Biology subjects

Shachar-Hill, Y.

Publications and source records attributed to Shachar-Hill, Y..

5 recordsLinked to original sources

bioRxiv2024

Modeling With Uncertainty Quantification Identifies Essential Features of a Non-Canonical Algal Carbon-Concentrating Mechanism

The thermoacidophilic red alga Cyanidioschyzon merolae survives its challenging environment likely in part by operating a carbon-concentrating mechanism (CCM). Here, we demonstrated that C. merolaes cellular affinity for CO2 is stronger than its rubisco affinity for CO2. This provided further evidence that C. merolae operates a CCM while lacking structures and functions characteristic of CCMs in other organisms. To test how such a CCM could function, we created a mathematical compartmental model of a simple CCM distinct from those previously described in detail. The results supported the feasibility of this proposed minimal and non-canonical CCM in C. merolae. To facilitate robust modeling of this process, we incorporated new physiological and enzymatic data into the model, and we additionally trained a surrogate machine-learning model to emulate the mechanistic model and characterized the effects of model parameters on key outputs. This parameter exploration enabled us to identify model features that influenced whether the model met experimentally-derived criteria for functional carbon-concentration and efficient energy usage. Such parameters included cytosolic pH, bicarbonate pumping cost and kinetics, cell radius, carboxylation velocity, number of thylakoid membranes, and CO2 membrane permeability. Our exploration thus suggested that a novel CCM could exist in C. merolae and illuminated essential features necessary for CCMs to function. SignificanceCarbon-concentrating mechanisms (CCMs) are processes which boost photosynthetic efficiency. By developing modeling approaches to robustly describe CCMs in organisms where biochemical data is limited, such as extremophile algae, we can better understand how organisms survive environmental challenges. We demonstrate an interdisciplinary modeling approach which efficiently sampled from large parameter spaces and identified features (e.g., compartment permeability, pH, enzyme characteristics) which determine the function and energy cost of a simple CCM. This approach is new to compartmental photosynthetic modeling, and could facilitate effective use of models to inform experiments and rational engineering. For example, engineering CCMs into crops may improve agricultural productivity, and could benefit from models defining the structural and biochemical features necessary for CCM function.

plant biology↗

bioRxiv2024

Biophysical carbon concentrating mechanisms in land plants: insights from reaction-diffusion modeling

Carbon Concentrating Mechanisms (CCMs) have evolved numerous times in photosynthetic organisms. They elevate the concentration of CO2 around the carbon-fixing enzyme rubisco, thereby increasing CO2 assimilatory flux and reducing photorespiration. Biophysical CCMs, like the pyrenoid-based CCM of Chlamydomonas reinhardtii or carboxysome systems of cyanobacteria, are common in aquatic photosynthetic microbes, but in land plants appear only among the hornworts. To predict the likely efficiency of biophysical CCMs in C3 plants, we used spatially resolved reaction-diffusion models to predict rubisco saturation and light use efficiency. We find that the energy efficiency of adding individual CCM components to a C3 land plant is highly dependent on the permeability of lipid membranes to CO2, with values in the range reported in the literature that are higher than used in previous modeling studies resulting in low light use efficiency. Adding a complete pyrenoid-based CCM into the leaf cells of a C3 land plant is predicted to boost net CO2 fixation, but at higher energetic costs than those incurred by photorespiratory losses without a CCM. Two notable exceptions are when substomatal CO2 levels are as low as those found in land plants that already employ biochemical CCMs and when gas exchange is limited such as with hornworts, making the use of a biophysical CCM necessary to achieve net positive CO2 fixation under atmospheric CO2 levels. This provides an explanation for the uniqueness of hornworts CCM among land plants and evolution of pyrenoids multiple times.

plant biology↗

bioRxiv2022

Integrative Teaching of Metabolic Modeling and Flux Analysis with Interactive Python Modules

The modeling of rates of biochemical reactions - fluxes - in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and Flux Balance Analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, Metabolic Control Analysis, l3C-Metabolic Flux Analysis and Flux Balance Analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

scientific communication and education↗

bioRxiv2022

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

MotivationThe accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as Flux Balance Analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. ResultsRelative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into Flux Balance Analysis predictions of a multi-tissue, diel model of Arabidopsis thalianas central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C Metabolic Flux Analysis (MFA) compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps, as measured by weighted averaged percent error values, dropped from between 169-180% and 94-103% in high light and low light conditions, respectively, to between 10-12% and 9-11%, depending on the gene expression dataset used. The incorporation of gene expression data into the modeling process also substantially altered the predicted carbon and energy economy of the plant. AvailabilityCode is available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights Contactyairhill@msu.edu

systems biology↗

bioRxiv2022

Introduction of a condensed, reverse tricarboxylic acid cycle for additional CO2 fixation in plants

Plants employ the Calvin-Benson cycle (CBC) to fix atmospheric CO2 for the production of biomass. The flux of carbon through the CBC is limited by the activity and selectivity of ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Alternative pathways that do not use RuBisCO to fix CO2 exist but occur only in anaerobic microorganisms. Rather than modifying existing routes of carbon metabolism in plants, we have developed a synthetic carbon fixation cycle that does not exist in nature, but is inspired by metabolisms of bacterial autotrophs. This synthetic cycle uses endogenous plant metabolites to fix CO2 and yield glyoxylate as a product. In this work, we build and characterize a condensed, reverse tricarboxylic acid (crTCA) cycle in vitro and in planta. We demonstrate that a simple, synthetic cycle can be used to fix carbon in vitro under aerobic and mesophilic conditions and that these enzymes retain activity when expressed transiently in planta. We then evaluate stable transgenic lines of Camelina sativa that have both phenotypic and physiologic changes. Transgenic C. sativa are shorter than controls with increased rates of photosynthetic CO2 assimilation and changes in photorespiratory metabolism. This first iteration of a build-test-learn phase of the crTCA cycle provides promising evidence that this pathway can be used to increase photosynthetic capacity in plants.

plant biology↗