Biology subjects

Kohler, D.

Publications and source records attributed to Kohler, D..

5 recordsLinked to original sources

bioRxiv2026

MSstatsBioNet: Integrating Statistical Analyses with Prior Knowledge Biomolecular Networks for Quantitative Proteomics and Phosphoproteomics

A common outcome of quantitative mass spectrometry-based proteomic and phosphoproteomic experiments is a list of proteins that are differentially abundant between conditions. However, biological interpretation requires evaluation in the context of prior knowledge of biological mechanisms and protein function. One approach to facilitate mechanistic biological interpretation is to integrate such lists with biological network databases, built from manually curated resources and text mining systems. This manuscript automates this process with MSstatsBioNet, a Bioconductor package that integrates MSstats, a family of open-source packages for detecting differentially abundant proteins, and INDRA, a system that extracts biomolecular networks from biomedical literature using text mining and merges those networks with the content of curated knowledge bases. Taking as input a list of differentially abundant proteins from MSstats, MSstatsBioNet retrieves a protein subnetwork from INDRA and overlays experimental fold changes onto the underlying subnetwork. Users can then interact with the network and overlaid data, interrogating primary literature evidence to construct granular mechanistic narratives for iterative hypothesis generation. We demonstrate the utility of this approach with three case studies, two measuring changes in protein abundance and one measuring changes in phosphorylation.

bioinformatics↗

bioRxiv2026

MSstatsResponse: Semi-parametric statistical model enhances detection of drug-protein interactions in chemoproteomics experiments

Chemoproteomics is a popular approach for the identification of small molecule-protein interactions in biological systems. Several chemoproteomics workflows leverage functionalized chemical probes and mass spectrometry to measure protein engagement through direct protein enrichment or competition using a range of small molecule concentrations. Statistical methods for analysis of such dose-response chemoproteomics datasets are limited. For example, existing methods rely on fixed curve shapes and are sensitive to experimental variation, particularly when the number of doses or replicates is limited. Here, we present MSstatsResponse, a semi-parametric statistical framework for analyzing chemoproteomic dose-response experiments that uses isotonic regression that does not require a fixed curve shape. This approach improves the accuracy and robustness of curve fitting, target identification, and half-response estimation across diverse experimental designs. We evaluate MSstatsResponse by generating a benchmark chemoproteomic dataset that profiled the competition between the kinase-binding probe XO44 and the drug Dasatinib using three mass spectrometry acquisition strategies: data-independent acquisition, tandem mass tag-based data-dependent acquisition, and selected reaction monitoring. We further evaluate the method on simulated datasets that vary the number of doses, number of replicates, and levels of noise, and demonstrate that MSstatsResponse consistently improves sensitivity, specificity, and reproducibility compared to existing methods, particularly in low-replicate and low-dose settings. MSstatsResponse is implemented as an open-source R/Bioconductor package that integrates with the MSstats ecosystem for quantitative proteomics. It provides a unified workflow for preprocessing, curve fitting, target identification, and experimental design, enabling researchers to select the number of doses and replicates appropriate to their experimental goals. The software and documentation are freely available at https://bioconductor.org/packages/MSstatsResponse.

bioinformatics↗

bioRxiv2025

Accounting for longitudinal peak quality metrics with MSstats+ enhances differential analysis in proteomic experiments with data-independent acquisition

Mass spectrometry-based proteomics with data-independent acquisition benefits from advanced instrumentation and computational analysis. Despite continued improvements, the quality of quantification may be poor for some measurements. As the scale of proteomic experiments increases, these poor-quality measurements are challenging to characterize by hand, yet they undermine the detection of differentially abundant proteins and the downstream biological conclusions. We introduce MSstats+, a computational workflow that takes as input not only peak intensities reported by tools such as Spectronaut, but also quality metrics such as peak shape and retention time, and longitudinal run order profiles of these metrics. MSstats+ translates these metrics into a single measure of quality, and downweights poor quality measurements when detecting differentially abundant proteins. The method offers a natural treatment of missing values, weighting the imputed values according to the quality metrics in the run. We demonstrate the accuracy of the resulting differential analysis in four experiments: two custom benchmarking studies with intentionally induced anomalies, a controlled mixture of proteomes, and a large-scale clinical investigation. MSstats+ is implemented in the family of open-source R/Bioconductor packages MSstats.

bioinformatics↗

bioRxiv2022

SARS-CoV-2 exposure in Norwegian rats (Rattus norvegicus) from New York City

Millions of Norway rats (Rattus norvegicus)inhabit New York City (NYC), presenting the potential for transmission of SARS-CoV-2 from humans to rats and other wildlife. We evaluated SARS-CoV-2 exposure among 79 rats captured from NYC during the fall of 2021. Results showed that 13 of 79 rats (16.5%) tested IgG or IgM positive, and partial genomes of SARS-CoV-2 were recovered from four rats that were qRT-PCR positive. Using a virus challenge study, we also showed that Alpha, Delta, and Omicron variants can cause robust infections in wild-type Sprague Dawley (SD) rats, including high level replications in the upper and lower respiratory tracts and induction of both innate and adaptive immune responses. Additionally, the Delta variant resulted in the highest infectivity. In summary, our results indicated that rats are susceptible to infection with Alpha, Delta, and Omicron variants, and rats in the NYC municipal sewer systems have been exposed to SARS-CoV-2. Our findings highlight the potential risk of secondary zoonotic transmission from urban rats and the need for further monitoring of SARS-CoV-2 in those populations. ImportanceSince its emergence causing the COVID-19 pandemic, the host tropism expansion of SARS-CoV-2 raises a potential risk for reverse-zoonotic transmission of emerging variants into rodent species, including wild rat species. In this study, we presented both genetic and serological evidence for SARS-CoV-2 exposure in wild rat population from New York City, and these viruses are potentially linked to the viruses during the early stages of the pandemic. We also demonstrated that rats are susceptible to additional variants (i.e., Alpha, Delta, and Omicron) predominant in humans and that the susceptibility to different variants vary. Our findings highlight the potential risk of secondary zoonotic transmission from urban rats and the need for further monitoring of SARS-CoV-2 in those populations.

microbiology↗

bioRxiv2022

MSstatsPTM: Statistical relative quantification of post-translational modifications in bottom-up mass spectrometry-based proteomics

Liquid chromatography coupled with bottom up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in post-translational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site, and consider all the relevant sources of confounding and variation. In this manuscript we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with post-translational modifications and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag (TMT)-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

bioinformatics↗