A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coli

Chemical reservoir computation in a self-organizing reaction network

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.

OsTH1 is a key player in thiamin biosynthesis in rice

Thiamin is a vital nutrient that acts as a cofactor for several enzymes primarily localized in the mitochondria. These thiamin-dependent enzymes are involved in energy metabolism, nucleic acid biosynthesis, and antioxidant machinery. The enzyme HMP-P kinase/thiamin monophosphate synthase (TH1) holds a key position in thiamin biosynthesis, being responsible for the phosphorylation of HMP-P into HMP-PP and for the condensation of HMP-PP and HET-P to form TMP. Through mathematical kinetic model, we have identified TH1 as a critical player for thiamin biofortification in rice. We further focused on the functional characterization of OsTH1. Sequence and gene expression analysis, along with phylogenetic studies, provided insights into OsTH1 bifunctional features and evolution. The indispensable role of OsTH1 in thiamin biosynthesis was validated by heterologous expression of OsTH1 and successful complementation of yeast knock-out mutants impaired in thiamin production. We also proved that the sole OsTH1 overexpression in rice callus significantly improves B1 concentration, resulting in 50% increase in thiamin accumulation. Our study underscores the critical role of OsTH1 in thiamin biosynthesis, shedding light on its bifunctional nature and evolutionary significance. The significant enhancement of thiamin accumulation in rice callus upon OsTH1 overexpression constitutes evidence of its potential application in biofortification strategies.

Omics data for sampling thermodynamically feasible kinetic models

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

Metabolic engineering to improve production of 3-hydroxypropionic acid from corn-stover hydrolysate in Aspergillus species

BACKGROUND

Fuels and chemicals derived from non-fossil sources are needed to lessen human impacts on the environment while providing a healthy and growing economy. 3-hydroxypropionic acid (3-HP) is an important chemical building block that can be used for many products. Biosynthesis of 3-HP is possible; however, low production is typically observed in those natural systems. Biosynthetic pathways have been designed to produce 3-HP from a variety of feedstocks in different microorganisms.

RESULTS

In this study, the 3-HP β-alanine pathway consisting of aspartate decarboxylase, β-alanine-pyruvate aminotransferase, and 3-hydroxypropionate dehydrogenase from selected microorganisms were codon optimized for Aspergillus species and placed under the control of constitutive promoters. The pathway was introduced into Aspergillus pseudoterreus and subsequently into Aspergillus niger, and 3-HP production was assessed in both hosts. A. niger produced higher initial 3-HP yields and fewer co-product contaminants and was selected as a suitable host for further engineering. Proteomic and metabolomic analysis of both Aspergillus species during 3-HP production identified genetic targets for improvement of flux toward 3-HP including pyruvate carboxylase, aspartate aminotransferase, malonate semialdehyde dehydrogenase, succinate semialdehyde dehydrogenase, oxaloacetate hydrolase, and a 3-HP transporter. Overexpression of pyruvate carboxylase improved yield in shake-flasks from 0.09 to 0.12 C-mol 3-HP C-mol^-1 glucose in the base strain expressing 12 copies of the β-alanine pathway. Deletion or overexpression of individual target genes in the pyruvate carboxylase overexpression strain improved yield to 0.22 C-mol 3-HP C-mol^-1 glucose after deletion of the major malonate semialdehyde dehydrogenase. Further incorporation of additional β-alanine pathway genes and optimization of culture conditions (sugars, temperature, nitrogen, phosphate, trace elements) for 3-HP production from deacetylated and mechanically refined corn stover hydrolysate improved yield to 0.48 C-mol 3-HP C-mol^-1 sugars and resulted in a final titer of 36.0 g/L 3-HP.

CONCLUSIONS

The results of this study establish A. niger as a host for 3-HP production from a lignocellulosic feedstock in acidic conditions and demonstrates that 3-HP titer and yield can be improved by a broad metabolic engineering strategy involving identification and modification of genes participated in the synthesis of 3-HP and its precursors, degradation of intermediates, and transport of 3-HP across the plasma membrane.

Prospects for carbon-negative biomanufacturing

Biomanufacturing has the potential to reduce demand for petrochemicals and mitigate climate change. Recent studies have also suggested that some of these products can be net carbon negative, effectively removing CO₂ from the atmosphere and locking it up in products. This review explores the magnitude of carbon removal achievable through biomanufacturing and discusses the likely fate of carbon in a range of target molecules. Solvents, cleaning agents, or food and pharmaceutical additives will likely re-release their carbon as CO₂ at the end of their functional lives, while carbon incorporated into non-compostable polymers can result in long-term sequestration. Future research can maximize its impact by focusing on reducing emissions, achieving performance advantages, and enabling a more circular carbon economy.

Metformin action over gut microbiota is related to weight and glycemic control in gestational diabetes mellitus: A randomized trial

BACKGROUND

Metformin, which is known to produce profound changes in gut microbiota, is being increasingly used in gestational diabetes mellitus (GDM). The aim of this study was to elucidate the differences in gut microbiota composition and function in women with GDM treated with metformin compared to those treated with insulin.

METHODS

From May to December 2018, 58 women with GDM were randomized to receive insulin (INS; n = 28) or metformin (MET; n = 30) at the University Hospital Virgen de la Victoria, Málaga, Spain. Basal visits, with at least 1 follow-up visit and prepartum visit, were performed. At the basal and prepartum visits, blood and stool samples were collected. The gut microbiota profile was determined through 16S rRNA analysis.

RESULTS

Compared to INS, women on MET presented a lower mean postprandial glycemia and a lower increase in weight and body mass index (BMI). Firmicutes and Peptostreptococcaceae abundance declined, while Proteobacteria and Enterobacteriaceae abundance increased in the MET group. We found inverse correlations between changes in the abundance of Proteobacteria and mean postprandial glycemia (p = 0.023), as well as between Enterobacteriaceae and a rise in BMI and weight gain (p = 0.031 and p = 0.036, respectively). Regarding the metabolic profile of gut microbiota, predicted metabolic pathways related to propionate degradation and ubiquinol biosynthesis predominated in the MET group.

CONCLUSION

Metformin in GDM affects the composition and metabolic profile of gut microbiota. These changes could mediate, at least in part, its clinical effects. Studies designed to assess how these changes influence metabolic control during and after pregnancy are necessary.

Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

AIMS

Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach.

METHODS

An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin.

RESULTS

The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect.

CONCLUSIONS

We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome.