Ryne C. Ramaker

Genome-wide DNA methylation measurements in prostate tissues uncovers novel prostate cancer diagnostic biomarkers and transcription factor binding patterns

BackgroundCurrent diagnostic tools for prostate cancer lack specificity and sensitivity for detecting very early lesions. DNA methylation is a stable genomic modification that is detectable in peripheral patient fluids such as urine and blood plasma that could serve as a non-invasive diagnostic biomarker for prostate cancer.MethodsWe measured genome-wide DNA methylation patterns in 73 clinically annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate tissues using the Illumina Infinium HumanMethylation450 BeadChip array. We overlaid the most significantly differentially methylated sites in the genome with transcription factor binding sites measured by the Encyclopedia of DNA Elements consortium. We used logistic regression and receiver operating characteristic curves to assess the performance of candidate diagnostic models.ResultsWe identified methylation patterns that have a high predictive power for distinguishing malignant prostate tissue from benign-adjacent prostate tissue, and these methylation signatures were validated using data from The Cancer Genome Atlas Project. Furthermore, by overlaying ENCODE transcription factor binding data, we observed an enrichment of enhancer of zeste homolog 2 binding in gene regulatory regions with higher DNA methylation in malignant prostate tissues.ConclusionsDNA methylation patterns are greatly altered in prostate cancer tissue in comparison to benign-adjacent tissue. We have discovered patterns of DNA methylation marks that can distinguish prostate cancers with high specificity and sensitivity in multiple patient tissue cohorts, and we have identified transcription factors binding in these differentially methylated regions that may play important roles in prostate cancer development.

Distinct gene regulatory programs define the inhibitory effects of liver X receptors and PPARG on cancer cell proliferation

BackgroundThe liver X receptors (LXRs, NR1H2 and NR1H3) and peroxisome proliferator-activated receptor gamma (PPARG, NR1C3) nuclear receptor transcription factors (TFs) are master regulators of energy homeostasis. Intriguingly, recent studies suggest that these metabolic regulators also impact tumor cell proliferation. However, a comprehensive temporal molecular characterization of the LXR and PPARG gene regulatory responses in tumor cells is still lacking.MethodsTo better define the underlying molecular processes governing the genetic control of cellular growth in response to extracellular metabolic signals, we performed a comprehensive, genome-wide characterization of the temporal regulatory cascades mediated by LXR and PPARG signaling in HT29 colorectal cancer cells. For this analysis, we applied a multi-tiered approach that incorporated cellular phenotypic assays, gene expression profiles, chromatin state dynamics, and nuclear receptor binding patterns.ResultsOur results illustrate that the activation of both nuclear receptors inhibited cell proliferation and further decreased glutathione levels, consistent with increased cellular oxidative stress. Despite a common metabolic reprogramming, the gene regulatory network programs initiated by these nuclear receptors were widely distinct. PPARG generated a rapid and short-term response while maintaining a gene activator role. By contrast, LXR signaling was prolonged, with initial, predominantly activating functions that transitioned to repressive gene regulatory activities at late time points.ConclusionsThrough the use of a multi-tiered strategy that integrated various genomic datasets, our data illustrate that distinct gene regulatory programs elicit common phenotypic effects, highlighting the complexity of the genome. These results further provide a detailed molecular map of metabolic reprogramming in cancer cells through LXR and PPARG activation. As ligand-inducible TFs, these nuclear receptors can potentially serve as attractive therapeutic targets for the treatment of various cancers.

Genomic regulation of invasion by STAT3 in triple negative breast cancer

Breast cancer is a heterogeneous disease comprised of four molecular subtypes defined by whether the tumor-originating cells are luminal or basal epithelial cells. Breast cancers arising from the luminal mammary duct often express estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2). Tumors expressing ER and/or PR are treated with anti-hormonal therapies, while tumors overexpressing HER2 are targeted with monoclonal antibodies. Immunohistochemical detection of ER, PR, and HER2 receptors/proteins is a critical step in breast cancer diagnosis and guided treatment. Breast tumors that do not express these proteins are known as “triple negative breast cancer” (TNBC) and are typically basal-like. TNBCs are the most aggressive subtype, with the highest mortality rates and no targeted therapy, so there is a pressing need to identify important TNBC tumor regulators. The signal transducer and activator of transcription 3 (STAT3) transcription factor has been previously implicated as a constitutively active oncogene in TNBC. However, its direct regulatory gene targets and tumorigenic properties have not been well characterized. By integrating RNA-seq and ChIP-seq data from 2 TNBC tumors and 5 cell lines, we discovered novel gene signatures directly regulated by STAT3 that were enriched for processes involving inflammation, immunity, and invasion in TNBC. Functional analysis revealed that STAT3 has a key role regulating invasion and metastasis, a characteristic often associated with TNBC. Our findings suggest therapies targeting STAT3 may be important for preventing TNBC metastasis.

RNA sequencing of pancreatic adenocarcinoma tumors yields novel expression patterns associated with long‐term survival and reveals a role for ANGPTL4

Pancreatic adenocarcinoma patients have low survival rates due to late‐stage diagnosis and high rates of cancer recurrence even after surgical resection. It is important to understand the molecular characteristics associated with survival differences in pancreatic adenocarcinoma tumors that may inform patient care.

aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing environments

Summary: The wide range of RNA‐seq applications and their high‐computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project‐oriented pipeline for processing of RNA‐seq data in high‐performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high‐performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA‐seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project‐oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis. Availability and documentation: https://github.com/HudsonAlpha/aRNAPipe; DOI: 10.5281/zenodo.202950 Contact: rmyers@hudsonalpha.org Supplementary information: Supplementary data are available at Bioinformatics online.

RNA sequencing-based cell proliferation analysis across 19 cancers identifies a subset of proliferation-informative cancers with a common survival signature

Despite advances in cancer diagnosis and treatment strategies, robust prognostic signatures remain elusive in most cancers. Cell proliferation has long been recognized as a prognostic marker in cancer, but the generation of comprehensive, publicly available datasets allows examination of the links between cell proliferation and cancer characteristics such as mutation rate, stage, and patient outcomes. Here we explore the role of cell proliferation across 19 cancers (n = 6,581 patients) by using tissue-based RNA sequencing data from The Cancer Genome Atlas Project and calculating a ‘proliferative index’ derived from gene expression associated with Proliferating Cell Nuclear Antigen (PCNA) levels. This proliferative index is significantly associated with patient survival (Cox, p-value < 0.05) in 7 of 19 cancers, which we have defined as “proliferation-informative cancers” (PICs). In PICs, the proliferative index is strongly correlated with tumor stage and nodal invasion. PICs demonstrate reduced baseline expression of proliferation machinery relative to non-PICs. Additionally, we find the proliferative index is significantly associated with gross somatic mutation burden (Spearman, p = 1.76 × 10−23) as well as with mutations in individual driver genes. This analysis provides a comprehensive characterization of tumor proliferation indices and their association with disease progression and prognosis in multiple cancer types and highlights specific cancers that may be particularly susceptible to improved targeting of this classic cancer hallmark.

Characterization of coding/noncoding variants for SHROOM3 in patients with CKD

Background Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example, SHROOM3 is an associated risk gene for CKD, yet causative mechanism(s) of SHROOM3 allele(s) are unknown.Methods We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human SHROOM3 risk locus for CKD.Results We identified a novel SHROOM3 transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of SHROOM3 in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (P=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of SHROOM3 with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.Conclusions These data demonstrate multiple new SHROOM3-dependent genetic/molecular mechanisms that likely affect CKD.

R2DGC: threshold-free peak alignment and identification for 2D gas chromatography-mass spectrometry in R

Summary: Comprehensive 2D gas chromatography‐mass spectrometry is a powerful method for analyzing complex mixtures of volatile compounds, but produces a large amount of raw data that requires downstream processing to align signals of interest (peaks) across multiple samples and match peak characteristics to reference standard libraries prior to downstream statistical analysis. Very few existing tools address this aspect of analysis and those that do have shortfalls in usability or performance. We have developed an R package that implements retention time and mass spectra similarity threshold‐free alignments, seamlessly integrates retention time standards for universally reproducible alignments, performs common ion filtering and provides compatibility with multiple peak quantification methods. We demonstrate that our packages performance compares favorably to existing tools on a controlled mix of metabolite standards separated under variable chromatography conditions and data generated from cell lines. Availability and implementation: R2DGC can be downloaded at https://github.com/rramaker/R2DGC or installed via the Comprehensive R Archive Network (CRAN). Contact: sjcooper@hudsonalpha.org Supplementary information: Supplementary data are available at Bioinformatics online.

Post-mortem molecular profiling of three psychiatric disorders reveals widespread dysregulation of cell-type associated transcripts and refined disease-related transcription changes

Background Psychiatric disorders are multigenic diseases with complex etiology contributing significantly to human morbidity and mortality. Although clinically distinct, several disorders share many symptoms suggesting common underlying molecular changes exist that may implicate important regulators of pathogenesis and new therapeutic targets. Results We compared molecular signatures across brain regions and disorders in the transcriptomes of postmortem human brain samples. We performed RNA sequencing on tissue from the anterior cingulate cortex, dorsolateral prefrontal cortex, and nucleus accumbens from three groups of 24 patients each diagnosed with schizophrenia, bipolar disorder, or major depressive disorder, and from 24 control subjects, and validated the results in an independent cohort. The most significant disease differences were in the anterior cingulate cortex of schizophrenia samples compared to controls. Transcriptional changes were assessed in an independent cohort, revealing the transcription factor EGR1 as significantly down regulated in both cohorts and as a potential regulator of broader transcription changes observed in schizophrenia patients. Additionally, broad down regulation of genes specific to neurons and concordant up regulation of genes specific to astrocytes was observed in SZ and BPD patients relative to controls. We also assessed the biochemical consequences of gene expression changes with untargeted metabolomic profiling and identified disruption of GABA levels in schizophrenia patients. Conclusions We provide a comprehensive post-mortem transcriptome profile of three psychiatric disorders across three brain regions. We highlight a high-confidence set of independently validated genes differentially expressed between schizophrenia and control patients in the anterior cingulate cortex and integrate transcriptional changes with untargeted metabolite profiling.

De novo mutations in the GTP/GDP-binding region of RALA, a RAS-like small GTPase, cause intellectual disability and developmental delay

Mutations that alter signaling of RAS/MAPK-family proteins give rise to a group of Mendelian diseases known as RASopathies, but the matrix of genotype-phenotype relationships is still incomplete, in part because there are many RAS-related proteins, and in part because the phenotypic consequences may be variable and/or pleiotropic. Here, we describe a cohort of ten cases, drawn from six clinical sites and over 16,000 sequenced probands, with de novo protein-altering variation in RALA, a RAS-like small GTPase. All probands present with speech and motor delays, and most have intellectual disability, low weight, short stature, and facial dysmorphism. The observed rate of de novo RALA variants in affected probands is significantly higher (p=4.93 × 10−11) than expected from the estimated mutation rate. Further, all de novo variants described here affect conserved residues within the GTP/GDP-binding region of RALA; in fact, six alleles arose at only two codons, Val25 and Lys128. We directly assayed GTP hydrolysis and RALA effector-protein binding, and all but one tested variant significantly reduced both activities. The one exception, S157A, reduced GTP hydrolysis but significantly increased RALA-effector binding, an observation similar to that seen for oncogenic RAS variants. These results show the power of data sharing for the interpretation and analysis of rare variation, expand the spectrum of molecular causes of developmental disability to include RALA, and provide additional insight into the pathogenesis of human disease caused by mutations in small GTPases. Author Summary While many causes of developmental disabilities have been identified, a large number of affected children cannot be diagnosed despite extensive medical testing. Previously unknown genetic factors are likely to be the culprits in many of these cases. Using DNA sequencing, and by sharing information among many doctors and researchers, we have identified a set of individuals with developmental problems who all have changes to the same gene, RALA. The affected individuals all have similar symptoms, including intellectual disability, speech delay (or no speech), and problems with motor skills like walking. In nearly all of these cases (10 of 11), the genetic change found in the child was not inherited from either parent. The locations and biological properties of these changes suggest that they are likely to disrupt the normal functions of RALA and cause significant health problems. We also performed experiments to show that the genetic changes found in these individuals alter two key functions of RALA. Together, we have provided evidence that genetic changes in RALA can cause DD/ID. These results will allow doctors and researchers to identify additional children with the same condition, providing a clinical diagnosis to these families and leading to new research opportunities.