COVID-19 Cohort. We retrospectively analyzed data from COVID-19-confirmed hospitalized patients from D’Or São Luiz Network Hospitals in Rio de Janeiro (Brazil) and subject to clinically indicated CSF analysis from April to November 2020. Suspected COVID-19 was defined based on symptoms clinically compatible with COVID-19.10 Confirmation of COVID-19 was based on detecting SARS-CoV-2’s E, RDRP, or N genes RNA by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) assays (Allpex 2019 n-CoV assay #RP10252W) on the nasopharyngeal or nasal swabs or by blood detection of anti-SARS-CoV-2 IgG/IgM antibodies. The National Commission for Research Ethics (CONEP) from the Brazilian Ministry of Health and the Committee for Research Ethics of D’Or Institute of Research and Education (IDOR) approved the study protocol and all amendments, CAAE #29496920.8.0000.5262; CAAE #41576620.7.0000.5249. An independent data board reviewed clinical, laboratory, and imaging data with access to unblinded data.
COVID-19 severity classification followed the “Ordinal Scale for Clinical Improvement” proposed by a special World Health Organization (WHO) committee.11 Mild disease included hospitalized patients who did not receive oxygen therapy or received oxygen by masks or nasal cannula. Severe disease included hospitalized patients who required at least one of the following treatments: oxygen by non-invasive ventilation; high-flow oxygen; intubation; and mechanical ventilation with or without additional organ support.
Clinical data
Clinical data were extracted from the patients’ medical records, including medical history and comorbidities inquired by the medical team to patients and relatives at hospital admission anamnesis, clinical characteristics at hospital admission, in-hospital symptoms, complications, and medication used, according to an approved clinical research form (CRF)12 and clinical characterization protocol (CCP)13 created by members of the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) in collaboration with the World Health Organization. Detailed laboratory results were also collected, and qualitative and quantitative estimations of lung disease caused by COVID-19 were determined by computerized tomography (CT) scan examination.
An experienced neurologist reviewed patients’ clinical data and defined a major neurological complaint. Encephalitis was defined as presenting altered mental status (altered level of consciousness, lethargy, or personality change) for at least 24h and two or more of the following criteria: a) seizures not attributable to a pre-existing condition; b) new-onset focal neurologic finding; c) elevated CSF white blood cell count (above 5 cells/mm3); d) acute neuroimaging finding consistent with encephalitis; e) abnormal electroencephalography consistent with encephalitis, or f) fever (above 38°C) within 3-days of symptom onset.14
Neuroimaging protocols and analysis
All neuroimaging data were acquired during hospitalization. Brain CT and/or MRI images were analyzed by two experienced neuroradiologists blinded to patient clinical data, according to a pre-established structured report. Patients with normal brain scans or findings not associated with COVID-19 were classified as negative findings. Patients solely presenting intracranial hypertension signs were assigned to a specific group defined as moderate neuroimaging findings. Patients with other discoveries related to COVID-19 encephalitis, ischemic lesions, stroke, or demyelinating lesions (associated or not to intracranial hypertension) were read as pronounced neuroimaging changes and assigned to another group. CT scans (with or without contrast administration) were acquired in multidetector scanners with volumetric data acquisition. MRI acquisitions were performed on 1.5 Tesla equipment, following standardized multiplanar protocols for brain, spine, and/or vessel studies.
Follow-up
Surviving patients’ or family relatives’ contact information was retrieved from medical and billing records. Seven patients could not be reached, three refused to participate in the interview, and one presented communication impairments that precluded participation. A follow-up electronic survey (Portuguese version) assessing post-COVID clinical complaints and events was elaborated based on the ISARIC Post-COVID-19 Health and Wellbeing Follow-Up Survey that can be accessed at https://isaric.org/wp-content/uploads/2021/03/Initial-Freestanding-survey.pdf. All patients’ consent was obtained for follow-up interview participants.
Laboratory Data
Blood tests closest to CSF sampling were retrieved for all patients. COVID-19 hyperinflammatory syndrome score (cHIS)15 was computed considering reported fever at admission or hospitalization, and blood indicators of cytokinaemia (C-reactive protein (CRP) above 15 mg/dL or triglyceride concentration above 150 mg/dL or blood IL-6 concentration above 15 pg/mL); hematological dysfunction (neutrophil to lymphocyte ratio (NLR) above 10, less than 110 billion/L platelets, or hemoglobin bellow 9.2 g/dL); coagulopathy (D-dimer concentration above 1.5 µg/mL); hepatic injury (lactate dehydrogenase concentration above 400 U/L or aspartate aminotransferase above 100 U/L); macrophage activation (ferritin concentration above 700 µg/L). Systemic inflammatory index (SII)16 was calculated by multiplying neutrophils and platelet counts and dividing them by lymphocyte counts.
Cognitively healthy control, amnestic mild cognitive impairment (aMCI), and Alzheimer’s disease (AD) cohort. COVID-19 patients' CSF molecular biomarkers levels were compared with a pre-pandemic prospective and longitudinal aged cohort17 (study protocol and amends were approved by the CONEP from the Brazilian Ministry of Health and the Committee for Research Ethics of IDOR, CAAE #47163715.0.0000.5249). Inclusion criteria were 50 years old or above and eight years of scholarly or above. Participants were evaluated in a battery of cognitive and psychological tests that based their diagnosis as bellow described. For comparison with COVID-19 groups, we included participants diagnosed as cognitively healthy control, aMCI, or AD and that collected CSF samples.
Cognitive and behavioral assessments
The cognitive evaluation was performed by neuropsychologists using validated versions of tests. Normative data for the Brazilian population according to age and schooling was applied to define cases with impairment in each task. The Mini-Mental State Examination (MMSE) was adopted to measure global cognitive performance.18,19 Episodic memory was assessed using the Rey-Auditory Verbal Learning test (RAVLT).20 The Trail-Making Test was used to evaluate visual tracking/cognitive speed (TMT part A) and cognitive flexibility (TMT part B).21 Working memory was measured using the Backwards Digit Span test.22 The following scales were applied: the Geriatric Depression Scale23 and the Geriatric Anxiety Inventory.24
Diagnosis
Dementia was diagnosed according to the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for Major Neurocognitive Disorder25, as presenting a cognitive concern from the individual, a knowledgeable informant, or the clinician; evidence of substantial impairment in at least one cognitive domain, demonstrated by neuropsychological assessment; and significant functional difficulties, evidenced by clinical interview. The DSM-5 criteria for Mild Neurocognitive Disorder25 based aMCI diagnosis as presenting a cognitive concern of the individual, a knowledgeable informant, or the clinician; evidence of mild impairment in at least one cognitive domain, demonstrated by neuropsychological assessment; and overall preserved functional abilities, as evidenced by clinical interview. A probable AD etiology was indicated by the presence of all the following25: prominent impairment in memory tasks; insidious onset and gradual progression of symptoms; symptoms not consistent with cerebrovascular disease, psychiatric disorders, delirium, traumatic brain injury, normal pressure hydrocephalus (NPH), reversible dementias, neuroinfectious diseases or other neurodegenerative conditions, according to clinical assessment, laboratory exams (complete blood count, VDRL, thyroid function tests, vitamin B-12, and folate level tests) and brain MRI examination by board-certified radiologists.
NPH pre-pandemic uninfected controls. CSF samples from ten donors diagnosed with NPH and subject to lumbar puncture to drain fluid excess in 2019 were used as uninfected control in proteomics and SARS-CoV-2 spike protein Multiple Reaction Monitoring analysis.
Healthy donors with no clinical history compatible with COVID-19. Plasma samples from five healthy and uninfected donors were used as controls in serum cytokine panel evaluation by Luminex. Volunteers worked in a clinical laboratory and tested negative on weekly nasopharyngeal screening for SARS-CoV-2 mRNA by RT-qPCR since the arrival of COVID-19 in Brazil until donating blood samples.
CSF collection and routine laboratory analysis.
Irrespective of patients' grouping, after lumbar puncture, CSF was immediately processed for routine laboratory analysis consisting of cell counts, total protein, glucose, lactate, microbiological analysis, and the opening pressure estimation. For the COVID-19 group, 14 CSF samples were also investigated for the presence of SARS-CoV-2 RNA and other neuropathogens using the Biomanguinhos (E + P1) RT-qPCR kit (FIOCRUZ, Brazil), XGEN Master COVID-19 (Mobius Brazil), XGEN Viral Meningitis Panel (Mobius, Brazil) or FilmArray Meningitis/Encephalitis Panel (bioMérieux, Brazil). Oligoclonal bands IgG investigation results were available for four COVID-19 patients (HYDRASYS FOCUSING, Sebia, France). For all patients from all groups, the remaining cell-free CSF supernatants were stored in polypropylene tubes and immediately frozen at -80ºC until used for molecular analysis described next.
Molecular Analysis
Blood cytokine evaluation.
We retrieved 16 serum samples from COVID-19 patients collected during hospitalization (median 0 days, interquartile range 0-5.5, maximum 44 days apart CSF collection date), stored in polypropylene tubes, and frozen at -80ºC until molecular analysis. Control serum came from the above specified five healthy donors (negative RT-qPCR for SARS-CoV-2). Before assays, samples were thawed and kept on ice. Cytokines were measured using the Human Cytokine/Chemokine Magnetic Bead Panel kit (Millipore, #HCYTOMAG-60K) following manufacturers’ instructions. Results were read at a MagPix Luminex xMAP instrument and analyzed with XPonent software. Data is represented as a fold change computed by subtracting the control mean value (X) from COVID-19 individual values (Y) and dividing by the control mean, [(Y-X)/X]. Raw data (pg/mL) was used for statistical analysis. Multiple comparisons are corrected by FDR, Q = 5%. IL3 was only detected in one COVID-19 sample and was further excluded from the analysis.
CSF molecular biomarkers quantification.
Before assays, CSF samples were thawed and kept on ice. Samples were tested undiluted. IL6 levels were measured using a Human IL6 Quantikine ELISA kit (R&D Systems, #D6050). Before analysis, samples were diluted (1:2) in a solution provided by the kit (RD6F). CSF Alzheimer’s disease (AD)-related biomarkers were quantified using EuroImmun ELISA kits with the manufacturer’s sample dilution indications (Aβ1–40 # EQ 6511-9601-L, Aβ1–42 EQ 6521-9601-L, Tau EQ 6531-9601-L, and pTau181 EQ 6591-9601-L). Samples, quality controls, and calibrators were run in duplicates for all targets, following manufacturers’ indications. Standard curves were calculated using a 4-parameter logistic regression model. Low undetermined levels were expressed as 0 pg/mL. Two COVID-19 patients (#20 and #25) samples exceeded the total tau standard curve and were defined as the highest value (1321 pg/mL).
LC-MS/MS Shotgun Proteomics Analysis.
COVID-19 and control CSF samples first received a protease inhibitor cocktail (Halt Protease Inhibitor Cocktail, Thermo Scientific, #78430). The CSF samples’ protein amount was quantified using the BCA and diluted 1:1 v/v in buffer (Tris-HCL 100mM and 2M Urea). Aiming to obtain a higher quality of buffer exchange and protein digestion, we performed the FASP protocol for tryptic digestion26 in 20 µg of protein, briefly described in washing steps to buffer exchange in a microcolumn tip (10kDa MW cut off), and tryptic digestion performed in the column. Samples were reduced, alkylated, and later digested using trypsin. Digested peptides from each sample were resuspended in 0.1% FA. The separation of tryptic peptides was performed on an ACQUITY MClass System (Waters Corporation). 1 µg of each digested sample was loaded onto a Symmetry C18 5 µm, 180 µm × 20 mm precolumn (Waters Corp.) used as a trapping column and subsequently separated by a 120-min reversed-phase gradient at 300 nL/min (linear-gradient, 3–55% ACN over 90 min) using an HSS T3 C18 1.8 µm, 75 µm × 150 mm nanoscale and LC column (Waters Corp.) maintained at 30°C. The gradient elution Water-Formic Acid (99.9/0.1, v/v) was used as eluent A and Acetonitrile Formic Acid (99.9/0.1, v/v) as B. The Separated peptides were analyzed by a High Definition Synapt G2-Si Mass spectrometer directly coupled to the chromatographic system. Differential protein expression was evaluated with a data-independent acquisition (DIA) of shotgun proteomics analysis by Expression configuration mode (Mse). The mass spectrometer operated in “Expression Mode”, switching between low (4 eV) and high (25–60 eV) collision energies on the gas cell, using a 1.0s scan time per function over 50–2000 m/z. All spectra have been acquired in Ion Mobility Mode by applying a 1.000m/s wave velocity for the ion separation and a 175m/s transfer wave velocity. The processing of low and elevated energy added to the data of the reference lock mass ([Glu1]-Fibrinopeptide B Standard, Waters Corp.) provides a time-aligned inventory of accurate mass retention time components for both the low and elevated-energy (EMRT, exact mass retention time). Each sample was run in three technical replicates.
Continuum LC-MS data from three replicate experiments for each sample was processed for qualitative and quantitative analysis using the software Progenesis (Waters Corp.). The qualitative identification of proteins was obtained by searching the Homo sapiens database (UniProt KB/Swiss-Prot Protein reviewed). Search parameters were set as automatic tolerance for precursor ions and product ions, a minimum of one fragment ions matched per peptide, a minimum of three fragment ions matched per protein, a minimum of one unique peptide matched per protein, 2 missed cleavage, carbamidomethylation of cysteines as fixed modification and oxidation of methionines as variable modifications, FDR of the identification algorithm < 1%.
Label-free quantitative analysis was obtained using the relative abundance intensity integrated with Progenesis software, using all peptides identified for normalization. The expression analysis was performed considering technical replicates available for each experimental condition, following the hypothesis that each group is an independent variable. It was considered only proteins present in two out of three technical replicates, and a statistical cut-off of ANOVA > 0.05 was adopted. Protein Interaction and pathway-enrichment analysis were performed in Cytoscape, and in silico analyses were performed in an R environment. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD033979 and 10.6019/PXD033979.
Multiple Reaction Monitoring (MRM) for SARS-CoV-2’s spike protein.
Approximately 500 fmols of digested peptides from a recombinant spike protein produced by Cell Culture Engineering Lab (COPPER/UFRJ)27 were spiked in tryptic peptides from an E. Coli total protein extract and further loaded onto a Symmetry C18 5 µm, 180 µm × 20 mm precolumn (Waters Corp.) used as trapping column and subsequently separated by a 90 min reversed-phase gradient at 300 nL/min (linear-gradient, 3–55% ACN over 90 min) using an HSS T3 C18 1.8 µm, 75 µm × 150 mm nanoscale and LC column (Waters Corp.) at 40°C. For the gradient elution, Water-Formic Acid (99.9/0.1, v/v) was used as eluent A and Acetonitrile Formic Acid (99.9/0.1, v/v) as B. The Separated peptides were analyzed by a High Definition Synapt G2-Si Mass spectrometer directly coupled to the chromatographic system.
The generated raw file was imported into the Skyline software. The Spike protein was chosen as a FASTA file as a reference for the theoretical tryptic peptides, isolating the mass of the peptides of interest (parent ions) used for the MS/MS selection (fragment ions) and their retention time to create the MRM method. Four peptides’ transitions (543.2774++; 570.3035++; 609.7987++, and 679.8386++) were chosen, and their respective fragments of the recombinant spike protein considering only peptides with at least three fragment ions and 8–25 amino acids. The Skyline software created the collision energy to be applied for each peptide. The MRM method for searching for the Spike protein was applied to all CSF samples.
SARS-CoV-2’s genome sequencing, assembly, and phylogeny.
Five SARS-CoV-2 nasopharyngeal swabs positive samples (collected during hospitalization) with viral genes N1 or N2 amplification Ct < 30 were sequenced. Sequencing libraries were prepared using the QIAseq FX DNA Library Prep kit (QIAGEN, Germany), and reactions were sequenced on the Illumina MiSeq platform (Illumina, USA) with a v3 (600 cycles) cartridge. A custom pipeline for data quality control and consensus genome assembly was used.28 The viral genomes were classified into Pango lineages using the Pangolin tool v2.4.2. The assembly and classification are available in Suppl. Table 6. A dataset (n = 58) containing only lineages identified in Rio de Janeiro (Brazil) between April and September 2020 was created using genomes publicly available on the GISAID EpiCoV database to corroborate the classification (See Suppl. Table 6). The dataset was aligned with minimap18, and a maximum likelihood phylogeny was inferred with IQ-tree v2.0.319 under the GTR + F + I + G4 model.29
Statistical Analysis
Statistical analyses were performed using Prism 9 Software, v 9.0.2 or Matlab R2019b (Mathworks, USA). Categorical variables are expressed as frequency and proportion (No, %) and analyzed using Fisher’s exact or Chi-squared tests, followed by post-hoc Chi-Squared pairwise comparison. When necessary, multiple comparisons were corrected by Bonferroni's corrected alpha level (p = 0.017). Continuous variables were checked for normal distribution using the D’Agostino & Pearson normality test. As indicated, normally distributed data were expressed as the mean and standard error of the mean (SEM) or standard deviation (SD). Variances were compared using the F test. Groups were compared using a t-test, Welch’s t-test (correction for different variances), or one-way ANOVA followed by Tukey’s comparison test. Non-parametric data were expressed as the median and interquartile range (IQR) and analyzed using the Mann-Whitney test, Spearman correlation, or Kruskal-Wallis, followed by Dunn’s comparison test. Alternatively, multiple comparisons were corrected by the FDR method when indicated. The patient’s age and sex were considered possible confounding factors in CSF biomarkers analysis. Biomarkers levels did not differ in a comparison between sexes. The confounding effect of age was adjusted using multiple linear regression for AD-related biomarkers (Aβ and Tau species). The level of statistical significance was set at 5%. Missing or unavailable data were not included in the statistical analysis.