Integrative Genomic Analyses Identify LncRNA Regulatory Networks across Pediatric Leukemias and Solid Tumors

Long noncoding RNAs (lncRNA) play an important role in gene regulation and contribute to tumorigenesis. While pan-cancer studies of lncRNA expression have been performed for adult malignancies, the lncRNA landscape across pediatric cancers remains largely uncharted. Here, we curated RNA sequencing data for 1,044 pediatric leukemia and extracranial solid tumors and integrated paired tumor whole genome sequencing and epigenetic data in relevant cell line models to explore lncRNA expression, regulation, and association with cancer. A total of 2,657 lncRNAs were robustly expressed across six pediatric cancers, including 1,142 exhibiting histotype-elevated expression. DNA copy number alterations contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. Application of a multidimensional framework to identify and prioritize lncRNAs impacting gene networks revealed that lncRNAs dysregulated in pediatric cancer are associated with proliferation, metabolism, and DNA damage hallmarks. Analysis of upstream regulation via cell type-specific transcription factors further implicated distinct histotype-elevated and developmental lncRNAs. Integration of these analyses prioritized lncRNAs for experimental validation, and silencing of TBX2-AS1, the top-prioritized neuroblastoma-specific lncRNA, resulted in significant growth inhibition of neuroblastoma cells, confirming the computational predictions. Taken together, these data provide a comprehensive characterization of lncRNA regulation and function in pediatric cancers and pave the way for future mechanistic studies.

SIGNIFICANCE

Comprehensive characterization of lncRNAs in pediatric cancer leads to the identification of highly expressed lncRNAs across childhood cancers, annotation of lncRNAs showing histotype-specific elevated expression, and prediction of lncRNA gene regulatory networks.

Mu-opioid receptor-expressing neurons in the paraventricular thalamus modulate chronic morphine-induced wake alterations

Disrupted sleep is a symptom of many psychiatric disorders, including substance use disorders. Most drugs of abuse, including opioids, disrupt sleep. However, the extent and consequence of opioid-induced sleep disturbance, especially during chronic drug exposure, is understudied. We have previously shown that sleep disturbance alters voluntary morphine intake. Here, we examine the effects of acute and chronic morphine exposure on sleep. Using an oral self-administration paradigm, we show that morphine disrupts sleep, most significantly during the dark cycle in chronic morphine, with a concomitant sustained increase in neural activity in the Paraventricular Nucleus of the Thalamus (PVT). Morphine binds primarily to Mu Opioid Receptors (MORs), which are highly expressed in the PVT. Translating Ribosome Affinity Purification (TRAP)-Sequencing of PVT neurons that express MORs showed significant enrichment of the circadian entrainment pathway. To determine whether MOR + cells in the PVT mediate morphine-induced sleep/wake properties, we inhibited these neurons during the dark cycle while mice were self-administering morphine. This inhibition decreased morphine-induced wakefulness but not general wakefulness, indicating that MORs in the PVT contribute to opioid-specific wake alterations. Overall, our results suggest an important role for PVT neurons that express MORs in mediating morphine-induced sleep disturbance.

H3K27me3 Demethylases Maintain the Transcriptional and Epigenomic Landscape of the Intestinal Epithelium

BACKGROUND & AIMS

Although trimethylation of histone H3 lysine 27 (H3K27me3) by polycomb repressive complex 2 is required for intestinal function, the role of the antagonistic process-H3K27me3 demethylation-in the intestine remains unknown. The aim of this study was to determine the contribution of H3K27me3 demethylases to intestinal homeostasis.

METHODS

An inducible mouse model was used to simultaneously ablate the 2 known H3K27me3 demethylases, lysine (K)-specific demethylase 6A (Kdm6a) and lysine (K)-specific demethylase 6B (Kdm6b), from the intestinal epithelium. Mice were analyzed at acute and prolonged time points after Kdm6a/b ablation. Cellular proliferation and differentiation were measured using immunohistochemistry, while RNA sequencing and chromatin immunoprecipitation followed by sequencing for H3K27me3 were used to identify gene expression and chromatin changes after Kdm6a/b loss. Intestinal epithelial renewal was evaluated using a radiation-induced injury model, while Paneth cell homeostasis was measured via immunohistochemistry, immunoblot, and transmission electron microscopy.

RESULTS

We did not detect any effect of Kdm6a/b ablation on intestinal cell proliferation or differentiation toward the secretory cell lineages. Acute and prolonged Kdm6a/b loss perturbed expression of gene signatures belonging to multiple cell lineages (adjusted P value < .05), and a set of 72 genes was identified as being down-regulated with an associated increase in H3K27me3 levels after Kdm6a/b ablation (false discovery rate, <0.05). After prolonged Kdm6a/b loss, dysregulation of the Paneth cell gene signature was associated with perturbed matrix metallopeptidase 7 localization (P < .0001) and expression.

CONCLUSIONS

Although KDM6A/B does not regulate intestinal cell differentiation, both enzymes are required to support the full transcriptomic and epigenomic landscape of the intestinal epithelium and the expression of key Paneth cell genes.

Variant-to-gene-mapping analyses reveal a role for pancreatic islet cells in conferring genetic susceptibility to sleep-related traits

We investigated the potential role of sleep-trait associated genetic loci in conferring a degree of their effect via pancreatic α- and β-cells, given that both sleep disturbances and metabolic disorders, including type 2 diabetes and obesity, involve polygenic contributions and complex interactions. We determined genetic commonalities between sleep and metabolic disorders, conducting linkage disequilibrium genetic correlation analyses with publicly available GWAS summary statistics. Then we investigated possible enrichment of sleep-trait associated SNPs in promoter-interacting open chromatin regions within α- and β-cells, intersecting public GWAS reports with our own ATAC-seq and high-resolution promoter-focused Capture C data generated from both sorted human α-cells and an established human beta-cell line (EndoC-βH1). Finally, we identified putative effector genes physically interacting with sleep-trait associated variants in α- and EndoC-βH1cells running variant-to-gene mapping and establish pathways in which these genes are significantly involved. We observed that insomnia, short and long sleep-but not morningness-were significantly correlated with type 2 diabetes, obesity and other metabolic traits. Both the EndoC-βH1 and α-cells were enriched for insomnia loci (p = .01; p = .0076), short sleep loci (p = .017; p = .022) and morningness loci (p = 2.2 × 10-7; p = .0016), while the α-cells were also enriched for long sleep loci (p = .034). Utilizing our promoter contact data, we identified 63 putative effector genes in EndoC-βH1 and 76 putative effector genes in α-cells, with these genes showing significant enrichment for organonitrogen and organophosphate biosynthesis, phosphatidylinositol and phosphorylation, intracellular transport and signaling, stress responses and cell differentiation. Our data suggest that a subset of sleep-related loci confer their effects via cells in pancreatic islets.

Benchmarking AutoML frameworks for disease prediction using medical claims

Objectives

Ascertain and compare the performances of Automated Machine Learning (AutoML) tools on large, highly imbalanced healthcare datasets.

Materials and Methods

We generated a large dataset using historical de-identified administrative claims including demographic information and flags for disease codes in four different time windows prior to 2019. We then trained three AutoML tools on this dataset to predict six different disease outcomes in 2019 and evaluated model performances on several metrics.

Results

The AutoML tools showed improvement from the baseline random forest model but did not differ significantly from each other. All models recorded low area under the precision-recall curve and failed to predict true positives while keeping the true negative rate high. Model performance was not directly related to prevalence. We provide a specific use-case to illustrate how to select a threshold that gives the best balance between true and false positive rates, as this is an important consideration in medical applications.

Discussion

Healthcare datasets present several challenges for AutoML tools, including large sample size, high imbalance, and limitations in the available features. Improvements in scalability, combinations of imbalance-learning resampling and ensemble approaches, and curated feature selection are possible next steps to achieve better performance.

Conclusion

Among the three explored, no AutoML tool consistently outperforms the rest in terms of predictive performance. The performances of the models in this study suggest that there may be room for improvement in handling medical claims data. Finally, selection of the optimal prediction threshold should be guided by the specific practical application.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13040-022-00300-2).

α Cell dysfunction in islets from nondiabetic, glutamic acid decarboxylase autoantibody-positive individuals

BACKGROUNDMultiple islet autoantibodies (AAbs) predict the development of type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in only approximately 15% of individuals who are positive for single AAbs (generally against glutamic acid decarboxylase [GADA]); hence, the single GADA+ state may represent an early stage of T1D.METHODSHere, we functionally, histologically, and molecularly phenotyped human islets from nondiabetic GADA+ and T1D donors.RESULTSSimilar to the few remaining β cells in the T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, α cell glucagon secretion was dysregulated in both GADA+ and T1D islets, with impaired glucose suppression of glucagon secretion. Single-cell RNA-Seq of GADA+ α cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of cAMP-dependent protein kinase inhibitor β (PKIB), providing a molecular basis for the loss of glucose suppression and the increased effect of 3-isobutyl-1-methylxanthine (IBMX) observed in GADA+ donor islets.CONCLUSIONWe found that α cell dysfunction was present during the early stages of islet autoimmunity at a time when β cell mass was still normal, raising important questions about the role of early α cell dysfunction in the progression of T1D.FUNDINGThis work was supported by grants from the NIH (3UC4DK112217-01S1, U01DK123594-02, UC4DK112217, UC4DK112232, U01DK123716, and P30 DK019525) and the Vanderbilt Diabetes Research and Training Center (DK20593).

A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.

The promise of automated machine learning for the genetic analysis of complex traits

The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.

Abstract 3028: Integrative genomics reveals lncRNAs associated with pediatric cancer

Background: Long non-coding RNAs (lncRNAs) have emerged as key components of transcriptional and post-transcriptional gene regulation. Dysregulation of lncRNA expression has been widely observed in cancer and several lncRNAs are known to influence tumor initiation and progression. Despite this, the lncRNA landscape and regulatory networks across pediatric cancers remain relatively uncharted.

Methods: To characterize the lncRNA landscape of pediatric cancers, we first curated RNA sequencing data for 1,044 pediatric leukemia and solid tumors from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) project to identify known and novel expressed lncRNAs. This data set included: 280 acute myeloid leukemia (AML), 190 acute B-cell leukemia (B-ALL), 244 acute T-cell leukemia (T-ALL), 121 Wilm's tumor (WT), 48 rhabdoid tumors (RT), and 161 neuroblastoma (NBL). Histotype-specific expression was assessed using the tau score. Whole genome sequencing (WGS) from 826 matched normal-tumor pairs was integrated to identify somatic copy number alterations (SCNAs) disrupting lncRNA expression. To further implicate cancer-relevant drivers of lncRNA expression, we used a unique combination of epigenetic data in pediatric cell lines, including ChIP-sequencing for cancer-specific transcription factors and genome-scale chromatin capture data. A global analysis of lncRNA function was performed using the lncMod method, in which expression data is modelled to identify lncRNA modulators that perturb transcription factor regulation of target genes. Functional prioritization of lncRNAs was obtained through integration of analyses per cancer. Biochemical assays in human-derived cell line models were utilized to validate the function of the top prioritized lncRNA in NBL.

Results: We report a total of 2,657 robustly expressed lncRNAs across six pediatric cancers, including 1,142 lncRNAs exhibiting histotype-specific expression. SCNAs contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. There were 207 (28%) lncRNAs in regions with SCNA that had significant expression dysregulation. LncMod analysis across the cancers revealed context-specific transcriptional gene networks per dysregulated lncRNA and enrichment for proliferation, metabolic, and DNA damage hallmarks. We further identified 547 cancer-associated lncRNAs in NBL based on upstream regulation via oncogenic transcription factors. The top-prioritized lncRNA, TBX2-AS1, was predicted to impact proliferation in NBL. Silencing of TBX2-AS1 using siRNAs achieved &gt;90% knockdown in NBL cells and resulted in 46.6% decreased cell growth (p = 8.1 x 10-4).

Conclusion: This study defines the lncRNA landscape across six pediatric cancers and provides a detailed catalog of how lncRNAs impact regulatory gene networks. These data serve as a robust resource for future hypothesis-driven mechanistic studies.

Citation Format: Apexa Modi, Gonzalo Lopez, Karina L. Conkrite, Tsz Ching Leung, Sathvik Ramanan, Daphne Cheung, Chun Su, Matthew E. Johnson, Elisabetta Manduchi, Samantha Gadd, Jinghui Zhang, Malcolm A. Smith, Jaime M. Guidry Auvil, Daniela S. Gerhard, Soheil Meshinchi, Elizabeth J. Perlman, Stephen P. Hunger, John M. Maris, Andrew D. Wells, Struan F. Grant, Sharon J. Diskin. Integrative genomics reveals lncRNAs associated with pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3028.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving β-Cell Maturation

Newly differentiated pancreatic β cells lack proper insulin secretion profiles of mature functional β cells. The global gene expression differences between paired immature and mature β cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for β-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six β-cell developmental stages in mice, spanning from β-cell progenitor to mature β cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across β-cell maturation, such as the deregulation of β-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca²⁺ influx, which were established over a week before β-cell maturation completes. In particular, β cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal β cells displayed high basal insulin secretion, which decreased to the low levels found in mature β cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across β-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving β-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional β cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

A developmental lineage-based gene co-expression network for mouse pancreatic β-cells reveals a role for Zfp800 in pancreas development

To gain a deeper understanding of pancreatic β-cell development, we used iterative weighted gene correlation network analysis to calculate a gene co-expression network (GCN) from 11 temporally and genetically defined murine cell populations. The GCN, which contained 91 distinct modules, was then used to gain three new biological insights. First, we found that the clustered protocadherin genes are differentially expressed during pancreas development. Pcdhγ genes are preferentially expressed in pancreatic endoderm, Pcdhβ genes in nascent islets, and Pcdhα genes in mature β-cells. Second, after extracting sub-networks of transcriptional regulators for each developmental stage, we identified 81 zinc finger protein (ZFP) genes that are preferentially expressed during endocrine specification and β-cell maturation. Third, we used the GCN to select three ZFPs for further analysis by CRISPR mutagenesis of mice. Zfp800 null mice exhibited early postnatal lethality, and at E18.5 their pancreata exhibited a reduced number of pancreatic endocrine cells, alterations in exocrine cell morphology, and marked changes in expression of genes involved in protein translation, hormone secretion and developmental pathways in the pancreas. Together, our results suggest that developmentally oriented GCNs have utility for gaining new insights into gene regulation during organogenesis.

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Background

A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.

Results

We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj.

Conclusions

In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

Collapse of the hepatic gene regulatory network in the absence of FoxA factors

Here, Reizel et al. investigated the FoxA factor's role in maintaining the regulatory network needed for liver development, and ablated all FoxA genes in the adult mouse liver. They found that loss of FoxA caused rapid and massive reduction in the expression of critical liver genes, and that FoxA proteins are be required for maintaining enhancer activity, chromatin accessibility, nucleosome positioning, and binding of HNF4α.

The FoxA transcription factors are critical for liver development through their pioneering activity, which initiates a highly complex regulatory network thought to become progressively resistant to the loss of any individual hepatic transcription factor via mutual redundancy. To investigate the dispensability of FoxA factors for maintaining this regulatory network, we ablated all FoxA genes in the adult mouse liver. Remarkably, loss of FoxA caused rapid and massive reduction in the expression of critical liver genes. Activity of these genes was reduced back to the low levels of the fetal prehepatic endoderm stage, leading to necrosis and lethality within days. Mechanistically, we found FoxA proteins to be required for maintaining enhancer activity, chromatin accessibility, nucleosome positioning, and binding of HNF4α. Thus, the FoxA factors act continuously, guarding hepatic enhancer activity throughout adult life.

How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases

Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.

Embracing study heterogeneity for finding genetic interactions in large-scale research consortia

Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.

SNCA and mTOR Pathway Single Nucleotide Polymorphisms Interact to Modulate the Age at Onset of Parkinson's Disease

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the α-synuclein (SNCA) gene are associated with differential risk and age at onset (AAO) of both idiopathic and Leucine-rich repeat kinase 2 (LRRK2)-associated Parkinson's disease (PD). Yet potential combinatory or synergistic effects among several modulatory SNPs for PD risk or AAO remain largely underexplored.

OBJECTIVES

The mechanistic target of rapamycin (mTOR) signaling pathway is functionally impaired in PD. Here we explored whether SNPs in the mTOR pathway, alone or by epistatic interaction with known susceptibility factors, can modulate PD risk and AAO.

METHODS

Based on functional relevance, we selected a total of 64 SNPs mapping to a total of 57 genes from the mTOR pathway and genotyped a discovery series cohort encompassing 898 PD patients and 921 controls. As a replication series, we screened 4170 PD and 3014 controls available from the International Parkinson's Disease Genomics Consortium.

RESULTS

In the discovery series cohort, we found a 4-loci interaction involving STK11 rs8111699, FCHSD1 rs456998, GSK3B rs1732170, and SNCA rs356219, which was associated with an increased risk of PD (odds ratio = 2.59, P < .001). In addition, we also found a 3-loci epistatic combination of RPTOR rs11868112 and RPS6KA2 rs6456121 with SNCA rs356219, which was associated (odds ratio = 2.89; P < .0001) with differential AAO. The latter was further validated (odds ratio = 1.56; P = 0.046-0.047) in the International Parkinson's Disease Genomics Consortium cohort.

CONCLUSIONS

These findings indicate that genetic variability in the mTOR pathway contributes to SNCA effects in a nonlinear epistatic manner to modulate differential AAO in PD, unraveling the contribution of this cascade in the pathogenesis of the disease. © 2019 International Parkinson and Movement Disorder Society.

Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies

Background

The principal line of investigation in Genome Wide Association Studies (GWAS) is the identification of main effects, that is individual Single Nucleotide Polymorphisms (SNPs) which are associated with the trait of interest, independent of other factors. A variety of methods have been proposed to this end, mostly statistical in nature and differing in assumptions and type of model employed. Moreover, for a given model, there may be multiple choices for the SNP genotype encoding. As an alternative to statistical methods, machine learning methods are often applicable. Typically, for a given GWAS, a single approach is selected and utilized to identify potential SNPs of interest. Even when multiple GWAS are combined through meta-analyses within a consortium, each GWAS is typically analyzed with a single approach and the resulting summary statistics are then utilized in meta-analyses.

Results

In this work we use as case studies a Type 2 Diabetes (T2D) and a breast cancer GWAS to explore a diversity of applicable approaches spanning different methods and encoding choices. We assess similarity of these approaches based on the derived ranked lists of SNPs and, for each GWAS, we identify a subset of representative approaches that we use as an ensemble to derive a union list of top SNPs. Among these are SNPs which are identified by multiple approaches as well as several SNPs identified by only one or a few of the less frequently used approaches. The latter include SNPs from established loci and SNPs which have other supporting lines of evidence in terms of their potential relevance to the traits.

Conclusions

Not every main effect analysis method is suitable for every GWAS, but for each GWAS there are typically multiple applicable methods and encoding options. We suggest a workflow for a single GWAS, extensible to multiple GWAS from consortia, where representative approaches are selected among a pool of suitable options, to yield a more comprehensive set of SNPs, potentially including SNPs that would typically be missed with the most popular analyses, but that could provide additional valuable insights for follow-up.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0201-4) contains supplementary material, which is available to authorized users.

Genetic and Epigenetic Fine Mapping of Complex Trait Associated Loci in the Human Liver

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory elements of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the effects of genetic variation on histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 74 GWAS loci that have been associated with at least one complex phenotype. Our results reveal a repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass

A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs. Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait. These data are typically used post GWAS analyses to fine-map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow. Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses.

Genome-scale Capture C promoter interactions implicate effector genes at GWAS loci for bone mineral density

Osteoporosis is a devastating disease with an essential genetic component. GWAS have discovered genetic signals robustly associated with bone mineral density (BMD), but not the precise localization of effector genes. Here, we carry out physical and direct variant to gene mapping in human mesenchymal progenitor cell-derived osteoblasts employing a massively parallel, high resolution Capture C based method in order to simultaneously characterize the genome-wide interactions of all human promoters. By intersecting our Capture C and ATAC-seq data, we observe consistent contacts between candidate causal variants and putative target gene promoters in open chromatin for ~ 17% of the 273 BMD loci investigated. Knockdown of two novel implicated genes, ING3 at ‘CPED1-WNT16’ and EPDR1 at ‘STARD3NL’, inhibits osteoblastogenesis, while promoting adipogenesis. This approach therefore aids target discovery in osteoporosis, here on the example of two relevant genes involved in the fate determination of mesenchymal progenitors, and can be applied to other common genetic diseases.

GWAS have identified numerous genetic loci for bone mineral density (BMD) and fracture risk. Here, the authors map these variants to putative target genes using ATAC-seq and Capture C of human osteoblasts and confirm ING3 and EPDR1 as BMD genes in in vitro osteoblast differentiation experiments.

Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS

Although Genome Wide Association Studies (GWAS) have led to many valuable insights into the genetic bases of common diseases over the past decade, the issue of missing heritability has surfaced, as the discovered main effect genetic variants found to date do not account for much of a trait's predicted genetic component. We present a workflow, integrating epigenomics and topologically associating domain data, aimed at discovering trait-associated SNP pairs from GWAS where neither SNP achieved independent genome-wide significance. Each analyzed SNP pair consists of one SNP in a putative active enhancer and another SNP in a putative physically interacting gene promoter in a trait-relevant tissue. As a proof-of-principle case study, we used this approach to identify focused collections of SNP pairs that we analyzed in three independent Type 2 diabetes (T2D) GWAS. This approach led us to discover 35 significant SNP pairs, encompassing both novel signals and signals for which we have found orthogonal support from other sources. Nine of these pairs are consistent with eQTL results, two are consistent with our own capture C experiments, and seven involve signals supported by recent T2D literature.

A high-resolution, genome-scale promoter interactome in human T follicular helper cells implicates novel causal genes at SLE GWAS loci

Genome-Wide Association Studies (GWAS) have implicated &gt;60 loci in the susceptibility to systemic lupus erythematosus (SLE). However, GWAS reports signals in non-coding genomic regions, not the precise location of culprit genes. Chromatin conformation capture (3C) technologies that detect physical contacts between regions of the genome offer a powerful opportunity to map disease variants to target genes. We developed a massively parallel, high-resolution method to characterize the genome-wide interactomes of 36,691 promoters of protein-coding, noncoding, antisense, snRNA, miRNA, snoRNA and lincRNA genes in any cell type. Using this method, we generated promoter interactomes of primary human T follicular helper (TFH) cells from healthy tonsil, a cell type relevant to SLE as TFH operate upstream of pathogenic autoantibody-producing B cells. These sub-1kb TFH promoter interactome datasets were intersected with maps of TFH open chromatin generated by ATAC-seq and SLE SNPs from the 63 candidate loci, resulting in detection of consistent interactions between genes and accessible SNPs at 48 loci. We find that ~25% of accessible SLE SNPs interact with the nearest gene, e.g. STAT4 and IKZF3, while ~75% of accessible SNPs ‘skip’ the nearest gene to interact with distant genes, e.g. LCLAT1 at the ‘LBH’ locus, and the master TFH transcription factor BCL6 at the ‘LPP-TPRG1’ locus. Gene ontology analysis confirms that genes directly implicated by SNP interactions reside in SLE-relevant networks while ‘nearest to SNP’ genes do not. In conclusion, high-resolution, 3-dimensional promoter interactions with accessible, disease-associated SNPs in disease-relevant tissue connect variants to relevant genes with high apparent accuracy.

A Dementia-Associated Risk Variant near TMEM106B Alters Chromatin Architecture and Gene Expression

Neurodegenerative diseases pose an extraordinary threat to the world's aging population, yet no disease-modifying therapies are available. Although genome-wide association studies (GWASs) have identified hundreds of risk loci for neurodegeneration, the mechanisms by which these loci influence disease risk are largely unknown. Here, we investigated the association between common genetic variants at the 7p21 locus and risk of the neurodegenerative disease frontotemporal lobar degeneration. We showed that variants associated with disease risk correlate with increased expression of the 7p21 gene TMEM106B and no other genes; co-localization analyses implicated a common causal variant underlying both association with disease and association with TMEM106B expression in lymphoblastoid cell lines and human brain. Furthermore, increases in the amount of TMEM106B resulted in increases in abnormal lysosomal phenotypes and cell toxicity in both immortalized cell lines and neurons. We then combined fine-mapping, bioinformatics, and bench-based approaches to functionally characterize all candidate causal variants at this locus. This approach identified a noncoding variant, rs1990620, that differentially recruits CTCF in lymphoblastoid cell lines and human brain to influence CTCF-mediated long-range chromatin-looping interactions between multiple cis-regulatory elements, including the TMEM106B promoter. Our findings thus provide an in-depth analysis of the 7p21 locus linked by GWASs to frontotemporal lobar degeneration, nominating a causal variant and causal mechanism for allele-specific expression and disease association at this locus. Finally, we show that genetic variants associated with risk of neurodegenerative diseases beyond frontotemporal lobar degeneration are enriched in CTCF-binding sites found in brain-relevant tissues, implicating CTCF-mediated gene regulation in risk of neurodegeneration more generally.

Spatial phenotyping of the endocardial endothelium as a function of intracardiac hemodynamic shear stress

Despite substantial evidence for the central role of hemodynamic shear stress in the functional integrity of vascular endothelial cells, hemodynamic and molecular regulation of the endocardial endothelium lining the heart chambers remains understudied. We propose that regional differences in intracardiac hemodynamics influence differential endocardial gene expression leading to phenotypic heterogeneity of this cell layer. Measurement of intracardiac hemodynamics was performed using 4-dimensional flow MRI in healthy humans (n=8) and pigs (n=5). Local wall shear stress (WSS) and oscillatory shear indices (OSI) were calculated in three distinct regions of the LV - base, mid-ventricle (midV), and apex. In both the humans and pigs, WSS values were significantly lower in the apex and midV relative to the base. Additionally, both the apex and midV had greater oscillatory shear indices (OSI) than the base. To investigate regional phenotype, endocardial endothelial cells (EEC) were isolated from an additional 8 pigs and RNA sequencing was performed. A false discovery rate of 0.10 identified 1051 differentially expressed genes between the base and apex, and 321 between base and midV. Pathway analyses revealed apical upregulation of genes associated with translation initiation. Furthermore, tissue factor pathway inhibitor (TFPI; mean 50-fold) and prostacyclin synthase (PTGIS; 5-fold), genes prominently associated with antithrombotic protection, were consistently upregulated in LV apex. These spatio-temporal WSS values in defined regions of the left ventricle link local hemodynamics to regional heterogeneity in endocardial gene expression.

The type 2 diabetes presumed causal variant within TCF7L2 resides in an element that controls the expression of ACSL5

AIMS/HYPOTHESIS

One of the most strongly associated type 2 diabetes loci reported to date resides within the TCF7L2 gene. Previous studies point to the T allele of rs7903146 in intron 3 as the causal variant at this locus. We aimed to identify the actual gene(s) under the influence of this variant.

METHODS

Using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein-9 nuclease, we generated a 1.4 kb deletion of the genomic region harbouring rs7903146 in the HCT116 cell line, followed by global gene expression analysis. We then carried out a combination of circularised chromosome conformation capture (4C) and Capture C in cell lines, HCT116 and NCM460 in order to ascertain which promoters of these perturbed genes made consistent physical contact with this genomic region.

RESULTS

We observed 99 genes with significant differential expression (false discovery rate [FDR] cut-off:10%) and an effect size of at least twofold. The subsequent promoter contact analyses revealed just one gene, ACSL5, which resides in the same topologically associating domain as TCF7L2. The generation of additional, smaller deletions (66 bp and 104 bp) comprising rs7903146 showed consistently reduced ACSL5 mRNA levels across all three deletions of up to 30-fold, with commensurate loss of acyl-CoA synthetase long-chain family member 5 (ACSL5) protein. Notably, the deletion of this single-nucleotide polymorphism region abolished significantly detectable chromatin contacts with the ACSL5 promoter. We went on to confirm that contacts between rs7903146 and the ACSL5 promoter regions were conserved in human colon tissue. ACSL5 encodes ACSL5, an enzyme with known roles in fatty acid metabolism.

CONCLUSIONS/INTERPRETATION

This 'variant to gene mapping' effort implicates the genomic location harbouring rs7903146 as a regulatory region for ACSL5.

The Ontology for Biomedical Investigations

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

Integrated Regional Cardiac Hemodynamic Imaging and RNA Sequencing Reveal Corresponding Heterogeneity of Ventricular Wall Shear Stress and Endocardial Transcriptome

Background

Unlike arteries, in which regionally distinct hemodynamics are associated with phenotypic heterogeneity, the relationships between endocardial endothelial cell phenotype and intraventricular flow remain largely unexplored. We investigated regional differences in left ventricular wall shear stress and their association with endocardial endothelial cell gene expression.

Methods and Results

Local wall shear stress was calculated from 4‐dimensional flow magnetic resonance imaging in 3 distinct regions of human (n=8) and pig (n=5) left ventricle: base, adjacent to the outflow tract; midventricle; and apex. In both species, wall shear stress values were significantly lower in the apex and midventricle relative to the base; oscillatory shear index was elevated in the apex. RNA sequencing of the endocardial endothelial cell transcriptome in pig left ventricle (n=8) at a false discovery rate ≤10% identified 1051 genes differentially expressed between the base and the apex and 327 between the base and the midventricle; no differentially expressed genes were detected at this false discovery rate between the apex and the midventricle. Enrichment analyses identified apical upregulation of genes associated with translation initiation including mammalian target of rapamycin, and eukaryotic initiation factor 2 signaling. Genes of mitochondrial dysfunction and oxidative phosphorylation were also consistently upregulated in the left ventricular apex, as were tissue factor pathway inhibitor (mean 50‐fold) and prostacyclin synthase (5‐fold)—genes prominently associated with antithrombotic protection.

Conclusions

We report the first spatiotemporal measurements of wall shear stress within the left ventricle and linked regional hemodynamics to heterogeneity in ventricular endothelial gene expression, most notably to translation initiation and anticoagulation properties in the left ventricular apex, in which oscillatory shear index is increased and wall shear stress is decreased.

Arterial endothelial methylome: differential DNA methylation in athero-susceptible disturbed flow regions in vivo

BACKGROUND

Atherosclerosis is a heterogeneously distributed disease of arteries in which the endothelium plays an important central role. Spatial transcriptome profiling of endothelium in pre-lesional arteries has demonstrated differential phenotypes primed for athero-susceptibility at hemodynamic sites associated with disturbed blood flow. DNA methylation is a powerful epigenetic regulator of endothelial transcription recently associated with flow characteristics. We investigated differential DNA methylation in flow region-specific aortic endothelial cells in vivo in adult domestic male and female swine.

RESULTS

Genome-wide DNA methylation was profiled in endothelial cells (EC) isolated from two robust locations of differing patho-susceptibility:--an athero-susceptible site located at the inner curvature of the aortic arch (AA) and an athero-protected region in the descending thoracic (DT) aorta. Complete methylated DNA immunoprecipitation sequencing (MeDIP-seq) identified over 5500 endothelial differentially methylated regions (DMRs). DMR density was significantly enriched in exons and 5'UTR sequences of annotated genes, 60 of which are linked to cardiovascular disease. The set of DMR-associated genes was enriched in transcriptional regulation, pattern specification HOX loci, oxidative stress and the ER stress adaptive pathway, all categories linked to athero-susceptible endothelium. Examination of the relationship between DMR and mRNA in HOXA genes demonstrated a significant inverse relationship between CpG island promoter methylation and gene expression. Methylation-specific PCR (MSP) confirmed differential CpG methylation of HOXA genes, the ER stress gene ATF4, inflammatory regulator microRNA-10a and ARHGAP25 that encodes a negative regulator of Rho GTPases involved in cytoskeleton remodeling. Gender-specific DMRs associated with ciliogenesis that may be linked to defects in cilia development were also identified in AA DMRs.

CONCLUSIONS

An endothelial methylome analysis identifies epigenetic DMR characteristics associated with transcriptional regulation in regions of atherosusceptibility in swine aorta in vivo. The data represent the first methylome blueprint for spatio-temporal analyses of lesion susceptibility predisposing to endothelial dysfunction in complex flow environments in vivo.

Endothelial epigenetics in biomechanical stress: disturbed flow-mediated epigenomic plasticity in vivo and in vitro

Arterial endothelial phenotype is regulated by local hemodynamic forces that are linked to regional susceptibility to atherogenesis. A complex hierarchy of transcriptional, translational, and post-translational mechanisms is greatly influenced by the characteristics of local arterial shear stress environments. We discuss the emerging role of localized disturbed blood flow on epigenetic mechanisms of endothelial responses to biomechanical stress, including transcriptional regulation by proximal promoter DNA methylation, and post-transcriptional and translational regulation of gene and protein expression by chromatin remodeling and noncoding RNA-based mechanisms. Dynamic responses to flow characteristics in vivo and in vitro include site-specific differentially methylated regions of swine and mouse endothelial methylomes, histone marks regulating chromatin conformation, microRNAs, and long noncoding RNAs. Flow-mediated epigenomic responses intersect with cis and trans factor regulation to maintain endothelial function in a shear-stressed environment and may contribute to localized endothelial dysfunctions that promote atherosusceptibility.

Insm1 promotes endocrine cell differentiation by modulating the expression of a network of genes that includes Neurog3 and Ripply3

Insulinoma associated 1 (Insm1) plays an important role in regulating the development of cells in the central and peripheral nervous systems, olfactory epithelium and endocrine pancreas. To better define the role of Insm1 in pancreatic endocrine cell development we generated mice with an Insm1^GFPCre reporter allele and used them to study Insm1-expressing and null populations. Endocrine progenitor cells lacking Insm1 were less differentiated and exhibited broad defects in hormone production, cell proliferation and cell migration. Embryos lacking Insm1 contained greater amounts of a non-coding Neurog3 mRNA splice variant and had fewer Neurog3/Insm1 co-expressing progenitor cells, suggesting that Insm1 positively regulates Neurog3. Moreover, endocrine progenitor cells that express either high or low levels of Pdx1, and thus may be biased towards the formation of specific cell lineages, exhibited cell type-specific differences in the genes regulated by Insm1. Analysis of the function of Ripply3, an Insm1-regulated gene enriched in the Pdx1-high cell population, revealed that it negatively regulates the proliferation of early endocrine cells. Taken together, these findings indicate that in developing pancreatic endocrine cells Insm1 promotes the transition from a ductal progenitor to a committed endocrine cell by repressing a progenitor cell program and activating genes essential for RNA splicing, cell migration, controlled cellular proliferation, vasculogenesis, extracellular matrix and hormone secretion.

Emerging topic: flow-related epigenetic regulation of endothelial phenotype through DNA methylation

Atherosclerosis is a multi-focal disease; it is associated with arterial curvatures, asymmetries and branches/bifurcations where non-uniform arterial geometry generates patterns of blood flow that are considerably more complex than elsewhere, and are collectively referred to as disturbed flow. Such regions are predisposed to atherosclerosis and are the sites of 'athero-susceptible' endothelial cells that express regionally different cell phenotypes than endothelium in nearby athero-protected locations. The regulatory hierarchy of endothelial function includes control at the epigenetic level. MicroRNAs and histone modifications are established epigenetic regulators that respond to disturbed flow. However, very recent reports have linked transcriptional regulation by DNA methylation to endothelial gene expression in disturbed flow in vivo and in vitro. We outline these in the context of site-specific atherosusceptibility mediated by local hemodynamics.

Dual lineage-specific expression of Sox17 during mouse embryogenesis

Sox17 is essential for both endoderm development and fetal hematopoietic stem cell (HSC) maintenance. While endoderm-derived organs are well known to originate from Sox17-expressing cells, it is less certain whether fetal HSCs also originate from Sox17-expressing cells. By generating a Sox17(GFPCre) allele and using it to assess the fate of Sox17-expressing cells during embryogenesis, we confirmed that both endodermal and a part of definitive hematopoietic cells are derived from Sox17-positive cells. Prior to E9.5, the expression of Sox17 is restricted to the endoderm lineage. However, at E9.5 Sox17 is expressed in the endothelial cells (ECs) at the para-aortic splanchnopleural region that contribute to the formation of HSCs at a later stage. The identification of two distinct progenitor cell populations that express Sox17 at E9.5 was confirmed using fluorescence-activated cell sorting together with RNA-Seq to determine the gene expression profiles of the two cell populations. Interestingly, this analysis revealed differences in the RNA processing of the Sox17 mRNA during embryogenesis. Taken together, these results indicate that Sox17 is expressed in progenitor cells derived from two different germ layers, further demonstrating the complex expression pattern of this gene and suggesting caution when using Sox17 as a lineage-specific marker.

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute.

Database URL:http://www.cbil.upenn.edu/annotCompute/

Coronary artery endothelial transcriptome in vivo: identification of endoplasmic reticulum stress and enhanced reactive oxygen species by gene connectivity network analysis

BACKGROUND

Endothelial function is central to the localization of atherosclerosis. The in vivo endothelial phenotypic footprints of arterial bed identity and site-specific atherosusceptibility are addressed.

METHODS AND RESULTS

Ninety-eight endothelial cell samples from 13 discrete coronary and noncoronary arterial regions of varying susceptibilities to atherosclerosis were isolated from 76 normal swine. Transcript profiles were analyzed to determine the steady-state in vivo endothelial phenotypes. An unsupervised systems biology approach using weighted gene coexpression networks showed highly correlated endothelial genes. Connectivity network analysis identified 19 gene modules, 12 of which showed significant association with circulatory bed classification. Differential expression of 1300 genes between coronary and noncoronary artery endothelium suggested distinct coronary endothelial phenotypes, with highest significance expressed in gene modules enriched for biological functions related to endoplasmic reticulum (ER) stress and unfolded protein binding, regulation of transcription and translation, and redox homeostasis. Furthermore, within coronary arteries, comparison of endothelial transcript profiles of susceptible proximal regions to protected distal regions suggested the presence of ER stress conditions in susceptible sites. Accumulation of reactive oxygen species throughout coronary endothelium was greater than in noncoronary endothelium consistent with coronary artery ER stress and lower endothelial expression of antioxidant genes in coronary arteries.

CONCLUSIONS

Gene connectivity analyses discriminated between coronary and noncoronary endothelial transcript profiles and identified differential transcript levels associated with increased ER and oxidative stress in coronary arteries consistent with enhanced susceptibility to atherosclerosis.

Discovery approaches to UPR in athero-susceptible endothelium in vivo

The endothelium is a monolayer of cells that lines the entire inner surface of the cardiovascular and lymphatic circulations where it controls normal physiological functions through both systemic and local regulation. Endothelial phenotypes are heterogeneous, dynamic and malleable, properties that in large- and medium-sized arteries lead to a central role in the development of focal and regional atherosclerosis. The endothelial phenotype in athero-susceptible sites is different from that in nearby athero-resistant regions. Understanding the in vivo gene, protein, and metabolic expression profiles of susceptible endothelium is, therefore, an important spatiotemporal challenge in atherosclerosis research. Recent studies have demonstrated that endoplasmic reticulum (ER) stress and the UPR are characteristics of susceptible endothelium. Here, we outline global genomic profiling, pathway analyses, and gene connectivity approaches to the identification of UPR and associated pathways as discrete markers of athero-susceptibility in arterial endothelium.

Propagation of adipogenic signals through an epigenomic transition state

The transcriptional mechanisms by which temporary exposure to developmental signals instigates adipocyte differentiation are unknown. During early adipogenesis, we find transient enrichment of the glucocorticoid receptor (GR), CCAAT/enhancer-binding protein beta (CEBPbeta), p300, mediator subunit 1, and histone H3 acetylation near genes involved in cell proliferation, development, and differentiation, including the gene encoding the master regulator of adipocyte differentiation, peroxisome proliferator-activated receptor gamma2 (PPARgamma2). Occupancy and enhancer function are triggered by adipogenic signals, and diminish upon their removal. GR, which is important for adipogenesis but need not be active in the mature adipocyte, functions transiently with other enhancer proteins to propagate a new program of gene expression that includes induction of PPARgamma2, thereby providing a memory of the earlier adipogenic signal. Thus, the conversion of preadipocyte to adipocyte involves the formation of an epigenomic transition state that is not observed in cells at the beginning or end of the differentiation process.

LKB1 regulates pancreatic beta cell size, polarity, and function

Pancreatic beta cells, organized in the islets of Langerhans, sense glucose and secrete appropriate amounts of insulin. We have studied the roles of LKB1, a conserved kinase implicated in the control of cell polarity and energy metabolism, in adult beta cells. LKB1-deficient beta cells show a dramatic increase in insulin secretion in vivo. Histologically, LKB1-deficient beta cells have striking alterations in the localization of the nucleus and cilia relative to blood vessels, suggesting a shift from hepatocyte-like to columnar polarity. Additionally, LKB1 deficiency causes a 65% increase in beta cell volume. We show that distinct targets of LKB1 mediate these effects. LKB1 controls beta cell size, but not polarity, via the mTOR pathway. Conversely, the precise position of the beta cell nucleus, but not cell size, is controlled by the LKB1 target Par1b. Insulin secretion and content are restricted by LKB1, at least in part, via AMPK. These results expose a molecular mechanism, orchestrated by LKB1, for the coordinated maintenance of beta cell size, form, and function.

Chronic endoplasmic reticulum stress activates unfolded protein response in arterial endothelium in regions of susceptibility to atherosclerosis

RATIONALE

Endothelial function and dysfunction are central to the focal origin and regional development of atherosclerosis; however, an in vivo endothelial phenotypic footprint of susceptibility to atherosclerosis preceding pathological change remains elusive.

OBJECTIVE

To conduct a comparative multi-site genomics study of arterial endothelial phenotype in atherosusceptible and atheroprotected regions.

METHODS AND RESULTS

Transcript profiles of freshly isolated endothelial cells from 7 discrete arterial regions in normal swine were analyzed to determine the steady state in vivo endothelial phenotypes in regions of varying susceptibilities to atherosclerosis. The most abundant common feature of the endothelium of all atherosusceptible regions was the upregulation of genes associated with endoplasmic reticulum (ER) stress. The unfolded protein response pathway, induced by ER stress, was therefore investigated in detail in endothelium of the atherosusceptible aortic arch and was found to be partially activated. ER transmembrane signal transducers IRE1alpha and ATF6alpha and their downstream effectors, but not PERK, were activated concomitant with a higher transcript expression of protein folding enzymes and chaperones, indicative of ER stress in vivo.

CONCLUSIONS

The findings demonstrate the prevalence of chronic endothelial ER stress and activated unfolded protein response in vivo at atherosusceptible arterial sites. We propose that chronic localized biological stress is linked to spatial susceptibility of the endothelium to the initiation of atherosclerosis.

A flexible two-stage procedure for identifying gene sets that are differentially expressed

MOTIVATION

Microarray data analysis has expanded from testing individual genes for differential expression to testing gene sets for differential expression. The tests at the gene set level may focus on multivariate expression changes or on the differential expression of at least one gene in the gene set. These tests may be powerful at detecting subtle changes in expression, but findings at the gene set level need to be examined further to understand whether they are informative and if so how.

RESULTS

We propose to first test for differential expression at the gene set level but then proceed to test for differential expression of individual genes within discovered gene sets. We introduce the overall false discovery rate (OFDR) as an appropriate error rate to control when testing multiple gene sets and genes. We illustrate the advantage of this procedure over procedures that only test gene sets or individual genes.

Analysis and management of microarray gene expression data

Microarray experiments require careful planning and choice of analysis tools in order to get the most out of the data generated, especially considering the associated significant cost and effort. Microarray experiments also require careful documentation, often residing in local databases and/or submitted to public repositories. An often bewildering assortment of choices is available for experimental design, data preprocessing, data analysis (e.g., differential gene expression, classification), and data management. This unit covers the basic steps and common applications for planning, data processing, and data management of microarray experiments, and provides guidance to making choices based on the goals and practical realities of the experiment, as well as the authors' experience in this area.