key: cord-1046047-aknl3gvt
authors: Jang, Hyesun; Choudhury, Saibyasachi; Yu, Yanbao; Sievers, Benjamin L.; Gelbart, Terri; Singh, Harinder; Rawlings, Stephen A.; Proal, Amy; Tan, Gene S.; Smith, Davey; Freire, Marcelo
title: Persistent Immune and Clotting Dysfunction Detected in Saliva and Blood Plasma after COVID-19
date: 2022-04-18
journal: bioRxiv
DOI: 10.1101/2022.03.18.484814
sha: c7f786ae9174c37b06f9bf3efb7dd2cdf1b372be
doc_id: 1046047
cord_uid: aknl3gvt

A growing number of studies indicate that coronavirus disease 2019 (COVID-19) is associated with inflammatory sequelae, but molecular signatures governing the normal vs. pathologic convalescence process have not been well-delineated. We characterized global immune and proteome responses in matched plasma and saliva samples obtained from COVID-19 patients collected between 4-6 weeks after initial clinical symptoms resolved. Convalescent subjects showed robust IgA and IgG responses and positive antibody correlations between matched saliva and plasma samples. However, global shotgun proteomics revealed persistent inflammatory patterns in convalescent samples including dysfunction of salivary innate immune cells and clotting factors in plasma (e.g., fibrinogen and antithrombin), with positive correlations to acute COVID-19 disease severity. Saliva samples were characterized by higher concentrations of IgA, and proteomics showed altered pathways that correlated positively with IgA levels. Our study positions saliva as a viable fluid to monitor immunity beyond plasma to document COVID-19 immune, inflammatory, and coagulation-related sequelae.

. One 69 longitudinal study followed COVID-19 survivors for up to 6-, and 12-month after symptom onset. 70 While the majority of subjects returned to normal life and produced antibody levels, they exhibited 71 a dynamic range of recovery levels (4) and the complete molecular fingerprint caused by virus 72 exposure remains unknown. It is consequently important to document molecular signatures in 73 NP, and NL63 (p<0.0001, p=0.0036 S1, p=0.0009 S2, and p<0.0001, respectively) ( Fig. 2A) . The 130 IgG response showed an opposite trend in that the titers in convalescent plasma were significantly 131 higher than in convalescent saliva for SARS-CoV-2 RBD, S1, S2, and NP (p=0.0178, p<0.0001, 132 p<0.0001, and p<0.0001) (Fig. 2B) . The IgM response was also significantly higher in plasma 133 than saliva for all four SARS-CoV and NL63 antigens (p<0.0001 for RBD, p=0.0117 for S1, 134 p<0.0001 for S2, p=0.0338 for NP, and p<0.0001 for NL63). 135 136 We evaluated saliva and plasma for neutralizing activity against SARS-CoV-2 S bearing 137 pseudovirus particles (rVSV-GFPΔG*Spike). Saliva showed obviously lower neutralizing activity 138 in comparison to plasma (Fig. S1) . Neutralizing activity in plasma samples was surprisingly higher 139 than expected. Despite the fact that the donors in the healthy group were collected pre-COVID-19 140 era and may have never encountered the SARS-CoV-2 virus, more than half of their plasma 141 displayed cross reactivity with a significant level of neutralizing activity (62.5 %, median 142 IC50=271.10). Convalescent COVID-19 subjects showed increased neutralizing activity in plasma 143 for both positive rate and titer (92.16%, median IC50=317.30). In contrast, paired saliva samples 144 were poor at neutralizing the pseudoviral particles, despite the robust RBD S1-binding IgA 145 responses detected by ELISA ( Fig. 2A) . Only after purification and concentration of the IgAs in 146 saliva (16), neutralizing activity was detected within a limited range from two convalescent 147 COVID-19 salivary samples (16.22%, IC50=10.00). alpha-actin-1 (AC=P12814, fold changes= 2.450, p-value=0.004), nuclear transport factor 2 163 illness, clearly differentiated no-symptom participants but did not show a further increase in 176 moderate to severe cases. 177 178 The proteomic signature was evaluated by random forest machine learning and network 179 analyses (STRING enrichment analyses). The hierarchical clustering heatmap generated from the 180 random forest machine learning demonstrated the pathway is clustered into two groups based on 181 convalescent COVID-19 vs. healthy (Fig. 4A) . Network analyses performed based on 182 differentially expressed proteins between convalescent COVID-19 and healthy samples ( Table 1 ) 183 showed that the convalescent plasma proteome displayed suppressed biological functions involved 184 in oxidative damage response, and antimicrobial properties against opportunistic infection 185 (Staphylococcus aureus infection) and complement and coagulation cascades (Fig. 4B) . In 186 contrast, the pathways enriched in convalescent COVID-19 plasma were all associated with fibrin 187 clot formation (Fig. 4B) . Pathways enriched in convalescent samples included hemostasis, platelet 188 degranulation, immune system, interleukin-12 signaling, and leukocyte activation (Fig. 4B) . 189 Pathways related to granule or lysozyme formation were suppressed in saliva from convalescent 190 COVID-19 donors (Fig. 4B) . 191 192 Altered proteomic functions in COVID19 convalescent saliva directly correlated with the 193 expression of RBD-binding IgA response in saliva 194 Comparative proteomic analyses between healthy vs. convalescent COVID-19 suggest that 195 inflammatory markers induced by SARS-CoV-2 remain in both body fluids during the recovery 196 phase. For a better understanding of the inflammatory patterns occurring in the oral local mucosal 197 and systemic immune system, we performed a separate set of analyses that compared the salivary 198 and plasma proteome (Fig. 5A) . The PCA analysis showed a clear separation between the saliva 199 and plasma proteome for both healthy and convalescent COVID-19 samples (Fig.5B ). Hierarchical 200 clustering heatmaps were clustered into two groups based on the origin of samples (saliva vs. 201 plasma) while demographic factors (gender, age, and acute COVID-19 disease severity) did not 202 contribute to the clustering. (Fig. 5C) . In a healthy state, most DE proteins were higher in saliva, 203 but proteins related to coagulation pathways (complement C3, C5, antithrombin) were higher in 204 plasma (Fig. 5C) . The convalescent COVID-19 heatmap had a similar pattern to the healthy 205 heatmap, except that the expression pattern of apolipoprotein and fibrinogen was reversed 206 (saliva>plasma in healthy; plasma>saliva in COVID-19, Fig. 5C ). The cluster of saliva in the 207 convalescent COVID-19 heatmap further diverged into three subclusters (Blue arrows and roman 208 numerals on top of the Fig. 5C ), suggesting a link between the humoral immune response and the 209 innate inflammatory response in the oral mucosa. Each cluster was labeled as I, II, and III in 210 increasing order of protein expression level.

We then determined the influence of the immune subclusters in relation to serological 213 results for each fluid by investigating correlations with immunoglobulin levels ( Fig.6A-C) . 214 Strikingly, significant correlations were observed with the RBD-binding saliva IgA, IgM, and 215 plasma IgA titers, as in a linear increase by the order of subcluster numbers (p=0.0274, p=0.0038, 216 and p=0.0409, respectively, Fig. 6A-C) . Since we observed an increasing trend in the expression 217 level of DE protein, we also performed a correlation analysis between each RBD binding 218 immunoglobulin with each DE protein involved in the convalescent COVID-19 saliva sub-219 clustering ( Fig. 6D-H) . The Clusterin showed significant positive correlations with RBD binding 220 IgA in both saliva and plasma (Fig. 6D&E) . Fibrinogen beta chain was significantly correlated with RBD binding IgA in saliva (Fig. 6F) , and Apolipoprotein A1 was correlated with the RBD 222 binding salivary IgM and plasma IgA (Fig. 6G&H) Carefully designed serosurveillance studies aimed at implementing antibody testing by 250 investigations of blood-derived fluids (19, 20), but not saliva. Convalescent COVID-19 subjects 251 from our study successfully mounted antibody responses to SARS-CoV-2 in both blood plasma 252 and saliva fluids, confirming the clinical phase of our subjects and the feasibility of our 253 investigations. While the majority of serological testing in SARS-CoV-2 cases detect IgG 254 antibodies at individual and population levels (21), our study showed a significant increase in RBD 255 binding IgA in both convalescent saliva and plasma, S1 binding IgG in plasma, and RBD binding 256 IgM in saliva. Significant correlations between paired saliva and plasma showed positivity for 257 SARS-CoV-2 RBD or S1 binding immunoglobulins, indicating that saliva is an available biofluid 258 for monitoring the presence of protective antibody responses and immune responses. There were 259 several similarities detected among both fluid types, we also found unique patterns within saliva 260 when compared to blood plasma. The IgA response in convalescence was significantly higher in 261 saliva than in plasma, whereas the IgG response showed an opposite trend in that the titers in 262 convalescent plasma were significantly higher than in saliva. This is expected as IgG is the 263 dominant subtype in the blood (22), while IgA is found in mucosal tissues (23). To date, however, 264 evidence on antibody responses and neutralization levels to SARS-CoV-2 provided a limited range 265 of information regarding the immune responses and pathogenesis of subjects that recovered, or 266 not, from the natural infection.

Next-generation plasma profiling demonstrates a comprehensive overview of the immune 269 response and has the potential to elucidate the impact of COVID-19 on the host. Zhong et al. 270 showed that more than 200 proteins were found significantly different in plasma levels at the time 271 of infection as compared to 14 days later (24). In comparison to Zhong et al's findings, our plasma 272 proteome appears to reflect a recovery process, displaying much fewer numbers of significantly 273 enriched DE proteins (p-value<0.05, fold change>2)( Fig. 3D and Table 1 ). Yet, the participants of 274 our cohort still displayed a significant enrichment of fibrinogen in plasma. If not limited to the 275 proteins upregulated by 2 fold or higher, convalescent plasma showed an increase in numerous 276 proteins associated with neutrophil functions or migration, such as annexin 1 (25, 26), 277 antileukoproteinase (27), and Matrix metalloproteinase-9 (28, 29) ( Table 1A) . Interestingly, 278 salivary proteome appears to maintain activated inflammatory status longer than plasma, as severe inflammation and thrombosis (37-42). Abnormal fibrinolysis is known to impact networks 301 with neutrophil functions, including NET formation (38, 43, 44) . Indeed, excessive release of 302 NETs, with a low resolution of inflammation, can lead to immune thrombosis in blood vessels, 303 with NET-fibrin interactions contributing to the severity of tissue injury and pathogenesis (45). 304 Unique to the oral organ, the NET-fibrin axis also plays a unique role in regulating the constant 305 deposition of fibrin produced by the commensal microbiome-triggered inflammation (46). 306 Other specific drivers of the abnormal inflammatory and clotting responses observed in our 307 convalescent COVID-19 subjects require further study. Several research teams have identified 308 SARS-CoV-2 or protein in "viral reservoir" tissue samples collected from subjects months after 309 acute 48) . antigen S1 itself appears capable of directly interacting with platelets and fibrinogen to drive blood 313 hypercoagulation (50). This suggests that further studies of convalescent COVID-19 saliva and plasma would benefit from the measurement of SARS-CoV-2 RNA and spike antigen in addition 315 to inflammatory and proteomic signatures. SARS-CoV-2 persistence in intestinal tissue or the oral 316 mucosa, and possible shedding of spike antigen into saliva or blood, could also perpetuate chronic 317 inflammatory and clotting sequelae. 318 The molecular mechanisms underlying higher concentrations of IgA but lower IgA 319 neutralizing activity in convalescent saliva also require further exploration. It is possible that 320 higher salivary IgA concentrations represent some form of extended antibody-mediated disease 321 enhancement. Antibody-mediated disease enhancement has been reported in diverse RNA viral 322 diseases, such as influenza, SARS-CoV-2, Dengue, and human immunodeficiency viruses 323 infections (51-54). One team found that SARS-CoV-2 RBD-specific neutralizing dimeric IgAs 324 isolated from nasal turbinate could facilitate viral infection, transmission, and injury in Syrian 325 hamsters (55). Aleyd et al (56) demonstrated that IgA enhances NETosis as an effective defense 326 mechanism to eliminate pathogens at mucosal surfaces. In contrast, neutrophil activation by IgA 327 immune complex is also known to contribute to the immunopathogenesis of autoimmune diseases, 328 such as IgA vasculitis, and nephropathy (57-59). In respiratory viral disease models, such as 329 influenza and SARS-CoV-2, the formation of an IgA-virus immune complex led to exacerbated 330 NETosis of neutrophils isolated from peripheral blood mononuclear cells (PBMCs) ex vivo (60). 331 Our study has several limitations. Samples were collected at only a one-time point, and 332 antibody levels or proteomic responses were not adjusted by the different baseline of each 333 individual intervariability. It was also not possible to draw predictive conclusions from our 334 findings but instead predictive correlations. While study subjects were able to report the severity 335 of their acute COVID-19 illness (asymptomatic, mild, or moderate/severe), clinical symptom data 336 was not obtained after convalescent phase when saliva and plasma were collected. 337 Future studies would benefit from requiring convalescent COVID-19 subjects to report 338 possible chronic symptoms longitudinally. This is especially pressing since up to 30% of patients Blood and saliva samples were collected from convalescent COVID-19 donors who visited 360 the COVID clinic at the University of California, San Diego (n=34). Confirmed COVID-19 cases 361 were defined as previously described (65). Throughout the sample collection, the major SARS-362 CoV-2 strain circulating throughout the study was the original strain (USA-WA1/2020) and the 363 vaccine against SARS-CoV-2 was not available. For comparison, we included healthy donors 364 (n=13) from the pre-pandemic era, and subjects recruited to the study signed the institutional 365 review board (IRB)-approved consent form (# 2018-268) (66). 366 Peripheral blood samples were collected by venipuncture and collected into BD vacutainer 367 SST tubes (Vitality Medical, Salt Lake City, Utah). After 1hr., the collected blood sample was 368 centrifuged for serum separation. Saliva was collected by the "passive drool technique" using the parallel, separate sets of samples were processed and used for mass spectrometry to detect host 377 antiviral-, and microbial proteins and peptides ( Fig. 3-5) . In the end, all collected data were 378 collectively analyzed to verify the interaction among systemic and oral mucosal immune responses 379 to the SARS-CoV-2 infection (Fig. 6) . proteomics analysis, the proteins remaining on filters were digested using the filter aided sample 465 preparation (FASP) approach as described previously (66). 467 For the LC-MS/MS analysis, the Ultimate 3000 nanoLC coupled to Q Exactive mass 468 spectrometer (Thermo Scientific) was used as previously described (9 

For proteomics data analysis, protein identification and quantitation were performed using 484 the MaxQuant-Andromeda software suite (version 1.6.3.4) as previously described (71) 

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Fibrin is a critical regulator of neutrophil effector function at 644 mucosal barrier sites. bioRxiv (2021)

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SARS-CoV-2 spike protein S1 induces fibrin(ogen) 666 resistant to fibrinolysis: Implications for microclot formation in COVID-19

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in response to viral infection

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Funding: 748 The Conrad Prebys Foundation Grant

Public Health Service Grants (R00 DE0234804) from the National Institute of 750 Dental and Cranial Research (MF)

SR 756 Visualization: HS, MF, HJ, YY, GT 757 Supervision: MF, GT, DS 758 Writing-original draft: MF, HJ 759 Writing-review & editing: HJ, MF, GT

Competing interests: Dr. Davey Smith DMS has consulted for FluxErgy Inc, Kiadis 762 Pharmaceuticals, Bayer Pharmaceuticals, Linear Therapies

Marcelo Freire has consulted for Mars 764

All data are available in the main text or the 767 supplementary materials. The raw proteomic data that support the findings of this study are 768 shown in the source data file

MPO) (EC 1.11.2.2) [Cleaved into: Myeloperoxidase; 89 kDa myeloperoxidase

Myeloperoxidase light chain