Abstract This study aimed to identify candidate salivary metabolites for differentiating oral squamous cell carcinoma (OSCC) patients from healthy controls. Untargeted metabolomics was performed using an Orbitrap UHPLC-MS/MS system on saliva samples from nine OSCC patients and nine healthy controls following ethical approval. A total of 1,105 metabolite features, including those detected in quality control samples, were identified. Sequential filtering to exclude non-endogenous or non-human-associated compounds retained 291 endogenous metabolites. Following further preprocessing, 75 metabolite features were subjected to downstream statistical analysis using MetaboAnalyst 6.0. Of these, 34 metabolites exhibited differential abundance based on a nominal p value < 0.05 and a fold-change threshold of >1.2, comprising 21 upregulated and 13 downregulated metabolites. The ten most significantly upregulated metabolites were prioritized for further evaluation. L-iditol, lignoceric acid, D-glucose 1-phosphate, ManGlcNAc2, and Man3GlcNAc2 emerged as candidate discriminatory features. Receiver operating characteristic (ROC) analysis demonstrated promising discriminatory performance, with a three-metabolite panel comprising Man3GlcNAc2, lignoceric acid, and ManGlcNAc2 achieving an area under the curve (AUC) of 1.0 within this discovery cohort. Nevertheless, this apparently perfect discrimination should be interpreted cautiously because of the small sample size, potential overfitting, and exploratory nature of the analysis. The identified metabolites represent preliminary candidate biomarkers rather than clinically validated diagnostic markers. Larger independent cohorts, appropriate disease-control groups, and targeted analytical validation are essential to establish their reproducibility, disease specificity, and potential clinical utility for OSCC detection and monitoring.
KRAS is mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), where hotspot alterations in codons 12, 13, and 61 drive tumor initiation and progression. Although distinct biochemical properties have been described for individual KRAS mutants, whether they generate unique allele-specific signaling programs in PDAC cells remains unresolved. Here, we systematically interrogated the molecular consequences of seven common KRAS mutant variants in reconstituted isogenic, KRAS-deficient PDAC cell lines by integrated transcriptomic, proteomic, and phosphoproteomic profiling. We found that baseline cellular state, rather than allele identity, was the predominant driver of molecular variation. Comparisons with established KRAS reference signatures revealed significant but moderate overlap at the mRNA level and less so at the proteome level. Pathway analyses highlighted interferon response and mitochondrial translation-related proteins as recurrently altered across mutant alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust mutant allele-specific molecular programs were identified in our KRAS-reconstituted cell lines. Together, our study establishes a comprehensive multi-omics resource for KRAS signaling in PDAC and demonstrates that cellular context exerts a stronger influence than allele identity in shaping molecular profiles, with implications for interpreting putative allele-specific signaling dependencies.
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.
