Abstract Some people remain healthier throughout life than others but the underlying reasons are poorly understood. Here we hypothesize this advantage is attributable in part to optimal immune resilience (IR), defined as the capacity to preserve and/or rapidly restore immune functions that promote disease resistance (immunocompetence) and control inflammation in infectious diseases as well as other causes of inflammatory stress. We gauge IR levels with two distinct peripheral blood metrics that quantify the balance between (i) CD8 + and CD4 + T-cell levels and (ii) gene expression signatures tracking longevity-associated immunocompetence and mortality-associated inflammation. Profiles of IR metrics in ~48,500 individuals collectively indicate that some persons resist degradation of IR both during aging and when challenged with varied inflammatory stressors. With this resistance, preservation of optimal IR tracked (i) a lower risk of HIV acquisition, AIDS development, symptomatic influenza infection, and recurrent skin cancer; (ii) survival during COVID-19 and sepsis; and (iii) longevity. IR degradation is potentially reversible by decreasing inflammatory stress. Overall, we show that optimal IR is a trait observed across the age spectrum, more common in females, and aligned with a specific immunocompetence-inflammation balance linked to favorable immunity-dependent health outcomes. IR metrics and mechanisms have utility both as biomarkers for measuring immune health and for improving health outcomes.
Abstract The effects of HIV infection upon the thymus and peripheral T cell turnover have been implicated in the pathogenesis of AIDS. In this study, we investigated whether decreased thymic output, increased T cell proliferation, or both can occur in HIV infection. We measured peripheral blood levels of TCR rearrangement excision circles (TREC) and parameters of cell proliferation, including Ki67 expression and ex vivo bromodeoxyuridine incorporation in 22 individuals with early untreated HIV disease and in 15 HIV-infected individuals undergoing temporary interruption of therapy. We found an inverse association between increased T cell proliferation with rapid viral recrudescence and a decrease in TREC levels. However, during early HIV infection, we found that CD45RO−CD27high (naive) CD4+ T cell proliferation did not increase, despite a loss of TREC within naive CD4+ T cells. A possible explanation for this is that decreased thymic output occurs in HIV-infected humans. This suggests that the loss of TREC during HIV infection can arise from a combination of increased T cell proliferation and decreased thymic output, and that both mechanisms can contribute to the perturbations in T cell homeostasis that underlie the pathogenesis of AIDS.
Effective population screening of HIV and prevention of HIV transmission are only part of the global fight against AIDS. Community-level effects, for example those aimed at thwarting future transmission, are potential outcomes of treatment and may be important in stemming the epidemic. However, current clinical trial designs are incapable of detecting a reduction in future transmission due to treatment. We took advantage of the fact that HIV is an evolving pathogen whose transmission network can be reconstructed using genetic sequence information to address this shortcoming. Here, we use an HIV transmission network inferred from recently infected men who have sex with men (MSM) in San Diego, California. We developed and tested a network-based statistic for measuring treatment effects using simulated clinical trials on our inferred transmission network. We explored the statistical power of this network-based statistic against conventional efficacy measures and find that when future transmission is reduced, the potential for increased statistical power can be realized. Furthermore, our simulations demonstrate that the network statistic is able to detect community-level effects (e.g., reduction in onward transmission) of HIV treatment in a clinical trial setting. This study demonstrates the potential utility of a network-based statistical metric when investigating HIV treatment options as a method to reduce onward transmission in a clinical trial setting.
Abstract Background Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster–either directly or through intermediaries. Methods Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics–that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database. Results Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model. Conclusions Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.
The excessive viral diversity noted by Heath et al . in our sample of infected HIV patients prompted us to reexamine our data. Doing so uncovered several mislabeled samples, but no sources of contamination were identified. Investigations into additional transmission pairs will be needed to further investigate these issues.
Clinical effectiveness of coronavirus disease 2019 (COVID-19) vaccination in solid organ transplant recipients (SOTRs) is not well documented despite multiple studies demonstrating sub-optimal immunogenicity.
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