Whole-exome Sequencing and an iPSC-Derived Cardiomyocyte Model Provides a Powerful Platform for Gene Discovery in Left Ventricular Hypertrophy

Rationale: Left ventricular hypertrophy (LVH) is a heritable predictor of cardiovascular disease, particularly in blacks. Objective: Determine the feasibility of combining evidence from two distinct but complementary experimental approaches to identify novel genetic predictors of increased LV mass. Methods: Whole-exome sequencing (WES) was conducted in seven African-American sibling trios ascertained on high average familial LV mass indexed to height (LVMHT) using Illumina HiSeq technology. Identified missense or nonsense (MS/NS) mutations were examined for association with LVMHT using linear mixed models adjusted for age, sex, body weight, and familial relationship. To functionally assess WES findings, human induced pluripotent stem cell-derived cardiomyocytes (induced pluripotent stem cell-CM) were stimulated to induce hypertrophy; mRNA sequencing (RNA-seq) was used to determine gene expression differences associated with hypertrophy onset. Statistically significant findings under both experimental approaches identified LVH candidate genes. Candidate genes were further prioritized by seven supportive criteria that included additional association tests (two criteria), regional linkage evidence in the larger HyperGEN cohort (one criterion), and publically available gene and variant based annotations (four criteria). Results: WES reads covered 91% of the target capture region (of size 37.2 MB) with an average coverage of 65×. WES identified 31,426 MS/NS mutations among the 21 individuals. A total of 295 MS/NS variants in 265 genes were associated with LVMHT with q-value <0.25. Of the 265 WES genes, 44 were differentially expressed (P < 0.05) in hypertrophied cells. Among the 44 candidate genes identified, 5, including HLA-B, HTT, MTSS1, SLC5A12, and THBS1, met 3 of 7 supporting criteria. THBS1 encodes an adhesive glycoprotein that promotes matrix preservation in pressure-overload LVH. THBS1 gene expression was 34% higher in hypertrophied cells (P = 0.0003) and a predicted conserved and damaging NS variant in exon 13 (A2099G) was significantly associated with LVHMT (P = 4 × 10−6). Conclusion: Combining evidence from cutting-edge genetic and cellular experiments can enable identification of novel LVH risk loci

Identification of Gene Modules Associated with Drought Response in Rice by Network-Based Analysis

Understanding the molecular mechanisms that underlie plant responses to drought stress is challenging due to the complex interplay of numerous different genes. Here, we used network-based gene clustering to uncover the relationships between drought-responsive genes from large microarray datasets. We identified 2,607 rice genes that showed significant changes in gene expression under drought stress; 1,392 genes were highly intercorrelated to form 15 gene modules. These drought-responsive gene modules are biologically plausible, with enrichments for genes in common functional categories, stress response changes, tissue-specific expression and transcription factor binding sites. We observed that a gene module (referred to as module 4) consisting of 134 genes was significantly associated with drought response in both drought-tolerant and drought-sensitive rice varieties. This module is enriched for genes involved in controlling the response of the plant to water and embryonic development, including a heat shock transcription factor as the key regulator in the expression of ABRE-containing genes. These results suggest that module 4 is highly conserved in the ABA-mediated drought response pathway in different rice varieties. Moreover, our study showed that many hub genes clustered in rice chromosomes had significant associations with QTLs for drought stress tolerance. The relationship between hub gene clusters and drought tolerance QTLs may provide a key to understand the genetic basis of drought tolerance in rice

A general co-expression network-based approach to gene expression analysis: comparison and applications

<p>Abstract</p> <p>Background</p> <p>Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric.</p> <p>Results</p> <p>Here, we develop a general co-expression network-based approach for analyzing both genes and samples in microarray data. Our approach consists of a simple but robust rank-based network construction method, a parameter-free module discovery algorithm and a novel reference network-based metric for module evaluation. We report some interesting topological properties of rank-based co-expression networks that are very different from that of value-based networks in the literature. Using a large set of synthetic and real microarray data, we demonstrate the superior performance of our approach over several popular existing algorithms. Applications of our approach to yeast, Arabidopsis and human cancer microarray data reveal many interesting modules, including a fatal subtype of lymphoma and a gene module regulating yeast telomere integrity, which were missed by the existing methods.</p> <p>Conclusions</p> <p>We demonstrated that our novel approach is very effective in discovering the modular structures in microarray data, both for genes and for samples. As the method is essentially parameter-free, it may be applied to large data sets where the number of clusters is difficult to estimate. The method is also very general and can be applied to other types of data. A MATLAB implementation of our algorithm can be downloaded from <url>http://cs.utsa.edu/~jruan/Software.html</url>.</p

Identification of gene modules associated with low temperatures response in Bambara groundnut by network-based analysis

Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties

Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry

We evaluated interactions of SNP-by-ACE-I/ARB and SNP-by-TD on serum potassium (K+) among users of antihypertensive treatments (anti-HTN). Our study included seven European-ancestry (EA) (N = 4835) and four African-ancestry (AA) cohorts (N = 2016). We performed race-stratified, fixed-effect, inverse-variance-weighted meta-analyses of 2.5 million SNP-by-drug interaction estimates; race-combined meta-analysis; and trans-ethnic fine-mapping. Among EAs, we identified 11 significant SNPs (P &lt; 5 × 10 −8 ) for SNP-ACE-I/ARB interactions on serum K+ that were located between NR2F1-AS1 and ARRDC3-AS1 on chromosome 5 (top SNP rs6878413 P = 1.7 × 10 −8 ; ratio of serum K+ in ACE-I/ARB exposed compared to unexposed is 1.0476, 1.0280, 1.0088 for the TT, AT, and AA genotypes, respectively). Trans-ethnic fine mapping identified the same group of SNPs on chromosome 5 as genome-wide significant for the ACE-I/ARB analysis. In conclusion, SNP-by-ACE-I /ARB interaction analyses uncovered loci that, if replicated, could have future implications for the prevention of arrhythmias due to anti-HTN treatment-related hyperkalemia. Before these loci can be identified as clinically relevant, future validation studies of equal or greater size in comparison to our discovery effort are needed

Comprehensive Network Analysis of Anther-Expressed Genes in Rice by the Combination of 33 Laser Microdissection and 143 Spatiotemporal Microarrays

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events

Gene coexpression clusters and putative regulatory elements underlying seed storage reserve accumulation in Arabidopsis

Abstract Background In Arabidopsis, a large number of genes involved in the accumulation of seed storage reserves during seed development have been characterized, but the relationship of gene expression and regulation underlying this physiological process remains poorly understood. A more holistic view of this molecular interplay will help in the further study of the regulatory mechanisms controlling seed storage compound accumulation. Results We identified gene coexpression networks in the transcriptome of developing Arabidopsis (Arabidopsis thaliana) seeds from the globular to mature embryo stages by analyzing publicly accessible microarray datasets. Genes encoding the known enzymes in the fatty acid biosynthesis pathway were found in one coexpression subnetwork (or cluster), while genes encoding oleosins and seed storage proteins were identified in another subnetwork with a distinct expression profile. In the triacylglycerol assembly pathway, only the genes encoding diacylglycerol acyltransferase 1 (DGAT1) and a putative cytosolic "type 3" DGAT exhibited a similar expression pattern with genes encoding oleosins. We also detected a large number of putative cis-acting regulatory elements in the promoter regions of these genes, and promoter motifs for LEC1 (LEAFY COTYLEDON 1), DOF (DNA-binding-with-One-Finger), GATA, and MYB transcription factors (TF), as well as SORLIP5 (Sequences Over-Represented in Light-Induced Promoters 5), are overrepresented in the promoter regions of fatty acid biosynthetic genes. The conserved CCAAT motifs for B3-domain TFs and binding sites for bZIP (basic-leucine zipper) TFs are enriched in the promoters of genes encoding oleosins and seed storage proteins. Conclusions Genes involved in the accumulation of seed storage reserves are expressed in distinct patterns and regulated by different TFs. The gene coexpression clusters and putative regulatory elements presented here provide a useful resource for further experimental characterization of protein interactions and regulatory networks in this process.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries