Biology subjects

Arend, M.

Publications and source records attributed to Arend, M..

6 recordsLinked to original sources

bioRxiv2024

Phototropin connects blue light perception to starch metabolism in green algae

In photosynthetic organisms light acts as an environmental signal to control their development and physiology, and as energy source to drive the conversion of CO2 into carbohydrates used for growth or storage. The main storage carbohydrate in green algae is starch, which accumulates during the day and is broken down at night to meet cellular energy demands. The signalling role of light quality in the regulation of starch accumulation remains unexplored. Here, we identify PHOTOTROPIN-MEDIATED SIGNALLING KINASE 1 (PMSK1) as a key regulator of starch metabolism in Chlamydomonas reinhardtii. In its phosphorylated form (PMSK1-P), it activates GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE (GAP1), promoting starch biosynthesis. We show that blue light, perceived by PHOTOTROPIN, induces PMSK1 dephosphorylation that in turn represses GAP1 mRNA levels and reduces starch accumulation. These findings reveal a novel blue light-mediated signaling pathway that advances our understanding of photoreceptor-controlled carbon metabolism in microalgae. One-Sentence SummaryBlue light perception by PHOTOTROPIN triggers kinase-mediated signaling to inhibit starch accumulation in the green alga Chlamydomonas.

plant biology↗

bioRxiv2023

Accurate prediction of in vivo protein abundances by coupling constraint-based modeling and machine learning

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

bioinformatics↗

bioRxiv2022

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale

Biofuels produced from microalgae offer a promising solution for carbon neutral economy, and integration of turnover numbers into metabolic models can improve the design of metabolic engineering strategies towards achieving this aim. However, the coverage of enzyme turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic model Saccharomyces cerevisiae often used as a cell factory. Here we generated protein abundance data from Chlamydomonas reinhardtii cells grown in various experiments, covering between 2337 and 3708 proteins, and employed these data with constraint-based metabolic modeling approaches to estimate in vivo maximum apparent turnover numbers for this model organism. The gathered data allowed us to estimate maximum apparent turnover numbers for 568 reactions, of which 46 correspond to transporters that are otherwise difficult to characterize. The resulting, largest-to-date catalogue of proxies for in vivo turnover numbers increased the coverage for C. reinhardtii by more than 10-fold. We showed that incorporation of these in vivo turnover numbers into a protein-constrained metabolic model of C. reinhardtii improves the accuracy of predicted enzyme usage in comparison to predictions resulting from the integration on in vitro turnover numbers. Together, the integration of proteomics and physiological data allowed us to extend our knowledge of previously uncharacterized enzymes in the C. reinhardtii genome and subsequently increase predictive performance for biotechnological applications. Significance statementCurrent metabolic modelling approaches rely on the usage of in vitro turnover numbers (kcat) that provide limited information on enzymes operating in their native environment. This knowledge gap can be closed by data-integrative approaches to estimate in vivo kcat values that can improve metabolic modelling and design of metabolic engineering strategies. In this work, we assembled a high-quality proteomics data set containing 27 samples of various culture conditions and strains of Chlamydomonas reinhardtii. We used this resource to create the largest data set of estimates for in vivo turnover numbers to date. Subsequently, we showed that metabolic models parameterized with these estimates provide better predictions of enzyme abundance than those obtained by using in vitro turnover numbers.

systems biology↗

bioRxiv2022

Estimates of in vivo turnover numbers by simultaneously considering data from multiple conditions improve metabolic predictions

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by ranking of estimates obtained by integrating reaction rate and enzyme abundance measurements from individual experiments; yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly in the ultimate test scenario when protein abundances are integrated in the model. We demonstrate that in vivo estimation of turnover number by simultaneous consideration of heterogeneous physiological data leads to improved prediction of condition-specific growth rates. Moreover, the obtained estimates are more precise than the available in vivo turnover numbers. Therefore, our approach provides the means to decrease the bias of in vivo turnover numbers and paves the way towards cataloguing in vivo kcatomes of other organisms.

bioinformatics↗

bioRxiv2022

Widening the landscape of transcriptional regulation of algal photoprotection

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomics data compendium of Chlamydomonas reinhardtiis responses to different light and carbon supply, we reconstructed a consensus genome-scale gene regulatory network from complementary inference approaches and used it to elucidate transcriptional regulators of photoprotection. We showed that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO2-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

plant biology↗

bioRxiv2021

Photoprotection is regulated by light-independent CO2 availability

Photosynthetic algae have evolved mechanisms to cope with suboptimal light and CO2 conditions. When light energy exceeds CO2 fixation capacity, Chlamydomonas reinhardtii activates photoprotection, mediated by LHCSR1/3 and PSBS, and the CO2 Concentrating Mechanism (CCM). How light and CO2 signals converge to regulate these processes remains unclear. Here, we show that excess light activates photoprotection- and CCM-related genes by altering intracellular CO2 concentrations and that depletion of CO2 drives these responses, even in total darkness. High CO2 levels, derived from respiration or impaired photosynthetic fixation, repress LHCSR3/CCM genes while stabilizing the LHCSR1 protein. Finally, we show that the CCM regulator CIA5 also regulates photoprotection, controlling LHCSR3 and PSBS transcript accumulation while inhibiting LHCSR1 protein accumulation. This work has allowed us to dissect the effect of CO2 and light on CCM and photoprotection, demonstrating that light often indirectly affects these processes by impacting intracellular CO2 levels.

plant biology↗