Initiating a Human Variome Project Country Node.

Genetic diseases are a pressing global health problem that requires comprehensive access to basic clinical and genetic data to counter. The creation of regional and international databases that can be easily accessed by clinicians and diagnostic labs will greatly improve our ability to accurately diagnose and treat patients with genetic disorders. The Human Variome Project is currently working in conjunction with human genetics societies to achieve this by establishing systems to collect every mutation reported by a diagnostic laboratory, clinic, or research laboratory in a country and store these within a national repository, or HVP Country Node. Nodes have already been initiated in Australia, Belgium, China, Egypt, Malaysia, and Kuwait. Each is examining how to systematically collect and share genetic, clinical, and biochemical information in a country-specific manner that is sensitive to local ethical and cultural issues. This article gathers cases of genetic data collection within countries and takes recommendations from the global community to develop a procedure for countries wishing to establish their own collection system as part of the Human Variome Project. We hope this may lead to standard practices to facilitate global collection of data and allow efficient use in clinical practice, research and therapy.

Identification of microbial mixtures by LC-selective proteotypic-peptide analysis (SPA).

This paper describes a method--using a combination of LC-MS/MS of selected bacteria-specific peptides and database search--for determining the species of bacteria present in a mixture. We identified the proteotypic peptides that were associated with specific bacteria by searching protein databases for the LC-MS/MS data. The retention time windows for specific peptide markers were used as an extra constraint so that the peptide markers of many bacterial species could be analyzed in a single LC-selective proteotypic-peptide analysis (SPA). We performed LC-MS/MS analyses on the proteolytic digest of cell extracts and monitored only the selected marker peptide ions at given elution time windows. The corresponding bacterial species could be characterized when the selected peptides that eluted at expected elution windows were identified correctly from the database. We managed to identify up to eight bacterial species simultaneously during a single LC-MS/MS analysis, as well as bacteria mixed in various abundances. Two marker ions having similar values of m/z, but obtained from two different bacterial samples, which would otherwise be selected as precursors within mass tolerance and would complicate the MS/MS data, were time-resolved using LC and then used to correctly identify their bacterial sources. The coupling of selective MS/MS monitoring with separation methods, such as LC, provides a highly selective and accurate analytical method for characterizing complex mixtures of bacterial species.

Identifying bacterial species using CE-MS and SEQUEST with an empirical scoring function.

CE-MS/MS analysis of proteolytic digests of bacterial cell extracts was combined with SEQUEST searching and a new scoring system to identify bacteria species in bacterial mixtures. Searches of MS/MS spectra against protein databases enabled the identification of bacterial species by the matching of the proteins associated with the corresponding species. An empirical scoring function was obtained by evaluating the SEQUEST search results of 38 samples that contained single bacterial species. The scoring by the empirical function helped move up the positive identification results from their original positions in the ranking based on Xcorr values alone. Therefore, the identification of bacteria in the samples that contained bacterial mixtures was improved. Bacterial species in 20 bacterial mixtures, including one real sample, were correctly identified by database searches and the new scoring function.