Biology subjects

Misra, B. B.

Publications and source records attributed to Misra, B. B..

8 recordsLinked to original sources

bioRxiv2026

TIMS-Bench: Towards community standards for benchmarking untargeted trapped ion mobility metabolomics tools and datasets

Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics is an important technology for unbiased discovery of small molecules in biomedical (e.g., drug discovery to diagnostics), animal, plant, environmental, and microbial research. Over the past decade, ion mobility has added an additional dimension to the triplet of MS1, MS2, and retention time, helping resolve co-eluting or isomeric features in an LC-MS/MS that aid in compound identification. Here, we focused on evaluating the current trapped ion mobility spectrometry (TIMS)-amenable feature-finding tools (MZmine 4.9, MS-DIAL 5.5, and MetaboScape 2025 14.0.3) for pre-processing of metabolomics data generated using a popular ion mobility mass spectrometry (IM-MS) technique, TIMS. We leveraged ten public and three benchmark TIMS datasets to evaluate these tools for their strengths and weaknesses. Our results show that MZmine consistently identified the highest number of features and confidently annotated features; however, this performance was accompanied by an increased number of false positives, due to peak splitting, as well as reduced accuracy in collision cross section (CCS) measurements. In contrast, MetaboScape achieved the highest fraction of high-quality MS2 spectra, reflecting a more conservative feature detection strategy. MS-DIAL demonstrated balanced performance, identifying features that other tools missed. Finally, we publicly release the ground-truth datasets and code to support future developments in improving IMS data analysis.

bioinformatics↗

bioRxiv2025

Defining the limits of plant chemical space: challenges and estimations

The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species have been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches--a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering--to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, conservatively estimating that the total plant chemical space likely spans millions, if not more, with the vast majority still unexplored.

bioinformatics↗

bioRxiv2021

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two applications showing how one can deconvolute the predictions made by RPath as well as predict novel targets. Finally, we have made the source code and data publicly available at https://github.com/enveda/RPath.

bioinformatics↗

bioRxiv2020

Time-course analysis of Streptococcus sanguinis after manganese depletion reveals changes in glycolytic, nucleotide, and redox metabolites

IntroductionManganese is important for the endocarditis pathogen, Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci. ObjectivesWe applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion. MethodsEDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC-MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM). ResultsWe observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Products of glycolysis such as pyruvate and fructose-1,6-bisphosphate increased, suggesting that enzymes that act on them may require manganese for activity or expression. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases. Simultaneous accumulation of ortho-tyrosine and reduced glutathione suggests that cells were unable to utilize glutathione as a reductant. ConclusionDifferential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which may be connected to carbon catabolite repression.

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↗

bioRxiv2019

Comparison of a GC-Orbitrap-MS with Parallel GC-FID Capabilities for Metabolomics of Human Serum

Gas chromatography mass spectrometry (GC-MS) platforms for use in high throughput and discovery metabolomics have heavily relied on time of flight (ToF), and low resolution quadrupole and ion trap mass spectrometers and are typically run in electron ionization (EI) modes for matching spectral libraries. Traditionally, detectors such as flame ionization detection (FID), have also helped in identification and quantification of compounds in complex samples for diverse clinical applications, i.e., fatty acids. We probed if combination of FID in line with a high-resolution instrument like a GC-Orbitrap-MS may confer advantages over traditional mass spectrometry using EI.\n\nWe used a commercially available human serum sample to enhance the chemical space of serum using an advanced high resolution mass spectrometry (HR-MS) platform (QExactive Orbitrap-MS) with an FID feature for confident metabolite identification to assess the suitability of the platform for routine clinical metabolomics research. Using the EI mode, we quantified 294 metabolites in human serum using GC-Orbitrap-MS. These metabolites belonged to 89 biological pathways in KEGG. Following a sample split, using an in-line FID analysis, 1117 peaks were quantified. Moreover, representative peaks from FID and their corresponding MS counterparts showed a good correspondence when compared for relative abundance.\n\nOur study highlights the benefits of the use of a higher mass accuracy instrument for untargeted GC-MS-based metabolomics not only with EI mode but also orthogonal detection method such as FID, for robust and orthogonal quantification, in future studies addressing complex biological samples in clinical set ups.

systems biology↗

bioRxiv2019

Metabolic Reprogramming: Short-term Western Diet Exposure Induces Sustained Changes in Plasma Metabolites

Dietary patterns are well known to contribute to the metabolic syndrome and related disorders. We evaluated the impact of a short-term Westernized diet (WD) exposure on systemic plasma metabolomic changes in a cohort of adult male baboons. In this pilot study, five male baboons (n=5) raised and maintained on a standard monkey chow diet (high complex carbohydrates, low fat) were exposed to a challenge WD (high in saturated fats and simple carbohydrates) for 7 weeks (49 d), followed by a 57-day washout period on monkey chow (106 d). In addition to monitoring clinical measures, we used a comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (2D GC-ToF-MS) platform to assess the metabolomic changes in plasma at three time points. Twenty-three metabolites were changed in response to the WD (49 d), but the response across the animals was highly variable. All animals presented a very different metabolic profile than at baseline (0 d), and the washout period resulted in a relatively homogenous metabolic state across all animals. A short-term exposure to a WD led to long-term changes in the metabolic profile, suggesting a "reset" of the metabolic program.

systems biology↗

bioRxiv2018

DIMEdb: an integrated database and web service for metabolite identification in direct infusion mass spectrometery

MotivationMetabolomics involves the characterisation, identification, and quantification of small molecules (metabolites) that act as the reaction intermediates of biological processes. Over the past few years, we have seen wide scale improvements in data processing, database, and statistical analysis tools. Direct infusion mass spectrometery (DIMS) is a widely used platform that is able to produce a global fingerprint of the metabolome, without the requirement of a prior chromatographic step - making it ideal for wide scale high-throughput metabolomics analysis. In spite of these developments, metabolite identification still remains a key bottleneck in untargeted mass spectrometry-based metabolomics studies. The first step of the metabolite identification task is to query masses against a metaboite database to get putative metabolite annotations. Each existing metabolite database differs in a number of aspects including coverage, format, and accessibility - often limiting the user to a rudimentary web interface. Manually combining multiple search results for a single experiment where there may be potentially hundreds of masses to investigate becomes an incredibly arduous task.\n\nResultsTo facilitate unified access to metabolite information we have created the Direct Infusion MEtabolite database (DIMEdb), a comprehensive web-based metabolite database that contains over 80,000 metabolites sourced from a number of renowned metabolite databases of which can be utilised in the analysis and annotation of DIMS data. To demostrate the efficacy of DIMEdb, a simple use case for metabolic identification is presented. DIMEdb aims to provide a single point of access to metabolite information, and hopefully facilitate the development of much needed bioinformatic tools.\n\nAvailabilityDIMEdb is freely available at https://dimedb.ibers.aber.ac.uk.\n\nContactkeo7@aber.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗