Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.
Escherichia
coli is a common inhabitant of the
human microbiota and a beacon model organism in biology. However,
an understanding of its signaling systems that regulate population-level
phenotypes known as quorum sensing remain incomplete. Here, we define
the structure and biosynthesis of autoinducer-3 (AI-3), a metabolite
of previously unknown structure involved in the pathogenesis of enterohemorrhagic E. coli (EHEC). We demonstrate that novel AI-3 analogs are
derived from threonine dehydrogenase (Tdh) products and “abortive”
tRNA synthetase reactions, and they are distributed across a variety
of Gram-negative and Gram-positive bacterial pathogens. In addition
to regulating virulence genes in EHEC, we show that the metabolites
exert diverse immunological effects on primary human tissues. The
discovery of AI-3 metabolites and their biochemical origins now provides
a molecular foundation for investigating the diverse biological roles
of these elusive yet widely distributed bacterial signaling molecules.
Cell-cell interactions drive essential biological processes critical to cell and tissue development, function, pathology, and disease outcome. The growing appreciation of immune cell interactions within disease environments has led to significant efforts to develop protein-and cell-based therapeutic strategies. A better understanding of these cell-cell interactions will enable the development of effective immunotherapies. However, characterizing these complex cellular interactions at molecular resolution in their native biological contexts remains challenging. To address this, we introduce photocatalytic cell tagging (PhoTag), a modality agnostic platform for profiling cell-cell interactions. Using photoactivatable flavin-based cofactors, we generate phenoxy radical tags for targeted labeling at the cell surface. Through various targeting modalities (e.g. MHC-Multimer, antibody, single domain antibody (VHH)) we deliver a flavin photocatalyst for cell tagging within monoculture, co-culture, and peripheral blood mononuclear cells. PhoTag enables highly selective tagging of the immune synapse between an immune cell and an antigen-presenting cell through targeted labeling at the cell-cell junction. This allowed for the ability to profile gene expression-level differences between interacting and bystander cell populations. Given the modality agnostic and spatio-temporal nature of PhoTag, we envision its broad utilization to detect and profile intercellular interactions within an immune synapse and other confined cellular regions for any biological system..
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