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High-throughput sequencing platforms provide an approach for detecting rare HIV-1 variants and documenting more fully quasispecies diversity. We applied this technology to the V3 loop-coding region of env in samples collected from 4 chronically HIV-infected subjects in whom CCR5 antagonist (vicriviroc [VVC]) therapy failed. Between 25,000-140,000 amplified sequences were obtained per sample. Profound baseline V3 loop sequence heterogeneity existed; predicted CXCR4-using populations were identified in a largely CCR5-using population. The V3 loop forms associated with subsequent virologic failure, either through CXCR4 use or the emergence of high-level VVC resistance, were present as minor variants at 0.8-2.8% of baseline samples. Extreme, rapid shifts in population frequencies toward these forms occurred, and deep sequencing provided a detailed view of the rapid evolutionary impact of VVC selection. Greater V3 diversity was observed post-selection. This previously unreported degree of V3 loop sequence diversity has implications for viral pathogenesis, vaccine design, and the optimal use of HIV-1 CCR5 antagonists.

Alveolar macrophages (AMs) are critical for defense against airborne pathogens and AM dysfunction is thought to contribute to the increased burden of pulmonary infections observed in individuals living with HIV-1 (HIV). While HIV nucleic acids have been detected in AMs early in infection, circulating HIV during acute and chronic infection is usually CCR5 T cell-tropic (T-tropic) and enters macrophages inefficiently in vitro. The mechanism by which T-tropic viruses infect AMs remains unknown. We collected AMs by bronchoscopy performed in HIV-infected, antiretroviral therapy (ART)-naive and uninfected subjects. We found that viral constructs made with primary HIV envelope sequences isolated from both AMs and plasma were T-tropic and inefficiently infected macrophages. However, these isolates productively infected macrophages when co-cultured with HIV-infected CD4+ T cells. In addition, we provide evidence that T-tropic HIV is transmitted from infected CD4+ T cells to the AM cytosol. We conclude that AM-derived HIV isolates are T-tropic and can enter macrophages through contact with an infected CD4+ T cell, which results in productive infection of AMs. CD4+ T cell-dependent entry of HIV into AMs helps explain the presence of HIV in AMs despite inefficient cell-free infection, and may contribute to AM dysfunction in people living with HIV.

We investigated the prevalence of xenotropic murine leukemia virus-related virus (XMRV) among 293 participants seen at academic hospitals in Boston, Massachusetts. Participants were recruited from the following 5 groups of patients: chronic fatigue syndrome (n = 32), human immunodeficiency virus infection (n = 43), rheumatoid arthritis (n = 97), hematopoietic stem-cell or solid organ transplant (n = 26), or a general cohort of patients presenting for medical care (n = 95). XMRV DNA was not detected in any participant samples. We found no association between XMRV and patients with chronic fatigue syndrome or chronic immunomodulatory conditions.

Human immunodeficiency virus 1 (HIV-1) is a retrovirus with a ten-kilobase single-stranded RNA genome. HIV-1 must express all of its gene products from a single primary transcript, which undergoes alternative splicing to produce diverse protein products that include structural proteins and regulatory factors 1 , 2 . Despite the critical role of alternative splicing, the mechanisms that drive the choice of splice site are poorly understood. Synonymous RNA mutations that lead to severe defects in splicing and viral replication indicate the presence of unknown cis -regulatory elements 3 . Here we use dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) to investigate the structure of HIV-1 RNA in cells, and develop an algorithm that we name ‘detection of RNA folding ensembles using expectation–maximization’ (DREEM), which reveals the alternative conformations that are assumed by the same RNA sequence. Contrary to previous models that have analysed population averages 4 , our results reveal heterogeneous regions of RNA structure across the entire HIV-1 genome. In addition to confirming that in vitro characterized 5 alternative structures for the HIV-1 Rev responsive element also exist in cells, we discover alternative conformations at critical splice sites that influence the ratio of transcript isoforms. Our simultaneous measurement of splicing and intracellular RNA structure provides evidence for the long-standing hypothesis 6 – 8 that heterogeneity in RNA conformation regulates splice-site use and viral gene expression. Dimethyl sulfate mutational profiling with sequencing, combined with the newly developed DREEM algorithm, reveals that heterogeneity of RNA structure in HIV-1 regulates the use of splice sites and expression of viral genes.

HIV-1 infection is a major health threat in both developed and developing countries. The integration of mobile health approaches and bioengineered catalytic motors can allow the development of sensitive and portable technologies for HIV-1 management. Here, we report a platform that integrates cellphone-based optical sensing, loop-mediated isothermal DNA amplification and micromotor motion for molecular detection of HIV-1. The presence of HIV-1 RNA in a sample results in the formation of large-sized amplicons that reduce the motion of motors. The change in the motors motion can be accurately measured using a cellphone system as the biomarker for target nucleic acid detection. The presented platform allows the qualitative detection of HIV-1 (n = 54) with 99.1% specificity and 94.6% sensitivity at a clinically relevant threshold value of 1000 virus particles/ml. The cellphone system has the potential to enable the development of rapid and low-cost diagnostics for viruses and other infectious diseases. Micromotors have a range of potential healthcare applications. Here, the authors describe the development of a metal nanoparticle DNA micromotor which can be used to detect human HIV-1 by a change in the motion of the micromotors, monitored by cell phone camera, triggered by binding of HIV-1 RNA.

While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.

The relationship between SARS-CoV-2 viral load and risk of disease progression remains largely undefined in coronavirus disease 2019 (COVID-19). Here, we quantify SARS-CoV-2 viral load from participants with a diverse range of COVID-19 disease severity, including those requiring hospitalization, outpatients with mild disease, and individuals with resolved infection. We detected SARS-CoV-2 plasma RNA in 27% of hospitalized participants, and 13% of outpatients diagnosed with COVID-19. Amongst the participants hospitalized with COVID-19, we report that a higher prevalence of detectable SARS-CoV-2 plasma viral load is associated with worse respiratory disease severity, lower absolute lymphocyte counts, and increased markers of inflammation, including C-reactive protein and IL-6. SARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality. Our data show that SARS-CoV-2 viral loads may aid in the risk stratification of patients with COVID-19, and therefore its role in disease pathogenesis should be further explored. In this study, Massachusetts Consortium for Pathogen Readiness (MassCPR) investigators assess the relationship between SARS-CoV-2 viral load and COVID-19 disease severity and report that the levels of detectable viral RNA, especially in plasma, correlates with severity of respiratory disease, inflammatory markers and predicted risk of death.

Several clinical studies show that individuals with HIV are at an increased risk for worsened lung function and for the development of COPD, although the mechanism underlying this increased susceptibility is poorly understood. The airway epithelium, situated at the interface between the external environment and the lung parenchyma, acts as a physical and immunological barrier that secretes mucins and cytokines in response to noxious stimuli which can contribute to the pathobiology of chronic obstructive pulmonary disease (COPD). We sought to determine the effects of HIV on the lung epithelium. We grew primary normal human bronchial epithelial (NHBE) cells and primary lung epithelial cells isolated from bronchial brushings of patients to confluence and allowed them to differentiate at an air- liquid interface (ALI) to assess the effects of HIV on the lung epithelium. We assessed changes in monolayer permeability as well as the expression of E-cadherin and inflammatory modulators to determine the effect of HIV on the lung epithelium. We measured E-cadherin protein abundance in patients with HIV compared to normal controls. Cell associated HIV RNA and DNA were quantified and the p24 viral antigen was measured in culture supernatant. Surprisingly, X4, not R5, tropic virus decreased expression of E-cadherin and increased monolayer permeability. While there was some transcriptional regulation of E-cadherin, there was significant increase in lysosome-mediated protein degradation in cells exposed to X4 tropic HIV. Interaction with CXCR4 and viral fusion with the epithelial cell were required to induce the epithelial changes. X4 tropic virus was able to enter the airway epithelial cells but not replicate in these cells, while R5 tropic viruses did not enter the epithelial cells. Significantly, X4 tropic HIV induced the expression of intercellular adhesion molecule-1 (ICAM-1) and activated extracellular signal-regulated kinase (ERK). We demonstrate that HIV can enter airway epithelial cells and alter their function by impairing cell-cell adhesion and increasing the expression of inflammatory mediators. These observed changes may contribute local inflammation, which can lead to lung function decline and increased susceptibility to COPD in HIV patients.

In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network’s performance can degrade substantially when applied to a dataset with a different distribution. Here, we show that adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality. Specifically, we used low-quality images acquired using inexpensive portable optical systems to train networks for the evaluation of human embryos, the quantification of human sperm morphology and the diagnosis of malarial infections in the blood, and show that the networks performed well across different data distributions. We also show that adversarial learning can be used with unlabelled data from unseen domain-shifted datasets to adapt pretrained supervised networks to new distributions, even when data from the original distribution are not available. Adaptive adversarial networks may expand the use of validated neural-network models for the evaluation of data collected from multiple imaging systems of varying quality without compromising the knowledge stored in the network. Adversarial learning can be used to develop high-performing networks trained on unannotated medical images of varying image quality, and to adapt pretrained supervised networks to new domain-shifted datasets.