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Abstract
Background The SARS-CoV-2 pandemic is a global health problem. Beside the specific pathogenic effect of SARS-CoV-2, incompletely understood deleterious and aberrant host immune responses play critical roles in severe disease. Our objective was to summarise the available information on the pathophysiology of COVID-19.
Methods Two reviewers independently identified eligible studies according to the following PICO framework: P (population): patients with SARS-CoV-2 infection; I (intervention): any intervention/no intervention; C (comparator): any comparator; O (outcome) any clinical or serological outcome including but not limited to immune cell phenotype and function and serum cytokine concentration.
Results Of the 55 496 records yielded, 84 articles were eligible for inclusion according to question-specific research criteria. Proinflammatory cytokine expression, including interleukin-6 (IL-6), was increased, especially in severe COVID-19, although not as high as other states with severe systemic inflammation. The myeloid and lymphoid compartments were differentially affected by SARS-CoV-2 infection depending on disease phenotype. Failure to maintain high interferon (IFN) levels was characteristic of severe forms of COVID-19 and could be related to loss-of-function mutations in the IFN pathway and/or the presence of anti-IFN antibodies. Antibody response to SARS-CoV-2 infection showed a high variability across individuals and disease spectrum. Multiparametric algorithms showed variable diagnostic performances in predicting survival, hospitalisation, disease progression or severity, and mortality.
Conclusions SARS-CoV-2 infection affects both humoral and cellular immunity depending on both disease severity and individual parameters. This systematic literature review informed the EULAR ‘points to consider’ on COVID-19 pathophysiology and immunomodulatory therapies.
- COVID-19
- cytokines
- inflammation
- polymorphism
- genetic
- T-lymphocyte subsets
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Key messages
What is already known about this subject?
The SARS-CoV-2 pandemic is a global health issue and disease pathogenesis along with mechanisms leading to severe COVID-19 are yet poorly understood.
A deleterious excessive and aberrant non-effective host immune response may play an important role throughout the course of severe disease.
What does this study add?
Cytokine profiles, cellular and humoral immune response are highly heterogeneous across individuals and specific patterns are associated with the evolution to severe COVID-19 and a poor prognosis.
Failure to maintain high interferon (IFN) levels is characteristic of severe forms of COVID-19 and could be related to loss-of-function mutations in the IFN pathway and/or the presence of anti-IFN antibodies.
Immune and non-immune-mediated mechanisms play an important role in COVID-19 thrombotic manifestations.
Multiparametric algorithms including clinical and biological features can predict poor outcomes in SARS-CoV-2 infected individuals.
How might this impact on clinical practice?
The emerging knowledge on immune pathways and severe SARS-CoV-2 infection indicate distinct cytokine pathway perturbations compared with other rheumatological disorders including the interleukin-6 and type I IFN pathway.
Significant knowledge gaps exist that will stimulate further research.
Introduction
The SARS-CoV-2 pandemic has led to the scientific and global communities facing an unprecedented challenge.1 The rapid spread of the virus along with the lack of effective antiviral drugs to treat COVID-19 has so far resulted in more than 65 000 000 confirmed cases and 1 500 000 deaths (COVID-19.who.int/; 15 December 2020).2 SARS-CoV-2 infection encompasses a broad spectrum of clinical phenotypes, from asymptomatic or mild diseases with little or no respiratory symptoms to severe COVID-19 with life-threatening manifestations such as acute respiratory distress syndrome (ARDS) leading to multiorgan failure and death.3 Lung damage in severe COVID-19 is linked to inflammatory alveolar and interstitial immune cell infiltration and activation.4 The cellular and humoral immune response to SARS-CoV-2 appears to inadequately control viral spread or may be evident in tissue where there is no detectable virus with both scenarios being potentially deleterious consequent to severe inflammation.5 Excessive production and release of proinflammatory mediators, including interleukin (IL)-1β, IL-6, tumour necrosis factor-α and monocyte chemoattractant protein 1 (MCP-1) and many other molecules, occurs in more patients with severe COVID-19.6 In severe cases, these features resemble other systemic severe inflammatory states such as macrophage activation syndrome (MAS) or secondary haemophagocytic lymphohistocytosis.6 7
A massive research effort to better understand the complex viral–host interactions has resulted in an extremely high volume of publications in a very short timeframe. The high heterogeneity and variety in the quality of the literature require a systematic appraisal; in order to propose a synthesis of existing evidence towards improved COVID-19 understanding and therapy. This systematic literature review (SLR) aimed to summarise the available information on the pathogenesis of SARS-CoV-2 infection from the rheumatological perspective, given that this specialty is intimately involved in investigation of aberrant and severe immunological reactions in many organ systems and in heterogeneous autoimmune and autoinflammatory disorders. An SLR addressing therapeutic aspects on the repurposing of rheumatic drugs as potential COVID-19 therapy is addressed elsewhere.8 This SLR informed the EULAR points to consider (PtC) on COVID-19 pathophysiology and immunomodulatory therapies.9
Methods
Search methodology
The scope of the systematic literature search on pathophysiology according to the Population, Intervention, Comparator and Outcome (PICO) approach was determined by the EULAR task force aiming at developing PtCs on COVID-19 pathophysiology and immunomodulatory therapies (online supplemental text S1).10 Three separate searches (online supplemental text S2, S3 and S4) were performed, one for studies on pathophysiology of COVID-19, the second on studies on COVID-19 treatment and the third on COVID-19 and rheumatic and musculoskeletal diseases (RMDs), with this SLR reporting on pathophysiology. The databases explored were MEDLINE, Embase, The Cochrane Database of Systematic Reviews, CENTRAL and CINAHL. Hand search for individual original research studies and crosscheck for references from specific Rheumatology, Haematology and Immunology journals were selected as described in the online supplemental material.
Supplemental material
Study selection, data collection and assessment of risk of bias
Two reviewers (AA and AN) independently assessed titles and abstracts of the retrieved papers. General eligibility criteria were described as follows: original research articles, published in peer-reviewed journals in English language, on adult and paediatric patients with proven SARS-CoV-2 infection according to the reference standard (nucleic acid amplification tests such as RT-qPCR) presenting with signs/symptoms of COVID-19 or asymptomatic and no diagnosis of RMDs prior to SARS-CoV-2 infection. In addition, different predetermined eligibility criteria were set according to the research questions (online supplemental text S5). Among other, unsupervised clustering methods (defined as multiparametric flow cytometry, mass cytometry, multiplex-luminex technologies, single cell RNA seq) were a pre-requisite for cells population, chemokines and cytokines assessment. In addition, for humoral response assessment, only studies using validated commercially available antibodies testing kits were included. For multiparametric algorithm studies, a minimum size of 200 patients was chosen. The agreement between reviewers, calculated with the Cohen’s kappa, was 0.95. Discrepancies were resolved by discussion. The task force methodologist (PMM) was consulted in the case of uncertainties. Data on patients’ characteristics, scientific methods, parameters assessed and outcomes were extracted. The risk of bias was calculated with validated tools according to the study design (online supplemental text S6). The structure of reporting this SLR follows the structure of the PtCs,8 as decided by the task force members following a consensus process.
Results
Of the 55 496 records yielded by the three searches, 290 were selected for detailed review. Of these, 84 articles met the inclusion criteria for the research questions on the pathogenesis of COVID-19 (online supplemental table S1 and S2).
Genetic variants and SARS-CoV-2 severity
As far as genes involved in the immune response are concerned, Zhang et al demonstrated that known variants of toll-like receptor 3 (TLR3)–and interferon regulatory factor 7 (IRF7)–dependent type I interferon (IFN) immunity associated with life-threatening influenza are present in a subset of patients with life-threatening COVID-19 (table 1).11 In addition, new TLR3 variants have been identified in life-threatening COVID-19 and linked to hampered IFN immunity in vivo and in vitro.11 Variants of the IFN-related genes were also identified by a study sequencing and genotyping interferon-induced transmembrane protein 3 (IFITM3) rs12252 sequence that observed an association between homozygosity for the C allele (CC vs CT/TT) and disease severity (OR 6.37; p<0.0001).12 A first genome-wide association study (GWAS) conducted in 1980 patients with severe COVID-19 identified cross-replicating associations with rs11385942 at locus 3p21.31 spanning genes involved in the immune response such as CCR9, CXCR6 and CXCR1.4 While writing this manuscript, an important GWAS study came to our attention.13 Although outside the review period, we highlight it due to its relevance and the exceptionality of the rapid pace of publications on the topic of this SLR. This GWAS made on 2244 critically ill patients revealed association with single nuclear polymorphism (SNP) involved in the IFN pathway (IFNAR2, TYK2, OAS) and CCR2. Mendelian randomisation supported a causal link from low expression of IFNAR2 and high expression of TYK2 to life-threatening disease, and high expression of CCR2 as well.13 Sequencing and genotyping of perforin rs35947132 (A91V) sequence in patients with severe COVID-19 was also performed showing that both patients carrying the sequence died.14 Of interest, previous studies reported a higher prevalence of the A91V variant in patients with haemophagocytic lymphohistocytosis,15 suggesting a possible common mechanism. Data on human leucocyte antigen (HLA) haplotypes are scarce and only showed a higher prevalence of some haplotypes (B*27:07, DRB1*15:01 and DQB1*06:02) in 99 COVID-19 patients versus 107 healthy donors.16 In addition, the only available GWAS failed to identify any SNP association signals at the HLA complex that met the significance threshold of suggestive association or any significant allele associations with either COVID-19 infection or disease severity (1980 and 2381 patients, respectively).17
Genetic variants and disease severity
Other genes that are not directly involved in the immune response but may be related to SARS-CoV-2 infection have been explored. The ACE-2 facilitates SARS-CoV-2 entry in human cells by binding of the virus spike protein.18 Low ACE2 allelic variability has been reported,19 20 along with a different distribution of variants versus controls.19 However, no solid association between ACE-2 variants and disease severity has been demonstrated.17 19–21 Finally, with regard to blood type, the only available data come from a GWAS study which identified the rs657152 A or C SNP at locus 9q34.2 (OR for the A allele 1.32; 95% CI 1.20 to 1.47; p<0.0001) and estimated a higher risk of severe COVID-19 in blood group A versus other blood groups and a lower risk of severe COVID-19 in blood group O versus other blood groups.17 All data pertaining to this research question are reported in table 1.
Myeloid cellular response to SAR-CoV-2 infection according to disease phenotype
Innate and adaptive cellular immune response has been thoroughly assessed. It is worth noting that only a few studies used unsupervised clustering approaches (single cell RNA sed, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq)) while most used multiparametric flow cytometry. Since different gating strategies were used, different ‘unique’ subsets were reported in several studies and are shown in table 2. Data detailed in the text were reported in at least two individual studies. Neutrophils were reported to be overall increased in patients with COVID-19 regardless of disease severity versus healthy donor (HD).22 23 Of interest, circulating immature neutrophils were reported to be increased, similarly to bacterial sepsis.22 The monocyte compartment was affected by SARS-CoV-2 infection in different manners. A shift towards classical CD14+ inflammatory monocytes producing TNFα and IL-1β was observed in all patients with COVID-19 versus HD.22–25 In addition, the expression of HLA-DR was strongly reduced, especially in severe patients and monocytes response to stimulation in vitro with bacterial or viral ligand cocktail was impaired.23 26–28 The dendritic cells (DCs) pool was decreased in all patients with COVID-19 compared with HD27 and especially severe patients compared with both HD and moderate forms.25 28
Cellular immune response to SARS-CoV-2 infection according to disease phenotype
Lymphoid cellular response to SAR-CoV-2 infection according to disease phenotype
The lymphoid compartment was also affected by SARS-CoV-2 infection. Lymphopenia was frequently reported with both CD4+ and CD8+ lymphocytes consistently reduced compared with HD.26 27 29 The same results were observed in mild30 or severe28 versus HD and in recovered patients24 versus HD. Other studies showed various modulation of T-cell subsets as detailed in table 2.24 31 Blood CD8+ T cell cytotoxicity was decreased in mild30 or all COVID-19 patients compared with HD,32 as shown by a reduction in perforin, granzyme A and B production. An increase in PD-1 expression by CD8+ T cells was reported in severe patients.33 To summarise, two major abnormalities were described in the lymphoid compartment: a relative percentage increase of both central memory CD4+ cells and terminal effector CD8+ cells expressing PD1 suggesting a possible exhausted phenotype. NK cells were decreased in COVID-19 patients versus HD, and in severe COVID-19 versus both HD and mildly affected individuals.24 28 29 34
Finally, results regarding recovered versus active COVID-19 were conflicting, with one study reporting no differences in the lymphoid population,28 while two other studies showed a reduction of NK cells and of different T lymphocyte populations in acutely infected patients followed by a recovery in lymphocytes level during the convalescent phase.29 34 B cells were less often studied but an increase in circulating plasmablasts was reported, while other results were inconsistent.24 28 29 In addition, one study identified immunotypes associated with disease severity.35 More specifically, the Immunotype 1 associating activated CD4 and CD8 T effector memory cells, along with a reduction of circulating follicular helper cells, hyperactivated or exhausted CD8 T cells and plasmablasts was associated with severe diseases, while Immunotype 3 lacking activated T and B cells was associated with milder forms.35
Circulating and tissue neutrophil extracellular traps during SARS-CoV-2 infection
Five studies assessed serum and tissue neutrophil extracellular traps (NETs) release and the results are detailed in table 3. All of them reported an increase in circulating NETs in COVID-19 regardless of disease severity, when compared with healthy donors or convalescent COVID-19 patients.36–39 Moreover, NETs levels in tracheal fluid were higher than plasma levels36 37 and large NETs infiltrating area were reported within the lung tissue of deceased patients, along with small vessel clot occlusion with material composed of Cit-H3+ MPO+ cells and NETs.36 38 40 Functionally, neutrophils isolated from COVID-19patients with displayed a higher baseline production of NETs in vitro.36 37 Circulating platelet-neutrophil aggregates were also observed. It has been suggested that they contribute to the hypercoagulability state observed in COVID-19 and so offer insights into the extensive pulmonary and systemic immunothrombosis that emerges in severe COVID-19.36 38
Circulating and tissue neutrophil extracellular traps (NETs) during SARS-CoV-2 infection
Cytokine and chemokine profiles associated with COVID-19 severity
Studies using unbiased approaches such as mass cytometry or assessing several cytokines through Multiplex or Luminex techniques were included. Five studies assessing the cytokine release in COVID-19 regardless of disease severity showed consistent (reported in at least two manuscript) increase of IL-1α, IL-1β, IL-1Ra, IL-6, IL-8, IL-10, IL-17, IL-18, TNF-α, IFN-α 2, IFN-γ, G-CSF, M-CSF, TRAIL, FGF, VEGF and PGDF when compared with HDs.26 27 41–44 The following chemokines were also reported to be consistently increased: Eotaxin, MCP3, MIP-1α. Additional components were reported to be increased or decreased only in one study and are reported in table 4. Although few disparities in cytokine profiles were highlighted in COVID-19 compared with other infections (sepsis, ARDS or influenza), no cytokines were consistent reported to be differentially expressed.27 45 Variations in cytokines and chemokines released were also reported; depending on disease severity when comparing mild with moderate disease,27 42 mild or moderate vs severe.26 27 41 42 46 Of interest, patients with severe COVID-19 displayed higher levels of IFN-γ, IL-1RA, IL-6, IL-10, M-CSF, MCP-1, MCP-3 and ENRAGE when compared with milder forms.26 27 42 46 In addition, one study showed that IL-1α, IL-1β, IL-17A, IL-12 p70 and IFN-α were decreasing steadily after 10 days in patients with moderate forms of COVID-19, while severe patients maintained higher levels.26 Del Valle et al have also shown that high serum IL-6 and TNF-α levels at the time of hospitalisation were strong and independent predictors of patient survival (p<0.0001 and p=0.0140, respectively), adjusted on prognostic factors in a large cohort of patients.47
Cytokine and chemokine profiles associated with COVID-19 severity
Interferon response to SARS-CoV-2 infection at the transcriptional and protein level
Three studies explored IFN response in patients with COVID-19 using CyTOF48 or multiparametric flow cytometry32 (table 5). Of interest, type I IFN responses were not sustained over time in severe and critical patients.32 In one study, plasma levels of IFN-α2 protein and IFN activity were significantly reduced in severe and critical patients compared with patients with mild-to-moderate disease.48 In another study, impaired mechanistic target of rapamycin (mTOR) signalling and IFN-α production by plasmacytoid DCs was shown, and single-cell RNA sequencing revealed a lack of type I IFNs in patients with severe COVID-19 and transient expression of IFN-stimulated genes.32 The failure to maintain high IFN production in severe forms of COVID-19 could also be related to loss-of-function mutations in the interferon pathway49 and/or the presence of anti-IFN antibodies associated with more severe forms of the disease.13 Noting the aforementioned loss of function in IFN pathways, the data were contradictory regarding IFN production by monocytes, while an IFN signature was reported in classical inflammatory monocytes in one study, a reduction of IFN production was reported in another study.22 25 Similarly, IFN-α and IFN-β production in response to stimulation in vitro were impaired in acute COVID-19 patients’ DCs, while in convalescent patients, DCs could only produce IFN-β. Conversely, serum levels of IFN-α and IFN-γ were increased in another study, and a correlation between viral load and IFN levels was reported.26 However, methods used for cytokines measurement (Simoa or Luminex) and timing of samples (early vs late timepoints) were different between studies. In addition, cytokines were assessed at both transcriptional or protein levels depending on the study and this could partly explain the observed differences.
Interferon response to SARS-CoV-2 infection
Humoral immune response to SAR-CoV-2 infection according to disease phenotype
Five longitudinal studies assessing anti-SARS CoV-2 IgM and IgG using commercially available assays were included (table 6).50–54 Three studies used ELISA,50 51 53 while two studies used chemiluminescence immunoassays (CLIA)52 targeting various SARS-CoV-2 antigens. A variable timing of appearance for IgM within the first 2 weeks after symptom onset has been described and one study reported that patients with mild COVID-19 did not show any IgM response up to 4 weeks after symptom onset.50 As far as IgGs are concerned, studies using ELISA agreed that these antibodies appear by the second/third week after symptom onset while those using CLIA identified IgGs as early as week 1.52 54 IgGs were still detectable up to 6–8 weeks after symptom onset.50 52–54 The studies assessing neutralising antibodies (Nab) provided highly heterogeneous data and since assays were not standardised, comparison across studies was not possible.50 55–57 The SLR did not retrieve any article identifying a role of antibody dependent enhancement and detrimental effect of anti-SARS-CoV-2 antibodies.
Humoral immune response to SAR-CoV-2 infection according to disease phenotype
Platelets, endothelial dysfunction and thrombosis and SARS-CoV-2 infection
A clear pathophysiological link between lung inflammation in COVID-19 and extensive immunothrombosis that has been associated with severe disease and mortality exists pointing towards potential involvement of platelets and endothelial cells. One study sequenced total RNA from platelets isolated from SARS-CoV-2 infected individuals identifying specific clusters of expression in patients with COVID-19, regardless of severity, compared with normal subjects. In particular, enriched pathways observed in COVID-19 associated with protein ubiquitination, antigen presentation and mitochondrial dysfunction. Of interest, one of the top significantly overexpressed genes was IFITM3, whose variants have been associated with disease severity as mentioned above.11 In addition, a comparison of data obtained in patients with COVID-19 with existing RNA-Seq data in H1N1 influenza and sepsis revealed that numerous gene changes were unique for each disease condition. Of the differentially expressed genes that were shared, >96% changed in the same direction.58 Data regarding the detection of platelets positive for SARS-CoV-2 RNA revealed that they were present only in a small subset of patients with COVID-19.59 60 With regard to platelet function, two studies showed higher basal activation in COVID-19 as demonstrated by P-selectin expression, compared with normal subjects,58 59 with basal hyperactivation and stimulated in vitro responses being more pronounced in severe COVID-19.58 59 Since P-selectin is also responsible for interaction between platelets and monocytes, it is not surprising that Hottz et al also demonstrated that platelets form higher numbers of aggregates with monocytes in severe COVID-19. In addition, while aggregated, platelets induce monocyte expression of tissue factor (TF) via P-selecting and integrin αIIb/β3.59 Finally, while data on in vitro platelet aggregation are conflicting,58 61 two studies agreed on a greater adhesion and spreading on fibrinogen and collagen compared with normal subjects58 and in severe vs mild COVID-19.60
Regarding circulating endothelial cells (CECs), a marker of endothelial injury, data are conflicting with two studies reporting increased numbers in COVID-19 vs normal subjects,62 63 one study reported numbers similar to those of normal controls64 and one study observed higher numbers of CECs in patients with COVID-19 in intensive care unit (ICU) versus those not admitted to ICU.65 Only one study investigated circulating endothelial progenitors (CEPs) and observed that they were higher in COVID-19 compared with normal subjects but there was no difference between mild and severe disease. Of interest, apoptotic CEPs/mL positively correlated with the copies of SARS‐CoV‐2 RNA in severe COVID-19.64 All data pertaining to these research questions are presented in table 7.
Endothelial dysfunction, thrombosis and SARS-CoV-2 infection
Multiparametric algorithms for prediction of disease outcome and progression
Several algorithms have been published, using mostly a retrospective design on both inception and validation cohorts (table 8). Most algorithms included clinical parameters such as: demographics (age, race, ethnicity, gender, socioeconomic status, smoking, body mass index), symptoms (fever, fatigue, shortness of breath, diarrhoea, vomiting, haemoptysis, dyspnoea, unconsciousness), comorbidities (asthma, diabetes, hypertension, immunosuppressive disease, cancer history) and treatment (nonsteroidal anti-inflammatory drugs, immunomodulatory therapies). Biological parameters were also included as follows: immune cells (white cell count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio), inflammatory markers (C reactive protein, ferritin), coagulation markers (platelets, procalcitonin, NT-proBNP, AT) and others (haemoglobin, ALT, AST, direct bilirubin, albumin, chloride, potassium, anion gap, glomerular filtration rate, blood urea nitrogen, myoglobin, troponin, lactate dehydrogenase). Imaging parameters including severe chest X-ray radiographic abnormalities and diffuse pulmonary infiltration on CT that have also been linked to severe disease.
Multiparametric algorithms for prediction of disease outcome and progression
One multiparametric model aimed at predicting the risk of hospitalisation with an area under the curve (AUC) of 0.9,66 while three studies aimed at predicting survival with AUC between 0.879 and 0.955,67–69 and two other aimed at predicting disease mortality with AUC between 0.871 and 0.975.70 71 Other algorithms were developed, aiming at predicting disease progression towards a severe phenotype with AUC from 0.77 to 0.9.72–79 Each algorithm is detailed in table 8.
Difference in pathogenesis of SARS-CoV-2 infection between adults and children
Very few studies compared adult and paediatric patients with SARS-CoV-2 infection and all of them evaluated very small cohorts. Some differences were observed with regard to clinical (eg, diarrhoea and vomiting more frequent in children) and haematological (eg, neutropenia more frequent in children) features. This may hint possible different pathogenic mechanisms in response to SARS-CoV-2 infection; none of the studies specifically explored them.80–82
Gut and SARS-CoV-2 infection
Only four publications about two studies investigating the gut microbiome of patients with COVID-19 were retrieved by the SLR and both of them identified a dysbiosis (online supplemental table S3). A highly heterogeneous configuration that was different according to the faecal SARS-CoV-2 viral load was observed, along with depletion of beneficial commensals and abundance of opportunistic pathogens. Of interest, these abnormalities persisted even after recovery from COVID-19.83–86 In addition, when comparing the gut microbiome of patients with COVID-19 with that of patients with H1N1 influenza, differing overall compositions were observed. Opportunistic pathogens were reported in with more pronounced abundance in COVID-19.84 Interestingly, a specific set of bacterial species allowed to discriminate the two patient groups.86 One study identified increased levels of biomarkers of gut leakage and gut homing, while no difference in biomarkers of enterocyte damage were observed in patients with COVID-19 compared with normal subjects.87
Histological lesions related to SARS-CoV-2 infections
Most histological studies have assessed lung tissue damage linked to COVID-19 in deceased individuals (online supplemental table S4). Two studies reported viral inclusions assessed by electronic microscopy immunohistochemistry with or without in situ hybridisation.4 40 Viral inclusions were observed mainly in airways and tracheal epithelium and pneumocytes. Histological studies of autopsy specimens from patients with identified cause of death being various among which ARDS, consistently reported the following tissular lesions: exudative, proliferative, mixed, organising or fibrosing diffuse alveolar damage (DAD); associated with microvascular and macrovascular thrombi.4 88 89 Of interest in the study from Li et al, patients with fibrosing DAD were younger (p=0.034) and had a longer duration of illness (p=0.033), hospitalisation (p=0.037) and mechanical ventilation (p=0.014) compared with those with acute DAD. Similarly, patients displaying organising DAD features had a longer duration of illness (p=0.032) and hospitalisation (p=0.023) compared with those with acute DAD.89 De Michele et al90 have identified different histological patterns associated with COVID-19 severity. In their autopsy series, 29 (73%) of 40 patients presented with acute lung injury (ALI) defined by the presence of hyaline membranes, DAD—with or without—an organising (proliferative) phase. This pattern was associated with longer hospitalisation (p=0.02), longer ventilation (p=0.003) and more radiographic alveolar infiltrates (p=0.01).
Comorbidities and immune response to SARS-CoV-2 infection
Although many studies assessed the impact of comorbidities on clinical outcomes of COVID-19,91 only one study explored the effect of comorbidities, namely, type 2 diabetes (T2D) on immune response in patients with COVID-19 (online supplemental table S5). By means of unsupervised analyses of cytometry data and principal component analysis) including lymphocyte and monocyte subpopulations, the authors identified three distinct clusters of patients corresponding to COVID-19 without T2D, COVID-19+T2D and T2D without COVID-19‐19. In more detail, an increase of CD14+ monocytes, increased phenotypically switched monocytes and decreased classical monocytes were observed in in patients with COVID-19+T2D compared with those with COVID-19 without T2D.92
Immunosenescence and SARS-CoV-2 infection
Three studies assessed the impact of immunosenescence on immune response to SARS-CoV-2 infection using different age cut-offs.30 93 94 Among other, CD4+ and CD8+ T cells were reduced compared with younger patients, and CD8+ T cell cytotoxicity was reduced as demonstrated by a decrease in granzyme and perforin expression. Two studies reported that CD8+ T cells displayed an exhausted phenotype as shown by higher PD-1 expression.93 94 However, it is worth noting that PD-1 is known to be increased in older patients regardless of their SARS-CoV-2 infection status.95 Other cell population are reported in only one study, and the results are detailed in online supplemental table S6.
Consequences of immunomodulatory drugs on viral load and host antiviral immune response
Only a few studies assessed the effects of immunomodulatory drugs on viral load and host antiviral immune response (online supplemental table S7). Several cytokines levels, including ↓ IL-6, MCP-3 and IFN-γ were reduced after baricitinib treatment in four patients.44 After tocilizumab treatment, two studies reported an increase in lymphocyte counts in small groups of five patients.29 96 This is in line with data from clinical trials.97 In addition, the administration of Tocilizumab restored of NK cell cytotoxicity29 and rescued HLA-DR expression in conventional monocytes.96
Discussion
This SLR summarises current evidence on SARS-CoV-2 infection pathogenesis as viewed from the Rheumatology perspective. We gathered a large amount of publications showing how SARS-CoV-2 infection affects immune and non-immune cells. While some features were consistently reported across studies for both the lymphoid and the myeloid compartment, the heterogeneity of results prevented any firm conclusion being drawn in many publications. We did not retrieve data on mast cell and eosinophils since studies remain scarce.98 From a cytokines’ point of view, IL-6, TNFα and IL-1β production and release were associated with COVID-19 severity.99 It is noteworthy that the elevations in cytokine levels including IL-6 were reported to be mild or modest in general compared with sepsis, or oncoimmunotherapy-related cytokine storm and MAS.6
In addition, genetic predisposition, especially linked to the type I IFN pathway, was shown to be responsible for more severe phenotypes in different cohorts, highlighting molecules and pathways deemed essential for a functional anti-SARS-CoV-2 response. Despite the clear genetic evidence incriminating loss-of-function mutations in the IFN pathway and the strong history of IFN link to viral immunity, a clear beneficial role of type I IFN cannot be determined so far and may vary depending on the timing and the stage of the disease. In fact, type I IFN response appeared to be variably described across studies, probably because of variable methods (transcriptomic data vs protein measurements) and timing of analysis. It is very likely that, while IFN response is initiated in all patients with COVID-19, the magnitude of IFN production and its duration to clear the virus may differ according to disease severity, and it probably fails to remain high in severe and critical patients, therefore contributing to impaired viral response and worse outcome. Longitudinal studies measuring IFN response across time and disease severity are warranted to confirm this hypothesis. In addition, IFN protein measurement in blood may not reflect disease in tissue.
Humoral response to SARS-CoV-2 tended to be variable among individuals and the presence of IgM was inconsistently reported, suggesting that some individuals do not develop an IgM response. Non-immune cells such as platelets and endothelial cells exhibit an activated phenotype favouring clotting along with a hypercoagulability state. Of interest, children and young adults were displaying different features compared with infected adults, presenting with extremely common mild forms of the disease and more rarely severe disease termed multisystem inflammatory syndrome in children. All these findings taken together address partly the knowledge gap in understanding SARS-CoV-2 infection mechanisms.
While conducting this SLR, we were faced with several limitations that prevented from drawing conclusions in several aspects of disease pathogenesis. First, limitations related to study design itself or methods used since most studies were assessing only a few randomly selected cytokines or cell subsets. Such approaches are biased and could potentially lead to inaccurate or non-generalisable results. In this SLR, we included only studies using unsupervised clustering approaches through single cell RNA seq or similar techniques; or at least multiparametric flow cytometry for cell assessment. Similarly, only mass spectrometry or multiplexed cytokine assays that could simultaneously assess several cytokines (eg, Luminex, Muliplex) were included and analysed. Through this strict approach, we aimed at reducing the risk of biased results. The second type of limitations pertained to the heterogeneity of inclusion criteria and treatments received by individuals included in the studies. In fact, the definition of disease severity was extremely variable across studies, since the WHO scale was not always used, and also two WHO scales exist, classifying patient of moderate severity differently.100
In addition, in several studies, patients who were enrolled could receive several treatments including immunomodulators such as steroids or IL-6 receptor antagonists; and the results were presented without clustering or subgroup analysis, hereby leading to high risk of results’ misinterpretation. Another aspect pertains to multiparametric algorithm studies, where in addition of the very common retrospective design, most of the algorithms published were not validated in other cohorts, while those who did, were in fact validated in temporally different cohorts but in the same population. Although the current context of the pandemic and associated rush in delivering useful science to better understand and treat the disease might explain some of these issues, the interpretation of the results needs to be guarded.
In conclusion, the results of the present SLR highlight the aberrant immune and non-immune cellular response to SARS-CoV-2 infection. Despite the massive amount of literature published, the knowledge gap in SARS-CoV-2 disease mechanisms as viewed from the Rheumatology perspective on how immunity is contributing to severe outcomes remains incompletely understood. Future studies should endeavour to address pending questions such as a better description of host–virus interactions across disease spectrum, and differences in immune response between mild and severe forms. Another important aspect to be further explored is the identification of new therapeutic targets and the study of humoral immune response to vaccination compared with infected individuals. This SLR informs the EULAR PtCs on COVID-19 pathophysiology and immunomodulatory therapies.
References
Supplementary materials
Supplementary Data
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Footnotes
Twitter @AurelieRheumo, @pedrommcmachado
AN and AA contributed equally.
Contributors AN, AA, XM, BT, GDM, JE, LM, DGMG and PMM contributed to study design and contributed to the final manuscript. AN and AA analysed the data.
Funding This work was funded by the European League Against Rheumatism (CLI122). PMM is supported by the National Institute for Health Research (NIHR), University College London Hospitals (UCLH), Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the (UK) National Health Service, NIHR or the Department of Health.
Competing interests AA, AN, JE, LM and GDM have nothing to declare. XM has received consulting and/or speaker’s fees from BMS, Eli Lilly, Galapagos, Gilead, GSK, Janssen, Novartis, Pfizer, Servier and UCB, all unrelated to this manuscript. BT has received consulting and/or speaker’s fees from Roche, Chugai, Vifor Pharma, GSK, AstraZeneca, Terumo BCT, LFB and Grifols. DGMG has received consulting and/or speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this manuscript. PMM has received consulting and/or speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.