775 papers
Microbiology explores the invisible world of tiny life forms that shape our health, environment, and even the air we breathe. From bacteria and viruses to fungi and parasites, this field investigates how these microscopic organisms interact with us and each other, driving everything from disease outbreaks to beneficial fermentation processes. Understanding them is crucial for developing new medicines, improving food safety, and combating global health challenges.
At Gist.Science, we make the latest discoveries in this dynamic field accessible to everyone. We process every new preprint uploaded to bioRxiv in this category, transforming dense academic findings into both clear, plain-language explanations and detailed technical summaries. This ensures that whether you are a student, a researcher, or simply curious, you can grasp the significance of cutting-edge science without getting lost in jargon.
Below are the most recent papers in microbiology, curated and summarized directly from the bioRxiv server to keep you at the forefront of discovery.
This study utilizes spatial transcriptomics to reveal that the joint microenvironment in a Lyme arthritis mouse model dictates distinct, region-specific immune signatures and fibroblast phenotypes that evolve from pro-inflammatory states during peak infection to resolution following antibiotic treatment.
This study demonstrates that SOS-mediated prophage induction acts as an evolutionary bottleneck that constrains the frequency of resistance mutations to DNA-damaging antibiotics by eliminating hosts with strong SOS responses, thereby selecting for rarer, high-effect mutations that dampen the SOS pathway and ultimately lead to higher levels of resistance.
This study reveals that the acquisition of Stickland-metabolizing bacteria, such as *Clostridioides difficile*, during infant gut maturation confers resistance to *Clostridium botulinum* infection by competing for shared amino acid-dependent nutritional niches.
By applying a comprehensive dual-method profiling approach to 1,110 longitudinal samples from the Lifelines NEXT cohort, this study reveals that the dynamic infant gut virome is shaped by host factors and temperate phage lifecycle switching, with specific associations between induced temperate phages and the development of eczema in the first year of life.
This paper proposes two novel stacking-based hybrid ensemble models (EM1 and EM2) that integrate multiple machine learning algorithms to significantly improve the accuracy and robustness of predicting gut microbiome-related diseases compared to traditional single-algorithm and deep learning approaches.
This study characterizes the pathogenesis and host response of Jamaican fruit bats to a novel, wild-type Tacaribe virus isolate (DOM2014), revealing a nonfatal, persistent infection with mild liver disease and limited adaptive immunity, thereby establishing a more biologically relevant reservoir model than the historically used, mouse-passaged TRVL-11573 strain.
This paper introduces FRAPPPE, a machine learning-based workflow using 13C flux ratio analysis to reveal distinct metabolic differences in central carbon and nucleoside co-metabolism between gut Bacteroidota and Escherichia coli.
This study elucidates how *Escherichia coli* integrates the dietary advanced glycation end product Nε-carboxymethyllysine (CML) into nitrogen metabolism through a branched transamination network involving the distinct substrate specificities of PatA, GabT, and IlvE, which convert CML and its decarboxylated metabolite into novel intermediates.
This study expands the known diversity and host range of the Sarthroviridae family by discovering ten novel, highly divergent sarthrovirus species in diverse Australian hosts and environments, revealing a broader ecological distribution, multiple distinct lineages, and potential alternative replication strategies.
This European collaboration study identifies and evaluates critical technical variables—including inoculum preparation, culture agitation, and vessel size—to establish standardized protocols that improve the reproducibility and consistency of Time Kill Curve experiments across multiple laboratories.