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Generation and analysis of a gene regulatory network for Chlamydomonas reinhardtii
Generation and analysis of a gene regulatory network for Chlamydomonas reinhardtii
Stopping climate change requires renewable energy sources. Microalgae are a promising biotechnological resource for biofuels. Under stress conditions they produce large quantities of storage lipids as triacylglycerides (TAGs), which can be further converted into biodiesel. To optimize the biotechnological production of valuable compounds such as TAGs in microalgae, a thorough system-level understanding of the underlying regulatory mechanisms is required. Current knowledge is based on mutant screens, co-expression analyses, and predictions but a systems-level perspective is missing. The present work is dedicated to expanding the existing knowledge of fundamental regulatory mechanisms in microalgae and enabling advanced biotechnological use of microalgae. All regulatory processes in a cell are mediated and controlled by interactions between macromolecules, such as protein-protein interactions (PPI), protein-DNA interactions (PDI), or interactions between proteins and RNA or small molecules. These interactions are organized in complex networks, and the systematic mapping of these interactomes and regulomes is essential for understanding complex biological relationships in an organism. In order to enable large-scale mapping of PPIs and PDIs for the model organism of microalgae, Chlamydomonas reinhardtii (C. reinhardtii), a comprehensive set of protein-encoding open-reading frames (ORFs) was generated. This ORFeome allows for an unbiased and high-throughput approach to performing proteomic studies. The successfully generated ORFeome collection consists of 8,627 Gateway® Entry clones. It enables systematic investigations of fundamental biological and regulatory processes in C. reinhardtii. PDIs are mediated by transcription factors (TFs) that bind to specific genomic DNA sequences in promoter regions, enhancers, and other cis-regulatory elements, thereby regulating the expression of target genes. These interactions are described in gene regulatory networks (GRN). Using the Yeast-1-hybrid (Y1H) method, the first experimental-based large-scale GRN of C. reinhardtii was constructed, consisting of 1,451 interactions between 142 TFs binding to 200 promoter regions. To determine the quality of the Y1H network, a second orthogonal assay, called PampDAP-seq, was performed. This modified DAP-seq method identified 320 unique PDIs for six TFs. The comparison of DAP-seq interactions with found Y1H interactions showed an overlap of 23.8 %, which highly supports the Y1H results. The generated network map was hierarchically structured according to the outdegree of the TFs. 15 TFs represent the highest level in this network and are responsible for 66.7 % of all interactions. In addition, multiple TFs, including previously known stress-related TFs, were found to form an intermediate level of regulation that combines various signals from other TFs to mediate a subsequent cellular response. Moreover, a distributed regulation structure for metabolic pathway promoters was observed. 84.5 % of metabolic promoters are targeted by more than two TFs. This indicates that each metabolic pathway is regulated by a large number of TFs, a distinct characteristic of higher plants like Arabidopsis thaliana (A. thaliana). In addition, four promising key regulators of the C. reinhardtii lipid metabolism were identified. The successfully generated ORFeome collection provides a resource for the in-depth exploration of fundamental biological processes. Furthermore, the first experimental-based GRN for any microalgae provides valuable insights into the global structure of transcriptional regulation and can pave the way for advanced biotechnological engineering in microalgae.
Chlamydomonas reinhardtii, microalgae, lipid metabolism, gene regulatory network, GRN, Y1H, gene regulation, protein-DNA interaction, networks
Sauer, Mayra Anna
2024
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Sauer, Mayra Anna (2024): Generation and analysis of a gene regulatory network for Chlamydomonas reinhardtii. Dissertation, LMU München: Faculty of Biology
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Abstract

Stopping climate change requires renewable energy sources. Microalgae are a promising biotechnological resource for biofuels. Under stress conditions they produce large quantities of storage lipids as triacylglycerides (TAGs), which can be further converted into biodiesel. To optimize the biotechnological production of valuable compounds such as TAGs in microalgae, a thorough system-level understanding of the underlying regulatory mechanisms is required. Current knowledge is based on mutant screens, co-expression analyses, and predictions but a systems-level perspective is missing. The present work is dedicated to expanding the existing knowledge of fundamental regulatory mechanisms in microalgae and enabling advanced biotechnological use of microalgae. All regulatory processes in a cell are mediated and controlled by interactions between macromolecules, such as protein-protein interactions (PPI), protein-DNA interactions (PDI), or interactions between proteins and RNA or small molecules. These interactions are organized in complex networks, and the systematic mapping of these interactomes and regulomes is essential for understanding complex biological relationships in an organism. In order to enable large-scale mapping of PPIs and PDIs for the model organism of microalgae, Chlamydomonas reinhardtii (C. reinhardtii), a comprehensive set of protein-encoding open-reading frames (ORFs) was generated. This ORFeome allows for an unbiased and high-throughput approach to performing proteomic studies. The successfully generated ORFeome collection consists of 8,627 Gateway® Entry clones. It enables systematic investigations of fundamental biological and regulatory processes in C. reinhardtii. PDIs are mediated by transcription factors (TFs) that bind to specific genomic DNA sequences in promoter regions, enhancers, and other cis-regulatory elements, thereby regulating the expression of target genes. These interactions are described in gene regulatory networks (GRN). Using the Yeast-1-hybrid (Y1H) method, the first experimental-based large-scale GRN of C. reinhardtii was constructed, consisting of 1,451 interactions between 142 TFs binding to 200 promoter regions. To determine the quality of the Y1H network, a second orthogonal assay, called PampDAP-seq, was performed. This modified DAP-seq method identified 320 unique PDIs for six TFs. The comparison of DAP-seq interactions with found Y1H interactions showed an overlap of 23.8 %, which highly supports the Y1H results. The generated network map was hierarchically structured according to the outdegree of the TFs. 15 TFs represent the highest level in this network and are responsible for 66.7 % of all interactions. In addition, multiple TFs, including previously known stress-related TFs, were found to form an intermediate level of regulation that combines various signals from other TFs to mediate a subsequent cellular response. Moreover, a distributed regulation structure for metabolic pathway promoters was observed. 84.5 % of metabolic promoters are targeted by more than two TFs. This indicates that each metabolic pathway is regulated by a large number of TFs, a distinct characteristic of higher plants like Arabidopsis thaliana (A. thaliana). In addition, four promising key regulators of the C. reinhardtii lipid metabolism were identified. The successfully generated ORFeome collection provides a resource for the in-depth exploration of fundamental biological processes. Furthermore, the first experimental-based GRN for any microalgae provides valuable insights into the global structure of transcriptional regulation and can pave the way for advanced biotechnological engineering in microalgae.