SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the lung, liver, adipose tissue, and pancreatic cells via IRF1.

BACKGROUND: COVID-19 can cause multiple organ damages as well as metabolic abnormalities such as hyperglycemia, insulin resistance, and new onset of diabetes. The insulin/IGF signaling pathway plays an important role in regulating energy metabolism and cell survival, but little is known about the impact of SARS-CoV-2 infection. The aim of this work was to investigate whether SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the host cell/tissue, and if so, the potential mechanism and association with COVID-19 pathology. METHODS: To determine the impact of SARS-CoV-2 on insulin/IGF signaling pathway, we utilized transcriptome datasets of SARS-CoV-2 infected cells and tissues from public repositories for a wide range of high-throughput gene expression data: autopsy lungs from COVID-19 patients compared to the control from non-COVID-19 patients; lungs from a human ACE2 transgenic mouse infected with SARS-CoV-2 compared to the control infected with mock; human pluripotent stem cell (hPSC)-derived liver organoids infected with SARS-CoV-2; adipose tissues from a mouse model of COVID-19 overexpressing human ACE2 via adeno-associated virus serotype 9 (AAV9) compared to the control GFP after SARS-CoV-2 infection; iPS-derived human pancreatic cells infected with SARS-CoV-2 compared to the mock control. Gain and loss of IRF1 function models were established in HEK293T and/or Calu3 cells to evaluate the impact on insulin signaling. To understand the mechanistic regulation and relevance with COVID-19 risk factors, such as older age, male sex, obesity, and diabetes, several transcriptomes of human respiratory, metabolic, and endocrine cells and tissue were analyzed. To estimate the association with COVID-19 severity, whole blood transcriptomes of critical patients with COVID-19 compared to those of hospitalized noncritical patients with COVID-19. RESULTS: We found that SARS-CoV-2 infection impaired insulin/IGF signaling pathway genes, such as IRS, PI3K, AKT, mTOR, and MAPK, in the host lung, liver, adipose tissue, and pancreatic cells. The impairments were attributed to interferon regulatory factor 1 (IRF1), and its gene expression was highly relevant to risk factors for severe COVID-19; increased with aging in the lung, specifically in men; augmented by obese and diabetic conditions in liver, adipose tissue, and pancreatic islets. IRF1 activation was significantly associated with the impaired insulin signaling in human cells. IRF1 intron variant rs17622656-A, which was previously reported to be associated with COVID-19 prevalence, increased the IRF1 gene expression in human tissue and was frequently found in American and European population. Critical patients with COVID-19 exhibited higher IRF1 and lower insulin/IGF signaling pathway genes in the whole blood compared to hospitalized noncritical patients. Hormonal interventions, such as dihydrotestosterone and dexamethasone, ameliorated the pathological traits in SARS-CoV-2 infectable cells and tissues. CONCLUSIONS: The present study provides the first scientific evidence that SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in respiratory, metabolic, and endocrine cells and tissues. This feature likely contributes to COVID-19 severity with cell/tissue damage and metabolic abnormalities, which may be exacerbated in older, male, obese, or diabetic patients.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging.

INTRODUCTION: Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently. METHODS: In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject's whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown. RESULTS: The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level. CONCLUSION: The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.