Biomedical subjects

Yongxiang Fang

Publications and source records attributed to Yongxiang Fang.

3 recordsLinked to original sources

PubMed2006

Hypoxia-inducible myoglobin expression in nonmuscle tissues.

Myoglobin (Myg) is an oxygen-binding hemoprotein that is widely thought to be expressed exclusively in oxidative skeletal and cardiac myocytes, where it plays a key role in coping with chronic hypoxia. We now show in a hypoxia-tolerant fish model, that Myg is also expressed in a range of other tissues, including liver, gill, and brain. Moreover, expression of Myg transcript was substantially enhanced during chronic hypoxia, the fold-change induction being far greater in liver than muscle. By using 2D gel electrophoresis, we have confirmed that liver expresses a protein corresponding to the Myg-1 transcript and that it is significantly up-regulated during hypoxia. We have also discovered a second, unique Myg isoform, distinct from neuroglobin, which is expressed exclusively in the neural tissue but whose transcript expression was unaffected by environmental hypoxia. Both observations of nonmuscle expression and a brain-specific isoform are unprecedented, indicating that Myg may play a much wider role than previously understood and that Myg might function in the protection of tissues from deep hypoxia and ischemia as well as in reoxygenation and reperfusion injury.

Amino Acid Sequence↗

PubMed2004

Coping with cold: An integrative, multitissue analysis of the transcriptome of a poikilothermic vertebrate.

How do organisms respond adaptively to environmental stress? Although some gene-specific responses have been explored, others remain to be identified, and there is a very poor understanding of the system-wide integration of response, particularly in complex, multitissue animals. Here, we adopt a transcript screening approach to explore the mechanisms underpinning a major, whole-body phenotypic transition in a vertebrate animal that naturally experiences extreme environmental stress. Carp were exposed to increasing levels of cold, and responses across seven tissues were assessed by using a microarray composed of 13,440 cDNA probes. A large set of unique cDNAs (approximately 3,400) were affected by cold. These cDNAs included an expression signature common to all tissues of 252 up-regulated genes involved in RNA processing, translation initiation, mitochondrial metabolism, proteasomal function, and modification of higher-order structures of lipid membranes and chromosomes. Also identified were large numbers of transcripts with highly tissue-specific patterns of regulation. By unbiased profiling of gene ontologies, we have identified the distinctive functional features of each tissue's response and integrate them into a comprehensive view of the whole-body transition from one strongly adaptive phenotype to another. This approach revealed an expression signature suggestive of atrophy in cooled skeletal muscle. This environmental genomics approach by using a well studied but nongenomic species has identified a range of candidate genes endowing thermotolerance and reveals a previously unrecognized scale and complexity of responses that impacts at the level of cellular and tissue function.

Adaptation, Physiological↗

PubMed2003

A model-based analysis of microarray experimental error and normalisation.

A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. Through analysis of the model and two-dye microarray data sets, this study found the following. The systematic error introduced by microarray experiments mainly involves spot intensity-dependent, feature-specific and spot position-dependent contributions. It is difficult to remove all these errors effectively without a suitable combined normalisation operation. Adaptive normalisation using a suitable regression technique is more effective in removing spot intensity-related dye bias than self-normalisation, while regional normalisation (block normalisation) is an effective way to correct spot position-dependent errors. However, dye-flip replicates are necessary to remove feature-specific errors, and also allow the analyst to identify the experimentally introduced dye bias contained in non-self-self data sets. In this case, the bias present in the data sets may include both experimentally introduced dye bias and the biological difference between two samples. Self-normalisation is capable of removing dye bias without identifying the nature of that bias. The performance of adaptive normalisation, on the other hand, depends on its ability to correctly identify the dye bias. If adaptive normalisation is combined with an effective dye bias identification method then there is no systematic difference between the outcomes of the two methods.

Algorithms↗