Identification of a gene-pathway associated with non-alcoholic steatohepatitis.

BACKGROUND/AIMS: We have integrated gene expression profiling of liver biopsies of NASH patients with liver samples of a mouse model of steatohepatitis (MAT1A-KO) to identify a gene-pathway associated with NASH. METHODS: Affymetrix U133 Plus 2.0 microarrays were used to evaluate nine patients with NASH, six patients with steatosis, and six control subjects; Affymetrix MOE430A microarrays were used to evaluate wild-type and MAT1A-KO mice at 15 days, 1, 3, 5 and 8 months after birth. Transcriptional profiles of patients with NASH and MAT1A-KO mice were compared with those of their proficient controls. RESULTS: We identified a gene-pathway associated with NASH, that accurately distinguishes between patients with early-stage NASH and controls. Patients with steatosis have a gene expression pattern intermediate between that of NASH and controls. Promoter analysis revealed that 34 of the genes associated with NASH contained an Sp1 element. We found that Sp1 binding to these genes is increased in MAT1A-KO mice. Sp1 is also hyperphosphorylated in MAT1A-KO as well as in patients with NASH and steatosis. CONCLUSIONS: A gene-pathway associated with NASH has been identified. We speculate that hyperphosphorylation of Sp1 may be involved in the genesis of steatosis and that other factors, such as oxidative stress, may trigger its progression to NASH.

GARBAN II: an integrative framework for extracting biological information from proteomic and genomic data.

Genomic and proteomic analyses generate a massive amount of data that requires specific bioinformatic tools for its management and interpretation. GARBAN II, developed from the previous GARBAN platform, provides an integrated framework to simultaneously analyse and compare multiple datasets from DNA microarrays and proteomic studies. The general architecture, gene classification and comparison, and graphical representation have been redesigned to ensure a user-friendly feature and to improve the capabilities and efficiency of this system. Additionally, GARBAN II has been extended with new applications to display networks of coexpressed genes and to integrate access to BioRag and MotifScanner so as to facilitate the holistic analysis of users' data.

Correlation between gene expression and GO semantic similarity.

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.