A review of disease risk prediction methods and applications in the omics era.

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA.

BACKGROUND: IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level. METHODS: Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm. RESULTS: We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control. CONCLUSION: In this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.

Effects of genetic variation on the structure of RNA and protein.

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

Integrating multi-omics data to analyze the potential pathogenic mechanism of CTSH gene involved in type 1 diabetes in the exocrine pancreas.

Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of insulin-producing pancreatic islet beta cells. Despite significant advancements, the precise pathogenesis of the disease remains unknown. This work integrated data from expression quantitative trait locus (eQTL) studies with Genome wide association study (GWAS) summary data of T1D and single-cell transcriptome data to investigate the potential pathogenic mechanisms of the CTSH gene involved in T1D in exocrine pancreas. Using the summary data-based Mendelian randomization (SMR) approach, we obtained four potential causative genes associated with T1D: BTN3A2, PGAP3, SMARCE1 and CTSH. To further investigate these genes'roles in T1D development, we validated them using a scRNA-seq dataset from pancreatic tissues of both T1D patients and healthy controls. The analysis showed a significantly high expression of the CTSH gene in T1D acinar cells, whereas the other three genes showed no significant changes in the scRNA-seq data. Moreover, single-cell WGCNA analysis revealed the strongest positive correlation between the module containing CTSH and T1D. In addition, we found cellular ligand-receptor interactions between the acinar cells and different cell types, especially ductal cells. Finally, based on functional enrichment analysis, we hypothesized that the CTSH gene in the exocrine pancreas enhances the antiviral response, leading to the overexpression of pro-inflammatory cytokines and the development of an inflammatory microenvironment. This process promotes ß cells injury and ultimately the development of T1D. Our findings offer insights into the underlying pathogenic mechanisms of T1D.

Epigenome-wide methylation haplotype association analysis identified HLA-DRB1, HLA-DRB5 and HLA-DQB1 as risk factors for rheumatoid arthritis.

The aim of this study was to compare nonrandom associations between physically adjacent single methylation polymorphism loci among rheumatoid arthritis (RA) and normal subjects for investigating RA-risk methylation haplotypes (meplotype). With 354 ACPA-positive RA patients and 335 normal controls selected from a case-control study based on Swedish population, we conducted the first RA epigenome-wide meplotype association study using our software EWAS2.0, mainly including (i) converted the ß value to methylation genotype (menotype) data, (ii) identified methylation disequilibrium (MD) block, (iii) calculated frequent of each meplotypes in MD block and performed case-control association test and (iv) screened for RA-risk meplotypes by odd ratio (OR) and p-values. Ultimately, 545 meplotypes on 334 MD blocks were identified significantly associated with RA (p-value < .05). These meplotypes were mapped to 329 candidate genes related to RA. Subsequently, combined with gene optimization, eight RA-risk meplotypes were identified on three risk genes: HLA-DRB1, HLA-DRB5 and HLA-DQB1. Our results reported the relationship between DNA methylation pattern on HLA-DQB1 and the risk of RA for the first time, demonstrating the co-demethylation of 'cg22984282' and 'cg13423887' on HLA-DQB1 gene (meplotype UU, p-value = 2.90E - 6, OR = 1.68, 95% CI = [1.35, 2.10]) may increase the risk of RA. Our results demonstrates the potential of methylation haplotype analysis to identify RA-related genes from a new perspective and its applicability to the study of other disease.

RABC: Rheumatoid Arthritis Bioinformatics Center.

Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA.

Exploring effects of DNA methylation and gene expression on pan-cancer drug response by mathematical models.

Since genetic alteration only accounts for 20%-30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with P < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and P < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as EGFR, MET, and TET2, synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs' responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.

Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data.

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

Upregulation Of Protein Tyrosine Phosphatase Receptor Type C Associates To The Combination Of Hashimoto's Thyroiditis And Papillary Thyroid Carcinoma And Is Predictive Of A Poor Prognosis.

INTRODUCTION: PTC is not generally considered a lethal disease, but prone to recurrence as the prognosis. Hashimoto's thyroiditis (HT) is an important factor that affects the prognosis of papillary thyroid carcinoma (PTC). It is crucial to find biomarkers to identify the combination of HT with PTC and to predict the prognosis. METHODS: RNASeq data from the Cancer Genome Atlas (TCGA) database was used to screen differentially expressed genes (DEGs) of PTC with HT via the edgeR package of R software version 3.3.0. Also, the DEGs were applied to the DAVID web-based tool to determine the enrichment of gene functions via Gene Ontology (GO) analysis and to identify associated pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. By constructing protein interaction networks within Cytoscape software, we screened candidate genes and explored possible relationships with the clinical phenotype of PTC. Finally, additional thyroid tissue samples were collected to verify the results above. RESULTS: After analyzing the RNA-Seq data of PTC patients from the Cancer Genomic Atlas, 497 differentially expressed PTC genes were found to be associated with HT, of which protein tyrosine phosphatase receptor type C (PTPRC), KIT, and COL1A1 were associated with tumor size and lymph node metastasis (p < 0.05). Verification of these results with another 30 thyroid tissues of clinical PTC patients revealed that the expression level of PTPRC in the PTC with HT group was higher than that in the PTC without HT group (p < 0.05) and the ROC curve showed a good discrimination (area under the curve = 0.846). However, the correlation with the clinical phenotype was not statistically significant (p > 0.05). DISCUSSION: These data suggest that upregulation of PTPRC enhances the incidence of HT associated with PTC and is also predictive of a poor prognosis.

Facile Synthesis of Z-Scheme Ag3PO4/Sulfur-Doped g-C3N4 Heterojunction Hybrids with Enhanced Visible Light Photocatalytic Performance.

Ag3PO4/sulfur-doped g-C3N4 heterojunctions were fabricated by the means of a facile calcination and co-precipitation method. Structural characterization suggested that Ag3PO4 was successfully loaded onto sulfur-doped g-C3N4. The absorption band edges of sulfur-doped g-C3N4 were shifted to the longer wavelength in comparison with bulk g-C3N4. The Ag3PO4/sulfur-doped g-C3N4 heterojunctions manifested substantially higher visible-light photocatalytic performance as compared with Ag3PO4/bulk g-C3N4. Photoluminescence spectra suggested that the stable Ag3PO4/SGCN heterojunctions could effectively address the electron-hole recombination rate, together with remarkably enhancing the photocatalytic activity. The enhancement of light absorption and better dispersion in Ag3PO4/sulfur-doped g-C3N4 provide more migration channels, together with posing crucial responsibility for the enhanced photocatalytic performance.

Prediction of circRNAs Based on the DNA Methylation-Mediated Feature Sponge Function in Breast Cancer.

Several studies have found that DNA methylation is associated with transcriptional regulation and affect sponge regulation of non-coding RNAs in cancer. The integration of circRNA, miRNA, DNA methylation and gene expression data to identify sponge circRNAs is important for revealing the role of DNA methylation-mediated regulation of sponge circRNAs in cancer progression. We established a DNA methylation-mediated circRNA crosstalk network by integrating gene expression, DNA methylation and non-coding RNA data of breast cancer in TCGA. Four modules (26 candidate circRNAs) were mined. Next, 10 DNA methylation-mediated sponge circRNAs (sp_circRNAs) and five sponge driver genes (sp_driver genes) in breast cancer were identified in the CMD network using a computational process. Among the identified genes, ERBB2 was associated with six sponge circRNAs, which illustrates its better sponge regulatory function. Survival analysis showed that DNA methylations of 10 sponge circRNA host genes are potential prognostic biomarkers in the TCGA dataset (p = 0.0239) and GSE78754 dataset (p = 0.0377). In addition, the DNA methylation of two sponge circRNA host genes showed a significant negative correlation with their driver gene expressions. We developed a strategy to predict sponge circRNAs by DNA methylation mediated with playing the role of regulating breast cancer sponge driver genes.

Exploration of drug-response mechanism by integrating genetics and epigenetics across cancers.

AIM: To discover CpG island methylator phenotype (CIMP) as a predictor for cancer drug-response mechanism. MATERIALS & METHODS: CIMP classification of 966 cancer cell lines was determined according to identified copy number alteration and differential methylation by DNA methylation profiles. CIMP-related drugs were analyzed by analysis of variance. Tissue-cell-drug networks were developed to predict drug response of individual samples. RESULTS & CONCLUSION: One hundred and thirty-six copy number gain and 142 copy number loss cell lines were classified into CIMP-high and CIMP-low groups, meanwhile 9 and 24 CIMP-associated drugs were identified, respectively. Specially, breast invasive carcinoma samples primarily composed by HCC1419 were predicted to be sensitive to GSK690693. The study provides guidance for drug response in cancer therapy through genome-wide DNA methylation.

Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns.

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal-like subtype into two different prognosis-subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.

A (001) dominated conjugated polymer with high-performance of hydrogen evolution under solar light irradiation.

A two-dimensional imide-based conjugated polymer with a preferred (001) orientation was constructed by solvent-induced assembly. A high performance of 1640 µmol h-1 g-1 for solar-driven photocatalytic hydrogen evolution and an excellent stability were achieved due to tunnelling charge transport between the neighbouring molecular sheets.

Multilayer gyroid cubic membrane organization in green alga Zygnema.

Biological cubic membranes (CM), which are fluid membranes draped onto the 3D periodic parallel surface geometries with cubic symmetry, have been observed within subcellular organelles, including mitochondria, endoplasmic reticulum, and thylakoids. CM transition tends to occur under various stress conditions; however, multilayer CM organizations often appear associated with light stress conditions. This report is about the characterization of a projected gyroid CM in a transmission electron microscopy study of the chloroplast membranes within green alga Zygnema (LB923) whose lamellar form of thylakoid membrane started to fold into multilayer gyroid CM in the culture at the end of log phase of cell growth. Using the techniques of computer simulation of transmission electron microscopy (TEM) and a direct template matching method, we show that these CM are based on the gyroid parallel surfaces. The single, double, and multilayer gyroid CM morphologies are observed in which space is continuously divided into two, three, and more subvolumes by either one, two, or several parallel membranes. The gyroid CM are continuous with varying amount of pseudo-grana with lamellar-like morphology. The relative amount and order of these two membrane morphologies seem to vary with the age of cell culture and are insensitive to ambient light condition. In addition, thylakoid gyroid CM continuously interpenetrates the pyrenoid body through stalk, bundle-like, morphologies. Inside the pyrenoid body, the membranes re-folded into gyroid CM. The appearance of these CM rearrangements due to the consequence of Zygnema cell response to various types of environmental stresses will be discussed. These stresses include nutrient limitation, temperature fluctuation, and ultraviolet (UV) exposure.

The drug target genes show higher evolutionary conservation than non-target genes.

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Genome-wide haplotype association analysis identifies SERPINB9, SERPINE2, GAK, and HSP90B1 as novel risk genes for oral squamous cell carcinoma.

The oral squamous cell carcinoma (OSCC) is one of the most common malignant epithelial neoplasms and considered to be caused by the genetic damage. In addition, smoking habit and excessive alcohol consumption have been estimated to be the main risk factors. Although the association between OSCC and genetic susceptibility loci has been observed in many different populations, most of these studies simply focused on the single nucleotide polymorphism. Therefore, we made a contrast analysis between the 112 OSCC patients from the GEO database and 245 normal samples from the HapMap project. First, we performed a genome-wide haplotype association study by comparing the frequency of the haplotypes in the case-control experiment. Then, we mapped the haplotypes to the corresponding genes, screened the risk genes according to significant haplotypes (P < 1E-04), and prioritized the OSCC genes based on their similarity to the known OSCC susceptibility genes. We filtered four OSCC genes including SERPINB9, SERPINE2, GAK, and HSP90B1 through the gene global prioritization score (P < 0.005). SERPINB9 ranked first in the candidate gene list and contained a significant haplotype TAGGA (P value = 3.12E-11). The second risk gene was SERPINE2 with the haplotype GGGCCCTTT, which was closely similar to the SERPINB9.

Genes with stable DNA methylation levels show higher evolutionary conservation than genes with fluctuant DNA methylation levels.

Different human genes often exhibit different degrees of stability in their DNA methylation levels between tissues, samples or cell types. This may be related to the evolution of human genome. Thus, we compared the evolutionary conservation between two types of genes: genes with stable DNA methylation levels (SM genes) and genes with fluctuant DNA methylation levels (FM genes). For long-term evolutionary characteristics between species, we compared the percentage of the orthologous genes, evolutionary rate dn/ds and protein sequence identity. We found that the SM genes had greater percentages of the orthologous genes, lower dn/ds, and higher protein sequence identities in all the 21 species. These results indicated that the SM genes were more evolutionarily conserved than the FM genes. For short-term evolutionary characteristics among human populations, we compared the single nucleotide polymorphism (SNP) density, and the linkage disequilibrium (LD) degree in HapMap populations and 1000 genomes project populations. We observed that the SM genes had lower SNP densities, and higher degrees of LD in all the 11 HapMap populations and 13 1000 genomes project populations. These results mean that the SM genes had more stable chromosome genetic structures, and were more conserved than the FM genes.

Prioritization of rheumatoid arthritis risk subpathways based on global immune subpathway interaction network and random walk strategy.

The initiation and development of rheumatoid arthritis (RA) is closely related to mutual dysfunction of multiple pathways. Furthermore, some similar molecular mechanisms are shared between RA and other immune diseases. Therefore it is vital to reveal the molecular mechanism of RA through searching for subpathways of immune diseases and investigating the crosstalk effect among subpathways. Here we exploited an integrated approach combining both construction of a subpathway-subpathway interaction network and a random walk strategy to prioritize RA risk subpathways. Our research can be divided into three parts: (1) acquisition of risk genes and identification of risk subpathways of 85 immune diseases by using subpathway-lenient distance similarity (subpathway-LDS) method; (2) construction of a global immune subpathway interaction (GISI) network with subpathways identified by subpathway-LDS; (3) optimization of RA risk subpathways by random walk strategy based on GISI network. The results showed that our method could effectively identify RA risk subpathways, such as MAPK signaling pathway, prostate cancer pathway and chemokine signaling pathway. The integrated strategy considering crosstalk between immune subpathways significantly improved the effect of risk subpathway identification. With the development of GWAS, our method will provide insight into exploring molecular mechanisms of immune diseases and might be a promising approach for studying other diseases.