Only a small proportion of cancers result from familial cancer syndromes with Mendelian inheritance. Nonfamilial, 'sporadic' cancers, which represent most cancer cases, also have a significant hereditary component, but the genes involved have low penetrance and are extremely difficult to detect. Therefore, mapping and cloning of quantitative trait loci (QTLs) for cancer susceptibility in animals could help identify homologous genes in humans. Several cancer-susceptibility QTLs have been mapped in mice and rats, but none have been cloned so far. Here we report the positional cloning of the mouse gene Scc1 (Susceptibility to colon cancer 1) and the identification of Ptprj, encoding a receptor-type protein tyrosine phosphatase, as the underlying gene. In human colon, lung and breast cancers, we show frequent deletion of PTPRJ, allelic imbalance in loss of heterozygosity (LOH) and missense mutations. Our data suggest that PTPRJ is relevant to the development of several different human cancers.

Besides the established selection criteria based on embryo morphology and blastomere number, new parameters for embryo viability are needed to improve the clinical outcome of IVF and more particular of elective single-embryo transfer. Genome-wide gene expression in cumulus cells was studied, since these cells surround the oocyte inside the follicle and therefore possibly reflect oocyte developmental potential. Early cleavage (EC) was chosen as a parameter for embryo viability. Gene expression in cumulus cells from eight oocytes resulting in an EC embryo (EC-CC; n = 8) and from eight oocytes resulting in a non-EC (NEC) embryo (NEC-CC; n = 8) was analysed using microarrays (n = 16). A total of 611 genes were differentially expressed (P < 0.01), mainly involved in cell cycle, angiogenesis, apoptosis, epidermal growth factor, fibroblast growth factor and platelet-derived growth factor signalling, general vesicle transport and chemokine and cytokine signalling. Of the 25 selected differentially expressed genes analysed by quantitative real-time PCR 15 (60%) genes could be validated in the original samples. Of these 8 (53%) could also be validated in 24 (12-EC-CC and 12 NEC-CC) extra independent samples. The most differentially expressed genes among these were CCND2, CXCR4, GPX3, CTNND1 DHCR7, DVL3, HSPB1 and TRIM28, which probably point to hypoxic conditions or a delayed oocyte maturation in NEC-CC samples. This opens up perspectives for new molecular embryo or oocyte selection parameters which might also be useful in countries where the selection has to be made at the oocyte stage before fertilization instead of at the embryonic stage.

For the study of biological phenomena influenced by multiple genes in mice, the Recombinant Congenic Strains (RCS) have been developed. An RCS series comprises approximately 20 homozygous strains, each of which contains on average 87.5% genes of a common background strain and 12.5% of a common donor strain. In an RCS series, non-linked genes involved in the control of a multigenic trait become distributed into different recombinant congenic strains. In this way a multigenic trait is transformed into a series of single gene traits in which each gene can be studied individually. For the ability to use the strength of the recombinant congenic strains system to its full extent, a thorough genetic characterization is indispensable. We have typed the CcS/Dem and OcB/Dem series for 611 and 550 markers, respectively. This results in a genetic characterization sufficient to detect most donor strain genes. In addition, we report the genetic characterization of the HcB/Dem and HcB(N4)/Dem series. Strains of the latter series contain on average 6.25% of the donor strain genome. Both series have been typed for 130 markers. All the typing data have been deposited in the Mouse Genome Database at The Jackson Laboratory.

To dissect the multigenic control of colon tumour susceptibility in the mouse we used the set of 20 CcS/Dem (CcS) recombinant congenic (RC) strains. Each CcS strain carries a unique, random subset of approximately 12.5% of the genome of strain STS/A (STS) on the genetic background of BALB/cHeA (BALB/c). Previously, applying a protocol of 26 injections of 1,2-dimethylhydrazine (DMH), we detected two susceptibility loci, Scc1 and Scc2, on chromosome 2 (refs 4, 5). Using a shorter tumour-induction procedure, combining DMH and N-ethyl-N-nitrosourea (ENU) treatment, we demonstrate that BALB/c, STS and most CcS strains are relatively resistant. The strain CcS-19, however, is susceptible, probably due to a combination of BALB/c and STS alleles at several loci. Analysis of 192 (BALB/c x CcS-19) F2 mice revealed, in addition to the Scc1/Scc2 region, three new susceptibility loci: Scc3 on chromosome 1, Scc4 on chromosome 17 and Scc5 on chromosome 18. Scc4 and Scc5 have no apparent individual effect, but show a strong reciprocal interaction. Their BALB/c and STS alleles are not a priori susceptible or resistant but the genotype at one locus determines the effect of the allele at the second locus and vice versa. These findings and the accompanying paper on lung tumour susceptibility show that interlocus interactions are likely to be an important component of tumour susceptibility.

Mitochondrial disorders, characterized by clinical symptoms and/or OXPHOS deficiencies, are caused by pathogenic variants in mitochondrial genes. However, pathogenic variants in some of these genes can lead to clinical manifestations which overlap with other neuromuscular diseases, which can be caused by pathogenic variants in non-mitochondrial genes as well. Mitochondrial pathogenic variants can be found in the mitochondrial DNA (mtDNA) or in any of the 1,500 nuclear genes with a mitochondrial function. We have performed a two-step next-generation sequencing approach in a cohort of 117 patients, mostly children, in whom a mitochondrial disease-cause could likely or possibly explain the phenotype. A total of 86 patients had a mitochondrial disorder, according to established clinical and biochemical criteria. The other 31 patients had neuromuscular symptoms, where in a minority a mitochondrial genetic cause is present, but a non-mitochondrial genetic cause is more likely. All patients were screened for pathogenic variants in the mtDNA and, if excluded, analyzed by whole exome sequencing (WES). Variants were filtered for being pathogenic and compatible with an autosomal or X-linked recessive mode of inheritance in families with multiple affected siblings and/or consanguineous parents. Non-consanguineous families with a single patient were additionally screened for autosomal and X-linked dominant mutations in a predefined gene-set. We identified causative pathogenic variants in the mtDNA in 20% of the patient-cohort, and in nuclear genes in 49%, implying an overall yield of 68%. We identified pathogenic variants in mitochondrial and non-mitochondrial genes in both groups with, obviously, a higher number of mitochondrial genes affected in mitochondrial disease patients. Furthermore, we show that 31% of the disease-causing genes in the mitochondrial patient group were not included in the MitoCarta database, and therefore would have been missed with MitoCarta based gene-panels. We conclude that WES is preferable to panel-based approaches for both groups of patients, as the mitochondrial gene-list is not complete and mitochondrial symptoms can be secondary. Also, clinically and genetically heterogeneous disorders would require sequential use of multiple different gene panels. We conclude that WES is a comprehensive and unbiased approach to establish a genetic diagnosis in these patients, able to resolve multi-genic disease-causes.

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The “Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium” is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1,300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across bo...

Methanopterin is a coenzyme involved in methanogenesis. From 2 kg wet cells of Methanobacterium thermoautotrophicum about 35 μmol methanopterin were isolated. The structure of this compound was elucidated by various two‐dimensional nuclear‐magnetic‐resonance techniques. Methanopterin was identified as N‐[1′‐(2″‐amino‐4″‐hydroxy‐7″‐ methyl‐6″‐ pteridinyl)ethyl]‐4‐[2′,3′,4′,5′‐ tetrahydroxypent‐1′‐ yl (5′→1″) O‐α‐ribofuranosyl‐5″‐phosphoric acid] aniline, in which the phosphate group is esterified with α‐hydroxyglutaric acid. The molecular formula of the sodium salt of methanopterin at pH 7.0 is C30H38O16N6PNa3·xH2O (x is about 4). The anhydrous sodium salt of methanopterin has a molecular mass of 838.60 Da and the molar absorption coefficient at 342 nm is 7.4 mM−1 cm−1 at pH 7.0.

Supplementary data are available at Bioinformatics online.