Genomics Commons^™

A Novel Multi-Network Approach Reveals Tissue-Specific Cellular Modulators Of Fibrosis In Systemic Sclerosis, Jaclyn N. Taroni, Casey S. Greene, Viktor Martyanov, Tammara A. Wood

Dartmouth Scholarship

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology.We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast …

Familial Lung Cancer: A Brief History From The Earliest Work To The Most Recent Studies, Anthony Musolf, Claire Simpson, Mariza De Andrade, Diptasri Mandal, Colette Gaba, Ping Yang, Yafang Li

Dartmouth Scholarship

Lung cancer is the deadliest cancer in the United States, killing roughly one of four cancer patients in 2016. While it is well-established that lung cancer is caused primarily by environmental effects (particularly tobacco smoking), there is evidence for genetic susceptibility. Lung cancer has been shown to aggregate in families, and segregation analyses have hypothesized a major susceptibility locus for the disease. Genetic association studies have provided strong evidence for common risk variants of small-to-moderate effect. Rare and highly penetrant alleles have been identified by linkage studies, including on 6q23–25. Though not common, some germline mutations have also been identified …

Application Of Rnai-Induced Gene Expression Profiles For Prognostic Prediction In Breast Cancer, Yue Wang, Kenneth . M. K. Mark, Matthew H. Ung, Arminja Kettenbach, Todd Miller, Wei Xu, Wenqing Cheng Cheng, Tian Xia, Chao Cheng

Dartmouth Scholarship

Homologous recombination (HR) is the primary pathway for repairing double-strand DNA breaks implicating in the development of cancer. RNAi-based knockdowns of BRCA1 and RAD51 in this pathway have been performed to investigate the resulting transcriptomic profiles. Here we propose a computational framework to utilize these profiles to calculate a score, named RNA-Interference derived Proliferation Score (RIPS), which reflects cell proliferation ability in individual breast tumors. RIPS is predictive of breast cancer classes, prognosis, genome instability, and neoadjuvant chemosensitivity. This framework directly translates the readout of knockdown experiments into potential clinical applications and generates a robust biomarker in breast cancer.

A Comprehensive Benchmarking Study Of Protocols And Sequencing Platforms For 16s Rrna Community Profiling, Rosalinda D’Amore, Umer Zeeshan Ijaz, Melanie Schirmer, John G. Kenny, Richard Gregory, Alistair C. Darby, Migun Shakya

Dartmouth Scholarship

In the last 5 years, the rapid pace of innovations and improvements in sequencing technologies has completely changed the landscape of metagenomic and metagenetic experiments. Therefore, it is critical to benchmark the various methodologies for interrogating the composition of microbial communities, so that we can assess their strengths and limitations. The most common phylogenetic marker for microbial community diversity studies is the 16S ribosomal RNA gene and in the last 10 years the field has moved from sequencing a small number of amplicons and samples to more complex studies where thousands of samples and multiple different gene regions are interrogated. …

Leveraging Global Gene Expression Patterns To Predict Expression Of Unmeasured Genes, James Rudd, René A. Zelaya, Eugene Demidenko, Ellen L. Goode, Casey S. Greene S. Greene, Jennifer A. Doherty

Dartmouth Scholarship

BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Integrative Analysis Of Survival-Associated Gene Sets In Breast Cancer, Frederick S. Varn, Matthew H. Ung, Shao Ke Lou, Chao Cheng

Dartmouth Scholarship

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient’s cancer. Identifying robust gene sets that are consistently predictive of a patient’s clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that …

Methylation Of Leukocyte Dna And Ovarian Cancer: Relationships With Disease Status And Outcome, Brooke L. Fridley, Sebastian M. Armasu, Mine S. Cicek, Melissa C. Larson, Chen Wang, Stacey J. Winham, Kimberly R. Kalli, Devin C. Koestler

Dartmouth Scholarship

Genome-wide interrogation of DNA methylation (DNAm) in blood-derived leukocytes has become feasible with the advent of CpG genotyping arrays. In epithelial ovarian cancer (EOC), one report found substantial DNAm differences between cases and controls; however, many of these disease-associated CpGs were attributed to differences in white blood cell type distributions. We examined blood-based DNAm in 336 EOC cases and 398 controls; we included only high-quality CpG loci that did not show evidence of association with white blood cell type distributions to evaluate association with case status and overall survival.

How To Get The Most From Microarray Data: Advice From Reverse Genomics, Ivan P. Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y. Gorlova, Kim-Anh Do, Christopher Amos

Dartmouth Scholarship

Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data–derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes—identifying differentially expressed genes—is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results …

Reactin: Regulatory Activity Inference Of Transcription Factors Underlying Human Diseases With Application To Breast Cancer, Mingzhu Zhu, Chun-Chi Liu, Chao Cheng

Dartmouth Scholarship

Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs. Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between …

Machine Learning And Genome Annotation: A Match Meant To Be?, Kevin Y. Yip, Chao Cheng, Mark Gerstein

Dartmouth Scholarship

By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.

Micrornas And The Advent Of Vertebrate Morphological Complexity, Alysha M. Heimberg, Lorenzo F. Sempere, Vanessa N. Moy, Phillip C. J. Donoghue, Kevin J. Peterson

Dartmouth Scholarship

The causal basis of vertebrate complexity has been sought in genome duplication events (GDEs) that occurred during the emergence of vertebrates, but evidence beyond coincidence is wanting. MicroRNAs (miRNAs) have recently been identified as a viable causal factor in increasing organismal complexity through the action of these ≈22-nt noncoding RNAs in regulating gene expression. Because miRNAs are continuously being added to animalian genomes, and, once integrated into a gene regulatory network, are strongly conserved in primary sequence and rarely secondarily lost, their evolutionary history can be accurately reconstructed. Here, using a combination of Northern analyses and genomic searches, we show …

Gene Response Profiles For Daphnia Pulex Exposed To The Environmental Stressor Cadmium Reveals Novel Crustacean Metallothioneins, Joseph R. Shaw, John K. Colbourne, Jennifer C. Davey, Stephen P. Glaholt, Thomas H. Hampton, Celia Y. Chen, Carol L. Folt, Joshua W. Hamilton

Dartmouth Scholarship

Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant.

Sampling Daphnia's Expressed Genes: Preservation, Expansion And Invention Of Crustacean Genes With Reference To Insect Genomes, John K. Colbourne, Brian D. Eads, Joseph Shaw, Elizabeth Bohuski, Darren Bauer, Justen Andrews

Dartmouth Scholarship

Functional and comparative studies of insect genomes have shed light on the complement of genes, which in part, account for shared morphologies, developmental programs and life-histories. Contrasting the gene inventories of insects to those of the nematodes provides insight into the genomic changes responsible for their diversification. However, nematodes have weak relationships to insects, as each belongs to separate animal phyla. A better outgroup to distinguish lineage specific novelties would include other members of Arthropoda. For example, crustaceans are close allies to the insects (together forming Pancrustacea) and their fascinating aquatic lifestyle provides an important comparison for understanding the genetic …

Characterization Of The Yeast Ionome: A Genome-Wide Analysis Of Nutrient Mineral And Trace Element Homeostasis In Saccharomyces Cerevisiae, David J. Eide, Suzanne Clark, T . Murlidharan Nair, Mathias Gehl, Michael Gribskov, Mary Lou Guerinot, Jeffrey Harper

Dartmouth Scholarship

Nutrient minerals are essential yet potentially toxic, and homeostatic mechanisms are required to regulate their intracellular levels. We describe here a genome-wide screen for genes involved in the homeostasis of minerals in Saccharomyces cerevisiae. Using inductively coupled plasma-atomic emission spectroscopy (ICP-AES), we assayed 4,385 mutant strains for the accumulation of 13 elements (calcium, cobalt, copper, iron, potassium, magnesium, manganese, nickel, phosphorus, selenium, sodium, sulfur, and zinc). We refer to the resulting accumulation profile as the yeast 'ionome'.

A Gene Expression Fingerprint Of C. Elegans Embryonic Motor Neurons, Rebecca M. Fox, Stephen E. Von Stetina, Susan J. Barlow, Christian Shaffer, Kellen L. Olszewski, Jason H. Moore

Dartmouth Scholarship

Differential gene expression specifies the highly diverse cell types that constitute the nervous system. With its sequenced genome and simple, well-defined neuroanatomy, the nematode C. elegans is a useful model system in which to correlate gene expression with neuron identity. The UNC-4 transcription factor is expressed in thirteen embryonic motor neurons where it specifies axonal morphology and synaptic function. These cells can be marked with an unc-4::GFP reporter transgene. Here we describe a powerful strategy, Micro-Array Profiling of C. elegans cells (MAPCeL), and confirm that this approach provides a comprehensive gene expression profile of unc-4::GFP motor neurons in vivo.

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Chemical Genomics In Yeast, Charles Brenner

Dartmouth Scholarship

Four recent 'chemical genomic' studies, using genome-scale collections of yeast gene deletions, have presented complementary approaches to identifying gene-drug and pathway-drug interactions.Many drugs have unknown, controversial or multiple mechanisms of action. Four recent 'chemical genomic' studies, using genome-scale collections of yeast gene deletions that were either arrayed or barcoded, have presented complementary approaches to identifying gene-drug and pathway-drug interactions.