The KMDB/MutationView: a mutation database for human disease genes

The KMDB/MutationView is a graphical database of mutations in human disease-causing genes and its current version consists of nine category-based sub-databases including diseases of eye, heart, ear, brain, cancer, syndrome, autoimmunity, muscle and blood. The KMDB/MutationView stores mutation data of 97 genes involved in 87 different disease and is accessible through http://mutview.dmb.med. keio.ac.jp.     

Keio Mutation Database for eye disease genes (KMeyeDB).

A database of mutations in human eye disease genes has been constructed. This KMeyeDB employs a database software MutationView which provides graphical data presentation and analysis as a smooth user-interface. Currently, the KMeyeDB contains mutation data of 16 different genes for 18 eye diseases. The KMeyeDB is accessible through http://mutview.dmb.med.keio.ac.jp with advanced internet browsers.     

A mutation spectra database for bacterial and mammalian genes.

Each mutation spectrum in this database is a dataset of changes in DNA base sequence in mutations induced in a gene by a particular mutagen (including spontaneous processes) under defined conditions. There are 240 datasets with 24 500 mutants in nine bacterial genes, two phage genes, five mammalian genes and one yeast gene. The database is available on the Web at http://info.med.yale.edu/mutbase/ . The data tables can be viewed on the Web and downloaded in text form for local use. The data are also available in dBASE III, a format which can be utilized by essentially any desktop computer database program or spreadsheet, and makes feasible analyses of a large number of mutants. Researchers are invited to submit additional data. A data entry program, MUTSIN, diagrams each mutation on the computer screen as the data are entered and alerts the user to any discrepancies between the entry and the gene sequence.     

Glaucoma database

Glaucoma, a complex heterogenous disease, is the leading cause for optic nerve-related blindness worldwide. Primary open angle glaucoma (POAG) is the most common subset and by the year 2020 it is estimated that approximately 60 million people will be affected. MYOC, OPTN, CYP1B1 and WDR36 are the important candidate genes. Nearly 4% of the glaucoma patients have mutation in any one of these genes. Mutation in any of these genes causes disease either directly or indirectly and the severity of the disease varies according to position of the genes. We have compiled all the related mutations and SNPs in the above genes and developed a database, to help access statistical and clinical information of particular mutation. This database is available online at http:bicmku.in:8081/glaucoma The database, constructed using SQL, contains data pertaining to the SNPs and mutation information involved in the above genes and relevant study data. AVAILABILITY: The database is available for free at http:bicmku.in:8081/glaucoma.     

SNP and mutation data on the web - hidden treasures for uncovering

SNP data has grown exponentially over the last two years, SNP database evolution has matched this growth, as initial development of several independent SNP databases has given way to one central SNP database, dbSNP. Other SNP databases have instead evolved to complement this central database by providing gene specific focus and an increased level of curation and analysis on subsets of data, derived from the central data set. By contrast, human mutation data, which has been collected over many years, is still stored in disparate sources, although moves are afoot to move to a similar central database. These developments are timely, human mutation and polymorphism data both hold complementary keys to a better understanding of how genes function and malfunction in disease. The impending availability of a complete human genome presents us with an ideal framework to integrate both these forms of data, as our understanding of the mechanisms of disease increase, the full genomic context of variation may become increasingly significant.     

Ionizing radiation and genetic risks. XI. The doubling dose estimates from the mid-1950s to the present and the conceptual change to the use of human data on spontaneous mutation rates and mouse data on induced mutation rates for doubling dose calculations

This paper provides an overview of the concept of doubling dose, changes in the database employed for calculating it over the past 30 years and recent advances in this area. The doubling dose is estimated as a ratio of the average rates of spontaneous and induced mutations in a defined set of genes. The reciprocal of the doubling dose is the relative mutation risk per unit dose and is one of the quantities used in estimating genetic risks of radiation exposures. Most of the doubling dose estimates used thus far have been based on mouse data on spontaneous and induced rates of mutations. Initially restricted to mutations in defined genes (with particular focus on the seven genes at which induced recessive mutations were studied in different laboratories), the doubling dose concept was subsequently expanded to include other endpoints of genetic damage. At least during the past 20 years, the magnitude of the doubling dose has remained unchanged at approximately 1 Gy for chronic low LET radiation exposures.One of the assumptions underlying the use of the doubling dose based on mouse data for predicting genetic risks in humans, namely, that the spontaneous rates of mutations in mouse and human genes are similar, is incorrect; this is because of the fact that, unlike in the mouse, the mutation rate in humans differs between the two sexes (being higher in males than in females) and increases with paternal age. Further, an additional source of uncertainty in spontaneous mutation rate estimates in mice has been uncovered. This is related to the non-inclusion of mutations which arise as germinal mosaics and which result in clusters of identical mutations in the following generation. In view of these reasons, it is suggested that a prudent way forward is to revert to the use of human data on spontaneous mutation rates and mouse data on induced mutation rates for doubling dose calculations as was first done in the 1972 BEIR report of the US National Academy of Sciences. The advantages of this procedure are the following: (i) estimates of spontaneous mutation rates in humans, which are usually presented as sex-averaged rates, automatically include sex differences and paternal age-effects; (ii) since human geneticists count all mutations that arise anew irrespective of whether they are part of a cluster or not, had clusters occurred, they would have been included in mutation rate calculations and (iii) one stays close to the aim of risk estimation, namely, estimation of the risk of genetic diseases in humans.On the basis of detailed analyses of the pertinent data, it is now estimated that the average spontaneous mutation rate of human genes (n=135 genes) is: (2.95+/-0.64)x10(-6) per gene and the average induced mutation rate of mouse genes (n=34) is: (0.36+/-0.10)x10(-5) per gene per Gy for chronic low LET radiation. The resultant doubling dose is (0.82+/-0.29) Gy. The standard error of the doubling dose estimate incorporates sampling variability across loci for estimates of spontaneous and induced mutation rates as well as variability in induced mutation rates in individual mouse experiments on radiation-induced mutations. We suggest the use of a rounded doubling dose value of 1 Gy for estimating genetic risks of radiation. Although this value is the same as that used previously, its conceptual basis is different and the present estimate is based on more extensive data than has so far been the case.     

A mutation spectra database for bacterial and mammalian genes: 1998.

This database consists of over 24 000 mutations in 18 viral, bacterial, yeast or mammalian genes. The data are grouped as sets of DNA base sequence changes or spectra caused by a particular mutagen under defined conditions. The spectra are available on the World Wide Web at http://info.med.yale.edu/mutbase/ in two formats; in text format that can be browsed on-line or downloaded for use with a text editor and in dBASEIII format for use, after downloading, by relational database programs or by spreadsheets. Researchers are encouraged to submit DNA sequence changes to a suitable mutation database such as ours. A data entry program, MUTSIN, can be retrieved from this site. MUTSIN diagrams each mutation on the computer screen and alerts the user to any discrepancies.     

The human gene mutation database.

The Human Gene Mutation Database (HGMD) represents a comprehensive core collection of data on published germline mutations in nuclear genes underlying human inherited disease. By September 1997, the database contained nearly 12 000 different lesions in a total of 636 different genes, with new entries currently accumulating at a rate of over 2000 per annum. Although originally established for the scientific study of mutational mechanisms in human genes, HGMD has acquired a much broader utility to researchers, physicians and genetic counsellors so that it was made publicly available at http://uwcm.ac.uk/uwcm/mg/hgmd0.html in April 1996. Mutation data in HGMD are accessible on the basis of every gene being allocated one web page per mutation type, if data of that type are present. Meaningful integration with phenotypic, structural and mapping information has been accomplished through bi-directional links between HGMD and both the Genome Database (GDB) and Online Mendelian Inheritance in Man (OMIM), Baltimore, USA. Hypertext links have also been established to Medline abstracts through Entrez , and to a collection of 458 reference cDNA sequences also used for data checking. Being both comprehensive and fully integrated into the existing bioinformatics structures relevant to human genetics, HGMD has established itself as the central core database of inherited human gene mutations.     

A general database for DNA sequence changes induced by mutagenesis of several bacterial and mammalian genes.

This electronic database is a collection of 225 sets of data on mutations in more than twenty-three thousand mutants (October, 1995) in eleven bacterial genes, five mammalian genes and one gene in yeast cells. Each dataset consists of the changes in DNA sequence in the mutants, typically tens to hundreds, induced by mutagenesis of a particular cell line under specific conditions. The database is available on the Internet and on diskettes, and is periodically updated. Researchers are invited to submit additional data. A data entry program, MUTSIN, is available that diagrams each mutation on the computer screen as entered and alerts the user to any inconsistency between the entry and the wild type gene sequence.     

基因突变在结直肠癌诊疗及预后中的研究进展

结直肠癌是常见的恶性肿瘤之一,其发病率居全球恶性肿瘤发病率的第三位,死亡率呈逐年上升趋势。中国已成为全球结直肠癌每年新发病例数和死亡病例数最多的国家。对结直肠癌基因突变状态的识别以及对结直肠癌发生发展过程进行精确分类,可实现对患者进行个性化精准治疗的目的,而精准治疗的实现有赖于基因测序技术。目前,二代测序技术(Next generation sequencing,NGS)结合基因捕获技术,集中对研究者感兴趣的候选基因或外显子进行平行测序,极大拓展了对肿瘤特征基因的认识,为发展新的治疗手段和治疗策略奠定了基础。整合癌症基因组数据库IntOgen已明确72个结直肠癌驱动突变基因,包括“TP53”、“KRAS”、“PIK3CA”等;癌基因数据库Cancer Gene Census目前收录的结直肠癌突变基因有59个,包括原癌基因“BRAF”、抑癌基因“SMAD4”等;在线人类孟德尔遗传OMIM数据库已收录55个与结直肠癌相关的体细胞突变基因,包括“SRC”、“APC”等。本文通过26篇国内外文献,对结直肠癌基因突变检测的共识基因进行综述,并总结了与结直肠癌患者临床诊断、分型、预后、治疗等临床病理特征相关的突变基因标志物。     

Mutational profiling of low-grade gliomas identifies prognosis and immunotherapy-related biomarkers and tumour immune microenvironment characteristics

Low-grade glioma (LGG) is a heterogeneous tumour with the median survival rate less than 10 years. Therefore, it is urgent to develop efficient immunotherapy strategies of LGG. In this study, we analysed mutation profiles based on the data of 510 LGG patients from the Cancer Genome Atlas (TCGA) database and investigated the prognostic value of mutated genes and evaluate their immune infiltration. Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to indicate the characteristics of gliomas that respond to immune checkpoint blockade (ICB) therapy. Univariate and multivariate cox regression analysis was performed to identify indicators to construct the nomogram model. 485 (95.47%) of 508 LGG samples showed gene mutation, and 9 mutated genes were significantly related to overall survival (OS), among which 6 mutated genes were significantly correlated with OS between mutation and wildtypes. Immune infiltration and immune score analyses revealed that these six mutated genes were significantly associated with tumour immune microenvironment in LGG. The response of LGG with different characteristics to ICB was evaluated by TIDE algorithm. Finally, CIC gene was screened through both univariate and multivariate Cox regression analyses, and the nomogram model was established to determine the potential prognostic value of CIC in LGG. Our study provides comprehensive analysis of mutated genes in LGG, supporting modulation of mutated genes in the management of LGG.     

PGDB: a curated and integrated database of genes related to the prostate

The Prostate Gene Database (PGDB: http://www.ucsf.edu/pgdb) is a curated and integrated database of genes or genomic loci related to the human prostate and prostatic diseases. Currently, PGDB covers genes involved in a number of molecular and genetic events of the prostate including gene amplification, mutation, gross deletion, methylation, polymorphism, linkage and over-expression, as published in the literature. Genes that are specifically expressed in prostate, as evidenced by analysis of data from expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), are also included. There are a total of 165 unique entries in the database. Users can either browse or query the PGDB through a web interface. For each gene, in addition to basic gene information and rich cross-references to other databases, inclusive and relevant literature references are provided to support the inclusion of the gene in the database. Detailed expression data calculated from the UniGene and SAGEmap databases are also presented.     

An Excess of G over C Nucleotides in Mutagenesis of Human Genetic Diseases

Strand asymmetries in DNA evolution, including indel and single nucleotide substitutions, were reported in prokaryotes. Recently, an excess of G>A over C>T substitutions in hemophilia B patients was recognized in our molecular diagnostic practices. Further analysis demonstrated biased point mutations between sense and antisense strands when unique changes in factor IX were counted. Similar mutation spectra of factor IX and the HGMD prompted us to speculate that the excess of G>A over C>T may be present in genes other than factor IX. Data from nine genes (each has ≥100 missense mutations) retrieved from HGMD, international factor IX database, and Dr. Sommer’s lab database in the City of Hope National Medical Center, Duarte, CA, USA were analyzed for their point mutation spectra. Similar to factor IX, all genes selected in this study have biased G>A over C>T unique mutations when nonsense mutations were excluded. The biased missense point mutations were recently convincingly documented by the statistic data of categorized missense mutation in HGMD. The consistence of the genetic observation and the genomic data from HGMD strongly indicate that biased point mutations, possibly a phenotypic selection, are more widespread than previously thought. The biased mutations have immediate clinical impact in molecular diagnostics.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.     

Mutation spectra in supF: approaches to elucidating sequence context effects

Shuttle vectors carrying the supF suppressor tRNA gene were originally developed for mutagenesis experiments in primate and human cells. Since then, the supF gene has been used as a mutation reporter in other mammalian cells, yeast, Escherichia coli, and transgenic mice. The widespread use of the vector for studies of many DNA reactive agents has produced a large database of mutation spectra. These provide primary information on the kinds and distribution of mutations provoked by many agents and, in many instances, allow comparisons between related agents or the same agent in different cell backgrounds. In this review we will discuss some of these data with a primary focus on the interpretation of UV mutation spectra. We will also describe our development and application of custom supF marker genes as an approach to studying the effect of sequence context on mutation hotspots and cold spots. Our studies suggest that C-C photoproducts are not mutagenic in certain sequence contexts in which T-C photoproducts are mutation hotspots. In addition, we have found several examples of sequence context effects acting as much as 80 bases away from the site of mutation. We will consider some of the problems raised by these studies and the possible resolution of some of them offered by the newly discovered family of damage bypass DNA polymerases.     

Statistical analysis of pathogenicity of somatic mutations in cancer

Recent large-scale sequencing studies have revealed that cancer genomes contain variable numbers of somatic point mutations distributed across many genes. These somatic mutations most likely include passenger mutations that are not cancer causing and pathogenic driver mutations in cancer genes. Establishing a significant presence of driver mutations in such data sets is of biological interest. Whereas current techniques from phylogeny are applicable to large data sets composed of singly mutated samples, recently exemplified with a p53 mutation database, methods for smaller data sets containing individual samples with multiple mutations need to be developed. By constructing distinct models of both the mutation process and selection pressure upon the cancer samples, exact statistical tests to examine this problem are devised. Tests to examine the significance of selection toward missense, nonsense, and splice site mutations are derived, along with tests assessing variation in selection between functional domains. Maximum-likelihood methods facilitate parameter estimation, including levels of selection pressure and minimum numbers of pathogenic mutations. These methods are illustrated with 25 breast cancers screened across the coding sequences of 518 kinase genes, revealing 90 base substitutions in 71 genes. Significant selection pressure upon truncating mutations was established. Furthermore, an estimated minimum of 29.8 mutations were pathogenic.     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果：NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的...     

A Network of Cancer Genes with Co-Occurring and Anti-Co-Occurring Mutations

Certain cancer genes contribute to tumorigenesis in a manner of either co-occurring or mutually exclusive (anti-co-occurring) mutations; however, the global picture of when, where and how these functional interactions occur remains unclear. This study presents a systems biology approach for this purpose. After applying this method to cancer gene mutation data generated from large-scale and whole genome sequencing of cancer samples, a network of cancer genes with co-occurring and anti-co-occurring mutations was constructed. Analysis of this network revealed that genes with co-occurring mutations prefer direct signaling transductions and that the interaction relations among cancer genes in the network are related with their functional similarity. It was also revealed that genes with co-occurring mutations tend to have similar mutation frequencies, whereas genes with anti-co-occurring mutations tend to have different mutation frequencies. Moreover, genes with more exons tend to have more co-occurring mutations with other genes, and genes having lower local coherent network structures tend to have higher mutation frequency. The network showed two complementary modules that have distinct functions and have different roles in tumorigenesis. This study presented a framework for the analysis of cancer genome sequencing outputs. The presented data and uncovered patterns are helpful for understanding the contribution of gene mutations to tumorigenesis and valuable in the identification of key biomarkers and drug targets for cancer.     

基于合成致死策略寻找ARID1A突变肝细胞癌的治疗靶点

ARID1A编码的BAF250a蛋白是SWI/SNF(SWItch/Sucrose Non-Fermentable)染色质重组复合物BAF(BRG1-associated factors)的亚基之一,参与改变染色体的结构和可接近性。ARID1A在肝细胞癌(hepatocellular carcinoma,HCC)中的突变率高达13%,但目前尚无有效的治疗药物。本研究旨在利用合成致死策略寻找携带ARID1A突变HCC的治疗新靶标。首先,本研究通过分析ARID1A突变与肿瘤恶性程度的相关性发现ARID1A突变的肿瘤恶性度增加;进而分析Achilles和NCI-60癌症细胞系中ARID1A突变和野生型细胞系的基因表型值(gene phenotype value,GPV)和高表达基因,获得ARID1A突变细胞低GPV和高表达的重叠基因,再扩大样本使用CCLE(Cancer Cell Line Encyclopedia)细胞系的高表达基因进行重叠基因分析;最后并在TCGA(the Cancer Genome Atlas)肝癌数据库中进行筛选,获得116个潜在的ARID1A合成致死基因。本研究运用生物信息学方法计算获得多个ARID1A的潜在合成性致死基因,为ARID1A突变HCC患者提供新的治疗靶点,也为靶向药物研发提供了新靶标和新策略。