Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood

Abstract

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 29, 2021
Source ID
10.1093/g3journal/jkab141

Entities

People

  • Björn Oskarsson
  • Christopher D. Link
  • Joanne Wuu
  • Leonard Petrucelli
  • Marko Melnick
  • Mercedes Prudencio
  • Michael Benatar
  • Patrick Gonzales
  • Robin D. Dowell
  • Thomas J Larocca
  • Yuping Song

Organizations

  • ALS Association
  • ALS Recovery Fund
  • Colorado State University
  • Mayo Clinic
  • Muscular Dystrophy Association
  • National Institute of Neurological Disorders and Stroke
  • National Institutes of Health
  • United States Department of Defense
  • University of Colorado
  • University of Miami

Tags

Fields of Study

  • Biology

Readers

  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • Biotechnology