The dynamics of simian immunodeficiency virus after depletion of CD8+ cells

CD8+ T cells control SIV infection using both cytolytic effects and non-cytolytic suppression of virus production

Whether CD8+ T lymphocytes control human immunodeficiency virus infection by cytopathic or non-cytopathic mechanisms is not fully understood. Multiple studies highlighted non-cytopathic effects, but one hypothesis is that cytopathic effects of CD8+ T cells occur before viral production. Here, to examine the role of CD8+ T cells prior to virus production, we treated SIVmac251-infected macaques with an integrase inhibitor combined with a CD8-depleting antibody, or with either reagent alone. We analyzed the ensuing viral dynamics using a mathematical model that included infected cells pre- and post- viral DNA integration to compare different immune effector mechanisms. Macaques receiving the integrase inhibitor alone experienced greater viral load decays, reaching lower nadirs on treatment, than those treated also with the CD8-depleting antibody. Models including CD8+ cell-mediated reduction of viral production (non-cytolytic) were found to best explain the viral profiles across all macaques, in addition an effect in killing infected cells pre-integration (cytolytic) was supported in some of the best models. Our results suggest that CD8+ T cells have both a cytolytic effect on infected cells before viral integration, and a direct, non-cytolytic effect by suppressing viral production.

What explains the poor contraction of the viral load during paediatric HIV infection?

An acute HIV infection in young children differs markedly from that in adults: Children have higher viral loads (VL), and a poor contraction to a setpoint VL that is not much lower than the peak VL. As a result, children progress faster towards AIDS in the absence of treatment. We used a classical ordinary differential equation model for viral infection dynamics to study why children have a lower viral contraction ratio than adults. We performed parameter sweeps to identify factors explaining the observed difference between children and adults. We grouped parameters associated with the host, the infection, or the immune response. Based on paediatric data available from datasets within the EPIICAL project (https://www.epiical.org/), we refuted that viral replication rates differ between young children and adults, and therefore these cannot be responsible for the low VL contraction ratios seen in children. The major differences in lowering VL contraction ratio resulted from sweeping the parameters linked to the immune response. Thus, we postulate that an "ineffective" (late and/or weak) immune response is the most parsimonious explanation for the higher setpoint VL in young children, and hence the reason for their fast disease progression.

Protocol for evaluating CD8+ T cell-mediated immunity in latently SHIV-infected rhesus macaques with HIV fusion-inhibitory lipopeptide monotherapy

Strong cellular immunity contributes to the control of HIV infection. Here, we describe a step-by-step protocol to assess the simian immunodeficiency virus (SIV)-specific CD8⁺ T cell responses by quantifying the degranulation, cytokine and chemokine production from SHIV_SF162P3-infected rhesus macaques with an HIV fusion-inhibitory lipopeptide (LP-98) monotherapy. We also present the steps for adoptive transfer of an anti-CD8 antibody into a stable virologic control (SVC) group of LP-98-treated monkeys, confirming a direct role of CD8⁺ T cells in SVC macaques.

For complete details on the use and execution of this protocol, please refer to Xue et al. (2022).

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Recover PBMCs from latently SHIV_SF162P3 infected rhesus macaques treated with LP-98

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Evaluation of SIVmac239 Gag-specific CD8⁺ T cell responses

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Adoptive transfer of anti-CD8 antibody to stable virologic control (SVC) macaques

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Quantitative detection of CD8⁺ T cell counts and plasma viral RNA

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Highly dampened HIV-specific cytolytic effector T cell responses define viremic non-progression

This study reports on HIV-specific T cell responses in HIV-1 infected Viremic Non-Progressors (VNPs), a rare group of people living with HIV that exhibit asymptomatic infection over several years accompanied by stable CD4+ T cell counts in spite of ongoing viral replication. We attempted to identify key virus-specific functional attributes that could underlie the apparently paradoxical virus-host equilibrium observed in VNPs. Our results revealed modulation of HIV-specific CD4+ and CD8+ effector T cell responses in VNPs towards a dominant non-cytolytic profile with concomitantly diminished degranulation (CD107a+) ability. Further, the HIV specific CD8+ effector T cell response was primarily enriched for MIP-1β producing cells. As expected, concordant with better viral suppression, VCs exhibit a robust cytolytic T cell response. Interestingly, PuPs shared features common to both these responses but did not exhibit a CD4+ central memory IFN-γ producing Gag-specific response that was shared by both non-progressor (VC and VNP) groups, suggesting CD4 helper response is critical for non-progression. Our study also revealed that cytolytic response in VNPs is primarily limited to polyfunctional cells while both monofunctional and polyfunctional cells significantly contribute to cytolytic responses in VCs. To further understand mechanisms underlying the unique HIV-specific effector T cell response described here in VNPs we also evaluated and demonstrated a possible role for altered gut homing in these individuals. Our findings inform immunotherapeutic interventions to achieve functional cures in the context of ART resistance and serious non AIDS events.

The CD8+ T Cell Noncytotoxic Antiviral Responses

The CD8+ T cell noncytotoxic antiviral response (CNAR) was discovered during studies of asymptomatic HIV-infected subjects more than 30 years ago. In contrast to CD8+ T cell cytotoxic lymphocyte (CTL) activity, CNAR suppresses HIV replication without target cell killing. This activity has characteristics of innate immunity: it acts on all retroviruses and thus is neither epitope specific nor HLA restricted. The HIV-associated CNAR does not affect other virus families. It is mediated, at least in part, by a CD8+ T cell antiviral factor (CAF) that blocks HIV transcription. A variety of assays used to measure CNAR/CAF and the effects on other retrovirus infections are described. Notably, CD8+ T cell noncytotoxic antiviral responses have now been observed with other virus families but are mediated by different cytokines. Characterizing the protein structure of CAF has been challenging despite many biologic, immunologic, and molecular studies. It represents a low-abundance protein that may be identified by future next-generation sequencing approaches. Since CNAR/CAF is a natural noncytotoxic activity, it could provide promising strategies for HIV/AIDS therapy, cure, and prevention.

Thresholds for post-rebound SHIV control after CCR5 gene-edited autologous hematopoietic cell transplantation

Autologous, CCR5 gene-edited hematopoietic stem and progenitor cell (HSPC) transplantation is a promising strategy for achieving HIV remission. However, only a fraction of HSPCs can be edited ex vivo to provide protection against infection. To project the thresholds of CCR5-edition necessary for HIV remission, we developed a mathematical model that recapitulates blood T cell reconstitution and plasma simian-HIV (SHIV) dynamics from SHIV-1157ipd3N4-infected pig-tailed macaques that underwent autologous transplantation with CCR5 gene editing. The model predicts that viral control can be obtained following analytical treatment interruption (ATI) when: (1) transplanted HSPCs are at least fivefold higher than residual endogenous HSPCs after total body irradiation and (2) the fraction of protected HSPCs in the transplant achieves a threshold (76–94%) sufficient to overcome transplantation-dependent loss of SHIV immunity. Under these conditions, if ATI is withheld until transplanted gene-modified cells engraft and reconstitute to a steady state, spontaneous viral control is projected to occur.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Virtual memory CD8+ T cells restrain the viral reservoir in HIV-1-infected patients with antiretroviral therapy through derepressing KIR-mediated inhibition

The viral reservoir is the major hurdle in developing and establishing an HIV cure. Understanding factors affecting the size and decay of this reservoir is crucial for the development of therapeutic strategies. Recent work highlighted that CD8+ T cells are involved in the control of viral replication in ART-treated HIV-1-infected individuals, but how CD8+ T cells sense and restrict the HIV reservoir are not fully understood. Here, we demonstrate that a population of unconventional CD45RA+, PanKIR+, and/or NKG2A+ virtual memory CD8+ T cells (T_VM cells), which confer rapid and robust protective immunity against pathogens, plays an important role in restraining the HIV DNA reservoir in HIV-1-infected patients with effective ART. In patients undergoing ART, T_VM cells negatively correlate with HIV DNA and positively correlate with circulating IFN-α2 and IL-15. Moreover, T_VM cells constitutively express high levels of cytotoxic granule components, including granzyme B, perforin and granulysin, and demonstrate the capability to control HIV replication through both cytolytic and noncytolytic mechanisms. Furthermore, by using an ex vivo system, we showed that HIV reactivation is effectively suppressed by T_VM cells through KIR-mediated recognition. This study suggests that T_VM cells are a promising target to predict posttreatment virological control and to design immune-based interventions to reduce the reservoir size in ART-treated HIV-1-infected individuals.

Modeling the immune response to HIV infection

The interplay between immune response and HIV is intensely studied via mathematical modeling, with significant insights but few direct answers. In this short review, we highlight advances and knowledge gaps across different aspects of immunity. In particular, we identify the innate immune response and its role in priming the adaptive response as ripe for modeling. The latter have been the focus of most modeling studies, but we also synthesize key outstanding questions regarding effector mechanisms of cellular immunity and development of broadly neutralizing antibodies. Thus far, most modeling studies aimed to infer general immune mechanisms; we foresee that significant progress will be made next by detailed quantitative fitting of models to data, and prediction of immune responses.

Validated Models of Immune Response to Virus Infection

Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.