Integrating Dynamic 3D Chromatin Architecture and Gene Expression Alterations Reveal Heterosis in Brassica rapa.

Heterosis plays a significant role in enhancing variety, boosting yield, and raising economic value in crops, but the molecular mechanism is still unclear. We analyzed the transcriptomes and 3D genomes of a hybrid (F1) and its parents (w30 and 082). The analysis of the expression revealed a total of 485 specially expressed genes (SEGs), 173 differentially expressed genes (DEGs) above the parental expression level, more actively expressed genes, and up-regulated DEGs in the F1. Further study revealed that the DEGs detected in the F1 and its parents were mainly involved in the response to auxin, plant hormone signal transduction, DNA metabolic process, purine metabolism, starch, and sucrose metabolism, which suggested that these biological processes may play a crucial role in the heterosis of Brassica rapa. The analysis of 3D genome data revealed that hybrid F1 plants tend to contain more transcriptionally active A chromatin compartments after hybridization. Supplementaryly, the F1 had a smaller TAD (topologically associated domain) genome length, but the number was the highest, and the expression change in activated TAD was higher than that of repressed TAD. More specific TAD boundaries were detected between the parents and F1. Subsequently, 140 DEGs with genomic structural variants were selected as potential candidate genes. We found two DEGs with consistent expression changes in A/B compartments and TADs. Our findings suggested that genomic structural variants, such as TADs and A/B chromatin compartments, may affect gene expression and contribute to heterosis in Brassica rapa. This study provides further insight into the molecular mechanism of heterosis in Brassica rapa.

Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function.

Bronchodilator (BD) drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of BD medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken before BD medication have been extensively studied, and the genetics of the BD response itself have received some attention. However, few studies have focused on the genetics of post-BD lung function. To address this gap, we analyzed lung function phenotypes in 1103 subjects from the Study of African Americans, Asthma, Genes, and Environment, a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (forced expiratory volume in 1 s [FEV1 ], forced vital capacity [FVC], and FEV1 /FVC) taken before and after albuterol BD administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and five additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-BD lung function: ADAMTS1, RAD54B, and EGLN3.

Genome-Wide Correlation of DNA Methylation and Gene Expression in Postmortem Brain Tissues of Opioid Use Disorder Patients.

BACKGROUND: Opioid use disorder (OUD) affects millions of people, causing nearly 50 000 deaths annually in the United States. While opioid exposure and OUD are known to cause widespread transcriptomic and epigenetic changes, few studies in human samples have been conducted. Understanding how OUD affects the brain at the molecular level could help decipher disease pathogenesis and shed light on OUD treatment. METHODS: We generated genome-wide transcriptomic and DNA methylation profiles of 22 OUD subjects and 19 non-psychiatric controls. We applied weighted gene co-expression network analysis to identify genetic markers consistently associated with OUD at both transcriptomic and methylomic levels. We then performed functional enrichment for biological interpretation. We employed cross-omics analysis to uncover OUD-specific regulatory networks. RESULTS: We found 6 OUD-associated co-expression gene modules and 6 co-methylation modules (false discovery rate <0.1). Genes in these modules are involved in astrocyte and glial cell differentiation, gliogenesis, response to organic substance, and response to cytokine (false discovery rate <0.05). Cross-omics analysis revealed immune-related transcription regulators, suggesting the role of transcription factor-targeted regulatory networks in OUD pathogenesis. CONCLUSIONS: Our integrative analysis of multi-omics data in OUD postmortem brain samples suggested complex gene regulatory mechanisms involved in OUD-associated expression patterns. Candidate genes and their upstream regulators revealed in astrocyte, and glial cells could provide new insights into OUD treatment development.

Sliced inverse regression for integrative multi-omics data analysis.

Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.

Successful targeting of the NRG1 pathway indicates novel treatment strategy for metastatic cancer.

BACKGROUND: NRG1 fusion-positive lung cancers have emerged as potentially actionable events in lung cancer, but clinical support is currently limited and no evidence of efficacy of this approach in cancers beyond lung has been shown. PATIENTS AND METHODS: Here, we describe two patients with advanced cancers refractory to standard therapies. Patient 1 had lung adenocarcinoma and patient 2 cholangiocarcinoma. Whole-genome and transcriptome sequencing were carried out for these cases with select findings validated by fluorescence in situ hybridization. RESULTS: Both tumors were found to be positive for NRG1 gene fusions. In patient 1, an SDC4-NRG1 gene fusion was detected, similar gene fusions having been described in lung cancers previously. In patient 2, a novel ATP1B1-NRG1 gene fusion was detected. Cholangiocarcinoma is not a disease type in which NRG1 fusions had been described previously. Integrative genome analysis was used to assess the potential functional significance of the detected genomic events including the gene fusions, prioritizing therapeutic strategies targeting the HER-family of growth factor receptors. Both patients were treated with the pan HER-family kinase inhibitor afatinib and both displayed significant and durable response to treatment. Upon progression sites of disease were sequenced. The lack of obvious genomic events to describe the disease progression indicated that broad transcriptomic or epigenetic mechanisms could be attributed to the lack of prolonged response to afatinib. CONCLUSION: These observations lend further support to the use of pan HER-tyrosine kinase inhibitors for the treatment of NRG1 fusion-positive in both cancers of lung and hepatocellular origin and indicate more broadly that cancers found to be NRG1 fusion-positive may benefit from such a clinical approach regardless of their site of origin. CLINICAL TRIAL INFORMATION: Personalized Oncogenomics (POG) Program of British Columbia: Utilization of Genomic Analysis to Better Understand Tumour Heterogeneity and Evolution (NCT02155621).

Integrative Analysis of Genome, 3D Genome, and Transcriptome Alterations of Clinical Lung Cancer Samples.

Genomic studies of cancer cell alterations, such as mutations, copy number variations (CNVs), and translocations, greatly promote our understanding of the genesis and development of cancers. However, the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties. To explore the 3D genome structure in clinical lung cancer, we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer, combining with RNA sequenceing analysis. We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells (1 × 104), compared the genome architecture between clinical samples and cell lines of lung cancer, and identified conserved and changed spatial chromatin structures between normal and cancer samples. We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer. By integrating those different types of cancer alterations, we showed significant associations between CNVs, 3D genome, and gene expression. We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures. Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.

Integrative molecular analysis of metastatic hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the major type of primary liver cancer. Intrahepatic metastasis, such as portal vein tumor thrombosis (PVTT), strongly indicates poor prognosis of HCC. But now, there are limited understandings of the molecular features and mechanisms of those metastatic HCCs. METHODS: To characterize the molecular alterations of the metastatic HCCs, we implemented an integrative analysis of the copy number variations (CNVs), DNA methylations and transcriptomes of matched adjacent normal, primary tumor and PVTT samples from 19 HCC patients. RESULTS: CNV analysis identified a frequently amplified focal region chr11q13.3 and a novel deletion peak chr19q13.41 containing three miRNAs. The integrative analysis with RNA-seq data suggests that CNVs and differential promoter methylations regulate distinct oncogenic processes. Then, we used individualized differential analysis to identify the differentially expressed genes between matched primary tumor and PVTT of each patient. Results show that 5 out of 19 studied patients acquire evidential progressive alterations of gene expressions (more than 1000 differentially expressed genes were identified in each patient). While, another subset of eight patients have nearly identical gene expressions between the corresponding matched primary tumor and PVTT. Twenty genes were found to be recurrently and progressively differentially expressed in multiple patients. These genes are mainly associated with focal adhesion, xenobiotics metabolism by cytochrome P450 and amino acid metabolism. For several differentially expressed genes in metabolic pathways, their expressions are significantly associated with overall survivals and vascular invasions of HCC patients. The following transwell assay experiments validate that they can regulate invasive phenotypes of HCC cells. CONCLUSIONS: The metastatic HCCs with PVTTs have significant molecular alterations comparing with adjacent normal tissues. The recurrent alteration patterns are similar to several previously published general HCC cohorts, but usually with higher severity. By an individualized differential analysis strategy, the progressively differentially expressed genes between the primary tumor and PVTT were identified for each patient. A few patients aquire evidential progressive alterations of gene expressions. And, experiments show that several recurrently differentially expressed genes can strongly regulate HCC cell invasions.

Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes.

Identifying oncogenic drivers and tumor suppressors remains a challenge in many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations resulting from DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a Bayesian algorithm and CRISPR/Cas9 "mini-pool" screen that enables both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell-cycle and nucleic-acid-binding activities. Functional studies showed that many iExCN genes represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask disease genes in cancer.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data.

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies.