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• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Human Microbiome Project Demonstration Study of Cutaneous Microbiome in Psoriasis

  Psoriasis, a highly prevalent disease of humans of unknown cause, is a chronic inflammatory disorder primarily involving skin, with distinctive clinical characteristics. With the newly developed tools that facilitate microbiome research, it now is possible to assess whether the cutaneous microbiome plays a role in the pathogenesis of this disorder. Preliminary data from our studies suggest that the cutaneous microbiome in psoriasis is complex and possibly different from normal. To deal with this complexity, we propose to examine the cutaneous microbiome in relation to psoriasis with explorations at several taxonomic and informatic levels. Our overall objective is to examine how changes in the normal cutaneous microbiome contribute to the pathogenesis of psoriasis. Since causality is complex and often difficult to prove, and beyond the scope of this RFP, our overall hypothesis is that there are alterations in the cutaneous microbiome in areas of skin affected by psoriasis in comparison with the range observed in clinically unaffected areas, or in healthy persons. We also hypothesize that the characteristics of the microbiome may affect clinical responses to the immunomodulatory agents used to treat psoriasis. An alternative hypothesis is that effective treatment of psoriasis with systemic immunomodulatory agents will not substantially affect the disordered microbial ecosystem. Such observations would provide evidence for the roles of the microbiota in this disorder. Since an important consideration in microbiome research is the optimal level (e.g. phylum, genus, species, strain, gene) at which to examine a scientific question, and we are not yet certain what are the optimal levels for psoriasis, this also will be examined. Our studies of psoriasis should allow development of both approaches and tools that will have general utility for Microbiome research. To test our hypothesis, we propose the following specific aims: 1) To understand the cutaneous microbiome species composition overlaying psoriatic lesions; 2)To investigate differences in metagenome content for psoriatic lesions compared to normal skin; 3) To identify differences in the transcriptional profiles of the microbiome and the host between normal skin and psoriatic lesions using high-throughput sequencing; and 4) To estimate the effects of systemic immunomodulatory therapy for psoriasis on microbiome composition. In total, these studies should help us understand the role of the microbiome in psoriasis pathogenesis. We sought to characterize and compare the cutaneous microbiota of psoriatic lesions (lesion), unaffected contralateral skin from psoriatic patients (normal), and similar skin loci in matched healthy controls (control) in order to discern patterns that govern skin colonization and their relationship to clinical diagnosis. Using high-throughput 16S rRNA sequencing, we assayed the cutaneous bacterial communities of 51 matched triplets and characterized these samples using community data analysis techniques.

  Study phs000251

• A Clone's Genomic Stability as a Biomarker of Its DNA-Damage Resilience

  A main problem in the treatment of advanced cancers, including gastric cancers and glioblastoma, is the incertitude at which we predict how individual patients will respond to DNA-damaging agents, especially on the long run. Knowing the mechanism behind a patient's response, or the lack thereof, will help us depart from the oversimplified “more-is-better” and “one-size-fits-all” principles according to which DNA-damaging agents are administered. This will improve clinical outcome by allowing us to pinpoint those who would respond better and longer to lower doses than to higher doses of DNA-damaging agents. Under the assumption that the success of DNA-damaging therapy increases with the proliferation rate of a relatively homogeneous tumor population, there was little reason to assume anything other than monotonic dose-response relations. With the recent paradigm shift that most cancers are in fact DNA mosaic products of ongoing evolution, comes the urgency to reconsider these fundamental principles behind DNA-damaging therapy administration. As the developers of one of the first DNA deconvolution methods and with access to technologies to profile the transcriptomes of up to 10,000 cells simultaneously, we are equipped to embark on first personalized dose-finding strategies for DNA-damaging therapies. We will test the potential of the very long-term legacy that DNA-damage entails on a cell “genomic instability” as new biomarker of DNA-damage response. Our preliminary studies showed that, for most cancer types, DNA-damaging agents change a clone's genomic instability and that clones succumb to a limit in the amount of genomic instability they can tolerate. In particular, our results showed that patients with intermediate genomic instability have a very poor outcome and that this relation is only evident among treatment-naive patients, but not among patients treated with DNA-damaging agents. Further they show that we can measure genomic instability per clone and that clones with extreme genomic instability typically don't grow large. Our hypothesis that genomic instability, rather than proliferation rate, determines how sensitive a tumor is to DNA-damaging agents on the long-term, is founded on two unexpected findings. Patients with extremely high genomic instability per tumor clone have an exceptionally good outcome. Aim 1 will integrate exome- and single cell RNA-Seq (scRNA-Seq) data to characterize clones and to measure how much genomic instability they can tolerate. Low genomic instability is associated with reduced benefit from DNA-damaging agents. Aim 2 will use comet assays and treatment history to quantify DNA damage per clone, relating it to the clones' ability to tolerate DNA damage and to changes in the genomic instability of therapy-surviving clones.

  Study phs003762

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Study EGAS00001000761

• Impact of Respiratory Virus Infections and Bacterial Microbiome Shifts on Lymphocyte and Respiratory Function in Infants Born Prematurely or Full Term

  Public health importance: Babies born preterm, approximately 1 out of every 9 live births in the United States, have significant respiratory morbidity over the first two years of life, exacerbated by respiratory viral infections. Many (<50%) return to pediatricians, emergency rooms and pulmonologists with symptoms of respiratory dysfunction (SRD): intermittent or chronic wheezing, poor growth and an excess of upper and lower respiratory tract infections (LRTI). SRD correlate inversely with gestational age and weight at birth and is more common in those with chronic lung disease of prematurity, yet its incidence and severity varies widely among both the prematurely born and those born at term. There is evidence from clinical studies and animal models that risks of LRTI and recurrent wheezing is influenced by gut and respiratory flora and by T cell responses to infection. Information gained from this study will be used to identify characteristics, risk factors and potential mechanisms for early and persistent lung disease in children born at term and born preterm. This Clinical Research Study will investigate the relationships between sequential respiratory viral infections, patterns of intestinal and respiratory bacterial colonization, and adaptive cellular immune phenotypes which are associated with increased susceptibility to respiratory infections and long term respiratory morbidity in preterm and full term infants. We hypothesize that the timing and acquisition of specific viral infections and bacterial species are directly related to respiratory morbidity in the first year of life as defined by SRD and by measures of pulmonary function. We hypothesize that cellular and molecular immuno-maturity are altered due to factors presented by premature birth in such a way as to promote chronic inflammatory and cytotoxic damage to the lung, with subsequent enhanced, damaging responses to infectious agents and environmental irritants. Our preliminary studies demonstrate both feasibility and expertise in mutiparameter immunophenotyping of small volume peripheral blood samples obtained from premature infants including gene expression arrays of flow cytometry sorted cells. We will use new technologies for known viral identification, as well as high-throughput metagenome sequencing of RNA and DNA virus like particles (VLP) to be used for viral discovery in infant respiratory sample and use of high-throughput pyrosequencing (454T) of bacterial 16S rRNA to determine shifts in bacterial community structure, occurring in pre-term (PT) as compared to full term (FT) infants, over the first year of life. Finally, we present statistical approaches to stratify disease risk predictors using multivariate logistic regression modeling approaches. We propose to evaluate T cell phenotypic and functional profiles relative to viral and predominant bacterial exposures according to highly complementary, but independent, Specific Objectives. Objective 1: To determine if viral respiratory infections and patterns of respiratory and gut bacterial community structure (microbiome) in prematurely born babies predict the rate and degree of immunologic maturation, and pulmonary dysfunction, measured from birth to 36 weeks corrected gestational age (CGA). Objective 2: To determine the relationship between respiratory viral infections and disease severity up to one year CGA, and the lymphocyte (Lc) phenotypes documented at term gestation (birth for term infants and 36 wks/NICU discharge in preterm infants) and at one year CGA. Three secondary outcomes of this objective will be to a) relate the quantity, type and severity of viral infections with pulmonary function at one and three years of life, b) relate the viral community structure to severity of viral infections and c) to seek evidence of modulation of viral susceptibility by bacterial respiratory and gut community structure (microbiome). The relationship of colonization with known and non-identified bacterial species in both the respiratory tract and the gut will be evaluated. Flow cytometry data corresponding to this study can be found within Immport study SDY1302. Positive and negative controls for microbiome samples are uploaded under SRA bioproject PRJNA474485. Microbiome samples corresponding to PRISM2 are distinguished from PRISM1 via "_PRISM2" appended to the sample name. Within the positive and negative controls, PRISM1 controls are uploaded as bam files and PRISM2 controls are uploaded as paired fastq. Samples ending in -08 correspond to TLDA qPCR results for a given sample. There is a column for each pathogen tested and a column to indicate where that pathogen was bacteria or virus.

  Study phs001347

• Determining Genetic Role in Treatment Response to Anti-Platelet Interventions (The PAPI Study)

  CHD is the leading cause of death in the United States. One of the most common ways to prevent CHD is to take an anti-platelet agent, which lessens platelet aggregation. Two of the most common anti-platelet agents are aspirin and clopidogrel. However, up to 25% to 30% of people do not respond to these medications. Evidence indicates that treatment response may be related to genetics. The purpose of this study is to determine specific gene variants that predict response to aspirin and clopidogrel therapy. This study is part of a larger group of studies called the Pharmacogenomics Research Network (PGRN). Participants are from the Old Order Amish of Lancaster, Pennsylvania. They are well suited for genetic studies because they are a homogenous, closed, founder population. Participants received 300 mg of clopidogrel on the first day, then 75 mg of clopidogrel per day for the next 6 days. On the last day of clopidogrel treatment, participants took a single dose of 324 mg aspirin. Participants underwent platelet function tests before and after clopidogrel alone, and then again after taking clopidogrel plus aspirin. Using the gene variation profiles across the genome, researchers analyzed which variants correspond to treatment response.

  Study phs000391

• Intraductal transplantation models of human pancreatic ductal adenocarcinoma reveal progressive transition of molecular subtypes

  Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past few decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated. However, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present an orthotopic xenotransplantation model of human patient-derived PDAC organoids in which organoids are grafted via direct injection into the pancreatic duct of immunocompromised (NSG) mice. Tumors that arise from these intraductally grafted organoids (IGOs) enable the distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively, depending on which organoid they originate from. RNA-sequencing was used to characterize the tumors that resulted from IGO xenografts, and to compare the fast- and slow-progressing IGO tumors. Tumors generated using a traditional orthotopic xenograft model, in which organoids are injected into the parenchyma of the pancreas (i.e. orthotopically grafted organoids, or OGOs), were also sequenced for comparison with the IGO tumors. To further characterize the model, laser-capture microdissection followed by RNA-sequencing was used to compare intraductal to invasive lesions resulting from IGOs. To better understand the connection between KRAS signaling and the fast-progressing phenotype, RNA-sequencing was used to compare cultured organoids in which oncogenic KRAS (GeneID: 3845) was hyperactivated to control organoids, and to compare laser-capture microdissected lesions derived from IGOs of KRAS-hyperactivated organoids to lesions derived from control organoids. To ascertain copy number, low-coverage, whole genome DNA-sequencing was performed on organoids cultured in vitro, and on laser-capture microdissected lesions derived from IGO tumors.

  Study phs002045

• The Human Pancreas Analysis Program (HPAP)

  The past decade has seen a dramatic improvement in our ability to phenotype and molecularly profile human tissues with unprecedented resolution at the genomic, epigenomic, protein, and functional levels. The Human Pancreas Analysis Program (HPAP), part of the Human Islet Research Network and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through multiple NIH grants, is performing deep phenotyping of the human endocrine pancreas to better understand the cellular and molecular events that precede and lead to beta-cell loss and/or dysfunction in type 1 diabetes (T1D) and type 2 diabetes (T2D) as well as to accumulate, analyze, and distribute high-value data sets to the diabetes research community through the HPAP PANC-DB database (and additional details provided at that site). To this end, HPAP employs state-of-the-art technologies to perform comprehensive analyses of pancreas biology as it pertains to organ donors with T1D, autoantibody-positive donors without diabetes, donors with T2D, and control donors. Pancreas procurement and analyses take advantage of the expertise and extensive network of Organ Procurement Organizations (OPOs) and autoantibody screening centers established through the JDRF/The Lenona M. and Harry B. Helmsley Charitable Trust (HCT) – funded nPOD program. In contrast to nPOD, the major product provided by HPAP is not archived biomaterial subject to broad distribution, but rather, the delivery of extensive and high-quality molecular data sets to the diabetes research community in order to facilitate further discovery. Together, nPOD and HPAP are complementary programs that assist the diabetes community and afford the maximal opportunity for advancing knowledge about the pathogenesis of T1D and T2D.

  Study phs002465

• Systems biology of Colorectal Cancer

  Colorectal cancer (CRC) is one of the most common cancers in both males and females, and it is perhaps the best understood of all epithelial tumors in terms of its molecular origin. Yet, despite large amount of work that has concentrated on understanding of colon tumorigenesis, we still do not know the full complement of molecular lesions that are individually necessary – and together sufficient – to cause colorectal cancer. Neither do we understand why some specific mutations that are relatively rare in other tumors (e.g. loss of the APC tumor suppressor) are extremely common in colorectal cancer. We propose here to use the tools of systems biology to develop a quantitative and comprehensive model of colorectal tumorigenesis. The model will include identification of cell-type specific and oncogenic pathways that contribute to colon tumorigenesis, and explain in molecular detail how a genotype of an individual CRC leads to activation of downstream genes that drive uncontrolled cell growth. This model will subsequently be used to find novel therapeutic targets, to guide genetic screening to identify individuals with elevated risk for developing CRC, and to classify patients into molecular subgroups to select the treatment combination that is optimal for each patient (personalized medicine). The specific objectives of the SYSCOL project are: 1. Identify genetic markers for individual risk using genotyping and sequencing of constitutional DNA from sporadic and familial CRC cases and controls 2. Identify genes and regulatory elements that contribute to colorectal cancer cell growth 3. Use data from Aims 1-2 to develop a quantitative model for colorectal tumorigenesis 4. Apply the model for identification of high-risk individuals, for molecular classification of the disease, and for identification of novel molecular treatment targets

  Study EGAS00001000854

• Neuron-specifc methylome analysis of Alzheimer's disease brain

  Neuron-specific methylome analysis of Alzheimer's disease brain to detect epigenetic changes related to the pathomechanism.

  Study JGAS000125