key: cord-0897860-7h4obetv
authors: Goliwas, K. F.; Simmons, C. S.; Khan, S. A.; Wood, A. M.; Wang, Y.; Berry, J. L.; Athar, M.; Mobley, J. A.; Kim, Y.-i.; Thannickal, V. J.; Harrod, K. S.; Donahue, J. M.; Deshane, J. S.
title: Local SARS-CoV-2 peptide-specific Immune Responses in Convalescent and Uninfected Human Lung Tissue Models
date: 2021-09-06
journal: medRxiv : the preprint server for health sciences
DOI: 10.1101/2021.09.02.21263042
sha: 043f4d80a864f81a47eaa35f7cdc3b213bd90155
doc_id: 897860
cord_uid: 7h4obetv

Multi-specific and long-lasting T cell immunity have been recognized as indicators for long term protection against pathogens including the novel coronavirus SARS-CoV-2, the causative agent of the COVID-19 pandemic. Functional significance of peripheral memory T cell subsets in COVID-19 convalescents (CONV) are beginning to be appreciated; but little is known about lung resident memory T cell (lung TRM) responses and their role in limiting the severity of SARS-CoV-2 infection. Here, we utilize a perfusion three dimensional (3D) human lung tissue model and identify pre-existing local T cell immunity against SARS-CoV-2 spike protein and structural antigens in the lung tissues. We report ex vivo maintenance of functional multi-specific IFN-{gamma} secreting lung TRM in CONV and their induction in lung tissues of vaccinated CONV. Importantly, we identify SARS-CoV-2 spike peptide-responding B cells in lung tissues of CONV in ex vivo 3D-tissue models. Our study highlights a balanced and local anti-viral immune response in the lung and persistent induction of TRM cells as an essential component for future protection against SARS-CoV-2 infection. Further, our data suggest that inclusion of multiple viral antigens in vaccine approaches may broaden the functional profile of memory T cells to combat the severity of coronavirus infection.

COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has been a global health concern worldwide for the past two years 1-3 . This pandemic has claimed millions of lives and has caused significant economic impacts 1-3 . Infected individuals develop lymphopenia and demonstrate hyperactivated and exhausted T cell responses that contribute to the prolonged period of immune dysregulation, an established hallmark of SARS-CoV-2 infection [4] [5] [6] [7] [8] . Although vaccine efforts have been successful, the emergence of variants 9, 10 , persistence of infection rates and lack of adequate vaccine intake continue to pose a problem for eradication and management of COVID-19. All current COVID-19 vaccines utilize SARS-CoV-2 spike protein to elicit humoral immunity. However, whether these approaches will induce long-term protection is largely unknown.

Evidence from previous zoonotic coronaviruses indicate that an important determinant for recovery and long-term protection is coronavirus-specific T cell immunity [11] [12] [13] . During the initial phase of the pandemic, 20-50% unexposed individuals had significant T cell reactivity to SARS-CoV-2 antigen peptide pools 6, 7, [14] [15] [16] [17] . Several studies reported pre-existing CD4 and CD8 T cell responses in peripheral blood mononuclear cells against both structural (nucleocapsid, N) and non-structural regions of SARS-CoV-2 in COVID-19 convalescent individuals 6, 7, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] . In majority of the convalescents (CONV), SARS-CoV-2-specific CD4 and CD8 T cell responses are seen with significantly larger overall T cell responses in those who had severe compared with mild disease 18, 22, 23 . However, a greater proportion of CD8 + compared with CD4 + T cell responses were noted in mild cases 18, 22, 23 . Additionally, polyfunctional CD8 + T cells were increased in mild versus severe disease. Furthermore, long-lasting memory T cells reactive to N protein of SARS-CoV, displayed robust cross-reactivity to N protein of SARS-CoV-2 24, 25 . Interestingly, SARS-CoV-2specfic T cells are present in individuals with no prior history of SARS suggesting the possibility of pre-existing cross-reactive immune memory to seasonal coronaviruses 7, 17 .

As memory T cell response induced by previous viral pathogens can shape susceptibility to subsequent viral infections including SARS-CoV-2, and/or influence clinical severity of COVID-19, pre-existing memory T cells that recognize SARS-CoV-2 have been evaluated recently 22, [24] [25] [26] [27] [28] [29] . Upon SARS-CoV-2 exposure, individuals with pre-existing T cell immunity are anticipated to have a faster and stronger immune response that could limit disease severity 7 . Additionally, increased memory T follicular helper CD4 + T cells could facilitate a rapid SARS-CoV-2 neutralizing antibody response 30 . The increased presence of both memory CD4 + and CD8 + T cells may enable direct antiviral immunity in the lungs and nasopharynx; pre-existing CD4 + T cell memory could influence the outcome of vaccination, leading to a quicker robust immune response and development of neutralizing antibodies 31 . Alternatively, pre-existing immunity could be detrimental due to antibody-mediated disease enhancement or from an inferior immune response 32 . Thus accurate measurements of pre-existing T cell immunity are essential to correlate with prospective infection, disease severity and effective vaccine responses.

The assessment of a complete SARS-CoV-2 reactive T cell pool in circulation of uninfected (UN) and CONV individuals has been challenging. A full understanding of both the breadth and depth of the SARS-CoV-2 specific T cell responses have not been accomplished so far, as studies have been restricted to circulating T cells which may not adequately represent lung-specific responses to viral infection and/or may not reflect direct anti-viral immunity in the lungs or the nasopharynx. We have developed a perfused three dimensional lung tissue culture model that maintains the tissue architecture of the human lung and enables assessment of preexisting T cell immunity in the lung tissue. Utilizing this model established with lung tissue cores from both UN and CONV individuals, we demonstrate the presence of local immune responses to SARS-CoV-2 peptide pools in the lungs of UN individuals induced by pre-existing T cell immunity and significant memory T cell response in the lungs of CONV individuals.

We first collected remnant surgical specimen from individuals without any history of SARS-CoV-2 infection, who were undergoing lung resection surgeries. For ex vivo culture, 5 mm diameter tissue cores were generated and one tissue core was placed into the central chamber of a polydimethylsiloxane (PDMS) bioreactor containing a mixture of extracellular matrix (ECM) components for structural support. The tissue/ECM support was penetrated with five 400 micron Teflon coated stainless steel wires to generate through-channels for adequate tissue perfusion.

Wires were removed following ECM polymerization. The bioreactor was then connected to a perfusion system and peristaltic pump, where a serum-free defined tissue culture media was perfused from a media reservoir through the tissue volume and collected in a collection reservoir ( Figure 1A-B) . Using this culture system, we have observed maintenance of histologic tissue architecture (Extended Fig. 1A) , as well as cell density (cells/area, Extended Fig. 1B ) over a two week culture period. Further, lactate dehydrogenase (LDH) remained unchanged during culture (Extended Fig. 1C) , indicating sustained viability. The ECM composition utilized in this model has been used with earlier prototype bioreactors to generate cell culture models of lung and breast carcinoma (Goliwas et. al. 2021; Goliwas et al, 2016) , where cell growth and sustained viability have been observed. These platforms were adapted for the ex vivo culture of human lung tissue for these studies and cell phenotyping showed maintenance of lung epithelial and endothelial cells, as well as fibroblasts, lymphocytes including CD8 + T cells (Extended Figs. 1D-UN) and four individuals who previously tested positive for SARS-CoV-2 and cleared the virus (convalescent (CONV), Extended Data Table 1 with subject demographics). Tissue cores were cultured ex vivo using the bioreactor platform and following four to five days perfusion culture, peptide pools covering the SARS-CoV-2 membrane glycoprotein (M peptide), nucleocapsid phosphoprotein (N peptide), or the immunodominant sequence of the spike protein (S peptide) were added and cellular response was compared to vehicle control exposed tissues ( Figure 1C ). Fig 8S) .

Following exposure to the three peptide pools developed against structural proteins of SARS-CoV-2, the lung tissue cores were collagenase digested and alterations in the cellular landscape and immune response were evaluated. As noted at baseline, no differences were observed in the epithelial cell populations, but an increase in CD31 + endothelial cells was found with M peptide treatment in CONV samples (Extended Fig. 2E & Extended Fig. 3D ). No changes were noted in the CD45 + immune cells, TNF-α producing immune cells, or the CD4 + T cells with peptide exposure within UN or CONV lung tissues (Figures 2H-I, Extended Fig. 4C ). The change in frequency of the PD-1 + CD4 + T cells between control and N peptide-exposed tissues was greater in CONV compared to UN lung tissues ( Figure 2J) . Additionally, the hyperactivated CD4 + T cells showed an increasing trend in CONV tissues (p=0.075) exposed to the M peptide when compared to control; this change was not observed in UN tissues ( Figure 2K ). No significant changes were noted in proliferating, antigen specific, or IFN-γ producing CD4 + T cells (Extended 

We assessed T cell memory subsets within the lung tissues of UN and CONV individuals, as they play a vital role in viral clearance during re-infection and recent studies identified functional memory T cells within the peripheral blood of CONV patients 22, [24] [25] [26] [27] [28] [29] . At baseline, CD4 + or CD8 + lung memory T cell subsets were not significantly different comparing UN and CONV tissues Following ex vivo culture, TRM responses to all peptide pools were noted within the UN tissues, All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.02.21263042 doi: medRxiv preprint with decreasing CD4 + TRM frequency with peptide exposure (Figure 3K) . We determined the % IFN-γ + subsets within the CD4 + and CD8 + TRM as well as % TRM within the overall IFN-γ + CD4 + and CD8 + cells. Although statistical significance was not observed when comparing UN and CONV, patient-specific differences were noted in CONV. At baseline, the average % IFN-γ + CD8 + TRM in CONV was 1.71 fold higher than UN average ( Figure 3G ). Within the CONV, % IFNγ + CD8 + TRM and % IFN-γ + CD4 + TRM in Subject #10 who received COVID-19 vaccination, were 3.69 and 4.65 fold higher, respectively, than average of UN ( Figure 3Q, 3L) . While meaningful fold change in average %TRM within CD8 + IFN-γ + was not noted, the average %TRM within CD4 + IFN-γ + in CONV was 2 fold higher than UN average ( Figure 3M ). Of the total IFN-γ + CD8 + T cells in the lungs of CONV individuals #11 (COVID + twice) and #8, 40-50% were TRM ( Figure   3R ). Both % IFN-γ + of the CD8 + TRM (2.55 fold) and % TRM within CD4 + IFN-γ + (2.26 fold) were higher in Subject #11 (Figure 3R, 3L) . In CONV Subject #10, S-peptide responding % IFN-γ + CD8 + TRM was 1.5 fold higher compared to control (Figure 3Q) , and the %TRM within CD8 + IFNγ + cells was 3.25 fold and 2.72 fold higher in M-peptide and N-peptide exposed tissues, respectively ( Figure 3R ). Interestingly, in CONV #5, % IFN-γ + CD8 + TRM was 5.88 fold and 10 fold higher in the S-peptide and M-peptide treated samples compared to the respective controls; robust response was not noted with N-peptide ( Figure 3Q ). The % TRM CD4 + IFN-γ + S-peptide responders in CONV #5 was 12.5 fold higher than the control, whereas other CONV did not demonstrate a robust response in ( Figure 3M ); in CONV #8, the M-peptide responders were 3.39

fold higher compared to control ( Figure 3M ). The % IFN-γ + within the CD4 + TRM cells that are Speptide responders were 14.3 fold higher compared to controls in CONV #5, while the responders were minimal in CONV #10 and 11 ( Figure 3L ). But the % IFN-γ producing M-peptide responder CD4 + TRM were 2.87 fold and N-peptide responder TRM were 3.31 fold higher in vaccinated CONV #10 and CONV #11 (COVID + twice) respectively compared to control ( Figure 3L ). All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.02.21263042 doi: medRxiv preprint Additionally, stem cell like CD4 + memory T cells increased in frequency (p=0.078) with M peptide exposure (Extended Fig. 8H ), whereas both naïve and central memory CD4 + T cell frequencies reduced with M-peptide exposure; similar reduction in N-peptide responders within naïve CD4 + T cells was noted in UN tissues (Figures 3N, Extended Fig. 8I) . Interestingly, the change in the frequency of naïve CD4 + T cells, when comparing the control and S peptide responders, was significantly increased in CONV when compared to UN ( Figure 3N ). No significant differences were observed in CD4 + EM cells ( Figure 3O ). Amongst CD8 + memory subsets, a trend towards a reduction in S peptide-responding TRM CD8 + T cells was noted (p=0.076), when compared to respective controls in UN tissues. This trend coincided with an increasing trend in the change in frequency of TRM between control and S peptide exposure when comparing UN and CONV; this cell population increased with peptide stimulation in CONV tissues ( Figure 3P ). No differences were observed in CD8 + stem cell like memory T cells and minimal changes were observed in CD8 + naïve and central memory subsets (Extended Fig. 8J -K, Figure 3S ). Importantly, CD8 + effector memory cells tended to increase with peptide exposure in CONV tissues, with a significant increase in the change between control and S peptide treated samples observed in CONV tissues when compared to UN tissues ( Figure 3T ).

As the humoral immune response to infection is essential for protection and has not been evaluated in SARS-CoV-2 convalescence at the tissue level, we evaluated B cell subsets in a subset of the lung tissues from UN and CONV individuals. No significant differences in B cell subsets were noted at baseline (Figure 4A-G, Extended Figs. 9A-B) . When CD19 + B cells were evaluated following ex vivo culture and peptide exposure, a trend towards an increase (p=0.068) was observed in CONV tissues with S peptide exposure when compared to control. Furthermore, the change between control and S peptide exposed samples was significantly different in CONV when compared to UN tissues ( Figure 4H ). When evaluating memory B cells, opposing changes between control and M peptide exposed samples were noted in UN and CONV ( Figure 4I ). While All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.02.21263042 doi: medRxiv preprint transitional B cells were boosted in UN, plasmablasts tended to be higher in CONV samples, and all other B cell subsets remained unchanged with peptide exposure (Figures 4J-N) .

Towards optimal assessment of the impact of specific T cell and humoral responses on host protection, T cell dynamics both during SARS-CoV-2 infection and memory phase should be addressed; it is essential to evaluate the role of SARS-CoV-2 specific effector T cells for viral clearance and potential accumulation of these in CONV and recovered individuals over time. But modeling local immune responses against SARS-CoV-2 has so far posed challenges for lack of appropriate small animal models of infection and access to lung tissues from infected and convalescent individuals during this pandemic. We demonstrate here the establishment of a three dimensional perfused human lung tissue model that maintains the cellular heterogeneity, viability and human matrix components over an extended culture period. In this model, we successfully evaluate local immune response to SARS-CoV-2 peptide pools that represent membrane, nucleocapsid and spike proteins of SARS-CoV-2. We provide evidence for pre-existing T cell immunity and SARS-CoV-2 peptide-specific local lymphocyte memory responses in lung tissues from UN and CONV that validates this model for evaluating local immune responses to viral anew and previously encountered viral antigens.

Evidence of elevated frequencies of PD-1 + CD4 + and/or CD8+ T cells in our CONV lung tissue models are consistent with the reported increase of PD-1 + cells in circulation of CONV [33] [34] [35] [36] suggesting that these models are relevant for evaluation of antigen primed responses. Preexisting T cell Immunity in lungs of UN individuals in our study, however, was represented by Mpeptide responder CD8 + stem cell like memory cells, and not N-peptide responding CD8 + T cells as reported by several studies in circulation of UN individuals 6, 7, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] . Interestingly, the change in frequencies of overall N-peptide responding CD8 + T cells were increased signific [33] [34] [35] [36] antly in CONV compared to UN. Additionally, the change in N-peptide responding % PD-1 + CD4 + T cells All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. ;  was significantly higher in CONV compared to UN; both not consistent with what is reported in circulation which perhaps reflects the differences in the lung niche that may support local immune responses different from that in circulation.

Previous studies in SARS-CoV recovered individuals have identified persistent memory T cell responses that suggested vaccine-mediated induction of TRMs could be a long-term protection strategy for this pandemic 12 These observations are consistent with vaccine mediated induction of TRM as a potential long term protection strategy 12, 40 . Additionally, vaccine induced TRM localized to lung tissue of CONV suggest potential beneficial effects in the respiratory tract. Further, the predominant IFN-γ secretion by CD8 + T cells was in response to S-peptide in CONV and the overall TRM response to S-peptide was higher in CONV. In this context, high frequency of spike protein-specific CD4 + T cell responses has been reported in blood of COVID- 19 CONV 7, 14, 15, 28, 41 . Importantly, CD4 + T cells are necessary for the formation of protective CD8 + TRM during influenza infection; IFN-γ is an essential signal for this process 42 . Consistent with this, despite the small size in our study, % TRM within CD4 + IFN-γ producers was increased in CONV; a robust 12.5 fold increase in S-All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. For peptide exposure studies, 12 tissue specimen were obtained from patients with no history of SARS-CoV-2 infection and 4 tissue specimen were obtained from patients who had previously tested positive for SARS-CoV-2 and cleared the infection. This study was approved by the University of Alabama at Birmingham Institutional Review Board (IRB-300003092 and IRB-300003384) and conducted following approved guidelines and regulations. Written informed consent was obtained from all participants. Patient demographics are described in Table 1 .

Sample Processing and Ex Vivo Perfusion Culture: 5 mm diameter tissue cores were generated from remnant surgical specimen using a tissue coring press (Alabama Research and Development, USA). One tissue core was placed into the central chamber of a polydimethylsiloxane (PDMS, Krayden, USA) bioreactor containing a mixture of extracellular matrix (ECM, 90% collagen type 1 (Advanced Biomatrix, USA) + 10% growth factor reduced Matrigel (Corning, USA)) components for structural support as previously described 44 . The tissue/ECM volume was then penetrated with five 400 micron Teflon coated stainless steel wires to generate through-channels for tissue perfusion. Following ECM polymerization, the wires were removed and the through-channels were filled with tissue culture media (1:1 mixture of X-Vivo15 and Bronchial Epithelial Growth media (Lonza, USA) with antibiotics (MP Biomedicals, USA)).

The bioreactor was then connected to a perfusion system, that contained a media reservoir, peroxide cured silicon tubing (Cole Parmer, USA), a collection reservoir and peristaltic pump (ESI, USA), and tissue culture media was perfused through the tissue volume for 5 to 14 days (37°C, 5% CO2), with media changed every 3 days. At the end of each experiment, a portion of each tissue was fixed separately for histologic processing and collagenase B (Roche, Switzerland) digestion for flow cytometry analysis All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. Histologic Processing and Analysis: Following ex vivo culture, a portion of each cultured tissue was fixed with neutral buffered formalin, processed to paraffin, and histological sections were prepared, as previously described 45 . 5 micron sections were stained with hematoxylin and eosin (H&E) to evaluate tissue morphology and cell density (number of cells per cross-sectional area) as described before 46 .

Matrix Proteomics: For matrix protein enrichment and extraction, tissue samples were prepared as described before 47 . Briefly, tissues were processed using the Millipore Compartment Protein Extraction Kit with some modifications of the described methodology 47 and all fractions were stored at -80° overnight. The ECM fraction was then reconstituted in 8M urea and deglycosylated.

The urea-insoluble fraction was collected by centrifugation, reconstituted in 1x LDS sample buffer, and sonicated for 20 minutes in an ultrasonic water bath. Both urea-soluble and insoluble fractions were quantified via EZQ protein assay, and an equal amount per sample was loaded onto 10% Bis-tris gels and gels were stained overnight with Colloidal Coomassie. Each sample was then digested in 3 fractions with trypsin overnight and high resolution LC-ESI-MS/MS analysis was completed. Data was searched against human subset of Uniref100 database with Carbamidomethylation, Oxidation, and Hydroxyproline. All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.02.21263042 doi: medRxiv preprint Measuring Lactate Dehydrogenase: Lactate dehydrogenase (LDH) was measured in conditioned media using the Invitrogen CyQUANT LDH Cytotoxicity Assay (Thermo Fisher, Germany) following manufacturer's instruction.

Statistical Analysis: The measured flow data was summarized by presenting descriptive statistics, such as mean with standard error of the mean (SEM), in the uninfected and convalescent groups. Changes of the measurement between control and each of peptides were computed separately. Two sample t-tests and Wilcoxon rank-sum tests were performed to determine if means of the changes were different between uninfected and convalescent groups.

Mean and SD of the outcome measured were estimated by control and peptides within uninfected and convalescent groups respectively. To evaluate difference in the outcome between control and each of peptides within each of the groups, paired t-test and Wilcoxon signed-rank tests were used. All other statistical analyses were performed using SAS 9.4 (SAS Institute, USA). Statistical significance was determined at P-value < 0.05.

School of Medicine COVID-19 pilot grant awarded to J.S.D. and was also supported by the UAB Comprehensive Flow Cytometry Core Facility (NIH P30 AR048311 and NIH P30 AI27667), the UAB Tissue Biorepository, and the UAB Pathology Core Research Laboratories. The authors would like to thank Dr. Paul Geopfert for providing insights in T cell immunology in infection.

K.F.G. was involved in bioreactor model set up, experimental design and execution, data collection, data analysis, and manuscript and figure preparation. C.S.S., S.A.K., and A.M.W. were involved in data collection, data analysis, and manuscript editing. Y.W. was involved in data collection and manuscript editing. J.L.B. was involved in bioreactor design and production and All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. Correspondence and requests for materials should be addressed to Jessy Deshane (jessydeshane@uabmc.edu) All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 6, 2021. n=4 UN and n=3 CONV. Statistics shown in blue are comparisons between control and peptide exposed samples within each group (UN and CONV). Statistics shown in black are the change in response between UN and CONV for each peptide when compared to the corresponding controls.

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