Quantifying the effects of light intensity on bioproduction and maintenance energy during photosynthetic growth of Rhodobacter sphaeroides

Engineering photo-methylotrophic Methylobacterium for enhanced 3-hydroxypropionic acid production during non-growth stage fermentation

This study explored the potential of methanol as a sustainable feedstock for biomanufacturing, focusing on Methylobacterium extorquens, a well-established representative of methylotrophic cell factories. Despite this bacterium's long history, its untapped photosynthetic capabilities for production enhancement have remained unreported. Using genome-scale flux balance analysis, it was hypothesized that introducing photon fluxes could boost the yield of 3-hydroxypropionic acid (3-HP), an energy- and reducing equivalent-consuming chemicals. To realize this, M. extorquens was genetically modified by eliminating the negative regulator of photosynthesis, leading to improved ATP levels and metabolic activity in non-growth cells during a two-stage fermentation process. This modification resulted in a remarkable 3.0-fold increase in 3-HP titer and a 2.1-fold increase in its yield during stage (II). Transcriptomics revealed that enhanced light-driven methanol oxidation, NADH transhydrogenation, ATP generation, and fatty acid degradation were key factors. This development of photo-methylotrophy as a platform technology introduced novel opportunities for future production enhancements.

Light intensity defines growth and photopigment content of a mixed culture of purple phototrophic bacteria

Purple bacteria (PPB), anoxygenic photoorganoheterotrophic organisms with a hyper-versatile metabolism and high biomass yields over substrate, are promising candidates for the recovery of nutrient resources from wastewater. Infrared light is a pivotal parameter to control and design PPB-based resource recovery. However, the effects of light intensities on the physiology and selection of PPB in mixed cultures have not been studied to date. Here, we examined the effect of infrared irradiance on PPB physiology, enrichment, and growth over a large range of irradiance (0 to 350 W m−2) in an anaerobic mixed-culture sequencing batch photobioreactor. We developed an empirical mathematical model that suggests higher PPB growth rates as response to higher irradiance. Moreover, PPB adapted to light intensity by modulating the abundances of their phototrophic complexes. The obtained results provide an in-depth phylogenetic and metabolic insight the impact of irradiance on PPB. Our findings deliver the fundamental information for guiding the design of light-driven, anaerobic mixed-culture PPB processes for wastewater treatment and bioproduct valorization.

Polyhydroxyalkanoates from organic waste streams using purple non-sulfur bacteria

Many microorganisms can produce intracellular and extracellular biopolymers, such as polyhydroxyalkanoates (PHA). Despite PHA's benefits, their widespread at the industrial level has not occurred due mainly to high production costs. PHA production under a biorefinery scheme is proposed to improve its economic viability. In this context, purple non-sulfur bacteria (PNSB) are ideal candidates to produce PHA and other substances of economic interest. This review describes the PHA production by PNSB under different metabolic pathways, by using a wide range of wastes and under diverse operational conditions such as aerobic and anaerobic metabolism, irradiance level, light or dark conditions. Some strategies, such as controlling the feed regime, biofilm reactors, and open photobioreactors in outdoor conditions, were identified from the literature review as the approach needed to improve the process's economic viability when using mixed cultures of PNSB and wastes as substrates.

Solar Radiation Allocation and Spatial Distribution in Chinese Solar Greenhouses: Model Development and Application

Solar radiation is the sole energy source for Chinese solar greenhouse agriculture. A favorable light environment is the foundation of a desirable crop growth environment, and it is key in solar greenhouse design. In this study, a mathematical model is established to quantitatively evaluate the solar greenhouse light environment. The model was developed considering the greenhouse shape parameters, materials’ optical properties, and interior solar radiation evolution, including the beam radiation, diffuse radiation, and multi-reflection. The model was validated under different weather conditions, and the results reveal a mean percentage error of 1.67 and 10.30% for clear sunny weather and cloudy weather, respectively, and a determination coefficient of 0.9756. By using this model, the solar radiation allocation in a solar greenhouse was calculated to determine the solar radiation availability for the heat-storage north wall and the entire greenhouse, and the dynamical spatial distribution of the solar radiation was obtained to describe the light environment quality. These allow the optimization of the greenhouse lighting regulation and planting pattern. Moreover, several optimizing measures are derived according to the model for improving the low-light environment near the north wall and maximizing the north wall’s heat storage/release capacity in a solar greenhouse.

Optimization and control of the light environment for greenhouse crop production

Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is influenced by changes in temperature and CO₂ concentration is an important challenge for the development of greenhouse light environment control system. In view of this challenge, this paper describes a light environment optimization and control model based on a crop growth model for predicting cucumber photosynthesis. The photosynthetic rate values for different photosynthetic photon flux densities (PPFD), CO₂ concentration, and temperature conditions provided to cucumber seedlings were obtained by using an LI-6400XT portable photosynthesis system during multi-factorial experiments. Based on the measured data, photosynthetic rate predictions were determined. Next, a support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO₂ conditions. The light saturation point was used to establish the light environment optimization and control model and to perform model validation. The slope of the fitting straight line comparing the measured and predicted light saturation point was 0.99, the intercept was 23.46 and the coefficient of determination was 0.98. The light control model was able to perform dynamic acquisition of the light saturation point and provide a theoretical basis for the efficient and accurate control of the greenhouse light environment.

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival.

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosynthetic growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.

IMPORTANCE Knowledge about the role of genes under a particular growth condition is required for a holistic understanding of a bacterial cell and has implications for health, agriculture, and biotechnology. We developed the Tn-seq analysis software (TSAS) package to provide a flexible and statistically rigorous workflow for the high-throughput analysis of insertion mutant libraries, advanced the knowledge of gene essentiality in R. sphaeroides, and illustrated how Tn-seq data can be used to more accurately identify genes that play important roles in metabolism and other processes that are essential for cellular survival.

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Integrated In Silico Analysis of Pathway Designs for Synthetic Photo-Electro-Autotrophy

The strong advances in synthetic biology enable the engineering of novel functions and complex biological features in unprecedented ways, such as implementing synthetic autotrophic metabolism into heterotrophic hosts. A key challenge for the sustainable production of fuels and chemicals entails the engineering of synthetic autotrophic organisms that can effectively and efficiently fix carbon dioxide by using sustainable energy sources. This challenge involves the integration of carbon fixation and energy uptake systems. A variety of carbon fixation pathways and several types of photosystems and other energy uptake systems can be chosen and, potentially, modularly combined to design synthetic autotrophic metabolism. Prior to implementation, these designs can be evaluated by the combination of several computational pathway analysis techniques. Here we present a systematic, integrated in silico analysis of photo-electro-autotrophic pathway designs, consisting of natural and synthetic carbon fixation pathways, a proton-pumping rhodopsin photosystem for ATP regeneration and an electron uptake pathway. We integrated Flux Balance Analysis of the heterotrophic chassis Escherichia coli with kinetic pathway analysis and thermodynamic pathway analysis (Max-min Driving Force). The photo-electro-autotrophic designs are predicted to have a limited potential for anaerobic, autotrophic growth of E. coli_, given the relatively low ATP regenerating capacity of the proton pumping rhodopsin photosystems and the high ATP maintenance of E. coli. If these factors can be tackled, our analysis indicates the highest growth potential for the natural reductive tricarboxylic acid cycle and the synthetic pyruvate synthase–pyruvate carboxylate -glyoxylate bicycle. Both carbon fixation cycles are very ATP efficient, while maintaining fast kinetics, which also results in relatively low estimated protein costs for these pathways. Furthermore, the synthetic bicycles are highly thermodynamic favorable under conditions analysed. However, the most important challenge identified for improving photo-electro-autotrophic growth is increasing the proton-pumping rate of the rhodopsin photosystems, allowing for higher ATP regeneration. Alternatively, other designs of autotrophy may be considered, therefore the herein presented integrated modeling approach allows synthetic biologists to evaluate and compare complex pathway designs before experimental implementation.