Identification of Hepatotropic Viruses from Plasma Using Deep Sequencing: A Next Generation Diagnostic Tool

Contributors: Gane Ka-Shu Wong Research Group, Eric John Carpenter, Glenn Ford, Jordan Dacey Lee Patterson, Sandra Lynn O'Keefe, Tracy Nicole Jordan, Troy Anthony Mitchell, Weiwei Wang, PhD


PLoS One. 2013 Apr 17;8(4):e60595. doi: 10.1371/journal.pone.0060595. Print 2013.

Law J1, Jovel J, Patterson J, Ford G, O’keefe S, Wang W, Meng B, Song D, Zhang Y, Tian Z, Wasilenko ST, Rahbari M, Mitchell T, Jordan T, Carpenter E, Mason AL, Wong GK.


We conducted an unbiased metagenomics survey using plasma from patients with chronic hepatitis B, chronic hepatitis C, autoimmune hepatitis (AIH), non-alcoholic steatohepatitis (NASH), and patients without liver disease (control). RNA and DNA libraries were sequenced from plasma filtrates enriched in viral particles to catalog virus populations. Hepatitis viruses were readily detected at high coverage in patients with chronic viral hepatitis B and C, but only a limited number of sequences resembling other viruses were found. The exception was a library from a patient diagnosed with hepatitis C virus (HCV) infection that contained multiple sequences matching GB virus C (GBV-C). Abundant GBV-C reads were also found in plasma from patients with AIH, whereas Torque teno virus (TTV) was found at high frequency in samples from patients with AIH and NASH. After taxonomic classification of sequences by BLASTn, a substantial fraction in each library, ranging from 35% to 76%, remained unclassified. These unknown sequences were assembled into scaffolds along with virus, phage and endogenous retrovirus sequences and then analyzed by BLASTx against the non-redundant protein database. Nearly the full genome of a heretofore-unknown circovirus was assembled and many scaffolds that encoded proteins with similarity to plant, insect and mammalian viruses. The presence of this novel circovirus was confirmed by PCR. BLASTx also identified many polypeptides resembling nucleo-cytoplasmic large DNA viruses (NCLDV) proteins. We re-evaluated these alignments with a profile hidden Markov method, HHblits, and observed inconsistencies in the target proteins reported by the different algorithms. This suggests that sequence alignments are insufficient to identify NCLDV proteins, especially when these alignments are only to small portions of the target protein. Nevertheless, we have now established a reliable protocol for the identification of viruses in plasma that can also be adapted to other patient samples such as urine, bile, saliva and other body fluids.



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goes to…APCaRI member Russ Greiner

Image of DREAM challenge winners, Russ Greiner pictured on far left.

Dr. Russ Greiner, Canada CIFAR AI Chair, Fellow-in-Residence at Amii, University of Alberta Professor, and APCaRI member, received the CAIAC Lifetime Achievement Award announced at the Canadian AI Conference on May 27, 2021. This the highest honour bestowed by CAIAC, given in recognition to researchers who have distinguished themselves through outstanding research excellence in AI during the course of their academic career. APCaRI congratulates Russ Greiner for his well-deserved CAIAC Lifetime Achievement Award!

“Using machine learning techniques to produce effective, evidence-based personalized treatment”

The main foci of Russ Greiner’s current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability. He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes.

One of these four papers was an entry into an international machine learning competition hosted by DREAM, an open-science effort dedicated to improving health and health care through crowdsourcing problem-solving. DREAM’s challenge was to develop an algorithm to predict which prostate cancer patients would respond to certain treatments and which would follow the medication regimen. The algorithm could be used by clinicians to help chose the best treatment plans for the patient.

Greiner and a team of students tied for the top place in the competition against over 50 teams from around the world. Then the winners collaborated to create an even better solution to the problem!







- Perrin Beatty