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An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits.

ABSTRACT: With mounting availability of genomic and phenotypic databases, data integration and mining become increasingly challenging. While efforts have been put forward to analyze prokaryotic phenotypes, current computational technologies either lack high throughput capacity for genomic scale analysis, or are limited in their capability to integrate and mine data across different scales of biology. Consequently, simultaneous analysis of associations among genomes, phenotypes, and gene functions is prohibited. Here, we developed a high throughput computational approach, and demonstrated for the first time the feasibility of integrating large quantities of prokaryotic phenotypes along with genomic datasets for mining across multiple scales of biology (protein domains, pathways, molecular functions, and cellular processes). Applying this method over 59 fully sequenced prokaryotic species, we identified genetic basis and molecular mechanisms underlying the phenotypes in bacteria. We identified 3,711 significant correlations between 1,499 distinct Pfam and 63 phenotypes, with 2,650 correlations and 1,061 anti-correlations. Manual evaluation of a random sample of these significant correlations showed a minimal precision of 30% (95% confidence interval: 20%-42%; n = 50). We stratified the most significant 478 predictions and subjected 100 to manual evaluation, of which 60 were corroborated in the literature. We furthermore unveiled 10 significant correlations between phenotypes and KEGG pathways, eight of which were corroborated in the evaluation, and 309 significant correlations between phenotypes and 166 GO concepts evaluated using a random sample (minimal precision = 72%; 95% confidence interval: 60%-80%; n = 50). Additionally, we conducted a novel large-scale phenomic visualization analysis to provide insight into the modular nature of common molecular mechanisms spanning multiple biological scales and reused by related phenotypes (metaphenotypes). We propose that this method elucidates which classes of molecular mechanisms are associated with phenotypes or metaphenotypes and holds promise in facilitating a computable systems biology approach to genomic and biomedical research.

SUBMITTER: Liu Y

PROVIDER: S-EPMC1636675 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOvarian (OV) cancer is considered as one of the most deadly malignancies in women, since it is unfortunately diagnosed in advanced stages. Nowadays, the importance of bioinformatics tools and their frequent usage in tracking dysregulated cancer-related genes and pathways have been highlighted in researches.AimThe aim of this study is to investigate dysregulated miRNAs-genes network and its function in OV tumors based on the integration of microarray data through a system biology approach.MethodsTwo microarray data (GSE119056 and GSE4122) were analyzed to explore the differentially expressed miRNAs (DEmiRs) and genes among OV tumors and normal tissues. Then, through the help of TargetScan, miRmap, and miRTarBase databases, the dysregulated miRNA-gene network in OV tumors was constructed by Cytoscape. In the next step, co-expression and protein-protein interaction networks were made using GEPIA and STRING databases. Moreover, the functional analysis of the hub genes was done by DAVID, KEGG, and Enrichr databases. Eventually, the regulatory network of TF-miRNA-gene was constructed.ResultsThe potential dysregulated miRNAs-genes network in OV tumors has been constructed, including 109 differentially expressed genes (DEGs), 25 DEmiRs, and 213 interactions. Two down-regulated microRNAs, miR-660-3p and hsa-miR-4510, have the most interactions with up-expressed oncogenic DEGs. CDK1, PLK1, CCNB1, CCNA2, and EZH2 are involved in protein module, which show significant overexpression in OV tumors according to The Cancer Genome Atlas (TCGA) data. EZH2 shows amplification in OV tumors with remarkable percentage. The transcription factors TFAP2C and GATA4 have the pivotal regulatory functions in oncotranscriptomic profile of OV tumors.ConclusionIn current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs-DEGs and TF-miRNA-gene networks reveal the potential interactions that could be a significant piece of the OV onco-puzzle.

Project description:BACKGROUND:Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations. RESULTS:Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1-4, 6-7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. CONCLUSIONS:The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.

Project description:MotivationAccurate prediction of genes encoding small proteins (on the order of 50 amino acids or less) remains an elusive open problem in bioinformatics. Some of the best methods for gene prediction use either sequence composition analysis or sequence similarity to a known protein coding sequence. These methods often fail for small proteins, however, either due to a lack of experimentally verified small protein coding genes or due to the limited statistical significance of statistics on small sequences. Our approach is based upon the hypothesis that true small proteins will be under selective pressure for encoding the particular amino acid sequence, for ease of translation by the ribosome and for structural stability. This stability can be achieved either independently or as part of a larger protein complex. Given this assumption, it follows that small proteins should display conserved local protein structure properties much like larger proteins. Our method incorporates neural-net predictions for three local structure alphabets within a comparative genomic approach using a genomic alignment of 22 closely related bacteria genomes to generate predictions for whether or not a given open reading frame (ORF) encodes for a small protein.ResultsWe have applied this method to the complete genome for Escherichia coli strain K12 and looked at how well our method performed on a set of 60 experimentally verified small proteins from this organism. Out of a total of 11 407 possible ORFs, we found that 6 of the top 10 and 27 of the top 100 predictions belonged to the set of 60 experimentally verified small proteins. We found 35 of all the true small proteins within the top 200 predictions. We compared our method to Glimmer, using a default Glimmer protocol and a modified small ORF Glimmer protocol with a lower minimum size cutoff. The default Glimmer protocol identified 16 of the true small proteins (all in the top 200 predictions), but failed to predict on 34 due to size cutoffs. The small ORF Glimmer protocol made predictions for all the experimentally verified small proteins but only contained 9 of the 60 true small proteins within the top 200 predictions.Contactjsamayoa@jhu.edu

Project description:Neurodegenerative diseases represent a heterogeneous group of disorders that share common features like abnormal protein aggregation, perturbed Ca2+ homeostasis, excitotoxicity, impairment of mitochondrial functions, apoptosis, inflammation, and oxidative stress. Despite recent advances in the research of biomarkers, early diagnosis, and pharmacotherapy, there are no treatments that can halt the progression of these age-associated neurodegenerative diseases. Numerous epidemiological studies indicate that long-term intake of a Mediterranean diet, characterized by a high consumption of extra virgin olive oil, correlates with better cognition in aged populations. Olive oil phenolic compounds have been demonstrated to have different biological activities like antioxidant, antithrombotic, and anti-inflammatory activities. Oleocanthal, a phenolic component of extra virgin olive oil, is getting more and more scientific attention due to its interesting biological activities. The aim of this research was to characterize the neuroprotective effects of oleocanthal against H?O?-induced oxidative stress in neuron-like SH-SY5Y cells. Moreover, protein expression profiling, combined with pathways analyses, was used to investigate the molecular events related to the protective effects. Oleocanthal was demonstrated to counteract oxidative stress, increasing cell viability, reducing reactive oxygen species (ROS) production, and increasing reduced glutathione (GSH) intracellular level. Proteomic analysis revealed that oleocanthal significantly modulates 19 proteins in the presence of H?O?. In particular, oleocanthal up-regulated proteins related to the proteasome, the chaperone heat shock protein 90, the glycolytic enzyme pyruvate kinase, and the antioxidant enzyme peroxiredoxin 1. Moreover, oleocanthal protection seems to be mediated by Akt activation. These data offer new insights into the molecular mechanisms behind oleocanthal protection against oxidative stress.

Project description:RationaleMean platelet volume (MPV) and platelet count (PLT) are platelet measures that have been linked to cardiovascular disease (CVD) and mortality risk. Identifying protein biomarkers for these measures may yield insights into CVD mechanisms.ObjectiveWe aimed to identify causal protein biomarkers for MPV and PLT among 71 CVD-related plasma proteins measured in FHS (Framingham Heart Study) participants.Methods and resultsWe conducted integrative analyses of genetic variants associated with PLT/MPV with protein quantitative trait locus variants associated with plasma proteins followed by Mendelian randomization to infer causal relations of proteins for PLT/MPV. We also tested protein-PLT/MPV association in FHS participants. Using induced pluripotent stem cell-derived megakaryocyte clones that produce functional platelets, we conducted RNA-sequencing and analyzed expression differences between low- and high-platelet producing clones. We then performed small interfering RNA gene knockdown experiments targeting genes encoding proteins with putatively causal platelet effects in megakaryocyte clones to examine effects on platelet production. In protein-trait association analyses, ten proteins were associated with MPV and 31 with PLT. Mendelian randomization identified 4 putatively causal proteins for MPV and 4 for PLT. GP-5 (Glycoprotein V), GRN (granulin), and MCAM (melanoma cell adhesion molecule) were associated with PLT, while MPO (myeloperoxidase) showed significant association with MPV in both analyses. RNA-sequencing analysis results were directionally concordant with observed and Mendelian randomization-inferred associations for GP-5, GRN, and MCAM. In siRNA gene knockdown experiments, silencing GP-5, GRN, and MPO decreased PLTs. Genome-wide association study results suggest several of these may be linked to CVD risk.ConclusionsWe identified 4 proteins that are causally linked to PLTs. These proteins may also have roles in the pathogenesis of CVD via a platelet/blood coagulation-based mechanism.

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An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits.

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