The main obstacle to cure HIV-1 is the latent reservoir. Antiretroviral therapy effectively controls viral replication, however, it does not eradicate the latent reservoir. Latent CD4
+
T cells are ...extremely rare in HIV-1 infected patients, making primary CD4
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T cell models of HIV-1 latency key to understanding latency and thus finding a cure. In recent years several primary CD4
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T cell models of HIV-1 latency were developed to study the underlying mechanism of establishing, maintaining and reversing HIV-1 latency. In the search of biomarkers, primary CD4
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T cell models of HIV-1 latency were used for bulk and single-cell transcriptomics. A wealth of information was generated from transcriptome analyses of different primary CD4
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T cell models of HIV-1 latency using latently- and reactivated HIV-1 infected primary CD4
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T cells. Here, we performed a pooled data-analysis comparing the transcriptome profiles of latently- and reactivated HIV-1 infected cells of 5
in vitro
primary CD4
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T cell models of HIV-1 latency and 2
ex vivo
studies of reactivated HIV-1 infected primary CD4
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T cells from HIV-1 infected individuals. Identifying genes that are differentially expressed between latently- and reactivated HIV-1 infected primary CD4
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T cells could be a more successful strategy to better understand and characterize HIV-1 latency and reactivation. We observed that natural ligands and coreceptors were predominantly downregulated in latently HIV-1 infected primary CD4
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T cells, whereas genes associated with apoptosis, cell cycle and HLA class II were upregulated in reactivated HIV-1 infected primary CD4
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T cells. In addition, we observed 5 differentially expressed genes that co-occurred in latently- and reactivated HIV-1 infected primary CD4
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T cells, one of which, MSRB2, was found to be differentially expressed between latently- and reactivated HIV-1 infected cells. Investigation of primary CD4
+
T cell models of HIV-1 latency that mimic the
in vivo
state remains essential for the study of HIV-1 latency and thus providing the opportunity to compare the transcriptome profile of latently- and reactivated HIV-1 infected cells to gain insights into differentially expressed genes, which might contribute to HIV-1 latency.
Genomic safe harbors (GSH) are defined as sites in the host genome that allow stable expression of inserted transgenes while having no adverse effects on the host cell, making them ideal for use in ...basic research and therapeutic applications. Silencing and fluctuations in transgene expression would be highly undesirable effects. We have previously shown that transgene expression in Jurkat T cells is not silenced for up to 160 days after CRISPR-Cas9-mediated insertion of reporter genes into the adeno-associated virus site 1 (AAVS1), a commonly used GSH. Here, we studied fluctuations in transgene expression upon targeted insertion into the GSH AAVS1. We have developed an efficient method to generate and validate highly complex barcoded plasmid libraries to study transgene expression on the single-cell level. Its applicability is demonstrated by inserting the barcoded transgene Cerulean into the AAVS1 locus in Jurkat T cells via the CRISPR-Cas9 technology followed by next-generation sequencing of the transcribed barcodes. We observed large transcriptional variations over two logs for transgene expression in the GSH AAVS1. This barcoded transgene insertion model is a powerful tool to investigate fluctuations in transgene expression at any GSH site.
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Inderbitzin, Loosli, and colleagues employ a novel method to construct and extensively characterize DNA barcode libraries and apply CRISPR-Cas9 technology for targeted insertion into the safe-harbor gene AAVS1 in Jurkat T cells. This technique revealed high fluctuations in gene expression in AAVS1, spanning over two logs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Background
Next-generation sequencing (NGS) is gradually replacing Sanger sequencing (SS) as the primary method for HIV genotypic resistance testing. However, there are limited systematic ...data on comparability of these methods in a clinical setting for the presence of low-abundance drug resistance mutations (DRMs) and their dependency on the variant-calling thresholds.
Methods
To compare the HIV-DRMs detected by SS and NGS, we included participants enrolled in the Swiss HIV Cohort Study (SHCS) with SS and NGS sequences available with sample collection dates ≤7 days apart. We tested for the presence of HIV-DRMs and compared the agreement between SS and NGS at different variant-calling thresholds.
Results
We included 594 pairs of SS and NGS from 527 SHCS participants. Males accounted for 80.5% of the participants, 76.3% were ART naive at sample collection and 78.1% of the sequences were subtype B. Overall, we observed a good agreement (Cohen’s kappa >0.80) for HIV-DRMs for variant-calling thresholds ≥5%. We observed an increase in low-abundance HIV-DRMs detected at lower thresholds 28/417 (6.7%) at 10%–25% to 293/812 (36.1%) at 1%–2% threshold. However, such low-abundance HIV-DRMs were overrepresented in ART-naive participants and were in most cases not detected in previously sampled sequences suggesting high sequencing error for thresholds <3%.
Conclusions
We found high concordance between SS and NGS but also a substantial number of low-abundance HIV-DRMs detected only by NGS at lower variant-calling thresholds. Our findings suggest that a substantial fraction of the low-abundance HIV-DRMs detected at thresholds <3% may represent sequencing errors and hence should not be overinterpreted in clinical practice.
The widespread use of the integrase strand transfer inhibitor (INSTI) dolutegravir in first-line and second-line antiretroviral therapy (ART) might facilitate emerging resistance. The DTG RESIST ...study combined data from HIV cohorts to examine patterns of drug resistance mutations (DRMs) and identify risk factors for dolutegravir resistance.
We included cohorts with INSTI resistance data from two collaborations (ART Cohort Collaboration, International epidemiology Databases to Evaluate AIDS in Southern Africa), and the UK Collaborative HIV Cohort. Eight cohorts from Canada, France, Germany, Italy, the Netherlands, Switzerland, South Africa, and the UK contributed data on individuals who were viraemic on dolutegravir-based ART and underwent genotypic resistance testing. Individuals with unknown dolutegravir initiation date were excluded. Resistance levels were categorised using the Stanford algorithm. We identified risk factors for resistance using mixed-effects ordinal logistic regression models.
We included 599 people with genotypic resistance testing on dolutegravir-based ART between May 22, 2013, and Dec 20, 2021. Most had HIV-1 subtype B (n=351, 59%), a third had been exposed to first-generation INSTIs (n=193, 32%), 70 (12%) were on dolutegravir dual therapy, and 18 (3%) were on dolutegravir monotherapy. INSTI DRMs were detected in 86 (14%) individuals; 20 (3%) had more than one mutation. Most (n=563, 94%) were susceptible to dolutegravir, seven (1%) had potential low, six (1%) low, 17 (3%) intermediate, and six (1%) high-level dolutegravir resistance. The risk of dolutegravir resistance was higher on dolutegravir monotherapy (adjusted odds ratio aOR 34·1, 95% CI 9·93-117) and dolutegravir plus lamivudine dual therapy (aOR 9·21, 2·20-38·6) compared with combination ART, and in the presence of potential low or low (aOR 5·23, 1·32-20·7) or intermediate or high-level (aOR 13·4, 4·55-39·7) nucleoside reverse transcriptase inhibitor (NRTI) resistance.
Among people with viraemia on dolutegravir-based ART, INSTI DRMs and dolutegravir resistance were rare. NRTI resistance substantially increased the risk of dolutegravir resistance, which is of concern, notably in resource-limited settings. Monitoring is important to prevent resistance at the individual and population level and ensure the long-term sustainability of ART.
US National Institutes of Health, Swiss National Science Foundation.
Despite effective prevention approaches, ongoing human immunodeficiency virus 1 (HIV-1) transmission remains a public health concern indicating a need for identifying its drivers.
We combined a ...network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission.
We found that only a minority of clusters and patients acquired links to new infections between 2007 and 2020. While the growth of clusters and the probability of individual patients acquiring new links in the transmission network was associated with epidemiological, behavioral, and virological predictors, the strength of these associations decreased substantially when adjusting for network characteristics. Thus, these network characteristics can capture major heterogeneities beyond classical epidemiological parameters. When modeling the probability of a newly diagnosed patient being linked with future infections, we found that the best predictive performance (median area under the curve receiver operating characteristic AUCROC = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUCROC = 0.54).
These results highlight the utility of molecular epidemiology-based network approaches for analyzing and predicting ongoing HIV transmission dynamics. This approach may serve for real-time prospective assessment of HIV transmission.
