Biomedical subjects

Zhengqing Ouyang

Publications and source records attributed to Zhengqing Ouyang.

4 recordsLinked to original sources

PubMed2026

Targeting eIF4A-dependent translation in genetically complex sarcoma.

Dedifferentiated liposarcoma (DDLS), myxofibrosarcoma (MFS), and undifferentiated pleomorphic sarcoma (UPS) are the most common types of genetically complex sarcoma. There is an urgent need to develop effective targeted therapy for these deadly sarcoma types. Despite their genetic complexity, these sarcomas share genomic alterations causing PI3K/Akt/mTOR and MAPK pathway activation, and both pathways control translation mediated by the RNA helicase eIF4A. We therefore investigated eIF4A inhibition as a therapeutic strategy. The eIF4A inhibitor CR-1-31B effectively suppressed tumor growth and induced apoptosis in DDLS, MFS, and UPS patient-derived cell lines and mouse xenografts. Transcriptome-scale ribosome footprinting identified eIF4A-dependent mRNAs such as the Hippo pathway transcriptional coactivators YAP1 (YAP) and WWTR1 (TAZ). Combined knockdown of YAP and TAZ induced apoptosis in DDLS, MFS, and UPS cell lines, and their ectopic expression partially rescued cells from apoptosis induced by CR-1-31B. Genomic analysis of patient tumors revealed that YAP and WWTR1 were frequently amplified or gained in DDLS, MFS, and UPS and were associated with worse clinical outcomes. Together, our findings identify a strategy for targeting the Hippo pathway in incurable forms of sarcoma based on inhibition of eIF4A-dependent translation of the key oncogenic transcription factors YAP and TAZ.

Humans↗

PubMed2005

Hierarchical structure analysis describing abnormal base composition of genomes.

Abnormal base compositional patterns of genomic DNA sequences are studied in the framework of a hierarchical structure (HS) model originally proposed for the study of fully developed turbulence [She and Lévêque, Phys. Rev. Lett. 72, 336 (1994)]. The HS similarity law is verified over scales between 10(3)bp and 10(5)bp, and the HS parameter beta is proposed to describe the degree of heterogeneity in the base composition patterns. More than one hundred bacteria, archaea, virus, yeast, and human genome sequences have been analyzed and the results show that the HS analysis efficiently captures abnormal base composition patterns, and the parameter beta is a characteristic measure of the genome. Detailed examination of the values of beta reveals an intriguing link to the evolutionary events of genetic material transfer. Finally, a sequence complexity (S) measure is proposed to characterize gradual increase of organizational complexity of the genome during the evolution. The present study raises several interesting issues in the evolutionary history of genomes.

Animals↗

PubMed2004

Scaling and hierarchical structures in DNA sequences.

A method of analyzing DNA correlation structure is introduced. Density fluctuations of nucleotides are shown to display an extended self-similarity scaling when the scale varies between 100 and 8000 base pairs. The scaling is accurately described by a hierarchical structure model of She and Leveque [Phys. Rev. Lett. 72, 336 (1994)]]. The derived model parameter beta is able to quantify moderately large-scale correlations which exist in a true DNA sequence but are absent in its randomly shuffled sequence and in a simulated model sequence by an evolution model of Hsieh et al. [Phys. Rev. Lett. 90, (2003)]]. Finally, it is shown that beta varies with the evolution category and measures the organizational complexity of the genome.

Animals↗

PubMed2004

Multivariate entropy distance method for prokaryotic gene identification.

A new simple method is found for efficient and accurate identification of coding sequences in prokaryotic genome. The method employs a Shannon description of artificial language for DNA sequences. It consists in translating a DNA sequence into a pseudo-amino acid sequence with 20 fundamental words according to the universal genetic code. With an entropy-density profile (EDP), the method maps a sequence of finite length to a vector and then analyzes its position in the 20-dimensional phase space depending on its nature. It is found that the ratio of the relative distance to an averaged coding and non-coding EDP over a small number (up to one) of open reading frames (ORFs) can serve as a good coding potential. An iterative algorithm is designed for finding a set of "root" sequences using this coding potential. A multivariate entropy distance (MED) algorithm is then proposed for the identification of prokaryotic genes; it has a feature to combine the use of a coding potential and an EDP-based sequence similarity analysis. The current version of MED is unsupervised, parameter-free and simple to implement. It is demonstrated to be able to detect 95-99% genes with 10-30% of additional genes when tested against the RefSeq database of NCBI and to detect 97.5-99.8% of confirmed genes with known functions. It is also shown to be able to find a set of (functionally known) genes that are missed by other well-known gene finding algorithms. All measurements show that the MED algorithm reaches a similar performance level as the algorithms like GeneMark and Glimmer for prokaryotic gene prediction.

Algorithms↗