Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression data from uninfected and rhinovirus-infected peripheral blood mononuclear cells from 98 individuals

ABSTRACT: Rhinovirus (RV) is the most prevalent human respiratory virus. Each year, RV infects billions of people and is responsible for at least half of all common colds, the most common illness of humans. RV infection also affects the morbidity of a range of respiratory illnesses, such as bronchiolitis, pneumonia, asthma, chronic obstructive pulmonary disease, and cystic fibrosis. Despite its biological importance and public health significance, little is known about the genetic architecture of response to RV. To address this, we obtained genome-wide genotype and gene expression data in uninfected and RV-infected peripheral blood mononuclear cells (PBMCs) from 98 individuals. We characterized gene expression differences in response to RV infection and mapped expression quantitative trait loci (eQTLs) in both uninfected and RV-infected PBMCs. The study includes data from uninfected and rhinovirus-infected peripheral blood mononuclear cells (PBMCs) from 98 individuals. Twenty ml of blood was drawn from each participant. PBMCs were isolated from whole blood samples by Ficoll-Paque separation. From each subject, 4 million PBMCs were treated with media alone for 24 hours and 4 million PBMCs were treated with media containing RV16 for 24 hours. The multiplicity of infection was 10 plaque-forming units per cell. Total RNA was extracted after 24-hour incubation, using the RNeasy Plus Mini Kit; concentrations were measured on a Nanodrop ND-100 Spectrophotometer and quality was assessed using an Agilent 2100 Bioanalyzer. Genome wide gene expression profiling of uninfected and rhinovirus-infected PBMCs was obtained using Illumina HumanHT-12 v4 Expression BeadChip arrays at the University of Chicago Functional Genomics Core.

ORGANISM(S): Homo sapiens

SUBMITTER: Minal Caliskan

PROVIDER: E-GEOD-53543 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Background: Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Indeed, a subjectM-bM-^@M-^Ys epigenetic, transcriptomic and proteomic profiles dynamically respond to environmental challenges according to their intrinsic specific genetics. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obviously limitations stemming from ethical considerations of the clinical risk. In this rhinovirus study, we first establish that ex vivo human cells response to virus can serve as proxy for otherwise controversial in vivo human experimentation of viral infection. We further demonstrate that the N-of-1-pathways framework, that we previously validated for single patient M-bM-^@M-^Xomics analyses in cancer, can be utilized to understand individualized ex vivo response to viral stress and inform clinicians involved in personalized therapeutics. Method: N-of-1-pathways, validated retrospectively in whole tumors and experimentally in cancer cell lines, is a framework designed for identifying deregulated pathways at the single patient level between two M-bM-^@M-^Xomics profiles. It computes a significance score for a list of given genesets, using the M-bM-^@M-^Xomics profiles of a mere two samples as input (e.g. normal/tumoral, pre/post-treatment). We hypothesized that its usage could be extended to virus exposed cells. Additionally, we developed the Similarity Venn Diagram, an efficient and deceptively simple method for comparing results expressed in an ontology organized as a directed acyclic graph. We extracted the peripheral blood of four human subjects, with and without an ex vivo infection to rhinovirus. Their deregulated pathways were compared to those of 9 human subjects prior and after intranasal inoculation M-bM-^@M-^\in vivoM-bM-^@M-^] with rhinovirus. Results: We compared the N-of-1-pathways results using two established cohort-level methodologies: GSEA and enrichment of differentially expressed genes. Results are significantly and biologically similar between in vivo and ex vivo studies, both at the genes and enriched pathways levels. ROC curves demonstrate that deregulated pathways identified by N-of-1-pathways in cells each single subject infected ex vivo recapitulate the biologically relevant pathways observed in vivo in a whole cohort. Conclusion: In the context of less than five published transcriptomes of human viral infections in vivo and one ex vivo, the latter can be supplemented by 2-sample analyses yielding increased insight in individualized response without clinical risks. PBMCs incubated with viruses: The live PBMCs had been isolated from blood samples collected from four human subjects under a protocol approved by The University of Arizona Internal Review Board. Whole blood was obtained from donors and placed in heparin tubes that were centrifuged according to standard protocols to obtain PBMCs, then each aliquoted in two paired samples. Each sample of the pair was subsequently exposed to and incubated with either (i) Human Rhinovirus serotype 16 obtained from the American Type Culture Collection (RV; ATCCM-BM-. VR-283; ex vivo infected sample), or to (ii) sterile medium (control ex vivo non-infected sample) and incubated at 35M-BM-0C. This protocol resulted in 4 ex vivo infected + 4 ex vivo controls = 8 paired samples. RNA was extracted from these samples, amplified, tagged and hybridized on Affymetrix Human Gene 1.0 ST microarrays according to standard operating procedures. Dataset and preprocessing: Robust Multiple-array Average (RMA) normalization was applied on each patient data independently (2 paired samples at a time, to avoid bias in the single-patient experiments) using Affymetrix Power Tools (APT)

Dataset Information

Gene expression data from uninfected and rhinovirus-infected peripheral blood mononuclear cells from 98 individuals

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure