The HIV-1 reservoir landscape in persistent elite controllers and transient elite controllers

FUNDING. Instituto de Salud Carlos III (FI17/00186, FI19/00083, MV20/00057, PI18/01532, PI19/01127 and PI22/01796), Gilead Fellowships (GLD22/00147). NIH grants AI155171, AI116228, AI078799, HL134539, DA047034, MH134823, amfAR ARCHE and the Bill and Melinda Gates Foundation.BACKGROUND. Persistent controllers (PCs) maintain antiretroviral-free HIV-1 control indefinitely over time, while transient controllers (TCs) eventually lose virological control. It is essential to characterize the quality of the HIV reservoir in terms of these phenotypes in order to identify the factors that lead to HIV progression and to open new avenues toward an HIV cure. METHODS. The characterization of HIV-1 reservoir from peripheral blood mononuclear cells was performed using next-generation sequencing techniques, such as full-length individual and matched integration site proviral sequencing (FLIP-Seq; MIP-Seq). RESULTS. PCs and TCs, before losing virological control, presented significantly lower total, intact, and defective proviruses compared with those of participants on antiretroviral therapy (ART). No differences were found in total and defective proviruses between PCs and TCs. However, intact provirus levels were lower in PCs compared with TCs; indeed the intact/defective HIV-DNA ratio was significantly higher in TCs. Clonally expanded intact proviruses were found only in PCs and located in centromeric satellite DNA or zinc-finger genes, both associated with heterochromatin features. In contrast, sampled intact proviruses were located in permissive genic euchromatic positions in TCs. CONCLUSIONS. These results suggest the need for, and can give guidance to, the design of future research to identify a distinct proviral landscape that may be associated with the persistent control of HIV-1 without ART.Instituto de Salud Carlos III (FI17/00186, FI19/00083, MV20/00057, PI18/01532, PI19/01127, PI22/01796)Gilead Fellowships (GLD22/00147)NIH grants AI155171, AI116228, AI078799, HL134539, DA047034, MH134823, amfAR ARCHEBill and Melinda Gates Foundatio

The HIV-1 reservoir landscape in persistent elite controllers and transient elite controllers.

BACKGROUNDPersistent controllers (PCs) maintain antiretroviral-free HIV-1 control indefinitely over time, while transient controllers (TCs) eventually lose virological control. It is essential to characterize the quality of the HIV reservoir in terms of these phenotypes in order to identify the factors that lead to HIV progression and to open new avenues toward an HIV cure.METHODSThe characterization of HIV-1 reservoir from peripheral blood mononuclear cells was performed using next-generation sequencing techniques, such as full-length individual and matched integration site proviral sequencing (FLIP-Seq; MIP-Seq).RESULTSPCs and TCs, before losing virological control, presented significantly lower total, intact, and defective proviruses compared with those of participants on antiretroviral therapy (ART). No differences were found in total and defective proviruses between PCs and TCs. However, intact provirus levels were lower in PCs compared with TCs; indeed the intact/defective HIV-DNA ratio was significantly higher in TCs. Clonally expanded intact proviruses were found only in PCs and located in centromeric satellite DNA or zinc-finger genes, both associated with heterochromatin features. In contrast, sampled intact proviruses were located in permissive genic euchromatic positions in TCs.CONCLUSIONSThese results suggest the need for, and can give guidance to, the design of future research to identify a distinct proviral landscape that may be associated with the persistent control of HIV-1 without ART.FUNDINGInstituto de Salud Carlos III (FI17/00186, FI19/00083, MV20/00057, PI18/01532, PI19/01127 and PI22/01796), Gilead Fellowships (GLD22/00147). NIH grants AI155171, AI116228, AI078799, HL134539, DA047034, MH134823, amfAR ARCHE and the Bill and Melinda Gates Foundation

Migration and activation marker expressing in monocytes subset in mild and several/critical patients (S/C)

S2 Fig. Migration and activation marker expressing in monocytes subset in mild and several/critical patients (S/C).Peer reviewe

S1 Fig - Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients

A, Predictive models for hospital discharge during the first week in mild patients. B, Predictive models for worsening of clinical status during the first week in patients who were admitted mildly ill. AUC, area under the curve.Peer reviewe

Activation, homing and maturation marker expression in different monocyte subsets

Data are expressed by percentage and interquartile range. Medians fluorescence intensitive (MFI) were calculated in those markets that have a high rate of expression.Peer reviewe

Baseline characteristics of the mild patients who were discharged and worsened during the first week

Quantitative variables are expressing as number (percentage) or median (interquartile range). Pa value for differences between patients who were or not discharged. Pb value for differences between patients who who did and did not get wore. SpO2, peripheral capillary oxygen saturation; CRP, C-reactive protein; LDH, Lactate dehydrogenase; NLR, neutrophil/lymphocyte ratio.Peer reviewe

Receiver operating curve (ROC) analyses to evaluate the ability of clinical and laboratory data to predict worse prognosis during the first week

AUC, area under the curve; SE, sensitivity; S, specificity; PPV, positive predictive value; NPV, negative predictive value. SpO2, peripheral capillary oxygen saturation; CRP, C-reactive protein; LDH, Lactate dehydrogenase; NLR, neutrophil/lymphocyte ratio; TNF-α; tumor necrosis factor α; IL-6, interleukine-6; IL-8, interleukine-8; IL-1β, interleukine-1β; MIP-1β, macrophage inflammatory proteins 1β; sCD25, soluble receptor interleukine-2; IP-10, interferon γ-induced protein 10.Peer reviewe

Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients

Background The SARS-CoV-2 pandemic has overwhelmed hospital services due to the rapid transmission of the virus and its severity in a high percentage of cases. Having tools to predict which patients can be safely early discharged would help to improve this situation. Methods Patients confirmed as SARS-CoV-2 infection from four Spanish hospitals. Clinical, demographic, laboratory data and plasma samples were collected at admission. The patients were classified into mild and severe/critical groups according to 4-point ordinal categories based on oxygen therapy requirements. Logistic regression models were performed in mild patients with only clinical and routine laboratory parameters and adding plasma pro-inflammatory cytokine levels to predict both early discharge and worsening. Results 333 patients were included. At admission, 307 patients were classified as mild patients. Age, oxygen saturation, Lactate Dehydrogenase, D-dimers, neutrophil-lymphocyte ratio (NLR), and oral corticosteroids treatment were predictors of early discharge (area under curve (AUC), 0.786; sensitivity (SE) 68.5%; specificity (S), 74.5%; positive predictive value (PPV), 74.4%; and negative predictive value (NPV), 68.9%). When cytokines were included, lower interferon-γ-inducible protein 10 and higher Interleukin 1 beta levels were associated with early discharge (AUC, 0.819; SE, 91.7%; S, 56.6%; PPV, 69.3%; and NPV, 86.5%). The model to predict worsening included male sex, oxygen saturation, no corticosteroids treatment, C-reactive protein and Nod-like receptor as independent factors (AUC, 0.903; SE, 97.1%; S, 68.8%; PPV, 30.4%; and NPV, 99.4%). The model was slightly improved by including the determinations of interleukine-8, Macrophage inflammatory protein-1 beta and soluble IL-2Rα (CD25) (AUC, 0.952; SE, 97.1%; S, 98.1%; PPV, 82.7%; and NPV, 99.6%). Conclusions Clinical and routine laboratory data at admission strongly predict non-worsening during the first two weeks; therefore, these variables could help identify those patients who do not need a long hospitalization and improve hospital overcrowding. Determination of pro-inflammatory cytokines moderately improves these predictive capacities.This work was supported by Consejeria de Salud y Familia (research Project COVID-0005-2020 and Research Contract RH-0037-2020 to JV); Consejería de Transformación Económica, Industria, Conocimiento y Universidades (PY20/01276 to APG); Instituto de Salud Carlos III (CP19/00159 to AGV, CP19/00146 to AR, FI19/00304 to EMM, FI19/00083 to MCGC, "a way to make Europe, and COV20/00698 to support COHVID-GS), Red Temática de Investigación Cooperativa en SIDA (RD16/0025/0020, RD16/0025/0006 and RD16/0025/0026), Fondos FEDER; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas-ISCIII (CB21/13/00020) Madrid, Spain. ERM was supported by the Spanish Research Council (CSIC). AR is also supported by a grant from IISPV through the project “2019/IISPV/05” (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Receiver operating curve (ROC) analyses to evaluate the ability of clinical and laboratory data to predict discharge during the first week

AUC, area under the curve; SE, sensitivity; S, specificity; PPV, positive predictive value; NPV, negative predictive value. SpO2, peripheral capillary oxygen saturation; CRP, C-reactive protein; LDH, Lactate dehydrogenase; NLR, neutrophil/lymphocyte ratio; TNF-α; tumor necrosis factor α; IL-6, interleukine-6; IL-8, interleukine-8; IL-1β, interleukine-1β; MIP-1β, macrophage inflammatory proteins 1β; sCD25, soluble receptor interleukine-2; IP-10, interferon γ-induced protein 10.Peer reviewe