1. Introduction
The recent emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, responsible for the disease caused by the coronavirus disease 2019 (COVID-19), has already affected nearly seven hundred million individuals worldwide, with 6,919,573 deaths until September 2023 [1]. As evidenced in recent years, the occurrence of viral epidemics characterized by unpredictable clinical outcomes has been highly frequent on a global scale [2]. Therefore, the end of the public health emergency of international concern declared on 5 May 2023 by the World Health Organization (WHO) does not mean that COVID-19 has ceased to be a serious public health problem. Extensive studies have provided strong evidence of the frequent occurrence of neurological manifestations that resemble clinical patterns seen in autoimmune para- or post-infectious diseases triggered by SARS-CoV-2 [3,4]. This pattern is seen in other viral infections, such as Zika and Chikungunya infection [5,6,7]. Moreover, the hypothesis that viral infections can act as triggers for the development of autoimmune diseases is not a new one [8]. Due to the association between infection with other coronaviruses and autoimmunity, it is reasonable to assume that there is a connection between SARS-CoV-2 infection and certain autoimmune diseases that will be diagnosed later [9,10,11].
It is already well discussed in the literature that viral diseases have been identified as potential triggers of inflammatory demyelinating diseases (IDDs) and autoimmune encephalitis (AE) [12,13]. Numerous demyelinating disorders such as multiple sclerosis (MS), neuromyelitis optica spectrum diseases (NMOSDs), acute disseminated encephalomyelitis (ADEM), myelitis, and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) have been described as post and para-infectious complications of COVID-19 [6], with the most common forms of AE following SARS-CoV-2 infection being limbic encephalitis and Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis [14]. Important studies have identified the presence of autoantibodies anti-glutamic acid decarboxylase 65-kilodalton isoform (anti-GAD65) [15], anti-myelin oligodendrocyte glycoprotein (anti-MOG), and others [16,17,18,19].
Several hypotheses have been proposed to explain the molecular basis of the loss of immune tolerance and induction of autoimmune mechanisms, including hyperinflammation syndrome caused by SARS-CoV-2, molecular mimicry (MM) by viral proteins, immune cell activation through bystander effect, the release of autoantigens from virus-damaged tissues, lymphocyte activation mediated by superantigens, and epitope spreading [20,21,22,23]. Some recent studies on MM have shown similarities between SARS-CoV-2 protein sequences and human proteins found in multiple organs/tissues (neurological, vascular, and cardiac), indicating the potential for cross-reactive immune recognition of these regions by T cells and antibodies produced by B cells [20,21,24,25]. However, the true spectrum of autoimmune conditions, their pathophysiology, prevalence, and the risk of their development in individuals after SARS-CoV-2 infection remain unknown, representing just the tip of the iceberg.
In this study, we intend to investigate the potential role of MM between SARS-CoV-2 antigens and human autoantigens of CNS autoimmune diseases.
2. Materials and Methods
2.1. Study Population
A prospective cohort study was performed with patients admitted to the COVID-19 Intense Care Unity (ICU) of Clementino Fraga Filho University Hospital in Rio de Janeiro, RJ, Brazil. This work was approved by the National Council for Ethics in Research (CAAE 33659620.1.1001.5258) and accompanied afterward on the post-COVID ambulatory of the same hospital. All subjects signed an informed consent agreeing to participate in this research. From 2020 to 2022, COVID-19 patients were evaluated with complete physical and neurological examinations and searched for CNS autoimmune diseases. They were also actively asked for long COVID symptoms, with a focus on cognitive impairment. In addition, all patients were assessed for objective alterations with the following neurocognitive battery tests: the Symbol Digit Modalities Test (SDMT) and Montreal Cognitive Assessment (MoCA). Patients with previously known CNS autoimmune diseases or neurocognitive disturbances were excluded from the study.
2.2. Linear Sequence Analysis
Peptide sharing between SARS-CoV-2 antigens and autoantigens was analyzed in accordance with França et al. (2023) [26]. Briefly, a viral polyprotein library was constructed using the major viral antigens reported in the literature and protein sequences available in the National Center for Biotechnology Information (NCBI) Protein Reference Sequences
The sequence alignment was performed using EMBOSS WATER
2.3. Three-Dimensional Comparative Modelling
The combinations that shared significant linear identity according to BLASTp were then investigated for three-dimensional similarities. The three-dimensional models were built using the Swiss Model, an online modeling server
2.4. Antigenic Prediction
To confirm whether the SARS-CoV-2 sequences studied have antigenic properties, VaxiJen version 2.0
2.5. Search for Potential T Cell Epitopes
The sequences with significant TM-Score and antigenic properties were used as the input in a neural network–based algorithm to predict T cell epitopes showing binding capacity to human leukocyte antigen (HLA) subtypes using the Immune Epitope Database and Analysis (IEDB) Major Histocompatibility Complex-I (MHC-I) Binding Predictions
The prediction method used was the stabilization matrix alignment method (SMM-align) version 1.1. The predicted output is given in units of half maximal inhibitory concentration (IC50nM). Therefore, a lower number indicates higher affinity. As a rough guideline, peptides with IC50 values <50 nM are considered high affinity, <500 nM intermediate affinity, and <5000 nM low affinity. Since most known epitopes have high or intermediate affinity, we only considered a noteworthy result for the epitopes with <500 nM [31].
3. Results
3.1. Study Population
A total of 112 patients were evaluated. The mean age was 65.95 (17–95), and 58 (51.78%) were women. Among them, three were classified as having IDD. As our population consisted mainly of patients from the first and second wave of the COVID-19 pandemic, and all of them were hospitalized in the ICU, 51 (45.53%) patients progressed to death during or nearly after the hospitalization. The patients with IDD survived and remained with controlled disease for one year after the episodes. Among the survivors, 32 patients (52.45%) presented cognitive complaints during long COVID. Regarding the neurocognitive battery, 15 (24.59%) patients had cognitive impairment according to the 2 test results, while 20 had alteration only in SDMT and 33 had alteration only in MoCA.
3.1.1. Patient 1
Patient 1, male, 24 years old, with a history of IDD in the family (mother diagnosed with MS) was admitted to the hospital presenting paresthesia in the upper and lower limbs on the left side. The Magnetic Resonance Imaging (MRI) showed two white-matter lesions hyperintense in T2 and fluid-attenuated inversion recovery (FLAIR) with gadolinium enhancement, one on the periventricular region and one on the medullary bulb transition (Figure 1). The cerebrospinal fluid (CSF) exam was negative for infections, including SARS-CoV-2, and showed oligoclonal bands with normal cell and protein count. Serum research was positive for anti-MOG antibodies through flow cytometry and negative for anti-AQP4 antibodies. Screening for metabolic and other autoimmune diseases was negative. Although asymptomatic, as part of the hospital protocol, he was tested for COVID-19 with a Polymerase Chain Reaction (PCR) test, which was positive. He was treated with intravenous glucocorticoids, evolving with complete recovery. On the long COVID assessment, he presented new symptoms and new lesions on the MRI, being diagnosed with MOGAD.
3.1.2. Patient 2
Patient 2, female, 19 years old, evolved 20 days after SARS-CoV-2 m-RNA vaccination with subacute paresthesia in hands and feet, followed by urinary incontinence, visual disturbance, mental confusion, appendicular ataxia, progressive tetraparesis, and coma within days. Her Expanded Disability Status Scale (EDSS) during the acute state was 9.5. The MRI showed countless white-matter lesions hyperintense in T2/FLAIR, several with gadolinium enhancement (Figure 2). CSF exam was positive for oligoclonal bands, and negative for infections, including SARS-CoV-2. Screening for autoimmune diseases, including MOG and AQP4 antibodies, and metabolic diseases were negative. First, she received a diagnosis of ADEM following COVID-19 vaccination and was treated with pulse therapy with glucocorticoids, partially recovering from the attack. Nonetheless, after 6 months she once again evolved with a new aggressive demyelinating event, being diagnosed with MS and treated with natalizumab. The patient stabilized with the treatment, and, after a year, she recovered nearly a hundred percent (EDSS 3.0).
3.1.3. Patient 3
Patient 3, female, 39 years old, with a history of psoriasis, spontaneous miscarriages, and reducing gastroplasty was admitted due to paresthesia in the right hemiface and left lower limb, as well as deviation of the labial commissure, right auricular fullness, vertigo, and headache. A diagnostic investigation with complementary tests was initiated. Eighteen days before, she had COVID-19 confirmed with PCR, with headache, cough, and sore throat. The MRI showed hyperintense T2/FLAIR oval white-matter lesions on the right cortex, periventricular region, and brainstem. (Figure 3). The CSF exam was positive for oligoclonal bands, and negative for infections, including SARS-CoV-2 and autoantibodies. Screening for autoimmune diseases, including MOG and AQP4 antibodies, and metabolic diseases were negative. At first, she was diagnosed with ADEM following SARS-CoV-2 infection. Nonetheless, three months later, she evolved with new focal symptoms and new lesions on the MRI, with gadolinium enhancement. Therefore, she was diagnosed with MS and treated with dimethyl fumarate. The disease stabilized and she stayed asymptomatic since then.
3.2. Sequence Identification
The extended literature research led to the selection of eight viral proteins and ten self-proteins associated with CNS demyelinating diseases and autoimmune encephalitis, listed as follows:
SARS-CoV-2 proteins: spike protein (S), envelope protein (E), leader protein/non-structural protein 1 (Nsp1), non-structural protein 2 (Nsp2), non-structural protein 3 (Nsp3), non-structural protein 13/helicase (Nsp13), ORF7a, and nucleocapsid (N) (Table 1).
Self-proteins: glutamic acid decarboxylase 65-kilodalton isoform (GAD65), myelin proteolipid protein (PLP), myelin basic protein (MBP), myelin-oligodendrocyte glycoprotein (MOG), myelin-associated glycoprotein (MAG), myelin-associated oligodendrocyte basic protein (MOBP), transaldolase, 2’,3’-Cyclic-nucleotide 3’-phosphodiesterase (CNP), aquaporin-4 (AQP4), N-methyl-D-aspartate receptor 1 (NMDAR1), and myeloperoxidase (MPO) (Table 2).
The FASTA archive of all proteins can be found in the Supplementary Materials.
3.3. Linear and Three-Dimensional Analysis
A total of 80 possible arrangements were made through the bioinformatics approach to identify the sequences that shared linear and three-dimensional identity with human autoantigens of demyelinating brain diseases and autoimmune encephalitis.
The resulting arrangements were ranked based on the highest TM-scores, excluding randomly arranged and unrelated proteins (TM-score < 0.3), leaving 29 arrangements (Table 3). It is noteworthy that we only considered the SARS-CoV-2 sequences with reported linear identity on BLASTp meaningful, varying from 62.50% to 100% of identity, with significant E-score and antigenic properties according to VaxiJen (threshold antigenic score of 0.4).
Among these arrangements, eleven three-dimensional models had a significant linear and three-dimensional overlap of autoimmune CNS proteins and SARS-CoV-2 proteins (TM-score ≥ 0.5). The most similar structures were M and NMDAR1 (TM-score: 0.89), M and MPO (TM-Score = 0.73), nsp2 and NMDAR1 (TM-score = 0.69), S and MOG (TM-score = 0.63), ORF7a and MOG (TM-score = 0.62), N and MPO (TM-score = 0.59), nsp13 and GAD65 (TM-score = 0.52), nsp1 and GAD65 (TM-score = 0.52), nsp1 and MOG (TM-score = 0.50), nsp3 and MPO (TM-score = 0.50), and S and NMDAR1 (TM-score = 0.50) (Figure 1).
NMDAR1, MOG, and MPO were the self-antigens with more significant identity with SARS-CoV-2 antigens, each one with three different proteins. GAD65 also had significant identity with two virus antigens. PLP, MBP, MOBP, MAG, AQP4, and transaldolase demonstrated significant linear identity with at least one virus protein, along with three-dimensional overlap not considered random. Nonetheless, their TM-scores were below 0.5, meaning they are not on the same fold. This means that their MM is possible, yet less achievable in practice.
3.4. Search for Potential T Cell Epitopes
The sequences of the eleven combinations were used as the input in a neural network–based algorithm to predict their binding capacity to HLA subtypes related to autoimmune CNS diseases. All sequences presented at least one epitope with strong or intermediate binding capacity to the chosen HLA subtypes (Table 4). The binding capacity of all 29 arrangements with TM-Score > 0.3 can be seen on the Supplementary Materials of this paper. The arrangements with the strongest binding capacities were seen with the nsp1 and GAD65 epitopes of HLA-A*31:01 (Ic50 31.44 and 27.31, respectively) and with the nsp13 and GAD65 epitopes of HLA-A*31:01 (Ic50 18.72 and 35.44, respectively).
NMDAR1 combinations had the highest number of epitopes with strong or intermediate binding capacity with two combinations binding to all four selected HLA subtypes (nsp2 and NMDAR1, and S and NMDAR1), and the same epitope had identity with different virus antigens (M and nsp2).
Interestingly, different combinations shared the same viral epitope, such as M with NMDAR1 and MPO, and nsp1 with GAD65 and MOG (Table 4). This reinforces the potential of such a region to possibly trigger an unwanted autoimmune response by mimicking distinct self-proteins.
4. Discussion
SARS-CoV-2 is widely studied for the generation of multi-system autoimmune reactions [32]. In this sense, the triggering of CNS autoimmune diseases seems to be a consequence of an imprecise adaptive immune system response to the presence of viral antigens. It is well known that some viruses demonstrated neurotropic features [33,34,35,36] and replication within the brain tissue, as shown by our team with the Zika virus (ZIKV) [33]. However, even ZIKV nervous system manifestation is not always associated with acute infection, and MM seems to justify these events [37,38]. Similarly, in COVID-19, viral load or severe acute infection does not seem to be the only mechanism to justify CNS involvement [39,40]. The occurrence of IDD phenotypes and encephalitis as para or postinfectious events seems to be an immune-mediated response induced by SARS-CoV-2 [41].
A large study from various global health organizations found that the incidence of autoimmune diseases was significantly higher in the COVID-19 cohort compared to the non-COVID-19 group after a 6-month follow-up period [42]. Another similar study identified a 43% higher likelihood of developing an autoimmune disease between 3 to 15 months after infection compared to a non-COVID-19 cohort [43]. Despite the progress made, cases of CNS autoimmunity after COVID-19 are rare and mainly consist of isolated case reports or case series, which provide limited information regarding clinical outcomes [44].
Although the target of such supposed autoimmune mechanisms, precisely regarding the CNS manifestations, is still not fully understood, our findings suggest that cross-reaction with selected CNS proteins associated with autoimmune brain diseases is possible to occur secondary to the immune response to SARS-CoV-2 infection. However, the risk of developing these diseases or experiencing relapses in the setting of COVID-19 remains relatively low [45]. In our cohort, three patients developed IDD following SARS-CoV-2 infection or vaccination. Since the CNS autoimmune manifestations after COVID-19 are rare, it is expected that genetic predisposition plays an essential role in the disease mechanism [46]. Despite the low frequency, the identification of IDD in such circumstances is primordial, taking into consideration the high prevalence of SARS-CoV-2 infection and the possible critical state in which the patients may encounter it. For example, our first patient evolved with EDSS 9.5, a near-death experience in a young individual with no previous comorbidity. Moreover, is important to consider SARS-CoV-2 infection as a possible trigger of IDDs because some patients present demyelinating events as the only symptom of COVID-19, as happened with our second patient.
Molecular mimicry has been described as an essential immune mechanism involved in autoimmune reactions, especially from viruses [8]. The sharing of a linear amino acid sequence or a three-dimensional conformation fit between an antigen of the virus and a host self-protein can trigger a cross-reaction from the adaptive immune system and, therefore, have a major role in initiating an autoimmune response in genetically susceptible individuals [47]. Several researchers have recognized molecular mimicry as a component of COVID-19 pathophysiology [48,49,50]. For example, Lucchese et al. observed that molecular mimicry between SARS-CoV-2 antigens and respiratory pacemaker neurons may contribute to understanding respiratory failure [51]. Hence, MM may be a key component of the immune system dysregulated response in the CNS.
In this study, 80 possible arrangements of identity among SARS-CoV-2 antigens and self-antigens related to autoimmune CNS diseases were made. Among these arrangements, eleven models had a significant linear and three-dimensional overlap of autoimmune CNS proteins and SARS-CoV-2 proteins (TM-score ≥ 0.5). NMDAR1, MOG, and MPO were the self-antigens with more significant identity with SARS-CoV-2 antigens, followed by GAD65. Notably, dysregulated serum levels of autoantibodies NMDAR, GAD65, and MOG were detected in patients with severe COVID-19 compared with healthy controls and mild COVID-19 patients [52].
MS is a classic example of an autoimmune CNS disease characterized by chronic inflammation and demyelination [53]. SARS-CoV-2 most likely acts as a precipitating factor rather than being a direct cause of MS, triggering autoimmunity in genetically predisposed individuals. In our cohort, two patients had SARS-CoV-2-related events (vaccination and infection) as the trigger for MS. Both patients were women; however, their ages, comorbidities, symptoms, and MRI lesions were considerably different, highlighting the importance of genetic predisposal and other environmental factors on the course of the disease [54].
MBP, MOBP, PLP, and MAG are myelin proteins known to be critical autoantigens in causing demyelination in CNS leading to MS [55,56]. In our study, the TM-scores among these proteins and SARS-CoV-2 antigens were low, but not considered randomly arranged, unrelated proteins. This can mean that MM among these MS autoantigens is feasible; however, the evidence is not strong. Nevertheless, MPO, a pro-oxidative enzyme associated with immune-inflammatory, oxidative stress pathways, and cortical demyelination [57], has been gaining acceptance as an important modulator of MS activity [58]. Higher-expressing MPO genotype is overrepresented in early-onset MS in women [44], and immunohistochemical analysis shows that MPO is present in microglia in and around MS lesions [44]. This study found a significant overlap of MPO among three different SARS-CoV-2 antigens (M, N, nsp3). SARS-CoV-2’s ability to mimic MPO seems to provide a greater threat for triggering new-onset MS or worsening of MS symptoms in genetically predisposed patients, as seen during the COVID-19 pandemic [59,60].
MOGAD is an emerging subset of CNS demyelinating disease [61,62], and has also been related to COVID-19 [6]. In our study, one patient evolved with MOGAD during an asymptomatic SARS-CoV-2 infection. In addition, our in-silico analysis showed that MOG shared significant linear and three-dimensional identity with three different virus antigens (S, ORF7a, and nsp1), having the most prominent overlap with the S protein. This finding agrees with recent literature, which identified anti-MOG antibodies in the acute and post-infectious phase of SARS-CoV-2 infection and COVID-19 vaccination [63,64]. Moreover, the literature has shown that diseases associated with anti-MOG almost tripled during the COVID-19 pandemic [65].
Our study highlights that NMDAR1 has three domains with significant linear and three-dimensional identity with SARS-CoV-2 antigens, including the spike protein, and all of them have the binding capacity to T-cells to be considered epitopes. It has been proposed that SARS-CoV-2’s molecular mimicry may induce anti-NMDAR encephalitis after COVID-19 [66]. This may be a key mechanism beneath CNS manifestations of COVID-19 disease and vaccination associated with anti-NMDAR antibodies [14,67].
Moreover, GAD65 had the strongest binding capacity to HLA in this work, with two different combinations of mimicry. Cases of autoimmune encephalitis associated with GAD65 have been described following SARS-CoV-2 infection [15,68]. This finding reinforces the association of SARS-CoV-2 MM and the clinical findings related to anti-GAD65 antibody.
A growing body of evidence has demonstrated the relationship between ADEM and SARS-CoV-2 infection [69]. ADEM following SARS-CoV-2 infection and vaccination have been associated with MOG [70,71,72,73] and NMDAR [74] antibodies. Interestingly, in our study, both MOG and NMDAR are associated with S protein, the most common antigen presented in SARS-CoV-2 vaccination, which may indicate the relevance of MM in post-COVID ADEM manifestations.
In this cohort, 52.45% of the COVID-19 ICU patients presented cognitive impairment during the post-acute phase of COVID-19. Notably, NMDAR, GAD65, and MPO may be involved not only in acute encephalitis or demyelinating events but also in neurocognitive and psychiatric manifestations, frequently seen in long COVID patients [66,75,76,77]. Pathological results in cognitive screening were associated with the presence of antibodies against NMDAR and GAD65 in CSF of long COVID patients [78]. The MM between these autoantibodies and SARS-CoV-2 antigens may be a prominent asset to understanding the pathogenesis of long COVID cognitive and psychiatric symptoms.
The putative epitopes of SARS-CoV-2 that form MM are rich in alanine (Ala) and leucine (Leu) (Table 4), considerably responsible for the loop conformation seen in Figure 4 [79]. Notably, an increase in Ala has been associated with several neurological diseases such as MS, Guillain–Barré syndrome, and motor neuron disease [80]. The peptide that corresponds to the glycine/alanine repeat sequence of Epstein–Barr virus nuclear antigen-1, named P62, has been found to generate cross-reactive autoantibodies in MS patients [81] and other autoimmune diseases [82,83]. Similarly, the presence of Leu on the V3 loop region of the envelope gene of HIV-infected patients was associated with dementia [84]. Also, Leu mutations in the dengue type 4 virus envelope sequence were determinants of neurovirulence in mice [85].
Genetic susceptibility seems to explain the heterogeneity of response to immune tolerance breakdown and molecular mimicry between autoantigens and viral proteins [47]. Due to the limited knowledge about genetic susceptibility to explain mechanisms involved in the pathophysiology of AE, we chose HLA variants with known associations with CNS autoimmune diseases [56,78,79,80,81,82]. Interestingly enough, all eleven combinations with significant linear and three-dimensional identity presented at least one epitope with strong or intermediate binding capacity to the chosen HLA subtypes. In this manner, the investigation of the connection between HLA alleles related to CNS autoimmune diseases and the MM found in this paper can strengthen the results and possibly help elucidate the pathophysiology of these manifestations.
It is worth highlighting the results regarding the S protein. The spike, or its fragments, has the ability to cross the blood-brain barrier (BBB), irrespective of the presence of the viral RNA [83]. Furthermore, some cases have reported an association between CNS demyelination events and the use of vaccines with the S protein as the main antigen for the generation of immunological memory, which has become a major concern for health authorities worldwide [84]. Thus, the MM regarding S may be more common than the others described in this article. Indeed, both MOG and NMDAR1, which presented significant linear and three-dimensional overlap with spike, have been associated with COVID-19 in a more expressive way than the other autoimmune affection [14,65,66] and have been related to COVID-19 vaccination [67,85,86,87]. One of our patients triggered IDD following COVID-19 vaccination. Although is not possible to affirm causality, MM must be considered as a possible mechanism for this phenomenon.
As a limitation of this study, it is important to mention that it is a theoretical work; however, it is based on our cohort findings and recent literature studies regarding SARS-CoV-2. In addition, it uses validated software to give results as close as possible to reality. Additionally, the study used a limited number of HLA alleles in the prediction of T-cell binding capacity (only the most common HLA alleles in the literature associated with CNS autoimmune diseases) in order to increase the specificity of the results. Thereby, it is possible that some epitopes of rarer HLAs were not included in this study. Further studies are needed to validate the in-silico work described here, as well as to understand the probable genetic susceptibility some individuals have that causes the development of such manifestations.
5. Conclusions
The presented study proposes a demonstration of possible molecular mimicry between SARS-CoV-2 antigens and CNS autoimmune self-antigens, especially MOG, NMDAR1, GAD65, and MPO, in genetically susceptible individuals. The results agree with our cohort, with three cases of IDD, and with the most recent literature. Therefore, advancing our understanding of the key mechanisms of SARS-CoV-2-mediated autoimmunity is urgent.
Conceptualization, E.G.G., S.V.A.-L., M.M.d.S. and F.L.F.-D.; methodology, E.G.G., F.L.F.-D., P.R.V.B., J.V.R.-V. and R.A.F.; software, E.G.G. and F.L.F.-D.; validation, A.L.S., R.A.F., P.R.V.B., J.V.R.-V., C.F.T., V.G.C.C., V.C.S.R.P., M.M.d.S. and F.L.F.-D.; formal analysis, E.G.G.; investigation, E.G.G., A.L.S., L.A.D., S.V.A.-L., P.R.V.B., C.F.T., V.G.C.C., V.C.S.R.P., M.M.d.S. and J.V.R.-V.; resources, S.V.A.-L. and P.R.V.B.; data curation, A.L.S., E.G.G., R.A.F., L.A.D., F.L.F.-D., P.R.V.B., J.V.R.-V., C.F.T., V.G.C.C., V.C.S.R.P., M.M.d.S. and S.V.A.-L.; writing—original draft preparation, E.G.G.; writing—review and editing, A.L.S., R.A.F., S.V.A.-L., P.R.V.B., F.L.F.-D. and J.V.R.-V.; visualization, E.G.G., L.A.D., A.L.S., R.A.F., F.L.F.-D., P.R.V.B., J.V.R.-V., C.F.T., V.G.C.C., V.C.S.R.P., M.M.d.S. and S.V.A.-L.; supervision, F.L.F.-D., P.R.V.B., M.M.d.S. and S.V.A.-L.; project administration, S.V.A.-L.; funding acquisition, S.V.A.-L. All authors have read and agreed to the published version of the manuscript.
The results presented in this article are supported by data in other articles published in MDPI journals. The clinical profile and risk factors for severe COVID-19 in our cohort of hospitalized patients comparing the first and second pandemic waves were published in the Journal of Clinical Medicine in 2023, under the [
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
| ADEM | Acute disseminated encephalomyelitis |
| AE | Autoimmune encephalitis |
| Ala | Alanine |
| AQP4 | Aquaporin-4 |
| anti-GAD65 | Anti-glutamic acid decarboxylase 65-kilodalton isoform |
| anti-MOG | Anti-myelin oligodendrocyte glycoprotein |
| anti-NMDAR | Anti- N-methyl-D-aspartate receptor |
| CNP | 2’,3’-Cyclic-nucleotide 3’-phosphodiesterase |
| COVID-19 | Coronavirus disease 2019 |
| E | Envelope protein |
| EDSS | Expanded Disability Status Scale |
| FLAIR | Fluid-attenuated inversion recovery |
| GAD65 | Glutamic acid decarboxylase 65-kilodalton isoform |
| HLA | Human leukocyte antigen |
| IC50nM | Half maximal inhibitory concentration |
| ICU | Intense care unit |
| IDDs | Inflammatory demyelinating diseases |
| IEDB | Immune Epitope Database and Analysis |
| Leu | Leucine |
| MAG | |
| Myelin-associated Glycoprotein | |
| MBP | Myelin basic protein |
| MHC-I | Major histocompatibility complex-I |
| MHC-II | Major histocompatibility complex-II |
| MM | Molecular mimicry |
| MOBP | Myelin-associated oligodendrocyte basic protein |
| MoCA | Montreal Cognitive Assessment |
| MOG | |
| Myelin-oligodendrocyte Glycoprotein | |
| MOGAD | Myelin oligodendrocyte glycoprotein antibody-associated Disease |
| MPO | Myeloperoxidase |
| MRI | Magnetic resonance imaging |
| MS | Multiple sclerosis |
| N | Nucleocapsid |
| NCBI | National Center for Biotechnology Information |
| NMDAR1 | N-methyl-D-aspartate receptor 1 |
| NMOSD | Neuromyelitis optica spectrum diseases |
| Nsp1 | Non-structural protein 1 |
| Nsp2 | Non-structural protein 2 |
| Nsp3 | Non-structural protein 3 |
| Nsp13 | Non-structural protein 13 |
| PCR | Polymerase chain reaction |
| PDB | Protein data bank |
| PLP | Myelin proteolipid protein |
| RMSD | Root mean square deviation |
| S | Spike protein |
| SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
| SDMT | Symbol Digit Modalities Test |
| SMM-align | Stabilization matrix alignment method |
| TM-scores | Template modeling scores |
| WHO | World Health Organization |
| ZIKV | Zika virus |
Footnotes
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Enlarge this image.Figure 1. MRI of patient 1. (A)—axial flair sequence reveals hyperintense lesion near the posterior horn of the right lateral ventricle (red arrows). (B,C)—axial flair and sagittal T2 sequences demonstrate hyperintense lesion in the pons (red arrows). (D–F)—axial and sagittal T1 sequences with contrast demonstrating impregnation of the lesions (yellow arrows).
Enlarge this image.Figure 2. MRI of patient 2. (A–D)—sagittal and axial flair sequences and sagittal T2, showing multiple lesions (red arrows) with hypersignal, distributed in the periventricular and subcortical regions and in the spinal cord. Some of these lesions are impregnated with contrast (yellow arrows) (E,F).
Enlarge this image.Figure 3. MRI of patient 3—(A–F)—flair sequence in the axial plane showing multiple lesions in the inferior cerebellar peduncle and the subcortical and periventricular regions, predominantly in the right hemisphere (red arrows).
Enlarge this image.Figure 4. Models had a significant linear and three-dimensional overlap of autoimmune CNS proteins (in red) and SARS-CoV-2 (in blue) proteins according to TM-align, with their respective amino acid sequences. The arrows indicate the sequence region with three-dimensional overlap. (a) M and NMDAR1 (TM-score: 0.89). (b) M and MPO (TM-Score = 0.73). (c) nsp2 and NMDAR1 (TM-score = 0.69). (d) S and MOG (TM-score = 0.63). (e) ORF7a and MOG (TM-score = 0.62). (f) N and MPO (TM-score = 0.59). (g) nsp13 and GAD65 (TM-score = 0.52). (h) nsp1 and GAD65 (TM-score = 0.52). (i) nsp1 and MOG (TM-score = 0.50). (j) nsp3 and MPO (TM-score = 0.50). (k) S and NMDAR1 (TM-score = 0.50).
SARS-CoV-2 proteins selected.
| Protein | Number of Aminoacids | Gene | NCBI Reference Sequence | Uniprot ID |
|---|---|---|---|---|
| E | 75aa | E | YP_009724392 | P0DTC4 |
| Nsp1 | 180aa | ORF1a | YP_009742608.1 | P0DTD1 |
| M | 222aa | M | YP_009724393.1 | P0DTC5 |
| Nsp2 | 638aa | ORF1a | YP_009742609.1 | P0DTD1 |
| Nsp3 | 1945aa | ORF1a | YP_009742610.1 | P0DTD1 |
| Nsp13 | 601aa | ORF1a | NP_828870.1 | P0DTD1 |
| ORF7a | 121aa | ORF7a | YP_009724395.1 | P0DTC7 |
| S | 1273aa | S | YP_009724390.1 | P0DTC2 |
Self-proteins associated with IDDs and autoimmune encephalitis.
| Protein | Number of Aminoacids | Gene | NCBI Reference Sequence | Uniprot ID |
|---|---|---|---|---|
| 2’,3’-Cyclic-nucleotide 3’-phosphodiesterase (CNP) | 421aa | CNP | NP_149124.3 | P09543 |
| Aquaporin-4 (AQP4) | 323aa | AQP4 | NP_001641.1 | P55087 |
| Glutamic acid decarboxylase 65-kilodalton isoform (GAD65) | 585aa | GAD2 | NP_001127838.1 | Q05329 |
| Myelin-associated glycoprotein (MAG) | 626aa | MAG | NP_002352.1 | P20916 |
| Myelin basic protein (MBP) | 304aa | MBP | NP_001020272.1 | P02686 |
| Myelin oligodendrocyte glycoprotein (MOG) | 247aa | MOG | NP_996532.2 | Q16653 |
| Myelin-associated oligodendrocytic basic protein (MOBP) | 183aa | MOBP | NP_001380633.1 | Q13875 |
| Myeloperoxidase (MPO) | 745aa | MPO | NP_000241.1 | P05164 |
| N-methyl-D-aspartate receptor 1 (NMDAR1) | 938aa | GRIN1 | NP_015566.1 | Q05586 |
| Transaldolase | 337aa | TALDO1 | NP_006746.1 | P37837 |
Linear and three-dimensional identity between SARS-CoV-2 antigens and self-antigens, and antigenic properties of each combination.
| SARS-CoV-2 Antigens | Autoantigens | Region of the SARS-CoV-2 Antigen with More Identity | Region of the Autoantigen with More Identity | % Identity | E-Value | SWISS MODEL SARS-CoV-2 Antigen | SWISS MODEL Autoantigen | TM-SCORE | RMSD | Overall Prediction Vaxijen Linear | Overall Prediction VaxiJen Three-Dimensional Model |
|---|---|---|---|---|---|---|---|---|---|---|---|
| M | NMDAR1 | 54–70 | 562–577 | 100 | 3.00 × 10−4 | 74–106 | 626–658 | 0.89 | 0.55 | 0.9306 | 0.5324 |
| M | MPO | 134–162 | 57–85 | 100 | 1.0 | 76–105 | 67–96 | 0.73 | 1.21 | 0.4740 | 0.5177 |
| Nsp2 | NMDAR1 | 448–465 | 325–344 | 71.43 | 0.038 | 549–584 | 621–650 | 0.69 | 1.35 | 0.5983 | 0.4174 |
| S | MOG | 249–278 | 83–102 | 83.33 | 0.069 | 944–974 | 150–180 | 0.63 | 2.06 | 0.4706 | 0.4059 |
| ORF7a | MOG | 25–32 | 95–102 | 75 | 2.00 × 10−4 | 17–81 | 30–153 | 0.62 | 2.83 | 0.4846 | 0.6598 |
| N | MPO | 227–236 | 149–157 | 70 | 0.002 | 388–419 | 67–98 | 0.59 | 1.91 | 0.4117 | 0.4124 |
| Nsp13 | GAD65 | 466–472 | 439–445 | 66.67 | 3.00 × 10−4 | 290–349 | 312–389 | 0.52 | 3.22 | 0.6555 | 0.4695 |
| Nsp1 | GAD65 | 131–138 | 137–139 | 100 | 0.005 | 32–61 | 302–331 | 0.52 | 1.57 | −0.3299 | 0.6325 |
| Nsp1 | MOG | 103–114 | 210–221 | 71.43 | 0.003 | 33–62 | 204–232 | 0.50 | 1.59 | 0.6013 | 0.6757 |
| Nsp3 | MPO | 903–908 | 613–618 | 83.33 | 2.00 × 10−5 | 180–209 | 71–112 | 0.50 | 2.79 | 1.8236 | 0.8823 |
| S | NMDAR1 | 1020–1027 | 223–230 | 100 | 0.017 | 1020–1050 | 221–250 | 0.50 | 1.47 | 0.8726 | 0.7359 |
| Nsp3 | NMDAR1 | 1800–1809 | 766–774 | 100 | 5.00 × 10−4 | 399–535 | 153–278 | 0.49 | 3.86 | 0.4238 | 0.4044 |
| Nsp13 | PLP | 88–94 | 99–105 | 71.43 | 1.00 × 10−4 | 310–342 | 175–210 | 0.49 | 2.82 | −0.0624 | 0.4685 |
| Nsp1 | PLP | 34–62 | 196–211 | 100 | 0.006 | 33–64 | 240–272 | 0.47 | 1.69 | 0.7645 | 0.5854 |
| Nsp1 | Transaldolase | 84–99 | 139–154 | 100 | 0.001 | 33–62 | 145–176 | 0.46 | 2.18 | 0.6898 | 0.6757 |
| Nsp2 | MPO | 419–425 | 159–165 | 100 | 0.15 | 672–707 | 67–95 | 0.46 | 1.16 | 0.5813 | 0.5890 |
| Nsp3 | PLP | 88–94 | 99–105 | 71.43 | 1.00 × 10−4 | 180–209 | 34–63 | 0.45 | 2.07 | −0.0624 | 0.8823 |
| M | MAG | 132–138 | 369–375 | 100 | 2.00 × 10−4 | 156–186 | 295–325 | 0.43 | 2.59 | 0.4549 | 0.6527 |
| S | Transaldolase | 1110–1117 | 37–44 | 62.50 | 0.002 | 276–305 | 125–156 | 0.42 | 2.10 | 0.8734 | 0.6476 |
| Nsp13 | Transaldolase | 146–151 | 307–312 | 83.33 | 0.001 | 367–396 | 135–164 | 0.41 | 2.60 | 1.0726 | 1.1104 |
| N | CNP | 243–252 | 191–200 | 100 | 0.001 | 195–239 | 322–363 | 0.41 | 2.72 | 0.7056 | 0.6601 |
| Nsp13 | NMDAR1 | 301–306 | 628–636 | 100 | 3.00 × 10−4 | 503–532 | 683–712 | 0.41 | 2.90 | 0.9060 | 0.4818 |
| Nsp2 | Transaldolase | 389–403 | 95–109 | 66.67 | 0.19 | 249–280 | 134–163 | 0.40 | 1.90 | 0.5146 | 0.7524 |
| Nsp2 | CNP | 270–278 | 348–356 | 77.78 | 4.00 × 10−7 | 395–432 | 349–382 | 0.39 | 2.38 | 0.9295 | 0.4387 |
| N | MOBP | 198–209 | 168–178 | 77.78 | 5.00 × 10−6 | 202–233 | 29–74 | 0.38 | 2.38 | 0.4291 | 0.6320 |
| N | PLP | 170–184 | 120–134 | 75 | 0.0003 | 212–241 | 75–98 | 0.37 | 1.44 | 0.4459 | 0.7169 |
| Nsp1 | MBP | 76–102 | 198–116 | 100 | 0.010 | 32–67 | 211–240 | 0.36 | 3.22 | 0.6583 | 0.4770 |
| Nsp3 | AQP4 | 63–77 | 51–65 | 100 | 0.27 | 2667–2697 | 197–227 | 0.35 | 2.36 | 0.4168 | 0.5551 |
| Nsp2 | MBP | 61–73 | 107–119 | 100 | 8.00 × 10−4 | 280–309 | 200–229 | 0.33 | 2.79 | 0.6625 | 0.5624 |
The viral epitopes for MHC-I and MHC-II and the corresponding homologous human proteins.
| SARS-CoV-2 Antigen | Autoantigens | Allele | Potential SARS-CoV-2 Epitope | Corresponding Human Epitope | IC50 Virus Peptide | IC50 Human Peptide |
|---|---|---|---|---|---|---|
| M | NMDAR1 | HLA-DQA1*01:02/DQB1*06:02 | NWITGGIAIAMACLV | VWAGFAMIIVASYTA | 57.00 | 60.00 |
| HLA-DRB1*15:01 | LMWLSYFIASFRLFA | GFAMIIVASYTANLA | 68.00 | 49.00 | ||
| HLA-A*31:01 | LSYFIASFR | LGMVWAGFAM | 12.20 | 262.06 | ||
| M | MPO | HLA-A*31:01 | LSYFIASFR | KQLVDKAYK | 12.20 | 68.78 |
| HLA-B*07:02 | GGIAIAMACLV | RLRSGSASPM | 321.23 | 159.89 | ||
| Nsp2 | NMDAR1 | HLA-DQA1*01:02/DQB1*06:02 | RVLQKAAITILDGIS | VWAGFAMIIVASYTA | 93.00 | 60.00 |
| HLA-DRB1*15:01 | ITILDGISQYSLRLI | VWAGFAMIIVASYTA | 146.00 | 60.00 | ||
| HLA-A*31:01 | RTLETAQNSVR | GAPRSFSAR | 309.40 | 129.86 | ||
| HLA-B*07:02 | SVRVLQKAAI | APRSFSARIL | 357.95 | 29.30 | ||
| S | MOG | HLA-DRB1*15:01 | LNTLVKQLSSNFGAI | GVLVLLAVLPVLLLQ | 89.00 | 58.00 |
| HLA-DQA1*01:02/DQB1*06:02 | QNAQALNTLVKQLSS | WVSPGVLVLLAVLPV | 319.00 | 229.00 | ||
| ORF7a | MOG | HLA-DQA1*01:02/DQB1*06:02 | YQECVRGTTVLLKEP | YWVSPGVLVLLAVLP | 261.00 | 210.00 |
| N | MPO | HLA-A*31:01 | FSKQLQQSMSS | KQLVDKAYK | 46.72 | 68.78 |
| Nsp13 | GAD65 | HLA-DRB1*15:01 | AIGLALYYPSARIVY | AKQKGFVPFLVSATA | 71.00 | 196.00 |
| HLA-DQA1*01:02/DQB1*06:02 | IVYTACSHAAVDALC | LVSATAGTTVYGAFD | 347.00 | 52.00 | ||
| HLA-A*31:01 | KYLPIDKCSR | KHKWKLSGVER | 18.72 | 35.44 | ||
| HLA-B*07:02 | LPIDKCSR | VPFLVSAT | 187.50 | 69.82 | ||
| Nsp1 | GAD65 | HLA-DRB1*03:01 | SVEEVLSEARQHLKD | RGKMIPSDLERRILE | 489.00 | 448.00 |
| HLA-A*31:01 | HLKDGTCGLVE | KMIPSDLERR | 31.44 | 27.31 | ||
| Nsp1 | MOG | HLA-A*31:01 | HLKDGTCGLVE | CWKITLFVIVP | 31.44 | 284.79 |
| Nsp3 | MPO | HLA-A*31:01 | SYKDWSYSGQS | RLRSGSASPME | 38.77 | 111.56 |
| S | NMDAR1 | HLA-DQA1*01:02/DQB1*06:02 | ASANLAATKMSECVL | ASEDDAATVYRAAAM | 213.00 | 121.00 |
| HLA-A*31:01 | KMSECVLGQSKR | LSASEDDAATVYR | 297.9 | 495.7 | ||
| HLA-DQA1*01:02/DQB1*06:02 | AQYTSALLAGTITSG | SRRVLLLAGRLAAQS | 122.00 | 199.00 | ||
| HLA-B*07:02 | MIAQYTSAL | MAAESRRVL | 61.88 | 54.29 |
Supplementary Materials
The following supporting information can be downloaded at
References
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Details
; Fernandes, Renan Amphilophio 2
; Jéssica Vasques Raposo-Vedovi 2 ; Andreza Lemos Salvio 2
; Larissa Araujo Duarte 1 ; Caio Faria Tardim 3 ; Vinicius Gabriel Coutinho Costa 4 ; Valéria Coelho Santa Rita Pereira 3 ; Valle Bahia, Paulo Roberto 5 ; Marcos Martins da Silva 3
; Fabrícia Lima Fontes-Dantas 6
; Soniza Vieira Alves-Leon 7
1 Translational Neuroscience Laboratory (LabNet), Biomedical Institute, Federal University of the State of Rio de Janeiro, Rio de Janeiro 20211-030, RJ, Brazil;
2 Translational Neuroscience Laboratory (LabNet), Biomedical Institute, Federal University of the State of Rio de Janeiro, Rio de Janeiro 20211-030, RJ, Brazil;
3 Department of Neurology, Clementino Fraga Filho University Hospital, Federal University of Rio de Janeiro, Rio de Janeiro 21941-913, RJ, Brazil;
4 Morphological Sciences Post-Graduation Program, Federal University of Rio de Janeiro, Rio de Janeiro 21941-913, RJ, Brazil;
5 Department of Radiology, Clementino Fraga Filho University Hospital, Federal University of Rio de Janeiro, Rio de Janeiro 21941-913, RJ, Brazil;
6 Department of Pharmacology, Institute of Biology, Rio de Janeiro State University, Rio de Janeiro 20950-000, RJ, Brazil
7 Translational Neuroscience Laboratory (LabNet), Biomedical Institute, Federal University of the State of Rio de Janeiro, Rio de Janeiro 20211-030, RJ, Brazil;