{"id":3919,"date":"2022-03-16T21:37:31","date_gmt":"2022-03-16T21:37:31","guid":{"rendered":"http:\/\/wp.cov19longhaulfoundation.org\/?p=3919"},"modified":"2022-03-16T21:37:31","modified_gmt":"2022-03-16T21:37:31","slug":"sars-cov-2-infects-human-pancreatic-%ce%b2-cells-and-elicits-%ce%b2-cell-impairment","status":"publish","type":"post","link":"https:\/\/cov19longhaulfoundation.org\/?p=3919","title":{"rendered":"SARS-CoV-2 infects human pancreatic \u03b2 cells and elicits \u03b2 cell impairment"},"content":{"rendered":"\n

aUTHORS: Chien-Ting Wu<\/a>,1,13<\/sup>Peter V. Lidsky<\/a>,2,13<\/sup>Yinghong Xiao<\/a>,2,13<\/sup>Ivan T. Lee<\/a>,3,4,5,13<\/sup>Ran Cheng<\/a>,1,6,13<\/sup>Tsuguhisa Nakayama<\/a>,5,7,13<\/sup>Sizun Jiang<\/a>,3,13<\/sup>Janos Demeter<\/a>,1<\/sup>Romina J. Bevacqua<\/a>,8<\/sup>Charles A. Chang<\/a>,8,9,10,11<\/sup>Robert L. Whitener<\/a>,8<\/sup>Anna K. Stalder<\/a>,12<\/sup>Bokai Zhu<\/a>,3<\/sup>Han Chen<\/a>,3<\/sup><\/p>\n\n\n\n

Cell Metab.<\/a>\u00a02021 Aug 3; 33(8): 1565\u20131576.e5.Published online 2021 May18.\u00a0doi:\u00a010.1016\/j.cmet.2021.05.013<\/a> : \u00a0PMC8130512PMID:\u00a034081912<\/a><\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

Emerging evidence points toward an intricate relationship between the pandemic of coronavirus disease 2019 (COVID-19) and diabetes. While preexisting diabetes is associated with severe COVID-19, it is unclear whether COVID-19 severity is a cause or consequence of diabetes. To mechanistically link COVID-19 to diabetes, we tested whether insulin-producing pancreatic \u03b2 cells can be infected by SARS-CoV-2 and cause \u03b2 cell depletion. We found that the SARS-CoV-2 receptor, ACE2, and related entry factors (TMPRSS2, NRP1, and TRFC) are expressed in \u03b2 cells, with selectively high expression of NRP1. We discovered that SARS-CoV-2 infects human pancreatic \u03b2 cells in patients who succumbed to COVID-19 and selectively infects human islet \u03b2 cells\u00a0in\u00a0vitro<\/em>. We demonstrated that SARS-CoV-2 infection attenuates pancreatic insulin levels and secretion and induces \u03b2 cell apoptosis, each rescued by NRP1 inhibition. Phosphoproteomic pathway analysis of infected islets indicates apoptotic \u03b2 cell signaling, similar to that observed in type 1 diabetes (T1D). In summary, our study shows SARS-CoV-2 can directly induce \u03b2 cell killing.Keywords:\u00a0<\/strong>SARS-CoV-2, COVID-19, ACE2, type 1 diabetes, neuropilin 1, phosphoproteomics, apoptosis, SARS-CoV-2 spike protein, insulin, pancreatic beta cell <\/p>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic infection caused by the positive-sense RNA virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Zhu et al., 2020b<\/a>). Although initial studies focused on lung injury and cardiovascular manifestations (Yang et al., 2020<\/a>; Zheng et al., 2020<\/a>), other organ dysfunctions have been observed, notably in the kidney, pancreas, intestine, and olfactory epithelia (Fang et al., 2020<\/a>; Giacomelli et al., 2020<\/a>; Lamers et al., 2020<\/a>; Puelles et al., 2020<\/a>). With regard to diabetes, several recent clinical studies suggested a significant increase in new-onset hyperglycemia, diabetic ketoacidosis (DKA), and diabetes in patients with COVID-19 (Chee et al., 2020<\/a>; Ebekozien et al., 2020<\/a>; Hollstein et al., 2020<\/a>; Naguib et al., 2021<\/a>; Rubino et al., 2020<\/a>; Singh and Singh, 2020<\/a>; Unsworth et al., 2020<\/a>), although some studies question the statistical significance of the effect (Boddu et al., 2020<\/a>). Conceptually, \u03b2 cell damage could be a consequence of direct virally induced cell death or T cell autoreactivity. Therefore, the clinical association between COVID-19 and diabetes raises the first question of whether SARS-CoV-2 can infect pancreatic islet endocrine cells, particularly insulin-secreting \u03b2 cells, and cause cell death or dysfunction to initiate diabetes. The binding of SARS-CoV-2 to the host cell membrane is mediated primarily by the interaction between the viral spike glycoprotein (S) and its main entry host receptor, angiotensin-converting enzyme 2 (ACE2) (Hoffmann et al., 2020<\/a>). Accordingly, many recent studies have focused on analyzing the expression levels of ACE2 in pancreatic endocrine cells. Recent RNA and protein expression studies suggested low ACE2 expression levels in \u03b1, \u03b2, and \u03b4 cells in the pancreas, leading some to postulate that SARS-CoV-2 is unable to infect \u03b2 cells (Arda et al., 2016<\/a>; Baron et al., 2016<\/a>; Blodgett et al., 2015<\/a>; Coate et al., 2020<\/a>; Kusmartseva et al., 2020<\/a>; Segerstolpe et al., 2016<\/a>). However, these characterizations are incomplete, and a more direct evaluation of cellular SARS-CoV-2 tropism is needed. Here, we suggest that the clinical severity of diabetes in patients with COVID-19 may be notably influenced by showing direct viral infection of endocrine cells, particularly \u03b2 cells.Go to:<\/a><\/p>\n\n\n\n

Results<\/h2>\n\n\n\n

Pancreatic \u03b2 cells selectively express SARS-CoV-2 entry factor proteins<\/h3>\n\n\n\n

Recently published studies have been discordant in terms of whether the SARS-CoV-2 receptor, ACE2, is present within insulin-secreting \u03b2 cells of the pancreas (Coate et al., 2020<\/a>; Kusmartseva et al., 2020<\/a>). While technical differences can explain the discrepancies, several of the studies found low levels of ACE2 mRNA expression in pancreatic islets, leading to speculation that SARS-CoV-2 is unable to infect \u03b2 cells. However, SARS-CoV-2 entry is thought to be not only mediated by ACE2 but also by transmembrane serine protease 2 (TMPRSS2), neuropilin 1 (NRP1) (Cantuti-Castelvetri et al., 2020<\/a>; Daly et al., 2020<\/a>), and transferrin receptor (TFRC) (Tang et al., 2020<\/a>). We first evaluated the mRNA expression level of ACE2<\/em>, TMPRSS2<\/em>, NRP1<\/em>, TFRC<\/em>, and FURIN<\/em> in three previously published single-cell RNA sequencing (RNA-seq) datasets (Arda et al., 2016<\/a>; Blodgett et al., 2015<\/a>; Kim et al., 2020<\/a>) in order to assess their expression within the two major pancreatic islet cell populations: insulin-secreting \u03b2 cells and glucagon-secreting \u03b1 cells (Figures S1<\/a>A\u2013S1C). We observed that ACE2<\/em> and TMPRSS2<\/em> transcripts, while expressed at low levels, are nonetheless readily measurable within both \u03b2 cells and \u03b1 cells. Additionally, the transcripts of other SARS-CoV-2 entry factors, NRP1<\/em>, TFRC<\/em>, and FURIN<\/em>, are expressed abundantly in pancreatic islets. We next investigated the protein expression of these SARS-CoV-2 entry factors by co-staining ACE2 (Lee et al., 2020<\/a>), TMPRSS2 (Su\u00e1rez-Fari\u00f1as et al., 2021<\/a>), NRP1 (Cantuti-Castelvetri et al., 2020<\/a>; Daly et al., 2020<\/a>), and TFRC (Haberger et al., 2020<\/a>) in combination with insulin (INS), a \u03b2 cell marker, or glucagon (GLU), an \u03b1 cell marker, in pancreatic autopsy samples from 5 non-COVID-19 donors negative for COVID-19 (by PCR test). The characteristics of these donors are summarized in Table 1<\/a> . Consistent with recent mRNA work, ACE2 and TMPRSS2 were generally expressed within \u03b2 cells and \u03b1 cells but at low protein levels (Figures 1<\/a> A and S1<\/a>D) (Coate et al., 2020<\/a>; Kusmartseva et al., 2020<\/a>). Strikingly, we found robust NRP1 and TFRC protein expression within \u03b2 cells, but not \u03b1 cells, suggesting a potential mechanism for SARS-CoV-2 tropism for \u03b2 cells (Figures 1<\/a>A and S1<\/a>D). For orthogonal confirmation of this result, we utilized a different anti-NRP1 antibody to confirm the NRP1 expression in the pancreas and arrived at the same conclusion (Figure S2<\/a>A). Pre-incubation of the anti-NRP1 and anti-TFRC antibodies with the immunizing peptides drastically reduced staining in the pancreas, further validating the specificity of the result (Figures S2<\/a>B and S2C). To further explore this, we compared the differential protein expression of SARS-CoV-2 entry factors within \u03b2 cells and \u03b1 cells. Similar to the mRNA data, no major differences in ACE2 and TMPRSS2 protein expression were observed between \u03b2 cells and \u03b1 cells (Figure 1<\/a>B), suggesting that the expression levels of these receptors are unlikely to be singularly responsible for a propensity for SARS-CoV-2 to infect \u03b2 cells. Importantly, though, NRP1 and TFRC proteins were significantly increased in \u03b2 cells as compared with \u03b1 cells (Figure 1<\/a>B). NRP1 and TFRC were recently found to facilitate ACE2-mediated SARS-CoV-2 entry (Cantuti-Castelvetri et al., 2020<\/a>; Daly et al., 2020<\/a>; Tang et al., 2020<\/a>), whereas stable SARS-CoV-2 spike-ACE2 interactions depended on both NRP1 and TFRC. These results indicate that \u03b2 cells contain the necessary molecular components for SARS-CoV-2 viral entry and that the higher expression of NRP1 and TFRC entry factors may in part explain the tropism of SARS-CoV-2 for \u03b2 cells.<\/p>\n\n\n\n

Table 1<\/h3>\n\n\n\n

Non-COVID-19 pancreatic tissue donor characteristics<\/p>\n\n\n\n

Donor<\/th>Gender<\/th>Age<\/th>BMI<\/th>Diabetes<\/th>Cause of death<\/th>COVID-19<\/th><\/tr><\/thead>
1<\/td>male<\/td>78<\/td>26<\/td>no<\/td>acute cardiac arrest<\/td>negative<\/td><\/tr>
2<\/td>male<\/td>50<\/td>29<\/td>no<\/td>advanced cancer<\/td>negative<\/td><\/tr>
3<\/td>male<\/td>82<\/td>18<\/td>no<\/td>left-sided cardiac failure<\/td>negative<\/td><\/tr>
4<\/td>female<\/td>86<\/td>24<\/td>no<\/td>combined hemorrhagic and cardiogenic shock<\/td>negative<\/td><\/tr>
5<\/td>male<\/td>81<\/td>28.4<\/td>no<\/td>progressing coronary insufficiency<\/td>negative<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Open in a separate window<\/a><\/a><\/p>\n\n\n\n

\"An<\/figure>\n\n\n\n

<\/a><\/a>Open in a separate window<\/a>Figure 1<\/a><\/p>\n\n\n\n

SARS-CoV-2-associated receptors are expressed in pancreatic \u03b2 cells<\/p>\n\n\n\n

(A) Representative double immunofluorescence staining of ACE2, TMPRSS2, NRP1, and TFRC with the \u03b2 cell marker, insulin (INS), and \u03b1 cell marker, glucagon (GLU), in the normal human pancreas, donor 1. See Table 1<\/a>.<\/p>\n\n\n\n

(B) Quantification of ACE2, TMPRSS2, NRP1, and TFRC in \u03b2 cells (INS +) and \u03b1 cells (GLU +) from a normal pancreas. No statistically significant changes in ACE2 and TMPRSS2 expression were detected between \u03b2 and \u03b1 cells. NRP1 and TFRC expression was statistically significantly higher in \u03b2 cells compared with \u03b1 cells. Rabbit anti-NRP1 (Abcam, ab81321, 1:200) and mouse anti-TFRC (Thermo Fisher, # 13-6800, 1:200) were used for the experiments shown here.<\/p>\n\n\n\n

Error bars represent mean \u00b1 SD (~10\u201315 islets from the pancreas of 5 non-COVID-19 donors; see Table 1<\/a>). \u2217\u2217<\/sup>p < 0.001, one-way ANOVA with Tukey\u2019s post-test. Each dot represents one donor. Scale bars, 5 \u03bcm (A) and 2\u2009\u03bcm (insets). See also Figures S1<\/a> and S2<\/a> and Table 1<\/a>.<\/p>\n\n\n\n

SARS-CoV-2 infects \u03b2 cells ex vivo<\/em> and requires NRP1<\/h3>\n\n\n\n

To test our hypothesis regarding the increased tropism of SARS-CoV-2 for pancreatic \u03b2 cells, we isolated human islets from healthy donors and infected them with SARS-CoV-2 ex vivo<\/em>. The characteristics of the islet donors are summarized in Table 2<\/a> . Two or 6 dpi, infected pancreatic islets were fixed and stained with antibodies against the SARS-CoV-2 nucleocapsid protein (NP) in combination with antibodies against cell-type-specific markers: insulin (\u03b2 cells), glucagon (\u03b1 cells), somatostatin (\u03b4 cells), or CD31 (endothelial cells). Interestingly, SARS-CoV-2 NP was primarily observed in insulin-positive \u03b2 cells at both 2 and 6 dpi (Figures 2<\/a>A, 2C, and 2D), indicating preferential infection of \u03b2 cells by SARS-CoV-2. Similar results were obtained using an antibody raised against the SARS-CoV-2 spike protein (SP) (Figures 2<\/a>B\u20132D). In contrast, the presence of SARS-CoV-2 was notably lower in other pancreatic cell types, namely \u03b1 and \u03b4 cells, and endothelial cells (Figures 2<\/a>A\u20132D). These results strongly support the increased susceptibility of human pancreatic \u03b2 cells for SARS-CoV-2.<\/p>\n\n\n\n

Table 2<\/h3>\n\n\n\n

Non-COVID-19 pancreatic islet donor characteristics<\/p>\n\n\n\n

Donor<\/th>Gender<\/th>Age<\/th>BMI<\/th>Diabetes<\/th>Cause of death<\/th>COVID-19<\/th>Islet RRID<\/th><\/tr><\/thead>
1<\/td>male<\/td>27<\/td>25.3<\/td>no<\/td>anoxia<\/td>negative<\/td>SAMN15314807<\/td><\/tr>
2<\/td>female<\/td>48<\/td>30.9<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>SAMN15770453<\/td><\/tr>
3<\/td>male<\/td>52<\/td>24.5<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>SAMN15850322<\/td><\/tr>
4<\/td>female<\/td>63<\/td>26.9<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>SAMN15942269<\/td><\/tr>
5<\/td>male<\/td>35<\/td>31.3<\/td>no<\/td>head trauma<\/td>negative<\/td>SAMN16191825<\/td><\/tr>
6<\/td>female<\/td>42<\/td>31.2<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>SAMN16427178<\/td><\/tr>
7<\/td>female<\/td>51<\/td>25.2<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>SAMN16515959<\/td><\/tr>
8<\/td>male<\/td>37<\/td>25<\/td>no<\/td>head trauma<\/td>negative<\/td>UNOS ID: AHJX486<\/td><\/tr>
9<\/td>female<\/td>53<\/td>27.4<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>UNOS ID: AHK2444<\/td><\/tr>
10<\/td>male<\/td>60<\/td>29.9<\/td>no<\/td>anoxia<\/td>negative<\/td>SAMN17528599<\/td><\/tr>
11<\/td>female<\/td>44<\/td>23.8<\/td>no<\/td>cerebrovascular\/stroke<\/td>negative<\/td>UNOS ID: AIA3480<\/td><\/tr>
12<\/td>male<\/td>62<\/td>25.9<\/td>no<\/td>head trauma<\/td>negative<\/td>SAMN17928660<\/td><\/tr>
13<\/td>male<\/td>56<\/td>24.2<\/td>no<\/td>anoxia<\/td>negative<\/td>SAMN18021384<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Open in a separate window<\/a><\/a><\/p>\n\n\n\n

\"An<\/figure>\n\n\n\n

<\/a><\/a>Open in a separate window<\/a>Figure 2<\/a><\/p>\n\n\n\n

SARS-CoV-2 preferentially infects \u03b2 cells of human pancreatic islets ex vivo<\/em><\/p>\n\n\n\n

(A\u2013D) Mock-treated or SARS-CoV-2-infected human pancreatic islets were stained after 2 or 6 dpi. (A) Representative double immunofluorescence staining of SARS-CoV-2 nucleocapsid protein (NP) in combination with \u03b2 cell marker, insulin (INS); \u0251 cell marker, glucagon (GLU); \u03b4 cell marker, somatostatin (SST); and endothelial cell marker (CD31).<\/p>\n\n\n\n

(B) Representative double immunofluorescence staining of SARS-CoV-2 spike protein (SP) in combination with a similar combination of markers as (A). The nuclei were stained using DAPI (blue) as a counterstain.<\/p>\n\n\n\n

(C) Quantified percentages of SARS-CoV-2 NP and SP within \u03b1, \u03b2, \u03b4, and endothelial cells of pancreatic islets. Around 40% to 60% NP and SP staining, respectively, are present within \u03b2 cells.<\/p>\n\n\n\n

(D) Quantified percentages of SARS-CoV-2 NP- and SP-positive \u03b1, \u03b2, \u03b4, and endothelial cells.<\/p>\n\n\n\n

(C and D) Error bars represent mean \u00b1 SD (~500\u20131,000 cells were quantified from healthy isolated human islets from donors 1\u20135; see Table 2<\/a>).<\/p>\n\n\n\n

(E) Representative double immunofluorescence staining of SARS-CoV-2 NP in combination with insulin after pre-treating islets with dimethyl sulfoxide (DMSO) or 100 \u03bcM EG00229 for 1 h before infection with SARS-CoV-2. Islets were fixed at 2 dpi and stained for SARS-CoV-2 NP and \u03b2 cell marker, insulin (INS). Quantification of the percentages of \u03b2 cells containing NP-positive \u03b2 cells (right).<\/p>\n\n\n\n

Error bars represent mean \u00b1 SD (~500\u20131,000 cells were quantified from healthy isolated human islets from donors 10\u201313; see Table 2<\/a>). \u2217<\/sup>p < 0.05, two-tailed Student\u2019s t test. Each dot represents one donor. Scale bars, 5\u2009\u03bcm (A, B, and E) and 2 \u03bcm (insets). See also Table 2<\/a>.<\/p>\n\n\n\n

Given the selectively high expression of NRP1 in \u03b2 cells, we hypothesized that inhibition of NRP1 would be sufficient to block infection, even if other co-receptors were important. It has been shown that the treatment of the small molecule EG00229, a selective NRP1 antagonist, reduced the efficiency of SARS-CoV-2 infection in vitro<\/em> (Daly et al., 2020<\/a>). Here, we also found that incubation of ex vivo<\/em> pancreatic islets with EG00229 notably reduced the efficiency of SARS-CoV-2 infection (Figure 2<\/a>E). This result supports a critical role of NRP1 protein in the increased tropism of SARS-CoV-2 for pancreatic \u03b2 cells. Additional studies will be needed to further establish the relationship between levels of NRP1 and the levels of other viral receptors and the efficiency of infection.<\/p>\n\n\n\n

SARS-CoV-2 infects \u03b2 cells in subjects with COVID-19<\/h3>\n\n\n\n

Next, we determined whether SARS-CoV-2 tropism for \u03b2 cells is also observed in patients with COVID-19. We obtained pancreatic autopsy samples from 9 patients who died from severe COVID-19-related complications. The characteristics of these patients are summarized in Table 3<\/a> . Histological analysis revealed lipomatosis, fibrosis, or autolysis in some of the samples, whereas acute or chronic pancreatitis was not observed in any patient (Table 1<\/a>), tending to exclude that broad pancreatic damage is a universal feature. The pancreas of 7 out of 9 of these patients had SARS-CoV-2 viral positivity as measured by RT-PCR. We observed SARS-CoV-2 NP staining selective to insulin-positive \u03b2 cells in 4 of 7 patients, while the remaining 3 pancreatic samples and healthy control samples were negative for NP staining (Figure 3<\/a> A). The specificity of the anti-NP antibody was validated through peptide blocking assays (Figure S2<\/a>D). The 3 negative samples (staining not shown) from patients with COVID-19 had extensive autolysis\/atrophy (Table 3<\/a>), which may explain the lack of NP signal due to rapid proteolysis of tissue by digestive enzymes. As an orthogonal confirmation of our observations of viral presence in \u03b2 cells, we performed in situ<\/em> hybridization (ISH) using a validated SARS-CoV-2 spike mRNA probe in combination with an antibody targeting insulin on the four positive SARS-CoV-2-infected human pancreatic tissues (see Figures S3<\/a>A and S3B for SARS-CoV-2 probe validation) (Lee et al., 2020<\/a>). Similar to the NP staining results, SARS-CoV-2 spike transcripts were detected in \u03b2 cells of these autopsied pancreatic islets (Figure 3<\/a>B). These results confirm SARS-CoV-2 tropism for \u03b2 cells, supporting a model in which SARS-CoV-2 infects and replicates in \u03b2 cells to induce pancreatic dysfunction, thus leading to hyperglycemia or diabetes.<\/p>\n\n\n\n

Table 3<\/h3>\n\n\n\n

COVID-19 patient characteristics, pancreas viral load, percentage of NP+ islets, and histological analysis<\/p>\n\n\n\n

Patient<\/th>Age<\/th>BMI<\/th>Diabetes<\/th>Viral load PCR (mean)<\/th>Ct values (ORF, S, N)<\/th>NP-positive islets (%)<\/th>Histological analysis<\/th><\/tr><\/thead>
1<\/td>67<\/td>35<\/td>no<\/td>29<\/td>33\/32\/35<\/td>4<\/td>well preserved, mild fibrosis<\/td><\/tr>
2<\/td>85<\/td>26<\/td>type II<\/td>108<\/td>35\/35\/35<\/td>31<\/td>mild fibrosis, lipomatosis, and autolysis<\/td><\/tr>
3<\/td>95<\/td>23<\/td>no<\/td>24<\/td>34\/33\/33<\/td>5<\/td>well preserved, lipomatosis, and mild fibrosis<\/td><\/tr>
4<\/td>71<\/td>24<\/td>no<\/td>15<\/td>35\/35\/indet<\/td>12<\/td>mild lipomatosis and autolysis<\/td><\/tr>
5<\/td>88<\/td>28<\/td>no<\/td>6<\/td>31\/32\/32<\/td>0<\/td>extensive atrophy, lipomatosis, and moderate fibrosis<\/td><\/tr>
6<\/td>85<\/td>30<\/td>no<\/td>11<\/td>38\/37\/36<\/td>0<\/td>extensive autolysis<\/td><\/tr>
7<\/td>58<\/td>47<\/td>no<\/td>16<\/td>38\/35\/36<\/td>0<\/td>extensive autolysis<\/td><\/tr>
8<\/td>66<\/td>29<\/td>no<\/td>indeterminate<\/td>indeterminate<\/td>0<\/td>mild lipomatosis, fibrosis, autolysis<\/td><\/tr>
9<\/td>54<\/td>30<\/td>no<\/td>indeterminate<\/td>indeterminate<\/td>0<\/td>extensive autolysis<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Open in a separate window<\/a><\/p>\n\n\n\n

Pancreatic autopsies were obtained from 9 patients who died from COVID-19-related complications. The pancreas of 7 out of 9 of these patients had SARS-CoV-2 viral positivity by RT-PCR. Ct values of RT-PCR targeting three different SARS-CoV-2 genomic regions (ORF1ab, spike [S], and nucleocapsid [N]) are shown. Ct values of the pancreas from patients 8 and 9 were between 37 and 40 and therefore considered \u201cindeterminate\u201d and not positive. The percentages of the nucleocapsid (NP)-positive islets by immunofluorescence staining as described in the manuscript are also shown for each patient sample. Pancreas from patients 5\u20137 did not have NP positivity, possibly due to extensive autolysis\/atrophy. Histological analysis was performed by a board-certified pathologist (M.S.M.).<\/a><\/p>\n\n\n\n

\"An<\/figure>\n\n\n\n

<\/a><\/a>Open in a separate window<\/a>Figure 3<\/a><\/p>\n\n\n\n

SARS-CoV-2 infects pancreatic \u03b2 cells of patients with COVID-19<\/p>\n\n\n\n

(A) Representative double immunofluorescence staining of pancreatic islets from patients with COVID-19 and healthy controls using antibodies against SARS-CoV-2 NP and INS.<\/p>\n\n\n\n

(B) Representative multiplexed images of in situ<\/em> hybridization against the SARS-CoV-2 spike mRNA, in combination with immunofluorescence staining of insulin (INS). SARS-CoV-2 spike mRNA expression (red dots) was detected within pancreatic \u03b2 cells. The nuclei were stained using DAPI (blue) as a counterstain.<\/p>\n\n\n\n

Scale bars, 5 \u03bcm (A and B) and 2 \u03bcm (insets). See also Figure S3<\/a> and Table 3<\/a>.<\/p>\n\n\n\n

We next investigated whether ACE2 and NRP1 are differentially expressed in the pancreatic \u03b2 cells of patients with COVID-19 compared with non-COVID-19 donors as a potential explanation for why \u03b2 cells are more susceptible to viral infection. ACE2 expression remained low in individuals with COVID-19 with no statistically significant difference compared with non-COVID-19 donors (Figures S3<\/a>C and S3D). Conversely, NRP1 expression is upregulated in patients with COVID-19 compared with non-COVID-19 donors (Figures S3<\/a>C and S3D). These results support a potential role of NRP1 in \u03b2 cell susceptibility of viral infection. However, it is also possible that this is due to the increased NRP1 expression in the organ caused by SARS-CoV-2 infection, which in turn causes the cells to be more susceptible to infection. Further research is needed to establish the generality and mechanism by which SARS-CoV-2 may require either (1) preexisting or (2) virally induced upregulation of NRP1 levels.<\/p>\n\n\n\n

SARS-CoV-2 infection suppresses insulin secretion and kills \u03b2 cells ex vivo<\/em><\/h3>\n\n\n\n

To determine whether SARS-CoV-2 infection affected pancreatic islet function, we quantified the insulin content and glucose-stimulated insulin secretion (GSIS), a functional assay for \u03b2 cell insulin release, in infected islets. We observed a dramatic decrease in insulin content and GSIS in SARS-CoV-2-infected human islets, compared with mock-treated islets (Figures 4<\/a>A and 4B). Notably, this effect is partially reversed upon treatment with the NRP1 antagonist EG00229 (Figure S3<\/a>E). In type 1 diabetes (T1D), virus-induced \u03b2 cell damage can be a result of either virus-triggered cell death or immune-mediated loss of infected pancreatic \u03b2 cell mass. Previous reports of SARS-CoV-1\/2-induced apoptosis in ACE2-expressing A549 and Vero E6 cells (Diemer et al., 2008<\/a>; Li et al., 2020<\/a>; Zhu et al., 2020a<\/a>) suggested a similar mechanism of virus-mediated cell death in pancreatic \u03b2 cells ex vivo<\/em>. To this end, we performed the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay on SARS-CoV-2-infected human islets 6 dpi. TUNEL signal was significantly increased in infected \u03b2 cells, compared with mock-infected \u03b2 cells (Figures 4<\/a>C and 4D). Since SARS-CoV-2 also infects a small number of other cells, such as \u03b1 cells, a population that accounts for the second-largest number of cells in the islet, we next investigated whether SARS-CoV-2 infection can cause apoptosis of \u03b1 cells (Figure S3<\/a>F). The increased TUNEL signal in infected \u03b1 cells suggests that viral infection-induced cell death was agnostic to cell type, although the percentage of \u03b2 cells undergoing apoptosis was higher due to higher susceptibility. SARS-CoV-2 spike protein treatment was sufficient to induce apoptosis in \u03b2 cells, as indicated by an increase in TUNEL signal (Figures 4<\/a>E and 4F). This observation is consistent with past findings that SARS-CoV-1 SP can induce apoptosis in Vero E6 cells (Chow et al., 2005<\/a>). Altogether, these results support a model in which SARS-CoV-2-induced \u03b2 cell apoptosis leads to dysregulation in insulin production and secretion.<\/a><\/p>\n\n\n\n

\"An<\/figure>\n\n\n\n

<\/a><\/a>Figure 4<\/a><\/p>\n\n\n\n

SARS-CoV-2 infection interferes with insulin content\/secretion and induces \u03b2 cell apoptosis<\/p>\n\n\n\n

(A\u2013F) Pancreatic islet functionality was analyzed by insulin content, glucose-stimulated insulin secretion (GSIS), and TUNEL staining ex vivo<\/em>.<\/p>\n\n\n\n

(A) Insulin content is decreased in SARS-CoV-2-infected islets compared with mock-treated islets.<\/p>\n\n\n\n

(B) GSIS is decreased in SARS-CoV-2-infected islets compared with mock-treated islets.<\/p>\n\n\n\n

(A and B) Error bars represent mean \u00b1 SD (data were collected from 7 healthy isolated human islets, donors 2\u20138; see Table 2<\/a>). \u2217<\/sup>p < 0.05, two-tailed Student\u2019s t test.<\/p>\n\n\n\n

(C) Representative staining of \u03b2 cell apoptosis by in situ<\/em> TUNEL and DAPI staining in \u03b2 cells (INS) of mock- or SARS-CoV-2-treated human islets. DNase-treated sections were used as a positive control in the TUNEL assay.<\/p>\n\n\n\n

(D and F) Quantification of the percentages of islets containing TUNEL-positive \u03b2 cells. Error bars represent mean \u00b1 SD (~500\u20131,000 cells were quantified from each of 3\u20135 separate healthy isolated human islets, donors 1\u20135 [D] and 7\u20139 [F]; see Table 2<\/a>).<\/p>\n\n\n\n

(E) Representative staining of \u03b2 cell apoptosis by in situ<\/em> TUNEL and DAPI staining in \u03b2 cells (INS) of mock-treated versus SARS-CoV-2-SP-treated human islets.<\/p>\n\n\n\n

\u2217<\/sup>p < 0.05, \u2217\u2217<\/sup>p < 0.01, two-tailed Student\u2019s t test. Scale bars, 5\u2009\u03bcm (C and E). See also Figures S3\u2013S5<\/a> and Tables 2<\/a>, S1<\/a>, S2<\/a>, and S3<\/a>.<\/p>\n\n\n\n

Given the central role of regulatory kinases in the control of apoptosis, we next asked whether the binding of SARS-CoV-2 to its receptors is sufficient to trigger apoptosis-related signaling pathways independent of additional cellular stress as a result of viral infection and replication. We first leveraged assaying global phosphoproteomics to determine potential kinase regulatory roles of the SARS-CoV-2 SP, which directly binds the viral receptors. Isolated human islets were incubated with SARS-CoV-2 SP from SARS-CoV-2 for 15 or 30 min in parallel with vehicle control treatment. Cells were then harvested, and extracts were prepared for phosphoproteomic mass spectrometry and analysis of signaling (Figure S4<\/a>A). We employed a substrate-based kinase activity prediction model to determine the activity levels of specific kinases from this large-scale phosphoproteomic data. This analysis is based on the underlying assumption that the activity levels of upstream kinases can be inferred through the measured abundance of known downstream target phosphorylation events (Hernandez-Armenta et al., 2017<\/a>). Using kinase set enrichment analysis (KSEA) (Drake et al., 2012<\/a>; Ochoa et al., 2016<\/a>), we were able to assign an enrichment score (ES) value (weighted Kolmogorov-Smirnov statistic; STAR Methods<\/a>) to each kinase to reflect its activity in a manner analogous to that of gene set enrichment analysis (GSEA) (Subramanian et al., 2005<\/a>). Differential expression of phosphosites in the 15- and 30-min SARS-CoV-2 SP-treated human islets was calculated by comparing them to vehicle control, and KSEA was performed using a kinase-substrate database created using PhosphoSitePlus (Hornbeck et al., 2015<\/a>) and NetworKin (Linding et al., 2008<\/a>).<\/p>\n\n\n\n

Quantification of the activity of 67 kinases (Figure S4<\/a>B; Tables S1<\/a> and S2<\/a>) revealed an upregulation of stress-response MAP kinases, including JNK\/p38 (MAPK8\/11) (Wada and Penninger, 2004<\/a>) and cytoskeleton reorganizing p21-activated kinases (PAK) (Manser et al., 1994<\/a>), which are two classic pathways triggering cell death by the apoptosis pathways. Additionally, multiple members of the protein kinase C (PKC) family were downregulated in response to SARS-CoV-2 SP treatment. Through pathway analysis using gene ontology (GO) biological process over-representation analysis (ORA), apoptosis emerged as a recurring top hit (Figures S4<\/a>C and S5<\/a>A\u2013S5D; Table S3<\/a>) as early as 30 min post-SARS-CoV-2 spike protein incubation. Apoptotic kinases, including PAK, were upregulated, as was activation of GSK3\u03b2 and the proapoptotic kinase JNK1, both previously linked to apoptosis in \u03b2 cells (Dhanasekaran and Reddy, 2008<\/a>; Guo et al., 2016<\/a>; Tournier et al., 2000<\/a>) (Figures S5<\/a>E\u2013S5I).<\/p>\n\n\n\n

To understand whether the same pathways were activated by a viral infection, we repeated the phosphoproteomics analysis on cells infected with SARS-CoV-2 (24 h post-infection) to measure the activity levels of specific kinases triggered by the virus. As with SARS-CoV-2 SP treatment, we also observed upregulation of JNK and PAK in SARS-CoV-2-infected islet cells (Figure S4<\/a>B). As before, GO analysis pointed to apoptosis and programmed cell death as the most significantly enriched categories (Figure S4<\/a>D). To validate that JNK and PAK are indeed activated in virus-infected islets, we co-stained phosphorylated JNK1\/2 (pJNK1\/2) and phosphorylated PAK1\/2 (pPAK1\/2) in combination with the SARS-CoV-2 SP in infected pancreatic islets. Our results confirm that pJNK1\/2 and pPAK1\/2 were primarily observed in SARS-CoV-2 SP-positive cells at 24 h post-infection (Figure S4<\/a>E), confirming that the SARS-CoV-2 infection induces the activation of JNK and PAK. The kinase activities induced by SARS-CoV-2 SP and SARS-CoV-2 infection support a mechanism through which SARS-CoV-2 induces apoptosis via the JNK-MAPK apoptosis pathway, allowing a potential window for therapeutic intervention.Go to:<\/a><\/p>\n\n\n\n

Discussion<\/h2>\n\n\n\n

Emerging clinical reports have noted a significant increase in new-onset hyperglycemia, DKA, and diabetes in patients with COVID-19. Understanding how SARS-CoV-2 affects the normal function of the pancreas is an urgent unmet need with fundamental healthcare implications. In this study, we discovered that SARS-CoV-2 preferentially infects \u03b2 cells in isolated human pancreatic islets ex vivo<\/em> and in patients who succumbed to COVID-19. Building on recent studies that identified low-level ACE2 expression in pancreatic islets (Fignani et al., 2020<\/a>; Kusmartseva et al., 2020<\/a>), we observed that ACE2 as well as TMPRSS2 are indeed modestly expressed in \u03b2 cells. Importantly, we uncover the selective expression of other critical SARS-CoV-2 entry factors, NRP1 and TFRC, in \u03b2 cells. We propose that this enrichment of NRP1, and possibly TFRC, is a potential mechanism underlying SARS-CoV-2 tropism for \u03b2 cells. Further studies incorporating more robust reagents, such as well-validated antibodies, will be needed to better understand the additional roles of other factors implicated in SARS-CoV-2 entry, including TMPRSS4, Furin, and heparan sulfate (Clausen et al., 2020<\/a>). Heparan sulfate, in particular, has been shown to be highly expressed in pancreatic \u03b2 cells and plays an important role in regulating \u03b2 cell survival (Simeonovic et al., 2018<\/a>; Ziolkowski et al., 2012<\/a>) and would be another leading candidate in determining SARS-CoV-2 tropism for \u03b2 cells.<\/p>\n\n\n\n

Of note, while we found SARS-CoV-2 localization within \u03b2 cells from 4 autopsied patients, SARS-CoV-2 NP was not detected in pancreatic islets from 3 autopsied patient samples from a separate report (Kusmartseva et al., 2020<\/a>). This discrepancy is likely due to the issue that pancreatic tissues are highly prone to autolysis, resulting in the rapid proteolysis of proteins due to the abundance of digestive enzymes. Indeed, we also did not detect SARS-CoV-2 NP signal in 3 out of 7 pancreas tissues with extensive pathologist-verified autolysis\/atrophy, further suggesting the importance of rapid tissue preservation and documentation during COVID-19 autopsies. For these samples, we were able to confirm viral genomic expression by ISH in \u03b2 cells as evidence of viral infection. Finally, we showed that SARS-CoV-2 infection leads to dysregulation of insulin homeostasis, induction of apoptosis-associated signaling pathways, along with cell apoptosis, mainly in \u03b2 cells. These key observations support a mechanism through which SARS-CoV-2 can directly drive \u03b2 cell damage to cause clinical T1D linked to hyperglycemia. These effects of the virus are not mutually exclusive with the possibility that SARS-CoV-2 can also induce autoimmune-mediated \u03b2 cell destruction and is the subject of further investigation. Nonetheless, at least our microscopic histological observations do not suggest ongoing insulitis. Additional limitations of our study are the small sample size of pancreas samples from patients with COVID-19 and the lack of pancreas from children due to challenges in procuring these tissues. Moreover, since we utilized the pancreas from patients who succumbed to severe COVID-19, we are unable to generalize the SARS-CoV-2 \u03b2 cell tropism to all patients with COVID-19, particularly those with mild COVID-19 due to the invasiveness of such a biopsy. Indeed, only a minority of patients with COVID-19 develop hyperglycemia, DKA, or T1D (Chee et al., 2020<\/a>; Ebekozien et al., 2020<\/a>; Hollstein et al., 2020<\/a>; Naguib et al., 2021<\/a>; Rubino et al., 2020<\/a>; Singh and Singh, 2020<\/a>; Unsworth et al., 2020<\/a>).<\/p>\n\n\n\n

Although we identify a mechanism explaining \u03b2 cell-selective cell death, many details of how the virus migrates to the pancreas in patients with severe COVID-19 remain unclear. We suspect that following the initial infection of the upper airway and secondary expansion of the virus to the lungs, viral particles can be taken up by the vasculature and propagated to vascularized organs including the pancreas, kidney, and brain. From there, the route of vascular exit and viral entry to the tissue itself may require additional steps or preconditions to favor viral attack of secondary tissues like the pancreas. It would be of value to evaluate patient records to determine the time of onset of COVID-19-induced pneumonia, marking severe lung infection, compared with the evolution of hyperglycemia as a marker of pancreatic damage and a diabetes-like effect on insulin secretion.<\/p>\n\n\n\n

While this manuscript was under review, M\u00fcller and colleagues (Ulm University Medical Center, Germany) reported that SARS-CoV-2 infects human pancreatic endocrine cells ex vivo<\/em> and in vivo<\/em> and interferes with \u03b2 cell functions ex vivo<\/em> (M\u00fcller et al., 2021<\/a>). While the general findings are similar to ours, there are some important key discrepancies. First, inconsistent with previous studies, these authors found that ACE2 and TMPRSS2 expression are higher in \u03b2 cells than \u0251 and \u03b4 cells. We and others found that ACE2 and TMPRSS2 expression are low in islets (Figure 1<\/a>) (Coate et al., 2020<\/a>; Kusmartseva et al., 2020<\/a>) both at the mRNA and protein levels. Furthermore, we identified the expression of two other SARS-CoV-2 entry receptors, NRP1 and TFRC, to be higher in \u03b2 cells than \u0251 cells (Figure 1<\/a>). NRP1 expression, and not ACE2, was found to be upregulated in patients with COVID-19 compared with non-COVID-19 donors (Figure S3<\/a>). Treatment of human islets with an NRP1 inhibitor reduces infection by SARS-CoV-2 and partially rescues GSIS in the islets, showing the critical role of NRP1 for enabling SARS-CoV-2 infection. Second, although the authors confirmed that SARS-CoV-2 infects \u03b2 cells by staining pancreatic tissue sections from COVID-19 autopsies, the authors did not find \u03b2 cells to be selectively infected by the SARS-CoV-2, but rather a large number of endocrine cells were infected in isolated islets. In contrast, we found that \u03b2 cells are more susceptible to SARS-CoV-2 infection. Finally, the authors measured apoptosis via cleaved caspase-3 staining at 3 dpi and found no increased apoptosis in infected islets at 3 dpi. Our results indicate a significant increase in apoptotic \u03b2 cells in infected islets at 2 and 6 dpi (Figure 4<\/a>). This is further confirmed through phosphoproteomic analysis and subsequent identification of the specific upregulated kinases involved in apoptotic processes in isolated pancreatic islet post-SARS-CoV-2 SP treatment or virus infection (Figures S4<\/a> and S5<\/a>).<\/p>\n\n\n\n

There are a number of variations between our studies, one of which is the source of the SARS-CoV-2 used. Although we used the same SARS-CoV-2 D614G variant, the source is quite different. The clinically isolated virus used by M\u00fcller et al. originated from the Amsterdam University, Netherlands (010V-03903), while the clinical virus strain used in this paper was from UCSF, United States (CA-UCSF-0001C). Further studies are required to determine whether these different virus strains may affect pathological outcomes. In addition, our source of human islets is different. Our human islets are from patients of United States origin and may reflect different ethnicities. Given the scale of the ongoing epidemic, our findings emphasize the urgent need for the development of therapies to prevent COVID-19-induced diabetes, here informed through a combination of ex vivo<\/em> tissue culture models, retrospective autopsy samples, and unbiased phosphoproteomic analysis.<\/p>\n\n\n\n

Limitations of study<\/h3>\n\n\n\n

There are expected limitations to this study. First, the localization and quantification of pancreatic ACE2, TMPRSS2, NRP1, and TFRC are largely based on ex vivo<\/em> and in situ<\/em> analyses of protein (IHC and IF) expression in pancreatic tissues from a limited cohort of healthy or COVID-19 subjects. The histological analysis of these pancreatic tissue sections can only provide approximate predictions for ACE2, TMPRSS2, NRP1, and TFRC protein expression in isolated islets and islet cells and a general signature for their susceptibility to in vitro<\/em> SARS-CoV-2 infection. This limitation is improved by our ability to infect human islet ex vivo<\/em> and reconstitute several critical aspects of viral infection and pathogenesis, increasing our confidence in the results. For this study, we could not directly translate our ex vivo<\/em> observations of the dramatic decrease in insulin content and GSIS in SARS-CoV-2-infected human islets to the COVID-19 clinical samples in this study, as the insulin and blood glucose levels of these patients were not measured. Additional studies correlating patient serum markers of islet secretion of insulin, glucose levels, and COVID-19 status would be of considerable benefit to better understand virus effects on patients with typical versus severe COVID-19 disease.<\/p>\n\n\n\n

Finally, phosphoproteomics was performed across the entire population of islet cell types, since single-cell phosphoproteomics is currently technically challenging. Therefore, we are unable to deconvolute any diversity of signaling pathways caused by the treatment of SARS-CoV-2 SP or SARS-CoV-2 infection in individual cell types in this study. Given the measured selectivity of infection in \u03b2 cells and preponderance of \u03b2 cells, we can reasonably estimate that the strong effects we observe reflect \u03b2 cell alterations, rather than bystander effects.Go to:<\/a><\/p>\n\n\n\n

STAR\u2605Methods<\/h2>\n\n\n\n

Key resources table<\/h3>\n\n\n\n
REAGENT or RESOURCE<\/th>SOURCE<\/th>IDENTIFIER<\/th><\/tr><\/thead>
Antibodies<\/strong><\/td><\/tr>
<\/td><\/tr>
Rabbit anti-SARS-CoV-2-NP<\/td>GeneTex<\/td>Cat# GTX135361; RRID: AB_2887484<\/a><\/td><\/tr>
Mouse anti-SARS-CoV-2-NP<\/td>Thermo Fisher<\/td>Cat# MA1-7403; RRID: AB_1018420<\/a><\/td><\/tr>
Mouse anti-SARS-CoV-2-SP<\/td>GeneTex<\/td>Cat# GTX632604; RRID: AB_2864418<\/a><\/td><\/tr>
Rabbit anti-ACE2<\/td>Abcam<\/td>Cat# ab15348; RRID: AB_301861<\/a><\/td><\/tr>
Mouse anti-TMPRSS2<\/td>Millipore<\/td>Cat# MABF2158<\/td><\/tr>
Mouse anti-NRP1<\/td>Santa Cruz<\/td>Cat# sc-5307; RRID: AB_2282634<\/a><\/td><\/tr>
Rabbit anti-NRP1<\/td>Abcam<\/td>Cat# ab81321; RRID: AB_1640739<\/a><\/td><\/tr>
Rabbit anti-NRP1<\/td>Atlas<\/td>Cat# HPA030278; RRID: AB_10601976<\/a><\/td><\/tr>
Mouse anti-TFRC<\/td>Thermo Fisher<\/td>Cat# 13-6800; RRID: AB_2533029<\/a><\/td><\/tr>
Rabbit anti-TFRC<\/td>Atlas<\/td>Cat# HPA028598; RRID: AB_10601599<\/a><\/td><\/tr>
Mouse anti-Insulin<\/td>Cell Signaling<\/td>Cat# 8138S; RRID: AB_10949314<\/a><\/td><\/tr>
Mouse anti-Insulin<\/td>Santa Cruz<\/td>Cat# sc-8033; RRID: AB_627285<\/a><\/td><\/tr>
Rabbit anti-glucagon<\/td>ProteinTech<\/td>Cat# 15954-1-AP; RRID: AB_2878200<\/a><\/td><\/tr>
Mouse anti-glucagon<\/td>Abcam<\/td>Cat# ab10988; RRID: AB_297642<\/a><\/td><\/tr>
Mouse-somatostatin<\/td>Santa Cruz<\/td>Cat# sc-55565; RRID: AB_831726<\/a><\/td><\/tr>
Mouse-somatostatin<\/td>Santa Cruz<\/td>Cat# sc-74556; RRID: AB_2271061<\/a><\/td><\/tr>
Mouse anti-CD31<\/td>BD<\/td>Cat# 550389; RRID: AB_2252087<\/a><\/td><\/tr>
Mouse anti-CD31<\/td>Novus<\/td>Cat# NBP2-47785; RRID: AB_2864381<\/a><\/td><\/tr>
Rabbit anti- Phospho-JNK1\/2<\/td>Cell Signaling<\/td>Cat# 4668T<\/td><\/tr>
Rabbit anti- Phospho-PAK1\/2<\/td>Cell Signaling<\/td>Cat# 2601S; RRID: AB_330220<\/a><\/td><\/tr>
<\/td><\/tr>
Bacterial and virus strains<\/strong><\/td><\/tr>
<\/td><\/tr>
SARS-CoV-2<\/td>Joe DeRisi Lab, UCSF<\/td>SARS-CoV-2\/human\/USA\/CA-UCSF-0001C\/2020<\/td><\/tr>
<\/td><\/tr>
Biological samples<\/strong><\/td><\/tr>
<\/td><\/tr>
Human pancreatic islets<\/td>Integrated Islet Distribution Program (IIDP)<\/td>Table 2<\/a><\/td><\/tr>
<\/td><\/tr>
Chemicals, peptides, and recombinant proteins<\/strong><\/td><\/tr>
<\/td><\/tr>
DMSO<\/td>Sigma-Aldrich<\/td>Cat# 276855<\/td><\/tr>
EG00229<\/td>Sigma-Aldrich<\/td>Cat# SML1367<\/td><\/tr>
DAPI<\/td>Bio Trend<\/td>Cat# 40043<\/td><\/tr>
Pen\/Strep<\/td>Thermo Fisher<\/td>Cat# 15140163<\/td><\/tr>
Paraformaldehyde<\/td>AlfaAesar<\/td>Cat# 433689M<\/td><\/tr>
Normal Donkey Serum<\/td>Jackson ImmunoResearch<\/td>Cat# 017-000-121<\/td><\/tr>
NP40<\/td>Sigma<\/td>Cat# 11332473001<\/td><\/tr>
The unrelated mock peptide: KKHKNQRSRKKHKNQRSR<\/td>Genscript<\/td>N\/A<\/td><\/tr>
The NRP1 peptide for blocking assay<\/td>Abcam<\/td>Cat# ab189308<\/td><\/tr>
The TFRC peptide for blocking assay: DQARSAFSNLFGGEPLSYTRFSLARQ<\/td>GenScript<\/td>N\/A<\/td><\/tr>
The SARS-CoV-2 NP peptide for blocking assay: STGSNQNGERSGARSK<\/td>GenScript<\/td>N\/A<\/td><\/tr>
BSA<\/td>Sigma<\/td>Cat# A6003-25G<\/td><\/tr>
1X PBS<\/td>Corning<\/td>Cat# 46-013-CM<\/td><\/tr>
Triton X-100<\/td>USB<\/td>Cat# 22686<\/td><\/tr>
OCT compound<\/td>VWR<\/td>Cat# 25608-930<\/td><\/tr>
Saponin<\/td>Sigma-Aldrich<\/td>Cat# S7900<\/td><\/tr>
Fluoromount-G<\/td>SouthernBiotech<\/td>Cat# 0100-01<\/td><\/tr>
Dako Target Retrieval Solution, pH 9<\/td>DAKO Agilent<\/td>Cat# S236784-2<\/td><\/tr>
ProLong Gold Antifade mounting medium with DAPI<\/td>Thermo Fisher<\/td>Cat# P36931<\/a><\/td><\/tr>
Hoechst<\/td>Thermo Fisher<\/td>Cat# 33342<\/td><\/tr>
GlutaMax<\/td>life<\/td>Cat# 35050-079<\/td><\/tr>
<\/td><\/tr>
Critical commercial assays<\/strong><\/td><\/tr>
<\/td><\/tr>
Human insulin ELISA kit<\/td>Mercodia<\/td>Cat# 10-1113-01<\/td><\/tr>
In Situ Cell Death Detection Kit, TMR red<\/td>Sigma-Aldrich<\/td>Cat# 12156792910<\/td><\/tr>
TaqMan 2019-nCoV Control Kit v1<\/td>Thermo Fisher<\/td>Cat# A47533<\/td><\/tr>
RNAscope Multiplex Fluorescent Reagent Kit v2<\/td>Bio-Techne<\/td>Cat# 323100<\/td><\/tr>
TSA Cyanine 3<\/td>Akoya Biosciences<\/td>Cat# NEL744001KT<\/td><\/tr>
High-Select Fe-NTA Phosphopeptide Enrichment Kit<\/td>Thermo Scientific<\/td>Cat# A32992<\/td><\/tr>
<\/td><\/tr>
Deposited data<\/strong><\/td><\/tr>
<\/td><\/tr>
RNA-seq data for FACS purified human alpha and beta cells (Blodgett et al., 2015<\/a>)<\/td>NCBI Gene Expression Omnibus<\/td>GEO: GSE67543<\/a><\/td><\/tr>
RNA-seq data for FACS purified human alpha and beta cells (Arda et al., 2016<\/a>)<\/td>NCBI Gene Expression Omnibus<\/td>GEO: GSE57973<\/a><\/td><\/tr>
RNA-seq data for FACS purified human alpha, beta, and delta cells (Kim et al., 2020<\/a>)<\/td>NCBI Gene Expression Omnibus<\/td>GEO: GSE143889<\/a><\/td><\/tr>
Phosphoproteomics data of SARS-CoV-2 infected and Spike protein treated human pancreatic islets<\/td>This paper<\/td>PRIDE: PXD025629<\/a><\/td><\/tr>
<\/td><\/tr>
Oligonucleotides<\/strong><\/td><\/tr>
<\/td><\/tr>
SARS-CoV-2 spike mRNA<\/td>Bio-Techne<\/td>Cat# 848561<\/td><\/tr>
<\/td><\/tr>
Software and algorithms<\/strong><\/td><\/tr>
<\/td><\/tr>
ImageJ<\/td>NIH<\/td>https:\/\/imagej.nih.gov\/ij\/<\/a><\/td><\/tr>
Zen black<\/td>Carl Zeiss<\/td>https:\/\/www.zeiss.com\/microscopy\/us\/products\/microscope-software\/zen.html<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Open in a separate window<\/a><\/p>\n\n\n\n

Resource availability<\/h3>\n\n\n\n

Lead contact<\/h4>\n\n\n\n

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Peter Jackson (pjackson@stanford.edu<\/a>).<\/p>\n\n\n\n

Materials availability<\/h4>\n\n\n\n

This study did not generate new unique reagents.<\/p>\n\n\n\n

Data and code availability<\/h4>\n\n\n\n

The analysis code used to support the findings of this study are available at https:\/\/github.com\/bmyury\/membrane_ACE2_quantitation<\/a>.<\/p>\n\n\n\n

Experimental model and subject details<\/h3>\n\n\n\n

Human pancreas tissue collection<\/h4>\n\n\n\n

De-identified human pancreatic islets were obtained from adult organ donors without a history of diabetes or glucose intolerance. Islets were procured through the Integrated Islet Distribution Program, Alberta Diabetes Institute IsletCore, and the International Institute for the Advancement of Medicine. SARS-CoV-2 infected pancreas tissue was obtained during autopsy and processed as previously described (Menter et al., 2020<\/a>); the procedure was approved by the ethics commission of Northern Switzerland (EKNZ; study ID: 2020-00969). All patients with COVID-19 or their relatives consented to the use of tissue for research purposes. The characteristics of the autopsy and islet donors are summarized in Tables 1<\/a>, \u200b,2,2<\/a>, and \u200band33<\/a>.<\/p>\n\n\n\n

Method details<\/h3>\n\n\n\n

Virus production, islet infection, and drug treatment<\/h4>\n\n\n\n

A549 cells stably expressing ACE2<\/em> under CMV promoter were infected with SARS-CoV-2 clinical isolate of the pandemic D614G variant (SARS-CoV-2\/human\/USA\/CA-UCSF-0001C\/2020, kindly provided by Sara Sunshine and Joe DeRisi) with MOI ~0.05 in MEM medium supplemented with 2% FBS and penicillin\/streptavidin (Gibco). 3 days after infection, the medium was collected and cleared from cell debris by centrifugation at 3,000 g for 10 min at 4 C. The virus titers were measured by plaque assay. To infect the islets, 100 ul of virus suspension (5-10×106<\/sup> PFU per ml) was added to 1 ml of the pancreatic islet culture and incubated at 37\u00b0C for the indicated time. Small molecules were dissolved in DMSO (276855, Sigma-Aldrich). Islets were first treated with 100 \u03bcM EG00229 (SML1367, Sigma-Aldrich) or DMSO for 1 hour before infection with SARS-CoV-2. Islets were fixed at 2 dpi.<\/p>\n\n\n\n

Immunofluorescence immunohistochemistry (IF IHC) of cryosections<\/h4>\n\n\n\n

For cryosections, isolated human islets were fixed in 4% paraformaldehyde in 1X PBS for 1 h, embedded in collagen I (Becton-Dickinson, San Jose, CA, USA) 4% (wt\/vol.) and then fixed on ice in 4% paraformaldehyde in 1X PBS for another 20 min, followed by three 5 min washes with 1X PBS and equilibration in 30% sucrose\/1X PBS overnight. Tissue specimens were processed for OCT-embedded, 6-\u03bcm cryosections.<\/p>\n\n\n\n

Cryosections were blocked with 5% normal donkey serum (017-000-121, Jackson ImmunoResearch) in IF buffer (3% BSA and 0.4% saponin in PBS; for all else: 3% BSA and 0.1% NP-40 in PBS) at room temperature for 1 hour. Samples were incubated with primary antibody in IF buffer at room temperature for overnight at 4\u00b0C, followed by 5 washes with IF buffer. Samples were then incubated with fluorescent-labeled secondary antibody at room temperature for 1 hour, followed by a 5 min incubation with 4\u2019,6-dia-midino-2-phenylindole (DAPI) in PBS at room temperature for 5min and 3 washes with IF buffer. Coverslips were mounted with Fluoromount-G (0100-01, SouthernBiotech) onto glass slides followed by image acquisition.<\/p>\n\n\n\n

Immunofluorescence immunohistochemistry (IF IHC) of FFPE sections<\/h4>\n\n\n\n

Sections were cut to 4\u2009\u03bcm thickness onto frosted glass slides at the Stanford University Histology Service Center and University Hospital Basel. H&E-stained sections were obtained from each formalin-fixed paraffin-embedded (FFPE) block. Deparaffinization, rehydration, and heat-induced epitope retrieval (HIER) were performed on a ST4020 small linear stainer (Leica). For deparaffinization, slides were baked at 70\u2009\u00b0C for 1 h, followed by rehydration in descending concentrations of ethanol (100% twice, 95% twice, 80%, 70%, ddH2O twice; each step for 3\u2009min). Washes were performed using a Leica ST4020 Linear Stainer (Leica Biosystems, Wetzlar, Germany) programmed to three dips per wash for 30\u2009s each. HIER was performed in a Lab VisionTM PT module (Thermo Fisher) using Dako Target Retrieval Solution, pH 9 (S236784-2, DAKO Agilent) at 97\u2009\u00b0C for 10\u2009min and cooled down to 65\u2009\u00b0C. After further cooling to room temperature for 30\u2009min, slides were washed for 5-10\u2009min three times in Tris-Buffered Saline (TBS), containing 0.1% Tween 20 (Cell Marque; TBS-T). Sections were then blocked in 5% normal donkey serum ((D9663, Sigma-Aldrich) in TBS-T at room temperature for 1\u2009h, followed by incubation with primary antibodies in the blocking solution. After one overnight incubation of primary antibodies in 4\u2009\u00b0C, sections were washed three times with TBS-T and stained with the appropriate secondary antibodies in PBS with 3% bovine serum albumin, 0.4% saponin, and 0.02% sodium azide at room temperature for 1\u2009h. Following this, sections were washed three times with TBS-T and mounted with ProLong Gold Antifade mounting medium with DAPI (Invitrogen). Hoechst (33342, Thermo) was also used in the second to last TBS-T wash for additional nuclear staining. For the peptide blocking assay, the NRP1 (Abcam, ab81321), TFRC (Thermo, # 13-6800), and SARS-CoV-2 NP (GeneTex, GTX135361) antibodies were preincubated with a 20-fold molar excess of the immunizing peptide or an unrelated mock peptide for 3 hours with rotation at room temperature immediately before primary antibody staining. The peptides used are described below.<\/p>\n\n\n\n

SARS-COV-2 PCR<\/h4>\n\n\n\n

For detection of SARS-CoV-2, RNA was first isolated from formalin-fixed and paraffin embedded pancreas tissue by using the Maxwell RSC RNA FFPE Kit (Promega, Madison, WI, USA) according to the manufacturer\u2019s protocol. Afterwards, TaqMan reverse transcription polymerase chain reaction (RT-PCR) was performed by using the TaqMan 2019-nCoV Control Kit v1 (A47533, ThermoFisher Scientific) to target three different viral genomic regions (ORF1ab, S and N) and the human RPPH1 gene (RNAse-P). According to the manufacturer\u2019s protocol, a C\u0442 value below 37 in at least two out of three viral genomic regions was considered positive. A case was considered negative if C\u0442 values were above 40. Values between 37 and 40 were considered indeterminate and the assay was repeated. Samples were always run as duplicates.<\/p>\n\n\n\n

Antibodies and reagents<\/h4>\n\n\n\n

Antibodies used include the following: rabbit anti-SARS-CoV-2-NP (GeneTex, GTX135361, 1:2,000), mouse anti-SARS-CoV-2-NP (Thermo Fisher, MA1-7403, 1:200), mouse anti-SARS-CoV-2-SP (GeneTex, GTX632604, 1:500), rabbit anti-ACE2 (Abcam, ab15348, 1:200), mouse anti-TMPRSS2 (Millipore, MABF2158, 1:200), mouse anti-NRP1 (Santa Cruz, sc-5307, 1:200), rabbit anti-NRP1 (Abcam, ab81321, 1:200), rabbit anti-NRP1 (Atlas, HPA030278, 1:200), mouse anti-TFRC (Thermo Fisher, # 13-6800, 1:200), rabbit anti-TFRC (Atlas, HPA028598, 1:200), mouse anti-Insulin (Cell Signaling, 8138S, 1:4,000), mouse anti-Insulin (Santa Cruz, sc-8033, 1:1,000), rabbit anti-glucagon (ProteinTech, 15954-1-AP, 1:3,000), mouse anti-glucagon (Abcam, ab10988, 1:4,000), mouse-somatostatin (Santa Cruz, sc-55565, 1:6,000), mouse-somatostatin (Santa Cruz, sc-74556, 1:2,000), mouse anti-CD31 (BD, 550389, 1:100), mouse anti-CD31 (Novus, NBP2-47785, 1:200), rabbit anti- Phospho-JNK1\/2 (Cell Signaling, 4668T, 1:100), and rabbit anti- Phospho-PAK1\/2 (Cell Signaling, 2601S, 1:100). Spike protein (BPS Bioscience, 100688) was used in the apoptosis experiment in Figures 4<\/a>E and 4F. The immunizing peptides for the peptide blocking assay were NRP1 (Abcam, ab189308), TFRC (custom synthesized through GenScript using sequences provided in confidence by Thermo Fisher), and SARS-CoV-2 NP (custom synthesized through GenScript using sequences provided in confidence by GeneTex). The sequence of the unrelated mock peptide used for the peptide blocking assay was KKHKNQRSRKKHKNQRSR (Genscript).<\/p>\n\n\n\n

In situ<\/em> hybridization staining<\/h4>\n\n\n\n

Rehydration and HIER of tissue sections were performed as described above and in Lee et al. (2020)<\/a>. After cooling to room temperature, slides were washed for 2\u2009\u00d7\u20092\u2009min ddH2O before a 15 min H2O2 block at 40\u2009\u00b0C (322335, Bio-Techne). Slides were then washed for 2\u2009\u00d7\u20092\u2009min ddH2O before an overnight hybridization at 40\u2009\u00b0C with probes against the SARS-CoV-2 spike mRNA (848561, Bio-Techne). Amplification of the ISH probes was performed the next day according to manufacturer\u2019s protocol (323100, Bio-Techne), with the final deposition of Cyanine 3 for SARS-CoV-2 spike mRNA probe targets (NEL744001KT, Akoya Biosciences). Slides were then processed as described above for IF IHC staining for mouse anti-insulin (Cell Signaling, 8138S, 1:4000).<\/p>\n\n\n\n

Microscopy<\/h4>\n\n\n\n

Fluorescence-immunolabeled images were acquired using a Zeiss AxioImager Z1 microscope or a Marianas spinning disk confocal (SDC) microscopy (Intelligent Imaging Innovations). Post-imaging processing was performed using ZEN (Carl Zeiss). Final figures were organized using Adobe Illustrator.<\/p>\n\n\n\n

In vitro<\/em> insulin secretion assays<\/h4>\n\n\n\n

GSIS was performed 6 days post infection. Batches of 25 islets were used for in vitro secretion assays. Islets were incubated at a glucose concentration of 2.8 mM for 1 h as an initial equilibration period. Subsequently, islets were incubated at 2.8 mM glucose concentration for 1 h. Supernatant was taken and stored for insulin quantification. Islets were incubated at 16.7 mM glucose concentration for another 1 h. Supernatant was taken and stored for insulin quantification. Islets were then lysed in an acid-ethanol solution (1.5% HCL in 75% ethanol) to extract the total cellular insulin or glucagon content. Secreted human insulin in the supernatants and islet lysates were quantified using a human insulin ELISA kit (Mercodia). Secreted insulin levels were divided by total insulin content and presented as a percentage of total insulin content and normalized to values obtained at 2.8 mM glucose. All secretion assays were carried out in RPMI 1640 (Gibco) and the above-mentioned glucose concentrations.<\/p>\n\n\n\n

TUNEL staining<\/h4>\n\n\n\n

Cellular apoptosis was measured by TUNEL staining according to the manufacturer’s instructions (Roche, Berlin, Germany). The proportion of TUNEL-positive nuclei in pancreatic \u03b2 cell was determined through image analysis of the cryosections. The number of TUNEL-positive cells in pancreatic islets was counted under an Everest deconvolution workstation (Intelligent Imaging Innovations) equipped with a Zeiss AxioImager Z1 microscope and a CoolSnapHQ cooled CCD camera (Roper Scientific).<\/p>\n\n\n\n

Phosphopeptide shotgun proteomics<\/h4>\n\n\n\n

Isolated human islet cells were treated with SARS-CoV-2 spike protein for 15, 30 minutes or with vehicle (water) for 30 minutes. Cells were harvested, lysed, reduce, and alkylated using 100 \u03bcl of lysis buffer (6M Guanidine Hydrochloride, 100 mM Tris-HCl pH 8.0, 10 mM TCEP, 10 mM CAA) and boiled 60\u00b0C for 1 hour. Proteins were precipitated by adding 100 \u03bcl methanol, vortexed, 50 \u03bcl chloroform, vortexed, 100 \u03bcl water, vortexed, and centrifuged at 13,000g for 2 minutes. The top aqueous layer was removed, 200 \u03bcl of methanol was added, vortexed, and centrifuged at 13,000g for 3 minutes. Methanol was removed and dried proteins were resuspended using 200mM HEPES pH 8.5. Proteins were digested using Trypsin\/Lys-C overnight at 37\u00b0C 250 RPM. Sample was acidified using TFA and cleaned using stage tips. Stage tips were created using 5 layers of C18 filters packed into a P200 tip. The stage tips were activated using methanol, equilibrated twice with equilibration buffer (5% ACN, 0.5% TFA). Sample were bound, washed twice with equilibration buffer, and eluted using elution buffer (50% ACN, 0.1% FA). Eluted samples were dried using a Speed-Vac and resuspended using Binding\/Wash Buffer in High-Select Fe-NTA Phosphopeptide Enrichment Kit (Thermo Scientific, A32992). The peptides were enriched for phosphopeptides according to the manufacturer\u2019s instructions. Eluted samples were eluted and resuspended using Solution A (2% ACN, 0.1% FA).<\/p>\n\n\n\n

Samples were analyzed using the timsTOF Pro (Bruker Daltonics) (Meier et al., 2018<\/a>), an ion-mobility spectrometry quadrupole time of flight mass spectrometer. Specifically, a nanoElute (Bruker Daltonics) high pressure nanoflow system was connected to the timsTOF Pro. Peptides were delivered to a reversed phase analytical column (10 cm x 75 \u03bcm i.d., Bruker 1866154). Liquid chromatography was performed at 50 \u00b0C and peptides were separated on the analytical column using a 48 min gradient (solvent A: 2% ACN, 0.1% FA; solvent B: 0.1% FA, in ACN) at a flow rate of 500 nl\/min. A linear gradient from 2-35 % B was applied for 45 min, followed by a step to 95% B for 1.5 min and 3 min of washing at 95% B. The timsTOF Pro was operated in PASEF mode with the following settings: Mass Range 100 to 1700m\/z, 1\/K0 Start 0.85 V\u00b7s\/cm2, End 1.3 V\u00b7s\/cm2, Ramp time 100ms, Lock Duty Cycle to 100%, Capillary Voltage 1700, Dry Gas 3 l\/min, Dry Temp 200\u00b0C, PASEF settings: 4 MS\/MS, charge range 0-5, active exclusion for 0.04 min, Scheduling Target intensity 20000, Intensity threshold 500, CID collision energy 10eV.<\/p>\n\n\n\n

For analysis, Bruker raw data files were processed using Byonic software (Protein Metrics) to identify peptides and proteins using the NCBI Homo sapiens refseq protein database. Data were searched with 20 ppm error tolerance for precursor and 40 ppm for fragment ions using QTOF\/HCD fragmentation type. Besides standard variable modifications, we searched for S\/T\/Y phosphorylation and set 1 % FDR for protein identifications.<\/p>\n\n\n\n

Kinase set enrichment analysis (KSEA Analysis)<\/h4>\n\n\n\n

Counts for phosphosites between two technical replicates were summed after the total counts per sample were normalized to the median of total counts. The counts were then log2<\/sub>-transformed and quantile normalized. Batch correction was done using ComBat function from the sva package from Bioconductor and the log2<\/sub>-transformation was undone to obtain the counts for differential expression analysis. Differential expression analysis was conducted with the msms.glm.pois function from the msmsTests package from Bioconductor (Josep Gregori, Alex Sanchez and Josep Villanueva (2020). msmsTests: LC-MS\/MS Differential Expression Tests. R package version 1.26.0.). KSEA was calculated with the ksea function from ksea package from GitHub (David Ochoa (2020). ksea: Kinase Activity Prediction based in Quantitative Phosphoproteomic Data. R package version 0.1.2.) using a kinase substrate database created from PhosphositePlus (Hornbeck et al., 2015<\/a>) and NetworKin (Linding et al., 2008<\/a>). p-values were adjusted using Benjamini-Hochberg procedure. GO ORA was conducted using enrichGO function from the clusterProfiler package from Bioconductor (Yu et al., 2012<\/a>) and the database org.Hs.eg.db package from Bioconductor (Marc Carlson (2020). org.Hs.eg.db: Genome wide annotation for Human. R package version 3.11.4.).<\/p>\n\n\n\n

Quantification and statistical analysis<\/h3>\n\n\n\n

For signal quantification, samples were stained simultaneously in batch with the primary antibodies (ex. ACE2, TMPRSS2, NRP1, TFRC, insulin, glucagon as described above) using the same master mixes and identical incubation times under similar staining conditions described above. Exposure times under confocal microscopy were identical for the quantified samples. Quantification was performed using a custom script developed in the FIJI package of ImageJ as previously described (Lee et al., 2020<\/a>). Briefly, a binary mask was created by thresholding the insulin and glucagon channels using selected cutoff values to generate a comprehensive outline of each channel. Insulin- and glucagon-positive regions were segmented using continuity of high signal regions on a binary mask. Finally, the signals of proteins of interest within the segmented regions were computed. While the experimenters were not strictly blinded to the samples, all sample processing, staining, and data acquisition were performed in parallel under identical conditions without regard to the specific identity of the samples. Quantification used a custom script developed in the FIJI package of ImageJ as previously described (Lee et al., 2020<\/a>). Experimental sample sizes were not predetermined given the exploratory nature of the work and the limited availability of tissue specimens. No pancreatic samples were excluded from experimentation\/analyses unless otherwise stated in the manuscript (see Table 3<\/a>). Mann-Whitney U test was used when the data were not normally distributed by Shapiro-Wilk normality test and were not at equal variance by F-test. When the normal distribution and equal variance were confirmed, Student\u2019s t test were used. Kruskal-Wallis test and post-hoc Dunn\u2019s multiple comparison test were used for comparisons of more than two groups. Analyses were performed with GraphPad Prism 6.0 (GraphPad Software, La Jolla, CA) software and IBM SPSS Statistics version 23 (IBM, Armonk, NY).Go to:<\/a><\/p>\n\n\n\n

Acknowledgments<\/h2>\n\n\n\n

The authors acknowledge members of the Kim laboratory, especially Jonathan Lam and Dr. Sangbin Park, for helpful discussions and assistance with islet experiments. We thank the Stanford Diabetes Research Center\/Stanford Islet Research Core (supported by # P30DK116074). We thank the Stanford Diabetes Research Center\/Stanford Islet Research Core (SDRC\/SIRC), Alberta Diabetes Institute Islet (ADI) Research Core, IIDP, NDRI, and IIAM for islet and\/or pancreas procurement, and especially the organ donors and their families. This work was supported by the National Institutes of Health R01DK127665 (P.K.J.), R01HD085901 (P.K.J.), R01GM121565 (P.K.J.), P30DK116074 (P.K.J.), R01AI149672-01 (G.P.N.), and U54-CA209971 (G.P.N.); Stanford Diabetes Research Center (SDRC) Pilot and Feasibility Research grant (P.K.J.); the Fast Grant Funding for COVID-19 Science (P.K.J. and G.P.N.); the Botnar Research Centre for Child Health Emergency Response to COVID-19 grant (S.J., M.S.M., G.P.N., and A.T.); a Bill and Melinda Gates Foundation COVID-19 Pilot Award (S.J. and G.P.N.); the Rachford & Carlotta A. Harris Endowed Chair (G.P.N.); California Institute for Regenerative Medicine (DISC2-09637) (J.V.N.); Defense Advanced Research Project Agency (HR001118S0037-PREPARE-FP-001) (J.V.N.); the Operndorf Foundation (J.V.N.); Stanford Respond. Innovate. Scale. Empower (RISE) COVID-19 crisis response trainee seed grant (C.-T.W., R.C., I.T.L., S.J., and T.N.); Stanford Translational Research and Applied Medicine (TRAM) pilot grant (I.T.L.); Thrasher Research Fund Early Career Award (I.T.L.); Stanford Maternal and Child Health Research Institute (MCHRI) Clinical (MD) Trainee Support Award (I.T.L., Ernest and Amelia Gallo endowed postdoctoral fellow); Leukemia & Lymphoma Society Career Development Program (S.J.); Cellular and Molecular Biology Training grant (NIH 5 T32 GM007276) (R.C.); and the Swiss National Science Foundation (SNSF; grant no. 320030_189275) (M.S.M.).<\/p>\n\n\n\n

Author contributions<\/h3>\n\n\n\n

C.-T.W. conceived and coordinated the study. C.-T.W., P.V.L., Y.X., I.T.L., R.C., S.J., and T.N. designed and performed the experiments. C.-T.W., I.T.L., and T.N. performed the microscopy imaging. P.V.L. and Y.X. made virus and infected isolated human pancreatic islets. I.T.L., S.J., A.K.S., A.T., and M.S.M. obtained patient consent, and collected, processed, banked, and\/or evaluated the human samples. R.C. and J.D. performed phosphopeptide shotgun proteomics. C.-T.W., I.T.L., R.C., J.D., T.N., and S.J. analyzed the data. C.-T.W., T.N., and S.J. conducted statistical analyses. R.J.B., R.L.W., and C.A.C. provided isolated pancreatic islets. B.Z. and H.C. assisted in experiments. Y.G. contributed novel tools that enabled the analysis. C.-T.W. and T.N. prepared the final figures. C.-T.W., I.T.L., S.J., and R.C. wrote the manuscript with contributions by P.V.L., J.V.N., A.T., M.S.M., and P.K.J. The co-first authors, C.-T.W., P.V.L., Y.X., I.T.L., R.C., S.J., and T.N. contributed equally and have the right to list their name first in their CV. Funding and supervision were provided by P.K.J. All authors reviewed and agreed with the content of this manuscript.<\/p>\n\n\n\n

Declaration of interests<\/h3>\n\n\n\n

The authors declare no competing interests.Go to:<\/a><\/p>\n\n\n\n

Notes<\/h2>\n\n\n\n

Published: May 18, 2021Go to:<\/a><\/p>\n\n\n\n

Footnotes<\/h2>\n\n\n\n

Supplemental information can be found online at https:\/\/doi.org\/10.1016\/j.cmet.2021.05.013<\/a>.<\/a>Go to:<\/a><\/p>\n\n\n\n

Supplemental information<\/h2>\n\n\n\n

Document S1. Figures S1\u2013S5:<\/strong>Click here to view.<\/a>(22M, pdf)<\/sup>Table S1. Phosphoproteomic data, related to Figures 4 and S4:<\/strong><\/p>\n\n\n\n

Contains raw count of filtered phosphoproteomic data and fold change analysis of phosphosites for human islet cells treated with purified Spike protein (PhosphoSpike) or SARS-CoV-2 (PhosphoSars). Unfiltered phosphoproteomic raw count data is also included (UnfilteredPhospho).Click here to view.<\/a>(5.6M, xlsx)<\/sup>Table S2. Predicted kinase activity, related to Figures 4 and S4:<\/strong><\/p>\n\n\n\n

Kinase activity as predicted by KSEA of various kinases in human islet cells treated with purified Spike protein (KSEA.Spike) or SARS-CoV-2 (KSEA.Sars). Kinase activity is reported as ES as calculated by weighted Kolmogorov-Smirnov statistics.Click here to view.<\/a>(22K, xlsx)<\/sup>Table S3. Gene ontology pathway analysis, related to Figures 4 and S5:<\/strong><\/p>\n\n\n\n

Gene ontology enrichments for upregulated\/downregulated kinases upon treatment by Spike protein or SARS-CoV-2.Click here to view.<\/a>(397K, xlsx)<\/sup>Document S2. Article plus supplemental information:<\/strong>Click here to view.<\/a>(26M, pdf)<\/sup>Go to:<\/a><\/p>\n\n\n\n

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