OBJECTIVES:We assessed the effect of early ART during acute HIV infection on reservoir dynamics, cytokine profile, and T-cell metabolism. METHODS:We studied a longitudinal cohort of PWH starting ART during early (ET) or chronic (CT) infection, and a cross-sectional cohort including matched ET and CT participants (≥36 months virologically suppressed) and HIV-negative controls. We analysed total HIV DNA, intact and defective proviruses, cell-associated HIV RNA, plasma cytokines, and metabolomic profiles of CD4+ and CD8+ T-cells. RESULTS:Over 75% of ET participants started ART in Fiebig stages IV-VI. Early ART was associated with lower total HIV DNA and cell-associated RNA. Although intact proviruses were similar between groups, they represented a larger proportion of the reservoir in ET participants. Worse pre-ART immune status correlated with a larger and more transcriptionally active reservoir. Regulatory, inflammatory, and homeostatic cytokines negatively correlated with the intact reservoir, particularly in CT participants. Metabolomic profiling of T-cells demonstrated ART timing-dependent alterations in several metabolic pathways. Metabolites involved in glycolysis, amino-acid metabolism, and polyol pathways positively correlated with HIV transcription in CD4⁺ T-cells, especially in CT participants. CONCLUSION:Early ART limits the HIV reservoir size, shapes its composition, and influences immunometabolic pathways, though it might not be enough to reduce the intact reservoir.

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.

While the general immune response to Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) is well-understood, the long-term effects of Human Immunodeficiency Virus-1/Severe Acute Respiratory Syndrome-Coronavirus-2 (HIV-1/SARS-CoV-2) co-infection on the immune system remain unclear. This study investigates the immune response in people with HIV-1 (PWH) co-infected with SARS-CoV-2 to understand its long-term health consequences. A retrospective longitudinal study of PWH with suppressed viral load and SARS-CoV-2 infection was conducted. Cryopreserved peripheral blood mononuclear cells and plasma samples were collected at three time-points: HIV-1/pre-SARS-CoV-2 (n = 18), HIV-1/SARS-CoV-2 (n = 46), and HIV-1/post-SARS-CoV-2 (n = 36). Plasma levels of 25 soluble cytokines and chemokines, and anti-S/anti-N-IgG-SARS-CoV-2 antibodies were measured. Immunophenotyping of innate and adaptive immune components and HIV-1 and SARS-CoV-2-specific T/B-cell responses were assessed by flow cytometry. HIV-1/SARS-CoV-2 co-infection was associated with long-lasting immune dysfunction, characterized by elevated levels of pro-inflammatory cytokines and a decrease in the MIG-IP10-ITAC chemokine axis at the HIV/SARS-CoV-2 time-point, which persisted one year later. Additionally, alterations in the distribution of subsets and increased activation (NKG2D/NKG2C) and maturation (TIM3) markers of NK and dendritic cells were observed at the HIV-1/SARS-CoV-2 time-point, persisting throughout the study. Effector memory CD4 T-cell subsets were decreased, while exhaustion/senescence (PD1/TIM3/CD57) markers were elevated at all three time-points. SARS-CoV-2-specific T/B-cell responses remained stable throughout the study, while HIV-1-specific T-cell responses decreased at the HIV-1/SARS-CoV-2 time-point and remained so. Persistent immune dysfunction in HIV-1/SARS-CoV-2 co-infection increases the risk of future complications, even in PWH with mild symptoms. Exacerbated inflammation and alterations in immune cells may contribute to reduce vaccine efficacy and potential reinfections.

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

Hypertension is one of the most common risk factors for COVID-19 clinical progression. The identification of plasma biomarkers for anticipating worse clinical outcomes and to better understand the shared mechanisms between hypertension and COVID-19 are needed. A hypothesis-generating study was designed to compare plasma proteomics and metabolomics between 22 hypertensives (HT) and 41 non-hypertensives (nHT) patients with the most unfavorable COVID-19 progression. A total of 43 molecules were significantly differed between HT (n = 22) and nHT (n = 41). Random Forest (RF) analysis identified myo-inositol, gelsolin and phosphatidylcholine (PC) 32:1 as the top molecules for distinguishing between HT and nHT. Plasma myo-inositol and gelsolin were higher (P = 0.03 and P = 0.02, respectively) and plasma PC 32:1 was lower (P = 0.03) in HT compared to nHT. Biological processes like stress response and blood coagulation, along with KEGG pathways including ascorbate and aldarate metabolism (P = 0.021) and linoleic acid metabolism (P = 0.028), were altered in hypertensive patients with the most unfavorable COVID-19 progression. There is a clear link between hypertension and severe COVID-19. Key biological pathways to consider for improving the prognosis and quality of life of hypertensive patients who become infected with SARS-CoV-2 include oxidative stress, ascorbate and aldarate metabolism, lipid metabolism, immune system and inflammation.

Background:People living with chronic HIV (PLWH) show immune dysfunction, despite viral suppression and normal CD4 recovery, particularly those with low CD4/CD8 ratios. Subjacent cellular alterations of such a reliable marker of clinical progression remain elusive. Methods:Categorization by CD4/CD8 ratio after three year of therapy (R < 0.8/R > 1.2, n = 28/n = 24) and post-hoc reclassification by nadir-CD4 (N ≤ 350/N > 350) were performed in PLWH achieving viral suppression and CD4 ≥ 500. CD4 T cell-associated viral reservoir, as well as metabolism-related gene expression, glucose uptake ability, relative telomere length (RTL), and thymic output for CD4 and CD8 T cells, were determined. Results:Patients with a CD4/CD8 ratio < 0.8 exhibited reduced CD8 T-cell glucose uptake ability after stimulation (p = 0.007) and trends to shorter RTL (p = 0.093) and to larger CD4-associated viral reservoir (p = 0.068) than R > 1.2. Differently, patients with nadir ≤350 exhibited altered CD4 and CD8 T-cell expression of metabolism-related genes, although no differences in glucose uptake ability, and shorter RTL in both cell subsets, but similar viral reservoir to patients with nadir >350. Remarkably, viral reservoir and both CD4 and CD8 thymic output showed inverse associations (r = -0.623, p = 0.01 and r = -0.661, p = 0.038, respectively). Conclusion:A low CD4/CD8 ratio in chronic PLWH stands on a larger viral reservoir in CD4 T cells and metabolic alterations in CD8 T cells, probably related to its exhaustion and compromised effector functionality, and thymic output could contribute to such alterations. Patients with lower nadir-CD4 showed a resting-like CD4 phenotype and a metabolically active CD8 subset, without further viral reservoir extension. Persistence of low CD4/CD8 ratio and low nadir-CD4 counts seems to rely on different immune damage.

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.

Background Persistence of a low CD4/CD8 ratio is associated with an increased morbimortality in people living with HIV (PLWH) under effective antiretroviral therapy. We aimed to explore the immunological significance of a persistently low CD4/CD8 ratio, even despite normal CD4 levels, and assess whether these features vary from those associated to a low nadir-CD4, another well-established predictor of disease progression. Methods CD4-recovered PLWH were classified by CD4/CD8 ratio after three-years of ART (viral suppression, CD4≥500; R<0.8, n=24 and R>1.2, n=28). sj/β-TRECs ratio and inflammatory-related markers were quantified. PBMCs were immunophenotyped by CyTOF and functionally characterized by ELISPOT. Subjects were also reclassified depending on nadir-CD4 (N≤350/N>350). Results R<0.8 showed a differential inflammatory profile compared to R>1.2 (increased β2-microglobulin, D-dimers and IP-10 before ART). R<0.8 presented lower baseline thymic function, being inversely correlated with post-ART inflammation. R<0.8 at follow-up showed most alterations in CD8 subsets (increasing frequency and exhibiting a senescent phenotype [e.g., CD57+, CD95+]) and enhanced T-cell IFNγ/IL-2 secretion. However, comparing N≤350 to N>350, the main features were altered functional markers in CD4 T-cells, despite no differences in maturational subsets, together with a restricted T-cell cytokine secretion pattern. Conclusion Persistence of low CD4/CD8 ratio in successfully-treated PLWH, with normal CD4 counts, is associated with baseline inflammation and low thymic function, and it features post-therapy alterations specific to CD8 T-cells. Differently, subjects recovered from low nadir-CD4 in this setting feature post-therapy alterations on CD4 T-cells. Hence, different mechanisms of disease progression could underlie these biomarkers, potentially requiring different clinical approaches.

Abstract Background COVID-19 pneumonia causes hyperinflammatory response that culminates in acute respiratory syndrome (ARDS) related to increased multiorgan dysfunction and mortality risk. Antiviral-neutralizing immunoglobulins production reflect the host humoral status and illness severity, and thus, immunoglobulin (Ig) circulating levels could be evidence of COVID-19 prognosis. Methods The relationship among circulating immunoglobulins (IgA, IgG, IgM) and COVID-19 pneumonia was evaluated using clinical information and blood samples in a COVID-19 cohort composed by 320 individuals recruited during the acute phase and followed up to 4 to 8 weeks (n = 252) from the Spanish first to fourth waves. Results COVID-19 pneumonia development depended on baseline Ig concentrations. Circulating IgA levels together with clinical features at acute phase was highly associated with COVID-19 pneumonia development. IgM was positively correlated with obesity (ρb = 0.156, P = 0.020), dyslipemia (ρb = 0.140, P = 0.029), COPD (ρb = 0.133, P = 0.037), cancer (ρb = 0.173, P = 0.007) and hypertension (ρb = 0.148, P = 0.020). Ig concentrations at recovery phase were related to COVID-19 treatments. Conclusions Our results provide valuable information on the dynamics of immunoglobulins upon SARS-CoV-2 infection or other similar viruses.

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.

[This corrects the article DOI: 10.1371/journal.pone.0269875.].

BackgroundThe pathological mechanisms of SARS-CoV-2 in humans remain unclear and the unpredictability of COVID-19 progression may be attributed to the absence of biomarkers that contribute to the prognosis of this disease. Therefore, the discovery of biomarkers is needed for reliable risk stratification and to identify patients who are more likely to progress to a critical stage.MethodsAiming to identify new biomarkers we analysed N-glycan traits in plasma from 196 patients with COVID-19. Samples were classified into three groups according to their severity (mild, severe and critical) and obtained at diagnosis (baseline) and at 4 weeks of follow-up (postdiagnosis), to evaluate their behaviour through disease progression. N-glycans were released with PNGase F and labelled with Rapifluor-MS, followed by their analysis by LC-MS/MS. The Simglycan structural identification tool and Glycostore database were employed to predict the structure of glycans.ResultsWe determined that plasma from SARS-CoV-2-infected patients display different N-glycosylation profiles depending on the disease severity. Specifically, levels of fucosylation and galactosylation decreased with increasing severity and Fuc1Hex5HexNAc5 was identified as the most suitable biomarker to stratify patients at diagnosis and distinguish mild from critical outcomes.ConclusionIn this study we explored the global plasma glycosignature, reflecting the inflammatory state of the organs during the infectious disease. Our findings show the promising potential of glycans as biomarkers of COVID-19 severity.

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.

The metabolic alterations caused by SARS-CoV-2 infection reflect disease progression. To analyze molecules involved in these metabolic changes, a multiomics study was performed using plasma from 103 patients with different degrees of COVID-19 severity during the evolution of the infection. With the increased severity of COVID-19, changes in circulating proteomic, metabolomic, and lipidomic profiles increased. Notably, the group of severe and critical patients with high HRG and ChoE (20:3) and low alpha-ketoglutaric acid levels had a high chance of unfavorable disease evolution (AUC = 0.925). Consequently, patients with the worst prognosis presented alterations in the TCA cycle (mitochondrial dysfunction), lipid metabolism, amino acid biosynthesis, and coagulation. Our findings increase knowledge regarding how SARS-CoV-2 infection affects different metabolic pathways and help in understanding the future consequences of COVID-19 to identify potential therapeutic targets.

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.

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. 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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.