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PubMed2006

Systematic proteome and transcriptome analysis of stem cell populations.

Methods for relative assessment of the transcriptional activity of the cell are now routinely employed and obtain large amounts of information regarding process such as transformation or development. These approaches have great impact and are of significant value. Nonetheless, mRNA is an intermediate in the process of protein synthesis and changes in mRNA expression do not reflect absolute or relative changes in protein levels. The mechanisms which translate mRNA to protein are highly regulated, and it remains unclear how the transcriptome reflects the functional state of the cell, as defined by its protein output. Large scale analyses of the proteome are now becoming a reality due to technical advances in protein arrays and mass spectrometry. Thus for the first time data on large numbers of mRNA transcripts and the levels of expression of their associated proteins is available in dynamic systems. Analysis of one such comparison, the transcriptome and proteome of primary haematopoietic stem cells, reveals post-translational regulation of the proteome in stem cell populations. The factors which must be considered when comparing two systematically acquired 'omics' datasets are reviewed and the relative merits of transcriptome and proteome approaches are discussed.

Animals↗
PubMed2025

Integration of multiple omics reveals key targets and cellular mechanisms for intervention in sarcopenia.

BACKGROUND: Sarcopenia, an age-related syndrome characterized by progressive loss of muscle mass, strength, and function, presents a significant global health burden with limited therapeutic interventions. This study integrates genomic causality, multi-tissue omics, and cellular mediation analyses to identify and prioritize mechanistically grounded therapeutic targets. METHODS: A multi-tiered analytical framework was applied, beginning with two-sample Mendelian randomization (MR) to infer causal relationships between 4907 plasma proteins (cis-pQTLs from 35,559 individuals) and sarcopenia traits in Pan-UK Biobank participants. Bayesian colocalization and transcriptomic validation in human sarcopenia muscle biopsies were employed to prioritize targets. Cellular mediation analysis quantified contributions of immune and stromal cell subtypes to protein-trait pathways using transcriptomic deconvolution. RESULTS: MR identified 1237 plasma proteins causally associated with sarcopenia traits, with six targets (HGFAC, GATM, HMOX2, F2, LMAN2L, HPGDS) validated through colocalization, transcriptomic expression, and sarcopenia-related dysregulation. Cellular mediation revealed immune mechanisms underlying HGFAC's effects, with CD4+ regulatory T cells mediating 3.49 % of its impact on sarcopenia traits. Prothrombin exhibited muscle-protective effects independent of coagulation. CONCLUSION: This study establishes a causal map linking plasma proteins to sarcopenia through immune-stromal interactions. The integration of MR, multi-omics validation, and cellular mediation prioritizes six proteins as actionable targets, supporting repurposing of thrombin inhibitors and development of immunometabolic therapies. The framework bridges genomic causality with cellular pathophysiology, advancing precision strategies for age-related muscle decline.

Humans↗
PubMed2026

Harnessing metabolomics and proteomics in a clinical trial for pulmonary arterial hypertension: insights from post-hoc analysis of the REHAB-PH trial.

BACKGROUND: The significant clinical and molecular heterogeneity of pulmonary arterial hypertension (PAH) poses challenges in identifying effective therapies. Advanced multidimensional profiling offers an opportunity to capture molecular responses and assess biomarker stability, yet its application in randomised trials remains limited. METHODS: We evaluated the multi-omic profiles of participants with PAH in a randomised, placebo-controlled trial of famotidine. Plasma metabolomic and proteomic profiling was performed at enrolment and 24 weeks. Baseline profiles were compared between treatment arms to assess randomisation balance. Intraclass correlation coefficients quantified within-subject stability over time. Linear regression models adjusting for age, sex, body mass index and PAH aetiology evaluated famotidine's molecular effects. False discovery rate was controlled for multiple comparisons. FINDINGS: For the 79 participants, baseline multi-omic profiles were similar between groups. At 24 weeks, 34 and 37 participants remained in the famotidine and placebo groups respectively. The placebo group showed high molecular stability, while greater variability was observed in the famotidine group. Famotidine treatment was associated with significant changes across 191 proteomic pathways (q-value <0.05), but no metabolomic changes remained significant after multiple-testing correction. INTERPRETATION: Integrating multi-omics into a prospective clinical trial is feasible and yields stable longitudinal profiles in the absence of intervention. While famotidine did not yield clinical benefit, associated proteomic changes illustrate how molecular profiling can reveal treatment-related biology and inform future trial design. These findings highlight the broader utility of multi-omics for evaluating drug responses and identifying molecular endotypes in PAH and beyond. FUNDING: US National Institutes of Health.

Humans↗
PubMed2025

Integrative omics of the genetic basis for wheat WUE and drought resilience reveal the function of TaMYB7-A1.

Improving wheat&#xa0;drought resilience and water use efficiency (WUE) is critical for sustaining productivity under increasing water scarcity. Here, we integrate genome-wide association&#xa0;study (GWAS), expression quantitative trait locus (eQTL) mapping, population-transcriptome analysis, and summary-data-based mendelian randomization (SMR), followed by functional validation using indexed EMS mutants and transgenic lines, to systematically identify key WUE regulators. GWAS across water conditions in 228 accessions identifies 73 quantitative trait loci (QTLs) for WUE-traits. Transcriptome profiling of 110 diverse accessions reveals 28 drought-responsive modules. eQTL mapping uncovers 146,966 regulatory variants, including condition-specific hotspots associated with key drought-related pathways. Integrative analysis underscores 85 high-confidence candidate genes, notably TaMYB7-A1. Overexpression of TaMYB7-A1 enhances photosynthesis, WUE, root development, and grain yield under drought condition by activating TaPIP2;2-B1 (water transport), TaRD20-D1 (stomatal regulation), and TaABCB4-B1 (root growth), reflecting reduced water loss and improved physiological resilience. Our study presents a comprehensive regulatory map and robust targets for wheat drought adaptation and resilient cultivar breeding.

Triticum↗
PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗
PubMed2026

Microbial partnerships and molecular mechanisms in plant stress physiology for climate-resilient and sustainable farming.

Plant-microbial partnerships and their underlying molecular mechanisms are indispensable, natural drivers of improved nutrient acquisition and stress tolerance in the face of climate-driven environmental challenges. Modern multi-omics tools, when coupled with artificial intelligence and synthetic biology, enable the precise design of targeted bioinoculants and synthetic microbial consortia. Translating these advanced microbiome-based strategies into scalable, field-level agricultural applications provides a sustainable path toward securing global food production while maintaining soil health. Global climate change imposes multifaceted abiotic and biotic stresses on crops, disrupting physiological and molecular processes and threatening agricultural productivity. Plant-associated microbes represent an underexplored yet powerful ally in enhancing crop resilience. This review presents current knowledge of plant-microbe interactions and the molecular mechanisms governing plant stress physiology, with an emphasis on climate-resilient and sustainable farming. Hence, ever-changing environmental cues pose a significant burden on agricultural productivity, and plant-associated microbial communities modulate a cascade of physiological and molecular responses, including production of phytohormones, signaling, regulation of reactive oxygen species homeostasis, and activation of plant immune responses to help plants withstand stress and enhance productivity. Moreover, root exudates, phytohormones, and quorum sensing mediate the central communication networks, facilitating plant-microbe cross talk. Additionally, the advances in OMICs approaches aid in disentangling the molecular underpinnings of these interactions by providing mechanistic insights and potential candidate gene targets for crop improvement and stress resilience. In the post-genomic era, integrating artificial intelligence and big data analysis to optimize microbiome-based strategies for sustainable agriculture is a new frontier for disentangling plant-microbe symbiosis to improve soil health, enhance crop yields, and improve stress tolerance. Thus, by integrating the ecological, physiological, and molecular perspectives, this review highlights the transformative potential of harnessing plant-microbe symbiosis for climate-resilient and sustainable agriculture.

Stress, Physiological↗
PubMed2006

Protein biomarkers associated with acute renal failure and chronic kidney disease.

Acute renal failure (ARF) as well as chronic kidney disease (CKD) are currently categorized according to serum creatinine concentrations. Serum creatinine, however, has shortcomings because of its low predictive values. The need for novel markers for the early diagnosis and prognosis of renal diseases is imminent, particularly for markers reflecting intrinsic organ injury in stages when glomerular filtration is not impaired. This review summarizes protein markers discussed in the context of ARF as well as CKD, and provides an overview on currently available discovery results following 'omics' techniques. The identified set of candidate marker proteins is discussed in their cellular and functional context. The systematic review of proteomics and genomics studies revealed 56 genes to be associated with acute or chronic kidney disease. Context analysis, i.e. correlation of biological processes and molecular functions of reported kidney markers, revealed that 15 genes on the candidate list were assigned to the most significant ontology groups: immunity and defence. Other significantly enriched groups were cell communication (14 genes), signal transduction (22 genes) and apoptosis (seven genes). Among 24 candidate protein markers, nine proteins were also identified by gene expression studies. Next generation candidate marker proteins with improved diagnostic and prognostic values for kidney diseases will be derived from whole genome scans and protemics approaches. Prospective validation still remains elusive for all proposed candidates.

Acute Kidney Injury↗
PubMed2026

Histone deacetylases: From acetylation homeostasis to oncogenic and neurodegenerative disorders.

Histone deacetylases (HDACs) are central regulators of acetylation homeostasis, governing chromatin architecture, transcriptional dynamics, and diverse cellular processes through reversible lysine deacetylation. Dysregulation of HDAC activity disrupts epigenetic balance and is strongly implicated in oncogenic transformation and the progression of neurodegenerative disorders. This chapter provides a comprehensive overview of HDAC biology with a particular emphasis on experimental and analytical methodologies used to investigate their function. We describe the structural and functional diversity of HDAC classes and their roles in multiprotein complexes that regulate gene expression and cellular signaling. A major focus is placed on screening-compatible and mechanistic assays, including fluorometric, colorimetric, radiometric, fluorescence polarization, TR-FRET, AlphaScreen/AlphaLISA, and differential scanning fluorimetry approaches for quantitative measurement of enzymatic activity and inhibitor profiling. In addition, advanced methodologies such as mass spectrometry-based acetylome analysis, chromatin immunoprecipitation sequencing (ChIP-seq), recombinant enzyme assays, and cell-based reporter systems are discussed in the context of functional genomics and drug discovery. The integration of high-throughput screening, structural biology, and multi-omics strategies is highlighted as essential for dissecting HDAC-mediated regulatory networks. Collectively, this chapter serves as a methodological framework for studying HDAC function and developing targeted epigenetic therapies in cancer and neurodegenerative diseases.

Histone Deacetylases↗
PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2005

Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools.

The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.

Animals↗
PubMed2002

"A system biology" approach to bioinformatics and functional genomics in complex human diseases: arthritis.

Human and other annotated genome sequences have facilitated generation of vast amounts of correlative data, from human/animal genetics, normal and disease-affected tissues from complex diseases such as arthritis using gene/protein chips and SNP analysis. These data sets include genes/proteins whose functions are partially known at the cellular level or may be completely unknown (e.g. ESTs). Thus, genomic research has transformed molecular biology from "data poor" to "data rich" science, allowing further division into subpopulations of subcellular fractions, which are often given an "-omic" suffix. These disciplines have to converge at a systemic level to examine the structure and dynamics of cellular and organismal function. The challenge of characterizing ESTs linked to complex diseases is like interpreting sharp images on a blurred background and therefore requires a multidimensional screen for functional genomics ("functionomics") in tissues, mice and zebra fish model, which intertwines various approaches and readouts to study development and homeostasis of a system. In summary, the post-genomic era of functionomics will facilitate to narrow the bridge between correlative data and causative data by quaint hypothesis-driven research using a system approach integrating "intercoms" of interacting and interdependent disciplines forming a unified whole as described in this review for Arthritis.

Animals↗
PubMed2004

Interaction networks: coordinating responses to xenobiotic exposure.

In the last decade the increased usage of '-omic' technologies, plus the sequencing of over 800 complete genomes has led to a vast increase in the amount of information available to the researcher for examining cellular responses to xenobiotics. Much effort has been put into the identification and analysis of expression profiles associated with pathobiological conditions and/or xenobiotic exposure. These profiles are commonly used in two applications. Firstly, comparative profile experiments are used to classify pathobiological states and for the screening of novel chemical entities to predict their action(s) on the body. Secondly, mechanistic investigations will gain information on the molecular mechanisms underlying toxic responses/pathobiological states. During the course of such analysis it has become increasingly clear that a series of highly refined interaction networks exist within the body, regulating both the sensitivity and selectivity of the body's response to pathobiological states/xenobiotic exposure. These interaction networks exist at several levels: Firstly, within individual cells, the interaction between factors that transmit xenobiotics signals will determine the overall cellular response. Secondly, intraorgan communication occurs between the different cell types/sub-types which makes up an organ, coordinating the overall organ response. Finally, interorgan interactions provide axes of response through the body.

Animals↗
PubMed2024

Integrative Omics Reveals the Metabolic Patterns During Oocyte Growth.

Well-controlled metabolism is associated with high-quality oocytes and optimal development of a healthy embryo. However, the metabolic framework that controls mammalian oocyte growth remains unknown. In the present study, we comprehensively depict the temporal metabolic dynamics of mouse oocytes during in&#xa0;vivo growth through the integrated analysis of metabolomics and proteomics. Many novel metabolic features are discovered during this process. Of note, glycolysis is enhanced, and oxidative phosphorylation capacity is reduced in the growing oocytes, presenting a Warburg-like metabolic program. For nucleotide biosynthesis, the salvage pathway is markedly activated during oocyte growth, whereas the de novo pathway is evidently suppressed. Fatty acid synthesis and channeling into phosphoinositides are specifically elevated in oocytes accompanying primordial follicle activation; nevertheless, fatty acid oxidation is reduced in these oocytes simultaneously. Our data establish the metabolic landscape during in&#xa0;vivo oocyte growth and serve as a broad resource for probing mammalian oocyte metabolism.

Animals↗
PubMed2025

DNA methylation and multi-omics profiling of T cells uncovers chemotactic pathways and proliferation-linked hypomethylation in narcolepsy type 1.

Narcolepsy type 1 (NT1) is a chronic sleep disorder caused by a loss of orexin-producing cells in the brain and involves autoimmune mechanisms, including the presence of autoreactive T cells. In this study, we performed genome-wide DNA methylation analysis using both CD4+/CD8+ T cells from 42 NT1 patients and 42 controls across discovery and replication cohorts. To identify methylation changes more robustly associated with the disease, we prioritized differentially methylated regions (DMRs) over single-site differentially methylated positions (DMPs). Furthermore, to validate and interpret DMP-level associations, we integrated genome-wide genotype and gene expression data obtained from the same individuals. As a result, the DMR analysis identified 15 reproducible DMRs in CD4+ T cells and 5 in CD8+ T cells, with most DMRs shared between the two cell types. Shared DMRs included regions associated with CCL5 (p&#xa0;=&#x2009;2.1E-02) and CCR4 (p&#xa0;=&#x2009;8.3E-03). Integrative analysis with genotype and gene expression data also showed that the DMP related to S100A4, which promotes lymphocyte migration through CCR5 and CXCR3 receptors, was associated with the disease in CD4+ T cells. Pathway analysis of genes identified through both the DMR and integrative analyses indicated enrichment in cell chemotaxis-related pathways, suggesting that aberrant chemokine-mediated cell migration plays a central role in NT1 pathogenesis. Further, NT1-associated methylation changes were predominantly hypomethylation events, significantly enriched in non-promoter, non-CpG island regions (p&#xa0;=&#x2009;1.74E-102). We further observed that global hypomethylation levels were correlated with hypoSC, a mitotic index estimated from methylation data, highlighting increased T cell proliferation in NT1.

Humans↗
PubMed2026

[Applications and Challenges of Deep Learning in Human Genome Research].

In recent years, the advent of high-throughput omics technologies has fueled an explosive growth in human genomic data. Uncovering the latent functions within this vast data has become a significant challenge in functional genomics research. While traditional statistical methods have proved successful for analyzing smaller-scale datasets in the past, they exhibit clear limitations in analytical efficiency and integrating multi-dimensional data, struggling to meet the escalating demands of contemporary genomic analysis. The introduction of deep learning (DL) technologies offers a novel paradigm for this field. This review systematically examines the advances in applying deep learning to human genomics research. Studies demonstrate that when ample labeled data is available, discriminative DL computational methods-such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs)-achieve high accuracy and efficiency in genomic variant discovery tasks. Furthermore, generative DL methods, particularly Large Language Models (LLMs) leveraging self-supervised pre-training strategies, effectively integrate complex genomic information and exhibit superior performance in functional genomic sequence annotation and gene regulation studies. This review also explores the application of LLMs in multi-omics data integration and prediction. Looking ahead, the continued accumulation of long-read sequencing and high-dimensional data is expected to enable DL technologies to integrate increasingly complex and heterogeneous genomic information, playing an increasingly crucial role in human genomics research.

Deep Learning↗
PubMed2026

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗
PubMed2006

The identification of human tumour antigens: Current status and future developments.

The biggest challenge facing us today in cancer control and prevention is the identification of novel biomarkers for detection and improved therapeutic interventions to reduce mortality and morbidity rates. Biomarkers are important indicators to inform us of the physiological state of the cell at a specific time. It is now clear that malignant transformation occurs by changes in cellular DNA and protein expression with subsequent clonal proliferation of the altered cells. The affected genes and their expressed protein products or biomarkers are those involved in the normal growth and maintenance of the cancerous cells. These biomarkers could prove pivotal for the identification of early cancer and people at risk of developing cancer. Altered proteins or changes in gene expression in malignant cells may lead to the expression of tumour antigens recognised by host immune system. In this review we discuss current research into the molecular technologies making possible the global genomic-wide analysis of changes in DNA (genotyping), RNA expression (transcriptomics) and protein expression (proteomics) that have accelerated the rate of new biomarker/tumour antigen discovery. To gain a comprehensive understanding of the physiology and pathophysiology of cancer an approach that harmoniously integrates the various 'omic' platforms are key to unraveling the complexity 'needle-in-a-haystack' quality of biomarker/tumour antigen discovery.

Antigens, Neoplasm↗