Evidence for Structural Variants of a- and b-Type Peptide Fragment Ions Using Combined Ion Mobility/Mass Spectrometry

Tandem mass spectrometry (MS/MS) of peptides plays a key role in the field of proteomics, and an understanding of the fragmentation mechanisms involved is vital for data interpretation. Not all the fragment ions observed by low-energy collision-induced dissociation of protonated peptides are readily explained by the generally accepted structures for a- and b-ions. The possibility of a macrocyclic structure for b-type ions has been recently proposed. In this study, we have undertaken investigations of linear protonated YAGFL-NH2, N-acetylated-YAGFL-NH2, and cyclo-(YAGFL) peptides and their fragments using a combination of ion mobility (IM) separation and mass spectrometry. The use of IM in this work both gives insight into relative structural forms of the ion species and crucial separation of isobaric species. Our study provides compelling evidence for the formation of a stable macrocyclic structure for the b5 ion generated by fragmentation of protonated linear YAGFL-NH2. Additionally we demonstrate that the a4 ion fragment of protonated YAGFL-NH2 has at least two structures; one of which is attributable to a macrocyclic structure on the basis of its subsequent fragmentation. More generally, this work emphasizes the value of combined IM-MS/MS in probing the detailed fragmentation mechanisms of peptide ions, and illustrates the use of combined ion mobility/collisional activation/mass spectrometry analysis in achieving an effective enhancement of the resolution of the mobility separator

Genome-Wide Analysis of the Effects of Heat Shock on a Saccharomyces cerevisiae Mutant With a Constitutively Activated cAMP-Dependent Pathway

We have used DNA microarray technology and 2-D gel electrophoresis combined with mass spectrometry to investigate the effects of a drastic heat shock from 30℃ to 50℃ on a genome-wide scale. This experimental condition is used to differentiate between wild-type cells and those with a constitutively active cAMP-dependent pathway in Saccharomyces cerevisiae. Whilst more than 50% of the former survive this shock, almost all of the latter lose viability. We compared the transcriptomes of the wildtype and a mutant strain deleted for the gene PDE2, encoding the high-affinity cAMP phosphodiesterase before and after heat shock treatment. We also compared the two heat-shocked samples with one another, allowing us to determine the changes that occur in the pde2Δ mutant which cause such a dramatic loss of viability after heat shock. Several genes involved in ergosterol biosynthesis and carbon source utilization had altered expression levels, suggesting that these processes might be potential factors in heat shock survival. These predictions and also the effect of the different phases of the cell cycle were confirmed by biochemical and phenotypic analyses. 146 genes of previously unknown function were identified amongst the genes with altered expression levels and deletion mutants in 13 of these genes were found to be highly sensitive to heat shock. Differences in response to heat shock were also observed at the level of the proteome, with a higher level of protein degradation in the mutant, as revealed by comparing 2-D gels of wild-type and mutant heat-shocked samples and mass spectrometry analysis of the differentially produced proteins

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

<p>Abstract</p> <p>Background</p> <p>Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide <it>prima facie </it>evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced <it>Aspergillus niger </it>fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another <it>A.niger </it>sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR).</p> <p>Results</p> <p>405 identified peptide sequences were mapped to 214 different <it>A.niger </it>genomic <it>loci </it>to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these <it>loci </it>either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models.</p> <p>Conclusion</p> <p>This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of <it>A.niger </it>sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.</p

Longitudinal association of dietary acid load with kidney function decline in an older adult population with metabolic syndrome

Background: Diets high in acid load may contribute to kidney function impairment. This study aimed to investigate the association between dietary acid load and 1-year changes in glomerular filtration rate (eGFR) and urine albumin/creatinine ratio (UACR). Methods: Older adults with overweight/obesity and metabolic syndrome (mean age 65 ± 5 years, 48% women) from the PREDIMED-Plus study who had available data on eGFR (n = 5,874) or UACR (n = 3,639) at baseline and after 1 year of follow-up were included in this prospective analysis. Dietary acid load was estimated as potential renal acid load (PRAL) and net endogenous acid production (NEAP) at baseline from a food frequency questionnaire. Linear and logistic regression models were fitted to evaluate the associations between baseline tertiles of dietary acid load and kidney function outcomes. One year-changes in eGFR and UACR were set as the primary outcomes. We secondarily assessed ≥ 10% eGFR decline or ≥10% UACR increase. Results: After multiple adjustments, individuals in the highest tertile of PRAL or NEAP showed higher one-year changes in eGFR (PRAL, β: -0.64 ml/min/1.73 m2; 95% CI: -1.21 to -0.08 and NEAP, β: -0.56 ml/min/1.73 m2; 95% CI: -1.13 to 0.01) compared to those in the lowest category. No associations with changes in UACR were found. Participants with higher levels of PRAL and NEAP had significantly higher odds of developing ≥10% eGFR decline (PRAL, OR: 1.28; 95% CI: 1.07-1.54 and NEAP, OR: 1.24; 95% CI: 1.03-1.50) and ≥10 % UACR increase (PRAL, OR: 1.23; 95% CI: 1.04-1.46) compared to individuals with lower dietary acid load. Conclusions: Higher PRAL and NEAP were associated with worse kidney function after 1 year of follow-up as measured by eGFR and UACR markers in an older Spanish population with overweight/obesity and metabolic syndrome. Keywords: albuminuria; chronic kidney disease (CKD); dietary acid load; glomerular filtration rate (GFR); kidney function; net endogenous acid production (NEAP); potential renal acid load (PRAL); renal nutrition

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article