Integrative Multi-omics Approach Research Articles

Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Integrative multi-omics approach using random forest and artificial neural network models for early diagnosis and immune infiltration characterization in ischemic stroke.

Ischemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings. It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches. We utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration. A total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. 10 key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls. The study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease.

Integrative Multi-Omics Approach in Vascular Ehlers–Danlos Syndrome: Further Insights into the Disease Mechanisms by Proteomic Analysis of Patient Dermal Fibroblasts

Background: Dominant mutations in COL3A1 are known to cause vascular Ehlers–Danlos syndrome (vEDS) by impairing extracellular matrix (ECM) homeostasis. This disruption leads to the fragility of soft connective tissues and a significantly increased risk of life-threatening arterial and organ ruptures. Currently, treatments for vEDS are primarily symptomatic, largely due to a limited understanding of its underlying pathobiology and molecular mechanisms. Methods: In this study, we conducted a comprehensive analysis of the intracellular proteome of vEDS fibroblasts, integrating these findings with our previous transcriptome results to identify key molecular pathways that drive the disease. Additionally, we explored the therapeutic potential of inhibiting miR-29b-3p as a proof of concept. Results: Our integrative multi-omics analysis revealed complex pathological networks, emphasizing the critical role of miRNAs, particularly miR-29b-3p, in impairing ECM organization, autophagy, and cellular stress responses, all of which contribute to the pathogenesis of vEDS. Notably, the inhibition of miR-29b-3p in vEDS fibroblasts resulted in the upregulation of several differentially expressed target genes involved in these critical processes, as well as increased protein expression of essential ECM components, such as collagen types V and I. These changes suggest potential therapeutic benefits aimed at improving ECM integrity and restoring intracellular homeostasis. Conclusions: Overall, our findings advance our understanding of the complex biological mechanisms driving vEDS and lay a solid foundation for future research focused on developing targeted and effective treatment strategies for this life-threatening disorder.

Understanding the biological processes of kidney carcinogenesis: an integrative multi-omics approach

Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial–mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.

An integrative multi-omics approach reveals metabolic mechanism of flavonoids during anaerobic fermentation of de'ang pickled tea

Anaerobic fermentation (AF) is critical process for Yunnan De'ang pickled tea production. Therefore, widely targeted metabolomics and metagenomics were integrated to reveal the AF mechanism. Lactic acid bacteria (LAB) (e.g. Lactiplantibacillus plantarum, Lactobacillus vaccinostercus and Lactobacillus paracollinoides) and yeasts like Candida metapsilosis and Cyberlindnera fabianii dominated in the AF. Based on bacterial community succession and metabolites variation, the whole AF processes were divided into two phases, i.e., before and after four months. A total of 327 characteristic metabolites (VIP >1.0, P < 0.05, and FC > 1.50 or < 0.67) were selected from the AF. Besides amino acids increase, LAB and yeasts also promoted non-galloylated catechins, and several simple flavones/flavonols, flavanones/flavanonols and methoxy flavones/flavonols accumulations along with galloylated catechins, flavonol/flavone glycosides and anthocyanins decrease during the AF. This study would improve the understanding about AF mechanism of tea-leaves from the perspectives of flavonoids metabolism and microbial community succession.

Integrating multi-omics data reveals neuroblastoma subtypes in the tumor microenvironment

Neuroblastoma (NB) is a severe pediatric tumor originating from the developing sympathetic nervous system, characterized by diverse clinical outcomes, including spontaneous regression and aggressive metastasis. This variability suggests the existence of different NB subtypes, necessitating accurate classification for effective targeted treatment. In this study, we employed the similarity network fusion (SNF) algorithm and identified three NB subtypes, including mesenchymal-like (MES), MYCN-like (MYCN), and neurogenic-like (Neurogenic). The MES subtype exhibited the highest activation of immune-related pathways. The MYCN subtype demonstrated the worst prognosis, with enrichment in cell growth and proliferation pathways. Conversely, the Neurogenic subtype showed the best prognosis, with enrichment in sympathetic nervous system development processes. Through single-cell RNA sequencing (scRNA-seq) analysis, we examined the tumor microenvironments of these distinct NB subtypes, revealing divergent differentiation trajectories for adrenergic cells within the MYCN and Neurogenic subtypes. We also identified a significant presence of naïve T cells in the MES subtype, as well as mesenchymal cell subtypes associated with the unique plasticity observed in both the MES and MYCN subtypes. Drug sensitivity prediction analysis suggested that the MES subtype may respond favorably to MEK inhibitors, while the MYCN subtype may be susceptible to Bcl-2 inhibitors. Our integrative multi-omics approach enabled precise stratification of NB into biologically distinct subtypes, potentially facilitating the development of subtype-specific therapeutic strategies for improved patient management and survival outcomes.

Integrative metabolomic and microbiomic profiling: Unveiling the therapeutic mechanisms of Yangyinqingfei Decoction in acute lung injury

Acute lung injury (ALI) is a severe inflammatory condition characterized by disruption of the alveolar-capillary barrier, leading to impaired gas exchange and respiratory failure. The gut-lung axis has emerged as a crucial player in the pathogenesis of ALI, highlighting the potential therapeutic value of modulating gut microbiota and associated metabolic pathways. This study aimed to investigate the mechanisms underlying the protective effects of Yangyinqingfei Decoction (YYQFD) in treating ALI from the perspective of the gut-lung axis using an integrative multi-omics approach. Eight key gut microbiota genera were identified and associated with YYQFD treatment, including Lachnospiraceae, Prevotellaceae_NK3B31_group, Lachnospiraceae_NK4A136_group, and unclassified_Eubacterium_coprostanoligenes_group, etc. Metabolomic analysis revealed significant regulation of purine metabolism and pyrimidine metabolism pathways by YYQFD, involving metabolites such as xanthine, inosine, hypoxanthine, guanine, deoxyadenosine, thymine, and thymidine. Correlation analysis demonstrated significant associations between specific gut microbiota and metabolites, suggesting their potential as diagnostic or therapeutic targets. Furthermore, the mediation analysis revealed that YYQFD might regulate deoxyadenosine and L-isoleucine levels via the unclassified_Eubacterium_Coprostanoligenes_group and unclassified_Clostridia_vadinBB60_group, contributing to its therapeutic effects in ALI. This finding highlights the importance of the gut-lung axis in the therapeutic mechanisms of YYQFD and provides insights for future research and development of targeted interventions.

Therapeutic potential of omaveloxolone in counteracting muscle atrophy post-denervation: a multi-omics approach

BackgroundMuscle atrophy caused by denervation is common in neuromuscular diseases, leading to loss of muscle mass and function. However, a comprehensive understanding of the overall molecular network changes during muscle denervation atrophy is still deficient, hindering the development of effective treatments.MethodIn this study, a sciatic nerve transection model was employed in male C57BL/6 J mice to induce muscle denervation atrophy. Gastrocnemius muscles were harvested at 3 days, 2 weeks, and 4 weeks post-denervation for transcriptomic and proteomic analysis. An integrative multi-omics approach was utilized to identify key genes essential for disease progression. Targeted proteomics using PRM was then employed to validate the differential expression of central genes. Combine single-nucleus sequencing results to observe the expression levels of PRM-validated genes in different cell types within muscle tissue.Through upstream regulatory analysis, NRF2 was identified as a potential therapeutic target. The therapeutic potential of the NRF2-targeting drug Omaveloxolone was evaluated in the mouse model.ResultThis research examined the temporal alterations in transcripts and proteins during muscle atrophy subsequent to denervation. A comprehensive analysis identified 54,534 transcripts and 3,218 proteins, of which 23,282 transcripts and 1,852 proteins exhibited statistically significant changes at 3 days, 2 weeks, and 4 weeks post-denervation. Utilizing multi-omics approaches, 30 hubgenes were selected, and PRM validation confirmed significant expression variances in 23 genes. The findings highlighted the involvement of mitochondrial dysfunction, oxidative stress, and metabolic disturbances in the pathogenesis of muscle atrophy, with a pronounced impact on type II muscle fibers, particularly type IIb fibers. The potential therapeutic benefits of Omaveloxolone in mitigating oxidative stress and preserving mitochondrial morphology were confirmed, thereby presenting novel strategies for addressing muscle atrophy induced by denervation. GSEA analysis results show that Autophagy, glutathione metabolism, and PPAR signaling pathways are significantly upregulated, while inflammation-related and neurodegenerative disease-related pathways are significantly inhibited in the Omaveloxolone group.GSR expression and the GSH/GSSG ratio were significantly higher in the Omaveloxolone group compared to the control group, while MuSK expression was significantly lower than in the control group.ConclusionIn our study, we revealed the crucial role of oxidative stress, glucose metabolism, and mitochondrial dysfunction in denervation-induced muscle atrophy, identifying NRF2 as a potential therapeutic target. Omaveloxolone was shown to stabilize mitochondrial function, enhance antioxidant capacity, and protect neuromuscular junctions, thereby offering promising therapeutic potential for treating denervation-induced muscle atrophy.Graphical

Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.

Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns. We performed machine learning-based, integrative unsupervised clustering to identify proteomics- and metabolomics-defined subpopulations of individuals living with obesity (BMI ≥ 30 kg/m2), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits. We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high-density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2. Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.

Integrative Multi-Omics Approach for Improving Causal Gene Identification.

Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

Multi-omics data and model integration reveal the main mechanisms associated with respiro-fermentative metabolism and ethanol stress responses in Kluyveromyces marxianus

Kluyveromyces marxianus is a yeast capable of fermenting sugars into ethanol and growing at high temperatures (>37ºC). However, it is less tolerant to ethanol than Saccharomyces cerevisiae, which limits its application in second-generation ethanol production. Since the mechanisms of ethanol stress response are still poorly described, especially compared to S. cerevisiae, we used an integrative multi-omics approach, combining transcriptomics, co-expression networks, gene regulation, and genome-scale metabolic modelling to gain insights about these mechanisms. Through metabolic modelling, we predicted the occurrence of a respiro-fermentative metabolism and its onset as the dilution rate increased. From gene co-expression networks, we detected that the protein quality control system is a main mechanism involved in the ethanol stress response. Further, we identified key regulators in the ethanol stress response, such as HAP3, MET4, and SNF2, and assessed how disturbances in their gene expression affect cellular metabolism. We also found that amino acid metabolism, membrane lipid metabolism, and ergosterol exhibit increased metabolic flux under the explored conditions, along with usage of enzymes related to these pathways. These findings provide useful cues to develop and implement genetic and metabolic engineering strategies to enhance ethanol tolerance and point for future research in stress responses of K. marxianus.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

A multi-omics approach to elucidate okadaic acid-induced changes in human HepaRG hepatocarcinoma cells

Okadaic acid (OA), a prevalent marine biotoxin found in shellfish, is known for causing acute gastrointestinal symptoms. Despite its potential to reach the bloodstream and the liver, the hepatic effects of OA are not well understood, highlighting a significant research gap. This study aims to comprehensively elucidate the impact of OA on the liver by examining the transcriptome, proteome, and phosphoproteome alterations in human HepaRG liver cells exposed to non-cytotoxic OA concentrations. We employed an integrative multi-omics approach, encompassing RNA sequencing, shotgun proteomics, phosphoproteomics, and targeted DigiWest analysis. This enabled a detailed exploration of gene and protein expression changes, alongside phosphorylation patterns under OA treatment. The study reveals concentration- and time-dependent deregulation in gene and protein expression, with a significant down-regulation of xenobiotic and lipid metabolism pathways. Up-regulated pathways include actin crosslink formation and a deregulation of apoptotic pathways. Notably, our results revealed that OA, as a potent phosphatase inhibitor, induces alterations in actin filament organization. Phosphoproteomics data highlighted the importance of phosphorylation in enzyme activity regulation, particularly affecting proteins involved in the regulation of the cytoskeleton. OA's inhibition of PP2A further leads to various downstream effects, including alterations in protein translation and energy metabolism. This research expands the understanding of OA's systemic impact, emphasizing its role in modulating the phosphorylation landscape, which influences crucial cellular processes. The results underscore OA's multifaceted effects on the liver, particularly through PP2A inhibition, impacting xenobiotic metabolism, cytoskeletal dynamics, and energy homeostasis. These insights enhance our comprehension of OA's biological significance and potential health risks.

Hepatokine ITIH3 Protects Against Hepatic Steatosis by Downregulating Mitochondrial Bioenergetics and De Novo Lipogenesis

Accumulating evidence suggest that mitochondrial functions are altered and contribute to the pathogenesis of non-alcoholic fatty liver disease (NAFLD). As a result, research is being conducted to discover targets that alter mitochondrial functions to ameliorate NAFLD, and its progression towards non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Recent studies demonstrate that liver secretory proteins regulate normal development, obesity, simple steatosis to NASH progression. Inter-α-trypsin inhibitors (ITI) are one such proteins secreted by liver, altered in NAFLD, but their functional importance in mitochondrial metabolism is poorly understood. Here, we have identified that ITIH3 is a potential candidate gene that protects against NAFLD by targeting mitochondrial metabolism. We used an integrative multiomics approach using transcriptomic data from approximately 100 different mouse strains called hybrid mouse diversity panel (HMDP) and a cohort of bariatric surgery patients and identified Itih3/ITIH3 expression to be inversely associated with NAFLD/NASH progression and hepatic TG accumulation. Our network enrichment analyses revealed NAFLD-related pathways such as mitochondria, TCA cycle, fatty acid metabolism, cholesterol metabolism, coagulation and fibrinolysis. Next, disease enrichments by ToppGene revealed metabolic syndrome-related disease traits including dyslipidemia, steatosis, NASH and fibrosis. Our in vitro and in vivo studies using loss and gain of function mouse and cell culture models demonstrated that hepatic ITIH3 reduced liver triglyceride accumulation and de novo lipogenesis (DNL). Remarkably, hepatic ITIH3 also lowered mitochondrial respiration and lipid droplet accumulation. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Together, we conclude that ITIH3 plays a unique role in regulating the hepatic lipid profile, DNL, and mitochondrial metabolism, all of which have implications for NAFLD treatment. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD. R00DK120875. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank with balanced sex representation, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Furthermore, both DNAmEMRAge and OMICmAge exhibit greater accuracy (average ROC = 0.88) in predicting 5-year and 10-year mortality than current biological age predictors. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process. This work was funded in part by TruDiagnostic for data generation and manuscript writing. Efforts for RK, KM, YC, SB, and JALS are supported by R01HL123915 from the NIH/NHLBI; PK is supported by K99HL159234 from the NIH/NHLBI; RSK is supported by K01HL146980 from the NIH/NHLBI; SHC is supported by K01HL153941 from the NIH/NHLBI; YC and JAILS are supported by R01HL141826 from the NIH/NHLBI; AD is supported by K01HL130629 from the NIH/NHLBI; AD and JALS are supported by 1R01HL152244 from the NIH/NHLBI; MM and JALS are supported by R01HL155742 from the NIH/NHLBI; MM is supported by R01HL139634 from the NIH/NHLBI; JALS, STW, EWK are supported by the NIH U01HG008685; VNG is supported by NIH AG065403 and AG064223; CEW is supported by the JSPS KAKENHI Grant (JP19K21239), the Japanese Environment Research and Technology Development Fund (No. 5- 1752), JPJ SBP-1201854), HLF 20180290, HLF 20200693), and the Swedish Research Council (2016-02798); NJWR is supported by MR/Y010736/1 and IF\R1\231034; EM is supported by the NIH (ZICTR000410-03). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT.

Abstract 409: Deciphering chromosomal instability in consensus molecular subtypes (CMS) in CRC: Insights from an integrative multi-omics approach

Abstract CRC is a heterogeneous disease with different molecular subtypes, which can have a profound impact on treatment response and patient outcomes. CMS classification based on gene expression profiles has been shown to differentiate patients into clinically relevant sub-groups. However, deep multi-omics characterization of the CMS classes based on high quality patient data is lacking. Through the integration of multi-omics data, we aim to enhance our understanding of CRC’s molecular heterogeneity and thereby facilitate the development of personalized therapeutic strategies. We performed a multi-omics analysis of CRC patients to identify potential new features of CMS groups. 890 fresh-frozen, surgically resected CRC tumor and adjacent normal samples were used to analyze whole genome and RNA sequencing data. DNA and RNA was prepared using Qiagen AllPrep Universal and KAPA Hyper Prep kit (DNA), as well as TruSeq Stranded Total mRNA kits and sequenced on a NovaSeq6000 system. We identified CMS subtypes based on RNA-Seq data and performed all statistical analyses using R. Our analysis revealed distinct molecular characteristics across the four CMS subtypes, including unique driver mutations, signaling cascades, and immune cell infiltration profiles. The poor prognosis associated with CMS4 patients was corroborated by our clinical data. Notably, we observed high global chromosomal instability (CIN) profiles for both CMS2 and CMS4 and identified recurrent global aneuploidy patterns within these subtypes that link alterations of known cancer genes to these large-scale structural events. We found higher levels of CIN in CMS2 compared to CMS4, which appears to be predominantly driven by numerical, rather than structural CIN. Specifically, in CMS2, SMAD2/4 is commonly impacted by deletion on chromosome arm 18q, while PLCG1 is more influenced by amplifications on 20q respectively, suggesting potential values as CRC CMS biomarkers. In conclusion, our integrative multi-omics analysis offers a comprehensive understanding of CRC molecular subtypes, which can inform personalized treatment strategies. Our findings underscore the importance of considering not only specific driver mutations but also large-scale structural rearrangements in the context of CMS subtypes. Citation Format: Julia Bischof, Jonathan Woodsmith, David Church. Deciphering chromosomal instability in consensus molecular subtypes (CMS) in CRC: Insights from an integrative multi-omics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 409.

Abstract 1937: YAP-TAZ-TEAD transcriptional program is a prominent driver of acquired resistance to KRASG12C-based therapies in patients with metastatic colorectal cancer

Abstract Background: KRAS mutations are present in 50% of patients with metastatic colorectal cancer (mCRC) and play a critical role in mCRC tumor biology. KRASG12C mutations are present in 6% of patients with mCRC. While KRASG12C inhibitors (G12Ci) have entered the clinic, clinical responses to G12Ci are transient even when combined with EGFRi, highlighting the need to optimize therapeutic interventions. We have characterized our KRASG12C mCRC patient-derived xenograft (PDX) models and paired biopsies from progressing patients that have acquired resistance to G12Ci/G12Ci+EGFRi through an integrative multi-omics approach and have validated them through wet-lab experiments. Experimental Procedures: qPCR analysis was performed on 13 PDX models that were established from paired tumor biopsies from patients pre and at progression of treatment. We also treated 5 KRASG12C mCRC PDX models with G12Ci +/- EGFRi until acquired resistance was achieved. Tumors were harvested post-treatment and whole-exome, RNAseq, and RPPA were performed on them. In parallel, 3 paired parental and G12Ci/EGFRi-resistant 2D/3D in vitro models were established and treated with G12Ci +/- EGFRi +/- pan-TEADi. Expression and response profiles were assessed through cell viability assays, western blot, qPCR, and IncuCyte analyses. We simultaneously re-established in vivo acquired resistance to G12Ci + EGFRi in 2 PDX models and treated them with pan-TEADi triple combo. Finally, we performed spatial transcriptomics (Xenium) from 6 paired pre-/post-treatment patient samples to characterize transcriptomic profiles at the single-cell resolution. Results: In our PDX models, the acquisition of a new NRAS mutation and KRAS amplification in response to therapy were found, which are commonly known genomic resistance mechanisms to MAPK targeted therapies. 3 of the 5 PDX models (p &amp;lt; 0.05, p &amp;lt; 0.001) and 2 of 4 patients (p &amp;lt; 0.01, p &amp;lt; 0.001) from the PDX models established from paired tumor biopsies from patients pre and at progression of treatment exhibited YAP1-TEAD signaling as an acquired response to G12Ci-based therapies. In our in vitro work, pan-TEADi triple combo provided efficacy over G12Ci/therapy in 3D in vitro models while both 2D/3D in vitro resistant models were sensitized to pan-TEADi monotherapy, suggesting the combination drives a state change to be YAP dependent where YAP-TEAD signaling may now be driving MAPK signaling. Detailed spatial transcriptomic data will be presented. Conclusion: Clinical responses to G12Ci are transient and require optimizing therapeutic modalities. In patients, where genomic mechanisms are not found, YAP-TAZ-TEAD signaling was found to be a common feature of resistance to inhibitors targeting the KRAS pathway. This work suggests that pan-TEADi may provide patients with improved therapeutic options. Citation Format: Oluwadara Coker, Alexey Sorokin, Neal Akhave, Kelsey Pan, Fengqin Gao, Zhensheng Liu, Preeti Kanikarla, HeyMin Lee, Oscar Villarreal, Jumanah Alshenaifi, Giulia Maddalena, Alaa Mohamed, Dionne Prescod, David Menter, Saikat Chowdury, Ryan Corcoran, Kunal Rai, David Hong, Scott Kopetz. YAP-TAZ-TEAD transcriptional program is a prominent driver of acquired resistance to KRASG12C-based therapies in patients with metastatic colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1937.

Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?

The skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut-brain-skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other 'omics' technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest 'omics' sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.