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Wurtele, E. S.

Publications and source records attributed to Wurtele, E. S..

10 recordsLinked to original sources

A pan-tissue, pan-disease compendium of human orphan genes

Species-specific genes are ubiquitous in evolution, with functions ranging from prey paralysis to survival in subzero temperatures. Because they are typically expressed under limited conditions and lack canonical features, such genes may be vastly under-identified, even in humans. Here, we leverage terabytes of human RNA-Seq data to identify thousands of highly-expressed transcripts that do not correspond to any Gencode-annotated gene. Many may be novel ncRNAs although 80% of them contain ORFs that have the potential of encoding proteins unique to Homo sapiens (orphan genes). We validate our findings with independent strand-specific and single-cell RNA-seq datasets. Hundreds of these novel transcripts overlap with deleterious genomic variants; thousands show significant association with disease-specific patient survival. Most are dynamically regulated and accumulate selectively in particular tissues, cell-types, developmental stages, tumors, COVID-19, sex, and ancestries. As such, these transcripts hold potential as diagnostic biomarkers or therapeutic targets. To empower future discovery, we provide a compendium of these huge RNA-Seq expression data, and RiboSeq data, with associated metadata. Further, we supply the gene models for the novel genes as UCSC Genome Browser tracks.

evolutionary biology↗

Accuracy of functional gene community detection in Saccharomyces cerevisiae by maximizing Generalized Modularity Density

Identifying functionally-cohesive gene communities from large data sets of expression data for individual genes is a key approach to understanding the molecular components of biological processes. Here, we compare the accuracy of twelve different approaches to infer gene co-expression networks and then find gene communities within the networks. Among the approaches used are ones involving a recently developed clustering method that identifies communities by maximizing Generalized Modularity Density (Qg). RNA-Seq data from 691 samples of S. cerevisiae (yeast) are analyzed. These data have been obtained from organisms grown under diverse environmental and developmental conditions and encompass varied mutant lines. To assess the accuracy of different approaches, we introduce a statistical measure, the Average Adjusted Rand Index (AARI) score, which compares their results to Gene Ontology (GO) term associations. Inferring gene networks using the Context Likelihood of Relatedness (CLR) and subsequently clustering by maximizing Generalized Modularity Density is found to identify the most significant functional communities. Also, to quantify the extent to which the identified communities are biologically relevant, a GO term enrichment analysis is performed. The results indicate that many of the communities found by maximizing Generalized Modularity Density are enriched in genes with known biological functions. Furthermore, some of the communities contain genes of unknown function, enabling inference of potentially novel functional interactions involving these genes. Furthermore, some genes are species-specific orphan genes; assignment of these orphan genes to communities enriched in a particular biological process provides a method to infer the biological process in which they are involved. We focus on a few communities that are highly significantly enriched in a particular biological process, and develop experimentally-testable predictions about the orphan genes in these communities. Author summaryFinding gene communities that are of biological relevance from expression profiles of individual genes is a critical approach to understanding biological processes and their molecular components. Various computational methods have been developed to infer underlying metabolic and regulatory networks and to identify functional communities of genes. Which network inference and clustering methods works best to achieve this goal has largely remained an open question. Here, using genome-wide transcriptomic data for S. cerevisiae, we systematically compare the effectiveness of several commonly used network inference and clustering methods. We rank these methods by comparing the clusters obtained by different methods to Gene Ontology (GO) terms. We find that inferring gene networks using a method known as the Context Likelihood of Relatedness (CLR) and subsequently clustering by maximizing Generalized Modularity Density identifies the most significant functional communities.

systems biology↗

The Great Deceiver: miR-2392's Hidden Role in Driving SARS-CoV-2 Infection

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provides an exciting avenue towards antiviral therapeutics. From patient transcriptomic data, we have discovered a circulating miRNA, miR-2392, that is directly involved with SARS-CoV-2 machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia as well as promoting many symptoms associated with COVID-19 infection. We demonstrate miR-2392 is present in the blood and urine of COVID-19 positive patients, but not detected in COVID-19 negative patients. These findings indicate the potential for developing a novel, minimally invasive, COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we have developed a novel miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters and may potentially inhibit a COVID-19 disease state in humans.

systems biology↗

Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

microbiology↗

orfipy: a fast and flexible tool for extracting ORFs

SummarySearching for ORFs in transcripts is a critical step prior to annotating coding regions in newly-sequenced genomes and to search for alternative reading frames within known genes. With the tremendous increase in RNA-Seq data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in fasta sequences. The search is rapid and is fully customizable, with a choice of Fasta and BED output formats. Availability and implementationorfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy). Contactusingh@iastate.edu, mash@iastate.edu Supplementary informationSupplementary data are available at https://github.com/urmi-21/orfipy

bioinformatics↗

Differential expression of COVID-19-related genes in European Americans and African Americans

The Coronavirus disease 2019 (COVID-19) pandemic has affected African American populations disproportionately in regards to both morbidity and mortality. A multitude of factors likely account for this discrepancy. Gene expression represents the interaction of genetics and environment. To elucidate whether levels of expression of genes implicated in COVID-19 vary in African Americans as compared to European Americans, we re-mine The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) RNA-Seq data. Multiple genes integral to infection, inflammation and immunity are differentially regulated across the two populations. Most notably, F8A2 and F8A3, which encode the HAP40 protein that mediates early endosome movement in Huntingtons Disease, are more highly expressed by up to 24-fold in African Americans. Such differences in gene expression can establish prognostic signatures and have critical implications for precision treatment of diseases such as COVID-19. We advocate routine inclusion of information such as postal code, education level, and profession (as a proxies for socioeconomic condition) and race in the metadata about each individual sampled for sequencing studies. This relatively simple change would enable large-scale data-driven approaches to dissect relationships among race, socio-economic factors, and disease.

bioinformatics↗

pyrpipe: a python package for RNA-Seq workflows

The availability of terabytes of RNA-Seq data and continuous emergence of new analysis tools, enable unprecedented biological insight. However, implementing RNA-Seq analysis pipelines in a reproducible, flexible manner is challenging as data gets bigger and more complex. Thus, there is a pressing requirement for frameworks that allows for fast, efficient, easy-to-manage, and reproducibile analysis. Simple scripting has many challenges and drawbacks. We have developed a python package, python RNA-Seq Pipeliner (pyrpipe) that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in python, in an object-oriented manner. pyrpipe provides access to popular RNA-Seq tools, within python, via easy-to-use high level APIs. Pipelines can be customized by integrating new python code, third-party programs, or python libraries. Users can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in python and is compatible with python versions 3.6 and higher. To illustrate the rich functionality of pyrpipe, we provide case studies using RNA-Seq data from GTEx, SARS-CoV-2-infected human cells, and Zea mays. All source code is freely available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source or from PyPI (https://pypi.org/project/pyrpipe). Documentation is available at (http://pyrpipe.rtfd.io).

bioinformatics↗

Maximizing prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ("orphan genes") share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene annotation pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes and 99% of ancient genes in Arabidopsis. We provide a light weight, flexible, reproducible solution to improve gene prediction.

bioinformatics↗

Recycling RNA-Seq Data to Identify Candidate Orphan Genes for experimental analysis

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

bioinformatics↗

phylostratr: A framework for phylostratigraphy

MotivationThe goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. Currently, there are no general pipelines for this task. We present an R package, phylostratr, to fill this gap, making high-quality phylostratigraphic analysis accessible to non-specialists.\n\nResultsPhylostratigraphic analysis entails searching for homologs within increasingly broad clades. The highest clade that contains all homologs of a gene is that genes phylostratum. We have created a general R-based framework, phylostratr, for estimating the phylostratum of every gene in a species. The program can fully automate an analysis: select species for a balanced representation of each strata, retrieve the sequences from UniProt, build BLAST databases, run BLAST, infer homologs for each gene against each subject species, determine phylostrata, and return summaries and diagnostics. phylostratr allows extensive customization. A user may: modify the automatically-generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. phylostratr also offers proteome quality assessments, false-positive diagnostics, and checks for missing organelle genomes. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.\n\nAvailabilityphylostratr source code and vignettes are available on GitHub at https://github.com/arendsee/phylostratr\n\nContactevewurtele@gmail.com

bioinformatics↗