The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.
Depression is a prevalent and disabling mental disorder associated with increased suicide risk and impaired quality of life. Herbal medicines such as Hypericum perforatum L. (St. John's Wort [SJW]) and Chaihu Shugan San (CSGS) have been widely used to treat depressive symptoms, yet their system-level molecular mechanisms remain incompletely understood. In this study, we applied a multi-omics strategy centered on quantitative proteomics, combined with metabolomics and microbiota profiling, to investigate the antidepressant mechanisms of CSGS and SJW in a chronic unpredictable mild stress rat model. Behavioral assessments confirmed that both treatments significantly alleviated depressive-like behaviors. Proteomic analysis identified 204 differentially expressed proteins (DEPs) in the CSGS group and 473 DEPs in the SJW group. Functional enrichment analyses suggested that CSGS was more closely associated with lipid metabolism, whereas SJW was linked to broader metabolic processes. Integrative metabolomic and microbiota analyses further supported these proteomics-driven pathway distinctions, with SJW showing stronger associations with gut microbiota alterations. CSGS and SJW alleviated depressive-like behaviors through divergent yet partially overlapping molecular mechanisms. These findings highlight the utility of multi-omics approaches for elucidating system-level mechanisms of complex herbal medicines.
DNA sequencing has revolutionized biological and biomedical research, offering profound insights into genome organization, function, and variability. From the pioneering Sanger capillary electrophoresis method to the advent of next-generation sequencing, the field has evolved toward unprecedented speed, scalability, and cost decreases over the years. These advancements have enabled diverse applications across genomics, transcriptomics, metagenomics, epigenomics, and precision medicine, powering global initiatives such as the Human Genome Project, the Human Microbiome Project, and the 1000 Genomes Project. Bioinformatics has also advanced in data processing, variant detection, and functional annotation, helping transform raw sequencing data into biologically meaningful insights and knowledge. Although highly advanced, sequencing technologies still encounter challenges, including accuracy trade-offs and the need for efficient management of rapidly increasing volumes of data. Leveraging the genomic revolution, this review explores the shifts toward next-generation phenomics (NGP), an archetype that uses artificial intelligence that integrates multi-omics data with digital phenotyping, the Internet of Things, and real-time analytics. The goal of NGP is to integrate genotypic and phenotypic data to support predictive modeling of health, disease, and environmental interactions. By tracing history, advances in sequencing technologies, and future perspectives on NGP, this article offers a comprehensive overview for researchers and clinicians, highlighting how the integration of omics and digital data will drive the generation of personalized and systems-level biology.
Metabolomics is the comprehensive analysis of small-molecule metabolites in living systems and is increasingly being applied in forensic science and health diagnostics. This review broadly integrates the foundational principles of metabolomics, key analytical techniques, and translational applications across forensic toxicology, postmortem interval estimation, and disease biomarker discovery. Advanced methodologies, such as mass spectrometry, nuclear magnetic resonance spectroscopy, and single-cell metabolomics, have knowingly enhanced sensitivity and resolution, enabling accurate detection of drug-related biomarkers, metabolic perturbations, and trauma-induced molecular signatures. Moreover, integrating metabolomics with cellular and molecular biology offers novel insights into disease pathophysiology, particularly in cancer, neurodegeneration, and metabolic disorders. Hence, emphasis is placed on the role of metabolite-mediated signaling and epigenetic regulation in bridging diagnostic gaps. The review delves deeper into recent advances in DNA-based phenotypic prediction, trace-based evidence collection strategies, and artificial-intelligence-driven analytical models. The conceptual and regulatory fundamentals of forensic and clinical metabolomics are compared in this review, exposing potential trends for transdisciplinary innovation.
Lung cancer remains a leading cause of cancer-related mortality worldwide due to its extensive molecular heterogeneity, late-stage diagnosis, and therapeutic resistance. Advances in high-throughput omics technologies have enabled comprehensive characterization of tumors across multiple biological layers, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, single-omics analyses provide only fragmented insights into tumor biology, highlighting the need for integrative multiomics approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for integrating heterogeneous datasets and uncovering biologically and clinically relevant patterns. This review summarizes recent advances in AI-driven multiomics integration for lung cancer, highlighting its applications in molecular subtyping, biomarker discovery, prognosis prediction, therapeutic response modeling, and precision oncology. We also discuss current challenges, including data heterogeneity, model interpretability, reproducibility, and clinical translation, together with emerging strategies for integrating multimodal data such as radiomics and digital pathology. Finally, we introduce precision phenomics as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management. Collectively, AI-driven multiomics integration has the potential to transform lung cancer research and improve patient outcomes.
Metabolomics is a powerful systems-level approach and has the potential to serve as an important part for understanding the biochemical pathways and metabolic phenotypes in physiological and pathological states. Extracellular vesicles (EVs), which include apoptotic bodies, microvesicles, and exosomes, have emerged alongside metabolomics as active mediators of intercellular communication, transporting diverse cargo that mirrors the cellular origin and metabolic status of their parent cell. EVs are enriched with lipids, proteins, nucleic acids, and biologically active metabolites involve in signal transduction, metabolic regulation, and pathogenic mechanisms of a disease. Nevertheless issues related to heterogeneity of vesicles, purity of isolation, and detection sensitivity of metabolites, the study of EV metabolomics still methodologically and analytically challenging. This review offers a critical synthesis of current knowledge in EV metabolomics including analytical technology, statistical and computational approaches, and emerging clinical applications. In addition, a specific focus on methodological variability, contamination chances, and limitations in existing research study design that affect reproducibility and translation. Furthermore, multi-omics integration and machine learning are reviewed as promising approaches to enhance the discovery of biomarkers and interpretation of the biological system. Finally, highlighting the key research gap and future research directions to steer the advancement of clinically related applications in translational medicine.
Inflammatory bowel disease (IBD) is a chronic and recurrent gastrointestinal disease, the pathogenesis of which has not been fully elucidated. Increasing evidence suggests that the disorder of mitochondrial metabolism is closely related to the pathogenesis of IBD, but its specific regulatory network and key genes remain to be further investigated. IBD-related transcriptome datasets (GSE3365 and GSE75214) and single-cell sequencing dataset (GSE134809) were obtained from the Gene Expression Omnibus database. Differentially expressed genes and hub genes were identified through differential expression analysis and weighted gene co-expression network analysis, and candidate genes were obtained by intersecting these with mitochondrial metabolism-related genes, followed by functional enrichment analysis. Machine learning algorithms were used to screen key genes and construct risk prediction models. Additionally, analysis of GSE134809 single-cell data identified characteristic cell types and expression distribution of key genes in IBD and explored communication between different cell types. Furthermore, immune cell infiltration, competitive endogenous RNA (ceRNA) network, and transcription factor prediction were performed. Finally, the diagnostic performance of key genes was validated in GSE75214 and reverse transcription-quantitative polymerase chain reaction. Two key genes, mitochondrial ribosomal protein L35 (MRPL35) and MRPL39, were identified, which were downregulated in IBD, and had good diagnostic potential. Single-cell analysis revealed that key genes were predominantly highly expressed in mononuclear phagocyte (MNP) cells. MNP cells communicated with other cells through receptor ligands including MIF-(CD74 + CXCR4), MDK-SDC1, and ITGB2-ICAM2, which are complexly related to mitochondrial metabolism. With the progression of IBD, infiltration levels of resting natural killer cells, naive B cells, M2 macrophages, and naive CD4 T cells decreased, and correlations between different cells continuously changed. A ceRNA network centered on XIST, hsa-miR-103a-3p, and MRPL35 was constructed. Additionally, therapeutic drugs targeting key genes were predicted, including cimetidine, eugenol, chlortetracycline, vincristine, irinotecan, bisacodyl, and sulpiride, with molecular docking validating high affinity between these drugs and key targets. This study constructed a multiomics integrated analysis strategy and identified MRPL35 and MRPL39 as potential markers and therapeutic targets, providing new insights for the diagnosis and treatment of IBD.
Statins are widely prescribed lipid-lowering agents that also exert pleiotropic anticancer effects, such as induction of apoptosis and ferroptosis, modulation of autophagy, and remodeling of the tumor microenvironment. Consistent with these multifaceted actions, statins have demonstrated synergistic activity with several chemotherapeutic agents. Emerging evidence indicates that statins modulate the activity of key DNA damage response kinases, such as ataxia-telangiectasia (ATM) and checkpoint kinase 2 (CHK2), in colorectal cancer cells, suggesting a potential impact on DNA repair pathways. To investigate this possibility, publicly available transcriptomic, proteomic, and phosphoproteomic datasets derived from colorectal cancer models treated with atorvastatin or lovastatin were systematically analyzed. Genes and proteins associated with DNA repair exhibiting differential expression, as well as proteins with altered phosphorylation status, were identified. These datasets were subsequently subjected to pathway enrichment and protein-protein interaction network analyses to determine whether statin exposure preferentially affected specific DNA repair pathways. Integrated multi-omics analysis revealed coordinated perturbation of tumor protein p53 (TP53)-centered DNA repair signaling, including pathways involved in TP53 regulation and double-strand break repair. Taken together, these findings suggest that statin-induced alteration of TP53-mediated DNA repair signaling may promote the persistence of DNA damage, thereby increasing the sensitivity of tumor cells to chemotherapy and potentially mitigating resistance mechanisms in colorectal cancer.
Fibromyalgia is a chronic pain syndrome characterized by widespread musculoskeletal pain, fatigue, sleep disturbances, and cognitive dysfunction, with substantial impact on quality of life and functional capacity. Despite its high prevalence, its underlying molecular mechanisms remain incompletely understood, and reliable biomarkers are lacking. This study performed an integrative analysis of publicly available transcriptomic datasets combined with protein-protein interaction network analysis, hub gene identification, and investigation of microRNA (miRNA)-mediated posttranscriptional regulation, in addition to evaluating molecular modulation following therapeutic intervention. Differentially expressed genes consistently identified across independent cohorts revealed two major molecular axes: An inflammatory-immune axis involving cytokine and interferon-related pathways, including IL6, TNF, CXCL8, and STAT1, and a structural axis associated with extracellular matrix organization, including COL1A1, COL3A1, FN1, and ITGB1. Integration with miRNA data demonstrated reduced expression of regulatory miRNAs linked to inflammatory and structural pathways, suggesting impaired posttranscriptional control. Therapeutic modulation analysis further demonstrated reduced expression of inflammatory genes and increased expression of structural genes following manual therapy, indicating partial reversibility of these molecular alterations. Collectively, these findings support the presence of multilevel molecular dysregulation in fibromyalgia and highlight potential biomarkers and therapeutic targets associated with inflammatory and peripheral structural mechanisms.
Mesial temporal lobe epilepsy (MTLE) is the most common form of drug-resistant epilepsy in adults, yet its molecular pathogenesis remains elusive. While iron dysregulation has been implicated in MTLE, transcriptome-level regulation of iron-related genes in MTLE brain, including regional, subcellular, and pathology-specific patterns, remains largely unexplored. We analyzed publicly available nuclear and cytoplasmic RNA-sequencing data from hippocampal and cortical tissues of patients with MTLE with and without hippocampal sclerosis and controls. We identified differential expression among 562 curated iron-related genes, which constituted 1.46-2.95% of all differentially expressed genes across regions and compartments. These genes showed region- and compartment-specific expression profiles, with recurrent upregulation of CH25H, TAL1, BTG2, TNF, and PTGIS and consistent downregulation of OGFOD3. Protein-protein interaction and hub gene network analysis identified SLC40A1, CH25H, HBB, PTGIS, and CYP2C19 as central hubs linking iron transport, lipid metabolism, oxidative stress, and neuroprotection. Upstream regulatory analysis revealed enrichment of seizure-responsive immediate early genes (EGR2, ATF3, JUN) and neurogenic transcription factors (NEUROD1, ASCL1), with the former upregulated and the latter downregulated, indicating seizure-driven transcriptional reprogramming. Our analyses suggest potential regulatory links connecting iron homeostasis with apoptosis, osmotic balance, cholesterol metabolism, and pH/CO2 buffering. Exploratory analysis showed a negative association between several iron-related genes, including CYP26B1, and seizure frequency in MTLE. Collectively, these findings reveal complex transcriptional programs governing iron dysregulation in MTLE. The results underscored coordinated regulation of inflammatory and metabolic pathways converging on iron homeostasis and neuronal stress responses in MTLE pathophysiology, providing a systems-level framework for potential prognosis and therapeutic targeting.
By 2050, nearly 20% of the global population will exceed 60 years old, experiencing compromised physiological and functional abilities, neurological disorders, and sarcopenia. Geroscience has evolved immensely through OMICS approaches and high-throughput technologies, generating massive datasets requiring efficient management, annotation, and storage. This highlights the need for user-friendly databases integrated with machine learning (ML) and artificial intelligence (AI). This review provides a comparative synthesis of the state-of-the-art databases on longevity genes, age-related signaling pathways, model organism phenotypes, and manually curated aging/antiaging experimental studies. We delineate the architecture, methodology, and functional features of contemporary geroscience databases, detailing the objectives, content, and dataset size, including multiomics information. Critically, these databases facilitate geriatric interventions: from biomarker discovery to drug repositioning, significantly impacting aging-associated conditions like muscle loss and Alzheimer's. In addition, we present updated insights into the increasing use of deep aging clocks and multiomics databases, coupled with ML- and AI-dependent analyses, fostering advanced dataset development. Interestingly, these tools are capable of dataset pattern recognition, predictive modeling, and the generation of research hypotheses. By bridging manually curated and AI-driven tools, this review offers a holistic view of the complementary strengths of aging databases, paving the way for the next generation of geroscience.
Diabetic peripheral neuropathy (DPN) is a prevalent complication of type 2 diabetes that is typically diagnosed after irreversible nerve damage, largely due to the lack of molecular biomarkers that distinguish neuropathy-specific changes from general diabetic pathology. From an integrative biology perspective, the network-level molecular mechanisms underlying DPN remain incompletely defined. In this study, transcriptomic profiles from peripheral blood mononuclear cells of healthy controls, patients with type 2 diabetes, and patients with DPN were analyzed using a systems-level, network-based bioinformatics framework. Comparative analysis identified genes specifically associated with DPN, distinct from broader diabetic alterations. Protein-protein interaction and network topology analyses prioritized key hub genes enriched in immune signaling, calcium transport, lipid metabolism, and inflammatory pathways implicated in neuronal dysfunction. Among these, Toll-like receptor 9 (TLR9) emerged as a prominent biomarker candidate, demonstrating high network centrality and strong diagnostic performance (area under the curve = 1.0). TLR9 was significantly upregulated in DPN and functionally linked to mitogen-activated protein kinase and nuclear factor kappa-light-chain-enhancer of activated B-cell signaling pathways, consistent with immune-mediated mechanisms of neuropathic injury. Collectively, these findings define a DPN-specific molecular network and support TLR9 as a biomarker candidate, providing a systems-level foundation for future experimental validation and translational research in diabetic neuropathy.
Primary ovarian failure (POF) is linked to diabetes-related metabolic dysregulation, including inflammation, oxidative stress, and mitochondrial dysfunction. Summary-data-based Mendelian Randomization and colocalization analysis were employed to explore causal relationships between hypoglycemic drug targets and POF risk, integrating multi-omics data to uncover underlying genetic and metabolic mechanisms. A significant association was revealed between elevated dipeptidyl peptidase-IV (DPP4) expression levels and reduced POF risk. This association remained robust following multiple testing correction and colocalization analysis. In subsequent methylation level analysis, three CpG sites in DPP4 were identified, where elevated methylation levels were associated with increased POF risk. Furthermore, increased DPP4 protein levels were demonstrated to be associated with reduced POF risk. Through the integration of multi-omics evidence and two-sample Mendelian randomization analysis, the findings were further validated, with sensitivity analyses confirming the stability of the results and the absence of significant pleiotropy or heterogeneity. It was demonstrated that increased DPP4 gene expression and protein levels have protective effects against POF, whereas elevated methylation at specific CpG sites is associated with increased POF risk. Evidence is provided supporting DPP4 as a potential therapeutic target for POF prevention.
The classification of immune and nonimmune genes in cattle is crucial for understanding immune mechanisms and their link to disease resistance. Traditional methods rely on manual curation and conventional bioinformatics tools, which are often time-consuming and labor-intensive. We introduce ImmFinder, a multimodal fully connected neural network (FCNN) framework designed to classify immune genes by integrating genomic and transcriptomic datasets. ImmFinder achieved an accuracy of 85.67%, an F1-score of 0.85, a precision of 0.86, and a recall of 0.85, demonstrating strong predictive performance. Additionally, the area under the curve-receiver operating characteristic (AUC-ROC) curve scores of 0.9250 (test set) and 0.9264 (validation set) further validate its robustness. These findings highlight the potential of a multimodal deep learning approach for immune gene classification, advancing functional genomics in cattle. The limitations of ImmFinder include reliance on the available bovine genomic and transcriptomic datasets used for training and evaluation, which may constrain immediate generalization to other breeds or species; additional external validation and experimental follow-up will be required to confirm biological hypotheses derived from model predictions. Currently, ImmFinder demonstrates the value of multimodal data fusion for functional gene annotation and provides a scalable baseline for integrating data types, such as genomics and transcriptomics. In future work, we will expand the training cohorts, broaden the range of data modalities, and pursue experimental validation of high-confidence model predictions. ImmFinder is implemented in Python, and all datasets, training models, preprocessing, and model development scripts are available on GitHub.
Colon adenocarcinoma (COAD) is a heterogeneous malignancy whose molecular complexity limits effective therapy. Existing transcriptome-based classifications capture only part of this diversity. To refine COAD stratification, we integrated genomic, epigenomic, and transcriptomic data from 297 The Cancer Genome Atlas patients. Ten complementary clustering algorithms were combined through a consensus ensemble framework to ensure robust and unbiased subtype discovery. The resulting molecular subtypes were characterized by genomic alterations, signaling pathways, tumor microenvironment features, and predicted therapeutic responses. As a result, four reproducible molecular subtypes (CS1-CS4) were identified. CS1 displayed enrichment of extracellular matrix organization and epithelial-mesenchymal transition signatures, suggesting invasive potential. CS2 exhibited transcriptional similarity to PD-1 responders, indicating potential benefit from immune checkpoint blockade. CS3 represented a mutation-driven subtype with frequent APC, TP53, and KRAS alterations and extensive copy number gains. CS4 showed the highest immune infiltration, elevated tumor mutational burden, and enhanced sensitivity to 5-fluorouracil and cetuximab. Validation across four independent cohorts confirmed the reproducibility of these subtypes. This integrative multi-omics framework refines the molecular taxonomy of COAD, revealing immunologically active and therapeutically distinct subgroups. The classification not only bridges genomic, epigenomic, and transcriptomic regulation but also provides a practical roadmap for precision oncology by linking molecular features to potential treatment strategies.
The discussion on precision oncology integrates multiomics technologies and artificial intelligence, specifically addressing biomarker discovery and personalized therapeutic strategies. In this way, clinical translation and multiomics biomarkers are reconstructed challenges such as heterogeneity, validate, algorithmic bias, regulatory complexities, and ethical issues. This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics. To conduct the study, we performed literature search using standard databases such as PubMed, Web of Science, and Scopus, focusing on papers published between 2020 and 2025. We address the all aspects of biomarker identification and clinical applications; we employed a five-stage framework comprising multiparametric data generation, integration, biomarker discovery, rigorous validation, and regulatory implementation. To further examine this review, we have employed emerging computational approaches, including machine learning and deep learning and graph neural networks alongside regulatory frameworks and ethical, legal, and social considerations. Discussing translation barriers, we consider factors such as limited reproducibility, validation, and critical discussion particularly studies. Ultimately, a future model based on standardized, validated, and transparent learning strategies accelerates and fosters the development of clinically reliable standards. Our review provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Lung adenocarcinoma (LUAD) is the most prevalent subtype of nonsmall cell lung cancer. Cigarette smoking, the primary etiological factor, introduces mutagenic and epigenetic changes that promote tumorigenesis, with nicotine acting as a key bioactive component modulating cellular signaling rather than directly causing mutations. In this study, differential transcriptomic profiling of smoker and nonsmoker LUAD samples from the PanCancer Atlas identified neurotensin (NTS) and calcitonin-related polypeptide alpha (CALCA) as the most significantly upregulated genes in smokers. The analysis included 495 LUAD tumor samples with annotated smoking history, comprising 209 never smokers and 286 current smokers. A dataset from the NCBI Gene Expression Omnibus (GSE10072) was used to validate the results. Only samples from current smokers and never smokers were considered to unravel direct molecular impact of active smoking, and the analysis confirmed the observed differential expression patterns of key genes, including NTS and CALCA, between smoker- and nonsmoker-derived LUAD samples. Pathway enrichment analysis revealed G protein-coupled receptor-mediated neuroendocrine signaling activation, suggesting a nicotine-driven reprogramming of tumor cells toward a secretory phenotype. Molecular docking simulations demonstrated stable interactions of (S)-nicotine with NTS and CALCA, suggesting these proteins as potential mediators of nicotine-induced oncogenic signaling. Kaplan-Meier analysis indicated that high expression of NTS and CALCA was associated with poorer overall survival, warranting further investigation in independent cohorts. Collectively, this integrative bioinformatics and structural study informs the molecular consequences of smoking in LUAD, identifies nicotine-responsive neuropeptides as potential signatures of tumor aggressiveness, and can provide a foundation for drug repurposing strategies to mitigate smoking-associated malignancy.
This study investigated the connection between the transcriptome and proteome by integrating eQTL (Expression Quantitative Trait Locus) and pQTL (Protein Quantitative Trait Locus) datasets generated using different technologies. eQTL data were obtained from the eQTLGen (microarray-based) and INTERVAL (RNA-Seq-based) studies, while pQTL data were derived from the UK Biobank (Olink platform) and deCODE (SomaScan platform) studies. A total of 1162 genes common to all four datasets were analyzed. Mendelian randomization (MR) identified 211 genes whose transcript levels significantly (p < 5e-8) predicted protein levels, whereas genetic correlation analysis detected 67 genes with shared genetic regulation. Negative transcript-protein associations were observed for 12% of genes identified by MR and 7% of those identified through genetic correlation. Cross-platform comparisons showed the strongest concordance between eQTL and pQTL effect sizes in the INTERVAL-UK Biobank panel and the weakest in the eQTLGen-deCODE panel. Colocalization analysis further confirmed these findings and indicated genes with strong eQTL-pQTL overlap predominantly encode intracellular proteins, whereas genes with weak overlap tend to encode glycosylated secreted proteins. Integrating both the transcriptome and proteome for biomarker discovery and locus annotation is important, as the overall genetic architectures of the blood transcriptome and proteome are not the same. RNA-Seq and Olink platforms provide more accurate measurements of RNA and protein levels.
Ankylosing spondylitis (AS) is a prevalent autoimmune disorder that primarily affects the spinal joints, leading to chronic pain. Defining immune cell programs that contribute to AS is essential for the discovery of translational targets. Because immune function depends on metabolic state, we analyzed single-cell RNA sequencing data from peripheral blood mononuclear cells (PBMCs) of patients with AS and healthy controls (HCs), and constructed genome-scale metabolic models for each immune cell type to quantify differences in pathway activity and reaction fluxes between diseased and healthy conditions. Across datasets, AS showed consistent metabolic reprogramming, with increased flux through purine metabolism, fatty acid degradation, and glycolysis in CD14 monocytes and T cell subsets, including CD4 memory, CD4 naive, and CD8 T cells, indicating coordinated shifts in energy production and biosynthetic demand. To extend these findings, we constructed cell-type-specific protein-protein interaction networks and evaluated differential connectivity. We identified 63 rewired protein hubs across nine immune cell types. RPS11 emerged as a central hub linked to translation and prior evidence of AS pathogenesis. This integrative systems biology approach connects cell-type-specific metabolic alterations with rewired interaction hubs in an accessible clinical compartment, suggesting that PBMC-based multiomics signatures may support future biomarker development and therapeutic prioritization in autoimmune diseases.