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Abstract In this work our aim was to identify early biomarkers in plasma samples associated with mortality in children with perinatal HIV treated early in life, to potentially inform early intervention targeting this vulnerable group. 20/215 children (9.3%) with perinatal HIV, enrolled within 3 months of age died prematurely within the first year of the study, despite early ART initiation. Using a propensity score, we selected 40 alive study participants having similar clinical and virological records compared to the deceased group. 13 HIV unexposed (HU) healthy children were additionally used as controls. Baseline plasma samples were analyzed using a targeted proteomic approach, and to assess pathogen-associated and damage-associated molecular patterns (PAMPs, DAMPs) levels. Data from deceased participants were compared to both control groups, with multivariate logistic regression models used to evaluate the association between mortality and plasma proteins. We developed a machine learning model to predict mortality risk, finding that IL-6 and CXCL11 not only were higher in deceased children than Matched-children with HIV (p

Summary: Background: Despite antiretroviral treatment (ART), immune dysfunction persists in children with perinatal HIV infection (HEI). Here we investigated the impact of HIV status on maternal antibody (Ab) passage, long-term vaccine induced immunity and B-cell maturation. Methods: 46 HIV Exposed Uninfected (HEU), 43 HEI, and 15 HIV unexposed uninfected (HUU) infants were vaccinated with 3 doses of DTaP-HepB-Hib-PCV10-OP at 2, 3, and 4 months at Matola Provincial Hospital, Maputo, Mozambique. Tetanus toxoid specific (TT) IgG, HIV Ab and B-cell phenotype characteristics were evaluated at entry, pre-ART, 5, 10, and 18 months in this longitudinal cohort study. Findings: Baseline (maternal) plasma TT Ab levels were significantly lower in HEI compared to both HEU and HUU and a faster decay of TT Ab was observed in HEI compared to HEU with significantly lower TT Ab levels at 10 and 18 months of age. TT unprotected (UP) (≤0.1 IU/mL) HEI showed higher HIV-RNA at entry and higher longitudinal HIV viremia (Area Under the Curve) compared to TT protected (P) HEI. A distinct HIV-Ab profile was found at entry in HEI compared to HEU. B-cell phenotype showed a B-cell perturbation in HEI vs HEU infants at entry (mean age 40.8 days) with lower transitional CD10+CD19+ B-cells and IgD+CD27− naive B-cells and an overall higher frequency of IgD−CD27− double negative B-cell subsets in HEI. Interpretation: B-cell perturbation, presenting with higher double negative IgD−CD27− B-cells was observed in neonatal age and may play a major role in the B-cell exhaustion in HEI. The ability to maintain TT protective Ab titers over time is impaired in HEI with uncontrolled viral replication and the current vaccination schedule is insufficient to provide long-term protection against tetanus. Funding: This work was supported by: NIH grant to SP (5R01AI127347-05); Children’s Hospital Bambino Gesú (Ricerca corrente 2019) to NC, and Associazione Volontari Bambino Gesù to PP.

BackgroundDespite a successful antiretroviral therapy (ART), adolescents living with perinatally acquired HIV (PHIV) experience signs of B-cell hyperactivation with expansion of ‘namely’ atypical B-cell phenotypes, including double negative (CD27-IgD-) and termed age associated (ABCs) B-cells (T-bet+CD11c+), which may result in reduced cell functionality, including loss of vaccine-induced immunological memory and higher risk of developing B-cells associated tumors. In this context, perinatally HIV infected children (PHIV) deserve particular attention, given their life-long exposure to chronic immune activation.MethodsWe studied 40 PHIV who started treatment by the 2nd year of life and maintained virological suppression for 13.5 years, with 5/40 patients experiencing transient elevation of the HIV-1 load in the plasma (Spike). We applied a multi-disciplinary approach including immunological B and T cell phenotype, plasma proteomics analysis, and serum level of anti-measles antibodies as functional correlates of vaccine-induced immunity.ResultsPhenotypic signs of B cell hyperactivation were elevated in subjects starting ART later (%DN T-bet+CD11c+ p=0.03; %AM T-bet+CD11c+ p=0.02) and were associated with detectable cell-associated HIV-1 RNA (%AM T-bet+CD11c+ p=0.0003) and transient elevation of the plasma viral load (spike). Furthermore, B-cell hyperactivation appeared to be present in individuals with higher frequency of exhausted T-cells, in particular: %CD4 TIGIT+ were associated with %DN (p=0.008), %DN T-bet+CD11c+ (p=0.0002) and %AM T-bet+CD11c+ (p=0.002) and %CD4 PD-1 were associated with %DN (p=0.048), %DN T-bet+CD11c+ (p=0.039) and %AM T-bet+CD11c+ (p=0.006). The proteomic analysis revealed that subjects with expansion of these atypical B-cells and exhausted T-cells had enrichment of proteins involved in immune inflammation and complement activation pathways. Furthermore, we observed that higher levels of ABCs were associated a reduced capacity to maintain vaccine-induced antibody immunity against measles (%B-cells CD19+CD10- T-bet+, p=0.035).ConclusionWe identified that the levels of hyperactivated B cell subsets were strongly affected by time of ART start and associated with clinical, viral, cellular and plasma soluble markers. Furthermore, the expansion of ABCs also had a direct impact on the capacity to develop antibodies response following routine vaccination.

Early initiation of antiretroviral therapy and adherence to achieve viral load suppression (VLS) are crucial for reducing morbidity and mortality of perinatally HIV-infected infants. In this descriptive cohort study of 39 HIV perinatally infected infants, who started treatment at one month of life in Mozambique, we aimed to describe the viral response over 2 years of follow up. VLS ≤ 400 copies/mL, sustained VLS and viral rebound were described using a Kaplan–Meier estimator. Antiretroviral drug transmitted resistance was assessed for a sub-group of non-VLS infants. In total, 61% of infants reached VLS, and 50% had a rebound. Cumulative probability of VLS was 36%, 51%, and 69% at 6, 12 and 24 months of treatment, respectively. The median duration of VLS was 7.4 months (IQR 12.6) and the cumulative probability of rebound at 6 months was 30%. Two infants had resistance biomarkers to drugs included in their treatment regimen. Our findings point to a low rate of VLS and high rate of viral rebound. More frequent viral response monitoring is advisable to identify infants with rebound and offer timely adherence support. It is urgent to tailor the psychosocial support model of care to this specific age group and offer differentiated service delivery to mother–baby pairs.

Background: Melanoma is a heterogeneous tumour, but the impact of this heterogeneity upon therapeutic response is not well understood. Methods: Single cell mRNA analysis was used to define the transcriptional heterogeneity of melanoma and its dynamic response to BRAF inhibitor therapy and treatment holidays. Discrete transcriptional states were defined in cell lines and melanoma patient specimens that predicted initial sensitivity to BRAF inhibition and the potential for effective re-challenge following resistance. A mathematical model was developed to maintain competition between the drug-sensitive and resistant states, which was validated in vivo. Findings: Our analyses showed melanoma cell lines and patient specimens to be composed of >3 transcriptionally distinct states. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy and drug holidays. Transcriptional state composition predicted for therapy response. The differences in fitness between the different transcriptional states were leveraged to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation demonstrated that the personalized adaptive dosing schedules outperformed continuous or fixed intermittent BRAF inhibitor schedules. Interpretation: Our study provides the first evidence that transcriptional heterogeneity at the single cell level predicts for initial BRAF inhibitor sensitivity. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules can delay the time to resistance. Funding: This work was funded by the National Institutes of Health. The funder played no role in assembly of the manuscript. Keywords: Melanoma, MITF, Resistance, Heterogeneity, Mathematical modelling

The size of the latent HIV reservoir is associated with the timing of therapeutic interventions and overall health of the immune system. Here, we demonstrate that T cell phenotypic signatures associate with viral reservoir size in a cohort of HIV vertically infected children and young adults under durable viral control, and who initiated anti-retroviral therapy (ART)

The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from in-vitro stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.

Modern technologies and their increased accessibility have shifted ‘benchtop’ medical research to the larger dimension of ‘omics. The huge amount of data derived from gene expression and sequencing experiments has propelled physicians, basic scientists and bioinformaticians towards a common goal to transform ‘big data’ into predictive constructs that are readily available and will offer clinical utility. Although most of the studies available in the literature have been performed on healthy subjects and in peripheral blood mononuclear cells (PBMC), which are a heterogenous and extremely variable pool of cells, scientists are now trying to address mechanistic questions in purified cell subsets in pathological conditions. In the field of HIV, few attempts have been made to comprehensively evaluate gene-expression profiles of infected patients with different disease status. With the view of discovering a path towards remission or viral eradication, perinatally HIV-infected children represent a unique model. In fact the well-defined time of infection and the resulting opportunity to start early treatment, thereby generating a smaller size of viral reservoir and a more intact immune system, allow for investigation of therapeutic strategies to defeat the virus. In this scenario, ‘transcriptomic’ or gene expression technologies and supporting bioinformatics applications need to be strategically integrated to provide novel information about immune correlates of virus control following treatment interruption. Here we review modern techniques for gene expression analysis and discuss the best transcriptomic strategies applicable to the field of functional cure in paediatric HIV infection.

T follicular helper (Tfh) cells are a subset of CD4 T cells that provide critical signals to antigen-primed B cells in germinal centers to undergo proliferation, isotype switching, and somatic hypermutation to generate long-lived plasma cells and memory B cells during an immune response. The quantity and quality of Tfh cells therefore must be tightly controlled to prevent immune dysfunction in the form of autoimmunity and, on the other hand, immune deficiency. Both Tfh and B cell perturbations appear during HIV infection resulting in impaired antibody responses to vaccines such as seasonal trivalent influenza vaccine, also seen in biologic aging. Although many of the HIV-associated defects improve with antiretroviral therapy (ART), excess immune activation and antigen-specific B and T cell responses including Tfh function are still impaired in virologically controlled HIV-infected persons on ART. Interestingly, HIV infected individuals experience increased risk of age-associated pathologies. This review will discuss Tfh and B cell dysfunction in HIV infection and highlight the impact of chronic HIV infection and aging on Tfh–B cell interactions.

Despite effective antiretroviral therapy (ART), HIV-infected individuals with apparently similar clinical and immunological characteristics can vary in responsiveness to vaccinations. However, molecular mechanisms responsible for such impairment, as well as biomarkers able to predict vaccine responsiveness in HIV-infected children, remain unknown. Following the hypothesis that a B cell qualitative impairment persists in HIV-infected children (HIV) despite effective ART and phenotypic B cell immune reconstitution, the aim of the current study was to investigate B cell gene expression of HIV compared to age-matched healthy controls (HCs) and to determine whether distinct gene expression patterns could predict the ability to respond to influenza vaccine. To do so, we analyzed prevaccination transcriptional levels of a 96-gene panel in equal numbers of sort-purified B cell subsets (SPBS) isolated from peripheral blood mononuclear cells using multiplexed RT-PCR. Immune responses to H1N1 antigen were determined by hemaglutination inhibition and memory B cell ELISpot assays following trivalent-inactivated influenza vaccination (TIV) for all study participants. Although there were no differences in terms of cell frequencies of SPBS between HIV and HC, the groups were distinguishable based upon gene expression analyses. Indeed, a 28-gene signature, characterized by higher expression of genes involved in the inflammatory response and immune activation was observed in activated memory B cells (CD27+CD21−) from HIV when compared to HC despite long-term viral control (>24 months). Further analysis, taking into account H1N1 responses after TIV in HIV participants, revealed that a 25-gene signature in resting memory (RM) B cells (CD27+CD21+) was able to distinguish vaccine responders from non-responders (NR). In fact, prevaccination RM B cells of responders showed a higher expression of gene sets involved in B cell adaptive immune responses (APRIL, BTK, BLIMP1) and BCR signaling (MTOR, FYN, CD86) when compared to NR. Overall, these data suggest that a perturbation at a transcriptional level in the B cell compartment persists despite stable virus control achieved through ART in HIV-infected children. Additionally, the present study demonstrates the potential utility of transcriptional evaluation of RM B cells before vaccination for identifying predictive correlates of vaccine responses in this population.