A subset of people living with HIV (PLHIV), known as immunological non-responders (INR), fails to achieve adequate CD4⁺ T cell recovery despite viral suppression with antiretroviral therapy (ART). These individuals face an increased risk of adverse clinical outcomes. This study aims to explore the intracellular mechanisms and molecular signatures underlying this incomplete immune recovery using a multi-omics approach. The study analysed CD4⁺ and CD8⁺ T cells from 100 ART-naïve PLHIV. Participants were classified into controls (baseline CD4⁺ > 200 cells/µL, n = 49) and cases (baseline CD4⁺ ≤ 200 cells/µL, n = 51). Cases were further categorised after 48 weeks of ART as immunological responders (IR, n = 34) or INR (n = 16) based on a recovery cut-off of 250 cells/µL. The research employed integrative proteomic and metabolomic analyses, adjusted for sex, complemented by miRNA and qRT-PCR validation in a subset of samples to identify differential molecular signatures. In CD4⁺ T cells, a baseline signature of 381 proteins and 45 metabolites significantly differentiated controls from cases; notably, 34 proteins and 16 metabolites specifically discriminated future IR from INR before starting treatment. CD8⁺ T cells showed a more limited signature with minimal stratification between IR and INR. Integrative analysis revealed coordinated dysregulation in CD4⁺ T cells of INR, characterised by altered glycolytic flux, metabolic exhaustion, and oxidative stress. Exploratory analyses via miRNA and qRT-PCR confirmed these findings and suggested post-transcriptional mechanisms contribute to the impairment. Incomplete immune recovery in PLHIV is primarily driven by pre-existing metabolic dysfunction and oxidative stress within CD4⁺ T cells, rather than CD8⁺ T cells. These findings identify specific molecular signatures present before ART initiation, highlighting early potential therapeutic targets and strategies to enhance immune reconstitution in this vulnerable population.

In adults living with HIV, non-invasive biomarkers have been described for the early identification of metabolic dysfunction-associated steatotic liver diseases (MASLD). However, this issue remains unexplored in children and young people with vertical HIV (YWVH), among whom MASLD prevalence is around 30

OBJECTIVES:We aimed to describe health-related quality of life (HRQoL), overall and across its dimensions, identify associated factors, and assess changes over time among people with HIV (PWH) from the Spanish multicentre CoRIS cohort. METHODS:We developed a mobile app to collect HRQoL data every 3 months using the WHOQOL-HIV-BREF questionnaire (31 items across six domains), among PWH followed in CoRIS in 2021-2023. Factors associated with good/very good global HRQoL and with domain-specific mean scores were identified using multivariable logistic and linear regression, respectively. RESULTS:Of 414 PWH (94.2% on antiretroviral treatment, 91.1% virally suppressed), 51.2% reported good/very good HRQoL. Latin American migrants (adjusted OR: 0.60 [95% CI: 0.36; 1.00]), and participants with lower educational level (0.36 [0.21; 0.64]), a previous AIDS diagnosis (0.56 [0.29; 1.11]) and a history of non-AIDS-related cancers (0.40 [0.14; 1.14]) were less likely to report good/very good global HRQoL. The most affected items included sexual satisfaction, forgiveness and blame, sleep and rest, and concerns about the future, with spirituality, religion and personal beliefs as the most affected domain. Latin American origin, lower educational level and shorter (<2 years) or longer (>15 years) time since HIV diagnosis were associated with poorer HRQoL in specific domains. No significant changes in HRQoL were observed after 12 months except slightly higher scores in physical health. CONCLUSIONS:Only half of PWH reported good/very good global HRQoL. This highlights the need to develop targeted strategies to improve HRQoL among PWH, focusing on addressing the most affected dimensions and supporting the most vulnerable groups.

Introduction The DOLAM trial revealed that switching from triple antiretroviral therapy (three-drug regimen; 3DR) to dolutegravir plus lamivudine (two-drug regimen; 2DR) was virologically non-inferior to continuing 3DR after 48 weeks of follow-up. Weight increased with 2DR relative to 3DR but it did not impact on metabolic parameters.Methods Multiomics plasma profile was performed to gain further insight into whether this therapy switch might affect specific biological pathways. DOLAM (EudraCT 201500027435) is a Phase 4, randomized, open-label, non-inferiority trial in which virologically suppressed persons with HIV treated with 3DR were assigned (1:1) to switch to 2DR or to continue 3DR for 48 weeks. Untargeted proteomics, metabolomics and lipidomics analyses were performed at baseline and at 48 weeks. Univariate and multivariate analyses were performed to identify changes in key molecules between both therapy arms.Results Switching from 3DR to 2DR showed a multiomic impact on circulating plasma concentration of N-acetylmuramoyl-L-alanine amidase (Q96PD5), insulin-like growth factor-binding protein 3 (A6XND0), alanine and triglyceride (TG) (48:0). Correlation analyses identified an association among the up-regulation of these four molecules in persons treated with 2DR.Conclusions Untargeted multiomics profiling studies identified molecular changes potentially associated with inflammation immune pathways, and with lipid and glucose metabolism. Although these changes could be associated with potential metabolic or cardiovascular consequences, their clinical significance remains uncertain. Further work is needed to confirm these findings and to assess their long-term clinical consequences.

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.

BACKGROUND To determine by multi-omic analysis changes in metabolites, lipids and proteins as consequence of transient viral rebound (tVR) in children with perinatally acquired HIV-1 (PHIV). METHODS Plasma samples from children with PHIV and with tVR (first episode of transient RNA-HIV viral load >20 copies/ml followed by suppression) on the time-point immediately before (pre-tVR) and after (post-tVR) the tVR were assessed. Multi-omic analyses were performed using nLC-Orbitrap, GC-qTOF-MS and LC-qTOF-MS. RESULTS Comparing pre- and post-tVR time-points, HIV-1-children with tVR (n=5) showed a trend to a decrease in ratio CD4/CD8 (p=0.08) but no significant differences were observed in plasma metabolites, lipids or proteins. Post-tVR condition was compared with a reference group of children with PHIV with persistent viral control (n=9), paired by sex, age and time under antiretroviral treatment. A total of 10 proteins, 8 metabolites and 2 lipids showed significant differences (p<0.05): serotransferrin, clusterin, kininogen-1, succinic acid, threonine, 2-hydroxyisovaleric acid, methionine, 2-hydroxyglutaric, triacylglyceride 50:0 (TG50:0) and diacylglyceride 34:1 (DG34:1) were up-regulated while alpha-2-macroglobulin, apolipoprotein A-II, carboxylic ester hydrolase, apolipoprotein D, coagulation factor IX, peptidase inhibitor 16, SAA2-SAA4 readthrough, oleic acid, palmitoleic acid and D-sucrose downregulated on post-tVR time-point compared to reference group. Ratio CD4/CD8 correlated with apolipoprotein A-II, DG34:1 and methionine (p=0.004;ρ=0.71, p=0.016;ρ=-0.63 and p=0.032;ρ=-0.57, respectively). Nadir CD4+ correlated inversely with kininogen-1 (p=0.022;ρ=-0.60) and positively with D-sucrose (p=0.001;ρ=0.77). CONCLUSIONS tVR followed by suppression implies changes in soluble proteins, lipids and metabolites that correlate with immunological parameters, mainly ratio CD4/CD8, that decreased after tVR. These distinct soluble biomarkers could be considered potential biomarkers of immune progression.

Antiretroviral therapy (ART) induces persistent suppression of HIV-1 replication and gradual recovery of T-cell counts, and consequently, morbidity and mortality from HIV-related illnesses have been significantly reduced. However, in approximately 30% of people living with HIV (PLHIV) on ART, CD4+ T-cell counts fail to normalize despite ART and complete suppression of HIV viral load, resulting in severe immune dysfunction, which may represent an increased risk of clinical progression to AIDS and non-AIDS events as well as increased mortality. These patients are referred to as "immune inadequate responders", "immunodiscordant responders" or "immune nonresponders (INR)". The molecular mechanisms underlying poor CD4+ T-cell recovery are still unclear. In this sense, the use of omics sciences has shed light on possible factors involved in the activity and metabolic dysregulation of immune cells during the failure of CD4+ T-cell recovery in INR. Moreover, identification of key molecules by omics approaches allows for the proposal of potential biomarkers or therapeutic targets to improve CD4+ T-cell recovery and the quality of life of these patients. Hence, this review aimed to summarize the information obtained through different omics concerning the molecular factors and pathways associated with the INR phenotype to better understand the complexity of this immunological status in HIV infection.

Clinical and Translational MedicineVolume 12, Issue 1 e704 LETTER TO EDITOROpen Access Fetuin-A, inter-α-trypsin inhibitor, glutamic acid and ChoE (18:0) are key biomarkers in a panel distinguishing mild from critical coronavirus disease 2019 outcomes Laia Reverté, Laia Reverté Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain These authors contributed equally to this paper, and should be considered as primary co-authors.Search for more papers by this authorElena Yeregui, Elena Yeregui Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain These authors contributed equally to this paper, and should be considered as primary co-authors.Search for more papers by this authorMontserrat Olona, Montserrat Olona Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorAlicia Gutiérrez-Valencia, Alicia Gutiérrez-Valencia orcid.org/0000-0003-3445-1574 Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorMaria José Buzón, Maria José Buzón orcid.org/0000-0003-4427-9413 Department of Infectious Disease, Hospital Universitari Vall d'Hebron, Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, SpainSearch for more papers by this authorAnna Martí, Anna Martí Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, SpainSearch for more papers by this authorFrederic Gómez-Bertomeu, Frederic Gómez-Bertomeu Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorTeresa Auguet, Teresa Auguet Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorLuis F. López-Cortés, Luis F. López-Cortés Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorJoaquin Burgos, Joaquin Burgos Department of Infectious Disease, Hospital Universitari Vall d'Hebron, Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, SpainSearch for more papers by this authorClara Benavent-Bofill, Clara Benavent-Bofill Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, SpainSearch for more papers by this authorCarme Boqué, Carme Boqué Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorGraciano García-Pardo, Graciano García-Pardo Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorEzequiel Ruiz-Mateos, Ezequiel Ruiz-Mateos orcid.org/0000-0001-6747-7813 Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorMaria Teresa Mestre, Maria Teresa Mestre Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, SpainSearch for more papers by this authorFrancesc Vidal, Corresponding Author Francesc Vidal fvidalmarsal.hj23.ics@gencat.cat orcid.org/0000-0002-6692-6186 Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain Correspondence Francesc Vidal and Anna Rull, Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain. Email: fvidalmarsal.hj23.ics@gencat.cat and anna.rull@iispv.catSearch for more papers by this authorConsuelo Viladés, Consuelo Viladés Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorJoaquim Peraire, Joaquim Peraire Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain These authors contributed equally to this paper, and should both be considered as senior co-authors.Search for more papers by this authorAnna Rull, Corresponding Author Anna Rull anna.rull@iispv.cat orcid.org/0000-0002-8907-7754 Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain These authors contributed equally to this paper, and should both be considered as senior co-authors. Correspondence Francesc Vidal and Anna Rull, Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain. Email: fvidalmarsal.hj23.ics@gencat.cat and anna.rull@iispv.catSearch for more papers by this authorCOVIDOMICS Study Group, COVIDOMICS Study Group The clinical centres and research groups which contribute to COVIDOMICS Study Group : Hospital Universitari Joan XXIII / IISPV / URV: Jenifer Masip, Verónica Alba, Montserrat Vargas, Laia Bertrán, Carmen Aguilar, Miguel López-Dupla, José Antonio Porras, Sergi Veloso, Ajla Alibalic, David Riesco, Mónica Real, Jessica Binetti, Judit Poblet, Mercé Sirisi, Gaspar Dalmau, Vanessa Gázquez, Esther Rodriguez, Antonia Garcia, Esther Picó, Cristina Gutiérrez, Gemma Recio-Comí, Carla Martin-Grau, Teresa Sans, Carlos Chiapella, Pilar Carbajo, Mireia Cramp, Carme Bes, Rosalia Bote, Nuria Alba, Blanca Rosich, Cristina Varillas, Catherine Cabrejo, Rosaura Reig, Llorenç Mairal, Jesús Esteve Ferrán, Neus Camañes, Angela Cortés and Rafael Gracia. Hospital Universitari Vall d'Hebrón / VHIR / UAB: Meritxell Genescà, Anna Falcó, Cristina Kirkegard, Jordi Navarro, Vicente Descalzo, Paula Suanzes, Judith Grau, Nerea Sanchez, Antonio Astorga and Vicenç Falcó. IBiS/Virgen del Rocío Hospital COVID-19 Working Team: José Miguel Cisneros, Luis E. López-Cortés, Alberto Pérez-Gómez, Joana Vitallé, Carmen Gasca-Capote, María Trujillo-Rodriguez, Ana Serna-Gallego, Esperanza Muñoz-Muela, María de los Reyes Jiménez-Leon, Mohamed Rafii-El-Idrissi Benhnia, Francisco Ostos, Laura Herrera-Hidalgo, Karin Neukam, Inmaculada Rivas-Jeremias, Silvia Llaves, Montserrat Dominguez, Cesar Sotomayor, Cristina Roca-Oporto, Nuria Espinosa, Carmen Infante-Domínguez, Juan Carlos Crespo-Rivas, Abraham Saborido, Sara Bachiller, Sonsoles Salto-Alejandre, Judith Berastegui-Cabrera, Pedro Camacho-Martínez, Carmen Infante Domínguez, Marta Carretero-Ledesma, Juan Carlos Crespo-Rivas, Eduardo Márquez, José Manuel Lomas, Claudio Bueno, Rosario Amaya, José Antonio Lepe, Jerónimo Pachón, Elisa Cordero, Javier Sánchez-Céspedes, Manuela Aguilar Guisado, Almudena Aguilera, Clara Aguilera, Teresa Aldabo-Pallas, Verónica Alfaro-Lara, Cristina Amodeo, Javier Ampuero, María Dolores Avilés, Maribel Asensio, Bosco Barón-Franco, Lydia Barrera-Pulido, Rafael Bellido-Alba, Máximo Bernabeu-Wittel, Candela Caballero-Eraso, Macarena Cabrera, Enrique Calderón, Jesús Carbajal-Guerrero, Manuela Cid-Cumplido, Yael Corcia Palomo, Juan Delgado, Antonio Domínguez-Petit, Alejandro Deniz, Reginal Dusseck-Brutus, Ana Escoresca-Ortega, Fátima Espinosa, Michelle Espinoza, Carmen Ferrándiz-Millón, Marta Ferrer, Teresa Ferrer, Ignacio Gallego-Texeira, Rosa Gámez-Mancera, Emilio García, Horacio García-Delgado, Manuel García-Gutiérrez, María Luisa Gascón-Castillo, Aurora González-Estrada, Demetrio González, Carmen Gómez-González, Rocío González-León, Carmen Grande-Cabrerizo, Sonia Gutiérrez, Carlos Hernández-Quiles, Inmaculada Concepción Herrera-Melero, Marta Herrero-Romero, Luis Jara, Carlos Jiménez-Juan, Silvia Jiménez-Jorge, Mercedes Jiménez-Sánchez, Julia Lanseros-Tenllado, Carmina López, Isabel López, Álvaro López Barrios, Rafael Luque-Márquez, Daniel Macías-García, Guillermo Martín-Gutiérrez, Luis Martín-Villén, José Molina, Aurora Morillo, María Dolores Navarro-Amuedo, Dolores Nieto-Martín, Francisco Ortega, María Paniagua-García, Amelia Peña-Rodríguez, Esther Pérez, Manuel Poyato, Julia Praena-Segovia, Rafaela Ríos, Jesús F. Rodríguez, María Jesús Rodríguez-Hernández, Santiago Rodríguez-Suárez, Ángel Rodríguez-Villodres, Nieves Romero Rodríguez, Ricardo Ruiz, Zida Ruiz de Azua, Celia Salamanca, Sonia Sánchez, Víctor Manuel Sánchez-Montagut, Alejandro Suárez Benjumea and Javier Toral.Search for more papers by this author Laia Reverté, Laia Reverté Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain These authors contributed equally to this paper, and should be considered as primary co-authors.Search for more papers by this authorElena Yeregui, Elena Yeregui Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain These authors contributed equally to this paper, and should be considered as primary co-authors.Search for more papers by this authorMontserrat Olona, Montserrat Olona Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorAlicia Gutiérrez-Valencia, Alicia Gutiérrez-Valencia orcid.org/0000-0003-3445-1574 Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorMaria José Buzón, Maria José Buzón orcid.org/0000-0003-4427-9413 Department of Infectious Disease, Hospital Universitari Vall d'Hebron, Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, SpainSearch for more papers by this authorAnna Martí, Anna Martí Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, SpainSearch for more papers by this authorFrederic Gómez-Bertomeu, Frederic Gómez-Bertomeu Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorTeresa Auguet, Teresa Auguet Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorLuis F. López-Cortés, Luis F. López-Cortés Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorJoaquin Burgos, Joaquin Burgos Department of Infectious Disease, Hospital Universitari Vall d'Hebron, Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, SpainSearch for more papers by this authorClara Benavent-Bofill, Clara Benavent-Bofill Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, SpainSearch for more papers by this authorCarme Boqué, Carme Boqué Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorGraciano García-Pardo, Graciano García-Pardo Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorEzequiel Ruiz-Mateos, Ezequiel Ruiz-Mateos orcid.org/0000-0001-6747-7813 Unit of Infectious Diseases, Microbiology and Preventive Medicine, Virgen del Rocío University Hospital, Seville, Spain Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, SpainSearch for more papers by this authorMaria Teresa Mestre, Maria Teresa Mestre Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, SpainSearch for more papers by this authorFrancesc Vidal, Corresponding Author Francesc Vidal fvidalmarsal.hj23.ics@gencat.cat orcid.org/0000-0002-6692-6186 Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain Correspondence Francesc Vidal and Anna Rull, Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain. Email: fvidalmarsal.hj23.ics@gencat.cat and anna.rull@iispv.catSearch for more papers by this authorConsuelo Viladés, Consuelo Viladés Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, SpainSearch for more papers by this authorJoaquim Peraire, Joaquim Peraire Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain These authors contributed equally to this paper, and should both be considered as senior co-authors.Search for more papers by this authorAnna Rull, Corresponding Author Anna Rull anna.rull@iispv.cat orcid.org/0000-0002-8907-7754 Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain Institut Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain Universitat Rovira i Virgili (URV), Tarragona, Spain These authors contributed equally to this paper, and should both be considered as senior co-authors. Correspondence Francesc Vidal and Anna Rull, Hospital Universitari de Tarragona Joan XXIII (HJ23), Tarragona, Spain. Email: fvidalmarsal.hj23.ics@gencat.cat and anna.rull@iispv.catSearch for more papers by this authorCOVIDOMICS Study Group, COVIDOMICS Study Group The clinical centres and research groups which contribute to COVIDOMICS Study Group : Hospital Universitari Joan XXIII / IISPV / URV: Jenifer Masip, Verónica Alba, Montserrat Vargas, Laia Bertrán, Carmen Aguilar, Miguel López-Dupla, José Antonio Porras, Sergi Veloso, Ajla Alibalic, David Riesco, Mónica Real, Jessica Binetti, Judit Poblet, Mercé Sirisi, Gaspar Dalmau, Vanessa Gázquez, Esther Rodriguez, Antonia Garcia, Esther Picó, Cristina Gutiérrez, Gemma Recio-Comí, Carla Martin-Grau, Teresa Sans, Carlos Chiapella, Pilar Carbajo, Mireia Cramp, Carme Bes, Rosalia Bote, Nuria Alba, Blanca Rosich, Cristina Varillas, Catherine Cabrejo, Rosaura Reig, Llorenç Mairal, Jesús Esteve Ferrán, Neus Camañes, Angela Cortés and Rafael Gracia. Hospital Universitari Vall d'Hebrón / VHIR / UAB: Meritxell Genescà, Anna Falcó, Cristina Kirkegard, Jordi Navarro, Vicente Descalzo, Paula Suanzes, Judith Grau, Nerea Sanchez, Antonio Astorga and Vicenç Falcó. IBiS/Virgen del Rocío Hospital COVID-19 Working Team: José Miguel Cisneros, Luis E. López-Cortés, Alberto Pérez-Gómez, Joana Vitallé, Carmen Gasca-Capote, María Trujillo-Rodriguez, Ana Serna-Gallego, Esperanza Muñoz-Muela, María de los Reyes Jiménez-Leon, Mohamed Rafii-El-Idrissi Benhnia, Francisco Ostos, Laura Herrera-Hidalgo, Karin Neukam, Inmaculada Rivas-Jeremias, Silvia Llaves, Montserrat Dominguez, Cesar Sotomayor, Cristina Roca-Oporto, Nuria Espinosa, Carmen Infante-Domínguez, Juan Carlos Crespo-Rivas, Abraham Saborido, Sara Bachiller, Sonsoles Salto-Alejandre, Judith Berastegui-Cabrera, Pedro Camacho-Martínez, Carmen Infante Domínguez, Marta Carretero-Ledesma, Juan Carlos Crespo-Rivas, Eduardo Márquez, José Manuel Lomas, Claudio Bueno, Rosario Amaya, José Antonio Lepe, Jerónimo Pachón, Elisa Cordero, Javier Sánchez-Céspedes, Manuela Aguilar Guisado, Almudena Aguilera, Clara Aguilera, Teresa Aldabo-Pallas, Verónica Alfaro-Lara, Cristina Amodeo, Javier Ampuero, María Dolores Avilés, Maribel Asensio, Bosco Barón-Franco, Lydia Barrera-Pulido, Rafael Bellido-Alba, Máximo Bernabeu-Wittel, Candela Caballero-Eraso, Macarena Cabrera, Enrique Calderón, Jesús Carbajal-Guerrero, Manuela Cid-Cumplido, Yael Corcia Palomo, Juan Delgado, Antonio Domínguez-Petit, Alejandro Deniz, Reginal Dusseck-Brutus, Ana Escoresca-Ortega, Fátima Espinosa, Michelle Espinoza, Carmen Ferrándiz-Millón, Marta Ferrer, Teresa Ferrer, Ignacio Gallego-Texeira, Rosa Gámez-Mancera, Emilio García, Horacio García-Delgado, Manuel García-Gutiérrez, María Luisa Gascón-Castillo, Aurora González-Estrada, Demetrio González, Carmen Gómez-González, Rocío González-León, Carmen Grande-Cabrerizo, Sonia Gutiérrez, Carlos Hernández-Quiles, Inmaculada Concepción Herrera-Melero, Marta Herrero-Romero, Luis Jara, Carlos Jiménez-Juan, Silvia Jiménez-Jorge, Mercedes Jiménez-Sánchez, Julia Lanseros-Tenllado, Carmina López, Isabel López, Álvaro López Barrios, Rafael Luque-Márquez, Daniel Macías-García, Guillermo Martín-Gutiérrez, Luis Martín-Villén, José Molina, Aurora Morillo, María Dolores Navarro-Amuedo, Dolores Nieto-Martín, Francisco Ortega, María Paniagua-García, Amelia Peña-Rodríguez, Esther Pérez, Manuel Poyato, Julia Praena-Segovia, Rafaela Ríos, Jesús F. Rodríguez, María Jesús Rodríguez-Hernández, Santiago Rodríguez-Suárez, Ángel Rodríguez-Villodres, Nieves Romero Rodríguez, Ricardo Ruiz, Zida Ruiz de Azua, Celia Salamanca, Sonia Sánchez, Víctor Manuel Sánchez-Montagut, Alejandro Suárez Benjumea and Javier Toral.Search for more papers by this author First published: 24 January 2022 https://doi.org/10.1002/ctm2.704 AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Dear Editor, The mechanistic pathways leading to immune dysregulation and complications driven by uncontrolled severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remain major challenges.1, 2 Hence, a detailed analysis of the proteome, metabolome and lipidome profile of coronavirus disease 2019 (COVID-19) patients showing different severity grades might shed light on the disease pathophysiology and unveil new predictive biomarkers to promptly ascertain patient's outcomes. Our COVID-19 study cohort included 273 SARS-CoV-2 infected individuals recruited during the first wave (March–April 2020) in three different hospitals and grouped by the disease severity following the medical inclusion criteria3 in mild, severe or critical (Figure 1A), from whom demographic, preexisting clinical conditions and COVID-19 treatments are summarized in Table S1. The greatest significant differences were observed between mild and critically ill patients. These findings indicated that older individuals with comorbidities such as hypertension, obesity, diabetes and cardiovascular disorders, mostly presenting dyspnea (Figure 1B), may be at higher risk of suffering from severe respiratory distress with subsequent oxygen and drug requirements and, eventually, died. Similarly, the serum biochemical composition analysis revealed a well-differentiated blood pattern previously defined for critically ill patients (Figure S1). FIGURE 1Open in figure viewerPowerPoint Study design and clinical characterization of the study cohort. (A) Flowchart of the clinical strategy followed to categorize patients of the coronavirus disease-2019 (COVID-19) study cohort. (B) Incidence of comorbidities (a), COVID-19 symptoms (b), medication (c), and oxygen & intensive care (d), grouped by disease severity as mild, severe and critical patients. The size of bars (a, c) and circular (b, d) portions is proportional to the percentage of the corresponding comorbidity, symptom, medication or treatment. While patients with mild disease presented mostly anosmia, were treated with antibiotics and did not require oxygen supply, the incidence of dyspnea was significantly higher in the severe and critical groups, many of the latter requiring corticosteroids, hydroxychloroquine, lopinavir/ritonavir. Low-flow oxygen therapies were mainly necessary for severe patients, some of who required high-flow oxygen administration and, a high proportion of critical patients were intubated and required vasopressor administration or dialysis. Please note that COVID-19-related medication (b) was dispensed after blood sample collection, so that is assumed the subsequent analysis are not biased due to exposure to medication at the time of blood collection In light of the promising results already provided by omic technologies in the search for predictive biomarkers of COVID-19 severity,4, 5 we conducted a nontargeted multi-omic, including proteomic, metabolomic and lipidomic analyses, in the serum from patients of the COVID-19 study cohort. The proteomics analysis identified 65 proteins with a significant increase or decrease in abundance according to the disease severity (Figure 2A), which resulted to be highly interconnected (Figure 2B). Hence, the complement and coagulation cascades were markedly the most significantly enriched pathways related to COVID-19 severity (Figure 2C). Other protein-coding genes such as carboxypeptidases, protease inhibitors, acute phase proteins, extracellular matrix stabilizers and antimicrobial enzymes, were also significantly up-regulated in severely and critically ill patients. These results showed the essential contribution of these proteins in the coagulopathy phenomenon and hyperinflammatory state that subsequently enhances SARS-CoV-2 endocytosis and infectivity and promotes secondary bacterial infections, previously described as aggravators of severe and critical COVID-19 cases.6 Proteins with reduced abundance in critically ill patients with COVID-19 were mostly associated with lipid transport (apolipoproteins), which dysfunction seems to increase SARS-CoV-2 infectivity in patients with COVID-19.7 For the first time, fetuin-A (AHSG) and inter-α-trypsin inhibitor 3 (ITIH3) were determined as the most accurate biomarkers (random forest) of the critical clinical progression of COVID-19 (Figure 2D). FIGURE 2Open in figure viewerPowerPoint Serum proteomics profile of coronavirus disease-2019 (COVID-19) study cohort. (A) Heatmap showing significant proteins increasing or decreasing in accordance with disease severity. Columns correspond to the degree of disease severity: mild (left), severe (centre) and critical (right) groups. Mean values for each compound in each coronavirus disease-2019 (COVID-19) group (columns) are colour-coded based on relative abundance, low (red) & high (green). Among the 65 significant proteins, 33 increased and 32 decreased with disease severity. (B) Up-(upper) and down-(bottom) regulated protein networks sorted by gene-name showing a tight interconnection within the up- and down-regulated proteins. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis sorted by pathway impact and -log10 (p). The interconnected up- and down-regulated genes were enriched in 77 pathways, and the 10 relevant pathways whose impact values were greater than 0.1 (p < .05) were further considered. The bubble diagram shows matched pathways according to the p-values and pathway impact values. The size of bubbles shows pathway impact value and the colour denote the level of significance by means of p-values. Numbers in circles correspond to the significantly enriched pathways ordered from the highest to the lowest pathway impact value. The number of matched proteins, impact value and p-value corresponding to each pathway are indicated on the inserted table. (D) Random forest analysis showing the 15 protein-encoding genes ranked by classification accuracy to distinguish between a mild and critical group of patients. Squares on the right represent the COVID-19 (1 = mild, 2 = severe and 3 = critical) and the colours depict the accuracy power (red and blue for high and low accuracy, respectively) The metabolomic and lipidomic analyses revealed 34 metabolites and 28 lipids that were significantly increased or decreased in relation to severity (Figure 3A). Interestingly, many of the altered metabolites were amino acids and sugars involved in central carbon metabolism. In line with previous reports,8, 9 critically ill patients showed a significant increase in glucose and glutamic acid (GA) levels but a reduction in glutamine, citrate and uric acid levels, suggesting mitochondrial dysfunction, an enhanced glutaminolysis and a shift from anaerobic to aerobic glycolysis (Warburg effect). Accordingly, D-glutamine and D-glutamate metabolism were the most significantly enriched pathways (Figure 3B, left panel), and were significantly related to seizures disorders, anoxia, heart failure, diabetes, obesity and inflammatory diseases (Figure 3B, right panel). Lipid levels that increased with severity were mainly triglycerides (TGs) and diacylglycerols, and those that decreased were predominantly sphingomyelins (SMs), cholesteryl esters (ChoEs) and lysophosphatidylcholines. Lipoproteins rich in TGs may trigger dysfunction in innate immunity and impair the defence mechanism against COVID-1910 and a reduced abundance of SMs and ChoEs may interfere in signal transduction and in key immune and cellular processes. Among them, GA and ChoE (18:0) resulted in the most powerful (random forest) predictive biomarkers for COVID-19 evolution (Figure 3C), confirmed by the prognosis accuracy determined by the receiver operating characteristic (ROC) analysis (Figure S2A–C, respectively). The highest accuracy was attained when combining both compounds in the distinction of mild from critical illnesses (Figure 2SD). To provide insights into the biological pathways related to the pathophysiology of the disease, we study the linkage and co-regulation between the distinct classes of biomolecules by integrating the most significant demographical and clinical data (Table S1 and Figure S1) and the top omic molecules determined above (Figure S3A,B) in Spearman correlation matrix analyses (Figure 4A1–3). Despite all three groups showing a similar association pattern for most of the variables analyzed, patients with mild illness (Figure 4A1) significantly differed from those of the severe and critical groups (Figure 4A2,3, respectively). In brief, significant correlations were obtained across the omic data, which were more intense between lipidomics than within the protein-encoding genes, and nearly negligible through metabolomics. The predictive power of the selected omics biomolecules as biomarkers for the severe disease was subsequently demonstrated by the high accuracy, sensitivity and specificity obtained by combining the four molecules in the ROC analysis (Figure 4B) to effectively distinguish critical COVID-19 patients from patients with mild disease (area under the curve [AUC] = 0.994). To precisely predict whether a patient will progress from severe to a life-threatening disease, not only the four but all the top-omic selected biomarkers need to be integrated into the ROC analysis (AUC = 0.811; Figure 4C). FIGURE 3Open in figure viewerPowerPoint Metabolomic and lipidomic profile of coronavirus disease-2019 (COVID-19) patients grouped by disease severity. (A) Heatmap plots significant relative abundance of metabolites & lipids increasing or decreasing in accordance with disease severity. Significant differences (p-values < .05) were determined by ANOVA test followed by post-hoc Bonferroni correction for mean relative abundance between mild, severe and critical COVID-19 groups of patients. Columns correspond to the degree of disease severity: mild (left), severe (centre) and critical (right) groups. Mean values for each compound in each COVID-19 group (columns) are colour-coded based on relative abundance, low (red) & high (green). (B) Metabolomic and lipidomic Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and related blood diseases in COVID-19 patient cohort. Functional metabolic enrichment pathway of 15 metabolites selected from random forest modelling (Fisher's exact test. p < .05) (left), and the corresponding enriched blood pathway diseases (right). (C) Random forest modelling of significant metabolites and lipids with the highest classification accuracy. Right-legend indicates the capacity of the compounds to differentiate groups of severity, blue (low) and red (high) FIGURE 4Open in figure viewerPowerPoint Evaluation of potential biomarkers to be a part of a panel to distinguish coronavirus disease-2019 (COVID-19) outcomes. (A) Heatmap showing the Spearman correlation coefficient of pairwise comparison between demographic, clinical and most enriched proteomic, metabolomic and lipidomic biomolecules determined in the blood of patients with mild (1), severe (2) and critical (3) COVID-19. Spearman matrices are colour-coded (-1:1, orange:purple through white) and correlations with p-values < .05 were considered statistically significant. (B) Receiver operating characteristic (ROC) curves analysis for the predictive power of top selected protein-encoding genes, lipids and metabolites in random forest analysis to differentiate patients with mild from those with a critical illness. (C) ROC curve analysis for the most enriched proteomic, metabolomic and lipidomic biomolecules to differentiate severe from critically-ill patients with COVID-19. (D) Binary logistic regression modelling analysis testing the accuracy of the four selected biomarkers to differentiate mild from critically ill patients with COVID-19 in a randomly selected set of patients Taking a step further, the inclusion of AHSG, ITIH, GA and ChoE (18:0) in a predictive biomarker panel for COVID-19 severity was validated in a randomly selected subset of patients. The regression modelling analysis confirmed the usefulness (classification accuracy >90%) of the biomarker panel in distinguishing mild to critical COVID-19 outcomes (Figure 4D). Once more, all these findings highlighted the complex interactions between certain biological processes and the most serious complications arising from SARS-CoV-2 infections and revealed their potential as predictive biomarkers of disease severity. Limitations are the small sample size to perform subgroup analyses and the lack of a non-infected SARS-CoV-2 group of subjects. However, this study was conducted in a representative symptomatic well-characterized Spanish cohort to determine predictive biomarkers of COVID-19 severity. In conclusion, the multi-omic analysis identified new specific molecules related to complement and coagulation cascades, platelet activation, cell adhesion, acute inflammation, energy production (Krebs cycle and Warburg effect), amino acid catabolism and lipid transport as fingerprints of the acute disease. A novel biomarker panel consisting of AHSG, ITIH3, GA and ChoE (18:0) was proposed for the accurate differentiation of mild from critical COVID-19 outcomes. ACKNOWLEDGEMENTS This study would not have been possible without the generous collaboration of all the patients and their families and medical and nursing staff who have taken part in the project. We want to particularly acknowledge the collaboration of the Departments of Preventive Medicine and Epidemiology, Internal Medicine, Critical Care, Emergency, Occupational Health, Laboratory Medicine and Molecular Biology, and BioBank-IISPV (B.0000853 and B.0000854) integrated into the Spanish National Biobanks Platform (PT20/00197) and CERCA Programme (Generalitat de Catalunya) and IISPV for their collaboration. We also thank Pol Herrero, Maria Guirro and Antoni del Pino from the Proteomics and Metabolomics facilities of the Centre for Omic Sciences (COS) Joint Unit of the Universitat Rovira i Virgili-Eurecat for their contribution to mass spectrometry analyses. FUNDING INFORMATION This work has been developed in the framework of the COVIDOMICS’ project supported by Direcció General de Recerca i Innovació en Salut (DGRIS), Departament de Salut, Generalitat de Catalunya (PoC-6-17 and PoC1-5). The research has also been funded by the Programa de Suport als Grups de Recerca AGAUR (2017SGR948), the SPANISH AIDS Research Network [RD16/0025/0006, RD16/0025/0007 and RD16/0025/0020]-ISCIII-FEDER (Spain), the Centro de Investigación Biomédica en Red de Enfermedades Infecciosas-ISCIII [CB21/13/00020], Madrid, Spain and Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades Junta de Andalucía (research Project CV20-85418). Elena Yeregui was supported by the Instituto de Salud Carlos III (ISCIII) under grant agreement “FI20/00118″ through the programme “Contratos Predoctorales de Formación en Investigación en Salud”. Laia Reverté was supported by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CD20/00105″ through the programme “Contratos Sara Borrell”. Francesc Vidal was supported by grants from the Programa de Intensificación de Investigadores (INT20/00031)-ISCIII and by “Premi a la Trajectòria Investigadora dels Hospitals de l'ICS 2018″. Anna Rull was supported by a grant from IISPV through the project “2019/IISPV/05″ (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores 2019″ and by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CP19/00146″ through the Miguel Servet Program. Maria José Buzón was supported by the Miguel Servet Program (CP17/00179). Ezequiel Ruiz-Mateos was supported by the Spanish Research Council (CSIC). Alicia Gutiérrez-Valencia was supported by the Instituto de Salud Carlos III, cofinanced by the European Development Regional Fund (“A way to achieve Europe”), Subprograma Miguel Servet (grant CP19/00159). This project was also funded by a donation from the city Council of Perafort (to Teresa Auguet). CONFLICT OF INTEREST The authors declare that they have no conflict of interest. Supporting Information Filename Description ctm2704-sup-0001-SuppMat.docx2.3 MB Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. REFERENCES 1 WHO. COVID-19 therapeutic trial synopsis. World Health Organization. 2020; 1- 9Google Scholar 2Domingo P, Mur I, Pomar V, et al. The four horsemen of a viral apocalypse: the pathogenesis of SARS-CoV-2 infection (COVID-19). EBioMedicine. 2020; 58:102887. https://doi.org/10.1016/J.EBIOM.2020.102887. CrossrefPubMedWeb of Science®Google Scholar 3Wang GQ, Zhao L, Wang X, Jiao YM, Wang FS. Diagnosis and treatment protocol for COVID-19 patients (tentative 8th edition). Infect Dis Immun. 2021; 1: 8- 16. https://doi.org/10.1097/01.id9.0000733564.21786.b0. Google Scholar 4Wang X, Xu G, Liu X, et al. Multiomics: unraveling the panoramic landscapes of SARS-CoV-2 infection. Cell Mol Immunol. 2021; 18: 2313- 2324. CrossrefCASPubMedWeb of Science®Google Scholar 5McArdle A, Washington KE, Chazarin Orgel B, et al. Discovery proteomicsfor COVID-19: where we are now. J Proteome Res. 2021; 20: 4627. https://doi.org/10.1021/ACS.JPROTEOME.1C00475. CrossrefCASPubMedWeb of Science®Google Scholar 6Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395: 1054- 1062. https://doi.org/10.1016/S0140-6736(20)30566-3. CrossrefCASPubMedWeb of Science®Google Scholar 7Wang H, Yuan Z, Pavel MA, et al. The role of high cholesterol in age-related COVID19 lethality. BioRxiv. 2020. https://doi.org/10.1101/2020.05.09.086249. Google Scholar 8Wu D, Shu T, Yang X, et al. Plasma metabolomic and lipidomic alterations associated with COVID-19. Nat Sci Rev. 2020; 7: 1157- 1168. https://doi.org/10.1093/NSR/NWAA086. CrossrefCASPubMedWeb of Science®Google Scholar 9Bharadwaj S, Singh M, Kirtipal N, Kang SG. SARS-CoV-2 and Glutamine: SARS-CoV-2 triggered pathogenesis via metabolic reprograming of glutamine in host cells. Front Mol Biosci. 2021; 7. https://doi.org/10.3389/fmolb.2020.627842. CrossrefPubMedWeb of Science®Google Scholar 10McKechnie JL, Blish CA. The innate immune system: fighting on the front lines or fanning the flames of COVID-19? Cell Host Microbe. 2020; 27: 863- 869. CrossrefCASPubMedWeb of Science®Google Scholar Volume12, Issue1January 2022e704 FiguresReferencesRelatedInformation

A significant proportion of people living with HIV (PLHIV) who successfully achieve virological suppression fail to recover CD4+ T-cell counts. Since adipose tissue has been discovered as a key immune organ, this study aimed to assess the role of adipokines in the HIV immunodiscordant response. This is a multicenter prospective study including 221 PLHIV starting the first antiretroviral therapy (ART) and classified according to baseline CD4+ T-cell counts/µL (controls > 200 cells/µL and cases ≤ 200 cells/µL). Immune failure recovery was considered when cases did not reach more than 250 CD4+ T cells/µL at 144 weeks (immunological nonresponders, INR). Circulating adipokine concentrations were longitudinally measured using enzyme-linked immunosorbent assays. At baseline, apelin receptor (APLNR) and zinc-alpha-2-glycoprotein (ZAG) concentrations were significantly lower in INRs than in immunological responders (p = 0.043 and p = 0.034), and they remained lower during all ART follow-up visits (p = 0.044 and p = 0.028 for APLNR, p = 0.038 and p = 0.010 for ZAG, at 48 and 144 weeks, respectively). ZAG levels positively correlated with retinol-binding protein 4 (RBP4) levels (p < 0.01), and low circulating RBP4 concentrations were related to a low CD4+ T-cell gain (p = 0.018 and p = 0.039 at 48 and 144 weeks, respectively). Multiple regression adjusted for clinical variables and adipokine concentrations confirmed both low APLNR and RBP4 as independent predictors for CD4+ T cells at 144 weeks (p < 0.001). In conclusion, low APLNR and RBP4 concentrations were associated with poor immune recovery in treated PLHIV and could be considered predictive biomarkers of a discordant immunological response.

Long-term elite controllers (LTECs) are a fascinating small subset of HIV individuals with viral and immunological HIV control in the long term that have been designated as models of an HIV functional cure. However, data on the LTEC phenotype are still scarce, and hence, the metabolomics and lipidomics signatures in the LTEC-extreme phenotype, LTECs with more than 10 years of viral and immunological HIV control, could be pivotal to finding the keys for functional HIV remission. Metabolomics and lipidomics analyses were performed using high-resolution mass spectrometry (ultra-high-performance liquid chromatography–electrospray ionization–quadrupole time of flight [UHPLC-(ESI) qTOF] in plasma samples of 13 patients defined as LTEC-extreme, a group of 20 LTECs that lost viral and/or immunological control during the follow-up study (LTEC-losing) and 9 EC patients with short-term viral and immunological control (less than 5 years; no-LTEC patients). Long-term viral and immunological HIV-1 control was found to be strongly associated with elevated tricarboxylic acid (TCA) cycle function. Interestingly, of the nine metabolites identified in the TCA cycle, α-ketoglutaric acid (p = 0.004), a metabolite implicated in the activation of the mTOR complex, a modulator of HIV latency and regulator of several biological processes, was found to be a key metabolite in the persistent control. On the other hand, a lipidomics panel combining 45 lipid species showed an optimal percentage of separation and an ability to differentiate LTEC-extreme from LTEC-losing, revealing that an elevated lipidomics plasma profile could be a predictive factor for the reignition of viral replication in LTEC individuals.

Nuclear magnetic resonance (NMR)-based advanced lipoprotein tests have demonstrated that LDL and HDL particle numbers (LDL-P and HDL-P) are more powerful cardiovascular (CV) risk biomarkers than conventional cholesterol markers. Of interest, in people living with HIV (PLHIV), predictors of preclinical atherosclerosis and vascular dysfunction may be associated with impaired immune function. We previously stated that immunological non-responders (INR) were at higher CV risk than immunological responders (IR) before starting antiretroviral therapy (ART). Using Liposcale® tests, we characterized the lipoprotein profile from the same cohort of PLHIV at month 12 and month 36 after starting ART, intending to explore what happened with these indicators of CV risk during viral suppression. ART initiation dissipates the differences in lipoprotein-based CV risk markers between INR and IR, and only an increase in the number of HDL-P was found in INR + IR when compared to controls (p = 0.047). Interestingly, CD4+ T-cell counts negatively correlated with medium HDL-P concentrations at month 12 in all individuals (ρ = −0.335, p = 0.003). Longitudinal analyses showed an important increase in LDL-P and HDL-P at month 36 when compared to baseline values in both IR and INR. A proper balance between a proatherogenic and atherogenic environment may be related to the reconstitution of CD4+ T-cell count in PLHIV.

People living with HIV (PLWH) who are able to maintain suppressed viral load (VL) for years in the absence of antiretroviral therapy (ART) are known as elite controllers (ECs). ECs represent a heterogeneous population in terms of virological, immunological, and clinical outcomes, and approximately 25% of ECs lose viral control overtime. The study of the mechanisms leading to the loss of viral control in ECs is crucial for the identification of differential markers for the design of novel eradication and immunotherapeutic strategies. Previously, we identified virological and immunological factors involved in the spontaneous loss of viral control,1 and we also demonstrated that proteomics and metabolomics are powerful tools to identify potential biomarkers and therapeutic targets in ECs.2, 3 Additionally, genome-wide associations and transcriptome analyses have also been described for ECs and compared to other phenotypes of PLWH,4, 5 in particular the study of specific microRNA (miRNA) expression profiles.5 miRNAs play vital roles in development, apoptosis, and oncogenesis by interfering with gene expression at the post-transcriptional level.6 In HIV/AIDS scenario, the most relevant feature is that some miRNAs can modulate HIV replication by directly targeting HIV RNA or targeting messenger RNA (mRNA) of cell factors necessary for HIV replication.7 Our study conducted in 18 ECs (Figure S1, Table S1), 12 individuals who experienced a loss of spontaneous viral HIV-1 control (transient controllers, TCs) and six ECs who persistently maintained viral control during the same follow-up period (persistent controllers, PCs), showed an upregulated plasma miRNA profile in TCs before and after the loss of viral control. First, 23 miRNAs were found differentially expressed in TCs compared to PCs at the preloss time point (Figure 1A, Table S2). From the 23 miRNAs, 22 miRNAs were positively correlated with viral load (VL), seven miRNAs were positively correlated with CD4+ T-cell counts, and 11 miRNAs were positively correlated with CD8+ T-cell counts (Table S3). Interestingly, the spontaneous loss of viral control in ECs can be defined by the expression of hsa-miR-27a-3p, hsa-miR-376a-3p, and hsa-miR-199a-3p (Figure 1B), as confirmed by the diagnostic accuracy determined by the ROC analysis (Figure 1C). Notably, hsa-miR-27a-3p, hsa-miR-376a-3p, and hsa-miR-199a are tightly connected and related to lipid metabolism (Figure 1D). Then, we also evaluated the plasma miRNA profile in TCs under the postloss condition and we found 38 miRNAs differentially expressed among groups, suggesting that viremia strongly influences the plasma miRNA profile of ECs (Figure 2A, Table S4). And again, these significantly expressed miRNAs among groups were also related to relevant genes linked to lipid pathways (Figure 2B), and some of them positively correlated with VL, CD4+ T-cell counts and CD8+ T-cell counts (Table S5). Moreover, of the 23 miRNAs significantly differentially expressed under the preloss condition and the 38 miRNAs significantly differentially expressed under the postloss condition, only the upregulation of 19 miRNAs overlapped (Figure 3A). Noteworthy, the expression of hsa-miR-199a-3p showed an optimal percentage of separation and an ability to differentiate between both groups of ECs before and after the loss of viral control (Figure 3B). Accordingly, the upregulation of hsa-miR-199a-3p could be related to lipid dysregulation in TCs, which in turn may potentiate the activation of a cytokine deregulation1 and in last term bias the virological control in TCs. Thus, our results confirmed differences in the expression of some miRNAs with target sites located in viral RNA regions encoding viral accessory proteins, suggesting the emerging concept that upregulated host-derived miRNAs in TCs might act as antiviral defence mechanism directly affecting important steps during HIV infection and also playing a key role in HIV immune pathogenesis. Notably, hsa-miR-423-3p targets the gag gene, hsa-miR-29a/29b and hsa-miR-326 target the nef gene, and hsa-miR-324-5p targets the vif gene6, 8 (Figure 4). One mechanism associated to the inhibitory effect of HIV replication could be the interaction of nef with the RISC complex, which leads to the inhibition of the translation of viral proteins and viral replication. On the other hand, Nef-containing exosomes, taken up by macrophages, have been suggested to impair lipid cholesterol efflux, causing intracellular cholesterol accumulation, which consequently affects the risk of cardiovascular diseases in PLWH.9, 10 Several miRNAs can regulate different steps of HDL-C metabolism, and recent studies have promoted the importance of miRNAs in controlling LDL metabolism and in the regulation of genes involved in very low-density lipoprotein (VLDL) secretion. Our results are in accord with these data since the LDL metabolism resulted to be increased in TCs under the postloss condition (Figure S2 and Figure S3).2 Additionally, some of the most representative host-derived miRNAs associated with the spontaneous loss of viral control overtime are liver-specific miRNAs, being implicated in fatty acid and cholesterol biosynthesis. Thus, our results suggest that disturbance in lipoprotein levels, mostly induced by upregulation of some liver-specific miRNAs, may be highly associated with the immunological factors behind the loss of viral control in TCs. It is known that HIV infection is characterized by a high energy demand to reprogram the cells to aerobic glycolytic pathways that consequently may increase anabolic metabolism.2 So, the increased lipid profile in TCs before the loss of viral control could be a consequence of the antiviral defence mechanism of the host against viral replication. The main limitation of this work is the small sample size. However, it must be highlighted that these patients are rare, and it is difficult to have a follow-up with sequentially stored samples. Despite the relatively low number of patients, we were able to have a tight follow-up of TCs with samples before and after the loss of virological control. Validation studies are needed to establish the proposed miRNAs as biomarkers for the loss of viral control in ECs. In conclusion, our study reveals a specific host-derived miRNA pattern in ECs that may be used as a biomarker for quick screening of the virological and immunological progression in ECs, and we confirmed that viremia induces increased LDL metabolism in TCs. Notably, the expression of liver-specific hsa-miR-199a-3p showed an optimal percentage of separation and an ability to differentiate between both groups of ECs before and after the loss of viral control. This study would not have been possible without the collaboration of all the patients and medical and nursing staff who took part in the project. These results were partially presented during the Virtual Conference on Retroviruses and Opportunistic Infections (CROI, 2021, Abstract number 2037). We also acknowledge the Biobanc IISPV (B.0000853 + B.0000854) integrated into the Biobanks and Biomodels Platform (PT20/00197) for its collaboration and CERCA Programme (Generalitat de Catalunya), ECRIS integrated in the Spanish AIDS Research Network (Annex S1), and BiosferTeslab, a spin-off company of the Rovira i Virgili University (URV) and the Pere Virgili Health Research Institute (IISPV). We also thank the comments and criticisms of the anonymous reviewers that greatly helped to improve the manuscript. Francesc Vidal is supported by grants from the Programa de Intensificación de Investigadores (INT20/00031)-ISCIII. Ezequiel Ruiz-Mateos was supported by Consejo Superior de Investigaciones Cientificas (CSIC). María del Carmen Gasca-Capote and María Reyes Jimenez-Leon were supported by ISCIII-FEDER, PFIS programme, FI19/00083 and FI17/00186, respectively. Anna Rull is supported by IISPV through the project “2019/IISPV/05″ (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores 2019,” and by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CP19/00146″ through the Miguel Servet Program. Jenifer Masip is supported by the Universitat Rovira i Virgili under grant agreement “2019PMF-PIPF-18” through the call “Martí Franquès Research Fellowship Programme.” Norma Rallón is a Miguel Servet II investigator supported by a grant from the Instituto de Salud Carlos III (ISCIII) “CPII19/00025.” All research protocols were approved and carried out according to the recommendations of the Ethical Committee for Clinical Research following the rules of Good Clinical Practice from the Institut d'Investigació Sanitària Pere Virgili (CEIm IISPV). The CEIM IISPV is an independent committee, made up of health and non-health professionals, which supervises the correct compliance of the ethical principles governing clinical trials and research projects that are carried out in our region, specifically in terms of methodology, ethics, and laws. All participants gave written informed consent in agreement with the Declaration of Helsinki. Fondo de Investigación Sanitaria-ISCIII-FEDER: PI13/0796, PI16/00503, PI16/0684, PI18/1532, PI19/01127, PI19/01337, PI20/00326, RD16/0025/0006, RD16/0025/0020, INT20/00031, FI19/00083, FI17/00186, CP19/00146, CPII19/00025. European Regional Development Fund/European Social Fund; Programa de Suport als Grups de Recerca AGAUR, Grant Number: 2017SGR948; Gilead Fellowship Program, Grant Number: GLD14/293; SPANISH AIDS Research Network, Grant Numbers: RD16/0025/0006, RD16/0025/0020 (ISCIII-FEDER, Spain). Programa de Intensificación de Investigadores, Grant Number: INT20/00031; Consejo Superior de Investigaciones Cientificas (CSIC); ISCIII-FEDER, PFIS programme, Grant Numbers: FI19/00083, FI17/00186; IISPV: 2019/IISPV/05 (Boosting Young Talent); GeSIDA “III Premio para Jóvenes Investigadores 2019″; Instituto de Salud Carlos III (ISCIII), Grant Number: CP19/00146; Universitat Rovira i Virgili, Grant Number: 2019PMF-PIPF-18; Instituto de Salud Carlos III (ISCIII), Grant Number: CPII19/00025 All authors reviewed and approved the submitted version of the manuscript. Experimental design: Jenifer Masip, Carmen Gasca-Capote, Ezequiel Ruiz-Mateos, and Anna Rull. Intellectual guidance: Consuelo Viladés, Joaquim Peraire, Francesc Vidal, Anna Rull, and Ezequiel Ruiz-Mateos. Recruitment of participants: Jenifer Masip, Ana-Irene Malo, Lorna Leal, Carmen Rodríguez Martín, Norma Rallón, Consuelo Viladés, Joaquim Peraire, Montserrat Olona, and Francesc Vidal. Sample procurement: Jenifer Masip and Verónica Alba. Data collection: Ezequiel Ruiz-Mateos, Ana-Irene Malo, Lorna Leal, Carmen Rodríguez Martín, Norma Rallón, Consuelo Viladés, Joaquim Peraire, and Montserrat Olona. Data analysis and interpretation: Jenifer Masip, María del Carmen Gasca-Capote, María Reyes Jimenez-Leon, Alberto Perez-Gomez, Ezequiel Ruiz-Mateos, and Anna Rull. Manuscript preparation: Jenifer Masip, María del Carmen Gasca-Capote, Ezequiel Ruiz-Mateos, and Anna Rull. Study design, data analysis, and article development: Jenifer Masip, Anna Rull, Ezequiel Ruiz-Mateos, and Francesc Vidal. Reviewed and edited the manuscript: Francesc Vidal, Anna Rull, and Ezequiel Ruiz-Mateos. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

BACKGROUND Early combined antiretroviral treatment (cART) in perinatally acquired HIV-1 children has been associated with a rapid viral suppression, small HIV-1 reservoir size and reduced mortality and morbidity. Immunometabolism has emerged as an important field in HIV-1 infection offering both relevant knowledge regarding immunopathogenesis and potential targets for therapies against HIV-1. OBJECTIVES To characterize the proteomic, lipidomic and metabolomic profile of HIV-1-infected children depending on their age at cART initiation. PATIENTS AND METHODS Plasma samples from perinatally HIV-1-infected children under suppressive cART who initiated an early cART (first 12 weeks after birth, EARLY, n = 10) and late cART (12-50 weeks after birth, LATE, n = 10) were analysed. Comparative plasma proteomics, lipidomics and metabolomics analyses were performed by nanoLC-Orbitrap, UHPLC-qTOF and GC-qTOF, respectively. RESULTS Seven of the 188 proteins identified exhibited differences comparing EARLY and LATE groups of HIV-1-infected children. Despite no differences in the lipidomic (n = 115) and metabolomic (n = 81) profiles, strong correlations were found between proteins and lipid levels as well as metabolites, including glucidic components and amino acids, with clinical parameters. The ratio among different proteins showed high discriminatory power of EARLY and LATE groups. CONCLUSIONS Protein signature show a different proinflammatory state associated with a late cART introduction. Its associations with lipid levels and the relationships found between metabolites and clinical parameters may potentially trigger premature non-AIDS events in this HIV-1 population, including atherosclerotic diseases and metabolic disorders. Antiretroviral treatment should be started as soon as possible in perinatally acquired HIV-1-infected children to prevent them from future long-life complications.

ABSTRACT: Background: The underlying mechanisms of incomplete immune reconstitution in treated HIV-positive patients are very complex and may be multifactorial, but perturbation of chemokine secretion could play a key role in CD4+ T-cell turnover. Methods: We evaluated the circulating baseline and 48-week follow-up concentrations of SDF-1/CXCL12, fractalkine/CX3CL1, MCP-1/CCL2, MIP-α/CCL3, MIP-β/CCL4 and RANTES/CCL5, and we estimated their association with CXCL12, CX3CR1, CCR2, CCL5 and CCR5 single nucleotide polymorphisms (SNPs) to investigate multiple chemokine-chemokine receptor signatures associated with immune dysregulation preceding poor immune recovery. Findings: The circulating concentrations and gene expression patterns of SDF-1/CXCL12 (CXCL12 rs1801157) and MCP-1/CCL2 (CCR2 rs1799864_814) were associated with immune recovery status. CCR2 rs1799864_814 and CCR5 rs333_814 (Δ32) determine the baseline plasma RANTES and MIP-α concentrations, respectively, in participants with poor immune response. Interpretation: SDF-1/CXCL12 and MCP-1/CCL2 could be considered prognostic markers of immune failure despite suppressive antiretroviral therapy. The strong linkage disequilibrium (LD) between CCR2 rs1799864_814 and CCR5 rs1800024 indicated that the alleles of each gene are inherited together more often than would be expected by chance. Funding: This work was supported by Fondo de Investigacion Sanitaria and SPANISH AIDS Research Network (ISCIII-FEDER); AGAUR and Gilead Fellowship. FV and YMP are supported by grants from the Programa de Intensificación (ISCIII) and Servicio Andaluz de Salud, respectively. JVG,EY and LR are supported by the Instituto de Salud Carlos III (ISCIII). AR is supported by Departament de Salut, Generalitat de Catalunya and by the Instituto de Salud Carlos III (ISCIII).

Objective: To determine the proportion of people infected by HIV or AIDS under follow-up in the VACH Cohort in 2012 who were lost to follow-up from 2013 to 2014, and to establish the sociodemographic features relating to this loss. Methods: We considered subjects with less than one recorded consultation per year studied to be lost to follow-up. We built logistic regression models to calculate the odds ratios (OR) and their 95% confidence intervals (95% CI), of the variables relating to loss to follow-up. Results: The overall percentage of losses to follow-up was 15.5% (95% CI 14.9-16-1). The variables associated with loss to follow up were: not receiving antiretroviral treatment (ART) (OR: 1.948, 95% CI: 1.651-2.298), being an immigrant (OR: 1.746; 95%Cl: 1.494-2.040), intravenous drug consumption being the mechanism for HIV transmission (OR: 1.498, 95% CI: 1.312-1.711), being unemployed (OR: 1.331; 95% CI: 1.179-1.503), being without a partner (OR: 1.948, 95% CI: 1.651-1.298), belonging to a low socioeconomic class (OR: 1.279; 95% Cl: 1.143-1.431), and being attended in a hospital with fewer than 1000 patients under follow-up (OR: 1.257, 95% CI: 1.121-1.457), as well as being under age and having spent less time under follow-up in the Cohort. Conclusions: 15.5% of the patients were lost to follow-up over a period of 2 years in the VACH Cohort. This was associated with a series of sociodemographic and epidemiological variables that it might be useful to identify to design initiatives targeting the populations most likely to abandon the circuits of care, and guide strategies towards achieving Objective 90-90-90. (C) 2018 Published by Elsevier Espana, S.L.U.

A relationship between polymorphisms in genes encoding interleukin 7 (IL-7) and its cellular receptor (IL-7R) and antiretroviral therapy (ART)-associated immune recovery in HIV subjects has been previously reported. However, details of this relationship remain unclear, and the association of these polymorphisms with circulating IL-7/IL-7R levels is scarce. Here, we explored whether IL-7/IL-7R axis was associated with quantitative CD4+ T-cell recovery in HIV-infected subjects. IL-7/IL-7R polymorphisms were assessed by genotyping, and multiple inheritance models were used to estimate both, their association with low pre-ART CD4+ T-cell counts and incomplete immune recovery status after 48 weeks of suppressive ART. Integrated data from genetic variants association and soluble plasma IL-7/IL-7R quantification suggest that IL-7/IL-7R genotype expression could alter the homeostatic balance between soluble and membrane-bound receptors. The haplotype analyses indicates that allele combinations impacts pre-ART circulating CD4+ T-cell counts, immune recovery status and the absolute increment of CD4+ T-cell counts. The knowledge about how IL-7/IL-7R axis is related to quantitative CD4+ T-cell recovery and immune recovery status after initiating ART could be useful regarding T-cell reservoirs investigations in HIV subjects.

The immunological, biochemical and molecular mechanisms associated with poor immune recovery are far from known, and metabolomic profiling offers additional value to traditional soluble markers. Here, we present novel and relevant data that could contribute to better understanding of the molecular mechanisms preceding a discordant response and HIV progression under suppressive combined antiretroviral therapy (cART). Integrated data from nuclear magnetic resonance (NMR)-based lipoprotein profiles, mass spectrometry (MS)-based metabolomics and soluble plasma biomarkers help to build prognostic and immunological progression tools that enable the differentiation of HIV-infected subjects based on their immune recovery status after 96 weeks of suppressive cART. The metabolomic signature of ART-naïve HIV subjects with a subsequent late immune recovery is the expression of pro-inflammatory molecules and glutaminolysis, which is likely related to elevate T-cell turnover in these patients. The knowledge about how these metabolic pathways are interconnected and regulated provides new targets for future therapeutic interventions not only in HIV infection but also in other metabolic disorders such as human cancers where glutaminolysis is the alternative pathway for energy production in tumor cells to meet their requirement of rapid proliferation.

ObjectivesThe aim of the study was to assess the rates of discontinuation of integrase inhibitor regimens because of any neuropsychiatric adverse event (NPAE) and the factors associated with discontinuation.MethodsA population‐based, prospective, multicentre cohort study was carried out. Treatment‐naïve subjects starting therapy with a regimen containing integrase inhibitors, or those switching to such a regimen, with plasma HIV‐1 RNA < 50 HIV‐1 RNA copies/mL in 14 hospitals in Catalonia or the Balearic Islands (Spain) were included in the study. Every discontinuation because of adverse events (AEs) was double‐checked directly with treating physicians. Multivariable Cox models identified factors correlated with discontinuation.ResultsA total of 4165 subjects (37% treatment‐naïve) started regimens containing dolutegravir (n = 1650; 91% with abacavir), raltegravir (n = 930) or elvitegravir/cobicistat (n = 1585). There were no significant differences among regimens in the rate of discontinuation because of any AE. Rates of discontinuation because of NPAEs were low but higher for dolutegravir/abacavir/lamivudine [2.1%; 2.9 (95% confidence interval (CI) 2.0, 4.2) discontinuations/100 patients/year] versus elvitegravir/cobicistat (0.5%; 0.8 (95% CI 0.3, 1.5) discontinuations/100 patients/year], with significant differences among centres for dolutegravir/abacavir/lamivudine and NPAEs (P = 0.003). We identified an association of female gender and lower CD4 count with increased risk of discontinuation because of any AE [Incidence ratio (IR) 2.3 (95% CI 1.4, 4.0) and 1.8 (95% CI 1.1, 2.8), respectively]. Female gender, age > 60 years and abacavir use were not associated with NPAE discontinuations. NPAEs were commonly grade 1–2, and had been present before and improved after drug withdrawal.ConclusionsIn this large prospective cohort study, patients receiving dolutegravir, raltegravir or elvitegravir/cobicistat did not show significant differences in the rate of discontinuation because of any toxicity. The rate of discontinuations because of NPAEs was low, but was significantly higher for dolutegravir than for elvitegravir/cobicistat, with significant differences among centres, suggesting that greater predisposition to believe that a given adverse event is caused by a given drug of some treating physicians might play a role in the discordance seen between cohorts.

BACKGROUNDElite controllers (ECs) spontaneously control plasma human immunodeficiency virus type 1 (HIV-1) RNA without antiretroviral therapy. However, 25% lose virological control over time. The aim of this work was to study the proteomic profile that preceded this loss of virological control to identify potential biomarkers.METHODSPlasma samples from ECs who spontaneously lost virological control (transient controllers [TCs]), at 2 years and 1 year before the loss of control, were compared with a control group of ECs who persistently maintained virological control during the same follow-up period (persistent controllers [PCs]). Comparative plasma shotgun proteomics was performed with tandem mass tag (TMT) isobaric tag labeling and nanoflow liquid chromatography coupled to Orbitrap mass spectrometry.RESULTSEighteen proteins exhibited differences comparing PC and preloss TC timepoints. These proteins were involved in proinflammatory mechanisms, and some of them play a role in HIV-1 replication and pathogenesis and interact with structural viral proteins. Coagulation factor XI, α-1-antichymotrypsin, ficolin-2, 14-3-3 protein, and galectin-3-binding protein were considered potential biomarkers.CONCLUSIONSThe proteomic signature associated with the spontaneous loss of virological control was characterized by higher levels of inflammation, transendothelial migration, and coagulation. Galectin-3 binding protein could be considered as potential biomarker for the prediction of virological progression and as therapeutic target in ECs.