Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension.

BACKGROUND: Integrative multiomics can elucidate pulmonary arterial hypertension (PAH) pathobiology, but procuring human PAH lung samples is rare. METHODS: We leveraged transcriptomic profiling and deep phenotyping of the largest multicenter PAH lung biobank to date (96 disease and 52 control) by integration with clinicopathologic data, genome-wide association studies, Bayesian regulatory networks, single-cell transcriptomics, and pharmacotranscriptomics. RESULTS: We identified 2 potentially protective gene network modules associated with vascular cells, and we validated ASPN, coding for asporin, as a key hub gene that is upregulated as a compensatory response to counteract PAH. We found that asporin is upregulated in lungs and plasma of multiple independent PAH cohorts and correlates with reduced PAH severity. We show that asporin inhibits proliferation and transforming growth factor-ß/phosphorylated SMAD2/3 signaling in pulmonary artery smooth muscle cells from PAH lungs. We demonstrate in Sugen-hypoxia rats that ASPN knockdown exacerbated PAH and recombinant asporin attenuated PAH. CONCLUSIONS: Our integrative systems biology approach to dissect the PAH lung transcriptome uncovered asporin as a novel protective target with therapeutic potential in PAH.

OpenSAFELY: The impact of COVID-19 on azathioprine, leflunomide and methotrexate monitoring, and factors associated with change in monitoring rate.

AIMS: The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic. METHODS: A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations. RESULTS: An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study. CONCLUSION: DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups.

A self-monitoring wellbeing screening methodology for keyworkers, 'My Personal Wellbeing', using an integrative wellbeing model.

BACKGROUND: The detrimental impact of Covid-19 has led to an urgent need to support the wellbeing of UK National Health Service and care workers. This research develops an online diary to support the wellbeing of staff in public healthcare in real-time, allowing the exploration of population wellbeing and pro-active responses to issues identified. METHODS: The diary was co-produced by NHS and care stakeholders and university researchers. It was based on an integrative model monitoring mental health symptoms as well as wellbeing indicators. Diary users were encouraged to reflect on their experience confidentially, empowering them to monitor their wellbeing. The data collected was analysed using Mann-Whitney-Wilcoxon and Kruskal-Wallis statistical tests to determine any significant wellbeing trends and issues. RESULTS: A statistically significant decline in wellbeing (P < 2.2E-16), and a significant increase in symptoms (P = 1.2E-14) was observed. For example, indicators of post-traumatic stress, including, flashbacks, dissociation, and bodily symptoms (Kruskal-Wallis P = 0.00081, 0.0083, and 0.027, respectively) became significantly worse and users reported issues with sleeping (51%), levels of alertness (46%), and burnout (41%). CONCLUSIONS: The wellbeing diary indicated the value of providing ways to distinguish trends and wellbeing problems, thus, informing how staff wellbeing services can determine and respond to need with timely interventions. The results particularly emphasised the pressing need for interventions that help staff with burnout, self-compassion, and intrusive memories.

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism.

BACKGROUND: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis. METHODS: Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse. RESULTS: The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression. CONCLUSIONS: The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.

Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States.

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.

The Genetic Architecture of Diet-Induced Hepatic Fibrosis in Mice.

We report the genetic analysis of a "humanized" hyperlipidemic mouse model for progressive nonalcoholic steatohepatitis (NASH) and fibrosis. Mice carrying transgenes for human apolipoprotein E*3-Leiden and cholesteryl ester transfer protein and fed a "Western" diet were studied on the genetic backgrounds of over 100 inbred mouse strains. The mice developed hepatic inflammation and fibrosis that was highly dependent on genetic background, with vast differences in the degree of fibrosis. Histological analysis showed features characteristic of human NASH, including macrovesicular steatosis, hepatocellular ballooning, inflammatory foci, and pericellular collagen deposition. Time course experiments indicated that while hepatic triglyceride levels increased steadily on the diet, hepatic fibrosis occurred at about 12 weeks. We found that the genetic variation predisposing to NASH and fibrosis differs markedly from that predisposing to simple steatosis, consistent with a multistep model in which distinct genetic factors are involved. Moreover, genome-wide association identified distinct genetic loci contributing to steatosis and NASH. Finally, we used hepatic expression data from the mouse panel and from 68 bariatric surgery patients with normal liver, steatosis, or NASH to identify enriched biological pathways. Conclusion: The pathways showed substantial overlap between our mouse model and the human disease.

Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems.

BACKGROUND: Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. RESULTS: We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. CONCLUSION: Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

A comprehensive comparison of association estimators for gene network inference algorithms.

MOTIVATION: Gene network inference (GNI) algorithms enable the researchers to explore the interactions among the genes and gene products by revealing these interactions. The principal process of the GNI algorithms is to obtain the association scores among genes. Although there are several association estimators used in different applications, there is no commonly accepted estimator as the best one for the GNI applications. In this study, 27 different interaction estimators were reviewed and 14 most promising ones among them were evaluated by using three popular GNI algorithms with two synthetic and two real biological datasets belonging to Escherichia coli bacteria and Saccharomyces cerevisiae yeast. Influences of the Copula Transform (CT) pre-processing operation on the performance of the interaction estimators are also observed. This study is expected to assist many researchers while studying with GNI applications. RESULTS: B-spline, Pearson-based Gaussian and Spearman-based Gaussian association score estimators outperform the others for all datasets in terms of the performance and runtime. In addition to this, it is observed that, when the CT operation is used, inference performances of the estimators mostly increase, especially for two synthetic datasets. Detailed evaluations and discussions are given in the experimental results. CONTACT: gokmen.altay@bahcesehir.edu.tr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some genomics datasets, we aggregate atypia patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA methylation as an integrative input source into a deep neural network for colon cancer stages classification, and samples stratification into low or high-risk survival groups. RESULTS: The genomics-only and integrated input features return Area Under Curve-Receiver Operating Characteristic curve (AUC-ROC) of 0.97 compared with AUC-ROC of 0.78 obtained when only image features are used for the stage's classification. A further analysis of prediction accuracy using the confusion matrix shows that the integrated features have a weakly improved accuracy of 0.08% more than the accuracy obtained with genomics features. Also, the extracted features were used to split the patients into low or high-risk survival groups. Among the 2,700 fused features, 1,836 (68%) features showed statistically significant survival probability differences in aggregating samples into either low or high between the two risk survival groups. Availability and Implementation: https://github.com/Ogundipe-L/EDCNN.

Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer.

Breast cancer (BC) is the most common cancer among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. GSE101124 and GSE182471 datasets were obtained from the Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray classification (PAM50). The circRNA-miRNA-mRNA relationship was investigated using the Cancer-Specific CircRNA, miRDB, miRTarBase, and miRWalk databases. The circRNA-miRNA-mRNA regulatory network was annotated using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The protein-protein interaction network was constructed by the STRING database and visualized by the Cytoscape tool. Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The Disease-Free Survival and Overall Survival analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. We conclude that hsa_circ_0000515/miR-486-5p/SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC subgroup of BC.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Integrative Multiomics to Dissect the Lung Transcriptional Landscape of Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) remains an incurable and often fatal disease despite currently available therapies. Multiomics systems biology analysis can shed new light on PAH pathobiology and inform translational research efforts. Using RNA sequencing on the largest PAH lung biobank to date (96 disease and 52 control), we aim to identify gene co-expression network modules associated with PAH and potential therapeutic targets. Co-expression network analysis was performed to identify modules of co-expressed genes which were then assessed for and prioritized by importance in PAH, regulatory role, and therapeutic potential via integration with clinicopathologic data, human genome-wide association studies (GWAS) of PAH, lung Bayesian regulatory networks, single-cell RNA-sequencing data, and pharmacotranscriptomic profiles. We identified a co-expression module of 266 genes, called the pink module, which may be a response to the underlying disease process to counteract disease progression in PAH. This module was associated not only with PAH severity such as increased PVR and intimal thickness, but also with compensated PAH such as lower number of hospitalizations, WHO functional class and NT-proBNP. GWAS integration demonstrated the pink module is enriched for PAH-associated genetic variation in multiple cohorts. Regulatory network analysis revealed that BMPR2 regulates the main target of FDA-approved riociguat, GUCY1A2, in the pink module. Analysis of pathway enrichment and pink hub genes (i.e. ANTXR1 and SFRP4) suggests the pink module inhibits Wnt signaling and epithelial-mesenchymal transition. Cell type deconvolution showed the pink module correlates with higher vascular cell fractions (i.e. myofibroblasts). A pharmacotranscriptomic screen discovered ubiquitin-specific peptidases (USPs) as potential therapeutic targets to mimic the pink module signature. Our multiomics integrative study uncovered a novel gene subnetwork associated with clinicopathologic severity, genetic risk, specific vascular cell types, and new therapeutic targets in PAH. Future studies are warranted to investigate the role and therapeutic potential of the pink module and targeting USPs in PAH.

Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.

A pilot study on the impacts of lung-strengthening Qigong on wellbeing.

BACKGROUND: Qigong embraces a range of self-care exercises originating from China. Lung-Strengthening Qigong (LSQ) is a specific technique for maintaining and improving physical and mental wellbeing. METHODS: We recruited 170 practitioners and 42 non-practitioner/control samples to investigate the impacts of LSQ practice on body, mind, thoughts, and feelings. This is a pilot study pursued to plan for an adequately powered, non-clinical randomized controlled trials (RCT) on overall wellbeing and health and to evaluate the adequacy of delivering the physical activity intervention with fidelity. Self-evaluation-based data collection schemes were developed by regularly requesting completion of a questionnaire from both practitioner and control group, and an online diary and end of study survey (EOS) completion only from the practitioners. Diverse types of analyses were conducted, including statistical tests, machine learning, and qualitative thematic models. RESULTS: We evaluated all different data resources together and observed that (a)the impacts are diverse, including improvements in physical (e.g., elevated sleep quality, physical energy, reduced fatigue), mental (e.g., increased positivity, reduced stress), and relational (e.g., enhanced connections to self and nature) wellbeing, which were not observed in control group; (b)measured by the level-of-effectiveness, four distinct clusters were identified, from no-effect to a high-level of effect; (c)a majority (84 %) of the LSQ practitioners experienced an improvement in wellbeing; (d)qualitative and quantitative analyses of the diary entries, questionnaires, and EOS were all found to be consistent, (e)majority of the positively impacted practitioners had no or some little prior experience with LSQ. CONCLUSIONS: Novel features of this study include (i)an increased sample size vis-à-vis other related studies; (ii)provision of weekly live-streamed LSQ sessions; (iii)integration of quantitative and qualitative type of analyses. The pilot study indicated that the proportion of practitioners who continued to engage in completing the regular-interval questionnaires over time was higher for practitioners compared to the control group. The engagement of practitioners may have been sustained by participation in the regular live LSQ sessions. To fully understand the impacts of LSQ on clinical/physiological outcomes, especially for specific patient groups, more objective biomarkers (e.g. respiratory rate, heart rate variation) could be tracked in future studies.

Refining colorectal cancer classification and clinical stratification through a single-cell atlas.

BACKGROUND: Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells. RESULTS: Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance. CONCLUSIONS: Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.

The Memory of Environmental Chemical Exposure in C. elegans Is Dependent on the Jumonji Demethylases jmjd-2 and jmjd-3/utx-1.

How artificial environmental cues are biologically integrated and transgenerationally inherited is still poorly understood. Here, we investigate the mechanisms of inheritance of reproductive outcomes elicited by the model environmental chemical Bisphenol A in C. elegans. We show that Bisphenol A (BPA) exposure causes the derepression of an epigenomically silenced transgene in the germline for 5 generations, regardless of ancestral response. Chromatin immunoprecipitation sequencing (ChIP-seq), histone modification quantitation, and immunofluorescence assays revealed that this effect is associated with a reduction of the repressive marks H3K9me3 and H3K27me3 in whole worms and in germline nuclei in the F3, as well as with reproductive dysfunctions, including germline apoptosis and embryonic lethality. Furthermore, targeting of the Jumonji demethylases JMJD-2 and JMJD-3/UTX-1 restores H3K9me3 and H3K27me3 levels, respectively, and it fully alleviates the BPA-induced transgenerational effects. Together, our results demonstrate the central role of repressive histone modifications in the inheritance of reproductive defects elicited by a common environmental chemical exposure.

Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) encompasses benign steatosis and more severe conditions such as non-alcoholic steatohepatitis (NASH), cirrhosis, and liver cancer. This chronic liver disease has a poorly understood etiology and demonstrates sexual dimorphisms. We aim to examine the molecular mechanisms underlying sexual dimorphisms in NAFLD pathogenesis through a comprehensive multi-omics study. We integrated genomics (DNA variations), transcriptomics of liver and adipose tissue, and phenotypic data of NAFLD derived from female mice of ~ 100 strains included in the hybrid mouse diversity panel (HMDP) and compared the NAFLD molecular pathways and gene networks between sexes. RESULTS: We identified both shared and sex-specific biological processes for NAFLD. Adaptive immunity, branched chain amino acid metabolism, oxidative phosphorylation, and cell cycle/apoptosis were shared between sexes. Among the sex-specific pathways were vitamins and cofactors metabolism and ion channel transport for females, and phospholipid, lysophospholipid, and phosphatidylinositol metabolism and insulin signaling for males. Additionally, numerous lipid and insulin-related pathways and inflammatory processes in the adipose and liver tissue appeared to show more prominent association with NAFLD in male HMDP. Using data-driven network modeling, we identified plausible sex-specific and tissue-specific regulatory genes as well as those that are shared between sexes. These key regulators orchestrate the NAFLD pathways in a sex- and tissue-specific manner. Gonadectomy experiments support that sex hormones may partially underlie the sexually dimorphic genes and pathways involved in NAFLD. CONCLUSIONS: Our multi-omics integrative study reveals sex- and tissue-specific genes, processes, and networks underlying sexual dimorphism in NAFLD and may facilitate sex-specific precision medicine.

Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-alcoholic Fatty Liver Disease.

The etiology of non-alcoholic fatty liver disease (NAFLD), the most common form of chronic liver disease, is poorly understood. To understand the causal mechanisms underlying NAFLD, we conducted a multi-omics, multi-tissue integrative study using the Hybrid Mouse Diversity Panel, consisting of ∼100 strains of mice with various degrees of NAFLD. We identified both tissue-specific biological processes and processes that were shared between adipose and liver tissues. We then used gene network modeling to predict candidate regulatory genes of these NAFLD processes, including Fasn, Thrsp, Pklr, and Chchd6. In vivo knockdown experiments of the candidate genes improved both steatosis and insulin resistance. Further in vitro testing demonstrated that downregulation of both Pklr and Chchd6 lowered mitochondrial respiration and led to a shift toward glycolytic metabolism, thus highlighting mitochondria dysfunction as a key mechanistic driver of NAFLD.

Estimation of the proteomic cancer co-expression sub networks by using association estimators.

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.