Accuracy of intraoperative aberrometry versus modern preoperative methods in post-myopic laser vision correction eyes undergoing cataract surgery with capsular tension ring placement.

PURPOSE: To assess the accuracy of intraoperative wavefront aberrometry (IWA) versus modern intraocular lens formulas in post-myopic laser vision correction (LVC) patients undergoing cataract surgery with capsular tension ring placement. METHODS: This is a retrospective chart review conducted at an academic outpatient center. All post-myopic LVC eyes undergoing cataract surgery with IWA from a single surgeon from 05/2017 to 12/2019 were included. All patients received a capsular tension ring (CTR). Mean numerical error (MNE), median numerical error (MedNE), and percentages of prediction error within 0.50D, 0.75D, and 1.00D were calculated for the above formulas. RESULTS: Twenty-seven post-myopic LVC eyes from 18 patients were included. In post-myopic LVC, MNE with Optiwave Refractive Analysis (ORA), Barrett True K (BTK), Haigis, Haigis-L, Shammas, SRK/T, Hill-RBF v3.0, and W-K AL-adjusted Holladay 1 were + 0.224, - 0.094, + 0.193, - 0.231, - 0.372, + 1.013, + 0.860, and + 0.630 (F = 8.49, p < 0.001). MedNE were + 0.125, - 0.145, + 0.175, + 0.333, + 0.333, + 1.100, + 0.880, and + 0.765 (F = 7.89, p < 0.001), respectively. BTK provided improved accuracy in both MNE (p < 0.001) and MedNE (p = .033) when compared to ORA in pairwise analysis. If the ORA vs. BTK-suggested IOL power were routinely selected, 30% and 15% of eyes would have projected hyperopic outcomes, respectively (p = 0.09). CONCLUSIONS: Our study suggests that in post-myopic LVC eyes undergoing cataract surgery with CTRs, BTK performed more accurately than ORA with regard to accuracy and yielded a lower percentage of eyes with hyperopic outcomes. Haigis, Haigis-L, and Shammas yielded similar results to ORA with regard to accuracy and percentage of eyes with hyperopic outcomes. On average, Shammas and Haigis-L suggested IOLs that would yield outcomes more myopic than expected when compared to BTK.

Validation of the Prognostic Usefulness of the Gene Expression Profiling Test in Patients with Uveal Melanoma.

PURPOSE: To validate the prognostic usefulness of gene expression profile (GEP) testing in patients with uveal melanoma. To determine whether combining tumor size with the GEP classification provides additional prognostic value. DESIGN: Retrospective analysis. PARTICIPANTS: Patients with a diagnosis of choroidal melanoma examined at Yale New Haven Hospital; University of California, San Diego; and Memorial Sloan Kettering Cancer Center. METHODS: Patients' demographic and clinical data and tumor characteristics were collected. Univariate and multivariate Cox hazard regression analysis were used to assess the association between tumor characteristics and GEP classification with metastasis as an outcome. MAIN OUTCOME MEASURES: Metastasis-free survival (MFS). RESULTS: Of the 337 individuals included in the study, 87 demonstrated metastases. The mean follow-up time was 37.2 (standard deviation [SD], 40.2) months for patients with metastases and 55.0 (SD, 49.3) months for those without metastases. Tumors of larger thickness and GEP class 2 (vs. class 1) were associated significantly with increased risk of metastasis. Tumor thickness showed better prognostic usefulness than GEP classification (Wald statistic, 40.7 and 24.2, respectively). Class 2 tumors with a thickness of 7.0 mm or more were associated with increased risk of metastasis than tumors with a thickness of < 7.0 mm (hazard ratio [HR], 3.23; 95% confidence interval [CI], 1.61-6.51), whereas class 1 tumors with a thickness of 9.0 mm or more were associated with increased risk of metastasis than tumors with a thickness of < 9.0 mm (HR, 2.07; 95% CI, 0.86-4.99). No difference in MFS was found between patients with class 1A tumors compared with those with class 1B tumors (P = 0.8). Patients with class 2 tumors showed an observed 5-year MFS of 47.5% (95% CI, 36.0%-62.8%). CONCLUSIONS: Tumor size was the most significant predictor of metastasis and provided additional prognostic value independent of GEP classification. In addition, rates of metastasis for class 2 tumors were lower than estimates reported by Castle Bioscience, and no difference in rates of metastasis were found between class 1A and 1B tumors. This indicates that tumor size should be accounted for when relying on GEP for prognostication and that patients with GEP class 1A or 1B tumors may benefit from the same metastatic surveillance protocols. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli.

Unraveling the mechanisms of microbial adaptive evolution following genetic or environmental challenges is of fundamental interest in biological science and engineering. When the challenge is the loss of a metabolic enzyme, adaptive responses can also shed significant insight into metabolic robustness, regulation, and areas of kinetic limitation. In this study, whole-genome sequencing and high-resolution 13C-metabolic flux analysis were performed on 10 adaptively evolved pgi knockouts of Escherichia coliPgi catalyzes the first reaction in glycolysis, and its loss results in major physiological and carbon catabolism pathway changes, including an 80% reduction in growth rate. Following adaptive laboratory evolution (ALE), the knockouts increase their growth rate by up to 3.6-fold. Through combined genomic-fluxomic analysis, we characterized the mutations and resulting metabolic fluxes that enabled this fitness recovery. Large increases in pyridine cofactor transhydrogenase flux, correcting imbalanced production of NADPH and NADH, were enabled by direct mutations to the transhydrogenase genes sthA and pntAB The phosphotransferase system component crr was also found to be frequently mutated, which corresponded to elevated flux from pyruvate to phosphoenolpyruvate. The overall energy metabolism was found to be strikingly robust, and what have been previously described as latently activated Entner-Doudoroff and glyoxylate shunt pathways are shown here to represent no real increases in absolute flux relative to the wild type. These results indicate that the dominant mechanism of adaptation was to relieve the rate-limiting steps in cofactor metabolism and substrate uptake and to modulate global transcriptional regulation from stress response to catabolism.

Colorectal carcinoma presenting in the orbit: mass effect from an uncommon cause.

An 84-year-old male with previously documented poor medical follow-up presented with progressive painless proptosis of the right eye. Right upper eyelid ptosis, limited motility, proptosis, and inferomedial displacement of the right globe were noted on the exam. Computed tomography (CT) imaging revealed a right retrobulbar extraconal heterogenous mass with ill-defined borders. Biopsy revealed a malignant adenocarcinoma with tumor markers suggestive of a colorectal primary. A rectal mass was identified during a systemic workup. After biopsy, the patient was diagnosed with stage IV metastatic rectal adenocarcinoma. He began palliative radiation therapy shortly following diagnosis.

Evaluation of Electronic Health Record Implementation in an Academic Oculoplastics Practice.

PURPOSE: Despite increasing electronic health record (EHR) adoption, perceptions of EHRs are negative among ophthalmologists due to concerns about productivity, costs, and documentation. The authors evaluated the effects of EHR adoption in an oculoplastics practice, which had not been previously studied. METHODS: Clinical volume, documentation time, time spent with patients, reimbursement, relative value units, and patient satisfaction were examined for 2 academic oculoplastics attendings between April 2018 and April 2019, with EHR implementation in September 2018. RESULTS: The mean number of patients seen in a half-day clinic was 31.8 versus 27.7 (p = 0.018) pre- and post-EHR implementation, respectively. EHR implementation had no effect on total monthly reimbursement (p = 0.88) or total monthly relative value units (p = 0.54). Average reimbursement (p = 0.004) and relative value units (p = 0.001) per patient encounter were significantly greater with EHR use. Patient satisfaction scores improved (p = 0.018). Mean physician time per patient increased from 6.4 to 9.0 minutes (p < 0.001). Mean documentation time per patient increased from 1.7 to 3.6 minutes (p < 0.001). Average patient wait times decreased by 9 minutes (p = 0.03) with EHR use. No scribes were used. CONCLUSIONS: EHR implementation was associated with decreased patient volume without significant differences in total reimbursement. Although EHR adoption was associated with increased physician time devoted to patients and greater time expenditure on documentation, patients experienced decreased wait times. This suggests that EHR use streamlined the overall clinic flow without sacrificing physicians' time with the patient. The author's findings suggest that EHR implementation can be accomplished in an academic oculoplastics setting without negative impact on patient experience or reimbursement considerations.

Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism.

Despite remarkable progress in mapping biochemistry and gene-protein-reaction relationships, quantitative systems-level understanding of microbial metabolism remains a persistent challenge. Here, 13C-metabolic flux analysis was applied to interrogate metabolic responses to 20 genetic perturbations in all viable Escherichia coli single gene knockouts in upper central metabolic pathways. Strains with severe growth defects displayed highly altered intracellular flux patterns and were the most difficult to predict using current constraint-based modeling approaches. In the ΔpfkA strain, an unexpected glucose-secretion phenotype was identified. The broad range of flux rewiring responses that were quantified suggest that some compensating pathways are more flexible than others, resulting in a more robust physiology. The fact that only 2 out of 20 strains displayed an increased net pathway-flux capacity points to a fundamental rate limitation of E. coli core metabolism. In cataloguing the various cellular responses, our results provide a critical resource for kinetic model development and efforts focused on genotype-to-phenotype predictions.

Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding.

In this study, we have investigated for the first time the metabolism of E. coli grown on agar using 13C metabolic flux analysis (13C-MFA). To date, all 13C-MFA studies on microbes have been performed with cells grown in liquid culture. Here, we extend the scope of 13C-MFA to biological systems where cells are grown in dense microbial colonies. First, we identified new optimal 13C tracers to quantify fluxes in systems where the acetate yield cannot be easily measured. We determined that three parallel labeling experiments with the tracers [1,2-13C]glucose, [1,6-13C]glucose, and [4,5,6-13C]glucose permit precise estimation of not only intracellular fluxes, but also of the amount of acetate produced from glucose. Parallel labeling experiments were then performed with wild-type E. coli and E. coli ΔackA grown in liquid culture and on agar plates. Initial attempts to fit the labeling data from wild-type E. coli grown on agar did not produce a statistically acceptable fit. To resolve this issue, we employed the recently developed co-culture 13C-MFA approach, where two E. coli subpopulations were defined in the model that engaged in metabolite cross-feeding. The flux results identified two distinct E. coli cell populations, a dominant cell population (92% of cells) that metabolized glucose via conventional metabolic pathways and secreted a large amount of acetate (~40% of maximum theoretical yield), and a second smaller cell population (8% of cells) that consumed the secreted acetate without any glucose influx. These experimental results are in good agreement with recent theoretical simulations. Importantly, this study provides a solid foundation for future investigations of a wide range of problems involving microbial biofilms that are of great interest in biotechnology, ecology and medicine, where metabolite cross-feeding between cell populations is a core feature of the communities.

Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis.

Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no 13C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in 13C metabolic flux analysis (13C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated 13C-MFA using the optimal tracers [1,2-13C]glucose, [1,6-13C]glucose, [1,2-13C]xylose and [5-13C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-13C]glucose and [U-13C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO2 originates from biomass turnover. Using knockout strains, we also demonstrated that ß-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH2, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.

Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis.

Vibrio natriegens is a fast-growing, non-pathogenic bacterium that is being considered as the next-generation workhorse for the biotechnology industry. However, little is known about the metabolism of this organism which is limiting our ability to apply rational metabolic engineering strategies. To address this critical gap in current knowledge, here we have performed a comprehensive analysis of V. natriegens metabolism. We constructed a detailed model of V. natriegens core metabolism, measured the biomass composition, and performed high-resolution 13C metabolic flux analysis (13C-MFA) to estimate intracellular fluxes using parallel labeling experiments with the optimal tracers [1,2-13C]glucose and [1,6-13C]glucose. During exponential growth in glucose minimal medium, V. natriegens had a growth rate of 1.70 1/h (doubling time of 24min) and a glucose uptake rate of 3.90g/g/h, which is more than two 2-fold faster than E. coli, although slower than the fast-growing thermophile Geobacillus LC300. 13C-MFA revealed that the core metabolism of V. natriegens is similar to that of E. coli, with the main difference being a 33% lower normalized flux through the oxidative pentose phosphate pathway. Quantitative analysis of co-factor balances provided additional insights into the energy and redox metabolism of V. natriegens. Taken together, the results presented in this study provide valuable new information about the physiology of V. natriegens and establish a solid foundation for future metabolic engineering efforts with this promising microorganism.

13C metabolic flux analysis of three divergent extremely thermophilic bacteria: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252.

Thermophilic organisms are being increasingly investigated and applied in metabolic engineering and biotechnology. The distinct metabolic and physiological characteristics of thermophiles, including broad substrate range and high uptake rates, coupled with recent advances in genetic tool development, present unique opportunities for strain engineering. However, poor understanding of the cellular physiology and metabolism of thermophiles has limited the application of systems biology and metabolic engineering tools to these organisms. To address this concern, we applied high resolution 13C metabolic flux analysis to quantify fluxes for three divergent extremely thermophilic bacteria from separate phyla: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. We performed 18 parallel labeling experiments, using all singly labeled glucose tracers for each strain, reconstructed and validated metabolic network models, measured biomass composition, and quantified precise metabolic fluxes for each organism. In the process, we resolved many uncertainties regarding gaps in pathway reconstructions and elucidated how these organisms maintain redox balance and generate energy. Overall, we found that the metabolisms of the three thermophiles were highly distinct, suggesting that adaptation to growth at high temperatures did not favor any particular set of metabolic pathways. All three strains relied heavily on glycolysis and TCA cycle to generate key cellular precursors and cofactors. None of the investigated organisms utilized the Entner-Doudoroff pathway and only one strain had an active oxidative pentose phosphate pathway. Taken together, the results from this study provide a solid foundation for future model building and engineering efforts with these and related thermophiles.

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring.

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

Heterotrophic bacteria from an extremely phosphate-poor lake have conditionally reduced phosphorus demand and utilize diverse sources of phosphorus.

Heterotrophic Proteobacteria and Actinobacteria were isolated from Lake Matano, Indonesia, a stratified, ferruginous (iron-rich), ultra-oligotrophic lake with phosphate concentrations below 50 nM. Here, we describe the growth of eight strains of heterotrophic bacteria on a variety of soluble and insoluble sources of phosphorus. When transferred to medium without added phosphorus (P), the isolates grow slowly, their RNA content falls to as low as 1% of cellular dry weight, and 86-100% of the membrane lipids are replaced with amino- or glycolipids. Similar changes in lipid composition have been observed in marine photoautotrophs and soil heterotrophs, and similar flexibility in phosphorus sources has been demonstrated in marine and soil-dwelling heterotrophs. Our results demonstrate that heterotrophs isolated from this unusual environment alter their macromolecular composition, which allows the organisms to grow efficiently even in their extremely phosphorus-limited environment.

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system.

13C-Metabolic flux analysis (13C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13C-MFA. The best single tracers were doubly 13C-labeled glucose tracers, including [1,6-13C]glucose, [5,6-13C]glucose and [1,2-13C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-13C]glucose and [1,2-13C]glucose. Combined analysis of [1,6-13C]glucose and [1,2-13C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-13C]glucose +20% [U-13C]glucose.

13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling.

13C metabolic flux analysis (13C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, 13C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for 13C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for 13C-MFA, we have used Escherichia coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for 13C-MFA will have widespread applicability in metabolic engineering and biomedical research.

Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism.

Understanding the impact of gene knockouts on cellular physiology, and metabolism in particular, is centrally important to quantitative systems biology and metabolic engineering. Here, we present a comprehensive physiological characterization of wild-type Escherichia coli and 22 knockouts of enzymes in the upper part of central carbon metabolism, including the PTS system, glycolysis, pentose phosphate pathway and Entner-Doudoroff pathway. Our results reveal significant metabolic changes that are affected by specific gene knockouts. Analysis of collective trends and correlations in the data using principal component analysis (PCA) provide new, and sometimes surprising, insights into E. coli physiology. Additionally, by comparing the data-to-model predictions from constraint-based approaches such as FBA, MOMA and RELATCH we demonstrate the important role of less well-understood kinetic and regulatory effects in central carbon metabolism.

Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing.

We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and (13)C-metabolic flux analysis ((13)C-MFA) with [1,6-(13)C]glucose, [5-(13)C]xylose, and [1,6-(13)C]glucose+[5-(13)C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90°C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81°C, the maximum growth rate on glucose and xylose was 0.44 and 0.46h(-1), respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. (13)C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, (13)C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, (13)C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.

Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli.

The use of parallel labeling experiments for (13)C metabolic flux analysis ((13)C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-(13)C]glucose and mixtures of [1-(13)C]glucose and [U-(13)C]glucose, four novel tracers were applied in this study: [2,3-(13)C]glucose, [4,5,6-(13)C]glucose, [2,3,4,5,6-(13)C]glucose and a mixture of [1-(13)C]glucose and [4,5,6-(13)C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-(13)C]glucose+20% [U-(13)C]glucose, while [4,5,6-(13)C]glucose and [5-(13)C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.

Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium.

We have isolated a new extremely thermophilic fast-growing Geobacillus strain that can efficiently utilize xylose, glucose, mannose and galactose for cell growth. When grown aerobically at 72 °C, Geobacillus LC300 has a growth rate of 2.15 h(-1) on glucose and 1.52 h(-1) on xylose (doubling time less than 30 min). The corresponding specific glucose and xylose utilization rates are 5.55 g/g/h and 5.24 g/g/h, respectively. As such, Geobacillus LC300 grows 3-times faster than E. coli on glucose and xylose, and has a specific xylose utilization rate that is 3-times higher than the best metabolically engineered organism to date. To gain more insight into the metabolism of Geobacillus LC300 its genome was sequenced using PacBio's RS II single-molecule real-time (SMRT) sequencing platform and annotated using the RAST server. Based on the genome annotation and the measured biomass composition a core metabolic network model was constructed. To further demonstrate the biotechnological potential of this organism, Geobacillus LC300 was grown to high cell-densities in a fed-batch culture, where cells maintained a high xylose utilization rate under low dissolved oxygen concentrations. All of these characteristics make Geobacillus LC300 an attractive host for future metabolic engineering and biotechnology applications.