OMPdb: a database of β-barrel outer membrane proteins from Gram-negative bacteria

We describe here OMPdb, which is currently the most complete and comprehensive collection of integral β-barrel outer membrane proteins from Gram-negative bacteria. The database currently contains 69 354 proteins, which are classified into 85 families, based mainly on structural and functional criteria. Although OMPdb follows the annotation scheme of Pfam, many of the families included in the database were not previously described or annotated in other publicly available databases. There are also cross-references to other databases, references to the literature and annotation for sequence features, like transmembrane segments and signal peptides. Furthermore, via the web interface, the user can not only browse the available data, but submit advanced text searches and run BLAST queries against the database protein sequences or domain searches against the collection of profile Hidden Markov Models that represent each family’s domain organization as well. The database is freely accessible for academic users at http://bioinformatics.biol.uoa.gr/OMPdb and we expect it to be useful for genome-wide analyses, comparative genomics as well as for providing training and test sets for predictive algorithms regarding transmembrane β-barrels

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P &lt; 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P &lt; 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio &gt;2, P &lt; 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.</p

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Objective We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). Research Design and Methods 732 recently diagnosed T2D patients from the IMI-DIRECT study were extensively phenotyped over three years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS) and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. Results Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS, and increasing CLIm; visceral or liver fat, HDL-cholesterol and triglycerides had further independent, though weaker, roles (R2=0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from AUROC=0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS and CLIm was relatively stable (odds ratios 0.07 to 0.09). T2D polygenic risk score and baseline pancreatic fat, GLP-1, glucagon, diet, and physical activity did not show an independent role. Conclusions Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of T2D patients in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression

Creating a specialist protein resource network:a meeting report for the protein bioinformatics and community resources retreat

During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue

Caractérisation des périodes de sécheresse sur le domaine de l'Afrique simulée par le Modèle Régional Canadien du Climat (MRCC5)

Les conséquences des changements climatiques sur la fréquence ainsi que sur l'intensité des précipitations auront un impact direct sur les périodes de sécheresse et par conséquent sur différents secteurs économiques tels que le secteur de l'agriculture. Ainsi, dans cette étude, l'habilité du Modèle Régional Canadien du Climat (MRCC5) à simuler les différentes caractéristiques des périodes de sécheresse est évaluée pour 4 seuils de précipitation soit 0.5 mm, 1 mm, 2 mm et 3 mm. Ces caractéristiques incluent le nombre de jours secs, le nombre de périodes de sécheresse ainsi que le maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans. Les résultats sont présentés pour des moyennes annuelles et saisonnières. L'erreur de performance est évaluée en comparant le MRCC5 piloté par ERA-Interim aux données d'analyses du GPCP pour le climat présent (1997-2008). L'erreur due aux conditions aux frontières c'est-à-dire les erreurs de pilotage du MRCC5, soit par CanESM2 et par ERA-Interim ainsi que l'évaluation de la valeur ajoutée du MRCC5 face au CanESM2 sont également analysées. L'analyse de ces caractéristiques est également faite dans un contexte de climat changeant pour deux périodes futures, soit 2041-2070 et 2071-2100 à l'aide du MRCC5 piloté par le modèle de circulation générale CanESM2 de même que par le modèle CanESM2 sous le scénario RCP 4.5. Les résultats suggèrent que le MRCC5 piloté par ERA-Interim a tendance à surestimer la moyenne annuelle du nombre de jours secs ainsi que le maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans dans la plupart des régions de l'Afrique et une tendance à sous-estimer le nombre de périodes de sécheresse. En général, l'erreur de performance est plus importante que l'erreur due aux conditions aux frontières pour les différentes caractéristiques de périodes de sécheresse. Pour les régions équatoriales, les changements appréhendés par le MRCC5 piloté par CanESM2 pour les différentes caractéristiques de périodes de sécheresse et pour deux périodes futures (2041-2070 et 2071-2100), suggèrent une augmentation significatives du nombre de jours secs ainsi que du maximum de jours consécutifs sans précipitation associé à une récurrence de 5 ans. Une diminution significative du nombre de périodes de sécheresse est aussi prévue.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Modèle Régional du Climat, Changement climatique, Jours secs, Nombre de périodes de sécheresse, Événement de faible récurrence, Afriqu

A guideline to proteome-wide alpha-helical membrane protein topology predictions

For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an unseen set of proteins of known structure and also on four genome-scale data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the Phobius group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.AuthorCount:4;</p