Biology subjects

Tabb, D. L.

Publications and source records attributed to Tabb, D. L..

5 recordsLinked to original sources

bioRxiv2026

mzQC: a versatile way to communicate quality information for biological mass spectrometry

Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.

bioinformatics↗

bioRxiv2024

Communicating mass spectrometry quality information in mzQC with Python, R, and Java

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intra-laboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organizations Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionality to operate on mzQC files, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries for extracting, analyzing, and visualizing QC metrics from different sources and show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

bioinformatics↗

bioRxiv2021

Phosphoproteomics reveals new insights into the role of PknG during the persistence of pathogenic mycobacteria in host macrophages

Pathogenic mycobacteria, such as Mycobacterium tuberculosis, modulate the host immune system to evade clearance and promote long-term persistence, resulting in disease progression or latent infection. Understanding the mechanisms pathogenic mycobacteria use to escape elimination by the host immune system is critical to better understanding the molecular mechanisms of mycobacterial infection. Protein kinase G (PknG) in pathogenic mycobacteria has been shown to play an important role in avoiding clearance by macrophages through blocking phagosome-lysosome fusion; however, the exact mechanism is not completely understood. Here, to further investigate the role of mycobacterial PknG during early events of macrophage infection, RAW 264.7 macrophage cell lines were infected with M. bovis BCG wild-type and PknG knock-out mutant strains. After proteolysis, phosphopeptides were enriched via TiO2 columns and subjected to LC-MS/MS to identify differentially phosphorylated peptides between the wild-type and PknG mutant infected macrophages. A total of 1401 phosphosites on 914 unique proteins were identified. Following phosphoproteome normalisation and differential expression analysis, a total of 149 phosphosites were differentially phosphorylated in the wild-type infected RAW 264.7 macrophages versus the PknG knock-out mutant. A subset of 95 phosphosites was differentially up-regulated in the presence of PknG. Functional analysis of our data revealed that PknG kinase activity reprograms normal macrophage function through interfering with host cytoskeletal organisation, spliceosomal machinery, translational initiation, and programmed cell death. Differentially phosphorylated proteins in this study serve as a foundation for further validation and PknG host substrate assignment. ImportanceTuberculosis (TB) remains one of the leading causes of death from infection worldwide, due to the ability of Mycobacterium tuberculosis (Mtb) to survive and replicate within the host, establishing reservoirs of live bacteria that promote persistence and recurrence of disease. Understanding the mechanisms that Mtb uses to evade the host immune system is thus a major goal in the TB field. Protein kinase G is thought to play an important role in Mtb avoiding clearance by the host through disruption of macrophage function, but the underlying molecular mechanisms of this are not well understood. Here, our new phosphoproteomic data reveals that mycobacterial PknG substantially reprograms normal macrophage function through extensive PknG-mediated post-translational control of critical host cellular processes. These novel findings therefore considerably increase our knowledge of mycobacterial pathogenicity, including specific host cellular pathways that might be re-activatable through host-directed therapy, thereby restoring macrophage ability to eliminate Mtb.

molecular biology↗

bioRxiv2020

Microbial function and genital inflammation in young South African women at high risk of HIV infection

BackgroundFemale genital tract (FGT) inflammation is an important risk factor for HIV acquisition. The FGT microbiome is closely associated with inflammatory profile, however, the relative importance of microbial activities has not been established. Since proteins are key elements representing actual microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT microbial function and inflammation in 113 young and adolescent South African women at high risk of HIV infection. Women were grouped as having low, medium or high FGT inflammation by K-means clustering according to pro-inflammatory cytokine concentrations. ResultsA total of 3,186 microbial and human proteins were identified in lateral vaginal wall swabs using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. However, differences in the predicted relative abundance of most bacteria were observed between 16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations (gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based on 16S rRNA gene sequence data (p<0.0001). The majority of microbial biological processes were underrepresented in women with high inflammation compared to those with low inflammation, including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 women nine weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with high FGT inflammation in an independent sample of ten women. ConclusionsBoth the presence of specific microbial taxa in the FGT and their properties and activities are critical determinants of FGT inflammation. Our findings support those of previous studies suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of microbial activities, we have developed the FGT-METAP application that is available at (http://immunodb.org/FGTMetap/).

microbiology↗

bioRxiv2019

MetaNovo: a probabilistic approach to peptide and polymorphism discovery in complex mass spectrometry datasets

BackgroundMicrobiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focussed search libraries based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing will only target the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. We describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored databases for target-decoy searches directly at the proteome level, enabling analyses without prior expectation of sample composition or metagenomic data generation, and compatible with standard downstream analysis pipelines. ResultsWe compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome database - but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying a known sample contaminant without prior expectation. ConclusionsBy estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence search databases. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. The pipeline source code is available on GitHub1 and documentation is provided to run the software as a singularity-compatible docker image available from the Docker Hub2.

bioinformatics↗