Basic Research And Applications Of High-throughput Sequencing In A Variety Of Complex Diseases

High-throughput sequencing technology has developed rapidly over the past 20 years and has been widely used in studies and applications[1,2].Different types of omics data provide interpretation of life at different levels[3].The paper describes its value in basic research and clinical application of complex diseases from the following three aspects.Chapter Ⅰ.Whole genome sequencing identified mutated genes associated with Venous thromboembolism in Chinese pedigrees and disease risk modelingObjective:Venous thromboembolism(VTE)is a complex disease caused by a variety of factors.Genetic factors of VTE vary significantly among different ethnic groups,and the large-scale sequencing study on Chinese VTE families have not been reported.In this part,we use large-scale whole-genome sequencing technology to study the genetic characteristics in Chinese VTE families and build VTE risk models,which will be helpful for VTE genetic testing and clinical prevention.Methods:1.Samples of whole genome sequencing experiment were from 27 VTE families(including 57 confirmed VTE cases and 25 healthy controls)in China;2.Data analysis:including whole genome sequencing data process,variants annotation and filtering,variants screening of known genes and candidate genes,gene load testing;3.VTE risk modeling:a genetic model based on feature genes and a combined model with non-genetic factors were established respectively based on logistic regression algorithm.Results:1.By analyzing the pattern of lineage co-segregation,we found new variants of VTE relevant known genes and candidate genes,which could account for almost all families(26/27,96.3%);2.The more common mutant genes in Chinese VTE families are SERPINC1 and PROS1,which can explain 8 families and 3 families respectively,while the common VTE-related mutations FV Leiden[4]and FII G20210A[5]in western populations are not found in our family members;3.The feature genes of the genetic model for VTE risk model were TSPY2,SERPINC1,ECM2,LAMA3 and HBG1,with an AUC of 0.882.If only the rare mutation sites were considered,there were only three features retained,TSPY2,SERPINC1 and GGT2,and the AUC of the model could reach 0.872;4.Based on genetic and environmental factors(age,gender),a combined risk model of VTE was constructed,and the AUC of the model could reach 0.952.Conclusion:It is the first large-scale whole-genome sequencing study of VTE families in China;We proved that genetic factors of VTE vary significantly among different ethnic groups;New genes/mutation sites associated with VTE were found;VTE risk model for Chinese VTE pedigree was constructed.Chapter Ⅱ.Single cell transcriptome sequencing identified the cell types in adrenal tumors and functional analysisObjective:We encountered a rare case with ectopic secreting ACTH and CRH pheochromocytoma associated with Cushing’s syndrome.In order to determine whether a unique cell type is responsible for multiple hormones secreting,we used single-cell transcriptome sequencing to identify rare tumor cell types,thereby revealing the underlying molecular mechanisms.Other 3 adrenal tumor specimens from common pheochromocytoma and adrenocortical adenomas were also involved for comparison,providing potential biomarkers in adrenal tumor diagnose.Methods:1.A total of seven samples of the single cell transcriptome sequencing experiment were from three patients with adrenal tumors,four samples from the rare case,one sample from a common pheochromocytoma case and two samples from a case with adrenocortical adenomas;2.Single-cell transcriptome data analysis:including data pre-processing and quality control,cell clustering,differential expression gene analysis,pseudo-time analysis,etc;3.Immunohistochemical and immunofluorescence staining were used to confirm the positive cells secreting multiple hormones.Results:1.A total of 16 cell types were identified by unbiased cell clustering of single-cell transcriptome,and there are composition differences of adrenal cell types,immune cell types and endothelial cell types among collected samples;2.A novel multi-functionally chromafin-like cell type(ACTH+&CRH+pheochromocytes)has been identified,which is only found in tumor samples of rare ectopic secreting ACTH and CRH pheochromocytoma;3.It revealed that the molecular mechanism of the rare case harbor Cushing’s syndrome is due to the novel tumor cell type of ACTH+&CRH+pheochromocyte,that is,the secretion of ACTH had a direct effect on the adrenal gland to produce cortisol,while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary;4.A new potential marker GAL was identified for the detection of rare ectopic ACTH&CRH secreting pheochromocytes;5.The differentially expressed genes of ACTH+&CRH+pheochromocytes and other adrenal cell types are mainly enriched in neuropeptide signal pathways,hormone secretion and transport pathways.Conclusion:Single-cell RNA sequencing identified a new chromaffin-like cell type with multiple hormones secreting function,called ACTH+&CRH+pheochromocytes.It revealed the molecular mechanism of the rare case with ectopic Cushing’s syndrome.A potential molecular marker GAL might be locally involved in the regulation of the hypothalamic-pituitary-adrenal axis.Chapter Ⅲ:Gene discovery knowledgebase and omics database for disorders of sexual developmentObjective:Disorders of sex development(DSD)is a spectrum of disease with heterogeneity,largely arisen from mutations of gene or chromosome.It is critical to study the pathogenesis of sexual dysplasia and assist clinical diagnosis from the genetic level.Some general databases and analysis tools lack specially designed for DSD diseases and sample grouping.We intended to establish a comprehensive platform for DSD,which will assist users to identify DSD-related genes and candidate biomarkers for deeper understanding of DSD.Methods:1.We proposed a new DSD classification system based on the recommendation of nomenclature and classification of DSD presented by 2006 WHO consensus conference.It accommodates the wide spectrum of phenotypic as well as reflecting most detailed known genetic cause;2.The knowledgebase of DSDatlas,based on the Phenolyzer toolkit,has integrated the knowledge of disease gene associations from Malacards,Genecards and DisGenet databases;3.The omics database has collected 25 experimental datasets related to DSD,including 1 self-produced dataset and 24 publicly available datasets from GEO and SRA.Each dataset has undergone manual review and secondary analysis;4.A web application DSDatlas has been developed to provide ranking and comparison of relevant genes of DSD disease subtypes,DSD related omics data analysis and visualization such as differential expressed gene analysis and gene mutation search and visualization in the DSD family.Results:1.A new DSD classification system was proposed,including four secondary classifications(46,XY DSD,46,XX DSD,Sex chromosome disorders of sexual development and Syndrome forms involving DSD),15 tertiary classifications and 44 specific DSD related disease names;2.Based on DSD classification,the DSDatlas platform(DSDatlas,http://dsd.geneworks.cn)maintains both a knowledgebase and a omics database for DSD.The knowledgebase provides the ranking DSD related genes with evidence display,and the database provides the gene-centered omics data analysis and visualization system.Conclusion:The new DSD classification system covers more complete subtypes of DSD.DSDatlas,a platform for gene discovery and omics data analysis by ranking DSD related genes,analyzing the mutation and differential expression of genes in each dataset,will contribute to further recognition of DSD and even help the clinical management of DSD.In summary,high-throughput sequencing technology has played an important role in various disease research fields.The first part detected genetic mutation and built VTE risk model at the genomic level;the second part identified rare adrenal tumor cells with multiple hormone secretion functions by single cell transcriptome technique;the third part constructed a gene discovery knowledge base and omics database for DSD by integrating gene-disease association knowledge and gene mutation and expression data.