Two novel mutations in the SLCO2A1 gene in a Chinese patient with primary hypertrophic osteoarthropathy

Primary hypertrophic osteoarthropathy (PHO) is a rare monogenetic disease characterized by digital clubbing, periostosis and pachydermia. Mutations in the 15-hydroxy-prostaglandin dehydrogenase (HPGD) gene and solute carrier organic anion transporter family member 2A1 (SLCO2A1) gene have been shown to be associated with PHO. Here, we described clinical characteristics in a Chinese patient with PHO, and identified two novel mutations in SLCO2A1: a heterozygous guanine-to-thymidine transition at the invariant − 1 position of the acceptor site of intron 2 (c.235-1G > T) and a heterozygous missense mutation p.Pro219Leu (c.656C > T) in exon 5.     

NemChR-DB: a database of parasitic nematode chemosensory G-protein coupled receptors

Nematode Chemosensory G-Protein Coupled Receptors have expanded within nematodes, where they play important roles in foraging and host-seeking behaviour. Nematode Chemosensory G-Protein Coupled Receptors are most highly expressed during free-living stages when chemosensory signalling is required for host detection and nematode activation in various parasitic nematodes, and therefore position Nematode Chemosensory G-Protein Coupled Receptors at the transition from infective to parasitic stages, making them important regulators to study in terms of host-seeking and host specificity. To facilitate the analysis of Nematode Chemosensory G-Protein Coupled Receptors, here we describe an integrative database of nematode chemoreceptors called NemChR-DB. This database enables users to study diverse parasitic nematode chemoreceptors, functionally explore sequence entries through structural and literature-based annotations, and perform cross-species comparisons.     

A method to search for similar protein local structures at ligand-binding sites and its application to adenine recognition

We have developed a method of searching for similar spatial arrangements of atoms around a given chemical moiety in proteins that bind a common ligand. The first step in this method is to consider a set of atoms that closely surround a given chemical moiety. Then, to compare the spatial arrangements of such surrounding atoms in different proteins, they are translated and rotated so that the chemical moieties are superposed on each other. Spatial arrangements of surrounding atoms in a pair of proteins are judged to be similar, when there are many corresponding atoms occupying similar spatial positions. Because the method focuses on the arrangements of surrounding atoms, it can detect structural similarities of binding sites in proteins that are dissimilar in their amino acid sequences or in their chain folds. We have applied this method to identify modes of nucleotide base recognition by proteins. An all-against-all comparison of the arrangements of atoms surrounding adenine moieties revealed an unexpected structural similarity between protein kinases, cAMP-dependent protein kinase (cAPK), and casein kinase-1 (CK1), and D-Ala:D-Ala ligase (DD-ligase) at their adenine-binding sites, despite a lack of similarity in their chain folds. The similar local structure consists of a four-residue segment and three sequentially separated residues. In particular the four-residue segments of these enzymes were found to have nearly identical conformations in their backbone parts, which are involved in the recognition of adenine. This common local structure was also found in substrate-free three-dimensional structures of other proteins that are similar to DD-ligase in the chain fold and of other protein kinases. As the proteins with different folds were found to share a common local structure, these proteins seem to constitute a remarkable example of convergent evolution for the same recognition mechanism. Received: 9 December 1996 / Accepted: 7 February 1997     

BiomarkerDigger: A versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers

We have developed a proteome database (DB), BiomarkerDigger ( http://biomarkerdigger.org ) that automates data analysis, searching, and metadata‐gathering function. The metadata‐gathering function searches proteome DBs for protein–protein interaction, Gene Ontology, protein domain, Online Mendelian Inheritance in Man, and tissue expression profile information and integrates it into protein data sets that are accessed through a search function in BiomarkerDigger. This DB also facilitates cross‐proteome comparisons by classifying proteins based on their annotation. BiomarkerDigger highlights relationships between a given protein in a proteomic data set and any known biomarkers or biomarker candidates. The newly developed BiomarkerDigger system is useful for multi‐level synthesis, comparison, and analyses of data sets obtained from currently available web sources. We demonstrate the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.     

MSDA,a proteomics software suite for in‐depth Mass Spectrometry Data Analysis using grid computing

One of the major bottlenecks in the proteomics field today resides in the computational interpretation of the massive data generated by the latest generation of high‐throughput MS instruments. MS/MS datasets are constantly increasing in size and complexity and it becomes challenging to comprehensively process such huge datasets and afterwards deduce most relevant biological information. The Mass Spectrometry Data Analysis (MSDA, https://msda.unistra.fr ) online software suite provides a series of modules for in‐depth MS/MS data analysis. It includes a custom databases generation toolbox, modules for filtering and extracting high‐quality spectra, for running high‐performance database and de novo searches, and for extracting modified peptides spectra and functional annotations. Additionally, MSDA enables running the most computationally intensive steps, namely database and de novo searches, on a computer grid thus providing a net time gain of up to 99% for data processing.     

Immunopeptidomic Analyses of Colorectal Cancers With and Without Microsatellite Instability

Colorectal cancer is the second leading cause of cancer death worldwide, and the incidence of this disease is expected to increase as global socioeconomic changes occur. Immune checkpoint inhibition therapy is effective in treating a minority of colorectal cancer tumors; however, microsatellite stable tumors do not respond well to this treatment. Emerging cancer immunotherapeutic strategies aim to activate a cytotoxic T cell response against tumor-specific antigens, presented exclusively at the cell surface of cancer cells. These antigens are rare and are most effectively identified with a mass spectrometry–based approach, which allows the direct sampling and sequencing of these peptides. Although the few tumor-specific antigens identified to date are derived from coding regions of the genome, recent findings indicate that a large proportion of tumor-specific antigens originate from allegedly noncoding regions. Here, we employed a novel proteogenomic approach to identify tumor antigens in a collection of colorectal cancer–derived cell lines and biopsy samples consisting of matched tumor and normal adjacent tissue. The generation of personalized cancer databases paired with mass spectrometry analyses permitted the identification of more than 30,000 unique MHC I–associated peptides. We identified 19 tumor-specific antigens in both microsatellite stable and unstable tumors, over two-thirds of which were derived from noncoding regions. Many of these peptides were derived from source genes known to be involved in colorectal cancer progression, suggesting that antigens from these genes could have therapeutic potential in a wide range of tumors. These findings could benefit the development of T cell–based vaccines, in which T cells are primed against these antigens to target and eradicate tumors. Such a vaccine could be used in tandem with existing immune checkpoint inhibition therapies, to bridge the gap in treatment efficacy across subtypes of colorectal cancer with varying prognoses. Data are available via ProteomeXchange with identifier PXD028309.     

根瘤菌资源数据库的更新和应用

对原有根瘤菌资源数据库进行扩充和更新。在原有7类信息子库的基础上增添了DNA同源性、全细胞蛋白电泳、多位点酶电泳、16SrRNA基因序列和付里叶变换红外光谱等5个新信息子库;并增加了图象功能和更换了新工作平台,使之运行更加灵活、快速和简便。目前库内存贮了已研究过的700余株的有关信息。     

Biallelic nonsense mutations in the otogelin-like gene (OTOGL) in a child affected by mild to moderate hearing impairment

Hearing impairment is characterized by great genetic heterogeneity. We report the identification, by whole exome sequencing, of two different nonsense mutations (c.1558C>T; p.Gln520* and c.2773C>T; p.Arg925*) in the otogelin-like gene (OTOGL), in a child affected by mild to moderate isolated deafness. Parental genotypes allowed us to conclude that these mutations are present in the compound heterozygous state in the patient. In addition, our clinical data establish that the tectorial membrane and/or the outer hair cells are defective in this form of deafness.