PubMed · 15685900
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James R McCarty. 2004. Check your TSBME listing.. https://pubmed.ncbi.nlm.nih.gov/15685900/
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Reliable identification of posttranslational modifications is key to understanding various cellular regulatory processes. We describe a tool, InsPecT, to identify posttranslational modifications using tandem mass spectrometry data. InsPecT constructs database filters that proved to be very successful in genomics searches. Given an MS/MS spectrum S and a database D, a database filter selects a small fraction of database D that is guaranteed (with high probability) to contain a peptide that produced S. InsPecT uses peptide sequence tags as efficient filters that reduce the size of the database by a few orders of magnitude while retaining the correct peptide with very high probability. In addition to filtering, InsPecT also uses novel algorithms for scoring and validating in the presence of modifications, without explicit enumeration of all variants. InsPecT identifies modified peptides with better or equivalent accuracy than other database search tools while being 2 orders of magnitude faster than SEQUEST, and substantially faster than X!TANDEM on complex mixtures. The tool was used to identify a number of novel modifications in different data sets, including many phosphopeptides in data provided by Alliance for Cellular Signaling that were missed by other tools.
AIMS/HYPOTHESIS: The genetic architecture of model-derived parameters of beta cell function has never been assessed. Therefore, we estimated heritability (h(2)) for model-derived phenotypes of insulin secretion in twins. METHODS: Thirty-three monozygotic (MZ) and 23 dizygotic (DZ) twin pairs from the Finnish Twin Cohort Study underwent an OGTT (plasma glucose/C-peptide at 0, 30, 60, 90 and 120 min). A subset of the twin pairs (21 MZ/20 DZ) also underwent an IVGTT (frequent sampling of plasma glucose/insulin from 0 to 60 min) followed by a 160-min euglycaemic-hyperinsulinaemic clamp (45 mU.min(-1).m(-2)). Mathematical modelling was applied to the IVGTT and the OGTT to assess first-phase (readily releasable insulin [RRI]) and second-phase (sigma) secretion (IVGTT), and a global index of beta cell performance (OGTT beta index). Intraclass correlation coefficients and genetic and non-genetic components for trait variances were computed to assess the h(2) of model-derived parameters. RESULTS: The intraclass correlation coefficients in MZ twins were 0.78 for RRI, 0.67 for sigma and 0.57 for OGTT beta index. In DZ twins the correlation coefficients were 0.23, 0.32 and 0.42, respectively. Using the most parsimonious model for each trait, the h(2)--the proportion of variance accounted for by genetic factors--was 76% (95% CI: 53-88%) for RRI, 28% (34-80%) for sigma and 53% (26-72%) for OGTT beta index. CONCLUSIONS/INTERPRETATION: Our findings demonstrate that model-derived parameters of insulin secretion have a substantial genetic component and may be used in the search for genetic determinants of beta cell function in humans.
Since the human genome was sequenced, there has been intense activity to understand the function of the 30,000 identified genes; attention has now turned to the products of genes--proteins. Proteomics is the large-scale study of the structure and function of proteins; it includes the rapidly evolving field of disease proteomics, which aims to identify proteins involved in human disease and to understand how their expression, structure and function cause illness. Proteomics has identified proteins that offer promise as diagnostic or prognostic markers, or as therapeutic targets in a range of illnesses, including cancer, immune rejection after transplantation, and infectious diseases such as tuberculosis and malaria; it has the potential to allow patient-tailored therapy. Some major challenges remain, both technical (eg, detecting "low-abundance" proteins, and maintaining sample stability) and in data management (eg, correlating changes in proteins with disease processes).