Genetic and Functional Differences of Escherichia coli Strains from Colorectal Cancer Mucosal Tissues

Machine learning determines the incidence of Alzheimer's disease based on population gut microbiome profile

The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases, such as Alzheimer's disease, motor neurone disease and Parkinson's disease (Boddy SL, Giovannelli I, Sassani M, et al. The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). BMC Med. 2021;19(1):13). We hypothesize that population level differences in the gut microbiome will predict the incidence of Alzheimer's disease using machine learning methods. Cross-sectional analyses were performed in R, using two large, open-access microbiome datasets (n = 959 and n = 2012). Countries in these datasets were grouped based on Alzheimer's disease incidence and the gut microbiome profiles compared. In countries with a high incidence of Alzheimer's disease, there is a significantly lower diversity of the gut microbiome (P < 0.05). A permutational analysis of variance test (P < 0.05) revealed significant differences in the microbiome profile between countries with high versus low incidence of Alzheimer's disease with several contributing taxa identified: at a species level Escherichia coli, and at a genus level Haemophilus and Akkermansia were found to be reproducibly protective in both datasets. Additionally, using machine learning, we were able to predict the incidence of Alzheimer's disease within a country based on the microbiome profile (mean area under the curve 0.889 and 0.927). We conclude that differences in the microbiome can predict the varying incidence of Alzheimer's disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.

Metagenomic analyses of gut microbiome composition and function with age in a wild bird; little change, except increased transposase gene abundance

Studies on wild animals, mostly undertaken using 16S metabarcoding, have yielded ambiguous evidence regarding changes in the gut microbiome (GM) with age and senescence. Furthermore, variation in GM function has rarely been studied in such wild populations, despite GM metabolic characteristics potentially being associated with host senescent declines. Here, we used 7 years of repeated sampling of individuals and shotgun metagenomic sequencing to investigate taxonomic and functional changes in the GM of Seychelles warblers (Acrocephalus sechellensis) with age. Our results suggest that taxonomic GM species richness declines with age and in the terminal year, with this terminal decline occurring consistently across all ages. Taxonomic and functional GM composition also shifted with host age. However, the changes we identified occurred linearly with age (or even mainly during early years prior to the onset of senescence in this species) with little evidence of accelerated change in later life or during their terminal year. Therefore, the results suggest that changes in the GM with age are not linked to senescence. Interestingly, we found a significant increase in the abundance of a group of transposase genes with age, which may accumulate passively or due to increased transposition induced as a result of stressors that arise with age. These findings reveal taxonomic and functional GM changes with age, but not senescence, in a wild vertebrate and provide a blueprint for future wild functional GM studies linked to age and senescence.

Comprehensive analysis of multiple cytokines to stratify uropathogenic Escherichia coli pathogenesis in mouse model of urinary tract infection

PURPOSE

Urinary tract infection (UTI) is one of the most common human bacterial infections primarily caused by uropathogenic E. coli (UPEC). Empiric treatment in UTI cause emergence of multidrug resistance and limit treatment options. Understanding UTI at the molecular level with respect to the causative pathogen as well as subsequent host response pose an absolute necessity towards appropriate clinical management. This study aimed to investigate host cytokine response in mouse UTI model with respect to bacterial colonization and associated virulence gene expression upon infection.

METHOD

Mouse UTI model was established with two clinical UPEC isolates E. coli NP105 and E. coli P025. UPEC colonization in bladder and kidney was evaluated by bacterial culture (CFU/ml). Histopathology of the tissues were examined by hematoxylin and eosin staining. PCR and real time PCR were used to detect the incidence and expression of respective bacterial genes. Cytokine concentrations in tissues and sera were evaluated using ELISA. GraphPad prism version 8.0.2 was used for statistical interpretation.

RESULT

Highest bacterial colonization was observed on 7th and 9th day post infection (p.i). in bladder and kidney of mouse infected with E. coli P025 and E. coli NP105 respectively with a distinct difference in relative expression of fimH and papC adhesin genes in vivo. IL-1β level in tissues and sera of E. coli NP105 and E. coli P025 infected mouse was significantly different but the IL-17A, GCSF, TGF-β levels were comparable.

CONCLUSION

These findings show a role of IL1β to stratify pathogenicity of UPEC in mouse UTI model.