Metagenomic evidence for a polymicrobial signature of sepsis

Interpretable machine learning decodes soil microbiome's response to drought stress

BACKGROUND

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

RESULTS

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.

CONCLUSIONS

In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Divergent bacterial landscapes: unraveling geographically driven microbiomes in Atlantic cod

Establishing microbiome signatures is now recognized as a critical step toward identifying genetic and environmental factors shaping animal-associated microbiomes and informing the health status of a given host. In the present work, we prospectively collected 63 blood samples of the Atlantic cod population of the Southern Gulf of Saint Lawrence (GSL) and characterized their 16S rRNA circulating microbiome signature. Our results revealed that the blood microbiome signature was dominated at the phylum level by Proteobacteria, Bacteroidetes, Acidobacteria and Actinobacteria, a typical signature for fish populations inhabiting the GSL and other marine ecosystems. At the genus level, however, we identified two distinct cod groups. While the microbiome signature of the first group was dominated by Pseudoalteromonas, a genus we previously found in the microbiome signature of Greenland and Atlantic halibut populations of the GSL, the second group had a microbiome signature dominated by Nitrobacter and Sediminibacterium (approximately 75% of the circulating microbiome). Cods harboring a Nitrobacter/Sediminibacterium-rich microbiome signature were localized in the most southern part of the GSL, just along the northern coast of Cape Breton Island. Atlantic cod microbiome signatures did not correlate with the weight, length, relative condition, depth, temperature, sex, and salinity, as previously observed in the halibut populations. Our study provides, for the first time, a unique snapshot of the circulating microbiome signature of Atlantic cod populations and the potential existence of dysbiotic signatures associated with the geographical distribution of the population, probably linked with the presence of nitrite in the environment.

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream.

Molecular Methodologies for Improved Polymicrobial Sepsis Diagnosis

Polymicrobial sepsis is associated with worse patient outcomes than monomicrobial sepsis. Routinely used culture-dependent microbiological diagnostic techniques have low sensitivity, often leading to missed identification of all causative organisms. To overcome these limitations, culture-independent methods incorporating advanced molecular technologies have recently been explored. However, contamination, assay inhibition and interference from host DNA are issues that must be addressed before these methods can be relied on for routine clinical use. While the host component of the complex sepsis host–pathogen interplay is well described, less is known about the pathogen’s role, including pathogen–pathogen interactions in polymicrobial sepsis. This review highlights the clinical significance of polymicrobial sepsis and addresses how promising alternative molecular microbiology methods can be improved to detect polymicrobial infections. It also discusses how the application of shotgun metagenomics can be used to uncover pathogen/pathogen interactions in polymicrobial sepsis cases and their potential role in the clinical course of this condition.