Alexandra T. Wilson

Abstract Many eukaryotic photosynthetic organisms enhance their carbon uptake by supplying concentrated CO 2 to the CO 2 -fixing enzyme Rubisco in an organelle called the pyrenoid. Ongoing efforts seek to engineer this pyrenoid-based CO 2 -concentrating mechanism (PCCM) into crops to increase yields. Here we develop a computational model for a PCCM on the basis of the postulated mechanism in the green alga Chlamydomonas reinhardtii . Our model recapitulates all Chlamydomonas PCCM-deficient mutant phenotypes and yields general biophysical principles underlying the PCCM. We show that an effective and energetically efficient PCCM requires a physical barrier to reduce pyrenoid CO 2 leakage, as well as proper enzyme localization to reduce futile cycling between CO 2 and HCO 3 − . Importantly, our model demonstrates the feasibility of a purely passive CO 2 uptake strategy at air-level CO 2 , while active HCO 3 − uptake proves advantageous at lower CO 2 levels. We propose a four-step engineering path to increase the rate of CO 2 fixation in the plant chloroplast up to threefold at a theoretical cost of only 1.3 ATP per CO 2 fixed, thereby offering a framework to guide the engineering of a PCCM into land plants.

Chloroplasts are eukaryotic photosynthetic organelles that drive the global carbon cycle. Despite their importance, our understanding of their protein composition, function, and spatial organization remains limited. Here, we determined the localizations of 1,034 candidate chloroplast proteins using fluorescent protein tagging in the model alga Chlamydomonas reinhardtii. The localizations provide insights into the functions of poorly characterized proteins; identify novel components of nucleoids, plastoglobules, and the pyrenoid; and reveal widespread protein targeting to multiple compartments. We discovered and further characterized cellular organizational features, including eleven chloroplast punctate structures, cytosolic crescent structures, and unexpected spatial distributions of enzymes within the chloroplast. We also used machine learning to predict the localizations of other nuclear-encoded Chlamydomonas proteins. The strains and localization atlas developed here will serve as a resource to accelerate studies of chloroplast architecture and functions.

Photosynthesis is central to food production and the Earth's biogeochemistry, yet the molecular basis for its regulation remains poorly understood. Here, using high-throughput genetics in the model eukaryotic alga Chlamydomonas reinhardtii, we identify with high confidence (false discovery rate [FDR] < 0.11) 70 poorly characterized genes required for photosynthesis. We then enable the functional characterization of these genes by providing a resource of proteomes of mutant strains, each lacking one of these genes. The data allow assignment of 34 genes to the biogenesis or regulation of one or more specific photosynthetic complexes. Further analysis uncovers biogenesis/regulatory roles for at least seven proteins, including five photosystem I mRNA maturation factors, the chloroplast translation factor MTF1, and the master regulator PMR1, which regulates chloroplast genes via nuclear-expressed factors. Our work provides a rich resource identifying regulatory and functional genes and placing them into pathways, thereby opening the door to a system-level understanding of photosynthesis.

Summary Chloroplasts are eukaryotic photosynthetic organelles that drive the global carbon cycle. Despite their importance, our understanding of their protein composition, function, and spatial organization remains limited. Here, we determined the localizations of 1,032 candidate chloroplast proteins by using fluorescent protein tagging in the model alga Chlamydomonas reinhardtii . The localizations provide insights into the functions of hundreds of poorly-characterized proteins, including identifying novel components of nucleoids, plastoglobules, and the pyrenoid. We discovered and further characterized novel organizational features, including eleven chloroplast punctate structures, cytosolic crescent structures, and diverse unexpected spatial distributions of enzymes within the chloroplast. We observed widespread protein targeting to multiple organelles, identifying proteins that likely function in multiple compartments. We also used machine learning to predict the localizations of all Chlamydomonas proteins. The strains and localization atlas developed here will serve as a resource to enable studies of chloroplast architecture and functions. Graphical Abstract Highlights 1,032 candidate chloroplast proteins localized by fluorescent tagging. This protein atlas reveals novel chloroplast structures, functional regions, and components. Prevalent dual-organelle localization suggests extensive cross-compartment coordination. Atlas-trained machine learning predicts localizations of all C. reinhardtii proteins.

Abstract Many photosynthetic organisms enhance the performance of their CO 2 -fixing enzyme Rubisco by operating a CO 2 -concentrating mechanism (CCM). Most CCMs in eukaryotic algae supply concentrated CO 2 to Rubisco in an organelle called the pyrenoid. Ongoing efforts seek to engineer an algal CCM into crops that lack a CCM to increase yields. To advance our basic understanding of the algal CCM, we develop a chloroplast-scale reaction-diffusion model to analyze the efficacy and the energy efficiency of the CCM in the green alga Chlamydomonas reinhardtii . We show that achieving an effective and energetically efficient CCM requires a physical barrier such as thylakoid stacks or a starch sheath to reduce CO 2 leakage out of the pyrenoid matrix. Our model provides insights into the relative performance of two distinct inorganic carbon uptake strategies: at air-level CO 2 , a CCM can operate effectively by taking up passively diffusing external CO 2 and catalyzing its conversion to HCO 3 − , which is then trapped in the chloroplast; however, at lower external CO 2 levels, effective CO 2 concentration requires active import of HCO 3 − . We also find that proper localization of carbonic anhydrases can reduce futile carbon cycling between CO 2 and HCO 3 − , thus enhancing CCM performance. We propose a four-step engineering path that increases predicted CO 2 saturation of Rubisco up to seven-fold at a theoretical cost of only 1.5 ATP per CO 2 fixed. Our system-level analysis establishes biophysical principles underlying the CCM that are broadly applicable to other algae and provides a framework to guide efforts to engineer an algal CCM into land plants. Significance Statement Eukaryotic algae mediate approximately one-third of CO 2 fixation in the global carbon cycle. Many algae enhance their CO 2 -fixing ability by operating a CO 2 -concentrating mechanism (CCM). Our model of the algal CCM lays a solid biophysical groundwork for understanding its operation. The model’s consistency with experimental observations supports existing hypotheses about the operating principles of the algal CCM and the functions of its component proteins. We provide a quantitative estimate of the CCM’s energy efficiency and compare the performance of two distinct CO 2 assimilation strategies under varied conditions. The model offers a quantitative framework to guide the engineering of an algal CCM into land plants and supports the feasibility of this endeavor.