Metabolomics-based analysis of gene-metabolite correlations in yeast transcription factor knockouts
Laboratory of Bioresource Engineering (Fukusaki Lab)
Zanariah Binti Hashim
Chapter 1: General introduction
Cellular functions are determined by integrative interactions between various constituents, i.e. genes, transcripts, proteins, and metabolites. Thus, it is important to study these interactions to understand the whole system. Genetic perturbations are often used to investigate the contribution of individual components. One of such components is transcription factor. Transcription factors (TFs) are regulatory proteins that interact with DNA to either promote or suppress gene expression. Due to the importance of TFs in gene regulation, they have been extensively studied and several databases dedicated to TFs were established. High throughput genome-wide expression of gene/transcript using microarrays and protein interaction via chromatin immunoprecipitation (ChIP) techniques have largely contributed to the current knowledge of TF regulation. However, our understanding is still lacking since many regulatory events that link gene expression to final phenotypic changes remain poorly characterized. In particular, systematic analyses of global metabolic alteration following a TF perturbation have been largely unexplored.
In this study, I applied metabolomics to investigate gene-metabolite correlations in yeast strains each lacking a gene that encodes a transcription factor. First, to demonstrate the utility of metabolomics in finding known and unknown correlations, a representative TF complex was examined. Next, a global metabolome analysis of 154 TF-knockout strains was performed. The correlations obtained from this study are expected to enhance our knowledge of TF regulation.
Chapter 2: Metabolic profiling of retrograde pathway transcription factors Rtg1 and Rtg3 knockout yeast
Mitochondrial function has been demonstrated to be closely related to aging. While slight dysfunction engages in pro-longevity processes, strong mitochondrial dysfunction can lead to neurodegeneration. Thus, the balance between mitochondrial damage and repair affects aging. To understand the aging process, the knowledge of intracellular signaling pathways in mitochondria is crucial. One of such signaling pathways is the retrograde response (RTG). RTG is a mitochondria-to-nucleus signaling pathway that is activated when respiratory function is reduced due to repressed mitochondria. In yeast, Rtg1 and Rtg3 are two basic helix-loop-helix regulators associated with RTG. Previous studies reported that Rtg1/Rtg3 regulates several tricarboxylic acid (TCA) genes and a prototypical target is CIT2 which encodes a peroxisomal citrate synthase. Using a widely-targeted metabolomics approach, polyamine biosynthesis and other amino acid metabolism were found to be significantly altered in RTG-deficient strains, apart from the expected TCA and glyoxylate cycles. A characteristic decrease of 2-oxoglutarate preceding the decreases of other TCA cycle intermediates in RTG deletion mutants was observed, suggesting that 2-oxoglutarate may play a pivotal role in controlling the flow and balance of TCA/glyoxylate cycles under RTG response.
Chapter 3: Global analysis of gene-metabolite correlations in 154 transcription factor deletion strains
To further evaluate gene/TF-metabolite correlations on a global scale, a comprehensive metabolome analysis of 154 TF-related unessential gene knockouts was conducted. Hierarchical clustering analysis was employed to characterize the strains according to their metabolic signatures. As a result, four clustering patterns of strains were observed: 1) differential and no-cluster, 2) differential and formed clusters, 3) not differential and formed clusters, and 4) not differential and no-cluster. Differential strains are those that showed significantly altered metabolic profile compared to wild-type. In addition, average deviation from the mutant median was also calculated as an alternative differential parameter that is independent from WT profile. About 30% of the strains were classified as differential, while 27 individual clusters consisting of differential and non-differential strains were observed. As TFs with similar function are assumed to share similar metabolic signatures, these clusters give us hints of possible TF association and regulatory pathways.
Chapter 4: Conclusions and future perspectives
The complete elucidation of cellular functions is an enormous effort and requires various strategies to capture the entire system. Metabolomics-based gene-metabolite correlation analysis offers a holistic view of the metabolic shifts under genetic or environmental perturbations, both qualitatively and quantitatively. Here, I demonstrated that metabolomics analysis of yeast knockout strains can yield known and unexpected metabolic alterations which will be interesting to follow up. The metabolome dataset presented in this study does not only provide information about key metabolites but also represents a useful resource for future research regarding transcriptional regulation. Metabolomics can aid the identification of important target genes and/or proteins, for example in the engineering of strains with improved phenotype or screening of target molecules for drug development. It is anticipated that metabolomics will be routinely performed, whether as a primary or complementary means in many gene regulation studies.
List of publications:
1) Hashim Z., Mukai Y., Bamba T. and Fukusaki E. Metabolic profiling of retrograde pathway transcription factors Rtg1 and Rtg3 knockout yeast. Metabolites 4, 580-598 (2014).
2) Hashim Z., Teoh S. T., Bamba T. and Fukusaki E. Construction of a metabolome library for transcription factor-related single gene mutants of Saccharomyces cerevisiae. Journal of Chromatography B (2014) (In press, DOI: 10.1016/j.jchromb.2014.05.041).