Inclusion of maintenance energy improves the intracellular flux predictions of CHO.
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ABSTRACT:
SUBMITTER: Szeliova D
PROVIDER: S-EPMC8221792 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell's investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.
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Project description:The rapidly growing mesophilic methanogen Methanococcus maripaludis S2 has a unique ability to consume both CO2 and N2, the main components of a flue gas, and produce methane with H2 as the electron donor. The existing literature lacks experimental measurements of CO2 and H2 uptake rates and CH4 production rates on M. maripaludis. Furthermore, it lacks estimates of maintenance energies for use with genome-scale models. In this paper, we performed batch culture experiments on M. maripaludis S2 using CO2 as the sole carbon substrate to quantify three key extracellular fluxes (CO2, H2, and CH4) along with specific growth rates. For precise computation of these fluxes from experimental measurements, we developed a systematic process simulation approach. Then, using an existing genome-scale model, we proposed an optimization procedure to estimate maintenance energy parameters: growth associated maintenance (GAM) and non-growth associated maintenance (NGAM).The measured extracellular fluxes for M. maripaludis showed excellent agreement with in silico predictions from a validated genome-scale model (iMM518) for NGAM = 7.836 mmol/gDCW/h and GAM = 27.14 mmol/gDCW. M. maripaludis achieved a CO2 to CH4 conversion yield of 70-95 % and a growth yield of 3.549 ± 0.149 g DCW/mol CH4 during the exponential phase. The ATP gain of 0.35 molATP/molCH4 for M. maripaludis, computed using NGAM, is in the acceptable range of 0.3-0.7 mol ATP/molCH4 reported for methanogens. Interestingly, the uptake distribution of amino acids, quantified using iMM518, confirmed alanine to be the most preferred amino acids for growth and methanogenesis.This is the first study to report experimental gas consumption and production rates for the growth of M. maripaludis on CO2 and H2 in minimal media. A systematic process simulation and optimization procedure was successfully developed to precisely quantify extracellular fluxes along with cell growth and maintenance energy parameters. Our growth yields, ATP gain, and energy parameters fall within acceptable ranges known in the literature for hydrogenotrophic methanogens.
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Project description:The reliability of molecular mechanics simulations to predict effects of ion binding to proteins depends on their ability to simultaneously describe ion-protein, ion-water, and protein-water interactions. Force fields (FFs) to describe protein-water and ion-water interactions have been constructed carefully and have also been refined routinely to improve accuracy. Descriptions for ion-protein interactions have also been refined, although in an a posteriori manner through the use of "nonbonded-fix (NB-fix)" approaches in which parameters from default Lennard-Jones mixing rules are replaced with those optimized against some reference data. However, even after NB-fix corrections, there remains a significant need for improvement. This is also true for polarizable FFs that include self-consistent inducible moments. Our recent studies on the polarizable AMOEBA FF suggested that the problem associated with modeling ion-protein interactions could be alleviated by recalibrating polarization models of cation-coordinating functional groups so that they respond better to the high electric fields present near ions. Here, we present such a recalibration of carbonyls, carboxylates, and hydroxyls in the AMOEBA protein FF and report that it does improve predictions substantially─mean absolute errors in Na+-protein and K+-protein interaction energies decrease from 8.7 to 5.3 and 9.6 to 6.3 kcal/mol, respectively. Errors are computed with respect to estimates from van der Waals-inclusive density functional theory benchmarked against high-level quantum mechanical calculations and experiments. While recalibration does improve ion-protein interaction energies, they still remain underestimated, suggesting that further improvements can be made in a systematic manner through modifications in classical formalism. Nevertheless, we show that by applying our many-body NB-fix correction to Lennard-Jones components, these errors are further reduced to 2.7 and 2.6 kcal/mol, respectively, for Na+ and K+ ions. Finally, we show that the recalibrated AMOEBA protein FF retains its intrinsic reliability in predicting protein structure and dynamics in the condensed phase.
| S-EPMC9817009 | biostudies-literature
Project description:The specific cellular physiology of hematopoietic stem cells (HSCs) is underexplored, and their maintenance in vitro remains challenging. We discovered that culture of HSCs in low calcium increased their maintenance as determined by phenotype, function, and single-cell expression signature. HSCs are endowed with low intracellular calcium conveyed by elevated activity of glycolysis-fueled plasma membrane calcium efflux pumps and a low-bone-marrow interstitial fluid calcium concentration. Low-calcium conditions inhibited calpain proteases, which target ten-eleven translocated (TET) enzymes, of which TET2 was required for the effect of low calcium conditions on HSC maintenance in vitro. These observations reveal a physiological feature of HSCs that can be harnessed to improve their maintenance in vitro.
| S-EPMC7649753 | biostudies-literature
Project description:The aim of the present study was to test the hypothesis that a prolonged refeeding duration after successful very-low-energy diet (VLED)-induced weight loss beneficially affects weight development and eating behaviour. Patients (n 269) were recruited to a 1-year obesity treatment programme with 12 weeks of an initial VLED. After the VLED, patients with >or= 10 % weight loss were randomly allocated to 1 week (group 1) or 6 weeks (group 6) refeeding to an ordinary, energy-reduced diet, and thereafter followed and actively treated for an additional 40 weeks. Eating behaviour (revised twenty-one-item Three-Factor Eating Questionnaire) was measured at baseline, during and after refeeding, and at week 52. Weight change over time in the two treatment groups was tested by repeated-measures analysis in completers and by intention to treat (ITT). Of the patients, 169 (109 women) lost >or= 10 % during the VLED and were randomised. At randomisation, weight loss was - 16.5 (SD 3.7) % in group 1 and - 16.7 (SD 4.3) % in group 6 (P = 0.73). Between weeks 12 and 52, completers in group 6 regained significantly less weight (3.9 (SD 9.1) %) as compared with group 1 (8.2 (SD 8.3) %; P = 0.006) (ITT, P = 0.05). Completers in group 6 also maintained a higher level of dietary restraint after refeeding was completed, but eating behaviour did not differ at week 52. Weight change after the refeeding periods were completed did not differ significantly between the groups (P = 0.06). Overall, longer refeeding duration after successful weight loss with a VLED improves weight maintenance in a 1-year perspective.
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Project description:BackgroundConstraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.ResultsAn alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.ConclusionDue to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method's ability to generate condition- and tissue-specific flux predictions in multicellular organisms.
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Project description:Macroecological patterns are likely the result of both stochastically neutral mechanisms and deterministic differences between species. In Madagascar, the simplest stochastically neutral hypothesis - the mid-domain effects (MDE) hypothesis - has already been rejected. However, rejecting the MDE hypothesis does not necessarily refute the existence of all other neutral mechanisms. Here, we test whether adding complexity to a basic neutral model improves predictions of biodiversity patterns. The simplest MDE model assumes that: (1) species' ranges are continuous and unfragmented, (2) are randomly located throughout the landscape, and (3) can be stacked independently and indefinitely. We designed a simulation based on neutral theory that allowed us to weaken each of these assumptions incrementally by adjusting the habitat capacity as well as the likelihood of short- and long-distance dispersal. Simulated outputs were compared to four empirical patterns of bird diversity: the frequency distributions of species richness and range size, the within-island latitudinal diversity gradient, and the distance-decay of species compositional similarity. Neutral models emulated empirical diversity patterns for Madagascan birds accurately. The frequency distribution of range size, latitudinal diversity gradient, and the distance-decay of species compositional similarity could be attributed to stochastic long-distance migration events and zero-sum population dynamics. However, heterogenous environmental gradients improved predictions of the frequency distribution of species richness. Patterns of bird diversity in Madagascar can broadly be attributed to stochastic long-distance migration events and zero-sum population dynamics. This implies that rejecting simple hypotheses, such as MDE, does not serve as evidence against stochastic processes in general. However, environmental gradients were necessary to explain patterns of species richness and deterministic differences between species are probably important for explaining the distributions of narrow-range and endemic species.
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Project description:MOTIVATION:Metabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates. RESULTS:We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis. AVAILABILITY AND IMPLEMENTATION:The COBRA compatible software is available at github.com/markusheinonen/bamfa. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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Project description:The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. It provides precursors for the biosynthesis of nucleotides and contributes to the production of reducing power in the form of NADPH. It has been hypothesized that mammalian cells may contain a hidden reaction in PPP catalyzed by transketolase-like protein 1 (TKTL1) that is closely related to the classical transketolase enzyme; however, until now there has been no direct experimental evidence for this reaction. In this work, we have applied state-of-the-art techniques in (13)C metabolic flux analysis ((13)C-MFA) based on parallel labeling experiments and integrated flux fitting to estimate the TKTL1 flux in CHO cells. We identified a set of three parallel labeling experiments with [1-(13)C]glucose+[4,5,6-(13)C]glucose, [2-(13)C]glucose+[4,5,6-(13)C]glucose, and [3-(13)C]glucose+[4,5,6-(13)C]glucose and developed a new method to measure (13)C-labeling of fructose 6-phosphate by GC-MS that allows intuitive interpretation of mass isotopomer distributions to determine key fluxes in the model, including glycolysis, oxidative PPP, non-oxidative PPP, and the TKTL1 flux. Using these tracers we detected a significant TKTL1 flux in CHO cells at the stationary phase. The flux results suggest that the main function of oxidative PPP in CHO cells at the stationary phase is to fuel the TKTL1 reaction. Overall, this study demonstrates for the first time that carbon atoms can be lost in the PPP, by means other than the oxidative PPP, and that this loss of carbon atoms is consistent with the hypothesized TKTL1 reaction in mammalian cells.
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Project description:Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps.
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