Biology subjects

Peterson, J. M.

Publications and source records attributed to Peterson, J. M..

4 recordsLinked to original sources

bioRxiv2024

Identification and Characterization of the Lipoprotein N-acyltransferase in Bacteroides

Members of the Bacteroidota compose a large portion of the human gut microbiota, contributing to overall gut health via the degradation of various polysaccharides. This process is facilitated by lipoproteins, globular proteins anchored to the cell surface by a lipidated N-terminal cysteine. Despite their importance, lipoprotein synthesis by these bacteria is understudied. In E. coli, the -amino linked lipid of lipoproteins is added by the lipoprotein N-acyltransferase Lnt. Herein, we have identified a protein distinct from Lnt responsible for the same process in Bacteroides, named lipoprotein N-acyltransferase in Bacteroides (Lnb). Deletion of Lnb yields cells that synthesize diacylated lipoproteins, with impacts on cell viability and morphology, growth on polysaccharides, and protein composition of membranes and outer membrane vesicles (OMVs). Our results not only challenge the accepted paradigms of lipoprotein biosynthesis in Gram-negative bacteria, but also support the establishment of a new family of lipoprotein N-acyltransferases. SignificanceBacteroidota are key members of the human gut microbiota that influence human health by degrading polysaccharides. This degradation is achieved by a suite of lipoproteins, a class of membrane protein characterized by lipidation. Lipoprotein synthesis in Bacteroidota is understudied. Here, we used a genetic screen to identify gene(s) responsible for N-acylation, the last step in lipoprotein biosynthesis. Our screen identified the lipoprotein N-acyltransferase in Bacteroides (Lnb) that performs this step. We show that deletion of Lnb negatively affects cellular growth and ability to degrade polysaccharides, deepening our understanding of Bacteroidota and lipoproteins.

microbiology↗

bioRxiv2024

A common set of structurally distinct endogenous retroviruses predict survival in multiple human tumor types

Accumulating evidence suggests that viral mimicry induction in cancer cells through the expression of human endogenous retroviral elements (ERVs) enhances tumor immunogenicity and improves response to immunotherapy. However, the characterization of ERV expression across multiple tumor types, their clinical relevance, and their role in tumor immunity remain limited. Here, we assessed the prognostic and immune-related consequences of ERV expression across 8 different tumor types from The Cancer Genome Atlas (TCGA). We applied a recently developed tool, Telescope, to quantify the expression of 14,968 ERVs in bulk tumor RNA-sequencing data. Approximately 15-22% of Telescopes ERVs (navg = 2,736) were expressed in each tumor cohort. We identified both tissue-specific and commonly expressed ERVs across cohorts. Over 50% of tissue-specific ERVs were ubiquitously expressed (n = 1,709) across all 8 tumor types. Using patient clinical data, we identified a subset of 94 ERVs whose expression levels were significantly associated with overall survival, of which 42 were favorably prognostic. Prognostic ERVs displayed unique structural characteristics, including more frequent inverted repeats and more stable secondary structures. Patients were classified based on ERV expression, allowing identification of patient cohorts with greater than 6-fold differences in overall survival. We also found that expression of positively prognostic ERVs was associated with enrichment for selected anti-tumor immune response gene expression signatures. This analysis extends previous results identifying patterns of ERV expression in bulk tumor RNA-seq datasets, providing further insight into the role of ERVs in tumor clinicopathology and immunogenicity.

cancer biology↗

bioRxiv2023

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and in some cases, new therapeutic leads. In select cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

biochemistry↗

bioRxiv2020

An in silico map of the SARS-CoV-2 RNA Structurome

SARS-CoV-2 is a positive-sense single-stranded RNA virus that has exploded throughout the global human population. This pandemic coronavirus strain has taken scientists and public health researchers by surprise and knowledge of its basic biology (e.g. structure/function relationships in its genomic, messenger and template RNAs) and modes for therapeutic intervention lag behind that of other human pathogens. In this report we used a recently-developed bioinformatics approach, ScanFold, to deduce the RNA structural landscape of the SARS-CoV-2 transcriptome. We recapitulate known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that the SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a huge reservoir of potential drug targets for RNA-binding small molecules. Our results also predict regions that are accessible for intermolecular interactions, which can aid in the design of antisense therapeutics. All results are made available via a public database (the RNAStructuromeDB) where they may hopefully drive drug discovery efforts to inhibit SARS-CoV-2 pathogenesis.

bioinformatics↗