Wim Trypsteen

Background The HIV reservoir persists in long-living immune cells after antiretroviral therapy initiation. Proviral DNA integrates at preferential host DNA sites, while HIV transcription continues under treatment, with potential biological consequences. Treated people with HIV remain at increased risk for premature aging and non-AIDS comorbidities compared to people without HIV. Whether HIV reservoir size and activity impact health outcomes during treatment remains unclear. Methods We analyzed 1895 people with HIV using antiretroviral therapy from the 2000HIV cohort to study associations between the reservoir and historic and prospective virologic and immunological response markers, comorbidity outcomes and biological aging scores. The reservoir was assessed by quantifying total and intact proviral HIV DNA in circulating CD4+ T cells, along with measures of HIV transcriptional activity, including cell-associated HIV RNA. Results HIV reservoir markers were highly correlating and associated with older aging scores and adverse virologic outcomes, including quantifiable and residual viremia at study entry and during follow-up. Total HIV DNA, unlike the minor fraction of intact HIV DNA, correlated with prevalent cardiovascular disease, subclinical atherosclerotic disease, liver fibrosis and incident malignancies independent of circulating inflammatory markers and lowest CD4 nadir. Total HIV RNA associated with prevalent cardiovascular disease and liver fibrosis. Conclusion The HIV reservoir associated with older aging scores and long-term health outcomes in people with HIV using antiretroviral therapy, independent of inflammation and immune suppression history. Whether reservoir-reducing interventions lower the risk of non-AIDS comorbidities should be studied in people with high reservoir markers. Funding and trial registration ViiV Healthcare; 2000HIV ClinicalTrials.gov number: NTC03994835

Background: Long non-coding RNAs (lncRNAs) are numerous, often lowly expressed, and highly context-dependent, making their functional characterization challenging using single-readout approaches. Integrating target repression assessment with complementary molecular and cellular phenotyping is therefore important for systematic lncRNA functional characterization across cellular contexts. Methods: To address this, we developed an arrayed CRISPR interference (CRISPRi)-based multimodal framework for functional lncRNA characterization across cellular contexts, pairing targeted knockdown with target repression assessment, shallow 3′-end RNA-seq, live-cell growth analysis, and Cell Painting-based morphological profiling. As a proof of concept, we applied this strategy to HEK293T and KELLY cells, targeting 68 expressed lncRNAs. Results: Targeted 3′-end RNA capture-seq stratified targets by baseline coverage and confirmed measurable repression for 18 lncRNAs in HEK293T cells and 21 lncRNAs in KELLY cells. Transcriptomic profiling captured the broadest layer of downstream response to lncRNA repression, whereas growth and morphology phenotypes were less frequent and restricted to a small subset of targets. Matched comparisons between HEK293T and KELLY showed that both molecular and cellular responses to the same lncRNA perturbation were often cell-line-dependent. As a representative multimodal hit, EPB41L4A-AS1 repression in KELLY cells was associated with reduced translation- and biosynthesis-related pathway activity, reduced cell growth, and morphological feature changes involving Golgi, actin and mitochondria. Conclusions: Together, these results demonstrate that arrayed CRISPRi coupled with multimodal profiling provides a structured framework for systematically characterizing lncRNA perturbation effects across complementary molecular and cellular readouts and prioritizing candidate functional lncRNAs for downstream mechanistic studies.

Digital PCR (dPCR) is a powerful technology for absolute quantification of nucleic acids, valued for its accuracy, sensitivity, and repeatability. Yet, the commercialization of different instruments with proprietary software has introduced challenges to data analysis, interoperability, and comparability. Therefore, we present the Digital PCR Data Essentials Standard (DDES) – a lightweight, human- and machine-readable, and cross-platform data standard developed in collaboration with the dPCR community. The standard consists of three file types designed to enable both manual inspection and automated analysis: (i) a main file summarizing experiment and reaction-level (meta-)data; (ii) an assay file describing targets and detection chemistry, and (iii) intensity files capturing partition-level raw fluorescence data per reaction. DDES supports a wide range of current dPCR applications, including singleplex and multiplex assays, endpoint and real-time readouts, and will be curated to implement future dPCR developments. By harmonizing the data structure, DDES lays out the foundation for FAIR dPCR data practices and supports improved software compatibility, collaborative and reproducible research, and future dPCR data repositories.

Digital polymerase chain reaction (dPCR) is a state-of-the-art targeted quantification method of nucleic acids. The technology is based on massive partitioning of a reaction mixture into individual PCR reactions. The resulting partition-level end-point fluorescence intensities are used to classify partitions as positive or negative, i.e. containing or not containing the target nucleic acid(s). Many automatic dPCR partition classification methods have been proposed, but they are limited to the analysis of single- or dual-color dPCR data. While general-purpose or flow cytometry clustering methods can be directly applied to multicolor dPCR data, these methods do not exploit the approximate prior knowledge on cluster center locations available in dPCR data. We present Polytect, a method that relies on crude cluster results from flowPeaks, previously shown to offer good partition classification performance, and subsequently refines flowPeaks’ results by automatic cluster merging and cluster labeling, exploiting the prior knowledge on cluster center locations. Comparative analyses with established methods such as flowPeaks, dpcp, and ddPCRclust reveal that Polytect often surpasses established methods, both on empirical and simulated data. Polytect manages to merge excess clusters, while also successfully identifying empty clusters when fewer than the maximally observable number of clusters are present. On par with recent developments in instruments, Polytect extends beyond two-color data. The method is available as an R package and R Shiny app (https://digpcr.shinyapps.io/Polytect/).

Background: Persistent latent reservoirs of intact HIV-1 proviruses, capable of rebounding despite suppressive antiretroviral therapy (ART), hinder efforts towards an HIV-1 cure. Hence, assays specifically quantifying intact proviruses are crucial to assess the impact of curative interventions. Two recent assays have been utilized in clinical trials: intact proviral DNA assay (IPDA) and quadruplex quantitative PCR (Q4PCR). While IPDA is more sensitive due to amplifying short fragments, it may overestimate intact fractions by relying only on quantification of 2 proviral regions. Q4PCR samples 4 proviral regions, yet is sequencing-based, favoring amplification of shorter, hence non-intact, proviral sequences. Methods: Leveraging digital PCR (dPCR) advancements, we developed the “Rainbow” 5-plex proviral HIV-1 DNA assay. This first-in-its-kind assay was evaluated using standard materials and samples from 83 people living with HIV-1, enabling simultaneous quantification of both total and intact HIV-1 DNA levels. HIV proviral unique molecular identifier (UMI)-mediated long-read sequencing (HIV-PULSE) was used to validate the specificity of the Rainbow HIV-1 DNA assay. Results: The Rainbow assay proved equally sensitive but more specific than IPDA and is not subjected to bias against full-length proviruses, enabling high-throughput quantification of total and intact reservoir size. The near full-length sequences allowed validation of the Rainbow specificity and the design of personalized Rainbow primer/probe sets, which enabled the detection of intact HIV-1 DNA. Conclusions: This innovation offers potential for targeted evaluation and monitoring of potential rebound-competent reservoirs, contributing to HIV-1 management and cure strategies. ClinicalTrials.gov Registration Numbers: NCT04553081, NCT04305665.

Digital PCR (dPCR) has been used in the field of virology since its inception. Technological innovations in microfluidics more than a decade ago caused a sharp increase in its use. There is an emerging consensus that dPCR now outperforms quantitative PCR (qPCR) in the basic parameters such as precision, sensitivity, accuracy, repeatability and resistance to inhibitors. These strengths have led to several current applications in quantification, mutation detection and environmental DNA and RNA samples. In high throughput scenarios, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the cost and throughput still significantly hampered the adaption of dPCR. There is much unexplored potential within the multiplexing capabilities of dPCR. This will allow simultaneous multi-target quantification and can also partially alleviate the throughput and cost drawback. In this review, we discuss the strengths and weaknesses of dPCR with a focus on virology applications and we discuss future applications. Finally, we discuss recent evolutions of the technology in the form of real-time dPCR and digital high-resolution melting.

Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy is highly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier’s partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR’s robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR’s use.

Reducing the number of active compounds for lifelong human immunodeficiency virus (HIV) treatment is of interest, especially to reduce potential long-term side effects. So far, available data assessing viral control support the robustness and safety of 2DR (2-drug regimen) antiretroviral therapy compared to 3DR. However, further in-depth investigations of the viral reservoirs are mandatory to guarantee long-term safety of these regimens regarding stable intact HIV-1 DNA copies, HIV-1 RNA transcripts, and sustained immunological control. The RUMBA study is the first prospective randomized controlled trial evaluating the impact of switch from 3DR to 2DR on the viral reservoir. Participants on any stable second-generation integrase strand transfer inhibitor-based 3DR regimen with HIV-1 RNA < 50 copies/mL plasma for at least 3 months were randomized to switch to dolutegravir/lamivudine (DTG/3TC, n = 89) or to switch or stay on bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF, n = 45). After 48 weeks, virological, immunological, and metabolic parameters were evaluated. We did not observe a significant difference in change over time in the mean number of intact HIV-1 DNA copies/million CD4⁺ T cells with DTG/3TC compared to B/F/TAF. There was no evidence in this study that switching to DTG/3TC increased the active reservoir by HIV-1 transcription. No significant changes in proinflammatory cytokines or major immune cell subsets were observed. Changes in exhaustion and activation of specific cellular subsets were small and bidirectional. Metabolic outcomes are similar between the treatment regimens. This study confirms the safety of DTG/3TC compared to B/F/TAF through viral control after in-depth investigations of the intact HIV-1 reservoir, HIV-1 transcription, and inflammatory markers. NCT04553081.