Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Mock or latently infected KSHV cells (BCBL, SLK and HFF)

ABSTRACT: [original title] Mock or latently infected KSHV cells (BCBL, SLK and HFF) vs common reference (mixture of RNA from both infected and uninfected cells). Expression profiling of latently infected cells using a custom tiling microarray. SLK and HFF cells were infected and selected for rKSHV.219. Mock infected SLK and HFF cells served as controls for each of these two stably infected cells, respectively. BJAB cells served as uninfected controls for the BCBL-1 cells. Biological replicates were harvested and analyzed. Two condition experiment: mock infected vs. latently infected cells. Three cell types.

ORGANISM(S): Homo sapiens

SUBMITTER: Sanjay Chandriani

PROVIDER: E-GEOD-20443 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Array-based transcript profiling and limiting-dilution reverse transcription-PCR analysis identify additional latent genes in Kaposi's sarcoma-associated herpesvirus.

Chandriani Sanjay S Ganem Don D

Journal of virology 20100310 11

Kaposi's sarcoma-associated herpesvirus (KSHV) is a B-lymphotropic herpesvirus strongly linked to both lymphoproliferative diseases and Kaposi's sarcoma. The viral latency program of KSHV is central to persistent infection and plays important roles in the pathogenesis of KSHV-related tumors. Up to six polypeptides and 18 microRNAs are known to be expressed in latency, but it is unclear if all major latency genes have been identified. Here, we have employed array-based transcript profiling and li ...[more]

PMID: 20219929

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Project description:Background: The cellular reservoir of latent HIV infection remains the main barrier to cure this virus. Elimination of this reservoir would be possible, if molecular identity of latently infected cells were fully elucidated. Biomarkers proposed previously were able to capture only a relatively small fraction of all reservoir cells. In the present study, we set out to conduct comprehensive molecular profiling, at the protein and RNA levels, of CD4+ T cells latently infected with HIV in vitro, using liquid chromatography-mass spectrometry (LC-MS) and RNA sequencing (RNA-Seq), respectively. Protein-based methods such as quantitative proteomic profiling using LC-MS may be more beneficial due to direct transferability of results to antibody-based approaches to capture latently infected cells. Integrated analysis of proteomic and transcriptomic data adds a level of validation and increases confidence in identified biomarkers. Flow cytometry and integrated HIV DNA assay were further used to enrich for latently infected cells with antibodies against selected biomarker proteins. Results: Using quantitative proteomics, we identified a total of 10,886 proteins (peptide level FDR < 0.05), of which 673 were up- and 780 down-regulated in latently infected compared to mock-infected cells in vitro (p < 0.05). Among these proteins, 21 were dysregulated at the RNA level in the same direction. Pathway analysis identified p53, mTOR, Wnt and NOTCH signaling, demonstrating that our in vitro model reflects known mechanisms of latency establishment and maintenance. Comparison of identified proteins with other proteomics studies revealed that identified molecular signatures of latency depend on technology and cell types used; however, a subset of proteins were identified both in the present, and at least one other study. Antibodies against selected protein markers, CEACAM1 and PLXNB2, could enrich for latently infected cells from mixed cell population 3-10 fold (5.8 fold average, p < 0.001). Conclusion: Two new molecules, CEACAM1 and PLXNB2, were identified as biomarkers for HIV latency. However, the level of enrichment for latently infected cells compared to biomarkers proposed previously was not improved. These results are consistent with the idea that each proposed biomarker defines only a subset of latently infected cells, and that a combined biomarker will be required to capture or target the latent HIV reservoir represented by different cell types.

Dataset Information

Mock or latently infected KSHV cells (BCBL, SLK and HFF)

Publications

Array-based transcript profiling and limiting-dilution reverse transcription-PCR analysis identify additional latent genes in Kaposi's sarcoma-associated herpesvirus.

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