RSG POSTER ABSTRACTS - 42 through 60
Complete list of RSG Poster Abstracts (.pdf) - Click here.
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Poster: P42
microRNA-mediated feed forward disinhibition of multiple functional pathways amplifies prohypertensive signaling
Danielle Decicco, Thomas Jefferson University, United States
James Schwaber, Thomas Jefferson University, United States
Rajanikanth Vadigepalli, Thomas Jefferson University, United States
microRNAs have emerged as novel post-transcriptional regulators of many cellular disease processes. However, in essential hypertension, there has been no characterization of the microRNA expression landscape in key neuroanatomical blood pressure control regions during hypertension development. Using a global analysis of microRNA expression levels in these regions, we quantified 419 well-annotated microRNAs in the brainstem, and we identified 24 microRNAs showing stage-dependent differential expression in hypertensive rats compared to controls. We constructed microRNA regulatory networks based on predicted targets from bioinformatic databases including RNA22 and miRWALK. Our microRNA regulatory networks indicated that predicted targets primarily fell into functional pathways previously associated with hypertension such as inflammation and Angiotensin II signaling. We measured the putative targets using high-throughput qPCR to evaluate correlations between microRNAs and their predicted gene targets. Our analysis revealed a similar extent of positive and negative correlations between the microRNA and predicted target transcript patterns suggesting regulatory relationships. We discovered a pair of microRNAs, previously shown to be enriched in different cells types: miR-135a in astrocytes and miR-376a in neurons, which demonstrated stronger anti-correlational relationships with their putative targets in the hypertensive state compared to controls. These microRNAs demonstrate expression levels which are negatively correlated with key target expression levels in the inflammation and Angiotensin II pathways. Interestingly, the key putative targets are known inhibitors of these functional pathways that show increased activity in hypertension. Such feed forward disinhibition by microRNA-135a and microRNA-376a of the inflammatory and Angiotensin II pathways occurred at the onset of hypertension suggesting a mechanistic role for this regulatory network. Given that both pathways are hyperactive in the chronic hypertensive stage, microRNA regulatory network-mediated disinhibition of those pathways at the onset stage is likely to have a causal effect of amplifying those pathways, contributing to the development of hypertension. This feed-forward disinhibition by miR-135a and miR-376a suggests synergistic network activity contributing to the development of hypertension.
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Poster: P43
Prioritizing animal models for human diseases using genome-wide functional networks
Max Homilius, Princeton University, United States
Arjun Krishnan, Princeton University, United States
Calum MacRae, Brigham and Women’s Hospital, Harvard Medical School, United States
Olga Troyanskaya, Princeton University, United States
Model organisms are key to studying the molecular basis of human traits and diseases. Therefore, for a rare or common disease defined by a group of implicated genes, it is valuable to identify relevant model organism phenotypes to transfer knowledge and propel further investigation. Yet, we lack tools to seamlessly search across organisms to identify the model phenotype equivalent to a human disease (or the human disease corresponding to a model phenotype of interest). The most straightforward approach – mapping disease to phenotype based on overlapping homologous genes – is severely limiting because, 1) our knowledge of associated genes for most diseases and phenotypes is largely incomplete, thus leaving many actual disease-phenotype pairs with little to no ‘common’ genes; 2) treating diseases and phenotypes as bags of genes ignores the underlying complex organism-specific biology. Here we present a framework for systematically matching diseases and phenotypes that overcomes both of these limitations. By jointly using genome-scale functional gene interaction networks in both human and the model organism, we create and match genome-wide representations of human diseases and model phenotypes, and further filter nonspecific matches to arrive at highly resolved disease-phenotype mappings. Further, for each disease-phenotype pair, in addition to known genes, we report the novel homologous genes most associated with the disease/phenotype, which are prime candidates for experimental follow-up. We have made our approach available through a dynamic web-interface that allows researchers to easily use their own gene set (or a previously known disease/phenotype) to query a large collection of resources containing disease-gene and phenotype-gene associations in human and five model organisms (mouse, zebrafish, fly, worm and yeast). Users can readily see prioritized diseases/phenotypes, list candidate genes, explore them in the context of the underlying networks, and export all results.
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Poster: P44
Discovery of bruchid resistance-related variations in regulatory regions by genome-wide sequence comparison
Dung-Chi Wu, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Mao-Sen Liu, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Tony Chien-Yen Kuo, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Kuan-Yi Li, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Roland Schafleitner, AVRDC-the World Vegetable Center, Taiwan
Hsiao-Feng Lo, Department of Horticulture and Landscape Architecture, Nation Taiwan University, Taiwan
Long-Fang O. Chen, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Chien-Yu Chen, Dept. of BIME, National Taiwan University, Taiwan
Chia-Yun Ko, Institute of Plant and Microbial Biology, Academia Sinica, Taiwan
Huei Mei Chen, AVRDC-the World Vegetable Center, Taiwan
Mungbean, Vigna radiata [L.] R. Wilczek, is one of the most important legume crops with valuable nutritional and medical value. The bruchid beetle (Callobruchus maculates), known as bean weevils, would attack mungbeans both in the field and in storage, resulting in great losses in the stored grains. A wild mungbean, V. radiata var sublobata (TC1966) from Madagascar, was resistant to many bean weevils and with the ability of crossing with V. radiata. Though little knowledge has been uncovered for weevil resistance, breeding for bruchid resistance is still a major goal in mungbean stuides. In this study, we first de novo assembled the genome of a bruchid-resistant recombinant inbreeding line 59 (RIL59) which derived from TC1966 and a bruchid-susceptible variety NM92. The primitive genomic data was combined with additional genome and transcriptome analysis for different levels of bruchid-resistance mungbean lines, including the two parent, TC1966 and NM92, and the other 12-inbred-generation progenies, to investigate where might the major distinct loci between bruchid-resistant and bruchid-susceptible lines. The ab initio predicted gene models of RIL59 consist of 44,317 genes, representing 49,952 transcripts. The genome-wide variation analysis performed on NM92, TC1966 and RIL59 revealed that 3,162 genes have sequence variants, including non-synonymous substitutions and INDELs, on exons to cause protein sequence changes. These genes were suspected to be related to the bruchid-resistance. On the other hand, a draft bruchid-susceptible mungbean (Vigna radiata var. radiata VC1973A) genome was previously published. We mapped the above-mentioned putative bruchid-resistance-related genes to this bruchid-susceptible draft genome and found a hot region on Vr05. This result was consistent with the genotype-by-sequencing (GBS) data which also suggested that the region from 5M bps to 12M bps of Vr05 is strongly related to bruchid-resistance. These two draft genomes were aligned to identify 127 scaffolds of RIL59 that together correspond to the Vr05 of VC1973A. Among them, about 50 scaffolds were considered associated with this region. In total, 508 genes were identified in these scaffolds. If considering the upstream 2,000 bps of each gene model as the promoter, there were 544 promoters falling in the suspected resistance-related region. Comparison on the promoters between the bruchid-resistant and bruchid-susceptible mungbean lines discovered some large structure variations, suggesting the gain or loss of regulatory elements might play key roles in bruchid resistance. In summary, the comparison of promoters of the two draft genomes reveals the potential impact of regulatory regions in affecting resistant phenotypes of mungbeans.
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Poster: P45
Empirical Evidence Supporting a Systematic Approach to Gene Network Identification
Sweta Sharma, Rutgers University, United States
Desmond Lun, Rutgers University, United States
A major cellular systems biology challenge of the past decade has been the development of a comprehensive model for gene regulatory networks (GRNs). Particularly, there is growing impetus for the extraction of regulatory information from expression data as it becomes increasingly available and accurate. Identifying networks from such information requires deciphering direct interactions from indirect ones. For instance, if gene A regulates gene B and B regulates gene C, then changing A's expression will directly affect B's expression while indirectly affecting C's.
Recently, Birget et al proposed a systematic approach for network identification. They consider a binary model that captures the non-linear dependencies of GRNs and reverse-engineer the network using assignments (perturbations to the expression level of a single gene) and whole transcriptome steady-state expression measurements. Under this model, their approach achieves identification of acyclic networks with worst-case complexity costs in terms of assignments and measurements that scale quadratically with the size of the network. For networks with cycles, the worst-case complexity cost scales cubically.
We conduct a proof-of-concept experiment for this approach by reverse-engineering a five-gene sub-network of the outer-membrane protein regulator (ompR) in E. coli. Through assignments achieved by gene deletions and expression measurements from qPCR, we successfully identify the regulatory relationships and discern direct from indirect interactions. We also performed computational experiments on in silico networks derived from known regulatory relationships in E. coli and S. cerevisiae, where gene regulation is thermodynamically modeled using the system of ODEs that was used to generate data for previous DREAM challenges. We achieve 100% identification for noiseless acyclic networks of size ranging from 100 to 4,000 genes. For noisy acyclic E. coli networks of size 100, we obtain an AUPR of .95. This is significantly improved from the .71 AUPR obtained by the top performer in the DREAM3 inference challenge for acyclic in silico networks. Furthermore, we achieve this using ten-fold fewer assignments and measurements. For noisy cyclic E. coli networks of size 100, we obtain an AUPR of .75, compared to .45 for the top performer in the DREAM4 InSilico_Size100 sub-challenge containing cyclic networks. We achieve this using roughly the same number of assignments and half as many measurements.
Taken together, our results imply that the reverse engineering method of Birget et al is not only experimentally feasible but uses reasonable resources. It can therefore serve as the basis for systematic, accurate reverse engineering of large-scale gene regulatory networks.
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Poster: P46
Dysregulation of co-regulatory microRNA networks by chronic ethanol consumption leads to impaired liver regeneration
Austin Parrish, Thomas Jefferson University, United States
Egle Juskeviciute, Thomas Jefferson University, United States
Jan Hoek, Thomas Jefferson University, United States
Rajanikanth Vadigepalli, Thomas Jefferson University, United States
microRNAs are a class of small, non-coding RNAs ~21 nucleotides long that regulate numerous cellular processes in a post-transcriptional manner. Previous research has identified several microRNAs of interest involved in liver regeneration and hepatocellular carcinoma, including miR-21, which has been shown by our lab to be significantly upregulated following liver damage by 70% partial hepatectomy, along with chronic ethanol consumption. Given that microRNAs often exert their effects in regulatory networks that display both positive and negative cooperation, we sought to identify additional microRNAs involved in liver regeneration alongside miR-21. In order to accomplish this, we performed in vivo knockdown of miR-21 using a locked nucleic acid (LNA) probe containing a complementary sequence to miR-21. Whole liver tissue samples were collected from both control- and ethanol-fed Sprague-Dawley rats at baseline conditions and 24 hours post-partial hepatectomy. These samples were analyzed for microRNA expression using the NanoString microRNA microarray platform. Analysis of the expression data reveals twelve microRNAs that show differential expression in response to miR-21 knockdown. Of these genes, three show positive correlations with miR-21 expression while eight are negatively correlated. Using target prediction software, we developed a network of putative microRNA-gene interactions and compared the predicted targets to genes identified as differentially expressed based on Affymetrix microarray analysis. This network of putative targets identifies a number of genes that are potentially regulated by these differentially expressed microRNAs. Gene ontology and pathway analysis reveals that multiple predicted targets are involved in processes relating to cell cycle progression. In conclusion, these studies identified a set of co-regulatory microRNAs whose dysregulation by chronic ethanol consumption may lead to impaired liver regeneration.
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Poster: P47
Furthering understanding of Parkinson's Disease through integrative analysis in C. elegans
Victoria Yao, Princeton University, United States
Rachel Kaletsky, Princeton University, United States
Coleen Murphy, Princeton University, United States
Olga Troyanskaya, Princeton University, United States
The etiology of complex human diseases, especially in the context of aging, such as Parkinson's disease, is likely a combination of many environmental and genetic factors. Elucidating the molecular basis of pathophysiologies of such diseases requires a combination of systems-level studies in human and model systems. The nematode C. elegans is an effective and efficient model for human disease due to its sufficient complexity and high genetic conservation with humans, combined with short lifespan and the abundance of genetic tools and assays. In particular, the complexity of C. elegans at the tissue level allows for in depth investigations of relevant diseases in a tissue-specific manner. To this end, we developed a novel semi-supervised regularized Bayesian integration method to integrate a large compendium of heterogenous datasets for the construction of 203 tissue- and cell-type specific networks in C. elegans. We demonstrate the accuracy of these networks in detecting tissue-specific functional signal, even for very small and specific tissues and cell types. We then use the dopaminergic neuron worm network combined with Parkinson's disease genes identified in quantitative genetic studies in human to predict new genes implicated in Parkinson's disease. A subset of these predictions has been experimentally confirmed to have Parkinson's disease endophenotypes in C. elegans and are conserved in human, providing potential therapeutic targets.
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Poster: P48
Transcription Network Inference using RNA Expression and Degradation Rate Data in S. cerevisiae
Konstantine Tchourine, NYU - Center for Genomics and Systems Biology, United States
Christian L. Mueller, Simons Center for Data Analysis, United States
Chirstine Vogel, NYU - Center for Genomics and Systems Biology, United States
Richard Bonneau, NYU - Center for Genomics and Systems Biology, United States
Despite many years of research and the availability of large-scale datasets, modeling RNA transcription and predicting transcriptional regulatory interactions on a systems level in eukaryotes remains a challenging problem and requires modeling changes in RNA abundance due to both the regulation of synthesis and degradation. Even Saccharomyces cerevisiae has several hundred putative TFs and ~6,000 potential targets, rendering the theoretical regulatory interaction space enormous. Further, eukaryotes are marked by extensive promoter regions, many response pathways, and additional regulatory layers, e.g. RNA decay, which further confound gene expression regulation. For these reasons, even the best network inference algorithms have so far performed very poorly in yeast. To address this challenge, we are taking several steps towards constructing the first high-quality, high-coverage yeast regulatory network. I am developing an expanded version of an existing gene regulatory inference framework, Inferelator-BBSR, that incorporates RNA decay rates to predict new regulatory interactions, estimate each interaction’s contribution to the dynamics of the transcription process, and estimate gene-dependent RNA decay rates. Incorporation of RNA decay rates can be done either computationally by finding optimal decay rates for different modes of regulation in yeast, or empirically by directly incorporating RNA decay rate data into the inference procedure. In this presentation, I will show that both ways of incorporating RNA decay rates into the inference framework improve regulatory network inference. Furthermore, I will show that the inferred regulatory network can help identify different modes of stress adaptation which require different average RNA decay rates.
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Poster: P49
The optimized high-throughput siRNA screening : Applications in cancer target discovery
Nayoung Kim, Sookmyung Women's University, Korea, The Republic of
Sukjoon Yoon, Sookmyung Women's University, Korea, The Republic of
RNA interference (RNAi) has become a powerful tool for drug target discovery, and the systematic loss-of-function screens using RNAi libraries can now be performed to identify the biological functions of specific genes or pathways in various diseases. Cancer target discovery studies on clinically relevant drug applications and their mode of actions can be accelerated by integrating multi-level omics data such as genome, transcriptome, proteome and phosphatome data together with siRNA screening data.
We introduce the siRNA screening platform composed of the image-based assay optimization, primary screening, data analysis and hit selection criteria using some studies to investigate novel therapeutic targets in cancer. We applied two different samples to siRNA screening. One example is a study using a specific gene-knockdown cell line. In this study, in order to identify novel therapeutic targets in STK11-deficient lung cancer cells, we utilized a large-scale siRNA screening to identify genes that would sensitize STK11-deficient lung cancer cells (A549) with or without AMPK. And another example is a genome-wide siRNA screening using a sphere-forming (3D) culture system similar to in vivo. 3D growths of cancer cells in vitro are more reflective of in situ cancer cell growth than growth in monolayer (2D). This study is designed to identify genes reducing sphere size on 3D as compared to 2D.
In the study using a stable knockdown cell line, the perturbation of several genes exhibited significant inhibitory effect on the growth of AMPK-knockdown cells. And we identified that specific hits inducing inhibition of cell growth with AMPK knockdown were related to metabolism and signal transduction among various functional categories. These results highlight the potential of synthetic lethal siRNA screens with AMPK inhibitors to define new determinants of potential therapeutic targets. And in another screening using 3D culture system, we found specific genes reducing sphere formation. These hits were related to lipid metabolism. From these results, we can find new therapeutic target-related drugs for inhibition of tumor progression and metastasis.
This screening platform can be provided as a valuable tool to find novel therapeutic targets and drugs for cancer therapy. We now provide this platform service to academic and industrial organizations.
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Poster: P50
MACE: a web-based application for analyzing mutation-specific drug response and gene expression in cancers
Yourae Hong, Sookmyung Women's University, Korea, The Republic of
Sukjoon Yoon, Sookmyung Women's University, Korea, The Republic of
Systematic understanding of mutation-oriented drug sensitivity on cancer cell lines will provides therapeutic benefits on the cancer therapy. Here, we present the MACE database as a web-based interactive tool for interpreting drug response and gene expression in the genotypic classification of cancer cell lines. Chemical screening and DNA microarray data on NCI60 cell lines were organized to identify mutation- or lineage-specific chemicals and gene expression signatures. In this system, users can perform the individual and combined analysis to find potential associations of chemicals and genes with major gene mutations of cancers. The present MACE database can be used to understand how gene mutation is interconnected with the drug response and gene expression in cancer subtypes. This database provides a valuable tool to predict and optimize the therapeutic window for anticancer agents and related gene targets. The MACE web database is available at http://mace.sookmyung.ac.kr/.
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Poster: P51
Reverse engineering gene regulatory networks from structural and epigenetic datasets
Brittany Baur, Marquette University, United States
Serdar Bozdag, Marquette University, United States
One of the major challenges in computational biology is the identification of “driver” copy number changes that promote cancer cell progression. The goal of this study is to identify genes within regions with aberrant copy number and DNA methylation changes that have widespread downstream effects, and their associated targets. We first identified these aberrant regions by integrating DNA methylation or copy number datasets with gene expression datasets in luminal A breast cancer patients. We then identified candidate genes within the aberrated regions which could act as regulators of downstream targets by integrating the expression levels of the regulators and potential targets with pathway analysis. Based on gene ontology, we established that genes associated with aberrant copy number and DNA methylation changes are enriched in terms associated with the regulation of various biological processes. This indicates that these genes are potentially regulators of other genes. We identified several candidate genes within these regions that are likely regulators strongly affected by copy number or DNA methylation aberrations. By identifying causal genes within the aberrant regions, this study could aid in the discovery of therapeutic targets of cancer drugs.
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Poster: P52
Alternative Splicing During Heat Stress in Arabidopsis thaliana
Gaurav Kandoi, Iowa State University, United States
Julie A Dickerson, Iowa State University, United States
Alternative splicing (AS) which produces multiple messenger RNAs by different combination of various regions of the precursor transcript is a major cause of diversity in gene products. Recent estimates suggest the rate of AS to be as high as 95% in humans and 60% in plants. Despite the prevalence of AS events, their functional consequences are largely unknown. Although the impact of abiotic stresses (temperature, salt, light etc.) on AS events in Arabidopsis thaliana has been widely studied, not much is known about how differential splicing affects the metabolic pathways under such stress conditions. High-throughput RNA sequencing (RNA-seq) data from a heat stress experiment in A. thaliana, was used to find regions which undergo differential splicing. Even though heat stress leads to an increase in the number of AS events, only ~90 of such alternatively spliced genes are also differentially spliced (DS) between the two conditions. Most of these are nuclear genes and have been annotated with biological processes such as response to stress, response to abiotic or biotic stimulus and cell organization, and biogenesis. A significant portion of these differentially spliced genes are also linked with molecular functions like binding (DNA or RNA, nucleotide, protein and nucleic acid) and enzymatic activity (transferase, hydrolase and kinase). For the most part, the novel spliced isoforms are predicted to be more abundant than the normal transcript in the heat stress condition. Conserved domain analyses indicate that novel spliced isoforms share similar domain architecture with the normal transcripts more often than not. By studying the effect of such alternative splicing events on protein function, we can identify important metabolic networks. Combination of these differential networks across the spectrum of stress conditions generates metabolic models with a high-level regulatory framework.
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Poster: P53
An integrated computational pipeline for analysing genetic, molecular, and functional variations in complex diseases
Bajuna Salehe, University of Reading, United Kingdom
Chris Jones, University of Reading, United Kingdom
Giuseppe Di Fatta, University of Reading, United Kingdom
Liam McGuffin, University of Reading, United Kingdom
The ongoing advancement of the technologies used for generating ‘omic data has led to the flood in biological data. This ‘big data’ phenomenon has increased the challenge for biologists and biomedical experts of finding a better analytical strategies that are capable of integrating variation data from different omic states, using integrated computational approaches to further understanding phenotypes (complex diseases/polygenic traits). Here, we have proposed an 'omic variation framework focusing initially on single nucleotide polymorphisms (SNPs), which is one of the key 'omic variation types that are studied in order to understand the relationships underpinning complex traits. However, this framework should also be adaptable to other 'omic variation data, such as methylomic, transcriptomic and copy number variation (CNVs). Furthermore, we have designed a pipeline for an integrated computational approach to implement this framework, which we have applied to study platelet proteomic data sets. In this case study the aim is to understand the association of SNPs at different levels with the adenosine diphosphate (ADP) activated platelet response. Platelets play key roles in the thrombus formation, which is one of the major risks for cardiovascular diseases (CVDs), and ADP activated platelet response is highly involved during the thrombus formation, as well as being variable among individuals. Using the initial implementation of this pipeline we have been able to identify key genetic variants (SNPs) such as rs6141803 and rs7007145 in PTK2B and COMMD7 genes respectively that are significantly associated with platelet aggregation. Many of our identified SNPs were previously unidentified, and have been independently reported to be associated with the risk of CVDs.
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Poster: P54
Exploration of Breast Cancer Genes and Bioinformatics Analyses
Shahrzad Eslamian, Grand Valley State University, United States
Leidig Jonathan P, Grand Valley State University, United States
Information visualization may be applied to bioinformatics research tools to assist in understanding the complex (often textual) datasets. The main goal of this work was to design an interactive visualization tools to detail the genes potentially responsible for breast cancer as they are discovered through bioinformatics analysis. The dataset is derived from the publically shared research as maintained by the bioinformatics research community. The visualization aims to detail the explicit relationships and existing analyses of these target genes and their related micro RNA, considering the distributed nature of this field of research and disaggregation of the underlying datasets.
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Poster: P55
Spectral coherence classification of uORF translation in a neuroblastoma cell model of differentiation
Sang Chun, University of Michigan, United States
Caitlin Rodriguez, University of Michigan, United States
Peter Todd, University of Michigan, United States
Ryan Mills, University of Michigan, United States
Upstream open reading frames (uORFs) are prevalent in the human transcriptome and may negatively regulate the abundance of canonically encoding proteins through the promotion of mRNA decay and competitive expression, among other mechanisms. uORFs are conserved across species and have been annotated to genes with diverse biological functions, including but not limited to oncogenes, cell cycle control and differentiation, and stress response. As such, the aberrant expression of certain uORFs has been implicated in the development and progression of various diseases. Therefore, the positive identification and validation of uORFs as translational products is critical for understanding their role in complex biological processes and disease etiology. Where mRNA-Seq has been used to approximate the transcriptomic content of a cell, or group of cells, the recently developed method of sequencing ribosome-protected fragments aims to profile the translational landscape of a sample. In concert, various algorithms have been developed to differentiate coding transcripts from non-coding transcripts based on the alignment of ribosome-protected fragments to a reference transcriptome. We have developed a classification algorithm based on the magnitude of coherence between the aligned ribosome profiling reads and tri-nucleotide periodic signal inherent to protein-coding sequences. In this study, we compare our spectral coherence-based classification algorithm (SPECtre) against existing methods and apply our approach to positively identify variably translated uORFs related to differentiation of SH-SY5Y neuroblastoma cells.
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Poster: P56
Transcriptional Regulatory Networks During the Endothelial-to-Hematopoietic Transition in the Mouse Embryo
Long Gao, The University of Iowa, United States
Joanna Tober, University of Pennsylvania, United States
Peng Gao, The University of Iowa, United States
Jianshu Zhang, The University of Iowa, United States
Changya Chen, The University of Iowa, United States
Nancy A. Speck, University of Pennsylvania, United States
Kai Tan, The University of Iowa, United States
Hematopoietic stem cells (HSCs) in the embryo are derived from hemogenic endothelia (HE) of the arterial wall from the aorta/gonad/mesonephros (AGM) region and yolk sac (YS). HE from AGM and YS has different developmental potentials. HE from YS primarily produces committed erythroid/myeloid progenitor and HE from AGM can produce lymphoid progenitors and HSCs. The transcriptional regulatory networks (TRN) that control the endothelial-to-hemogenic transition in AGM and YS are poorly understood. Here we compared the transcriptomes of endothelium and hemogenic endothelium from embryonic (E) day 9.5 and E10.5 AGM and YS by RNA-Seq. We developed a novel computational method for constructing condition-specific transcriptional regulatory networks (TRNs) by sample elimination and network comparison with limited number of samples. By modeling developmental-stage-specific TRNs, we identified 73 gene modules (1429 genes) with differential activities between E and HE and between AGM HE and YS HE. We further identified a number of transcription factors that regulate the endothelial-to-hemogenic transitions, including Runx1, Sox7, Hoxa7, and Hoxd9. Long intergenic noncoding RNAs (lincRNAs) have been shown to regulate the development of various lineages. However, nothing is known about the role of lincRNAs during embryonic hematopoiesis. We identified 18 and 41 novel lincRNAs that are specifically expressed in E and HE, respectively. Among them, 10 lincRNAs were differentially expressed between E and HE, suggesting a role in regulating the development of hemogenic endothelium. In summary, our systematic analysis of the transcriptomes during endothelial-to-hemogenic transition has uncovered a number of novel regulators and gene pathways of this critical developmental transition.
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Poster: P57
Dynamic organization and activation of enhancers and super-enhancers dictate effector and memory CD8+ T cell responses
Bing He, University of Iowa, United States
Haihui Xue, University of Iowa, United States
Kai Tan, University of Iowa, United States
CD8 T cells are critical in controlling infection by intracellular pathogens including viruses and intracellular bacteria. Differentiation of naïve CD8 T cells (TN) to effector (TE) and memory CD8 T cells (TCM) is accompanied by dynamic gene expression and epigenetic modification changes at promoters as revealed by previous analyses. However, there is virtually no information regarding the dynamics of enhancers during CD8 T cells responses to date. Here, we have mapped four histone modification marks in TN, TE, and TCM cells after viral infection. Our results suggest that the chromatin environment at regulatory DNA sequences in TCM is more permissive than in TN and TE. We further predicted the enhancers, super enhancers, and their targets, and constructed condition-specific transcriptional regulatory networks (TRNs) in three T cell stages. We have identified a highly dynamic repertoire of the enhancers and their targets during CD8 T cell responses, as 77% of the enhancers and 82% of the enhancer-promoter interactions are stage-specific. Our results suggest the dynamic change of enhancer activity during cell stage transition leads to TRN rewiring, which explains the expression change of the key factors of T cell function.
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Poster: P58
Data and Computing Platform to facilitate NCER- PD (National Centre of Excellence in Research on Parkinson’s Disease) Project
Venkata Satagopam, LCSB, University of Luxembourg, Luxembourg
Peter Banda, LCSB, University of Luxembourg, Luxembourg
Jan Martens, LCSB, University of Luxembourg, Luxembourg
Joachim Kutzera, LCSB, University of Luxembourg, Luxembourg
Kirsten Roomp, LCSB, University of Luxembourg, Luxembourg
Wei Gu, LCSB, University of Luxembourg, Luxembourg
Patrick May, LCSB, University of Luxembourg, Luxembourg
Reinhard Schneider, LCSB, University of Luxembourg, Luxembourg
The Data and Computing Platform provides key infrastructure for the integration, curation and interrogation of anonymized clinical and experimental data. The platform manages multidimensional data associated with clinical research, including patient data, sample-associated information, and high-throughput molecular readouts from these samples. These different data flows are integrated at their source with the help of advanced data capture and transfer approaches. Clinical data can be entered remotely, via electronic forms at the time of collection, assuring their integrity and standardization. To attain this goal, REDCap[1], a state-of-the-art clinical research data management system has been implemented. All entered data will be immediately anonymized and sample-associated data will be accessed directly at their storage location, the IBBL, via secure communication with the LIMS of the biobank. High-throughput experimental data will be uploaded directly to the database service provided by LCSB for handling large, heterogeneous biomedical datasets: the tranSMART system[2]. tranSMART enables sharing, integration, standardization and analysis of heterogeneous data from collaborative translational research. It is used in pharmaceutical industry and in Innovative Medicine Initiative projects (e.g. eTRIKS[3], AETIONOMY[4]) to store and share curated phenotypic data such as clinical observations and adverse events; omics data like transcriptomics, proteomics, metabolomics and genotyping. Well-grounded machine learning and computational modeling approaches will enable data analysis and interpretation.
1. REDcap(2015) /www.project-redcap.org
2. tranSMART(2015) http://transmartfoundation.org
3. eTRIKS(2015) www.etriks.org
4. AETIONOMY(2015) www.aetionomy.eu
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Poster: P59
Emergent Topological and Statistical Properties of Gene Regulatory Grids
Wilberforce Ouma, Ohio State University, United States
Mohammadmahdi Yousefi, Ohio State University, United States
Andrea Doseff, Ohio State University, United States
Erich Grotewold, Ohio State University, United States
Gene regulatory grids (GRGs) are static representations of gene regulatory networks (GRNs) encompassing all possible regulator-target gene interactions that provide a system-wide view of transcriptional gene regulation. To understand their architectural organization, we constructed and investigated emergent topological and statistical properties of GRGs of the following model organisms: Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. We implemented a formal statistical approach for fitting a power-law function to the empirical degree distribution of the grid and observed that the out-degree, and not the in-degree, follows a power-law distribution, suggesting a scale-free property of GRGs that have transcription factors as hubs. The four GRGs however exhibit different power-law exponents. A computational sub-sampling of sub-grids from the original grids showed that for D. melanogaster, the exponent was invariant for a large number of sub-grids. With this invariant property of the exponent, we derived a mathematical formulation that estimates the number of interactions in a fully-connected grid. We hypothesize that a consequence of the scale-free property in cellular networks is reduction of the average path-length in the grid, resulting in faster signal propagation.
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Poster: P60
Integrated Feature Detection From Chromatin State Measurements
Anastasia Shcherban, Institute of Biosciences and Medical Technology (BioMediTech), University of Tampere, Finland
Matti Nykter, Institute of Biosciences and Medical Technology (BioMediTech), University of Tampere, Finland
Juha Kesseli, Institute of Biosciences and Medical Technology (BioMediTech), University of Tampere, Finland
Identification and extraction of biologically relevant features, such as active regulatory regions from high throughput sequencing (HTS) data is a major challenge. A number of algorithms have been developed to perform feature detection on HTS data. In most cases, features are detected from one track of HTS data at a time, followed by further analysis and integration of peak detection results obtained from multiple sources. Thus, these methods fail to efficiently discover and utilize the complementary information found in multiple signals.
In this work we present our approach extending an existing algorithm called ZINBA (Zero-inflated negative binomial algorithm) to analyze multiple data tracks simultaneously. Our goal is to shed light on the relationships between data tracks using our statistical model while improving detection results for features that can be found from a selected pair of tracks. The statistical model is built by incorporating a correlation term for HTS data, so that the algorithm can be run for two tracks in parallel with improved results. To estimate the parameters of this model, the iterative algorithm (of EM-type) is extended from the original by including correlation estimation based on the results of logistic regression and generalized linear model fitting steps. As an output, our algorithm provides feature detection results for both tracks separately, a correlation model describing the interaction between the two tracks, and an optional consensus track output showing feature calls based on data from both of the two tracks used.
Here, we consider three examples of integrating HTS data across multiple data types and evaluate the performance of our algorithm with Receiver Operating Characteristics (ROC) curves and Area Under Curve (AUC) values. The example cases have been selected as pairs of tracks from ENCODE datasets that share promoter and enhancer activity information. First, we combine ChIP-seq histone modification marks H3k4me3 and H3k27ac (K562 cell line). Second, we apply our algorithm to ChIP-seq TFs P300 and FAIRE-seq open chromatin data (K562). Finally, we apply our algorithm to a pair of FAIRE-seq replicates (K562) and study the resulting consensus track. In all the cases the analysis of algorithm performance shows a significant improvement in comparison to single track analysis.