Molecular omics最新文献

Recent advances in genomic technologies have greatly enhanced our understanding of genotype-phenotype relationships and improved diagnostic of genetic diseases. However, the dissection of complex structural variants remains challenging due to the limitations of current methods in resolving their breakpoint and interpreting phenotypes involving multiple disrupted genes. In this study, we demonstrate how an integrative approach-combining molecular cytogenetics, genomic, and transcriptomic methods-enables the detection, structural and functional characterization of a complex structural variants affecting the MBD5, USP34, and XPO1 genes. Our findings underscore the utility of the Exo-C, a modified chromosome conformation capture technique, in resolving complex rearrangements. We also report, for the first time, a composite neurodevelopmental phenotype resulting from the combined effects of MBD5-associated intellectual disability and 2p15p16.1 microdeletion syndromes.

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 vs. muscle-invasive, NMIBC vs. MIBC). Following chemical derivatization, urinary samples were analysed by gas chromatography-mass spectrometry (GC-MS), and the resulting datasets were evaluated using univariate and multivariate statistical approaches. Among the 32 metabolites identified (e.g., amino acids, organic acids, alcohols, sugar-derivatives), lactate was identified as significantly upregulated in BC versus controls, particularly in MIBC cases. ROC analysis demonstrated 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.

Genome-scale metabolic models 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 bridges infer missing priors while retaining mechanistic structure. These categories translate into practical workflows, spanning enzyme-constrained modeling, 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 machine learning, 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.

Diabetic kidney disease (DKD) is the leading cause of kidney failure among diabetic patients. At the time of clinical diagnosis, kidney function has already significantly deteriorated, limiting treatment options. We developed a novel approach using TMT labeling to identify kidney proteins in plasma samples and putative protein biomarker signatures that distinguish patients with DKD or reduced kidney function from control individuals. Plasma samples from 28 patients from the NC A&T Men's Minority Health Initiative Cohort included 7 healthy controls, 7 patients with diabetes and microalbuminuria (DM), and 2 patients with DKD. In addition, our sample set included 12 individuals with DM but no detectable microalbuminuria. Plasma samples were depleted, and analyzed using TMT labeling with kidney lysate as a reference sample to identify potentially kidney-derived proteins in plasma that could indicate early kidney cell damage and protein leakage. A total of 424 proteins were identified in the plasma samples. Of these, the Human Protein Atlas labels 375 as proteins expressed in the kidney, and 4 proteins as kidney-enriched. We identified 13 proteins whose abundance levels were different between patients with kidney injury and controls (p<0.05). Using sPLS-DA analysis, we identified a biomarker signature of 4 plasma proteins that confidently distinguish samples from individuals with kidney injury and control individuals. Interestingly, samples from DM patients without any detectable kidney dysfunction align between the controls and individuals with kidney damage, suggesting that some of these individuals are more similar in their biomarker signature to DKD patients and may be progressing to microalbuminuria.

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 delayed diagnosis, limits treatment success. With the aim of improving early diagnosis of HNSCC, we analyzed urine samples from 19 male HNSCC patients and 10 healthy male subjects, identifying 1427 proteins by mass spectrometry-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 mass spectrometry-identified peptides with cancer variant databases suggested the presence of HNSCC-associated protein variants (e.g., GAA p.(Trp746Cys), SIAE p.(Pro210Leu)) as potential indicators of advanced disease. Functional analyzes linked the identified proteins to important tumor-related processes, including 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 at large volumes, and enables longitudinal monitoring of molecular changes over time, providing a convenient window into systemic and tumor-associated processes. This study provides proof of concept that tumor 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 underscores the translational promise of urinary proteogenomics in HNSCC management.

STUB1, also known as CHIP (C-terminus of Hsc70-Interacting Protein), plays a vital role in cellular protein homeostasis through its E3 ubiquitin ligase activity. Recent evidence suggests that STUB1 (CHIP) is implicated in various cancer types, influencing tumorigenesis by regulating the degradation of oncogenic and tumor suppressor proteins, viz., c-Myc, PTEN, p53 etc. This study investigates the prognostic value of STUB1 across multiple cancers through a comprehensive pan-cancer analysis utilizing large public databases, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) and further validation of results in multiple cancer cell lines. Our analysis reveals distinct expression patterns of STUB1 (CHIP) across different cancer types and highlights its correlation with clinical outcomes. In certain cancers, high STUB1 (CHIP) expression is associated with worse prognosis, likely due to its role in degrading tumor suppressor proteins. Conversely, in other cancer types, low STUB1 (CHIP) expression correlates with poor survival, possibly due to impaired degradation of oncogenic factors. The study provides crucial insights into the dual roles of STUB1 (CHIP) in several cancer types, establishing it as a potential prognostic marker. Our investigation into the contextual role of STUB1 (CHIP) within human tissue samples, employing immunoblotting and complementary assays, highlights its potential as a therapeutic target for restoring protein homeostasis and modulating cancer progression. Nonetheless, further research is necessary to comprehensively elucidate the mechanisms by which STUB1 (CHIP) regulates tumorigenesis across various cancer types.

Omics analysis has become an indispensable tool for researchers in the life sciences, enabling the study of DNA, RNA, and proteins and how they respond to cellular stimulus. Many methods of data analysis exist for the generation and characterization of gene lists, however, selection of genes for further investigation is still heavily influenced by prior knowledge, with practitioners often studying well characterized genes, reinforcing bias in the literature. Here, we have developed an open-source, R package for impartial ranking of gene lists derived from omics analysis that we term deciphering scientific discoveries (DeSciDe). We applied a pipeline that sorts a gene list first by precedence, which we define as co-occurrence of the gene with pre-defined search terms in publications. We then rank gene lists by connectivity, an underutilized metric for how related a gene is to other enriched genes. The combination of these rankings by scatterplot provides a method for gene selection by simple visual analysis. We apply this analysis method to published Omics datasets, identifying novel avenues for investigation. Further, using this method we have been able to assign a novel loss of function role for the histone mutation H2A E92K.

Helichrysum species, of which 35% are native to South Africa, are renowned for their diverse medicinal properties, yet their chemical composition remains largely unexplored. As such, continuous efforts are needed to comprehensively characterize the phytochemistry of Helichrysum species which will subsequently contribute to the discovery and exploration of Helichrysum-derived natural products for drug discovery. Thus, a computational metabolomics work is reported herein to comprehensively characterize the metabolic landscape of three medicinal species (H. italicum, H. petiolare, and H. splendidum), which are less studied. The metabolites were extracted using hexane, ethyl acetate, and methanol and analyzed on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. Different solvents were utilized to increase metabolome coverage in Helichrysum species. Spectral data were mined using molecular networking (MN) strategies. The results revealed that multiple extraction methods provide a more comprehensive analysis of the metabolome of the three plants. The measured metabolome of Helichrysum species is rich in phenylpropanoids, lipids and lipid-like molecules, pointing to a rich chemistry with potential bioactivities. Comparative analysis of the H. italicum, H. petiolare and H. splendidum metabolomes revealed that the flavonoid glucoside and triterpenoid profiles of the three species differ distinctively. These results expand the knowledge base on the chemistry of Helichrysum plants and provide deconvoluted details of the various chemical classes that differentially define the metabolome of the Helichrysum plants. Such actionable insights point to Helichrysum's potential as a valuable source of natural compounds with promising medicinal properties.

In pancreatic ductal adenocarcinoma, hypoxia is a crucial component of the tumour microenvironment and is associated with worse clinical outcomes. Adaptation to extreme hypoxic settings is based on abnormal lipid metabolism, but insights into how hypoxia-regulated lipid changes link with aggressive migratory potential in pancreatic cancer are lacking. This study investigates the molecular processes, pathways, and critical proteins involved in hypoxia-induced lipidic and polyunsaturated fatty acid alterations in pancreatic cancer. Our findings elucidate increased multilayer unsaturation in FA chains of major lipid classes associated with greater migration and invasion, as well as higher abundances of particular desaturases. The expression of these proteins was verified in clinical tumour samples by unsaturated fatty acid biosynthesis-related gene enrichment score. High unsaturated fatty acid clusters were shown to be associated with a low survival rate. Pathway correlation and protein–protein interaction analysis indicated that the PPAR-hypoxia axis and SCD/FADS2/APOC3-HDLBP protein network are implicated in mediating the observed alterations in lipid pools and poly-unsaturation levels in pancreatic cancer under hypoxia. These results provide novel therapeutic targets in pancreatic cancer while improving our understanding of hypoxia-induced migratory potential in pancreatic cancer.

Osteoarthritis (OA) is a complex and increasingly prevalent condition, affecting an estimated 600 million people worldwide and significantly reducing quality of life. Understanding OA as both an inflammatory and neurological disease presents challenges for diagnosis and treatment, as no curative therapy is currently available. The neuroactive effects of OA pain on neuropeptide systems, particularly within the spinal cord, remain underexplored, impeding therapeutic advances. Mass spectrometry (MS) was employed to characterize the spinal cord peptidome in a validated rat model of OA pain. The peptidome was analyzed longitudinally over 84 days using the Montreal Induction of Rat Arthritis Testing (MI-RAT©; n = 20) model, compared to arthrotomic (Sham; n = 4) and healthy (Naive; n = 9) groups. Label-free peptidome profiling using liquid chromatography coupled with tandem MS (LC-MS/MS) revealed dynamic changes in endogenous spinal peptides during OA progression, leading to the identification of 624 peptides derived from 29 prohormone precursors. The findings reveal substantial changes in peptide levels in the spinal cord, particularly involving neuropeptide substance P and peptides derived from proenkephalin, calcitonin gene-related peptide, and somatostatin. These results provide novel insights into the molecular mechanisms underlying OA-associated pain and identify potential targets for new therapeutic interventions in neurological pain conditions.