HDL proteome and apolipoproteins concentrations in severe ICU COVID-19 patients

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The severity of the disease has been associated with reduced levels of both high-density lipoprotein (HDL) and low-density lipoprotein cholesterol. Despite the crucial role that these nanoparticles play in SARS-CoV-2 infection, few studies have examined their structure during COVID-19 beyond HDL quantity. The study aimed to assess apolipoprotein levels in COVID-19 patients who either survived or died following ICU admission. In addition, ICU survivors and non-survivors were compared for HDL particle size and proteome. Methods : Between February and April 2020, our study enrolled 37 COVID-19 patients upon their intensive care unit admission. Among them, 18 survived the disease, while 19 succumbed to it. We used mass spectrometry to assess plasma levels of 14 apolipoproteins and LCAT. Additionally, we analyzed HDL subpopulation distribution by utilizing native polyacrylamide gel electrophoresis. HDL particles were isolated from both surviving and non-surviving patients using ultracentrifugation, followed by characterization of their proteomes with NanoLC-MS/MS. Results : Plasma apolipoproteins, including Apo A-II, Apo Cs (I, II, III), Apo H, Apo J, Apo M, and LCAT, were decreased in patients who did not survive COVID-19. However, no alterations were noted in the distribution of HDL subpopulations in relation to mortality. HDL composition was further altered based on mortality, displaying a decline in Apo H and paraoxonase 3. Conclusion : In conclusion, we have shown an alteration in plasma apolipoproteins and HDL composition between surviving COVID-19 patients and non-survivors. Some markers, such as Apo H, are more predictive than baseline lipid concentrations such as HDL-C. COVID-19 mortality HDL particle apolipoproteome apolipoprotein proteomic Intensive care unit Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China in December 2019, is caused by a Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and has caused more than 6 million deaths worldwide [ 1 , 2 ]. COVID-19 presents a broad range of symptoms and gravity, varying from ambulatory mild disease (including asymptomatic forms) to severe cases, that may result in systemic failure and death [ 3 , 4 ]. Severe forms of COVID-19 are associated with a hyperinflammatory response marked by a cytokine storm resulting in excessive production of pro-inflammatory cytokines like monocyte chemotactic protein-3 (MCP-3), interleukin − 1 (IL-1), and interferon α/β (IFNα/β). These three cytokines are closely associated with the severe form of COVID-19, although the cytokine profile of COVID-19 patients varies [ 5 – 7 ]. Lipoproteins are macromolecular complexes composed of apolipoproteins and lipids. Apolipoproteins regulate the lipoprotein structure, lipid transport, enzyme co-activation, and provide anti-inflammatory and antioxidant effects [ 8 ]. The apolipoproteins are classified into subfamilies, including Apo A, B, C, D, E, L, F, H, M, and (a). These proteins are distributed across various lipoproteins such as low-density lipoproteins (LDL) and high-density lipoproteins (HDL). [ 9 , 10 ]. During a SARS-CoV-2 infection, the concentrations of total cholesterol, LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) are reduced and are linked to the severity of the disease. [ 11 – 14 ]. Interestingly, numerous studies have also shown that lipid and lipoprotein metabolism are disrupted during the acute phase of SARS-CoV-2 infection, along with other illnesses [ 15 ]. Some apolipoproteins, including Apo A-I and Apo B100, can be dosed routinely like HDL-C, LDL-C, TC. Moreover, Apo A-I, A-II, and B are present in low levels in COVID-19 patients and are linked to an increased likelihood of developing severe forms of the disease [ 16 ]. In a previous study, our team found lower plasmatic concentrations of nine apolipoproteins in COVID-19 patients compared to controls [ 13 ]. Beyond quantitative changes, few studies have focused on the structure and function of HDL during COVID-19. Nonetheless, HDL possesses pleiotropic properties, including antioxidant and anti-inflammatory effects, in addition to its primary role in reverse cholesterol transport [ 11 , 15 , 17 – 19 ]. In a recent study of our team comparing COVID-19 patients versus controls, with an enrichment of acute phase proteins and a depletion of apolipoproteins, HDL particles proteome was profoundly altered in COVID-19 patients [ 20 ]. Moreover, in our in vitro analyses, we underlined that HDL from COVID-19 patients was much less endothelioprotective [ 20 ]. Whereas previous studies mostly compared COVID-19 patients with controls, the aim of the present work was to compare apolipoprotein concentrations of deceased patients with those of patients who survived an episode of COVID-19 leading to severe form of the disease. The second objective of this work was to characterize HDL particles (size and proteome) at admission to ICU between deceased patients and survivors. 2. Materials and methods 2.1. Study population We conducted an analysis with data from the prospective French COVID cohort (NCT04262921) [ 21 ]. The French COVID cohort includes hospitalized patients with a PCR-confirmed COVID‐19 and who gave written consent. The study was sponsored by INSERM, conducted in accordance with the Declaration of Helsinki, and Ethics approval was given by the French Ethics Committee CPP Ile‐de‐France VI (ID RCB: 2020‐A00256‐33). We selected adult patients from the French COVID cohort study, from February 1, 2020 until July 31, 2020 (corresponding to the first wave of pandemic in France). Pregnant women were excluded. None of the patients received corticosteroids. In this study, patients selected from the French COVID cohort had to meet the following criteria: - admitted in ICU; - at least 1 ml of plasmas for lipoprotein isolations and analyze; - not be on long-term statin therapy. 2.2. Quantification of lipoprotein subpopulation by tube gel electrophoresis (Quantimetrix Lipoprint) Plasma HDL subpopulations were determined by polyacrylamide gel electrophoresis (PAGE) using Quantimetrix Lipoprint HDL (Quantimetrix Corporation, Redondo Beach, CA, USA). Lipoprint was performed and analyzed according to the manufacturer's protocol. Briefly, 25 µL EDTA plasma was mixed with 300 µL loading gel, and this sample gel was applied to the Lipoprint HDL gel tube before photopolymerization for 35 minutes at RT. Gel tube electrophoresis was performed at 3 mA/gel tube until albumin reached 1.5 cm at the end of the gel tube (± 50 min). After resting in the dark for 30 min, the tubes were scanned and analyzed using Lipoware HDL software. After tube analysis, three main subpopulations were determined from the 10 subpopulations measured: large HDL (HDL-1 to HDL-3), intermediate HDL (HDL-4 to HDL-7) and large HDL (HDL-8 and HDL-10). 2.3. Lipoprotein isolation from plasma All subsequent steps were performed under a Class II microbial safety bench. Lipoproteins were isolated from EDTA plasma by three-step ultracentrifugation. All ultracentrifugation steps were performed at 252,000 g for 20 hours at 10°C using ultracentrifuge tubes placed in a 50.4 Ti rotor in a Beckman Coulter Optima L-80 XP ultracentrifuge. For the first ultracentrifugation step, plasma was weighed to a density of 1.019 using a potassium bromide (KBr) solution with a density of 1.35. The weighted plasma was covered with a 1.019 density KBr solution in ultracentrifugation tubes. After ultracentrifugation, the upper fraction corresponding to VLDL was collected. For the second ultracentrifugation step, plasma was collected and weighted to a density of 1.063 using a KBr solution with a density of 1.35. The weighted plasma was covered with a 1.063 density KBr solution in ultracentrifuge tubes. After ultracentrifugation, the upper fraction corresponding to LDL was collected. For the final ultracentrifugation step, plasma was weighted to a density of 1.21 using a KBr solution with a density of 1.21. The weighted plasma was covered with a 1.21 density KBr solution in ultracentrifugation tubes. After ultracentrifugation, the upper fraction corresponding to HDL was collected. After isolation, lipoproteins were washed five times with saline EN buffer (0.15 M NaCl, 1 mM EDTA, 0.025% NaN3, pH 7.3) using a centrifugal filtration tube (Amicon Ultra, 3 kDa cut-off, UFC500396). Tubes were centrifuged at 14,000g for 15 minutes at 4°C, and the volume was concentrated to 100 µL. Lipoprotein proteins were then assayed by the bicinchoninic acid method (BCA, Sigma), and HDL purity was verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) with Coomassie blue staining. 2.4. Fast protein Liquid Chromatography (FPLC) HDL isolated from each patient was purified in an ÄKTA Pure FPLC system (GE Healthcare) on a SuperdexTM 200 Increase 10/300 GL column (10 x 300 mm; Cytiva) in EN saline buffer. HDL was loaded into a 500 µL loop and injected at a flow rate of 0.250 mL/min. Elution was then performed with Saline EN buffer at a constant flow rate of 0.300 mL/min, and fractions were collected every 500 µL. Finally, the column was washed with two column volumes at a flow rate of 0.700 mL/min. 2.5. Label-free Quantification by Nano LC-MS/MS analysis Label-free protein quantification was performed on HDL at the time of ICU admission from patients who survived their COVID-19 (n = 14) and on HDL from patients who did not survive their severe COVID-19 (n = 15). 2.5.1. HDL sample preparation and proteolytic digestion The concentration of total HDL protein was measured using the BCA assay, with bovine serum albumin serving as the standard. As previously described [ 22 ], 5 µg HDL was combined with 25 µL digestion buffer (6 M urea, 50 mM ammonium bicarbonate) and denatured at 80°C for 10 minutes for sample preparation. Dithiothreitol was used for sample reduction, followed by iodoacetamide alkylation. Upon alkylation, the samples were diluted with four volumes of 50 mM ammonium bicarbonate to reduce the urea concentration. The HDLs were digested with 3 µg of ditosylphenylalanyl chloromethyl ketone trypsin (TPCK-treated Sigma) overnight at 37°C. Tryptic digestion was halted with 2% TFA and incubated at 4°C for 15 minutes. Subsequently, samples were centrifuged at 14,000 g for 15 minutes at 10°C and the desalted peptides in the supernatant were collected on peptide desalting spin columns (Thermo Fisher Scientific Pierce, part number 2162704). Peptides were eluted using a solution of 50% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA), followed by drying with a Speed Vacuum. Then, 20 µL of 40% ACN with 0.1% TFA was used to elute the peptide, which was subsequently vacuum-dried at 60°C prior to mass spectrometric analysis. 2.5.2. Nano LC-MS/MS method The method was carried out with two solvents, solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in 80% ACN). Digested peptides were solubilized in 20 µL of 4% ACN with 0.1% TFA. Mass spectrometry analysis was performed by nano-LC on a Thermo Fisher Ultimate 3000 Series NCS-3500 RS coupled to a nanospray ionization (NSI)-Q-Orbitrap mass spectrometer (Q Exactive Plus, Thermo Fisher Scientific, Bremen, Germany) [ 20 ]. Briefly, 5 µL of samples were injected into a C18 LC-EASY-spray column (2 µm, 100 Å, 75 µm x 50 cm, Thermo Fisher Scientific). Peptides were eluted on two linear elution gradients starting from 4–25% solution B (0.1% formic acid in 80% ACN) until 103 min, and then from 25–40% solution for 20 min. The column was then washed for 5 min with 90% solution B and equilibrated with 4% solution B for 20 min. Solution A was composed of 0.1% formic acid in water. To minimize contamination between samples, two blank runs containing solution A were conducted after every sample run. The flow rate was maintained at 0.300 µL/min, and the column temperature was maintained at 40°C. The NSI was set at 1.8 kV, the capillary temperature was set at 275°C, and the S lens RF level was set at 30% for mass spectrometry parameters. The mass spectra were obtained in positive ion mode using data-dependent acquisition within the m/z range of 350 to 2,000. Full scan mass spectra were acquired at a resolving power of 70,000 (at m/z 400) using automatic gain control (AGC) at a set value of 5e5 and a maximum injection time (Max IT) of 100 ms. MS/MS spectra of the top ten most intense precursor ions were acquired at a resolving power of 35,000 (at m/z 400) with an AGC set to 1e5 and a maximum IT of 100 ms. Peptide fragmentation was performed by setting the normalized collision energy to 28. Precursor ions were excluded dynamically for 60 seconds to prevent peptide selection repetition. The Orbitrap's performance was assessed weekly, and the mass spectrometer underwent external calibration before analysis using the LTQ ESI positive ion calibration solution (Pierce™). XCalibur 4.2.47 software (Thermo Fisher Scientific) was used to collect the data. 2.5.3. Protein identification and quantification Protein identification and quantification were performed automatically from the raw files with Proteome discoverer software (version 2.2.2.0). MS and MS/MS spectra were compared to the Uniprot Human reference proteome database containing only canonical sequences (26 559 sequences; 01 July 2021) using the SEQUEST HT search engine. The following features were defined in SEQUEST HT for identification: variable modifications are methionine oxidation and N-terminal acetylation of proteins, then, a fixed modification sets the carbamidomethylation of proteins and the specific enzyme selected is trypsin with two miscleavage allowed. A mass tolerance of 10 ppm for precursor ions and 0.02 Da for-product ions was used in SEQUEST HT. A decoy database search strategy with target inversion was applied with a false discovery rate (FDR) set at 1% for proteins and peptides. Then, only proteins with at least one unique peptide with a Sequest Score (Xcorr) ≥ 1.5 were used and for Xcorr < 1.5, manual verification of peptide identification was performed on the MS/MS spectrum. Identified peptides were quantified by label-free using the peak intensity of a validated peptide (unique and razor) for a given protein. After quantification, all proteins with more than 60% missing value were not considered in the analysis. For proteins with ≤ 60% missing values, they were replaced by the IQR of the corresponding proteins in the respective groups. The list of identified and quantified proteins and peptides is available in the supplemental table. 2.5.4. Data processing Proteome Discoverer (version 2.2.2.2.0) was utilized for the computations. Protein abundance refers to the mean of the protein intensities observed in all samples among each group (COVID-19 survivors versus non-survivors). Protein intensity was defined as the combination of protein and peptide intensities measured for each individual. All protein intensity values were subjected to logarithmic transformation (base 2). The log2 (Fold Change) is determined by taking the base 2 logarithm of the disparity between the average intensities of a specific protein in COVID-19 patients who did not survive and the average intensities of the same protein in COVID-19 patients who did survive. 2.6. Statistical analysis The Shapiro-Wilk test was used to test the normality of the data distributions. Mann-Whitney test or unpaired t-test was then performed, as appropriate, to compare patients who survived COVID-19 with those who did not. A volcano plot was generated by Prism to evaluate differential proteins on the basis of two indicators: the -log10 p-value of the Mann-Whitney test and the log2 (protein abundance fold change of non-survivors and survivors). Marker performance was measured using receiver operating characteristic (ROC) curves. A p-value < 0.05 is considered to indicate a statistically significant result. Statistical analyses were performed using Prism software (GraphPad Software Inc., San Diego, CA, USA). 3. Results 3.1 Study population 37 patients were included in this analysis. Survivors (n = 18) and non-survivors (n = 19) were matched based on age and gender. General characteristics of the patient population are presented in Table 1 and a flow chart is expressed on supplemental figure S1 . Table 1 Characteristics and outcomes of the study population Characterisitics Study population (n = 37) Survivors (INC) (n = 18) Non-survivors (n = 19) Survivors (INC) vs Non-survivors p-values Age, years, median [IQR] 61 [54; 68] 59 [51; 65] 61 [57; 68] 0.3047 (ns) Male sex, n (%) 37 (89%) 16 (89%) 17 (89%) > 0.9999 (ns) Obesity 13 (35%) 6 (33%) 7 (37%) > 0.9999 (ns) BMI, kg/m2, median [IQR] 28 [24; 31] 26 [24; 30] 29 [25; 33] 0.3677 (ns) High Blood pressure, n (%) 15 (41%) 5 (28%) 10 (53%) 0.1837 (ns) Diabetes mellitus, n (%) 13 (35%) 5 (28%) 8 (42%) 0.4951 (ns) Timing between fist symptoms and hospitalization (days), median [IQR] 8 [5; 9] 8 [5; 11] 7 [5; 9] 0.6781 (ns) Medication Statin, n (%) 0 0 0 > 0.9999 (ns) Corticosteroides, n (%) 0 0 0 > 0.9999 (ns) Outcomes Time between hospitalization and death (days), median [IQR] 13 [8; 27] N.A. 13 [8; 27] N.A. Clinical features TC, mmol/L, median [IQR] 3.45 [3.09; 3.91] 3.28 [2.81; 3.62] 3.62 [3.19; 4.24] 0.0426 (*) HDL-C, mmol/L, median [IQR] 0.49 [0.40; 0.58] 0.50 [0.43; 0.58] 0.48 [0.40; 0.63] 0.8381 (ns) LDL-C, mmol/L, median [IQR] 1.64 [1.37; 2.15] 1.98 [1.51; 2.26] 1.46 [1.34; 2.06] 0.1100 (ns) TG, mmol/L, median [IQR] 1.74 [1.51; 2.06] 1.71 [1.52; 2.17] 1,75 [1.46; 1.95] 0.0554 (ns) BMI: body mass index; TC: total cholesterol; HDL-C: high-density lipoprotein-cholesterol; LDL-C: low-density lipoprotein-cholesterol; TG: triglycerides 3.2 Comparison of plasma apolipoprotein concentrations between COVID-19 survivors and non-survivors We used mass spectrometry to measure 14 apolipoproteins and LCAT directly from the plasma of COVID-19 survivors and non-survivors. The following apolipoproteins were measured: Apo(a), Apo As (A-I, A-II, A-IV), Apo B100, Apo Cs (C-I, C-II, C-III), Apo D, Apo E, Apo H, Apo J, Apo L1, and Apo M. The concentration of seven apolipoproteins was significantly reduced in patients who did not survive compared with those who did. These apolipoproteins include Apo A-II (4.68 ± 3.76 vs. 2.30 ± 1.51; p = 0.0147), Apo Cs (C-I (2.29 ± 1.71 vs. 0.87 ± 0.62; p = 0.0060), C-II (1.64 ± 0.99 vs. 1.00 ± 0.58; p = 0.0269) and C-III (0.21 ± 0.18 vs. 0.09 ± 0.08; p = 0.0045), Apo H (11.45 ± 3.45 vs. 9.28 ± 2.52; p = 0.0351), Apo J (13.55 ± 3.99 vs. 11.03 ± 2.81; p = 0.0318) and Apo M (2.90 ± 1.09 vs. 2.12 ± 0.63; p = 0.0112). Furthermore, the concentration of LCAT ( p = 0.0137) is also significantly diminished in patients who did not survive COVID-19. Results are expressed in Fig. 1 . 3.3 Modulation of HDL subpopulations size between survivors and non-survivors COVID-19 patients The different HDL subpopulations size (large – intermediate – small) of COVID-19 patients were determined using the Lipoprint HDL tube gel electrophoresis technique. No difference was found between groups (Fig. 2 ). 3.4 HDL proteome analysis of surviving versus non-surviving patients of their COVID-19 HDL was isolated and prepared for shotgun proteomics analysis from 14 COVID-19 survivor patients and 15 non-survivor COVID-19 patients. Digested peptides obtained from 5 µg of HDL were prepared and analyzed using mass spectrometry combined with NanoLC. A first analysis of the MS/MS spectra by Proteome Discoverer software from the SEQUEST HT database identified 98 proteins that may be associated with HDL from either surviving or non-surviving patients (see Supplemental Table S2 ). After verification, our inclusion criteria ( see Materials and Methods ) indicate that 72 proteins are actually associated with patient HDL. To compare COVID-19 survivor and non-survivor groups, we conducted a fold change analysis of intensities associated with HDL proteins prior to log base-2 transformation. Out of the 72 proteins identified, the relative abundance of 11 proteins was lower in non-survivor HDL as compared to control HDL. These proteins included alpha-1 antitrypsin (AAT), cathelicidin antimicrobial peptide, complement C4 (A, B), serotransferrin, apolipoprotein C-I, heat shock protein HSP90-beta, protein-glutamine gamma-glutamyltransferase E, centrosomal protein, and vitronectin. In contrast, HDL from non-survivors showed an increased abundance of nine proteins, including serum amyloid A-2, CD99 antigen, mitochondria-associated proteins, and cellular debris (Fig. 2 ). The intensities of the 72 proteins associated with HDL in the patients were logarithmically transformed to base 2 to facilitate statistical analyses. The results indicate that HDL protein profile displays more significant alterations in non-surviving COVID-19 patients when compared to those who survived. Specifically, there were significant differences in the levels of 24 proteins in the HDL of non-survivors relative to COVID-19 survivors (Fig. 4 . A ). The levels of 19 of these proteins were significantly lower in the HDL of the non-survivors. The mentioned proteins comprise ribosomal protein 60S L7a (RL7A), adenosylhomocysteinase (ALDH1L1), alpha-1-antitrypsin (AAT), alpha-2-antiplasmin (SERPINF2), aminopeptidase N (ANPEP), apolipoprotein (a) (LPA), apolipoprotein H (APOH), 135 kDa centrosomal protein (CEP135), complement C4-A (CO4A), complement component C9 (CO9), haptoglobin-related protein (HPTR), heat shock protein HSP 90-beta (HS90B), prenylcysteine oxidase 1 (PCYOX1), protein S100-A8, protein-glutamine gamma-glutamyltransferase E, paraoxonase serum/lactonase 3 (PON3) and vitronectin (VTNC). However, five proteins are significantly elevated in HDL from the non-surviving group. The proteins that have increased are ATP synthase subunit beta mitochondrial (ATP5E), beta-2-microglobulin (B2M), CD99 antigen (CD99), cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) and secretoglobin family member 1 3A (SCGB3A2) (Fig. 4 . B ). 3.5 Diagnostic performance via receiver characteristic (ROC) curve ROC curves were used to assess the diagnostic accuracy of diverse lipid markers (Fig. 5 ). Except for TC (AUC = 0.71 ± 0.09, p = 0.0418), the regular lipid markers had areas under the curve (AUC) lower than 0.7 and a p-value over 0.05 (Fig. 5 A-D). Plasma apolipoproteins and LCAT provide a AUC score of greater than 0.7 for all except Apo H (AUC = 0.68 ± 0.09, p = 0.0056) and Apo J (AUC = 0.69 ± 0.09, p = 0.0483), which are unreliable indicators of patient outcomes (Fig. 5 E-L). Among the potential markers identified in HDL particles from COVID-19 patients are Apo H (AUC = 0.94 ± 0.06, p < 0.0001) and the 135 kDa CEP135 protein (AUC = 0.91 ± 0.05, p = 0.0001), all of which have shown significant effectiveness in predicting survival, with an AUC greater than 0.90. When using patient HDL proteins as markers, ATP5E, CO4A, PON3 and ITIH4 demonstrate effectiveness in predicting patient survival, while CO9 display lesser effectiveness with a lower AUC (Fig. 5 M-T). 4. Discussion In this present study, we have therefore shown that while lipid profiles on admission to ICU for COVID-19 cannot be used to stratify patients who died from those who survived, plasma analysis of certain apolipoproteins and proteomic analysis of HDL seem to be able to do so. Especially, APOH and CEP135 are potentially novel biomarkers for COVID-19 severity linked to HDL proteins. Lipid metabolism plays a key role in SARS-CoV-2 infection. Several hypotheses have been proposed to explain the changes in lipid metabolism, including viral use and modification of lipids to support replication, hemodilution, and increased vascular permeability. [ 23 , 24 ]. Lowering cholesterol levels in lipoproteins is also aided by the cytokine storm [ 25 ]. However, while in bacterial sepsis HDL-C concentrations are associated with patient outcome and in particular mortality, these links were not found in COVID-19 [ 13 , 14 , 20 , 26 – 30 ]. In this present study, we confirmed that HDL-C, LDL-C, and TG concentrations are similar between survivors and non-survivors COVID-19 patients. Similarly, Sun et al. reported no disparity in HDL-C, LDL-C, and TC levels between surviving and non-surviving patients with COVID-19, although TG levels were higher in non-surviving patients. We have also shown that a crude concentration of HDL-C does not appear to be sufficient to stratify patients during COVID-19. Thus, the first step was to characterize the plasma apolipoproteins which are key regulators of lipid metabolism and have anti-inflammatory, antioxidant and endothelial protective properties [ 9 , 31 ]. The mechanisms between apolipoproteins and the severity of COVID-19 are not yet fully understood. However, multiple studies have demonstrated a negative correlation between Apo A-I and Apo B levels and the severity of COVID-19 [ 16 , 32 , 33 ]. In our previous studies, we demonstrated that COVID-19 patients exhibit diminished levels of nine Apo forms, such as Apo A-II, C (I, II), H, J, M, as well as LCAT, alongside elevated levels of Apo E as compared to controls [ 13 ]. Our study confirms the previous one and highlights several apolipoproteins that are significantly more reduced on admission to ICU in patients who will die secondarily, such as Apo A-II, Apo Cs (C-I, C-II and C-III), Apo H, Apo J and Apo M. Most of these apolipoproteins are linked to reduced macrophage function [ 34 ]. Cholesterol efflux by macrophages induces production of Apo C through Liver X Receptor (LXR) activation [ 35 ]. Reduced Apo C concentrations indicate a decrease in macrophage cholesterol efflux function. Interestingly, we observed a decreased in Apo A-II but not in Apo A-I concentrations among non-survivors of COVID-19 when compared to survivors. This observation differs from most of the studies reporting essentially a decrease in Apo A-I concentration with an association with worth outcome [ 32 , 33 ]. This difference can be attributed to the sample size, which is significantly larger than ours. Moreover, corticosteroid therapy modifies lipidi profile. Our findings show a trend towards reduced Apo A-II concentrations. Our previous studies, however, observed apo A-II concentrations in both HDL and plasma of COVID-19 patients [ 13 , 20 ]. Additionally, other research has also seen lower Apo A-II levels in COVID-19 patients [ 34 , 36 ]. This decrease in Apo A-II suggests a potential alteration in cholesterol efflux, as well as a decrease in the anti-inflammatory and antioxidant properties of HDL [ 37 , 38 ]. It is interesting to note that ApoB100 and LCAT were associated with mortality in our last study [ 13 ] but not in this work. The role of Apo H, also known as beta-2-glycoprotein 1, is not well understood. According to earlier research [ 39 ], it has been observed that Apo H levels decrease in COVID-19 patients. This decrease in Apo H could potentially create a prothrombotic environment in COVID-19, leading to antiphospholipid syndrome. This decrease in Apo H could potentially create a prothrombotic environment in COVID-19, due to an antiphospholipid syndrome. Furthermore, Apo H seems to capture the SARS-CoV-2 virus, leading to the hypothesis that the virus utilizes Apo H [ 40 ]. Clusterin, also known as Apo J, acts as an inhibitor of the complement system [ 41 ]. Reductions in the concentration of Apo J led to complement system activation, which then causes a prothrombotic environment [ 42 , 43 ]. Our study indicates also that Apo M levels were decreased in COVID-19 patients who did not survive. A study conducted by Marfia et al [ 44 ] showed that the concentration of sphingosine-1-P (S1P) was closely related to the concentration of apo M and was able to accurately predict the prognosis of COVID-19 patients. Our findings reaffirm that Apo M can serve as a potential marker for assessing COVID-19 severity. Furthermore, Apo M depletion is associated with loss of anti-inflammatory and anti-atherogenic functions of S1P/ApoM complex [ 45 , 46 ]. Therefore, deficiency of these apolipoproteins promotes an overall thromboinflammatory environment [ 47 ]. Our study revealed a decrease in 19 proteins and an increase in five proteins in the HDL of non-surviving COVID-19 patients. Among the proteins increased in the HDL of COVID-19 patients, the CD99 antigen plays a key role in the T-cell adhesion process. CD99 is mainly localized on T lymphocytes and endothelial cells. The level of this particular protein is lower in COVID-19 patients with severe symptoms, indicating reduced endothelial integrity and recruitment of monocytes, neutrophils and T cells [ 48 ]. A hypothesis to explain its increase in HDL from non-survivors of COVID-19 is that it is captured by the latter. HDL levels in non-survivors are found to be enriched with mitochondrial ATP synthase subunit beta, which interacts with the ecto-F1-ATPase receptor on the cell surface [ 49 ]. SARS-CoV-2 is able to interact with ecto-F1-ATPase and ATP synthase [ 50 , 51 ]. An elevated beta-2-microglobulin has been identified as a potential biomarker of COVID-19 severity [ 52 , 53 ], which may explain the elevated levels in the HDL of non-surviving COVID-19 patients. Interestingly, the HDL proteins of non-survivors of COVID-19 exhibit a decrease in AAT, I'TIH4, Complement C4A, and C9, which are normally increased in plasma as COVID-19 severity increases. This increase could be explained by an increase in the compounds and proteins of the acute phase complement and a decrease in the proteins involved in the coagulation cascade [ 54 , 55 ]. Among the coagulation-related proteins, the levels of beta 2 glycoprotein 1 (Apo H) are reduced in the HDL of COVID-19 non-survivors, suggesting a disturbance in the coagulation cascades [ 34 , 55 ]. The remodeling of high-density lipoprotein in patients during the COVID-19 pandemic suggests a loss of high-density lipoprotein (HDL) functionality [ 20 ]. Our study is the first to investigate LDL and HDL distribution changes during COVID-19 using tube gel electrophoresis (Lipoprint) among patients who survived or did not survive the disease. The distribution of lipoproteins was similar regardless of patient outcome. COVID-19 brings about a modification in the lipoprotein subpopulations' distribution. This change is characterized by a reduction in the concentrations of small, intermediate, and large subpopulations of HDL and large LDL, accompanied by an escalation in small and dense LDL concentrations. [ 56 ]. Finally, routine lipid markers in our study demonstrated poor accuracy in assessing disease severity. It is surprising to observe this in light of the association between HDL-C and disease severity [ 57 ]. In contrast, apolipoproteins have superior performance to routine lipid assays in assessing patient outcome. This is expected since various studies have shown that apolipoproteins are decreased in COVID-19 [ 13 , 23 , 32 – 34 ]. However, it seems that HDL-associated Apo H is a more accurate indicator of COVID-19 severity than total plasma Apo H. Nevertheless, these results merit further analysis with more patients included. The primary obstacle we encountered was the small number of available samples, which hindered our ability to conduct experiments on a representative sample of the population under study. Nevertheless, our preliminary results are promising. This study is focused on a modest sample of 37 patients during the initial wave of COVID-19, providing supplementary information to the current knowledge on COVID-19. Another limitation is the low levels of HDL volume of patients included in our study. It would be pertinent to evaluate the lipoproteins' functionality to ascertain the effect of changes in their proteome. Further comparative studies carried out in various centers can help confirm and validate the conclusions presented in our research. In conclusion, in a cohort of patients hospitalized in ICU for severe COVID-19, we demonstrated major differences in plasma apolipoproteins and in protein composition of HDL by comparing surviving and non-surviving patients. These markers, measured on admission to ICU, also appear to be more predictive of mortality than BASIC lipid concentrations such as HDL-C. Nevertheless, these results should be interpreted with caution given the small number of patients included in this study. In this context, it is important to conduct a high-powered multicenter study in the future. Declarations Conflicts of interests The authors declare that they have no conflict of interest to disclose. Funding FB is supported by a grant from the Région Réunion for their PhD (European Union-Reunion Region). This research was funded by FEDER "LIPICOR" RE0027600 and "TFORCE-COVIR" RE0027601 (EU—Région Réunion—French State national counterpart), and by the BIOST Fédération. Author Contribution OM and ST designed the study. FB, BV acquired the data. FB, OM, ST analyzed the data. FB, ST, OM wrote the manuscript. PR, OM, ST, AF and SJ critically reviewed the manuscript. All authors reviewed the manuscript. Acknowledgement The sponsor of the LIPICOR and T-FORCE COVIR studies were respectively Assistance Publique - Hopitaux de Paris (Délégation à la Recherche Clinique et à l'Innovation) and FEDER Région Réunion. References Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. N Engl J Med. 2020;382:1199–207. https://doi.org/10.1056/NEJMoa2001316 . Zhu N, Zhang D, Wang W, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382:727–33. https://doi.org/10.1056/NEJMoa2001017 . Zhang X, Tan Y, Ling Y, et al. Viral and host factors related to the clinical outcome of COVID-19. Nature. 2020;583:437–40. https://doi.org/10.1038/s41586-020-2355-0 . WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20:e192–7. https://doi.org/10.1016/S1473-3099(20)30483-7 . Vaninov N. In the eye of the COVID-19 cytokine storm. Nat Rev Immunol. 2020;20:277–277. https://doi.org/10.1038/s41577-020-0305-6 . Hu B, Huang S, Yin L. The cytokine storm and COVID-19. J Med Virol. 2021;93:250–6. https://doi.org/10.1002/jmv.26232 . Yang Y, Shen C, Li J et al. (2020) Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcom. Jonas A, Phillips MC. Lipoprotein structure. Biochemistry of Lipids, Lipoproteins and Membranes. Elsevier; 2008. pp. 485–506. Dominiczak MH, Caslake MJ. Apolipoproteins: metabolic role and clinical biochemistry applications. Ann Clin Biochem. 2011;48:498–515. https://doi.org/10.1258/acb.2011.011111 . Alaupovic P. (1996) [2] Significance of apolipoproteins for structure, function, and classification of plasma lipoproteins. In: Methods in Enzymology. Academic Press, pp 32–60. (2020) Hypolipidemia is associated with the severity of COVID-19. J Clin Lipidol 14:297–304. https://doi.org/10.1016/j.jacl.2020.04.008 Almas T, Malik J, Alsubai AK, et al. Effect of COVID-19 on lipid profile parameters and its correlation with acute phase reactants: A single-center retrospective analysis. Annals Med Surg. 2022;78:103856. https://doi.org/10.1016/j.amsu.2022.103856 . Begue F, Chemello K, Veeren B, et al. Plasma Apolipoprotein Concentrations Are Highly Altered in Severe Intensive Care Unit COVID-19 Patients: Preliminary Results from the LIPICOR Cohort Study. Int J Mol Sci. 2023;24:4605. https://doi.org/10.3390/ijms24054605 . Tanaka S, Tymowski CD, Assadi M, et al. Lipoprotein concentrations over time in the intensive care unit COVID-19 patients: Results from the ApoCOVID study. PLoS ONE. 2020;15:e0239573. https://doi.org/10.1371/journal.pone.0239573 . Li Y, Zhang Y, Lu R, et al. Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta. 2021;517:66–73. https://doi.org/10.1016/j.cca.2021.02.011 . Ulloque-Badaracco JR, Hernandez-Bustamante EA, Herrera-Añazco P, Benites-Zapata VA. Prognostic value of apolipoproteins in COVID-19 patients: A systematic review and meta-analysis. Travel Med Infect Dis. 2021;44:102200. https://doi.org/10.1016/j.tmaid.2021.102200 . Tran-Dinh A, Diallo D, Delbosc S, et al. HDL and endothelial protection. Br J Pharmacol. 2013;169:493–511. https://doi.org/10.1111/bph.12174 . Wang G, Zhang Q, Zhao X, et al. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study. Lipids Health Dis. 2020;19:1–7. https://doi.org/10.1186/s12944-020-01382-9 . Tanaka S, Couret D, Tran-Dinh A, et al. High-density lipoproteins during sepsis: from bench to bedside. Crit Care. 2020;24:134. https://doi.org/10.1186/s13054-020-02860-3 . Begue F, Tanaka S, Mouktadi Z, et al. Altered high-density lipoprotein composition and functions during severe COVID-19. Sci Rep. 2021;11:2291. https://doi.org/10.1038/s41598-021-81638-1 . Yazdanpanah Y, French COVID, cohort investigators and study group. Impact on disease mortality of clinical, biological, and virological characteristics at hospital admission and overtime in COVID-19 patients. J Med Virol. 2021;93:2149–59. https://doi.org/10.1002/jmv.26601 . Tanaka S, Begue F, Veeren B, et al. First Recombinant High-Density Lipoprotein Particles Administration in a Severe ICU COVID-19 Patient, a Multi-Omics Exploratory Investigation. Biomedicines. 2022;10:754. https://doi.org/10.3390/biomedicines10040754 . Song J-W, Lam SM, Fan X, et al. Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis. Cell Metabol. 2020;32:188–e2025. https://doi.org/10.1016/j.cmet.2020.06.016 . Tian S, Hu W, Niu L, et al. Pulmonary Pathology of Early-Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients With Lung Cancer. J Thorac Oncol. 2020;15:700–4. https://doi.org/10.1016/j.jtho.2020.02.010 . Sorokin AV, Karathanasis SK, Yang Z-H, et al. COVID-19—Associated dyslipidemia: Implications for mechanism of impaired resolution and novel therapeutic approaches. FASEB J. 2020;34:9843–53. https://doi.org/10.1096/fj.202001451 . Sun JT, Chen Z, Nie P et al. (2020) Lipid Profile Features and Their Associations With Disease Severity and Mortality in Patients With COVID-19. Front Cardiovasc Med 7. Sampedro-Nuñez M, Aguirre-Moreno N, García-Fraile Fraile L, et al. Finding answers in lipid profile in COVID-19 patients. Endocrine. 2021;74:443–54. https://doi.org/10.1007/s12020-021-02881-0 . Chien J-Y, Jerng J-S, Yu C-J, Yang P-C. Low serum level of high-density lipoprotein cholesterol is a poor prognostic factor for severe sepsis*. Crit Care Med. 2005;33:1688–93. https://doi.org/10.1097/01.CCM.0000171183.79525.6B . Tanaka S, Stern J, Bouzid D, et al. Relationship between lipoprotein concentrations and short-term and 1-year mortality in intensive care unit septic patients: results from the HIGHSEPS study. Ann Intensiv Care. 2021;11:11. https://doi.org/10.1186/s13613-021-00800-0 . Barlage S, Gnewuch C, Liebisch G, et al. Changes in HDL-associated apolipoproteins relate to mortality in human sepsis and correlate to monocyte and platelet activation. Intensive Care Med. 2009;35:1877–85. https://doi.org/10.1007/s00134-009-1609-y . Mehta A, Shapiro MD. Apolipoproteins in vascular biology and atherosclerotic disease. Nat Reviews Cardiol. 2022;19:168–79. https://doi.org/10.1038/s41569-021-00613-5 . Poynard T, Deckmyn O, Rudler M, et al. Performance of serum apolipoprotein-A1 as a sentinel of Covid-19. PLoS ONE. 2020;15:e0242306. https://doi.org/10.1371/journal.pone.0242306 . Zhu Z, Yang Y, Fan L, et al. Low serum level of apolipoprotein A1 may predict the severity of COVID-19: A retrospective study. J Clin Lab Anal. 2021;35:e23911. https://doi.org/10.1002/jcla.23911 . Shen B, Yi X, Sun Y, et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020;182:59–e7215. https://doi.org/10.1016/j.cell.2020.05.032 . Mak PA, Laffitte BA, Desrumaux C, et al. Regulated Expression of the Apolipoprotein E/C-I/C-IV/C-II Gene Cluster in Murine and Human Macrophages: A CRITICAL ROLE FOR NUCLEAR LIVER X RECEPTORS α AND β*. J Biol Chem. 2002;277:31900–8. https://doi.org/10.1074/jbc.M202993200 . Kimhofer T, Lodge S, Whiley L, et al. Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection. J Proteome Res. 2020;19:4442–54. https://doi.org/10.1021/acs.jproteome.0c00519 . Melchior JT, Street SE, Andraski AB, et al. Apolipoprotein A-II alters the proteome of human lipoproteins and enhances cholesterol efflux from ABCA1. J Lipid Res. 2017;58:1374–85. https://doi.org/10.1194/jlr.M075382 . Zvintzou E, Xepapadaki E, Kalogeropoulou C, et al. Pleiotropic effects of apolipoprotein A-Ⅱ on high-density lipoprotein functionality, adipose tissue metabolic activity and plasma glucose homeostasis. J Biomed Res. 2020;34:14–26. https://doi.org/10.7555/JBR.33.20190048 . Serrano M, Espinosa G, Lalueza A, et al. Beta-2-Glycoprotein-I Deficiency Could Precipitate an Antiphospholipid Syndrome-like Prothrombotic Situation in Patients With Coronavirus Disease 2019. ACR Open Rheumatol. 2021;3:267–76. https://doi.org/10.1002/acr2.11245 . Kang J, Jang H, Kim TH, et al. Accurate Diagnosis of COVID-19 from Self-Collectable Biospecimens Using Synthetic Apolipoprotein H Peptide-Coated Nanoparticle Assay. Anal Chem. 2022;94:17186–94. https://doi.org/10.1021/acs.analchem.2c03813 . Tschopp J, Chonn A, Hertig S, French LE. Clusterin, the human apolipoprotein and complement inhibitor, binds to complement C7, C8 beta, and the b domain of C9. J Immunol. 1993;151:2159–65. de Bont CM, Boelens WC, Pruijn GJM. NETosis, complement, and coagulation: a triangular relationship. Cell Mol Immunol. 2019;16:19–27. https://doi.org/10.1038/s41423-018-0024-0 . Zhou Y, Xu Z, Liu Z. Impact of Neutrophil Extracellular Traps on Thrombosis Formation: New Findings and Future Perspective. Front Cell Infect Microbiol. 2022;12:910908. https://doi.org/10.3389/fcimb.2022.910908 . Marfia G, Navone S, Guarnaccia L, et al. Decreased serum level of sphingosine-1-phosphate: a novel predictor of clinical severity in COVID-19. EMBO Mol Med. 2021;13:e13424. https://doi.org/10.15252/emmm.202013424 . Wolfrum C, Poy MN, Stoffel M. Apolipoprotein M is required for prebeta-HDL formation and cholesterol efflux to HDL and protects against atherosclerosis. Nat Med. 2005;11:418–22. https://doi.org/10.1038/nm1211 . Yao Mattisson I, Christoffersen C. Apolipoprotein M and its impact on endothelial dysfunction and inflammation in the cardiovascular system. Atherosclerosis. 2021;334:76–84. https://doi.org/10.1016/j.atherosclerosis.2021.08.039 . Martins-Gonçalves R, Hottz ED, Bozza PT. Acute to post-acute COVID-19 thromboinflammation persistence: Mechanisms and potential consequences. Curr Res Immunol. 2023;4:100058. https://doi.org/10.1016/j.crimmu.2023.100058 . Siwy J, Wendt R, Albalat A, et al. CD99 and polymeric immunoglobulin receptor peptides deregulation in critical COVID-19: A potential link to molecular pathophysiology? Proteomics. 2021;21:e2100133. https://doi.org/10.1002/pmic.202100133 . Martinez LO, Najib S, Perret B, et al. Ecto-F1-ATPase/P2Y pathways in metabolic and vascular functions of high density lipoproteins. Atherosclerosis. 2015;238:89–100. https://doi.org/10.1016/j.atherosclerosis.2014.11.017 . Panfoli I, Esposito A. Beneficial effect of polyphenols in COVID-19 and the ectopic F1FO‐ATP synthase: Is there a link? J Cell Biochem. 2022;123:1281–4. https://doi.org/10.1002/jcb.30306 . Ramachandran K, Maity S, Muthukumar AR, et al. SARS-CoV-2 infection enhances mitochondrial PTP complex activity to perturb cardiac energetics. iScience. 2022;25:103722. https://doi.org/10.1016/j.isci.2021.103722 . Conca W, Alabdely M, Albaiz F, et al. Serum β2-microglobulin levels in Coronavirus disease 2019 (Covid-19): Another prognosticator of disease severity? PLoS ONE. 2021;16:e0247758. https://doi.org/10.1371/journal.pone.0247758 . Calvet J, Berenguer-Llergo A, Gay M, et al. Biomarker candidates for progression and clinical management of COVID-19 associated pneumonia at time of admission. Sci Rep. 2022;12:640. https://doi.org/10.1038/s41598-021-04683-w . Medjeral-Thomas NR, Troldborg A, Hansen AG, et al. Protease inhibitor plasma concentrations associate with COVID-19 infection. Oxf Open Immunol. 2021;2:iqab014. https://doi.org/10.1093/oxfimm/iqab014 . Geyer PE, Arend FM, Doll S, et al. High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion. EMBO Mol Med. 2021;13:e14167. https://doi.org/10.15252/emmm.202114167 . Ballout RA, Kong H, Sampson M, et al. The NIH Lipo-COVID Study: A Pilot NMR Investigation of Lipoprotein Subfractions and Other Metabolites in Patients with Severe COVID-19. Biomedicines. 2021;9:1090. https://doi.org/10.3390/biomedicines9091090 . Hu X, Chen D, Wu L, et al. Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection. Clin Chim Acta. 2020;510:105–10. https://doi.org/10.1016/j.cca.2020.07.015 . Additional Declarations No competing interests reported. Supplementary Files SupplementalFRENCHCOVID.pptx SupplementaryTableFRENCHCOVID.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Reviews received at journal 10 Sep, 2024 Reviews received at journal 09 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviews received at journal 02 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviewers invited by journal 28 Aug, 2024 Editor assigned by journal 26 Aug, 2024 Submission checks completed at journal 26 Aug, 2024 First submitted to journal 26 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4978039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352055827,"identity":"9eb195de-643b-4438-b70f-f9a152a14d1c","order_by":0,"name":"Floran BEGUE","email":"","orcid":"","institution":"INSERM, UMR 1188 Diabète Athérothrombose Réunion Océan Indien (DéTROI), Université de La Réunion","correspondingAuthor":false,"prefix":"","firstName":"Floran","middleName":"","lastName":"BEGUE","suffix":""},{"id":352055828,"identity":"f6326418-b32c-48cf-a686-844d6da14558","order_by":1,"name":"Bryan VEEREN","email":"","orcid":"","institution":"INSERM, UMR 1188 Diabète Athérothrombose Réunion Océan Indien (DéTROI), Université de La Réunion","correspondingAuthor":false,"prefix":"","firstName":"Bryan","middleName":"","lastName":"VEEREN","suffix":""},{"id":352055829,"identity":"b3ccf263-3028-48a6-b9c0-cc46259f5b73","order_by":2,"name":"Philippe RONDEAU","email":"","orcid":"","institution":"INSERM, UMR 1188 Diabète Athérothrombose Réunion Océan Indien (DéTROI), Université de La Réunion","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"RONDEAU","suffix":""},{"id":352055830,"identity":"8855a5ce-122a-4dee-a2e8-d9efb92935e4","order_by":3,"name":"Aline-Marie FLORENCE","email":"","orcid":"","institution":"Université Paris Cité, INSERM IAME UMR 1137","correspondingAuthor":false,"prefix":"","firstName":"Aline-Marie","middleName":"","lastName":"FLORENCE","suffix":""},{"id":352055831,"identity":"67af2def-ba14-4c0c-b415-288adb9b3dc8","order_by":4,"name":"Simon JAMARD","email":"","orcid":"","institution":"University Hospital of Tours","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"JAMARD","suffix":""},{"id":352055832,"identity":"7c13f9eb-0b11-4f0e-b3bc-f3834b26fa47","order_by":5,"name":"Philippe MONTRAVERS","email":"","orcid":"","institution":"AP-HP, Bichat-Claude Bernard Hospital","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"MONTRAVERS","suffix":""},{"id":352055833,"identity":"f6572d24-8378-47b6-ad68-77c1b9239e7b","order_by":6,"name":"Sébastien TANAKA","email":"","orcid":"","institution":"INSERM, UMR 1188 Diabète Athérothrombose Réunion Océan Indien (DéTROI), Université de La Réunion","correspondingAuthor":false,"prefix":"","firstName":"Sébastien","middleName":"","lastName":"TANAKA","suffix":""},{"id":352055834,"identity":"71a8639a-cf09-482b-ae2f-ce9245f75d71","order_by":7,"name":"Olivier MEILHAC","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACdsYGhgQwi7HxMJCUY4NK8OPUwozQ0gDSYgzTItmAUwsSG6QlEaYSpxZ+ZuY2iQcV9xh025sbDhfU2KX3Sbc/fFyZwyBhjkOPZDNjm0TCmWIGszMHGw7POJac2yZzxtjw7DYGCZkD2LUYHGZsNkhsS2Awu5HYcJiH7UBum0QOm2TjNoY6CRwOg2j5B9Ry/yFQy78D6WwS6c9/ArVI4NHS+CCxAWQLMMR42w4ksEkkmDHi0wL0S+ODhGMJPGZngA7j7Us2BDrMGOgwCZxa+NnbHxz8UZMgZ3b8+MPHPN/s5OVnpD/82LjNBqcWGOBBFyCkYRSMglEwCkYBPgAA4R5XvkuQNMcAAAAASUVORK5CYII=","orcid":"","institution":"INSERM, UMR 1188 Diabète Athérothrombose Réunion Océan Indien (DéTROI), Université de La Réunion","correspondingAuthor":true,"prefix":"","firstName":"Olivier","middleName":"","lastName":"MEILHAC","suffix":""}],"badges":[],"createdAt":"2024-08-26 12:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4978039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4978039/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-024-02381-w","type":"published","date":"2025-01-31T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66793524,"identity":"df350f1e-4f6c-41ae-8fcb-937a0cf1c9ea","added_by":"auto","created_at":"2024-10-16 13:52:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApolipoprotein changes in COVID-19 non-survivors in ICU. \u003c/strong\u003eA panel of 14 apolipoproteins and LCAT (\u003cstrong\u003eA-O\u003c/strong\u003e) was quantified by mass spectrometry in the plasma of 18 surviving and 19 non-surviving COVID-19 patients. A Shapiro-Wilk normality test was performed to determine whether to use an unpaired t-test for parametric data or a Mann-Whitney test for nonparametric data. \u003cem\u003e* p \u0026lt;0.05, ** p\u0026lt;0.01, ns p ≥0.05 \u003c/em\u003ecomparing survivors and non-survivors of COVID-19.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/726b682591c5b49b7f348833.png"},{"id":66792382,"identity":"bd5e098d-7ce2-4264-bd0f-e674a8f100ab","added_by":"auto","created_at":"2024-10-16 13:44:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHDL subpopulations remain unchanged in survivors and non-survivors. \u003c/strong\u003eThe distribution of HDL subpopulations (Large \u003cstrong\u003e(A)\u003c/strong\u003e, Intermediate \u003cstrong\u003e(B)\u003c/strong\u003e and Small \u003cstrong\u003e(C)\u003c/strong\u003e) in 15 survivors (gray dots) and 16 non-survivors (black dots) were found to be identical. A Shapiro-Wilk test was conducted to verify the normal distribution of small, intermediate and large HDL, followed by an unpaired t-test for Large HDL and a Mann Whitney test for Intermediate and Small HDL. Each point indicates the percentage of cholesterol per patient. No significant differences were observed in comparisons between survivor and non-survivor groups (p ≥0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/e15b9efc8d6296d9a64c08a1.png"},{"id":66795041,"identity":"bb1914f4-39d7-42fb-b082-9d389e15d403","added_by":"auto","created_at":"2024-10-16 14:00:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of proteins identified in HDL isolated from plasma of COVID-19 surviving (n=14) and non-surviving (n=15) patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/3bc937b8b51efb0e799e2f17.png"},{"id":66792384,"identity":"48e86b8b-e7c8-410a-a598-f35590e38be7","added_by":"auto","created_at":"2024-10-16 13:44:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAltered HDL proteome in COVID-19 patient non-survivors. (A) \u003c/strong\u003eVolcano plot showing proteins differentially expressed in HDL from survivors (n=14) and non-survivors (n=15). Red points represent proteins with higher levels in non-survivor HDL, while blue points represent proteins with lower levels in non-survivor HDL. The y-axis represents -log10 (Mann-Whitney \u003cem\u003ep-value\u003c/em\u003e) and the x-axis represents log2 (P.A.I fold change of non-survivors and survivors)\u003cstrong\u003e. (B) \u003c/strong\u003eProtein abundance of HDL isolated from the plasma of the 14 survivors and 15 non-survivors of COVID-19. Mann-Whitney test was used to compare HDL protein abundance in survivors and non-survivors. * \u003cem\u003ep\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001, **** p\u0026lt;0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/31c7f1b88fd63effb22aa888.png"},{"id":66793525,"identity":"1d5e0143-4e5b-4bd9-9c85-ff4bd3b0b30f","added_by":"auto","created_at":"2024-10-16 13:52:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) curve.\u003c/strong\u003e ROC curve of the routine lipid markers (A-D). ROC curve of plasma apolipoproteins varying significantly between COVID-19 survivor vs. non-survivor groups (E-L). ROC curve of altered proteins in the HDL proteome of patients surviving vs. not surviving their COVID-19 (M-T). The red line corresponds to random classifier.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/27369a76c80cb44ecd41d610.png"},{"id":75351274,"identity":"b81364bb-58ca-4ba8-8d3c-fc6a6e35a5fd","added_by":"auto","created_at":"2025-02-03 16:08:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1504563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/ee142102-89b3-4360-875f-435f4cb1cb30.pdf"},{"id":66793523,"identity":"82c5cb3b-2bf1-4c98-a603-e9b79c89f48a","added_by":"auto","created_at":"2024-10-16 13:52:14","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49923,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFRENCHCOVID.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/e3597072781f27dbaa2a8ce8.pptx"},{"id":66792387,"identity":"df7b1c65-3e45-4540-b3fe-7bf04af3f982","added_by":"auto","created_at":"2024-10-16 13:44:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":794422,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableFRENCHCOVID.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4978039/v1/a87b101e69daeb381ac4e61e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HDL proteome and apolipoproteins concentrations in severe ICU COVID-19 patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China in December 2019, is caused by a Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and has caused more than 6\u0026nbsp;million deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. COVID-19 presents a broad range of symptoms and gravity, varying from ambulatory mild disease (including asymptomatic forms) to severe cases, that may result in systemic failure and death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Severe forms of COVID-19 are associated with a hyperinflammatory response marked by a cytokine storm resulting in excessive production of pro-inflammatory cytokines like monocyte chemotactic protein-3 (MCP-3), interleukin \u0026minus;\u0026thinsp;1 (IL-1), and interferon α/β (IFNα/β). These three cytokines are closely associated with the severe form of COVID-19, although the cytokine profile of COVID-19 patients varies [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLipoproteins are macromolecular complexes composed of apolipoproteins and lipids. Apolipoproteins regulate the lipoprotein structure, lipid transport, enzyme co-activation, and provide anti-inflammatory and antioxidant effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The apolipoproteins are classified into subfamilies, including Apo A, B, C, D, E, L, F, H, M, and (a). These proteins are distributed across various lipoproteins such as low-density lipoproteins (LDL) and high-density lipoproteins (HDL). [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring a SARS-CoV-2 infection, the concentrations of total cholesterol, LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) are reduced and are linked to the severity of the disease. [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Interestingly, numerous studies have also shown that lipid and lipoprotein metabolism are disrupted during the acute phase of SARS-CoV-2 infection, along with other illnesses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome apolipoproteins, including Apo A-I and Apo B100, can be dosed routinely like HDL-C, LDL-C, TC. Moreover, Apo A-I, A-II, and B are present in low levels in COVID-19 patients and are linked to an increased likelihood of developing severe forms of the disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a previous study, our team found lower plasmatic concentrations of nine apolipoproteins in COVID-19 patients compared to controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond quantitative changes, few studies have focused on the structure and function of HDL during COVID-19. Nonetheless, HDL possesses pleiotropic properties, including antioxidant and anti-inflammatory effects, in addition to its primary role in reverse cholesterol transport [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In a recent study of our team comparing COVID-19 patients versus controls, with an enrichment of acute phase proteins and a depletion of apolipoproteins, HDL particles proteome was profoundly altered in COVID-19 patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, in our in vitro analyses, we underlined that HDL from COVID-19 patients was much less endothelioprotective [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhereas previous studies mostly compared COVID-19 patients with controls, the aim of the present work was to compare apolipoprotein concentrations of deceased patients with those of patients who survived an episode of COVID-19 leading to severe form of the disease. The second objective of this work was to characterize HDL particles (size and proteome) at admission to ICU between deceased patients and survivors.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eWe conducted an analysis with data from the prospective French COVID cohort (NCT04262921) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The French COVID cohort includes hospitalized patients with a PCR-confirmed COVID‐19 and who gave written consent. The study was sponsored by INSERM, conducted in accordance with the Declaration of Helsinki, and Ethics approval was given by the French Ethics Committee CPP Ile‐de‐France VI (ID RCB: 2020‐A00256‐33).\u003c/p\u003e \u003cp\u003eWe selected adult patients from the French COVID cohort study, from February 1, 2020 until July 31, 2020 (corresponding to the first wave of pandemic in France). Pregnant women were excluded. None of the patients received corticosteroids. In this study, patients selected from the French COVID cohort had to meet the following criteria: - admitted in ICU; - at least 1 ml of plasmas for lipoprotein isolations and analyze; - not be on long-term statin therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Quantification of lipoprotein subpopulation by tube gel electrophoresis (Quantimetrix Lipoprint)\u003c/h2\u003e \u003cp\u003ePlasma HDL subpopulations were determined by polyacrylamide gel electrophoresis (PAGE) using Quantimetrix Lipoprint HDL (Quantimetrix Corporation, Redondo Beach, CA, USA). Lipoprint was performed and analyzed according to the manufacturer's protocol. Briefly, 25 \u0026micro;L EDTA plasma was mixed with 300 \u0026micro;L loading gel, and this sample gel was applied to the Lipoprint HDL gel tube before photopolymerization for 35 minutes at RT. Gel tube electrophoresis was performed at 3 mA/gel tube until albumin reached 1.5 cm at the end of the gel tube (\u0026plusmn;\u0026thinsp;50 min). After resting in the dark for 30 min, the tubes were scanned and analyzed using Lipoware HDL software. After tube analysis, three main subpopulations were determined from the 10 subpopulations measured: large HDL (HDL-1 to HDL-3), intermediate HDL (HDL-4 to HDL-7) and large HDL (HDL-8 and HDL-10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Lipoprotein isolation from plasma\u003c/h2\u003e \u003cp\u003eAll subsequent steps were performed under a Class II microbial safety bench. Lipoproteins were isolated from EDTA plasma by three-step ultracentrifugation. All ultracentrifugation steps were performed at 252,000 g for 20 hours at 10\u0026deg;C using ultracentrifuge tubes placed in a 50.4 Ti rotor in a Beckman Coulter Optima L-80 XP ultracentrifuge. For the first ultracentrifugation step, plasma was weighed to a density of 1.019 using a potassium bromide (KBr) solution with a density of 1.35. The weighted plasma was covered with a 1.019 density KBr solution in ultracentrifugation tubes. After ultracentrifugation, the upper fraction corresponding to VLDL was collected. For the second ultracentrifugation step, plasma was collected and weighted to a density of 1.063 using a KBr solution with a density of 1.35. The weighted plasma was covered with a 1.063 density KBr solution in ultracentrifuge tubes. After ultracentrifugation, the upper fraction corresponding to LDL was collected. For the final ultracentrifugation step, plasma was weighted to a density of 1.21 using a KBr solution with a density of 1.21. The weighted plasma was covered with a 1.21 density KBr solution in ultracentrifugation tubes. After ultracentrifugation, the upper fraction corresponding to HDL was collected. After isolation, lipoproteins were washed five times with saline EN buffer (0.15 M NaCl, 1 mM EDTA, 0.025% NaN3, pH 7.3) using a centrifugal filtration tube (Amicon Ultra, 3 kDa cut-off, UFC500396). Tubes were centrifuged at 14,000g for 15 minutes at 4\u0026deg;C, and the volume was concentrated to 100 \u0026micro;L. Lipoprotein proteins were then assayed by the bicinchoninic acid method (BCA, Sigma), and HDL purity was verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) with Coomassie blue staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Fast protein Liquid Chromatography (FPLC)\u003c/h2\u003e \u003cp\u003eHDL isolated from each patient was purified in an \u0026Auml;KTA Pure FPLC system (GE Healthcare) on a SuperdexTM 200 Increase 10/300 GL column (10 x 300 mm; Cytiva) in EN saline buffer. HDL was loaded into a 500 \u0026micro;L loop and injected at a flow rate of 0.250 mL/min. Elution was then performed with Saline EN buffer at a constant flow rate of 0.300 mL/min, and fractions were collected every 500 \u0026micro;L. Finally, the column was washed with two column volumes at a flow rate of 0.700 mL/min.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Label-free Quantification by Nano LC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eLabel-free protein quantification was performed on HDL at the time of ICU admission from patients who survived their COVID-19 (n\u0026thinsp;=\u0026thinsp;14) and on HDL from patients who did not survive their severe COVID-19 (n\u0026thinsp;=\u0026thinsp;15).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. HDL sample preparation and proteolytic digestion\u003c/h2\u003e \u003cp\u003eThe concentration of total HDL protein was measured using the BCA assay, with bovine serum albumin serving as the standard. As previously described [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], 5 \u0026micro;g HDL was combined with 25 \u0026micro;L digestion buffer (6 M urea, 50 mM ammonium bicarbonate) and denatured at 80\u0026deg;C for 10 minutes for sample preparation. Dithiothreitol was used for sample reduction, followed by iodoacetamide alkylation. Upon alkylation, the samples were diluted with four volumes of 50 mM ammonium bicarbonate to reduce the urea concentration. The HDLs were digested with 3 \u0026micro;g of ditosylphenylalanyl chloromethyl ketone trypsin (TPCK-treated Sigma) overnight at 37\u0026deg;C. Tryptic digestion was halted with 2% TFA and incubated at 4\u0026deg;C for 15 minutes. Subsequently, samples were centrifuged at 14,000 g for 15 minutes at 10\u0026deg;C and the desalted peptides in the supernatant were collected on peptide desalting spin columns (Thermo Fisher Scientific Pierce, part number 2162704). Peptides were eluted using a solution of 50% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA), followed by drying with a Speed Vacuum. Then, 20 \u0026micro;L of 40% ACN with 0.1% TFA was used to elute the peptide, which was subsequently vacuum-dried at 60\u0026deg;C prior to mass spectrometric analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Nano LC-MS/MS method\u003c/h2\u003e \u003cp\u003eThe method was carried out with two solvents, solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in 80% ACN). Digested peptides were solubilized in 20 \u0026micro;L of 4% ACN with 0.1% TFA. Mass spectrometry analysis was performed by nano-LC on a Thermo Fisher Ultimate 3000 Series NCS-3500 RS coupled to a nanospray ionization (NSI)-Q-Orbitrap mass spectrometer (Q Exactive Plus, Thermo Fisher Scientific, Bremen, Germany) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Briefly, 5 \u0026micro;L of samples were injected into a C18 LC-EASY-spray column (2 \u0026micro;m, 100 \u0026Aring;, 75 \u0026micro;m x 50 cm, Thermo Fisher Scientific). Peptides were eluted on two linear elution gradients starting from 4\u0026ndash;25% solution B (0.1% formic acid in 80% ACN) until 103 min, and then from 25\u0026ndash;40% solution for 20 min. The column was then washed for 5 min with 90% solution B and equilibrated with 4% solution B for 20 min. Solution A was composed of 0.1% formic acid in water. To minimize contamination between samples, two blank runs containing solution A were conducted after every sample run. The flow rate was maintained at 0.300 \u0026micro;L/min, and the column temperature was maintained at 40\u0026deg;C. The NSI was set at 1.8 kV, the capillary temperature was set at 275\u0026deg;C, and the S lens RF level was set at 30% for mass spectrometry parameters.\u003c/p\u003e \u003cp\u003eThe mass spectra were obtained in positive ion mode using data-dependent acquisition within the m/z range of 350 to 2,000. Full scan mass spectra were acquired at a resolving power of 70,000 (at m/z 400) using automatic gain control (AGC) at a set value of 5e5 and a maximum injection time (Max IT) of 100 ms. MS/MS spectra of the top ten most intense precursor ions were acquired at a resolving power of 35,000 (at m/z 400) with an AGC set to 1e5 and a maximum IT of 100 ms. Peptide fragmentation was performed by setting the normalized collision energy to 28. Precursor ions were excluded dynamically for 60 seconds to prevent peptide selection repetition. The Orbitrap's performance was assessed weekly, and the mass spectrometer underwent external calibration before analysis using the LTQ ESI positive ion calibration solution (Pierce\u0026trade;). XCalibur 4.2.47 software (Thermo Fisher Scientific) was used to collect the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3. Protein identification and quantification\u003c/h2\u003e \u003cp\u003eProtein identification and quantification were performed automatically from the raw files with Proteome discoverer software (version 2.2.2.0). MS and MS/MS spectra were compared to the Uniprot Human reference proteome database containing only canonical sequences (26 559 sequences; 01 July 2021) using the SEQUEST HT search engine. The following features were defined in SEQUEST HT for identification: variable modifications are methionine oxidation and N-terminal acetylation of proteins, then, a fixed modification sets the carbamidomethylation of proteins and the specific enzyme selected is trypsin with two miscleavage allowed. A mass tolerance of 10 ppm for precursor ions and 0.02 Da for-product ions was used in SEQUEST HT. A decoy database search strategy with target inversion was applied with a false discovery rate (FDR) set at 1% for proteins and peptides. Then, only proteins with at least one unique peptide with a Sequest Score (Xcorr)\u0026thinsp;\u0026ge;\u0026thinsp;1.5 were used and for Xcorr\u0026thinsp;\u0026lt;\u0026thinsp;1.5, manual verification of peptide identification was performed on the MS/MS spectrum. Identified peptides were quantified by label-free using the peak intensity of a validated peptide (unique and razor) for a given protein. After quantification, all proteins with more than 60% missing value were not considered in the analysis. For proteins with \u0026le;\u0026thinsp;60% missing values, they were replaced by the IQR of the corresponding proteins in the respective groups. The list of identified and quantified proteins and peptides is available in the supplemental table.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4. Data processing\u003c/h2\u003e \u003cp\u003eProteome Discoverer (version 2.2.2.2.0) was utilized for the computations. Protein abundance refers to the mean of the protein intensities observed in all samples among each group (COVID-19 survivors versus non-survivors). Protein intensity was defined as the combination of protein and peptide intensities measured for each individual. All protein intensity values were subjected to logarithmic transformation (base 2). The log2 (Fold Change) is determined by taking the base 2 logarithm of the disparity between the average intensities of a specific protein in COVID-19 patients who did not survive and the average intensities of the same protein in COVID-19 patients who did survive.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test was used to test the normality of the data distributions. Mann-Whitney test or unpaired t-test was then performed, as appropriate, to compare patients who survived COVID-19 with those who did not. A volcano plot was generated by Prism to evaluate differential proteins on the basis of two indicators: the -log10 p-value of the Mann-Whitney test and the log2 (protein abundance fold change of non-survivors and survivors). Marker performance was measured using receiver operating characteristic (ROC) curves. A \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered to indicate a statistically significant result. Statistical analyses were performed using Prism software (GraphPad Software Inc., San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study population\u003c/h2\u003e \u003cp\u003e37 patients were included in this analysis. Survivors (n\u0026thinsp;=\u0026thinsp;18) and non-survivors (n\u0026thinsp;=\u0026thinsp;19) were matched based on age and gender. General characteristics of the patient population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and a flow chart is expressed on \u003cb\u003esupplemental figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and outcomes of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacterisitics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurvivors (INC) (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurvivors (INC) vs\u003c/p\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 [54; 68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 [51; 65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 [57; 68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3047 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9999 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9999 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 [24; 31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 [24; 30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 [25; 33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3677 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Blood pressure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1837 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4951 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming between fist symptoms and hospitalization (days), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 [5; 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 [5; 11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 [5; 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6781 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9999 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroides, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9999 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime between hospitalization and death (days), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 [8; 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 [8; 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.45 [3.09; 3.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28 [2.81; 3.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.62 [3.19; 4.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0426 (*)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 [0.40; 0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 [0.43; 0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 [0.40; 0.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8381 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 [1.37; 2.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98 [1.51; 2.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 [1.34; 2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1100 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 [1.51; 2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71 [1.52; 2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,75 [1.46; 1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0554 (ns)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eBMI: body mass index; TC: total cholesterol; HDL-C: high-density lipoprotein-cholesterol; LDL-C: low-density lipoprotein-cholesterol; TG: triglycerides\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of plasma apolipoprotein concentrations between COVID-19 survivors and non-survivors\u003c/h2\u003e \u003cp\u003eWe used mass spectrometry to measure 14 apolipoproteins and LCAT directly from the plasma of COVID-19 survivors and non-survivors. The following apolipoproteins were measured: Apo(a), Apo As (A-I, A-II, A-IV), Apo B100, Apo Cs (C-I, C-II, C-III), Apo D, Apo E, Apo H, Apo J, Apo L1, and Apo M. The concentration of seven apolipoproteins was significantly reduced in patients who did not survive compared with those who did. These apolipoproteins include Apo A-II (4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76 \u003cem\u003evs.\u003c/em\u003e 2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0147), Apo Cs (C-I (2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 \u003cem\u003evs.\u003c/em\u003e 0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0060), C-II (1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99 \u003cem\u003evs.\u003c/em\u003e 1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0269) and C-III (0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 \u003cem\u003evs.\u003c/em\u003e 0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045), Apo H (11.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45 \u003cem\u003evs.\u003c/em\u003e 9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0351), Apo J (13.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99 \u003cem\u003evs.\u003c/em\u003e 11.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0318) and Apo M (2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 \u003cem\u003evs.\u003c/em\u003e 2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0112). Furthermore, the concentration of LCAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0137) is also significantly diminished in patients who did not survive COVID-19. Results are expressed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Modulation of HDL subpopulations size between survivors and non-survivors COVID-19 patients\u003c/h2\u003e \u003cp\u003eThe different HDL subpopulations size (large \u0026ndash; intermediate \u0026ndash; small) of COVID-19 patients were determined using the Lipoprint HDL tube gel electrophoresis technique. No difference was found between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 HDL proteome analysis of surviving versus non-surviving patients of their COVID-19\u003c/h2\u003e \u003cp\u003eHDL was isolated and prepared for shotgun proteomics analysis from 14 COVID-19 survivor patients and 15 non-survivor COVID-19 patients. Digested peptides obtained from 5 \u0026micro;g of HDL were prepared and analyzed using mass spectrometry combined with NanoLC. A first analysis of the MS/MS spectra by Proteome Discoverer software from the SEQUEST HT database identified 98 proteins that may be associated with HDL from either surviving or non-surviving patients \u003cb\u003e(see Supplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). After verification, our inclusion criteria (\u003cb\u003esee Materials and Methods\u003c/b\u003e) indicate that 72 proteins are actually associated with patient HDL. To compare COVID-19 survivor and non-survivor groups, we conducted a fold change analysis of intensities associated with HDL proteins prior to log base-2 transformation. Out of the 72 proteins identified, the relative abundance of 11 proteins was lower in non-survivor HDL as compared to control HDL. These proteins included alpha-1 antitrypsin (AAT), cathelicidin antimicrobial peptide, complement C4 (A, B), serotransferrin, apolipoprotein C-I, heat shock protein HSP90-beta, protein-glutamine gamma-glutamyltransferase E, centrosomal protein, and vitronectin. In contrast, HDL from non-survivors showed an increased abundance of nine proteins, including serum amyloid A-2, CD99 antigen, mitochondria-associated proteins, and cellular debris (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe intensities of the 72 proteins associated with HDL in the patients were logarithmically transformed to base 2 to facilitate statistical analyses. The results indicate that HDL protein profile displays more significant alterations in non-surviving COVID-19 patients when compared to those who survived. Specifically, there were significant differences in the levels of 24 proteins in the HDL of non-survivors relative to COVID-19 survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). The levels of 19 of these proteins were significantly lower in the HDL of the non-survivors. The mentioned proteins comprise ribosomal protein 60S L7a (RL7A), adenosylhomocysteinase (ALDH1L1), alpha-1-antitrypsin (AAT), alpha-2-antiplasmin (SERPINF2), aminopeptidase N (ANPEP), apolipoprotein (a) (LPA), apolipoprotein H (APOH), 135 kDa centrosomal protein (CEP135), complement C4-A (CO4A), complement component C9 (CO9), haptoglobin-related protein (HPTR), heat shock protein HSP 90-beta (HS90B), prenylcysteine oxidase 1 (PCYOX1), protein S100-A8, protein-glutamine gamma-glutamyltransferase E, paraoxonase serum/lactonase 3 (PON3) and vitronectin (VTNC). However, five proteins are significantly elevated in HDL from the non-surviving group. The proteins that have increased are ATP synthase subunit beta mitochondrial (ATP5E), beta-2-microglobulin (B2M), CD99 antigen (CD99), cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) and secretoglobin family member 1 3A (SCGB3A2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Diagnostic performance via receiver characteristic (ROC) curve\u003c/h2\u003e \u003cp\u003eROC curves were used to assess the diagnostic accuracy of diverse lipid markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Except for TC (AUC\u0026thinsp;=\u0026thinsp;0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0418), the regular lipid markers had areas under the curve (AUC) lower than 0.7 and a \u003cem\u003ep-value\u003c/em\u003e over 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). Plasma apolipoproteins and LCAT provide a AUC score of greater than 0.7 for all except Apo H (AUC\u0026thinsp;=\u0026thinsp;0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0056) and Apo J (AUC\u0026thinsp;=\u0026thinsp;0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0483), which are unreliable indicators of patient outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-L). Among the potential markers identified in HDL particles from COVID-19 patients are Apo H (AUC\u0026thinsp;=\u0026thinsp;0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the 135 kDa CEP135 protein (AUC\u0026thinsp;=\u0026thinsp;0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.0001), all of which have shown significant effectiveness in predicting survival, with an AUC greater than 0.90. When using patient HDL proteins as markers, ATP5E, CO4A, PON3 and ITIH4 demonstrate effectiveness in predicting patient survival, while CO9 display lesser effectiveness with a lower AUC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM-T).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this present study, we have therefore shown that while lipid profiles on admission to ICU for COVID-19 cannot be used to stratify patients who died from those who survived, plasma analysis of certain apolipoproteins and proteomic analysis of HDL seem to be able to do so. Especially, APOH and CEP135 are potentially novel biomarkers for COVID-19 severity linked to HDL proteins.\u003c/p\u003e \u003cp\u003eLipid metabolism plays a key role in SARS-CoV-2 infection. Several hypotheses have been proposed to explain the changes in lipid metabolism, including viral use and modification of lipids to support replication, hemodilution, and increased vascular permeability. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Lowering cholesterol levels in lipoproteins is also aided by the cytokine storm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, while in bacterial sepsis HDL-C concentrations are associated with patient outcome and in particular mortality, these links were not found in COVID-19\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this present study, we confirmed that HDL-C, LDL-C, and TG concentrations are similar between survivors and non-survivors COVID-19 patients. Similarly, Sun et al. reported no disparity in HDL-C, LDL-C, and TC levels between surviving and non-surviving patients with COVID-19, although TG levels were higher in non-surviving patients.\u003c/p\u003e \u003cp\u003eWe have also shown that a crude concentration of HDL-C does not appear to be sufficient to stratify patients during COVID-19. Thus, the first step was to characterize the plasma apolipoproteins which are key regulators of lipid metabolism and have anti-inflammatory, antioxidant and endothelial protective properties [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The mechanisms between apolipoproteins and the severity of COVID-19 are not yet fully understood. However, multiple studies have demonstrated a negative correlation between Apo A-I and Apo B levels and the severity of COVID-19 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In our previous studies, we demonstrated that COVID-19 patients exhibit diminished levels of nine Apo forms, such as Apo A-II, C (I, II), H, J, M, as well as LCAT, alongside elevated levels of Apo E as compared to controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our study confirms the previous one and highlights several apolipoproteins that are significantly more reduced on admission to ICU in patients who will die secondarily, such as Apo A-II, Apo Cs (C-I, C-II and C-III), Apo H, Apo J and Apo M. Most of these apolipoproteins are linked to reduced macrophage function [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cholesterol efflux by macrophages induces production of Apo C through Liver X Receptor (LXR) activation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Reduced Apo C concentrations indicate a decrease in macrophage cholesterol efflux function.\u003c/p\u003e \u003cp\u003eInterestingly, we observed a decreased in Apo A-II but not in Apo A-I concentrations among non-survivors of COVID-19 when compared to survivors. This observation differs from most of the studies reporting essentially a decrease in Apo A-I concentration with an association with worth outcome [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This difference can be attributed to the sample size, which is significantly larger than ours. Moreover, corticosteroid therapy modifies lipidi profile. Our findings show a trend towards reduced Apo A-II concentrations. Our previous studies, however, observed apo A-II concentrations in both HDL and plasma of COVID-19 patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, other research has also seen lower Apo A-II levels in COVID-19 patients [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This decrease in Apo A-II suggests a potential alteration in cholesterol efflux, as well as a decrease in the anti-inflammatory and antioxidant properties of HDL [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is interesting to note that ApoB100 and LCAT were associated with mortality in our last study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] but not in this work. The role of Apo H, also known as beta-2-glycoprotein 1, is not well understood. According to earlier research [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], it has been observed that Apo H levels decrease in COVID-19 patients. This decrease in Apo H could potentially create a prothrombotic environment in COVID-19, leading to antiphospholipid syndrome. This decrease in Apo H could potentially create a prothrombotic environment in COVID-19, due to an antiphospholipid syndrome. Furthermore, Apo H seems to capture the SARS-CoV-2 virus, leading to the hypothesis that the virus utilizes Apo H [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Clusterin, also known as Apo J, acts as an inhibitor of the complement system [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Reductions in the concentration of Apo J led to complement system activation, which then causes a prothrombotic environment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study indicates also that Apo M levels were decreased in COVID-19 patients who did not survive. A study conducted by Marfia et al [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] showed that the concentration of sphingosine-1-P (S1P) was closely related to the concentration of apo M and was able to accurately predict the prognosis of COVID-19 patients. Our findings reaffirm that Apo M can serve as a potential marker for assessing COVID-19 severity. Furthermore, Apo M depletion is associated with loss of anti-inflammatory and anti-atherogenic functions of S1P/ApoM complex [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Therefore, deficiency of these apolipoproteins promotes an overall thromboinflammatory environment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed a decrease in 19 proteins and an increase in five proteins in the HDL of non-surviving COVID-19 patients. Among the proteins increased in the HDL of COVID-19 patients, the CD99 antigen plays a key role in the T-cell adhesion process. CD99 is mainly localized on T lymphocytes and endothelial cells. The level of this particular protein is lower in COVID-19 patients with severe symptoms, indicating reduced endothelial integrity and recruitment of monocytes, neutrophils and T cells [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A hypothesis to explain its increase in HDL from non-survivors of COVID-19 is that it is captured by the latter. HDL levels in non-survivors are found to be enriched with mitochondrial ATP synthase subunit beta, which interacts with the ecto-F1-ATPase receptor on the cell surface [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. SARS-CoV-2 is able to interact with ecto-F1-ATPase and ATP synthase [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. An elevated beta-2-microglobulin has been identified as a potential biomarker of COVID-19 severity [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], which may explain the elevated levels in the HDL of non-surviving COVID-19 patients. Interestingly, the HDL proteins of non-survivors of COVID-19 exhibit a decrease in AAT, I'TIH4, Complement C4A, and C9, which are normally increased in plasma as COVID-19 severity increases. This increase could be explained by an increase in the compounds and proteins of the acute phase complement and a decrease in the proteins involved in the coagulation cascade [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Among the coagulation-related proteins, the levels of beta 2 glycoprotein 1 (Apo H) are reduced in the HDL of COVID-19 non-survivors, suggesting a disturbance in the coagulation cascades [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The remodeling of high-density lipoprotein in patients during the COVID-19 pandemic suggests a loss of high-density lipoprotein (HDL) functionality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study is the first to investigate LDL and HDL distribution changes during COVID-19 using tube gel electrophoresis (Lipoprint) among patients who survived or did not survive the disease. The distribution of lipoproteins was similar regardless of patient outcome. COVID-19 brings about a modification in the lipoprotein subpopulations' distribution. This change is characterized by a reduction in the concentrations of small, intermediate, and large subpopulations of HDL and large LDL, accompanied by an escalation in small and dense LDL concentrations. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, routine lipid markers in our study demonstrated poor accuracy in assessing disease severity. It is surprising to observe this in light of the association between HDL-C and disease severity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In contrast, apolipoproteins have superior performance to routine lipid assays in assessing patient outcome. This is expected since various studies have shown that apolipoproteins are decreased in COVID-19 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, it seems that HDL-associated Apo H is a more accurate indicator of COVID-19 severity than total plasma Apo H. Nevertheless, these results merit further analysis with more patients included.\u003c/p\u003e \u003cp\u003eThe primary obstacle we encountered was the small number of available samples, which hindered our ability to conduct experiments on a representative sample of the population under study. Nevertheless, our preliminary results are promising. This study is focused on a modest sample of 37 patients during the initial wave of COVID-19, providing supplementary information to the current knowledge on COVID-19. Another limitation is the low levels of HDL volume of patients included in our study. It would be pertinent to evaluate the lipoproteins' functionality to ascertain the effect of changes in their proteome. Further comparative studies carried out in various centers can help confirm and validate the conclusions presented in our research.\u003c/p\u003e \u003cp\u003eIn conclusion, in a cohort of patients hospitalized in ICU for severe COVID-19, we demonstrated major differences in plasma apolipoproteins and in protein composition of HDL by comparing surviving and non-surviving patients. These markers, measured on admission to ICU, also appear to be more predictive of mortality than BASIC lipid concentrations such as HDL-C. Nevertheless, these results should be interpreted with caution given the small number of patients included in this study. In this context, it is important to conduct a high-powered multicenter study in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFB is supported by a grant from the R\u0026eacute;gion R\u0026eacute;union for their PhD (European Union-Reunion Region). This research was funded by FEDER \"LIPICOR\" RE0027600 and \"TFORCE-COVIR\" RE0027601 (EU\u0026mdash;R\u0026eacute;gion R\u0026eacute;union\u0026mdash;French State national counterpart), and by the BIOST F\u0026eacute;d\u0026eacute;ration.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOM and ST designed the study. FB, BV acquired the data. FB, OM, ST analyzed the data. FB, ST, OM wrote the manuscript. PR, OM, ST, AF and SJ critically reviewed the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe sponsor of the LIPICOR and T-FORCE COVIR studies were respectively Assistance Publique - Hopitaux de Paris (D\u0026eacute;l\u0026eacute;gation \u0026agrave; la Recherche Clinique et \u0026agrave; l'Innovation) and FEDER R\u0026eacute;gion R\u0026eacute;union.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus\u0026ndash;Infected Pneumonia. N Engl J Med. 2020;382:1199\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2001316\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2001316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu N, Zhang D, Wang W, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382:727\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2001017\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2001017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Tan Y, Ling Y, et al. Viral and host factors related to the clinical outcome of COVID-19. Nature. 2020;583:437\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-2355-0\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2355-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO Working Group on the Clinical Characterisation and Management of COVID-19 infection. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20:e192\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1473-3099(20)30483-7\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(20)30483-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaninov N. In the eye of the COVID-19 cytokine storm. Nat Rev Immunol. 2020;20:277\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41577-020-0305-6\u003c/span\u003e\u003cspan address=\"10.1038/s41577-020-0305-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu B, Huang S, Yin L. The cytokine storm and COVID-19. J Med Virol. 2021;93:250\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jmv.26232\u003c/span\u003e\u003cspan address=\"10.1002/jmv.26232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Shen C, Li J et al. (2020) Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcom.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJonas A, Phillips MC. Lipoprotein structure. Biochemistry of Lipids, Lipoproteins and Membranes. Elsevier; 2008. pp. 485\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominiczak MH, Caslake MJ. Apolipoproteins: metabolic role and clinical biochemistry applications. Ann Clin Biochem. 2011;48:498\u0026ndash;515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1258/acb.2011.011111\u003c/span\u003e\u003cspan address=\"10.1258/acb.2011.011111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlaupovic P. (1996) [2] Significance of apolipoproteins for structure, function, and classification of plasma lipoproteins. In: Methods in Enzymology. Academic Press, pp 32\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(2020) Hypolipidemia is associated with the severity of COVID-19. J Clin Lipidol 14:297\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacl.2020.04.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2020.04.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmas T, Malik J, Alsubai AK, et al. Effect of COVID-19 on lipid profile parameters and its correlation with acute phase reactants: A single-center retrospective analysis. Annals Med Surg. 2022;78:103856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amsu.2022.103856\u003c/span\u003e\u003cspan address=\"10.1016/j.amsu.2022.103856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegue F, Chemello K, Veeren B, et al. Plasma Apolipoprotein Concentrations Are Highly Altered in Severe Intensive Care Unit COVID-19 Patients: Preliminary Results from the LIPICOR Cohort Study. Int J Mol Sci. 2023;24:4605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms24054605\u003c/span\u003e\u003cspan address=\"10.3390/ijms24054605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka S, Tymowski CD, Assadi M, et al. Lipoprotein concentrations over time in the intensive care unit COVID-19 patients: Results from the ApoCOVID study. PLoS ONE. 2020;15:e0239573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0239573\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0239573\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang Y, Lu R, et al. Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta. 2021;517:66\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cca.2021.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2021.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlloque-Badaracco JR, Hernandez-Bustamante EA, Herrera-A\u0026ntilde;azco P, Benites-Zapata VA. Prognostic value of apolipoproteins in COVID-19 patients: A systematic review and meta-analysis. Travel Med Infect Dis. 2021;44:102200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tmaid.2021.102200\u003c/span\u003e\u003cspan address=\"10.1016/j.tmaid.2021.102200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran-Dinh A, Diallo D, Delbosc S, et al. HDL and endothelial protection. Br J Pharmacol. 2013;169:493\u0026ndash;511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bph.12174\u003c/span\u003e\u003cspan address=\"10.1111/bph.12174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Zhang Q, Zhao X, et al. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study. Lipids Health Dis. 2020;19:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12944-020-01382-9\u003c/span\u003e\u003cspan address=\"10.1186/s12944-020-01382-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka S, Couret D, Tran-Dinh A, et al. High-density lipoproteins during sepsis: from bench to bedside. Crit Care. 2020;24:134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-020-02860-3\u003c/span\u003e\u003cspan address=\"10.1186/s13054-020-02860-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegue F, Tanaka S, Mouktadi Z, et al. Altered high-density lipoprotein composition and functions during severe COVID-19. Sci Rep. 2021;11:2291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-81638-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-81638-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazdanpanah Y, French COVID, cohort investigators and study group. Impact on disease mortality of clinical, biological, and virological characteristics at hospital admission and overtime in COVID-19 patients. J Med Virol. 2021;93:2149\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jmv.26601\u003c/span\u003e\u003cspan address=\"10.1002/jmv.26601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka S, Begue F, Veeren B, et al. First Recombinant High-Density Lipoprotein Particles Administration in a Severe ICU COVID-19 Patient, a Multi-Omics Exploratory Investigation. Biomedicines. 2022;10:754. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biomedicines10040754\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines10040754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong J-W, Lam SM, Fan X, et al. Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis. Cell Metabol. 2020;32:188\u0026ndash;e2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2020.06.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2020.06.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian S, Hu W, Niu L, et al. Pulmonary Pathology of Early-Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients With Lung Cancer. J Thorac Oncol. 2020;15:700\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtho.2020.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2020.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorokin AV, Karathanasis SK, Yang Z-H, et al. COVID-19\u0026mdash;Associated dyslipidemia: Implications for mechanism of impaired resolution and novel therapeutic approaches. FASEB J. 2020;34:9843\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1096/fj.202001451\u003c/span\u003e\u003cspan address=\"10.1096/fj.202001451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun JT, Chen Z, Nie P et al. (2020) Lipid Profile Features and Their Associations With Disease Severity and Mortality in Patients With COVID-19. Front Cardiovasc Med 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampedro-Nu\u0026ntilde;ez M, Aguirre-Moreno N, Garc\u0026iacute;a-Fraile Fraile L, et al. Finding answers in lipid profile in COVID-19 patients. Endocrine. 2021;74:443\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12020-021-02881-0\u003c/span\u003e\u003cspan address=\"10.1007/s12020-021-02881-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChien J-Y, Jerng J-S, Yu C-J, Yang P-C. Low serum level of high-density lipoprotein cholesterol is a poor prognostic factor for severe sepsis*. Crit Care Med. 2005;33:1688\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.CCM.0000171183.79525.6B\u003c/span\u003e\u003cspan address=\"10.1097/01.CCM.0000171183.79525.6B\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka S, Stern J, Bouzid D, et al. Relationship between lipoprotein concentrations and short-term and 1-year mortality in intensive care unit septic patients: results from the HIGHSEPS study. Ann Intensiv Care. 2021;11:11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13613-021-00800-0\u003c/span\u003e\u003cspan address=\"10.1186/s13613-021-00800-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlage S, Gnewuch C, Liebisch G, et al. Changes in HDL-associated apolipoproteins relate to mortality in human sepsis and correlate to monocyte and platelet activation. Intensive Care Med. 2009;35:1877\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00134-009-1609-y\u003c/span\u003e\u003cspan address=\"10.1007/s00134-009-1609-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta A, Shapiro MD. Apolipoproteins in vascular biology and atherosclerotic disease. Nat Reviews Cardiol. 2022;19:168\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-021-00613-5\u003c/span\u003e\u003cspan address=\"10.1038/s41569-021-00613-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoynard T, Deckmyn O, Rudler M, et al. Performance of serum apolipoprotein-A1 as a sentinel of Covid-19. PLoS ONE. 2020;15:e0242306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0242306\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0242306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Yang Y, Fan L, et al. Low serum level of apolipoprotein A1 may predict the severity of COVID-19: A retrospective study. J Clin Lab Anal. 2021;35:e23911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcla.23911\u003c/span\u003e\u003cspan address=\"10.1002/jcla.23911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen B, Yi X, Sun Y, et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020;182:59\u0026ndash;e7215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2020.05.032\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.05.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMak PA, Laffitte BA, Desrumaux C, et al. Regulated Expression of the Apolipoprotein E/C-I/C-IV/C-II Gene Cluster in Murine and Human Macrophages: A CRITICAL ROLE FOR NUCLEAR LIVER X RECEPTORS α AND β*. J Biol Chem. 2002;277:31900\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M202993200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M202993200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimhofer T, Lodge S, Whiley L, et al. Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection. J Proteome Res. 2020;19:4442\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jproteome.0c00519\u003c/span\u003e\u003cspan address=\"10.1021/acs.jproteome.0c00519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelchior JT, Street SE, Andraski AB, et al. Apolipoprotein A-II alters the proteome of human lipoproteins and enhances cholesterol efflux from ABCA1. J Lipid Res. 2017;58:1374\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1194/jlr.M075382\u003c/span\u003e\u003cspan address=\"10.1194/jlr.M075382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZvintzou E, Xepapadaki E, Kalogeropoulou C, et al. Pleiotropic effects of apolipoprotein A-Ⅱ on high-density lipoprotein functionality, adipose tissue metabolic activity and plasma glucose homeostasis. J Biomed Res. 2020;34:14\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7555/JBR.33.20190048\u003c/span\u003e\u003cspan address=\"10.7555/JBR.33.20190048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerrano M, Espinosa G, Lalueza A, et al. Beta-2-Glycoprotein-I Deficiency Could Precipitate an Antiphospholipid Syndrome-like Prothrombotic Situation in Patients With Coronavirus Disease 2019. ACR Open Rheumatol. 2021;3:267\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/acr2.11245\u003c/span\u003e\u003cspan address=\"10.1002/acr2.11245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang J, Jang H, Kim TH, et al. Accurate Diagnosis of COVID-19 from Self-Collectable Biospecimens Using Synthetic Apolipoprotein H Peptide-Coated Nanoparticle Assay. Anal Chem. 2022;94:17186\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.analchem.2c03813\u003c/span\u003e\u003cspan address=\"10.1021/acs.analchem.2c03813\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTschopp J, Chonn A, Hertig S, French LE. Clusterin, the human apolipoprotein and complement inhibitor, binds to complement C7, C8 beta, and the b domain of C9. J Immunol. 1993;151:2159\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bont CM, Boelens WC, Pruijn GJM. NETosis, complement, and coagulation: a triangular relationship. Cell Mol Immunol. 2019;16:19\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41423-018-0024-0\u003c/span\u003e\u003cspan address=\"10.1038/s41423-018-0024-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Xu Z, Liu Z. Impact of Neutrophil Extracellular Traps on Thrombosis Formation: New Findings and Future Perspective. Front Cell Infect Microbiol. 2022;12:910908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2022.910908\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2022.910908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarfia G, Navone S, Guarnaccia L, et al. Decreased serum level of sphingosine-1-phosphate: a novel predictor of clinical severity in COVID-19. EMBO Mol Med. 2021;13:e13424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15252/emmm.202013424\u003c/span\u003e\u003cspan address=\"10.15252/emmm.202013424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfrum C, Poy MN, Stoffel M. Apolipoprotein M is required for prebeta-HDL formation and cholesterol efflux to HDL and protects against atherosclerosis. Nat Med. 2005;11:418\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nm1211\u003c/span\u003e\u003cspan address=\"10.1038/nm1211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao Mattisson I, Christoffersen C. Apolipoprotein M and its impact on endothelial dysfunction and inflammation in the cardiovascular system. Atherosclerosis. 2021;334:76\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atherosclerosis.2021.08.039\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2021.08.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartins-Gon\u0026ccedil;alves R, Hottz ED, Bozza PT. Acute to post-acute COVID-19 thromboinflammation persistence: Mechanisms and potential consequences. Curr Res Immunol. 2023;4:100058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crimmu.2023.100058\u003c/span\u003e\u003cspan address=\"10.1016/j.crimmu.2023.100058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiwy J, Wendt R, Albalat A, et al. CD99 and polymeric immunoglobulin receptor peptides deregulation in critical COVID-19: A potential link to molecular pathophysiology? Proteomics. 2021;21:e2100133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pmic.202100133\u003c/span\u003e\u003cspan address=\"10.1002/pmic.202100133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez LO, Najib S, Perret B, et al. Ecto-F1-ATPase/P2Y pathways in metabolic and vascular functions of high density lipoproteins. Atherosclerosis. 2015;238:89\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atherosclerosis.2014.11.017\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2014.11.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanfoli I, Esposito A. Beneficial effect of polyphenols in COVID-19 and the ectopic F1FO‐ATP synthase: Is there a link? J Cell Biochem. 2022;123:1281\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcb.30306\u003c/span\u003e\u003cspan address=\"10.1002/jcb.30306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamachandran K, Maity S, Muthukumar AR, et al. SARS-CoV-2 infection enhances mitochondrial PTP complex activity to perturb cardiac energetics. iScience. 2022;25:103722. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isci.2021.103722\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2021.103722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConca W, Alabdely M, Albaiz F, et al. Serum β2-microglobulin levels in Coronavirus disease 2019 (Covid-19): Another prognosticator of disease severity? PLoS ONE. 2021;16:e0247758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0247758\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0247758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvet J, Berenguer-Llergo A, Gay M, et al. Biomarker candidates for progression and clinical management of COVID-19 associated pneumonia at time of admission. Sci Rep. 2022;12:640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-04683-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-04683-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedjeral-Thomas NR, Troldborg A, Hansen AG, et al. Protease inhibitor plasma concentrations associate with COVID-19 infection. Oxf Open Immunol. 2021;2:iqab014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oxfimm/iqab014\u003c/span\u003e\u003cspan address=\"10.1093/oxfimm/iqab014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeyer PE, Arend FM, Doll S, et al. High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion. EMBO Mol Med. 2021;13:e14167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15252/emmm.202114167\u003c/span\u003e\u003cspan address=\"10.15252/emmm.202114167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallout RA, Kong H, Sampson M, et al. The NIH Lipo-COVID Study: A Pilot NMR Investigation of Lipoprotein Subfractions and Other Metabolites in Patients with Severe COVID-19. Biomedicines. 2021;9:1090. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biomedicines9091090\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines9091090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Chen D, Wu L, et al. Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection. Clin Chim Acta. 2020;510:105\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cca.2020.07.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2020.07.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, mortality, HDL particle, apolipoproteome, apolipoprotein, proteomic, Intensive care unit","lastPublishedDoi":"10.21203/rs.3.rs-4978039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4978039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSARS-CoV-2 infection affects both lipid metabolism and lung function. The severity of the disease has been associated with reduced levels of both high-density lipoprotein (HDL) and low-density lipoprotein cholesterol. Despite the crucial role that these nanoparticles play in SARS-CoV-2 infection, few studies have examined their structure during COVID-19 beyond HDL quantity.\u003c/p\u003e\n\u003cp\u003eThe study aimed to assess apolipoprotein levels in COVID-19 patients who either survived or died following ICU admission. In addition, ICU survivors and non-survivors were compared for HDL particle size and proteome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Between February and April 2020, our study enrolled 37 COVID-19 patients upon their intensive care unit admission. Among them, 18 survived the disease, while 19 succumbed to it. We used mass spectrometry to assess plasma levels of 14 apolipoproteins and LCAT. Additionally, we analyzed HDL subpopulation distribution by utilizing native polyacrylamide gel electrophoresis. HDL particles were isolated from both surviving and non-surviving patients using ultracentrifugation, followed by characterization of their proteomes with NanoLC-MS/MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Plasma apolipoproteins, including Apo A-II, Apo Cs (I, II, III), Apo H, Apo J, Apo M, and LCAT, were decreased in patients who did not survive COVID-19. However, no alterations were noted in the distribution of HDL subpopulations in relation to mortality. HDL composition was further altered based on mortality, displaying a decline in Apo H and paraoxonase 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: In conclusion, we have shown an alteration in plasma apolipoproteins and HDL composition between surviving COVID-19 patients and non-survivors. Some markers, such as Apo H, are more predictive than baseline lipid concentrations such as HDL-C.\u003c/p\u003e","manuscriptTitle":"HDL proteome and apolipoproteins concentrations in severe ICU COVID-19 patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 13:44:10","doi":"10.21203/rs.3.rs-4978039/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T13:00:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-10T12:57:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T10:02:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T08:20:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-02T18:25:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163566722083737634474351338202614787462","date":"2024-08-30T18:10:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285310498964467090298236019850781214901","date":"2024-08-30T10:50:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15264198840826720577840197858624211483","date":"2024-08-29T12:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55007996911995819334803769121504574706","date":"2024-08-29T08:19:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224880384136420838136084837031254126367","date":"2024-08-29T07:49:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296291577733769947560188786467223970149","date":"2024-08-28T11:46:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-28T08:01:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-26T15:20:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-26T13:25:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2024-08-26T12:42:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42985409-5a53-4e22-8906-96baa3844cce","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:02:32+00:00","versionOfRecord":{"articleIdentity":"rs-4978039","link":"https://doi.org/10.1186/s12944-024-02381-w","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-01-31 15:57:34","publishedOnDateReadable":"January 31st, 2025"},"versionCreatedAt":"2024-10-16 13:44:10","video":"","vorDoi":"10.1186/s12944-024-02381-w","vorDoiUrl":"https://doi.org/10.1186/s12944-024-02381-w","workflowStages":[]},"version":"v1","identity":"rs-4978039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4978039","identity":"rs-4978039","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}