Vincent Gardeux1, Anthony Bosco2, Jianrong Li1, Marilyn J Halonen3, Daniel Jackson4, Fernando D Martinez5, Yves A Lussier6. 1. Department of Medicine, University of Arizona, Tucson, AZ, USA. 2. Telethon Institute for Child Health Research, Perth, Australia. 3. Department of Pharmacology, University of Arizona, Tucson, AZ, USA. 4. Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA; The Childhood Asthma Research and Education Network (CARE). 5. The Childhood Asthma Research and Education Network (CARE); Department of Pediatrics, University of Arizona, Tucson, AZ, USA; BIO5 Institute, University of Arizona, Tucson, AZ, USA. Electronic address: fdmartin@email.arizona.edu. 6. Department of Medicine, University of Arizona, Tucson, AZ, USA; BIO5 Institute, University of Arizona, Tucson, AZ, USA; UA Cancer Center, University of Arizona, Tucson, AZ, USA. Electronic address: yves@email.arizona.edu.
Abstract
BACKGROUND: Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obvious limitations stemming from ethical considerations of the clinical risk. OBJECTIVE: In this rhinovirus study, we first hypothesized that ex vivo human cells response to virus can serve as a proxy for otherwise controversial in vivo human experimentation. We further hypothesized that the N-of-1-pathways framework, previously validated in cancer, can be effective in understanding the more subtle individual transcriptomic response to viral infection. METHOD: N-of-1-pathways computes a significance score for a given list of gene sets at the patient level, using merely the 'omics profiles of two paired samples as input. We extracted the peripheral blood mononuclear cells (PBMC) of four human subjects, aliquoted in two paired samples, one subjected to ex vivo rhinovirus infection. Their dysregulated genes and pathways were then compared to those of 9 human subjects prior and after intranasal inoculation in vivo with rhinovirus. Additionally, we developed the Similarity Venn Diagram, a novel visualization method that goes beyond conventional overlap to show the similarity between two sets of qualitative measures. RESULTS: We evaluated the individual N-of-1-pathways results using two established cohort-based methods: GSEA and enrichment of differentially expressed genes. Similarity Venn Diagrams and individual patient ROC curves illustrate and quantify that the in vivo dysregulation is recapitulated ex vivo both at the gene and pathway level (p-values⩽0.004). CONCLUSION: We established the first evidence that an interpretable dynamic transcriptome metric, conducted as an ex vivo assays for a single subject, has the potential to predict individualized response to infectious disease without the clinical risks otherwise associated to in vivo challenges. These results serve as a foundational work for personalized "virograms".
BACKGROUND: Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obvious limitations stemming from ethical considerations of the clinical risk. OBJECTIVE: In this rhinovirus study, we first hypothesized that ex vivo human cells response to virus can serve as a proxy for otherwise controversial in vivo human experimentation. We further hypothesized that the N-of-1-pathways framework, previously validated in cancer, can be effective in understanding the more subtle individual transcriptomic response to viral infection. METHOD: N-of-1-pathways computes a significance score for a given list of gene sets at the patient level, using merely the 'omics profiles of two paired samples as input. We extracted the peripheral blood mononuclear cells (PBMC) of four human subjects, aliquoted in two paired samples, one subjected to ex vivo rhinovirus infection. Their dysregulated genes and pathways were then compared to those of 9 human subjects prior and after intranasal inoculation in vivo with rhinovirus. Additionally, we developed the Similarity Venn Diagram, a novel visualization method that goes beyond conventional overlap to show the similarity between two sets of qualitative measures. RESULTS: We evaluated the individual N-of-1-pathways results using two established cohort-based methods: GSEA and enrichment of differentially expressed genes. Similarity Venn Diagrams and individual patient ROC curves illustrate and quantify that the in vivo dysregulation is recapitulated ex vivo both at the gene and pathway level (p-values⩽0.004). CONCLUSION: We established the first evidence that an interpretable dynamic transcriptome metric, conducted as an ex vivo assays for a single subject, has the potential to predict individualized response to infectious disease without the clinical risks otherwise associated to in vivo challenges. These results serve as a foundational work for personalized "virograms".
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Authors: Vincent Gardeux; Joanne Berghout; Ikbel Achour; A Grant Schissler; Qike Li; Colleen Kenost; Jianrong Li; Yuan Shang; Anthony Bosco; Donald Saner; Marilyn J Halonen; Daniel J Jackson; Haiquan Li; Fernando D Martinez; Yves A Lussier Journal: J Am Med Inform Assoc Date: 2017-11-01 Impact factor: 4.497