Mitochondrial ATP generation is more proteome efficient than glycolysis.

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states.

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973.

Cyanobacteria are photosynthetic organisms that have garnered significant recognition as potential hosts for sustainable bioproduction. However, their complex regulatory networks pose significant challenges to major metabolic engineering efforts, thereby limiting their feasibility as production hosts. Genome streamlining has been demonstrated to be a successful approach for improving productivity and fitness in heterotrophs but is yet to be explored to its full potential in phototrophs. Here, we present the systematic reduction of the genome of the cyanobacterium exhibiting the fastest exponential growth, Synechococcus elongatus UTEX 2973. This work, the first of its kind in a photoautotroph, involved an iterative process using state-of-the-art genome-editing technology guided by experimental analysis and computational tools. CRISPR-Cas3 enabled large, progressive deletions of predicted dispensable regions and aided in the identification of essential genes. The large deletions were combined to obtain a strain with 55-kb genome reduction. The strains with streamlined genome showed improvement in growth (up to 23%) and productivity (by 22.7%) as compared to the wild type (WT). This streamlining strategy not only has the potential to develop cyanobacterial strains with improved growth and productivity traits but can also facilitate a better understanding of their genome-to-phenome relationships.IMPORTANCEGenome streamlining is an evolutionary strategy used by natural living systems to dispense unnecessary genes from their genome as a mechanism to adapt and evolve. While this strategy has been successfully borrowed to develop synthetic heterotrophic microbial systems with desired phenotype, it has not been extensively explored in photoautotrophs. Genome streamlining strategy incorporates both computational predictions to identify the dispensable regions and experimental validation using genome-editing tool, and in this study, we have employed a modified strategy with the goal to minimize the genome size to an extent that allows optimal cellular fitness under specified conditions. Our strategy has explored a novel genome-editing tool in photoautotrophs, which, unlike other existing tools, enables large, spontaneous optimal deletions from the genome. Our findings demonstrate the effectiveness of this modified strategy in obtaining strains with streamlined genome, exhibiting improved fitness and productivity.

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.

Little Brown Bats (Myotis lucifugus) Support the Binding of SARS-CoV-2 Spike and Are Likely Susceptible to SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), believed to have originated from a bat species, can infect a wide range of non-human hosts. Bats are known to harbor hundreds of coronaviruses capable of spillover into human populations. Recent studies have shown a significant variation in the susceptibility among bat species to SARS-CoV-2 infection. We show that little brown bats (LBB) express angiotensin-converting enzyme 2 receptor and the transmembrane serine protease 2, which are accessible to and support SARS-CoV-2 binding. All-atom molecular dynamics (MD) simulations revealed that LBB ACE2 formed strong electrostatic interactions with the RBD similar to human and cat ACE2 proteins. In summary, LBBs, a widely distributed North American bat species, could be at risk of SARS-CoV-2 infection and potentially serve as a natural reservoir. Finally, our framework, combining in vitro and in silico methods, is a useful tool to assess the SARS-CoV-2 susceptibility of bats and other animal species.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis.

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (kapp) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis.

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PPi) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PPi sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PPi synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PPi-generating cycles. In all cases, the metabolic cost of PPi synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PPi could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PPi sources likely account for most PPi production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PPi levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

Metabolic engineering of low-pH-tolerant non-model yeast, Issatchenkia orientalis, for production of citramalate.

Methyl methacrylate (MMA) is an important petrochemical with many applications. However, its manufacture has a large environmental footprint. Combined biological and chemical synthesis (semisynthesis) may be a promising alternative to reduce both cost and environmental impact, but strains that can produce the MMA precursor (citramalate) at low pH are required. A non-conventional yeast, Issatchenkia orientalis, may prove ideal, as it can survive extremely low pH. Here, we demonstrate the engineering of I. orientalis for citramalate production. Using sequence similarity network analysis and subsequent DNA synthesis, we selected a more active citramalate synthase gene (cimA) variant for expression in I. orientalis. We then adapted a piggyBac transposon system for I. orientalis that allowed us to simultaneously explore the effects of different cimA gene copy numbers and integration locations. A batch fermentation showed the genome-integrated-cimA strains produced 2.0 g/L citramalate in 48 h and a yield of up to 7% mol citramalate/mol consumed glucose. These results demonstrate the potential of I. orientalis as a chassis for citramalate production.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale.

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD).

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

SARS-CoV-2 Omicron (B.1.1.529) Infection of Wild White-Tailed Deer in New York City.

There is mounting evidence of SARS-CoV-2 spillover from humans into many domestic, companion, and wild animal species. Research indicates that humans have infected white-tailed deer, and that deer-to-deer transmission has occurred, indicating that deer could be a wildlife reservoir and a source of novel SARS-CoV-2 variants. We examined the hypothesis that the Omicron variant is actively and asymptomatically infecting the free-ranging deer of New York City. Between December 2021 and February 2022, 155 deer on Staten Island, New York, were anesthetized and examined for gross abnormalities and illnesses. Paired nasopharyngeal swabs and blood samples were collected and analyzed for the presence of SARS-CoV-2 RNA and antibodies. Of 135 serum samples, 19 (14.1%) indicated SARS-CoV-2 exposure, and 11 reacted most strongly to the wild-type B.1 lineage. Of the 71 swabs, 8 were positive for SARS-CoV-2 RNA (4 Omicron and 4 Delta). Two of the animals had active infections and robust neutralizing antibodies, revealing evidence of reinfection or early seroconversion in deer. Variants of concern continue to circulate among and may reinfect US deer populations, and establish enzootic transmission cycles in the wild: this warrants a coordinated One Health response, to proactively surveil, identify, and curtail variants of concern before they can spill back into humans.

Development and Validation of Indirect Enzyme-Linked Immunosorbent Assays for Detecting Antibodies to SARS-CoV-2 in Cattle, Swine, and Chicken.

Multiple domestic and wild animal species are susceptible to SARS-CoV-2 infection. Cattle and swine are susceptible to experimental SARS-CoV-2 infection. The unchecked transmission of SARS-CoV-2 in animal hosts could lead to virus adaptation and the emergence of novel variants. In addition, the spillover and subsequent adaptation of SARS-CoV-2 in livestock could significantly impact food security as well as animal and public health. Therefore, it is essential to monitor livestock species for SARS-CoV-2 spillover. We developed and optimized species-specific indirect ELISAs (iELISAs) to detect anti-SARS-CoV-2 antibodies in cattle, swine, and chickens using the spike protein receptor-binding domain (RBD) antigen. Serum samples collected prior to the COVID-19 pandemic were used to determine the cut-off threshold. RBD hyperimmunized sera from cattle (n = 3), swine (n = 6), and chicken (n = 3) were used as the positive controls. The iELISAs were evaluated compared to a live virus neutralization test using cattle (n = 150), swine (n = 150), and chicken (n = 150) serum samples collected during the COVID-19 pandemic. The iELISAs for cattle, swine, and chicken were found to have 100% sensitivity and specificity. These tools facilitate the surveillance that is necessary to quickly identify spillovers into the three most important agricultural species worldwide.

Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling.

Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.

A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues.

The cellular entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the association of its receptor binding domain (RBD) with human angiotensin converting enzyme 2 (hACE2) as the first crucial step. Efficient and reliable prediction of RBD-hACE2 binding affinity changes upon amino acid substitutions can be valuable for public health surveillance and monitoring potential spillover and adaptation into non-human species. Here, we introduce a convolutional neural network (CNN) model trained on protein sequence and structural features to predict experimental RBD-hACE2 binding affinities of 8,440 variants upon single and multiple amino acid substitutions in the RBD or ACE2. The model achieves a classification accuracy of 83.28% and a Pearson correlation coefficient of 0.85 between predicted and experimentally calculated binding affinities in five-fold cross-validation tests and predicts improved binding affinity for most circulating variants. We pro-actively used the CNN model to exhaustively screen for novel RBD variants with combinations of up to four single amino acid substitutions and suggested candidates with the highest improvements in RBD-ACE2 binding affinity for human and animal ACE2 receptors. We found that the binding affinity of RBD variants against animal ACE2s follows similar trends as those against human ACE2. White-tailed deer ACE2 binds to RBD almost as tightly as human ACE2 while cattle, pig, and chicken ACE2s bind weakly. The model allows testing whether adaptation of the virus for increased binding with other animals would cause concomitant increases in binding with hACE2 or decreased fitness due to adaptation to other hosts.

Developmental changes in lignin composition are driven by both monolignol supply and laccase specificity.

The factors controlling lignin composition remain unclear. Catechyl (C)-lignin is a homopolymer of caffeyl alcohol with unique properties as a biomaterial and precursor of industrial chemicals. The lignin synthesized in the seed coat of Cleome hassleriana switches from guaiacyl (G)- to C-lignin at around 12 to 14 days after pollination (DAP), associated with a rerouting of the monolignol pathway. Lack of synthesis of caffeyl alcohol limits C-lignin formation before around 12 DAP, but coniferyl alcohol is still synthesized and highly accumulated after 14 DAP. We propose a model in which, during C-lignin biosynthesis, caffeyl alcohol noncompetitively inhibits oxidation of coniferyl alcohol by cell wall laccases, a process that might limit movement of coniferyl alcohol to the apoplast. Developmental changes in both substrate availability and laccase specificity together account for the metabolic fates of G- and C-monolignols in the Cleome seed coat.

Multiple spillovers from humans and onward transmission of SARS-CoV-2 in white-tailed deer.

Many animal species are susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and could act as reservoirs; however, transmission in free-living animals has not been documented. White-tailed deer, the predominant cervid in North America, are susceptible to SARS-CoV-2 infection, and experimentally infected fawns can transmit the virus. To test the hypothesis that SARS-CoV-2 is circulating in deer, 283 retropharyngeal lymph node (RPLN) samples collected from 151 free-living and 132 captive deer in Iowa from April 2020 through January of 2021 were assayed for the presence of SARS-CoV-2 RNA. Ninety-four of the 283 (33.2%) deer samples were positive for SARS-CoV-2 RNA as assessed by RT-PCR. Notably, following the November 2020 peak of human cases in Iowa, and coinciding with the onset of winter and the peak deer hunting season, SARS-CoV-2 RNA was detected in 80 of 97 (82.5%) RPLN samples collected over a 7-wk period. Whole genome sequencing of all 94 positive RPLN samples identified 12 SARS-CoV-2 lineages, with B.1.2 (n = 51; 54.5%) and B.1.311 (n = 19; 20%) accounting for ∼75% of all samples. The geographic distribution and nesting of clusters of deer and human lineages strongly suggest multiple human-to-deer transmission events followed by subsequent deer-to-deer spread. These discoveries have important implications for the long-term persistence of the SARS-CoV-2 pandemic. Our findings highlight an urgent need for a robust and proactive "One Health" approach to obtain enhanced understanding of the ecology, molecular evolution, and dissemination of SARS-CoV-2.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model.

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13C-metabolic flux analysis (13C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13C labeling data were used with 13C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Quantifying the propagation of parametric uncertainty on flux balance analysis.

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

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.

Functional Analysis of H+-Pumping Membrane-Bound Pyrophosphatase, ADP-Glucose Synthase, and Pyruvate Phosphate Dikinase as Pyrophosphate Sources in Clostridium thermocellum.

The atypical glycolysis of Clostridium thermocellum is characterized by the use of pyrophosphate (PPi) as a phosphoryl donor for phosphofructokinase (Pfk) and pyruvate phosphate dikinase (Ppdk) reactions. Previously, biosynthetic PPi was calculated to be stoichiometrically insufficient to drive glycolysis. This study investigates the role of a H+-pumping membrane-bound pyrophosphatase, glycogen cycling, a predicted Ppdk-malate shunt cycle, and acetate cycling in generating PPi. Knockout studies and enzyme assays confirmed that clo1313_0823 encodes a membrane-bound pyrophosphatase. Additionally, clo1313_0717-0718 was confirmed to encode ADP-glucose synthase by knockouts, glycogen measurements in C. thermocellum, and heterologous expression in Escherichia coli. Unexpectedly, individually targeted gene deletions of the four putative PPi sources did not have a significant phenotypic effect. Although combinatorial deletion of all four putative PPi sources reduced the growth rate by 22% (0.30 ± 0.01 h-1) and the biomass yield by 38% (0.18 ± 0.00 gbiomass gsubstrate-1), this change was much smaller than what would be expected for stoichiometrically essential PPi-supplying mechanisms. Growth-arrested cells of the quadruple knockout readily fermented cellobiose, indicating that the unknown PPi-supplying mechanisms are independent of biosynthesis. An alternative hypothesis that ATP-dependent Pfk activity circumvents a need for PPi altogether was falsified by enzyme assays, heterologous expression of candidate genes, and whole-genome sequencing. As a secondary outcome, enzymatic assays confirmed functional annotation of clo1313_1832 as ATP- and GTP-dependent fructokinase. These results indicate that the four investigated PPi sources individually and combined play no significant PPi-supplying role, and the true source(s) of PPi, or alternative phosphorylating mechanisms, that drive(s) glycolysis in C. thermocellum remain(s) elusive. IMPORTANCE Increased understanding of the central metabolism of C. thermocellum is important from a fundamental as well as from a sustainability and industrial perspective. In addition to showing that H+-pumping membrane-bound PPase, glycogen cycling, a Ppdk-malate shunt cycle, and acetate cycling are not significant sources of PPi supply, this study adds functional annotation of four genes and availability of an updated PPi stoichiometry from biosynthesis to the scientific domain. Together, this aids future metabolic engineering attempts aimed to improve C. thermocellum as a cell factory for sustainable and efficient production of ethanol from lignocellulosic material through consolidated bioprocessing with minimal pretreatment. Getting closer to elucidating the elusive source of PPi, or alternative phosphorylating mechanisms, for the atypical glycolysis is itself of fundamental importance. Additionally, the findings of this study directly contribute to investigations into trade-offs between thermodynamic driving force versus energy yield of PPi- and ATP-dependent glycolysis.