The mammary explant culture model has been a major experimental tool for studying hormonal requirements for milk protein gene expression as markers of secretory differentiation. Experiments with mammary explants from pregnant animals from many species have established that insulin, prolactin, and glucocorticoid are the minimal set of hormones required for the induction of maximal milk protein gene expression. However, the extent to which mammary explants mimic the response of the mammary gland in vivo is not clear. Recent studies have used microarray technology to study the transcriptome of mouse lactation cycle. It was demonstrated that the each phase of mouse lactation has a distinct transcriptional profile but making sense of microarray results requires analysis of large amounts of biological information which is increasingly difficult to access as the amount of literature increases. / The first objective is to examine the possibility of combining literature and genomic analysis to elucidate potentially novel hypotheses for further research into lactation biology. The second objective is to evaluate the strengths and limitations of the murine mammary explant culture for the study and understanding of murine lactogenesis. The underlying question to this objective is whether the mouse mammary explant culture is a good model or representation to study mouse lactogenesis. / The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biological text. However, this requirement for modification had not been examined. We have constructed Muscorian, using MontyLingua, a generic text processor. It uses a two-layered generalizationspecialization paradigm previously proposed where text was generically processed to a suitable intermediate format before domain-specific data extraction techniques are applied at the specialization layer. Evaluation using a corpus and experts indicated 86-90% precision and approximately 30% recall in extracting protein-protein interactions, which was comparable to previous studies using either specialized biological text processing tools or modified existing tools. This study also demonstrated the flexibility of the two-layered generalization-specialization paradigm by using the same generalization layer for two specialized information extraction tasks. / The performance of Muscorian was unexpected since potential errors from a series of text analysis processes is likely to adversely affect the outcome of the entire process. Most biomedical entity relationship extraction tools have used biomedical-specific parts-of-speech (POS) tagger as errors in POS tagging and are likely to affect subsequent semantic analysis of the text, such as shallow parsing. A comparative study between MontyTagger, a generic POS tagger, and MedPost, a tagger trained in biomedical text, was carried out. Our results demonstrated that MontyTagger, Muscorian's POS tagger, has a POS tagging accuracy of 83.1% when tested on biomedical text. Replacing MontyTagger with MedPost did not result in a significant improvement in entity relationship extraction from text; precision of 55.6% from MontyTagger versus 56.8% from MedPost on directional relationships and 86.1% from MontyTagger compared to 81.8% from MedPost on un-directional relationships. This is unexpected as the potential for poor POS tagging by MontyTagger is likely to affect the outcome of the information extraction. An analysis of POS tagging errors demonstrated that 78.5% of tagging errors are being compensated by shallow parsing. Thus, despite 83.1% tagging accuracy, MontyTagger has a functional tagging accuracy of 94.6%. This suggests that POS tagging error does not adversely affect the information extraction task if the errors were resolved in shallow parsing through alternative POS tag use. / Microarrays had been used to examine the transcriptome of mouse lactation and a simple method for microarray analysis is correlation studies where functionally related genes exhibit similar expression profiles. However, there has been no study to date using text mining to sieve microarray analysis to generate new hypotheses for further research in the field of lactational biology. Our results demonstrated that a previously reported protein name co-occurrence method (5-mention PubGene) which was not based on a hypothesis testing framework, is generally more stringent than the 99th percentile of Poisson distribution-based method of calculating co-occurrence. It agrees with previous methods using natural language processing to extract protein-protein interaction from text as more than 96% of the interactions found by natural language processing methods coincide with the results from 5-mention PubGene method. However, less than 2% of the gene co-expressions analyzed by microarray were found from direct co-occurrence or interaction information extraction from the literature. At the same time, combining microarray and literature analyses, we derive a novel set of 7 potential functional protein-protein interactions that had not been previously described in the literature. We conclude that the 5-mention PubGene method is more stringent than the 99th percentile of Poisson distribution method for extracting protein-protein interactions by co-occurrence of entity names and literature analysis may be a potential filter for microarray analysis to isolate potentially novel hypotheses for further research. / The availability of transcriptomics data from time-course experiments on mouse mammary glands examined during the lactation cycle and hormone-induced lactogenesis in mammary explants has permitted an assessment of similarity of gene expression at the transcriptional level. Global transcriptome analysis using exact Wilconox signed-rank test with continuity correction and hierarchical clustering of Spearman coefficient demonstrated that hormone-induced mammary explants behave differently to mammary glands at secretory differentiation. Our results demonstrated that the mammary explant culture model mimics in vivo glands in immediate responses, such as hormone-responsive gene transcription, but generally did not mimic responses to prolonged hormonal stimulus, such as the extensive development of secretory pathways and immune responses normally associated with lactating mammary tissue. Hence, although the explant model is useful to study the immediate effects of stimulating secretory differentiation in mammary glands, it is unlikely to be suitable for the study of secretory activation.
| Identifer | oai:union.ndltd.org:ADTP/269942 |
| Date | January 2009 |
| Creators | Ling, Maurice Han Tong |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Terms and Conditions: Copyright in works deposited in the University of Melbourne Eprints Repository (UMER) is retained by the copyright owner. The work may not be altered without permission from the copyright owner. Readers may only, download, print, and save electronic copies of whole works for their own personal non-commercial use. Any use that exceeds these limits requires permission from the copyright owner. Attribution is essential when quoting or paraphrasing from these works., Open Access |
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