Next Article in Journal
Low-Dose Radiotherapy for Patients with Pneumonia Due to COVID-19: A Single-Institution Prospective Study
Next Article in Special Issue
A Small Molecule That In Vitro Neutralizes Infection of SARS-CoV-2 and Its Most Infectious Variants, Delta, and Omicron
Previous Article in Journal
The Influence of Exercise-Associated Small Extracellular Vesicles on Trophoblasts In Vitro
Previous Article in Special Issue
Current SARS-CoV-2 Protective Strategies for Healthcare Professionals
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 3
10.3390/biomedicines11030856
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Bidirectional Relationship between Glycemic Control and COVID-19 and Perspectives of Islet Organoid Models of SARS-CoV-2 Infection

by
Tongran Zhang
1,2,
Nannan Wang
1,3,
Lingqiang Zhu
2,
Lihua Chen
1,4,* and
Huisheng Liu
1,3,4,5,*
1
Guangzhou Laboratory, Guangzhou 510006, China
2
Department of Pathophysiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
3
College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
4
School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 510180, China
5
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Biomedicines 2023, 11(3), 856; https://doi.org/10.3390/biomedicines11030856
Submission received: 11 January 2023 / Revised: 21 February 2023 / Accepted: 7 March 2023 / Published: 11 March 2023
(This article belongs to the Special Issue Emerging Trends in Pathophysiology and Therapy of COVID-19)
Browse Figure
Review Reports Versions Notes

Abstract

:
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to morbidity and mortality, with several clinical manifestations, and has caused a widespread pandemic. It has been found that type 2 diabetes is a risk factor for severe coronavirus disease 2019 (COVID-19) illness. Moreover, accumulating evidence has shown that SARS-CoV-2 infection can increase the risk of hyperglycemia and diabetes, though the underlying mechanism remains unclear because of a lack of authentic disease models to recapitulate the abnormalities involved in the development, regeneration, and function of human pancreatic islets under SARS-CoV-2 infection. Stem-cell-derived islet organoids have been valued as a model to study islets’ development and function, and thus provide a promising model for unraveling the mechanisms underlying the onset of diabetes under SARS-CoV-2 infection. This review summarized the latest results from clinical and basic research on SARS-CoV-2-induced pancreatic islet damage and impaired glycemic control. Furthermore, we discuss the potential and perspectives of using human ES/iPS cell-derived islet organoids to unravel the bidirectional relationship between glycemic control and SARS-CoV-2 infection.

1. Introduction

The storm of coronavirus disease 2019 (COVID-19) has been sweeping the globe since the end of 2019. Infection with the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), leads to morbidity and mortality, with several clinical manifestations such as pulmonary failure, cardiovascular disorder, pancreatic and other organ dysfunctions, and has caused a widespread pandemic at an unanticipated level [1,2,3,4]. Severe outcomes can include respiratory failure and even death induced by viral pneumonia, although there is a large population that manifests with asymptomatic infections or mild upper respiratory tract disease. Infection with the virus occurs through several receptors, mainly the angiotensin-converting enzyme-II receptor (ACE2) [5,6,7], with the help of other cofactors such as TMPRSS2, NRP1 [8,9], transferrin receptor [10], and FURIN [5,11]. Following viral proliferation after infection, the immune responses are triggered to defend against the infection. Through multiple infection pathways by binding different receptors, the infection can cause damage to different organs, leading to various consequences. These include pneumonia and acute respiratory distress syndrome caused by lung damage, arrhythmia caused by cardiac damage, acute pancreatitis and diarrhea caused by gastrointestinal damage, hyperglycemia caused by pancreatic islet damage, and many long-term complications [12]. Although the majority of people with a normal immune system are able to defend against this viral infection, there are often complications accompanied by tissue damage. Notably, it has been shown that elderly people with chronic diseases have a higher risk of severe clinical symptoms and a higher mortality rate than younger people [4].
A bidirectional link between COVID-19 and diabetes mellitus has extensively been noted. On one hand, substantial clinical data have reported the increased severity and mortality in COVID-19 patients with new-onset diabetes since 2020 [13,14,15,16,17]. One-third of deaths that occurred in individuals with pre-existing type 1 diabetes (T1D) and type 2 diabetes (T2D) were caused by COVID-19 from 1 March to 11 May 2020 [18]. On the other hand, an increasing amount of evidence has shown that SARS-CoV-2 infection can induce the development of hyperglycemia and new-onset diabetes [19,20,21,22]. It is therefore of great importance to focus on the long-term influence of COVID-19 on glycemic control.

2. Method

The authors reviewed the state of knowledge of using islet organoid models to study the bidirectional relationship between SARS-CoV-2 infection and glycemic control. The keywords used for the search were “diabetes and SARS-CoV-2”, or “organoid models and SARS-CoV-2”, or “pancreatic islet and SARS-CoV-2”. More than 100 papers were identified using PubMed as primary source for the literature review.

3. Human Infection and Organ Injury

3.1. Virus Entry

In the early 21st century, a coronavirus named SARS-CoV caused a worldwide pandemic. As a beta-coronavirus, SARS-CoV-2 is highly similar to SARS-CoV. Both of them bind the ACE2 receptor and require other virus entry factors or proteases to facilitate the cell entry of these viruses [23,24]. Briefly, the S protein of SARS-CoV-2 is cleaved by proprotein convertases such as furin in the virus-producing cells to generate the S1 and S2 subunits. The S1 subunit then binds the receptor and the S2 subunit facilitates the attachment of the S protein to the virion membrane, thereby mediating membrane fusion. TMPRSS2 and cathepsin L are the two major proteases involved in the activation of the S protein and further enhance the attachment and endocytosis through which the viruses then invade the host cell [10].

3.2. Organ damage

The poor outcomes of COVID-19 are caused by not only damage to the lungs but also multi-organ damage, including to the lung, kidney, heart, liver, intestines, eyes, and skin [25]. Among these, the lungs have been thought to be the nidus and niche for viral proliferation. The spillage of the virus enters the bloodstream and then leads to the failure of other organs [25,26], although several studies have reported viral RNA reads in these non-lung tissues [27,28]. Early infection activates the immune system [29] and the acute phase always results in respiratory tract symptoms, fever, and other respiratory manifestations caused by uncontrolled inflammation, which is referred to as “cytokine storm syndrome” (SCC) [30,31]. To focus on this hyper-stimulated immune response, a large amount of cytokines and chemokines such as interferon γ [32], IL-1α, IL-1β, IL-2, IL-6, IL-10 [33], and ferritin [34] are involved in the resistance against the bacterial and viral infection, ultimately resulting in severe tissue damage and multi-organ failure, as well as predisposing the patient to complications of the disease [35]. COVID-19, at the level of pulmonary damage, can involve interstitial inflammation; diffuse alveolar damage (DAD), mostly bilateral DAD; and necrotizing bronchitis/bronchiolitis [36]. It has to be mentioned that COVID-19 patients can also suffer from acute fibrinous and organizing pneumonia, although the proportion is low [37]. Widespread manifestations have been shown to be a peculiarity of COVID-19, including those in gastrointestinal tissues [38]. Chai et al. demonstrated that not only the hepatocytes but also the intra-hepatic bile duct can express ACE2 receptors [39].
Moreover, the spread of SARS-CoV-2 from the site of the lung to the kidney through blood circulation can induce acute kidney injury because of the wide distribution of ACE2 receptors in the proximal tubules and podocytes [40]. In the case of cardiac disease in COVID-19, similarly to the aforementioned organs, the presence of ACE2 receptors on the cardiac myocytes facilitates infection with the virus [41]. Delorey et al. reported that both cell composition and gene programs have changed in the hearts of COVID-19 patients compared with those of healthy subjects. However, the extent of myocardial involvement might not be as severe as in other organs damaged by SARS-CoV-2 [28]. Nonetheless, there are recent clinical data that reveal the development of post-COVID-19 syndrome, the morbidity of which commonly includes anxiety, intermittent fever, headache, sleep disturbances, cognitive dysfunction, and other neurological illness [42,43].

3.3. SARS-CoV-2 Infection Leading to Islet Damage

The pancreas is composed of alpha, beta, delta, polypeptide (PP), mesenchymal, acinar, ductal, and endothelial cells, together with immune cells. Each of these has specific marker genes, for example, glucagon (GCG) for alpha cells, insulin (INS) for beta cells, PRSS1 for acinar cells, and somatostatin for delta cells, which play robust roles in the classification of pancreatic cells. ACE2 receptor and TMPRSS2 effector protease are the two acknowledged proteins participating in the entry of SARS-CoV-2 [32,38], and have been shown to be present in alpha, beta, delta, mesenchymal, acinar, and ductal cells [44,45,46]. Recent studies using pancreases from deceased patients and human pluripotent-stem-cell-derived islet organoids have both demonstrated that SARS-CoV-2 is able to infect both alpha and beta cells [47]. Müller et al. reported the existence of SARS-CoV-2 spike proteins in pancreatic alpha and beta cells from SARS-CoV-2-infected patients [48,49]. ACE2 was also detected in hPSC-derived beta cells and alpha that had been transplanted into SCID beige mice for 2 months [48]. Nonetheless, there are contradictions about the expression of ACE2 in pancreatic islets, especially beta cells. For example, ACE2 and TMPRSS2 were not enriched in single beta cells when human islet cells were analyzed [46], whereas a moderate signal of ACE2 was detected in endocrine cells, including beta cells [48]. A low expression level of N-protein in the beta cells from islets of the infected patients has been reported [48]. Thus, more research is needed to clarify the molecular mechanism underlying whether the SARS-CoV-2 infection of the islets would trigger diabetes [50].
The damage to beta cells caused by SARS-CoV-2 infection includes the depression of the beta cells’ insulin secretion and the induction of beta cell apoptosis, with a similar signaling pathway to T1D, as demonstrated from phosphoproteomic pathway analysis. Therefore, SARS-CoV-2 is thought to directly kill beta cells [10]. Single-cell RNA-seq further revealed that the expression of INS was downregulated in SARS-CoV-2-infected islets, while the expression of other marker genes such as GCG, TM4SF4, and RFX6 in alpha cells, and PRSS2 in acinar cells, were increased in infected islets [10]. As well as cell death, the de-differentiation or trans-differentiation of beta cells may be other mechanisms for the damage induced by SARS-CoV-2 infection; for example, the trans-differentiation of beta cells to alpha cells may elicit another cell fate induced by SARS-CoV-2 infection, resulting in abnormal glucose control [51]. As suggested by Shin, the impairment of the insulin/IGF signaling pathway may contribute to COVID-19 pathology through interferon regulatory factor 1 (IRF1) [52]. Although clinical evidence has shown that SARS-CoV-2 infection impairs pancreatic islet function, more research should be conducted to reveal the molecular and biochemical relationships between glucose metabolism and SARS-CoV-2 infection.

4. The Bidirectional Relationship between SARS-CoV-2 Infection and Glucose Metabolism

4.1. Diabetic Patients Are Prone to Infection by SARS-CoV-2 and Have Increased COVID-19 Severity

Increasing evidence has suggested that patients with pre-existing diabetes are prone to be infected by SARS-CoV-2, with a higher severity of COVID-19. For instance, Guan et al. reported that people with poor glycemic control caused by diabetes are more susceptible to SARS-CoV-2 infection, and that individuals with diabetes were shown to have a higher risk of severe COVID-19 [53]. To investigate the reasons why people with diabetes are more prone to infection, it is necessary to elucidate the relationship between glycol metabolism and the immune response. People with T2D and obesity are characterized by metabolic disorders combined with immune dysfunction, resulting in the accumulation and activation of immune cells located in different tissues, such as T cells, dendritic cells, macrophages, neutrophils, and B cells, which then enhance the production of chemokines and cytokines [54]. Other metabolic factors including lipokines, adipokines, and branched-chain amino acids are at an abnormal level in patients with T2D and obesity, which also contribute to inflammation [54,55]. The dysfunctional immune system is probably responsible for the higher risk of viral infection through the activation of the NLRP3 inflammasome [56].
A number of studies have reported that diabetes increases the risk of many infections. Interestingly, the more dysregulated the glycemic control in infected people, the higher the mortality and morbidity [57,58]. Since the outbreak of the pandemic, it has transpired that there is a higher risk of severe COVID-19 outcomes and higher rates of mortality in people with metabolic disorders, as suggested by case series from different areas [13,14,15,16,17]. A retrospective analysis from the USA has reported that 58% of the patients with COVID-19 who were admitted to ICUs had diabetes mellitus before being infected [59]. Case series in England also reported increased COVID-19 mortality in people admitted to the intensive care unit (ICU) or high dependency unit (HDU) who had T2D than in those without diabetes [60]. Alongside the severity and mortality, the risk of reinfections, vaccine breakthrough infections, and long COVID appear to be higher in patients with diabetes [55]. Lim proposed several mechanisms that link clinical severity and T2D, including inflammatory cytokine production, endothelial damage, insulin resistance mediated by the renin–angiotensin–aldosterone system, and so on, thus leading to series of complications such as thrombosis and other organ damage [61]. In addition, SARS-CoV-2 infection may result in the aggravation of metabolic diseases [16].

4.2. SARS-CoV-2 Infection Predisposes COVID-19 Patients to Hyperglycemia

Regardless of pre-existing diabetes before SARS-CoV-2 infection, it has been suggested that hyperglycemia is a characteristic clinical manifestation for people with severe COVID-19 [17,62,63]. An analysis of the Glytec database suggested a close association between hyperglycemia/hypoglycemia and poor outcomes of COVID-19 [64,65]. In the report by Bode et al., 257 of 1122 people with COVID-19 were diagnosed with poorly controlled hyperglycemia, with glucose readings higher than 10 mM. The risk of hyperglycemia and hypoglycemia, long COVID-19, and even death was shown to be higher in people with uncontrolled glucose levels [66]. Furthermore, the drug therapy for COVID-19 such as RNA polymerase inhibitors and chloroquine also appears to contribute to hyperglycemia [67,68]. New-onset diabetes has been reported in several studies, suggesting possible links between the onset of T1D, T2D, severe diabetic ketoacidosis, and SARS-CoV-2 infection [69,70]. More clinical evidence linking SARS-CoV-2 and glucose metabolism disorder is shown in Table 1, and the possible mechanism of this could be the aggravated inflammation leading to beta cell dysfunction, the impairment of the action of insulin, and a deterioration in insulin resistance [54]. Furthermore, the expression of ACE2 is downregulated after SARS-CoV-2 infection, and this plays a critical role in the renin–angiotensin system (RAS), thus leading to more severe symptoms [71,72,73]. An understanding of these mechanisms would help to investigate promising therapeutic approaches such as blocking the binding of ACE2 to the S protein of SARS-CoV-2.

5. Remaining Questions

Clinical data and basic research have established the bidirectional relationship between glycemic control and COVID-19. However, the details and the underlying mechanisms are largely unknown (Table 2). ACE2 is expressed in the pancreas, with evidence showing that both the endocrine and exocrine function of the pancreas can be affected by SARS-CoV-2 infection [83], but its distribution and abundance in endocrine and non-endocrine cells is yet to be clarified. To address this question, multiple pancreatic cell types including delta cells, PP cells, acinar cells, and ductal cells should be generated from hPSCs, followed by cell sorting strategies to further investigate the distribution and abundance of ACE2 in each cell type. The dynamics of ACE2 during pancreas development are still unknown. To date, there is poor knowledge about the expression of ACE2 during the development of the human pancreas. Current islet organoid differentiation methods could mimic the natural development of the human pancreas and thus allow us to monitor the expression of ACE2 across the development of the pancreas. Addressing this question would help us understand the impact of SARS-CoV-2 infection on the development of the pancreas. Another remaining question is how the SARS-CoV-2 infection influences the pancreatic endocrine cells’ function and plasticity, and what the underlying mechanisms are. Clinical evidence has suggested that beta cell de-differentiation and trans-differentiation occur upon infection with SARS-CoV-2, but the details and mechanisms are not clear. An investigation into the molecular mechanisms of pancreatic dysfunction is in progress, for example, the activation of the Na+/H+ exchanger in the pancreas, leading to cell damage [84,85]. Moreover, inflammatory cytokines and molecular mimicry may also be correlated with pancreatic cell damage [85,86]. Clinical data have shown that SARS-CoV-2 infection can result in hyperglycemia and/or diabetes (Table 1). It is not clear whether the infection resulted in T1D or T2D, and, if so, whether the effect is reversible or not. Although a connection between T1D and viral infections has been reported [87], more cohort studies and experimental research based on a disease model are expected to solve this question. It remains to be discovered whether the severity of impaired glycemic control correlates with the virulence of SARS-CoV-2 variants. There has been poor evidence showing this correlation, and thus islet organoids could be an unlimited source for investigating this question. Monitoring the function of organoids and their glucose sensitivity as affected by different SARS-CoV-2 variants may be a practical solution. Moreover, we need to clarify multi-organ interactions in the context of SARS-CoV-2 infection and whether islet damage would also affect other organs. Furthermore, it would be valuable to investigate whether beta cells are infected by SARS-CoV-2 simultaneously with the lungs, or whether they are affected following damage and inflammation to other tissues. Figure 1 presents the applications of multi-organoid systems for addressing this question. Recent progress was made by Tao et al. in the successful establishment of a human liver–islet system [88], and further attempts in this field will help to address these unsolved questions.

6. Strengths and Challenges of Using Islet Organoid Models

As mentioned above, it has been reported in numerous studies that diabetes is a high-risk factor for SARS-CoV-2 infection (Table 1). Severe symptoms, increased comorbidities, and higher mortality in people with COVID-19 were shown to be associated with pre-existing T1D and T2D [77]. SARS-CoV-2 infection, on the other hand, can induce hyperglycemia, although the mechanism is still unclear [66].
In order to investigate the impacts of SARS-CoV-2 infection on the pancreas, human pluripotent stem cell (hPSC)-derived islet organoids have been used as a valuable model. Organoids are typically 3D tissue models derived from hPSCs or expanded islets extracted from human samples. These are self-arranged and characterized with a measure of complexity and organ-like cellularity as well as functions to a certain extent [89,90]. The advantages of organoids include their tissue-like structures and the involvement of cell–cell interactions; hence, they are thought to have authenticity for modeling viral infections and accelerating the discovery of antiviral therapeutics. The islet organoids derived from stem cells in vitro have been instrumental sources for understanding the pathogenesis of diabetes and pancreatic dysfunction [91,92]. By using this model, researchers have found the implications of transcription factor GATA6 for diabetes [93]. Moreover, studies using this model reported the acquirement of mature function in iPSC-derived beta cells in vivo and validated the capacity of therapies for diabetes through the transplantation of islet organoids under the kidney capsule [92] or in macro-device implants [94]. Consistent with human primary beta and alpha cells, hPSC-derived beta and alpha cells also express the SARS-CoV-2 spike protein receptor ACE2 and permit viral invasion and robust replication, suggesting the potentiality of islet organoids to represent viral infections [95]. As a proof-of-concept experiment, islet organoids and several other organoids were used to model SARS-CoV-2 infection in various human organs [90,95]. Firstly, the islet organoid model of SARS-CoV-2 infection showed increased insulin resistance and chemokine expression, and decreased endocrine functional pathways caused by increased apoptosis, resulting in insulin deficiency and even the onset of diabetes in some COVID-19 patients [95]. Moreover, by using human liver ductal organoids as models, Zhao et al. recapitulated the infection, transmission, and evolution of SARS-CoV-2 [96]. In addition, drugs, such as mycophenolic acid, can be identified by inhibiting the entry of SARS-CoV-2 in lung organoids derived from pluripotent stem cells as models [97]. Moreover, Lai et al. modeled SARS-CoV-2 infection with hiPSC-derived retinal organoids and found the expression of ACE2 and TMPRSS2 [9]. Moreover, a recent study modeled SARS-CoV-2 infections in hiPSC-derived kidney organoids and reported the direct kidney cell infection and subsequent kidney injury induced by SARS-CoV-2 infection [98]. To promote a further understanding of SARS-CoV-2 infection and the development of novel therapeutics using organoids as models, omics/proteomics analyses could first be performed to investigate the effects of viral infection on the expression profile. Western blot, immunocytochemistry, and other molecular techniques could help to identify the distribution of the entry factors of the virus. Glycemic control and islet cell damage in SARS-CoV-2-infected islet organoids could then be assessed both in vitro and in vivo, which would help us to further understand the mechanism of infection and define a road map for new therapeutics (Figure 1). The intravital imaging and microscopic techniques of islet organoids also allow the exploration of dynamic changes after transplantation into mouse models [99].
Nevertheless, our understanding of the relationship between glycemic control and COVID-19 is still elusive, partly because of the limitations of the current islet organoid models. There are still several obstacles to establishing an authentic islet organoid model that could best mimic the primary human islets. First, the human pancreas contains multiple cell types, while the current islet organoids mainly contain alpha and beta cells, and the ratio and distribution and cell–cell interactions are random. Second, because of the incomplete developmental systems, the cells of organoids are immature and may be characterized by different cell properties. Third, the lack of blood vessels as well as sympathetic nerves could also lead to disordered cell arrangements and intra-islet signal transduction. In addition, high heterogeneity is another limitation in the field of stem cell regeneration, which has been reduced by the construction of micro-well platforms and bio-printing, as well as by robust differentiation [100]. Furthermore, relatively low levels of oxygen and nutrients, and physiological and physical support lead to cell death, necrosis, and apoptosis. Lastly, the use of islet organoid models is also restricted by the lack of an immune system. Moreover, the expression of ACE2 is relatively low in endocrine compartments compared with exocrine compartments. Evidence has shown that the pancreatic ducts and microvasculature are more likely to be targeted in SARS-CoV-2 infection [5,46]. Therefore, whether islet organoids are an ideal SARS-CoV-2 infection model remains to be observed and determined.
Although there are plenty of advantages of using islet organoids, the existing differentiation methods are not advanced enough to induce the full range of cell types. Incomplete cell types, unstable induced differentiation, organoids that are not fully functional, erroneous cell proportions, and the lack of blood vessels, an immune system, and sympathetic nerves are regarded as the challenges, which are worth overcoming so that hESC-derived organoids could function better when modeling SARS-CoV-2 infection and COVID-19-related dysfunction. For instance, as a safe and convenient system, organ-on-a-chip technology can identify the cross-talk between different SARS-CoV-2-infected organs and elucidate the mechanisms underlying the elevated mortality in diabetic COVID-19 patients. More detailed improvements will be discussed in the next section.

7. Conclusions and Prospects

Since the outbreak of COVID-19, the application of organoids for modeling of the disease has rapidly progressed. Nevertheless, islet organoid models should be further optimized by additional explorations to better unravel the relationships between glycemic control and infectious diseases [89].
The prospective solutions for the improvement of islet models are recapitulated in Figure 1. Firstly, differentiation methods should be improved for the directed generation of other pancreatic cell types, including both endocrine and exocrine cells. FGF family member 7 (FGF7) not only enhances the expression of PDX1, thereby promoting the differentiation of beta cells [101], but may also regulate the differentiation of pancreatic exocrine cells [102,103]. As well as FGF7, other FGFs may have a different potential to induce other pancreatic cell types, so they need to be surveyed. Moreover, hepatocyte nuclear factor 6 (Hnf6) promotes the differentiation of duct cells and also regulates acinar cell development [104,105,106]. Furthermore, histone deacetylase (DHAC) inhibitors also play essential roles in amplifying the endocrine progenitors of NGN3+ and ductal differentiation, but abolish the differentiation of acinar cells [107,108]. In addition, cell purification can be used to reconstruct islet organoids with the affiliated vessels and nerves. CD49a and DPP4 have been identified as surface markers for beta cells and alpha cells, respectively [103,109,110]. Hence, they can be used to efficiently purify beta cells and alpha cells after the elimination of irrelevant cells through a magnetic sorting strategy [103,109]. In this regard, one task would be to identify the cell surface biomarkers for other pancreatic cell types. The purified cell types can be used to clarify the presence of ACE2 and other related factors in the pancreas. They can also be further used to study the transcriptomics and proteomics related to viral entry and infection. Analyses of the transcriptome profile, multi-omics, and organoids with genome editing can further reveal the viral pathogenesis involving alterations in a series of molecular and cellular events, for example the up- or downregulation mechanism of ACE2 after infection, and the investigation of other virus entry factors [73].
Moreover, animal models should be established in order to study the long-term effects of infection and/or impacts on diabetic subjects. For example, hACE2 transgenic mice [111,112] can be used to induce a T1D model with streptozotocin (STZ) [113] or a T2D model with a high-fat diet (HFD) [114]. After hACE2 mice that have been implanted with islet organoids are infected with SARS-CoV-2, the physiological and pathological changes in the pancreas can be observed to investigate the damage and dysfunction of pancreatic cells caused by viral infection. The transcriptomic profile and omics would help in analyzing the modification of genetic transcription after infection, which would lead to a further understanding of the mechanism of how the infection causes impaired glycemic control. Moreover, strengthening the techniques of multi-organ chips, with the layout/interaction of multiple organs such as islets, lungs, intestines, livers, and immune cells, will more closely resemble human physiology and accelerate the resolution of unsolved problems [88,115]. For example, Tao et al. successfully developed a novel microfluidic multi-organoid system replicating the human liver–islet axis in both healthy and diseased states [88]. This technology would allow the remaining questions such as multi-organ interactions after SARS-CoV-2 infection and future drug discovery to be resolved.
Islet organoids enable us to investigate the mechanisms of the association between diabetes and the poor outcomes of COVID-19. Considered as an unlimited platform, islet organoids are expected to have high potential for the study of viral infections, COVID-19 disease therapy, and therapy for other diseases such as diabetes. We believe that the use of islet organoids would be a breakthrough in the study of SARS-CoV-2 infection and COVID-19 treatments.

Author Contributions

H.L., L.C. and L.Z. conceived the project, and T.Z., N.W., L.C. and H.L. wrote the manuscript, which was further edited by the other co-authors. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2020YFA0908200, 2021YFA1101300 to H.L.). This study was also supported partially by the Major Project of “Brain Science and Brain-inspired Research” (2022ZD0211803 to L.Z.), the National Natural Science Foundation of China (82030032, 32070960, 82261138555 to L.Z.), the Natural Science Foundation of Hubei Province (2022CFA004 to L.Z.), the Natural Science Foundation of Guangdong Province (2023A1515010483 to L.C.)

Data Availability Statement

All data for the review are available within the article.

Conflicts of Interest

The authors declare no other competing financial interest.

References

  1. Rapkiewicz, A.V.; Mai, X.; Carsons, S.E.; Pittaluga, S.; Kleiner, D.E.; Berger, J.S.; Thomas, S.; Adler, N.M.; Charytan, D.M.; Gasmi, B.; et al. Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: A case series. EClinicalMedicine 2020, 24, 100434. [Google Scholar] [CrossRef] [PubMed]
  2. Fox, S.E.; Akmatbekov, A.; Harbert, J.L.; Li, G.; Quincy Brown, J.; Vander Heide, R.S. Pulmonary and cardiac pathology in African American patients with COVID-19: An autopsy series from New Orleans. Lancet Respir. Med. 2020, 8, 681–686. [Google Scholar] [CrossRef] [PubMed]
  3. Connors, J.M.; Levy, J.H. COVID-19 and its implications for thrombosis and anticoagulation. Blood 2020, 135, 2033–2040. [Google Scholar] [CrossRef]
  4. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef] [PubMed]
  5. Kusmartseva, I.; Wu, W.; Syed, F.; Van Der Heide, V.; Jorgensen, M.; Joseph, P.; Tang, X.; Candelario-Jalil, E.; Yang, C.; Nick, H.; et al. Expression of SARS-CoV-2 entry factors in the pancreas of normal organ donors and individuals with COVID-19. Cell Metab. 2020, 32, 1041–1051. [Google Scholar] [CrossRef]
  6. Chen, L.; Hao, G. The role of angiotensin-converting enzyme 2 in coronaviruses/influenza viruses and cardiovascular disease. Cardiovasc. Res. 2020, 116, 1932–1936. [Google Scholar] [CrossRef]
  7. Goldman, N.; Fink, D.; Cai, J.; Lee, Y.N.; Davies, Z. High prevalence of COVID-19-associated diabetic ketoacidosis in UK secondary care. Diabetes Res. Clin. Pract. 2020, 166, 108291. [Google Scholar] [CrossRef]
  8. Cantuti-Castelvetri, L.; Ojha, R.; Pedro, L.D.; Djannatian, M.; Franz, J.; Kuivanen, S.; van der Meer, F.; Kallio, K.; Kaya, T.; Anastasina, M.; et al. Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity. Science 2020, 370, 856–860. [Google Scholar] [CrossRef]
  9. Ahmad Mulyadi Lai, H.I.; Chou, S.J.; Chien, Y.; Tsai, P.H.; Chien, C.S.; Hsu, C.C.; Jheng, Y.C.; Wang, M.L.; Chiou, S.H.; Chou, Y.B.; et al. Expression of endogenous angiotensin-converting enzyme 2 in human induced pluripotent stem cell-derived retinal organoids. Int. J. Mol. Sci. 2021, 22, 1320. [Google Scholar] [CrossRef]
  10. Wu, C.T.; Lidsky, P.V.; Xiao, Y.; Lee, I.T.; Cheng, R.; Nakayama, T.; Jiang, S.; Demeter, J.; Bevacqua, R.J.; Chang, C.A.; et al. SARS-CoV-2 infects human pancreatic beta cells and elicits beta cell impairment. Cell Metab. 2021, 33, 1565–1576. [Google Scholar] [CrossRef]
  11. Zang, R.; Gomez Castro, M.F.; McCune, B.T.; Zeng, Q.; Rothlauf, P.W.; Sonnek, N.M.; Liu, Z.; Brulois, K.F.; Wang, X.; Greenberg, H.B.; et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci. Immunol. 2020, 5, eabc3582. [Google Scholar] [CrossRef]
  12. Rabaan, A.A.; Smajlović, S.; Tombuloglu, H.; Ćordić, S.; Hajdarević, A.; Kudić, N.; Al Mutai, A.; Turkistani, S.A.; Al-Ahmed, S.H.; Al-Zaki, N.A.; et al. SARS-CoV-2 infection and multi-organ system damage: A review. Biomol. Biomed. 2023, 23, 37–52. [Google Scholar] [CrossRef]
  13. Steenblock, C.; Schwarz, P.E.H.; Ludwig, B.; Linkermann, A.; Zimmet, P.; Kulebyakin, K.; Tkachuk, V.A.; Markov, A.G.; Lehnert, H.; de Angelis, M.H.; et al. COVID-19 and metabolic disease: Mechanisms and clinical management. Lancet Diabetes Endocrinol. 2021, 9, 786–798. [Google Scholar] [CrossRef]
  14. Mirani, M.; Favacchio, G.; Carrone, F.; Betella, N.; Biamonte, E.; Morenghi, E.; Mazziotti, G.; Lania, A.G. Impact of comorbidities and glycemia at admission and dipeptidyl peptidase 4 inhibitors in patients with type 2 diabetes with COVID-19: A case series from an academic hospital in Lombardy, Italy. Diabetes Care 2020, 43, 3042–3049. [Google Scholar] [CrossRef]
  15. Ceriello, A.; Schnell, O. COVID-19: Considerations of diabetes and cardiovascular disease management. J. Diabetes Sci. Technol. 2020, 14, 723–724. [Google Scholar] [CrossRef]
  16. Steenblock, C.; Hassanein, M.; Khan, E.G.; Yaman, M.; Kamel, M.; Barbir, M.; Lorke, D.E.; Rock, J.A.; Everett, D.; Bejtullah, S.; et al. Diabetes and COVID-19: Short- and long-term consequences. Horm. Metab. Res. 2022, 54, 503–509. [Google Scholar] [CrossRef] [PubMed]
  17. Khunti, K.; Del Prato, S.; Mathieu, C.; Kahn, S.E.; Gabbay, R.A.; Buse, J.B. COVID-19, hyperglycemia, and new-onset diabetes. Diabetes Care 2021, 44, 2645–2655. [Google Scholar] [CrossRef] [PubMed]
  18. Barron, E.; Bakhai, C.; Kar, P.; Weaver, A.; Bradley, D.; Ismail, H.; Knighton, P.; Holman, N.; Khunti, K.; Sattar, N.; et al. Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: A whole-population study. Lancet Diabetes Endocrinol. 2020, 8, 813–822. [Google Scholar] [CrossRef] [PubMed]
  19. Wang, F.; Wang, H.; Fan, J.; Zhang, Y.; Wang, H.; Zhao, Q. Pancreatic injury patterns in patients with coronavirus disease 19 pneumonia. Gastroenterology 2020, 159, 367–370. [Google Scholar] [CrossRef]
  20. Unsworth, R.; Wallace, S.; Oliver, N.S.; Yeung, S.; Kshirsagar, A.; Naidu, H.; Kwong, R.M.W.; Kumar, P.; Logan, K.M. New-onset type 1 diabetes in children during COVID-19: Multicenter regional findings in the U.K. Diabetes Care 2020, 43, e170–e171. [Google Scholar] [CrossRef]
  21. Li, J.; Wang, X.; Chen, J.; Zuo, X.; Zhang, H.; Deng, A. COVID-19 infection may cause ketosis and ketoacidosis. Diabetes Obes. Metab. 2020, 22, 1935–1941. [Google Scholar] [CrossRef]
  22. Boddu, S.K.; Aurangabadkar, G.; Kuchay, M.S. New onset diabetes, type 1 diabetes and COVID-19. Diabetes Metab. Syndr. 2020, 14, 2211–2217. [Google Scholar] [CrossRef] [PubMed]
  23. El Jamal, S.M.; Pujadas, E.; Ramos, I.; Bryce, C.; Grimes, Z.M.; Amanat, F.; Tsankova, N.M.; Mussa, Z.; Olson, S.; Salem, F.; et al. Tissue-based SARS-CoV-2 detection in fatal COVID-19 infections: Sustained direct viral-induced damage is not necessary to drive disease progression. Hum. Pathol. 2021, 114, 110–119. [Google Scholar] [CrossRef] [PubMed]
  24. Amendola, A.; Garoffolo, G.; Songia, P.; Nardacci, R.; Ferrari, S.; Bernava, G.; Canzano, P.; Myasoedova, V.; Colavita, F.; Castilletti, C.; et al. Human cardiosphere-derived stromal cells exposed to SARS-CoV-2 evolve into hyper-inflammatory/pro-fibrotic phenotype and produce infective viral particles depending on the levels of ACE2 receptor expression. Cardiovasc. Res. 2021, 117, 1557–1566. [Google Scholar] [CrossRef] [PubMed]
  25. Singh, A.; Zaheer, S.; Kumar, N.; Singla, T.; Ranga, S. Covid19, beyond just the lungs: A review of multisystemic involvement by Covid19. Pathol. Res. Pract. 2021, 224, 153384. [Google Scholar] [CrossRef] [PubMed]
  26. Christ-Crain, M.; Hoorn, E.J.; Sherlock, M.; Thompson, C.J.; Wass, J. Endocrinology in the time of COVID-19-2021 updates: The management of diabetes insipidus and hyponatraemia. Eur. J. Endocrinol. 2021, 185, G35–G42. [Google Scholar] [CrossRef] [PubMed]
  27. Lemieux, J.E.; Siddle, K.J.; Shaw, B.M.; Loreth, C.; Schaffner, S.F.; Gladden-Young, A.; Adams, G.; Fink, T.; Tomkins-Tinch, C.H.; Krasilnikova, L.A.; et al. Phylogenetic analysis of SARS-CoV-2 in Boston highlights the impact of superspreading events. Science 2021, 371, eabe3261. [Google Scholar] [CrossRef]
  28. Delorey, T.M.; Ziegler, C.G.K.; Heimberg, G.; Normand, R.; Yang, Y.; Segerstolpe, Å.; Abbondanza, D.; Fleming, S.J.; Subramanian, A.; Montoro, D.T.; et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 2021, 595, 107–113. [Google Scholar] [CrossRef]
  29. Sette, A.; Crotty, S. Adaptive immunity to SARS-CoV-2 and COVID-19. Cell 2021, 184, 861–880. [Google Scholar] [CrossRef]
  30. Dotan, A.; David, P.; Arnheim, D.; Shoenfeld, Y. The autonomic aspects of the post-COVID19 syndrome. Autoimmun. Rev. 2022, 21, 103071. [Google Scholar] [CrossRef]
  31. Tarke, A.; Coelho, C.H.; Zhang, Z.; Dan, J.M.; Yu, E.D.; Methot, N.; Bloom, N.I.; Goodwin, B.; Phillips, E.; Mallal, S.; et al. SARS-CoV-2 vaccination induces immunological T cell memory able to cross-recognize variants from Alpha to Omicron. Cell 2022, 185, 847–859. [Google Scholar] [CrossRef]
  32. Bastard, P.; Rosen, L.B.; Zhang, Q.; Michailidis, E.; Hoffmann, H.H.; Zhang, Y.; Dorgham, K.; Philippot, Q.; Rosain, J.; Béziat, V.; et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science 2020, 370, eabd4585. [Google Scholar] [CrossRef] [PubMed]
  33. Dotan, A.; Muller, S.; Kanduc, D.; David, P.; Halpert, G.; Shoenfeld, Y. The SARS-CoV-2 as an instrumental trigger of autoimmunity. Autoimmun. Rev. 2021, 20, 102792. [Google Scholar] [CrossRef]
  34. Cheng, L.; Li, H.; Li, L.; Liu, C.; Yan, S.; Chen, H.; Li, Y. Ferritin in the coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis. J. Clin. Lab. Anal. 2020, 34, e23618. [Google Scholar] [CrossRef] [PubMed]
  35. Hu, B.; Huang, S.; Yin, L. The cytokine storm and COVID-19. J. Med. Virol. 2021, 93, 250–256. [Google Scholar] [CrossRef] [PubMed]
  36. Jain, A. COVID-19 and lung pathology. Indian J. Pathol. Microbiol. 2020, 63, 171–172. [Google Scholar] [CrossRef] [PubMed]
  37. Copin, M.C.; Parmentier, E.; Duburcq, T.; Poissy, J.; Mathieu, D. Time to consider histologic pattern of lung injury to treat critically ill patients with COVID-19 infection. Intensive Care Med. 2020, 46, 1124–1126. [Google Scholar] [CrossRef] [Green Version]
  38. Suárez-Fariñas, M.; Tokuyama, M.; Wei, G.; Huang, R.; Livanos, A.; Jha, D.; Levescot, A.; Irizar, H.; Kosoy, R.; Cording, S.; et al. Intestinal inflammation modulates the expression of ACE2 and TMPRSS2 and potentially overlaps with the pathogenesis of SARS-CoV-2–related disease. Gastroenterology 2021, 160, 287–301. [Google Scholar] [CrossRef]
  39. Chai, X.; Hu, L.; Zhang, Y.; Han, W.; Lu, Z.; Ke, A.; Zhou, J.; Shi, G.; Fang, N.; Fan, J.; et al. Specific ACE2 expression in cholangiocytes may cause liver damage after 2019-nCoV infection. bioRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
  40. Ye, M.; Wysocki, J.; William, J.; Soler, M.J.; Cokic, I.; Batlle, D. Glomerular localization and expression of Angiotensin-converting enzyme 2 and Angiotensin-converting enzyme: Implications for albuminuria in diabetes. J. Am. Soc. Nephrol. 2006, 17, 3067–3075. [Google Scholar] [CrossRef] [Green Version]
  41. Li, G.; Hu, R.; Zhang, X. Antihypertensive treatment with ACEI/ARB of patients with COVID-19 complicated by hypertension. Hypertens. Res. 2020, 43, 588–590. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Lopez-Leon, S.; Wegman-Ostrosky, T.; Perelman, C.; Sepulveda, R.; Rebolledo, P.A.; Cuapio, A.; Villapol, S. More than 50 long-term effects of COVID-19: A systematic review and meta-analysis. Sci. Rep. 2021, 11, 16144. [Google Scholar] [CrossRef]
  43. Taquet, M.; Geddes, J.R.; Husain, M.; Luciano, S.; Harrison, P.J. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: A retrospective cohort study using electronic health records. Lancet Psych. 2021, 8, 416–427. [Google Scholar] [CrossRef] [PubMed]
  44. Mine, K.; Nagafuchi, S.; Mori, H.; Takahashi, H.; Anzai, K. SARS-CoV-2 infection and pancreatic β cell failure. Biology 2021, 11, 22. [Google Scholar] [CrossRef] [PubMed]
  45. Geravandi, S.; Mahmoudi-Aznaveh, A.; Azizi, Z.; Maedler, K.; Ardestani, A. SARS-CoV-2 and pancreas: A potential pathological interaction? Trends. Endocrinol. Metab. 2021, 32, 842–845. [Google Scholar] [CrossRef]
  46. Coate, K.C.; Cha, J.; Shrestha, S.; Wang, W.; Gonçalves, L.M.; Almaça, J.; Kapp, M.E.; Fasolino, M.; Morgan, A.; Dai, C.; et al. SARS-CoV-2 cell entry factors ACE2 and TMPRSS2 are expressed in the microvasculature and ducts of human pancreas but are not enriched in β cells. Cell Metab. 2020, 32, 1028–1040. [Google Scholar] [CrossRef] [PubMed]
  47. van der Heide, V.; Jangra, S.; Cohen, P.; Rathnasinghe, R.; Aslam, S.; Aydillo, T.; Geanon, D.; Handler, D.; Kelley, G.; Lee, B.; et al. Limited extent and consequences of pancreatic SARS-CoV-2 infection. Cell Rep. 2022, 38, 110508. [Google Scholar] [CrossRef]
  48. Müller, J.A.; Groß, R.; Conzelmann, C.; Krüger, J.; Merle, U.; Steinhart, J.; Weil, T.; Koepke, L.; Bozzo, C.P.; Read, C.; et al. SARS-CoV-2 infects and replicates in cells of the human endocrine and exocrine pancreas. Nat. Metab. 2021, 3, 149–165. [Google Scholar] [CrossRef]
  49. Hollstein, T.; Schulte, D.M.; Schulz, J.; Glück, A.; Ziegler, A.G.; Bonifacio, E.; Wendorff, M.; Franke, A.; Schreiber, S.; Bornstein, S.R.; et al. Autoantibody-negative insulin-dependent diabetes mellitus after SARS-CoV-2 infection: A case report. Nat. Metab. 2020, 2, 1021–1024. [Google Scholar] [CrossRef] [PubMed]
  50. Memon, B.; Abdelalim, E.M. ACE2 function in the pancreatic islet: Implications for relationship between SARS-CoV-2 and diabetes. Acta Physiol. 2021, 233, e13733. [Google Scholar] [CrossRef]
  51. Tang, X.; Uhl, S.; Zhang, T.; Xue, D.; Li, B.; Vandana, J.J.; Acklin, J.A.; Bonnycastle, L.L.; Narisu, N.; Erdos, M.R.; et al. SARS-CoV-2 infection induces beta cell transdifferentiation. Cell Metab. 2021, 33, 1577–1591. [Google Scholar] [CrossRef]
  52. Shin, J.; Toyoda, S.; Nishitani, S.; Onodera, T.; Fukuda, S.; Kita, S.; Fukuhara, A.; Shimomura, I. SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the lung, liver, adipose tissue, and pancreatic cells via IRF1. Metabolism 2022, 133, 155236. [Google Scholar] [CrossRef]
  53. Guan, W.J.; Ni, Z.Y.; Hu, Y.; Liang, W.H.; Ou, C.Q.; He, J.X.; Liu, L.; Shan, H.; Lei, C.L.; Hui, D.S.C.; et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef]
  54. Donath, M.Y.; Dinarello, C.A.; Mandrup-Poulsen, T. Targeting innate immune mediators in type 1 and type 2 diabetes. Nature Reviews Immunology 2019, 19, 734–746. [Google Scholar] [CrossRef]
  55. Nassar, M.; Daoud, A.; Nso, N.; Medina, L.; Ghernautan, V.; Bhangoo, H.; Nyein, A.; Mohamed, M.; Alqassieh, A.; Soliman, K.; et al. Diabetes mellitus and COVID-19: Review article. Diabetes Metab. Syndr. 2021, 15, 102268. [Google Scholar] [CrossRef] [PubMed]
  56. Rodrigues, T.S.; de Sá, K.S.G.; Ishimoto, A.Y.; Becerra, A.; Oliveira, S.; Almeida, L.; Gonçalves, A.V.; Perucello, D.B.; Andrade, W.A.; Castro, R.; et al. Inflammasomes are activated in response to SARS-CoV-2 infection and are associated with COVID-19 severity in patients. J. Exp. Med. 2021, 218, e20201707. [Google Scholar] [CrossRef] [PubMed]
  57. Critchley, J.A.; Carey, I.M.; Harris, T.; DeWilde, S.; Hosking, F.J.; Cook, D.G. Glycemic control and risk of infections among people with type 1 or type 2 diabetes in a large primary care cohort study. Diabetes Care 2018, 41, 2127–2135. [Google Scholar] [CrossRef] [Green Version]
  58. Carey, I.M.; Critchley, J.A.; DeWilde, S.; Harris, T.; Hosking, F.J.; Cook, D.G. Risk of infection in type 1 and type 2 diabetes compared with the general population: A matched cohort study. Diabetes Care 2018, 41, 513–521. [Google Scholar] [CrossRef] [Green Version]
  59. Bhatraju, P.K.; Ghassemieh, B.J.; Nichols, M.; Kim, R.; Jerome, K.R.; Nalla, A.K.; Greninger, A.L.; Pipavath, S.; Wurfel, M.M.; Evans, L.; et al. COVID-19 in critically ill patients in the Seattle region—Case series. N. Engl. J. Med. 2020, 382, 2012–2022. [Google Scholar] [CrossRef]
  60. Dennis, J.M.; Mateen, B.A.; Sonabend, R.; Thomas, N.J.; Patel, K.A.; Hattersley, A.T.; Denaxas, S.; McGovern, A.P.; Vollmer, S.J. Type 2 diabetes and COVID-19–related mortality in the critical care setting: A national cohort study in England, March–July 2020. Diabetes Care 2021, 44, 50–57. [Google Scholar] [CrossRef] [PubMed]
  61. Lim, S.; Bae, J.H.; Kwon, H.S.; Nauck, M.A. COVID-19 and diabetes mellitus: From pathophysiology to clinical management. Nat Rev Endocrinol 2021, 17, 11–30. [Google Scholar] [CrossRef]
  62. Cai, Y.; Shi, S.; Yang, F.; Yi, B.; Chen, X.; Li, J.; Wen, Z. Fasting blood glucose level is a predictor of mortality in patients with COVID-19 independent of diabetes history. Diabetes Res. Clin. Pract. 2020, 169, 108437. [Google Scholar] [CrossRef] [PubMed]
  63. Fadini, G.P.; Morieri, M.L.; Boscari, F.; Fioretto, P.; Maran, A.; Busetto, L.; Bonora, B.M.; Selmin, E.; Arcidiacono, G.; Pinelli, S.; et al. Newly-diagnosed diabetes and admission hyperglycemia predict COVID-19 severity by aggravating respiratory deterioration. Diabetes Res. Clin. Pract. 2020, 168, 108374. [Google Scholar] [CrossRef] [PubMed]
  64. Klonoff, D.C.; Messler, J.C.; Umpierrez, G.E.; Peng, L.; Booth, R.; Crowe, J.; Garrett, V.; McFarland, R.; Pasquel, F.J. Association between achieving inpatient glycemic control and clinical outcomes in hospitalized patients with COVID-19: A multicenter, retrospective hospital-based analysis. Diabetes Care 2021, 44, 578–585. [Google Scholar] [CrossRef] [PubMed]
  65. Huang, C.; Huang, L.; Wang, Y.; Li, X.; Ren, L.; Gu, X.; Kang, L.; Guo, L.; Liu, M.; Zhou, X.; et al. 6-month consequences of COVID-19 in patients discharged from hospital: A cohort study. Lancet 2021, 397, 220–232. [Google Scholar] [CrossRef] [PubMed]
  66. Bode, B.; Garrett, V.; Messler, J.; McFarland, R.; Crowe, J.; Booth, R.; Klonoff, D.C. Glycemic characteristics and clinical outcomes of COVID-19 patients hospitalized in the United States. J. Diabetes Sci. Technol. 2020, 14, 813–821. [Google Scholar] [CrossRef] [PubMed]
  67. Li, Y.N.; Su, Y. Remdesivir attenuates high fat diet (HFD)-induced NAFLD by regulating hepatocyte dyslipidemia and inflammation via the suppression of STING. Biochem. Biophys Res Commun 2020, 526, 381–388. [Google Scholar] [CrossRef] [PubMed]
  68. Wasko, M.C.; McClure, C.K.; Kelsey, S.F.; Huber, K.; Orchard, T.; Toledo, F.G. Antidiabetogenic effects of hydroxychloroquine on insulin sensitivity and beta cell function: A randomised trial. Diabetologia 2015, 58, 2336–2343. [Google Scholar] [CrossRef] [Green Version]
  69. Denina, M.; Trada, M.; Tinti, D.; Funiciello, E.; Novara, C.; Moretto, M.; Rosati, S.; Garazzino, S.; Bondone, C.; De Sanctis, L. Increase in newly diagnosed type 1 diabetes and serological evidence of recent SARS-CoV-2 infection: Is there a connection? Front. Med. 2022, 9, 927099. [Google Scholar] [CrossRef]
  70. Jedrzejak, A.P.; Urbaniak, E.K.; Wasko, J.A.; Ziojla, N.; Borowiak, M. Diabetes and SARS-CoV-2-is there a mutual connection? Front. Cell Dev. Biol. 2022, 10, 913305. [Google Scholar] [CrossRef] [PubMed]
  71. Verdecchia, P.; Cavallini, C.; Spanevello, A.; Angeli, F. The pivotal link between ACE2 deficiency and SARS-CoV-2 infection. Eur. J. Intern. Med. 2020, 76, 14–20. [Google Scholar] [CrossRef] [PubMed]
  72. Datta, P.K.; Liu, F.; Fischer, T.; Rappaport, J.; Qin, X. SARS-CoV-2 pandemic and research gaps: Understanding SARS-CoV-2 interaction with the ACE2 receptor and implications for therapy. Theranostics 2020, 10, 7448–7464. [Google Scholar] [CrossRef]
  73. Li, M.Y.; Li, L.; Zhang, Y.; Wang, X.S. Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues. Infect. Dis. Poverty 2020, 9, 45. [Google Scholar] [CrossRef] [PubMed]
  74. Moon, S.J.; Rhee, E.J.; Jung, J.H.; Han, K.D.; Kim, S.R.; Lee, W.Y.; Yoon, K.H. Independent impact of diabetes on the severity of coronavirus disease 2019 in 5,307 patients in South Korea: A nationwide cohort study. Diabetes Metab. J. 2020, 44, 737–746. [Google Scholar] [CrossRef]
  75. Altonen, B.L.; Arreglado, T.M.; Leroux, O.; Murray-Ramcharan, M.; Engdahl, R. Characteristics, comorbidities and survival analysis of young adults hospitalized with COVID-19 in New York City. PLoS ONE 2020, 15, e0243343. [Google Scholar] [CrossRef]
  76. Woolcott, O.O.; Castilla-Bancayán, J.P. Diabetes and mortality among 1.6 million adult patients screened for SARS-CoV-2 in Mexico. medRxiv 2020. [Google Scholar] [CrossRef]
  77. Zhu, L.; She, Z.-G.; Cheng, X.; Qin, J.-J.; Zhang, X.-J.; Cai, J.; Lei, F.; Wang, H.; Xie, J.; Wang, W.; et al. Association of blood glucose control and outcomes in patients with COVID-19 and pre-existing type 2 diabetes. Cell Metab. 2020, 31, 1068–1077. [Google Scholar] [CrossRef]
  78. Holman, N.; Knighton, P.; Kar, P.; O’Keefe, J.; Curley, M.; Weaver, A.; Barron, E.; Bakhai, C.; Khunti, K.; Wareham, N.J.; et al. Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: A population-based cohort study. Lancet Diabetes Endocrinol. 2020, 8, 823–833. [Google Scholar] [CrossRef] [PubMed]
  79. Wargny, M.; Gourdy, P.; Ludwig, L.; Seret-Bégué, D.; Bourron, O.; Darmon, P.; Amadou, C.; Pichelin, M.; Potier, L.; Thivolet, C.; et al. Type 1 diabetes in people hospitalized for COVID-19: New insights from the Coronado study. Diabetes Care 2020, 43, e174–e177. [Google Scholar] [CrossRef]
  80. He, X.; Liu, C.; Peng, J.; Li, Z.; Li, F.; Wang, J.; Hu, A.; Peng, M.; Huang, K.; Fan, D.; et al. COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors. Signal Transduct. Target 2021, 6, 427. [Google Scholar] [CrossRef]
  81. Montefusco, L.; Ben Nasr, M.; D’Addio, F.; Loretelli, C.; Rossi, A.; Pastore, I.; Daniele, G.; Abdelsalam, A.; Maestroni, A.; Dell’Acqua, M.; et al. Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection. Nat. Metab. 2021, 3, 774–785. [Google Scholar] [CrossRef] [PubMed]
  82. Marchand, L.; Pecquet, M.; Luyton, C. Type 1 diabetes onset triggered by COVID-19. Acta Diabetol. 2020, 57, 1265–1266. [Google Scholar] [CrossRef] [PubMed]
  83. Zippi, M.; Hong, W.; Traversa, G.; Maccioni, F.; De Biase, D.; Gallo, C.; Fiorino, S. Involvement of the exocrine pancreas during COVID-19 infection and possible pathogenetic hypothesis: A concise review. Infez. Med. 2020, 28, 507–515. [Google Scholar] [PubMed]
  84. Cure, E.; Cumhur Cure, M. COVID-19 may affect the endocrine pancreas by activating Na(+)/H(+) exchanger 2 and increasing lactate levels. J. Endocrinol. Investig. 2020, 43, 1167–1168. [Google Scholar] [CrossRef]
  85. Abramczyk, U.; Nowaczyński, M.; Słomczyński, A.; Wojnicz, P.; Zatyka, P.; Kuzan, A. Consequences of COVID-19 for the pancreas. Int. J. Mol. Sci. 2022, 23, 864. [Google Scholar] [CrossRef] [PubMed]
  86. Caruso, P.; Longo, M.; Esposito, K.; Maiorino, M.I. Type 1 diabetes triggered by COVID-19 pandemic: A potential outbreak? Diabetes Res. Clin. Pract. 2020, 164, 108219. [Google Scholar] [CrossRef]
  87. Ruiz, P.L.D.; Tapia, G.; Bakken, I.J.; Håberg, S.E.; Hungnes, O.; Gulseth, H.L.; Stene, L.C. Pandemic influenza and subsequent risk of type 1 diabetes: A nationwide cohort study. Diabetologia 2018, 61, 1996–2004. [Google Scholar] [CrossRef] [Green Version]
  88. Tao, T.; Deng, P.; Wang, Y.; Zhang, X.; Guo, Y.; Chen, W.; Qin, J. Microengineered multi-organoid system from hiPSCs to recapitulate human liver-islet axis in normal and type 2 diabetes. Adv. Sci. 2022, 9, 2103495. [Google Scholar] [CrossRef]
  89. Zhang, X.; Ma, Z.; Song, E.; Xu, T. Islet organoid as a promising model for diabetes. Protein Cell 2022, 13, 239–257. [Google Scholar] [CrossRef]
  90. Han, Y.; Yang, L.; Lacko, L.A.; Chen, S. Human organoid models to study SARS-CoV-2 infection. Nat. Methods 2022, 19, 418–428. [Google Scholar] [CrossRef]
  91. Imai, Y.; Soleimanpour, S.A.; Tessem, J.S. Editorial: Study of pancreatic islets based on human models to understand pathogenesis of diabetes. Front. Endocrinol. 2022, 13, 1128653. [Google Scholar] [CrossRef] [PubMed]
  92. Fantuzzi, F.; Toivonen, S.; Schiavo, A.A.; Chae, H.; Tariq, M.; Sawatani, T.; Pachera, N.; Cai, Y.; Vinci, C.; Virgilio, E.; et al. In depth functional characterization of human induced pluripotent stem cell-derived beta cells in vitro and in vivo. Front. Cell Dev. Biol. 2022, 10, 967765. [Google Scholar] [CrossRef] [PubMed]
  93. Lorberbaum, D.S.; Sarbaugh, D.; Sussel, L. Leveraging the strengths of mice, human stem cells, and organoids to model pancreas development and diabetes. Front. Endocrinol. 2022, 13, 1042611. [Google Scholar] [CrossRef] [PubMed]
  94. Amin, M.L.; Deng, K.; Tran, H.A.; Singh, R.; Rnjak-Kovacina, J.; Thorn, P. Glucose-dependent insulin secretion from β Cell spheroids is enhanced by embedding into softer alginate hydrogels functionalised with RGD peptide. Bioengineering 2022, 9, 722. [Google Scholar] [CrossRef] [PubMed]
  95. Yang, L.; Han, Y.; Nilsson-Payant, B.E.; Gupta, V.; Wang, P.; Duan, X.; Tang, X.; Zhu, J.; Zhao, Z.; Jaffre, F.; et al. A human pluripotent stem cell-based platform to study SARS-CoV-2 tropism and model virus infection in human cells and organoids. Cell Stem. Cell 2020, 27, 125–136. [Google Scholar] [CrossRef] [PubMed]
  96. Zhao, B.; Ni, C.; Gao, R.; Wang, Y.; Yang, L.; Wei, J.; Lv, T.; Liang, J.; Zhang, Q.; Xu, W.; et al. Recapitulation of SARS-CoV-2 infection and cholangiocyte damage with human liver ductal organoids. Protein Cell 2020, 11, 771–775. [Google Scholar] [CrossRef] [Green Version]
  97. Han, Y.; Duan, X.; Yang, L.; Nilsson-Payant, B.E.; Wang, P.; Duan, F.; Tang, X.; Yaron, T.M.; Zhang, T.; Uhl, S.; et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature 2021, 589, 270–275. [Google Scholar] [CrossRef]
  98. Jansen, J.; Reimer, K.C.; Nagai, J.S.; Varghese, F.S.; Overheul, G.J.; de Beer, M.; Roverts, R.; Daviran, D.; Fermin, L.A.S.; Willemsen, B.; et al. SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids. Cell Stem. Cell 2022, 29, 217–231. [Google Scholar] [CrossRef]
  99. Wagner, L.E.; Melnyk, O.; Duffett, B.E.; Linnemann, A.K. Mouse models and human islet transplantation sites for intravital imaging. Front. Endocrinol. 2022, 13, 992540. [Google Scholar] [CrossRef] [PubMed]
  100. Hogrebe, N.J.; Maxwell, K.G.; Augsornworawat, P.; Millman, J.R. Generation of insulin-producing pancreatic β cells from multiple human stem cell lines. Nat. Protoc. 2021, 16, 4109–4143. [Google Scholar] [CrossRef]
  101. Pagliuca, F.W.; Millman, J.R.; Gürtler, M.; Segel, M.; Van Dervort, A.; Ryu, J.H.; Peterson, Q.P.; Greiner, D.; Melton, D.A. Generation of functional human pancreatic β cells in vitro. Cell 2014, 159, 428–439. [Google Scholar] [CrossRef] [Green Version]
  102. Shirasawa, S.; Yoshie, S.; Yokoyama, T.; Tomotsune, D.; Yue, F.; Sasaki, K. A novel stepwise differentiation of functional pancreatic exocrine cells from embryonic stem cells. Stem. Cells Dev. 2011, 20, 1071–1078. [Google Scholar] [CrossRef] [Green Version]
  103. Veres, A.; Faust, A.L.; Bushnell, H.L.; Engquist, E.N.; Kenty, J.H.; Harb, G.; Poh, Y.C.; Sintov, E.; Gürtler, M.; Pagliuca, F.W.; et al. Charting cellular identity during human in vitro β-cell differentiation. Nature 2019, 569, 368–373. [Google Scholar] [CrossRef]
  104. Kropp, P.A.; Zhu, X.; Gannon, M. Regulation of the pancreatic exocrine differentiation program and morphogenesis by onecut 1/Hnf6. Cell. Mol. Gastroenterol. Hepatol. 2019, 7, 841–856. [Google Scholar] [CrossRef] [Green Version]
  105. Pierreux, C.E.; Poll, A.V.; Kemp, C.R.; Clotman, F.; Maestro, M.A.; Cordi, S.; Ferrer, J.; Leyns, L.; Rousseau, G.G.; Lemaigre, F.P. The transcription factor hepatocyte nuclear factor-6 controls the development of pancreatic ducts in the mouse. Gastroenterology 2006, 130, 532–541. [Google Scholar] [CrossRef]
  106. Zhang, H.; Ables, E.T.; Pope, C.F.; Washington, M.K.; Hipkens, S.; Means, A.L.; Path, G.; Seufert, J.; Costa, R.H.; Leiter, A.B.; et al. Multiple, temporal-specific roles for HNF6 in pancreatic endocrine and ductal differentiation. Mech. Dev. 2009, 126, 958–973. [Google Scholar] [CrossRef] [Green Version]
  107. Zhang, Y.; Lei, Y.; Honarpisheh, M.; Kemter, E.; Wolf, E.; Seissler, J. Butyrate and class I histone deacetylase inhibitors promote differentiation of neonatal porcine islet cells into beta cells. Cells 2021, 10, 3249. [Google Scholar] [CrossRef] [PubMed]
  108. Haumaitre, C.; Lenoir, O.; Scharfmann, R. Histone deacetylase inhibitors modify pancreatic cell fate determination and amplify endocrine progenitors. Mol. Cell Biol. 2008, 28, 6373–6383. [Google Scholar] [CrossRef] [Green Version]
  109. Ameri, J.; Borup, R.; Prawiro, C.; Ramond, C.; Schachter, K.A.; Scharfmann, R.; Semb, H. Efficient generation of glucose-responsive beta cells from isolated GP2(+) human pancreatic progenitors. Cell Rep. 2017, 19, 36–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  110. Augstein, P.; Naselli, G.; Loudovaris, T.; Hawthorne, W.J.; Campbell, P.; Bandala-Sanchez, E.; Rogers, K.; Heinke, P.; Thomas, H.E.; Kay, T.W.; et al. Localization of dipeptidyl peptidase-4 (CD26) to human pancreatic ducts and islet alpha cells. Diabetes Res. Clin. Pract. 2015, 110, 291–300. [Google Scholar] [CrossRef] [PubMed]
  111. Sun, S.H.; Chen, Q.; Gu, H.J.; Yang, G.; Wang, Y.X.; Huang, X.Y.; Liu, S.S.; Zhang, N.N.; Li, X.F.; Xiong, R.; et al. A mouse model of SARS-CoV-2 infection and pathogenesis. Cell Host Microbe 2020, 28, 124–133. [Google Scholar] [CrossRef]
  112. Israelow, B.; Song, E.; Mao, T.; Lu, P.; Meir, A.; Liu, F.; Alfajaro, M.M.; Wei, J.; Dong, H.; Homer, R.J.; et al. Mouse model of SARS-CoV-2 reveals inflammatory role of type I interferon signaling. J. Exp. Med. 2020, 217, 2547–2562. [Google Scholar] [CrossRef] [PubMed]
  113. Jiang, Y.; Xie, F.; Lv, X.; Wang, S.; Liao, X.; Yu, Y.; Dai, Q.; Zhang, Y.; Meng, J.; Hu, G.; et al. Mefunidone ameliorates diabetic kidney disease in STZ and db/db mice. FASEB J. 2021, 35, e21198. [Google Scholar] [CrossRef] [PubMed]
  114. Wang, M.; Song, L.; Strange, C.; Dong, X.; Wang, H. Therapeutic effects of adipose stem cells from diabetic mice for the treatment of type 2 diabetes. Mol. Ther. 2018, 26, 1921–1930. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Ronaldson-Bouchard, K.; Teles, D.; Yeager, K.; Tavakol, D.N.; Zhao, Y.; Chramiec, A.; Tagore, S.; Summers, M.; Stylianos, S.; Tamargo, M.; et al. A multi-organ chip with matured tissue niches linked by vascular flow. Nat. Biomed. Eng. 2022, 6, 351–371. [Google Scholar] [CrossRef] [PubMed]
Biomedicines 11 00856 g001 550
Figure 1. Islet organoids for unraveling the relationship between glycemic control and SARS-CoV-2 infection. The generation and purification of pancreatic cell types would help clarify the distribution and abundance of ACE2 and other related factors in the pancreas. They would also allow the engineering of human islet organoids that are physiologically similar to primary human islets. Mature human islet organoids can be used for studies of viral infections and multi-organ chip assessments. The identification of islet dysfunction and glycemic control in SARS-CoV-2-infected normal islet organoids in healthy or diabetic mouse models would help unravel the bidirectional relationship between glycemic control and SARS-CoV-2-infection.
Figure 1. Islet organoids for unraveling the relationship between glycemic control and SARS-CoV-2 infection. The generation and purification of pancreatic cell types would help clarify the distribution and abundance of ACE2 and other related factors in the pancreas. They would also allow the engineering of human islet organoids that are physiologically similar to primary human islets. Mature human islet organoids can be used for studies of viral infections and multi-organ chip assessments. The identification of islet dysfunction and glycemic control in SARS-CoV-2-infected normal islet organoids in healthy or diabetic mouse models would help unravel the bidirectional relationship between glycemic control and SARS-CoV-2-infection.
Biomedicines 11 00856 g001
Table
Table 1. Clinical studies showing the bidirectional relationship between glycemic control and SARS-CoV-2 infection.
Table 1. Clinical studies showing the bidirectional relationship between glycemic control and SARS-CoV-2 infection.
RegionSource of DataMain FindingsReference
STUDY 1England13,809 patients admitted for COVID-19 to the HDU/5447 admitted to the ICU
Mean age: 70/58		34.7% mortality with T2D;
25.5% mortality without T2D		[60]
STUDY 2England23,698 COVID-19-related deaths (with and without diabetes)31.4% mortality with T2D;
1.5% mortality with T1D		[18]
STUDY 3Korea5307 people with COVID-19Increased severity and higher mortality in 14.5% individuals with T2D[74]
STUDY 4USA395 patients with COVID-19
Age: 18–35		3.8% mortality without comorbidity;
13.6% mortality with diabetes (deceased);
18.5% mortality with diabetes (diagnosed)		[75]
STUDY 5Mexico757,210 patients with COVID-19Patients with diabetes had a 49% risk of death higher than those without diabetes;
Diagnosis of T2D as COVID-19 outcome in both young and old		[76]
STUDY 6USA1544 patients with COVID-19Hyperglycemia and hypoglycemia both contribute to poor outcomes of COVID-19[64]
STUDY 7China7337 with COVID-19
Ages: 18–75		13% of patients with T2D;
Death rate is 1.49-fold higher in the T2D cohort		[77]
STUDY 8China1099 patients
639 male/460 female
Mean age: 47		Individuals with diabetes are more susceptible to SARS-CoV-2 and more easily develop a severe course of COVID-19[53]
STUDY 9EnglandPopulation-based cohort studyCOVID-19-related mortality increases in people with a higher glycosylated hemoglobin level[78]
STUDY 10France2,608 patients with COVID-19
Age: 56.0 (±16.4)		Patients with T1D (age > 65–75) had higher rates of COVID-19-related mortality[79]
STUDY 11China92 patients with COVID-19 without metabolic-related diseasesNew-onset insulin resistance, hyperglycemia, and decreased HDL-C in these patients[80]
STUDY 12Italy551 patients with COVID-19
344 male/207 female
Age: 61  ±  0.7		46% overt hyperglycemia;
12% new-onset diabetes;
glycemic abnormalities last for 2 months after recovery		[81]
STUDY 13UK30 children with new-onset T1D
Age: 23 months–16 years		The number of children with new-onset T1D increased since the COVID-19 pandemic. Some of these patients had been infected/exposed to SARS-CoV-2[20]
STUDY 14France1 woman with COVID-19 and a history of gastric bypass
Age 29		The COVID-19 patient was diagnosed with new-onset diabetes after 1.5 months[82]
STUDY 15Italy413 patients with COVID-19 21 of 413 (5.1%) had new-onset diabetes;
Patients with new-onset diabetes reported higher severity and mortality than those with pre-existing diabetes		[63]
Table
Table 2. Remaining questions and proposed solutions (* indicates progresses that have been made by researchers).
Table 2. Remaining questions and proposed solutions (* indicates progresses that have been made by researchers).
Remaining QuestionsProposed Solutions/Progress *
1The distribution and abundance of ACE2 in endocrine and non-endocrine cellsGenerating missing pancreatic cell types, biochemical analyses
2The dynamics of ACE2 during the development of the pancreasBiochemical analyses
3The mechanisms of how SARS-CoV-2 infection influences the pancreatic endocrine cells’ functionTransplantation of islet organoids
Activation of the Na+/H+ exchanger *
Inflammatory cytokines *
4Whether infection results in T1D or T2DMore cohort studies and experimental research based on disease models
5The correlation between impaired glycemic control and the virulence of SARS-CoV-2 variants Infection model of islet organoids
6Multi-organ interactions after infection?Multi-organoid systems, co-culture systems
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, T.; Wang, N.; Zhu, L.; Chen, L.; Liu, H. Bidirectional Relationship between Glycemic Control and COVID-19 and Perspectives of Islet Organoid Models of SARS-CoV-2 Infection. Biomedicines 2023, 11, 856. https://doi.org/10.3390/biomedicines11030856

AMA Style

Zhang T, Wang N, Zhu L, Chen L, Liu H. Bidirectional Relationship between Glycemic Control and COVID-19 and Perspectives of Islet Organoid Models of SARS-CoV-2 Infection. Biomedicines. 2023; 11(3):856. https://doi.org/10.3390/biomedicines11030856

Chicago/Turabian Style

Zhang, Tongran, Nannan Wang, Lingqiang Zhu, Lihua Chen, and Huisheng Liu. 2023. "Bidirectional Relationship between Glycemic Control and COVID-19 and Perspectives of Islet Organoid Models of SARS-CoV-2 Infection" Biomedicines 11, no. 3: 856. https://doi.org/10.3390/biomedicines11030856

APA Style

Zhang, T., Wang, N., Zhu, L., Chen, L., & Liu, H. (2023). Bidirectional Relationship between Glycemic Control and COVID-19 and Perspectives of Islet Organoid Models of SARS-CoV-2 Infection. Biomedicines, 11(3), 856. https://doi.org/10.3390/biomedicines11030856

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop