Life Sciences Commons^™

The Trnaval Half: A Strong Endogenous Toll-Like Receptor 7 Ligand With A 5′-Terminal Universal Sequence Signature, Kamlesh Ganesh Pawar, Takuya Kawamura, Yohei Kirino

Computational Medicine Center Faculty Papers

Toll-like receptors (TLRs) are crucial components of the innate immune system. Endosomal TLR7 recognizes single-stranded RNAs, yet its endogenous ssRNA ligands are not fully understood. We previously showed that extracellular (ex-) 5'-half molecules of tRNA^HisGUG (the 5'-tRNA^HisGUG half) in extracellular vesicles (EVs) of human macrophages activate TLR7 when delivered into endosomes of recipient macrophages. Here, we fully explored immunostimulatory ex-5'-tRNA half molecules and identified the 5'-tRNA^ValCAC/AAC half, the most abundant tRNA-derived RNA in macrophage EVs, as another 5'-tRNA half molecule with strong TLR7 activation capacity. Levels of the ex-5'-tRNA^ValCAC/AAC half were highly up-regulated in macrophage EVs …

Integrated Transcriptomics And Histopathology Approach Identifies A Subset Of Rejected Donor Livers With Potential Suitability For Transplantation, Ankita Srivastava, Alexandra Manchel, John Waters, Manju Ambelil, Benjamin K. Barnhart, Jan B. Hoek, Ashesh P. Shah, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

BACKGROUND: Liver transplantation is an effective treatment for liver failure. There is a large unmet demand, even as not all donated livers are transplanted. The clinical selection criteria for donor livers based on histopathological evaluation and liver function tests are variable. We integrated transcriptomics and histopathology to characterize donor liver biopsies obtained at the time of organ recovery. We performed RNA sequencing as well as manual and artificial intelligence-based histopathology (10 accepted and 21 rejected for transplantation).

RESULTS: We identified two transcriptomically distinct rejected subsets (termed rejected-1 and rejected-2), where rejected-2 exhibited a near-complete transcriptomic overlap with the accepted livers, …

Radiation Exposure Determination In A Secure, Cloud-Based Online Environment, Ben C. Shirley, Eliseos J. Mucaki, Peter Rogan

Biochemistry Publications

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI …

Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman

Computer Science Faculty Publications

Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific assembly called Regional Extension of Assemblies Using Linked-Reads (REXTAL). In this study, we evaluate REXTAL and genome-wide assembly (Supernova) approaches on 10X Genomics linked-reads data sets partitioned and barcoded using the …

Identifying Potential Cancer Driver Genes By Genomic Data Integration., Yong Chen, Jingjing Hao, Wei Jiang, Tong He, Xuegong Zhang, Tao Jiang, Rui Jiang

Yong Chen

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis …

Incorporating Pathway Information Into Feature Selection Towards Better Performed Gene Signatures, Suyan Tian, Chi Wang, Bing Wang

Biostatistics Faculty Publications

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, …

A Longitudinal Cline Characterizes The Genetic Structure Of Human Populations In The Tibetan Plateau, Choongwon Jeong, Benjamin M. Peter, Buddha Basnyat, Maniraj Neupane, Geoff Childs, Sienna Craig, John Novembre, Anna Di Rienzo

Dartmouth Scholarship

Indigenous populations of the Tibetan plateau have attracted much attention for their good performance at extreme high altitude. Most genetic studies of Tibetan adaptations have used genetic variation data at the genome scale, while genetic inferences about their de- mography and population structure are largely based on uniparental markers. To provide genome-wide information on population structure, we analyzed new and published data of 338 individuals from indigenous populations across the plateau in conjunction with world- wide genetic variation data. We found a clear signal of genetic stratification across the east- west axis within Tibetan samples. Samples from more eastern locations …

Discovery And Validation Of Information Theory-Based Transcription Factor And Cofactor Binding Site Motifs., Ruipeng Lu, Eliseos J Mucaki, Peter K Rogan

Biochemistry Publications

Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, …

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.

Loregic: A Method To Characterize The Cooperative Logic Of Regulatory Factors, Daifeng Wang, Koon-Kiu Yan, Cristina Sisu, Chao Cheng, Joel Rozowsky, William Meyerson, Mark B. Gerstein

Dartmouth Scholarship

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. …

Machine Learning Methods Enable Predictive Modeling Of Antibody Feature:Function Relationships In Rv144 Vaccinees, Ickwon Choi, Amy W. Chung, Todd J. Suscovich, Supachai Rerks-Ngarm, Punnee Pitisuttithum, Sorachai Nitayapha, Jaranit Kaewkungwal, Robert J. O'Connell, Donald Francis, Merlin L. Robb, Nelson L. Michael, Jerome H. Kim, Galit Alter, Margaret E. Ackerman, Chris Bailey-Kellogg

Dartmouth Scholarship

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine …

Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond

Dartmouth Scholarship

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from …

A Quick Guide For Building A Successful Bioinformatics Community., Aidan Budd, Manuel Corpas, Michelle D Brazas, Jonathan C Fuller, Jeremy Goecks, Nicola J Mulder, Magali Michaut, B F Francis Ouellette, Aleksandra Pawlik, Niklas Blomberg

Computational Biology Institute

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding …

Mapping The Pareto Optimal Design Space For A Functionally Deimmunized Biotherapeutic Candidate, Regina S. Salvat, Andrew S. Parker, Yoonjoo Choi, Chris Bailey-Kellogg, Karl E. Griswold

Dartmouth Scholarship

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the …

Systems Level Analysis Of Systemic Sclerosis Shows A Network Of Immune And Profibrotic Pathways Connected With Genetic Polymorphisms, J. Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A. A. Wood, Casey S. Greene, Patricia A. Pioli, Monique E. Hinchcliff, Michael L. Whitfield

Dartmouth Scholarship

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes …

Trail-Based High Throughput Screening Reveals A Link Between Trail-Mediated Apoptosis And Glutathione Reductase, A Key Component Of Oxidative Stress Response., Dmitri Rozanov, Anton Cheltsov, Eduard Sergienko, Stefan Vasile, Vladislav Golubkov, Alexander E Aleshin, Trevor Levin, Elie Traer, Byron Hann, Julia Freimuth, Nikita Alexeev, Max A Alekseyev, Sergey P Budko, Hans Peter Bächinger, Paul Spellman

Computational Biology Institute

A high throughput screen for compounds that induce TRAIL-mediated apoptosis identified ML100 as an active chemical probe, which potentiated TRAIL activity in prostate carcinoma PPC-1 and melanoma MDA-MB-435 cells. Follow-up in silico modeling and profiling in cell-based assays allowed us to identify NSC130362, pharmacophore analog of ML100 that induced 65-95% cytotoxicity in cancer cells and did not affect the viability of human primary hepatocytes. In agreement with the activation of the apoptotic pathway, both ML100 and NSC130362 synergistically with TRAIL induced caspase-3/7 activity in MDA-MB-435 cells. Subsequent affinity chromatography and inhibition studies convincingly demonstrated that glutathione reductase (GSR), a key …

Integrated Assessment Of Predicted Mhc Binding And Cross-Conservation With Self Reveals Patterns Of Viral Camouflage, Lu He, Anne S. De Groot, Andres H. Gutierrez, William D. Martin, Lenny Moise, Chris Bailey-Kellogg

Dartmouth Scholarship

Immune recognition of foreign proteins by T cells hinges on the formation of a ternary complex sandwiching a constituent peptide of the protein between a major histocompatibility complex (MHC) molecule and a T cell receptor (TCR). Viruses have evolved means of "camouflaging" themselves, avoiding immune recognition by reducing the MHC and/or TCR binding of their constituent peptides. Computer-driven T cell epitope mapping tools have been used to evaluate the degree to which articular viruses have used this means of avoiding immune response, but most such analyses focus on MHC-facing ‘agretopes'. Here we set out a new means of evaluating the …

Identifying Potential Cancer Driver Genes By Genomic Data Integration., Yong Chen, Jingjing Hao, Wei Jiang, Tong He, Xuegong Zhang, Tao Jiang, Rui Jiang

Faculty Scholarship for the College of Science & Mathematics

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis …

Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson

Computational Biology Institute

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence …

A Unified Framework Integrating Parent-Of-Origin Effects For Association Study, Feifei Xiao, Jianzhong Ma, Christopher I. I. Amos

Dartmouth Scholarship

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including …

Key Genes For Modulating Information Flow Play A Temporal Role As Breast Tumor Coexpression Networks Are Dynamically Rewired By Letrozole, Nadia M. Penrod, Jason H. Moore

Dartmouth Scholarship

Genes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another. Using transcriptomic data generated from sequential tumor biopsy samples, taken at diagnosis, following 10-14 days and following 90 days of letrozole treatment, and a pairwise partial orrelation statistic, we build temporal gene coexpression networks. We characterize the structure of each network and identify genes that hold prominent positions for maintaining …

Phylogenetic Search Through Partial Tree Mixing., Kenneth Sundberg, Mark Clement, Quinn Snell, Dan Ventura, Michael Whiting, Keith Crandall

Computational Biology Institute

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques.

RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda

CONCLUSIONS: The use of Partial Tree Mixing …

A Genomic Island In Salmonella Enterica Ssp. Salamae Provides New Insights On The Genealogy Of The Locus Of Enterocyte Effacement., P Scott Chandry, Simon Gladman, Sean C Moore, Torsten Seemann, Keith A Crandall, Narelle Fegan

Computational Biology Institute

The genomic island encoding the locus of enterocyte effacement (LEE) is an important virulence factor of the human pathogenic Escherichia coli. LEE typically encodes a type III secretion system (T3SS) and secreted effectors capable of forming attaching and effacing lesions. Although prominent in the pathogenic E. coli such as serotype O157:H7, LEE has also been detected in Citrobacter rodentium, E. albertii, and although not confirmed, it is likely to also be in Shigella boydii. Previous phylogenetic analysis of LEE indicated the genomic island was evolving through stepwise acquisition of various components. This study describes a new LEE region from two …

Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg

Dartmouth Scholarship

Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.

Improved Ibd Detection Using Incomplete Haplotype Information, Giulio Genovese, Gregory Leibon, Martin R. Pollak, Daniel N. Rockmore

Dartmouth Scholarship

The availability of high density genetic maps and genotyping platforms has transformed human genetic studies. The use of these platforms has enabled population-based genome-wide association studies. However, in inheritance-based studies, current methods do not take full advantage of the information present in such genotyping analyses. In this paper we describe an improved method for identifying genetic regions shared identical-by-descent (IBD) from recent common ancestors. This method improves existing methods by taking advantage of phase information even if it is less than fully accurate or missing. We present an analysis of how using phase information increases the accuracy of IBD detection …

Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota

Dartmouth Scholarship

The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …