Background: MicroRNAs are small non-coding RNAs that regulate gene expression and thus play important roles in mammalian development. However, the comprehensive lists of microRNAs, as well as, molecular mechanisms by which microRNAs regulate gene expression during gamete and embryo development are poorly defined. The objectives of this study were to determine microRNAs in bull sperm and predict their functions. Methods: To accomplish our objectives we isolated miRNAs from sperm of high and low fertility bulls, conducted microRNA microarray experiments and validated expression of a panel of microRNAs using real time RT-PCR. Bioinformatic approaches were carried out to identify regulated targets. Results: We demonstrated that an abundance of microRNAs were present in bovine spermatozoa, however, only seven were differentially expressed; hsa-aga-3155, -8197, -6727, -11796, -14189, -6125, -13659. The abundance of miRNAs in the spermatozoa and the differential expression in sperm from high vs. low fertility bulls suggests that the miRNAs possibly play important functions in the regulating mechanisms of bovine spermatozoa. Conclusion: Identification of specific microRNAs expressed in spermatozoa of bulls with different fertility phenotypes will help better understand mammalian gametogenesis and early development.

Genome-wide association studies (GWAS) query the entire genome in a hypothesis-free, unbiased manner. Since they have the potential for identifying novel genetic variants, they have become a very popular approach to the investigation of complex diseases. Nonetheless, since the success of the GWAS approach varies widely, the identification of genetic variants for complex diseases remains a difficult problem. We developed a novel bioinformatics approach to identify the nominal genetic variants associated with complex diseases. To test the feasibility of our approach, we developed a web-based aggregation tool to organize the genes, genetic variations and pathways involved in preterm birth. We used semantic data mining to extract all published articles related to preterm birth. All articles were reviewed by a team of curators. Genes identified from public databases and archives of expression arrays were aggregated with genes curated from the literature. Pathway analysis was used to impute genes from pathways identified in the curations. The curated articles and collected genetic information form a unique resource for investigators interested in preterm birth. The Database for Preterm Birth exemplifies an approach that is generalizable to other disorders for which there is evidence of significant genetic contributions. Database URL:http://ptbdb.cs.brown.edu/dbPTBv1.php

SNPs located within the open reading frame of a gene that result in an alteration in the amino acid sequence of the encoded protein [nonsynonymous SNPs (nsSNPs)] might directly or indirectly affect functionality of the protein, alone or in the interactions in a multi-protein complex, by increasing/decreasing the activity of the metabolic pathway. Understanding the functional consequences of such changes and drawing conclusions about the molecular basis of diseases, involves integrating information from multiple heterogeneous sources including sequence, structure data and pathway relations between proteins. The data from NCBI's SNP database (dbSNP), gene and protein databases from Entrez, protein structures from the PDB and pathway information from KEGG have all been cross referenced into the StSNP web server, in an effort to provide combined integrated, reports about nsSNPs. StSNP provides ‘on the fly’ comparative modeling of nsSNPs with links to metabolic pathway information, along with real-time visual comparative analysis of the modeled structures using the Friend software application. The use of metabolic pathways in StSNP allows a researcher to examine possible disease-related pathways associated with a particular nsSNP(s), and link the diseases with the current available molecular structure data. The server is publicly available at http://glinka.bio.neu.edu/StSNP/.

OBJECTIVE: To identify candidate genes and genetic variants for preeclampsia using a bioinformatic approach to extract and organize genes and variants from the published literature. METHODS: Semantic data-mining and natural language processing were used to identify articles from the published literature meeting criteria for potential association with preeclampsia. Articles were manually reviewed by trained curators. Cluster analysis was used to aggregate the extracted genes into gene sets associated with preeclampsia or severe preeclampsia, early or late preeclampsia, maternal or fetal tissue sources, and concurrent conditions (ie, fetal growth restriction, gestational hypertension, or hemolysis, elevated liver enzymes, and low platelet count [HELLP]). Gene ontology was used to organize this large group of genes into ontology groups. RESULTS: From more than 22 million records in PubMed, with 28,000 articles on preeclampsia, our data-mining tool identified 2,300 articles with potential genetic associations with preeclampsia-related phenotypes. After curation, 729 articles were “accepted” that contained “statistically significant” associations with 535 genes. We saw distinct segregation of these genes by severity and timing of preeclampsia, by maternal or fetal source, and with associated conditions (eg, gestational hypertension, fetal growth restriction, or HELLP syndrome). CONCLUSION: The gene sets and ontology groups identified through our systematic literature curation indicate that preeclampsia represents several distinct phenotypes with distinct and overlapping maternal and fetal genetic contributions. LEVEL OF EVIDENCE: III

PRKAG2 encodes the γ₂-subunit isoform of 5′-AMP-activated protein kinase (AMPK), a heterotrimeric enzyme with major roles in the regulation of energy metabolism in response to cellular stress. Mutations in PRKAG2 have been implicated in a unique hypertrophic cardiomyopathy (HCM) characterized by cardiac glycogen overload, ventricular preexcitation, and hypertrophy. We identified a novel, de novo PRKAG2 mutation (K475E) in a neonate with prenatal onset of HCM. We aimed to investigate the cellular impact, signaling pathways involved, and therapeutic options for K475E mutation using cells stably expressing human wild-type (WT) or the K475E mutant. In human embryonic kidney-293 cells, the K475E mutation induced a marked increase in the basal phosphorylation of T172 and AMPK activity, reduced sensitivity to AMP in allosteric activation, and a loss of response to phenformin. In H9c2 cardiomyocytes, the K475E mutation induced inhibition of AMPK and reduced the response to phenformin and increases in the phosphorylation of p70S6 kinase (p70S6K) and eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1). Primary fibroblasts from the patient with the K475E mutation also showed marked increases in the phosphorylation of p70S6K and 4E-BP1 compared with those from age-matched, nondiseased controls. Moreover, overexpression of K475E induced hypertrophy in H9c2 cells, which was effectively reversed by treatment with rapamycin. Taken together, we have identified a novel, de novo infantile-onset PRKAG2 mutation causing HCM. Our study suggests the K475E mutation induces alteration in basal AMPK activity and results in a hypertrophy phenotype involving the mechanistic target of rapamycin signaling pathway, which can be reversed with rapamycin. NEW & NOTEWORTHY We identified a novel, de novo PRKAG2 mutation (K475E) in the cystathionine β-synthase 3 repeat, a region critical for AMP binding but with no previous reported mutation. Our data suggest the mutation affects AMP-activated protein kinase activity, activates cell growth pathways, and results in cardiac hypertrophy, which can be reversed with rapamycin.