The latent HIV reservoir is generated following HIV infection of activated effector CD4 T cells, which then transition to a memory phenotype. Here, we describe an
method, called QUECEL (
iescent
...ffector
ll
atency), that mimics this process efficiently and allows production of large numbers of latently infected CD4
T cells. Naïve CD4
T cells were polarized into the four major T cell subsets (Th1, Th2, Th17, and Treg) and subsequently infected with a single-round reporter virus which expressed GFP/CD8a. The infected cells were purified and coerced into quiescence using a defined cocktail of cytokines, including tumor growth factor beta, interleukin-10 (IL-10), and IL-8, producing a homogeneous population of latently infected cells. Flow cytometry and transcriptome sequencing (RNA-Seq) demonstrated that the cells maintained the correct polarization phenotypes and had withdrawn from the cell cycle. Key pathways and gene sets enriched during transition from quiescence to reactivation include E2F targets, G
M checkpoint, estrogen response late gene expression, and c-myc targets. Reactivation of HIV by latency-reversing agents (LRAs) closely mimics RNA induction profiles seen in cells from well-suppressed HIV patient samples using the envelope detection of
transcription sequencing (EDITS) assay. Since homogeneous populations of latently infected cells can be recovered, the QUECEL model has an excellent signal-to-noise ratio and has been extremely consistent and reproducible in numerous experiments performed during the last 4 years. The ease, efficiency, and accuracy of the mimicking of physiological conditions make the QUECEL model a robust and reproducible tool to study the molecular mechanisms underlying HIV latency.
Current primary cell models for HIV latency correlate poorly with the reactivation behavior of patient cells. We have developed a new model, called QUECEL, which generates a large and homogenous population of latently infected CD4
memory cells. By purifying HIV-infected cells and inducing cell quiescence with a defined cocktail of cytokines, we have eliminated the largest problems with previous primary cell models of HIV latency: variable infection levels, ill-defined polarization states, and inefficient shutdown of cellular transcription. Latency reversal in the QUECEL model by a wide range of agents correlates strongly with RNA induction in patient samples. This scalable and highly reproducible model of HIV latency will permit detailed analysis of cellular mechanisms controlling HIV latency and reactivation.
Understanding why some people establish and maintain effective control of HIV-1 and others do not is a priority in the effort to develop new treatments for HIV/AIDS. Using a whole-genome association ...strategy, we identified polymorphisms that explain nearly 15% of the variation among individuals in viral load during the asymptomatic set-point period of infection. One of these is found within an endogenous retroviral element and is associated with major histocompatibility allele human leukocyte antigen (HLA)-B*5701, whereas a second is located near the HLA-C gene. An additional analysis of the time to HIV disease progression implicated two genes, one of which encodes an RNA polymerase I subunit. These findings emphasize the importance of studying human genetic variation as a guide to combating infectious agents.
...this huge volume of laboratory results and associated metadata has reached the sweet spot for machine learning techniques. A quick inspection of PubMed reveals the growing number ofpublications ...that include the concepts of genomics and machine learning or, more recently, deep learning (Fig. 1). ...it is useful to remind ourselves of the basic concepts: ...this example shows how machine learning could be weaved into the analytics ofa laboratory for the broader capture of clinical, laboratory, genomics, and also operational data (6). ...prioritizing variants on the basis of the likelihood that they are pathogenic is the current challenge for applying machine learning.
Single-Cell Genomics for Virology Ciuffi, Angela; Rato, Sylvie; Telenti, Amalio
Viruses,
05/2016, Letnik:
8, Številka:
5
Journal Article, Book Review
Recenzirano
Odprti dostop
Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that ...single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review.
Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery ...in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication. Comprehensive functional analyses of these genes confirmed the previously described roles of some of the genes and revealed novel key virus-host interactions affecting multiple molecular processes within the host cell, including signal transduction, metabolism, cell cycle, and immune system. The results of our analysis are accessible through a freely available, dedicated and user-friendly R/Shiny application, called PEACHi2.0. This resource constitutes a catalogue of dynamic host responses to HIV-1 infection that provides a basis for a more comprehensive understanding of virus-host interactions.
Allotypes of the natural killer (NK) cell receptor KIR3DL1 vary in both NK cell expression patterns and inhibitory capacity upon binding to their ligands, HLA-B Bw4 molecules, present on target ...cells. Using a sample size of over 1,500 human immunodeficiency virus (HIV)+ individuals, we show that various distinct allelic combinations of the KIR3DL1 and HLA-B loci significantly and strongly influence both AIDS progression and plasma HIV RNA abundance in a consistent manner. These genetic data correlate very well with previously defined functional differences that distinguish KIR3DL1 allotypes. The various epistatic effects observed here for common, distinct KIR3DL1 and HLA-B Bw4 combinations are unprecedented with regard to any pair of genetic loci in human disease, and indicate that NK cells may have a critical role in the natural history of HIV infection.
HIV latency is a major obstacle to curing infection. Current strategies to eradicate HIV aim at increasing transcription of the latent provirus. In the present study we observed that latently ...infected CD4+ T cells from HIV-infected individuals failed to produce viral particles upon ex vivo exposure to SAHA (vorinostat), despite effective inhibition of histone deacetylases. To identify steps that were not susceptible to the action of SAHA or other latency reverting agents, we used a primary CD4+ T cell model, joint host and viral RNA sequencing, and a viral-encoded reporter. This model served to investigate the characteristics of latently infected cells, the dynamics of HIV latency, and the process of reactivation induced by various stimuli. During latency, we observed persistence of viral transcripts but only limited viral translation. Similarly, the reactivating agents SAHA and disulfiram successfully increased viral transcription, but failed to effectively enhance viral translation, mirroring the ex vivo data. This study highlights the importance of post-transcriptional blocks as one mechanism leading to HIV latency that needs to be relieved in order to purge the viral reservoir.
High levels of HIV-1 replication during the chronic phase of infection usually correlate with rapid progression to severe immunodeficiency. However, a minority of highly viremic individuals remains ...asymptomatic and maintains high CD4⁺ T cell counts. This tolerant profile is poorly understood and reminiscent of the widely studied nonprogressive disease model of SIV infection in natural hosts. Here, we identify transcriptome differences between rapid progressors (RPs) and viremic nonprogressors (VNPs) and highlight several genes relevant for the understanding of HIV-1-induced immunosuppression. RPs were characterized by a specific transcriptome profile of CD4⁺ and CD8⁺ T cells similar to that observed in pathogenic SIV-infected rhesus macaques. In contrast, VNPs exhibited lower expression of interferon-stimulated genes and shared a common gene regulation profile with nonpathogenic SIV-infected sooty mangabeys. A short list of genes associated with VNP, including CASP1, CD38, LAG3, TNFSF13B, SOCS1, and EEF1D, showed significant correlation with time to disease progression when evaluated in an independent set of CD4⁺ T cell expression data. This work characterizes 2 minimally studied clinical patterns of progression to AIDS, whose analysis may inform our understanding of HIV pathogenesis.
We evaluated the fraction of variation in HIV-1 set point viral load attributable to viral or human genetic factors by using joint host/pathogen genetic data from 541 HIV infected individuals. We ...show that viral genetic diversity explains 29% of the variation in viral load while host factors explain 8.4%. Using a joint model including both host and viral effects, we estimate a total of 30% heritability, indicating that most of the host effects are reflected in viral sequence variation.
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