Biology subjects

Burnet, M. C.

Publications and source records attributed to Burnet, M. C..

6 recordsLinked to original sources

bioRxiv2026

PTMOverlay: A Proteomic Tool to Visualize Post-Translational Modifications Across Evolution

Evolutionary conservation has been considered a hallmark of essential basic functions in cells. Therefore, the study of evolutionarily conserved post-translational modifications (PTMs) can provide insight into their role in protein function. In this context, mass spectrometry can identify and quantify thousands of PTM sites. However, a major bottleneck lies in analyzing the large amounts of data collected by the mass spectrometer. Here we address the need for a protein sequence alignment tool for multiple PTMs across several species. We developed a tool named PTMOverlay that takes peptide identification output files and overlays PTM sites onto multiple protein sequence alignments. Examining 31 bacteria isolates, we combined their protein sequences with select PTM types, including acetylation, phosphorylation, monomethylation, dimethylation, and trimethylation. The tool revealed a variety of conserved modification sites on the bacterial central carbon metabolism. Further structural analysis revealed possible interactions between methylated arginine and lysine residues with phosphothreonine/serine sites on the homodimer interface of enolase. Overall, this tool can parse large amounts of mass spectrometry data and allows for more informed and efficient selection of sites for future studies of protein function.

systems biology↗

bioRxiv2023

Regulation of bacterial stringent response by an evolutionary conserved ribosomal protein L11 methylation

Lysine and arginine methylation is an important regulator of enzyme activity and transcription in eukaryotes. However, little is known about this covalent modification in bacteria. In this work, we investigated the role of methylation in bacteria. By reanalyzing a large phyloproteomics dataset from 48 bacterial strains representing 6 phyla, we found that almost a quarter of the bacterial proteome is methylated. Many of these methylated proteins are conserved across diverse bacterial lineages, including those involved in central carbon metabolism and translation. Among the proteins with the most conserved methylation sites is ribosomal protein L11 (bL11). bL11 methylation has been a mystery for five decades, as the deletion of its methyltransferase PrmA causes no cell growth defects. A comparative proteomics analysis combined with a guanosine polyphosphate assay of the {Delta}prmA mutant in Escherichia coli revealed that bL11 methylation is important for stringent response signaling. Moreover, we show that the {Delta}prmA mutant has an abnormal polysome profile, suggesting a role in ribosomal homeostasis during stationary growth phase. Overall, our investigation demonstrates that the evolutionary conserved bL11 methylation is important for stringent response signaling and ribosomal homeostasis. ImportanceProtein methylation in bacteria was first identified over sixty years ago. Since then, its functional role has been identified for only a few proteins. To better understand the functional role of methylation in bacteria, we analyzed a large phyloproteomics dataset encompassing 48 diverse bacteria. Our analysis revealed that ribosomal proteins are often methylated at conserved residues, suggesting that methylation of these sites may have a functional role in translation. Further analysis revealed that methylation of ribosomal protein L11 is important for stringent response signaling and ribosomal homeostasis.

systems biology↗

bioRxiv2020

A lipid metabolic map of the pathogenic fungus Histoplasma capsulatum

Lipids play a fundamental role in fungal cell biology, being essential cell membrane components and major targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here we built a comprehensive map of the Histoplasma capsulatum lipid metabolic pathway by incorporating proteomic and lipidomic analyses. We performed genetic complementation and overexpression of H. capsulatum genes in Saccharomyces cerevisiae to validate reactions identified in the map and to determine enzymes responsible for catalyzing orphan reactions. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We found that the sphingolipid biosynthesis inhibitor myriocin, the fatty acid desaturase inhibitor thiocarlide and the fatty acid analog 10-thiastearic acid inhibit H. capsulatum growth in nanomolar to low micromolar concentrations. These compounds also reduced the intracellular infection in an alveolar macrophage cell line. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development.

microbiology↗

bioRxiv2020

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗

bioRxiv2018

Proteomics of natural bacterial isolates powered by deep learning-based de novo identification.

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing. Significance StatementProteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.

bioinformatics↗

bioRxiv2017

Listeria monocytogenes virulence factors are secreted in biologically active Extracellular Vesicles

Outer membrane vesicles produced by Gram-negative bacteria have been studied for half a century but the possibility that Gram-positive bacteria secreted extracellular vesicles (EVs) was not pursued due to the assumption that the thick peptidoglycan cell wall would prevent their release to the environment. However, following discovery in fungi, which also have cell walls, EVs have now been described for a variety of Gram-positive bacteria. EVs purified from Gram-positive bacteriaare implicated in virulence, toxin release and transference to host cells, eliciting immune responses, and spread of antibiotic resistance. Listeria monocytogenes is a Gram-positive bacterium that is the etiological agent of listeriosis. Here we report that L. monocytogenes produces EVs with diameter ranging from 20-200 nm, containing the pore-forming toxin listeriolysin O(LLO) and phosphatidylinositol-specific phospholipase C (PI-PLC). Using simultaneous metabolite, protein, and lipid extraction (MPLEx) multi-omics we characterized protein, lipid and metabolite composition of bacterial cells and secreted EVs and found that EVs carry the majority of listerial virulence proteins. Cell-free EV preparations were toxic to the murine macrophage cell line J774.16, in a LLO-dependent manner, evidencing EV biological activity. The deletion of plcA increased EV toxicity, suggesting PI-PLC can restrain LLO activity. Using immunogold electron microscopy we detect LLO localization at several organelles within infected human epithelial cells and with high-resolution fluorescence imaging we show that dynamic lipid structures are released from L. monocytogenes that colocalize with LLO during infection. Our findings demonstrate that L. monocytogenes utilize EVs for toxin release and implicate these structures in mammalian cytotoxicity.

microbiology↗