Functional Impact of Alternative Metabolic Substrates in Failing Human Cardiomyocytes.

Recent studies suggest that metabolic dysregulation in patients with heart failure might contribute to myocardial contractile dysfunction. To understand the correlation between function and energy metabolism, we studied the impact of different fuel substrates on human nonfailing or failing cardiomyocytes. Consistent with the concept of metabolic flexibility, nonfailing myocytes exhibited excellent contractility in all fuels provided. However, impaired contractility was observed in failing myocytes when carbohydrates alone were used but was improved when additional substrates were added. This study demonstrates the functional significance of fuel utilization shifts in failing human cardiomyocytes.

Extra-cardiac BCAA catabolism lowers blood pressure and protects from heart failure.

Pharmacologic activation of branched-chain amino acid (BCAA) catabolism is protective in models of heart failure (HF). How protection occurs remains unclear, although a causative block in cardiac BCAA oxidation is widely assumed. Here, we use in vivo isotope infusions to show that cardiac BCAA oxidation in fact increases, rather than decreases, in HF. Moreover, cardiac-specific activation of BCAA oxidation does not protect from HF even though systemic activation does. Lowering plasma and cardiac BCAAs also fails to confer significant protection, suggesting alternative mechanisms of protection. Surprisingly, activation of BCAA catabolism lowers blood pressure (BP), a known cardioprotective mechanism. BP lowering occurred independently of nitric oxide and reflected vascular resistance to adrenergic constriction. Mendelian randomization studies revealed that elevated plasma BCAAs portend higher BP in humans. Together, these data indicate that BCAA oxidation lowers vascular resistance, perhaps in part explaining cardioprotection in HF that is not mediated directly in cardiomyocytes.

Integrated landscape of cardiac metabolism in end-stage human nonischemic dilated cardiomyopathy.

Heart failure (HF) is a leading cause of mortality. Failing hearts undergo profound metabolic changes, but a comprehensive evaluation in humans is lacking. We integrate plasma and cardiac tissue metabolomics of 678 metabolites, genome-wide RNA-sequencing, and proteomic studies to examine metabolic status in 87 explanted human hearts from 39 patients with end-stage HF compared with 48 nonfailing donors. We confirm bioenergetic defects in human HF and reveal selective depletion of adenylate purines required for maintaining ATP levels. We observe substantial reductions in fatty acids and acylcarnitines in failing tissue, despite plasma elevations, suggesting defective import of fatty acids into cardiomyocytes. Glucose levels, in contrast, are elevated. Pyruvate dehydrogenase, which gates carbohydrate oxidation, is de-repressed, allowing increased lactate and pyruvate burning. Tricarboxylic acid cycle intermediates are significantly reduced. Finally, bioactive lipids are profoundly reprogrammed, with marked reductions in ceramides and elevations in lysoglycerophospholipids. These data unveil profound metabolic abnormalities in human failing hearts.

Truncated titin proteins in dilated cardiomyopathy.

Truncating variants in TTN (TTNtvs) are the most common known cause of nonischemic dilated cardiomyopathy (DCM), but how TTNtvs cause disease has remained controversial. Efforts to detect truncated titin proteins in affected human DCM hearts have failed, suggesting that disease is caused by haploinsufficiency, but reduced amounts of titin protein have not yet been demonstrated. Here, we leveraged a collection of 184 explanted posttransplant DCM hearts to show, using specialized electrophoretic gels, Western blotting, allelic phasing, and unbiased proteomics, that truncated titin proteins can quantitatively be detected in human DCM hearts. The sizes of truncated proteins corresponded to that predicted by their respective TTNtvs; the truncated proteins were encoded by the TTNtv-bearing allele; and no degradation fragments from protein encoded by either allele were detectable. In parallel, full-length titin was less abundant in TTNtv+ than in TTNtv− DCM hearts. Disease severity or need for transplantation did not correlate with TTNtv location. Transcriptomic profiling revealed few differences in splicing or allelic imbalance of the TTN transcript between TTNtv+ and TTNtv− DCM hearts. Studies with isolated human adult cardiomyocytes revealed no defects in contractility in cells from TTNtv+ compared to TTNtv− DCM hearts. Together, these data demonstrate the presence of truncated titin protein in human TTNtv+ DCM, show reduced amounts of full-length titin protein in TTNtv+ DCM hearts, and support combined dominant-negative and haploinsufficiency contributions to disease.

Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer.

Current literature suggests that epigenetically regulated super-enhancers (SEs) are drivers of aberrant gene expression in cancers. Many tumor types are still missing chromatin data to define cancer-specific SEs and their role in carcinogenesis. In this work, we develop a simple pipeline, which can utilize chromatin data from etiologically similar tumors to discover tissue-specific SEs and their target genes using gene expression and DNA methylation data. As an example, we applied our pipeline to human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV + OPSCC). This tumor type is characterized by abundant gene expression changes, which cannot be explained by genetic alterations alone. Chromatin data are still limited for this disease, so we used 3627 SE elements from public domain data for closely related tissues, including normal and tumor lung, and cervical cancer cell lines. We integrated the available DNA methylation and gene expression data for HPV + OPSCC samples to filter the candidate SEs to identify functional SEs and their affected targets, which are essential for cancer development. Overall, we found 159 differentially methylated SEs, including 87 SEs that actively regulate expression of 150 nearby genes (211 SE-gene pairs) in HPV + OPSCC. Of these, 132 SE-gene pairs were validated in a related TCGA cohort. Pathway analysis revealed that the SE-regulated genes were associated with pathways known to regulate nasopharyngeal, breast, melanoma, and bladder carcinogenesis and are regulated by the epigenetic landscape in those cancers. Thus, we propose that gene expression in HPV + OPSCC may be controlled by epigenetic alterations in SE elements, which are common between related tissues. Our pipeline can utilize a diversity of data inputs and can be further adapted to SE analysis of diseased and non-diseased tissues from different organisms.

Functional characterization of alternatively spliced GSN in head and neck squamous cell carcinoma.

We have recently performed the characterization of alternative splicing events (ASEs) in head and neck squamous cell carcinoma, which allows dysregulation of protein expression common for cancer cells. Such analysis demonstrated a high ASE prevalence among tumor samples, including tumor-specific alternative splicing in the GSN gene.In vitro studies confirmed that overall expression of either ASE-GSN or wild-type GSN (WT-GSN) isoform inversely correlated with cell proliferation, whereas the high ratio of ASE-GSN to WT-GSN correlated with increased cellular invasion. Additionally, a change in expression of either isoform caused compensatory changes in expression of the other isoform. Our results suggest that the overall expression and the balance between GSN isoforms are mediating factors in proliferation, while increased overall expression of ASE-GSN is specific to cancer tissues. As a result, we propose ASE-GSN can serve not only as a biomarker of disease and disease progression, but also as a neoantigen for head and neck squamous cell carcinoma treatment, for which only a limited number of disease-specific targeted therapies currently exist.

Discovery and development of differentially methylated regions in human papillomavirus-related oropharyngeal squamous cell carcinoma.

Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC) exhibits a different composition of epigenetic alterations. In this study, we identified differentially methylated regions (DMRs) with potential utility in screening for HPV-positive OPSCC. Genome wide DNA methylation was measured using methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) in 50 HPV-positive OPSCC tissues and 25 normal tissues. Fifty-one DMRs were defined with maximal methylation specificity to cancer samples. The Cancer Genome Atlas (TCGA) methylation array data was used to evaluate the performance of the proposed candidates. Supervised hierarchical clustering of 51 DMRs found that HPV-positive OPSCC had significantly higher DNA methylation levels compared to normal samples, and non-HPV-related head and neck squamous cell carcinoma (HNSCC). The methylation levels of all top 20 DNA methylation biomarkers in HPV-positive OPSCC were significantly higher than those in normal samples. Further confirmation using quantitative methylation specific PCR (QMSP) in an independent set of 24 HPV-related OPSCCs and 22 controls showed that 16 of the 20 candidates had significant higher methylation levels in HPV-positive OPSCC samples compared with controls. One candidate, OR6S1, had a sensitivity of 100%, while 17 candidates (KCNA3, EMBP1, CCDC181, DPP4, ITGA4, BEND4, ELMO1, SFMBT2, C1QL3, MIR129-2, NID2, HOXB4, ZNF439, ZNF93, VSTM2B, ZNF137P and ZNF773) had specificities of 100%. The prediction accuracy of the 20 candidates rang from 56.2% to 99.8% by receiver operating characteristic analysis. We have defined 20 highly specific DMRs in HPV-related OPSCC, which can potentially be applied to molecular-based detection tests and improve disease management.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Motivation: Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results: We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation: SEVA is implemented in the R/Bioconductor package GSReg. Contact: bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis.

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies.

Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks.

Chromatin alterations mediate mutations and gene expression changes in cancer. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has been utilized to study genome-wide chromatin structure in human cancer cell lines, yet numerous technical challenges limit comparable analyses in primary tumors. Here we have developed a new whole-genome analytic pipeline to optimize ChIP-Seq protocols on patient-derived xenografts from human papillomavirus-related (HPV+) head and neck squamous cell carcinoma (HNSCC) samples. We further associated chromatin aberrations with gene expression changes from a larger cohort of the tumor and normal samples with RNA-Seq data. We detect differential histone enrichment associated with tumor-specific gene expression variation, sites of HPV integration in the human genome, and HPV-associated histone enrichment sites upstream of cancer driver genes, which play central roles in cancer-associated pathways. These comprehensive analyses enable unprecedented characterization of the complex network of molecular changes resulting from chromatin alterations that drive HPV-related tumorigenesis. Cancer Res; 77(23); 6538-50. ©2017 AACR.

A Novel Functional Splice Variant of AKT3 Defined by Analysis of Alternative Splice Expression in HPV-Positive Oropharyngeal Cancers.

The incidence of HPV-related oropharyngeal squamous cell carcinoma (OPSCC) has increased more than 200% in the past 20 years. Recent genetic sequencing efforts have elucidated relevant genes in head and neck cancer, but HPV-related tumors have consistently shown few DNA mutations. In this study, we sought to analyze alternative splicing events (ASE) that could alter gene function independent of mutations. To identify ASE unique to HPV-related tumors, RNA sequencing was performed on 46 HPV-positive OPSCC and 25 normal tissue samples. A novel algorithm using outlier statistics on RNA-sequencing junction expression identified 109 splicing events, which were confirmed in a validation set from The Cancer Genome Atlas. Because the most common type of splicing event identified was an alternative start site (39%), MBD-seq genome-wide CpG methylation data were analyzed for methylation alterations at promoter regions. ASE in six genes showed significant negative correlation between promoter methylation and expression of an alternative transcriptional start site, including AKT3 The novel AKT3 transcriptional variant and methylation changes were confirmed using qRT-PCR and qMSP methods. In vitro silencing of the novel AKT3 variant resulted in significant growth inhibition of multiple head and neck cell lines, an effect not observed with wild-type AKT3 knockdown. Analysis of ASE in HPV-related OPSCC identified multiple alterations likely involved in carcinogenesis, including a novel, functionally active transcriptional variant of AKT3 Our data indicate that ASEs represent a significant mechanism of oncogenesis with untapped potential for understanding complex genetic changes that result in the development of cancer. Cancer Res; 77(19); 5248-58. ©2017 AACR.

PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF.

SUMMARY: Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g. time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel patternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with patternMarkers requires whole-genome data. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTEx data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data. AVAILABILITY AND IMPLEMENTATION: PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license. CONTACT: gsteinobrien@jhmi.edu or ccolantu@jhmi.edu or ejfertig@jhmi.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.