Biomedical subjects

Sun Tian

Publications and source records attributed to Sun Tian.

2 recordsLinked to original sources

PubMed2006

Application of a sensitive collection heuristic for very large protein families: evolutionary relationship between adipose triglyceride lipase (ATGL) and classic mammalian lipases.

BACKGROUND: Manually finding subtle yet statistically significant links to distantly related homologues becomes practically impossible for very populated protein families due to the sheer number of similarity searches to be invoked and analyzed. The unclear evolutionary relationship between classical mammalian lipases and the recently discovered human adipose triglyceride lipase (ATGL; a patatin family member) is an exemplary case for such a problem. RESULTS: We describe an unsupervised, sensitive sequence segment collection heuristic suitable for assembling very large protein families. It is based on fan-like expanding, iterative database searches. To prevent inclusion of unrelated hits, additional criteria are introduced: minimal alignment length and overlap with starting sequence segments, finding starting sequences in reciprocal searches, automated filtering for compositional bias and repetitive patterns. This heuristic was implemented as FAMILYSEARCHER in the ANNIE sequence analysis environment and applied to search for protein links between the classical lipase family and the patatin-like group. CONCLUSION: The FAMILYSEARCHER is an efficient tool for tracing distant evolutionary relationships involving large protein families. Although classical lipases and ATGL have no obvious sequence similarity and differ with regard to fold and catalytic mechanism, homology links detected with FAMILYSEARCHER show that they are evolutionarily related. The conserved sequence parts can be narrowed down to an ancestral core module consisting of three beta-strands, one alpha-helix and a turn containing the typical nucleophilic serine. Moreover, this ancestral module also appears in numerous enzymes with various substrate specificities, but that critically rely on nucleophilic attack mechanisms.

Adipose Tissue↗

PubMed2005

Molecular processes during fat cell development revealed by gene expression profiling and functional annotation.

BACKGROUND: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. Detailed characterization of the sequences of identified gene products has not been done and global mechanisms have not been investigated. We evaluated the extent to which molecular processes can be revealed by expression profiling and functional annotation of genes that are differentially expressed during fat cell development. RESULTS: Mouse microarrays with more than 27,000 elements were developed, and transcriptional profiles of 3T3-L1 cells (pre-adipocyte cells) were monitored during differentiation. In total, 780 differentially expressed expressed sequence tags (ESTs) were subjected to in-depth bioinformatics analyses. The analysis of 3'-untranslated region sequences from 395 ESTs showed that 71% of the differentially expressed genes could be regulated by microRNAs. A molecular atlas of fat cell development was then constructed by de novo functional annotation on a sequence segment/domain-wise basis of 659 protein sequences, and subsequent mapping onto known pathways, possible cellular roles, and subcellular localizations. Key enzymes in 27 out of 36 investigated metabolic pathways were regulated at the transcriptional level, typically at the rate-limiting steps in these pathways. Also, coexpressed genes rarely shared consensus transcription-factor binding sites, and were typically not clustered in adjacent chromosomal regions, but were instead widely dispersed throughout the genome. CONCLUSIONS: Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.

3T3-L1 Cells↗