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Evans MV, Drake JM. A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface. ECOHEALTH 2022; 19:246-258. [PMID: 35666334 PMCID: PMC9168633 DOI: 10.1007/s10393-022-01599-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.
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Affiliation(s)
- Michelle V Evans
- MIVEGEC, Institut de Recherche pour le Développement, 34000, Montpellier, France.
- Odum School of Ecology, University of Georgia, Athens, 30606, USA.
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, 30606, USA.
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, 30606, USA
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, 30606, USA
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Su Y, Wang HK, Gan XP, Chen L, Cao YN, Cheng DC, Zhang DY, Liu WY, Li FF, Xu XM. Alterations of gut microbiota in gestational diabetes patients during the second trimester of pregnancy in the Shanghai Han population. J Transl Med 2021; 19:366. [PMID: 34446048 PMCID: PMC8394568 DOI: 10.1186/s12967-021-03040-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/11/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The causes of gestational diabetes mellitus (GDM) are still unclear. Recent studies have found that the imbalance of the gut microbiome could lead to disorders of human metabolism and immune system, resulting in GDM. This study aims to reveal the different gut compositions between GDM and normoglycemic pregnant women and find the relationship between gut microbiota and GDM. METHODS Fecal microbiota profiles from women with GDM (n = 21) and normoglycemic women (n = 32) were assessed by 16S rRNA gene sequencing. Fasting metabolic hormone concentrations were measured using multiplex ELISA. RESULTS Metabolic hormone levels, microbiome profiles, and inferred functional characteristics differed between women with GDM and healthy women. Additionally, four phyla and seven genera levels have different correlations with plasma glucose and insulin levels. Corynebacteriales (order), Nocardiaceae (family), Desulfovibrionaceae (family), Rhodococcus (genus), and Bacteroidetes (phylum) may be the taxonomic biomarkers of GDM. Microbial gene functions related to amino sugar and nucleotide sugar metabolism were found to be enriched in patients with GDM. CONCLUSION Our study indicated that dysbiosis of the gut microbiome exists in patients with GDM in the second trimester of pregnancy, and gut microbiota might be a potential diagnostic biomarker for the diagnosis, prevention, and treatment of GDM.
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Affiliation(s)
- Yao Su
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Hong-Kun Wang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Xu-Pei Gan
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Li Chen
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Yan-Nan Cao
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - De-Cui Cheng
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Dong-Yao Zhang
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Wen-Yu Liu
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China
| | - Fei-Fei Li
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China.
| | - Xian-Ming Xu
- Department of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 of Haining Road, Hongkou District, Shanghai, 201600, China.
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