Biology subjects

Wilson, A. T.

Publications and source records attributed to Wilson, A. T..

3 recordsLinked to original sources

bioRxiv2022

Systematic identification and characterization of novel genes in the regulation and biogenesis of photosynthetic machinery

Photosynthesis is central to food production and the Earths 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 (FDR<0.11) 70 previously-uncharacterized genes required for photosynthesis. We then provide a resource of mutant proteomes that enables functional characterization of these novel genes by revealing their relationship to known genes. The data allow assignment of 34 novel genes to the biogenesis or regulation of one or more specific photosynthetic complexes. Additional analysis uncovers at least seven novel critical regulatory proteins, including five Photosystem I mRNA maturation factors and two master regulators: MTF1, which impacts chloroplast gene expression directly; and PMR1, which impacts expression via nuclear-expressed factors. Our work provides a rich resource identifying novel regulatory and functional genes and placing them into pathways, thereby opening the door to a system-level understanding of photosynthesis. HighlightsO_LIHigh-confidence identification of 70 previously-uncharacterized genes required for photosynthesis C_LIO_LIProteomic analysis of mutants allows assignment of function to novel genes C_LIO_LICharacterization of 5 novel Photosystem I mRNA maturation factors validates this resource C_LIO_LIMTF1 and PMR1 identified as master regulators of photosynthesis C_LI

cell biology↗

bioRxiv2022

A Chloroplast Protein Atlas Reveals Novel Structures and Spatial Organization of Biosynthetic Pathways

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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/493820v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19c871eorg.highwire.dtl.DTLVardef@16ac46corg.highwire.dtl.DTLVardef@859b7dorg.highwire.dtl.DTLVardef@1a3446e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI1,032 candidate chloroplast proteins localized by fluorescent tagging. C_LIO_LIThis protein atlas reveals novel chloroplast structures, functional regions, and components. C_LIO_LIPrevalent dual-organelle localization suggests extensive cross-compartment coordination. C_LIO_LIAtlas-trained machine learning predicts localizations of all C. reinhardtii proteins. C_LI

plant biology↗

bioRxiv2021

Diffusion barriers and adaptive carbon uptake strategies enhance the modeled performance of the algal CO2-concentrating mechanism

Many photosynthetic organisms enhance the performance of their CO2-fixing enzyme Rubisco by operating a CO2-concentrating mechanism (CCM). Most CCMs in eukaryotic algae supply concentrated CO2 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 CO2 leakage out of the pyrenoid matrix. Our model provides insights into the relative performance of two distinct inorganic carbon uptake strategies: at air-level CO2, a CCM can operate effectively by taking up passively diffusing external CO2 and catalyzing its conversion to HCO3-, which is then trapped in the chloroplast; however, at lower external CO2 levels, effective CO2 concentration requires active import of HCO3-. We also find that proper localization of carbonic anhydrases can reduce futile carbon cycling between CO2 and HCO3-, thus enhancing CCM performance. We propose a four-step engineering path that increases predicted CO2 saturation of Rubisco up to seven-fold at a theoretical cost of only 1.5 ATP per CO2 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 StatementEukaryotic algae mediate approximately one-third of CO2 fixation in the global carbon cycle. Many algae enhance their CO2-fixing ability by operating a CO2-concentrating mechanism (CCM). Our model of the algal CCM lays a solid biophysical groundwork for understanding its operation. The models 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 CCMs energy efficiency and compare the performance of two distinct CO2 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.

biophysics↗