Next Article in Journal
Engineered mRNA and the Rise of Next-Generation Antibodies
Next Article in Special Issue
Fc-Independent Protection from SARS-CoV-2 Infection by Recombinant Human Monoclonal Antibodies
Previous Article in Journal
A Single Animal Species-Based Prediction of Human Clearance and First-in-Human Dose of Monoclonal Antibodies: Beyond Monkey
Previous Article in Special Issue
Viroinformatics-Based Analysis of SARS-CoV-2 Core Proteins for Potential Therapeutic Targets
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Antibodies
Volume 10
Issue 4
10.3390/antib10040036
antibodies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

From Anti-SARS-CoV-2 Immune Response to the Cytokine Storm via Molecular Mimicry

by
Darja Kanduc
Department of Biosciences, Biotechnologies, and Biopharmaceutics, University of Bari, 70125 Bari, Italy
Antibodies 2021, 10(4), 36; https://doi.org/10.3390/antib10040036
Submission received: 19 June 2021 / Revised: 20 August 2021 / Accepted: 8 September 2021 / Published: 24 September 2021
(This article belongs to the Special Issue Antibodies, B Cell Responses and Immune Responses to SARS-CoV-2 Infections)
Versions Notes

Abstract

:
The aim of this study was to investigate the role of molecular mimicry in the cytokine storms associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human proteins endowed with anti-inflammatory activity were assembled and analyzed for peptide sharing with the SARS-CoV-2 spike glycoprotein (gp) using public databases. It was found that the SARS-CoV-2 spike gp shares numerous pentapeptides with anti-inflammatory proteins that, when altered, can lead to cytokine storms characterized by diverse disorders such as systemic multiorgan hyperinflammation, macrophage activation syndrome, ferritinemia, endothelial dysfunction, and acute respiratory syndrome. Immunologically, many shared peptides are part of experimentally validated epitopes and are also present in pathogens to which individuals may have been exposed following infections or vaccinal routes and of which the immune system has stored memory. Such an immunologic imprint might trigger powerful anamnestic secondary cross-reactive responses, thus explaining the raging of the cytokine storm that can occur following exposure to SARS-CoV-2. In conclusion, the results support molecular mimicry and the consequent cross-reactivity as a potential mechanism in SARS-CoV-2-induced cytokine storms, and highlight the role of immunological imprinting in determining high-affinity, high-avidity, autoimmune cross-reactions as a pathogenic sequela associated with anti-SARS-CoV-2 vaccines.

1. Introduction

Inflammation is a protective attempt by the organism to remove injurious stimuli, often represented by an infectious pathogen, and initiate healing processes. Ordinarily, inflammation is a stereotyped response and is considered as a mechanism of innate immunity [1]. However, as seen during the current SARS-Cov-2 pandemic, physiological inflammation may escalate into hyperinflammation [2] and associate with severe immunopathologies, from acute lung injury to thrombosis, vasculitis, and vasculopathy [3,4,5,6,7]. The molecular mechanisms that subvert a beneficial innate inflammatory response to SARS-CoV-2 into a pathological hyperinflammatory process are unknown.
Here, this study analyzes the degraded immunity associated with SARS-CoV-2 infection using the research paradigm developed since 1999 [8], according to which molecular mimicry between pathogen antigens and human proteins can trigger immune responses not only against the pathogen antigens, but also against the human host, with consequent cross-reactivity and autoimmune pathologies [9,10,11]. Accordingly, cross-reactive reactions between the SARS-CoV-2 spike gp and human surfactant seem to explain, for example, why SARS-CoV-2 so heavily attacks the respiratory system [12]. Such a cross-reactivity hypothesis appears to be substantiated by clinical data documenting that immunization with SARS-CoV antigens causes severe pneumonia [13], in this way also suggesting a dominant pathogenic role of anti-SARS-CoV antibodies in COVID-19. Moreover, importantly, SARS-CoV-2-reactive CD4+ T cells have been detected in ~40–60% of unexposed individuals, thus indicating cross-reactive T-cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2 [14], most possibly due to the phenomenon of ‘bystander activation’, whereby the infection may lead to activation of T cells that may in turn activate preprimed autoreactive T cells [15]. Furthermore, it has to be considered that comorbidities such as hypertension and diabetes induce chronic stress on endothelial cells with consequent abnormal expression of the molecules on their plasma membranes as an effect of post-translational modifications of intracellular proteins, including some heat-shock proteins. This can predispose cells and tissues to molecular mimicry phenomena that may occur during an infection [16].
Within this framework, in order to understand whether molecular mimicry and the consequent cross-reactivity might be capable of overturning the defensive innate immunity into a degenerated autoimmune self-attack, the present study explored human proteins related to anti-inflammatory activity for peptide sharing with the main antigen of SARS-CoV-2, i.e., the spike gp.

2. Materials and Methods

Peptide sharing between anti-inflammatory human proteins and the SARS-CoV-2 spike gp (NCBI, GenBank Protein Accession ID = QHD43416.1) was analyzed using pentapeptides as sequence probes since a peptide grouping formed by five amino acid (aa) residues defines a minimal immune determinant that can (1) induce highly specific antibodies, and (2) determine antigen–antibody specific interaction [17].
A library of 219 human proteins related to anti-inflammatory activity was assembled at random from the UniProtKB database (www.uniprot.org/, accessed on 1 June 2021) [18] using “anti-inflammatory” as a keyword. The 219 human proteins are listed in Supplementary Table S1.
The SARS-CoV-2 spike gp primary sequence was dissected into pentapeptides offset by one residue (that is, MFVFL, FVFLV, VFLVL, FLVLL, and so forth), and the resulting viral pentapeptides were analyzed for occurrences within the 219 human proteins that are related to anti-inflammatory activity.
The human proteins involved in the peptide sharing (53 in total) were analyzed for functions/diseases using UniProtKB (www.uniprot.org/, accessed on 1 June 2021) [18], Pubmed, and OMIM (www.omim.org/, accessed on 1 June 2021) resources.
Successively, using the Pir Peptide Match program (research.bioinformatics.udel.edu/peptidematch/index.jsp, accessed on 1 June 2021) [18], the shared peptides (namely, 59) were also investigated for occurrences in coronaviruses that were used as controls and are listed with NCBI:txid as follows: SARS-CoV (694009); Middle East respiratory syndrome (MERS)-CoV (1335626); human (H) CoV-229E (11137); HCoV-NL63 (277944).
Then, the immunological potential of the peptide sharing was analyzed by searching the Immune Epitope Database (IEDB, www.iedb.org/, accessed on 1 June 2021) [19] for immunoreactive epitopes hosting the shared pentapeptides.
As a last step, pentapeptides common to SARS-CoV-2–derived epitopes and anti-inflammatory proteins were also controlled for occurrences in the bacterial pathogens Bordetella pertussis (257313), Corynebacterium diphtheriae (257309), Clostridium tetani (212717), Haemophilus influenzae (71421), and Neisseria meningitides (122586).

3. Results

3.1. Description of the Peptide Sharing between SARS-CoV-2 Spike Gp and Anti-Inflammatory Human Proteins

The peptide sharing between the SARS-CoV-2 spike gp and anti-inflammatory human proteins is quantitatively and qualitatively described in Table 1 and Table 2, respectively.
Table 1 shows that the SARS-CoV-2 spike gp vs. anti-inflammatory human protein peptide overlap amounts to 59 minimal immune determinants. When compared to the CoV controls, Table 1 shows that peptide sharing also occurs—to a lesser extent, but still a remarkable one—with SARS-CoV, which emerged in 2003 and, even if endowed with a lethal potential, could be controlled with public health measures [20]. None of the 59 pentapeptides are present in the additional CoV controls, i.e., the pathogenic MERS-CoV [21] or the scarcely pathogenic HCoV-OC43 and HCoV-229E CoVs that cause only mild symptoms [22]. In sum, Table 1 provides a quantitative description of a common, vast, and specific molecular platform that joins the SARS-CoV-2 spike gp and anti-inflammatory human proteins and, as well, supports the possibility of autoimmune cross-reactions between the SARS-CoV-2 antigen and the human host.
Table 2 describes the distribution of the 59 SARS-CoV-2 spike gp pentapeptides among 53 anti-inflammatory human proteins.
At first glance, Table 2 reveals that the majority of the anti-inflammatory proteins involved in the peptide sharing are receptors implicated in cellular signaling pathways that regulate crucial processes such as proliferation, differentiation, apoptosis, and immune response. Space reasons do not allow a detailed one-by-one analysis of the 53 anti-inflammatory proteins listed in Table 2; hence, the discussion is here circumscribed to a few of the tabulated proteins.

3.1.1. Adenosine Receptor A2b (AA2BR)

Table 2 begins with AA2BR, a seven-transmembrane receptor that binds adenosine, a regulator of innate immunity released into the extracellular (EC) space in response to metabolic stress and cell damage [23]. Following adenosine binding, AA2BR initiates the canonical cyclic adenosine monophosphate (cAMP) signaling pathway that, in succession, comprises activation of the heterotrimeric protein Gs, activation of adenylate cyclase (AC), production of cAMP, activation of protein kinase A (PKA), and phosphorylation of the cAMP responsive element-binding (CREB) protein that triggers gene transcription [24]. Immunologically, cAMP is a primary regulator of innate immune cell function [25]. High levels of cAMP reduce the production of proinflammatory mediators tumor necrosis factor (TNF)-α, interleukin (IL)-17, and interferon (IFN)-γ, whilst increase the production of anti-inflammatory factors such as IL-10 [26].
Consequently, hitting AA2BR equates to altering the cAMP levels and disintegrating the intracellular signaling network that regulates innate immune response. Simply put, cross-reactions hitting AA2BR would decrease cAMP production with consequent high levels of the proinflammatory TNF-α, IL-17, and IFN-γ. Clinically, pathological consequences would be suffered by almost all organs and, in particular, by the pulmonary and cardiovascular systems, since deficiency/alterations/improper functioning of AA2BR characterize acute lung injury and vasculopathies. In fact, AA2BR
  • is expressed in all tested organs, including spleen, lung, colon, and kidney. Importantly, in all of these organs, the primary site of expression is the vasculature [27];
  • is highly expressed on type II alveolar epithelial cells [28]. Of note, type II alveolar epithelial cells produce and secrete a pulmonary surfactant that reduces alveolar surface tension and prevents alveolus collapse during respiration [12,29]:
  • has a potent anti-inflammatory role during acute lung injury [30,31];
  • dampens inflammation particularly during tissue hypoxia [32];
  • protects the colonic epithelial barrier during acute colitis [33];
  • mediates different types of cardioprotection [34];
  • when activated in the pre-adipocyte, inhibits adipogenesis [35];
  • enhances the abundance of T regulatory cells (Tregs), a cell type critical in constraining inflammation [36,37]. Tregs constrain inflammation via multiple mechanisms depending on the tissue and nature of injury/inflammation (i.e., production of the anti-inflammatory cytokines IL-10, IL-35, and transforming growth factor β) [38,39].
In sum, cross-reactivity against AA2BR alone would be sufficient in itself to explain the acute respiratory syndrome and the systemic multiorgan hyperinflammation associated with SARS-CoV-2 infection.

3.1.2. Adiponectin

Adiponectin is a versatile player of innate immunity [40]. This anti-inflammatory protein is an adipocyte-derived plasma protein [41] and a beneficial modulator for endothelial adhesion molecules [42] since it inhibits TNF-α-induced activation of the nuclear transcription factor κB (NF-κB) through a cAMP-dependent pathway [41]. Specifically, NF-κB is involved in the transcriptional regulation of vascular cell adhesion molecule-1, endothelial leukocyte adhesion molecule-1, and intracellular adhesion molecule-1. These adhesion molecules participate in the recruitment of leukocytes to inflammatory lesions and play a crucial role in monocyte adhesion to arterial endothelium [42,43,44]. The inhibitory effect of adiponectin and the concomitant cAMP accumulation are blocked by either AC inhibitors or PKA inhibitors [41].
Adiponectin stimulates expression of the potent anti-inflammatory IL-10 [45] and inhibits endothelial synthesis of inflammatory IL-8 [46,47]. Of note, hypo-adiponectinemia induces NLRP3 inflammasome activation [48,49,50]. Lastly, adiponectin paradoxically decreases in obesity [51].
On the whole, cross-reactions hitting adiponectin might lead to obesity, metabolic syndrome, diabetes, endothelial dysfunction, atherosclerosis, hypertension, and coronary artery disease [52].

3.1.3. C163A Protein

C163A protein is a membrane-bound receptor, selectively expressed in macrophages. C163A acts as a hemoglobin (Hb) scavenger [53], and altered C163A is related to severe inflammatory syndromes. Indeed, under physiological conditions, senescent erythrocytes are phagocytosed and catabolized in macrophages [54], excepted for 10–20% of them that undergo intravascular hemolysis before being phagocytosed [55]. The release of Hb and its degradation products, heme and iron, in nonhematopoietic tissues can lead to oxidative stress because of the Fenton reaction [56], thus causing severe inflammatory pathologies such as stroke [56,57]. Physiologically, such inflammatory pathologies are prevented by the scavenger C163A protein that binds the complex Hb–haptoglobin (Hp) and mediates the uptake of the complex for catabolism in macrophages, thereby neutralizing and protecting from intravascular Hb/heme toxicity due to the Fe++-induced oxidative stress.
Therefore, C163A deficiencies/alterations break down the protection against oxidative stress and cause intravascular accumulation of heme and iron with severe pathological outcomes. Indeed, the iron overload triggers the macrophage expression of C163A, while also triggering the expression of a disintegrin and metalloproteinase domain-containing protein 17 (ADAM17; alias: TNF-α-converting enzyme, TACE) [58,59]. The consequences can be devastating.
In fact, ADAM17 is a sheddase enzyme that cuts and sheds the EC domain of C163A [60], thus transforming the membrane-bound C163A receptor into the soluble C163A (sC163A) [61]. sC163A is still able to bind Hp–Hb complexes [62]; however, due to its being no longer bound to the macrophage membrane, it cannot mediate the uptake of the complex into the macrophages [63]. This creates a paroxysmal feedback loop in which the iron overload cannot be eliminated but instead further increases and further stimulates C163A and ADAM17 expression in macrophages, with ADAM17 nullifying the scavenger function of C163A and continuously producing sC163A. In parallel, the continuously increasing iron overload not only increases the oxidative stress but, in the attempt to neutralize the iron overload, also stimulates a continuous expression of ferritin with consequent hyper-ferritinemia [64]. To aggravate the pathological scenario, the increased ADAM17 sheddase can act on several transmembrane proteins with inflammatory properties such as TNF-α, the master-regulator of inflammatory cytokine production [65], thus unleashing powerful cytokine storms.
Such a succession of molecular events is supported by the fact that ADAM17 deficiency augments C163A-dependent apoptotic cell uptake into macrophages and the linked anti-inflammatory phenotype [66]. These events logically explain why high levels of sC163A are associated with the large spectrum of severe inflammatory disorders that are comprehensively grouped in the macrophage activation syndrome (MAS) and clinically include persistent high-grade fever, hepatosplenomegaly, lymphadenopathy, cytopenia, hemorrhagic manifestations, and a sepsis-like condition [67,68,69].

3.1.4. ACs, CREB1, IL-10, IL27B, MY18A, and NLRC

The immune dysregulated scenario that might originate from cross-reactivity against AA2BR, adiponectin, and C163A can be further disintegrated when one considers that targets of cross-reactions might include the following:
  • ACs 4, 7, and 9, the alterations of which lead to cAMP decrease and consequent high levels of the proinflammatory TNF-α, IL-17, and IFN-γ [26];
  • CREB1, a key transcription factor that, by inhibiting NF-κB activation, induces macrophage survival, and promotes the proliferation, survival, and regulation of T and B lymphocytes [70];
  • IL-10, a major immune regulatory cytokine with profound anti-inflammatory functions [71];
  • IL27B (aka EBI), which forms a heterodimer with IL12A known as IL-35 and a heterodimer with IL-27A known as IL-27 [72]. IL-35 and IL-27 are anti-inflammatory ILs. IL-35 promotes the proliferation and activation of Tregs and suppresses the function of T helper 17 cells and other inflammatory cells to inhibit immune responses [73], while IL-27 regulates innate immunity and controls microbial growth [74];
  • MY18A, which is predominantly expressed in alveolar macrophages and plays an important role in pulmonary immunity by enhancing opsonization and clearance of pathogens and by modulating macrophage inflammatory responses [75];
  • NLRC3 protein, which inhibits inflammation by disrupting NALP3 inflammasome assembly [76].

3.2. Immunogenic Potential and Immunologic Imprinting

3.2.1. Immunogenic Potential of the Peptide Sharing

Obviously, cross-reactions hitting the above-described anti-inflammatory human proteins might lead to the cytokine storm and hyperinflammation syndromes that accompany exposure to SARS-CoV-2. This possibility is solidly supported by analyses of the immunological potential of the peptide sharing illustrated in Table 2. Indeed, Table 3 shows that, according to IEDB [19], almost all of the shared pentapeptides, with the exception of nine (namely, CGDST, FLVLL, GIAVE, LVLLP, NATRF, NDPFL, TGIAV, VAVLY, and VFLVL), occur and recur in 80 SARS-CoV-2 spike gp-derived epitopes that have been experimentally validated as immunoreactive, thus highlighting the concrete possibility of cross-reactions between the SARS-CoV-2 spike gp and anti-inflammatory human proteins.

3.2.2. Immunologic Imprinting

Immunologically, it has to also be considered that, as already described [77,78,79,80,81], the extent and violence of the potential cross-reactions might considerably be intensified and heightened by phenomena of immunologic imprinting due to inter-pathogen peptide sharing. In fact, a distinctive property of the immune system is the memory for the immune determinants it has previously encountered so that, as a rule, the immune system reacts by recalling memory of the responses toward past infections rather than inducing ex novo responses toward the recent ones [82,83]. Such immunologic imprinting of immune determinants can be by and large common, in light of the massive and wide peptide overlap among pathogen proteomes [84,85].
Given this context, the 50 minimal immune determinants common to SARS-CoV-2 spike gp-derived epitopes and anti-inflammatory human proteins were analyzed for occurrences in bacterial pathogens such as B. pertussis, C. diphtheriae, C. tetani, H. influenzae, and N. meningitides, i.e., in pathogens to which, in general, individuals have already been exposed during their life due to infections or vaccination. The results reported in Table 4 illustrate an intense viral vs. bacterial peptide overlap capable of unchaining powerful and violent cross-reactions against the human host.

4. Discussion

In agreement with previous reports [77,78,79,80,81,84,85,86], the data described in Table 1 and Table 2 confirm that a vast peptide commonality joins viral and human proteins. Here, this study shows that the SARS-CoV-2 spike gp shares 59 minimal immune determinants with 53 anti-inflammatory human proteins that, consequently, may become susceptible to be targeted and altered/inactivated by autoimmune cross-reactions. The unexpected vastness and specificity of such peptide sharing can be mathematically appreciated when considering that the probability of two proteins sharing one pentapeptide on the basis of 20 aa is equal to 1 out of 20 raised to the power of 5, i.e., it is equal to 0.0000003125. Hence, the high level of the specific peptide sharing described in Table 1 and Table 2 opposes a casual peptide sharing phenomenon dictated by the pure mathematical laws of distribution and rather, once more, indicates an evolutionary process where viruses played specific roles in the origin of the eukaryotic cell [86].
Immunologically, a large part of the peptide sharing described in Table 2 is not only endowed with immunoreactivity (Table 3), but also has the potential to recall past infections and already targeted determinants (Table 4). Simply put, the inter-pathogen peptide sharing reported in Table 4 suggests that pre-existing immune responses to previously encountered pathogens—considering, in this case, B. pertussis, C. tetani, C. diphtheriae, H. influenzae, and/or N. meningitides—might be boosted following exposure to SARS-CoV-2. Therefore, the primary response to the virus might be transformed into a secondary (or even tertiary) response to the previously encountered pathogens of which the immune system has stored memory. This implies that the anamnestic and, by definition, extremely potent response against the bacterial immune determinants previously encountered may be predominant, whilst the immune response against the pathogen lastly encountered, i.e., SARS-CoV-2, may be weak or unsuccessful. As an additional consequence, the attack against the early sensitizing pathogens might fail because those early sensitizing pathogens are no longer present in the organism. In the end, a logical unavoidable result might be that the anamnestic, high-affinity, high-avidity, and extremely potent secondary immune response triggered by the lastly encountered pathogen, i.e., SARS-CoV-2, and addressed toward past infections can hit the only available targets, i.e., the immune determinants that, in this instance, are present in the human anti-inflammatory proteins. According to this sequence of events, molecular mimicry and immunological memory might explain the different pathological burden of the autoimmune responses—from zero or mild symptoms to severe, even lethal pathologies [80,81]—following exposure to SARS-CoV-2. In practice, the past history of the infections/vaccinations of each individual might be the main factor dictating the pathologic outcome.
Therefore, from a logical point of view, Table 3 and Table 4 powerfully support molecular mimicry and immunologic memory as the likely mechanistic links between SARS-CoV-2 infection and the immune dysregulation characterized by increased proinflammatory cytokines that affect COVID-19 patients. In this regard, the fact that peptide sharing specifically involves SARS-CoV-2, but not MERS-CoV (Table 1), is clinically noteworthy thus allowing the establishment of a causality relationship among shared peptides, potential cross-reactivity, and autoimmune pathological sequelae. Indeed, the lack of peptide commonalities between MERS-CoV and auto-inflammatory proteins described in Table 1 might explain the different immunological characteristics of the pathogenic SARS-CoV-2 and MERS-CoV [87].

5. Conclusions

The present report has special scientific-–clinical relevance in light of the consideration that, after decades of research [88,89,90,91,92,93], we still remain ignorant of the mechanism(s) via which respiratory infectious agents alter the immune defense of the human host by activating so-called ‘cytokine storms’ and cause physical occlusion of the pulmonary airways, with devastating pathologic sequelae in the systemic circulation. Indeed, scientifically, the present data indicate a molecular platform, i.e., peptide sharing, and a mechanism, i.e., molecular mimicry-induced cross-reactivity, to understand the etiology of the cytokine storm syndromes. Moreover, therapeutically, these data warn that antigens for vaccine formulation must be selected with some care. As repeatedly highlighted [9,12,17,80,81,86,94], the approach for specific, effective, and safe immunotherapies appears to reside in non-cross-reactive peptides.

Supplementary Materials

The following is available online at https://www.mdpi.com/article/10.3390/antib10040036/s1: Table S1. List of 219 human proteins assembled at random from UniprotKB database by using “anti-inflammatory” as keyword.

Funding

This research received no funding.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Signore, A. About inflammation and infection. EJNMMI Res. 2013, 3, 8. [Google Scholar] [CrossRef] [Green Version]
  2. Gustine, J.N.; Jones, D. Immunopathology of hyperinflammation in COVID-19. Am. J. Pathol. 2021, 191, 4–17. [Google Scholar] [CrossRef]
  3. Vabret, N.; Britton, G.J.; Gruber, C.; Hegde, S.; Kim, J.; Kuksin, M.; Levantovsky, R.; Malle, L.; Moreira, A.; Park, M.D.; et al. Immunology of COVID-19: Current State of the Science. Immunity 2020, 52, 910–941. [Google Scholar] [CrossRef]
  4. García, L.F. Immune Response, Inflammation, and the Clinical Spectrum of COVID-19. Front. Immunol. 2020, 11, 1441. [Google Scholar] [CrossRef] [PubMed]
  5. Tay, M.Z.; Poh, C.M.; Rénia, L.; MacAry, P.A.; Ng, L.F.P. The trinity of COVID-19: Immunity, inflammation and intervention. Nat. Rev. Immunol. 2020, 20, 363–374. [Google Scholar] [CrossRef] [PubMed]
  6. Yeh, C.H.; de Wit, K.; Levy, J.H.; Weitz, J.I.; Vaezzadeh, N.; Liaw, P.C.; Fox-Robichaud, A.; Soliman, K.; Kim, P.Y. Hypercoagulability and COVID-19 associated hypoxemic respiratory failure: Mechanisms and emerging management paradigms. J. Trauma Acute Care Surg. 2020, 89, e177–e181. [Google Scholar] [CrossRef] [PubMed]
  7. Becker, R.C. COVID-19-associated vasculitis and vasculopathy. J. Thromb. Thrombolysis 2020, 50, 499–511. [Google Scholar] [CrossRef] [PubMed]
  8. Willers, J.; Lucchese, A.; Kanduc, D.; Ferrone, S. Molecular mimicry of phage displayed peptides mimicking GD3 ganglioside. Peptides 1999, 20, 1021–1026. [Google Scholar] [CrossRef]
  9. Kanduc, D. Peptide cross-reactivity: The original sin of vaccines. Front. Biosci. 2012, 4, 1393–1401. [Google Scholar] [CrossRef] [Green Version]
  10. Adiguzel, Y. Molecular mimicry between SARS-CoV-2 and human proteins. Autoimmun. Rev. 2021, 20, 102791. [Google Scholar] [CrossRef]
  11. Angileri, F.; Legare, S.; Gammazza, A.M.; de Macario, E.C.; Macario, A.J.; Cappello, F. Molecular mimicry may explain multi-organ damage in COVID-19. Autoimmun. Rev. 2020, 19, 102591. [Google Scholar] [CrossRef]
  12. Kanduc, D.; Shoenfeld, Y. On the molecular determinants of the SARS-CoV-2 attack. Clin. Immunol. 2020, 215, 108426. [Google Scholar] [CrossRef] [PubMed]
  13. Agrawal, A.S.; Tao, X.; Algaissi, A.; Garron, T.; Narayanan, K.; Peng, B.H.; Couch, R.B.; Tseng, C.T. Immunization with inactivated Middle East Respiratory Syndrome coronavirus vaccine leads to lung immunopathology on challenge with live virus. Hum. Vaccin. Immunother. 2016, 12, 2351–2356. [Google Scholar] [CrossRef]
  14. Grifoni, A.; Weiskopf, D.; Ramirez, S.I.; Mateus, J.; Dan, J.M.; Moderbacher, C.R.; Rawlings, S.A.; Sutherland, A.; Premkumar, L.; Jadi, R.S.; et al. Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals. Cell 2020, 181, 1489–1501.e15. [Google Scholar] [CrossRef] [PubMed]
  15. Fujinami, R.S.; von Herrath, M.G.; Christen, U.; Whitton, J.L. Molecular mimicry, bystander activation, or viral persistence: Infections and autoimmune disease. Clin. Microbiol. Rev. 2006, 19, 80–94. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Cappello, F. Is COVID-19 a proteiform disease inducing also molecular mimicry phenomena? Cell Stress Chaperones 2020, 25, 381–382. [Google Scholar] [CrossRef] [PubMed]
  17. Kanduc, D. Pentapeptides as minimal functional units in cell biology and immunology. Curr. Protein Pept. Sci. 2013, 14, 111–120. [Google Scholar] [CrossRef] [PubMed]
  18. UniProt Consortium. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 2019, 47, D506–D515. Available online: http://www.uniprot.org (accessed on 1 June 2021). [CrossRef] [PubMed] [Green Version]
  19. Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2019, 47, D339–D343. Available online: www.iedb.org (accessed on 1 June 2021). [CrossRef] [PubMed] [Green Version]
  20. Petersen, E.; Koopmans, M.; Go, U.; Hamer, D.H.; Petrosillo, N.; Castelli, F.; Storgaard, M.; Al Khalili, S.; Simonsen, L. Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. Lancet Infect. Dis. 2020, 20, e238–e244. [Google Scholar] [CrossRef]
  21. Choudhry, H.; Bakhrebah, M.A.; Abdulaal, W.H.; Zamzami, M.A.; Baothman, O.A.; Hassan, M.A.; Zeyadi, M.; Helmi, N.; Alzahrani, F.; Ali, A.; et al. Middle East respiratory syndrome: Pathogenesis and therapeutic developments. Future Virol. 2019, 14, 237–246. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Su, S.; Wong, G.; Shi, W.; Liu, J.; Lai, A.C.K.; Zhou, J.; Liu, W.; Bi, Y.; Gao, G.F. Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses. Trends Microbiol. 2016, 24, 490–502. [Google Scholar] [CrossRef] [Green Version]
  23. Antonioli, L.; Fornai, M.; Blandizzi, C.; Haskó, G. Adenosine Regulation of the Immune System. In The Adenosine Receptors; Borea, P.A., Varani, K., Gessi, S., Merighi, S., Vincenzi, F., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2018; pp. 499–514. ISBN 978-3-319-90807-6. [Google Scholar]
  24. Krauss, G. Biochemistry of Signal Transduction and Regulation; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2014; ISBN 9783527333660. [Google Scholar]
  25. Serezani, C.H.; Ballinger, M.N.; Aronoff, D.M.; Peters-Golden, M. Cyclic AMP: Master regulator of innate immune cell function. Am. J. Respir. Cell. Mol. Biol. 2008, 39, 127–132. [Google Scholar] [CrossRef]
  26. Raker, V.K.; Becker, C.; Steinbrink, K. The cAMP Pathway as Therapeutic Target in Autoimmune and Inflammatory Diseases. Front. Immunol. 2016, 7, 123. [Google Scholar] [CrossRef] [Green Version]
  27. Haskó, G.; Csóka, B.; Németh, Z.H.; Vizi, E.S.; Pacher, P. A(2B) adenosine receptors in immunity and inflammation. Trends Immunol. 2009, 30, 263–270. [Google Scholar] [CrossRef] [Green Version]
  28. Cagnina, R.E.; Ramos, S.I.; Marshall, M.A.; Wang, G.; Frazier, C.R.; Linden, J. Adenosine A2B receptors are highly expressed on murine type II alveolar epithelial cells. Am. J. Physiol. Lung Cell. Mol. Physiol. 2009, 297, L467–L474. [Google Scholar] [CrossRef] [Green Version]
  29. Pérez-Gil, J. Structure of pulmonary surfactant membranes and films: The role of proteins and lipid-protein interactions. Biochim. Biophys. Acta 2008, 1778, 1676–1695. [Google Scholar] [CrossRef] [Green Version]
  30. Karmouty-Quintana, H.; Xia, Y.; Blackburn, M.R. Adenosine signaling during acute and chronic disease states. J. Mol. Med. 2013, 91, 173–181. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Hoegl, S.; Brodsky, K.S.; Blackburn, M.R.; Karmouty-Quintana, H.; Zwissler, B.; Eltzschig, H.K. Alveolar Epithelial A2B Adenosine Receptors in Pulmonary Protection during Acute Lung Injury. J. Immunol. 2015, 195, 1815–1824. [Google Scholar] [CrossRef] [Green Version]
  32. Aherne, C.M.; Kewley, E.M.; Eltzschig, H.K. The resurgence of A2B adenosine receptor signaling. Biochim. Biophys. Acta 2011, 1808, 1329–1339. [Google Scholar] [CrossRef] [Green Version]
  33. Aherne, C.M.; Saeedi, B.; Collins, C.B.; Masterson, J.C.; McNamee, E.N.; Perrenoud, L.; Rapp, C.R.; Curtis, V.F.; Bayless, A.; Fletcher, A.; et al. Epithelial-specific A2B adenosine receptor signaling protects the colonic epithelial barrier during acute colitis. Mucosal Immunol. 2015, 8, 1324–1338. [Google Scholar] [CrossRef] [PubMed]
  34. Seo, S.W.; Koeppen, M.; Bonney, S.; Gobel, M.; Thayer, M.; Harter, P.N.; Ravid, K.; Eltzschig, H.K.; Mittelbronn, M.; Walker, L.; et al. Differential Tissue-Specific Function of Adora2b in Cardioprotection. J. Immunol. 2015, 195, 1732–1743. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Gharibi, B.; Abraham, A.A.; Ham, J.; Evans, B.A.J. Contrasting effects of A1 and A2b adenosine receptors on adipogenesis. Int. J. Obes. 2012, 36, 397–406. [Google Scholar] [CrossRef] [Green Version]
  36. Ehrentraut, H.; Westrich, J.A.; Eltzschig, H.K.; Clambey, E.T. Adora2b adenosine receptor engagement enhances regulatory T cell abundance during endotoxin-induced pulmonary inflammation. PLoS ONE 2012, 7, e32416. [Google Scholar]
  37. Schingnitz, U.; Hartmann, K.; Macmanus, C.F.; Eckle, T.; Zug, S.; Colgan, S.P.; Eltzschig, H.K. Signaling through the A2B adenosine receptor dampens endotoxin-induced acute lung injury. J. Immunol. 2010, 184, 5271–5279. [Google Scholar] [CrossRef] [Green Version]
  38. Rubtsov, Y.P.; Rasmussen, J.P.; Chi, E.Y.; Fontenot, J.; Castelli, L.; Ye, X.; Treuting, P.; Siewe, L.; Roers, A.; Henderson, W.R., Jr.; et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 2008, 28, 546–558. [Google Scholar] [CrossRef] [PubMed]
  39. Vignali, D.A.; Collison, L.W.; Workman, C.J. How regulatory T cells work. Nat. Rev. Immunol. 2008, 8, 523–532. [Google Scholar] [CrossRef] [Green Version]
  40. Luo, Y.; Liu, M. Adiponectin: A versatile player of innate immunity. J. Mol. Cell. Biol. 2016, 8, 120–128. [Google Scholar] [CrossRef]
  41. Ouchi, N.; Kihara, S.; Arita, Y.; Okamoto, Y.; Maeda, K.; Kuriyama, H.; Hotta, K.; Nishida, M.; Takahashi, M.; Muraguchi, M.; et al. Adiponectin, an adipocyte-derived plasma protein, inhibits endothelial NF-kappaB signaling through a cAMP-dependent pathway. Circulation 2000, 102, 1296–1301. [Google Scholar] [CrossRef]
  42. Ouchi, N.; Kihara, S.; Arita, Y.; Maeda, K.; Kuriyama, H.; Okamoto, Y.; Hotta, K.; Nishida, M.; Takahashi, M.; Nakamura, T.; et al. Novel modulator for endothelial adhesion molecules: Adipocyte-derived plasma protein, adiponectin. Circulation 1999, 100, 2473–2476. [Google Scholar] [CrossRef] [Green Version]
  43. Ross, R. The pathogenesis of atherosclerosis: A perspective for the 1990s. Nature 1993, 362, 801–809. [Google Scholar] [CrossRef] [PubMed]
  44. Collins, T.; Read, M.A.; Neish, A.S.; Whitley, M.Z.; Thanos, D.; Maniatis, T. Transcriptional regulation of endothelial cell adhesion molecules: NF-kB and cytokine-inducible enhancers. FASEB J. 1995, 9, 899–909. [Google Scholar] [CrossRef] [Green Version]
  45. Park, P.H.; Huang, H.; McMullen, M.R.; Bryan, K.; Nagy, L.E. Activation of cyclic-AMP response element binding protein contributes to adiponectin-stimulated interleukin-10 expression in RAW 264.7 macrophages. J. Leukoc. Biol. 2008, 83, 1258–1266. [Google Scholar] [CrossRef] [PubMed]
  46. Kobashi, C.; Urakaze, M.; Kishida, M.; Kibayashi, E.; Kobayashi, H.; Kihara, S.; Funahashi, T.; Takata, M.; Temaru, R.; Sato, A.; et al. Adiponectin inhibits endothelial synthesis of interleukin-8. Circ. Res. 2005, 97, 1245–1252. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Asfaha, S.; Dubeykovskiy, A.N.; Tomita, H.; Yang, X.; Stokes, S.; Shibata, W.; Friedman, R.A.; Ariyama, H.; Dubeykovskaya, Z.A.; Muthupalani, S.; et al. Mice that express human interleukin-8 have increased mobilization of immature myeloid cells, which exacerbates inflammation and accelerates colon carcinogenesis. Gastroenterology 2013, 144, 155–166. [Google Scholar] [CrossRef] [Green Version]
  48. Wang, F.; Liu, Y.; Yang, W.; Yuan, J.; Mo, Z. Adiponectin inhibits NLRP3 inflammasome by modulating the AMPK-ROS pathway. Int. J. Clin. Exp. Pathol. 2018, 11, 3338–3347. [Google Scholar]
  49. Kim, M.J.; Kim, E.H.; Pun, N.T.; Chang, J.H.; Kim, J.A.; Jeong, J.H.; Choi, D.Y.; Kim, S.H.; Park, P.H. Globular Adiponectin Inhibits Lipopolysaccharide-Primed Inflammasomes Activation in Macrophages via Autophagy Induction: The Critical Role of AMPK Signaling. Int. J. Mol. Sci. 2017, 18, 1275. [Google Scholar] [CrossRef] [Green Version]
  50. Zhang, J.; Xia, L.; Zhang, F.; Zhu, D.; Xin, C.; Wang, H.; Zhang, F.; Guo, X.; Lee, Y.; Zhang, L.; et al. A novel mechanism of diabetic vascular endothelial dysfunction: Hypoadiponectinemia-induced NLRP3 inflammasome activation. Biochim. Biophys. Acta Mol. Basis Dis. 2017, 1863, 1556–1567. [Google Scholar] [CrossRef]
  51. Arita, Y.; Kihara, S.; Ouchi, N.; Takahashi, M.; Maeda, K.; Miyagawa, J.; Hotta, K.; Shimomura, I.; Nakamura, T.; Miyaoka, K.; et al. Paradoxical decrease of an adipose-specific protein, adiponectin, in obesity. Biochem. Biophys. Res. Commun. 1999, 425, 560–564. [Google Scholar] [CrossRef]
  52. Han, S.H.; Sakuma, I.; Shin, E.K.; Koh, K.K. Antiatherosclerotic and anti-insulin resistance effects of adiponectin: Basic and clinical studies. Prog. Cardiovasc. Dis. 2009, 52, 126–140. [Google Scholar] [CrossRef]
  53. Yang, H.; Wang, H.; Levine, Y.A.; Gunasekaran, M.K.; Wang, Y.; Addorisio, M.; Zhu, S.; Li, W.; Li, J.; de Kleijn, D.P.; et al. Identification of CD163 as an antiinflammatory receptor for HMGB1-haptoglobin complexes. JCI Insight 2016, 1, e85375. [Google Scholar] [CrossRef] [Green Version]
  54. Bratosin, D.; Mazurier, J.; Tissier, J.P.; Estaquier, J.; Huart, J.J.; Ameisen, J.C.; Aminoff, D.; Montreuil, J. Cellular and molecular mechanisms of senescent erythrocyte phagocytosis by macrophages. A review. Biochimie 1998, 80, 173–195. [Google Scholar] [CrossRef]
  55. Thomsen, J.H.; Etzerodt, A.; Svendsen, P.; Moestrup, S.K. The haptoglobin-CD163-heme oxygenase-1 pathway for hemoglobin scavenging. Oxid. Med. Cell. Longev. 2013, 2013, 523652. [Google Scholar] [CrossRef] [Green Version]
  56. Wu, J.; Hua, Y.; Keep, R.F.; Nakamura, T.; Hoff, J.T.; Xi, G. Iron and iron handling proteins in the brain after intracerebral hemorrhage. Stroke 2003, 34, 2964–2969. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Aronowski, J.; Zhao, X. Molecular pathophysiology of cerebral hemorrhage: Secondary brain injury. Stroke 2011, 42, 1781–1786. [Google Scholar] [CrossRef] [PubMed]
  58. Maras, J.S.; Das, S.; Sharma, S.; Sukriti, S.; Kumar, J.; Vyas, A.K.; Kumar, D.; Bhat, A.; Yadav, G.; Choudhary, M.C.; et al. Iron-Overload triggers ADAM-17 mediated inflammation in Severe Alcoholic Hepatitis. Sci. Rep. 2018, 8, 10264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Lee, J.K.; Shin, J.H.; Gwag, B.J.; Choi, E.J. Iron accumulation promotes TACE-mediated TNF-alpha secretion and neurodegeneration in a mouse model of ALS. Neurobiol. Dis. 2015, 80, 63–69. [Google Scholar] [CrossRef]
  60. Menghini, R.; Fiorentino, L.; Casagrande, V.; Lauro, R.; Federici, M. The role of ADAM17 in metabolic inflammation. Atherosclerosis 2013, 228, 12–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Etzerodt, A.; Moestrup, S.K. CD163 and inflammation: Biological, diagnostic, and therapeutic aspects. Antioxid. Redox. Signal. 2013, 18, 2352–2363. [Google Scholar] [CrossRef] [Green Version]
  62. Møller, H.J.; Peterslund, N.A.; Graversen, J.H.; Moestrup, S.K. Identification of the hemoglobin scavenger receptor/CD163 as a natural soluble protein in plasma. Blood 2002, 99, 378–380. [Google Scholar] [CrossRef]
  63. Møller, H.J.; Nielsen, M.J.; Maniecki, M.B.; Madsen, M.; Moestrup, S.K. Soluble macrophage-derived CD163: A homogenous ectodomain protein with a dissociable haptoglobin-hemoglobin binding. Immunobiology 2010, 215, 406–412. [Google Scholar] [CrossRef]
  64. Moreira, A.C.; Mesquita, G.; Gomes, M.S. Ferritin: An inflammatory player keeping iron at the core of pathogen-host interactions. Microorganisms 2020, 8, 589. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Parameswaran, N.; Patial, S. Tumor necrosis factor-α signaling in macrophages. Crit. Rev. Eukaryot. Gene Expr. 2010, 20, 87–103. [Google Scholar] [CrossRef] [PubMed]
  66. Driscoll, W.S.; Vaisar, T.; Tang, J.; Wilson, C.L.; Raines, E.W. Macrophage ADAM17 deficiency augments CD36-dependent apoptotic cell uptake and the linked anti-inflammatory phenotype. Circ. Res. 2013, 113, 52–61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Gao, Z.; Wang, Y.; Wang, J.; Zhang, J.; Wang, Z. Soluble ST2 and CD163 as Potential Biomarkers to Differentiate Primary Hemophagocytic Lymphohistiocytosis from Macrophage Activation Syndrome. Mediterr. J. Hematol. Infect. Dis. 2019, 11, e2019008. [Google Scholar] [CrossRef] [Green Version]
  68. Lerkvaleekul, B.; Vilaiyuk, S. Macrophage activation syndrome: Early diagnosis is key. Open Access Rheumatol. 2018, 10, 117–128. [Google Scholar] [CrossRef] [Green Version]
  69. Karakike, E.; Giamarellos-Bourboulis, E.J. Macrophage Activation-Like Syndrome: A distinct entity leading to early death in sepsis. Front. Immunol. 2019, 10, 55. [Google Scholar] [CrossRef] [Green Version]
  70. Wen, A.Y.; Sakamoto, K.M.; Miller, L.S. The role of the transcription factor CREB in immune function. J. Immunol. 2010, 185, 6413–6419. [Google Scholar] [CrossRef] [Green Version]
  71. Saraiva, M.; Vieira, P.; O’Garra, A. Biology and therapeutic potential of interleukin-10. J. Exp. Med. 2020, 217, e20190418. [Google Scholar] [CrossRef] [Green Version]
  72. Vignali, D.A.; Kuchroo, V.K. IL-12 family cytokines: Immunological playmakers. Nat. Immunol. 2012, 13, 722–728. [Google Scholar] [CrossRef] [Green Version]
  73. Wang, D.; Lei, L. Interleukin-35 regulates the balance of Th17 and Treg responses during the pathogenesis of connective tissue diseases. Int. J. Rheum. Dis. 2020, 24, 21–27. [Google Scholar] [CrossRef]
  74. Povroznik, J.M.; Robinson, C.M. IL-27 regulation of innate immunity and control of microbial growth. Future Sci. 2020, 6, FSO588. [Google Scholar] [CrossRef]
  75. Yang, L.; Carrillo, M.; Wu, Y.M.; DiAngelo, S.L.; Silveyra, P.; Umstead, T.M.; Halstead, E.S.; Davies, M.L.; Hu, S.; Floros, J.; et al. SP-R210 (Myo18A) Isoforms as Intrinsic Modulators of Macrophage Priming and Activation. PLoS ONE 2015, 10, e0126576. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Eren, E.; Berber, M.; Özören, N. NLRC3 protein inhibits inflammation by disrupting NALP3 inflammasome assembly via competition with the adaptor protein ASC for pro-caspase-1 binding. J. Biol. Chem. 2017, 292, 12691–12701. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Lucchese, G.; Kanduc, D. The Guillain–Barrè peptide signatures: From Zika virus to Campylobacter, and beyond. Virus Adapt. Treat. 2017, 9, 1–11. [Google Scholar] [CrossRef] [Green Version]
  78. Lucchese, G.; Kanduc, D. Minimal immune determinants connect Zika virus, Human Cytomegalovirus, and Toxoplasma gondii to microcephaly-related human proteins. Am. J. Reprod. Immunol. 2017, 77, e12608. [Google Scholar] [CrossRef] [Green Version]
  79. Kanduc, D.; Shoenfeld, Y. Inter-Pathogen Peptide Sharing and the Original Antigenic Sin: Solving a Paradox. Open Immunol. J. 2018, 8, 16–27. [Google Scholar] [CrossRef] [Green Version]
  80. Kanduc, D. Anti-SARS-CoV-2 Immune Response and Sudden Death: Titin as a Link. Adv. Stud. Biol. 2021, 13, 37–44. [Google Scholar] [CrossRef]
  81. Kanduc, D. Thromboses and Haemostasis Disorders Associated with Coronavirus Disease 2019 (COVID 19): The Possible Causal Role of Cross-Reactivity and Immunological Imprinting. Glob. Med. Genet. 2021. [Google Scholar] [CrossRef]
  82. Francis, T.; Salk, J.E.; Quilligan, J.J. Experience with vaccination against influenza in the spring of 1947: A preliminary report. Am. J. Public Health Nations Health 1947, 37, 1013–1016. [Google Scholar] [CrossRef] [Green Version]
  83. Davenport, F.M.; Hennessy, A.V.; Francis, T., Jr. Epidemiologic and immunologic significance of age distribution of antibody to antigenic variants of influenza virus. J. Exp. Med. 1953, 98, 641–656. [Google Scholar] [CrossRef]
  84. Kanduc, D.; Stufano, A.; Lucchese, G.; Kusalik, A. Massive peptide sharing between viral and human proteomes. Peptides 2008, 29, 1755–1766. [Google Scholar] [CrossRef] [PubMed]
  85. Trost, B.; Lucchese, G.; Stufano, A.; Bickis, M.; Kusalik, A.; Kanduc, D. No human protein is exempt from bacterial motifs, not even one. Self Nonself 2010, 1, 328–334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Kanduc, D. The comparative biochemistry of viruses and humans: An evolutionary path towards autoimmunity. Biol. Chem. 2019, 400, 629–638. [Google Scholar] [CrossRef] [PubMed]
  87. Zhang, Y.Y.; Li, B.R.; Ning, B.T. The Comparative Immunological Characteristics of SARS-CoV, MERS-CoV, and SARS-CoV-2 Coronavirus Infections. Front. Immunol. 2020, 11, 2033. [Google Scholar] [CrossRef] [PubMed]
  88. Theron, M.; Huang, K.J.; Chen, Y.W.; Liu, C.C.; Lei, H.Y. A probable role for IFN-gamma in the development of a lung immunopathology in SARS. Cytokine 2005, 32, 30–38. [Google Scholar] [CrossRef] [PubMed]
  89. Ioi, H.; Kawashima, H.; Nishimata, S.; Watanabe, Y.; Yamanaka, G.; Kashiwagi, Y.; Yamada, N.; Tsuyuki, K.; Takekuma, K.; Hoshika, A.; et al. A case of Reye syndrome with rotavirus infection accompanied with high cytokines. J. Infect. 2006, 52, e124–e128. [Google Scholar] [CrossRef]
  90. Satarker, S.; Tom, A.A.; Shaji, R.A.; Alosious, A.; Luvis, M.; Nampoothiri, M. JAK-STAT pathway inhibition and their implications in COVID-19 therapy. Postgrad. Med. 2021, 133, 489–507. [Google Scholar] [CrossRef]
  91. Torabi-Rahvar, M.; Rezaei, N. Storm at the Time of Corona: A Glimpse at the Current Understanding and Therapeutic Opportunities of the SARS-CoV-2 Cytokine Storm. Curr. Pharm. Des. 2021, 27, 1549–1552. [Google Scholar] [CrossRef]
  92. Liu, Y.; Chen, D.; Hou, J.; Li, H.; Cao, D.; Guo, M.; Ling, Y.; Gao, M.; Zhou, Y.; Wan, Y.; et al. An inter-correlated cytokine network identified at the center of cytokine storm predicted COVID-19 prognosis. Cytokine 2020, 138, 155365. [Google Scholar] [CrossRef]
  93. Zhang, Y.; Chen, Y.; Meng, Z. Immunomodulation for Severe COVID-19 Pneumonia: The State of the Art. Front. Immunol. 2020, 11, 577442. [Google Scholar] [CrossRef] [PubMed]
  94. Kanduc, D. The role of proteomics in defining autoimmunity. Expert Rev. Proteom. 2021, 18, 177–184. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Quantitative description of the pentapeptide sharing between CoV spike gps and anti-inflammatory human proteins.
Table 1. Quantitative description of the pentapeptide sharing between CoV spike gps and anti-inflammatory human proteins.
Spike Gp fromNumber of Shared PentapeptidesPentapeptide aa Sequence
SARS-CoV-259AAAYY, AEIRA, AGAAA, AISSV, ALLAG, ASFST, AVRDP, CGDST, EKGIY, FLVLL, FNGLT, FSALE, FSQIL, GIAVE, GICAS, GTITS, GVLTE, IRAAE, IYQTS, KLQDV, KNLRE, KQLSS, KVEAE, LEILD, LGFIA, LIRAA, LLPLV, LPPLL, LVLLP, LVRDL, NATRF, NDPFL, NIIRG, NNTVY, NTFVS, PDKVF, PFFSN, PIGAG, QDSLS, QLSSN, QQFGR, QSIIA, RAAEI, SKVGG, SSNFG, SSVLH, TFVSG, TGIAV, TLADA, TLEIL, TLLAL, TMSLG, TNGVG, TSPDV, VAVLY, VFLHV, VFLVL, VLPPL, YSVLY
SARS-CoV30AEIRA, AISSV, CGDST, FNGLT, FSQIL, GICAS, IRAAE, IYQTS, KLQDV, KQLSS, KVEAE, LGFIA, LIRAA, LPPLL, NNTVY, NTFVS, PIGAG, QLSSN, QQFGR, RAAEI, SSNFG, TFVSG, TGIAV, TLADA, TMSLG, TSPDV, VAVLY, VFLHV, VLPPL, YSVLY
MERS-CoV −−
hCoV-229E−−
hCoV-NL63 −−
Table
Table 2. Peptide sharing between SARS-CoV-2 spike gp and anti-inflammatory human proteins.
Table 2. Peptide sharing between SARS-CoV-2 spike gp and anti-inflammatory human proteins.
Peptides aAnti-Inflammatory Human Proteins b
VLPPLLAA2BR. Adenosine receptor A2b.
ASFST, ALLAGADCY4. Adenylate cyclase type 4
LGFIAADCY7. Adenylate cyclase type 7
KQLSSNADCY9. Adenylate cyclase type 9
VLLPLADIPO. Adiponectin
SSNFGC163A. Scavenger receptor cysteine-rich type 1 protein M130
FLVLLPCALRL. Calcitonin gene-related peptide type 1 receptor
IYQTSCREB1. Cyclic AMP-responsive element-binding protein 1
LVLLPLCRFR1. Corticotropin-releasing factor receptor 1
IYQTSDRD5. D(1B) dopamine receptor
VFLVL, TNGVGFURIN. Furin
CGDST, TFVSGGBB2. Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-2
TFVSGGBB4. Guanine nucleotide-binding protein subunit β-4
NDPFLGBGT2. Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit γ-T2
VFLHVGLHA. Glycoprotein hormones α chain
QDSLSGLP1R. Glucagon-like peptide 1 receptor
QSIIAGNAI1. Guanine nucleotide-binding protein G(i) subunit α-1
KNLREGNAI2. Guanine nucleotide-binding protein G(i) subunit α-2
QSIIAGNAI3. Guanine nucleotide-binding protein G(i) subunit α
NTFVSGGNB5. Guanine nucleotide-binding protein subunit β-5
YSVLY, GICASGP176. G-protein coupled receptor 176
ALLAGGPR25. Probable G-protein coupled receptor 25
LLPLVGPR83. Probable G-protein coupled receptor 83
TLLALGPR84. G-protein coupled receptor 84
EKGIYIL10. Interleukin-10
TMSLGIL27B. Interleukin-27 subunit β (EBI)
VLLPL, KVEAEISK5. Serine protease inhibitor Kazal-type 5
QLSSNKS6A5. Ribosomal protein S6 kinase α-5
VLPPLLSHB. Lutropin subunit β
PFFSNMC3R. Melanocortin receptor 3
GIAVEMC5R. Melanocortin receptor 5
FNGLTMXRA5. Matrix-remodeling-associated protein 5
SSVLH, NATRF, FSQIL, LIRAAEIMY18A. Unconventional myosin-XVIIIa
FSALEMYH9. Myosin-9
LVRDLNFKB1. Nuclear factor NF-kappa-B p105 subunit
TLEILD, NIIRG, SKVGG, AAAYY, ALLAGNLRC3. NLR family CARD domain-containing protein 3
LPPLLNR1H2. Oxysterol receptor LXR-β
GVLTENR1H4. Bile acid receptor
PIGAGNR5A2. Nuclear receptor subfamily 5 group A member 2
VAVLYPACR. Pituitary adenylate cyclase-activating polypeptide type I receptor
YSVLYPD2R. Prostaglandin D2 receptor
AISSVPE2R2. Prostaglandin E2 receptor EP2 subtype
TLADAPTH2R. Parathyroid hormone 2 receptor
AEIRAPTHR. Parathyroid hormone-related protein
KLQDVPTHY. Parathyroid hormone
AVRDPRAMP1. Receptor activity-modifying protein 1
NNTVYRORA. Nuclear receptor ROR-α
GTITSRXFP1. Relaxin receptor 1
PDKVFRXFP2. Relaxin receptor 2
QQFGR, TGIAVSBNO2. Protein strawberry notch homolog 2
VAVLYSCTR. Secretin receptor
AGAAASECR. Secretin
VAVLY, TSPDVVIPR2. Vasoactive intestinal polypeptide receptor 2
a Hexa- and heptapeptides composed of overlapping pentapeptides are given in bold. b Proteins given by Uniprot entry and name. Disease association and references are available at Uniprot, PubMed, and OMIM public databases.
Table
Table 3. Immunoreactive SARS-CoV-2 spike gp-derived epitopes containing peptides shared between the SARS-CoV-2 spike gp and anti-inflammatory human proteins.
Table 3. Immunoreactive SARS-CoV-2 spike gp-derived epitopes containing peptides shared between the SARS-CoV-2 spike gp and anti-inflammatory human proteins.
IEDB ID 1Epitope Sequence 2IEDB ID 1Epitope Sequence 2
1069137aqytsALLAGTITSg1309574rSSVLHstqdlflPFFSNvt
1069290ctlksftvEKGIYqt1309578sfiedllfnkvTLADAgfik
1069291cvadYSVLYnsasfs1309585sssgwtAGAAAYYvgylqpr
1069378ecdiPIGAGICASyq1309589sygfqpTNGVGyqpyrvvvl
1071585nLVRDLpqgFSALEp1309600tyvtqqLIRAAEIRAsanla
1072807skhtpinLVRDLpqg1309603vknkcvnfnFNGLTgtgvlt
1073281tesnkkflpfQQFGRdia1309604vlndilsrldKVEAEvqidr
1074838AEIRAsanlaatk1309606vTLADAgfikqygdclgdia
1074847aphgvVFLHVtyv1309616yeqyikwpwyiwLGFIAgli
1074888flPFFSNvtwfhai1309621yskhtpinLVRDLpqgfsal
1075041rsvasQSIIAytmsl1310281aphgvVFLHVtyvpa
1075079tpinLVRDL1310282aqalntlvKQLSSNf
1075094VLPPLLtdemiaqyt1310303caqkFNGLTVLPPLL
1075117wtAGAAAYYvgy1310415FNGLTVLPPLLtdem
1075125YSVLYnsASFSTfk1310434gAISSVlndilsrld
1087359iPIGAGICASy1310444givNNTVYdplqpel
1087679pikdfggfnFSQILpdps1310445gIYQTSnfrvqptes
1087680pinLVRDLpqgFSALEpl1310448gKLQDVvnqnaqaln
1125063gltVLPPLL1310487iginitrfqTLLALh
1309123khtpinLVRDLpqgf1310513itrfqTLLALhrsyl
1309132nFSQILpdpskpskr1310551krisncvadYSVLYn
1309140tdemiaqytsALLAG1310593llfnkvTLADAgfik
1309418AEIRAsanlaatkmsecvlg1310611LPPLLtdemiaqyts
1309444dAVRDPqTLEILDitpcsfg1310612lpqgFSALEplvdlp
1309447dfggfnFSQILpdpskpskr1310787sASFSTfkcygvspt
1309450dplsetkctlksftvEKGIY1310803siiayTMSLGaensv
1309451dsfkeeldkyfknhTSPDVd1310825svasQSIIAyTMSLG
1309461ehvnnsyecdiPIGAGICAS1310828svlynsASFSTfkcy
1309464esnkkflpfQQFGRdiadtt1310850TLEILDitpcsfggv
1309469fknhTSPDVdlgdisginas1310852tlvKQLSSNFGaiss
1309490iawnsnnldSKVGGnynyly1310865trfqTLLALhrsylt
1309522lppaytnsftrgvyyPDKVF1310899vLLPLVssqcvnltt
1309523lSSNFGAISSVlndilsrld1313244nsASFSTfk
1309531ngltgtGVLTEsnkkflpfq1313285pinLVRDLpqgfsal
1309532ngltVLPPLLtdemiaqyts1313286pinLVRDLpqgfwal
1309534nitrfqTLLALhrsyltpgd1316945FSQILpdpskpskrsfie
1309546pflmdlegkqgnfKNLREfv1321084LPPLLtdem
1309558qfnsaigkiQDSLSstasal1325128svasQSIIAy
1309561qrnfyepqiittdNTFVSGn1326261vasQSIIAy
1309567rdlpqgFSALEplvdlpigi1328800yTMSLGaensvay
1 Epitopes listed according to the IEDB ID number.2 Shared peptides are capitalized.
Table
Table 4. Pentapeptides occurring in microbial organisms and common to the SARS-CoV-2 spike gp, SARS-CoV-2 spike gp-derived epitopes, and anti-inflammatory proteins.
Table 4. Pentapeptides occurring in microbial organisms and common to the SARS-CoV-2 spike gp, SARS-CoV-2 spike gp-derived epitopes, and anti-inflammatory proteins.
OrganismShared Pentapeptides
B. pertussisAEIRA, AGAAA, AISSV, ALLAG, IRAAE, LEILD, LIRAA, LLPLV, LVRDL, SKVGG
C. diphtheriaeAEIRA, AGAAA, ALLAG, AVRDP, LLPLV, TLADA, VLPPL
C. tetaniAGAAA, EKGIY, GVLTE, RAAEI, TLADA
H. influenzaeAEIRA, AGAAA, AISSV, FNGLT, FSALE, GTITS, GVLTE, IRAAE, KNLRE, KQLSS, KVEAE, LEILD, LGFIA, LIRAA, LLPLV, LPPLL, LVRDL, QDSLS, QLSSN, QSIIA, RAAEI, TLADA, TLEIL, TLLAL, TMSLG, TNGVG, TSPDV, VLPPL
N. meningitidisAEIRA, AGAAA, ALLAG, ASFST, FNGLT, FSALE, LEILD, LGFIA, LPPLL, LVRDL, QDSLS, SKVGG, TLLAL, TMSLG
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kanduc, D. From Anti-SARS-CoV-2 Immune Response to the Cytokine Storm via Molecular Mimicry. Antibodies 2021, 10, 36. https://doi.org/10.3390/antib10040036

AMA Style

Kanduc D. From Anti-SARS-CoV-2 Immune Response to the Cytokine Storm via Molecular Mimicry. Antibodies. 2021; 10(4):36. https://doi.org/10.3390/antib10040036

Chicago/Turabian Style

Kanduc, Darja. 2021. "From Anti-SARS-CoV-2 Immune Response to the Cytokine Storm via Molecular Mimicry" Antibodies 10, no. 4: 36. https://doi.org/10.3390/antib10040036

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop