key: cord-0723963-idtmsszz
authors: Deb, Brototo; O’Brien, Daniel R; Bharucha, Adil E
title: Duodenal mucosal expression of COVID-19-related genes in health, diabetes gastroenteropathy and functional dyspepsia
date: 2022-01-28
journal: J Clin Endocrinol Metab
DOI: 10.1210/clinem/dgac038
sha: b0d20b37b1afd827a539ca0895230d25badef686
doc_id: 723963
cord_uid: idtmsszz

CONTEXT: SARS-CoV-2 infects the gastrointestinal tract and may be associated with symptoms that resemble diabetic gastroparesis. Why patients with diabetes who contract COVID-19 are more likely to have severe disease is unknown. OBJECTIVES: To compare the duodenal mucosal expression of SARS-CoV-2 and inflammation-related genes in healthy controls, diabetes gastroenteropathy (DGE), and functional dyspepsia (FD). DESIGN: Gastrointestinal transit, and duodenal mucosal mRNA expression of selected genes were compared in 21 controls, 39 DGE patients, and 37 FD patients. Pathway analyses were performed. SETTING: Tertiary referral center RESULTS: Patients had normal, delayed (5 FD [13%] and 13 DGE patients [33%], P=.03 vs controls), or rapid (5 FD[12%] and 5 DGE patients[12%]) gastric emptying. Compared to control participants, 100 SARS-CoV-2-related genes were increased in DGE (FDR<0.05) vs 13 genes in FD; 71 of these 100 genes were differentially expressed in DGE vs FD but only 3 between DGE patients with normal vs delayed GE. Upregulated genes in DGE include the SARS-CoV2 viral entry genes CTSL (|(Fold change) |=1.16; FDR<0.05) and CTSB (|Fold Change|=1.24; FDR<0.05) and selected genes involved in viral replication (eg, EIF2 pathways) and inflammation (CCR2, CXCL2, and LCN2, but not other inflammation-related pathways eg, IL-2 and IL-6 signaling). CONCLUSIONS: Several SARS-CoV-2-related genes were differentially expressed between DGE vs healthy controls and vs FD but not between DGE patients with normal vs delayed GE, which suggests that the differential expression is related to diabetes per se. The upregulation of CTSL and CTSB and replication genes may predispose to SARS-CoV2 infection of the gastrointestinal tract in diabetes.

A c c e p t e d M a n u s c r i p t Background 1 2 Diabetes and obesity are independent and synergistic risk factors for worse outcomes 3 in patients with COVID-19 infection (1) (2) (3) (4) (5) (6) . Both conditions affect the gastrointestinal tract (7-9). 4 Up to 50% of patients infected with SARS-CoV-2 have gastrointestinal symptoms (e.g., nausea, 5 vomiting, diarrhea, and abdominal pain) (10), which are similar to the symptoms of diabetic 6 gastroenteropathy (7, 8, (11) (12) (13) . The more severe gastrointestinal complications of COVID-19 7 (e.g., ileus, mesenteric ischemia) are also associated with diabetes (14,15). It has been 8 suggested that "the underlying chronic inflammatory state in diabetes may be "locked and 9 loaded" for virus-induced damage, promoting a vicious cycle of cytokine release and 10 hyperglycemic surges, leading to more widespread multiorgan damage, including injury to 11 tissues already weakened by preexisting diabetes complications (16) ." However, the extent to 12 which the increased severity of SARS-CoV-2 infection in diabetes is explained by underlying 13 host factors (e.g., dysimmunity) is unclear. 14 Gastrointestinal involvement may also affect the severity of COVID-19 independent of 15 diabetes. In China, gastrointestinal involvement was associated with less favorable outcomes 16 (17). By contrast, in the United States, patients with COVID-19 and gastrointestinal symptoms 17 were more likely to have a favorable outcome, reduced small intestine mucosal expression of 18 several inflammatory cytokines and chemokines (e.g., interleukin-1 gamma [IFN-, and reduced circulating levels of key inflammatory proteins than patients with 20 COVID-19 who did not have gastrointestinal symptoms (9). 21 After SARS-CoV-2 binds to the angiotensin-converting-enzyme 2 (ACE2) receptors on 22 epithelial cells, the proteases transmembrane-serine-protease-2 (TMPRSS2) and, to a lesser 23 extent, CTSL and CTSB (Cathepsin L and B) are responsible for priming the spike (Increased) 24 protein of SARS-CoV-2 (18,19). It has been suggested that "old age, obesity, and diabetes 25 A c c e p t e d M a n u s c r i p t produce a deadly symbiosis of dysregulated immunometabolism and chronic systemic 26 inflammation that intensifies virally induced hyperinflammation associated with SARS-CoV-2 27 infection" (20) . The bronchial and alveolar expression of the ACE2 protein but not mRNA is 28 increased in diabetes patients who were not infected with SARS-CoV-2 (21). Since the 29 gastrointestinal tract provides a route for SARS-CoV2, the aims of this study were to compare 30 the duodenal mucosal expression of genes and pathways that are involved in viral replication 31 and the pathogenesis of illness related to SARS-CoV-2 infection between patients with 1) 32 diabetic gastroenteropathy and healthy controls and 2) functional dyspepsia, which served as 33 a disease-control group, and healthy controls. 36 All participants provided informed consent to participate in this study, which was 37 approved by the Institutional Review Board at Mayo Clinic. From June 2014 to April 2017, 40 38 patients with diabetic gastroenteropathy and 40 patients with functional dyspepsia, who were 39 identified from our clinical practice, and 24 healthy volunteers, who were identified by public 40 advertisement, participated in this study that primarily sought to evaluate the effects of the 41 GLP-1 antagonist exendin 9-39 on symptoms during enteral lipid infusion (22, 23 (24, 25) . This paper is focused on the genes of putative relevance to COVID-19 infection. may be more important than an overwhelming increase in a single gene (30). In order to 96 address these gaps, the pathway analysis was also performed with GSEA using the MsigDB 97 A c c e p t e d M a n u s c r i p t database (30). An FDR cut-off of 0.25 was used to identify the significant gene-sets in GSEA. 98 The IPA and GSEA analyses catalogued the differentially expressed genes into pathways. The 99 analysis focused on those pathways that are known to be relevant to the pathogenesis and/or 100 manifestations of SARS-CoV-2 infection. 101 The IPA and GSEA uncover pathways that are associated with differentially expressed 102 genes in the literature. However, they do not consider the strength of the association between 103 individual genes and disease processes or pathways. Hence, some of the differentially 104 expressed genes are not strongly connected with the relevant disease mechanisms or 105 pathways identified by the IPA and GSEA. Hence, we used the nferX software (nFerence.ai), 106 which is based on a comprehensive corpus of the literature, to quantify the association 107 between genes and disease processes (eg, diabetes mellitus, viral infections) (31). This 108 software provides a local score that measures how frequently two tokens A and B, which, in 109 this instance, respectively reflect a differentially expressed gene and a disease process, are 4]) than in diabetic gastroenteropathy (2.6 [1.6, 3.5]). Compared to controls, the gastric 140 emptying t ½ of solids was longer in diabetic gastroenteropathy (P=.008) and in functional 141 dyspepsia (P=.04); differences between diabetic gastroenteropathy and functional dyspepsia 142 were also significant (P=.02) ( Table 1 ). The GE of solids was normal in 18 controls (75%), 30 166 Compared to healthy controls, the mRNA expression of CTSL (|Fold Change|=1.16) and 167 CTSB (|Fold Change|=1.24) were upregulated (FDR<0.05) in diabetic gastroenteropathy; 168 however, differences were modest. By contrast, the expression of ACE2 (|Fold Change|=1.16, 169 FDR=0.31), TMPRSS2 (|Fold Change|=1.07, FDR=0.55), and FURIN (|Fold Change|=1.05, 170 FDR=0.54) were not different in diabetic gastroenteropathy versus controls ( Table 2) . None of 171 A c c e p t e d M a n u s c r i p t the viral entry or SARS-CoV-2 viral replication genes shown in Table 2 were differentially 172 expressed between patients who were taking vs not taking metformin (data not shown).

174 diabetic gastroenteropathy 175 Compared to controls, the IPA identified upregulation of several genes in pathways 176 that are responsible for viral replication, inflammation, coagulation, and metabolism in 177 diabetic gastroenteropathy (Table 2) Table 3 shows the genes for which the nFerence local score was greater than 3, which 205 suggests a significant association between these genes and disease processes that are broadly 206 relevant to the pathogenesis of COVID-19. For example, the local score for DEAD-Box Helicase 207 3 X-Linked (DDX3X) and viral infection is 3.6. In some pathways (eg, EIF2 signaling, cytokine 208 storm, and interferon-gamma response), the local score for several genes, which are indicated 209 in bold font in Table 2 , was greater than 3. In other pathways (eg, IL-2 and 6 signaling), most 210 differentially expressed genes had a local score less than 3, which suggests that the connection 211 between these genes and the disease process (eg, inflammation) is weak. 213 Among diabetic gastroenteropathy patients, the a1c was inversely correlated with the 214 mRNA expression of ACE2 (r = -0.33, P < 0.04) and CTSB (r = -0.33, P < 0.04) but not with CTSL, However, by comparison to diabetic gastroenteropathy, there were fewer such genes (Table   242 2). (Table 2 ). However, in the nFerX 294 A c c e p t e d M a n u s c r i p t analysis, only two of these 7 genes (ie, CTSL and HNRNPA1) had a local score greater than 3. 295 For example, the expression of COPB1, which is a protein coding gene that transports proteins 296 and lipids from the Golgi apparatus to the endoplasmic reticulum (ER) has been implicated in 297 the replication of SARS-CoV not SARS-CoV-2, which might explain why the local score for this 298 gene is less than 3 (39). M a n u s c r i p t A c c e p t e d M a n u s c r i p t BMI, body mass index; IQR, interquartile range; GE, Gastric Emptying

Values are mean (SD) unless specified otherwise 1 Average of nausea and vomting, satiety, and bloating subscores M a n u s c r i p t 

COPB2***(1.19), CTSL*** (1.25)

TUBA1A (1.09), TUBA1B (1.04) EIF2 signaling AGO2* (0.88), AGO4* (0.89)

MT-RNR1*** (2.62)

MT-RNR1 (0.21), MT-RNR2 (0.37)

DHX9*** (1.13), NME1 (1.17), NUDT15*** (1.13), UMPS* (1.08)

NUDT5*** (1.16)

DHX9*** (1.13), NME1 (1.17), NUDT15*** (1.13)

NME1 (1.18), NME2 (1.10), NUDT15 (0.100), NUDT5 (1.03), SLC25A42 (1.04), UMPS (1.05) tRNA charging DARS2* (1.11), FARSA* (1.17)

FARSA*** (1.14), MARS2** (1.13), YARS2** (1.08)

Anti-viral response BCL3** (0.75), BNIP3* (0.80), BRD4*** (0.83), CANX* (1.14)

BRD4*** (0.84), CANX*** (1.26)

CANX (0.90), DAPK1 (1.00), FYCO1 (0.96), GZMB (0.87), HAT1 (0.93), LCN2 (0.93), SAE1 (0.95), TGFB1 (0.90) Inflammation Cytokine storm CCR2* (1.53)

IL-6 signaling HAX1*** † (1.24), INHBE*

IFN-gamma response BPGM* (1.18), BST2* (1.33), GCH1* (1.34), IFI30*** (1.31), PLA2G4A* (1.20)

PLA2G4A*** (1.33), PSMB2***(1.16), PSMB8*** (1.25)

BPGM (1.07), BST2 (1.17), GCH1 (0.86)

IFN-alpha response BST2* (1.33)

PSMB8*** (1.25), TXNIP (1.37) BST2 (1.17)

CHUK* (1.13), CREBBP*** (0.82), DIRAS3* (1.40), IKBKB* (0.90)

DIRAS3*** (1.49), IKBKB*** (0.82), MAP3K12* (1.16), NOS2(1.17), PIK3C2B*** (0.84), PIK3R2*** (1.25), PIK3R4*** (1.10), PPP1R14D*** (1.43), PPP1R7*** (1.15), RBP4*** (1.53)

CHUK (0.89), CREBBP (0.97), DIRAS3 (0.90), IKBKB* (0.90), MAP3K12 (0.97)

CTSB*** (1.28), CTSH*** (1.32), PEF1*** (1.44) CTSB (0.96), CTSH (0.92)

ACAT2*** (1.39), ACSL1** (1.64), CEL** (3.05), COX7A2*** (1.37), FABP2* (1.44), G0S2* †

MT-CO2** (0.79)

COX7A2*** (1.41), FABP2*** (1.51), G0S2*** (1.58), HMGCS1*** (1.50), MT-CO1*** (1.32), MT-CO2*** (1.51)

CEL (1.07), COX7A2 (0.96), FABP2 (0.93), G0S2 (1.11), HMGCS1 (0.93), IDIL1 (0.84)

DLD (0.87), MDH1 (0.95), SDHC (1.06), SDHD (0.87) Cholesterol synthesis ACAA2* (1.22), ACAT2** (1.39), DHCR7* (1.46), FDFT1** (1.31), FDPS* (1.37), FH** (1.19)

ACAT2*** (1.25), DHCR7* (1.34), FDFT1*** (1.38), FDPS*** (1.33), FH*** (1.19)

M a n u s c r i p t

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, 496Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-497based approach for interpreting genome-wide expression profiles. Proceedings of the National The genes with a local score >3.0 are shown in bold font.FDR, false discovery rate; *FDR: 0.01-0.05 **FDR: 0.005-0.01,***FDR: <0.005 † : Also differentially expressed between diabetic gastroenteropathy with delayed GE and diabetic gastroenteropathy with normal GE with FC changes being HAX1*** (1.23), G0S2*** (2.14), and PMVK*** (1.36) Of note, there are 100 differentially expressed genes between diabetic gastroenteropathy vs controls. Genes that pertain to two or more pathways are listed more then once in the table.A c c e p t e d M a n u s c r i p t Table 3 . Local scores and relevant disease processes of differentially expressed genes in diabetic gastroenteropathy Infection DDX3X (3.6), DHX9(3.6), HNRNPA1(Rhinovirus:3.4) A c c e p t e d M a n u s c r i p t Differentially expressed genes in diabetic gastroenteropathy with a local score < 3.0 COPB1, COPB2, DDX1, NME1, NME2, TUBA1A, NUDT15, TRIB3, NUDT5, UMPS, DARS2, FARSA, RPL23A, RPL39, RPL34, MARS2, YARS2, HAX1 IFI30, HAT1, BNIP3, PPP1R7, PLA2G4A, BPGM, DIRAS3, BPGM, GCH1, PIK3R2, DAPK1, FYCO1, PMVK, FDPS, COX7A2, HMGCS1, IDI1 Genes for which a local score is unavailable SLC254A2, RPLI8A, RHOBTB1, PSMB2, PPP1R14D, CHUK, UQCR11