write a literature review paper

timer Asked: Oct 23rd, 2018
account_balance_wallet $40

Question description


Topic: Public health microbiology: recent (2015-2018) - Cases of pandemic outbreaks, how were they contained

Attached are the guidelines.

Attached are the approved references (11 references in total. I can only download 4 as of now, when accepted, I will send the rest of the references)

Students are expected to write a literature review paper on a topic relevant to microbiology. The chosen topic may not cover an event more than 5 years old. You are encouraged to start your research paper as early in the semester as you possibly can. Your choice of topic may reflect your personal interests, support your career goals or nurture your inherent intuition and curiosity. Have enough time to gather literature, write, revise, revise some more, and turn in a scholarly paper. Maximum page limit is 10 (MINIMUM 8 pages), double spaced, AMA (American Medical Association) format.

FAIRLEIGH DICKINSON UNIVERSITY SCHOOL OF NATURAL SCIENCES BIOL 3225 General Microbiology  Literature review paper and presentation: 15% Students are expected to write a literature review paper on a topic relevant to microbiology. The chosen topic may not cover an event more than 5 years old. You are encouraged to start your research paper as early in the semester as you possibly can. Your choice of topic may reflect your personal interests, support your career goals or nurture your inherent intuition and curiosity. Have enough time to gather literature, write, revise, revise some more, and turn in a scholarly paper. Maximum page limit is 10, double spaced, AMA (American Medical Association) format. - Presentations should be 7-10 minutes long and should include all pertinent information as per their literature review. The Scientific Literature Review  OBJECTIVES/RATIONALE Background research is a vital component of the scientific research process. The students will be able to locate, identify and critique key elements of scientific literature to discover what is known and what remains to be learned about a topic. Books and articles are categorized as either primary or secondary sources. It is always more desirable to use primary sources whenever possible. Primary sources are first-hand accounts of events Secondary sources are second-hand accounts. The competent researcher never relies on secondary sources but always reviews the primary source as a check against possible errors. The researcher then makes records of the sources studied and summarizes the pertinent information found in each. The most common and useful method for recording bibliographical information is to write the complete information for each publication on a separate 3X5 card (or file). These cards are kept in alphabetical order in a file to be a ready source to cite relevant works in the body of the research paper. The researcher must develop an orderly and systematic approach to coding their index cards. It is important to summarize an article or book using the same format each time. This will enable quick comparisons. For an article, examine the author’s credentials, scan the hypothesis, and focus on the methods of research used (how the sample was selected and the data was analyzed). Read the conclusion and summary. Retain sound and pertinent articles. To summarize and record the information, note author, title, and year of publication. • Title of article • Authors name(s) - all authors MUST be recorded. • Full reference – year of publication, FULL name of the journal or book, # of issue or edition and page numbers. • Summary of information of interest NOTE: no everything may be of importance to your research, concentrate only on the issues that are pertinent to your work.  REVIEWS For the past three or four years you have learned how to review a scientific paper, now is time to learn how to use this information in a more productive form. Scientist must keep up with the advances on their area of interest by constantly reading new publications. This will give them an overview of what is being accomplished by the scientific community. Each individual paper you read will give you a piece of the puzzle. During this exercise, you will learn how to put all the pieces of the puzzle together as a BIG PICTURE – Scientific Literature Review. ELEMENTS OF SCIENTIFIC LITERATURE Title short, specific, clear Introduction WHY are you doing this research?- state the propose of what you are expecting to find (Present tense), review the individual literature and compile what information already exists on this subject (background information reported by the papers you read regarding your topic) Materials & Methods HOW was the research carried out? (Past tense) (design, target population, treatment, analytical methods ---- brief explanation on how the different authors conducted their experiments) – compare their methods), please NOTE that this may not be appropriated for your research. Results WHAT kind of results were reported by the papers you read (past tense) again, this may not apply to your research or it could be combine with your discussion and conclusion. Discussion/Conclusions WHAT do the results published by the different researchers mean to you? (present tense) References / Literature cited Paper Guidelines: The project will be based on new literature, at least seven original research articles from scientific journals (primary source). Beside the new scientific literature (primary sources), background references like books, popular science magazines or older articles from science journals should be used to complete your work; to create the BIG PICTURE. (Minimum 10-12 references) The steps required for the completion of the project are: 1. Sign-up for a topic provided by your instructor or if you have a particular topic or interest, you may clear it with your instructor. Due (in class) by the 2nd week of class. Please note that no two person will be allow to research the same topic. • When deciding on a topic, you should ask yourself the question….. What do you want to know about this subject? * Once you define your topic, you will not be allowed to change it. • Find primary references – at least 7 original research scientific articles from reputable scientific journals (use the library services!). – NOTE: scientific reviews and mini-reviews are considered as secondary source. -- Copies of the scientific papers PDF’s must be e-mailed to the instructor for approval no later than September 23, 2018. late delivers will be penalized • NOTE that I cannot open any link! You must download the paper into your computer as a PDF file and then forward the PDF as an email attachment. 2. Read the papers (followed guide provided in the beginning of this assignment) and look for additional scientific articles and background material (minimum 10 references should be included on your paper). • Prepare individual summary for each paper (like single pieces of a puzzle) write a two page “resume” in your own words for each reference. E-mail then to the instructor no later than October 6, 2018. – late delivers will be penalized 3. Compile all your information to give an overview of the studies done in a particular area of interest. “the entire puzzle” -4. Start writing your scientific literature review. First draft must be e-mailed to your instructor no later than 11:59pm October 27, 2018. • You may re-submit via e-mail working drafts until November 17. NO MORE DRAFTS WILL BE ACCEPTED AFTER THAT DAY. Make sure you have addressed all the recommendations and that your paper is free of grammatical errors and of good scientific quality as per the guide provided at the beginning of the assignment. • Points to remember: i. You are not to review each paper independently. DO NOT DESCRIBE INDIVIDUAL RESEARCHES, YOU MUST SUMMARIZE AND COMPARE ALL THE PAPERS. ii. You must compile all the information as a single unit iii. Don’t forget to cite in text what each author have to say. When citing in text use author(s) name(s) coma and year of publication. e.g. (Johnson, 2015) or (Johnson & Smith, 2016) or if more than two authors use (Willey et al., 2014) • It MUST include: o Title page -- (Title of your paper, your name and course number) o Introduction (background information reported by the papers you read regarding your topic) o Material & Methods if appropriate to your topic (brief explanation on how the different authors conducted their experiments) – compare their methods o Result / Discussion what do the results published by the different researchers mean to you. You may compare the data in tables, schemes or figures. BUT it has to be selfexplanatory and created by YOU. (do not copy them from the literature – that is considered plagiarism!) o Conclusion (what did you learn from your research) o Reference -- In text references and literature cited. In text references should be done by using the author(s) last name followed by a coma and the year of publication (ex. Johns, 2016; Johns & Willian, 2016; if more than three authors use the first authors name and et al. - Johns et al., 2016) End of the paper reference (literature cited) can be written as follow…Make sure to write the name of all authors here. -- do not use et al.! • Authors’ names (last name, Initial(s).and ; between the authors and before the last one)) Year of publication. Article title (capitalize only the first letter of the first word and proper nouns), Journal name (in italic), Volume number; full page numbers. o Li, B.; Li, W.; Chen, X.; Jiang, M.; and Dong, M. (2012). In vitro antibiofilm activity of the melanin from Auricularia auricula, an edible jelly mushroom. Annals of Microbiology, 62(4), 1523-1530. • Article in an online journal: Author(s) (same as above). Year Title. Journal Name; volume (issue NO.):inclusive pages. URL [provide the URL in this field; no need to use “URL:” preceding it]. Published [date]. Updated [date]. Accessed [date]. o Drake A.J., Smith A., and Betts P.R., (2002). Type 2 Diabetes in Obese White Children. Archives of Disease in Childhood 86(3), 207-208. http://vsearch.nlm.nih.gov/vivisimo/cgibin/query-meta?v:project=nlm-main-website&query=Archives+of+disease+in+childhood. Accessed April 5, 2016. o Langer, S., D. Schropp,, F. R. Bengelsdorf,, M. Othman, and M. Kazda. 2014. Dynamics of biofilm formation during anaerobic digestion of organic waste. Anaerobe 29 , p 44-51. http://dx.doi.org/10.1016/j.anaerobe.2013.11.013. Accessed April 5, 2016 • When citing data from a Web site, include the following elements, if available, in the order shown below: Author(s), if given (often, no authors are given). Title of the specific item cited (if none is given, use the name of the organization responsible for the site). Name of the Web site. URL [provide URL and verify that the link still works as close as possible to publication]. Published [date]. Updated [date]. Accessed [date]. o Living With Type 1 Diabetes. Diabetes.org. http://www.diabetes.org/living-withdiabetes/recently-diagnosed/living-with-type-1-diabetes.html. Published February 9, 2015. Accessed April 7, 2015. o Why Immunize? cdc.gov. http://www.cdc.gov/vaccines/vac-gen/why.htm. Updated September 23, 2014. Accessed April 7, 2015. o Yale University. Science Daily. http://www.sciencedaily.com/relesases/2015/01/1501733950. Published January 7, 2015. Accessed April 5, 2015. • When referring to an entire book, not pages or specific sections, use the following format: References should include the last name and first and middle initials of the author(s), italicized title case format for all titles (capitalize all words except prepositions such as of, between, through), articles (such as a, the, and an), and conjunctions (such as but, and, or; however, capitalize them if they begin the title or the subtitle) the city and state of publication, the publisher, and the year of publication/creation. o Silverstein A., Silverstein V.B., Nunn L.S. Cancer. Minneapolis, MN: Twenty-First Century Books; 2006. o Maul-Mellott, S.K. and Adams, J.N. Childhood Cancer: A Nursing Overview. Boston, MA: Jones and Bartlett; 1987. o Willey,J. M., L. M. Eherwood and C. J. Woolverton. Prescott’s MICROBIOLOGY 9th. Edition. McGraw-Hill. 2014. • Chapters from books should be capitalized in the same format as journal articles (sentence case format) and should not use quotation marks. Additionally, inclusive page numbers for the each chapter should be provided. The title of the book, however, should be title cased and italicized, following the print book format. A colon should follow the publication date and no space should be provided between the colon or the page number(s) and hypen. o Yagyu, S. and Iehara, T. MYCN nonamplified neuroblastoma: Detection of tumor-derived cellfree DNA in serum for predicting prognosis of neuroblastoma. In Hayat MA, ed. Pediatric Cancer Diagnosis, Therapy, and Prognosis. Dordrecht, NY: Springer; 2013:11-17. 5. You must prepare the material to be presented to your classmates. All Presentations (ppt, prezi, etc.. and an one page summary) regardless of your presentation date are due via e-mail to your instructor on November 25, 2018 NO EXCEPTIONS. Points will be deducted for late submissions  You will be presenting your topic during the class schedule time (based on the topic) and credit will be given for the presentation and for answered questions.  Presentations should be 7-10 min. long and should include all pertinent information as per their literature review.  A summary (one page) of your presentation should be prepared and e-mailed to your instructor before your presentation date for distribution to your classmates. All presentation materials will be included in your final examination. 6. Final paper is to be submitted via Safe-Assignment by 11:59PM DECEMBER 1, 2018- Once you submit it via Safe-assignment, you can no longer re-write it or fix it. EVALUATION OF THE PROJECT (15%) • • • • • Primary references delivered on time (1 pt each) Two page summary for each reference (2 pts each) First draft complete and delivered on time Final paper o Paper depth o Format o Spelling, paragraph and sentence structure, grammar and punctuation Oral presentation o One page summary o Presentation content o Timing + Question and answers Penalties: • Late submission(s) • No approved topic or literature • No drafts • No oral presentation 30pts 5pts 5pts 5pts 10pts 5pts - 50% of original pts. - 50 pts - 25 pts - 30 pts. 10pts 20pts 10pts 40pts 20pts SAMPLE LITERATURE REVIEW Prion propagation and their effect in humans Introduction Prions cause diseases like Creutzfeldt-Jakob disease (CJD), Gerstmann-Strässler-Scheinker syndrome (GSS), kuru, and fatal familial insomnia in humans (Barbisin et al. 2014). Diseases caused by prions in animals include bovine spongiform encephalopathy (BSE), also known as Mad Cow Disease, in cattle (Strom et al. 2014), scrapie in sheep, chronic wasting disease (CWD) in cervids (which includes deer), transmissible mink encephalopathy, and feline spongiform encephalopathy (Barbisin et al. 2014). Prion diseases are both fatal and currently incurable. These neurodegenerative diseases are collectively referred to as transmissible spongiform encephalopathies (TSEs). Prions cannot be seen by the naked eye and are naturally found in cells as normal, functional proteins. Their microbiological influence affects organisms on the cellular level, with neurons as the primary focus for most studies……………………….. Materials and Methods The researchers, Krejciova et al.(2014), simulated PrP conversion from the normal form to the abnormal form (that would normally occur in cells) in vitro using a technique called protein misfolding cyclic amplification (PMCA). PMCA works by incubating normally folded protein mixed with a small amount of misfolded protein. Then the conversion is sped up with a blast of ultrasound. The cycle is repeated until the desired end result is achieved (Zou & Pierluigi, 2012)……………….. Barbisin et al. (2014) were also concerned about humans becoming infected with prions. They had worked under certain assumptions. The primate Macaca fasicularis is known to be a good research subject for the understanding prion diseases inflicting humans………. Another group of scientists (Belay et al. 2015) found that prion studying from RNA studies had problems in maintaining RNA integrity, specifically from the heat produced using a rotating biopsy stamp when extracting cells…………………………………………………... Results/Discussion The findings of these studies indicate that prions continue to boggle the minds of researchers. These studies do, however, give more insight to the capabilities of TSE-related prions……………………………. Conclusion Researchers Krejciova et al. (2014) have concluded that prions from cattle infected with BSE can effectively afflict sheep of certain genotypes. However, the prion types found in these sheep have lost the capability to infect humans…………. Ultimately the ways prions propagate and influence cells and other microbes continues to elude researchers. The current level of knowledge surrounding prions highlights that there is little information on how to protect livestock and people from TSEs and other prion related neurodegenerative disorders………. Literature cited: Barbisin, M., Silvia Vanni, Ann-Christin Schmädicke, Judith Montag, Dirk Motzkus, Lennart Opitz, Gabriela Salinas-Riester, Giuseppe Legname (2014) Gene expression profiling of brains from bovine spongiform encephalopathy (BSE)infected cynomolgus macaques BMC Genomics. 2014; 15: 434. Published online 2014 Jun 5. doi: 10.1186/14712164-15-434
9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis Martyn D. Kirk , Sara M. Pires, Robert E. Black, Marisa Caipo, John A. Crump, Brecht Devleesschauwer, Dörte Döpfer, Aamir Fazil, Christa L. Fischer-Walker, Tine Hald, Aron J. Hall, Karen H. Keddy, Robin J. Lake, Claudio F. Lanata, Paul R. Torgerson, Arie H. Havelaar, Frederick J. Angulo Published: December 3, 2015 https://doi.org/10.1371/journal.pmed.1001921 Correction 23 Dec 2015: Kirk MD, Pires SM, Black RE, Caipo M, Crump JA, et al. (2015) Correction: World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis. PLOS Medicine 12(12): e1001940. https://doi.org/10.1371/journal.pmed.1001940 | View correction Abstract Background Foodborne diseases are important worldwide, resulting in considerable morbidity and mortality. To our knowledge, we present the first global and regional estimates of the disease burden of the most important foodborne bacterial, protozoal, and viral diseases. Methods and Findings We synthesized data on the number of foodborne illnesses, sequelae, deaths, and Disability Adjusted Life Years (DALYs), for all diseases with sufficient data to support global and regional estimates, by age and region. The data sources included varied by pathogen and included systematic reviews, cohort studies, surveillance studies and other burden of disease assessments. We sought relevant data circa 2010, and included sources from 1990–2012. The number of studies per pathogen ranged from as few as 5 studies for bacterial intoxications through to 494 studies for diarrheal pathogens. To estimate mortality for Mycobacterium bovis infections and morbidity and mortality for invasive non-typhoidal Salmonella enterica infections, we excluded cases attributed to HIV infection. We excluded stillbirths in our estimates. We estimate that the 22 diseases included in our study resulted in two billion (95% uncertainty interval [UI] 1.5–2.9 billion) cases, over one million (95% UI 0.89–1.4 million) deaths, and 78.7 million (95% UI 65.0–97.7 million) DALYs in 2010. To estimate the burden due to contaminated food, we then applied proportions of infections that were estimated to be foodborne from a global expert elicitation. Waterborne transmission of disease was not included. We estimate that 29% (95% UI 23–36%) of cases caused by diseases in our study, or 582 million (95% UI 401–922 million), were transmitted by contaminated food, resulting in 25.2 million (95% UI 17.5–37.0 million) DALYs. Norovirus was the leading cause of foodborne illness causing 125 million (95% UI 70–251 million) cases, while Campylobacter spp. caused 96 million (95% UI 52–177 million) foodborne illnesses. Of all foodborne diseases, diarrheal and invasive infections due to non-typhoidal S. enterica infections resulted in the highest burden, causing 4.07 million (95% UI 2.49–6.27 million) DALYs. Regionally, DALYs per 100,000 population were highest in the African region followed by the South East Asian region. Considerable burden of foodborne disease is borne by children less than five years of age. Major limitations of our study include data gaps, particularly in middle- and high-mortality countries, and uncertainty around the proportion of diseases that were foodborne. Conclusions Foodborne diseases result in a large disease burden, particularly in children. Although it is known that diarrheal diseases are a major burden in children, we have demonstrated for the first time the importance of contaminated food as a cause. There is a need to focus food safety interventions on preventing foodborne diseases, particularly in low- and middle-income settings. Editors' Summary Background Foodborne diseases are responsible for a large burden of illness (morbidity) and death (mortality) in both resource-rich and resource-poor countries. More than 200 diseases can be transmitted to people through the ingestion of food contaminated with microorganisms (bacteria, viruses, and parasites) or with chemicals. Contamination of food can occur at any stage of food production—on farms where crops are grown and animals raised, in factories where food is processed, and during food storage and preparation in shops, restaurants and the home. Contamination can arise because of pollution of the water, soil or air or through poor food-handling practices such as failing to wash one’s hands before preparing food. Many foodborne diseases (for example, norovirus, Escherichia coli, and campylobacter infections) present with gastrointestinal symptoms—stomach cramps, https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 1/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… diarrhea, and vomiting. However, some foodborne illnesses cause symptoms affecting other parts of the body and some have serious sequelae (abnormal bodily conditions or diseases arising from a pre-existing disease). For example, infection with some strains of E. coli can lead to kidney failure. Why Was This Study Done? Accurate regional and global estimates of the disease burden of foodborne illnesses are needed to guide governmental and international efforts to improve food safety. However, estimates of the number of cases of foodborne illness, sequelae, deaths, and disability adjusted life years (a DALY represents the disease-related loss of one year of full health because of premature death or disability; DALYs provide a measure of the burden of a disease) are only available for a few countries. Consequently, in 2007, the World Health Organization (WHO) established the Foodborne Disease Burden Epidemiology Reference Group (FERG) to estimate the global and regional burden of disease attributable to foodborne illnesses. Here, researchers involved in one of the constituent task forces of FERG—the Enteric Diseases Task Force—undertake a data synthesis (the combination of information from many different sources) to provide global and regional estimates of the disease burden of several important foodborne bacterial, protozoal (parasitic), and viral diseases. What Did the Researchers Do and Find? The researchers combined national estimates of foodborne diseases and data from systematic reviews (studies that identify all the research on a given topic using predefined criteria), national surveillance programs, and other sources to estimate the number of illnesses, sequelae, deaths and DALYs globally and regionally for 22 diseases with sufficient data to support such estimations. Together, these 17 bacterial infections, two viral infections, and three protozoal infections caused 2 billion cases of illness, more than 1 million deaths, and almost 80 million DALYs in 2010. Using information on the proportions of infections considered to be foodborne by expert panels, the researchers estimated that nearly a third of these cases of illness (582 million cases), resulting in 25 million DALYs, were transmitted by contaminated food. Notably, 38% of the cases of foodborne illness, 33% of deaths from these diseases, and 43% of the disease burden from contaminated food (11 million DALYs) occurred in children under 5 years old. The leading cause of foodborne illness was norovirus (125 million cases), closely followed by campylobacter (96 million); diarrheal and invasive infections caused by non-typhoidal Salmonella enterica infections caused the largest burden of disease (4.07 million DALYs). Finally, the burden of foodborne illness was highest in WHO’s African region. What Do These Findings Mean? The lack of reliable data on the 22 illnesses considered in this analysis for many regions of the world, including some of the most populous regions, and uncertainty about the proportion of the cases of each illness that is foodborne may limit the accuracy of these findings. Nevertheless, these results provide new information about the regional and global disease burden caused by foodborne illnesses. In particular, these estimates reveal an unexpectedly high disease burden caused by foodborne illnesses among young children. Thus, although children under the age of 5 years represent only 9% of the global population, nearly half of the disease burden from contaminated food may occur in this age group. Overall, the findings of this study suggest that governments and international agencies should prioritize food safety to prevent foodborne illness, particularly among young children, and highlight the need to identify effective food hygiene interventions that can be implemented in low- and middle-income countries. Additional Information This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001921.  The World Health Organization provides information about foodborne diseases, food safety and the estimation of the global burden of foodborne diseases (available in several languages)  The US National Institute of Allergy and Infectious Diseases provides detailed information about several foodborne illnesses  The US Centers for Disease Control and Prevention provides information about foodborne disease outbreaks in the US and elsewhere and information about food safety in the US  The UK National Health Service Choices website provides information about food poisoning (another name for foodborne illness) and about food safety  STOP Foodborne Illness STOP Foodborne Illness, a US non-profit public-health organization, provides personal stories about foodborne illness Citation: Kirk MD, Pires SM, Black RE, Caipo M, Crump JA, Devleesschauwer B, et al. (2015) World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis. PLoS Med 12(12): e1001921. https://doi.org/10.1371/journal.pmed.1001921 Academic Editor: Lorenz von Seidlein, Mahidol-Oxford Tropical Medicine Research Unit, THAILAND Received: March 20, 2015; Accepted: November 3, 2015; Published: December 3, 2015 Copyright: © . 2015 World Health Organization. This is an open access article distributed under the Creative Commons Attribution IGO License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/3.0/igo/. This article should not be reproduced for use in association with the promotion of commercial products, services or any legal entity. Data Availability: All relevant data are within the paper and its Supporting Information files. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 2/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Funding: This study was commissioned and paid for by the World Health Organization (WHO). Copyright in the original work on which this article is based belongs to WHO. The authors have been given permission to publish this article. We acknowledge the support from the Bill & Melinda Gates Foundation that funded CFL, CFW, and REB through the Child Health Epidemiology Reference Group (CHERG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The findings and conclusions of this report are those of the authors and do not necessarily represent the official views, decisions or policies of the World Health Organization, US Centers for Disease Control and Prevention, the Department of the Navy, Department of Defense, the US Government or other institutions listed. CFL, AJH, and FJA are employees of the US Government. MDK, REB, MC, BD, DD, AF, TH, KHK, RL, CFL, PRT, AHH, and FJA serve as members of the World Health Organization advisory body—the Foodborne Disease Epidemiology Reference Group—without remuneration. MDK is a member of the Editorial Board for PLOS ONE. PRT is a member of the Editorial Board for PLOS Neglected Tropical Diseases. Introduction Foodborne diseases are globally important, as they result in considerable morbidity, mortality, and economic costs [1,2]. Many different diseases, including those due to bacteria, viruses, parasites, chemicals, and prions, may be transmitted to humans by contaminated food [3]. Outbreaks and sporadic cases of foodborne disease are regular occurrences in all countries of the world. In recent decades, globalization of the food supply has also meant that pathogens causing foodborne diseases are rapidly transported across international borders [4]. Foodborne disease outbreaks have led to adverse impacts on trade and food security [5,6]. In response to foodborne diseases, national governments and international bodies have established elaborate systems to control and improve food safety [7]. Recognizing that contaminated food is an important cause of human disease, estimates of disease burden of the various foodborne diseases has been sought to enable advocacy for improved food safety and to assist governments to prioritize efforts for enhancing food safety. Although several countries have estimated the number of cases, sequelae, deaths, and Disability Adjusted Life Years (DALYs) resulting from foodborne diseases at the national level, most have not [8]. Furthermore, global and regional estimates of the burden of foodborne diseases have not been available [1,2]. In 2007, the World Health Organization (WHO) established the Foodborne Disease Burden Epidemiology Reference Group (FERG) to estimate the global and regional burden of disease attributable to food from all causes [9]. FERG consists of thematic task forces to estimate the human health burden of (1) enteric bacterial and viral infections, (2) parasitic infections, and (3) illnesses due to chemicals and toxins. The Director General of WHO nominated members of the FERG following an open call for applications to governments, in the scientific press, and through global networks [1]. The Enteric Diseases Task Force (EDTF) of the FERG comprised 14 experts in the epidemiology of viral, bacterial and parasitic infections transmitted by food, and was supported by various resource advisors who were expert in various aspects of infectious disease transmission. Methods In this study, the EDTF estimates the global and regional disease health burden in 2010 resulting from the most common foodborne diseases. For a glossary of terms used in this paper see S1 Text. FERG EDTF reviewed the epidemiology of all bacterial and viral diseases potentially transmitted by food and identified those for inclusion based on public health importance and data availability. We excluded enteroaggerative Escherichia coli, Vibrio parahaemolyticus, V. vulnificus, and Yersinia spp., which are infrequent causes of foodborne diseases, due to a lack of sufficient data for global estimation, ie–not commonly reported in systematic reviews of etiological agents of diarrhea [10]. After excluding these agents, we included 19 bacterial or viral diseases in our study. Of these 19 diseases, four are distinct manifestations of Salmonella enterica infection: invasive infections due to S. enterica serotype Typhi (Salmonella Typhi); invasive infections due to S. enterica serotype Paratyphi A (Salmonella Paratyphi A); invasive infections due to non-typhoidal S. enterica (iNTS); and diarrheal disease due to non-typhoidal S. enterica (NTS). We then determined that our approach for estimating the burden for diarrheal diseases could also be applied to protozoal diseases and included 3 protozoal diseases. Diarrhea is a dominant feature for 14 of these diseases—ten caused by bacteria, three by protozoa, and one by a virus. One or more extra-intestinal manifestations including bacteremia, hepatitis, and meningitis are the dominant feature for the other eight diseases—seven caused by bacteria and one caused by a virus. To identify data for estimation of incidence, mortality and sequelae for different agents, we conducted literature reviews to identify relevant studies, consulted with academics and expert committees, and evaluated systematic reviews that were conducted circa 2010. Where data sources were not available, the EDTF recommended that WHO commission systematic reviews into incidence and outcomes of specific agents, which were subsequently published in the peer-reviewed literature [11–22]. Where multiple data sources were available for a single agent, the EDTF made decisions to use the most contemporaneous and comprehensive sources, or incorporate both into the estimation process. In the following sections, we outline how we synthesized data in accordance with a documented framework reported elsewhere [23]. Table 1 summarizes our approach, while S2 Text provides a comprehensive description of the methods and distribution for all parameters used for estimating cases, sequelae, deaths and DALYs for each of the 22 diseases. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 3/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Table 1. Summary of enteric pathogens included and methods of estimating incidence of infection. For more information, see S2 Text. https://doi.org/10.1371/journal.pmed.1001921.t001 Estimating Cases, Sequelae, and Deaths for Diarrheal Diseases For diarrheal diseases caused by Campylobacter spp., Cryptosporidium spp., Entamoeba histolytica, enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), Giardia lamblia, norovirus, NTS, and Shigella spp., national estimates of foodborne diseases were only available from a limited number of countries. We therefore used two approaches, documented in Pires et al. [11], depending on the level of development of the country. The first approach, based on national estimates of the incidence of foodborne diseases from seven studies published between 2005–2014, was applied to the 61 countries in low-mortality (European Region (EUR) and other subregion A [27]) countries [3,28–35]. For countries with national estimates of incidence and mortality, we used these data. We used the median and associated uncertainty intervals for diarrheal diseases for the subregion to estimate incidence and mortality of diarrheal diseases for other countries within the subregion without national data [10]. The second approach was applied to the remaining 133 countries not in EUR or other subregion A categories (see S1 Text for a country list by subregion). For this approach, we modified the WHO Child Health Epidemiology Reference Group (CHERG) method to estimate diarrheal incidence and mortality for all age groups [10]. First, we estimated the overall incidence of diarrhea from all causes (i.e., “envelope” of diarrheal incidence) for 2010 by combining estimates of diarrheal incidence for children <5 years of age and persons ≥5 years of age [13,36]. We used the overall diarrheal mortality (i.e., envelope for diarrheal deaths) derived by WHO [37] for 2010. We derived an etiological proportion for each disease by region from systematic reviews of stool sample isolation or detection proportions from inpatient, outpatient, and community-based studies of persons with diarrhea. We followed the CHERG standard approach because there is limited information on pathogens among people who have died, and assumed that the distribution of pathogens observed among inpatients hospitalized with severe diarrhea represented the pathogen prevalence among diarrheal deaths [10]. To derive etiological proportions for children <5 years of age, we assumed that the distribution of pathogens in outpatient and community studies represented the pathogen prevalence among diarrheal episodes for those who did not die. We made the same assumption for persons ≥5 years of age but due to sparseness of data also included inpatient studies. For more details of methods, see Pires et al. [11]. For some pathogens we assumed that different etiological agents, such as Shigella spp., NTS and Campylobacter spp., had similar clinical profiles (S2 Text). Estimates for cholera were based on the incidence among populations at risk for cholera in endemic and non-endemic countries [24]. The case fatality ratio (CFR) for cholera was 1% in Western Pacific Region (WPR) subregion B, 1% in South-East Asian Region (SEAR) subregion B (except 1.5% in Bangladesh), 1.3% in Eastern Mediterranean Region (EMR) subregion B, 3% in SEAR subregion D, 3.2% in EMR subregion D, and 3.8% in African Region (AFR) [24]. For all other countries, we assumed cholera occurred only among international travellers and did not result in deaths. In this instance, we applied the median incidence from non-endemic countries with available data for cholera. Shiga-toxin producing E. coli (STEC) infection incidence and mortality were based on a systematic review described elsewhere [19]. Sequelae, more common among O157 infections, were hemolytic uremic syndrome (HUS) and end stage renal disease (ESRD). Based on review, we estimated 0.8% of O157 infections and 0.03% of infections caused by other serotypes result in HUS, and 3% of HUS cases result in ESRD. We estimated that the CFR for HUS was 3.7%; for ESRD the CFR was 20% in the 35 subregion A countries and 100% in other countries. We used data on the incidence and mortality of foodborne intoxications caused by Bacillus cereus, Clostridium perfringens, and Staphylococcus aureus from national studies conducted in low-mortality countries. We applied the median incidence from national studies to the 61 countries in EUR and other subregion A countries. We did not attempt to estimate burden due to these three foodborne intoxications in high- and middle-mortality countries due to the absence of data on diseases caused by these pathogens in these countries. The median CFR from national studies was 0.003% for C. perfringens and 0.0025% for S. aureus; there were no B. cereus deaths. We considered that 31% of Guillain-Barré Syndrome (GBS) cases globally were associated with antecedent Campylobacter infection and that the CFR for GBS was 4.1% [38,39]. Estimating Cases, Sequelae, and Deaths of Extra-Intestinal Diseases For diseases caused by hepatitis A virus, Brucella spp., Listeria monocytogenes, Mycobacterium bovis, iNTS, Salmonella Paratyphi A, and Salmonella Typhi we used a variety of approaches depending on availability of data. We used Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease (GBD) 2010 data to estimate the burden of disease for typhoid, paratyphoid, and hepatitis A [25]. IHME provided country-specific age-standardized prevalence data of typhoid and paratyphoid fever. These data were converted to incidence by dividing by duration, and partitioned into typhoid and paratyphoid assuming a 1.0 to 0.23 ratio [40]. Country-specific hepatitis A mortality data, stratified by age and sex, were converted to incidence assuming a CFR of 0.2%. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 4/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Rates of iNTS are highly correlated with HIV prevalence and malaria risk [22]. To estimate iNTS incidence globally, we used agespecific estimates of incidence from a systematic review [22] to construct a random effect log linear model using covariates of country specific HIV and malaria deaths, and the log of Gross Domestic Product. As data were sparse, we predicted incidence for all ages, which was converted to age-specific incidence based on age profiles for iNTS cases in low and high incidence settings [22]. From this, we predicted iNTS incidence among persons not infected with HIV [41,42]. To estimate deaths, we assumed that the CFR for iNTS in non-HIV infected individuals was a uniform distribution with a range 5–20% in sub-region B-E countries and range 3.9–6.6% in sub-region A countries [43]. Estimates for M. bovis infections were based on a systematic review where the proportion of human tuberculosis (TB) infections due to M. bovis ranged from 0.3% in Region of the Americas (AMR) to 2.8% in AFR [ 17]. We identified 51 countries that were free from M. bovis in cattle based on European Union certification and the World Organization for Animal Health [44]. All countries in a region except those free from M. bovis in cattle were assumed to have the same proportion of human TB infections due to M. bovis. To account for internationally acquired infections, all countries free of M. bovis in cattle were assigned the lowest observed proportion of human TB infections due to M. bovis (0.3%). To derive estimates of human M. bovis incidence, we multiplied WHO country-specific human TB incidence by the estimate of the proportion of human TB infections that were due to M. bovis [45]. To estimate mortality associated with M. bovis that accounted for HIV co-morbidity we used estimates of mortality due to human TB in HIV negative persons from WHO. We adjusted mortality data by assuming that the CFR for M. bovis was 20% lower than human TB, as M. bovis infections are more likely to be extrapulmonary [16]. To estimate the incidence and mortality for brucellosis we updated a systematic review and included additional data on 32 countries that were considered Brucella-free in livestock (free of B. arbortus in cattle and B. melitensis in sheep and goats) [15]. We imputed incidence data to countries without estimates using a Bayesian log-normal random effects model, except for countries that were Brucella-free in livestock [23]. To account for internationally acquired infections, all countries that were Brucella-free in livestock were assigned the median incidence of human brucellosis reported from these countries. The CFR for brucellosis was 0.05%, and 40% of cases resulted in chronic infections and 10% of cases in males resulted in orchitis [14]. We estimated the incidence and mortality for listeriosis using a systematic review that is described elsewhere [18]. In accordance with standard burden of disease practice, we excluded stillbirths, in our baseline burden estimates. The CFR was 14.9% for perinatal cases and 25.9% for other cases. Incidence and mortality data for botulism were only available from countries in Europe and North America. We limited our estimation to the 55 countries in EUR and AMR subregion A, which was based on the median incidence derived from countries with national estimates of botulism. We estimated that 35% of botulism cases were severe and that the CFR of severe botulism was 15%. Estimation of Foodborne Proportion We assumed that all infections from L. monocytogenes, M. bovis, and from the four foodborne intoxications (B. cereus, C. botulinum, C. perfringens, and S. aureus) were foodborne. To estimate the proportion of the other enteric diseases that were transmitted by contaminated food, we relied upon results of a FERG structured expert elicitation [46]. In this expert elicitation, foodborne and waterborne were considered separate transmission pathways. Proportions of brucellosis and hepatitis A infections and intestinal protozoa acquired from contaminated food were estimated using global panels of experts. Foodborne proportions for the remaining enteric diseases were estimated using a panel of experts for each region separately, although several experts were serving on more than one panel. Disability Weights Disability weights for each clinical outcome for the enteric diseases were taken from the GBD 2010 study [47]. Disability weights for listeriosis meningitis and neurological sequelae were derived from GBD 2010 disability weights using a multiplicative methodology and expert judgment. Disability weights for individual conditions are specified in S2 Text. Data Analysis The FERG approach to computing DALYs from the estimated cases, sequelae, deaths, and other parameters described in this paper is described in more detail elsewhere, including the modelling of uncertainty intervals incorporating (where relevant) uncertainty of estimated foodborne proportions [23]. Estimates were produced using the 2012 revisions for United Nations countrylevel population data for 2010 [48]. Calculations followed disease-specific models defined by incidence and probability parameters, each with a distribution [23]. Uncertainty around input parameters were propagated using Monte Carlo simulations; 10,000 values were sampled from each input parameter to calculate 10,000 estimates of cases, deaths or DALYs. The 2.5th and 97.5th percentile of each set of the 10,000 estimates yielded a 95% Uncertainty Interval (UI). Further details of analyses and modelling of DALYs and components described in this paper are in S2 Text. Results Cases We estimated that the 22 diseases in our study caused 2.0 billion (95% UI 1.5–3.0 billion) illnesses in 2010, 39% (95% UI 26–53%) in children <5 years of age. Among the 1.9 billion (95% UI 1.4–2.8 billion) cases of diarrheal diseases, norovirus was responsible for 684 million (95% UI 491–1,112 million) illnesses; the largest number of cases for any pathogen (Table 2). The pathogens resulting in the next largest number of cases were ETEC, Shigella spp., G. lamblia, Campylobacter spp. and NTS. Campylobacter spp. cases included 31,700 (95% UI 25,400–40,200) GBS cases. There were also 2.48 million (95% UI 1.69–5.38 million) STEC cases which included 3,330 (95% UI 2,160–6,550) with HUS and 200 (95% UI 15–760) with ESRD. Among the extra-intestinal diseases, the pathogens resulting in the most infections were hepatitis A virus, Salmonella Typhi and Salmonella Paratyphi A. Brucella spp. resulted in 0.83 million (95% UI 0.34–19.6 million) illnesses, which included almost 333,000 (95% UI 135,000– https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 5/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… 7,820,000) chronic infections and 83,300 (95% UI 33,800–1,960,000) episodes of orchitis. L. monocytogenes resulted in 14,200 (95% UI 6,100–91,200) illnesses which included 7830 (95% UI 3,400–50,500) cases of septicaemia, 3,920 (95% UI 1,650–24,900) cases of meningitis, and 666 (95% UI 207–4,710) cases with neurological sequelae. Table 2. Median number of foodborne illnesses, deaths, and Disability Adjusted Life Years (DALYs), with 95% uncertainty intervals, 2010. https://doi.org/10.1371/journal.pmed.1001921.t002 Overall, 29% (95% UI 23–36%) of all 22 diseases were estimated to be transmitted by contaminated food equating to 582 million (95% UI 400–922 million) foodborne cases in 2010; 38% (95% UI 24–53%) in children <5 years of age. The pathogens resulting in the most foodborne cases were norovirus, Campylobacter spp., ETEC, NTS, and Shigella spp. A high proportion of foodborne infections caused by V. cholerae, Salmonella Typhi, and Salmonella Paratyphi A occurred in the African region ( Table 3). A high proportion of foodborne infections caused by EPEC, Cryptosporidium spp., and Campylobacter spp. occurred among children <5 years of age (Table 4). Among the 11 diarrheal diseases, the rate ratio of foodborne cases occurring among children <5 years of age compared to those ≥5 years of age was 6.44 (95%UI 3.15–12.46). Table 3. Median rates of foodborne illnesses, deaths and Disability Adjusted Life Years (DALYs) per 100,000 persons, by region, with 95% uncertainty intervals, 2010. https://doi.org/10.1371/journal.pmed.1001921.t003 https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 6/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Table 4. Median number of foodborne illnesses, deaths, and Disability Adjusted Life Years (DALYs) by age group, with 95% uncertainty intervals, 2010. https://doi.org/10.1371/journal.pmed.1001921.t004 Deaths We estimated that the 22 diseases in our study caused 1.09 million (95% UI 0.89–1.37 million) deaths in 2010, 34% (95% UI 29– 38%) in children <5 years of age. Among the diarrheal diseases, norovirus was responsible for the most deaths. Other diarrheal pathogens responsible for large numbers of deaths were EPEC, V. cholerae, and Shigella spp. The 37,600 (95% UI 27,700– 55,100) deaths attributed to Campylobacter spp. included 1,310 (95% UI 887–1,880) deaths from GBS. Among the extra-intestinal enteric diseases, the pathogens resulting in the most deaths were Salmonella Typhi, hepatitis A virus, iNTS and Salmonella Paratyphi A. Overall, the 22 diseases in our study resulted in 351,000 (95% UI 240,000–524,000) deaths due to contaminated food in 2010; 33% (95% UI 27–40%) in children <5 years of age. The enteric pathogens resulting in the most foodborne deaths were Salmonella Typhi, EPEC, norovirus, iNTS, NTS, and hepatitis A. The mortality rates of foodborne diseases were consistently highest in the African region followed by the South Eastern Asian region (Table 3). Among the 11 diarrheal diseases due to contaminated food, the rate ratio of deaths in children <5 years of age compared to those ≥5 years of age was 8.37 (95%UI 5.90–11.4). For all 22 diseases, the rate ratio of deaths in children <5 years of age compared to those ≥5 years of age was 4.85 (95%UI 3.54–6.59). DALYs We estimated that the 22 diseases in our study caused 78.7 million (95% UI 65.0–97.7 million) DALYS in 2010, 43% (95% UI 38– 48%) in children <5 years of age. The pathogens resulting in the most DALYs were norovirus, Salmonella Typhi, EPEC, V. cholerae, ETEC, and hepatitis A (Table 2). We estimate that the 22 diseases in our study resulted in 25.2 million (95% UI 17.5–37.0 million) DALYs due to contaminated food; 43% (95% UI 36–50%) in children <5 years of age. Fig 1 shows the relative burden of foodborne enteric infections, if iNTS and NTS were grouped together. The pathogen resulting in the most foodborne DALYs was nontyphoidal S. enterica, if iNTS were included (4.07 million DALYs; 95% UI 2.49–6.27 million DALYs). Other pathogens resulting in substantial foodborne DALYs included: Salmonella Typhi, EPEC, norovirus, and Campylobacter spp. The rates of DALYs for foodborne diseases were highest in the African region. Overall, the 22 diseases transmitted by contaminated food resulted in 10.8 million (95% UI 7.59–15.3 million) DALYs in children <5 years of age compared to 14.3 million (95% UI 9.42–22.5 million) DALYs in those ≥5 years of age. Fig 1. Disability Adjusted Life Years for each pathogen acquired from contaminated food ranked from lowest to highest with 95% Uncertainty Intervals, 2010. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 7/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Note figure is on a logarithmic scale. The figure shows the median (white dot); Inter-Quartile Range = 50%UI = 25%/75% percentiles (thick black line); 90% UI = 5%/95% percentiles (thin black line); 95% UI = 2.5%/97.5% percentiles (thin grey line). Note, figure does not include four foodborne intoxications due to Clostridium botulinum, C. perfringens, S. aureus, and Bacillus cereus due to a lack of data for global estimation. In addition, data for non-typhoidal Salmonella enterica infections and invasive non-typhoidal S. enterica have been combined. https://doi.org/10.1371/journal.pmed.1001921.g001 Discussion To our knowledge, we estimate for the first time the substantial worldwide burden of foodborne bacterial, viral, and protozoal diseases in humans, particularly among children. Although children <5 years of age represent only 9% of the global population, we found that 43% of the disease burden from contaminated food occurred in this group. Foodborne illnesses from diarrheal and invasive non-typhoidal S. enterica resulted in the largest disease burden reflecting the ubiquitous nature of Salmonella, the severe nature of illness, and the fact that young children are commonly infected [19]. Large human disease burdens are also imposed by foodborne infections due to norovirus and typhoid. It is important to recognize that diseases with lower burden may still warrant intervention. In particular, certain foodborne diseases may represent a larger problem in some regions. For example, the most substantial burden due to foodborne cholera occurs in African and Asian regions. Similarly, the burden of brucellosis and M. bovis infections were highest in the Middle Eastern and African regions. To develop these comprehensive estimates of the disease burden of foodborne diseases, we adopted an innovative approach to incorporate the highest quality data available for each foodborne disease [11]. Due to their quality, we gave highest priority to studies with national estimates of foodborne diseases. Since studies with national estimates were only available in a few countries, we adapted the CHERG approach for estimating the disease burden of diarrheal diseases [10,36]. This approach was facilitated by the availability of estimates of the envelope of diarrheal deaths, along with recent advances in diarrheal disease diagnosis, such as widespread application of polymerase chain reaction (PCR) for norovirus detection [20,49]. In our study, norovirus resulted in the largest number of cases of foodborne diseases and overall burden, highlighting the global importance of this agent [20]. However, the disease model we used in the 135 middle- and high-mortality counties included only norovirus infections that resulted in a diarrheal illness. If we also included estimates for norovirus infections that resulted in vomiting without diarrhea, there would be an estimated additional 163 million norovirus cases in these countries [50]. We also found, similar to what has been reported in national studies, that in the countries where we applied the modified CHERG approach, the etiological cause of almost half of diarrheal cases and deaths remained unknown. This was likely due, in large part, to pathogens that are possibly foodborne but with insufficient data for estimation, and unknown agents not yet discovered. In this study, we focused our attention on the burden due to pathogens that were known to be transmitted by contaminated food. When we examined the human health impact of different pathogens, various serotypes of Salmonella resulted in the greatest foodborne burden. If we consider the combined burden attributable to S. enterica from all invasive (including iNTS, Salmonella Typhi and Salmonella Paratyphi A) and diarrheal infections, there were an estimated 8.76 million (95% UI 5.01–15.6 million) DALYs from all transmission sources and 6.43 million (95% UI 3.08–13.2 million) DALYs from contaminated food. This highlights the significant public health importance of Salmonella infections and the urgency of control, particularly for invasive infections in lowand middle-income settings where most of the mortality occurs [51–53]. Twelve of the diseases included in our study were also included in the GBD 2010 study [25,26,54]. For three diseases (typhoid, paratyphoid and hepatitis A) we used GBD 2010 data to derive estimates of incidence. For the other nine diarrheal diseases, we elected to conduct our own analysis or used updated data from commissioned systematic reviews to derive estimates. Our study builds upon the GBD 2010 study by providing estimates of the proportion of each disease acquired from food; we also provide, in addition to estimates of deaths, estimates of the number of illnesses for each of the diseases [26]. Before we applied our estimate of foodborne proportions to each pathogen, our estimates of the disease burden for a few pathogens, in terms of the estimated number of deaths and DALYs were relatively similar for diseases in common with GBD 2010 and FERG. However, there were important differences in other estimates. The GBD 2010 study estimates of deaths due to E. histolytica, Cryptosporidium spp., and Campylobacter spp. were 10 times, 4 times and 3 times greater, respectively, than the FERG estimates [25]. The FERG estimate for DALYs for nontyphoidal S. enterica combining diarrheal and invasive infections was 4 times greater than GBD 2010 [54]. The FERG estimates are relatively similar to previous global estimates of cholera, typhoid fever, salmonellosis, and shigellosis [19,24,40,55]. The GBD 2010 study estimates included ‘cysts and liver abscesses’ as a complication of typhoid fever, which has been questioned [56]. However, we understood this categorization to be a proxy for serious typhoid fever and incorporated these data into our estimates. The CFR for each of the diseases included in our estimate are comparable to those reported in national studies. There is a continuing need for high quality studies assessing the foodborne disease burden at the national level. Our methodology for estimating the disease burden attributable to foodborne transmission can be used in future studies. In comparing the overall burden of our findings, the diseases we included in our study resulted in 79 million DALYs in 2010. This represent approximately 3% of the 2.49 billion DALYs reported in the GBD 2010 study [54]. The GBD 2010 study estimated that approximately 25% of DALYs globally were due to deaths and disability in children younger than 5 years of age, while we estimated that 43% of the DALYs in our study were among children <5 years of age. There are obvious policy implications of our findings. Countries and international agencies must prioritize food safety to prevent foodborne illness, particularly among young children. The highest burden of disease due to contaminated food was in the African region, largely due to iNTS in children. The considerable regional difference in the burden of foodborne diseases suggests that current control methods exist to avoid an important proportion of the current burden. Our results should stimulate research into the epidemiology of foodborne diseases, with a view to informing prevention efforts. There is an urgent need to identify and implement effective food hygiene interventions in low- and middle-income countries. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 8/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… A limitation of our estimates of the consequences for human health of foodborne diseases is that, due to data gaps particularly in middle- and high-mortality countries, we included only a few sequelae in our estimates. We did not include data on post-infectious sequelae, such as reactive arthritis and irritable bowel syndrome following foodborne infections. Studies of the burden of enteric pathogens in low-mortality countries highlight that excluding these sequelae under-represents the true burden of disease, but reliable data were not available from middle- and high-mortality countries [57]. Where we did include sequelae, there were insufficient data to account for age-specific effects. We also excluded stillbirths; this exclusion only affected disease burden estimates for L. monocytogenes. A recent review estimated that listeriosis, which we assume is all foodborne, causes 273 stillbirths globally annually [18]. We were unable to distinguish between the effects on health from the condition under study and that of co-morbid conditions, which is common to many studies of human health. For example, salmonellosis and M. bovis infections may occur as HIV co-infections. If FERG included deaths among HIV-infected persons, there would have substantial additional deaths due to invasive nontyphoidal S. enterica deaths, some of which could presumably be averted by improvements in food safety. For some other pathogens in our estimates, such as L. monocytogenes and Cryptosporidium spp., we were unable to account for the excess mortality due to HIV infection due to a lack of reliable data. Another important limitation of our attempt to quantify the disease burden due of foodborne disease is the inherent difficulty in estimating the proportion of illness acquired from food [58]. We relied on a structured expert elicitation study for these estimates. We were unable to estimate differences in mode of transmission by age, despite this potentially being important. Expert elicitation studies can result in highly variable proportions attributed to food, depending on the nature of experts included in elicitation studies [59,60]. Without specific studies attributing sources of infection, it is difficult to obtain accurate estimates of foodborne transmission, but this finding regarding the need for more attribution studies is an important outcome of our study [61]. For example, the FERG expert elicitation study estimated that 18% of norovirus was foodborne compared with 14% estimated from a recent study based on outbreak genotyping [21]. The major limitation of our study was the lack of reliable data from many regions of the world. In particular, for the most populous regions of the world we had the least data for some pathogens [10]. We tried to use the best data available and attempted to make reasoned assumptions wherever possible [8]. For some agents, such as toxin-mediated illnesses, we elected to limit our estimates to countries where diseases were endemic or where there was sufficient data. Further data on burden of enteric diseases from lowand middle-income settings, particularly high quality epidemiological data, are needed to improve our understanding of foodborne diseases [8,11]. Despite these limitations, our estimates of the disease burden due to 22 foodborne diseases should provide policy makers with information for advocacy for improved regulation and control of foodborne diseases. Of particular importance, we estimate that almost half of the burden of foodborne disease occurs in children under 5 years of age. This previously underappreciated disease burden requires greater attention from governments and resourcing to improve food safety. In our study we highlight regions where diseases, such as tuberculosis and brucellosis are still transmitted by contaminated food. Our results highlight the benefits of countries conducting national studies estimating and attributing the incidence, hospitalizations and deaths due to foodborne diseases to improve understanding of burden and improve control measures. Our regional estimates could be used to fill data gaps for countries attempting to estimate the burden of disease due to contaminated food. Supporting Information S1 Text. Glossary and regions. https://doi.org/10.1371/journal.pmed.1001921.s001 (DOCX) S2 Text. Information sheets for foodborne agents. https://doi.org/10.1371/journal.pmed.1001921.s002 (DOCX) Acknowledgments We would like to acknowledge the assistance and leadership of the WHO Secretariat over the life of the FERG initiative, particularly Amy Cawthorne, Natsumi Chiba, Tim Corrigan, Tanja Kuchenmüller, Yuki Minato, and Claudia Stein. We acknowledge the Institute for Health Metrics and Evaluation (Seattle, WA, USA) for providing data on the global burden of selected diseases. Our work is based on the excellent contribution of commissioned scientists in providing data for these estimates, including: Marion Koopmans, Benjamin Lopman, Shannon Majowicz, Charline Maertens de Noordhout, Elaine Scallan, Linda Verhoef, along with many others. Anita McGrogan provided an update of her GBS review for FERG. Daniel Graciaa, Danielle Dang, Gregory Mak, and Joshua Kolbert provided exceptional research assistance. We thank other members of the EDTF who contributed to the work of the taskforce, including George Nasinyama, but not to the preparation of this manuscript. Author Contributions Conceived and designed the experiments: MDK SMP REB BD DD AF CLFW TH KHK RL CFL PRT AHH FJA. Performed the experiments: SMP JAC BD CLFW TH AJH CFL FJA. Analyzed the data: MDK SMP JAC BD CLFW TH AJH CFL FJA. Wrote the first draft of the manuscript: FJA MDK. Contributed to the writing of the manuscript: MDK SMP REB MC JAC BD DD AF CLFW TH AJH KHK RL CFL PRT AHH FJA. Agree with the manuscript’s results and conclusions: MDK SMP REB MC JAC BD DD AF CLFW TH AJH KHK RL CFL PRT AHH FJA. All authors have read, and confirm that they meet, ICMJE criteria for authorship. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 9/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… References 1. Stein C, Kuchenmuller T, Hendrickx S, Prüss-Űstün A, Wolfson L, Engels D, et al. The Global Burden of Disease assessments—WHO is responsible? PLoS Negl Trop Dis. 2007;1:e161. pmid:18160984 View Article PubMed/NCBI Google Scholar 2. Tauxe RV, Doyle MP, Kuchenmuller T, Schlundt J, Stein CE. Evolving public health approaches to the global challenge of foodborne infections. Int J Food Microbiol. 2010;139 Suppl 1:S16–28. pmid:19931203 View Article PubMed/NCBI Google Scholar 3. Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, et al. Foodborne illness acquired in the United States—major pathogens. Emerg Infect Dis. 2011;17:7–15. pmid:21192848 View Article PubMed/NCBI Google Scholar 4. Coulombier D, Takkinen J. From national to international—challenges in cross-border multi-country, multi-vehicle foodborne outbreak investigations. Eurosurveillance. 2013;18:20423. pmid:23517867 View Article PubMed/NCBI Google Scholar 5. Bernard H, Faber M, Wilking H, Haller S, Hohle M, Schielke A, et al. Large multistate outbreak of norovirus gastroenteritis associated with frozen strawberries, Germany, 2012. Eurosurveillance. 2014;19:20719. pmid:24602278 View Article PubMed/NCBI Google Scholar 6. Frank C, Werber D, Cramer JP, Askar M, Faber M, an der Heiden M, et al. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. New Engl J Med. 2011;365:1771–1780. pmid:21696328 View Article PubMed/NCBI Google Scholar 7. Hathaway SC. Food control from farm to fork: implementing the standards of Codex and the OIE. Rev Sci Tech Oie. 2013;32:479–485. View Article Google Scholar 8. Haagsma JA, Polinder S, Stein CE, Havelaar AH. Systematic review of foodborne burden of disease studies: quality assessment of data and methodology. Int J Food Microbiol. 2013;166:34–47. pmid:23827806 View Article PubMed/NCBI Google Scholar 9. Kuchenmuller T, Hird S, Stein C, Kramarz P, Nanda A, Havelaar AH. Estimating the global burden of foodborne diseases—a collaborative effort. Eurosurveillance. 2009;14. View Article Google Scholar 10. Lanata CF, Fischer-Walker CL, Olascoaga AC, Torres CX, Aryee MJ, Black RE, et al. Global causes of diarrheal disease mortality in children <5 years of age: a systematic review. PLoS ONE. 2013;8:e72788. pmid:24023773 View Article PubMed/NCBI Google Scholar 11. Pires SM, Fischer-Walker CL, Lanata CF, Devleesschauwer B, Hall AJ, Kirk MD, et al. Aetiology-specific estimates of the global and regional incidence and mortality of diarrhoeal diseases commonly transmitted through food. PLoS ONE. 2015; 10: e 0142927. View Article Google Scholar 12. Fischer Walker CL, Sack D, Black RE. Etiology of diarrhea in older children, adolescents and adults: a systematic review. PLoS Negl Trop Dis. 2010;4:e768. pmid:20689809 View Article PubMed/NCBI Google Scholar 13. Walker CL, Black RE. Diarrhoea morbidity and mortality in older children, adolescents, and adults. Epidemiol Infect. 2010;138:1215–1226. pmid:20307341 View Article PubMed/NCBI Google Scholar https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 10/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… 14. Dean AS, Crump L, Greter H, Hattendorf J, Schelling E, Zinsstag J. Clinical manifestations of human brucellosis: a systematic review and meta-analysis. PLoS Negl Trop Dis. 2012;6:e1929. pmid:23236528 View Article PubMed/NCBI Google Scholar 15. Dean AS, Crump L, Greter H, Schelling E, Zinsstag J. Global burden of human brucellosis: a systematic review of disease frequency. PLoS Negl Trop Dis. 2012;6:e1865. pmid:23145195 View Article PubMed/NCBI Google Scholar 16. Durr S, Muller B, Alonso S, Hattendorf J, Laisse CJ, van Helden PD, et al. Differences in primary sites of infection between zoonotic and human tuberculosis: results from a worldwide systematic review. PLoS Negl Trop Dis. 2013;7:e2399. pmid:24009789 View Article PubMed/NCBI Google Scholar 17. Muller B, Durr S, Alonso S, Hattendorf J, Laisse CJ, Parsons SD, et al. Zoonotic Mycobacterium bovis-induced tuberculosis in humans. Emerg Infect Dis. 2013;19:899–908. pmid:23735540 View Article PubMed/NCBI Google Scholar 18. Maertens-de-Noordhout C, Devleesschauwer B, Angulo FJ, Verbeke G, Haagsma J, Kirk M, et al. The global burden of listeriosis: a systematic review and meta-analysis. Lancet Infect Dis. 2014;14:1073–1082. pmid:25241232 View Article PubMed/NCBI Google Scholar 19. Majowicz SE, Scallan E, Jones-Bitton A, Sargeant JM, Stapleton J, Angulo FJ, et al. Global Incidence of Human Shiga Toxin-Producing Escherichia coli Infections and Deaths: A Systematic Review and Knowledge Synthesis. Foodborne Pathog Dis. 2014;11:447–455. pmid:24750096 View Article PubMed/NCBI Google Scholar 20. Ahmed SM, Hall AJ, Robinson AE, Verhoef L, Premkumar P, Parashar UD, et al. Global Prevalence of Norovirus Among Cases of Gastroenteritis. Lancet Infect Dis. 2014;14:725–730. pmid:24981041 View Article PubMed/NCBI Google Scholar 21. Verhoef L, Hewitt J, Barclay L, Ahmed SM, Lake R, Hall AJ, et al. Norovirus Genotype Profiles Associated with Foodborne transmission. Emerg Infect Dis. 2015;21:592–599. pmid:25811368 View Article PubMed/NCBI Google Scholar 22. Ao T, Feasy N, Gordon M, Keddy K, Angulo F, Crump J. Global burden of invasive nontyphoidal Salmonella disease, 2010. Emerg Infect Dis. 2015;21:941–949. View Article Google Scholar 23. Devleesschauwer B, Haagsma JA, Angulo FJ, Bellinger DC, Cole D, Döpfer D, et al. (2015) Methodological Framework for World Health Organization Estimates of the Global Burden of Foodborne Disease. PLoS ONE 10: e 0142498. View Article Google Scholar 24. Ali M, Lopez AL, You YA, Kim YE, Sah B, Maskery B, et al. The global burden of cholera. Bulletin World Health Organization. 2012;90:209–218A. View Article Google Scholar 25. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2095–2128. pmid:23245604 View Article PubMed/NCBI Google Scholar 26. Murray CJ, Ezzati M, Flaxman AD, Lim S, Lozano R, Michaud C, et al. GBD 2010: design, definitions, and metrics. Lancet. 2012;380:2063–2066. pmid:23245602 View Article PubMed/NCBI Google Scholar 27. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJL. Selected major risk factors and global and regional burden of disease. Lancet. 2002;360(9343):1347–1360. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 11/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… View Article Google Scholar 28. Scallan E, Griffin PM, Angulo FJ, Tauxe RV, Hoekstra RM. Foodborne illness acquired in the United States—unspecified agents. Emerg Infect Dis. 2011;17:16–22. pmid:21192849 View Article PubMed/NCBI Google Scholar 29. Thomas MK, Murray R, Flockhart L, Pintar K, Pollari F, Fazil A, et al. Estimates of the burden of foodborne illness in Canada for 30 specified pathogens and unspecified agents, circa 2006. Foodborne Pathog Dis. 2013;10:639–648. pmid:23659355 View Article PubMed/NCBI Google Scholar 30. Havelaar AH, Haagsma JA, Mangen MJ, Kemmeren JM, Verhoef LP, Vijgen SM, et al. Disease burden of foodborne pathogens in the Netherlands, 2009. Int J Food Microbiol. 2012;156:231–238. pmid:22541392 View Article PubMed/NCBI Google Scholar 31. Ford L, Glass K, Kirk M, Hall K. The burden of sequelae due to five pathogens acquired from contaminated food in Australia Circa 2010. Emerg Infect Dis. 2014;20:1865–1871. pmid:25340885 View Article PubMed/NCBI Google Scholar 32. Kirk M, Ford L, Glass K, Hall K. Foodborne illness, Australia, circa 2000 and circa 2010. Emerg Infect Dis. 2014;20:1857–1864. pmid:25340705 View Article PubMed/NCBI Google Scholar 33. Vaillant V, de Valk H, Baron E, Ancelle T, Colin P, Delmas MC, et al. Foodborne infections in France. Foodborne Pathog Dis. 2005;2:221–232. pmid:16156703 View Article PubMed/NCBI Google Scholar 34. Tam CC, Rodrigues LC, Viviani L, Dodds JP, Evans MR, Hunter PR, et al. Longitudinal study of infectious intestinal disease in the UK (IID2 study): incidence in the community and presenting to general practice. Gut. 2012;61:69–77. pmid:21708822 View Article PubMed/NCBI Google Scholar 35. Lake RJ, Cressey PJ, Campbell DM, Oakley E. Risk ranking for foodborne microbial hazards in New Zealand: burden of disease estimates. Risk Analysis. 2010;30:743–752. pmid:19645753 View Article PubMed/NCBI Google Scholar 36. Walker CL, Rudan I, Liu L, Nair H, Theodoratou E, Bhutta ZA, et al. Global burden of childhood pneumonia and diarrhoea. Lancet. 2013;381:1405–1416. pmid:23582727 View Article PubMed/NCBI Google Scholar 37. Global Health Observatory (GHO) data [Internet]. World Health Organization. [cited 6 June 2014]. Available: http://www.who.int/gho/en/ 38. McGrogan A, Madle GC, Seaman HE, de Vries CS. The epidemiology of Guillain-Barre syndrome worldwide. A systematic literature review. Neuroepidemiology. 2009;32:150–163. pmid:19088488 View Article PubMed/NCBI Google Scholar 39. Poropatich KO, Walker CL, Black RE. Quantifying the association between Campylobacter infection and Guillain-Barre syndrome: a systematic review. J Health Popul Nutr. 2010;28:545–552. pmid:21261199 View Article PubMed/NCBI Google Scholar 40. Crump JA, Luby SP, Mintz ED. The global burden of typhoid fever. Bulletin World Health Organization. 2004;82:346–353. View Article Google Scholar 41. Biggs HM, Lester R, Nadjm B, Mtove G, Todd JE, Kinabo GD, et al. Invasive Salmonella infections in areas of high and low malaria transmission intensity in Tanzania. Clin Infect Dis. 2014;58:638–647. pmid:24336909 View Article PubMed/NCBI https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 12/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… Google Scholar 42. Mtove G, Amos B, von Seidlein L, Hendriksen I, Mwambuli A, Kimera J, et al. Invasive salmonellosis among children admitted to a rural Tanzanian hospital and a comparison with previous studies. PLoS ONE. 2010;5:e9244. pmid:20168998 View Article PubMed/NCBI Google Scholar 43. Koch K, Kristensen B, Holt HM, Ethelberg S, Mølbak K, Schønheyder HC. International travel and the risk of hospitalization with non-typhoidal Salmonella bacteremia. A Danish population-based cohort study, 1999–2008. BMC Infect Dis. 2011;11:277. pmid:22011371 PubMed Central PMCID: PMC3206861. View Article PubMed/NCBI Google Scholar 44. World Animal Health Information System (WAHIS) [Internet]. 2012. Available: http://www.oie.int/wahis_2/public/index.php/home 45. Anon. Global tuberculosis report 2013. WHO/HTM/TB/201311. Geneva: The World Health Organization; 2014. 46. Hald T, Aspinall W, Devleesschauwer B, Cooke R, Corrigan T, Havelaar AH, et al. (2015) World Health Organization estimates of the relative contributions of food to the burden of disease due to selected foodborne hazards: a structured expert elicitation. PLoS ONE. In Press. View Article Google Scholar 47. Salomon JA, Vos T, Hogan DR, Gagnon M, Naghavi M, Mokdad A, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. Lancet. 2012;380:2129–2143. pmid:23245605 View Article PubMed/NCBI Google Scholar 48. Country-level population data for 2010 [Internet]. United Nations. [cited 14 August 2014]. Available: http://esa.un.org/wpp/ 49. Kotloff KL, Nataro JP, Blackwelder WC, Nasrin D, Farag TH, Panchalingam S, et al. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. Lancet. 2013;382:209–222. pmid:23680352 View Article PubMed/NCBI Google Scholar 50. Hall AJ. Estimating the global burden of foodborne norovirus. Foodborne Disease Epidemiology Reference Group meeting; Geneva, Switzerland: World Health Organization; 2013. 51. Crump JA, Heyderman RS. Invasive Salmonella infections in Africa. T Roy Soc Trop Med H. 2014;108:673–675. View Article Google Scholar 52. Feasey NA, Dougan G, Kingsley RA, Heyderman RS, Gordon MA. Invasive non-typhoidal salmonella disease: an emerging and neglected tropical disease in Africa. Lancet. 2012;379:2489–2499. pmid:22587967 View Article PubMed/NCBI Google Scholar 53. Majowicz SE, Musto J, Scallan E, Angulo FJ, Kirk M, O'Brien SJ, et al. The global burden of nontyphoidal Salmonella gastroenteritis. Clin Infect Dis. 2010;50:882–889. pmid:20158401 View Article PubMed/NCBI Google Scholar 54. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2197–2223. pmid:23245608 View Article PubMed/NCBI Google Scholar 55. Ram PK, Crump JA, Gupta SK, Miller MA, Mintz ED. Part II. Analysis of data gaps pertaining to Shigella infections in low and medium human development index countries, 1984–2005. Epidemiol Infect. 2008;136:577–603. pmid:17686195 View Article PubMed/NCBI Google Scholar 56. Crump JA. Updating and refining estimates of typhoid fever burden for public health action. Lancet Global Health. 2014;2:e551–553. pmid:25304622 View Article PubMed/NCBI Google Scholar https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 13/14 9/22/2018 World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseas… 57. Plass D, Mangen MJ, Kraemer A, Pinheiro P, Gilsdorf A, Krause G, et al. The disease burden of hepatitis B, influenza, measles and salmonellosis in Germany: first results of the Burden of Communicable Diseases in Europe Study. Epidemiol Infect. 2014:1–12. View Article Google Scholar 58. Pires SM. Assessing the applicability of currently available methods for attributing foodborne disease to sources, including food and food commodities. Foodborne Pathog Dis. 2013;10:206–213. pmid:23489045 View Article PubMed/NCBI Google Scholar 59. Vally HG, K., Ford L, Hall G, Kirk M, Shadbolt C, Veitch M, et al. The proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia: an expert elicitation Foodborne Pathog Dis. 2014;11:727–733. pmid:25072416 View Article PubMed/NCBI Google Scholar 60. Ravel A, Davidson VJ, Ruzante JM, Fazil A. Foodborne proportion of gastrointestinal illness: estimates from a Canadian expert elicitation survey. Foodborne Pathog Dis. 2010;7:1463–1472. pmid:20704505 View Article PubMed/NCBI Google Scholar 61. Pires SM, Vieira AR, Hald T, Cole D. Source Attribution of Human Salmonellosis: An Overview of Methods and Estimates. Foodborne Pathog Dis. 2014. View Article Google Scholar https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001921 14/14
MINIREVIEW Advances in Laboratory Methods for Detection and Typing of Norovirus Jan Vinjé Division of Viral Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA N orovirus is a good example of a pathogen where improved diagnostics has increased its recognition from a relatively unknown virus before the mid-1990s to the leading cause of epidemic and sporadic gastroenteritis in people of all ages worldwide (1, 2). The majority of norovirus outbreaks occur in health care settings (including long-term care facilities and hospitals), where the virus is predominantly spread from person to person. In addition, noroviruses have also been identified in over 58% of the reported foodborne outbreaks in which an etiologic agent was determined (3). In the most recent disease burden estimates in the United States (US), norovirus causes 570 to 800 deaths, 56,000 to 71,000 hospitalizations, 400,000 emergency room visits, and 1.7 to 1.9 million outpatient visits annually (4). In pediatric populations in industrialized countries where a rotavirus vaccine has been introduced, noroviruses are rapidly replacing rotavirus as the most common cause of medically attended acute gastroenteritis (2, 5). After an incubation period of 12 to 48 h, norovirus illness is characterized by projectile vomiting, nonbloody diarrhea, nausea, abdominal cramps, and low-grade fever. Some persons might experience only vomiting or diarrhea. In healthy individuals, the duration of symptoms is usually not longer than 48 h, and the disease is self-limiting in most patients. However, young children and the elderly are at increased risk for more-severe and prolonged illness leading to hospitalization, while the disease is increasingly recognized as an important cause of chronic gastroenteritis for immunocompromised patients (6). In countries that belong to temperate latitudes, most infections occur in the fall and winter and at least 70% of outbreaks are reported in semiclosed communities such as long-term-care facilities, schools, hospitals, and cruise ships. Noroviruses can infect humans via multiple routes, including the oral route, transmitted through contact with fecal matter or aerosolized vomitus from infected people, as well as contaminated surfaces, food, or water. Upon infection, noroviruses replicate in cells in the upper small intestinal tract (duodenum and upper jejunum), leading to both epithelial barrier and secretory pathway dysfunction. T cells are likely required for virus clearance from the intestine, and, as was reported in a case study of an immunocompromised patient, after more than 1 year of chronic norovirus diarrhea, increasing levels of T cells were associated with resolution of symptoms (7). Outside the human host, the virus is environmentally stable and has an estimated 50% human infectious dose (HID50) ranging from 18 to 1,015 genome February 2015 Volume 53 Number 2 equivalents, although a recent study estimated that the HID50 is more similar to those of other RNA viruses (1,320 to 2,800 particles) (8). This article reviews methods based on antigen and molecular detection of human noroviruses. Although other molecular methods such as those employing isothermal amplification (nucleic acid sequence-based amplification [NASBA], loop-mediated isothermal amplification [LAMP]) and microarrays have been described, this review focuses on immunological and reverse transcription-PCR (RT-PCR)-based molecular methods. Noroviruses are a group of nonenveloped single-stranded positive-sense RNA viruses classified in the family Caliciviridae. The virus particles are 27 to 40 nm in diameter, and the genome is 7.5 to 7.7 kb in length and, except for murine norovirus, contains 3 open reading frames (ORF1, ORF2, and ORF3). ORF1 encodes a polyprotein that is posttranslationally cleaved into seven nonstructural mature proteins (NS1 to NS7) that are involved in viral replication. ORF2 encodes the major structural protein (VP1) of approximately 60,000 Da, and ORF3 encodes a minor structural protein (VP2). The viral capsid contains 90 dimers of VP1 and a few copies of VP2. X-ray crystallographic structure studies using Norwalk virus-like particles have revealed that the VP1 has a shell (S) and a protruding (P) domain (9). The S domain surrounds the viral RNA, and the P domain, which is linked to the S domain through a flexible hinge, corresponds to the C-terminal part of the VP1. The P domain is further divided into P1 and the highly variable P2 subdomain which contains the putative neutralization sites and interacts with histoblood group antigens (HBGAs). VP2 is located interior to the virus particle and is most likely involved in capsid assembly and genome encapsidation (10). Except for murine strains, noroviruses cannot be cultivated in vitro, which prevents their classification into distinct serotypes. Therefore, they are genetically classified into 6 established geno- Accepted manuscript posted online 2 July 2014 Citation Vinjé J. 2015. Advances in laboratory methods for detection and typing of norovirus. J Clin Microbiol 53:373–381. doi:10.1128/JCM.01535-14. Editor: G. V. Doern Address correspondence to jvinje@cdc.gov. Copyright © 2015, American Society for Microbiology. All Rights Reserved. doi:10.1128/JCM.01535-14 Journal of Clinical Microbiology jcm.asm.org 373 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest Human noroviruses are the leading cause of epidemic and sporadic gastroenteritis across all age groups. Although the disease is usually self-limiting, in the United States norovirus gastroenteritis causes an estimated 56,000 to 71,000 hospitalizations and 570 to 800 deaths each year. This minireview describes the latest data on laboratory methods (molecular, immunological) for norovirus detection, including real-time reverse transcription-quantitative PCR (RT-qPCR) and commercially available immunological assays as well as the latest FDA-cleared multi-gastrointestinal-pathogen platforms. In addition, an overview is provided on the latest nomenclature and molecular epidemiology of human noroviruses. Minireview phylogenetic tree, capsid sequences from 105 strains representing the spatial and temporal sequence diversity of noroviruses from diverse geographic regions across the world were selected. Viruses belonging to GI, GII, and GIV infect humans, except GII.11, GII.18, and GII.19 viruses, which infect porcine species, and GIV.2 viruses, which infect canine species. GII.15 viruses, which have been detected only in humans, form a tentative new genogroup (dotted circle). GIII viruses infect cows and sheep, GIV.2 infects canines, GV.1 and GV.2 infect mice and rats, respectively, and GVI and GVII infect canine species. GII.4 viruses (arrow) are responsible for the majority of norovirus infections worldwide. The scale bar reflects the number of amino acid substitutions per site. groups (GI to GVI) (11), while tentative genogroup VII is proposed in this paper (12) (Fig. 1). GI and GII viruses are responsible for the majority of disease in humans, whereas GIV viruses are rarely detected as the cause of epidemic or sporadic disease. Based on the most recent phylogenetic analysis, GII.15 viruses may need to be reclassified as a separate genogroup, but this would need consensus approval from the international norovirus working group (13). Each genogroup is based on phylogenetic clustering of the complete VP1 amino acid sequence and is further divided into genotypes (13, 14) (Fig. 1). To date, nine capsid genotypes have been recognized in GI and 22 in GII, and three genotypes of GII (GII.11, GII.18, and GII.19) have been uniquely detected in swine. GIV viruses consist of 2 genotypes, of which GIV.1 has been detected in humans and GIV.2 in feline and canine species (15). GII viruses are most frequently detected (89%), whereas GI viruses, which include virus of the GI.1 prototype Norwalk virus strain, cause approximately 11% of the outbreaks (16). 374 jcm.asm.org Despite the extensive genetic diversity among noroviruses, viruses from a single genotype, GII.4, are responsible for the majority of the norovirus outbreaks worldwide (17). Due to epochal evolution, novel pandemic GII.4 variants have emerged every 2 to 3 years since the mid-1990s, replacing previous predominant GII.4 strains but not other endemic strains (17). These global GII.4 variant strains include the GII.4 US95/96 strain in 1995, GII.4 Farmington Hills in 2002, GII.4 Hunter in 2004, GII.4 Den Haag in 2006, GII.4 New Orleans in 2009, and GII.4 Sydney in 2012. These new GII.4 variants are often, but not always, associated with an increase in the number of outbreaks (18). In the United States, GII.4 Sydney has continued to cause the majority of the norovirus outbreaks during the 2013-to-2014 season (unpublished data). Several mechanisms that could explain the evolution of GII.4 viruses have been proposed, including the host herd immunity that drives antigenic variation in the hypervariable P2 domain of VP1. This domain of the viral capsid binds HBGAs, which serve as Journal of Clinical Microbiology February 2015 Volume 53 Number 2 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest FIG 1 Classification of noroviruses into 7 genogroups (GI to GVII) based on amino acid sequence diversity in the complete VP1 capsid protein. To build the Minireview TABLE 1 Overview laboratory assays for detection of norovirus Time (sample to result) Laboratory test(s) Advantage Disadvantage Electron microscopy Ability to detect multiple viral pathogens Expensive equipment and training; low throughput; insensitive 15 min High specificity, high throughput 57–76% sensitivity 60–90 min High specificity, no special equipment Single sample can be tested 35–52% sensitivity 15 min PCR amplicons can be sequenced and used for typing Results must to be confirmed by sequencing or hybridization PCR equipment required; reduced clinical specificity 5–6 ha 3 ha Tests in pipeline Public health, clinical laboratories Expensive equipment and kit format 5 ha Luminex Corporation Expensive equipment and kit format 2h Biofire Diagnostics Inc.; Nanosphere Inc.; tests from other companies pending 510k clearance Public health, clinical laboratories Clinical laboratories Immunological Enzyme immunoassay Immunochromatographic Real-time RT-PCR Molecular multiple enteric pathogen xTAG GPP FilmArray GI Panel, Verigene Enteric Pathogens Test a High specificity, sensitivity and throughput; possibility to multiplex mulitple targets High sensitivity, high throughput; detects 11 different enteric pathogens Includes nucleic acid extraction; detects 23 (FilmArray) and 9 (Verigene) different enteric pathogens; single sample can be tested Market Reference laboratories R-Biopharm Public health, clinical laboratories Point of care Reference laboratories Without nucleic acid extraction. cell attachment factors for noroviruses (19). Expression of HBGAs on cell surfaces is affected by the ABO, Secretor, and Lewis genotypes. Because GII.4 viruses can bind a wider range of HBGAs than other genotypes, they are able to infect a larger susceptible population. Another mechanism which may explain the emergence of new variants is homologous recombination, with most breakpoints identified in the ORF1-ORF2 junction region. Intergenotype and intragenotype recombination is also widespread, suggesting that both escape from herd immunity and recombination are important factors that drive the emergence of novel GII.4 viruses (20). DIAGNOSTIC METHODS Although norovirus can be detected in rectal swabs and vomitus, whole-stool samples are the preferred clinical specimen for the detection of norovirus because they contain a higher quantity of virus. Until the cloning and sequencing of the Norwalk virus genome in 1990 (21) followed by the development and application of the first RT-PCR assays for norovirus, electron microscopy (EM) was the only method to detect the virus. Initially named Norwalk-like viruses or small-round structured viruses, based on their morphology in EM, this group of viruses is now officially known as noroviruses, with Norwalk virus as its prototype. Although EM can also visualize other established gastroenteritis viruses such as rotaviruses, adenoviruses, astroviruses, and sapovi- February 2015 Volume 53 Number 2 ruses, the method is costly and insensitive and therefore not widely available in diagnostic microbiology laboratories. Because the rapid spread of norovirus is a major public health issue, rapid laboratory diagnosis is essential to assist implementation of appropriate control measures to reduce the spread of the virus and the magnitude of outbreaks. Hence, a simple rapid norovirus test would be an attractive alternative to more technically demanding assays such as enzyme immunoassays (EIAs) and reverse transcriptase PCR (Table 1). Immunochromatographic (ICG) lateral flow assays do not require specialized laboratory equipment and are designed for rapid (15-min) testing of individual samples. In a recent evaluation of 4 norovirus ICG tests, using a comprehensive panel of a wide variety of norovirus genotypes, the specificity of all tests was 100%. However, the overall sensitivity ranged from 35% to 52% and was strongly genogroup dependent, as the sensitivities ranged from 17% to 52% for GI strains to 59% to 78% for the predominant GII.4 viruses (22). These results were significantly lower than the sensitivities reported by other investigators as well as by the different manufacturers of the ICG kits, suggesting that robust evaluation of norovirus test requires validation with a norovirus stool panel that includes a wide variety of different GI and GII genotypes (22). The development of a broadly reactive EIA for noroviruses has been challenging because of the number of antigenically distinct Journal of Clinical Microbiology jcm.asm.org 375 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest Molecular Conventional RT-PCR FDA (510k)-cleared test jcm.asm.org Journal of Clinical Microbiology February 2015 Volume 53 Number 2 Region C/region D genotypeb GI.1 GI.2 GI.3a GI.3b GI.3c GI.3d GI.4 GI.5a GI.5b GI 6a GI.6b GI.7a GI.7b GI.7c GI.8 GI.9 GII.1 GII.2 GII.3a GII.3b GII.3c GII.4 GII.4 New Orleans GII.4 Apeldoorn GII.4 NSW001P GII.4 Sydney GII.5 GII.6a GII.6b GII.6c GII.7 GII.8 GII.9 GII.10 GI.1 GI.2 GI.3 GI.4 GI.5 GI.6 GI.7 GI.8 GI.9 GII.1 GII.2 GII.3 GII.4 Bristol GII.4 New Orleans GII.4 Sydney GII.5 GII.6 GII.7 GII.8 GII.9 GII.10 GII/Hu/GB/1990/GII.7/Leeds GII/Hu/NL/1998/GII.8/Amsterdam GII/Hu/US/1997/GII.9/VA97207 GII/Hu/DE/2000/GII.10/Erfurt546 GII/Hu/GB/1990/GII.6/Seacroft GII/Hu/US/1994/GII.6/Miami292 GII/Hu/JP/2008/GII.6/Shizuoka GII/Hu/AU/2012/GII.4/Sydney GII/Hu/GB/1990/GII.5/Hillingdon GII/Hu/US/2009/GII.4/NewOrleans GII/Hu/NL/2007/GII.4/Apeldoorn GII/Hu/AU/2008/GII.4/NSW001P GII/Hu/GB/1993/GII.4/Bristol GII/Hu/CA/1991/GII.3/Toronto GII/Hu/AR/1999/GII.3/Arg320 GII/Hu/NL/2006/GII.3/Rotterdam GI/Hu/US/2001/GI.8/Boxer GI/Hu/CA/2004/GI.9/Vancouver730 GII/Hu/US/1971/GII.1/Hawaii GII/Hu/GB/1994/GII.2/Melksham GI/Hu/GB/1994/GI.7/Winchester GI/Hu/US/2010/GI.7/Providence GI/Hu/JP/2003/GI.7/Chiba030100 GI/Hu/DE/1997/GI.6/BS5(Hesse) GI/Hu/GB/1995/GI.6/Sindlesham GI/Hu/GB/1989/GI.5/Musgrove GI/Hu/JP/1999/GI.5/SzUG1 GI/Hu/JP/1987/GI.4/Chiba407 GI/Hu/SA/1990/GI.3/DesertShield395 GI/Hu/NO/1995/GI.3/Stavanger GI/Hu/JP/1979/GI.3/Otofuke GI/Hu/JP/1999/GI.3/Akabane GI/Hu/US/1968/GI.1/Norwalk GI/Hu/GB/1991/GI.2/Southampton Reference strainc AJ277608 AF195848 AY038599 AF427118 AJ277620 AF414410 HM633213 JX459908 AJ277607 GU445325 AB445395 GQ845367 X76716 U02030 AF190817 AB385626 AF538679 HQ637267 U07611 X81879 AJ277609 JN899243 AJ844469 AJ277615 AF093797 AJ277614 AB039774 AB042808 U04469 AF145709 AB187514 EF547396 M87661 L07418 GenBank accession no. GII.P22 GII.Pa GII.Pc GII.Pe GII.P18 GII.P20 GII.P21 GII.P15 GII.P16 GII.P11 GII.P12 GII.P13 GII.P8 GII.P5 GII.P6 GII.P7 GII.P1 GII.P2 GII.P3 GII.P4 GI.Pc GI.Pd GI.Pf GI.Pa GI.Pb GI.P8 GI.P9 GI.P7 GI.P3 GI.P4 GI.P5 GI.P6 GI.P1 GI.P2 POL genotype AB360387 AY772730 AY823304 EU424333 AY682549 AB212306 AB190457 AY134748 AB434770 GII/Po/US/2003/GII.P18/OH-QW101 GII/Hu/GE/2005/GII.P20/Leverkusen267 GII/Hu/FR/2004/GII.P21/Pont de Roide673 GII/Hu/JP/2003/GII.P22/Hokkaido133 GII/Hu/JP/2004/GII.Pa/SN2000JA GII/Hu/US/1976/GII.Pc/SnowMountain GII/Hu/JP/2007/GII.Pe/OC07138 AB074893 AB220922 EF529741 AB039780 AF397156 AB039778 AB039777 U07611 X81879 U02030 X76716 AB039774 EF529738 AB187514 U04469 AB081723 GU299761 EF529737 JN603251 AY038598 AB042808 EU007765d AF093797 M87661 L07418 GenBank accession no. GII/Hu/JP/2006/GII.P15/Hiroshima66 GII/Hu/DE/2000/GII.P16/Neustrelitz260 GII/Po/US/1997/GII.P11/Sw918 GII/Hu/JP/2005/GII.P12/Sakai/04-179 GII/Hu/FR/2004/GII.P13/Briancon870 GII/Hu/JP/2002/GII.P8/Saitama U25 GII/Hu/HU/1999/GII.P5/MOH GII/Hu/JP/2002/GII.P6/Saitama U16 GII/Hu/JP/2002/GII.P7/Saitama U4 GII/Hu/US/1971/GII.P1/Hawaii GII/Hu/GB/1994/GII.P2/Melksham GII/Hu/CA/1991/GII.P3/Toronto GII/Hu/GB/1993/GII.P4/Bristol GI/Hu/JP/2000/GI.Pc/SzUG1 GI/Hu/FR/2003/GI.Pd/Vesoul576 GI/Hu/JP/1979/GI.Pf/Otofuke GI/Hu/SA/1990/GI.Pa/DesertShield GI/Hu/JP/2002/GI.Pb/WUG1 GI/Hu/US/2008/GI.P8/890321 GI/Hu/FR/2004/GI.P9/Chatellerault709 GI/Hu/SE/2008/GI.P7/Lilla Edet GI/Hu/US/1998/GI.P3/VA98115 GI/Hu/JP/1987/GI.P4/Chiba407 GI/Hu/SE/2005/GI.P5/07_1 GI/Hu/DE/1997/GI.P6/BS5(Hesse) GI/Hu/US/1968/GI.P1/Norwalk GI/Hu/GB/1991/GI.P2/Southampton Reference strain Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest 376 VP1 genotypea TABLE 2 Norovirus genogroups and genotypes as determined by phylogeny-based cluster analysis of capsid protein VP1, partial capsid nucleotide region (region C or region D), and RNA polymerase region (POL genotype) Minireview Journal of Clinical Microbiology N/A N/A N/A GVI.2 GVII GVII/Ca/HK/2007/GVII/026F GVI/Ca/PT/2007/GVI.2/Viseu GVI/Ca/IT/2007/GVI.1/Bari91 GV/Rn/HK/2011/GV.2/HKU_CT2 GV/Mu/US/2002/GV.1/MNV-1 GIV/Ca/IT/2006/GIV.2/Pistoia GIV/Hu/NL/1998/GIV.1/Alphatron GIII/Ov/NZ/2007/GIII.3/Norsewood30 GIII/Bo/GB/1976/GIII.2/Newbury GIII/Bo/DE/1980/GIII.1/Jena GII/Hu/JP/2003/GII.22/Yuri GII/Hu/IQ/2002/GII.21/IF1998 GII/Hu/DE/2002/GII.20/Luckenwalde591 GII/Po/US/2003/GII.19/OH-QW170 GII/Po/US/2003/GII.18/OH-QW101 AY113106 FJ692500 GQ443611 FJ875027 JX486101 AY228235 EF450827 AF195847 EU193658 AF097917 AJ011099 AB083780 AY675554 EU373815 AY823306 AY823304 AY502009 AY502010 AY130762 AY130761 GII.Pf GII.Pn GII.Pm GII.Pk GII.Pj GII.Ph GII.Pg GII/Hu/FR/1999/GII.Pf/S63 GII/Hu/CN/2007/GII.Pn/Beijing53931 GII/Hu/IN/2006/GII.Pm/PunePC24 GII/Hu/JP/1996/GII.Pk/OC96065 GII/Hu/GR/1997/GII.Pj/E3 GII/Hu/JP/1997/GII.Ph/OC97007 GII/Hu/AU/1983/GII.Pg/Goulburn Valley AY682550 GQ856469 EU921353 AF315813 AY682552 AB089882 DQ379714 b Genotypes based on phylogenetic clustering of complete VP1 amino acids (13). Genotypes based on partial capsid sequences (region C and region D) as used by CaliciNet surveillance network (27). N/A, not available. c Country abbreviations are as follows: AR, Argentina; AU, Australia; CA, Canada; CN, China; DE, Germany; FR, France; GB, United Kingdom; GR, Greece; HK, Hong Kong; HU, Hungary; IQ, Iraq; IN, India; JP, Japan; NL, Netherlands; NZ, New Zealand; NO, Norway; PT, Portugal; SA, Saudi Arabia; SE, Sweden; US, United States. d This strain is 100% identical to the actual reference strain, which is pending GenBank submission. a N/A N/A GIII.3 GVI.1 N/A GIII.2 GV.2 N/A GIII.1 N/A GII.22 GII.22 GV.1 GII.21 GII.21 N/A GII.20 GII.20 N/A GII.19 GII.19 GIV.2 GII.18 GII.18 GII/Hu/US/2002/GII.17/CS-E1 AB074893 AJ277618 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest February 2015 Volume 53 Number 2 GIV.1 GII.17 GII.17 GII/Hu/US/1999/GII.16/Tiffin GII/Hu/US/1999/GII.15/J23 GII.15 GII.16 GII.15 GII.16 GII/Hu/US/1998/GII.13/Fayetteville GII/Hu/US/1999/GII.14/M7 GII.13 GII.14 GII.13 GII.14 GII/Po/JP/1997/GII.11/Sw918 GII/Hu/GB/1990/GII.12/Wortley GII.11 GII.12 GII.11 GII.12 Minireview jcm.asm.org 377 Minireview 378 jcm.asm.org study in the United Kingdom, higher viral loads were found in norovirus-positive cases than in asymptomatic controls and a clinically significant cutoff value of 31 cycles for all ages resulted in a sensitivity of 72% (31). However, in other studies, including studies in low-income countries, norovirus was as commonly detected in stools from cases with moderate to severe diarrhea as in those from healthy controls and was present in similar viral loads (reference 32 and unpublished data). This makes interpretation of positive RT-qPCR results, particularly those determined from samples with low viral loads (high threshold cycle [CT] values), a challenge. Additional data from studies of considerable sample size are required to determine robust CT cutoff values to interpret norovirus RT-qPCR results. Such cutoff values may depend on variables such as the sample collection date, PCR platform, reagent or kit used, and study population (e.g., outbreak versus sporadic samples). Alternatively, data from outbreak studies in which multiple samples have been collected from norovirus-positive patients after they have become asymptomatic may help in establishing a clinically relevant cutoff value. In recent years, several different multi-gastrointestinal-pathogen diagnostic platforms have been developed for the simultaneous detection of pathogenic enteric viruses, bacteria, and parasites (Table 1). The xTAG GPP (Luminex Corporation, Toronto, Canada), FilmArray GI Panel (BioFire Diagnostics Inc., Salt Lake City, UT, USA), and Verigene Enteric Pathogens Test (EP) (Nanosphere, Northbrook, IL, USA) platforms have recently been FDA cleared and currently provide the most comprehensive commercial multiplex molecular diagnostic tests available for gastroenteritis diagnosis. The FDA-cleared version of the xTAG GPP platform simultaneously detects and identifies norovirus GI and GII, rotavirus group A, 7 bacterial species, and 2 parasite species (33), while the FilmArray GI Panel detects 23 enteric pathogens, including norovirus GI and GII, rotavirus group A, group F adenovirus, sapovirus, and astrovirus, 14 bacterial species, and 4 parasite species. The Verigene EP assay detects 5 bacterial species, 2 Shiga toxins, rotavirus, and norovirus. The Biofire and Luminex platforms are able to distinguish between GI and GII noroviruses. However, there are significant differences between these tests, including workflow and throughput differences (Table 1). The xTAG GPP can complete testing of 24 samples within 5 h, but this does not include preparation and extraction of the samples. In contrast, the FilmArray and Verigene systems have a turnaround time from unprocessed sample to results of 2 h, with minimal hands-on time. The drawback of the FilmArray and Verigene systems in the current formats is their low throughput, as only a single sample can be processed on the instrument at one time, which may not be an issue for clinical laboratories but limits the overall utility of the test in laboratories with moderate to high throughput. Another challenge of these multipathogen systems is that of data interpretation, specifically with high numbers of mixed infections and the lack of quantitative data to determine which pathogen is responsible for the gastrointestinal disease (34). If no laboratory diagnosis can be performed (e.g., when no specimens are available for testing), norovirus infections can also be detected on the basis of the clinical and epidemiological profile, which has been used successfully to differentiate norovirus causing gastroenteritis from other causes of enteric disease. These Kaplan criteria (35) are based on (i) a mean duration of illness of between 12 and 60 h, (ii) a mean incubation period of 24 to 48 h, (iii) vomiting in ⬎50% of patients, and (iv) the absence of bacte- Journal of Clinical Microbiology February 2015 Volume 53 Number 2 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest humans norovirus genotypes (n ⫽ 29) and the antigenic drift of certain strains (e.g., GII.4) over time. Although genogroup-specific monoclonal antibodies have been described, most commercial kits, including the IDEIA Norovirus EIA (Oxoid, Hampshire, United Kingdom), SRSV (II)-AD (Denka Seiken Co. Ltd., Tokyo, Japan), and RIDASCREEN (r-Biopharm AG, Darmstadt, Germany), include combinations of several cross-reactive monoclonal and polyclonal antibodies. The sensitivity of these kits is typically ⬍70%, while the specificity is usually ⬎90%, depending on the diagnostic goal (outbreak or sporadic cases), the number of samples tested per outbreak, and the time after the onset of symptoms that clinical samples were collected. The general scientific consensus is that EIA may be useful for rapid screening of multiple fecal samples collected during an outbreak of acute gastroenteritis for norovirus but, because of the low sensitivity, caution should be exercised in interpreting test results from sporadic cases (23). In the mid-1990s, the first conventional or endpoint RT-PCR assays were developed, targeting a relatively conserved small region of the RNA polymerase (POL) gene in ORF1 (region A). With the increasing number of sequences that became available during those early years, those assays were rapidly replaced by second-generation assays that proved to be more broadly reactive and able to detect the majority of the circulating norovirus strains. One of those early assays is, in a slightly modified format, still being used successfully for the detection and typing of noroviruses (24). Increased specificity and sensitivity are accomplished by the use of real-time RT-quantitative PCR assays (RT-qPCR) that do not require agarose gel analysis and subsequent confirmation and, in most protocols, use fluorescently labeled oligonucleotide probes. One-step RT-qPCR assays, in which both reverse transcription and cDNA amplification are performed in a single reaction, require less sample handling and therefore decrease the risk of cross-contamination, making them a preferred format in clinical laboratories. Because only one small region of the norovirus genome is sufficiently conserved for the development of genogroup-specific oligonucleotide primers and probes (25), most of the reported norovirus RT-qPCR assays target this ORF1-ORF2 junction region (26, 27). And although no commercial standalone norovirus RT-qPCR assay has yet been FDA cleared, in recent years these assays have become the gold standard for the rapid and sensitive detection of norovirus in clinical (stool, vomitus, serum) samples as well as in food, water, and environmental samples. Increasingly, RT-qPCR assays include an internal extraction control to reduce false-negative results and are able to simultaneously detect GI and GII strains (28) or GI, GII, and GIV strains (29). In addition to the high analytical sensitivity, RT-qPCR assays can also be used to determine the amount of nucleic acid in a sample in a semiquantitative way as a proxy to determine the viral load. Patients with higher viral loads have been reported to excrete the virus longer, and data from several studies suggest that GII viruses (i.e., GII.4) are shed in larger amounts than GI viruses (30). A significant number of patients excrete the virus 3 weeks after clinical symptoms have disappeared, and noroviruses are also frequently detected in fecal samples from asymptomatic patients, in particular, in children under the age of 5. Hence, virus detection by RT-qPCR does not always correlate with clinical norovirus disease but assessment of a possible difference in viral load in samples from clinical and asymptomatic cases may be a helpful tool to assess a causal relationship with clinical symptoms. In a Minireview FIG 2 GII.4 norovirus variants with a global distribution and the first season in which they emerged. New GII.4 variants emerge approximately every 2 to 3 years and replace the previously predominant strains. They include US95_96 in 1995, Farmington Hills in 2002, Hunter in 2004, Yerseke in 2006, Den Haag in 2006, New Orleans in 2009, and Sydney in 2012. GENOTYPING Noroviruses are classified into genogroups and genotypes based on amino acid diversity in the complete VP1 protein, but, as recombination in the ORF1-ORF2 junction region is common and as some capsid genotypes seem to be more prone to recombination than others, a dual-nomenclature system has been proposed using both the RNA polymerase (POL) region in ORF1 and VP1 sequences (13) (Table 2). Currently, 9 genotypes in GI, 22 in GII, 2 in GIII, 2 in GIV, 1 in GV, 2 in GVI, and 1 in the tentative new GVII have been recognized on the basis of complete VP1 amino acids (Fig. 1). The nomenclature system includes information on genogroup, genotype, and, for GII.4 strains, variant type. For example, if both POL and capsid (CAP) sequences are known, the strain name should be written as follows: norovirus GII/Hu/US/ 2010/GII.P12-GII.12/HS206. When only CAP sequences are available, the strain should be written as follows: norovirus GII/ Hu/AU/2012/GII.4 Sydney/Melbourne456. Because sequencing of the complete VP1 gene is currently not a routine procedure, nucleotide sequences of relatively small regions of ORF1 (POL or region A) or ORF2 (CAP or regions C and D) of the norovirus genome are used to genotype strains. Region C assays are in general more robust because the lower (40°C) annealing temperature required for the region D assays increases the likelihood of nonspecific amplification and because region D is located in a more variable part of ORF2. As determined on the basis of nucleotide sequence diversity in region C and region D, several genotypes consist of up to 4 different subclusters (e.g., GI.3a to GI.3d); therefore, reference sequences representative of each subcluster are required for correct typing of these strains (Table 2). An online norovirus typing tool is available for both polymerase and capsid typing (36). GII viruses, in particular, GII.4 viruses, are responsible for the majority of the norovirus outbreaks in people of all ages worldwide, whereas GI strains are more often detected in foodborne and waterborne outbreaks. For example, the GI.6 virus that emerged in 2012 was more often associated with foodborne disease outbreaks than the GII.4 viruses, which are strongly associated with person-to-person transmission and outbreaks in health care settings, resulting in an increased risk of more-severe disease outcomes such as hospitalization and death compared to other GI and GII viruses (37). GII.4 viruses have an epidemiology different those of other GI and GII genotypes. Since the mid-1990s, 7 dif- February 2015 Volume 53 Number 2 ferent GII.4 variants have successively emerged every 2 to 3 years, replacing previous dominant variants, and most of them have produced global epidemics of gastroenteritis. The first reported GII.4 pandemic (caused by GII.4 US95_96) occurred in 1995, followed by the emergence of GII.4 Farmington Hills in 2002, GII.4 Hunter in 2004, GII.4 Yerseke and GII.4 Den Haag in 2006, GII.4 New Orleans in 2009, and GII.4 Sydney in 2012 (Fig. 2). Although media coverage often suggests otherwise, studies in the United States have shown that neither the emergence of GII.4 New Orleans in 2009 nor that of GII.4 Sydney in 2012 led to an increase in norovirus activity compared to previous years. These findings underscore the importance of conducting well-designed studies to better understand the contributions that individual genotypes may make to norovirus disease burden. Between 2009 and 2013, several non-GII.4 strains (GII.12, GII.1, GI.6) have emerged that cocirculated with the predominant GII.4 viruses and caused 11% to 15% of all outbreaks, but each strain did not circulate longer than one norovirus season (16). Genotype distribution in sporadic norovirus disease usually follows the same trends as in outbreaks (2), although rare genotypes are often reported in children under 5 years of age. Continuous norovirus outbreak surveillance through surveillance networks such as NoroNet and CaliciNet will be important to identify changing trends in genotype distribution and identify emerging new strains. FUTURE PERSPECTIVES Noroviruses are the leading cause of epidemic and sporadic cases of acute gastroenteritis worldwide and a leading cause of foodborne disease. Therefore, rapid laboratory diagnosis is a critical tool to guide controlling norovirus outbreaks by choosing the most appropriate intervention and control practices such as enhanced cleaning and disinfection protocols, isolation, grouping patients based on symptoms, exclusion of symptomatic staff members or food handlers, or, ultimately, closing of units in hospitals (38). Over the last decade, significant progress has been made in the development of diagnostic methods for the routine detection of human noroviruses. RT-qPCR assays have become the gold standard for norovirus detection in most public health and research laboratories and are increasingly commercially available. Continued improvement of rapid, sensitive, and broadly reactive point-of-care assays, such as ICG assays, will be required to allow simple and reliable norovirus diagnosis where no laboratory facilities are available. Use of the multi-gastrointestinal-pathogen molecular platforms that are now available for the rapid detection of a suite of different enteric pathogens, including norovirus, in a single sample will become routine in many clinical laboratories over the next couple of years. Journal of Clinical Microbiology jcm.asm.org 379 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest rial pathogens detected in stool specimens. The criteria are highly specific (99%) and moderately sensitive (68%) for foodborne outbreaks but may not be valid for hospital outbreaks, where the duration of symptoms can be longer than 72 h. Minireview ACKNOWLEDGMENTS Many thanks to my colleagues at CDC and the CaliciNet surveillance network for their continued efforts to improve our understanding of the public health burden of norovirus, to Everardo Vega for help with Fig. 1, and to Harry Vennema (RIVM) for sharing polymerase gene reference sequences used in NoroNet. The findings and conclusions in this article are mine and do not necessarily represent the official position of the Centers for Disease Control and Prevention. REFERENCES 1. Glass RI, Parashar UD, Estes MK. 2009. Norovirus gastroenteritis. N Engl J Med 361:1776 –1785. http://dx.doi.org/10.1056/NEJMra0804575. 2. Payne DC, Vinjé J, Szilagyi PG, Edwards KM, Staat MA, Weinberg GA, Hall CB, Chappell J, Bernstein DI, Curns AT, Wikswo M, Shirley SH, Hall AJ, Lopman B, Parashar UD. 2013. Norovirus and medically attended gastroenteritis in U.S. children. N Engl J Med 368:1121–1130. http: //dx.doi.org/10.1056/NEJMsa1206589. 3. Hall AJ, Wikswo ME, Manikonda K, Roberts VA, Yoder JS, Gould LH. 2013. Acute gastroenteritis surveillance through the National Outbreak Reporting System, United States. Emerg Infect Dis 19:1305–1309. http: //dx.doi.org/10.3201/eid1908.130482. 4. Hall AJ, Lopman BA, Payne DC, Patel MM, Gastañaduy PA, Vinjé J, Parashar UD. 2013. Norovirus disease in the United States. Emerg Infect Dis 19:1198 –1205. http://dx.doi.org/10.3201/eid1908.130465. 5. Koo HL, Neill FH, Estes MK, Munoz FM, Cameron A, Dupont HL, Atmar RL. 2013. Noroviruses: the most common pediatric viral enteric pathogen at a large university hospital after introduction of rotavirus vaccination. J Pediatric Infect Dis Soc 2:57– 60. http://dx.doi.org/10.1093 /jpids/pis070. 6. Bok K, Green KY. 2012. Norovirus gastroenteritis in immunocompromised patients. N Engl J Med 367:2126 –2132. http://dx.doi.org/10.1056 /NEJMra1207742. 7. Wingfield T, Gallimore CI, Xerry J, Gray JJ, Klapper P, Guiver M, Blanchard TJ. 2010. Chronic norovirus infection in an HIV-positive patient with persistent diarrhoea: a novel cause. J Clin Virol 49:219 –222. http://dx.doi.org/10.1016/j.jcv.2010.07.025. 8. Atmar RL, Opekun AR, Gilger MA, Estes MK, Crawford SE, Neill FH, Ramani S, Hill H, Ferreira J, Graham DY. 2014. Determination of the 50% human infectious dose for Norwalk virus. J Infect Dis 209:1016 – 1022. http://dx.doi.org/10.1093/infdis/jit620. 9. Prasad BV, Hardy ME, Dokland T, Bella J, Rossmann MG, Estes MK. 1999. X-ray crystallographic structure of the Norwalk virus capsid. Science 286:287–290. http://dx.doi.org/10.1126/science.286.5438.287. 380 jcm.asm.org 10. Vongpunsawad S, Venkataram Prasad BV, Estes MK. 2013. Norwalk virus minor capsid protein VP2 associates within the VP1 shell domain. J Virol 87:4818 – 4825. http://dx.doi.org/10.1128/JVI.03508-12. 11. Green K. 2013. Caliciviridae: the noroviruses, p 583– 609. In Knipe DM, Howley PM, Cohen JI, Griffin DE, Lamb RA, Martin MA, Racaniello VR, Roizman B (ed), Fields virology, 6th ed. Lippincott Williams & Wilkins, Philadelphia, PA. 12. Tse H, Lau SK, Chan WM, Choi GK, Woo PC, Yuen KY. 2012. Complete genome sequences of novel canine noroviruses in Hong Kong. J Virol 86:9531–9532. http://dx.doi.org/10.1128/JVI.01312-12. 13. Kroneman A, Vega E, Vennema H, Vinjé J, White PA, Hansman G, Green K, Martella V, Katayama K, Koopmans M. 2013. Proposal for a unified norovirus nomenclature and genotyping. Arch Virol 158:2059 – 2068. http://dx.doi.org/10.1007/s00705-013-1708-5. 14. Zheng DP, Ando T, Fankhauser RL, Beard RS, Glass RI, Monroe SS. 2006. Norovirus classification and proposed strain nomenclature. Virology 346:312–323. http://dx.doi.org/10.1016/j.virol.2005.11.015. 15. Martella V, Decaro N, Lorusso E, Radogna A, Moschidou P, Amorisco F, Lucente MS, Desario C, Mari V, Elia G, Banyai K, Carmichael LE, Buonavoglia C. 2009. Genetic heterogeneity and recombination in canine noroviruses. J Virol 83:11391–11396. http://dx.doi.org/10.1128/JVI .01385-09. 16. Vega E, Barclay L, Gregoricus N, Shirley SH, Lee D, Vinjé J. 2014. Genotypic and epidemiologic trends of norovirus outbreaks in the United States, 2009 to 2013. J Clin Microbiol 52:147–155. http://dx.doi.org/10 .1128/JCM.02680-13. 17. Siebenga JJ, Vennema H, Zheng DP, Vinjé J, Lee BE, Pang XL, Ho EC, Lim W, Choudekar A, Broor S, Halperin T, Rasool NB, Hewitt J, Greening GE, Jin M, Duan ZJ, Lucero Y, O’Ryan M, Hoehne M, Schreier E, Ratcliff RM, White PA, Iritani N, Reuter G, Koopmans M. 2009. Norovirus illness is a global problem: emergence and spread of norovirus GII.4 variants, 2001–2007. J Infect Dis 200:802– 812. http://dx.doi .org/10.1086/605127. 18. Leshem E, Wikswo M, Barclay L, Brandt E, Storm W, Salehi E, Desalvo T, Davis T, Saupe A, Dobbins G, Booth HA, Biggs C, Garman K, Woron AM, Parashar UD, Vinjé J, Hall AJ. 2013. Effects and clinical significance of GII.4 Sydney norovirus, United States, 2012–2013. Emerg Infect Dis 19:1231–1238. http://dx.doi.org/10.3201/eid1908.130458. 19. Lindesmith LC, Beltramello M, Donaldson EF, Corti D, Swanstrom J, Debbink K, Lanzavecchia A, Baric RS. 2012. Immunogenetic mechanisms driving norovirus GII.4 antigenic variation. PLoS Pathog 8:e1002705. http://dx.doi.org/10.1371/journal.ppat.1002705. 20. Eden JS, Tanaka MM, Boni MF, Rawlinson WD, White PA. 2013. Recombination within the pandemic norovirus GII.4 lineage. J Virol 87: 6270 – 6282. http://dx.doi.org/10.1128/JVI.03464-12. 21. Xi JN, Graham DY, Wang KN, Estes MK. 1990. Norwalk virus genome cloning and characterization. Science 250:1580 –1583. http://dx.doi.org /10.1126/science.2177224. 22. Ambert-Balay K, Pothier P. 2013. Evaluation of 4 immunochromatographic tests for rapid detection of norovirus in faecal samples. J Clin Virol 56:194 –198. http://dx.doi.org/10.1016/j.jcv.2012.11.001. 23. Costantini V, Grenz L, Fritzinger A, Lewis D, Biggs C, Hale A, Vinjé J. 2010. Diagnostic accuracy and analytical sensitivity of IDEIA Norovirus assay for routine screening of human norovirus. J Clin Microbiol 48: 2770 –2778. http://dx.doi.org/10.1128/JCM.00654-10. 24. Vennema H, de Bruin E, Koopmans M. 2002. Rational optimization of generic primers used for Norwalk-like virus detection by reverse transcriptase polymerase chain reaction. J Clin Virol 25:233–235. http://dx.doi .org/10.1016/S1386-6532(02)00126-9. 25. Katayama K, Shirato-Horikoshi H, Kojima S, Kageyama T, Oka T, Hoshino F, Fukushi S, Shinohara M, Uchida K, Suzuki Y, Gojobori T, Takeda N. 2002. Phylogenetic analysis of the complete genome of 18 Norwalk-like viruses. Virology 299:225–239. http://dx.doi.org/10.1006 /viro.2002.1568. 26. Kageyama T, Kojima S, Shinohara M, Uchida K, Fukushi S, Hoshino FB, Takeda N, Katayama K. 2003. Broadly reactive and highly sensitive assay for Norwalk-like viruses based on real-time quantitative reverse transcription-PCR. J Clin Microbiol 41:1548 –1557. http://dx.doi.org/10 .1128/JCM.41.4.1548-1557.2003. 27. Vega E, Barclay L, Gregoricus N, Williams K, Lee D, Vinjé J. 2011. Novel surveillance network for norovirus gastroenteritis outbreaks, United States. Emerg Infect Dis 17:1389 –1395. http://dx.doi.org/10.3201 /eid1708.101837. Journal of Clinical Microbiology February 2015 Volume 53 Number 2 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest Recent advances in nucleic acid sequencing technologies, such as “next-generation” sequencing (NGS), have opened new perspectives for research and diagnostic applications because of the high speed and throughput of data generation. NGS has been applied for the discovery of novel viruses and the characterization of viral communities as well as whole-viral-genome sequencing and detection of the viral genome variability of RNA viruses. Although challenges remain, including difficulties in sample preparation and high cost, NGS is a potentially powerful method for the rapid identification, and characterization of any infectious agent, including norovirus, directly from stool could assist in infection control of outbreaks. Genotyping of norovirus strains is important, as certain genotypes are more often associated with foodborne transmission whereas others (e.g., GII.4) have led to more-severe disease outcomes. Standardized genotyping as performed by surveillance networks such as CaliciNet and NoroNet will make it easier to identify new emerging strains or common-source outbreaks and provide useful information on the distribution of strains in different populations, which is important for norovirus vaccine formulations (39). Minireview 34. 35. 36. 37. 38. 39. tion of Luminex xTAG gastrointestinal pathogen analyte-specific reagents for high-throughput, simultaneous detection of bacteria, viruses, and parasites of clinical and public health importance. J Clin Microbiol 51:3018 – 3024. http://dx.doi.org/10.1128/JCM.00896-13. Khare R, Espy MJ, Cebelinski E, Boxrud D, Sloan LM, Cunningham SA, Pritt BS, Patel R, Binnicker MJ. 2014. Comparative evaluation of two commercial multiplex panels for detection of gastrointestinal pathogens by use of clinical stool specimens. J Clin Microbiol 52:3667–3673. http: //dx.doi.org/10.1128/JCM.01637-14. Kaplan JE, Gary GW, Baron RC, Singh N, Schonberger LB, Feldman R, Greenberg HB. 1982. Epidemiology of Norwalk gastroenteritis and the role of Norwalk virus in outbreaks of acute nonbacterial gastroenteritis. Ann Intern Med 96:756 –761. Kroneman A, Vennema H, Deforche K, v d Avoort H, Peñaranda S, Oberste MS, Vinjé J, Koopmans M. 2011. An automated genotyping tool for enteroviruses and noroviruses. J Clin Virol 51:121–125. http://dx.doi .org/10.1016/j.jcv.2011.03.006. Desai R, Hembree CD, Handel A, Matthews JE, Dickey BW, McDonald S, Hall AJ, Parashar UD, Leon JS, Lopman B. 2012. Severe outcomes are associated with genogroup 2 genotype 4 norovirus outbreaks: a systematic literature review. Clin Infect Dis 55:189 –193. http://dx.doi.org/10.1093 /cid/cis372. Barclay L, Park GW, Vega E, Hall A, Parashar U, Vinjé J, Lopman B. 2014. Infection control for norovirus. Clin Microbiol Infect 20:731–740. http://dx.doi.org/10.1111/1469-0691.12674. Debbink K, Lindesmith LC, Baric RS. 2014. The state of norovirus vaccines. Clin Infect Dis 58:1746 –1752. http://dx.doi.org/10.1093/cid/ciu120. Jan Vinjé, Ph.D., is head of the National Calicivirus Laboratory and Director of CaliciNet at the Centers for Disease Control and Prevention (CDC) in Atlanta, GA. Dr. Vinjé received his Ph.D. degree at the University of Utrecht, the Netherlands, in 1999. After receiving a postdoctoral fellowship and an appointment as research assistant professor at the University of North Carolina in Chapel Hill, he joined CDC in 2006. Over the past 10 years, he has served on several program advisory committees from several European research projects (FP6, FP7). He is serving as technical expert on the norovirus subcommittee of the National Advisory Committee on Microbiological Criteria for Foods and is a member of the International Committee on Taxonomy of Viruses study groups on Caliciviridae (chair as of 2014) and Astroviridae. He is currently a member of the editorial board of the Journal of Clinical Microbiology and associate editor of the journal Food and Environmental Virology, and he serves as an ad hoc reviewer for multiple high-impact journals. Dr. Vinjé has published over 100 peer-reviewed publications and several book chapters. His research interests include all aspects of viral gastrointestinal disease, including detection, characterization, and prevention and control of norovirus infections. February 2015 Volume 53 Number 2 Journal of Clinical Microbiology jcm.asm.org 381 Downloaded from http://jcm.asm.org/ on September 22, 2018 by guest 28. Rolfe KJ, Parmar S, Mururi D, Wreghitt TG, Jalal H, Zhang H, Curran MD. 2007. An internally controlled, one-step, real-time RT-PCR assay for norovirus detection and genogrouping. J Clin Virol 39:318 –321. http://dx .doi.org/10.1016/j.jcv.2007.05.005. 29. Miura T, Parnaudeau S, Grodzki M, Okabe S, Atmar RL, Le Guyader FS. 2013. Environmental detection of genogroup I, II, and IV noroviruses by using a generic real-time reverse transcription-PCR assay. Appl Environ Microbiol 79:6585– 6592. http://dx.doi.org/10.1128/AEM.02112-13. 30. Chan MC, Sung JJ, Lam RK, Chan PK, Lee NL, Lai RW, Leung WK. 2006. Fecal viral load and norovirus-associated gastroenteritis. Emerg Infect Dis 12:1278 –1280. http://dx.doi.org/10.3201/eid1208.060081. 31. Phillips G, Lopman B, Tam CC, Iturriza-Gomara M, Brown D, Gray J. 2009. Diagnosing norovirus-associated infectious intestinal disease using viral load. BMC Infect Dis 9:63. http://dx.doi.org/10.1186/1471-2334-9-63. 32. Kotloff KL, Nataro JP, Blackwelder WC, Nasrin D, Farag TH, Panchalingam S, Wu Y, Sow SO, Sur D, Breiman RF, Faruque AS, Zaidi AK, Saha D, Alonso PL, Tamboura B, Sanogo D, Onwuchekwa U, Manna B, Ramamurthy T, Kanungo S, Ochieng JB, Omore R, Oundo JO, Hossain A, Das SK, Ahmed S, Qureshi S, Quadri F, Adegbola RA, Antonio M, Hossain MJ, Akinsola A, Mandomando I, Nhampossa T, Acácio S, Biswas K, O’Reilly CE, Mintz ED, Berkeley LY, Muhsen K, Sommerfelt H, Robins-Browne RM, Levine MM. 2013. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. Lancet 382:209 –222. http://dx.doi.org/10.1016/S0140 -6736(13)60844-2. 33. Navidad JF, Griswold DJ, Gradus MS, Bhattacharyya S. 2013. Evalua-
Rapid communications Genetic analyses of GII.17 norovirus strains in diarrheal disease outbreaks from December 2014 to March 2015 in Japan reveal a novel polymerase sequence and amino acid substitutions in the capsid region Y Matsushima1,2, M Ishikawa1, T Shimizu1, A Komane1, S Kasuo3, M Shinohara4 , K Nagasawa5, H Kimura5, A Ryo2, N Okabe1, K Haga6, Y H Doan6, K Katayama6, H Shimizu (shimizu-h@city.kawasaki.jp)1 1. Division of Virology, Kawasaki City Institute for Public Health, Kanagawa, Japan 2. Department of Microbiology, Yokohama City University School of Medicine, Kanagawa, Japan 3. Division of Infectious Diseases, Nagano Environmental Conservation Research Institute, Nagano, Japan 4. Division of Virology, Saitama Institute of Public Health, Saitama, Japan 5. Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan 6. Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan Citation style for this article: Matsushima Y, Ishikawa M, Shimizu T, Komane A, Kasuo S, Shinohara M, Nagasawa K, Kimura H, Ryo A, Okabe N, Haga K, Doan YH, Katayama K, Shimizu H. Genetic analyses of GII.17 norovirus strains in diarrheal disease outbreaks from December 2014 to March 2015 in Japan reveal a novel polymerase sequence and amino acid substitutions in the capsid region. Euro Surveill. 2015;20(26):pii=21173. Available online: http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=21173 Article submitted on 03 June 2015 / published on 02 July 2015 A novel GII.P17-GII.17 variant norovirus emerged as a major cause of norovirus outbreaks from December 2014 to March 2015 in Japan. Named Hu/GII/JP/2014/ GII.P17-GII.17, this variant has a newly identified GII. P17 type RNA-dependent RNA polymerase, while the capsid sequence displays amino acid substitutions around histo-blood group antigen (HBGA) binding sites. Several variants caused by mutations in the capsid region have previously been observed in the GII.4 genotype. Monitoring the GII.17 variant’s geographical spread and evolution is important. The present study uses complete genome sequences and phylogenetic and in silico analyses to characterise GII.P17 norovirus strains contributing to gastroenteritis outbreaks in Japan from December 2014 to March 2015. Norovirus outbreaks from October 2014 to March 2015 in Japan In Japan, numbers of norovirus cases are reported to the Infectious Agents Surveillance Report (IASR) system, which is the national surveillance system overseen by the National Institute of Infectious Diseases (http://www.nih.go.jp/niid/ja/iasr-noro.html). In the six months from October 2014 to March 2015, a total of 2,133 norovirus cases in the country were reported to IASR, including 373 cases caused by genotype GII.4, 146 cases caused by GII.3 and 100 cases caused by GII.17. Other genotypes (GI.2, GI.3, GI.6, GI.7, GII.2, GII.7, GII.12, GII.13 and GII.14) were also detected in this season. Although for most of the six months of the 2014/15 winter season GII.4 norovirus predominated, the www.eurosurveillance.org number of GII.17 cases presented a dramatic increase compared to the previous winter, whereby only three GII.17 cases had been detected from October 2013 to March 2014. In the previous five years, the average and standard deviation (SD) numbers of norovirus cases during the same months were moreover 2,727±340 for all norovirus cases, 589±256 for GII.4 cases, 130±216 for GII.3 cases and 1±1 for GII.17 cases. The first GII.17 cases in the 2014/15 winter season were observed in December 2014. In the subsequent months, more cases with this genotype continued to occur across the whole country. A sharp rise in GII.17 cases was moreover noted between January (n=11 cases) and February (n=55 cases) 2015, making GII.17 the most prevalent genotype in March (n=31 cases) 2015. To investigate the GII.P17 norovirus strains responsible for outbreaks in Japan, and to also look for any changes in the viral genome of strains circulating between 2013 and 2015, six specimens from the Kanagawa, Nagano and Saitama prefectures that were available for further characterisation were used in this study. Genotyping GII.P17 strains From the norovirus GII.17 specimens collected from January 2013 to March 2015, six available specimens (i.e. Kawasaki308 (collected in February, 2015), Kawasaki323 (collected in March, 2014), Nagano7–1 (collected in August, 2014), Nagano8–1 (collected in August, 2014), Saitama5203 (collected in April, 2013) and Saitama5309 (collected in July, 2013)) were selected for full genome analyses by next-generation 1 Figure 1 Time-scaled phylogenetic tree obtained with the Bayesian Markov chain Monte Carlo method of RNA-dependent RNA polymerase (RdRp) sequences (1,283 nt) of norovirus GII strains Hu/GII/JP/2001/GII.P22/Saitama/T49_(KJ196277) Sw/GII/US/2003/GII.P18/OH -QW125_(AY823305) Po/GII/CN/2009/GII.P11/Ch6_(HQ392821) Hu/GII/US/1988/GII.P8/CHDC3936_(JX846926) Hu/GII/JP/2007/GII.P15/Sapporo/HK299_(KJ196290) Hu/GII/DE/2005/GII.P20/Leverkusen267_(EU424333) Hu/GII/SN/1976/GII.P6/S9c_(KC576910) Hu/GII/CN/2011/GII.P6/Guangzhou/GZ2010- L96_(JX989075) Hu/GII/US/1975/GII.P6/CHDC2685_(KC597146) Hu/GII/VN/2010/GII.P7/20486_(KM198530) Hu/GII/US/2010/GII.P7/HS245_(KJ407072) Hu/GII/US/1984/GII.P7/CHDC4073_(JX846927) Hu/GII/VN/2010/GII.P7/30443_(KM198534) Hu/GII/VN/2009/GII.P7/30082_(KM198519) Hu/GII/JP/2010/GII.P7/Musashimurayama/TAKAsanKimchi_(KJ196295) Hu/GII/JP/2007/GII.P7/Sendai/YG99_(KJ196278) Hu/GII/JP/2008/GII.P7/Maizuru/8599_(GU017908) Hu/GII/IN/2006/GII.P13/Pune/PC25_(EU921354) Hu/GII/NL/1995/GII.P3/Amsterdam/1_(KJ194500) Hu/GII/NL/1994/GII.P3/Amsterdam_(KJ194504) Hu/GII/NL/2006/GII.P3/Maastricht021_(JN176920) Hu/GII/HK/2014/GII.P17/CUHK-NS-463_(KP998539) Hu/GII/JP/2015/GII.P17/Kawasaki308_(LC037415) Hu/GII/CN/2014/GII.P17/Guangzhou/41621_(KR020503) Hu/GII/JP/2014/GII.P17/Nagano8 -1_(LC043305) Hu/GII/JP/2014/GII.P17/Nagano7 -1_(LC043139) 1970 (95% HPD interval 1944-1988) Hu/GII/JP/2014/GII.P17/Kawasaki323 _(AB983218) Hu/GII/JP/2013/GII.P17/Saitama5309_(LC043168) 2002 (95% HPD interval 1990-2011) Hu/GII/TW/2013/GII.P17/13 -BH -1_(KJ156329) Hu/GII/JP/2013/GII.P17/Saitama5203 _(LC043167) Hu/GII/JP/2002/GII.P5/Saitama/T52_(KJ196288) Hu/GII/JP/2002/GII.P16/Saitama/T87_(KJ196286) Hu/GII/US/2011/GII.P16/HS255_(KJ407074) Hu/GII/JP/2004/GII.P2/MK04_(DQ456824) Hu/GII/JP/2007/GII.P21/Kawasaki/YO284_(KJ196284) Hu/GII/VN/2011/GII.P21/C2H-39_(KM198590) Hu/GII/NL/1995/GII.Pm/Amsterdam/3_(KJ194507) Hu/GII/US/1971/GII.Pm/Hawaii/7EK_(JX289822) Hu/GII/MY/1978/GII.P1/KL45_(KC576915) Hu/GII/US/1974/GII.P1/CHDC5191_(JX023286) Hu/GII/US/1977/GII.P1/CHDC4871_(FJ537138) Hu/GII/US/1974/GII.P1/CHDC2094_(FJ537135) Hu/GII/US/1988/GII.P4/CHDC3967_(FJ537136) Hu/GII/AU/2007/GII.P4/Sutherland/NSW505G_(GQ845368) Hu/GII/US/1987/GII.P4/MD145-12_(AY032605) Hu/GII/US/2001/GII.P4/HS66_(KJ407076) Hu/GII/TW/2012/GII.P4/Taipei/108_(KJ196285) Hu/GII/NZ/2006/GII.P4/Kenepuru/NZ327_(EF187497) Hu/GII/HK/2004/GII.P4/CU041213_(HM802541) Hu/GII/US/2012/GII.P4/NIHIC28.5_(KF429787) Hu/GII/US/2012/GII.P4/NIHIC1.7_(KF712494) Hu/GII/US/2002/GII.P4/Farmington_Hills_(AY502023) Hu/GII/US/2012/GII.P4/Ohio/7I_(JX126912) Hu/GII/JP/2006/GII.P4/Ehime1_(AB447453) Hu/GII/AU/2011/GII.P4/Randwick/NSW938K_(JX459905) Hu/GII/US/2002/GII.P4/Houston/TCH186_(EU310927) Hu/GII/GF/1978/GII.P_unassigned/C142_(KC597139) Hu/GII/HK/2005/GII.P12/CU051013_(HM802553) Hu/GII/JP/2002/GII.P12/Saitama/T80_(KJ196276) Hu/GII/AU/2008/GII.Pe/Armidale/NSW390I_(GQ845369) Hu/GII/TW/2012/GII.Pe/Taipei/106_(KJ196283) Hu/GII/HK/2013/GII.Pe/CUHK-NS-141_(KJ649705) Hu/GII/CF/1977/GII.Pj/B17_(KC576911) Hu/GII/ZA/2010/GII.P_unassigned/Bushbuckridge6387_(KC962458) Hu/GII/CN/1978/GII.Pg/HK71_(JX846924) Hu/GII/US/2010/GII.Pg/NIHIC6_(KC597145) Hu/GII/AU/2009/GII.Pg/Wahroonga/NSW004P_(JQ613568) Hu/GII/US/1975/GII.Pc/SnowMountRS_(KF429769) Hu/GII/MY/1978/GII.Pc/KL109_(JX846925) Hu/GII/JP/2011/GII.P_unassigned/Yuzawa/Gira2HS_(KJ196291) 50 years 600.0 550.0 500.0 450.0 400.0 350.0 300.0 Year 250.0 200.0 150.0 100.0 50.0 0.0 The phylogenetic analysis includes the nucleotide sequences of six Japanese GII.P17-GII.17 strains (indicated by black dots). The oldest GII.17 strain (C142) detected in 1978 and a previous Japanese GII.17 (Saitama T87) detected in 2002 are indicated by rectangular boxes. The arrows point to some nodes, for which the node ages are indicated with 95% highest posterior density (HPD) intervals. The scale bar represents time in years. sequencing as described [1,2]. The data analysis was performed with CLC Genomics Workbench v8.0.1 (CLC Bio). Contigs were assembled from the obtained sequence reads by de novo assembly. The nucleotide sequences for the GII.P17 strains in this study were deposited in GenBank and assigned accession numbers AB983218, LC037415, LC043139, LC043167, LC043168, and LC043305. When the Norovirus Genotyping Tool was used (http://www.rivm.nl/mpf/norovirus/typingtool) [3], the capsid genotypes of all six strains were assigned to GII.17, but the RNA-dependent RNA polymerase (RdRp) genotypes of some strains could not be assigned to any known genotype in the database. This observation suggested the genetic novelty of the virus in this region. Upon first noticing this with the sequence of Kawasaki323 strain in June 2014, we sought the advice of NoroNet, who coordinate norovirus nomenclature through a global network of research scientists, clinicians and public health officials [4]. After discussions with them, this variant was assigned to the RdRp genotype, GII.P17, in August, 2014. Finally, 2 we named the emergent variants of norovirus as Hu/ GII/JP/2014/GII.P17-GII.17. Phylogenetic and molecular dating analyses The phylogenetic analyses, the time of most recent common ancestor (tMRCA), and the divergence times were estimated for emergent GII.P17-GII.17 variants, along with other representative norovirus GII strains, by the Bayesian Markov chain Monte Carlo (MCMC) method implemented in Bayesian Evolutionary Analysis Sampling Trees (BEAST) v1.8.1 [5]. As a result of the marginal likelihood calculation in the four clock (strict clock, uncorrelated lognormal relaxed clock, uncorrelated exponential relaxed clock and random local clock) and demographic models (constant size, exponential growth, logistic growth and expansion growth), the datasets were analysed using the Tamura and Nei 1993 (TN93) + Gamma + Proportion Invariant (for RdRp) and the generalised time-reversible (GTR) + Gamma + Proportion Invariant (for capsid) nucleotide substitution models, with an uncorrelated exponential relaxed clock model under a constant size www.eurosurveillance.org Figure 2 Time-scaled phylogenetic tree obtained with the Bayesian Markov chain Monte Carlo method of full length capsid gene (virus protein 1 gene) sequences of norovirus GII strains Hu/GII/JP/2007/GII.15/Sapporo/HK299_(KJ196290) Hu/GII/JP/2011/GII_unassigned/Yuzawa/Gira2HS_(KJ196291) Hu/GII/JP/2007/GII.14/Fukuoka/KK282_(KJ196297) Hu/GII/CN/1978/GII.14/HK74_(JN699038) Hu/GII/1997/GII.9/VA97207_(AY038599) Hu/GII/NL/1998/GII.8/Amsterdam/98-18_(AF195848) Hu/GII/CN/1976/GII.7/HK4_(JN699042) Hu/GII/JP/2010/GII.7/Musashimurayama/TAKAsanKimchi_(KJ196295) Sw/GII/US/2003/GII.18/OH - QW101_(AY823304) Sw/GII/US/2003/GII.19/OH -QW218_(AY823307) Sw/GII/JP/1997/GII.11/Sw43_(AB074892) Hu/GII/US/1975/GII.3/CHDC2005_(HM072045) Hu/GII/HK/2014/GII.3/CUHK-NS-232_(KJ499445) Hu/GII/JP/2012/GII.6/Ehime120246_(AB818400) Hu/GII/SN/1976/GII.6/S9c_(JN699035) Hu/GII/TN/1976/GII.4/T091_(JX401281) Hu/GII/MY/1978/GII.4/KL45_(JX401280) Hu/GII/US/2010/GII.4/patient_A_(KF806495) Hu/GII/US/2012/GII.4/NIHIC28.5_(KF429787) Hu/GII/HK/2005/GII.4/CU051146_(HM802544) Hu/GII/JP/2006/GII.4/Aomori5_(AB447435) Hu/GII/US/2012/GII.4/NIHIC27.1_(KF429777) Hu/GII/JP/2008/GII.4/Niigata2_(AB541312) Hu/GII/TW/2012/GII.4/Taoyuan/CGMH67_(KC517377) Hu/GII/US/2010/GII.4/patient_C_(KF806514) Hu/GII/US/2002/GII.4/Houston/TCH186_(EU310927) Hu/GII/JP/2006/GII.4/Kimitsu/061146_(AB294792) Hu/GII/US/2005/GII.4/SSCS_(FJ411171) Hu/GII/AU/2007/GII.4/Sutherland/NSW505G_(GQ845368) Hu/GII/JP/2002/GII.4/Matsudo/021071_(AB294778) Hu/GII/US/1995/GII.4/Wellington_(FJ411169) Hu/GII/JP/2003/GII.4/Kaiso/030556_(AB294779) Hu/GII/US/1974/GII.4/CHDC2094_(FJ537135) Hu/GII/JP/2007/GII.20/OC07118_(AB542917) Hu/GII/JP/2007/GII.21/Kawasaki/YO284_(KJ196284) Hu/GII/JP/2002/GII.13/Saitama/T80_(KJ196276) 1988 (95% HPD Interval 1949–2011) 1861 (95% HPD Interval 1700–1957) Hu/GII/CN/2007/GII.22/Beijing/53931_(GQ856469) Hu/GII/GF/1978/GII.5/C15_(JN699044) Hu/GII/JP/2002/GII.5/Saitama/T52_(KJ196288) Hu/GII/MY/1978/GII.2/KL109_(JN699037) Hu/GII/TW/2011/GII.2/CGMH47_(KC464505) Hu/GII/DE/2000/GII.10/Erfurt/546_(AF427118) Hu/GII/DE/2000/GII.16/Neustrelitz260_(AY772730) Hu/GII/US/2012/GII.1/Ohio/509_(KC463911) Hu/GII/TW/2010/GII.12/CGMH38_(KC464496) Hu/GII/JP/2000/GII.12/Saitama/KU16_(KJ196294) 100 years 1,000.0 Hu/GII/TW/2013/GII.17/13 -BH -1_(KJ156329) Hu/GII/JP/2013/GII.17/Saitama5203_(LC043167) Hu/GII/JP/2014/GII.17/Kawasaki323 _(AB983218) Hu/GII/JP/2014/GII.17/Nagano8 -1_(LC043305) Hu/GII/JP/2014/GII.17/Nagano7 -1_(LC043139) Hu/GII/JP/2013/GII.17/Saitama5309_(LC043168) Hu/GII/HK/2014/GII.17/CUHK -NS-463_(KP998539) Hu/GII/CN/2014/GII.17/Guangzhou/41621_(KR020503) Hu/GII/JP/2015/GII.17/Kawasaki308_(LC037415) Hu/GII/US/2005/GII.17/Katrina -17_(DQ438972) Hu/GII/GF/1978/GII.17/C142_(JN699043) Hu/GII/JP/2002/GII.17/Saitama/T87_(KJ196286) Hu/GII/US/2002/GII.17/CS-E1_(AY502009) 750.0 500.0 250.0 0.0 Year The phylogenetic analysis includes the nucleotide sequences of six Japanese GII.P17-GII.17 strains (indicated by black dots). The oldest GII.17 strain (C142) detected in 1978 and previous Japanese GII.17 (Saitama T87) detected in 2002 are indicated by rectangular boxes. The arrows point to some nodes, for which the node ages are indicated with 95% highest posterior density (HPD) intervals. The scale bar represents time in years. tree prior. Convergence was assessed by the effective sample size (ESS) after a 2% burn-in. Only parameters with an ESS above 150 were accepted. Phylogenetic analysis using the maximum likelihood (ML) method showed no differences of topology to the Bayesian MCMC trees (data not shown). In terms of the RdRp gene, the emergent Japanese GII. P17 belonged to a single cluster including other strains detected in Asia in 2013 (Taiwan: KJ156329) and in 2014 (Guangzhou: KR020503; Hong Kong: KP998539). The tMRCA for the emergent GII.P17 cluster was estimated to be 2002 (95% highest posterior density (HPD) interval: 1990–2011 This suggests that the emergent GII.P17 has been circulating around Asia for ca 13 years (Figure 1). In terms of the RdRp gene, the emergent GII.P17 was most closely related to GII.P3, and evolved from a common ancestor in 1970 (95% HPD interval: 1944–1988). Nearly 32 years elapsed between the tMRCA of the emergent GII.P17 cluster, and the divergence of emergent GII.P17 strains www.eurosurveillance.org from the GII.P3 cluster (Figure 1). Interestingly, the oldest GII.17 (C142 detected in French Guiana in 1978) and the oldest Japanese GII.17 (Saitama T87 detected in 2002) had genotype combinations that were different from the emergent GII.17 forms in this study, the GII.P_unassigned-GII.17 for C142 and GII.P16-GII.17 for Saitama T87 (Figure 1). These results suggested that capsid GII.17 genotype evolved by exchanging the RdRp gene through at least two recombination events. In terms of the capsid gene, the emergent GII.P17-GII.17 strains also belonged to a single cluster that was different from the cluster formed by the older GII.17 strains (Figure 2). Notably, the emerging GII.17 cluster diverged from the old GII.17 cluster around the year 1861 (95% HPD interval: 1700–1957), yet the tMRCA was in the year 1988 (95% HPD interval: 1949–2011) (Figure 2). The two independent clusters formed by the old and emerging GII.17 strains partially arose from changes in the amino acids of the major epitopes (Figure 2 and Table). Thus, we thought that the emerging GII.17 3 4 www.eurosurveillance.org . . . . . . . . . . . . Saitama T87 CS-E1 Katrina-17 Saitama5203c Saitama5309c 13-BH-1 Kawasaki323c Nagano7-1c Nagano8-1c CUHK-NS-463 Guangzhou41621 Kawasaki308c . . . . . . . . . . . . P . . . . . . . . . . . . S . . . . . . . . . . . . V . . . . . . . . . . . . E . . . . . . . . . . . . S 217–225 (I) . . . . . . . . . . . . K . . . . . . . . . . . . T . . . . . . . . . . . . K . . . . . . . . . S . . T . . . . . . . . . . . . A Q Q Q E E E E E E . . . D I I I T T T T T T . . . V – – – – – – – – – Q D D H – – – – – – – – – N G G Q . . . S N N N D D D D D D 291–298 (II) Q Q Q . . . . . . . . . H R R R R R R R R R Q D D D D D D D D D D D D D D D D . . . . . . . . . . . . S P P P P P P P P P T . . S . . . . . . . . . . . . Q . . . . . . . . . . . . F 359–363 (III) . . . . . . . . . . . . V L L L . . . . . . . . . F Amino acid number (major epitopes)b R R R R R R R R R . . . G I I I . . . . . . . . . S S S S N N N N N N E . . T D D D D D D D D D . . . S N N N N N N N N N E T T D D D D – – – – – – – – – – 371–379 (IV) . . . . . . . . . . . . D . . . . . . . . . . . . F . . . . . . . . . . . . Q V V V . . . . . . . . . I N N N N N N N N N . . . K D D D D D D D D D I I I V D D D D D D D D D . . . E D D D G G G G G G T . . S D D D D D D D D D . . . G 390–396 (V) G G G – – – – – – – – – – H H H H H H H H H H H H H New GII.P17-GII.17 variants are highlighted in red. Previous GII.17 strains are highlighted in black. Dots indicate sequence identity among sequences presented. A dash represents the relative deletion of an amino acid at a certain position, compared to other sequences in the alignment. a The GenBank accession numbers of the sequences are as follow: C142 (JN699043); CS-E1 (AY502009); Katrina-17 (DQ438972); Saitama T87 (KJ196286); Kawasaki308 (LC037415); Kawasaki323 (AB983218); Nagano7–1 (LC043139); Nagano8–1 (LC043305); Saitama5203 (LC043167); Saitama5309 (LC043168). b Amino acid numbering is based on the sequence of the Kawasaki323 strain. c Japanese GII.P17-GII.17 strains in this study. P C142 Name of strainsa Table Amino acid substitutions in the major epitopes of virus protein 1 between novel GII.17 variants and previously identified GII.17 strains of norovirus Figure 3 Structural model of the dimer formed by virus protein 1 (VP1) of Kawasaki323 GII.17 strain Side view Estimation of positive selection sites and B-cell epitopes in virus protein 1 sequence of the GII.P17-GII.17 variant Top view 217–225 (I) 371–379 (IV) 291–298 (II) 390–396 (V) 359–363 (III) 354 (episodic diversifying selection) The GII.17 VP1 model was constructed by homology modelling with the crystal structure of norovirus capsid (1IHM). Five predicted epitopes (I–V) are shown by each colour with the amino acid positions based on the sequence of Kawasaki323 strain. Amino acids with episodic diversifying selection are highlighted in dark blue. strains may have variants of the capsid gene, as has been observed with some GII.4 strains [6]. Moreover within the emergent GII.P17-GII.17 cluster, diversification of strains further led to two sub-clusters in the capsid and RdRp genes, with changes to the amino acids of the major epitopes of the virus protein 1 (VP1) protein (Figures 1 and 2 and Table). One subcluster comprised the Kawasaki308 strain, while the other sub-cluster included the Kawasaki323 strain as well as all the other four GII.P17-GII.17 strains from this study. These results clearly suggest that the new GII. P17-GII.17 variants have different evolutionary histories than previously identified GII.17 strains, and that rapid evolution may occur within the emergent GII. P17-GII.17 variants. The GII.17 genotype may produce other variants whereby mutations lead to changes www.eurosurveillance.org in the antigenicity of the P2 domain while still being constrained by host immunity, in the same way as has been observed for GII.4 [6]. We analysed the evolutionary constraints on the GII. P17-GII.17 variants from host-immune pressure, based on the single likelihood ancestor counting (SLAC), fixed effect likelihood (FEL), internal FEL (IFEL), fast unconstrained bayesian approximation for inferring selection (FUBAR), random effects likelihood (REL) and mixed effects model of evolution (MEME) with only the GII.17 sequences in the dataset used in phylogenetic analyses for VP1 gene, but using another alignment [7]. One positive selection (V354W) (V, sub-cluster including Kawasaki323 strain; W, sub-cluster including Kawasaki308) was identified with the MEME analysis. This non-synonymous mutation was only observed in the sub-cluster of Kawasaki308 strain, suggesting that the selection was episodic but not pervasive. Moreover, the BepiPred and DiscoTope servers [8,9] predicted B-cell epitopes associated with humoral immunity at the amino acid positions 217–225 (I), 291– 298 (II), 359–363 (III), 371–379 (IV), and 390–396 (V) in the VP1 protein of the Kawasaki323 strain (Table). Epitope I was conserved in all sequences within the GII.17 cluster, whereas the others (II-V) were variable with amino acid substitutions, deletions, and insertions not only between the novel GII.P17-GII.17 variant and old GII.17 strains, but also within the GII.P17-GII.17 variant cluster, between sub-clusters represented by the Kawasaki323 and Kawasaki308 strains (Table). To identify these amino acid positions on the capsid structure, we calculated and constructed a capsid 3D model of the Kawasaki323 strain with the MODELLER 9.13 programme [10]. The epitopes were located on the exterior surface of the shell (epitope I) and the protruding 2 (P2) domains (epitope II-V), including the binding pocket for the histo-blood group antigens (Figure 3). Additionally, the mutation (V354W) (V, sub-cluster including Kawasaki323 strain; W, sub-cluster including Kawasaki308) associated with episodic positive selection was close to the 372R within the epitope IV (Figure 3). These results indicate that our GII.P17-GII.17 variants might have the potential to escape from host neutralising antibody by amino acid alterations of four putative B-cell epitopes in the P2 domain top, and to improve the binding capacity of the histo-blood group antigens. During the winter 2014/15, noroviruses similar to the Kawasaki308 strain became extremely prevalent in Japan and China [11], and this observation may imply a high infectivity of the strain because a number of mutations, including positive selection, were observed between strains detected in the 2013/14 and 2014/15 seasons. 5 Conclusions During the season from October 2014 to March 2015, a novel norovirus variant GII.P17-GII.17 was prevalent in Japan from December 2014 onwards. While, in general, the total number of norovirus cases during this season was lower than previous years beyond a range of average±SD, the number of cases affected by the GII.17 genotype appeared to be higher and increased dramatically in February 2015, making this the predominant genotype in the country in March 2015. Further characterisation GII.17 available strains from January 2013 to March 2015 assigned these to the novel GII.P17GII.17 variant. Because this variant was detected from a few cases in Japan and Taiwan, prior to becoming prevalent and causing outbreaks during the 2014/15 winter season in Japan and China [11], early surveillance of sporadic cases caused by this or any other potential variants may assist in anticipating outbreaks. Molecular and phylogenetic analyses conducted here show that the novel GII.17 variant has a different evolutionary history to previously identified GII.17 strains. As it might have the potential to spread globally in the near future, presumably by escaping host immunity as GII.4 variants do [6], monitoring trends in the geographical spread and evolution of the variant is important. 2. 3. 4. 5. 6. 7. 8. 9. 10. Acknowledgments This work was partially supported by a commissioned project for the Research on Emerging and Re-emerging Infectious Diseases from the Japanese Ministry of Health, Labour and Welfare and for the Research Program on Emerging and Reemerging Infectious Diseases from Japan Agency for Medical Research and development (AMED). The research was approved by research and ethical committee in Kawasaki City Institute for Public Health. 11. sequencing. PLoS ONE. 2014;9(6):e100699. http://dx.doi. org/10.1371/journal.pone.0100699 PMID:24971993 Ide T, Komoto S, Higo-Moriguchi K, Htun KW, Myint YY, Myat TW, et al. Whole Genomic Analysis of Human G12P[6] and G12P[8] Rotavirus Strains that Have Emerged in Myanmar. PLoS ONE. 2015;10(5):e0124965. http://dx.doi.org/10.1371/journal. pone.0124965 PMID:25938434 Kroneman A, Vennema H, Deforche K, v d Avoort H, Peñaranda S, Oberste MS, et al. An automated genotyping tool for enteroviruses and noroviruses. J Clin Virol. 2011;51(2):121-5. http://dx.doi.org/10.1016/j.jcv.2011.03.006 PMID: 21514213 Kroneman A, Vega E, Vennema H, Vinjé J, White PA, Hansman G, et al. Proposal for a unified norovirus nomenclature and genotyping. Arch Virol. 2013;158(10):2059-68. http://dx.doi. org/10.1007/s00705-013-1708-5 PMID:23615870 Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol. 2012;29(8):1969-73. http://dx.doi.org/10.1093/molbev/mss075 PMID:22367748 Debbink K, Lindesmith LC, Donaldson EF, Baric RS. Norovirus immunity and the great escape. PLoS Pathog. 2012;8(10):e1002921. http://dx.doi.org/10.1371/journal. ppat.1002921 PMID:23093932 Delport W, Poon AF, Frost SD, Kosakovsky Pond SL. Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics. 2010;26(19):24557. http://dx.doi.org/10.1093/bioinformatics/btq429 PMID:20671151 Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLOS Comput Biol. 2012;8(12):e1002829. http://dx.doi.org/10.1371/journal. pcbi.1002829 PMID:23300419 JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006;2(1):2. http://dx.doi. org/10.1186/1745-7580-2-2 PMID:16635264
Lee Virology Journal (2015) 12:193 DOI 10.1186/s12985-015-0421-2 REVIEW Open Access Porcine epidemic diarrhea virus: An emerging and re-emerging epizootic swine virus Changhee Lee Abstract The enteric disease of swine recognized in the early 1970s in Europe was initially described as “epidemic viral diarrhea” and is now termed “porcine epidemic diarrhea (PED)”. The coronavirus referred to as PED virus (PEDV) was determined to be the etiologic agent of this disease in the late 1970s. Since then the disease has been reported in Europe and Asia, but the most severe outbreaks have occurred predominantly in Asian swine-producing countries. Most recently, PED first emerged in early 2013 in the United States that caused high morbidity and mortality associated with PED, remarkably affecting US pig production, and spread further to Canada and Mexico. Soon thereafter, large-scale PED epidemics recurred through the pork industry in South Korea, Japan, and Taiwan. These recent outbreaks and global re-emergence of PED require urgent attention and deeper understanding of PEDV biology and pathogenic mechanisms. This paper highlights the current knowledge of molecular epidemiology, diagnosis, and pathogenesis of PEDV, as well as prevention and control measures against PEDV infection. More information about the virus and the disease is still necessary for the development of effective vaccines and control strategies. It is hoped that this review will stimulate further basic and applied studies and encourage collaboration among producers, researchers, and swine veterinarians to provide answers that improve our understanding of PEDV and PED in an effort to eliminate this economically significant viral disease, which emerged or re-emerged worldwide. Keywords: PED, PEDV, Review, Molecular epidemiology, Diagnosis, Pathogenesis, Preventive measures Background Historical perspective In 1971, British veterinary clinicians noted the appearance of a previously unrecognized enteric disease in growing and fattening pigs [1]. A clinical presentation of watery diarrhea was similar to symptoms of the porcine transmissible gastroenteritis virus (TGEV) infection. However, in the latter case, nursing piglets were only mildly affected. The disease, named epidemic viral diarrhea (EVD), then spread to multiple swine-producing countries in Europe. Five years later, TGE-like EVD reemerged and in contrast to previous outbreaks, the disease occurred in pigs of all ages including suckling pigs. Therefore, EVD in 1976 was classified as EVD type 2 in Correspondence: changhee@knu.ac.kr Animal Virology Laboratory, School of Life Sciences, BK21 Plus KNU Creative BioResearch Group, Kyungpook National University, Daegu 41566, Republic of Korea order to differentiate it from the initial EVD type 1 condition [2, 3]. In 1978, scientists at the Ghent University in Belgium were the first research group, which partially fulfilled Koch’s postulates and described a coronaviruslike agent (CV777) as the causative pathogen. Furthermore, they provided evidence that this novel virus was distinct from the two known porcine coronaviruses, TGEV and hemaggultinating encephalomyelitits virus [4, 5]. Since then, the EVD disease has been known as “porcine epidemic diarrhea (PED)”. Since the 1990s, PED cases have become rare in Europe and PED virus (PEDV)-associated diarrhea has been usually observed in adult pigs, whereas suckling piglets were generally spared or developed only mild symptoms [6]. PED was first reported in Asia in 1982 and since then it has had a great economic impact on the Asian pork industry [7–11]. In contrast to the present situation in Europe, PED epizootics in Asia are © 2016 Lee. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Lee Virology Journal (2015) 12:193 more severe causing high mortality in neonatal piglets and the disease has become an endemic pattern recently. However, despite a notorious reputation in Asian swineproducing countries, PED was not a well-known disease worldwide. For example, the disease had never occurred in the United States until 2013. In May 2013, PED suddenly appeared in the United States and rapidly spread across the country, as well as to Canada and Mexico, causing deaths of more than 8 million newborn piglets in the United States alone during a 1 year-epidemic period [12–15]. Subsequently, severe PED outbreaks recurred in Asian countries including South Korea, Taiwan, and Japan [16–18]. PEDV has now emerged or re-emerged as one of the most devastating viral diseases of swine in the world, leading to significant financial concerns in the global pork industry. This paper is a brief review focusing on current understanding of the molecular biology, epidemiology, diagnosis, and pathogenesis of PEDV, as well as control strategies to prevent PEDV infection. Review The virus PEDV genome and virion structures PEDV, the etiological agent of PED, is a large-enveloped RNA virus, which is a member of the genus Alphacoronavirus within the Coronaviridae family placed with the Arteriviridae family in the order Nidovirales on the basis of similarities in genome organization and replication strategy [4, 6, 19]. The PEDV genome is approximately 28 kb long with a 5’ cap and a 3’ polyadenylated tail and comprises a 5’ untranslated region (UTR), at least 7 open reading frames (ORF1a, ORF1b, and ORF2–6), and a 3’ UTR [20]. The two large ORFs 1a and 1b occupy the 5’-proximal two-thirds of the genome coding for nonstructural proteins (nsps). ORF1a translation yields a replicase polyprotein (pp) la, whereas ORF1b is expressed by a −1 ribosomal frame shift (RFS), which Cterminally extends ppla into pp1ab. These ppla and pplab are post-translationally cleaved by internal proteases generating 16 processing end products, named nsp1–16. The remaining ORFs in the 3’-proximal genome region encode four structural proteins expressed from the respective 3’-co-terminal nested set of subgenomic (sg) mRNAs: the 150–220 kDa glycosylated spike (S) protein, 20–30 kDa membrane (M) protein, 7 kDa envelope (E) protein, 58 kDa nucleocapsid (N) protein, and one accessory gene ORF3 (Fig. 1a) [6, 20–22]. The PEDV genome is encapsulated by a single N protein, forming a long and helical coil structure that is wrapped in a lipid envelope containing 3 surfaceassociated structural proteins, S, M, and E (Fig. 1b). Enveloped virions are roughly spherical and pleomorphic with a diameter ranging from 95 to 190 nm, including Page 2 of 16 the widely spaced, club-shaped, trimerized S projections measuring 18–23 nm in length [4]. PEDV has a buoyant density of 1.18 g/ml in sucrose and is sensitive to ether and chloroform. The virus is stable at 4–50 °C and is absolutely inactivated at pH values beyond pH 4–9 range [23]. Therefore, PEDV is inactivated by various acidic or alkaline disinfectants [24]. PEDV structural proteins Among viral structural proteins, the S protein of PEDV is the major envelope type I glycoprotein of the virion, which interacts with the cellular receptor during virus entry and stimulates induction of neutralizing antibodies in the natural host [22, 25, 26]. In addition, it is associated with growth adaptation in vitro and attenuation in vivo [27]. Thus, the PEDV S glycoprotein is known to be an appropriate viral gene for determining the genetic relatedness among PEDV isolates and for developing diagnostic assays and effective vaccines [16, 26, 28–30]. The M protein, the most abundant component of the viral envelope, is required for the assembly process and can also elicit the production of protective antibodies with virus-neutralizing activity [31, 32]. The small envelope E protein plays an important role during coronavirus budding, and coexpression of E and M proteins can form spike-less coronavirus-like virions [33]. PEDV E and N proteins are found in the endoplasmic reticulum (ER) where they independently induce ER stress [34, 35]. The N protein has multiple functions in viral replication and pathogenesis in coronavirology [36]. Generally, N proteins of coronaviruses interact with viral genomic RNA and associate with other N protein molecules to protect the viral genome, serving as the critical basis for the helical nucleocapsid during coronavirus assembly [36]. The PEDV N protein also perturbs antiviral responses by antagonizing interferon production, as part of the immune evasion strategy, and activates NF-κB [35, 37]. The product of ORF3, the only accessory gene in PEDV, is thought to function as an ion channel and to influence virus production and virulence [38, 39]. PEDV-host interactions Coronaviruses can infect a wide range of mammals, including humans, bats, and whales, and birds, but typically they have a limited host range, infecting only their specific natural host. Furthermore, coronaviruses exhibit a marked tropism for epithelial cells of the respiratory and enteric tracts, as well as for macrophages [40–42]. PEDV also has a restricted tissue tropism and replicates efficiently in porcine small intestinal villous epithelial cells or enterocytes. Porcine aminopeptidase N (pAPN) predominantly expressed on the surface of epithelial cells of small intestine has been identified as the cellular receptor for PEDV [43, 44]. The N-terminal region of Lee Virology Journal (2015) 12:193 Page 3 of 16 Fig. 1 Schematic representations of PEDV genome organization and virion structure. a The structure of PEDV genomic RNA. The 5’-capped and 3’-polyadenylated genome of approximately 28 kb is shown at the top. The viral genome is flanked by UTRs and is polycistronic, harboring replicase ORFs 1a and 1b followed by the genes encoding the envelope proteins, the N protein, and the accessory ORF3 protein. S, spike; E, envelope; M, membrane; N, nucleocapsid. Expression of the ORF1a and 1b yields two known polyproteins (pp1a and pp1ab) by −1 programmed RFS, which are co-translationally or post-translationally processed into at least 16 distinct nsps designated nsp1–16 (bottom). PLpro, papain-like cysteine protease; 3CLpro, the main 3C-like cysteine protease; RdRp; RNA-dependent RNA polymerase; Hel, helicase; ExoN, 3’ → 5’ exonuclease; NendoU, nidovirus uridylate-specific endoribonuclease; 2’OMT, ribose-2’-O-methyltransferase. b Model of PEDV structure. The structure of the PEDV virion is illustrated on the left. Inside the virion is the RNA genome associated with the N protein to form a long, helical ribonucleoprotein (RNP) complex. The virus core is enclosed by a lipoprotein envelope, which contains S, E, and M proteins. The predicted molecular sizes of each structural protein are indicated in parentheses. A set of corresponding sg mRNAs (sg mRNA; 2–6), through which canonical structural proteins or nonstructural ORF3 protein are exclusively expressed via a co-terminal discontinuous transcription strategy, are also depicted on the right the PEDV spike protein S1 domain is important for recognizing the pAPN receptor [45]. Thus, PEDV entry begins with the binding to pAPN followed by internalization of the virus into target cells by direct membrane fusion, and a subsequent release of the viral genome into the cytosol after uncoating to start the genome replication (Fig. 2). In addition to replicating in primary target cells from the natural host, PEDV can grow in some African green monkey kidney cell lines (Vero and MARC-145) [46, 47]. Although it still remains to be determined whether APN acts as the functional receptor for PEDV on these cells, overexpression of exogenous pAPN renders non-permissive cells susceptible to PEDV infection. This observation suggests a functional significance of the APN receptor density for PEDV propagation in cell culture [44]. A recent study also revealed that cell-surface heparan sulfate acts as the attachment factor of PEDV in Vero cells [48]. The addition of trypsin is indispensable for the isolation and serial passages in Vero cells [28, 46, 49, 50]. Trypsin facilitates PEDV entry and release by cleaving the S protein into S1 and S2 subunits, enabling efficient viral replication and spreading in vitro [51, 52]. However, some celladapted attenuated PEDV strains, such as SM98-1 and 83P-5, can support PEDV propagation in the absence of trypsin [44, 53]. As a result of viral infection, distinct cytopathic effects (CPEs) including cell fusion, vacuolation, syncytium, and detachment are produced in infected Vero cells [49]. Since viruses are obligate intracellular parasites, they may adjust the activity of cellular factors or signaling pathways to benefit their own multiplication in host cells. Proteome analysis showed that the expression of proteins involved in apoptosis, signal transduction, and stress responses is affected in PEDV-infected Vero cells Lee Virology Journal (2015) 12:193 Page 4 of 16 Fig. 2 Overview of the PEDV replication cycle. PEDV binds pAPN via the spike protein. Penetration and uncoating occur after the S protein-mediated fusion of the viral envelope with the plasma membrane. Following disassembly, the viral genome is released into the cytoplasm and immediately translated to yield replicases ppla and pp1ab. These polyproteins are proteolytically cleaved into 16 nsps comprising the replication and transcription complex (RTC) that first engages in the minus-strand RNA synthesis using genomic RNA. Both full- and sg-length minus strands are produced and used to synthesize full-length genomic RNA and sg mRNAs. Each sg mRNA is translated to yield only the protein encoded by the 5’-most ORF of the sg mRNA. The envelope S, E, and M proteins are inserted in the ER and anchored in the Golgi apparatus. The N protein interacts with newly synthesized genomic RNA to form helical RNP complexes. The progeny virus is assembled by budding of the preformed RNP at the ER-Golgi intermediate compartment (ERGIC) and then released by the exocytosis-like fusion of smooth-walled, virion-containing vesicles with the plasma membrane [22] [54]. PEDV induces apoptotic cell death in vitro and in vivo through the caspase-independent mitochondrial apoptosis-inducing factor (AIF) pathway [55]. PEDV infection activates the three major mitogen-activated protein kinase (MAPK) cascades involving extracellular signaling-regulated kinase (ERK), p38 MAPK, and c-Jun N-terminal kinase (JNK) [55, 56] (Kim Y, Lee C, unpublished data). In addition, PEDV appears to induce ER stress and activate NF-κB [34, 35]. Therefore, viral replication and subsequent pathological changes rely on PEDV ability to exploit multiple intracellular processes, such as apoptosis, MAPK signaling, and ER stress, which emerge in response to various extracellular stimuli. Heterogeneity Coronaviruses possess the largest known RNA genomes. Nonetheless, they maintain the stability and high fidelity replication of their large genomes while concomitantly generating genetic diversity required for adaptation and emergence. These properties can be ascribed to the 3’to-5’ proofreading exoribonuclease activity within nsp14 [57, 58]. Considering the high fidelity of coronavirus Lee Virology Journal (2015) 12:193 RNA replication, PEDV is assumed to undergo a slow evolutionary process accumulating mutations or recombination events necessary for viral fitness [59]. Genetic and phylogenetic analyses using the whole-genome or some individual genes have been conducted to determine diversity and relationships of global PEDV isolates. Among these, the full-length S gene and its S1 portion (aa 1–735) have been known to be suitable loci for sequencing to investigate genetic relatedness and molecular epidemiology of PEDV [16, 26, 28, 60, 61]. Although only one serotype of PEDV has been reported, phylogenetic studies of the S gene suggested that PEDV can be genetically separated into 2 groups: genogroup 1 (G1; classical) and genogroup 2 (G2; field epidemic or pandemic). Each genogroup can be further divided into subgroups 1a and 1b, and 2a and 2b, respectively (Fig. 3). G1a includes the prototype PEDV strain CV777, vaccine strains, and other cell culture-adapted strains, whereas G1b comprises new variants that were first identified in China [9], later in the United States [62] and South Korea [61], and recently in European countries [63–65]. G2 contains global field isolates, which are further clustered into 2a and 2b subgroups responsible for previous local epidemic outbreaks in Asia and recent pandemic outbreaks in North America and Asia, respectively. The global outbreak of virulent G2b strains appears to have resulted from point mutations in resident virulent field G1a populations. S genes of most PEDV field strains within the G2 group consist of 4161 nucleotides (nt) encoding 1386 amino acid (aa) residues. These genes are 9-nt (3-aa) longer than the homologous gene in the prototype CV777 strain. Compared to the sequences of CV777, G2 PEDV strains possess distinct genetic signatures, S insertions-deletions (S indels) that involve 2 notable 4-aa and 1-aa insertions at positions 55 and 56 and positions 135 and 136, respectively, and a unique 2-aa deletionlocated between positions 160 and 161 within the Nterminal hypervariable region of the S protein [26] (Fig. 4). In addition, this S indel pattern in G2 strains is identical to other novel G2 variants, MF3809 [GenBank:KF779469] and FL2013 [GenBank:KP765609], identified in South Korea and China, respectively, which harbored a large 204-aa S deletion at positions 713–916 or a 7-aa S deletion in the C-terminus, respectively (Fig. 4) [66, 67]. New variant strains within G1b are genetically divergent from G1a and G2 PEDV strains. Although G1b strains were isolated from epidemic cases, they do not contain genetic S signatures typical for G2 field strains. Sequence comparison of the N-terminal one-third of the S gene revealed that G1b strains share more than 95 % of their sequence with G1a classical strains, but their identity with G2 epidemic strains is less than 89 %. In Page 5 of 16 contrast, the analysis of the remaining portion of the S gene indicated that G1b strains exhibit more than 99 % identity with G2 field strains [61]. Furthermore, the entire genome-based phylogenetic analysis showed that G1b strains are clustered closely together with G2 epidemic PEDVs (Fig. 3b). Collectively, these data suggest that novel G1b variants appear to have resulted from a recombination event between classical G1a and epidemic G2 viruses, possibly during viral sg mRNA transcription, which most probably geographically happened in China. The US G1b variants have been named S INDEL strains because of the presence of insertions and a deletion in the S gene compared to sequences of original US PEDV strains [15]. This nomenclature might be incorrect since genomic sequences of PEDV isolates should be initially compared to that of the prototype PEDV strain CV777. Considering this issue, all G2 epidemic isolates include specific S indels, which make them different from CV777. It is therefore recommended that those strains be termed S INDEL strains. Molecular epidemiology Epidemiology of PEDV in Europe Although PEDV first appeared in the United Kingdom and spread to other European countries in the 1970s, the disease impact caused by PEDV in Europe and its economic importance were negligible compared to its effects on the industry in Asian countries and the United States. Over the last decades, therefore, the presence of PEDV was not intensely studied. In 1980s and 1990s, PEDV outbreaks became infrequent, while the virus persisted in an endemic form in the pig population at a low rate. Sporadic outbreaks were reported in some European countries, causing diarrhea in weaner or feeder pigs. A number of serological surveys indicated that seroprevalence of PEDV had become low in European pigs [68–73]. Interestingly, despite low immunity of pigs in European countries, the virus has not been causing severe outbreaks in these susceptible populations although the exact resistance mechanism has not been elucidated. However, PEDV that affected in pigs of all ages, including suckling piglets, re-emerged in a typical epidemic form in Italy in 2006 [74]. In 2014, a case of PED, which occurred on a fattening farm, was reported in Germany [63]. Shortly thereafter, outbreaks of PEDV were identified in a farrow-finish herd in France and in fattening pigs in Belgium [64, 65]. These German, French, and Belgian PEDV strains were found to be genetically almost identical to each other (99.9 % identity) and most closely related to G1b variants identified in China, the United States, and South Korea (Fig. 3). Further surveillance studies are needed to determine whether the G1b strains have already been circulating in Europe or were Lee Virology Journal (2015) 12:193 Page 6 of 16 Fig. 3 Phylogenetic analyses of global PEDV strains based on nucleotide sequences of the spike genes (a) and full-length genomes (b). A putative similar region of the spike protein and the complete genome sequence of TGEV was included as an outgroup in each panel. Multiple sequence alignments were performed using ClustalX 2.0 program and the phylogenetic tree was constructed from aligned nucleotide sequences using the distance-based neighbor-joining method of MEGA5.2 software. Numbers at each branch represent bootstrap values greater than 50 % of 1000 replicates. Names of the strains, countries and years of isolation, GenBank accession numbers, genogroups, and subgroups are shown. PEDV isolates identified in different countries are indicated by corresponding symbols: Europe (solid triangles), South Korea (sold circles), Thailand and Vietnam, (sold diamonds), and the United States (solid squares). Scale bars indicate nucleotide substitutions per site recently introduced from the United States or Asia. Since PED outbreaks may occur periodically in those countries, the implementation of proper biosecurity protocols would be necessary in order to prevent further spread of PEDV domestically or internationally in Europe. In addition, it would be important to investigate if high virulent G2 PEDV is present in certain areas of Europe. Lee Virology Journal (2015) 12:193 Page 7 of 16 Fig. 4 Amino acid sequence alignment of the N-terminal region of the S protein of global PEDV strains. The top illustration represents the organization of the PEDV genome. Only the corresponding alignment of amino acid sequences of the N-terminal region containing hypervariable regions [26] is shown. Dashes (−) indicate deleted sequences. Potential N glycosylation sites predicted by GlycoMod Tool (http://www.expasy.ch/tools/glycomod/) are shown in boldface type. Genetic subgroups of PEDV were marked with different colors: G1a (red), G1b (blue), G2a (green), and G2b (black). Insertions and deletions (indels) within PEDV isolates compared to the prototype CV777 strain are shaded. Amino acids representing potential hypervariable domains are indicated by solid boxes Epidemiology of PEDV in Asia In Asia, PED epidemics first occurred in 1982 in Japan and since then, PED caused severe epidemics in adjacent Asian countries, particularly in China and South Korea, resulting in heavy losses of piglets [8, 11, 75]. In the late 2000s, PEDV has been reported and become increasingly problematic in the Philippines, Thailand, Taiwan, and Vietnam [10, 17, 76]. In Thailand, several outbreaks of severe PEDV infection have emerged since late 2007. PEDV isolates responsible for epidemics in Thailand had S genetic signatures typical for field epidemic G2 strains and were placed in the cluster adjoining to South Korean and Chinese strains in the G2a or G2b subgroup ([77] see also Fig. 3a). PED was first observed in southern provinces of Vietnam and soon after, the disease spread throughout all major swine-producing regions in that country [76]. Vietnamese strains also had unique S indel characteristics and could be classified as the G2b sublineage, which continues to cause sporadic outbreaks in Vietnam [78]. PED still remains a devastating enteric disease leading to serious losses in China since its first identification. In the early 1990s, a vaccine containing the inactivated prototype CV777 strain was developed and has since been widely used throughout the swine industry in China. Until 2010, outbreaks of PED became infrequent with only a limited number of incidents. However, a remarkable increase in PED epidemics occurred in pigproducing provinces in late 2010 [9]. During that period, new variants of PEDV belonging to the G1b genogroup were first reported in China [9]. In addition, PED outbreaks in vaccinated herds questioned the effectiveness of the CV777-based vaccine [9]. Since then, severe Lee Virology Journal (2015) 12:193 PEDV epidemics have been reported in various regions in China [79–81]. At present, PED outbreaks in China were caused by both G1b variants and field epidemic G2 strains that differed genetically from the prototype CV777 strain [81]. One of the G2b strains, AH2012, was later found to be a potential progenitor of US PEDV strains that emerged subsequently during 2013 [15, 82]. Prior to late 2013, the prevalence of PEDV infection was relatively low with only sporadic outbreaks in Taiwan and Japan. In late 2013, severe large-scale PED epizootics suddenly re-emerged in these countries, which led to tremendous financial losses in their pork industry [17, 18]. Taiwan and Japanese isolates during 2013 to 2014 were phylogenetically related to the same clade as global G2b PEDV strains [17, 18]. Epidemiology of PEDV in the United States PEDV has been exotic in the United States until its sudden and explosive emergence in May 2013. Since then, PEDV has spread rapidly in swine farms across the United States, causing significant financial losses [14]. Genetic and phylogenetic analyses of the emergent US PEDV strains identified during the initial outbreak revealed a close relationship with Chinese strains, especially the AH2012 strain isolated in 2012 from Anhui Province in China, suggesting their origin [82]. Recently, it was suggested that the emergent PEDV strains in the United States potentially descended from 2 Chinese strains, AH2012 [GenBank:KC210145] and CH/ZMDZY/ 11 [GenBank:KC196276] in G2b sublineage through recombination [15, 80]. Furthermore, PEDV strains similar to those found in the United States appear to be responsible for subsequent large-scale PED outbreaks in South Korea, Taiwan, and Japan in late 2013 [16–18]. In January 2014, other novel US PEDV strains, such as OH851 [GenBank:KJ399978], without typical S protein genetic signatures of the epidemic G2 virus were reported. They phylogenetically clustered closely to novel Chinese strains in the G1b subgroup based on the similarities of the S gene or with the emergent US PEDV strains in the G2 group based on the whole genome characteristics [62]. Novel variants from the United States had a low nucleotide identity in their first 1170 nucleotides of the S1 region and a high similarity in the remaining S gene, compared to the PEDV strains mainly circulating in the United States, suggesting a rapid evolution of US PEDV variants through possible recombination events [62]. However, a retrospective study demonstrated that the new US variants were already present in June 2013, indicating a possibility that multiple parental PEDV strains were introduced into the United States at about the same time [15]. Although another PEDV variant TC-PC22A [GenBank:KM392224] with a 197-aa deletion in the N-terminal region of the S Page 8 of 16 protein was isolated, the large deletion was found to occur during cell adaptation, suggesting that such variants might not circulate naturally in US swine [50]. Epidemiology of PEDV in South Korea The first PED epizootic in South Korea was confirmed in 1992 [8]. However, a retrospective study revealed that PEDV already existed since as early as 1987 [83]. PED outbreaks have since occurred every year and became endemic, which resulted in high rates of death among piglets and substantial economic losses to domestic swine industry until 2010. In a serological survey carried out in 2007, 91.8 % of 159 tested farms had sero-positive pigs in wean to finish periods (30–150 days of age), indicating that the majority of farms were affected with endemic PEDV infection [84]. Since early 2000s, both modified attenuated and inactivated vaccines based on domestic isolates SM98-1 or DR-13 have been introduced nationwide, leading to a decline in the incidence of PEDV-associated diarrheal disease outbreaks compared to the past years. However, continuous PED epidemics in vaccinated farms have raised problems related to the efficacy of Korean commercial vaccines. PED isolates, which prevalently circulated in South Korea during the same period, were classified as G2a strains that contained S indels compared to CV777 and were distantly related to CV777 or Korean vaccine strains belonging to the G1a subgroup [26]. After South Korea experienced severe outbreaks of the foot-and-mouth disease (FMD) in 2010–2011, there was a state of lull during PED emergence. The prevalence of PEDV infections was occasional with only intermittent outbreaks in South Korea from 2011 to early 2013. This epidemic situation likely resulted from the mass culling of more than 3 million pigs (one-third of the entire domestic pig population) in South Korea during the 2010–2011 FMD outbreaks. However, beginning from November 2013, severe PED epidemics increased remarkably and swept through more than 40 % of pig farms across mainland South Korea [16]. Four months later, PEDV hit Jeju Island, which was PEDV-free since 2004 [60]. The re-emergent PEDV isolates responsible for massive epidemics in South Korea in 2013–2014 were classified into the G2b subgroup, where they clustered closely with emergent US PEDV strains [16, 60]. The source of PEDV incursion into the South Korean swine population has not yet been determined. The importation of pig breeding stock during or after the sudden emergence of PEDV in the United States might be one of the possible sources, but it remains unclear whether G2b PEDVs similar to US strains had preexisted in South Korea. Indeed, the G2b isolates KDJN12YG [GenBank:KJ857475] and KNU-1303 [GenBank:KJ451038] have been identified independently in Lee Virology Journal (2015) 12:193 November 2012 [59] and May 2013 [16], respectively. The former was similar to Chinese G2b strains, whereas the latter resembled emergent US G2b strains (Fig. 3a). Given these results, it is also conceivable that the virus, which has evolved independently by recombination or point mutations might have already been present in South Korea as a minor lineage before the emergence of outbreaks in the United States. Alternatively, it might have originated directly from China. Under suitable circumstances, G2b strains have subsequently become dominant, leading to a number of recent acute outbreaks nationwide [16, 59]. In March of 2014, novel variant G1b PEDV isolates have been found in South Korea, which were similar to the variants reported in China, the United States, and recently in several European countries [61]. They had common genetic and phylogenetic features of G1b strains (no S indels compared to CV777, different phylogenetic subgroup [G1b or G2] depending on the sequence of the S protein or whole-genome, and evidence of recombination), and were most closely related to the US variant strain OH851 among other G1b strains [61]. Although a temporal study will be needed to verify the presence of the G1b virus in earlier periods before its first identification, it is possible that similar to the causes of outbreaks in the United States, 2 G1b and G2b ancestor strains resembling US strains could have been simultaneously transmitted into South Korea. Another novel PEDV G2 strain (MF3809) with a large S deletion was found in South Korea, however, this isolate was identified from only 3 diarrhea samples out of 2634 on 1 out of 569 farms obtained in 2008 [66]. Thus, probability of the existence of this variant in South Korean pigs is very low at present. Nonetheless, we need to continue surveying yet-unidentified PEDV variants that may emerge locally or globally through genetic drift (e.g., after point mutations) or genetic shift (e.g., recombination events). Transmission PEDV infection among pigs occurs principally by a direct or indirect fecal-oral route. Airborne transmission may also play a role in PEDV dissemination under certain conditions [85]. PEDV can mainly enter farms by diarrheal feces or vomitus and contaminated environmental sources via clinically or subclinically infected pigs, trailers (transporting pigs, manures, or food), people (pig owners or visitors, such as swine practitioners or trailer drivers in contaminated work clothing and footwear), or wild animals and birds [6, 86]. Other contaminated fomites, such as sow milk, feed, food items, or food additives or ingredients, including spraydried porcine plasma, could all be potential sources of the virus [9, 87–89]. Page 9 of 16 After an acute (epidemic) outbreak, PEDV may disappear, remain in the farrowing unit because of inadequate hygiene management (e.g., improper disinfection and poor biosecurity), or persist in pigs in weaning or growing-finishing units where the virus is circulating, causing mild post-weaning diarrhea with very low mortality rates. In this endemic status, if newly born pigs are unable to obtain sufficient levels of maternal immunity from their dams due to incomplete sow vaccination or defective lactation performance owing to mastitis or agalactia, the virus circulating on the farm will infect susceptible piglets, which serve as the source of recurrence of epidemic outbreaks leading to a high number of pig deaths [90, 91]. Such endemic PED circumstances are not restricted to Asia: they may equally happen in North America, where sudden PED epidemics recently emerged. Clinical signs, lesions, and pathogenesis PEDV can infect pigs of all ages, causing watery diarrhea and vomiting accompanied by anorexia and depression. Morbidity approaches 100 % in piglets, but can vary in sows [6]. The incubation period of PEDV is approximately 2 days, ranging from 1 to 8 days depending on field or experimental conditions. The interval between the onset and cessation of clinical signs is 3–4 weeks [4, 6, 49, 81, 92, 93]. Fecal shedding of PEDV can be detected within 48 h and may last for up to 4 weeks. The severity of the disease and mortality rates might be inversely associated with the age of the pigs [6, 94]. PEDV infection in piglets up to 1 week of age causes severe watery diarrhea and vomiting for 3–4 days followed by extensive dehydration and electrolyte imbalance leading to death. Mortality rate averages 50 %, often approaching 100 % in 1- to 3-day-old piglets, and decreases to 10 % thereafter. In older animals, including weaner to finisher pigs, clinical signs are self-limiting within 1 week after the onset of the disease. However, PED may affect growth performance of growing pigs. Sows may not have diarrhea and often they manifest depressive symptoms and anorexia. If farrowing sows lose their offspring, they may subsequently suffer from reproductive disorders including agalactia or delayed estrus, which result from the absence of suckling piglets during the lactation period. Gross lesions are confined to the gastrointestinal tract and characterized by distended stomach filled with completely undigested milk curd and thin, transparent intestine walls with accumulation of yellowish fluids [49, 92, 95]. Histological hallmarks of the PEDV infection include severe diffuse atrophic enteritis, superficial villous enterocyte swelling with mild cytoplasmic vacuolation, necrosis of scattered enterocytes followed by sloughing, and contraction of the subjacent villous lamina Lee Virology Journal (2015) 12:193 propria containing apoptotic cells [49, 92, 93, 95]. The intestinal villi become reduced to two-thirds or more of their original length (villous height to crypt depth ratios change to less than 3:1 in affected pigs) with the extent of the pathology depending on the stage of the infection or disease process [92, 93, 95]. PEDV replicates in the cytoplasm of villous epithelial cells throughout the small intestine, destroying target enterocytes as a result of massive necrosis or apoptosis. These processes lead to villous atrophy and vacuolation as well as a marked reduction in the enzymatic activity [6, 55]. This sequence of events interrupts digestion and absorption of nutrients and electrolytes, thereby causing malabsorptive watery diarrhea followed by serious and fatal dehydration in piglets [6, 96]. Upon infection with PEDV, the disease outcome and deaths usually occur age-dependently. Although the reasons why PEDV causes more severe disease in nursing piglets in comparison to weaned pigs have not been clearly elucidated, slower regeneration of enterocytes in neonatal pigs may an important factor [97]. PEDV infection increases the number of crypt stem cells and proliferation of crypt cells, pointing to the accelerated epithelial cell renewal [98]. Enterocyte turnover rate was slower in normal nursing piglets than in weaned pigs, suggesting that the speed of crypt stem cell replacement appears to be associated with the age-dependent resistance to PED [98]. Diagnosis Since signs of the PEDV infection were clinically and pathologically indistinguishable from those caused by TGEV and the recently described porcine deltacoronavirus [96, 99, 100], PED diagnosis cannot be made purely on the basis of clinical signs and histopathological lesions. Therefore, differential diagnosis to demonstrate the presence of PEDV and/or its antigens must be conducted in the laboratory. A variety of PEDV detection methods, which include immunofluorescence (IF) or immunohistochemistry (IHC) tests, in situ hybridization, electron microscopy, virus isolation, enzyme-linked immunosorbent assays (ELISA), and various reversetranscription polymerase chain reaction (RT-PCR) techniques, have been used. Taking into account their fast turnaround times and sensitivity, conventional and realtime RT-PCR systems available as commercial kits are most widely used for PEDV detection during epidemic or endemic outbreaks, as well as for quarantine or slaughter policies. In addition, nucleotide sequencing of the S gene region may be useful in determining the genotype of PEDV circulating in herds. The combination of RT-PCR and S gene sequencing could well become the optimal tool for monitoring genetic diversity among PEDV isolates. Page 10 of 16 A number of serological assays have been used for the detection of PEDV antibodies, including indirect fluorescent antibody (IFA) staining, ELISA, and virus neutralization (VN) tests. Due to the special protection strategy (passive immunization) for neonatal piglets against PEDV, determining the presence or absence of anti-PEDV antibodies may be meaningless in sow herds. Instead, measuring quantities (or titers) of neutralizing antibodies against PEDV or, especially, against the S protein in serum and colostrum should be necessary to monitor the immunity level following sow immunization. In this regard, VN test could be essential for estimating levels of protective antibodies, which piglets would receive from sows. However, this method is time-consuming and cannot selectively detect only secretory IgA antibodies representing mucosal immunity. In contrast, IFA and indirect ELISA approaches for antibody detection are equally specific but less timeconsuming and easier to perform than VN test. Most assays that are currently in use have been developed on the basis of either whole virus [101–103] or viral protein antigens [29, 104]. Whole virus-based IFA and ELISA tests may be inappropriate for detecting protective antibodies regardless of the antigen (S, M, or N proteins) since they can only detect exposure due to natural infection or vaccination. However, these tools may still be useful for monitoring endemic situation with the PEDV infection in affected farms by determining infection status in weaner to finisher pigs. On the other hand, the entire S protein or its S1 portion could be used as viral antigens for developing ELISA because the S1 domain has been reported to contain the receptor binding region and main neutralizing epitopes [45, 105]. Recently, a recombinant S1 proteinbased indirect ELISA has been developed to detect anti-PEDV antibodies [29]. Although this method is a useful, sensitive and specific tool for the detection of antiPEDV S IgG and IgA antibodies in serum and colostrum samples, it remains to be determined whether concentrations of these antibodies correlate with levels of immune protection. Prevention and control measures Biosecurity One of the most important measures for prevention and control of acute PED outbreaks is strict biosecurity that blocks in principle the entrance of PEDV into pig farms (fattening and farrowing units) by minimizing introduction of any material or any person, which could be in contact with the virus. To accomplish this, disinfection must be thoroughly applied to all fomites, personnel, and external visitors that could be contaminated with PEDV. Although PEDV is inactivated by most virucidal disinfectants [24], PEDV RNA can still be detected by RT-PCR even after disinfection with several commercially available disinfectants [106]. Thus, we may need to Lee Virology Journal (2015) 12:193 evaluate disinfectants in vivo or under various field conditions, especially during the winter season, in order to select suitable disinfectant compositions and appropriate procedures. The following order of disinfection steps is recommended to pork producers attempting to disinfect transportation equipment or affected farrowing units: (i) proper cleaning by a high pressure washer using warm water at temperatures over 70 °C; (ii) disinfection by an appropriate disinfectant according to directions on the label; and (iii) overnight drying [90, 91]. Other biosecurity measures include restricting human traffic between fattening and farrowing units and limiting contact between trailers or drivers and the farm interior during the loading process at the pig farm or between drivers and the slaughter facilities during the unloading process at the collection point [86, 90, 91]. All newly arriving or replacement animals including gilts should be isolated for a certain period to monitor their health status [90, 91]. PEDV is a transboundary virus that seems to spread readily to neighboring or distant countries even across continents. Since PEDV is not a World Organization for Animal Health reportable disease, quarantine inspection might not properly implement potential sources or routes that mediate virus transmission between countries. During large-scale severe PED epidemics in adjacent or trading countries, quarantine (or international biosecurity) procedures should be adequately reinforced with a particular attention to any risk factors of international disease transmission in order to prevent the Page 11 of 16 entrance of PEDV as well as other emerging or reemerging pathogens. On the basis of available genetic and phylogenetic data on global PEDV strains, I propose several prospective paths through which PEDV could have spread to Asia and North America (Fig. 5). The classical G1a isolates might have emerged in China due to a careless use of cell-adapted strains as autogenous vaccines or illegal importation of attenuated live vaccines from South Korea. The epidemic G2a South Korean strains might have been initially introduced into China. Chinese G2a viruses were later transmitted to Southeast Asian countries including Thailand and Vietnam. It is also possible that G2a strains in these countries could have been directly transported from South Korea. In China, novel classical G1b and pandemic G2b viruses appear to have arisen concurrently in late 2010 to early 2011 probably via recombination between local G1a and G2a strains or point mutations in resident G2a viruses, respectively. These strains were likely coincidentally introduced into the United States. US-like G1b and G2b strains later landed in South Korea. The genesis of epidemic G2b strains might be ascribed to the evolutionary drift in local G2a lineages in South Korea. In addition, US-like G2b viruses spread further to other North American countries and also to Taiwan and Japan. Currently, there have been no official reports about the emergence of G1b viruses in these countries. Novel G1b and G2b strains may already be present in or they may yet to be brought to Southeast Asian countries from Fig. 5 Potential international PEDV transmission routes. Genetic subgroups of PEDV were marked with different colors as described in the legend to Fig. 4. Solid lines indicate PEDV spreads that have already occurred between countries; dotted lines indicate PEDV spreads that are expected to happen eventually; dashed circular arrows denote genetic mutations or recombination events that lead to the emergence of the novel subtypes Lee Virology Journal (2015) 12:193 China or South Korea. Considering this expected international dissemination route, we may recheck the importance of quarantine policy against PEDV and other epizootic agents. Vaccines Vaccination of sows is a fundamental tool in a strategy to control and eradicate PED during epidemic or endemic outbreaks. Piglets are then protected by a transfer of maternal antibodies via colostrum and milk from immune dams. Although PED first emerged in Europe, the disease has not caused sufficient economic losses to justify the vaccine development. In contrast, PED outbreaks in Asian countries have been serious, and therefore several PEDV vaccines have been developed. In China, CV777-attenuated or -inactivated vaccines have been routinely applied against PED. Attenuation of the virulence of the Japanese PEDV strain 83P-5 was achieved after 100 passages in Vero cells [27]. Subsequently, the cell-adapted 83P-5 strain has been employed as an intramuscular (IM) live virus vaccine (P-5 V) in Japan and it is now also available in South Korea. The cell-culture adaptation method was also applied to attenuate two South Korean virulent PEDV strains, SM98-1 (93 passages) and DR-13 (100 passages) [107, 108]. The SM98-1 strain has been used as an IM live or killed vaccine, whereas DR-13 is available as an oral live vaccine. Although these attenuated or inactivated vaccines have been demonstrated to provide protection under experimental conditions, their effectiveness in the field, as well as pros and cons of their use, are still being debated. In South Korea, the multiple dose vaccination program (3 or 4 IM administrations of vaccines in the following order: live-killed-killed or live-live-killed-killed, correspondingly) at 2- or 3-week intervals starting before farrowing or mating is commonly recommended in pregnant sows or gilts to maintain high levels of neutralizing antibodies in serum and colostrum [90, 91]. Recently, the Korean Animal and Plant Quarantine Agency evaluated the efficacy of domestic and imported PED vaccines commercially available in South Korea. The administration of each commercial vaccine according to corresponding manuals (twice at both 3 and 5 weeks prior to farrowing) increased the survival rate of piglets challenged with a virulent wild-type PEDV from 18.2 % to over 80 %. However, all vaccines did not significantly reduce the morbidity rate of diarrhea including virus shedding in feces [109]. Although protection against the enteric disease is primarily dependent on the presence of secretory IgA antibodies in the intestinal mucosa, the vaccine efficacy might be associated with maintaining high levels of PEDV-specific neutralizing antibodies in the serum and colostrum of vaccinated sows [90, 91, 109]. In addition to a direct vaccination, Page 12 of 16 another crucial aspect is passive colostral and lactogenic immunity of neonatal piglets by ample quantities of protective antibodies obtained from sow colostrums and milk. Thus, sanitation and health conditions of lactating sows have to be monitored to eliminate potential factors negatively affecting lactation performance, such as mastitis or agalactia, so that sows could constantly provide high-quality colostrum and milk to their litters. Suckling piglets lose their source of lactogenic immunity at weaning and soon thereafter become vulnerable to PEDV. Following an acute PED epidemic, the virus may persist in susceptible animals or pigs that survived, leading to the circulation of the virus on the farm (endemic PED). Thus, active immunization of weaner to finisher pigs may be necessary for the control of endemic PEDV infections [6]. The low to moderate effectiveness of current PEDV vaccines appears to be due to antigenic, genetic (>10 % amino acid variation between respective S proteins) and phylogenetic (G1 vs. G2) differences between vaccine and field epidemic strains [16, 26, 30, 49, 59]. Therefore, G2b epidemic PEDV or related strains prevalent in the field should be used for the development of next generation vaccines to control PED. The recombinant S1 protein derived from the field G2 PEDV isolate efficiently protected newborn piglets against PEDV, so it could be potentially used as a subunit vaccine for PED prevention in the future [30]. Isolation of PEDV that is phenotypically and genotypically identical to field strains responsible for PED epidemics worldwide is critical for developing effective vaccines. A number of culturable PEDV strains associated with recent outbreaks were obtained in the United States [28, 50]. On the basis of these isolates, an inactivated PEDV vaccine has been developed, which is currently available on the US market. It is expected that commercial live attenuated vaccines will soon be ready for pig producers. In South Korea, a field epidemic PEDV strain has been recently isolated in my laboratory, and we are now utilizing this isolate to spur the development of new effective and safe vaccines [49]. Interestingly, a recent study indicated that previous exposure of sows with “mild” G1b PEDV provides cross-protective lactogenic immunity against piglet challenge with virulent G2b virus [110]. This finding suggests that the vaccine or route of administration may also affect the efficacy of vaccines. Although it may be hard to predict the efficacy of new vaccines in the field, they will be promising practical tools for prevention and/or control of PED if their use is accompanied by tightened biosecurity and optimal farm management. Alternative immunoprophylactic and therapeutic strategies In acute PED outbreaks with rapidly increasing mortality rates, we may consider intentional exposure (feedback) Lee Virology Journal (2015) 12:193 of pregnant sows to the autogenous virus using watery feces or minced intestines from infected neonatal piglets, which will artificially stimulate rapid lactogenic immunity and, hopefully, shorten the outbreak on the farm [6]. However, there are several complications that need to be considered before the application of this approach. Wide-spread circulation of other viral pathogens, such as PRRSV or PCV2, contained in the intestinal or fecal contents may occur among sows or piglets [111, 112]. Since autogenous viral materials do not have homogenous infectious titers of PEDV, sow immunity may not be induced to a level sufficient for offspring protection. Following artificial exposure of sows, infectious viruses will be shed in feces, which, in turn, could be a potential source for PEDV transmission within the contaminated establishment and between different farms. Artificial passive immunization by oral administration of specific antibodies represents an attractive approach against gastrointestinal pathogens such as PEDV. Chicken egg yolk immunoglobulin against PEDV or the S1 domain has been found to protect neonatal piglets following challenge exposure [113, 114]. The immunoprophylactic effect of colostrum from cows immunized with PEDV has also led to a reduced mortality in newborn piglets [115]. Single chain variable fragments (scFvs) of the mouse monoclonal antibody or E. coli expressing scFvs were verified to neutralize PEDV in vitro [116]. This suggests that recombinant E. coli harboring scFvs might be an alternative prophylactic measure against PEDV infection. Pharmacological, biological, or natural agents that shorten epithelial cell renewal by stimulating proliferation or reorganization of crypt stem cells could be potential therapeutic targets to reduce PEDV-associated mortality from dehydration following severe villous atrophy [98]. For instance, the epidermal growth factor (EGF) has been shown to stimulate proliferation of intestinal crypt epithelial cells and to mitigate atrophic enteritis induced in piglets by PEDV infection. This finding suggested that treatment with EGF could be another therapeutic option [117]. Broad-spectrum antiviral drugs, such as ribavirin, which suppress PEDV infection in vitro, are of interest for their potential to treat PED [53]. Chemical inhibitors as well as compounds from medicinal plants or natural sources, which block the activation of mitochondrial AIF or MAPK signaling pathways required for PEDV replication, could be new therapeutic candidates to reduce PEDV-associated symptoms and mortality [55, 56]. In addition, nutritional supplements, which reduce stress and enhance resistance to the disease, may be useful for PED control in neonatal piglets. Conclusions For the last two or three decades, PED has continued to plague the pork industry in Europe and Asia. In early 2013, the disease struck North America resulting in Page 13 of 16 great economic losses, especially in the United States. Shortly thereafter, massive nationwide PED outbreaks reoccurred in South Korea, Japan, and Taiwan. PED is now globally recognized as an emerging and re-emerging disease and it has become a major financial issue for the swine industry worldwide. Despite geographically limited PED studies in Europe and Asia, a better knowledge of the virology, pathogenesis, immunology, epidemiology, and vaccinology has been gained. Since the emergence of PED in North America, much has been learned from research in the United States. Nevertheless, further studies are warranted to decipher the virus and the associated disease, and more extensive academic and practical studies are needed for a comprehensive understanding of the molecular and pathogenic biology of PEDV in order to develop effective vaccines and establish control measures including biosecurity in affected areas. Lastly, we should bear in mind that a combined application of vaccines, biosecurity protocols, and husbandry management must be the key step to prevent and control PED. Integrated and coordinated efforts in various disciplines among researchers, swine veterinarians, producers, swine industry specialists, producer associations, and authorities are required to achieve effective implementation of necessary measures. Competing interests The authors declare that they have no competing interests. Authors’ contributions CL drafted and wrote the manuscript, and designed the artwork. All authors read and approved the finalmanuscript. Acknowledgments I thank Ms. Sunhee Lee for her assistance in the preparation of references and figures. This paper was supported by Bio-industry Technology Development Program through the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (iPET) funded by the Ministry of Agriculture, Food and Rural Affairs (315021–04). Received: 6 August 2015 Accepted: 10 November 2015 References 1. Oldham J. Letter to the editor. Pig Farming. 1972;10:72–3. 2. Chasey D, Cartwright SF. Virus-like particles associated with porcine epidemic diarrhea. Res Vet Sci. 1978;25:255–6. 3. Wood EN. An apparently new syndrome of porcine epidemic diarrhoea. Vet Rec. 1977;100:243–4. 4. Pensaert MB, de Bouck P. A new coronavirus-like particle associated with diarrhea in swine. Arch Virol. 1978;58:243–7. 5. Debouck P, Pensaert M. Experimental infection of pigs with a new porcine enteric coronavirus, CV777. Am J Vet Res. 1980;41:219–23. 6. Saif LJ, Pensaert MB, Sestack K, Yeo SG, Jung K. Coronaviruses. In: Straw BE, Zimmerman JJ, Karriker LA, Ramirez A, Schwartz KJ, Stevenson GW, editors. Diseases of Swine. Ames: Wiley-Blackwell; 2012. p. 501–24. 7. Chen JF, Sun DB, Wang CB, Shi HY, Cui XC, Liu SW, et al. Molecular characterization and phylogenetic analysis of membrane protein genes of porcine epidemic diarrhea virus isolates in China. Virus Genes. 2008; 36:355–64. 8. Kweon CH, Kwon BJ, Jung TS, Kee YJ, Hur DH, Hwang EK, et al. Isolation of porcine epidemic diarrhea virus (PEDV) in Korea. Korean J Vet Res. 1993;33: 249–54 (in Korean). Lee Virology Journal (2015) 12:193 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. Li W, Li H, Liu Y, Pan Y, Deng F, Song Y, et al. New variants of porcine epidemic diarrhea virus, China, 2011. Emerg Infect Dis. 2012;8:1350–3. Puranaveja S, Poolperm P, Lertwatcharasarakul P, Kesdaengsakonwut S, Boonsoongnern A, Urairong K, et al. Chinese-like strain of porcine epidemic diarrhea virus. Thailand Emerg Infect Dis. 2009;15:1112–5. Takahashi K, Okada K, Ohshima K. An outbreak of swine diarrhea of a newtype associated with coronavirus-like particles in Japan. Jpn J Vet Sci. 1983; 45:829–32. Mole B. Deadly pig virus slips through US borders. Nature. 2013;499:388. Ojkic D, Hazlett M, Fairles J, Marom A, Slavic D, Maxie G, et al. The first case of porcine epidemic diarrhea in Canada. Can Vet J. 2015;56: 149–52. Stevenson GW, Hoang H, Schwartz KJ, Burrough ER, Sun D, Madson D, et al. Emergence of porcine epidemic diarrhea virus in the United States: Clinical signs, lesions, and viral genomic sequences. J Vet Diagn Invest. 2013;25:649–54. Vlasova AN, Marthaler D, Wang Q, Culhane MR, Rossow KD, Rovira A, et al. Distinct characteristics and complex evolution of PEDV strains, North America, May 2013–February 2014. Emerg Infect Dis. 2014;20:1620–8. Lee S, Lee C. Outbreak-related porcine epidemic diarrhea virus strains similar to US strains, South Korea, 2013. Emerg Infect Dis. 2014;20:1223–6. Lin CN, Chung WB, Chang SW, Wen CC, Liu H, Chien CH, et al. US-like strain of porcine epidemic diarrhea virus outbreaks in Taiwan, 2013–2014. J Vet Med Sci. 2014;76:1297–9. MAFF. Ministry of agriculture, Forestry, and Fisheries, Japan. www.maff.go.jp/ j/syouan/douei/ped/ped.html. 2013. Accessed 20 Jul 2015 (in Japanese). Cavanagh D. Nidovirales: A new order comprising Coronaviridae and Arteriviridae. Arch Virol. 1997;142:629–33. Kocherhans R, Bridgen A, Ackermann M, Tobler K. Completion of the porcine epidemic diarrhoea coronavirus (PEDV) genome sequence. Virus Genes. 2001;23:137–44. Duarte M, Tobler K, Bridgen A, Rasschaert D, Ackermann M, Laude H. Sequence analysis of the porcine epidemic diarrhea virus genome between the nucleocapsid and spike protein genes reveals a polymorphic ORF. Virology. 1994;198:466–76. Lai MC, Perlman S, Anderson LJ. Coronaviridae. In: Knipe DM, Howley PM, Griffin DE, Martin MA, Lamb RA, Roizman B, Straus SE, editors. Fields Virology. 5th ed. Philadelphia: Lippincott Williams & Wilkins; 2007. p. 1306–36. Hofmann M, Wyler R. Quantitation, biological and physicochemical properties of cell culture-adapted porcine epidemic diarrhea coronavirus (PEDV). Vet Microbiol. 1989;20:131–42. Pospischil A, Stuedli A, Kiupel M. Diagnostic notes update on porcine epidemic diarrhea. J Swine Health Prod. 2002;10:81–5. Jackwood MW, Hilt DA, Callison SA, Lee CW, Plaza H, Wade E. Spike glycoprotein cleavage recognition site analysis of infectious bronchitis virus. Avian Dis. 2001;45:366–72. Lee DK, Park CK, Kim SH, Lee C. Heterogeneity in spike protein genes of porcine epidemic diarrhea viruses isolated in Korea. Virus Res. 2010; 149:175–82. Sato T, Takeyama N, Katsumata A, Tuchiya K, Kodama T, Kusanagi K. Mutations in the spike gene of porcine epidemic diarrhea virus associated with growth adaptation in vitro and attenuation of virulence in vivo. Virus Genes. 2011;43:72–8. Chen Q, Li G, Stasko J, Thomas JT, Stensland WR, Pillatzki AE, et al. Isolation and characterization of porcine epidemic diarrhea viruses associated with the 2013 disease outbreak among swine in the United States. J Clin Microbiol. 2014;52:234–43. Gerber PF, Gong Q, Huang YW, Wang C, Holtkamp D, Opriessnig T. Detection of antibodies against porcine epidemic diarrhea virus in serum and colostrum by indirect ELISA. Vet J. 2014;202:33–6. Oh J, Lee KW, Choi HW, Lee C. Immunogenicity and protective efficacy of recombinant S1 domain of the porcine epidemic diarrhea virus spike protein. Arch Virol. 2014;159:2977–87. de Haan CA, Vennema H, Rottier PJ. Assembly of the coronavirus envelope: Homotypic interactions between the M proteins. J Virol. 2000;74:4967–78. Zhang Z, Chen J, Shi H, Chen X, Shi D, Feng L, et al. Identification of a conserved linear B-cell epitope in the M protein of porcine epidemic diarrhea virus. Virol J. 2012;9:225. Baudoux P, Carrat C, Besnardeau L, Charley B, Laude H. Coronavirus pseudoparticles formed with recombinant M and E proteins induce alpha interferon synthesis by leukocytes. J Virol. 1998;72:8636–43. Page 14 of 16 34. Xu X, Zhang H, Zhang Q, Dong J, Liang Y, Huang Y, et al. Porcine epidemic diarrhea virus E protein causes endoplasmic reticulum stress and up-regulates interleukin-8 expression. Virol J. 2013;10:26. 35. Xu X, Zhang H, Zhang Q, Huang Y, Dong J, Liang Y, et al. Porcine epidemic diarrhea virus N protein prolongs S-phase cell cycle, induces endoplasmic reticulum stress, and up-regulates interleukin-8 expression. Vet Microbiol. 2013;164:212–21. 36. McBride R, van Zyl M, Fielding BC. The coronavirus nucleocapsid is a multifunctional protein. Viruses. 2014;6:2991–3018. 37. Ding Z, Fang L, Jing H, Zeng S, Wang D, Liu L, et al. Porcine epidemic diarrhea virus nucleocapsid protein antagonizes beta interferon production by sequestering the interaction between IRF3 and TBK1. J Virol. 2014;88: 8936–45. 38. Song DS, Yang JS, Oh JS, Han JH, Park BK. Differentiation of a Vero cell adapted porcine epidemic diarrhea virus from Korean field strains by restriction fragment length polymorphism analysis of ORF 3. Vaccine. 2003; 21:1833–42. 39. Wang K, Lu W, Chen J, Xie S, Shi H, Hsu H, et al. PEDV ORF3 encodes an ion channel protein and regulates virus production. FEBS Lett. 2012;586:384–91. 40. Woo PC, Lau SK, Lam CS, Lau CC, Tsang AK, Lau JH, et al. Discovery of seven novel Mammalian and avian coronaviruses in the genus deltacoronavirus supports bat coronaviruses as the gene source of alphacoronavirus and betacoronavirus and avian coronaviruses as the gene source of gammacoronavirus and deltacoronavirus. J Virol. 2012;86:3995–4008. 41. McVey DS, Kennedy M, Chengappa MM. Veterinary Microbiology. 3rd ed. Ames: Wiley-Blackwell; 2013. 42. Reguera J, Mudgal G, Santiago C, Casasnovas JM. A structural view of coronavirus-receptor interactions. Virus Res. 2014;194:3–15. 43. Li BX, Ge JW, Li YJ. Porcine aminopeptidase N is a functional receptor for the PEDV coronavirus. Virology. 2007;365:166–72. 44. Nam E, Lee C. Contribution of the porcine aminopeptidase N (CD13) receptor density to porcine epidemic diarrhea virus infection. Vet Microbiol. 2010;144:41–50. 45. Lee DK, Cha SY, Lee C. The N-terminal region of the porcine epidemic diarrhea virus spike protein is important for the receptor binding. Korean J Microbiol Biotechnol. 2011;39:140–5. 46. Hofmann M, Wyler R. Propagation of the virus of porcine epidemic diarrhea in cell culture. J Clin Microbiol. 1988;26:2235–9. 47. Lawrence PK, Bumgardner E, Bey RF, Stine D, Bumgarner RE. Genome sequences of porcine epidemic diarrhea virus: In vivo and in vitro phenotypes. Genome Announc. 2014;2:e00503. 48. Huan CC, Wang Y, Ni B, Wang R, Huang L, Ren XF, et al. Porcine epidemic diarrhea virus uses cell-surface heparan sulfate as an attachment factor. Arch Virol. 2015;160:1621–8. 49. Lee S, Kim Y, Lee C. Isolation and characterization of a Korean porcine epidemic diarrhea virus strain KNU-141112. Virus Res. 2015;208:215–24. 50. Oka T, Saif LJ, Marthaler D, Esseili MA, Meulia T, Lin CM, et al. Cell culture isolation and sequence analysis of genetically diverse US porcine epidemic diarrhea virus strains including a novel strain with a large deletion in the spike gene. Vet Microbiol. 2014;173:258–69. 51. Shirato K, Matsuyama S, Ujike M, Taguchi F. Role of proteases in the release of porcine epidemic diarrhea virus from infected cells. J Virol. 2011;85:7872–80. 52. Wicht O, Li W, Willems L, Meuleman TJ, Wubbolts RW, van Kuppeveld FJ, et al. Proteolytic activation of the porcine epidemic diarrhea coronavirus spike fusion protein by trypsin in cell culture. J Virol. 2014;88:7952–61. 53. Kim Y, Lee C. Ribavirin efficiently suppresses porcine nidovirus replication. Virus Res. 2013;171:44–53. 54. Zeng S, Zhang H, Ding Z, Luo R, An K, Liu L, et al. Proteome analysis of porcine epidemic diarrhea virus (PEDV)-infected Vero cells. Proteomics. 2015;15:1819–28. 55. Kim Y, Lee C. Porcine epidemic diarrhea virus induces caspase-independent apoptosis through activation of mitochondrial apoptosis-inducing factor. Virology. 2014;460–461:180–93. 56. Kim Y, Lee C. Extracellular signal-regulated kinase (ERK) activation is required for porcine epidemic diarrhea virus replication. Virology. 2015;484:181–93. 57. Smith EC, Denison MR. Implications of altered replication fidelity on the evolution and pathogenesis of coronaviruses. Curr Opin Virol. 2012;2:519–24. 58. Smith EC, Denison MR. Coronaviruses as DNA wannabes: A new model for the regulation of RNA virus replication fidelity. PLoS Pathog. 2013;9: e1003760. Lee Virology Journal (2015) 12:193 59. Kim SH, Lee JM, Jung J, Kim IJ, Hyun BH, Kim HI, et al. Genetic characterization of porcine epidemic diarrhea virus in Korea from 1998 to 2013. Arch Virol. 2015;160:1055–64. 60. Lee S, Ko DH, Kwak SK, Lim CH, Moon SU, Lee DS, et al. Reemergence of porcine epidemic diarrhea virus on Jeju Island. Korean J Vet Res. 2014;54: 185–8. 61. Lee S, Park GS, Shin JH, Lee C. Full-genome sequence analysis of a variant strain of porcine epidemic diarrhea virus in South Korea. Genome Announc. 2014;2:e01116. 62. Wang L, Byrum B, Zhang Y. New variant of porcine epidemic diarrhea virus, United States, 2014. Emerg Infect Dis. 2014;20:917–9. 63. Hanke D, Jenckel M, Petrov A, Ritzmann M, Stadler J, Akimkin V, et al. Comparison of porcine epidemic diarrhea viruses from Germany and the United States, 2014. Emerg Infect Dis. 2015;21:493–6. 64. Grasland B, Bigault L, Bernard C, Quenault H, Toulouse O, Fablet C, et al. Complete genome sequence of a porcine epidemic diarrhea S gene indel strain isolated in France in December 2014. Genome Announc. 2015;3:e00535. 65. Theuns S, Conceição-Neto N, Christiaens I, Zeller M, Desmarets LM, Roukaerts ID, et al. Complete genome sequence of a porcine epidemic diarrhea virus from a novel outbreak in Belgium, january 2015. Genome Announc. 2015;3:e00506. 66. Park S, Kim S, Song D, Park B. Novel porcine epidemic diarrhea virus variant with large genomic deletion. South Korea Emerg Infect Dis. 2014;20:2089–92. 67. Zhang X, Pan Y, Wang D, Tian X, Song Y, Cao Y. Identification and pathogenicity of a variant porcine epidemic diarrhea virus field strain with reduced virulence. Virol J. 2015;12:88. 68. Carvajal A, Lanza I, Diego R, Rubio P, Cármenes P. Seroprevalence of porcine epidemic diarrhea virus infection among different types of breeding swine farms in Spain. Prev Vet Med. 1995;1–2:33–40. 69. Pijpers A, van Nieuwstadt AP, Terpstra C, Verheijden JH. Porcine epidemic diarrhoea virus as a cause of persistent diarrhoea in a herd of breeding and finishing pigs. Vet Rec. 1993;132:129–31. 70. Nagy B, Nagy G, Meder M, Mocsári E. Enterotoxigenic Escherichia coli, rotavirus, porcine epidemic diarrhoea virus, adenovirus and calici-like virus in porcine postweaning diarrhoea in Hungary. Acta Vet Hung. 1996;44:9–19. 71. Pensaert MB, Van Reeth K. Porcine epidemic diarrhea and porcine respiratory coronavirus. Proc Am Assoc Swine Practitioners. 1998; 433–6. 72. Pritchard GC, Paton DJ, Wibberley G, Ibata G. Transmissible gastroenteritis and porcine epidemic diarrhoea in Britain. Vet Rec. 1999;144:616–8. 73. Van Reeth K, Pensaert M. Prevalence of infections with enzootic respiratory and enteric viruses in feeder pigs entering fattening herds. Vet Rec. 1994; 135:594–7. 74. Martelli P, Lavazza A, Nigrelli AD, Merialdi G, Alborali LG, Pensaert MB. Epidemic of diarrhoea caused by porcine epidemic diarrhoea virus in Italy. Vet Rec. 2008;162:307–10. 75. Jinghui F, Yijing L. Cloning and sequence analysis of the M gene of porcine epidemic diarrhea virus LJB/03. Virus Genes. 2005;30:69–73. 76. Duy DT, Toan NT, Puranaveja S, Thanawongnuwech R. Genetic characterization of porcine epidemic diarrhea virus (PEDV) isolates from southern Vietnam during 2009–2010 outbreaks. Thai J Vet Med. 2011;41: 55–64. 77. Temeeyasen G, Srijangwad A, Tripipat T, Tipsombatboon P, Piriyapongsa J, Phoolcharoen W, et al. Genetic diversity of ORF3 and spike genes of porcine epidemic diarrhea virus in Thailand. Infect Genet Evol. 2014;21:205–13. 78. Vui DT, Tung N, Inui K, Slater S, Nilubol D. Complete genome sequence of porcine epidemic diarrhea virus in Vietnam. Genome Announc. 2014;2: e00753. 79. Sun RQ, Cai RJ, Chen YQ, Liang PS, Chen DK, Song CX. Outbreak of porcine epidemic diarrhea in suckling piglets. China Emerg Infect Dis. 2012;18:161–3. 80. Tian PF, Jin YL, Xing G, Qv LL, Huang YW, Zhou JY. Evidence of recombinant strains of porcine epidemic diarrhea virus, United States, 2013. Emerg Infect Dis. 2014;20:1735–8. 81. Wang XM, Niu BB, Yan H, Gao DS, Yang X, Chen L, et al. Genetic properties of endemic Chinese porcine epidemic diarrhea virus strains isolated since 2010. Arch Virol. 2013;158:2487–94. 82. Huang YW, Dickerman AW, Piñeyro P, Li L, Fang L, Kiehne R, et al. Origin, evolution, and genotyping of emergent porcine epidemic diarrhea virus strains in the United States. MBio. 2013;4:e00737. 83. Park NY, Lee SY. Retrospective study of porcine epidemic diarrhea virus (PEDV) in Korea by in situ hybridization. Korean J Vet Res. 1997;37:809–16 (in Korean). Page 15 of 16 84. Park CK, Pak SI. Infection patterns of porcine epidemic diarrhea virus (PEDV) by sera-epidemiological analysis in Korean pig farms. J Life Sci. 2009;19: 1304–8 (in Korean). 85. Alonso C, Goede DP, Morrison RB, Davies PR, Rovira A, Marthaler DG, et al. Evidence of infectivity of airborne porcine epidemic diarrhea virus and detection of airborne viral RNA at long distances from infected herds. Vet Res. 2014;45:73. 86. Lowe J, Gauger P, Harmon K, Zhang J, Connor J, Yeske P, et al. Role of transportation in spread of porcine epidemic diarrhea virus infection. United States Emerg Infect Dis. 2014;20:872–4. 87. Dee S, Clement T, Schelkopf A, Nerem J, Knudsen D, Christopher-Hennings J, et al. An evaluation of contaminated complete feed as a vehicle for porcine epidemic diarrhea virus infection of naïve pigs following consumption via natural feeding behavior: Proof of concept. BMC Vet Res. 2014;10:176. 88. Opriessnig T, Xiao CT, Gerber PF, Zhang J, Halbur PG. Porcine epidemic diarrhea virus RNA present in commercial spray-dried porcine plasma is not infectious to naïve pigs. PLoS One. 2014;9:e104766. 89. Pasick J, Berhane Y, Ojkic D, Maxie G, Embury-Hyatt C, Swekla K, et al. Investigation into the role of potentially contaminated feed as a source of the first-detected outbreaks of porcine epidemic diarrhea in Canada. Transbound Emerg Dis. 2014;61:397–410. 90. Park CK, Lee C. Clinical examination and control measures in a commercial pig farm persistently infected with porcine epidemic diarrhea virus. J Vet Clin. 2009;26:463–6 (in Korean). 91. Park CK, Lee KK, Lee C. PED past, present, and future. Proc Asian Pig Vet Soc Congr. 2011; S19–20. 92. Jung K, Wang Q, Scheuer KA, Lu Z, Zhang Y, Saif LJ. Pathology of US porcine epidemic diarrhea virus strain PC21A in gnotobiotic pigs. Emerg Infect Dis. 2014;20:662–5. 93. Madson DM, Magstadt DR, Arruda PH, Hoang H, Sun D, Bower LP, et al. Pathogenesis of porcine epidemic diarrhea virus isolate (US/Iowa/18984/ 2013) in 3-week-old weaned pigs. Vet Microbiol. 2014;174:60–8. 94. Shibata I, Tsuda T, Mori M, Ono M, Sueyoshi M, Uruno K. Isolation of porcine epidemic diarrhea virus in porcine cell cultures and experimental infection of pigs of different ages. Vet Microbiol. 2000;72:173–82. 95. Debouck P, Pensaert M, Coussement W. The pathogenesis of an enteric infection in pigs, experimentally induced by the coronavirus-like agent, CV777. Vet Microbiol. 1981;6:157–65. 96. Ducatelle R, Coussement W, Debouck P, Hoorens J. Pathology of experimental CV777 coronavirus enteritis in piglets II Electron microscopic study. Vet Pathol. 1982;19:57–66. 97. Moon HW, Norman JO, Lambert G. Age dependent resistance to transmissible gastroenteritis of swine (TGE). I. Clinical signs and some mucosal dimensions in small intestine. Can J Comp Med. 1973;37:157–66. 98. Jung K, Saif LJ. Porcine epidemic diarrhea virus infection: Etiology, epidemiology, pathogenesis and immunoprophylaxis. Vet J. 2015;204:134–43. 99. Haelterman EO. On the pathogenesis of transmissible gastroenteritis of swine. J Am Vet Med Assoc. 1972;160:534–40. 100. Ma Y, Zhang Y, Liang X, Lou F, Oglesbee M, Krakowka S, et al. Origin, evolution, and virulence of porcine deltacoronaviruses in the United States. MBio. 2015;6:e00064. 101. Carvajal A, Lanza I, Diego R, Rubio P, Cármenes PJ. Evaluation of a blocking ELISA using monoclonal antibodies for the detection of porcine epidemic diarrhea virus and its antibodies. J Vet Diagn Invest. 1995;7:60–4. 102. Hofmann M, Wyler R. Enzyme-linked immunosorbent assay for the detection of porcine epidemic diarrhea coronavirus antibodies in swine sera. Vet Microbiol. 1990;21:263–73. 103. Oh JS, Song DS, Yang JS, Song JY, Moon HJ, Kim TY, et al. Comparison of an enzyme-linked immunosorbent assay with serum neutralization test for serodiagnosis of porcine epidemic diarrhea virus infection. J Vet Sci. 2005;6: 349–52. 104. Knuchel M, Ackermann M, Müller HK, Kihm U. An ELISA for detection of antibodies against porcine epidemic diarrhoea virus (PEDV) based on the specific solubility of the viral surface glycoprotein. Vet Microbiol. 1992;32: 117–34. 105. Sun DB, Feng L, Shi HY, Chen JF, Liu SW, Chen HY, et al. Spike protein region (aa 636–789) of porcine epidemic diarrhea virus is essential for induction of neutralizing antibodies. Acta Virol. 2007;51:149–56. 106. Bowman AS, Nolting JM, Nelson SW, Bliss N, Stull JW, Wang Q, et al. Effects of disinfection on the molecular detection of porcine epidemic diarrhea virus. Vet Microbiol. 2015;179:213–8. Lee Virology Journal (2015) 12:193 Page 16 of 16 107. Kweon CH, Kwon BJ, Lee JG, Kwon GO, Kang YB. Derivation of attenuated porcine epidemic diarrhea virus (PEDV) as vaccine candidate. Vaccine. 1999; 17:2546–53. 108. Song DS, Oh JS, Kang BK, Yang JS, Moon HJ, Yoo HS, et al. Oral efficacy of Vero cell attenuated porcine epidemic diarrhea virus DR13 strain. Res Vet Sci. 2007;82:134–40. 109. QIA. Animal and Plant Quarantine Agency, Republic of Korea. 2014. http://www.qia.go.kr/viewwebQiaCom.do?id=36111&type=6_18_1bdsm. Accessed 11 July 2014 (in Korean). 110. Goede D, Murtaugh MP, Nerem J, Yeske P, Rossow K, Morrison R. Previous infection of sows with a “mild” strain of porcine epidemic diarrhea virus confers protection against infection with a “severe” strain. Vet Microbiol. 2015;176:161–4. 111. Jung K, Ha Y, Ha SK, Kim J, Choi C, Park HK, et al. Identification of porcine circovirus type 2 in retrospective cases of pigs naturally infected with porcine epidemic diarrhoea virus. Vet J. 2006;171:166–8. 112. Park JS, Ha Y, Kwon B, Cho KD, Lee BH, Chae C. Detection of porcine circovirus 2 in mammary and other tissues from experimentally infected sows. J Comp Pathol. 2009;140:208–11. 113. Kweon CH, Kwon BJ, Woo SR, Kim JM, Woo GH, Son DH, et al. Immunoprophylactic effect of chicken egg yolk immunoglobulin (Ig Y) against porcine epidemic diarrhea virus (PEDV) in piglets. J Vet Med Sci. 2000;62:961–4. 114. Lee DH, Jeon YS, Park CK, Kim SJ, Lee DS, Lee C. Immunoprophylactic effect of chicken egg yolk antibody (IgY) against a recombinant S1 domain of the porcine epidemic diarrhea virus spike protein in piglets. Arch Virol. 2015;160: 2197–207. 115. Shibata I, Ono M, Mori M. Passive protection against porcine epidemic diarrhea (PED) virus in piglets by colostrum from immunized cows. J Vet Med Sci. 2001;63:655–8. 116. Pyo HM, Kim IJ, Kim SH, Kim HS, Cho SD, Cho IS, et al. Escherichia coli expressing single-chain Fv on the cell surface as a potential prophylactic of porcine epidemic diarrhea virus. Vaccine. 2009;27:2030–6. 117. Jung K, Kang BK, Kim JY, Shin KS, Lee CS, Song DS. Effects of epidermal growth factor on atrophic enteritis in piglets induced by experimental porcine epidemic diarrhoea virus. Vet J. 2008;177:231–5. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit

Tutor Answer

School: UT Austin


Cases of Pandemic Outbreaks and their Containment
Student’s Name

Background Information
Throughout history, epidemics and pandemics have ravaged the human existence and
societies. Cholera, HIV-AIDS chicken pox, and small pox have killed tens of thousands (Sands,
Mundaca-Shah, Dzau, 2016). Ebola has a case fatality rate 40-50%, human noroviruses have
caused 500-800 deaths a year, while avian influenza epidemic has resulted to millions perishing
(Cenciarelli et al., 2015). Advancements in the medical field have revolutionized the defenses
against infectious diseases. Diagnostics, hygiene, vaccines and antibiotics are the most preferred
prevention and response tools to outbreaks. Despite this, the severe acute respiratory syndrome
(SARS) and the Ebola outbreak in West Africa have proved that we are not complacent (Sands,
Mundaca-Shah, Dzau, 2016). Therefore, it is important to study the pandemics and epidemics,
their course, diagnosis and control to advocate for policies, mechanisms of coordination and
resources to respond to crises from infectious diseases. Infectious diseases leading to potential
epidemics and pandemics cause huge disruptions in the economy and loss of life.
Ebola Virus Disease (EVD)
Certainly, Ebola demonstrated the ill-preparedness of crises for such infectious disease. It
iss one of the most fatal (40-50%) and serious viral diseases (Cenciarelli et al., 2015). There are
no strategies either therapeutic or prophylactics that could deal with this disease, hence, a great
community threat. The epidemic became a real concern in 2014 when it hit urban areas. The
World Health Organization declared EVD outbreak in western Africa an emergency of the public
health. There five classes of Ebola including Ebola virus (EBOV), Reston virus (RESTV),
Bundibugyo virus (BDBV), Tai forest virus (TAFV) and Sudan Virus (SUDV) (2). All except
RESTV are pathogenic to humans.

Several studies have indicated filoviruses as zoonotic, that is, transmission to humans
from animals’ ongoing life cycles and fruit bats were identified as probable disease vectors and
virus reservoir. Transmission is through close contact with organs, blood, other body fluids or
secretions of infected animals. It can also be transmitted through personal contact with
individuals having broken skins and that of a healthy individual with the body fluids of an
infected person (saliva, urine, fasces or semen). Ebola’s incubation period is between two to
twenty-one days and individuals are only infectious in the symptomatic phase (Cenciarelli et al.,
2015). After, a febrile illness developed with characteristics such as joint pain, weakness,
headache and joint pain. A major form of clinical manifestation is the hemorrhagic manifestation
in the gastrointestinal tract and many others develop a rash that is maculopapular in association
with arrythmia.
Disease diagnosis is through specific laboratory tests for example electron microscopy,
isolation of virus by the cell’s nature, antibody -capture enzyme linked immunosorbent assay
(ELISA), serum neutralization test, reverse transcriptase polymerase chain reaction (RT-PCR)
and serum neutralization test).
Norovirus Diarrheal Disease
Across all age groups, human noroviruses are the leading causes of erratic and epidemic
gastroenteritis (Matsushima et al, 2015). Although it is a self-limiting disease, it has resulted in a
whopping 56,000-71,000 in-patients flooding the health facilities, approximately 500-800 deaths
in a year, about 400,000 visits that demand emergency services and 1.7 million to 1.9 million
visits by out-patients (Kirk et al., 2010). In the 1990’s it was a relatively unrecognized virus
among individuals of all ages. Most of the norovirus outbreaks take place in the health facility
the predominant transmission mode is from one person to another. The characteristics of

noroviruses project after an incubation period of 22 hours to 48 hours. They include nausea, low
grade fever, vomiting, abdominal cramps and nonblood diarrhea (Matsushima et al, 2015). In
vulnerable populations for example the elderly or the pediatrics, they are prone to a more

flag Report DMCA

Excellent job

Similar Questions
Hot Questions
Related Tags

Brown University

1271 Tutors

California Institute of Technology

2131 Tutors

Carnegie Mellon University

982 Tutors

Columbia University

1256 Tutors

Dartmouth University

2113 Tutors

Emory University

2279 Tutors

Harvard University

599 Tutors

Massachusetts Institute of Technology

2319 Tutors

New York University

1645 Tutors

Notre Dam University

1911 Tutors

Oklahoma University

2122 Tutors

Pennsylvania State University

932 Tutors

Princeton University

1211 Tutors

Stanford University

983 Tutors

University of California

1282 Tutors

Oxford University

123 Tutors

Yale University

2325 Tutors