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An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites.

ABSTRACT: The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that short-read aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden. This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a "clean-and-recover" step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals. The optimized workflow and analysis recommendations can be implemented in the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets.

SUBMITTER: Rosewick N

PROVIDER: S-EPMC7606357 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites.

Rosewick Nicolas N Hahaut Vincent V Durkin Keith K Artesi Maria M Karpe Snehal S Wayet Jérôme J Griebel Philip P Arsic Natasa N Marçais Ambroise A Hermine Olivier O Burny Arsène A Georges Michel M Van den Broeke Anne A

Frontiers in microbiology 20201020

The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the applicati ...[more]

PMID: 33193242

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Project description:BackgroundHTLV-1 causes proliferation of clonal populations of infected T cells in vivo, each clone defined by a unique proviral integration site in the host genome. The proviral load is strongly correlated with odds of the inflammatory disease HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). There is evidence that asymptomatic HTLV-1 carriers (ACs) have a more effective CD8 + T cell response, including a higher frequency of HLA class I alleles able to present peptides from a regulatory protein of HTLV-1, HBZ. We have previously shown that specific features of the host genome flanking the proviral integration site favour clone survival and spontaneous expression of the viral transactivator protein Tax in naturally infected PBMCs ex vivo. However, the previous studies were not designed or powered to detect differences in integration site characteristics between ACs and HAM/TSP patients. Here, we tested the hypothesis that the genomic environment of the provirus differs systematically between ACs and HAM/TSP patients, and between individuals with strong or weak HBZ presentation.MethodsWe used our recently described high-throughput protocol to map and quantify integration sites in 95 HAM/TSP patients and 68 ACs from Kagoshima, Japan, and 75 ACs from Kumamoto, Japan. Individuals with 2 or more HLA class I alleles predicted to bind HBZ peptides were classified 'strong' HBZ binders; the remainder were classified 'weak binders'.ResultsThe abundance of HTLV-1-infected T cell clones in vivo was correlated with proviral integration in genes and in areas with epigenetic marks associated with active regulatory elements. In clones of equivalent abundance, integration sites in genes and active regions were significantly more frequent in ACs than patients with HAM/TSP, irrespective of HBZ binding and proviral load. Integration sites in genes were also more frequent in strong HBZ binders than weak HBZ binders.ConclusionClonal abundance is correlated with integration in a transcriptionally active genomic region, and these regions may promote cell proliferation. A clone that reaches a given abundance in vivo is more likely to be integrated in a transcriptionally active region in individuals with a more effective anti-HTLV-1 immune response, such those who can present HBZ peptides or those who remain asymptomatic.

Project description:BACKGROUND:Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. RESULTS:Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). CONCLUSIONS:We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a?>?6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ).

Dataset Information

An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites.

Publications

An Improved Sequencing-Based Bioinformatics Pipeline to Track the Distribution and Clonal Architecture of Proviral Integration Sites.

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