Jing Chen

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein-protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene-disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%.

BACKGROUND: -Methyladenosine (m6A) methylation is the most prevalent internal posttranscriptional modification on mammalian mRNA. The role of m6A mRNA methylation in the heart is not known. METHODS: To determine the role of m6A methylation in the heart, we isolated primary cardiomyocytes and performed m6A immunoprecipitation followed by RNA sequencing. We then generated genetic tools to modulate m6A levels in cardiomyocytes by manipulating the levels of the m6A RNA methylase methyltransferase-like 3 (METTL3) both in culture and in vivo. We generated cardiac-restricted gain- and loss-of-function mouse models to allow assessment of the METTL3-m6A pathway in cardiac homeostasis and function. RESULTS: We measured the level of m6A methylation on cardiomyocyte mRNA, and found a significant increase in response to hypertrophic stimulation, suggesting a potential role for m6A methylation in the development of cardiomyocyte hypertrophy. Analysis of m6A methylation showed significant enrichment in genes that regulate kinases and intracellular signaling pathways. Inhibition of METTL3 completely abrogated the ability of cardiomyocytes to undergo hypertrophy when stimulated to grow, whereas increased expression of the m6A RNA methylase METTL3 was sufficient to promote cardiomyocyte hypertrophy both in vitro and in vivo. Finally, cardiac-specific METTL3 knockout mice exhibit morphological and functional signs of heart failure with aging and stress, showing the necessity of RNA methylation for the maintenance of cardiac homeostasis. CONCLUSIONS: -adenosines as a dynamic modification that is enhanced in response to hypertrophic stimuli and is necessary for a normal hypertrophic response in cardiomyocytes. Enhanced m6A RNA methylation results in compensated cardiac hypertrophy, whereas diminished m6A drives eccentric cardiomyocyte remodeling and dysfunction, highlighting the critical importance of this novel stress-response mechanism in the heart for maintaining normal cardiac function.

BACKGROUND: Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses. RESULTS: For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds"), and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings - for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method - the three methods achieved a comparable AUC value, suggesting a similar performance. CONCLUSION: Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.