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Significant advances in the treatment of HIV infection have been made in the last three decades. Antiretroviral therapy (ART) is now potent enough to prevent virus replication and stop disease progression. However, ART alone does not cure the infection, primarily because HIV can persist in stable long-term reservoir cells including latently-infected CD4 + T cells. A central goal of the HIV research field is to devise strategies to eliminate these reservoirs and thereby develop a cure for HIV. This requires robust in vivo model systems to facilitate both the further characterization of persistent HIV reservoirs and evaluation of methods for eliminating latent virus. Humanized mice have proven to be versatile experimental models for studying many basic aspects of HIV biology. These models consist of immunodeficient mice transplanted with human cells or tissues, which allows development of a human immune system that supports robust infection with HIV. There are many potential applications for new generations of humanized mouse models in investigating HIV reservoirs and latency, but these models also involve caveats that are important to consider in experimental design and interpretation. This review briefly discusses some of the key strengths and limitations of humanized mouse models in HIV persistence studies.

In macromolecular crystallography, the rigorous detection of changed states (for example, ligand binding) is difficult unless signal is strong. Ambiguous (‘weak’ or ‘noisy’) density is experimentally common, since molecular states are generally only fractionally present in the crystal. Existing methodologies focus on generating maximally accurate maps whereby minor states become discernible; in practice, such map interpretation is disappointingly subjective, time-consuming and methodologically unsound. Here we report the PanDDA method, which automatically reveals clear electron density for the changed state—even from inaccurate maps—by subtracting a proportion of the confounding ‘ground state’; changed states are objectively identified from statistical analysis of density distributions. The method is completely general, implying new best practice for all changed-state studies, including the routine collection of multiple ground-state crystals. More generally, these results demonstrate: the incompleteness of atomic models; that single data sets contain insufficient information to model them fully; and that accuracy requires further map-deconvolution approaches. Building a ligand into a weak region of an electron density map of a protein is a subjective process. Here, the authors present a new method to obtain a clear electron density for a bound ligand based on multi-crystal experiments and 3D background correction.

Population bottlenecks, inbreeding, and artificial selection can all, in principle, influence levels of deleterious genetic variation. However, the relative importance of each of these effects on genome-wide patterns of deleterious variation remains controversial. Domestic and wild canids offer a powerful system to address the role of these factors in influencing deleterious variation because their history is dominated by known bottlenecks and intense artificial selection. Here, we assess genome-wide patterns of deleterious variation in 90 whole-genome sequences from breed dogs, village dogs, and gray wolves. We find that the ratio of amino acid changing heterozygosity to silent heterozygosity is higher in dogs than in wolves and, on average, dogs have 2–3% higher genetic load than gray wolves. Multiple lines of evidence indicate this pattern is driven by less efficient natural selection due to bottlenecks associated with domestication and breed formation, rather than recent inbreeding. Further, we find regions of the genome implicated in selective sweeps are enriched for amino acid changing variants and Mendelian disease genes. To our knowledge, these results provide the first quantitative estimates of the increased burden of deleterious variants directly associated with domestication and have important implications for selective breeding programs and the conservation of rare and endangered species. Specifically, they highlight the costs associated with selective breeding and question the practice favoring the breeding of individuals that best fit breed standards. Our results also suggest that maintaining a large population size, rather than just avoiding inbreeding, is a critical factor for preventing the accumulation of deleterious variants.

The latent reservoir remains a major roadblock to curing human immunodeficiency virus (HIV) infection. Currently available antiretroviral therapy (ART) can suppress active HIV replication, reduce viral loads to undetectable levels, and halt disease progression. However, antiretroviral drugs are unable to target cells that are latently infected with HIV, which can seed viral rebound if ART is stopped. Consequently, a major focus of the field is to study the latent viral reservoir and develop safe and effective methods to eliminate it. Here, we provide an overview of the major mechanisms governing the establishment and maintenance of HIV latency, the key challenges posed by latent reservoirs, small animal models utilized to study HIV latency, and contemporary cure approaches. We also discuss ongoing efforts to apply these approaches in combination, with the goal of achieving a safe, effective, and scalable cure for HIV that can be extended to the tens of millions of people with HIV worldwide.

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design. Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI’s potential in the field.

Approximately 38 million people were living with human immunodeficiency virus (HIV) in 2020 and 53% of those infected were female. A variety of virological and immunological sex-associated differences (sexual dimorphism) in HIV infection have been recognized in males versus females. Social, behavioral, and societal influences play an important role in how the HIV pandemic has affected men and women differently. However, biological factors including anatomical, physiologic, hormonal, and genetic differences in sex chromosomes can each contribute to the distinct characteristics of HIV infection observed in males versus females. One striking example of this is the tendency for women to have lower HIV plasma viral loads than their male counterparts early in infection, though both progress to AIDS at similar rates. Sex differences in acquisition of HIV, innate and adaptive anti-HIV immune responses, efficacy/suitability of specific antiretroviral drugs, and viral pathogenesis have all been identified. Sex differences also have the potential to affect viral persistence, latency, and cure approaches. In this brief review, we summarize the major biological male/female sex differences in HIV infection and their importance to viral acquisition, pathogenesis, treatment, and cure efforts.

Pattern recognition underpins innate immunity; the accurate identification of danger, including infection, injury, or tumor, is key to an appropriately targeted immune response. Pathogen detection is increasingly well defined mechanistically, but the discrimination of endogenous inflammatory triggers remains unclear. Tenascin-C, a matrix protein induced upon tissue damage and expressed by tumors, activates toll-like receptor 4 (TLR4)-mediated sterile inflammation. Here we map three sites within tenascin-C that directly and cooperatively interact with TLR4. We also identify a conserved inflammatory epitope in related proteins from diverse families, and demonstrate that its presence targets molecules for TLR detection, while its absence enables escape of innate immune surveillance. These data reveal a unique molecular code that defines endogenous proteins as inflammatory stimuli by marking them for recognition by TLRs. Although detection of pathogens by pattern recognition receptors is increasingly well defined, recognition of endogenous triggers remains poorly understood. By examining the interface between tenascin-C and TLR4, the authors identify a molecular code that identifies endogenous proteins as inflammatory stimuli.

Previous research has linked conservative political ideology with homophobia. Political ideology has also been linked to differences in moral decision-making, with research suggesting that conservatives and liberals may use different values in their moral decision-making processes. Moral foundations theory is a model of moral decision-making that proposes that individuals emphasize different domains in moral decision-making. Conservatives tend to emphasize binding foundations, while liberals tend to emphasize individualizing foundations. Utilizing large, ethnically diverse college samples, the purpose of these two cross-sectional studies (Study 1  N  = 492; Study 2  N  = 861) was to explore whether moral foundations mediate the relationship between political ideology and homophobia. These studies explored economic and social political ideology separately and utilized a two-factor model of moral foundations theory (individualizing and binding foundations). Results of both studies found that conservative economic and social political ideology was positively associated with homophobia. Study 1 found that both conservative economic and social political ideology had an indirect effect on homophobia through binding foundations. Study 2 found that both economic and social political ideology had an indirect effect on homophobia through both binding and individualizing foundations. Overall, the results were consistent with the notion that moral foundations may explain the relationship between political ideology and homophobia.

HIV is difficult to eradicate due to the persistence of a long-lived reservoir of latently infected cells. Previous studies have shown that natural killer cells are important to inhibiting HIV infection, but it is unclear whether the administration of natural killer cells can reduce rebound viremia when anti-retroviral therapy is discontinued. Here we show the administration of allogeneic human peripheral blood natural killer cells delays viral rebound following interruption of anti-retroviral therapy in humanized mice infected with HIV-1. Utilizing genetically barcoded virus technology, we show these natural killer cells efficiently reduced viral clones rebounding from latency. Moreover, a kick and kill strategy comprised of the protein kinase C modulator and latency reversing agent SUW133 and allogeneic human peripheral blood natural killer cells during anti-retroviral therapy eliminated the viral reservoir in a subset of mice. Therefore, combinations utilizing latency reversal agents with targeted cellular killing agents may be an effective approach to eradicating the viral reservoir. Here, Kim et al. describe a new kick and kill strategy utilizing a single administration of a protein kinase C modulator and latency reversing agent in combination with injections of allogeneic peripheral blood natural killer cells diminishes the HIV reservoir in HIV-infected humanized mice.

Chimeric antigen receptor (CAR) T cell therapy shows promise for various diseases. Our studies in humanized mice and nonhuman primates demonstrate that hematopoietic stem cells (HSCs) modified with anti-HIV CAR achieve lifelong engraftment, providing functional antiviral CAR-T cells that reduce viral rebound after antiretroviral therapy (ART) withdrawal. However, T cell exhaustion due to chronic immune activation remains a key obstacle to sustained CAR-T efficacy, necessitating additional measures to achieve functional cure. We recently showed that low-dose rapamycin treatment reduced inflammation and improved anti-HIV T cell function in HIV-infected humanized mice. Here, we report that rapamycin improved CAR-T cell function both in vitro and in vivo. In vitro treatment with rapamycin enhanced CAR-T cell mitochondrial respiration and cytotoxicity. In vivo treatment with low-dose rapamycin in HIV-infected, CAR-HSC mice decreased chronic inflammation, prevented exhaustion of CAR-T cells, and improved CAR-T control of viral replication. RNA-sequencing analysis of CAR-T cells from humanized mice showed that rapamycin downregulated multiple checkpoint inhibitors and upregulated key survival genes. Mice treated with CAR-HSCs and rapamycin had delayed viral rebound after ART and reduced HIV reservoir compared with those treated with CAR-HSCs alone. These findings suggest that HSC-based anti-HIV CAR-T cells combined with rapamycin treatment are a promising approach for treating persistent inflammation and improving immune control of HIV replication.