Reconstructing the Phylogeny of Corynebacteriales while Accounting for Horizontal Gene Transfer

A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface

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.

Alterations of gut microbiota in gestational diabetes patients during the second trimester of pregnancy in the Shanghai Han population

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.