Marten Jäger

Identity-by-descent filtering of exome sequence data identifies PIGV mutations in hyperphosphatasia mental retardation syndrome

Hyperphosphatasia mental retardation (HPMR) syndrome is an autosomal recessive form of mental retardation with distinct facial features and elevated serum alkaline phosphatase. We performed whole-exome sequencing in three siblings of a nonconsanguineous union with HPMR and performed computational inference of regions identical by descent in all siblings to establish PIGV, encoding a member of the GPI-anchor biosynthesis pathway, as the gene mutated in HPMR. We identified homozygous or compound heterozygous mutations in PIGV in three additional families.

Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome

Patients with genetic disease of unknown causes can be rapidly diagnosed by bioinformatic analysis of disease-associated DNA sequences and phenotype. Efficient Diagnosis of Genetic Disease We know which genes are mutated in almost 3000 inherited human diseases and have good descriptions of how these mutations affect the human phenotype. Now, Zemojtel et al. have coupled this knowledge with rapid sequencing of these genes in a group of 40 patients with undiagnosed genetic diseases. Bioinformatic matching of the patients’ clinical characteristics and their disease gene sequences to databases of current genetic and phenotype knowledge enabled the authors to successfully diagnose almost 30% of the patients. The process required only about 2 hours of a geneticists’ time. Zemojtel et al. have made their tools available to the community, enabling a fast straightforward process by which clinicians and patients can easily identify the genetic basis of inherited disease in certain people. Less than half of patients with suspected genetic disease receive a molecular diagnosis. We have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical data into an effective diagnostic workflow. We used variants in the 2741 established Mendelian disease genes [the disease-associated genome (DAG)] to develop a targeted enrichment DAG panel (7.1 Mb), which achieves a coverage of 20-fold or better for 98% of bases. Furthermore, we established a computational method [Phenotypic Interpretation of eXomes (PhenIX)] that evaluated and ranked variants based on pathogenicity and semantic similarity of patients’ phenotype described by Human Phenotype Ontology (HPO) terms to those of 3991 Mendelian diseases. In computer simulations, ranking genes based on the variant score put the true gene in first place less than 5% of the time; PhenIX placed the correct gene in first place more than 86% of the time. In a retrospective test of PhenIX on 52 patients with previously identified mutations and known diagnoses, the correct gene achieved a mean rank of 2.1. In a prospective study on 40 individuals without a diagnosis, PhenIX analysis enabled a diagnosis in 11 cases (28%, at a mean rank of 2.4). Thus, the NGS of the DAG followed by phenotype-driven bioinformatic analysis allows quick and effective differential diagnostics in medical genetics.

Microindel detection in short-read sequence data

MOTIVATION Several recent studies have demonstrated the effectiveness of resequencing and single nucleotide variant (SNV) detection by deep short-read sequencing platforms. While several reliable algorithms are available for automated SNV detection, the automated detection of microindels in deep short-read data presents a new bioinformatics challenge. RESULTS We systematically analyzed how the short-read mapping tools MAQ, Bowtie, Burrows-Wheeler alignment tool (BWA), Novoalign and RazerS perform on simulated datasets that contain indels and evaluated how indels affect error rates in SNV detection. We implemented a simple algorithm to compute the equivalent indel region eir, which can be used to process the alignments produced by the mapping tools in order to perform indel calling. Using simulated data that contains indels, we demonstrate that indel detection works well on short-read data: the detection rate for microindels (<4 bp) is >90%. Our study provides insights into systematic errors in SNV detection that is based on ungapped short sequence read alignments. Gapped alignments of short sequence reads can be used to reduce this error and to detect microindels in simulated short-read data. A comparison with microindels automatically identified on the ABI Sanger and Roche 454 platform indicates that microindel detection from short sequence reads identifies both overlapping and distinct indels. CONTACT peter.krawitz@googlemail.com; peter.robinson@charite.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

MicroRNAs Differentially Expressed in Postnatal Aortic Development Downregulate Elastin via 3′ UTR and Coding-Sequence Binding Sites

Elastin production is characteristically turned off during the maturation of elastin-rich organs such as the aorta. MicroRNAs (miRNAs) are small regulatory RNAs that down-regulate target mRNAs by binding to miRNA regulatory elements (MREs) typically located in the 3′ UTR. Here we show a striking up-regulation of miR-29 and miR-15 family miRNAs during murine aortic development with commensurate down-regulation of targets including elastin and other extracellular matrix (ECM) genes. There were a total of 14 MREs for miR-29 in the coding sequences (CDS) and 3′ UTR of elastin, which was highly significant, and up to 22 miR-29 MREs were found in the CDS of multiple ECM genes including several collagens. This overrepresentation was conserved throughout mammalian evolution. Luciferase reporter assays showed synergistic effects of miR-29 and miR-15 family miRNAs on 3′ UTR and coding-sequence elastin constructs. Our results demonstrate that multiple miR-29 and miR-15 family MREs are characteristic for some ECM genes and suggest that miR-29 and miR-15 family miRNAs are involved in the down-regulation of elastin in the adult aorta.

Composite transcriptome assembly of RNA-seq data in a sheep model for delayed bone healing

BackgroundThe sheep is an important model organism for many types of medically relevant research, but molecular genetic experiments in the sheep have been limited by the lack of knowledge about ovine gene sequences.ResultsPrior to our study, mRNA sequences for only 1,556 partial or complete ovine genes were publicly available. Therefore, we developed a composite de novo transcriptome assembly method for next-generation sequence data to combine known ovine mRNA and EST sequences, mRNA sequences from mouse and cow, and sequences assembled de novo from short read RNA-Seq data into a composite reference transcriptome, and identified transcripts from over 12 thousand previously undescribed ovine genes. Gene expression analysis based on these data revealed substantially different expression profiles in standard versus delayed bone healing in an ovine tibial osteotomy model. Hundreds of transcripts were differentially expressed between standard and delayed healing and between the time points of the standard and delayed healing groups. We used the sheep sequences to design quantitative RT-PCR assays with which we validated the differential expression of 26 genes that had been identified by RNA-seq analysis. A number of clusters of characteristic expression profiles could be identified, some of which showed striking differences between the standard and delayed healing groups. Gene Ontology (GO) analysis showed that the differentially expressed genes were enriched in terms including extracellular matrix, cartilage development, contractile fiber, and chemokine activity.ConclusionsOur results provide a first atlas of gene expression profiles and differentially expressed genes in standard and delayed bone healing in a large-animal model and provide a number of clues as to the shifts in gene expression that underlie delayed bone healing. In the course of our study, we identified transcripts of 13,987 ovine genes, including 12,431 genes for which no sequence information was previously available. This information will provide a basis for future molecular research involving the sheep as a model organism.

Next-generation diagnostics and disease-gene discovery with the Exomiser

Exomiser is an application that prioritizes genes and variants in next-generation sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian disease. Exomiser comprises a suite of algorithms for prioritizing exome sequences using random-walk analysis of protein interaction networks, clinical relevance and cross-species phenotype comparisons, as well as a wide range of other computational filters for variant frequency, predicted pathogenicity and pedigree analysis. In this protocol, we provide a detailed explanation of how to install Exomiser and use it to prioritize exome sequences in a number of scenarios. Exomiser requires ∼3 GB of RAM and roughly 15–90 s of computing time on a standard desktop computer to analyze a variant call format (VCF) file. Exomiser is freely available for academic use from http://www.sanger.ac.uk/science/tools/exomiser.

Jannovar: A Java Library for Exome Annotation

Transcript‐based annotation and pedigree analysis are two basic steps in the computational analysis of whole‐exome sequencing experiments in genetic diagnostics and disease‐gene discovery projects. Here, we present Jannovar, a stand‐alone Java application as well as a Java library designed to be used in larger software frameworks for exome and genome analysis. Jannovar uses an interval tree to identify all transcripts affected by a given variant, and provides Human Genome Variation Society‐compliant annotations both for variants affecting coding sequences and splice junctions as well as untranslated regions and noncoding RNA transcripts. Jannovar can also perform family‐based pedigree analysis with Variant Call Format (VCF) files with data from members of a family segregating a Mendelian disorder. Using a desktop computer, Jannovar requires a few seconds to annotate a typical VCF file with exome data. Jannovar is freely available under the BSD2 license. Source code as well as the Java application and library file can be downloaded from http://compbio.charite.de (with tutorial) and https://github.com/charite/jannovar.

MiR‐497∼195 Cluster MicroRNAs Regulate Osteoblast Differentiation by Targeting BMP Signaling

MicroRNAs play important roles during cell reprogramming and differentiation. In this study, we identified the miR‐497∼195 cluster, a member of the miR‐15 family, as strongly upregulated with age of postnatal bone development in vivo and late differentiation stages of primary osteoblasts cultured in vitro. Early expression of miR‐195–5p inhibits differentiation and mineralization. Microarray analyses along with quantitative PCR demonstrate that miR‐195–5p alters the gene regulatory network of osteoblast differentiation and impairs the induction of bone morphogenetic protein (BMP) responsive genes. Applying reporter gene and Western blot assays, we show that miR‐195–5p interferes with the BMP/Smad‐pathway in a dose‐dependent manner. Systematically comparing the changes in mRNA levels in response to miR‐195–5p overexpression with the changes observed in the natural course of osteoblast differentiation, we demonstrate that microRNAs of the miR‐15 family affect several target genes involved in BMP signaling. Predicted targets including Furin, a protease that cleaves pro‐forms, genes encoding receptors such as Acvr2a, Bmp1a, Dies1, and Tgfbr3, molecules within the cascade like Smad5, transcriptional regulators like Ski and Zfp423 as well as Mapk3 and Smurf1 were validated by quantitative PCR. Taken together, our data strongly suggest that miR‐497∼195 cluster microRNAs act as intracellular antagonists of BMP signaling in bone cells.

Distinct global shifts in genomic binding profiles of limb malformation-associated HOXD13 mutations

Gene regulation by transcription factors (TFs) determines developmental programs and cell identity. Consequently, mutations in TFs can lead to dramatic phenotypes in humans by disrupting gene regulation. To date, the molecular mechanisms that actually cause these phenotypes have been difficult to address experimentally. ChIP-seq, which couples chromatin immunoprecipitation with high-throughput sequencing, allows TF function to be investigated on a genome-wide scale, enabling new approaches for the investigation of gene regulation. Here, we present the application of ChIP-seq to explore the effect of missense mutations in TFs on their genome-wide binding profile. Using a retroviral expression system in chicken mesenchymal stem cells, we elucidated the mechanism underlying a novel missense mutation in HOXD13 (Q317K) associated with a complex hand and foot malformation phenotype. The mutated glutamine (Q) is conserved in most homeodomains, a notable exception being bicoid-type homeodomains that have lysine (K) at this position. Our results show that the mutation results in a shift in the binding profile of the mutant toward a bicoid/PITX1 motif. Gene expression analysis and functional assays using in vivo overexpression studies confirm that the mutation results in a partial conversion of HOXD13 into a TF with bicoid/PITX1 properties. A similar shift was not observed with another mutation, Q317R, which is associated with brachysyndactyly, suggesting that the bicoid/PITX1-shift observed for Q317K might be related to the severe clinical phenotype. The methodology described can be used to investigate a wide spectrum of TFs and mutations that have not previously been amenable to ChIP-seq experiments.

Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis

BackgroundGlycosylphosphatidylinositol biosynthesis defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of hyperphosphatasia with mental retardation syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed multiple congenital anomalies hypotonia seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.MethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.ResultsWe found that certain malformations such as Morbus Hirschsprung and diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.ConclusionsDue to the overlapping clinical spectrum of both HPMRS and MCAHS in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.