Enhancing Fluorescence in Both Solution and Solid States Induced by Imine Cage Formation.

Developing luminescent materials that exhibit strong emissions in both solution and solid phases is highly desirable and challenging. Herein, we report imine-bond directed formation of a rigid organic cage (TPE-cage) that was synthesized by [2+4] imine condensation of a TPE-cored tetra-aldehyde (TPE-TA) with a clip-like diamine (XA) to illustrate confinement-induced fluorescence enhancement. Compared to the non-emissive TPE-TA (ϕF =0.26 %) in the dichloromethane (DCM) solution, the TPE-cage achieved a remarkable (~520-fold) emission enhancement (ϕF =70.38 %). In contrast, a monomeric tetra-imine model compound (TPE-model) showed only a minor enhancement (ϕF =0.56 %) in emission compared to the parent tetra-aldehyde TPE-TA. The emission of TPE-cage was further enhanced by ~1.5-fold (ϕF =80.96 %) in the aggregated state owing to aggregation-induced emission enhancement (AIEE). This approach establishes the potential for synthesizing luminescent materials with high emission in both solution and solid-state by employing a single-step imine condensation reaction.

A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation.

Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

Quantitative Dynamic Analysis of de novo Sphingolipid Biosynthesis in Arabidopsis thaliana.

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. We explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing 15N isotope labeling and quantitative dynamic modeling approach, we developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase (LCBK) to be critical for maintaining sphingolipid homeostasis. Disruptions in these enzymes were found to affect cellular viability and increase the potential for programmed cell death (PCD). Thus, this work enhances our understanding of sphingolipid metabolism and demonstrates the utility of dynamic modeling in analyzing complex metabolic pathways.

Erratum: Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.

[This corrects the article DOI: 10.1016/j.isci.2022.104483.].

Protocol to develop a synthetic biology toolkit for the non-model bacterium R. palustris.

Numerous biology tools are developed to work for model organisms, which, however, do not work effectively in non-model organisms. Here, we present a protocol for developing a synthetic biology toolkit for Rhodopseudomonas palustris CGA009, a non-model bacterium with unique metabolic properties. We describe steps for introducing and characterizing biological devices in non-model bacteria, such as the utilization of fluorescence markers and RT-qPCR. This protocol may also be applicable for other non-model organisms. For complete details on the use and execution of this protocol, please refer to Immethun et al..1.

Synthesis of Mixed-Phase TiO2-ZrO2 Nanocomposite for Photocatalytic Wastewater Treatment.

The use of TiO2 nanoparticles for photocatalysis for the degradation of organic dyes under UV light for wastewater treatment has been widely studied. However, the photocatalytic characteristics of TiO2 nanoparticles are inadequate due to their UV light response and higher band gap. In this work, three nanoparticles were synthesized: (i) TiO2 nanoparticle was synthesized by a sol-gel process. (ii) ZrO2 was prepared using a solution combustion process and (iii) mixed-phase TiO2-ZrO2 nanoparticles were synthesized by a sol-gel process to remove Eosin Yellow (EY) from aqueous solutions in the wastewater. XRD, FTIR, UV-VIS, TEM, and XPS analysis methods were used to examine the properties of the synthesized products. The XRD investigation supported the tetragonal and monoclinic crystal structures of the TiO2 and ZrO2 nanoparticles. TEM studies identified that mixed-phase TiO2-ZrO2 nanoparticles have the same tetragonal structure as pure mixed-phase. The degradation of Eosin Yellow (EY) was examined using TiO2, ZrO2, and mixed-phase TiO2-ZrO2 nanoparticles under visible light. The results confirmed that the mixed-phase TiO2-ZrO2nanoparticles show a higher level of photocatalytic activity, and the process is accomplished at a high degradation rate in lesser time and at a lower power intensity.

Supplementation of Sulfide or Acetate and 2-Mercaptoethane Sulfonate Restores Growth of the Methanosarcina acetivorans ΔhdrABC Deletion Mutant during Methylotrophic Methanogenesis.

Methanogenic archaea are important organisms in the global carbon cycle that grow by producing methane gas. Methanosarcina acetivorans is a methanogenic archaeum that can grow using methylated compounds, carbon monoxide, or acetate and produces renewable methane as a byproduct. However, there is limited knowledge of how combinations of substrates may affect metabolic fluxes in methanogens. Previous studies have shown that heterodisulfide reductase, the terminal oxidase in the electron transport system, is an essential enzyme in all methanogens. Deletion of genes encoding the nonessential methylotrophic heterodisulfide reductase enzyme (HdrABC) results in slower growth rate but increased metabolic efficiency. We hypothesized that increased sulfide, supplementation of mercaptoethanesulfonate (coenzyme M, CoM-SH), or acetate would metabolically alleviate the effect of the ΔhdrABC mutation. Increased sulfide improved growth of the mutant as expected; however, supplementation of both CoM-SH and acetate together were necessary to reduce the effect of the ΔhdrABC mutation. Supplementation of CoM-SH or acetate alone did not improve growth. These results support our model for the role of HdrABC in methanogenesis and suggest M.acetivorans is more efficient at conserving energy when supplemented with acetate. Our study suggests decreased Hdr enzyme activity can be overcome by nutritional supplementation with sulfide or coenzyme M and acetate, which are abundant in anaerobic environments.

Opening the door to nitrogen fixation in oxygenic phototrophs.

Conveying biological nitrogen fixation (BNF) to photosynthetic species may be the next agricultural revolution, yet poses major engineering challenges. Liu et al. created a diazotrophic strain of a previously non-nitrogen-fixing species, the cyanobacterium Synechocystis sp. PCC 6803, and uncovered critical aspects of nitrogen fixation in an oxygenic species.

Early detection of Parkinson disease using stacking ensemble method.

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.

Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence.

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrient-sufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL's stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.

Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.

Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.

Rhodopseudomonas palustris: A biotechnology chassis.

Rhodopseudomonas palustris is an attractive option for biotechnical applications and industrial engineering due to its metabolic versatility and its ability to catabolize a wide variety of feedstocks and convert them to several high-value products. Given its adaptable metabolism, R. palustris has been studied and applied in an extensive variety of applications such as examining metabolic tradeoffs for environmental perturbations, biodegradation of aromatic compounds, environmental remediation, biofuel production, agricultural biostimulation, and bioelectricity production. This review provides a holistic summary of the commercial applications for R. palustris as a biotechnology chassis and suggests future perspectives for research and engineering.

Characterizing the Interplay of Rubisco and Nitrogenase Enzymes in Anaerobic-Photoheterotrophically Grown Rhodopseudomonas palustris CGA009 through a Genome-Scale Metabolic and Expression Model.

Rhodopseudomonas palustris CGA009 is a Gram-negative purple nonsulfur bacterium that grows phototrophically by fixing carbon dioxide and nitrogen or chemotrophically by fixing or catabolizing a wide array of substrates, including lignin breakdown products for its carbon and fixing nitrogen for its nitrogen requirements. It can grow aerobically or anaerobically and can use light, inorganic, and organic compounds for energy production. Due to its ability to convert different carbon sources into useful products during anaerobic growth, this study reconstructed a metabolic and expression (ME) model of R. palustris to investigate its anaerobic-photoheterotrophic growth. Unlike metabolic (M) models, ME models include transcription and translation reactions along with macromolecules synthesis and couple these reactions with growth rate. This unique feature of the ME model led to nonlinear growth curve predictions, which matched closely with experimental growth rate data. At the theoretical maximum growth rate, the ME model suggested a diminishing rate of carbon fixation and predicted malate dehydrogenase and glycerol-3 phosphate dehydrogenase as alternate electron sinks. Moreover, the ME model also identified ferredoxin as a key regulator in distributing electrons between major redox balancing pathways. Because ME models include the turnover rate for each metabolic reaction, it was used to successfully capture experimentally observed temperature regulation of different nitrogenases. Overall, these unique features of the ME model demonstrated the influence of nitrogenases and rubiscos on R. palustris growth and predicted a key regulator in distributing electrons between major redox balancing pathways, thus establishing a platform for in silico investigation of R. palustris metabolism from a multiomics perspective. IMPORTANCE In this work, we reconstructed the first ME model for a purple nonsulfur bacterium (PNSB). Using the ME model, different aspects of R. palustris metabolism were examined. First, the ME model was used to analyze how reducing power entering the R. palustris cell through organic carbon sources gets partitioned into biomass, carbon dioxide fixation, and nitrogen fixation. Furthermore, the ME model predicted electron flux through ferredoxin as a major bottleneck in distributing electrons to nitrogenase enzymes. Next, the ME model characterized different nitrogenase enzymes and successfully recapitulated experimentally observed temperature regulations of those enzymes. Identifying the bottleneck responsible for transferring an electron to nitrogenase enzymes and recapitulating the temperature regulation of different nitrogenase enzymes can have profound implications in metabolic engineering, such as hydrogen production from R. palustris. Another interesting application of this ME model can be to take advantage of its redox balancing strategy to gain an understanding of the regulatory mechanism of biodegradable plastic production precursors, such as polyhydroxybutyrate (PHB).

Synthetic Biology Tool Development Advances Predictable Gene Expression in the Metabolically Versatile Soil Bacterium Rhodopseudomonas palustris.

Harnessing the unique biochemical capabilities of non-model microorganisms would expand the array of biomanufacturing substrates, process conditions, and products. There are non-model microorganisms that fix nitrogen and carbon dioxide, derive energy from light, catabolize methane and lignin-derived aromatics, are tolerant to physiochemical stresses and harsh environmental conditions, store lipids in large quantities, and produce hydrogen. Model microorganisms often only break down simple sugars and require low stress conditions, but they have been engineered for the sustainable manufacture of numerous products, such as fragrances, pharmaceuticals, cosmetics, surfactants, and specialty chemicals, often by using tools from synthetic biology. Transferring complex pathways has proven to be exceedingly difficult, as the cofactors, cellular conditions, and energy sources necessary for this pathway to function may not be present in the host organism. Utilization of unique biochemical capabilities could also be achieved by engineering the host; although, synthetic biology tools developed for model microbes often do not perform as designed in other microorganisms. The metabolically versatile Rhodopseudomonas palustris CGA009, a purple non-sulfur bacterium, catabolizes aromatic compounds derived from lignin in both aerobic and anaerobic conditions and can use light, inorganic, and organic compounds for its source of energy. R. palustris utilizes three nitrogenase isozymes to fulfill its nitrogen requirements while also generating hydrogen. Furthermore, the bacterium produces two forms of RuBisCo in response to carbon dioxide/bicarbonate availability. While this potential chassis harbors many beneficial traits, stable heterologous gene expression has been problematic due to its intrinsic resistance to many antibiotics and the lack of synthetic biology parts investigated in this microbe. To address these problems, we have characterized gene expression and plasmid maintenance for different selection markers, started a synthetic biology toolbox specifically for the photosynthetic R. palustris, including origins of replication, fluorescent reporters, terminators, and 5' untranslated regions, and employed the microbe's endogenous plasmid for exogenous protein production. This work provides essential synthetic biology tools for engineering R. palustris' many unique biochemical processes and has helped define the principles for expressing heterologous genes in this promising microbe through a methodology that could be applied to other non-model microorganisms.

Heterologous phasin expression in Rhodopseudomonas palustris CGA009 for bioplastic production from lignocellulosic biomass.

Rhodopseudomonas palustris CGA009 is a metabolically robust microbe that can utilize lignin breakdown products to produce polyhydroxyalkanoates (PHAs), biopolymers with the potential to replace conventional plastics. Our recent efforts suggest PHA granule formation is a limiting factor for maximum production of the bioplastic poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) by R. palustris. The Phap1 phasin (phaP1) from the PHB-producing model bacterium Cupriavidus necator H16 was expressed in R. palustris with the aim of overproducing PHBV from the lignin breakdown product p-coumarate by fostering smaller and more abundant granules. Expression of phaP1 yielded PHBV production from R. palustris aerobically (0.7 g/L), which does not occur in the wild-type strain, and led to a significantly higher PHBV titer than wild-type anaerobic production (0.41 g/L). The 3HV fractions were also significantly increased under both anaerobic and aerobic conditions, which boosts thermomechanical properties and potential for application. Thus, heterologous phasin expression in R. palustris provides flexibility for industrial processing and could foster compositional changes in copolymers with better thermomechanical properties compared to PHB alone.

Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions.

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches.

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Staphylococcal ClpXP protease targets the cellular antioxidant system to eliminate fitness-compromised cells in stationary phase.

The transition from growth to stationary phase is a natural response of bacteria to starvation and stress. When stress is alleviated and more favorable growth conditions return, bacteria resume proliferation without a significant loss in fitness. Although specific adaptations that enhance the persistence and survival of bacteria in stationary phase have been identified, mechanisms that help maintain the competitive fitness potential of nondividing bacterial populations have remained obscure. Here, we demonstrate that staphylococci that enter stationary phase following growth in media supplemented with excess glucose, undergo regulated cell death to maintain the competitive fitness potential of the population. Upon a decrease in extracellular pH, the acetate generated as a byproduct of glucose metabolism induces cytoplasmic acidification and extensive protein damage in nondividing cells. Although cell death ensues, it does not occur as a passive consequence of protein damage. Instead, we demonstrate that the expression and activity of the ClpXP protease is induced, resulting in the degeneration of cellular antioxidant capacity and, ultimately, cell death. Under these conditions, inactivation of either clpX or clpP resulted in the extended survival of unfit cells in stationary phase, but at the cost of maintaining population fitness. Finally, we show that cell death from antibiotics that interfere with bacterial protein synthesis can also be partly ascribed to the corresponding increase in clpP expression and activity. The functional conservation of ClpP in eukaryotes and bacteria suggests that ClpP-dependent cell death and fitness maintenance may be a widespread phenomenon in these domains of life.

Optimization-based Eukaryotic Genetic Circuit Design (EuGeneCiD) and modeling (EuGeneCiM) tools: Computational approach to synthetic biology.

Synthetic biology has the potential to revolutionize the biotech industry and our everyday lives and is already making an impact. Developing synthetic biology applications requires several steps including design and modeling efforts which may be performed by in silico tools. In this work, we have developed two such tools, Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM), which use optimization concepts and bioparts including promotors, transcripts, and terminators in designing and modeling genetic circuits. EuGeneCiD and EuGeneCiM preclude problematic designs leading to future synthetic biology application development pipelines. EuGeneCiD and EuGeneCiM are applied to developing 30 basic logic gates as genetic circuit conceptualizations which respond to heavy metal ions pairs as input signals for Arabidopsis thaliana. For each conceptualization, hundreds of potential solutions were designed and modeled. Demonstrating its time-dependence and the importance of including enzyme and transcript degradation in modeling, EuGeneCiM is used to model a repressilator circuit.

Using EuGeneCiD and EuGeneCiM computational tools for synthetic biology.

Synthetic biology often relies on the design of genetic circuits, utilizing "bioparts" (modular DNA pieces) to accomplish desired responses to external stimuli. While such designs are usually intuited, detailed here is a computational approach to synthetic biology design and modeling using optimization-based tools named Eukaryotic Genetic Circuit Design and Modeling. These allow for designing and subsequent screening of genetic circuits to increase the chances of in vivo success and contribute to the development of an application development pipeline. For complete details on the use and execution of this protocol, please refer to Schroeder, Baber, and Saha (2021).