Antimetabolites, primarily studied in cancer, are novel drugs targeting metabolic networks by mimicking and inhibiting disease-causing metabolites, enabling poly-pharmacologic effects essential for complex diseases. Moreover, predicting patients likely to positively respond to antimetabolite drugs is necessary to simplify clinical applications. However, existing computational approaches for antimetabolite target discovery lack incorporation of disease-induced metabolic state perturbations, and their applicability beyond cancer remains unexplored. We introduce MATADOR (Metabolite-centric Analysis of TArgets for Drug ORientation), a computational workflow that integrates patient-derived omic data with metabolic networks to identify, evaluate, and prioritize antimetabolite targets based on metabolic state transformation. Applying MATADOR to RNA-seq data from breast, colon, lung, and liver cancers, we achieved a 66 ± 6% sensitivity in recapturing known antimetabolite targets, strongly supported thioredoxin as a pan-cancer antimetabolite drug target, and linked top-ranked targets to poor 5-year survival in breast and liver cancers. Extending beyond cancer, MATADOR nominated metabolites with proinflammatory effects as potential antimetabolite targets in Alzheimer's and Parkinson's diseases, aligning well with their known pathological mechanisms. Finally, applying MATADOR on personalized metabolic networks, machine learning models trained on metabolic gene expression demonstrated the ability to leverage gene expression to personalize antimetabolite targets. The proposed approach may expedite prioritization and personalization of antimetabolite targets during pre-clinical studies across diseases with systemic metabolic alterations.
Platelets are blood components not regularly analysed with proteomics due to the conventional wisdom that plasma for platelet research must be citrate-treated and freshly sampled to minimize artifactual changes. Information from platelets is complementary to plasma, specifically regarding immunophenotyping of patients with challenged immune systems due to infection or inflammation. We sought to develop a sample-sparing, high-throughput-compatible platelet proteomics workflow applicable to previously frozen, non-citrate platelet-rich plasma, in contrast to the field's standard, and test its efficacy by applying it to a COVID-19 cohort. We examined centrifugation of whole blood and platelet-rich plasma and volume requirements of plasma for platelet analysis. Platelet and platelet-poor plasma samples were analysed from a cohort of 79 patients, consisting of COVID-19 negative non-ICU and ICU controls and patients with COVID-19 over time. Conventional platelet count was successfully performed using flow cytometry on previously frozen plasma, showing minimal platelet aggregation and cell debris, demonstrating viability of previously frozen plasma for platelet proteomic analysis. Protein counts in platelets mostly mirrored trends in platelet count, except in severe COVID-19 patients within three days of admission to the ICU. Proteins dysregulated in this group compared to controls were enriched in terms related to platelet activation, phagosome, and efferocytosis. This agrees with prior reports using conventional platelet proteomics methods. Such similar findings suggest that the method developed here can utilize non-citrate, previously frozen plasma down to 0.5 μL per sample. This will make platelet proteomics studies on already collected, banked plasma samples more accessible and increase biomolecular information gained.
Type 2 diabetes mellitus (T2DM) is a significant public health burden in India, where the disease presents with unique clinical characteristics, yet data from this high-risk population remain scarce. This study aimed to conduct a lipidomic analysis to identify candidate lipid species associated with different glycemic stages using a refined untargeted lipidomics approach. A cross-sectional study of 95 individuals from Mumbai was stratified into healthy, prediabetic, newly diagnosed diabetic, and advanced diabetic groups based on glycated hemoglobin levels. An untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry approach was applied to pooled serum samples to construct a comprehensive lipid library and enable comparative profiling. A stringent filtering pipeline was implemented to ensure robust lipid identification. Our analysis identified a core lipidome of 69 species shared across all cohorts, alongside an increase in lipidomic diversity in diabetic states, with the advanced diabetic group exhibiting the highest number of unique species. Ceramides (Cer) were detected across the glycemic spectrum and exhibited a distinct remodeling pattern. Partial least squares discriminant analysis identified 20 candidate lipid species with variable importance in projection scores > 1.0, prominently including Cer, phosphatidylinositol, and hexosylceramides. Additional lipid classes of interest included sphingomyelins, triacylglycerols, acylcarnitines, and fatty acids. These hypothesis-generating findings provide a prioritized framework for future large-scale quantitative validation studies aimed at early prediction and risk stratification of T2DM in the Indian population.
Early and accurate differentiation of prostate cancer (PC) from benign prostatic hyperplasia (BPH) remains challenging; metabolomics enables biomarker discovery by capturing disease-specific metabolic changes. The study includes 64 expressed prostatic secretion from 31 PC cases and 33 BPH cases. Nuclear magnetic resonance spectroscopy was used for metabolomics. Multivariate analyses, including principal component analysis, orthogonal partial least squares discriminant analysis, and artificial neural network modelling, were performed to identify discriminative metabolites. Diagnostic performance was assessed using receiver operating characteristic curve analysis. Clinical correlations with prostate-specific antigen (PSA) levels, multiparametric MRI (mpMRI), and Gleason score (GS) were executed. Citrate, glutamate, myo-inositol, and cis-aconitate were identified as key metabolites distinguishing PC from BPH, showing significant correlations with PSA, mpMRI-derived Prostate Imaging Reporting and Data System scores and apparent diffusion coefficient values as well as histopathology-based GS. The identified metabolic signature demonstrates strong potential as a noninvasive tool to support early PC detection and clinical decision-making, showing correlation with PSA, mpMRI indices and GS to enhance diagnostic accuracy before structural changes become evident.
Metaproteomics is an effective tool for characterizing the functional profiles of microbial communities by directly identifying and quantifying abundances. However, prospective power analysis and sample-size estimation are often overlooked at the study design stage in metaproteomics, which can result in underpowered experiments and reduced ability to detect biologically meaningful effects. In this study, we present a practical, end-to-end workflow for conducting power analysis prior to data collection. We focus on three common experimental designs: between-group comparisons, parallelized perturbation experiments, and beta diversity analyses. To tailored these experimental designs, we consider three major statistical approaches for power estimation: parametric tests (e.g. t-test, ANOVA), non-parametric tests (e.g. Wilcoxon rank-sum test, Kruskal-Wallis test), and distance-based multivariate methods (e.g. PERMANOVA using Bray-Curtis). By presenting detailed case studies, we provide practical guidance on how to calculate effect sizes, generate simulated datasets, and estimate statistical power across varying sample sizes. We also supply corresponding visualizations for each scenario to support sample-size determination and power assessment. This framework is intended to help researchers optimize sample size, improve experimental efficiency, and reduce costs, thereby enabling more reliable and interpretable biological insights from metaproteomic studies.
