Leachianone G

26833330

Congenital disorders of glycosylation (CDGs) form a genetically and clinically heterogeneous group of diseases with aberrant protein glycosylation as a hallmark. A subgroup of CDGs can be attributed to disturbed Golgi homeostasis. However, identification of pathogenic variants is seriously complicated by the large number of proteins involved. As part of a strategy to identify human homologs of yeast proteins that are known to be involved in Golgi homeostasis, we identified uncharacterized transmembrane protein 199 (TMEM199, previously called C17orf32) as a human homolog of yeast V-ATPase assembly factor Vph2p (also known as Vma12p). Subsequently, we analyzed raw exome-sequencing data from families affected by genetically unsolved CDGs and identified four individuals with different mutations in TMEM199. The adolescent individuals presented with a mild phenotype of hepatic steatosis, elevated aminotransferases and alkaline phosphatase, and hypercholesterolemia, as well as low serum ceruloplasmin. Affected individuals showed abnormal N- and mucin-type O-glycosylation, and mass spectrometry indicated reduced incorporation of galactose and sialic acid, as seen in other Golgi homeostasis defects. Metabolic labeling of sialic acids in fibroblasts confirmed deficient Golgi glycosylation, which was restored by lentiviral transduction with wild-type TMEM199. V5-tagged TMEM199 localized with ERGIC and COPI markers in HeLa cells, and electron microscopy of a liver biopsy showed dilated organelles suggestive of the endoplasmic reticulum and Golgi apparatus. In conclusion, we have identified TMEM199 as a protein involved in Golgi homeostasis and show that TMEM199 deficiency results in a hepatic phenotype with abnormal glycosylation.

Golgi homeostasis, COPI vesicular transport, hypercholesterolemia, Congenital Disorders of Glycosylation, alkaline phosphatase, elevated aminotransferases, V-ATPase assembly, Vph2p, TMEM199 deficiency

TMEM199 Deficiency Is a Disorder of Golgi Homeostasis Characterized by Elevated Aminotransferases, Alkaline Phosphatase, and Cholesterol and Abnormal Glycosylation

Jos C. Jansen,1,2 Sharita Timal,2,3 Monique van Scherpenzeel,2,3 Helen Michelakakis,4 Dorothee Vicogne,5 Angel Ashikov,2,3 Marina Moraitou,4 Alexander Hoischen,6 Karin Huijben,2 Gerry Steenbergen,2 Marjolein A.W. van den Boogert,7 Francesco Porta,8 Pier Luigi Calvo,8 Mersyni Mavrikou,9 Giovanna Cenacchi,10 Geert van den Bogaart,11 Jody Salomon,1 Adriaan G. Holleboom,7 Richard J. Rodenburg,2,12 Joost P.H. Drenth,1 Martijn A. Huynen,13 Ron A. Wevers,2 Eva Morava,14,15 Francois Foulquier,5 Joris A. Veltman,6,16 and Dirk J. Lefeber2,3,

2016 Feb 4;

27331905

Computer-based resources are central to much, if not most, biological and medical research. However, while there is an ever expanding choice of bioinformatics resources to use, described within the biomedical literature, little work to date has provided an evaluation of the full range of availability or levels of usage of database and software resources. Here we use text mining to process the PubMed Central full-text corpus, identifying mentions of databases or software within the scientific literature. We provide an audit of the resources contained within the biomedical literature, and a comparison of their relative usage, both over time and between the sub-disciplines of bioinformatics, biology and medicine. We find that trends in resource usage differs between these domains. The bioinformatics literature emphasises novel resource development, while database and software usage within biology and medicine is more stable and conservative. Many resources are only mentioned in the bioinformatics literature, with a relatively small number making it out into general biology, and fewer still into the medical literature. In addition, many resources are seeing a steady decline in their usage (e.g., BLAST, SWISS-PROT), though some are instead seeing rapid growth (e.g., the GO, R). We find a striking imbalance in resource usage with the top 5% of resource names (133 names) accounting for 47% of total usage, and over 70% of resources extracted being only mentioned once each. While these results highlight the dynamic and creative nature of bioinformatics research they raise questions about software reuse, choice and the sharing of bioinformatics practice. Is it acceptable that so many resources are apparently never reused? Finally, our work is a step towards automated extraction of scientific method from text. We make the dataset generated by our study available under the CC0 license here: http://dx.doi.org/10.6084/m9.figshare.1281371.

A Survey of Bioinformatics Database and Software Usage through Mining the Literature

Geraint Duck,1 Goran Nenadic,1,2 Michele Filannino,1 Andy Brass,1 David L. Robertson,3 and Robert Stevens1,*

2016;