Biallelic Mutations in ATP5F1D, which Encodes a Subunit of ATP Synthase, Cause a Metabolic Disorder.

ATP synthase, H+ transporting, mitochondrial F1 complex, δ subunit (ATP5F1D; formerly ATP5D) is a subunit of mitochondrial ATP synthase and plays an important role in coupling proton translocation and ATP production. Here, we describe two individuals, each with homozygous missense variants in ATP5F1D, who presented with episodic lethargy, metabolic acidosis, 3-methylglutaconic aciduria, and hyperammonemia. Subject 1, homozygous for c.245C>T (p.Pro82Leu), presented with recurrent metabolic decompensation starting in the neonatal period, and subject 2, homozygous for c.317T>G (p.Val106Gly), presented with acute encephalopathy in childhood. Cultured skin fibroblasts from these individuals exhibited impaired assembly of F1FO ATP synthase and subsequent reduced complex V activity. Cells from subject 1 also exhibited a significant decrease in mitochondrial cristae. Knockdown of Drosophila ATPsynδ, the ATP5F1D homolog, in developing eyes and brains caused a near complete loss of the fly head, a phenotype that was fully rescued by wild-type human ATP5F1D. In contrast, expression of the ATP5F1D c.245C>T and c.317T>G variants rescued the head-size phenotype but recapitulated the eye and antennae defects seen in other genetic models of mitochondrial oxidative phosphorylation deficiency. Our data establish c.245C>T (p.Pro82Leu) and c.317T>G (p.Val106Gly) in ATP5F1D as pathogenic variants leading to a Mendelian mitochondrial disease featuring episodic metabolic decompensation.

PPA2-associated sudden cardiac death: extending the clinical and allelic spectrum in 20 new families.

PURPOSE: Biallelic hypomorphic variants in PPA2, encoding the mitochondrial inorganic pyrophosphatase 2 protein, have been recently identified in individuals presenting with sudden cardiac death, occasionally triggered by alcohol intake or a viral infection. Here we report 20 new families harboring PPA2 variants. METHODS: Synthesis of clinical and molecular data concerning 34 individuals harboring five previously reported PPA2 variants and 12 novel variants, 11 of which were functionally characterized. RESULTS: Among the 34 individuals, only 6 remain alive. Twenty-three died before the age of 2 years while five died between 14 and 16 years. Within these 28 cases, 15 died of sudden cardiac arrest and 13 of acute heart failure. One case was diagnosed prenatally with cardiomyopathy. Four teenagers drank alcohol before sudden cardiac arrest. Progressive neurological signs were observed in 2/6 surviving individuals. For 11 variants, recombinant PPA2 enzyme activities were significantly decreased and sensitive to temperature, compared to wild-type PPA2 enzyme activity. CONCLUSION: We expand the clinical and mutational spectrum associated with PPA2 dysfunction. Heart failure and sudden cardiac arrest occur at various ages with inter- and intrafamilial phenotypic variability, and presentation can include progressive neurological disease. Alcohol intake can trigger cardiac arrest and should be strictly avoided.

Identification of a pathogenic TUBB1 variant in a Chinese family with congenital macrothrombocytopenia through whole genome sequencing.

Congenital macrothrombocytopenia is a genetically heterogeneous group of rare disorders. We herein report a large Chinese family presented with phenotypic variability involving thrombocytopenia and/or giant platelets. Whole genome sequencing (WGS) of the proband and one of his affected brothers identified a potentially pathogenic c.952 C > T heterozygous variant in the TUBB1 gene. This p.R318W ß1-tubulin variant was also identified in three additional siblings and five members of the next generation. These findings were consistent with an autosomal dominant inheritance with incomplete penetrance. Moreover, impaired platelet agglutination in response to ristocetin was detected in the patient's brother. Half of the family members harboring the p.R318W mutation displayed significantly decreased external release of p-selectin by stimulated platelets. The p.R318W ß1-tubulin mutation was identified for the first time in a Chinese family with congenital macrothrombocytopenia using WGS as an unbiased sequencing approach. Affected individuals within the family demonstrated impaired platelet aggregation and/or release functions.

Developing a genomics rotation: Practical training around variant interpretation for genetic counseling students.

With the wide adoption of next-generation sequencing (NGS)-based genetic tests, genetic counselors require increased familiarity with NGS technology, variant interpretation concepts, and variant assessment tools. The use of exome and genome sequencing in clinical care has expanded the reach and diversity of genetic testing. Regardless of the setting where genetic counselors are performing variant interpretation or reporting, most of them have learned these skills from colleagues, while on the job. Though traditional, lecture-based learning around these topics is important, there has been growing need for the inclusion of case-based, experiential training of genomics and variant interpretation for genetic counseling students, with the goal of creating a strong foundation in variant interpretation for new genetic counselors, regardless of what area of practice they enter. To address this need, we established a genomics and variant interpretation rotation for Stanford's genetic counseling training program. In response to changes in the genomics landscape, this has now evolved into three unique rotation experiences, each focused on variant interpretation in the context of various genomic settings, including clinical laboratory, research laboratory, and healthy genomic analysis studies. Here, we describe the goals and learning objectives that we have developed for these variant interpretation rotations, and illustrate how these concepts are applied in practice.

A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing.

There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses.

Saccharomyces Genome Database: the genomics resource of budding yeast.

The Saccharomyces Genome Database (SGD, http://www.yeastgenome.org) is the community resource for the budding yeast Saccharomyces cerevisiae. The SGD project provides the highest-quality manually curated information from peer-reviewed literature. The experimental results reported in the literature are extracted and integrated within a well-developed database. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine and rich genome browser. The acquisition, integration and retrieval of these data allow SGD to facilitate experimental design and analysis by providing an encyclopedia of the yeast genome, its chromosomal features, their functions and interactions. Public access to these data is provided to researchers and educators via web pages designed for optimal ease of use.

Comprehensive whole-genome sequencing of an early-stage primary myelofibrosis patient defines low mutational burden and non-recurrent candidate genes.

In order to identify novel somatic mutations associated with classic BCR/ABL1-negative myeloproliferative neoplasms, we performed high-coverage genome sequencing of DNA from peripheral blood granulocytes and cultured skin fibroblasts from a patient with MPL W515K-positive primary myelofibrosis. The primary myelofibrosis genome had a low somatic mutation rate, consistent with that observed in similar hematopoietic tumor genomes. Interfacing of whole-genome DNA sequence data with RNA expression data identified three somatic mutations of potential functional significance: i) a nonsense mutation in CARD6, implicated in modulation of NF-kappaB activation; ii) a 19-base pair deletion involving a potential regulatory region in the 5'-untranslated region of BRD2, implicated in transcriptional regulation and cell cycle control; and iii) a non-synonymous point mutation in KIAA0355, an uncharacterized protein. Additional mutations in three genes (CAP2, SOX30, and MFRP) were also evident, albeit with no support for expression at the RNA level. Re-sequencing of these six genes in 178 patients with polycythemia vera, essential thrombocythemia, and myelofibrosis did not identify recurrent somatic mutations in these genes. Finally, we describe methods for reducing false-positive variant calls in the analysis of hematologic malignancies with a low somatic mutation rate. This trial is registered with ClinicalTrials.gov (NCT01108159).

Saccharomyces Genome Database provides mutant phenotype data.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is a scientific database for the molecular biology and genetics of the yeast Saccharomyces cerevisiae, which is commonly known as baker's or budding yeast. The information in SGD includes functional annotations, mapping and sequence information, protein domains and structure, expression data, mutant phenotypes, physical and genetic interactions and the primary literature from which these data are derived. Here we describe how published phenotypes and genetic interaction data are annotated and displayed in SGD.

Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing.

Whole-genome sequencing (WGS) can identify variants that cause genetic disease, but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. In the present study, we develop an approach for ultra-rapid nanopore WGS that combines an optimized sample preparation protocol, distributing sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling and fast variant filtration for efficient manual review. Application to two example clinical cases identified a candidate variant in <8 h from sample preparation to variant identification. We show that this framework provides accurate variant calls and efficient prioritization, and accelerates diagnostic clinical genome sequencing twofold compared with previous approaches.

Gene Ontology annotations at SGD: new data sources and annotation methods.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current.

Expanded protein information at SGD: new pages and proteome browser.

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

Tetrahymena Genome Database (TGD): a new genomic resource for Tetrahymena thermophila research.

We have developed a web-based resource (available at www.ciliate.org) for researchers studying the model ciliate organism Tetrahymena thermophila. Employing the underlying database structure and programming of the Saccharomyces Genome Database, the Tetrahymena Genome Database (TGD) integrates the wealth of knowledge generated by the Tetrahymena research community about genome structure, genes and gene products with the newly sequenced macronuclear genome determined by The Institute for Genomic Research (TIGR). TGD provides information curated from the literature about each published gene, including a standardized gene name, a link to the genomic locus in our graphical genome browser, gene product annotations utilizing the Gene Ontology, links to published literature about the gene and more. TGD also displays automatic annotations generated for the gene models predicted by TIGR. A variety of tools are available at TGD for searching the Tetrahymena genome, its literature and information about members of the research community.

Genome Snapshot: a new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome.

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) has created the Genome Snapshot (http://db.yeastgenome.org/cgi-bin/genomeSnapShot.pl). The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae. The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible through SGD's Advanced Search tool (http://db.yeastgenome.org/cgi-bin/search/featureSearch), and all the data presented on this page are available from the SGD ftp site (ftp://ftp.yeastgenome.org/yeast/).

Fungal BLAST and Model Organism BLASTP Best Hits: new comparison resources at the Saccharomyces Genome Database (SGD).

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is a scientific database of gene, protein and genomic information for the yeast Saccharomyces cerevisiae. SGD has recently developed two new resources that facilitate nucleotide and protein sequence comparisons between S.cerevisiae and other organisms. The Fungal BLAST tool provides directed searches against all fungal nucleotide and protein sequences available from GenBank, divided into categories according to organism, status of completeness and annotation, and source. The Model Organism BLASTP Best Hits resource displays, for each S.cerevisiae protein, the single most similar protein from several model organisms and presents links to the database pages of those proteins, facilitating access to curated information about potential orthologs of yeast proteins.

A novel TRIP11-FLT3 fusion in a patient with a myeloid/lymphoid neoplasm with eosinophilia.

FLT3 fusions are associated with myeloid and lymphoid neoplasms with eosinophilia. We describe a patient presenting with clinicopathologic features of both chronic eosinophilic leukemia, not otherwise specified (CEL, NOS) and systemic mastocytosis (SM). The bone marrow demonstrated a myeloproliferative neoplasm with eosinophilia and aggregates of atypical mast cells. Cytogenetic analysis revealed a t(13;14)(q12;q32), which was subsequently molecularly characterized as a novel TRIP11-FLT3 rearrangement. A KIT D816V mutation was also identified. The patient rapidly transformed to T-lymphoblastic leukemia/lymphoma and expired shortly after diagnosis. This is the fifth FLT3 fusion gene described in the literature; the presence of both myeloid and lymphoid neoplasms implicates involvement of an early hematopoietic progenitor by rearranged FLT3. We suggest that leukemias and lymphomas with FLT3 fusion genes exhibit similar clinicopathologic features to, and should be included in, the WHO category of "Myeloid and lymphoid neoplasms with eosinophilia and abnormalities of PDGFRA, PDGFRB, or FGFR1, or with PCM1-JAK2."

Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO).

The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http://genome-www.stanford.edu/Saccharomyces/.