Dataset Information

HIV Integration Site Analysis of Cellular Models of HIV Latency with a Probe-Enriched Next-Generation Sequencing Assay.

ABSTRACT:

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Antiretroviral therapy (ART) is successful in the suppression of HIV but cannot target and eradicate the latent proviral reservoir. The location of retroviral integration into the human genome is thought to play a role in the clonal expansion of infected cells and HIV persistence. We developed a high-throughput targeted sequence capture assay that uses a pool of HIV-specific probes to enrich Illumina libraries prior to deep sequencing. Using an expanded clonal population of ACH-2 cells, we demonstrate that this sequence capture assay has an extremely low false-positive rate. This assay assessed four cellular models commonly used to study HIV latency and latency-reversing agents: ACH-2 cells, J-Lat cells, the Bcl-2-transduced primary CD4(+)model, and the cultured TCM(central memory) CD4(+)model. HIV integration site characteristics and genes were compared between these cellular models and to previously reported patient data sets. Across these cellular models, there were significant differences in integration site characteristics, including orientation relative to that of the host gene, the proportion of clonally expanded sites, and the proportion located within genic regions and exons. Despite a greater diversity of minority integration sites than expected in ACH-2 cells, their integration site characteristics consistently differed from those of the other models and from the patient samples. Gene ontology analysis of highly represented genes from the patient samples found little overlap with HIV-containing genes from the cell lines. These findings show that integration site differences exist among the commonly used cellular models of HIV latency and in comparison to integration sites found in patient samples.

Importance

Despite the success of ART, currently there is no successful therapy to eradicate integrated proviruses. Cellular models of HIV latency are used to test the efficacy of latency-reversing agents, but it is unclear how well these models reflect HIV integration into the human genome in vivo We have developed a novel probe-based sequence enrichment assay to sequence and analyze integrated HIV. We compared HIV integration site characteristics between four cellular models and to previously described patient data sets. Significant differences were detected in the distribution of HIV integration sites between cellular models of HIV latency and compared to data sets from patient samples. The results from this study have implications for how well these cellular models of HIV infection truly reflect HIV integration in vivo and their applicability in drug discovery for novel latency-reversing agents.

SUBMITTER: Sunshine S

PROVIDER: S-EPMC4836361 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

HIV Integration Site Analysis of Cellular Models of HIV Latency with a Probe-Enriched Next-Generation Sequencing Assay.

Sunshine Sara S Kirchner Rory R Amr Sami S SS Mansur Leandra L Shakhbatyan Rimma R Kim Michelle M Bosque Alberto A Siliciano Robert F RF Planelles Vicente V Hofmann Oliver O Ho Sui Shannan S Li Jonathan Z JZ

Journal of virology 20160414 9

<h4>Unlabelled</h4>Antiretroviral therapy (ART) is successful in the suppression of HIV but cannot target and eradicate the latent proviral reservoir. The location of retroviral integration into the human genome is thought to play a role in the clonal expansion of infected cells and HIV persistence. We developed a high-throughput targeted sequence capture assay that uses a pool of HIV-specific probes to enrich Illumina libraries prior to deep sequencing. Using an expanded clonal population of AC ...[more]

PMID: 26912621

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Project description:Purpose: Next-generation sequencing (NGS) has become a powerful microscope to study cell models of HIV. The goals of this study is to analyze latent HIV infection in the TCM model of HIV latency described in Martins et al 2015 and to evaluate potential markers for HIV infection. Methods: Peripheral blood mononuclear cells were collected from 4 healthy donors. Naive CD4 T cells were isolated and utilized to generate a model of latent HIV infection. During model generation, cells were sorted and collected for RNA extraction 10 days post infection along with their uninfected counterparts. 2 days after this, cells were activated with CD3/CD28 stimulation and collected for study for a total of 16 samples from 4 donors. After cell collection, RNA was extracted from infected cells and their uninfected counterparts for deep sequencing by Expression Analysis. Sequence reads that passed quality filters were mapped using Tophat and counted using HTSeq. In addition to human transcripts, we utilized 92 ERCC spikes as negative controls. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential expression for activated samples was not performed, but rather used to demonstrate upregulation of T-cell activation markers along with changing to the type and abundance of HIV transcripts produced. Results: Using an custom built data analysis pipeline, 82 million reads per sample to the human genome (build hg38) and identified 13,534 human transcripts 67 ERCC spike in transcripts, and HIV NL43 transcripts were identified with the Tophat/HTSeq workflow. Differential expression analysis was performed between uninfected and latently infected cells. 826 genes were found to be differentially expressed, 275 downregulated and 551 upregulated (FDR < 0.05) with EdgeR. GO and Pathway analysis of differential expressed genes revealed significant dysregulation of genes involved in the p53 signaling pathway. Subsequent studies with pifithrin demonstrated a reduction in latently infected cell during model generation. Conclusions: This study represents the first detailed analysis of HIV latency with this cell model using next generations sequencing. These results demonstrate that the TCM model of HIV latency of Martins et al 2015 is truly reflective of HIV latency. This study also provides a framework for which to analyze future cell models of HIV latency using next generation sequencing. Finally, this work demonstrates that the p53 signaling pathway is a key pathway dysregulated in latency for this cell model with several genes dysregulated in HIV latency.

Project description:Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.

Dataset Information

HIV Integration Site Analysis of Cellular Models of HIV Latency with a Probe-Enriched Next-Generation Sequencing Assay.

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Importance

Publications

HIV Integration Site Analysis of Cellular Models of HIV Latency with a Probe-Enriched Next-Generation Sequencing Assay.

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