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.

Predictive value of common genetic variants in idiopathic pulmonary fibrosis survival.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrosing interstitial pneumonia of unknown etiology. The role of genetic risk factors has been the focus of numerous studies probing for associations of genetic variants with IPF. We aimed to determine whether single-nucleotide polymorphisms (SNPs) of four candidate genes are associated with IPF susceptibility and survival in a Portuguese population. A retrospective case-control study was performed with 64 IPF patients and 74 healthy controls. Ten single-nucleotide variants residing in the MUC5B, TOLLIP, SERPINB1, and PLAU genes were analyzed. Single- and multi-locus analyses were performed to investigate the predictive potential of specific variants in IPF susceptibility and survival. Multifactor dimensionality reduction (MDR) was employed to uncover predictive multi-locus interactions underlying IPF susceptibility. The MUC5B rs35705950 SNP was significantly associated with IPF: T allele carriers were significantly more frequent among IPF patients (75.0% vs 20.3%, P < 1.0 × 10-6). Genotypic and allelic distributions of TOLLIP, PLAU, and SERPINB1 SNPs did not differ significantly between groups. However, the MUC5B-TOLLIP T-C-T-C haplotype, defined by the rs35705950-rs111521887-rs5743894-rs5743854 block, emerged as an independent protective factor in IPF survival (HR = 0.37, 95% CI 0.17-0.78, P = 0.009, after adjustment for FVC). No significant multi-locus interactions correlating with disease susceptibility were detected. MUC5B rs35705950 was linked to an increased risk for IPF, as reported for other populations, but not to disease survival. A haplotype incorporating SNPs of the MUC5B-TOLLIP locus at 11p15.5 seems to predict better survival and could prove useful for prognostic purposes and IPF patient stratification. KEY MESSAGES : The MUC5B rs35705950 minor allele is associated with IPF risk in the Portuguese. No predictive multi-locus interactions of IPF susceptibility were identified by MDR. A haplotype defined by MUC5B and TOLLIP SNPs is a protective factor in IPF survival. The haplotype may be used as a prognostic tool for IPF patient stratification.

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

Newly differentiated pancreatic ß cells lack proper insulin secretion profiles of mature functional ß cells. The global gene expression differences between paired immature and mature ß 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 ß-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 ß-cell developmental stages in mice, spanning from ß-cell progenitor to mature ß 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 ß-cell maturation, such as the deregulation of ß-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 ß-cell maturation completes. In particular, ß cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal ß cells displayed high basal insulin secretion, which decreased to the low levels found in mature ß 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 ß-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 ß-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional ß cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

Mapping effector genes at lupus GWAS loci using promoter Capture-C in follicular helper T cells.

Systemic lupus erythematosus (SLE) is mediated by autoreactive antibodies that damage multiple tissues. Genome-wide association studies (GWAS) link >60 loci with SLE risk, but the causal variants and effector genes are largely unknown. We generated high-resolution spatial maps of SLE variant accessibility and gene connectivity in human follicular helper T cells (TFH), a cell type required for anti-nuclear antibodies characteristic of SLE. Of the ~400 potential regulatory variants identified, 90% exhibit spatial proximity to genes distant in the 1D genome sequence, including variants that loop to regulate the canonical TFH genes BCL6 and CXCR5 as confirmed by genome editing. SLE 'variant-to-gene' maps also implicate genes with no known role in TFH/SLE disease biology, including the kinases HIPK1 and MINK1. Targeting these kinases in TFH inhibits production of IL-21, a cytokine crucial for class-switched B cell antibodies. These studies offer mechanistic insight into the SLE-associated regulatory architecture of the human genome.

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.

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.

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.

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.

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.

Pancreatic Inflammation Redirects Acinar to ß Cell Reprogramming.

Using a transgenic mouse model to express MafA, Pdx1, and Neurog3 (3TF) in a pancreatic acinar cell- and doxycycline-dependent manner, we discovered that the outcome of transcription factor-mediated acinar to ß-like cellular reprogramming is dependent on both the magnitude of 3TF expression and on reprogramming-induced inflammation. Overly robust 3TF expression causes acinar cell necrosis, resulting in marked inflammation and acinar-to-ductal metaplasia. Generation of new ß-like cells requires limiting reprogramming-induced inflammation, either by reducing 3TF expression or by eliminating macrophages. The new ß-like cells were able to reverse streptozotocin-induced diabetes 6 days after inducing 3TF expression but failed to sustain their function after removal of the reprogramming factors.

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.

Cell-specific determinants of peroxisome proliferator-activated receptor gamma function in adipocytes and macrophages.

The nuclear receptor peroxisome proliferator activator receptor gamma (PPARgamma) is the target of antidiabetic thiazolidinedione drugs, which improve insulin resistance but have side effects that limit widespread use. PPARgamma is required for adipocyte differentiation, but it is also expressed in other cell types, notably macrophages, where it influences atherosclerosis, insulin resistance, and inflammation. A central question is whether PPARgamma binding in macrophages occurs at genomic locations the same as or different from those in adipocytes. Here, utilizing chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq), we demonstrate that PPARgamma cistromes in mouse adipocytes and macrophages are predominantly cell type specific. In thioglycolate-elicited macrophages, PPARgamma colocalizes with the hematopoietic transcription factor PU.1 in areas of open chromatin and histone acetylation, near a distinct set of immune genes in addition to a number of metabolic genes shared with adipocytes. In adipocytes, the macrophage-unique binding regions are marked with repressive histone modifications, typically associated with local chromatin compaction and gene silencing. PPARgamma, when introduced into preadipocytes, bound only to regions depleted of repressive histone modifications, where it increased DNA accessibility, enhanced histone acetylation, and induced gene expression. Thus, the cell specificity of PPARgamma function is regulated by cell-specific transcription factors, chromatin accessibility, and histone marks. Our data support the existence of an epigenomic hierarchy in which PPARgamma binding to cell-specific sites not marked by repressive marks opens chromatin and leads to local activation marks, including histone acetylation.

Matching methods for observational microarray studies.

MOTIVATION: We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors are likely to be present. RESULTS: We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on two microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome. AVAILABILITY: R code (www.r-project.org) for implementing our approach is included as Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatial heterogeneity of endothelial phenotypes correlates with side-specific vulnerability to calcification in normal porcine aortic valves.

Calcific aortic valve sclerosis involves inflammatory processes and occurs preferentially on the aortic side of endothelialized valve leaflets. Although the endothelium is recognized to play critical roles in focal vascular sclerosis, the contributions of valvular endothelial phenotypes to aortic valve sclerosis and side-specific susceptibility to calcification are poorly understood. Using RNA amplification and cDNA microarrays, we identified 584 genes as differentially expressed in situ by the endothelium on the aortic side versus ventricular side of normal adult pig aortic valves. These differential transcriptional profiles, representative of the steady state in vivo, identify globally distinct endothelial phenotypes on opposite sides of the aortic valve. Several over-represented biological classifications with putative relevance to endothelial regulation of valvular homeostasis and aortic-side vulnerability to calcification were identified among the differentially expressed genes. Of note, multiple inhibitors of cardiovascular calcification were significantly less expressed by endothelium on the disease-prone aortic side of the valve, suggesting side-specific permissiveness to calcification. However, coexisting putative protective mechanisms were also expressed. Specifically, enhanced antioxidative gene expression and the lack of differential expression of proinflammatory molecules on the aortic side may protect against inflammation and lesion initiation in the normal valve. These data implicate the endothelium in regulating valvular calcification and suggest that spatial heterogeneity of valvular endothelial phenotypes may contribute to the focal susceptibility for lesion development.

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA.

Although mRNA amplification is necessary for microarray analyses from limited amounts of cells and tissues, the accuracy of transcription profiles following amplification has not been well characterized. We tested the fidelity of differential gene expression following linear amplification by T7-mediated transcription in a well-established in vitro model of cytokine [tumor necrosis factor alpha (TNFalpha)]-stimulated human endothelial cells using filter arrays of 13,824 human cDNAs. Transcriptional profiles generated from amplified antisense RNA (aRNA) (from 100 ng total RNA, approximately 1 ng mRNA) were compared with profiles generated from unamplified RNA originating from the same homogeneous pool. Amplification accurately identified TNFalpha-induced differential expression in 94% of the genes detected using unamplified samples. Furthermore, an additional 1,150 genes were identified as putatively differentially expressed using amplified RNA which remained undetected using unamplified RNA. Of genes sampled from this set, 67% were validated by quantitative real-time PCR as truly differentially expressed. Thus, in addition to demonstrating fidelity in gene expression relative to unamplified samples, linear amplification results in improved sensitivity of detection and enhances the discovery potential of high-throughput screening by microarrays.