RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts.

The physiology and behavior of living organisms are featured by time-related variations driven by molecular clockworks that arose during evolution stochastically and heterogeneously. Over the years, several high-throughput experiments were performed to evaluate time-dependent gene expression in different cell types across several species and experimental conditions. Here, these were retrieved, manually curated, and analyzed by two software packages, BioCycle and MetaCycle, to infer circadian or ultradian transcripts across different species. These transcripts were stored in RhythmicDB and made publically available.

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes.

Motivation: Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind. Results: In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome. Availability and implementation: http://akid.bio.uniroma2.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Mediterranean meal versus Western meal effects on postprandial ox-LDL, oxidative and inflammatory gene expression in healthy subjects: a randomized controlled trial for nutrigenomic approach in cardiometabolic risk.

AIMS: Inflammation and oxidative damage contribute significantly to the development of cardiovascular diseases (CVD). Postprandial oxidative stress and inflammation are characterized by an increased susceptibility of the organism toward oxidative damage after consumption of a meal rich in lipids and/or carbohydrates. Micronutrients modulate immune system and exert a protective action by reducing oxidized low-density lipoprotein (ox-LDL) level. The aim of the present study was to evaluate the postprandial plasma ox-LDL level and the gene expression of 13 genes related to oxidative stress (HOSp) and human inflammasome pathways (HIp), after a tocopherol-enriched Mediterranean meal (TEM), and a Western high-fat meal (HFM). Moreover, Mediterranean Adequacy Index was calculated to define the quality of both meals. METHODS: We set up a randomized and crossover trial in healthy human volunteers. Ox-LDL level was measured by enzyme-linked immunosorbent assay and the gene expression of 13 genes related to HOSp and HIp by qRT-PCR. RESULTS: Ox-LDL levels significantly decreased comparing HFM versus TEM (p < 0.05). Percentages of significantly overexpressed genes after each dietary treatment are as follows: (A) baseline versus HFM: 7.69 % HIp and 23.08 % HOSp; (B) baseline versus TEM: 7.69 % HIp and 7.69 % HOSp; (C) HFM versus TEM: 15.38 % HIp and 15.38 % HOSp. CONCLUSIONS: TEM reduced postprandial risk factors of CVD, such as ox-LDL, and the expression of inflammation and oxidative stress-related genes. Chronic studies on larger population are necessary before definitive conclusions. TRIAL REGISTRATION: ClinicalTrials.gov Id: NCT01890070.

Individually tailored screening of susceptibility to sarcopenia using p53 codon 72 polymorphism, phenotypes, and conventional risk factors.

BACKGROUND AND AIM: p53 activity plays a role in muscle homeostasis and skeletal muscle differentiation; all pathways that lead to sarcopenia are related to p53 activities. We investigate the allelic frequency of the TP53 codon 72 in exon 4 polymorphism in the Italian female population and the association with appendicular skeletal muscle mass index in normal weight (NW), normal weight obese (NWO), and preobese-obese (Preob-Ob) subjects. METHODS: We evaluated anthropometry, body composition, and p53 polymorphism in 140 women distinguished in NW, NWO, and Preob-Ob. RESULTS: *Arg/*Arg genotype increases sarcopenia risk up to 20% (*Arg/*Arg genotype OR = 1.20; 95% CI = 0.48-2.9; *proallele carriers OR = 0.83; 95% CI = 0.83-2.06). The risk of being sarcopenic for *Arg/*Arg genotype in NWO and Preob-Ob is 31% higher than NW carriers of *proallele (RR = 0,31, 95% CI = 0,15-0,66, P = 0,0079). We developed a model able to predict sarcopenia risk based on age, body fat, and p53 polymorphism. CONCLUSION: Our study evidences that genotyping TP53 polymorphism could be a useful new genetic approach, in association with body composition evaluations, to assess sarcopenia risk.

B-Pred, a structure based B-cell epitopes prediction server.

The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein's peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window's width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications.

Lysophosphatidic acid enhances antimycobacterial response during in vivo primary Mycobacterium tuberculosis infection.

Lysophospholipids may play an important protective role during primary infection of Mycobacterium tuberculosis (MTB) by enhancing innate antimycobacterial immune response of both macrophages and alveolar epithelial cells. Here, we show that treatment with lysophosphatidic acid (LPA) of mice aerogenically infected with MTB immediately after infection results in a significant early reduction of pulmonary CFUs and of histopathological damage in comparison with control mice. In contrast, treatment of acute disease does not result in any improvement of both microbiological and histopathological parameters. Altogether, these results show that LPA treatment can exert protective effect if administrated during primary infection, only.

Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens.

Large-scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co-purification of non-specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14-3-3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14-3-3 phospho-peptide partners. Notably, a proline next to the carboxy side of the phospho-amino acid functions as a potent inhibitor of 14-3-3 binding. The position-specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these "peptide features" with "protein features", such as protein co-expression and co-localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14-3-3 peptide target on the partner proteins.