Genome-wide Gene Expression Data Research Articles

The probability of chromatin to be at the nuclear lamina has no systematic effect on its transcription level in fruit flies

BackgroundMultiple studies have demonstrated a negative correlation between gene expression and positioning of genes at the nuclear envelope (NE) lined by nuclear lamina, but the exact relationship remains unclear, especially in light of the highly stochastic, transient nature of the gene association with the NE.ResultsIn this paper, we ask whether there is a causal, systematic, genome-wide relationship between the expression levels of the groups of genes in topologically associating domains (TADs) of Drosophila nuclei and the probabilities of TADs to be found at the NE. To investigate the nature of this possible relationship, we combine a coarse-grained dynamic model of the entire Drosophila nucleus with genome-wide gene expression data; we analyze the TAD averaged transcription levels of genes against the probabilities of individual TADs to be in contact with the NE in the control and lamins-depleted nuclei. Our findings demonstrate that, within the statistical error margin, the stochastic positioning of Drosophila melanogaster TADs at the NE does not, by itself, systematically affect the mean level of gene expression in these TADs, while the expected negative correlation is confirmed. The correlation is weak and disappears completely for TADs not containing lamina-associated domains (LADs) or TADs containing LADs, considered separately. Verifiable hypotheses regarding the underlying mechanism for the presence of the correlation without causality are discussed. These include the possibility that the epigenetic marks and affinity to the NE of a TAD are determined by various non-mutually exclusive mechanisms and remain relatively stable during interphase.ConclusionsAt the level of TADs, the probability of chromatin being in contact with the nuclear envelope has no systematic, causal effect on the transcription level in Drosophila. The conclusion is reached by combining model-derived time-evolution of TAD locations within the nucleus with their experimental gene expression levels.

MoS-TEC: A toxicogenomics database based on model selection for time-expression curves

MoS-TEC is a newly developed toxicogenomics database for time-expression curves fitted with a statistical model selection approach. Toxicogenomic data provide information on the response of the genome to compounds, often measured in terms of gene expression values. When such experimental data are available for different exposure times, the functional relationships between the exposure time and the expression values of genes might be of interest. The TG-GATEs (Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database provides such information for genomewide gene expression data for 170 compounds. We performed extensive model selection using MCP-Mod on these data. Specifically, gene expression data measured for eight time points from in vivo experiments on rat liver for 120 compounds with complete datasets were considered. MCP-Mod is a two-step approach, including a multiple comparison procedure (MCP) and a modelling (Mod) approach. The results are estimated time-expression curves that model the relationship between exposure time and gene expression values for all combinations of genes and compounds. We present an appropriate data normalization approach and report which models were selected per compound and in total. For high-quality model fits with a large value for the explained variance, the sigEmax model was most frequently selected. The new R Shiny application MoS-TEC provides easy access for researchers to the best curve fit for all genes individually for all compounds. It can be used online without installing additional software.

Modulation of bacterial membranes and cellular macromolecules by dimethyl sulfoxide: A dose-dependent study providing novel insights

Using Escherichia coli as a model, this manuscript delves into the intricate interactions between dimethyl sulfoxide (DMSO) and membranes, cellular macromolecules, and the effects on various aspects of bacterial physiology. Given DMSO's wide-ranging use as a solvent in microbiology, we investigate the impacts of both non-growth inhibitory (1.0 % and 2.5 % v/v) and slightly growth-inhibitory (5.0 % v/v) concentrations of DMSO. The results demonstrate that DMSO causes alterations in bacterial membrane potential, influences the electrochemical characteristics of the cell surface, and exerts substantial effects on the composition and structure of cellular biomolecules. Genome-wide gene expression data from DMSO-treated E. coli was used to further investigate and bolster the results. The findings of this study provide valuable insights into the complex relationship between DMSO and biological systems, with potential implications in drug delivery and cellular manipulation. However, it is essential to exercise caution when utilizing DMSO to enhance the solubility and delivery of bioactive compounds, as even at low concentrations, DMSO exerts non-inert effects on cellular macromolecules and processes.

Transcription Factor Regulation of Gene Expression Network by ZNF385D and HAND2 in Carotid Atherosclerosis.

Carotid intima-media thickness (CIMT) is a surrogate indicator for atherosclerosis and has been shown to predict cardiovascular risk in multiple large studies. Identification of molecular markers for carotid atheroma plaque formation can be critical for early intervention and prevention of atherosclerosis. This study performed transcription factor (TF) network analysis of global gene expression data focusing on two TF genes, ZNF385D and HAND2, whose polymorphisms have been recently reported to show association with CIMT. Genome-wide gene expression data were measured from pieces of carotid endarterectomy collected from 34 hypertensive patients (atheroma plaque of stages IV and above according to the Stary classification) each paired with one sample of distant macroscopically intact tissue (stages I and II). Transcriptional regulation networks or the regulons were reconstructed for ZNF385D (5644 target genes) and HAND2 (781 target genes) using network inference. Their association with the progression of carotid atheroma was examined using gene-set enrichment analysis with extremely high statistical significance for regulons of both ZNF385D and HAND2 (p < 6.95 × 10-7) suggesting the involvement of expression quantitative loci (eQTL). Functional annotation of the regulon genes found heavy involvement in the immune system's response to inflammation and infection in the development of atherosclerosis. Detailed examination of the regulation and correlation patterns suggests that activities of the two TF genes could have high clinical and interventional impacts on impairing carotid atheroma plaque formation and preventing carotid atherosclerosis.

Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research.

The integration of data from multiple genomic assays from humans and non-human model organisms is an effective approach to identify genes involved in skeletal fragility and fracture risk due to osteoporosis and other conditions. This review summarizes genome-wide genetic variation and gene expression data resources relevant to the discovery of genes contributing to skeletal fragility and fracture risk. Genome-wide association studies (GWAS) of osteoporosis-related traits are summarized, in addition to gene expression in bone tissues in humans and non-human organisms, with a focus on rodent models related to skeletal fragility and fracture risk. Gene discovery approaches using these genomic data resources are described. We also describe the Musculoskeletal Knowledge Portal (MSKKP) that integrates much of the available genomic data relevant to fracture risk. The available genomic resources provide a wealth of knowledge and can be analyzed to identify genes related to fracture risk. Genomic resources that would fill particular scientific gaps are discussed.

Abstract P390: E-value Over P-value And Its Adjustments: Improving Dna Methylation Analysis With An Application To Hypertension Studies

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data to detect differentially methylated regions (DMRs) that may be involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical literature applications, P-values and their adjustments are among the most widely reported statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism related to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we proposed a method to calculate E-value for DMR detection in RRBS data, which is defined as likelihood ratios falling into the null hypothesis over the entire parameter space. We also provided the corresponding R package ‘ metevalue ’ as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-value, we generated various RRBS benchmarking datasets using our simulator ‘ RRBSsim ’ with 8 samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-value not only significantly improved accuracy, AUC and power, over that of P-value or adjusted P-value, but also reduced false discovery rates and Type I errors. To illustrate the utility of E-value, we applied it to identify DMRs in two real RRBS datasets. One was genome-wide DNA methylome and transcriptome of renal T lymphocytes from Dahl Salt Sensitive rats treated with high- or low-salt diets. The other was genome-wide DNA methylation and gene expression data of human arterioles from our clinical trial in which 10 subjects were placed on 2-week low-salt diet (1200-mg sodium/day). Compared to the classical adjusted P-value, the use of E-value detected biologically more relevant DMRs and improved the negative association between DNA methylation and gene expression.

Correlation of gene expression with magnetic resonance imaging features of retinoblastoma: a multi-center radiogenomics validation study

ObjectivesTo validate associations between MRI features and gene expression profiles in retinoblastoma, thereby evaluating the repeatability of radiogenomics in retinoblastoma.MethodsIn this retrospective multicenter cohort study, retinoblastoma patients with gene expression data and MRI were included. MRI features (scored blinded for clinical data) and matched genome-wide gene expression data were used to perform radiogenomic analysis. Expression data from each center were first separately processed and analyzed. The end product normalized expression values from different sites were subsequently merged by their Z-score to permit cross-sites validation analysis. The MRI features were non-parametrically correlated with expression of photoreceptorness (radiogenomic analysis), a gene expression signature informing on disease progression. Outcomes were compared to outcomes in a previous described cohort.ResultsThirty-six retinoblastoma patients were included, 15 were female (42%), and mean age was 24 (SD 18) months. Similar to the prior evaluation, this validation study showed that low photoreceptorness gene expression was associated with advanced stage imaging features. Validated imaging features associated with low photoreceptorness were multifocality, a tumor encompassing the entire retina or entire globe, and a diffuse growth pattern (all p < 0.05). There were a number of radiogenomic associations that were also not validated.ConclusionsA part of the radiogenomic associations could not be validated, underlining the importance of validation studies. Nevertheless, cross-center validation of imaging features associated with photoreceptorness gene expression highlighted the capability radiogenomics to non-invasively inform on molecular subtypes in retinoblastoma.Clinical relevance statementRadiogenomics may serve as a surrogate for molecular subtyping based on histopathology material in an era of eye-sparing retinoblastoma treatment strategies.Key Points• Since retinoblastoma is increasingly treated using eye-sparing methods, MRI features informing on molecular subtypes that do not rely on histopathology material are important.• A part of the associations between retinoblastoma MRI features and gene expression profiles (radiogenomics) were validated.• Radiogenomics could be a non-invasive technique providing information on the molecular make-up of retinoblastoma.

Modeling tissue co-regulation estimates tissue-specific contributions to disease.

Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.

Pellino1 promotes immunity in the skin during infection with herpes simplex virus (HSV)

Abstract Pellino1 (Peli1) is a ubiquitin ligase involved in IL-1 and Toll-like receptor signaling; however, the role of Pellino1 in skin immunity against HSV is unknown. Here, using the mouse flank HSV-1 skin infection model, we demonstrate that Pellino1 has several critical functions during active viral replication. Analyses of single cell genome wide gene expression data (RNA-seq) revealed expression of Peli1mRNA throughout the skin including in epithelial, fibroblast and immune cells. Both histology and RNA-seq demonstrated normal development of the epidermis and dermis in Pellino1 knockout (KO) mice. The median survival of Pellino1 KO mice was 12 days compared to greater than 16 days in wild type mice. Pellino1 deficient mice also developed larger zosteriform skin lesions along affected dermatomes. During infection HSV-1 did not replicate in the dermis. Immunohistochemistry revealed delayed recruitment of myeloid and T cells to early infected epidermis in Pellino1 deficient mice. This correlated with lower expression of the inflammatory IL-1 and IL-36 cytokines and the poorly understood chemokine Grp15l. In Pellino1 KO mice, the virus spread extensively through the epidermis, follicular infundibulum into sebaceous glands where sebocytes were found positive for the virus. Histological analyses for HSV-1 and neuronal axons revealed nerve endings throughout the epidermis and axons wrapped around the hair follicles. Dissemination of HSV-1 into the sebaceous glands did not appear to involve a shift in how the virus migrated through these nerve axons as early lesions were only found in the interfollicular epidermis. In conclusion, Pellino1 plays important roles in restricting viral dissemination. Supported by grant from NIH (R01 AI125111)

Identification of TIAM1 as a Potential Synthetic-Lethal-like Gene in a Defined Subset of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, has very poor outcomes. Current therapies often have low efficacy and significant toxicities. Thus, there is a critical need for the development of novel therapeutic approaches for HCC. We have developed a novel bioinformatics pipeline, which integrates genome-wide DNA methylation and gene expression data, to identify genes required for the survival of specific molecular cancer subgroups but not normal cells. Targeting these genes may induce cancer-specific "synthetic lethality". Initially, five potential HCC molecular subgroups were identified based on global DNA methylation patterns. Subgroup-2 exhibited the most unique methylation profile and two candidate subtype-specific vulnerability or SL-like genes were identified for this subgroup, including TIAM1, a guanine nucleotide exchange factor encoding gene known to activate Rac1 signalling. siRNA targeting TIAM1 inhibited cell proliferation in TIAM1-positive (subgroup-2) HCC cell lines but had no effect on the normal hepatocyte HHL5 cell line. Furthermore, TIAM1-positive/subgroup-2 cell lines were significantly more sensitive to the TIAM1/RAC1 inhibitor NSC23766 compared with TIAM1-negative HCC lines or the normal HHL5 cell line. The results are consistent with a synthetic lethal role for TIAM1 in a methylation-defined HCC subgroup and suggest it may be a viable therapeutic target in this subset of HCC patients.

A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli.

Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.

Location of a topologically associating domain at the nuclear periphery has no systematic causal effect on its genes transcription levels in drosophila nuclei.

Several studies have demonstrated a correlation between expression of genes and their positioning at the nuclear periphery. However, no systematic genome-wide analysis of how the probability of location at the nuclear envelope (NE) affects gene expression is available. It is unclear whether there is a genome-wide correlation or causal relationship between the expression levels of groups of genes in topologically associating domains (TADs) of Drosophila nuclei and the probabilities of TADs to be found at the NE. To investigate the possible relationship, we have developed a coarse-grained dynamic model of the entire Drosophila melanogaster interphase nucleus that explicitly accounts for the different epigenetic classes of TADs and for the interactions between the NE and lamina-associated domains (LADs). The model predicts a highly dynamic positioning of LADs and TADs at the NE. The probability of a LAD-containing TAD to be found at the NE is determined by a highly variable local density of LADs along genome. Modeling of the lamin depleted nuclei predicts a significant reduction of TAD contacts with the NE and a substantial redistribution of the chromatin. We combine model derived probabilities of TADs to be found at the NE with the genome-wide experimental gene expression data for both wild type and lamin depleted nuclei. The analysis of the TAD averaged gene transcription levels against the probabilities of individual TADs to be at the NE suggests that, within statistical error margin, the stochastic positioning of Drosophila TADs at the NE does not, by itself, systematically affect the average level of gene expression in TADs. A weak anti-correlation exists between these expression levels and the probabilities of TADs to be found at the NE, but no causality.

An Integrated Approach of Learning Genetic Networks From Genome-Wide Gene Expression Data Using Gaussian Graphical Model and Monte Carlo Method.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets.

The rates of adult neurogenesis and oligodendrogenesis are linked to cell cycle regulation through p27-dependent gene repression of SOX2

Cell differentiation involves profound changes in global gene expression that often has to occur in coordination with cell cycle exit. Because cyclin-dependent kinase inhibitor p27 reportedly regulates proliferation of neural progenitor cells in the subependymal neurogenic niche of the adult mouse brain, but can also have effects on gene expression, we decided to molecularly analyze its role in adult neurogenesis and oligodendrogenesis. At the cell level, we show that p27 restricts residual cyclin-dependent kinase activity after mitogen withdrawal to antagonize cycling, but it is not essential for cell cycle exit. By integrating genome-wide gene expression and chromatin accessibility data, we find that p27 is coincidentally necessary to repress many genes involved in the transit from multipotentiality to differentiation, including those coding for neural progenitor transcription factors SOX2, OLIG2 and ASCL1. Our data reveal both a direct association of p27 with regulatory sequences in the three genes and an additional hierarchical relationship where p27 repression of Sox2 leads to reduced levels of its downstream targets Olig2 and Ascl1. In vivo, p27 is also required for the regulation of the proper level of SOX2 necessary for neuroblasts and oligodendroglial progenitor cells to timely exit cell cycle in a lineage-dependent manner.

Integrative genomics reveals pathogenic mediator of valproate-induced neurodevelopmental disability.

Prenatal exposure to the anti-seizure medication sodium valproate (VPA) is associated with an increased risk of adverse postnatal neurodevelopmental outcomes, including lowered intellectual ability, autism spectrum disorder and attention-deficit hyperactivity disorder. In this study, we aimed to clarify the molecular mechanisms underpinning the neurodevelopmental consequences of gestational VPA exposure using integrative genomics. We assessed the effect of gestational VPA on foetal brain gene expression using a validated rat model of valproate teratogenicity that mimics the human scenario of chronic oral valproate treatment during pregnancy at doses that are therapeutically relevant to the treatment of epilepsy. Two different rat strains were studied-inbred Genetic Absence Epilepsy Rats from Strasbourg, a model of genetic generalized epilepsy, and inbred non-epileptic control rats. Female rats were fed standard chow or VPA mixed in standard chow for 2 weeks prior to conception and then mated with same-strain males. In the VPA-exposed rats maternal oral treatment was continued throughout pregnancy. Foetuses were extracted via C-section on gestational Day 21 (1 day prior to birth) and foetal brains were snap-frozen and genome-wide gene expression data generated. We found that gestational VPA exposure via chronic maternal oral dosing was associated with substantial drug-induced differential gene expression in the pup brains, including dysregulated splicing, and observed that this occurred in the absence of evidence for significant neuronal gain or loss. The functional consequences of VPA-induced gene expression were explored using pathway analysis and integration with genetic risk data for psychiatric disease and behavioural traits. The set of genes downregulated by VPA in the pup brains were significantly enriched for pathways related to neurodevelopment and synaptic function and significantly enriched for heritability to human intelligence, schizophrenia and bipolar disorder. Our results provide a mechanistic link between chronic foetal VPA exposure and neurodevelopmental disability mediated by VPA-induced transcriptional dysregulation.

Rank-based Bayesian variable selection for genome-wide transcriptomic analyses.

Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Bayesian Mallows model for variable selection that allows for a full probabilistic analysis, leading to coherent quantification of uncertainties. Simulation studies demonstrate the versatility and robustness of the proposed method in a variety of scenarios, as well as its superiority with respect to several competitors when varying the data dimension or data generating process. We use the novel approach to analyze genome-wide RNAseq gene expression data from ovarian cancer patients: several genes that affect cancer development are correctly detected in a completely unsupervised fashion, showing the usefulness of the method in the context of signature discovery for cancer genomics. Moreover, the possibility to also perform uncertainty quantification plays a key role in the subsequent biological investigation.

Genomic and gene expression associations to morphology of a sexual ornament in the chicken.

How sexual selection affects the genome ultimately relies on the strength and type of selection, and the genetic architecture of the involved traits. While associating genotype with phenotype often utilizes standard trait morphology, trait representations in morphospace using geometric morphometric approaches receive less focus in this regard. Here, we identify genetic associations to a sexual ornament, the comb, in the chicken system (Gallus gallus). Our approach combined genome-wide genotype and gene expression data (&gt;30k genes) with different aspects of comb morphology in an advanced intercross line (F8) generated by crossing a wild-type Red Junglefowl with a domestic breed of chicken (White Leghorn). In total, 10 quantitative trait loci were found associated to various aspects of comb shape and size, while 1,184 expression QTL were found associated to gene expression patterns, among which 98 had overlapping confidence intervals with those of quantitative trait loci. Our results highlight both known genomic regions confirming previous records of a large effect quantitative trait loci associated to comb size, and novel quantitative trait loci associated to comb shape. Genes were considered candidates affecting comb morphology if they were found within both confidence intervals of the underlying quantitative trait loci and eQTL. Overlaps between quantitative trait loci and genome-wide selective sweeps identified in a previous study revealed that only loci associated to comb size may be experiencing on-going selection under domestication.

Abstract 810: HRD is inversely correlated with MSI and identifies immunologically cold tumors in most cancer types

Abstract Homologous recombination deficiency (HRD) leads to DNA double strand breaks and can be exploited by the use of PARP inhibitors to induce synthetic lethality of cancer cells. Over the last years, the clinical utility of this therapeutic concept was successfully demonstrated in ovarian, breast, prostate and pancreatic cancer. Currently, the role of HRD is investigated in trials testing immune checkpoint blockers alone or in combination with PARP inhibitors. But the relationship between HRD and immune cell contexture in cancer is incompletely understood. Here, we analyzed the association of HRD with tumor mutational burden (TMB), immune cell composition and gene expression in 10,000 tumors of 32 solid cancer types from the TCGA project. For each of the tumors, the HRD sum score (HRDsum) was calculated from allele-specific copy numbers (derived from genotyping data) and the TMB was calculated from the missense mutation calls (derived from WES data). HRDsum and tumor mutational burden (TMB) correlated positively pan-cancer (R=0.42) and within most cancer types. By contrast, HRD was absent in ultra-hypermutated (TMB &amp;gt;= 100 mut/Mb) tumors. Ultra-hypermutation was typically associated with microsatellite instability (MSI) or POLE/POLD1 mutation. Significant positive correlation of the HRDsum and immune cell infiltration was observed pan-cancer, but only within a few cancer types. Significant positive correlation of HRDsum and the T-cell inflamed gene expression profile was observed only in 7 of 32 cancer types: in breast cancer, ovarian cancer, low grade glioma, testicular germ cell tumors and three kinds of kidney cancer. A functional genomics analysis was carried out by correlating genome-wide gene expression data (derived from RNA-Seq) with HRDsum. The resulting lists of significantly correlating genes were analyzed for enrichment of 50 hallmark gene sets (catalog H, MSigDB). We detected simultaneous enrichment of two proliferation-related categories, E2F_TARGETS and G2M_checkpoint, for 13 of the 32 cancer types (ACC, BLCA, BRCA, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, PRAD, SARC, THYM). The study shows that HRD is associated with immunological activation of the tumor microenvironment only in a minority of cancer types. Our data support further studies of immune activating therapies in combination with immune checkpoint blockade in the not intrinsically activated cancer types and advocate to combine different genomic and transcriptomic biomarkers (including HRD and TMB) for comprehensive molecular diagnostics and therapy guidance. Citation Format: Jan Budczies, Klaus Kluck, Susanne Beck, Iordanis Ouralidis, Michael Allgäuer, Michael Menzel, Eugen Rempel, Daniel Kazdal, Lukas Perkhofer, Alexander Kleger, Peter Schirmacher, Thomas Seufferlein, Albrecht Stenzinger. HRD is inversely correlated with MSI and identifies immunologically cold tumors in most cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 810.

Transcriptome-wide association study identifies PSMB9 as a susceptibility gene for coal workers' pneumoconiosis.

Coal workers' pneumoconiosis (CWP) is a type of typical occupational lung disease caused by prolonged inhalation of coal mine dust. The individuals' different genetic background may underlie their different susceptibility to develop pneumoconiosis, even under the same exposure level. This study aimed to identify susceptibility genes associated with CWP. Based on our previous genome-wide association study (GWAS, 202 CWP cases vs. 198 controls) and gene expression data obtained by analyzing human lungs and whole blood from the Genotype-Tissue Expression (GTEx) Portal, a transcriptome-wide association study (TWAS) was applied to identify CWP risk-related genes. Luciferase report gene assay, qRT-PCR, Western blot, immunofluorescence assay, and TUNEL assay were conducted to explore the potential role of the candidate gene in CWP. Proteasome 20S subunit beta 9 (PSMB9) was identified as a strong risk-related gene of CWP in both lungs and whole blood (Lungs: PTWAS = 4.22 × 10-4 ; Whole blood: PTWAS = 2.11 × 10-4 ). Single nucleotide polymorphisms (SNPs) rs2071480 and rs1351383, which locate in the promoter region and the first intron of the PSMB9 gene, were in high linkage disequilibrium (LD, r2 = 0.98) with the best GWAS SNP rs4713600 (G>T, OR=0.55, 95% CI: 0.42-0.74, P= 6.86 × 10-5 ). Both rs2071480 and rs1351383 significantly enhanced the transcriptional activity of PSMB9. Functional experiments revealed that silica exposure remarkably reduced the PSMB9 expression and caused cell apoptosis, while overexpression of PSMB9 markedly abolished silica-induced cell apoptosis. We here identified PSMB9 as a novel susceptibility gene for CWP and provided important insights into the further exploration of the CWP pathogenesis.

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers.

BackgroundFor many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.Methods and resultsFrom a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants’ medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.ConclusionsOur developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.