Causal relationships between interleukins, interferons and COVID-19 risk: a Mendelian randomization study.

Observational studies have shown close associations between COVID-19 risk and cytokines, especially interleukins (ILs) and interferons (IFNs). However, the causal relationships between ILs, IFNs and COVID-19 were still unclear. To resolve the problem, we conducted a Mendelian randomization analysis between COVID-19 and 47 cytokines, including 35 ILs and 12 IFNs. First, three methods were applied to estimate causal effects by using single nucleotide polymorphisms as instrumental variables (IVs). Subsequently, the MR-Egger method was used to estimate the horizontal pleiotropy of IVs. Finally, sensitivity analyses were applied to assess the robustness of results. As a result, one IFN (IFN-W1) and five ILs (IL-5, IL-6, IL-13, IL-16 and IL-37) were identified to significantly decrease the COVID-19 risk. In contrast, one IFN (IFNG) and five ILs (IL-3, IL-8, IL-27, IL-31 and IL-36ß) were found to be significantly associated with an increased risk of COVID-19. In summary, the findings of this study provide insights into potential therapeutic interventions for COVID-19.

Microbiome characteristics description of COVID-19 patients based on bulk RNA-seq and scRNA-Seq data.

After infection with SARS-CoV-2, the microbiome inside the human body changes dramatically. By re-annotating microbial sequences in bulk RNA-seq and scRNA-seq data of COVID-19 patients, we described the cellular microbial landscape of COVID-19 patients and identified characteristic microorganisms in various tissues. We found that Acinetobacter lwoffii was highly correlated with COVID-19 symptoms and might disrupt some pathways of patients by interacting with the host and other microbes, such as Klebsiella pneumoniae. We further identified characteristic microorganisms specific to cell type, indicating the enrichment preference of some microbes. We also revealed the co-infection of SARS-CoV-2 with hMPV, which may cause the development of COVID-19. Overall, we demonstrated that the presence of intracellular microorganisms in COVID-19 patients and the synergies between microorganisms were strongly correlated with disease progression, providing a theoretical basis for COVID-19 treatment in a certain extent.

Genome-wide Mendelian randomization and single-cell RNA sequencing analyses identify the causal effects of COVID-19 on 41 cytokines.

The elevated levels of inflammatory cytokines have attracted much attention during the treatment of COVID-19 patients. The conclusions of current observational studies are often controversial in terms of the causal effects of COVID-19 on various cytokines because of the confounding factors involving underlying diseases. To resolve this problem, we conducted a Mendelian randomization analysis by integrating the GWAS data of COVID-19 and 41 cytokines. As a result, the levels of 2 cytokines were identified to be promoted by COVID-19 and had unsignificant pleiotropy. In comparison, the levels of 10 cytokines were found to be inhibited and had unsignificant pleiotropy. Among down-regulated cytokines, CCL2, CCL3 and CCL7 were members of CC chemokine family. We then explored the potential molecular mechanism for a significant causal association at a single cell resolution based on single-cell RNA data, and discovered the suppression of CCL3 and the inhibition of CCL3-CCR1 interaction in classical monocytes (CMs) of COVID-19 patients. Our findings may indicate that the capability of COVID-19 in decreasing the chemotaxis of lymphocytes by inhibiting the CCL3-CCR1 interaction in CMs.

A novel method to identify pre-microRNA in various species knowledge base on various species.

BACKGROUND: More than 1/3 of human genes are regulated by microRNAs. The identification of microRNA (miRNA) is the precondition of discovering the regulatory mechanism of miRNA and developing the cure for genetic diseases. The traditional identification method is biological experiment, but it has the defects of long period, high cost, and missing the miRNAs that but also many other algorithms only exist in a specific period or low expression level. Therefore, to overcome these defects, machine learning method is applied to identify miRNAs. RESULTS: In this study, for identifying real and pseudo miRNAs and classifying different species, we extracted 98 dimensional features based on the primary and secondary structure, then we proposed the BP-Adaboost method to figure out the overfitting phenomenon of BP neural network by constructing multiple BP neural network classifiers and distributed weights to these classifiers. The novel method we proposed, from the 4 evaluation terms, have achieved greatly improvement on the effect of identifying true pre-RNA compared to other methods. And from the respect of identifying species of pre-RNA, the novel method achieved more accuracy than other algorithms. CONCLUSIONS: The BP-Adaboost method has achieved more than 98% accuracy in identifying real and pseudo miRNAs. It is much higher than not only BP but also many other algorithms. In the second experiment, restricted by the data, the algorithm could not get high accuracy in identifying 7 species, but also better than other algorithms.

DisSetSim: an online system for calculating similarity between disease sets.

BACKGROUND: Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. RESULTS: Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik's, Lin's, Wang's, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then "pair-wise-best pairs-average" (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. CONCLUSIONS: The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/ .

SIDD: a semantically integrated database towards a global view of human disease.

BACKGROUND: A number of databases have been developed to collect disease-related molecular, phenotypic and environmental features (DR-MPEs), such as genes, non-coding RNAs, genetic variations, drugs, phenotypes and environmental factors. However, each of current databases focused on only one or two DR-MPEs. There is an urgent demand to develop an integrated database, which can establish semantic associations among disease-related databases and link them to provide a global view of human disease at the biological level. This database, once developed, will facilitate researchers to query various DR-MPEs through disease, and investigate disease mechanisms from different types of data. METHODOLOGY: To establish an integrated disease-associated database, disease vocabularies used in different databases are mapped to Disease Ontology (DO) through semantic match. 4,284 and 4,186 disease terms from Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (OMIM) respectively are mapped to DO. Then, the relationships between DR-MPEs and diseases are extracted and merged from different source databases for reducing the data redundancy. CONCLUSIONS: A semantically integrated disease-associated database (SIDD) is developed, which integrates 18 disease-associated databases, for researchers to browse multiple types of DR-MPEs in a view. A web interface allows easy navigation for querying information through browsing a disease ontology tree or searching a disease term. Furthermore, a network visualization tool using Cytoscape Web plugin has been implemented in SIDD. It enhances the SIDD usage when viewing the relationships between diseases and DR-MPEs. The current version of SIDD (Jul 2013) documents 4,465,131 entries relating to 139,365 DR-MPEs, and to 3,824 human diseases. The database can be freely accessed from: http://mlg.hit.edu.cn/SIDD.