Regulation of amino-acid metabolism controls flux to lipid accumulation in <i>Yarrowia lipolytica</i>

Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation is similar to the overflow metabolism observed in many other microorganisms, e.g. ethanol production by Sacchromyces cerevisiae at nitrogen limitation

Integrated analysis, transcriptome-lipidome, reveals the effects of INO-level (INO2 and INO4) on lipid metabolism in yeast

In the yeast Saccharomyces cerevisiae, genes containing UASINO sequences are regulated by the Ino2/Ino4 and Opi1 transcription factors, and this regulation controls lipid biosynthesis. The expression level of INO2 and INO4 genes (INO-level) at different nutrient limited conditions might lead to various responses in yeast lipid metabolism.In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components.In this study, we undertook a global study on how INO-levels (transcription level of INO2 and INO4) affect lipid metabolism in yeast and we also studied the effects of single and double deletions of the two INO-genes (deficient effect). Using 2 types of nutrient limitations (carbon and nitrogen) in chemostat cultures operated at a fixed specific growth rate of 0.1 h-1 and strains having different INO-level, we were able to see the effect on expression level of the genes involved in lipid biosynthesis and the fluxes towards the different lipid components. Through combined measurements of the transcriptome, metabolome, and lipidome it was possible to obtain a large dataset that could be used to identify how the INO-level controls lipid metabolism and also establish correlations between the different components.Our analysis showed the strength of using a combination of transcriptome and lipidome analysis to illustrate the effect of INO-levels on phospholipid metabolism and based on our analysis we established a global regulatory map

Metabolite biosensors for cell factory development

Through synergy with natural sciences and engineering disciplines, biotechnology has\ua0become a broad, interdisciplinary, scientific field with many applications. One such\ua0application is the sustainable production of industrially relevant products using living\ua0systems such as microorganisms. Transforming microorganisms to cell factories is, however,\ua0a labour-intensive and cost-ineffective process, requiring many years of extensive\ua0research. Several fields together known as systems metabolic engineering, including\ua0synthetic biology, have greatly facilitated the process of customizing microorganisms\ua0to benefit human interests. Among several emerging tools are metabolite biosensors,\ua0which can be employed in high-throughput screening endeavours for identifying productive\ua0cells and in dynamic pathway regulation for optimizing metabolic systems.\ua0Developing and engineering metabolite biosensors to fit a certain application is, however,\ua0challenging.This thesis focuses on different aspects of utilizing and engineering metabolite-responsive\ua0transcription factor-based biosensors for facilitating the development of\ua0Saccharomyces cerevisiae as a cell factory. To that end, we improved the dynamic\ua0range of a malonyl-CoA-responsive biosensor by i) evaluating different binding site\ua0locations of the bacterial transcription factor FapR within different yeast promoters\ua0and by ii) using a chimeric transcription factor based on a native repressor system\ua0from S. cerevisiae. Furthermore, we suggest the possibility of using the CRISPR (Clustered\ua0Regulatory Interspaced Short Palindromic Repeats)/Cas9 system to facilitate\ua0biosensor development by guiding binding site positioning. We also employed an acyl-CoA-responsive biosensor based on the bacterial transcription factor FadR to screen for\ua0genes boosting the fatty acyl-CoA levels, which are precursors for industrially relevant\ua0compounds such as fatty alcohols. The possibility of developing fatty acid-responsive\ua0biosensors based on other transcription factors, including the endogenous transcription\ua0factor Mga2, has also been addressed. Finally, we looked into the potential of\ua0developing an alkane-responsive biosensor based on a system from Yarrowia lipolytica.\ua0Overall, this thesis provides answers, discussions and potential future directions on\ua0using and engineering metabolite biosensors for cell factory development

Collective analysis of multiple high-throughput gene expression datasets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonModern technologies have resulted in the production of numerous high-throughput biological datasets. However, the pace of development of capable computational methods does not cope with the pace of generation of new high-throughput datasets. Amongst the most popular biological high-throughput datasets are gene expression datasets (e.g. microarray datasets). This work targets this aspect by proposing a suite of computational methods which can analyse multiple gene expression datasets collectively. The focal method in this suite is the unification of clustering results from multiple datasets using external specifications (UNCLES). This method applies clustering to multiple heterogeneous datasets which measure the expression of the same set of genes separately and then combines the resulting partitions in accordance to one of two types of external specifications; type A identifies the subsets of genes that are consistently co-expressed in all of the given datasets while type B identifies the subsets of genes that are consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets. This contributes to the types of questions which can addressed by computational methods because existing clustering, consensus clustering, and biclustering methods are inapplicable to address the aforementioned objectives. Moreover, in order to assist in setting some of the parameters required by UNCLES, the M-N scatter plots technique is proposed. These methods, and less mature versions of them, have been validated and applied to numerous real datasets from the biological contexts of budding yeast, bacteria, human red blood cells, and malaria. While collaborating with biologists, these applications have led to various biological insights. In yeast, the role of the poorly-understood gene CMR1 in the yeast cell-cycle has been further elucidated. Also, a novel subset of poorly understood yeast genes has been discovered with an expression profile consistently negatively correlated with the well-known ribosome biogenesis genes. Bacterial data analysis has identified two clusters of negatively correlated genes. Analysis of data from human red blood cells has produced some hypotheses regarding the regulation of the pathways producing such cells. On the other hand, malarial data analysis is still at a preliminary stage. Taken together, this thesis provides an original integrative suite of computational methods which scrutinise multiple gene expression datasets collectively to address previously unresolved questions, and provides the results and findings of many applications of these methods to real biological datasets from multiple contexts.National Institute for Health Research (NIHR) and the Brunel College of Engineering, Design and Physical Science

Deciphering the transcriptional response of saccharomyces cerevisiae to perturbations of lipid metabolism and graded endoplasmic reticulum stress

Systems Biology combines experimental biology with mathematics and computational simulations to better describe biological phenomena that emerge from the interaction of different players. Extensive prior knowledge and experimental feasibility make the eukaryotic single-cell organism S. cerevisiae the preferred model organism for systems biology, while the strongly conserved features might enable conclusions for more complex organisms. In this thesis, a ‘Systems Biology’-approach was taken to better understand how S. cerevisiae coordinates different transcriptional and metabolic responses to adapt to two exemplary environmental changes, i.e. inositol depletion and low-level ER stress. Firstly, a quantitative model guided the construction of fast-folding, actively degraded reporter proteins, which were able to rapidly indicate specific transcriptional changes in single cells. Secondly, the developed reporter proteins, a fluorescent sphingolipid (SL) intermediate and classical molecular biology techniques were used to investigate the interaction of the signaling pathways, which enable S. cerevisiae to survive after inositol depletion, and to understand the role of SL metabolism during this process. The results highlighted the temporal order of transcription factors that follows the removal of inositol, i.e. first INO2/4, then HAC1 and lastly RLM1, and suggested that decreased SL biosynthesis is probably not responsible for the delayed disruption of ER homoeostasis but perturbs cell wall integrity after HAC1 activation. Thirdly, the adaptation to low ER stress was studied with a reporter protein for HAC1 and established fluorescent labels. The experimental insights then motivated a quantitative model for the adaptation to new environments, which lower the growth rate and change the inheritance of essential resources during cytokinesis. From the results, it emerged that ER stress mainly affects G1 duration in daughter cells and reduces the amount of ER content that is inherited by them. This lower inheritance probably contributed to the daughter-specific HAC1 activation. The analysis of the model implied that such a lower resource inheritance increases the daughter: mother ratio and probably lowers the resource demand of the population. Overall, the results supported the idea that transcriptional adaptation is primarily performed by daughter cells and is often a multi-step process. This work moreover lays the foundation to investigate transcriptional dynamics during other environmental changes and to further study the role of lipid metabolism for ER homeostasis. It also provided a mathematical model for the long-term impact of changes in the distribution of limiting resources.Open Acces

Utilising Yeast as a Model Organism to Deconstruct the Regulation of Tumour Associated Lipogenesis

It is important for cells to respond to external signals. Central to these responses are the sensing and signalling pathways that communicate with the nucleus and facilitate necessary changes in gene expression. Of particular importance are the mitogen-activated protein kinase (MAPK) and the mammalian target of rapamycin (mTOR) pathways. Both of these pathways have been shown to be involved in cell growth, proliferation, motility and survival. They are under intensive investigation in connection with cancer with recent evidence suggests their role in mediating lipogenesis. Lipogenesis accompanies a variety of disease states, including the formation of brain tumours. Malignant brain tumours are rapidly growing and often invade surrounding healthy tissue, resulting in poor prognosis for the patient. The ability to limit tumour growth and reduce invasion through a better understanding of tumour associated lipid formation may offer targets for the development of new therapies. Yeast is frequently used as a paradimic organism for the study of human diseases. In this study a Nile red assay has been developed, optimised and validated to measure levels of both polar and neutral lipids within yeast cells. This method has been utilised in the yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe, to study the role of the MAPK pathways in regulating lipid accumulation. Data in this thesis demonstrates that stress-activated protein kinase pathways (SAPK) play a key role in regulating lipid accumulation upon nitrogen limitation, as cells enter the stationary phase of growth. Evidence from S. cerevisiae proposes that the lipogenic switch occurs in two phases, with the central MAPK (Hog1) activated in both a MAPKK (Pbs2) independent and dependent manner. Analysis of Hog1 phosphorylation during various growth phases, suggests that there are previously uncharacterised sites on Hog1 which are potentially phosphorylated during phase one by the protein kinase Sch9, a target of the Tor1 complex. The second phase results in Hog1 being dually phosphorylated by the canonical pathway, via Pbs2p. It is proposed that Hog1 may have a number of downstream cytoplasmic and nuclear targets, including lipid related enzymes (Dga1) and transcription factors (Msn2/4). Data also suggests that lipid accumulation in S. pombe is also regulated in a similar manner. The oleaginous yeast Lipomyces starkeyi is able to accumulate high levels of lipid and has similarity to lipid enzymes found in mammalian cells. As such, it was proposed that L. starkeyi may be utilised as a model organism to further characterise the role of MAPK in lipid accumulation. Information from stress response studies and bioinformatics suggests the MAPK pathway in L. starkeyi is highly conserved. However, the application of yeast molecular tools to L. starkeyi was unsuccessful, demonstrating that further work is required to develop its use as a model organism. Data in this thesis has shown a novel role for the SAPK pathways in regulating lipid accumulation in yeast. It has also demonstrated cross talk between the MAPK and TOR pathways, resulting in an integrated cellular response. The high level of conservation of these pathways across species, suggests that directly targeting these pathways in cancer cells may reduce tumour associated lipogenesis, therefore inhibiting growth of glioma. With current treatments only delivering limited results, this could help extend patient survival