Accurate prediction of in vivo protein abundances by coupling constraint-based modelling 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.

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

Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.

Widening the landscape of transcriptional regulation of green 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 transcriptomic data compendium of Chlamydomonas reinhardtii's responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show 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.

Light-independent regulation of algal photoprotection by 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.

Data integration across conditions improves turnover number estimates and metabolic predictions.

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be 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 when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

Cross-stress gene expression atlas of Marchantia polymorpha reveals the hierarchy and regulatory principles of abiotic stress responses.

Abiotic stresses negatively impact ecosystems and the yield of crops, and climate change will increase their frequency and intensity. Despite progress in understanding how plants respond to individual stresses, our knowledge of plant acclimatization to combined stresses typically occurring in nature is still lacking. Here, we used a plant with minimal regulatory network redundancy, Marchantia polymorpha, to study how seven abiotic stresses, alone and in 19 pairwise combinations, affect the phenotype, gene expression, and activity of cellular pathways. While the transcriptomic responses show a conserved differential gene expression between Arabidopsis and Marchantia, we also observe a strong functional and transcriptional divergence between the two species. The reconstructed high-confidence gene regulatory network demonstrates that the response to specific stresses dominates those of others by relying on a large ensemble of transcription factors. We also show that a regression model could accurately predict the gene expression under combined stresses, indicating that Marchantia performs arithmetic multiplication to respond to multiple stresses. Lastly, two online resources ( https://conekt.plant.tools and http://bar.utoronto.ca/efp_marchantia/cgi-bin/efpWeb.cgi ) are provided to facilitate the study of gene expression in Marchantia exposed to abiotic stresses.

Pluripotency reprogramming by competent and incompetent POU factors uncovers temporal dependency for Oct4 and Sox2.

Oct4, along with Sox2 and Klf4 (SK), can induce pluripotency but structurally similar factors like Oct6 cannot. To decode why Oct4 has this unique ability, we compare Oct4-binding, accessibility patterns and transcriptional waves with Oct6 and an Oct4 mutant defective in the dimerization with Sox2 (Oct4defSox2). We find that initial silencing of the somatic program proceeds indistinguishably with or without Oct4. Oct6 mitigates the mesenchymal-to-epithelial transition and derails reprogramming. These effects are a consequence of differences in genome-wide binding, as the early binding profile of Oct4defSox2 resembles Oct4, whilst Oct6 does not bind pluripotency enhancers. Nevertheless, in the Oct6-SK condition many otherwise Oct4-bound locations become accessible but chromatin opening is compromised when Oct4defSox2 occupies these sites. We find that Sox2 predominantly facilitates chromatin opening, whilst Oct4 serves an accessory role. Formation of Oct4/Sox2 heterodimers is essential for pluripotency establishment; however, reliance on Oct4/Sox2 heterodimers declines during pluripotency maintenance.