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At least 19 recordsLinked to original sources
PubMed2025

Discovering diabetes complications-related microRNAs: meta-analyses and pathway modeling approach.

PURPOSE: MicroRNAs(miRNA) play an important role in the pathogenesis of diabetic complications by regulating gene expression. The objective of this paper is to investigate micoRNA expression in diabetic nephropathy (DN), diabetic retinopathy (DR), diabetic neuropathy (DNP), and diabetic cardiopathy (DC). METHODS: We conducted this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and retrieved eligible microRNA-related studies of diabetic complications from PubMed, Embase, and Web of science databases. We enriched pathways corresponding to differentially expressed miRNAs using the miRPath tool on the DIANA website, and predicted their target genes with DIANA microT-CDS and TargetScan. RESULTS: Although many of the selected studies were of high scientific quality, the results were heterogeneous. Among the 71 selected articles, 79 miRNAs were differentially expressed in various complications of diabetes, of which miRNA126, miRNA192 and 17 others were reported in at least two or more studies. A total of 156 target genes were predicted and 103 pathways were obtained by KEGG enrichment analysis. CONCLUSION: This comprehensive systematic evaluation provides experimental evidence statistics for miRNAs as circulating biomarkers and highlights promising biomarkers. These results provide preliminary data to further investigate the role of miRNAs in the diagnosis and therapeutic targets of human diabetic complications and support future broader longitudinal studies to better substantiate the role of dysregulated miRNAs as potential biomarkers and therapeutic targets of diabetic complications.

Humans↗
PubMed2025

Distinguishing specific from broad genetic associations between external correlates and common factors.

MOTIVATION: Within the genomic structural equation modelling (genomic SEM) framework, common factors are often used to index shared genetic etiology across constellations of genome-wide associations studies (GWASs) phenotypes. A standard common pathway model, in which a genetic association is estimated between an external GWAS phenotype and a common factor, assumes that all genetic associations between the external GWAS phenotype and the individual indicator phenotypes are mediated through the factor. This assumption can be tested using the QTrait statistic, which compares the common pathway model to an independent pathways model that allows for direct genetic associations between the external GWAS phenotype and the individual indicators of the factor. However, QTrait is not designed to identify either the magnitude or the source of this heterogeneity. RESULTS: We expand upon the QTrait approach by describing an effect size index that quantifies the degree to which the common pathways model is violated, and we provide a systematic approach for empirically identifying specific direct pathways between an external trait and indicator traits. Our method comprises a series of omnibus tests and outlying indicator detection algorithms indexing the heterogeneity of associations between the genetic component of external traits and the individual indicators of common factors. We provide a set of automated functions which we apply to investigate the patterns of genetic associations across a set of external correlates with respect to indicators of general cognitive ability and case-control and proxy GWAS indices of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: The Genomic SEM R package and the QTrait function is available at https://github.com/GenomicSEM/GenomicSEM. The QTrait function tutorial is available at https://github.com/GenomicSEM/GenomicSEM/wiki/8.-Tutorials. To ensure reproducibility of the analyses presented in this manuscript, the exact version of the QTrait function used, along with input data and scripts, has been archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17186083).

Genome-Wide Association Study↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed2026

The influence of neuroprotector isatin on haloperidolinduced catalepsy and proteomic profile of mice brain.

Isatin (indol-2,3-dione) is an endogenous regulator found in humans and animals. It interacts with numerous target proteins and exhibits a wide range of biological activities, including neuroprotective action in animal models of Parkinson's disease (PD) induced by administration of neurotoxins MPTP (1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine) or rotenone. An antipsychotic drug haloperidol, which impairs neurotransmitter balance in the nigrostriatal pathway, models dopamine deficiency and promotes the development of motor disorders characteristic of PD. In this work, the effect of two doses of isatin (10 mg/kg and 80 mg/kg) on the haloperidol catalepsy and on the proteomic profile of mice brain was investigated. The pretreatment of animals with isatin (1 h before haloperidol administration) reduced the occurrence of haloperidol catalepsy. The administration of haloperidol and also isatin with haloperidol influenced the relative content of a number of proteins associated with PD and other neurodegenerative diseases.

Animals↗
PubMed2025

Associations on the Fly, a new feature aiming to facilitate exploration of the Open Targets Platform evidence.

MOTIVATION: The Open Targets Platform (https://platform.opentargets.org) is a unique, comprehensive, open-source resource supporting systematic identification and prioritisation of targets for drug discovery. The Platform combines, harmonizes and integrates data from >20 diverse sources to provide target-disease associations, covering evidence derived from genetic associations, somatic mutations, known drugs, differential expression, animal models, pathways and systems biology. An in-house target identification scoring framework weighs the evidence from each data source and type, contributing to an overall score for each of the 7.8M target-disease associations. However, the old infrastructure did not allow user-led dynamic adjustments in the contribution of different evidence types for target prioritisation, a limitation frequently raised by our user community. Furthermore, the previous Platform user interface did not support navigation and exploration of the underlying target-disease evidence on the same page, occasionally making the user journey counterintuitive. RESULTS: Here, we describe 'Associations on the Fly' (AOTF), a new Platform feature-developed with a user-centred vision-that enables the user to formulate more flexible therapeutic hypotheses through dynamic adjustment of the weight of contributing evidence from each source, altering the prioritisation of targets. AVAILABILITY AND IMPLEMENTATION: The codebases that power the Platform-including our pipelines, GraphQL API, and React UI-are all open source and licensed under the APACHE LICENSE, VERSION 2.0. You can find all of our code repositories on GitHub at https://github.com/opentargets and on Zenodo at https://zenodo.org/records/14392214. This tool was implemented using React v18 and its code is accessible here: (https://github.com/opentargets/ot-ui-apps). The tools are accessible through the Open Targets Platform web interface (https://platform.opentargets.org/) and GraphQL API (https://platform-docs.opentargets.org/data-access/graphql-api). Data is available for download here: (https://platform.opentargets.org/downloads) and from the EMBL-EBI FTP: (https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/).

Software↗
PubMed2026

Proteomic Profiling of Pulmonary Function and Cardiovascular Disease Risk in the Atherosclerosis Risk in Communities Study.

BACKGROUND: Pulmonary function is linked to cardiovascular disease risk; however, the underlying mechanisms remain unclear. We aimed to identify protein biomarkers associated with pulmonary function and examine their impact on incident chronic obstructive pulmonary disease, coronary heart disease, heart failure, and all-cause mortality. METHODS: Data from White and Black Americans in the Atherosclerosis Risk in Communities study (visit 2: N=11&#x2009;354, mean age=57 years; visit 5: N=3517, mean age=75 years), a prospective cohort, were analyzed. Linear regression assessed associations between protein levels and pulmonary function measures, including forced expiratory volume in 1 second and forced vital capacity. The impact of the identified proteins on incident chronic obstructive pulmonary disease, coronary heart disease, heart failure, and mortality was estimated using logistic regression and Cox proportional hazards models. Pathway enrichment and Mendelian randomization explored underlying biological functions and causal effects. RESULTS: Of 4766 proteins analyzed, 364 were cross-sectionally associated with forced expiratory volume in 1 second (and forced vital capacity (false discovery rate<0.05). Ninety-four and 270 proteins had concordant positive and negative effects, respectively. Five pathways related to pulmonary and cardiac function were enriched. Of the 364 proteins, 112 were linked to all 4 outcomes, where 86 were associated with increased risk (odds ratio/hazard ratio [OR/HR], 1.05-1.42) and 26 with reduced risk (OR/HR, 0.69-0.96). Six proteins (STAT3 [signal transducer and activator of transcription 3], MIC-1 [growth differentiation factor 15], apoA-II [apolipoprotein A-II], TPST1 [protein-tyrosine sulfotransferase 1], integrin a1b1 [integrin alpha-I: beta-1 complex], and BLC [C-X-C motif chemokine 13]) showed potential inverse causal effects on with forced expiratory volume in 1 second and forced vital capacity, and integrin a1b1 demonstrated consistent inverse associations with chronic obstructive pulmonary disease, coronary heart disease, and heart failure risks. CONCLUSIONS: Proteins associated with pulmonary function may influence CVD risk. Six proteins, including integrin a1b1, represent promising targets for future interventions.

Aged↗
PubMed2025

Multi-context modeling of driver pathways reveals common and specific mechanisms across 23 cancer types.

Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets.

Humans↗
PubMed2026

Small-Molecule Degradation of the MicroRNA-21 Precursor Rescues Pathogenic Pathways in Cellular Models of Fibrosis.

MicroRNAs (miRNAs) are short RNA molecules that bind to target mRNAs, resulting in translational repression and gene silencing. Overexpression of microRNA-21 (miR-21) is associated with various human diseases, including autosomal dominant polycystic kidney disease (ADPKD) and pulmonary fibrosis. In this study, a previously described heterobifunctional molecule, TGP-21-RiboTAC, that degrades the miR-21 precursor (pre-miR-21) in triple-negative breast cancer cells was investigated in polycystic kidney cell lines and a lung fibroblast cell line. In the former, TGP-21-RiboTAC degraded pre-miR-21 and derepressed miR-21's downstream targets, programmed cell death 4 (PDCD4) and peroxisome proliferator-activated receptor alpha (PPAR&#x3b1;), known drivers of ADPKD. The heterobifunctional molecule also inhibited cyst growth and rescued the metabolic alterations that occur in ADPKD. In the lung fibroblast cell line, MRC-5, TGP-21-RiboTAC also reduced pre- and mature miR-21 levels, rescued transforming growth factor &#x3b2; (TGF-&#x3b2;)-induced repression of SMAD family member 7 (SMAD7), and inhibited cell invasion. Collectively, these studies demonstrate the potential of targeted RNA degradation as therapeutic agents that retard the development of organ fibrosis.

MicroRNAs↗
PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗
PubMed2026

Molecular Pathways, Target Landscape, and Translational Models in Heart Failure with Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) is a substantial global health burden and the greatest unmet medical need for cardiovascular diseases. It is marked by pronounced clinical heterogeneity and complex multi-system pathophysiology with limited therapeutic options. Progress in developing effective therapeutics is constrained by the inadequacy of experimental models to fully recapitulate the multifactorial nature of the disease. Recent evidence underscores the significant involvement of inflammatory, oxidative, and mitochondrial pathways in the pathogenesis of HFpEF, with non-coding RNAs and epigenetic regulation serving as crucial modulators and prospective therapeutic targets. This review maps the HFpEF target landscape, while critically assessing the mechanistic contributions, translational fidelity, and limitations of existing in vivo and in vitro models. Further, advances are noted among the in vitro technologies, including human cardiac organoids and engineered heart tissues integrated with high-throughput multi-omics and computational modeling, enabling in-depth examination of HFpEF mechanisms. Finally, we underscore the necessity of integrative, systems-level approaches and multi-marker strategies to enhance translational relevance, improve risk stratification, and accelerate development of mechanism-based therapies. Collectively, this review supports phenotypic-guided and mechanism-informed therapeutic development for HFpEF, and provides a roadmap for next generation model development and therapeutic innovation.

Humans↗
PubMed2026

Modeling meningioma in vitro in the omics era.

Meningioma biology has been substantially clarified by recent omics-based studies, which have identified recurrent mutations, copy-number alterations, and distinct molecular subgroups. However, although these approaches have provided a valuable framework, they are inherently limited in their ability to establish direct causal relationships. The mechanistic studies are therefore indispensable for translating these molecular observations into biological understanding. Nevertheless, the mechanistic literature has often evolved in a fragmented manner, with individual pathways and model systems studied in relative isolation from the broader multi-omic landscape. In this review, we synthesize these complementary bodies of work into an integrated framework and outline a clear roadmap for future studies. We first review the historical development of established meningioma cell lines, their current molecular characterization, and the recent emergence of 3D models and organoids. Intrinsic challenges in modeling meningioma in vitro are discussed, including the difficulty of establishing immortalized cell lines from predominantly benign tumors, genetic alterations introduced during immortalization, and drift under culture conditions that differ substantially from those of the parental tumors. Next, insights from functional studies centered on these models are integrated within the molecular framework established by large-scale omics analyses. To avoid fragmentation and overemphasis on isolated findings, prior studies are organized into six categories based on major signaling pathways: Hippo, PI3K/Akt/mTOR, MAPK, Wnt/&#x3b2;-catenin, FOXM1, and Notch. Finally, lessons from other cancer models, including experimental approaches to chromosome-scale genomic disturbances, are considered to provide a more integrated view of meningioma biology and to highlight directions for future research.

Meningioma↗
PubMed2016

A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways.

We present a generative model, Lux, to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications. Analysis of targeted data from Tet2-knockdown embryonic stem cells and T cells during development demonstrates DNA modification quantification at unprecedented detail, quantifies active demethylation pathways and reveals 5hmC localization in putative regulatory regions.

5-Methylcytosine↗
PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗
PubMed2020

Human NK cell deficiency as a result of biallelic mutations in MCM10.

Human natural killer cell deficiency (NKD) arises from inborn errors of immunity that lead to impaired NK cell development, function, or both. Through the understanding of the biological perturbations in individuals with NKD, requirements for the generation of terminally mature functional innate effector cells can be elucidated. Here, we report a cause of NKD resulting from compound heterozygous mutations in minichromosomal maintenance complex member 10 (MCM10) that impaired NK cell maturation in a child with fatal susceptibility to CMV. MCM10 has not been previously associated with monogenic disease and plays a critical role in the activation and function of the eukaryotic DNA replisome. Through evaluation of patient primary fibroblasts, modeling patient mutations in fibroblast cell lines, and MCM10 knockdown in human NK cell lines, we have shown that loss of MCM10 function leads to impaired cell cycle progression and induction of DNA damage-response pathways. By modeling MCM10 deficiency in primary NK cell precursors, including patient-derived induced pluripotent stem cells, we further demonstrated that MCM10 is required for NK cell terminal maturation and acquisition of immunological system function. Together, these data define MCM10 as an NKD gene and provide biological insight into the requirement for the DNA replisome in human NK cell maturation and function.

Alleles↗
PubMed2025

Cross-feeding percolation phase transitions of intercellular metabolic networks.

Intercellular cross-talk is essential for the adaptation capabilities of populations of cells. While direct diffusion-driven cell-to-cell exchanges are difficult to map, current nanotechnology enables one to probe single-cell exchanges with the medium. We introduce a mathematical method to reconstruct the dynamic unfolding of intercellular exchange networks from these data, applying it to an experimental coculture system. The exchange network, initially dense, progressively fragments into small disconnected clusters. To explain these dynamics, we develop a maximum-entropy multicellular metabolic model with diffusion-driven exchanges. The model predicts a transition from a dense network to a sparse one as nutrient consumption shifts. We characterize this crossover both numerically, revealing a power-law decay in the cluster-size distribution, and analytically, by connecting to percolation theory. Comparison with data suggests that populations evolve toward the sparse phase by remaining near the crossover. These findings offer insights into the collective organization driving the adaptive dynamics of cell populations.

Metabolic Networks and Pathways↗
PubMed2025

Flux-sum coupling analysis of metabolic network models.

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Escherichia coli↗
PubMed2019

Increased acetylation of H3K14 in the genomic regions that encode trained immunity enzymes in lysophosphatidylcholine-activated human aortic endothelial cells - Novel qualification markers for chronic disease risk factors and conditional DAMPs.

To test our hypothesis that proatherogenic lysophosphatidylcholine (LPC) upregulates trained immunity pathways (TIPs) in human aortic endothelial cells (HAECs), we conducted an intensive analyses on our RNA-Seq data and histone 3 lysine 14 acetylation (H3K14ac)-CHIP-Seq data, both performed on HAEC treated with LPC. Our analysis revealed that: 1) LPC induces upregulation of three TIPs including glycolysis enzymes (GE), mevalonate enzymes (ME), and acetyl-CoA generating enzymes (ACE); 2) LPC induces upregulation of 29% of 31 histone acetyltransferases, three of which acetylate H3K14; 3) LPC induces H3K14 acetylation (H3K14ac) in the genomic DNA that encodes LPC-induced TIP genes (79%) in comparison to that of in LPC-induced effector genes (43%) including ICAM-1; 4) TIP pathways are significantly different from that of EC activation effectors including adhesion molecule ICAM-1; 5) reactive oxygen species generating enzyme NOX2 deficiency decreases, but antioxidant transcription factor Nrf2 deficiency increases, the expressions of a few TIP genes and EC activation effector genes; and 6) LPC induced TIP genes(81%) favor inter-chromosomal long-range interactions (CLRI, trans-chromatin interaction) while LPC induced effector genes (65%) favor intra-chromosomal CLRIs (cis-chromatin interaction). Our findings demonstrated that proatherogenic lipids upregulate TIPs in HAECs, which are a new category of qualification markers for chronic disease risk factors and conditional DAMPs and potential mechanisms for acute inflammation transition to chronic ones. These novel insights may lead to identifications of new cardiovascular risk factors in upregulating TIPs in cardiovascular cells and novel therapeutic targets for the treatment of metabolic cardiovascular diseases, inflammation, and cancers. (total words: 245).

Acetylation↗