Metabolic model guided CRISPRi identifies 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 reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a 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 that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence. IMPORTANCE: This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

Optimal Protein Allocation Controls the Inhibition of GltA and AcnB in Neisseria gonorrhoeae.

Neisseria gonorrhea (Ngo) is a major concern for global public health due to its severe implications for reproductive health. Understanding its metabolic phenotype is crucial for comprehending its pathogenicity. Despite Ngo's ability to encode TCA cycle proteins, GltA and AcnB, their activities are notably restricted. To investigate this phenomenon, we used the iNgo_557 metabolic model and incorporated a constraint on total cellular protein content. Our results indicate that low cellular protein content severely limits GltA and AcnB activity, leading to a shift towards acetate overflow for ATP production, which is more efficient in terms of protein usage. Surprisingly, increasing cellular protein content alleviates this restriction on GltA and AcnB and delays the onset of acetate overflow, highlighting protein allocation as a critical determinant in understanding Ngo's metabolic phenotype. These findings underscore the significance of Ngo's metabolic adaptation in light of optimal protein allocation, providing a blueprint to understand Ngo's metabolic landscape.

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.

The Inherited Intestinal Microbiota from Myeloid-Specific ZIP8KO Mice Impairs Pulmonary Host Defense against Pneumococcal Pneumonia.

Intestinal dysbiosis increases susceptibility to infection through the alteration of metabolic profiles, which increases morbidity. Zinc (Zn) homeostasis in mammals is tightly regulated by 24 Zn transporters. ZIP8 is unique in that it is required by myeloid cells to maintain proper host defense against bacterial pneumonia. In addition, a frequently occurring ZIP8 defective variant (SLC39A8 rs13107325) is strongly associated with inflammation-based disorders and bacterial infection. In this study, we developed a novel model to study the effects of ZIP8-mediated intestinal dysbiosis on pulmonary host defense independent of the genetic effects. Cecal microbial communities from a myeloid-specific Zip8 knockout mouse model were transplanted into germ-free mice. Conventionalized ZIP8KO-microbiota mice were then bred to produce F1 and F2 generations of ZIP8KO-microbiota mice. F1 ZIP8KO-microbiota mice were also infected with S. pneumoniae, and pulmonary host defense was assessed. Strikingly, the instillation of pneumococcus into the lung of F1 ZIP8KO-microbiota mice resulted in a significant increase in weight loss, inflammation, and mortality when compared to F1 wild-type (WT)-microbiota recipients. Similar defects in pulmonary host defense were observed in both genders, although consistently greater in females. From these results, we conclude that myeloid Zn homeostasis is not only critical for myeloid function but also plays a significant role in the maintenance and control of gut microbiota composition. Further, these data demonstrate that the intestinal microbiota, independent of host genetics, play a critical role in governing host defense in the lung against infection. Finally, these data strongly support future microbiome-based interventional studies, given the high incidence of zinc deficiency and the rs13107325 allele in humans.

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.

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.

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).

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.