Here, we developed and validated a high throughput proteomics workflow that enables combined profiling of global proteomes and post-translational modifications (PTMs) without conventional enrichment or fractionation. Using mass spectrometry-based methods, we identified PTM landscapes, particularly N-glycopeptides and non-enzymatic glycated peptides, from the same sample prepared for global proteomics. Applying this methodology to investigate longitudinal serum in women at 6 and 10 years post-gestational diabetes mellitus (GDM), we identified PTM signatures, including N-glycosylation and non-enzymatic glycation, associated with development of type 2 diabetes (T2D) and prediabetes, which were confirmed in an independent T2D cohort.
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.
Metabolomics is a powerful tool for understanding biological function at the cellular level. While this omics technology has found application in areas ranging from toxicology to cancer, its potential for the study of human pregnancy complications is in its infancy. Proper placental function is critical for a healthy pregnancy and assessing placental metabolism offers a direct window into placental physiology. This review aims to analyse placental metabolomics in pregnancy complications in humans. A systematic search was completed using PubMed and Google Scholar, and the Covidence software was used for the screening process. Following title and abstract screening and full-text review, 32 articles remained and were included in this review. Multiple articles performed metabolomics using mass spectrometry and/or nuclear magnetic resonance to study pre-eclampsia, gestational hypertension, foetal growth restriction, maternal obesity, gestational diabetes mellitus, intrahepatic cholestasis of pregnancy, and preterm birth. A subset of pregnancy complications was investigated in only a single study each (human immunodeficiency virus in pregnancy, neural tube defects, chromosomal disorders, chronic hypoxia, and recurrent pregnancy loss). Univariate analyses identified potential biomarkers of pregnancy complications. Multivariate analyses demonstrated that the placental global metabolic profiles could separate healthy and complicated pregnancies and identified metabolic pathways (e.g. amino acid metabolism, lipid and phospholipid metabolism) to explain pathogenesis and the mechanisms of disease. We highlight knowledge gaps and future directions, emphasizing the need for data replication and inclusion of variables such as biological sex in the field of placental metabolomics.
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.
Malaria remains a major public health challenge due to drug and insecticide resistance, underscoring the need for novel therapies with distinct mechanisms of action. In this study, silver nanoparticles were green-synthesized using the brown marine algae Padina tetrastromatica (Ag-PT) and evaluated through integrated in vitro, in vivo, metabolomics, network pharmacology, and in silico approaches. Ag-PT showed potent antiplasmodial activity, with significantly lower IC50 values and superior parasite suppression compared to chemically synthesized silver nanoparticles. Untargeted metabolomics revealed that Ag-PT treatment specifically restored malaria-induced disruptions in fatty acid, arginine, and arachidonic acid metabolism. This included elevating precursors of specialized pro-resolving mediators such as DHA, 14-HDHA, and 18-HEPE, and replenishing l-arginine to improve nitric oxide synthesis and vascular function. Integration with network pharmacology identified COX-2 (PTGS2) as a key hub gene. Molecular docking and dynamics confirmed strong binding of the Ag-PT phytochemical eriodictyol to COX-2, suggesting inhibition that shifts arachidonic acid metabolism toward anti-inflammatory specialized pro-resolving mediator production. Collectively, these findings reveal that Ag-PT offers a multifaceted therapeutic strategy by simultaneously targeting the parasite while modulating host inflammatory and metabolic pathways. This integrated therapeutic strategy highlights the potential of eco-friendly, plant-based nanomedicines as a next-generation intervention for malaria management.
Investigating host-pathogen interactions at the molecular level is critical for understanding infection mechanisms and identifying potential therapeutic targets. Mass spectrometry (MS)-based proteomics has rapidly become one of the most employed techniques for the study of almost every aspect of the proteome, hence offering strong potential to study protein dynamics during viral infections. This review presents an overview of key MS-based approaches used in host-virus research to study changes in protein expression, cell signalling, and protein-protein interactions. For each method, we outline its underlying principles, practical and analytical expertise required and key strengths and limitations, with a particular focus on how each can be applied to study specific aspects of the dynamic interplay between host and virus. By comparing these approaches side by side, the review aims to give researchers a conceptual and practical guide on how to select the adequate MS technique for their specific biological questions in infectious biology. Ultimately, this resource is intended to support informed experimental design in host-pathogen research, helping to harness the full potential of MS-based proteomics in uncovering the complexity of infection biology.
Breast cancer subtypes exhibit significant molecular and metabolic heterogeneity, influencing their aggressiveness and therapeutic responses. Among them, triple-negative breast cancer (TNBC) is highly aggressive and often resistant to conventional therapies. To investigate the metabolic programming of this aggressiveness, we conducted an integrated transcriptomics and metabolomics analysis comparing the MCF-7 (luminal A, ER+/PR+) and MDA-MB-231 (TNBC) breast cancer cell lines. Transcriptome analysis of MCF-7 and MDA-MB-231 revealed the differential expression of genes involved in key metabolic pathways. Metabolomics data, further corroborated by transcriptomics, suggest pathway enrichment in beta-alanine, histidine, glutathione, nucleotide metabolism, and the tricarboxylic acid cycle. MDA-MB-231 cells displayed a metabolically aggressive phenotype with enhanced oxidative phosphorylation, redox adaptation, and nucleotide turnover. In contrast, MCF-7 cells showed a more regulated amino acid and redox metabolism profile. The integration of transcriptomic and metabolite profiles highlighted potential metabolic vulnerabilities in TNBC, offering insights into subtype-specific differences at the molecular level.
Characterizing RNA modifications is crucial for understanding fundamental biological processes, such as RNA folding, stability, translation, and splicing. However, current systems for ribonucleoside sample preparation are limited to the solution phase. In this study, we employed the click reaction between methyltetrazine and trans-cyclooctene to immobilize RNases, including nuclease P1, phosphodiesterase I, and shrimp alkaline phosphatase, on agarose beads. Using this digestion method, RNA was fully converted to ribonucleosides within 30 min. Importantly, integrating these immobilized RNases with a microspin tube modified with porous graphitic carbon enabled direct downstream MS analysis, constituting a streamlined system. We applied this system to monitor RNA modification dynamics during transforming growth factor-β (TGF-β)-induced epithelial-mesenchymal transition in lung cancer cells and observed significant changes in several RNA modifications (e.g. m6A and m5U), which is consistent with the indispensable role of RNA modifications in tumour metastasis. Overall, our results demonstrate the efficiency and robustness of our method and highlight a promising direction for RNA modification analysis, supporting the development of automated, high-throughput workflows for future large-cohort studies.
Seronegative rheumatoid arthritis (negRA) is difficult to diagnose due to the absence of rheumatoid factor and anticitrullinated peptide antibodies. This observational case-control study analysed urine samples from 35 negRA patients and 25 healthy controls using integrated liquid chromatography-quadrupole time-of-flight mass spectrometry and gas chromatography-mass spectrometry. Data were processed using mass spectrometry-data independent analysis and evaluated with multivariate approaches, including orthogonal partial least squares discriminant analysis and receiver operating characteristic curve analysis. We identified distinct urinary metabolic alterations in negRA, with four metabolites showing moderate-to-excellent diagnostic accuracy (area under the curve: 0.78-0.91). Pathway analysis using the Kyoto Encyclopedia of Genes and Genomes indicated the involvement of redox regulation and nucleotide/cofactor metabolism. These findings support the potential of urine metabolomics as a non-invasive tool for biomarker discovery in negRA and warrant validation in larger cohorts.
Many diseases, including diabetes and hypertension, can lead to kidney damage, often resulting in long-term health complications and organ failure. Both animal and human studies have documented a causal relationship between high dietary sodium intake and elevated blood pressure. We explored a proteomics analysis of plasma samples to detect early kidney damage in response to a high-sodium (HS) diet through the quantification of putative kidney-derived proteins in plasma for early disease detection. Plasma samples from seven female baboons fed an HS diet for 6 weeks were collected before and after the HS diet challenge. Plasma samples were analysed using a novel nanoparticle enrichment protocol. Using Seer's Proteograph XT workflow, we identified 2294 plasma proteins across all the samples. Of these, 2139 proteins were annotated with gene IDs, including 453 proteins previously identified in kidney lysate. In the Human Protein Atlas, 1969 of these proteins were annotated as expressed in the kidney, and 37 were classified as elevated. Ninety-seven proteins were only detected in plasma samples collected after the 6-week HS diet. We also identified 35 nominally significant differentially abundant proteins (P < .05), with 84% of these detected at higher abundance after HS exposure. Additional proteins were identified in two animals that demonstrated an increase in blood pressure in response to the HS diet. Our analysis reports an increase in putative kidney-derived proteins in plasma after HS diet exposure, and demonstrates the suitability of the Seer's Proteograph technology to identify potential biomarkers for kidney tissue damage.
The ubiquitin-proteasome system (UPS) is the primary protein degradation machinery in eukaryotic cells, composed of multiple proteasome isoforms. It plays critical roles in cellular proliferation, metabolism, immune regulation, oxidative stress, and aging, making it a long-standing focus of biological and therapeutic research. Recently, the UPS has been harnessed for the targeted degradation of disease-relevant proteins using proximity-inducing agents such as proteolysis-targeting chimeras and molecular glue degraders, which have transformed our understanding of druggability. Despite these advances, major gaps remain, particularly in understanding the proteasome's functional heterogeneity across biological contexts. Traditional research emphasizes proteasome structure, subunit function, and substrate features to guide chemical tool and therapeutic development. However, an often-overlooked aspect is the proteasomal degradome, the repertoire of peptide fragments generated during protein degradation. These peptides can exhibit biological activities distinct from their parent proteins, and pathogens, including viruses, have evolved mechanisms to block their production to evade immune detection. Thus, degradome characterization is essential to fully appreciate the proteasome's role in shaping cellular phenotypes in both healthy and diseased states. This review highlights recent studies exploring the degradome, with particular attention to -omic technologies applied to profile and interrogate these peptide products. By focusing on degradation outcomes rather than only the machinery, we aim to underscore the importance of tracing proteasome-derived peptides and their biological consequences. Ultimately, this perspective will broaden our understanding of the UPS while suggesting new avenues for therapeutic exploitation beyond current strategies.
Spinal cord injury (SCI) is a destructive neurological condition that leads to significant functional deficits in the affected individual. To map the altered pathways within the lesion epicentre and the surrounding rostrocaudal segments, we performed RNA-sequencing on injured spinal cord tissue. Samples were collected from three regions-rostral, epicentre, and caudal-at Days 1, 14, and 28 post-injury to systematically profile the transcriptomic changes and identify pathways associated with angiogenesis following SCI. Gene set enrichment analysis revealed enriched pathways, including hepatocyte growth factor (HGF) receptor and alpha 6 beta 4 integrin signalling, indicating an active angiogenic response. The involvement of HGF receptor signalling was further validated by quantitative polymerase chain reaction (qPCR), confirming its role in pathological remodelling after SCI. Subsequently, we identified paxillin (Pxn) as a candidate gene that promotes endothelial cell migration via HGF receptor signalling. Immunohistochemistry demonstrated the role of Pxn in mediating endothelial cell migration and proliferation post-SCI. In summary, our findings indicate that Pxn is a key mediator of endothelial cell proliferation and migration in response to angiogenic factors, such as HGF, following SCI. However, further mechanistic studies are required to fully establish the role of Pxn in endothelial cell migration and proliferation after SCI.
Diabetic kidney disease (DKD) is a complication of diabetes and the leading cause of kidney failure among diabetic patients. Unfortunately, it is typically diagnosed after normal kidney function is already significantly impaired. We used gas chromatography-mass spectrometry (GC-MS) of urine and plasma to explore whether metabolites can serve as potential biomarkers for the early diagnosis of DKD. Urine and plasma samples were obtained from 41 individuals [11 healthy controls, 20 patients with diabetes (diabetes mellitus or DM) and microalbuminuria, 10 patients with DKD]. A total of 342 metabolites were identified in urine samples and 252 metabolites were identified in plasma samples, with 182 metabolites overlapping between the two sample types. Among these, 58 metabolites from urine and 22 metabolites from plasma showed suggestive evidence (P-value < .05) of differences between samples from patients with DKD and samples from healthy control individuals. Sparse partial least squares discriminant analysis (sPLS-DA) was applied to identify metabolomics profiles (in urine, plasma, or both) that maximize separation between DKD patients and controls. A set of four metabolites in plasma, including tyrosine and threo-hydroxyaspartic acid, shows promise as an early-stage biomarker signature that will need to be validated in a larger study. Using this set of metabolites, we estimated the probability of DKD for each of the DM patients, based on their metabolomic profiles, and found a significant correlation with DM designation (low, medium, and high) and estimated glomerular filtration rate. Further validation in larger cohorts is needed to confirm their clinical utility and the ability to predict individuals at risk for DKD.
The fungal symbiont of leaf-cutter ant Atta mexicana, Leucoagaricus gongylophorus LEU18496 has the capability to produce enzymes such as cellulases, hemicellulases, and ligninases for plant biomass degradation. In this study, the fungus has been cultivated in submerged culture conditions using glucose and cellulose as carbon sources to explore gene expression level and unravel the molecular mechanisms responsible of enzyme production and carbohydrate catabolism. The transcriptomic analysis of L. gongylophorus LEU18496 using RNA-seq data, allowed the examination of the gene expression profiles across different carbon sources and growth phases. During the exponential growth phase on glucose there is a constitutive expression of several CAZymes, including β-glucosidase, pectinase, and endo-β-1,3-glucanase. The transcriptome data showed high expression of the creA repressor gene in the presence of glucose, underscoring its regulatory role in carbohydrate degradation and suggesting a regulatory mechanism governing CAZyme production and secretion when glucose is used as a carbon source. This study offers detailed insights into the pathways of cellulose and glucose catabolism, emphasizing the expression of key components involved in carbohydrate metabolism unravelling the metabolic strategies of L. gongylophorus providing information of the CAZymes and FOLymes production as high-value product suitable for biotechnological applications.
Genome-scale metabolic models (GEMs) have progressed from stoichiometric reconstructions to predictive, constraint-aware platforms. In this review, we organize strategies for multi-omics integration not by data type, but by the constraint logic they impose on model solution spaces. Biomass functions enforce composition and maintenance demands, while transcriptomic switches prune network feasibility. Enzyme and expression valves cap flux capacity, proteome budgeting enforces allocation trade-offs, and thermodynamics and fluxomics provide physical and experimental calibration. Machine learning (ML) bridges to infer missing priors while retaining mechanistic structure. These categories translate into practical workflows, spanning enzyme-constrained modelling, thermodynamic embedding, and fluxomics-guided calibration, together with minimal reporting standards to ensure transparency and reproducibility. Emerging directions include the integration of single-cell and spatial data, physics-informed and graph-based ML, and translational pipelines that couple computational predictions with experimental validation. By framing omics integration through constraint architectures, this review provides a coherent agenda for making GEMs reproducible, portable, and biologically meaningful across biotechnology, medicine, agriculture, and environmental applications.
Bladder cancer (BC) is the ninth most prevalent malignancy worldwide. It remains a significant clinical burden due to high recurrence rates and the need for reliable, non-invasive diagnostic tools. Metabolomics is a powerful strategy for non-invasive cancer detection, with urine representing an ideal biofluid for biomarker discovery, given its direct contact with the urinary tract and its rich diversity of metabolites. This study aimed to identify urinary metabolites showing significant differences in urinary levels between BC patients and controls, and to evaluate their potential for diagnosis and disease monitoring. Beyond identifying metabolites differentiating BC patients from controls, we also assessed whether urinary metabolic patterns could distinguish BC subtypes [non-muscle invasive BC (NMIBC) versus muscle-invasive BC (MIBC)]. Following chemical derivatization, urinary samples were analysed by gas chromatography-mass spectrometry, and the resulting datasets were evaluated using univariate and multivariate statistical approaches. Among the 32 metabolites identified (e.g. amino acids, organic acids, alcohols, and sugar-derivatives), lactate was identified as significantly upregulated in BC versus controls, particularly in MIBC cases. Receiver operating characteristic analysis demonstrated a good performance for overall BC detection and in discriminating between MIBC and NMIBC cases. These results, independent of smoking status and sex, position lactate as a promising non-invasive biomarker for invasive BC.
Head and neck squamous cell carcinoma (HNSCC) is a major clinical challenge due to its aggressive nature and poor prognosis in advanced stages. Late detection, often due to a delayed diagnosis, limits treatment success. With the aim of improving the early diagnosis of HNSCC, we analysed urine samples from 19 male HNSCC patients and 10 healthy male subjects and identified 1427 proteins by mass spectrometry (MS)-based proteomics. Of these, 351 proteins were consistently detected in all subjects and selected for quantitative comparisons, which highlighted potential prognostic markers such as RNASE1, LRG1, and CD44. Proteogenomic cross-referencing of MS-identified peptides with cancer variant databases suggested the presence of HNSCC-associated protein variants [e.g. GAA p.(Trp746Cys) and SIAE p.(Pro210Leu)] as potential indicators of advanced disease. Functional analyses linked the identified proteins to important tumour-related processes, including the epithelial-mesenchymal transition and neutrophil degranulation. These results support urine as a valuable body fluid for proteogenomic profiling, as it can be collected non-invasively, is available in large volumes, and enables the longitudinal monitoring of molecular changes over time, providing a convenient window into systemic and tumour-associated processes. This study provides a proof of concept that tumour-related protein variants originating from HNSCC can be detected in urine, supporting its potential as a source of biomarkers for early detection. However, given the small, male-only cohort, these findings should be regarded as preliminary and will require validation in larger, sex-balanced cohorts, including patients with benign or inflammatory head and neck conditions, to confirm disease specificity. Altogether, our data underscore the translational promise of urinary proteogenomics in HNSCC management.
