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Munyeku-Bazitama Y, Okitale-Talunda P, Hattori T, Saito T, Lombe BP, Miyamoto H, Mori-Kajihara A, Kajihara M, Nkoy AB, Twabela AT, Masumu J, Ahuka-Mundeke S, Muyembe-Tamfum JJ, Igarashi M, Park ES, Morikawa S, Makiala-Mandanda S, Takada A. Seroprevalence of Bas-Congo virus in Mangala, Democratic Republic of the Congo: a population-based cross-sectional study. THE LANCET. MICROBE 2024; 5:e529-e537. [PMID: 38555924 DOI: 10.1016/s2666-5247(24)00021-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Bas-Congo virus (BASV), an emerging tibrovirus, was associated with an outbreak of acute haemorrhagic fever in Mangala, Democratic Republic of the Congo, in 2009. In 2012, neutralising antibodies to BASV were detected in the lone survivor and one of his close contacts. However, subsequent serological and molecular surveys were unsuccessful as neither BASV antibodies nor its RNA were detected. In this study, we determined the seroprevalence of BASV infection in Mangala 13 years after the initial outbreak. METHODS We conducted a population-based serological survey from Jan 17 to Jan 23, 2022. Consenting individuals at least 5 years of age, living in Mangala for at least 4 weeks, and who had no contraindication to venepuncture were enrolled. Participants were interviewed using a pre-tested questionnaire for sociodemographic and clinical characteristics. We supplemented the collected serum samples with 284 archived samples from Matadi and Kinshasa. All samples were tested for antibodies to BASV and other tibroviruses using a pseudovirus-based neutralisation test. FINDINGS Among the 267 individuals from Mangala, the prevalence of BASV antibodies was 55% (95% CI 49-61; n=147). BASV seropositivity odds significantly increased with age (5·2 [95% CI 2·1-12·9] to 83·9 [20·8-337·7] times higher in participants aged 20 years or older than participants aged 5-19 years). Some occupational categories (eg, farmer or public servant) were associated with seropositivity. Only nine (6%) of 160 samples from Matadi and one (<1%) of 124 samples from Kinshasa had neutralising antibodies to BASV. Moreover, we also detected neutralising antibodies to other tibroviruses-Ekpoma virus 1, Ekpoma virus 2, and Mundri virus-in 84 (31%), 251 (94%), and 219 (82%) of 267 Mangala samples; 14 (9%), 62 (39%), and 120 (75%) of 160 Matadi samples; and six (5%), five (4%), and 33 (27%) of 124 Kinshasa samples, respectively. INTERPRETATION Human infection with BASV and other tibroviruses seems common in Mangala, although no deadly outbreak has been reported since 2009. Exposure to BASV might be highly restricted to Mangala and the increasing prevalence of neutralising antibodies with age suggests regular contact with the virus in this city. Altogether, our findings suggest that human infection with tibroviruses could be common in the study areas and not associated with deadly haemorrhagic or debilitating syndromes. FUNDING Japan Agency for Medical Research and Development (AMED) and Japan International Cooperation Agency (JICA) under the Science and Technology Research Partnership for Sustainable Development (SATREPS) and Japan Program for Infectious Diseases Research and Infrastructure from AMED.
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Affiliation(s)
- Yannick Munyeku-Bazitama
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Département de Biologie Médicale, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Patient Okitale-Talunda
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Département de Biologie Médicale, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Takanari Hattori
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Takeshi Saito
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA
| | - Boniface Pongombo Lombe
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Central Veterinary Laboratory of Kinshasa, Kinshasa, Democratic Republic of the Congo; Faculté de Médecine Vétérinaire, Université Pédagogique Nationale, Kinshasa, Democratic Republic of the Congo
| | - Hiroko Miyamoto
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Akina Mori-Kajihara
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Masahiro Kajihara
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Agathe Bikupe Nkoy
- Division of Pediatric Nephrology, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Augustin Tshibwabwa Twabela
- Central Veterinary Laboratory of Kinshasa, Kinshasa, Democratic Republic of the Congo; Faculté de Médecine Vétérinaire, Université Pédagogique Nationale, Kinshasa, Democratic Republic of the Congo
| | - Justin Masumu
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Central Veterinary Laboratory of Kinshasa, Kinshasa, Democratic Republic of the Congo; Faculté de Médecine Vétérinaire, Université Pédagogique Nationale, Kinshasa, Democratic Republic of the Congo
| | - Steve Ahuka-Mundeke
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Département de Biologie Médicale, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Jean-Jacques Muyembe-Tamfum
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Département de Biologie Médicale, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Manabu Igarashi
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Eun-Sil Park
- Department of Veterinary Science, National Institute of Infectious Diseases, Tokyo, Japan
| | - Shigeru Morikawa
- Department of Microbiology, Faculty of Veterinary Medicine, Okayama University of Science, Imabari, Japan
| | - Sheila Makiala-Mandanda
- Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo; Département de Biologie Médicale, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Ayato Takada
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; One Health Research Center, Hokkaido University, Sapporo, Japan; Department of Disease Control, School of Veterinary Medicine, University of Zambia, Lusaka, Zambia.
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Breban R. Emergence failure of early epidemics: A mathematical modeling approach. PLoS One 2024; 19:e0301415. [PMID: 38809831 PMCID: PMC11135784 DOI: 10.1371/journal.pone.0301415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 03/16/2024] [Indexed: 05/31/2024] Open
Abstract
Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology.
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Affiliation(s)
- Romulus Breban
- Institut Pasteur, Unité d’Epidémiologie des Maladies Emergentes, Paris, France
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3
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Zhang S, Li YD, Cai YR, Kang XP, Feng Y, Li YC, Chen YH, Li J, Bao LL, Jiang T. Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus. Front Genet 2024; 15:1361952. [PMID: 38495668 PMCID: PMC10940399 DOI: 10.3389/fgene.2024.1361952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: The global headlines have been dominated by the sudden and widespread outbreak of monkeypox, a rare and endemic zoonotic disease caused by the monkeypox virus (MPXV). Genomic composition based machine learning (ML) methods have recently shown promise in identifying host adaptability and evolutionary patterns of virus. Our study aimed to analyze the genomic characteristics and evolutionary patterns of MPXV using ML methods. Methods: The open reading frame (ORF) regions of full-length MPXV genomes were filtered and 165 ORFs were selected as clusters with the highest homology. Unsupervised machine learning methods of t-distributed stochastic neighbor embedding (t-SNE), Principal Component Analysis (PCA), and hierarchical clustering were performed to observe the DCR characteristics of the selected ORF clusters. Results: The results showed that MPXV sequences post-2022 showed an obvious linear adaptive evolution, indicating that it has become more adapted to the human host after accumulating mutations. For further accurate analysis, the ORF regions with larger variations were filtered out based on the ranking of homology difference to narrow down the key ORF clusters, which drew the same conclusion of linear adaptability. Then key differential protein structures were predicted by AlphaFold 2, which meant that difference in main domains might be one of the internal reasons for linear adaptive evolution. Discussion: Understanding the process of linear adaptation is critical in the constant evolutionary struggle between viruses and their hosts, playing a significant role in crafting effective measures to tackle viral diseases. Therefore, the present study provides valuable insights into the evolutionary patterns of the MPXV in 2022 from the perspective of genomic composition characteristics analysis through ML methods.
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Affiliation(s)
- Sen Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
| | - Ya-Dan Li
- College of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Yu-Rong Cai
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
- College of the First Clinical Medical, Inner Mongolia Medical University, Hohhot, China
| | - Xiao-Ping Kang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
| | - Ye Feng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
| | - Yu-Chang Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
| | - Yue-Hong Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
| | - Jing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China
- College of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Li-Li Bao
- College of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot, China
| | - Tao Jiang
- College of Basic Medical Sciences, Anhui Medical University, Hefei, China
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4
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Riesle-Sbarbaro SA, Wibbelt G, Düx A, Kouakou V, Bokelmann M, Hansen-Kant K, Kirchoff N, Laue M, Kromarek N, Lander A, Vogel U, Wahlbrink A, Wozniak DM, Scott DP, Prescott JB, Schaade L, Couacy-Hymann E, Kurth A. Selective replication and vertical transmission of Ebola virus in experimentally infected Angolan free-tailed bats. Nat Commun 2024; 15:925. [PMID: 38297087 PMCID: PMC10830451 DOI: 10.1038/s41467-024-45231-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/16/2024] [Indexed: 02/02/2024] Open
Abstract
The natural reservoir of Ebola virus (EBOV), agent of a zoonosis burdening several African countries, remains unidentified, albeit evidence points towards bats. In contrast, the ecology of the related Marburg virus is much better understood; with experimental infections of bats being instrumental for understanding reservoir-pathogen interactions. Experiments have focused on elucidating reservoir competence, infection kinetics and specifically horizontal transmission, although, vertical transmission plays a key role in many viral enzootic cycles. Herein, we investigate the permissiveness of Angolan free-tailed bats (AFBs), known to harbour Bombali virus, to other filoviruses: Ebola, Marburg, Taï Forest and Reston viruses. We demonstrate that only the bats inoculated with EBOV show high and disseminated viral replication and infectious virus shedding, without clinical disease, while the other filoviruses fail to establish productive infections. Notably, we evidence placental-specific tissue tropism and a unique ability of EBOV to traverse the placenta, infect and persist in foetal tissues of AFBs, which results in distinct genetic signatures of adaptive evolution. These findings not only demonstrate plausible routes of horizontal and vertical transmission in these bats, which are expectant of reservoir hosts, but may also reveal an ancillary transmission mechanism, potentially required for the maintenance of EBOV in small reservoir populations.
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Affiliation(s)
- S A Riesle-Sbarbaro
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - G Wibbelt
- Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
| | - A Düx
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
- Helmholtz Institute for One Health, Greifswald, Germany
| | - V Kouakou
- LANADA, Laboratoire National d'Appui au Développement Agricole, Bingerville, Côte d'Ivoire
| | - M Bokelmann
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - K Hansen-Kant
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - N Kirchoff
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - M Laue
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - N Kromarek
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - A Lander
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - U Vogel
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - A Wahlbrink
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - D M Wozniak
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
- Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany
| | - D P Scott
- Rocky Mountain Laboratories, National Institutes of Health, Hamilton, MT, USA
| | - J B Prescott
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - L Schaade
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany
| | - E Couacy-Hymann
- LANADA, Laboratoire National d'Appui au Développement Agricole, Bingerville, Côte d'Ivoire
- Centre National de Recherches Agronomiques, LIRED, Abidjan, Côte d'Ivoire
| | - A Kurth
- Center for Biological Threats and Special Pathogens, Robert Koch Institute, Berlin, Germany.
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5
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Akinsulie OC, Idris I, Aliyu VA, Shahzad S, Banwo OG, Ogunleye SC, Olorunshola M, Okedoyin DO, Ugwu C, Oladapo IP, Gbadegoye JO, Akande QA, Babawale P, Rostami S, Soetan KO. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Front Vet Sci 2024; 11:1347550. [PMID: 38356661 PMCID: PMC10864457 DOI: 10.3389/fvets.2024.1347550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.
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Affiliation(s)
- Olalekan Chris Akinsulie
- Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | - Ibrahim Idris
- Faculty of Veterinary Medicine, Usman Danfodiyo University, Sokoto, Nigeria
| | | | - Sammuel Shahzad
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | | | - Seto Charles Ogunleye
- Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Population Medicine and Pathobiology, College of Veterinary Medicine, Mississippi State University, Starkville, MS, United States
| | - Mercy Olorunshola
- Department of Pharmaceutical Microbiology, University of Ibadan, Ibadan, Nigeria
| | - Deborah O. Okedoyin
- Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
| | - Charles Ugwu
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | | | - Joy Olaoluwa Gbadegoye
- Department of Physiology, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Qudus Afolabi Akande
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, United States
| | - Pius Babawale
- Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, United States
| | - Sahar Rostami
- Department of Population Medicine and Pathobiology, College of Veterinary Medicine, Mississippi State University, Starkville, MS, United States
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6
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Lin Y, Pascall DJ. Characterisation of putative novel tick viruses and zoonotic risk prediction. Ecol Evol 2024; 14:e10814. [PMID: 38259958 PMCID: PMC10800298 DOI: 10.1002/ece3.10814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 11/02/2023] [Accepted: 11/24/2023] [Indexed: 01/24/2024] Open
Abstract
Tick-associated viruses remain a substantial zoonotic risk worldwide, so knowledge of the diversity of tick viruses has potential health consequences. Despite their importance, large amounts of sequences in public data sets from tick meta-genomic and -transcriptomic projects remain unannotated, sequence data that could contain undocumented viruses. Through data mining and bioinformatic analysis of more than 37,800 public meta-genomic and -transcriptomic data sets, we found 83 unannotated contigs exhibiting high identity with known tick viruses. These putative viral contigs were classified into three RNA viral families (Alphatetraviridae, Orthomyxoviridae and Chuviridae) and one DNA viral family (Asfarviridae). After manual checking of quality and dissimilarity towards other sequences in the data set, these 83 contigs were reduced to five contigs in the Alphatetraviridae from four putative viruses, four in the Orthomyxoviridae from two putative viruses and one in the Chuviridae which clustered with known tick-associated viruses, forming a separate clade within the viral families. We further attempted to assess which previously known tick viruses likely represent zoonotic risks and thus deserve further investigation. We ranked the human infection potential of 133 known tick-associated viruses using a genome composition-based machine learning model. We found five high-risk tick-associated viruses (Langat virus, Lonestar tick chuvirus 1, Grotenhout virus, Taggert virus and Johnston Atoll virus) that have not been known to infect human and two viral families (Nairoviridae and Phenuiviridae) that contain a large proportion of potential zoonotic tick-associated viruses. This adds to the knowledge of tick virus diversity and highlights the importance of surveillance of newly emerging tick-associated diseases.
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Affiliation(s)
- Yuting Lin
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Royal Veterinary CollegeUniversity of LondonLondonUK
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7
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Ming Z, Chen X, Wang S, Liu H, Yuan Z, Wu M, Xia H. HostNet: improved sequence representation in deep neural networks for virus-host prediction. BMC Bioinformatics 2023; 24:455. [PMID: 38041071 PMCID: PMC10691023 DOI: 10.1186/s12859-023-05582-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand. RESULTS To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability. CONCLUSION HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.
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Affiliation(s)
- Zhaoyan Ming
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China
| | - Xiangjun Chen
- Polytechnic Institute, Zhejiang University, Hangzhou, 310058, China
| | - Shunlong Wang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Hong Liu
- Institute of Biomedicine, Shandong University of Technology, Zibo, 255000, China
| | - Zhiming Yuan
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Minghui Wu
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China.
| | - Han Xia
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China.
- University of Chinese Academy of Sciences, Beijing, 100190, China.
- Hubei Jiangxia Laboratory, Wuhan, 430200, China.
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Zhang YJ, Luo Z, Sun Y, Liu J, Chen Z. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zool Res 2023; 44:1115-1131. [PMID: 37933101 PMCID: PMC10802096 DOI: 10.24272/j.issn.2095-8137.2023.263] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
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Affiliation(s)
- Yu-Juan Zhang
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zeyu Luo
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Yawen Sun
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Junhao Liu
- Chongqing Key Laboratory of Vector Insects
- Chongqing Key Laboratory of Animal Biology
- College of Life Science, Chongqing Normal University, Chongqing 401331, China
| | - Zongqing Chen
- School of Mathematical Sciences
- National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China. E-mail:
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Tseng KK, Koehler H, Becker DJ, Gibb R, Carlson CJ, Fernandez MDP, Seifert SN. Viral genomic features predict orthopoxvirus reservoir hosts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564211. [PMID: 37961540 PMCID: PMC10634857 DOI: 10.1101/2023.10.26.564211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Orthopoxviruses (OPVs), including the causative agents of smallpox and mpox have led to devastating outbreaks in human populations worldwide. However, the discontinuation of smallpox vaccination, which also provides cross-protection against related OPVs, has diminished global immunity to OPVs more broadly. We apply machine learning models incorporating both host ecological and viral genomic features to predict likely reservoirs of OPVs. We demonstrate that incorporating viral genomic features in addition to host ecological traits enhanced the accuracy of potential OPV host predictions, highlighting the importance of host-virus molecular interactions in predicting potential host species. We identify hotspots for geographic regions rich with potential OPV hosts in parts of southeast Asia, equatorial Africa, and the Amazon, revealing high overlap between regions predicted to have a high number of potential OPV host species and those with the lowest smallpox vaccination coverage, indicating a heightened risk for the emergence or establishment of zoonotic OPVs. Our findings can be used to target wildlife surveillance, particularly related to concerns about mpox establishment beyond its historical range.
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Affiliation(s)
- Katie K. Tseng
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Heather Koehler
- School of Molecular Biosciences, Washington State University, Pullman, WA, USA
| | - Daniel J. Becker
- Department of Biology, School of Biological Sciences, University of Oklahoma, Norman, OK, USA
| | - Rory Gibb
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK
- People & Nature Lab, UCL East, University College London, Stratford, London, UK
| | - Colin J. Carlson
- Center for Global Health Science and Security, Georgetown University, Washington, DC, USA
| | | | - Stephanie N. Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
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10
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Mollentze N, Streicker DG. Predicting zoonotic potential of viruses: where are we? Curr Opin Virol 2023; 61:101346. [PMID: 37515983 DOI: 10.1016/j.coviro.2023.101346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/31/2023]
Abstract
The prospect of identifying high-risk viruses and designing interventions to pre-empt their emergence into human populations is enticing, but controversial, particularly when used to justify large-scale virus discovery initiatives. We review the current state of these efforts, identifying three broad classes of predictive models that have differences in data inputs that define their potential utility for triaging newly discovered viruses for further investigation. Prospects for model predictions of public health risk to guide preparedness depend not only on computational improvements to algorithms, but also on more efficient data generation in laboratory, field and clinical settings. Beyond public health applications, efforts to predict zoonoses provide unique research value by creating generalisable understanding of the ecological and evolutionary factors that promote viral emergence.
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Affiliation(s)
- Nardus Mollentze
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom; MRC-University of Glasgow Centre for Virus Research, G61 1QH, United Kingdom
| | - Daniel G Streicker
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom; MRC-University of Glasgow Centre for Virus Research, G61 1QH, United Kingdom.
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11
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Jiang S, Zhang S, Kang X, Feng Y, Li Y, Nie M, Li Y, Chen Y, Zhao S, Jiang T, Li J. Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor. Viruses 2023; 15:1556. [PMID: 37515242 PMCID: PMC10384923 DOI: 10.3390/v15071556] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.
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Affiliation(s)
- Shuyang Jiang
- College of Mathematics, Jilin University, Changchun, Jilin 130012, China
| | - Sen Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Xiaoping Kang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Ye Feng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Yadan Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Maoshun Nie
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Yuchang Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Yuehong Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Shishun Zhao
- College of Mathematics, Jilin University, Changchun, Jilin 130012, China
| | - Tao Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
| | - Jing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing 100071, China
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12
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Poisot T, Ouellet MA, Mollentze N, Farrell MJ, Becker DJ, Brierley L, Albery GF, Gibb RJ, Seifert SN, Carlson CJ. Network embedding unveils the hidden interactions in the mammalian virome. PATTERNS (NEW YORK, N.Y.) 2023; 4:100738. [PMID: 37409053 PMCID: PMC10318366 DOI: 10.1016/j.patter.2023.100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 01/19/2023] [Accepted: 03/31/2023] [Indexed: 07/07/2023]
Abstract
Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.
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Affiliation(s)
- Timothée Poisot
- Département de Sciences Biologiques, Université de Montréal, Montréal, QC, Canada
| | - Marie-Andrée Ouellet
- Département de Sciences Biologiques, Université de Montréal, Montréal, QC, Canada
| | - Nardus Mollentze
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
- MRC – University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
| | | | - Liam Brierley
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | | | - Rory J. Gibb
- Center for Biodiversity & Environment Research, University College, London, UK
| | - Stephanie N. Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Colin J. Carlson
- Center for Global Health Science and Security, Georgetown University, Washington, DC, USA
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13
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Walsh SK, Imrie RM, Matuszewska M, Paterson GK, Weinert LA, Hadfield JD, Buckling A, Longdon B. The host phylogeny determines viral infectivity and replication across Staphylococcus host species. PLoS Pathog 2023; 19:e1011433. [PMID: 37289828 PMCID: PMC10284401 DOI: 10.1371/journal.ppat.1011433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 06/21/2023] [Accepted: 05/18/2023] [Indexed: 06/10/2023] Open
Abstract
Virus host shifts, where a virus transmits to and infects a novel host species, are a major source of emerging infectious disease. Genetic similarity between eukaryotic host species has been shown to be an important determinant of the outcome of virus host shifts, but it is unclear if this is the case for prokaryotes where anti-virus defences can be transmitted by horizontal gene transfer and evolve rapidly. Here, we measure the susceptibility of 64 strains of Staphylococcaceae bacteria (48 strains of Staphylococcus aureus and 16 non-S. aureus species spanning 2 genera) to the bacteriophage ISP, which is currently under investigation for use in phage therapy. Using three methods-plaque assays, optical density (OD) assays, and quantitative (q)PCR-we find that the host phylogeny explains a large proportion of the variation in susceptibility to ISP across the host panel. These patterns were consistent in models of only S. aureus strains and models with a single representative from each Staphylococcaceae species, suggesting that these phylogenetic effects are conserved both within and among host species. We find positive correlations between susceptibility assessed using OD and qPCR and variable correlations between plaque assays and either OD or qPCR, suggesting that plaque assays alone may be inadequate to assess host range. Furthermore, we demonstrate that the phylogenetic relationships between bacterial hosts can generally be used to predict the susceptibility of bacterial strains to phage infection when the susceptibility of closely related hosts is known, although this approach produced large prediction errors in multiple strains where phylogeny was uninformative. Together, our results demonstrate the ability of bacterial host evolutionary relatedness to explain differences in susceptibility to phage infection, with implications for the development of ISP both as a phage therapy treatment and as an experimental system for the study of virus host shifts.
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Affiliation(s)
- Sarah K. Walsh
- Centre for Ecology and Conservation; Faculty of Environment, Science, and Economy; Biosciences; University of Exeter; Cornwall; United Kingdom
- Environment and Sustainability Institute; University of Exeter; Cornwall; United Kingdom
| | - Ryan M. Imrie
- Centre for Ecology and Conservation; Faculty of Environment, Science, and Economy; Biosciences; University of Exeter; Cornwall; United Kingdom
| | - Marta Matuszewska
- Department of Medicine; University of Cambridge; Cambridge; United Kingdom
| | - Gavin K. Paterson
- Royal (Dick) School of Veterinary Studies and the Roslin Institute; University of Edinburgh;Edinburgh; United Kingdom
| | - Lucy A. Weinert
- Department of Veterinary Medicine; University of Cambridge; Cambridge; United Kingdom
| | - Jarrod D. Hadfield
- Institute of Evolutionary Biology; The University of Edinburgh; Edinburgh; United Kingdom
| | - Angus Buckling
- Centre for Ecology and Conservation; Faculty of Environment, Science, and Economy; Biosciences; University of Exeter; Cornwall; United Kingdom
- Environment and Sustainability Institute; University of Exeter; Cornwall; United Kingdom
| | - Ben Longdon
- Centre for Ecology and Conservation; Faculty of Environment, Science, and Economy; Biosciences; University of Exeter; Cornwall; United Kingdom
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14
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Barrile GM, Augustine DJ, Porensky LM, Duchardt CJ, Shoemaker KT, Hartway CR, Derner JD, Hunter EA, Davidson AD. A big data-model integration approach for predicting epizootics and population recovery in a keystone species. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2023; 33:e2827. [PMID: 36846939 DOI: 10.1002/eap.2827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/21/2022] [Accepted: 01/10/2023] [Indexed: 06/02/2023]
Abstract
Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
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Affiliation(s)
- Gabriel M Barrile
- Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | | | | | - Courtney J Duchardt
- Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Kevin T Shoemaker
- Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada, USA
| | | | | | - Elizabeth A Hunter
- U.S. Geological Survey, Virginia Cooperative Fish and Wildlife Research Unit, Department of Fisheries and Wildlife Conservation, Virginia Tech, Blacksburg, Virginia, USA
| | - Ana D Davidson
- Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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15
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Fontdevila Pareta N, Khalili M, Maachi A, Rivarez MPS, Rollin J, Salavert F, Temple C, Aranda MA, Boonham N, Botermans M, Candresse T, Fox A, Hernando Y, Kutnjak D, Marais A, Petter F, Ravnikar M, Selmi I, Tahzima R, Trontin C, Wetzel T, Massart S. Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis. Front Microbiol 2023; 14:1181562. [PMID: 37323908 PMCID: PMC10265641 DOI: 10.3389/fmicb.2023.1181562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/25/2023] [Indexed: 06/17/2023] Open
Abstract
The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties.
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Affiliation(s)
| | - Maryam Khalili
- Univ. Bordeaux, INRAE, UMR BFP, Villenave d'Ornon, France
- EGFV, Univ. Bordeaux, INRAE, ISVV, Villenave d’Ornon, France
| | | | - Mark Paul S. Rivarez
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
- College of Agriculture and Agri-Industries, Caraga State University, Butuan, Philippines
| | - Johan Rollin
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- DNAVision (Belgium), Charleroi, Belgium
| | - Ferran Salavert
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Coline Temple
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Miguel A. Aranda
- Department of Stress Biology and Plant Pathology, Center for Edaphology and Applied Biology of Segura, Spanish National Research Council (CSIC), Murcia, Spain
| | - Neil Boonham
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marleen Botermans
- Netherlands Institute for Vectors, Invasive Plants and Plant Health (NIVIP), Wageningen, Netherlands
| | | | - Adrian Fox
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
- Fera Science Ltd, York Biotech Campus, York, United Kingdom
| | | | - Denis Kutnjak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Armelle Marais
- Univ. Bordeaux, INRAE, UMR BFP, Villenave d'Ornon, France
| | | | - Maja Ravnikar
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Ilhem Selmi
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Rachid Tahzima
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- Plant Sciences Unit, Institute for Agricultural, Fisheries and Food Research (ILVO), Merelbeke, Belgium
| | - Charlotte Trontin
- European and Mediterranean Plant Protection Organization, Paris, France
| | - Thierry Wetzel
- DLR Rheinpfalz, Institute of Plant Protection, Neustadt an der Weinstrasse, Germany
| | - Sebastien Massart
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- Bioversity International, Montpellier, France
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16
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Simmonds P, Adriaenssens EM, Zerbini FM, Abrescia NGA, Aiewsakun P, Alfenas-Zerbini P, Bao Y, Barylski J, Drosten C, Duffy S, Duprex WP, Dutilh BE, Elena SF, García ML, Junglen S, Katzourakis A, Koonin EV, Krupovic M, Kuhn JH, Lambert AJ, Lefkowitz EJ, Łobocka M, Lood C, Mahony J, Meier-Kolthoff JP, Mushegian AR, Oksanen HM, Poranen MM, Reyes-Muñoz A, Robertson DL, Roux S, Rubino L, Sabanadzovic S, Siddell S, Skern T, Smith DB, Sullivan MB, Suzuki N, Turner D, Van Doorslaer K, Vandamme AM, Varsani A, Vasilakis N. Four principles to establish a universal virus taxonomy. PLoS Biol 2023; 21:e3001922. [PMID: 36780432 PMCID: PMC9925010 DOI: 10.1371/journal.pbio.3001922] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Abstract
A universal taxonomy of viruses is essential for a comprehensive view of the virus world and for communicating the complicated evolutionary relationships among viruses. However, there are major differences in the conceptualisation and approaches to virus classification and nomenclature among virologists, clinicians, agronomists, and other interested parties. Here, we provide recommendations to guide the construction of a coherent and comprehensive virus taxonomy, based on expert scientific consensus. Firstly, assignments of viruses should be congruent with the best attainable reconstruction of their evolutionary histories, i.e., taxa should be monophyletic. This fundamental principle for classification of viruses is currently included in the International Committee on Taxonomy of Viruses (ICTV) code only for the rank of species. Secondly, phenotypic and ecological properties of viruses may inform, but not override, evolutionary relatedness in the placement of ranks. Thirdly, alternative classifications that consider phenotypic attributes, such as being vector-borne (e.g., "arboviruses"), infecting a certain type of host (e.g., "mycoviruses," "bacteriophages") or displaying specific pathogenicity (e.g., "human immunodeficiency viruses"), may serve important clinical and regulatory purposes but often create polyphyletic categories that do not reflect evolutionary relationships. Nevertheless, such classifications ought to be maintained if they serve the needs of specific communities or play a practical clinical or regulatory role. However, they should not be considered or called taxonomies. Finally, while an evolution-based framework enables viruses discovered by metagenomics to be incorporated into the ICTV taxonomy, there are essential requirements for quality control of the sequence data used for these assignments. Combined, these four principles will enable future development and expansion of virus taxonomy as the true evolutionary diversity of viruses becomes apparent.
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Affiliation(s)
- Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - F. Murilo Zerbini
- Departamento de Fitopatologia/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Nicola G. A. Abrescia
- Structure and Cell Biology of Viruses Lab, Center for Cooperative Research in Biosciences—BRTA, Derio, Spain
- Basque Foundation for Science, IKERBASQUE, Bilbao, Spain
| | - Pakorn Aiewsakun
- Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jakub Barylski
- Department of Molecular Virology, Adam Mickiewicz University, Poznan, Poland
| | - Christian Drosten
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Siobain Duffy
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers The State University of New Jersey, New Brunswick, New Jersey, United States of America
| | - W. Paul Duprex
- The Center for Vaccine Research, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Bas E. Dutilh
- Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich-Schiller-University, Jena, Germany
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, the Netherlands
| | - Santiago F. Elena
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, Valencia, Spain
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Maria Laura García
- Instituto de Biotecnología y Biología Molecular, CCT-La Plata, CONICET, UNLP, La Plata, Argentina
| | - Sandra Junglen
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Aris Katzourakis
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mart Krupovic
- Institut Pasteur, Université Paris Cité, CNRS UMR6047, Archaeal Virology Unit, Paris, France
| | - Jens H. Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, Maryland, United States of America
| | - Amy J. Lambert
- Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America
| | - Elliot J. Lefkowitz
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Małgorzata Łobocka
- Institute of Biochemistry and Biophysics of the Polish Academy of Sciences, Warsaw, Poland
| | - Cédric Lood
- Department of Biosystems, KU Leuven, Leuven, Belgium
| | - Jennifer Mahony
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Jan P. Meier-Kolthoff
- Department of Bioinformatics and Databases, Leibniz Institute DSMZ—German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany
| | - Arcady R. Mushegian
- Division of Molecular and Cellular Biosciences, National Science Foundation, Alexandria, Virginia, United States of America
| | - Hanna M. Oksanen
- Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Minna M. Poranen
- Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Alejandro Reyes-Muñoz
- Max Planck Tandem Group in Computational Biology, Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Simon Roux
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Luisa Rubino
- Istituto per la Protezione Sostenibile delle Piante, CNR, UOS Bari, Bari, Italy
| | - Sead Sabanadzovic
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, Mississippi, United States of America
| | - Stuart Siddell
- School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - Tim Skern
- Medical University of Vienna, Max Perutz Labs, Vienna Biocenter, Vienna, Austria
| | - Donald B. Smith
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Matthew B. Sullivan
- Departments of Microbiology and Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, Ohio, United States of America
| | - Nobuhiro Suzuki
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, Japan
| | - Dann Turner
- School of Applied Sciences, College of Health, Science and Society, University of the West of England, Bristol, United Kingdom
| | - Koenraad Van Doorslaer
- School of Animal and Comparative Biomedical Sciences, Department of Immunobiology, BIO5 Institute, and University of Arizona Cancer Center, Tucson, Arizona, United States of America
| | - Anne-Mieke Vandamme
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Leuven, Belgium
- Center for Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Nikos Vasilakis
- Department of Pathology, Center of Vector-Borne and Zoonotic Diseases, Institute for Human Infection and Immunity and World Reference Center for Emerging Viruses and Arboviruses, The University of Texas Medical Branch, Galveston, Texas, United States of America
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17
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Bajiya N, Dhall A, Aggarwal S, Raghava GPS. Advances in the field of phage-based therapy with special emphasis on computational resources. Brief Bioinform 2023; 24:6961791. [PMID: 36575815 DOI: 10.1093/bib/bbac574] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/07/2022] [Accepted: 11/25/2022] [Indexed: 12/29/2022] Open
Abstract
In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Suchet Aggarwal
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
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18
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Li J, Tian F, Zhang S, Liu SS, Kang XP, Li YD, Wei JQ, Lin W, Lei Z, Feng Y, Jiang JF, Jiang T, Tong Y. Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning. Front Microbiol 2023; 14:1157608. [PMID: 37213516 PMCID: PMC10198438 DOI: 10.3389/fmicb.2023.1157608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 04/03/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals. Methods The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, ORF1ab and Spike. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs. Results and discussion The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Chiroptera, Primates, Rodentia/Lagomorpha, and Suiformes. The DCR-based CNN with five host labels (without Chiroptera) predicted a dominant adaptation of bat CoVs to Artiodactyla hosts, then to Carnivora and Rodentia/Lagomorpha mammals, and later to primates. Moreover, a linear asymptotic adaptation of all CoVs (except Suiformes) from Artiodactyla to Carnivora and Rodentia/Lagomorpha and then to Primates indicates an asymptotic bats-other mammals-human adaptation. Conclusion Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.
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Affiliation(s)
- Jing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Fengjuan Tian
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Sen Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Shun-Shuai Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Xiao-Ping Kang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Ya-Dan Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Jun-Qing Wei
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Wei Lin
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Zhongyi Lei
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Ye Feng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
| | - Jia-Fu Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
- Jia-Fu Jiang
| | - Tao Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, AMMS, Beijing, China
- Tao Jiang
| | - Yigang Tong
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering (BAIC-SM), College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
- *Correspondence: Yigang Tong
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19
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Dunay E, Owens LA, Dunn CD, Rukundo J, Atencia R, Cole MF, Cantwell A, Emery Thompson M, Rosati AG, Goldberg TL. Viruses in sanctuary chimpanzees across Africa. Am J Primatol 2023; 85:e23452. [PMID: 36329642 PMCID: PMC9812903 DOI: 10.1002/ajp.23452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
Infectious disease is a major concern for both wild and captive primate populations. Primate sanctuaries in Africa provide critical protection to thousands of wild-born, orphan primates confiscated from the bushmeat and pet trades. However, uncertainty about the infectious agents these individuals potentially harbor has important implications for their individual care and long-term conservation strategies. We used metagenomic next-generation sequencing to identify viruses in blood samples from chimpanzees (Pan troglodytes) in three sanctuaries in West, Central, and East Africa. Our goal was to evaluate whether viruses of human origin or other "atypical" or unknown viruses might infect these chimpanzees. We identified viruses from eight families: Anelloviridae, Flaviviridae, Genomoviridae, Hepadnaviridae, Parvoviridae, Picobirnaviridae, Picornaviridae, and Rhabdoviridae. The majority (15/26) of viruses identified were members of the family Anelloviridae and represent the genera Alphatorquevirus (torque teno viruses) and Betatorquevirus (torque teno mini viruses), which are common in chimpanzees and apathogenic. Of the remaining 11 viruses, 9 were typical constituents of the chimpanzee virome that have been identified in previous studies and are also thought to be apathogenic. One virus, a novel tibrovirus (Rhabdoviridae: Tibrovirus) is related to Bas-Congo virus, which was originally thought to be a human pathogen but is currently thought to be apathogenic, incidental, and vector-borne. The only virus associated with disease was rhinovirus C (Picornaviridae: Enterovirus) infecting one chimpanzee subsequent to an outbreak of respiratory illness at that sanctuary. Our results suggest that the blood-borne virome of African sanctuary chimpanzees does not differ appreciably from that of their wild counterparts, and that persistent infection with exogenous viruses may be less common than often assumed.
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Affiliation(s)
- Emily Dunay
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Leah A Owens
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Christopher D Dunn
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Joshua Rukundo
- Ngamba Island Chimpanzee Sanctuary/Chimpanzee Trust, Entebbe, Uganda
| | - Rebeca Atencia
- Jane Goodall Institute Congo, Pointe-Noire, Republic of Congo
| | - Megan F Cole
- Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Averill Cantwell
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Alexandra G Rosati
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA.,Department of Anthropology, University of Michigan, Ann Arbor, Michigan, USA
| | - Tony L Goldberg
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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20
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Kawasaki J, Tomonaga K, Horie M. Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing. Microbiol Immunol 2023; 67:1-13. [PMID: 36259224 DOI: 10.1111/1348-0421.13033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 09/28/2022] [Accepted: 10/16/2022] [Indexed: 01/10/2023]
Abstract
Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.
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Affiliation(s)
- Junna Kawasaki
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.,Laboratory of RNA Viruses, Department of Mammalian Regulatory Network, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.,Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Keizo Tomonaga
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.,Laboratory of RNA Viruses, Department of Mammalian Regulatory Network, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.,Department of Molecular Virology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masayuki Horie
- Division of Veterinary Sciences, Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan.,Osaka International Research Center for Infectious Diseases, Osaka Prefecture University, Osaka, Japan
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21
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Pegg CL, Schulz BL, Neely BA, Albery GF, Carlson CJ. Glycosylation and the global virome. Mol Ecol 2023; 32:37-44. [PMID: 36217579 PMCID: PMC10947461 DOI: 10.1111/mec.16731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/25/2022] [Accepted: 09/29/2022] [Indexed: 12/29/2022]
Abstract
The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation - and harnessing it for predictive science - could help close the molecular gap in zoonotic risk assessment.
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Affiliation(s)
- Cassandra L. Pegg
- School of Chemistry and Molecular BiosciencesThe University of QueenslandSt LuciaQueenslandAustralia
| | - Benjamin L. Schulz
- School of Chemistry and Molecular BiosciencesThe University of QueenslandSt LuciaQueenslandAustralia
| | - Benjamin A. Neely
- National Institute of Standards and TechnologyCharlestonSouth CarolinaUSA
| | - Gregory F. Albery
- Department of BiologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Colin J. Carlson
- Department of BiologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
- Department of Microbiology and ImmunologyGeorgetown University Medical CenterWashingtonDistrict of ColumbiaUSA
- Center for Global Health Science and SecurityGeorgetown University Medical CenterWashingtonDistrict of ColumbiaUSA
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22
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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23
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Metavirome of 31 tick species provides a compendium of 1,801 RNA virus genomes. Nat Microbiol 2023; 8:162-173. [PMID: 36604510 PMCID: PMC9816062 DOI: 10.1038/s41564-022-01275-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 10/20/2022] [Indexed: 01/07/2023]
Abstract
The increasing prevalence and expanding distribution of tick-borne viruses globally have raised health concerns, but the full repertoire of the tick virome has not been assessed. We sequenced the meta-transcriptomes of 31 different tick species in the Ixodidae and Argasidae families from across mainland China, and identified 724 RNA viruses with distinctive virome compositions among genera. A total of 1,801 assembled and complete or nearly complete viral genomes revealed an extensive diversity of genome architectures of tick-associated viruses, highlighting ticks as a reservoir of RNA viruses. We examined the phylogenies of different virus families to investigate virome evolution and found that the most diverse tick-associated viruses are positive-strand RNA virus families that demonstrate more ancient divergence than other arboviruses. Tick-specific viruses are often associated with only a few tick species, whereas virus clades that can infect vertebrates are found in a wider range of tick species. We hypothesize that tick viruses can exhibit both 'specialist' and 'generalist' evolutionary trends. We hope that our virome dataset will enable much-needed research on vertebrate-pathogenic tick-associated viruses.
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24
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Enveloped viruses show increased propensity to cross-species transmission and zoonosis. Proc Natl Acad Sci U S A 2022; 119:e2215600119. [PMID: 36472956 PMCID: PMC9897429 DOI: 10.1073/pnas.2215600119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The transmission of viruses between different host species is a major source of emerging diseases and is of particular concern in the case of zoonotic transmission from mammals to humans. Several zoonosis risk factors have been identified, but it is currently unclear which viral traits primarily determine this process as previous work has focused on a few hundred viruses that are not representative of actual viral diversity. Here, we investigate fundamental virological traits that influence cross-species transmissibility and zoonotic propensity by interrogating a database of over 12,000 mammalian virus-host associations. Our analysis reveals that enveloped viruses tend to infect more host species and are more likely to be zoonotic than nonenveloped viruses, while other viral traits such as genome composition, structure, size, or the viral replication compartment play a less obvious role. This contrasts with the previous notion that viral envelopes did not significantly impact or even reduce zoonotic risk and should help better prioritize outbreak prevention efforts. We suggest several mechanisms by which viral envelopes could promote cross-species transmissibility, including structural flexibility of receptor-binding proteins and evasion of viral entry barriers.
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25
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Contrasting the Practices of Virus Isolation and Characterization between the Early Period in History and Modern Times: The Case of Japanese Encephalitis Virus. Viruses 2022; 14:v14122640. [PMID: 36560644 PMCID: PMC9781737 DOI: 10.3390/v14122640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/18/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
Japanese encephalitis is a serious disease transmitted by mosquitoes. With its recent spread beyond the traditional territory of endemicity in Asia, the magnitude of global threat has increased sharply. While much of the current research are largely focused on changing epidemiology, molecular genetics of virus, and vaccination, little attention has been paid to the early history of virus isolation and phenotypic characterization of this virus. In this review, using this piece of history as an example, I review the transition of the concept and practice of virus isolation and characterization from the early period of history to modern times. The spectacular development of molecular techniques in modern times has brought many changes in practices as well as enormous amount of new knowledge. However, many aspects of virus characterization, in particular, transmission mechanism and host relationship, remain unsolved. As molecular techniques are not perfect in all respects, beneficial accommodation of molecular and biologic data is critically important in many branches of research. Accordingly, I emphasize exercising caution in applying only these modern techniques, point out unrecognized communication problems, and stress that JE research history is a rich source of interesting works still valuable even today and waiting to be discovered.
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26
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Becker DJ, Albery GF, Sjodin AR, Poisot T, Bergner LM, Chen B, Cohen LE, Dallas TA, Eskew EA, Fagre AC, Farrell MJ, Guth S, Han BA, Simmons NB, Stock M, Teeling EC, Carlson CJ. Optimising predictive models to prioritise viral discovery in zoonotic reservoirs. THE LANCET. MICROBE 2022; 3:e625-e637. [PMID: 35036970 PMCID: PMC8747432 DOI: 10.1016/s2666-5247(21)00245-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.
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Affiliation(s)
- Daniel J Becker
- Department of Biology, University of Oklahoma, Norman, OK, USA
| | - Gregory F Albery
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Anna R Sjodin
- Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Timothée Poisot
- Université de Montréal, Département de Sciences Biologiques, Montréal, QC, Canada
| | - Laura M Bergner
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Medical Research Centre, University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Binqi Chen
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA
| | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad A Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna C Fagre
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
- Bat Health Foundation, Fort Collins, CO, USA
| | - Maxwell J Farrell
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON, Canada
| | - Sarah Guth
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Barbara A Han
- Cary Institute of Ecosystem Studies, Millbrook, NY, USA
| | - Nancy B Simmons
- Department of Mammalogy, Division of Vertebrate Zoology, American Museum of Natural History, New York, NY, USA
| | - Michiel Stock
- Research Unit Knowledge-based Systems, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Emma C Teeling
- School of Biology and Environmental Science, Science Centre West, University College Dublin, Dublin, Ireland
| | - Colin J Carlson
- Department of Biology, Georgetown University, Washington, DC, USA
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
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27
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Nan BG, Zhang S, Li YC, Kang XP, Chen YH, Li L, Jiang T, Li J. Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants. Viruses 2022; 14:v14051072. [PMID: 35632811 PMCID: PMC9147419 DOI: 10.3390/v14051072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 12/04/2022] Open
Abstract
The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.
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Affiliation(s)
| | | | | | | | | | | | | | - Jing Li
- Correspondence: (T.J.); (J.L.)
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28
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Guth S, Mollentze N, Renault K, Streicker DG, Visher E, Boots M, Brook CE. Bats host the most virulent-but not the most dangerous-zoonotic viruses. Proc Natl Acad Sci U S A 2022; 119:e2113628119. [PMID: 35349342 DOI: 10.1101/2021.07.25.453574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023] Open
Abstract
SignificanceThe clear need to mitigate zoonotic risk has fueled increased viral discovery in specific reservoir host taxa. We show that a combination of viral and reservoir traits can predict zoonotic virus virulence and transmissibility in humans, supporting the hypothesis that bats harbor exceptionally virulent zoonoses. However, pandemic prevention requires thinking beyond zoonotic capacity, virulence, and transmissibility to consider collective "burden" on human health. For this, viral discovery targeting specific reservoirs may be inefficient as death burden correlates with viral, not reservoir, traits, and depends on context-specific epidemiological dynamics across and beyond the human-animal interface. These findings suggest that longitudinal studies of viral dynamics in reservoir and spillover host populations may offer the most effective strategy for mitigating zoonotic risk.
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Affiliation(s)
- Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Nardus Mollentze
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, United Kingdom
| | - Katia Renault
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Daniel G Streicker
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Elisa Visher
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Mike Boots
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
- Centre for Ecology and Conservation, University of Exeter, Exeter TR10 9FE, United Kingdom
| | - Cara E Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
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29
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Guth S, Mollentze N, Renault K, Streicker DG, Visher E, Boots M, Brook CE. Bats host the most virulent-but not the most dangerous-zoonotic viruses. Proc Natl Acad Sci U S A 2022; 119:e2113628119. [PMID: 35349342 PMCID: PMC9168486 DOI: 10.1073/pnas.2113628119] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/09/2022] [Indexed: 01/06/2023] Open
Abstract
SignificanceThe clear need to mitigate zoonotic risk has fueled increased viral discovery in specific reservoir host taxa. We show that a combination of viral and reservoir traits can predict zoonotic virus virulence and transmissibility in humans, supporting the hypothesis that bats harbor exceptionally virulent zoonoses. However, pandemic prevention requires thinking beyond zoonotic capacity, virulence, and transmissibility to consider collective "burden" on human health. For this, viral discovery targeting specific reservoirs may be inefficient as death burden correlates with viral, not reservoir, traits, and depends on context-specific epidemiological dynamics across and beyond the human-animal interface. These findings suggest that longitudinal studies of viral dynamics in reservoir and spillover host populations may offer the most effective strategy for mitigating zoonotic risk.
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Affiliation(s)
- Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Nardus Mollentze
- Medical Research Council–University of Glasgow Centre for Virus Research, Glasgow G61 1QH, United Kingdom
| | - Katia Renault
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Daniel G. Streicker
- Medical Research Council–University of Glasgow Centre for Virus Research, Glasgow G61 1QH, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Elisa Visher
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
| | - Mike Boots
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
- Centre for Ecology and Conservation, University of Exeter, Exeter TR10 9FE, United Kingdom
| | - Cara E. Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637
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30
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Fagre AC, Cohen LE, Eskew EA, Farrell M, Glennon E, Joseph MB, Frank HK, Ryan SJ, Carlson CJ, Albery GF. Assessing the risk of human-to-wildlife pathogen transmission for conservation and public health. Ecol Lett 2022; 25:1534-1549. [PMID: 35318793 PMCID: PMC9313783 DOI: 10.1111/ele.14003] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 12/16/2022]
Abstract
The SARS‐CoV‐2 pandemic has led to increased concern over transmission of pathogens from humans to animals, and its potential to threaten conservation and public health. To assess this threat, we reviewed published evidence of human‐to‐wildlife transmission events, with a focus on how such events could threaten animal and human health. We identified 97 verified examples, involving a wide range of pathogens; however, reported hosts were mostly non‐human primates or large, long‐lived captive animals. Relatively few documented examples resulted in morbidity and mortality, and very few led to maintenance of a human pathogen in a new reservoir or subsequent “secondary spillover” back into humans. We discuss limitations in the literature surrounding these phenomena, including strong evidence of sampling bias towards non‐human primates and human‐proximate mammals and the possibility of systematic bias against reporting human parasites in wildlife, both of which limit our ability to assess the risk of human‐to‐wildlife pathogen transmission. We outline how researchers can collect experimental and observational evidence that will expand our capacity for risk assessment for human‐to‐wildlife pathogen transmission.
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Affiliation(s)
- Anna C Fagre
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, USA.,Bat Health Foundation, Fort Collins, Colorado, USA
| | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, New York City, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, Washington, USA
| | - Max Farrell
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Emma Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Maxwell B Joseph
- Earth Lab, University of Colorado Boulder, Boulder, Colorado, USA
| | - Hannah K Frank
- Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, Louisina, USA
| | - Sadie J Ryan
- Quantitative Disease Ecology and Conservation (QDEC) Lab Group, Department of Geography, University of Florida, Gainesville, Florida, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA.,School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, District of Columbia, USA.,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Gregory F Albery
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
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31
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Li J, Wu YN, Zhang S, Kang XP, Jiang T. Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants. Brief Bioinform 2022; 23:6540151. [PMID: 35233612 PMCID: PMC9116219 DOI: 10.1093/bib/bbac036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/23/2022] [Accepted: 01/25/2022] [Indexed: 12/27/2022] Open
Abstract
Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes—receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)—of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.
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Affiliation(s)
- Jing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology 10 and Epidemiology, Beijing 100071, China
| | - Ya-Nan Wu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology 10 and Epidemiology, Beijing 100071, China
| | - Sen Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology 10 and Epidemiology, Beijing 100071, China
| | - Xiao-Ping Kang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology 10 and Epidemiology, Beijing 100071, China
| | - Tao Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology 10 and Epidemiology, Beijing 100071, China
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32
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Gaunt ER, Digard P. Compositional biases in RNA viruses: Causes, consequences and applications. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1679. [PMID: 34155814 PMCID: PMC8420353 DOI: 10.1002/wrna.1679] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 01/05/2023]
Abstract
If each of the four nucleotides were represented equally in the genomes of viruses and the hosts they infect, each base would occur at a frequency of 25%. However, this is not observed in nature. Similarly, the order of nucleotides is not random (e.g., in the human genome, guanine follows cytosine at a frequency of ~0.0125, or a quarter the number of times predicted by random representation). Codon usage and codon order are also nonrandom. Furthermore, nucleotide and codon biases vary between species. Such biases have various drivers, including cellular proteins that recognize specific patterns in nucleic acids, that once triggered, induce mutations or invoke intrinsic or innate immune responses. In this review we examine the types of compositional biases identified in viral genomes and current understanding of the evolutionary mechanisms underpinning these trends. Finally, we consider the potential for large scale synonymous recoding strategies to engineer RNA virus vaccines, including those with pandemic potential, such as influenza A virus and Severe Acute Respiratory Syndrome Coronavirus Virus 2. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Evolution and Genomics > Computational Analyses of RNA RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition.
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Affiliation(s)
- Eleanor R. Gaunt
- Department of Infection and ImmunityThe Roslin Institute, The University of EdinburghEdinburghUK
| | - Paul Digard
- Department of Infection and ImmunityThe Roslin Institute, The University of EdinburghEdinburghUK
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33
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Edridge AWD, Abd-Elfarag G, Deijs M, Jebbink MF, Boele van Hensbroek M, van der Hoek L. Divergent Rhabdovirus Discovered in a Patient with New-Onset Nodding Syndrome. Viruses 2022; 14:v14020210. [PMID: 35215803 PMCID: PMC8880091 DOI: 10.3390/v14020210] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/14/2022] [Accepted: 01/16/2022] [Indexed: 12/15/2022] Open
Abstract
A divergent rhabdovirus was discovered in the bloodstream of a 15-year-old girl with Nodding syndrome from Mundri West County in South Sudan. Nodding syndrome is a progressive degenerative neuropathy of unknown cause affecting thousands of individuals in Sub-Saharan Africa. The index case was previously healthy until she developed head-nodding seizures four months prior to presentation. Virus discovery by VIDISCA-NGS on the patient’s plasma detected multiple sequence reads belonging to a divergent rhabdovirus. The viral load was 3.85 × 103 copies/mL in the patient’s plasma and undetectable in her cerebrospinal fluid. Further genome walking allowed for the characterization of full coding sequences of all the viral proteins (N, P, M, U1, U2, G, U3, and L). We tentatively named the virus “Mundri virus” (MUNV) and classified it as a novel virus species based on the high divergence from other known viruses (all proteins had less than 43% amino acid identity). Phylogenetic analysis revealed that MUNV forms a monophyletic clade with several human-infecting tibroviruses prevalent in Central Africa. A bioinformatic machine-learning algorithm predicted MUNV to be an arbovirus (bagged prediction strength (BPS) of 0.9) transmitted by midges (BPS 0.4) with an artiodactyl host reservoir (BPS 0.9). An association between MUNV infection and Nodding syndrome was evaluated in a case–control study of 72 patients with Nodding syndrome (including the index case) matched to 65 healthy households and 48 community controls. No subject, besides the index case, was positive for MUNV RNA in their plasma. A serological assay detecting MUNV anti-nucleocapsid found, respectively, in 28%, 22%, and 16% of cases, household controls and community controls to be seropositive with no significant differences between cases and either control group. This suggests that MUNV commonly infects children in South Sudan yet may not be causally associated with Nodding syndrome.
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Affiliation(s)
- Arthur W. D. Edridge
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (M.D.); (M.F.J.)
- Center for Global Child Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (G.A.-E.); (M.B.v.H.)
- Correspondence: (A.W.D.E.); (L.v.d.H.)
| | - Gasim Abd-Elfarag
- Center for Global Child Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (G.A.-E.); (M.B.v.H.)
| | - Martin Deijs
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (M.D.); (M.F.J.)
| | - Maarten F. Jebbink
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (M.D.); (M.F.J.)
| | - Michael Boele van Hensbroek
- Center for Global Child Health, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (G.A.-E.); (M.B.v.H.)
| | - Lia van der Hoek
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (M.D.); (M.F.J.)
- Correspondence: (A.W.D.E.); (L.v.d.H.)
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34
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Schaeffer R, Temeeyasen G, Hause BM. Alphacoronaviruses Are Common in Bats in the Upper Midwestern United States. Viruses 2022; 14:v14020184. [PMID: 35215778 PMCID: PMC8877427 DOI: 10.3390/v14020184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 02/05/2023] Open
Abstract
Bats are a reservoir for coronaviruses (CoVs) that periodically spill over to humans, as evidenced by severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. A collection of 174 bat samples originating from South Dakota, Minnesota, Iowa, and Nebraska submitted for rabies virus testing due to human exposure were analyzed using a pan-coronavirus PCR. A previously partially characterized CoV, Eptesicus bat CoV, was identified in 12 (6.9%) samples by nested RT-PCR. Six near-complete genomes were determined. Genetic analysis found a high similarity between all CoV-positive samples, Rocky Mountain bat CoV 65 and alphacoronavirus HCQD-2020 recently identified in South Korea. Phylogenetic analysis of genome sequences showed EbCoV is closely related to bat CoV HKU2 and swine acute diarrhea syndrome CoV; however, topological incongruences were noted for the spike gene that was more closely related to porcine epidemic diarrhea virus. Similar to some alphaCoVs, a novel gene, ORF7, was discovered downstream of the nucleocapsid, whose protein lacked similarity to known proteins. The widespread circulation of EbCoV with similarities to bat viruses that have spilled over to swine warrants further surveillance.
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35
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Fay EJ, Balla KM, Roach SN, Shepherd FK, Putri DS, Wiggen TD, Goldstein SA, Pierson MJ, Ferris MT, Thefaine CE, Tucker A, Salnikov M, Cortez V, Compton SR, Kotenko SV, Hunter RC, Masopust D, Elde NC, Langlois RA. Natural rodent model of viral transmission reveals biological features of virus population dynamics. J Exp Med 2021; 219:212940. [PMID: 34958350 PMCID: PMC8713297 DOI: 10.1084/jem.20211220] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 12/21/2022] Open
Abstract
Emerging viruses threaten global health, but few experimental models can characterize the virus and host factors necessary for within- and cross-species transmission. Here, we leverage a model whereby pet store mice or rats-which harbor natural rodent pathogens-are cohoused with laboratory mice. This "dirty" mouse model offers a platform for studying acute transmission of viruses between and within hosts via natural mechanisms. We identified numerous viruses and other microbial species that transmit to cohoused mice, including prospective new members of the Coronaviridae, Astroviridae, Picornaviridae, and Narnaviridae families, and uncovered pathogen interactions that promote or prevent virus transmission. We also evaluated transmission dynamics of murine astroviruses during transmission and spread within a new host. Finally, by cohousing our laboratory mice with the bedding of pet store rats, we identified cross-species transmission of a rat astrovirus. Overall, this model system allows for the analysis of transmission of natural rodent viruses and is a platform to further characterize barriers to zoonosis.
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Affiliation(s)
- Elizabeth J. Fay
- Biochemistry, Molecular Biology and Biophysics Graduate Program, University of Minnesota, Minneapolis, MN,Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN,Center for Immunology, University of Minnesota, Minneapolis, MN
| | - Keir M. Balla
- Department of Human Genetics, University of Utah, Salt Lake City, UT
| | - Shanley N. Roach
- Biochemistry, Molecular Biology and Biophysics Graduate Program, University of Minnesota, Minneapolis, MN,Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | - Frances K. Shepherd
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | - Dira S. Putri
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN,Microbiology, Immunology and Cancer Biology Graduate Program, University of Minnesota, Minneapolis, MN
| | - Talia D. Wiggen
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | | | - Mark J. Pierson
- Center for Immunology, University of Minnesota, Minneapolis, MN
| | - Martin T. Ferris
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Claire E. Thefaine
- Microbiology, Immunology and Cancer Biology Graduate Program, University of Minnesota, Minneapolis, MN
| | - Andrew Tucker
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | - Mark Salnikov
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | - Valerie Cortez
- Department of Molecular, Cellular and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA
| | - Susan R. Compton
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT
| | - Sergei V. Kotenko
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, Newark, NJ
| | - Ryan C. Hunter
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN
| | - David Masopust
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN,Center for Immunology, University of Minnesota, Minneapolis, MN
| | - Nels C. Elde
- Department of Human Genetics, University of Utah, Salt Lake City, UT
| | - Ryan A. Langlois
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN,Center for Immunology, University of Minnesota, Minneapolis, MN,Correspondence to Ryan A. Langlois:
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36
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Albery GF, Becker DJ, Brierley L, Brook CE, Christofferson RC, Cohen LE, Dallas TA, Eskew EA, Fagre A, Farrell MJ, Glennon E, Guth S, Joseph MB, Mollentze N, Neely BA, Poisot T, Rasmussen AL, Ryan SJ, Seifert S, Sjodin AR, Sorrell EM, Carlson CJ. The science of the host-virus network. Nat Microbiol 2021; 6:1483-1492. [PMID: 34819645 DOI: 10.1038/s41564-021-00999-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/18/2021] [Indexed: 01/21/2023]
Abstract
Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington DC, USA.
| | - Daniel J Becker
- Department of Biology, University of Oklahoma, Norman, OK, USA
| | - Liam Brierley
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Cara E Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad A Dallas
- Department of Biological Sciences, University of South Carolina, Columbia, SC, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Maxwell J Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Emma Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA
| | - Nardus Mollentze
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.,MRC - University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, SC, USA
| | - Timothée Poisot
- Québec Centre for Biodiversity Sciences, Montréal, Québec, Canada.,Département de Sciences Biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Angela L Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sadie J Ryan
- Department of Geography, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.,School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Stephanie Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Anna R Sjodin
- Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Erin M Sorrell
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA. .,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA.
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37
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Majewska AA, Huang T, Han B, Drake JM. Predictors of zoonotic potential in helminths. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200356. [PMID: 34538139 PMCID: PMC8450625 DOI: 10.1098/rstb.2020.0356] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Ania A Majewska
- Odum School of Ecology and the Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.,Biology Department, Emory University, Atlanta, GA, USA
| | - Tao Huang
- Cary Institute of Ecosystem Studies, Millbrook, NY, USA.,Ecology, Evolution, and Behavior, Boise State University, Boise, ID, USA
| | - Barbara Han
- Cary Institute of Ecosystem Studies, Millbrook, NY, USA
| | - John M Drake
- Odum School of Ecology and the Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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38
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Carlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, Rasmussen AL, Albery GF, Bett B, Brett-Major DM, Cohen LE, Dallas T, Eskew EA, Fagre AC, Forbes KM, Gibb R, Halabi S, Hammer CC, Katz R, Kindrachuk J, Muylaert RL, Nutter FB, Ogola J, Olival KJ, Rourke M, Ryan SJ, Ross N, Seifert SN, Sironen T, Standley CJ, Taylor K, Venter M, Webala PW. The future of zoonotic risk prediction. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200358. [PMID: 34538140 PMCID: PMC8450624 DOI: 10.1098/rstb.2020.0358] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2021] [Indexed: 01/26/2023] Open
Abstract
In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Colin J. Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Zoe Grange
- Public Health Scotland, Glasgow G2 6QE, UK
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA
| | - Nardus Mollentze
- Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Alexandra L. Phelan
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Angela L. Rasmussen
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Gregory F. Albery
- Department of Biology, Georgetown University, Washington, DC 20007, USA
| | - Bernard Bett
- Animal and Human Health Program, International Livestock Research Institute, PO Box 30709-00100, Nairobi, Kenya
| | - David M. Brett-Major
- Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
| | - Lily E. Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70806, USA
| | - Evan A. Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna C. Fagre
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - Kristian M. Forbes
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Rory Gibb
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sam Halabi
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Charlotte C. Hammer
- Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK
| | - Rebecca Katz
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Jason Kindrachuk
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada R3E 0J9
| | - Renata L. Muylaert
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
| | - Felicia B. Nutter
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA
- Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | | | | | - Michelle Rourke
- Law Futures Centre, Griffith Law School, Griffith University, Nathan, Queensland 4111, Australia
| | - Sadie J. Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Noam Ross
- EcoHealth Alliance, New York, NY 10018, USA
| | - Stephanie N. Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Tarja Sironen
- Department of Virology, University of Helsinki, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
| | - Claire J. Standley
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Kishana Taylor
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Marietjie Venter
- Zoonotic Arbo and Respiratory Virus Program, Centre for Viral Zoonoses, Department of Medical Virology, University of Pretoria, Pretoria, South Africa
| | - Paul W. Webala
- Department of Forestry and Wildlife Management, Maasai Mara University, Narok 20500, Kenya
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Ergünay K. New viruses on the rise: a One Health and ecosystem-based perspective on emerging viruses. Future Virol 2021. [PMID: 34659443 PMCID: PMC8516350 DOI: 10.2217/fvl-2021-0215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 09/20/2021] [Indexed: 11/21/2022]
Abstract
Empowered by interdisciplinary collaboration, we now have the tools to identify new viruses, contain future outbreaks and broadly understand natural processes toward a global health.
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Affiliation(s)
- Koray Ergünay
- Department of Medical Microbiology, Virology Unit, Hacettepe University, Faculty of Medicine, Ankara, 06100, Turkey
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Do HQ, Nguyen VG, Chung CU, Jeon YS, Shin S, Jang KC, Pham LBH, Kong A, Kim CU, Park YH, Park BK, Chung HC. Genomic Characterization of a Novel Alphacoronavirus Isolated from Bats, Korea, 2020. Viruses 2021; 13:v13102041. [PMID: 34696471 PMCID: PMC8540747 DOI: 10.3390/v13102041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 12/19/2022] Open
Abstract
Coronavirus, an important zoonotic disease, raises concerns of future pandemics. The bat is considered a source of noticeable viruses resulting in human and livestock infections, especially the coronavirus. Therefore, surveillance and genetic analysis of coronaviruses in bats are essential in order to prevent the risk of future diseases. In this study, the genome of HCQD-2020, a novel alphacoronavirus detected in a bat (Eptesicus serotinus), was assembled and described using next-generation sequencing and bioinformatics analysis. The comparison of the whole-genome sequence and the conserved amino acid sequence of replicated proteins revealed that the new strain was distantly related with other known species in the Alphacoronavirus genus. Phylogenetic construction indicated that this strain formed a separated branch with other species, suggesting a new species of Alphacoronavirus. Additionally, in silico prediction also revealed the risk of cross-species infection of this strain, especially in the order Artiodactyla. In summary, this study provided the genetic characteristics of a possible new species belonging to Alphacoronavirus.
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Affiliation(s)
- Hai-Quynh Do
- Virology Lab, Department of Veterinary Medicine, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea;
| | - Van-Giap Nguyen
- Department of Veterinary Microbiology and Infectious Diseases, Faculty of Veterinary Medicine, Vietnam National University of Agriculture, Hanoi 100000, Vietnam;
| | - Chul-Un Chung
- Department of Life Science, Dongguk University, Gyeongju 38066, Korea;
- Correspondence: (C.-U.C.); (B.-K.P.); (H.-C.C.); Tel.: +82-2-880-1255 (C.-U.C., B.-K.P. & H.-C.C.); Fax: +82-2-885-0263 (C.-U.C., B.-K.P. & H.-C.C.)
| | - Yong-Shin Jeon
- Department of Life Science, Dongguk University, Gyeongju 38066, Korea;
| | - Sook Shin
- Noah Biotech Research Unit, Noah Biotech Co. Ltd, Suwon 16612, Korea; (S.S.); (K.-C.J.); (Y.-H.P.)
| | - Kuem-Chan Jang
- Noah Biotech Research Unit, Noah Biotech Co. Ltd, Suwon 16612, Korea; (S.S.); (K.-C.J.); (Y.-H.P.)
| | - Le Bich Hang Pham
- Institute of Genome Research, Vietnam Academy of Science and Technology, Hanoi 100000, Vietnam;
| | - Aeri Kong
- Department of Medical Science, University of California, Los Angeles, CA 90095, USA;
| | - Cheong-Ung Kim
- Department of Veterinary Medicine Microbology Lab, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea;
| | - Yong-Ho Park
- Noah Biotech Research Unit, Noah Biotech Co. Ltd, Suwon 16612, Korea; (S.S.); (K.-C.J.); (Y.-H.P.)
| | - Bong-Kyun Park
- Virology Lab, Department of Veterinary Medicine, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea;
- Correspondence: (C.-U.C.); (B.-K.P.); (H.-C.C.); Tel.: +82-2-880-1255 (C.-U.C., B.-K.P. & H.-C.C.); Fax: +82-2-885-0263 (C.-U.C., B.-K.P. & H.-C.C.)
| | - Hee-Chun Chung
- Virology Lab, Department of Veterinary Medicine, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul 08826, Korea;
- Correspondence: (C.-U.C.); (B.-K.P.); (H.-C.C.); Tel.: +82-2-880-1255 (C.-U.C., B.-K.P. & H.-C.C.); Fax: +82-2-885-0263 (C.-U.C., B.-K.P. & H.-C.C.)
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Oeschger TM, McCloskey DS, Buchmann RM, Choubal AM, Boza JM, Mehta S, Erickson D. Early Warning Diagnostics for Emerging Infectious Diseases in Developing into Late-Stage Pandemics. Acc Chem Res 2021; 54:3656-3666. [PMID: 34524795 DOI: 10.1021/acs.accounts.1c00383] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaks-SARS, MERS, swine flu, and COVID-19-has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from pre-human predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.
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Affiliation(s)
| | | | | | | | | | - Saurabh Mehta
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York 10065, United States
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Alkhamis MA, Fountain‐Jones NM, Aguilar‐Vega C, Sánchez‐Vizcaíno JM. Environment, vector, or host? Using machine learning to untangle the mechanisms driving arbovirus outbreaks. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02407. [PMID: 34245639 PMCID: PMC9286057 DOI: 10.1002/eap.2407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 01/28/2021] [Accepted: 03/03/2021] [Indexed: 06/13/2023]
Abstract
Climatic, landscape, and host features are critical components in shaping outbreaks of vector-borne diseases. However, the relationship between the outbreaks of vector-borne pathogens and their environmental drivers is typically complicated, nonlinear, and may vary by taxonomic units below the species level (e.g., strain or serotype). Here, we aim to untangle how these complex forces shape the risk of outbreaks of Bluetongue virus (BTV); a vector-borne pathogen that is continuously emerging and re-emerging across Europe, with severe economic implications. We tested if the ecological predictors of BTV outbreak risk were serotype-specific by examining the most prevalent serotypes recorded in Europe (1, 4, and 8). We used a robust machine learning (ML) pipeline and 23 relevant environmental features to fit predictive models to 24,245 outbreaks reported in 25 European countries between 2000 and 2019. Our ML models demonstrated high predictive performance for all BTV serotypes (accuracies > 0.87) and revealed strong nonlinear relationships between BTV outbreak risk and environmental and host features. Serotype-specific analysis suggests, however, that each of the major serotypes (1, 4, and 8) had a unique outbreak risk profile. For example, temperature and midge abundance were as the most important characteristics shaping serotype 1, whereas for serotype 4 goat density and temperature were more important. We were also able to identify strong interactive effects between environmental and host characteristics that were also serotype specific. Our ML pipeline was able to reveal more in-depth insights into the complex epidemiology of BTVs and can guide policymakers in intervention strategies to help reduce the economic implications and social cost of this important pathogen.
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Affiliation(s)
- Moh A. Alkhamis
- Department of Epidemiology and BiostatisticsFaculty of Public HeathHealth Sciences CentreKuwait UniversityKuwait City13110Kuwait
| | - Nicholas M. Fountain‐Jones
- School of Natural SciencesUniversity of TasmaniaHobartTasmania7001Australia
- Department of Veterinary Population MedicineCollege of Veterinary MedicineUniversity of MinnesotaSt. PaulMinnesota55108USA
| | - Cecilia Aguilar‐Vega
- VISAVET Health Surveillance Centre and Animal Health DepartmentVeterinary SchoolComplutense University of MadridMadrid28040Spain
| | - José M. Sánchez‐Vizcaíno
- VISAVET Health Surveillance Centre and Animal Health DepartmentVeterinary SchoolComplutense University of MadridMadrid28040Spain
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Shaw AE, Rihn SJ, Mollentze N, Wickenhagen A, Stewart DG, Orton RJ, Kuchi S, Bakshi S, Collados MR, Turnbull ML, Busby J, Gu Q, Smollett K, Bamford CGG, Sugrue E, Johnson PCD, Da Silva AF, Castello A, Streicker DG, Robertson DL, Palmarini M, Wilson SJ. The antiviral state has shaped the CpG composition of the vertebrate interferome to avoid self-targeting. PLoS Biol 2021; 19:e3001352. [PMID: 34491982 PMCID: PMC8423302 DOI: 10.1371/journal.pbio.3001352] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 07/07/2021] [Indexed: 12/24/2022] Open
Abstract
Antiviral defenses can sense viral RNAs and mediate their destruction. This presents a challenge for host cells since they must destroy viral RNAs while sparing the host mRNAs that encode antiviral effectors. Here, we show that highly upregulated interferon-stimulated genes (ISGs), which encode antiviral proteins, have distinctive nucleotide compositions. We propose that self-targeting by antiviral effectors has selected for ISG transcripts that occupy a less self-targeted sequence space. Following interferon (IFN) stimulation, the CpG-targeting antiviral effector zinc-finger antiviral protein (ZAP) reduces the mRNA abundance of multiple host transcripts, providing a mechanistic explanation for the repression of many (but not all) interferon-repressed genes (IRGs). Notably, IRGs tend to be relatively CpG rich. In contrast, highly upregulated ISGs tend to be strongly CpG suppressed. Thus, ZAP is an example of an effector that has not only selected compositional biases in viral genomes but also appears to have notably shaped the composition of host transcripts in the vertebrate interferome. Our cells are poised to combat viral infection through antiviral effectors. This study proposes that as well as targeting viral RNAs, antiviral effectors sometimes target host mRNAs too; over millions of years, this has selected for compositional biases in the host’s transcriptional response to virus infection.
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Affiliation(s)
- Andrew E. Shaw
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
- The Pirbright Institute, Woking, United Kingdom
| | - Suzannah J. Rihn
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Nardus Mollentze
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Arthur Wickenhagen
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Douglas G. Stewart
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Richard J. Orton
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Srikeerthana Kuchi
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Siddharth Bakshi
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | | | - Matthew L. Turnbull
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Joseph Busby
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Quan Gu
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Katherine Smollett
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Connor G. G. Bamford
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Elena Sugrue
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Paul C. D. Johnson
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Ana Filipe Da Silva
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Alfredo Castello
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Daniel G. Streicker
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Massimo Palmarini
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
| | - Sam J. Wilson
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow, United Kingdom
- * E-mail:
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Mollentze N, Babayan SA, Streicker DG. Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biol 2021; 19:e3001390. [PMID: 34582436 PMCID: PMC8478193 DOI: 10.1371/journal.pbio.3001390] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 08/10/2021] [Indexed: 11/18/2022] Open
Abstract
Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.
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Affiliation(s)
- Nardus Mollentze
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Simon A. Babayan
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Daniel G. Streicker
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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Guo Q, Li M, Wang C, Guo J, Jiang X, Tan J, Wu S, Wang P, Xiao T, Zhou M, Fang Z, Xiao Y, Zhu H. Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic. Sci Rep 2021; 11:17422. [PMID: 34465838 PMCID: PMC8408148 DOI: 10.1038/s41598-021-96903-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/18/2021] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.
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Affiliation(s)
- Qian Guo
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Mo Li
- Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China
| | - Chunhui Wang
- Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China
| | - Jinyuan Guo
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Xiaoqing Jiang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Jie Tan
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
| | - Shufang Wu
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Peihong Wang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
| | - Tingting Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Man Zhou
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Zhencheng Fang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Yonghong Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310006, China.
| | - Huaiqiu Zhu
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Quantitative Biology, Peking University, Beijing, 100871, China.
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
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Gibb R, Albery GF, Becker DJ, Brierley L, Connor R, Dallas TA, Eskew EA, Farrell MJ, Rasmussen AL, Ryan SJ, Sweeny A, Carlson CJ, Poisot T. Data Proliferation, Reconciliation, and Synthesis in Viral Ecology. Bioscience 2021. [DOI: 10.1093/biosci/biab080] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
The fields of viral ecology and evolution are rapidly expanding, motivated in part by concerns around emerging zoonoses. One consequence is the proliferation of host–virus association data, which underpin viral macroecology and zoonotic risk prediction but remain fragmented across numerous data portals. In the present article, we propose that synthesis of host–virus data is a central challenge to characterize the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open database of mammal host–virus associations that reconciles four published data sets. We show that this offers a substantially richer view of the known virome than any individual source data set but also that databases such as these risk becoming out of date as viral discovery accelerates. We argue for a shift in practice toward the development, incremental updating, and use of synthetic data sets in viral ecology, to improve replicability and facilitate work to predict the structure and dynamics of the global virome.
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Affiliation(s)
- Rory Gibb
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, England, United Kingdom
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Gregory F Albery
- Department of Biology, Georgetown University, Washington, DC, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Daniel J Becker
- Department of Biology, University of Oklahoma, Norman Oklahoma, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Liam Brierley
- Department of Health Data Science, University of Liverpool, Liverpool, England, United Kingdom
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Ryan Connor
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Tad A Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, Washington, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Maxwell J Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Angela L Rasmussen
- Vaccine Infectious Disease Organization and International Vaccine Centre, University of Saskatchewan, Saskatchewan, Saskatoon, Canada
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Sadie J Ryan
- Quantitative Disease Ecology and Conservation Lab, Department of Geography and with the Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States, and with the College of Life Sciences, University of KwaZulu Natal, Durban, South Africa
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Amy Sweeny
- Institute of Evolutionary Biology, University of Edinburgh, in Edinburgh, Scotland, United Kingdom
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Colin J Carlson
- Global Health Science and Security, Georgetown University Medical Center, Georgetown University, Washington, DC, United States
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
| | - Timothée Poisot
- Département de Sciences Biologiques, Université de Montréal, and with the Québec Centre for Biodiversity Sciences, both in Montréal, Québec, Canada
- Viral Emergence Research Initiative consortium, a global scientific collaboration to predict which viruses could infect humans, which animals host them, and where they could emerge
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García San Miguel L, Sierra MJ, Vazquez A, Fernandez-Martínez B, Molina R, Sanchez-Seco MP, Lucientes J, Figuerola J, de Ory F, Monge S, Suarez B, Simón F. Phlebovirus-associated diseases transmitted by phlebotominae in Spain: Are we at risk? ENFERMEDADES INFECCIOSAS Y MICROBIOLOGIA CLINICA (ENGLISH ED.) 2021; 39:345-351. [PMID: 34353512 DOI: 10.1016/j.eimce.2021.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 02/20/2020] [Indexed: 06/13/2023]
Abstract
The genera Phlebovirus transmitted by Diptera belonging to the Psychodidae family are a cause of self-limited febrile syndrome in the Mediterranean basin in summer and autumn. Toscana virus can also cause meningitis and meningoencephalitis. In Spain, Toscana, Granada, Naples, Sicily, Arbia and Arrabida-like viruses have been detected. The almost widespread distribution of Phlebotomus genus vectors, and especially Phlebotomus perniciosus, in which several of these viruses have been detected, makes it very likely that there will be regular human infections in our country, with this risk considered moderate for Toscana virus and low for the other ones, in areas with the highest vector activity. Most of the infections would be undiagnosed, while only Toscana virus would have a greater impact due to the potential severity of the illness.
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Affiliation(s)
- Lucía García San Miguel
- Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES), Direccion General de Salud Pública, Calidad e Innovación, Ministerio de Sanidad, Consumo y Bienestar Social, Spain.
| | - M Jose Sierra
- Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES), Direccion General de Salud Pública, Calidad e Innovación, Ministerio de Sanidad, Consumo y Bienestar Social, Spain
| | - Ana Vazquez
- Laboratorio de arbovirus y enfermedades víricas importadas, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain
| | - Beatriz Fernandez-Martínez
- Centro Nacional de Epidemiología, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Spain
| | - Ricardo Molina
- Laboratorio de Entomología Médica, Centro Nacional de Microbiología. Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Majadahonda, Spain
| | - M Paz Sanchez-Seco
- Laboratorio de arbovirus y enfermedades víricas importadas, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain
| | - Javier Lucientes
- Departamento de Patología Animal (Sanidad Animal), Instituto de Investigación Agroalimentario de Aragon IA2, Facultad de Veterinaria, Universidad de Zaragoza, Spain
| | - Jordi Figuerola
- Estacion Biológica de Doñana, Consejo Superior de Investigaciones Científicas, Ministerio de Ciencia, Innovación y Universidades, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain
| | - Fernando de Ory
- Laboratorio de arbovirus y enfermedades víricas importadas, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades, Spain
| | - Susana Monge
- Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES), Direccion General de Salud Pública, Calidad e Innovación, Ministerio de Sanidad, Consumo y Bienestar Social, Spain
| | - Berta Suarez
- Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES), Direccion General de Salud Pública, Calidad e Innovación, Ministerio de Sanidad, Consumo y Bienestar Social, Spain
| | - Fernando Simón
- Centro de Coordinación de Alertas y Emergencias Sanitarias (CCAES), Direccion General de Salud Pública, Calidad e Innovación, Ministerio de Sanidad, Consumo y Bienestar Social, Spain
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48
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Towards a more healthy conservation paradigm: integrating disease and molecular ecology to aid biological conservation †. J Genet 2021. [PMID: 33622992 PMCID: PMC7371965 DOI: 10.1007/s12041-020-01225-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Parasites, and the diseases they cause, are important from an ecological and evolutionary perspective because they can negatively affect host fitness and can regulate host populations. Consequently, conservation biology has long recognized the vital role that parasites can play in the process of species endangerment and recovery. However, we are only beginning to understand how deeply parasites are embedded in ecological systems, and there is a growing recognition of the important ways in which parasites affect ecosystem structure and function. Thus, there is an urgent need to revisit how parasites are viewed from a conservation perspective and broaden the role that disease ecology plays in conservation-related research and outcomes. This review broadly focusses on the role that disease ecology can play in biological conservation. Our review specifically emphasizes on how the integration of tools and analytical approaches associated with both disease and molecular ecology can be leveraged to aid conservation biology. Our review first concentrates on disease-mediated extinctions and wildlife epidemics. We then focus on elucidating how host–parasite interactions has improved our understanding of the eco-evolutionary dynamics affecting hosts at the individual, population, community and ecosystem scales. We believe that the role of parasites as drivers and indicators of ecosystem health is especially an exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation. The review concludes with a broad overview of the current and potential applications of modern genomic tools in disease ecology to aid biological conservation.
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Wardeh M, Blagrove MSC, Sharkey KJ, Baylis M. Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations. Nat Commun 2021; 12:3954. [PMID: 34172731 PMCID: PMC8233343 DOI: 10.1038/s41467-021-24085-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 05/21/2021] [Indexed: 11/09/2022] Open
Abstract
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.
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Affiliation(s)
- Maya Wardeh
- Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK.
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK.
| | - Marcus S C Blagrove
- Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Kieran J Sharkey
- Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
| | - Matthew Baylis
- Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
- Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, UK
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50
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Mechanisms Underlying Host Range Variation in Flavivirus: From Empirical Knowledge to Predictive Models. J Mol Evol 2021; 89:329-340. [PMID: 34059925 DOI: 10.1007/s00239-021-10013-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 05/13/2021] [Indexed: 12/22/2022]
Abstract
Preventing and controlling epidemics caused by vector-borne viruses are particularly challenging due to their diverse pool of hosts and highly adaptive nature. Many vector-borne viruses belong to the Flavivirus genus, whose members vary greatly in host range and specificity. Members of the Flavivirus genus can be categorized to four main groups: insect-specific viruses that are maintained solely in arthropod populations, mosquito-borne viruses and tick-borne viruses that are transmitted to vertebrate hosts by mosquitoes or ticks via blood feeding, and those with no-known vector. The mosquito-borne group encompasses the yellow fever, dengue, and West Nile viruses, all of which are globally spread and cause severe morbidity in humans. The Flavivirus genus is genetically diverse, and its members are subject to different host-specific and vector-specific selective constraints, which do not always align. Thus, understanding the underlying genetic differences that led to the diversity in host range within this genus is an important aspect in deciphering the mechanisms that drive host compatibility and can aid in the constant arms-race against viral threats. Here, we review the phylogenetic relationships between members of the genus, their infection bottlenecks, and phenotypic and genomic differences. We further discuss methods that utilize these differences for prediction of host shifts in flaviviruses and can contribute to viral surveillance efforts.
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