Checklist for One Health Epidemiological Reporting of Evidence (COHERE).

One Health is defined as the intersection and integration of knowledge regarding humans, animals, and the environment, yet as the One Health scientific literature expands, there is considerable heterogeneity of approach and quality of reporting in One Health studies. In addition, many researchers who publish such studies do not include or integrate data from all three domains of human, animal, and environmental health. This points to a critical need to unify guidelines for One Health studies. This report details the Checklist for One Health Epidemiological Reporting of Evidence (COHERE) to guide the design and publication format of future One Health studies. COHERE was developed by a core writing team and international expert review group that represents multiple disciplines, including human medicine, veterinary medicine, public health, allied professionals, clinical laboratory science, epidemiology, the social sciences, ecohealth and environmental health. The twin aims of the COHERE standards are to 1) improve the quality of reporting of observational or interventional epidemiological studies that collect and integrate data from humans, animals and/or vectors, and their environments; and 2) promote the concept that One Health studies should integrate knowledge from these three domains. The 19 standards in the COHERE checklist address descriptions of human populations, animal populations, environmental assessment, spatial and temporal relationships of data from the three domains, integration of analyses and interpretation, and inclusion of expertise in the research team from disciplines related to human health, animal health, and environmental health

Global burden of colistin-resistant bacteria : mobilized colistin resistance genes study (1980-2018)

Colistin is considered to be an antimicrobial of last-resort for the treatment of multidrug-resistant Gram-negative bacterial infections. The recent global dissemination of mobilized colistin resistance (mcr) genes is an urgent public health threat. An accurate estimate of the global prevalence of mcr genes, their reservoirs and the potential pathways for human transmission are required to implement control and prevention strategies, yet such data are lacking. Publications from four English (PubMed, Scopus, the Cochrane Database of Systematic Reviews and Web of Science) and two Chinese (CNKI and WANFANG) databases published between 18 November 2015 and 30 December 2018 were identified. In this systematic review and meta-analysis, the prevalence of mcr genes in bacteria isolated from humans, animals, the environment and food products were investigated. A total of 974 publications were identified. 202 observational studies were included in the systematic review and 71 in the meta-analysis. mcr genes were reported from 47 countries across six continents and the overall average prevalence was 4.7% (0.1-9.3%). China reported the highest number of mcr-positive strains. Pathogenic Escherichia coli (54%), isolated from animals (52%) and harboring an IncI2 plasmid (34%) were the bacteria with highest prevalence of mcr genes. The estimated prevalence of mcr-1 pathogenic E. coli was higher in food-animals than in humans and food products, which suggests a role for foodborne transmission. This study provides a comprehensive assessment of prevalence of the mcr gene by source, organism, genotype and type of plasmid

Accelerating the Deactivation of \u3cem\u3eSalmonella enterica\u3c/em\u3e Serovar Newport and \u3cem\u3eEscherichia coli\u3c/em\u3e O157:H7 in Dairy Manure by Modifying pH or Temperature

To assess methods for control of disease-causing bacteria in animal manures prior to field application, we manipulated the temperature or adjusted pH of dairy manure to high (3.5 to 5) or low (10 to 12) values with aluminum sulfate or hydrated lime, and inoculated the manure with Salmonella enterica serovar Newport or Escherichia coli O157:H7, then incubated the manure at ambient temperature. At pH 4.2, S. Newport was eliminated within 6 days; however at pH \u3e4.2 S. Newport was suppressed only temporarily and recovered to concentrations near the unamended controls. pH required to eliminate E. coli O157:H7 was 4.5. Both pathogens were killed by pH 11.0. The pathogens were eliminated within 2 weeks when inoculated manure was incubated at 37°C, whereas at 22°C and 4°C, the organisms persisted for much longer periods. S. Newport survived for over 300 days at 4°C, which has implications for manure spreading in colder seasons

Checklist for One Health epidemiological reporting of evidence (COHERE)

One Health is defined as the intersection and integration of knowledge regarding humans, animals, and the environment, yet as the One Health scientific literature expands, there is considerable heterogeneity of approach and quality of reporting in One Health studies. In addition, many researchers who publish such studies do not include or integrate data from all three domains of human, animal, and environmental health. This points to a critical need to unify guidelines for One Health studies. This report details the Checklist for One Health Epidemiological Reporting of Evidence (COHERE) to guide the design and publication format of future One Health studies. COHERE was developed by a core writing team and international expert review group that represents multiple disciplines, including human medicine, veterinary medicine, public health, allied professionals, clinical laboratory science, epidemiology, the social sciences, ecohealth and environmental health. The twin aims of the COHERE standards are to 1) improve the quality of reporting of observational or interventional epidemiological studies that collect and integrate data from humans, animals and/or vectors, and their environments; and 2) promote the concept that One Health studies should integrate knowledge from these three domains. The 19 standards in the COHERE checklist address descriptions of human populations, animal populations, environmental assessment, spatial and temporal relationships of data from the three domains, integration of analyses and interpretation, and inclusion of expertise in the research team from disciplines related to human health, animal health, and environmental health

Allelic Variation Contributes to Bacterial Host Specificity

Understanding the molecular parameters that regulate cross-species transmission and host adaptation of potential pathogens is crucial to control emerging infectious disease. Although microbial pathotype diversity is conventionally associated with gene gain or loss, the role of pathoadaptive nonsynonymous single-nucleotide polymorphisms (nsSNPs) has not been systematically evaluated. Here, our genome-wide analysis of core genes within Salmonella enterica serovar Typhimurium genomes reveals a high degree of allelic variation in surface-exposed molecules, including adhesins that promote host colonization. Subsequent multinomial logistic regression, MultiPhen and Random Forest analyses of known/suspected adhesins from 580 independent Typhimurium isolates identifies distinct host-specific nsSNP signatures. Moreover, population and functional analyses of host-associated nsSNPs for FimH, the type 1 fimbrial adhesin, highlights the role of key allelic residues in host-specific adherence in vitro. Together, our data provide the first concrete evidence that functional differences between allelic variants of bacterial proteins likely contribute to pathoadaption to diverse hosts

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease