Integrated Data Analysis Research Articles

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

IntroductionSystemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. ObjectivesTo identify key genes potentially involved in SLE pathogenesis using in-silico approach. MethodsHigh-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8+ T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. ResultsOur analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. ConclusionThis study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8+ T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

Saline-alkali stress affects the accumulation of proanthocyanidins and sesquiterpenoids via the MYB5-ANR/TPS31 cascades in the rose petals.

Rose (Rosa rugosa) petals are rich in diverse secondary metabolites, which have important physiological functions as well as great economic values. Currently, it remains unclear how saline and/or alkaline stress(es) influence the accumulation of secondary metabolites in rose. In this study, we analyzed the transcriptome and metabolite profiles of rose petals under aline-alkali stress and uncovered the induction mechanism underlying major metabolites. Dramatic changes were observed in the expression of 1363 genes and the abundances of 196 metabolites in petals in response to saline-alkali stress. These differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) are mainly associated with flavonoid and terpenoid metabolism and the reconstruction of cell walls. Of them, TERPENE SYNTHASE 31 (TPS31) overexpression in tobacco leaves driven by its own promoter resulted in significant alterations in the levels of diverse terpenoids, which were differentially influenced by saline-alkali stress. An integrated analysis of metabolomic and transcriptomic data revealed a high correlation between the abundances of flavonoids/terpenoids and the expression of the transcription factor MYB5. MYB5 may orchestrate the biosynthesis of sesquiterpenoids and proanthocyanidins through direct regulation of TPS31 and ANR expression under aline-alkali stress. Our finding facilitates improving the bioactive substance accumulation of rose petals by metabolic engineering.

The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

Maternal immune cell gene expression associates with maternal gut microbiome, milk composition and infant gut microbiome

Background– Pre-pregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, the impact of pre-pregnancy overweight on immune cell gene expression during pregnancy and its association with maternal and infant outcomes is not well explored. Methods– Blood samples were collected from healthy normal weight (NW, pre-pregnancy BMI 18.5-24.9) or overweight (OW, pre-pregnancy BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. ResultsGene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions– These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Furthermore, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.

MosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

Targeting Galectin-1 Overcomes Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma.

Resistance to paclitaxel poses a major obstacle in esophageal squamous cell carcinoma (ESCC) treatment. A better understanding of the mechanisms underlying paclitaxel resistance could help identify prognostic biomarkers and improved therapeutic strategies. In this study, we established a patient-derived xenograft (PDX) model of acquired paclitaxel resistance and used RNA-sequencing to identify galectin-1, encoded by LGALS1, as a key mediator of resistance. Integrative analysis of clinical data and physiological studies indicated that serum galectin-1 levels were elevated in resistant patients and correlated with treatment outcomes before and during taxane therapy. Importantly, exposing cells to serum from resistant patients resulted in increased paclitaxel resistance compared to serum from sensitive patients, which was closely associated with galectin-1 concentrations in the serum. The specific clearance of galectin-1 from resistant patient serum significantly restored paclitaxel sensitivity, and inhibiting galectin-1, through knockdown or the pharmacologic inhibitor OTX008, increased sensitivity to paclitaxel. Galectin-1 inhibition reduced the activity of β-catenin, thereby inhibiting stem cell properties induced by the Wnt/β-catenin pathway. Furthermore, galectin-1 regulated MDR1 transcription through increased nuclear accumulation of β-catenin, thus increasing resistance to paclitaxel. Combining OTX008 with clinical taxane formulations effectively reversed paclitaxel resistance in vitro and in vivo. Elevated galectin-1 levels thus serve as an indicator of response to paclitaxel therapy in ESCC, offering a therapeutic intervention strategy to overcome drug resistance.

Foxg1 regulates translation of neocortical neuronal genes, including the main NMDA receptor subunit gene, Grin1

BackgroundMainly known as a transcription factor patterning the rostral brain and governing its histogenesis, FOXG1 has been also detected outside the nucleus; however, biological meaning of that has been only partially clarified.ResultsPrompted by FOXG1 expression in cytoplasm of pallial neurons, we investigated its implication in translational control. We documented the impact of FOXG1 on ribosomal recruitment of Grin1-mRNA, encoding for the main subunit of NMDA receptor. Next, we showed that FOXG1 increases GRIN1 protein level by enhancing the translation of its mRNA, while not increasing its stability. Molecular mechanisms underlying this activity included FOXG1 interaction with EIF4E and, possibly, Grin1-mRNA. Besides, we found that, within murine neocortical cultures, de novo synthesis of GRIN1 undergoes a prominent and reversible, homeostatic regulation and FOXG1 is instrumental to that. Finally, by integrated analysis of multiple omic data, we inferred that FOXG1 is implicated in translational control of hundreds of neuronal genes, modulating ribosome engagement and progression. In a few selected cases, we experimentally verified such inference.ConclusionsThese findings point to FOXG1 as a key effector, potentially crucial to multi-scale temporal tuning of neocortical pyramid activity, an issue with profound physiological and neuropathological implications.

MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics.

Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

Integrative Data Analysis Where Partial Covariates Have Complex Nonlinear Effects by Using Summary Information from an External Data

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and nonparametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this article, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure.

FluoAnalysis: An Open-Source MATLAB Toolbox for Analysis of Calcium Imaging Measurements of Oscillatory Astrocytic and Neuronal Networks.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Characterization of Structure-Surface Correlations in Ointments Using Surface Tensiometry within the Concept of an Integrated Analytical Approach.

In recent years, integrated data analysis has proven to be a valuable method for investigating complex systems, whether artificial or natural. The integrated analytical approach allows the simultaneous integration of data acquired from different analytical techniques employed for the same sample at the same time, leading to an expansion of the amount of information available. Surface tensiometry is a technique that was recently introduced in the Integrated Analytical Approach for investigating pharmaceutical dosage forms for topical application. Therefore, studies of rheological characterization, release, and skin permeation can be integrated with surface tensiometry measurements to develop chemical and rheological-surface correlation models, providing a new method for the quality control and process control of semisolid preparations. In this context, the aim of this research is to validate the utility of surface tensiometry measurements in the Integrated Analytical Approach and utilize these data to gain insights into the structure-surface correlation. The preparations chosen for this study were a PEG-gel, a lipogel, and an O/W cream containing carnitine as a model drug. The formulations were characterized using rheological measurements and evaluated for their carnitine release performance. Furthermore, surface tensiometry measurements were performed to rapidly and noninvasively assess the influence of carnitine on the surface properties of the semisolid preparations investigated. Our work demonstrated a close correlation between surface energy and structural data, showing the importance of surface tensiometry contribution in the noninvasive and rapid evaluation of the presence of carnitine in semisolid formulations.

Differences in hypersensitivity reactions and gadolinium deposition disease/symptoms associated with gadolinium exposure to gadolinium-based contrast agents: new insights based on global databases VigiBase, FAERS, and IQVIA-MIDAS

BackgroundHypersensitivity reactions (HSRs) can occur unexpectedly and be life-threatening when gadolinium-based contrast agents (GBCAs) are used. Gadolinium deposition disease (GDD) and symptoms associated with gadolinium exposure (SAGE) have been controversial for a long time. However, similar studies are currently incomplete or outdated. Therefore, comparing the safety of different GBCAs in terms of HSRs and GDD/SAGE using the latest post-marketing safety data should yield further insights into safely using GBCAs.MethodsThe safety differences between all GBCAs to GDD and the spectrum of GBCA-related HSRs were all compared and analyzed by using the World Health Organization database VigiBase and the FDA Adverse Event Reporting System (FAERS) database in this study. A further analysis of SAGE was also conducted using FAERS data. The lower limit of the reporting odds ratio (ROR) 95% confidence interval was used for signal detection. Moreover, the frequency of HSRs was calculated by dividing the number of reports in VigiBase by the total sales volume (measured in millions) from 2008 to 2022 in the IQVIA Multinational Integrated Data Analysis System. All adverse events were standardized using the Medical Dictionary for Drug Regulatory Activities (MedDRA) 26.0.ResultsThis study shows that all GBCAs have the potential to induce HSRs, with nonionic linear GBCAs exhibiting a comparatively lower signal. According to standardized MedDRA query stratification analysis, gadobutrol had a greater ROR025 for angioedema. The ROR025 of gadobenate dimeglumine and gadoteridol is larger for anaphylactic/anaphylactoid shock conditions. Regarding severe cutaneous adverse reactions, only gadoversetamide and gadodiamide showed signals in FAERS and VigiBase. There were also differences in the frequency of HSRs between regions. Regarding GDD, gadoterate meglumine, and gadoteridol had a lower ROR025. An analysis of the 29 preferred terms linked to SAGE indicated that special consideration should be given to the risk of skin induration associated with gadoversetamide, gadopentetate dimeglumine, gadobenate dimeglumine, gadodiamide, and gadoteridol. Additionally, gadodiamide and gadoteridol pose a greater risk of skin tightness compared to other GBCAs.ConclusionsThe risk differences among GBCAs using data from several sources were compared in this study. However, as a hypothesis-generating method, a clear causal relationship would require further research and validation.

Urolithin A attenuates hexavalent chromium-induced small intestinal injury by modulating PP2A/Hippo/YAP1 pathway

Hexavalent chromium (Cr(VI)) exposure has been linked with gastrointestinal toxicity, whereas the molecular pathways and key targets remain elusive. Computational toxicology analysis predicted the correlation between protein phosphatase 2A (PP2A) and genes regarding Cr(VI)-induced intestinal injury. Here, we generated a mouse model with intestinal epithelium-specific knock out of Ppp2r1a (encoding PP2A Aα subunit) to investigate the mechanisms underlying Cr(VI)-induced small intestinal toxicity. Heterozygous (HE) mice and matched WT littermates were administrated with Cr(VI) at 0, 5, 20, and 80mg/l for 28 successive days. Cr(VI) treatment led to crypt hyperplasia, epithelial cell apoptosis, and intestinal barrier dysfunction, accompanied by the decline of goblet cell counts and Occludin expression in WT mice. Notably, these effects were aggravated in HE mice, indicating that PP2A Aα deficiency conferred mice with susceptibility to Cr(VI)-induced intestinal injury. The combination of data analysis and biological experiments revealed Cr(VI) exposure could decrease YAP1 phosphorylation at Ser127 but increase protein expression and activity, together with elevated transcriptional coactivator with PDZ-binding motif protein driving epithelial crypt cells proliferation following damage, suggesting the involvement of Hippo/YAP1 signaling pathway in Cr(VI)-induced intestinal toxicity. Nevertheless, the enhanced phosphorylation of YAP1 in HE mice resulted in proliferation/repair defects in intestinal epithelium, thereby exacerbating Cr(VI)-induced gut barrier dysfunction. Notably, by molecular docking and further studies, we identified urolithin A, a microbial metabolite, attenuated Cr(VI)-induced disruption of intestinal barrier function, partly by modulating YAP1 expression and activity. Our findings reveal the novel molecular pathways participated in Cr(VI)-caused small intestinal injury and urolithin A could potentially protect against environmental hazards-induced intestinal diseases.

An integrated analysis of aeromagnetic and PRISMA hyperspectral imagery data for potential gold mineralization exploration in Mwanza, Malawi

An integrated analysis of aeromagnetic and PRISMA hyperspectral imagery data for potential gold mineralization exploration in Mwanza, Malawi

Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia.

Chronic Lymphocytic Leukemia (CLL) is the most common B-cell malignancy in the Western world, characterized by frequent relapses despite temporary remissions. Our study integrated publicly available proteomic, transcriptomic, and patient survival datasets to identify key differences between healthy and CLL samples. We exposed approximately 1000 proteins that differentiate healthy from cancerous cells, with 608 upregulated and 415 downregulated in CLL cases. Notable upregulated proteins include YEATS2 (an epigenetic regulator), PIGR (Polymeric immunoglobulin receptor), and SNRPA (a splicing factor), which may serve as prognostic biomarkers for this disease. Key pathways implicated in CLL progression involve RNA processing, stress resistance, and immune response deficits. Furthermore, we identified three existing drugs-Bosutinib, Vorinostat, and Panobinostat-for potential further investigation in drug repurposing in CLL. We also found limited correlation between transcriptomic and proteomic data, emphasizing the importance of proteomics in understanding gene expression regulation mechanisms. This generally known disparity highlights once again that mRNA levels do not accurately predict protein abundance due to many regulatory factors, such as protein degradation, post-transcriptional modifications, and differing rates of translation. These results demonstrate the value of integrating omics data to uncover deregulated proteins and pathways in cancer and suggest new therapeutic avenues for CLL.

TEAD2 Promotes Hepatocellular Carcinoma Development and Sorafenib Resistance via TAK1 Transcriptional Activation.

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer, yet the effectiveness of treatment for HCC patients is significantly hindered by the development of drug resistance to sorafenib. Through the application of ATAC-seq to examine drug-resistant HCC tissues, we identified substantial alterations in chromatin accessibility in sorafenib-resistant patient-derived xenograft (PDX) models. Employing multi-omics data integration analysis, we confirmed TEAD2 as a crucial transcriptional regulator in sorafenib-resistant HCC tissues. Functional assays illustrated that TEAD2 plays a role in promoting HCC progression and enhancing resistance to sorafenib. Mechanistically, we demonstrated that TEAD2 binds to the TAK1 promoter to modulate its expression. Furthermore, we established the involvement of TAK1 in mediating TEAD2-induced sorafenib resistance in HCC, a finding supported by the effectiveness of TAK1 inhibitors. Our research highlights that targeting the TEAD2-TAK1 axis can effectively mitigate drug resistance in HCC patients receiving sorafenib treatment, offering a novel approach for enhancing the treatment outcomes and prognosis of individuals with HCC. Implications: Targeting the TEAD2-TAK1 axis presents a promising therapeutic strategy to overcome sorafenib resistance in HCC, potentially improving treatment outcomes and prognosis for patients.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

Abstract We138: Single-nucleus and bulk multiomic analyses reveal the dynamics of individual cardiac cell types after myocardial infarction and the underlying gene regulatory networks

Cardiovascular diseases are the leading causes of death in the world, among which myocardial infarction (MI) is the most fatal form. Following MI, the death of cardiomyocytes induces cardiac remodeling, which is mainly mediated by cardiac fibroblasts but also involves other cardiac cell types. A deeper understanding of the dynamics of individual cell types among different stages after myocardial infarction may help identify novel therapeutic strategies. Here, through single-nucleus multiomic (snRNAseq and snATACseq) analysis of uninjured mouse hearts and mouse hearts at different time points (2 h, 1 d, 3 d, 7 d, and 4 w) after MI, we identified multiple cardiac cell types, including cardiomyocyte, fibroblast, epicardial cell, endothelial cell, mural cell, and immune cell, and their dynamic gene expression and chromatin accessibility after MI. Features specific to the acute and chronic responses of different cell types to MI were identified. Cell type-specific single-nucleus gene regulatory networks (snGRNs) constructed through integrated analysis of snRNAseq and snATACseq data revealed transcription factors possibly responsible for the gene expression changes in individual cell types. The cardiac fibroblast GRN was further enriched using bulk RNAseq and ATACseq data collected at more time points (uninjured, and 2 h, 12 h, 1 d, 3 d, 7 d, 2 w, and 4 w after MI) and non-coding RNA transcriptomics. Promoter and enhancer activities and chromatin interactions in cardiac fibroblasts were further verified by CUT&Tag targeting corresponding histone markers and Hi-C, respectively. The refined cardiac fibroblast GRN identified multiple key transcription factors that likely govern cardiac fibroblast activation and differentiation after MI.

The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.