Prediction of HIV-associated neurocognitive disorder (HAND) from three genetic features of envelope gp120 glycoprotein

Self-reported neurocognitive complaints in the Swiss HIV Cohort Study: a viral genome-wide association study

People with HIV may report neurocognitive complaints, with or without associated neurocognitive impairment, varying between individuals and populations. While the HIV genome could play a major role, large systematic viral genome-wide screens to date are lacking. The Swiss HIV Cohort Study biannually enquires neurocognitive complaints. We quantified broad-sense heritability estimates using partial 'pol' sequences from the Swiss HIV Cohort Study resistance database and performed a viral near full-length genome-wide association study for the longitudinal area under the curve of neurocognitive complaints. We performed all analysis (i) restricted to HIV Subtype B and (ii) including all HIV subtypes. From 8547 people with HIV with neurocognitive complaints, we obtained 6966 partial 'pol' sequences and 2334 near full-length HIV sequences. Broad-sense heritability estimates for presence of memory loss complaints ranged between 1% and 17% (Subtype B restricted 1-22%) and increased with the stringency of the phylogenetic distance thresholds. The genome-wide association study revealed one amino acid (Env L641E), after adjusting for multiple testing, positively associated with memory loss complaints (P = 4.3 * 10-6). Other identified mutations, while insignificant after adjusting for multiple testing, were reported in other smaller studies (Tat T64N, Env *291S). We present the first HIV genome-wide association study analysis of neurocognitive complaints and report a first estimate for the heritability of neurocognitive complaints through HIV. Moreover, we could identify one mutation significantly associated with the presence of memory loss complaints. Our findings indicate that neurocognitive complaints are polygenetic and highlight advantages of a whole genome approach for pathogenicity determination.

Different features identified by machine learning associated with the HIV compartmentalization in semen

Genetic compartmentalization in semen has been observed in previous studies. However, genetic signatures associated with compartmentalization in semen are only beginning to be explored. A total of 2071 partial HIV env sequences for paired blood and semen specimens were collected from 42 persons with HIV (24 for subtype B, 18 for subtype C). The HIV sequences datasets of subtype B and C were then divided to compartmentalization group and no-compartmentalization group by using the genetic compartmentalization tests. These datasets were used to construct a machine learning (ML) metadataset. AAIndex metrics were adopted as quantitative measures of the biophysicochemical properties of each amino acid. Five algorithm tests were applied, all of which are implemented in the caret package. For Subtype B, the accuracy for the compartmentalization group is 0.87 (range: 0.80-0.92), 0.69 (range: 0.58-0.79) for the no-compartmentlization group. The similar results were also showed in subtype C. The accuracy for the compartmentalization group is 0.74 (range: 0.64-0.83), 0.50 (range: 0.39-0.61) for the no-compartmentlization. The model identified six env features most significant in distinguishing between proviruses in blood and semen in subtype B and C. These features are related to CD4 binding, glycosylation sites and coreceptor selection, which further associated with the viral compartmentalization in semen. In summary, we describe a machine learning model that distinguishes semen-tropic virus based on env sequences and identify six different important features. These ML approach and models can help us better understand the semen-tropic virus phenotype, and therefore its reservoir component, guiding a new study direction toward eradication of the HIV reservoir.

HIV and Proteomics: What We Have Learned from High Throughput Studies

The accelerated development of technology over the last three decades has driven biological sciences to high-throughput profiling experiments, now broadly referred to as systems biology. The unprecedented improvement of analytical instrumentation has opened new avenues for more complex experimental designs and expands the knowledge in genomics, proteomics, and other omics fields. Despite the collective efforts of hundreds of researchers, gleaning all the expected information from omics experiments is still quite far. This paper summarizes what has been learned from high-throughput proteomics studies thus far, and what is believed should be done to reveal even more valuable information from such studies. It is drawn from the background in using proteomics to study human immunodeficiency virus 1 infection of macrophages and/or T cells, but it is believed that some conclusions will be more broadly applicable.

Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy

OBJECTIVE

To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV).

DESIGN

Machine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV.

METHODS

Two hundred and eighty-five children (ages 2-14 years at baseline; Mage = 6.4 years) with pHIV in Southeast Asia underwent neurocognitive assessment at study enrollment and twice annually thereafter for an average of 5.4 years. Neurocognitive slopes were modeled to establish two subgroups [above (n = 145) and below average (n = 140) trajectories). Gradient-boosted multivariate regressions (GBM) with five-fold cross validation were conducted to examine baseline (pre-ART) and longitudinal predictive features derived from demographic, HIV disease, immune, mental health, and physical health indices (i.e. complete blood count [CBC]).

RESULTS

The baseline GBM established a classifier of neurocognitive group designation with an average AUC of 79% built from HIV disease severity and immune markers. GBM analysis of longitudinal predictors with and without interactions improved the average AUC to 87 and 90%, respectively. Mental health problems and hematocrit levels also emerged as salient features in the longitudinal models, with novel interactions between mental health problems and both CD4 cell count and hematocrit levels. Average AUCs derived from each GBM model were higher than results obtained using logistic regression.

CONCLUSION

Our findings support the feasibility of machine learning to identify children with pHIV at risk for suboptimal neurocognitive development. Results also suggest that interactions between HIV disease and mental health problems are early antecedents to neurocognitive difficulties in later childhood among youth with pHIV.

New Potential Axes of HIV Neuropathogenesis with Relevance to Biomarkers and Treatment

Human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) affect approximately half of people living with HIV despite viral suppression with antiretroviral therapies and represent a major cause of morbidity. HAND affects activities of daily living including driving, using the Internet and, importantly, maintaining drug adherence. Whilst viral suppression with antiretroviral therapies (ART) has reduced the incidence of severe dementia, mild neurocognitive impairments continue to remain prevalent. The neuropathogenesis of HAND in the context of viral suppression remains ill-defined, but underlying neuroinflammation is likely central and driven by a combination of chronic intermittent low-level replication of whole virus or viral components, latent HIV infection, peripheral inflammation possibly from a disturbed gut microbiome or chronic cellular dysfunction in the central nervous system. HAND is optimally diagnosed by clinical assessment with imaging and neuropsychological testing, which can be difficult to perform in resource-limited settings. Thus, the identification of biomarkers of disease is a key focus of the field. In this chapter, recent advances in the pathogenesis of HAND and biomarkers that may aid its diagnosis and treatment will be discussed.

The Discovery and Development of Oxalamide and Pyrrole Small Molecule Inhibitors of gp120 and HIV Entry - A Review

Human immunodeficiency virus type-1 (HIV-1) is the causative agent responsible for the acquired immunodeficiency syndrome (AIDS) pandemic. More than 60 million infections and 25 million deaths have occurred since AIDS was first identified in the early 1980s. Advances in available therapeutics, in particular combination antiretroviral therapy, have significantly improved the treatment of HIV infection and have facilitated the shift from high mortality and morbidity to that of a manageable chronic disease. Unfortunately, none of the currently available drugs are curative of HIV. To deal with the rapid emergence of drug resistance, off-target effects, and the overall difficulty of eradicating the virus, an urgent need exists to develop new drugs, especially against targets critically important for the HIV-1 life cycle. Viral entry, which involves the interaction of the surface envelope glycoprotein, gp120, with the cellular receptor, CD4, is the first step of HIV-1 infection. Gp120 has been validated as an attractive target for anti-HIV-1 drug design or novel HIV detection tools. Several small molecule gp120 antagonists are currently under investigation as potential entry inhibitors. Pyrrole, piperazine, triazole, pyrazolinone, oxalamide, and piperidine derivatives, among others, have been investigated as gp120 antagonist candidates. Herein, we discuss the current state of research with respect to the design, synthesis and biological evaluation of oxalamide derivatives and five-membered heterocycles, namely, the pyrrole-containing small molecule as inhibitors of gp120 and HIV entry.

NLRP3-dependent pyroptosis is required for HIV-1 gp120-induced neuropathology

The human immunodeficiency virus-1 (HIV-1) envelope protein gp120 is the major contributor to the pathogenesis of HIV-associated neurocognitive disorder (HAND). Neuroinflammation plays a pivotal role in gp120-induced neuropathology, but how gp120 triggers neuroinflammatory processes and subsequent neuronal death remains unknown. Here, we provide evidence that NLRP3 is required for gp120-induced neuroinflammation and neuropathy. Our results showed that gp120-induced NLRP3-dependent pyroptosis and IL-1β production in microglia. Inhibition of microglial NLRP3 inflammasome activation alleviated gp120-mediated neuroinflammatory factor release and neuronal injury. Importantly, we showed that chronic administration of MCC950, a novel selective NLRP3 inhibitor, to gp120 transgenic mice not only attenuated neuroinflammation and neuronal death but also promoted neuronal regeneration and restored the impaired neurocognitive function. In conclusion, our data revealed that the NLRP3 inflammasome is important for gp120-induced neuroinflammation and neuropathology and suggest that NLRP3 is a potential novel target for the treatment of HAND.

HIV-1-Associated Neurocognitive Disorders: Is HLA-C Binding Stability to β2-Microglobulin a Missing Piece of the Pathogenetic Puzzle?

AIDS dementia complex (ADC) and HIV-associated neurocognitive disorders (HAND) are complications of HIV-1 infection. Viral infections are risk factors for the development of neurodegenerative disorders. Aging is associated with low-grade inflammation in the brain, i.e., the inflammaging. The molecular mechanisms linking immunosenescence, inflammaging and the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease, are largely unknown. ADC and HAND share some pathological features with AD and may offer some hints on the relationship between viral infections, neuroinflammation, and neurodegeneration. β2-microglobulin (β2m) is an important pro-aging factor that interferes with neurogenesis and worsens cognitive functions. Several studies published in the 80-90s reported high levels of β2m in the cerebrospinal fluid of patients with ADC. High levels of β2m have also been detected in AD. Inflammatory diseases in elderly people are associated with polymorphisms of the MHC-I locus encoding HLA molecules that, by associating with β2m, contribute to cellular immunity. We recently reported that HLA-C, no longer associated with β2m, is incorporated into HIV-1 virions, determining an increase in viral infectivity. We also documented the presence of HLA-C variants more or less stably linked to β2m. These observations led us to hypothesize that some variants of HLA-C, in the presence of viral infections, could determine a greater release and accumulation of β2m, which in turn, may be involved in triggering and/or sustaining neuroinflammation. ADC is the most severe form of HAND. To explore the role of HLA-C in ADC pathogenesis, we analyzed the frequency of HLA-C variants with unstable binding to β2m in a group of patients with ADC. We found a higher frequency of unstable HLA-C alleles in ADC patients, and none of them was harboring stable HLA-C alleles in homozygosis. Our data suggest that the role of HLA-C variants in ADC/HAND pathogenesis deserves further studies. If confirmed in a larger number of samples, this finding may have practical implication for a personalized medicine approach and for developing new therapies to prevent HAND. The exploration of HLA-C variants as risk factors for AD and other neurodegenerative disorders may be a promising field of study.