Integrated translation and metabolism in a partially self-synthesizing biochemical network.

One of the hallmarks of living organisms is their capacity for self-organization and regeneration, which requires a tight integration of metabolic and genetic networks. We sought to construct a linked metabolic and genetic network in vitro that shows such lifelike behavior outside of a cellular context and generates its own building blocks from nonliving matter. We integrated the metabolism of the crotonyl-CoA/ethyl-malonyl-CoA/hydroxybutyryl-CoA cycle with cell-free protein synthesis using recombinant elements. Our network produces the amino acid glycine from CO2 and incorporates it into target proteins following DNA-encoded instructions. By orchestrating ~50 enzymes we established a basic cell-free operating system in which genetically encoded inputs into a metabolic network are programmed to activate feedback loops allowing for self-integration and (partial) self-regeneration of the complete system.

Biliary atresia and cholestasis plasma non-targeted metabolomics unravels perturbed metabolic pathways and unveils a diagnostic model for biliary atresia.

The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.

BACKGROUND: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

A Combined Metabolome and Transcriptome Reveals the Lignin Metabolic Pathway during the Developmental Stages of Peel Coloration in the 'Xinyu' Pear.

Sand pear is the main cultivated pear species in China, and brown peel is a unique feature of sand pear. The formation of brown peel is related to the activity of the cork layer, of which lignin is an important component. The formation of brown peel is intimately associated with the biosynthesis and accumulation of lignin; however, the regulatory mechanism of lignin biosynthesis in pear peel remains unclear. In this study, we used a newly bred sand pear cultivar 'Xinyu' as the material to investigate the biosynthesis and accumulation of lignin at nine developmental stages using metabolomic and transcriptomic methods. Our results showed that the 30 days after flowering (DAF) to 50DAF were the key periods of lignin accumulation according to data analysis from the assays of lignin measurement, scanning electron microscope (SEM) observation, metabolomics, and transcriptomics. Through weighted gene co-expression network analysis (WGCNA), positively correlated modules with lignin were identified. A total of nine difference lignin components were identified and 148 differentially expressed genes (DEGs), including 10 structural genes (PAL1, C4H, two 4CL genes, HCT, CSE, two COMT genes, and two CCR genes) and MYB, NAC, ERF, and TCP transcription factor genes were involved in lignin metabolism. An analysis of RT-qPCR confirmed that these DEGs were involved in the biosynthesis and regulation of lignin. These findings further help us understand the mechanisms of lignin biosynthesis and provide a theoretical basis for peel color control and quality improvement in pear breeding and cultivation.

A Multi-Omics Approach to Disclose Metabolic Pathways Impacting Intestinal Permeability in Obese Patients Undergoing Very Low Calorie Ketogenic Diet.

A very low calorie ketogenic diet (VLCKD) impacts host metabolism in people marked by an excess of visceral adiposity, and it affects the microbiota composition in terms of taxa presence and relative abundances. As a matter of fact, there is little available literature dealing with microbiota differences in obese patients marked by altered intestinal permeability. With the aim of inspecting consortium members and their related metabolic pathways, we inspected the microbial community profile, together with the set of volatile organic compounds (VOCs) from untargeted fecal and urine metabolomics, in a cohort made of obese patients, stratified based on both normal and altered intestinal permeability, before and after VLCKD administration. Based on the taxa relative abundances, we predicted microbiota-derived metabolic pathways whose variations were explained in light of our cohort symptom picture. A totally different number of statistically significant pathways marked samples with altered permeability, reflecting an important shift in microbiota taxa. A combined analysis of taxa, metabolic pathways, and metabolomic compounds delineates a set of markers that is useful in describing obesity dysfunctions and comorbidities.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.

BACKGROUND AND OBJECTIVE: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Metabolomic changes in Cryptocaryon irritans from Larimichthys crocea after exposure to copper plate.

Cryptocaryon irritans is a highly detrimental parasite in mariculture, causing significant economic losses to the aquaculture industry of Larimichthys crocea. In recent years, copper and copper alloy materials have been used to kill parasites. In this study, the effect of copper plates on the tomont period of C. irritans was explored. The findings indicated that copper plates effectively eradicated tomonts, resulting in a hatching rate of 0. The metabolomic analysis revealed that a total of 2,663 differentially expressed metabolites (1,032 up-regulated and 1,631 down-regulated) were screened in the positive ion mode, and 2,199 differentially expressed metabolites (840 up-regulated and 1,359 down-regulated) were screened in the negative ion mode. L-arginine and L-aspartic acid could be used as potential biomarkers. Copper plate treatment affected 25 metabolic pathways in the tomont, most notably influencing histidine metabolism, retinol metabolism, the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as arginine and proline metabolism. It was shown that high concentrations of copper ions caused a certain degree of disruption to the metabolome of tomonts in C. irritans, thereby impacting their metabolic processes. Consequently, this disturbance ultimately leads to the rapid demise of tomonts upon exposure to copper plates. The metabolomic changes observed in this study elucidate the lethal impact of copper on C. irritans tomonts, providing valuable reference data for the prevention and control of C. irritans in aquaculture.

Catabolism of 2-keto-3-deoxy-galactonate and the production of its enantiomers.

2-Keto-3-deoxy-galactonate (KDGal) serves as a pivotal metabolic intermediate within both the fungal D-galacturonate pathway, which is integral to pectin catabolism, and the bacterial DeLey-Doudoroff pathway for D-galactose catabolism. The presence of KDGal enantiomers, L-KDGal and D-KDGal, varies across these pathways. Fungal pathways generate L-KDGal through the reduction and dehydration of D-galacturonate, whereas bacterial pathways produce D-KDGal through the oxidation and dehydration of D-galactose. Two distinct catabolic routes further metabolize KDGal: a nonphosphorolytic pathway that employs aldolase and a phosphorolytic pathway involving kinase and aldolase. Recent findings have revealed that L-KDGal, identified in the bacterial catabolism of 3,6-anhydro-L-galactose, a major component of red seaweeds, is also catabolized by Escherichia coli, which is traditionally known to be catabolized by specific fungal species, such as Trichoderma reesei. Furthermore, the potential industrial applications of KDGal and its derivatives, such as pyruvate and D- and L-glyceraldehyde, are underscored by their significant biological functions. This review comprehensively outlines the catabolism of L-KDGal and D-KDGal across different biological systems, highlights stereospecific methods for discriminating between enantiomers, and explores industrial application prospects for producing KDGal enantiomers. KEY POINTS: • KDGal is a metabolic intermediate in fungal and bacterial pathways • Stereospecific enzymes can be used to identify the enantiomeric nature of KDGal • KDGal can be used to induce pectin catabolism or produce functional materials.

Genetic causality and metabolite pathway identifying the relationship of blood metabolites and psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

Systematic analysis of microorganisms' metabolism for selective targeting.

Selective drugs with a relatively narrow spectrum can reduce the side effects of treatments compared to broad-spectrum antibiotics by specifically targeting the pathogens responsible for infection. Furthermore, combating an infectious pathogen, especially a drug-resistant microorganism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to identify multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in groups ranging from one to six members, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways highlighted the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist in the validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can theoretically address a substantial portion of the 665 cases. We expect that the obtained solutions provide valuable insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.

Metabolomics and transcriptomics combined with physiology reveal key metabolic pathway responses in tobacco roots exposed to NaHS.

Hydrogen sulfide (H2S) has emerged as a novel endogenous gas signaling molecule, joining the ranks of nitric oxide (NO) and carbon monoxide (CO). Recent research has highlighted its involvement in various physiological processes, such as promoting root organogenesis, regulating stomatal movement and photosynthesis, and enhancing plant growth, development, and stress resistance. Tobacco, a significant cash crop crucial for farmers' economic income, relies heavily on root development to affect leaf growth, disease resistance, chemical composition, and yield. Despite its importance, there remains a scarcity of studies investigating the role of H2S in promoting tobacco growth. This study exposed tobacco seedlings to different concentrations of NaHS (an exogenous H2S donor) - 0, 200, 400, 600, and 800 mg/L. Results indicated a positive correlation between NaHS concentration and root length, wet weight, root activity, and antioxidant enzymatic activities (CAT, SOD, and POD) in tobacco roots. Transcriptomic and metabolomic analyses revealed that treatment with 600 mg/L NaHS significantly effected 162 key genes, 44 key enzymes, and two metabolic pathways (brassinosteroid synthesis and aspartate biosynthesis) in tobacco seedlings. The addition of exogenous NaHS not only promoted tobacco root development but also potentially reduced pesticide usage, contributing to a more sustainable ecological environment. Overall, this study sheds light on the primary metabolic pathways involved in tobacco root response to NaHS, offering new genetic insights for future investigations into plant root development.

Causal impact of human blood metabolites and metabolic pathways on serum uric acid and gout: a mendelian randomization study.

Objective: Hyperuricaemia and gout are common metabolic disorders. However, the causal relationships between blood metabolites and serum urate levels, as well as gout, remain unclear. A systematic evaluation of the causal connections between blood metabolites, hyperuricemia, and gout could enhance early screening and prevention of hyperuricemia and gout in clinical settings, providing novel insights and approaches for clinical treatment. Methods: In this study, we employed a bidirectional two-sample Mendelian randomization analysis utilizing data from a genome-wide association study involving 7,286 participants, encompassing 486 blood metabolites. Serum urate and gout data were sourced from the Chronic Kidney Disease Genetics consortium, including 288,649 participants for serum urate and 9,819 African American and 753,994 European individuals for gout. Initially, LDSC methodology was applied to identify blood metabolites with a genetic relationship to serum urate and gout. Subsequently, inverse-variance weighting was employed as the primary analysis method, with a series of sensitivity and pleiotropy analyses conducted to assess the robustness of the results. Results: Following LDSC, 133 blood metabolites exhibited a potential genetic relationship with serum urate and gout. In the primary Mendelian randomization analysis using inverse-variance weighting, 19 blood metabolites were recognized as potentially influencing serum urate levels and gout. Subsequently, the IVW p-values of potential metabolites were corrected using the false discovery rate method. We find leucine (IVW P FDR = 0.00004), N-acetylornithine (IVW P FDR = 0.0295), N1-methyl-3-pyridone-4-carboxamide (IVW P FDR = 0.0295), and succinyl carnitine (IVW P FDR = 0.00004) were identified as significant risk factors for elevated serum urate levels. Additionally, 1-oleoylglycerol (IVW P FDR = 0.0007) may lead to a substantial increase in the risk of gout. Succinyl carnitine exhibited acceptable weak heterogeneity, and the results for other blood metabolites remained robust after sensitivity, heterogeneity, and pleiotropy testing. We conducted an enrichment analysis on potential blood metabolites, followed by a metabolic pathway analysis revealing four pathways associated with serum urate levels. Conclusion: The identified causal relationships between these metabolites and serum urate and gout offer a novel perspective, providing new mechanistic insights into serum urate levels and gout.

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability.

BACKGROUND: Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. METHODS: In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. RESULTS: Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. CONCLUSIONS: This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

PEMPS: a phylogenetic software tool to model the evolution of metabolic pathways.

BACKGROUND: Metabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype-phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function. RESULTS: A software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters. CONCLUSIONS: The PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS .

Mixotrophic growth of a ubiquitous marine diatom.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.

A Metabolomic Approach to Investigate the Effect of Phytonutrients on Proteostasis and Metabolic Pathways in Drosophila melanogaster.

The use of Drosophila melanogaster as a biological platform to study the effect of diet and food bioactives on the metabolome remains a highly unexplored subject. Aiming to establish alternative solutions for the investigation of nutritional interventions with bioactive natural products by employing LC-MS-based metabolomics approaches, we assessed the effect of a phytonutrient-rich extract from the endemic Mediterranean plant Cichorium spinosum (stamnagkàthi) on a Drosophila population. The extract's modulating effect on the proteostasis network and metabolism of young D. melanogaster flies was evaluated. Furthermore, an untargeted metabolomics approach, employing a C18 UPLC-ESI-Orbitrap-HRMS/MS platform, permitted the detection of several biomarkers in the metabolic profile of Drosophila's tissues; while targeted amino acid quantification in Drosophila tissue was simultaneously performed by employing aTRAQ labeling and an ion-pairing UPLC-ESI-SWATH-HRMS/MS platform. The detected metabolites belong to different chemical classes, and statistical analysis with chemometrics tools was utilized to reveal patterns and trends, as well as to uncover potential class-distinguishing features and possible biomarkers. Our findings suggest that Drosophila can serve as a valuable in vivo model for investigating the role of bioactive phytoconstituents, like those found in C. spinosum's decoction, on diverse metabolic processes. Additionally, the fruit fly represents a highly effective platform to investigate the molecular mechanisms underlying sex differences in diverse aspects of nutrition and physiology in higher metazoans.

Upregulated Palmitoleate and Oleate Production in Escherichia coli Promotes Gentamicin Resistance.

Metabolic reprogramming mediates antibiotic efficacy. However, metabolic adaptation of microbes evolving from antibiotic sensitivity to resistance remains undefined. Therefore, untargeted metabolomics was conducted to unveil relevant metabolic reprogramming and potential intervention targets involved in gentamicin resistance. In total, 61 metabolites and 52 metabolic pathways were significantly altered in gentamicin-resistant E. coli. Notably, the metabolic reprogramming was characterized by decreases in most metabolites involved in carbohydrate and amino acid metabolism, and accumulation of building blocks for nucleotide synthesis in gentamicin-resistant E. coli. Meanwhile, fatty acid metabolism and glycerolipid metabolism were also significantly altered in gentamicin-resistant E. coli. Additionally, glycerol, glycerol-3-phosphate, palmitoleate, and oleate were separately defined as the potential biomarkers for identifying gentamicin resistance in E. coli. Moreover, palmitoleate and oleate could attenuate or even abolished killing effects of gentamicin on E. coli, and separately increased the minimum inhibitory concentration of gentamicin against E. coli by 2 and 4 times. Furthermore, palmitoleate and oleate separately decreased intracellular gentamicin contents, and abolished gentamicin-induced accumulation of reactive oxygen species, indicating involvement of gentamicin metabolism and redox homeostasis in palmitoleate/oleate-promoted gentamicin resistance in E. coli. This study identifies the metabolic reprogramming, potential biomarkers and intervention targets related to gentamicin resistance in bacteria.

Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Historic obstacles and emerging opportunities in the field of developmental metabolism - lessons from Heidelberg.

The field of developmental metabolism is experiencing a technological revolution that is opening entirely new fields of inquiry. Advances in metabolomics, small-molecule sensors, single-cell RNA sequencing and computational modeling present new opportunities for exploring cell-specific and tissue-specific metabolic networks, interorgan metabolic communication, and gene-by-metabolite interactions in time and space. Together, these advances not only present a means by which developmental biologists can tackle questions that have challenged the field for centuries, but also present young scientists with opportunities to define new areas of inquiry. These emerging frontiers of developmental metabolism were at the center of a highly interactive 2023 EMBO workshop 'Developmental metabolism: flows of energy, matter, and information'. Here, we summarize key discussions from this forum, emphasizing modern developmental biology's challenges and opportunities.

[Advances in the biosynthesis and metabolic regulation of terpenoids in Saccharomyces cerevisiae].

Terpenoids are the one of most abundant natural products. With diverse varieties and biological activities, they are widely used in the food, medicine, chemical industry, and novel fuels. However, the conventional methods such as plant extraction and chemical synthesis cannot meet the current market demand for terpenoids. Efficient microbial cell factories, especially engineered Saccharomyces cerevisiae strains, have been constructed for the industrial production of terpenoids. In recent years, researchers have constructed multiple S. cerevisiae strains with increased yield and productivity via approaches of synthetic biology and metabolic engineering. This paper reviews the recent progress in the biosynthesis of terpenoids in S. cerevisiae cells and summarizes a variety of metabolic engineering strategies for the production of terpenoids in S. cerevisiae. These strategies include the construction and optimization of metabolic pathways, the mining and modification of key enzymes, the regeneration of cofactors, the engineering of cell localization and cell efflux, and the improvement of cell tolerance. Our review will provide information and strategies for the effective biosynthesis of terpenoids in S. cerevisiae.