COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges

Are inflammatory bowel diseases associated with an increased risk of COVID-19 susceptibility and severity? A two-sample Mendelian randomization study

Background: Due to inconsistent findings in observational studies regarding the relationship between inflammatory bowel disease (IBD), encompassing ulcerative colitis (UC) and Crohn's disease (CD), and COVID-19, our objective is to explore a potential causative correlation between IBD and COVID-19 susceptibility and its severity using a two-sample Mendelian randomization (MR) analysis. Methods: Using summary data from genome-wide association studies, IBD, including UC and CD, were used as exposure instruments, while COVID-19 susceptibility, hospitalization, and very severe illness were employed as the outcome. The five analysis methods were adopted to evaluate the causal relationship between two diseases, with the inverse variance weighted (IVW) method being the most important. Also, sensitivity analyses were done to make sure that the main results of the MR analyses were reliable. Results: In the analysis using five methods, all p-values were higher than 0.05. There was no association between IBD and COVID-19 susceptibility, hospitalization, and severity in our MR study. The random-effect model was applied due to the existence of heterogeneity. MR-Egger regression revealed no indication of directional pleiotropy, and sensitivity analysis revealed similar relationships. Conclusion: This MR study found no evidence to support that IBD (which includes UC and CD) increases the risk of COVID-19 susceptibility or severity. Our result needs further confirmation through larger epidemiological studies.

Susceptibility of Patients with Inflammatory Bowel Disease to COVID-19 Compared with Their Households

BACKGROUND: Immunosuppressive agents used in the treatment of inflammatory bowel diseases (IBDs) could potentially increase the risk of coronavirus disease 2019 (COVID-19). We aimed to compare COVID-19 frequency in patients with IBD with their households and identify the related risk factors. METHODS: Firstly, a multi-centered, observational study on 2110 patients with IBD and 2110 age-matched household members was conducted to compare COVID-19 frequency. Secondly, the data of patients with IBD and COVID-19 who had called the COVID-19 hotline were added. Multivariable logistic regression was used to evaluate the effect of age, type and severity of IBD, the number of comorbidities, and medications on the frequency of COVID-19 among the patients with IBD. RESULTS: The prevalence of COVID-19 in patients with IBD and household groups was similar (34 [1.61%] versus 35 [1.65%]; P = 0.995). The prevalence of COVID-19 increased from 2.1% to 7.1% in those with three or more comorbidities (P = 0.015) and it was significantly higher in those with severe IBD (P = 0.026). The multivariable analysis only showed a significant association with anti-TNF monotherapy (OR: 2.5, CI: 0.97-6.71, P = 0.05), and other medications were not associated with COVID-19. CONCLUSION: The prevalence of COVID-19 in patients with IBD was similar to the household members. Only patients with IBD receiving anti-TNF monotherapy had a higher risk of COVID-19 susceptibility. This finding could be attributed to the higher exposure to the virus during administration in health care facilities.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

CORD-19: The Covid-19 Open Research Dataset

The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many Covid-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for Covid-19.