CHDGKB: a knowledgebase for systematic understanding of genetic variations associated with non-syndromic congenital heart disease

Copy number variation-associated lncRNAs may contribute to the etiologies of congenital heart disease

Copy number variations (CNVs) have long been recognized as pathogenic factors for congenital heart disease (CHD). Few CHD associated CNVs could be interpreted as dosage effect due to disruption of coding sequences. Emerging evidences have highlighted the regulatory roles of long noncoding RNAs (lncRNAs) in cardiac development. Whereas it remains unexplored whether lncRNAs within CNVs (CNV-lncRNAs) could contribute to the etiology of CHD associated CNVs. Here we constructed coexpression networks involving CNV-lncRNAs within CHD associated CNVs and protein coding genes using the human organ developmental transcriptomic data, and showed that CNV-lncRNAs within 10 of the non-syndromic CHD associated CNVs clustered in the most significant heart correlated module, and had highly correlated coexpression with multiple key CHD genes. HSALNG0104472 within 15q11.2 region was identified as a hub CNV-lncRNA with heart-biased expression and validated experimentally. Our results indicated that HSALNG0104472 should be a main effector responsible for cardiac defects of 15q11.2 deletion through regulating cardiomyocytes differentiation. Our findings suggested that CNV-lncRNAs could potentially contribute to the pathologies of a maximum proportion of 68.4% (13/19) of non-syndromic CHD associated CNVs. These results indicated that explaining the pathogenesis of CHD associated CNVs should take account of the noncoding regions.

High-risk genes involved in common septal defects of congenital heart disease

The septation defect is one of the main categories of congenital heart disease (CHD). They can affect the septation of the atria leading to atrial septal defect (ASD), septation of ventricles leading to ventricular septal defect (VSD), and formation of the central part of the heart leading to atrioventricular septal defect (AVSD). Disruption of critical genetic factors involved in the proper development of the heart structure leads to CHD manifestation. Because of this, to identify the high-risk genes involved in common septal defects, a comprehensive search of the literature with the help of databases and the WebGestalt analysis tool was performed. The high-risk genes identified in the analysis were checked in 16 Indian whole-exome sequenced samples, including 13 VSD and three Tetralogy of Fallot for in silico validation. This data revealed three variations in GATA4, i.e., c.C1223A at exon 6: c.C602A and c.C1220A at exon 7; and one variation in MYH6, i.e., c.G3883C at exon 28 in two VSD cases. This study supports previously published studies that suggested GATA4 and MYH6 as the high-risk genes responsible for septal defects. Thus, this study contributes to a better understanding of the genes involved in heart development by identifying the high-risk genes and interacting proteins in the pathway.

CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications

MOTIVATION

In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance.

RESULTS

The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions.

AVAILABILITY AND IMPLEMENTATION

CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MIKB: A manually curated and comprehensive knowledge base for myocardial infarction

Myocardial infarction knowledge base (MIKB; http://www.sysbio.org.cn/mikb/; latest update: December 31, 2020) is an open-access and manually curated database dedicated to integrating knowledge about MI to improve the efficiency of translational MI research. MIKB is an updated and expanded version of our previous MI Risk Knowledge Base (MIRKB), which integrated MI-related risk factors and risk models for providing help in risk assessment or diagnostic prediction of MI. The updated MIRKB includes 9701 records with 2054 single factors, 209 combined factors, 243 risk models, 37 MI subtypes and 3406 interactions between single factors and MIs collected from 4817 research articles. The expanded functional module, i.e. MIGD, is a database including not only MI associated genetic variants, but also the other multi-omics factors and the annotations for their functional alterations. The goal of MIGD is to provide a multi-omics level understanding of the molecular pathogenesis of MI. MIGD includes 1782 omics factors, 28 MI subtypes and 2347 omics factor-MI interactions as well as 1253 genes and 6 chromosomal alterations collected from 2647 research articles. The functions of MI associated genes and their interaction with drugs were analyzed. MIKB will be continuously updated and optimized to provide precision and comprehensive knowledge for the study of heterogeneous and personalized MI.

An update on the CHDGKB for the systematic understanding of risk factors associated with non-syndromic congenital heart disease

The Congenital Heart Disease Genetic Knowledge Base (CHDGKB) was established in 2020 to provide comprehensive knowledge about the genetics and pathogenesis of non-syndromic CHD (NS-CHD). In addition to the genetic causes of NS-CHD, environmental factors such as maternal drug use and gene-environment interactions can also lead to CHD. There is a need to integrate this information into a platform for clinicians and researchers to better understand the overall risk factors associated with NS-CHD. The updated CHDGKB contains the genetic and non-genetic risk factors from over 4200 records from PubMed that was manually curated to include the information associated with NS-CHD. The current version of CHDGKB, named CHD-RF-KB (KnowledgeBase for non-syndromic Congenital Heart Disease-associated Risk Factors), is an important tool that allows users to evaluate the recurrence risk and prognosis of NS-CHD, to guide treatment and highlight the precautions of NS-CHD. In this update, we performed extensive functional analyses of the genetic and non-genetic risk information in CHD-RF-KB. These data can be used to systematically understand the heterogeneous relationship between risk factors and NS-CHD phenotypes.

HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications

MOTIVATION

Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary.

RESULTS

In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs, and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine.

AVAILABILITY

The platform is openly available at http://sysbio.org.cn/HFBD/.

Nanotechnology Responses to COVID-19

Researchers, engineers, and medical doctors are made aware of the severity of the COVID-19 infection and act quickly against the coronavirus SARS-CoV-2 using a large variety of tools. In this review, a panoply of nanoscience and nanotechnology approaches show how these disciplines can help the medical, technical, and scientific communities to fight the pandemic, highlighting the development of nanomaterials for detection, sanitation, therapies, and vaccines. SARS-CoV-2, which can be regarded as a functional core-shell nanoparticle (NP), can interact with diverse materials in its vicinity and remains attached for variable times while preserving its bioactivity. These studies are critical for the appropriate use of controlled disinfection systems. Other nanotechnological approaches are also decisive for the development of improved novel testing and diagnosis kits of coronavirus that are urgently required. Therapeutics are based on nanotechnology strategies as well and focus on antiviral drug design and on new nanoarchitectured vaccines. A brief overview on patented work is presented that emphasizes nanotechnology applied to coronaviruses. Finally, some comments are made on patents of the initial technological responses to COVID-19 that have already been put in practice.