Biology subjects

Mohanty, T.

Publications and source records attributed to Mohanty, T..

3 recordsLinked to original sources

bioRxiv2022

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (https://github.com/InfectionMedicineProteomics/gscore).

bioinformatics↗

bioRxiv2022

A pharmacoproteomic landscape of organotypic intervention responses in gram-negative sepsis

Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events remains unclear. This knowledge gap has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens to prevent or reverse organ damage. Here, we used pharmacoproteomics to score treatment effects in a murine Escherichia coli sepsis model based on changes in the organ, cell, and plasma proteome landscapes. A combination of pathophysiological read-outs and time-resolved proteome maps of organs and blood enabled us to define time-dependent and organotypic proteotypes of dysfunction and damage upon administration of several combinations of the broad-spectrum beta-lactam antibiotic meropenem (Mem) and/or the immunomodulatory glucocorticoid methylprednisolone (Gcc). Three distinct response patterns were identified, defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, which depended on the underlying proteotype and varied significantly across organs. In the later stages of the disease, Gcc enhanced some positive treatment effects of Mem with superior reduction of the inflammatory response in the kidneys and partial restoration of sepsis-induced metabolic dysfunction. Unexpectedly, Mem introduced sepsis-independent perturbations in the mitochondrial proteome that were to some degree counteracted by Gcc. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and blood, coupled to a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider when assessing therapeutic efficacy. Such information is critical for quantitative, objective, and organotypic assessment of benefits and unintended effects of candidate treatments in relationship to dosing, timing, and potential synergistic combinations in murine sepsis models.

systems biology↗

bioRxiv2020

Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis.

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae and treatment options. Here we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell- and plasma proteins in the CSF, where in particular a large increase of neutrophil derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multi-protein LASSO regression model resulted in an 18-protein panel of cell and tissue associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen specific host response patterns in CSF from different disease etiologies to support future classification of pathogen-type based on host response patterns in meningitis.

microbiology↗