Exploring alternative pathways for the in vitro establishment of the HOPAC cycle for synthetic CO2 fixation

Photosynthesis: Genetic Strategies Adopted to Gain Higher Efficiency

The global challenge of feeding an ever-increasing population to maintain food security requires novel approaches to increase crop yields. Photosynthesis, the fundamental energy and material basis for plant life on Earth, is highly responsive to environmental conditions. Evaluating the operational status of the photosynthetic mechanism provides insights into plants' capacity to adapt to their surroundings. Despite immense effort, photosynthesis still falls short of its theoretical maximum efficiency, indicating significant potential for improvement. In this review, we provide background information on the various genetic aspects of photosynthesis, explain its complexity, and survey relevant genetic engineering approaches employed to improve the efficiency of photosynthesis. We discuss the latest success stories of gene-editing tools like CRISPR-Cas9 and synthetic biology in achieving precise refinements in targeted photosynthesis pathways, such as the Calvin-Benson cycle, electron transport chain, and photorespiration. We also discuss the genetic markers crucial for mitigating the impact of rapidly changing environmental conditions, such as extreme temperatures or drought, on photosynthesis and growth. This review aims to pinpoint optimization opportunities for photosynthesis, discuss recent advancements, and address the challenges in improving this critical process, fostering a globally food-secure future through sustainable food crop production.

Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry

The forward design of in vitro enzymatic reaction networks (ERNs) requires a detailed analysis of network kinetics and potentially hidden interactions between the substrates and enzymes. Although flow chemistry allows for a systematic exploration of how the networks adapt to continuously changing conditions, the analysis of the reaction products is often a bottleneck. Here, we report on the interface between a continuous stirred-tank reactor, in which an immobilized enzymatic network made of 12 enzymes is compartmentalized, and an ion mobility-mass spectrometer. Feeding uniformly 13C-labeled inputs to the enzymatic network generates all isotopically labeled reaction intermediates and products, which are individually detected by ion mobility-mass spectrometry (IMS-MS) based on their mass-to-charge ratios and inverse ion mobilities. The metabolic flux can be continuously and quantitatively monitored by diluting the ERN output with nonlabeled standards of known concentrations. The real-time quantitative data obtained by IMS-MS are then harnessed to train a model of network kinetics, which proves sufficiently predictive to control the ERN output after a single optimally designed experiment. The high resolution of the time-course data provided by this approach is an important stepping stone to design and control sizable and intricate ERNs.

Integrated translation and metabolism in a partially self-synthesizing biochemical network

One of the hallmarks of living organisms is their capacity for self-organization and regeneration, which requires a tight integration of metabolic and genetic networks. We sought to construct a linked metabolic and genetic network in vitro that shows such lifelike behavior outside of a cellular context and generates its own building blocks from nonliving matter. We integrated the metabolism of the crotonyl-CoA/ethyl-malonyl-CoA/hydroxybutyryl-CoA cycle with cell-free protein synthesis using recombinant elements. Our network produces the amino acid glycine from CO2 and incorporates it into target proteins following DNA-encoded instructions. By orchestrating ~50 enzymes we established a basic cell-free operating system in which genetically encoded inputs into a metabolic network are programmed to activate feedback loops allowing for self-integration and (partial) self-regeneration of the complete system.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Perspectives for Using CO2 as a Feedstock for Biomanufacturing of Fuels and Chemicals

Microbial cell factories offer an eco-friendly alternative for transforming raw materials into commercially valuable products because of their reduced carbon impact compared to conventional industrial procedures. These systems often depend on lignocellulosic feedstocks, mainly pentose and hexose sugars. One major hurdle when utilizing these sugars, especially glucose, is balancing carbon allocation to satisfy energy, cofactor, and other essential component needs for cellular proliferation while maintaining a robust yield. Nearly half or more of this carbon is inevitably lost as CO2 during the biosynthesis of regular metabolic necessities. This loss lowers the production yield and compromises the benefit of reducing greenhouse gas emissions-a fundamental advantage of biomanufacturing. This review paper posits the perspectives of using CO2 from the atmosphere, industrial wastes, or the exhausted gases generated in microbial fermentation as a feedstock for biomanufacturing. Achieving the carbon-neutral or -negative goals is addressed under two main strategies. The one-step strategy uses novel metabolic pathway design and engineering approaches to directly fix the CO2 toward the synthesis of the desired products. Due to the limitation of the yield and efficiency in one-step fixation, the two-step strategy aims to integrate firstly the electrochemical conversion of the exhausted CO2 into C1/C2 products such as formate, methanol, acetate, and ethanol, and a second fermentation process to utilize the CO2-derived C1/C2 chemicals or co-utilize C5/C6 sugars and C1/C2 chemicals for product formation. The potential and challenges of using CO2 as a feedstock for future biomanufacturing of fuels and chemicals are also discussed.

Synergistic investigation of natural and synthetic C1-trophic microorganisms to foster a circular carbon economy

A true circular carbon economy must upgrade waste greenhouse gases. C1-based biomanufacturing is an attractive solution, in which one carbon (C1) molecules (e.g. CO2, formate, methanol, etc.) are converted by microbial cell factories into value-added goods (i.e. food, feed, and chemicals). To render C1-based biomanufacturing cost-competitive, we must adapt microbial metabolism to perform chemical conversions at high rates and yields. To this end, the biotechnology community has undertaken two (seemingly opposing) paths: optimizing natural C1-trophic microorganisms versus engineering synthetic C1-assimilation de novo in model microorganisms. Here, we pose how these approaches can instead create synergies for strengthening the competitiveness of C1-based biomanufacturing as a whole.

Improving photosynthetic efficiency toward food security: Strategies, advances, and perspectives

Photosynthesis in crops and natural vegetation allows light energy to be converted into chemical energy and thus forms the foundation for almost all terrestrial trophic networks on Earth. The efficiency of photosynthetic energy conversion plays a crucial role in determining the portion of incident solar radiation that can be used to generate plant biomass throughout a growth season. Consequently, alongside the factors such as resource availability, crop management, crop selection, maintenance costs, and intrinsic yield potential, photosynthetic energy use efficiency significantly influences crop yield. Photosynthetic efficiency is relevant to sustainability and food security because it affects water use efficiency, nutrient use efficiency, and land use efficiency. This review focuses specifically on the potential for improvements in photosynthetic efficiency to drive a sustainable increase in crop yields. We discuss bypassing photorespiration, enhancing light use efficiency, harnessing natural variation in photosynthetic parameters for breeding purposes, and adopting new-to-nature approaches that show promise for achieving unprecedented gains in photosynthetic efficiency.