Enteropathway: the metabolic pathway database for the human gut microbiota.

The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.

MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference.

Development of robust and effective strategies for synthesizing new compounds, drug targeting and constructing GEnome-scale Metabolic models (GEMs) requires a deep understanding of the underlying biological processes. A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https://github.com/guofei-tju/MVML-MPI. Contact:  jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

Multi-label classification with XGBoost for metabolic pathway prediction.

BACKGROUND: Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pathway prediction methods that conclude that machine learning-based approaches are similar in performance to the most used method, PathoLogic which is a rule-based method. One issue is that previous studies evaluated PathoLogic without taxonomic pruning which decreases its performance. RESULTS: In this study, we update the evaluation results from previous studies to demonstrate that PathoLogic with taxonomic pruning outperforms previous machine learning-based approaches and that further improvements in performance need to be made for them to be competitive. Furthermore, we introduce mlXGPR, a XGBoost-based metabolic pathway prediction method based on the multi-label classification pathway prediction framework introduced from mlLGPR. We also improve on this multi-label framework by utilizing correlations between labels using classifier chains. We propose a ranking method that determines the order of the chain so that lower performing classifiers are placed later in the chain to utilize the correlations between labels more. We evaluate mlXGPR with and without classifier chains on single-organism and multi-organism benchmarks. Our results indicate that mlXGPR outperform other previous pathway prediction methods including PathoLogic with taxonomic pruning in terms of hamming loss, precision and F1 score on single organism benchmarks. CONCLUSIONS: The results from our study indicate that the performance of machine learning-based pathway prediction methods can be substantially improved and can even outperform PathoLogic with taxonomic pruning.

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 .

Untargeted Metabolomics Analysis of Gingival Tissue in Patients with Severe Periodontitis.

The purpose of this study was to determine potential metabolic biomarkers and therapeutic drugs in the gingival tissue of individuals with periodontitis. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were used to analyze the gingival tissue samples from 20 patients with severe periodontitis and 20 healthy controls. Differential metabolites were identified using variable important in projection (VIP) values from the orthogonal partial least squares discrimination analysis (OPLS-DA) model and then verified for significance between groups using a two-tailed Student's t test. In total, 65 metabolites were enriched in 33 metabolic pathways, with 40 showing a significant increase and 25 expressing a significant decrease. In addition, it was found that patients with severe periodontitis have abnormalities in metabolic pathways, such as glucose metabolism, purine metabolism, amino acid metabolism, and so on. Furthermore, based on a multidimensional analysis, 12 different metabolites may be the potential biomarkers of severe periodontitis. The experiment's raw data have been uploaded to the MetaboLights database, and the project number is MTBLS8357. Moreover, osteogenesis differentiation characteristics were detected in the selected metabolites. The findings may provide a basis for the study of diagnostic biomarkers and therapeutic metabolites in severe periodontitis.

PTGES is involved in myofibroblast differentiation via HIF-1α-dependent glycolysis pathway.

Lung cancer is the leading cause of cancer-related deaths worldwide. Patients with lung cancer usually exhibit poor prognoses and low 5-year survival rates. Idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are both chronic lung dysfunctions resulting in lung fibrosis and increased risk of lung cancer. Myofibroblasts contribute to the progression of asthma, COPD and IPF, leading to fibrosis in the airway and lungs. A growing body of evidence demonstrates that metabolic reprogramming is a major hallmark of fibrosis, being important in the progression of fibrosis. Using gene expression microarray, we identified and validated that the lipid metabolic pathway was upregulated in lung fibroblasts upon interleukin (IL)-4, IL-13 and tumour necrosis factor (TNF)-α treatment. In this study, we described that prostaglandin E synthase (PTGES) was upregulated in lung fibroblasts after IL-4, IL-13 and TNF-α treatments. PTGES increased α-SMA levels and promoted lung fibroblast cell migration and invasion abilities. Furthermore, PTGES was upregulated in a lung fibrosis rat model in vivo. PTGES increased AKT phosphorylation, leading to activation of the HIF-1α-glycolysis pathway in lung fibroblast cells. HIF-1α inhibitor or 2-DG treatments reduced α-SMA expression in recombinant PTGES (rPTGES)-treated lung fibroblast cells. Targeting PGE2 signalling in PTGES-overexpressing cells by a PTGES inhibitor reduced α-SMA expression. In conclusion, the results of this study demonstrate that PTGES increases the expression of myofibroblast marker via HIF-1α-dependent glycolysis and contributes to myofibroblast differentiation.

Glyoxylate supplementation ameliorates colitis associated colon cancer progression.

Colon cancer is on the rise in younger adults. Despite multimodal treatment strategies, clinical outcomes in advanced stage colon cancer patients remain poor. Neoadjuvant/adjuvant chemotherapy efficacy is limited due to chemoresistance, toxicity, and negative side effects. Overwhelming evidence supporting the small-molecule metabolites derived from breakdown of food or microbial sources confer an extensive array of host benefits, including chemo-preventive role in colon cancer. Our previous study indicated that the introduction of glyoxylate (Glx), an intermediate product of microbial or plant metabolism, exerts a cytotoxic effect in colon cancer cells. This study was designed to evaluate the effects of Glx on colon cancer with molecular insights. For this, we established an AOM/DSS-induced colitis associated colon cancer model in mice. Supplementation of Glx in vivo reduced colitis associated tumor growth and altered the metabolic characteristics of tumor tissue in mice without initiating any severe liver or renal toxicity. More specifically, intake of glyoxylate accumulated glycine in the colon tissue by elevation of alanine-glyoxylate transferase (AGXT) activity. Glycine accumulation increased intracellular Ca2+ concentration via glycine receptor activation and dysregulation of Ca2+ homeostasis lead to induction of apoptosis that resulted in arresting tumor growth. Interestingly, elevation of AGXT activity or Glx related specific metabolic pathway provides better survival in colon cancer patients. Collectively, our exclusive findings support the exploration of Glx either as a preventive molecule or its inclusion in the treatment regimens for colon cancer.

Recruitment of a Middling Promiscuous Enzyme Drives Adaptive Metabolic Evolution in Escherichia coli.

A key step in metabolic pathway evolution is the recruitment of promiscuous enzymes to perform new functions. Despite the recognition that promiscuity is widespread in biology, factors dictating the preferential recruitment of one promiscuous enzyme over other candidates are unknown. Escherichia coli contains four sugar kinases that are candidates for recruitment when the native glucokinase machinery is deleted-allokinase (AlsK), manno(fructo)kinase (Mak), N-acetylmannosamine kinase (NanK), and N-acetylglucosamine kinase (NagK). The catalytic efficiencies of these enzymes are 103- to 105-fold lower than native glucokinases, ranging from 2,400 M-1 s-1 for the most active candidate, NagK, to 15 M-1 s-1 for the least active candidate, AlsK. To investigate the relationship between catalytic activities of promiscuous enzymes and their recruitment, we performed adaptive evolution of a glucokinase-deficient E. coli strain to restore glycolytic metabolism. We observed preferential recruitment of NanK via a trajectory involving early mutations that facilitate glucose uptake and amplify nanK transcription, followed by nonsynonymous substitutions in NanK that enhance the enzyme's promiscuous glucokinase activity. These substitutions reduced the native activity of NanK and reduced organismal fitness during growth on an N-acetylated carbon source, indicating that enzyme recruitment comes at a cost for growth on other substrates. Notably, the two most active candidates, NagK and Mak, were not recruited, suggesting that catalytic activity alone does not dictate evolutionary outcomes. The results highlight our lack of knowledge regarding biological drivers of enzyme recruitment and emphasize the need for a systems-wide approach to identify factors facilitating or constraining this important adaptive process.

Metabolism of toxic benzonitrile and hydroxybenzonitrile isomers via several distinct central pathway intermediates in a catabolically robust Burkholderia sp.

Aromatic nitriles are of considerable environmental concern, because of their hazardous impacts on the health of both humans and wildlife. In the present study, Burkholderia sp. strain BC1 was observed to be capable of utilizing toxic benzonitrile and hydroxybenzonitrile isomers singly, as sole carbon and energy sources. The results of chromatographic and spectrometric analyses in combination with oxygen uptake and enzyme activity studies, revealed the metabolism of benzonitrile as well as 2-, 3-, and 4-hydroxybenzonitriles by nitrile hydratase-amidase to the corresponding carboxylates. These carboxylates were further metabolized via central pathways, namely benzoate-catechol, salicylate-catechol, 3-hydroxybenzoate-gentisate and 4-hydroxybenzoate-protocatechute pathways in strain BC1, ultimately leading to the TCA cycle intermediates. Studies also evaluated substrate specificity profiles of both nitrile hydratase and amidase(s) involved in the denitrification of the nitriles. In addition, a few metabolic crosstalk events due to the induction of multiple operons by central metabolites were appraised in strain BC1. The present study illustrates the broad degradative potential of strain BC1, harboring diverse catabolic machinery of biotechnological importance, elucidating pathways for the assimilation of benzonitrile and that of hydroxybenzonitrile isomers for the first time.

Metabolic regulation of preimplantation embryo development in vivo and in vitro: Molecular mechanisms and insights.

Understanding of embryonic development has led to the clinical application of Assisted Reproductive technologies (ART), with the resulting birth of millions of children. Recent developments in metabolomics, proteomics, and transcriptomics have brought to light new insights into embryonic growth dynamics, with implications spanning reproductive medicine, stem cell research, and regenerative medicine. The review explores the key metabolic processes and molecular pathways active during preimplantation embryo development, including PI3K-Akt, mTOR, AMPK, Wnt/ß-catenin, TGF-ß, Notch and Jak-Stat signaling pathways. We focused on analyzing the differences occurring in vitro as opposed to in vivo development and we discussed significant physiological and clinical implications.

Combining transcriptome and metabolome analysis to understand the response of sorghum to Melanaphis sacchari.

BACKGROUND: The sorghum aphid Melanaphis sacchari (Zehntner) (Homoptera: Aphididae) is an important insect in the late growth phase of sorghum (Sorghum bicolor L.). However, the mechanisms of sorghum response to aphid infestation are unclear. RESULTS: In this paper, the mechanisms of aphid resistance in different types of sorghum varieties were revealed by studying the epidermal cell structure and performing a transcriptome and metabolome association analysis of aphid-resistant and aphid-susceptible varieties. The epidermal cell results showed that the resistance of sorghum to aphids was positively correlated with epidermal cell regularity and negatively correlated with the intercellular space and leaf thickness. Transcriptome and metabolomic analyses showed that differentially expressed genes in the resistant variety HN16 and susceptible variety BTX623 were mainly enriched in the flavonoid biosynthesis pathway and differentially expressed metabolites were mainly related to isoflavonoid biosynthesis and flavonoid biosynthesis. The q-PCR results of key genes were consistent with the transcriptome expression results. Meanwhile, the metabolome test results showed that after aphidinfestation, naringenin and genistein were significantly upregulated in the aphid-resistant variety HN16 and aphid-susceptible variety BTX623 while luteolin was only significantly upregulated in BTX623. These results show that naringenin, genistein, and luteolin play important roles in plant resistance to aphid infestation. The results of exogenous spraying tests showed that a 1‰ concentration of naringenin and genistein is optimal for improving sorghum resistance to aphid feeding. CONCLUSIONS: In summary, the physical properties of the sorghum leaf structure related to aphid resistance were studied to provide a reference for the breeding of aphid-resistant varieties. The flavonoid biosynthesis pathway plays an important role in the response of sorghum aphids and represents an important basis for the biological control of these pests. The results of the spraying experiment provide insights for developing anti-aphid substances in the future.

Proteomic analysis identified proteins that are differentially expressed in the flavonoid and carotenoid biosynthetic pathways of Camellia Nitidissima flowers.

BACKGROUND: Camellia nitidissima Chi is a popular ornamental plant because of its golden flowers, which contain flavonoids and carotenoids. To understand the regulatory mechanism of golden color formation, the metabolites of C. nitidissima petals at five different developmental stages were detected, a proteome map of petals was first constructed via tandem mass tag (TMT) analysis, and the accuracy of the sequencing data was validated via parallel reaction monitoring (PRM). RESULTS: Nineteen color components were detected, and most of these components were carotenoids that gradually accumulated, while some metabolites were flavonoids that were gradually depleted. A total of 97,647 spectra were obtained, and 6,789 quantifiable proteins were identified. Then, 1,319 differentially expressed proteins (DEPs) were found, 55 of which belong to the flavonoid and carotenoid pathways, as revealed by pairwise comparisons of protein expression levels across the five developmental stages. Notably, most DEPs involved in the synthesis of flavonoids, such as phenylalanine ammonium lyase and 4-coumarate-CoA ligase, were downregulated during petal development, whereas DEPs involved in carotenoid synthesis, such as phytoene synthase, 1-deoxy-D-xylulose-5-phosphate synthase, and ß-cyclase, tended to be upregulated. Furthermore, protein‒protein interaction (PPI) network analysis revealed that these 55 DEPs formed two distinct PPI networks closely tied to the flavonoid and carotenoid synthesis pathways. Phytoene synthase and chalcone synthase exhibited extensive interactions with numerous other proteins and displayed high connectivity within the PPI networks, suggesting their pivotal biological functions in flavonoid and carotenoid biosynthesis. CONCLUSION: Proteomic data on the flavonoid and carotenoid biosynthesis pathways were obtained, and the regulatory roles of the DEPs were analyzed, which provided a theoretical basis for further understanding the golden color formation mechanism of C. nitidissima.

Compartmentalization of pathway sequential enzymes into synthetic protein compartments for metabolic flux optimization in Escherichia coli.

Advancing the formation of artificial membraneless compartments with organizational complexity and diverse functionality remains a challenge. Typically, synthetic compartments or membraneless organelles are made up of intrinsically disordered proteins featuring low-complexity sequences or polypeptides with repeated distinctive short linear motifs. In order to expand the repertoire of tools available for the formation of synthetic membraneless compartments, here, a range of DIshevelled and aXin (DIX) or DIX-like domains undergoing head-to-tail polymerization were demonstrated to self-assemble into aggregates and generate synthetic compartments within E. coli cells. Then, synthetic complex compartments with diverse intracellular morphologies were generated by coexpressing different DIX domains. Further, we genetically incorporated a pair of interacting motifs, comprising a homo-dimeric domain and its anchoring peptide, into the DIX domain and cargo proteins, respectively, resulting in the alteration of both material properties and client recruitment of synthetic compartments. As a proof-of-concept, several human milk oligosaccharide biosynthesis pathways were chosen as model systems. The findings indicated that the recruitment of pathway sequential enzymes into synthetic compartments formed by DIX-DIX heterotypic interactions or by DIX domains embedded with specific interacting motifs efficiently boosted metabolic pathway flux and improved the production of desired chemicals. We propose that these synthetic compartment systems present a potent and adaptable toolkit for controlling metabolic flux and facilitating cellular engineering.

Complete pathway elucidation and heterologous reconstitution of (+)-nootkatone biosynthesis from Alpinia oxyphylla.

(+)-Nootkatone is a natural sesquiterpene ketone widely used in food, cosmetics, pharmaceuticals, and agriculture. It is also regarded as one of the most valuable terpenes used commercially. However, plants contain trace amounts of (+)-nootkatone, and extraction from plants is insufficient to meet market demand. Alpinia oxyphylla is a well-known medicinal plant in China, and (+)-nootkatone is one of the main components within the fruits. By transcriptome mining and functional screening using a precursor-providing yeast chassis, the complete (+)-nootkatone biosynthetic pathway in Alpinia oxyphylla was identified. A (+)-valencene synthase (AoVS) was identified as a novel monocot-derived valencene synthase; three (+)-valencene oxidases AoCYP6 (CYP71BB2), AoCYP9 (CYP71CX8), and AoCYP18 (CYP701A170) were identified by constructing a valencene-providing yeast strain. With further characterisation of a cytochrome P450 reductase (AoCPR1) and three dehydrogenases (AoSDR1/2/3), we successfully reconstructed the (+)-nootkatone biosynthetic pathway in Saccharomyces cerevisiae, representing a basis for its biotechnological production. Identifying the biosynthetic pathway of (+)-nootkatone in A. oxyphylla unravelled the molecular mechanism underlying its formation in planta and also supported the bioengineering production of (+)-nootkatone. The highly efficient yeast chassis screening method could be used to elucidate the complete biosynthetic pathway of other valuable plant natural products in future.

Plant metabolomics.

Plants are a treasure trove of metabolic compounds. The chemical diversity of plant cells has developed and been maintained through evolution and metabolic regulation, and plays a crucial role in plant physiology, development, and adaption to changing environmental situations. Metabolomics, when combined with genomics and proteomics, has opened up unprecedented opportunities to address the biological importance of metabolic diversity. It has also provided an avenue for metabolic engineering to produce a particular compound of interest to meet societal and economical demands, an important effort to achieve sustainable development. This Special Issue therefore focuses on current trends in plant metabolomics research, providing examples in the development of analytical technologies, the functional study of plant metabolism, and applications to synthetic and engineering biology.

Differential short-term and long-term metabolic and cytokine responses to infection of severe fever with thrombocytopenia syndrome virus.

INTRODUCTION: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV), which has a wide geographic distribution. The primary clinical manifestations of SFTS are fever and thrombocytopenia, with multiorgan failure being the leading cause of death. While most patients recover with treatment, little is known about the potential long-term metabolic effects of SFTSV infection. OBJECTIVES: This study aimed to shed light on dysregulated metabolic pathways and cytokine responses following SFTSV infection, which pose significant risks to the short-term and long-term health of affected individuals. METHODS: Fourteen laboratory-confirmed clinical SFTS cases and thirty-eight healthy controls including 18 SFTSV IgG-positive and 20 IgG-negative individuals were recruited from Taizhou city of Zhejiang province, Eastern China. Inclusion criteria of healthy controls included residing in the study area for at least one year, absence of fever or other symptoms in the past two weeks, and no history of SFTS diagnosis. Ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to obtain the relative abundance of plasma metabolites. Short-term metabolites refer to transient alterations present only during SFTSV infection, while long-term metabolites persistently deviate from normal levels even after recovery from SFTSV infection. Additionally, the concentrations of 12 cytokines were quantified through fluorescence intensity measurements. Differential metabolites were screened using orthogonal projections to latent structures discriminant analysis (OPLS-DA) and the Wilcoxon rank test. Metabolic pathway analysis was performed using MetaboAnalyst. Between-group differences of metabolites and cytokines were examined using the Wilcoxon rank test. Correlation matrices between identified metabolites and cytokines were analyzed using Spearman's method. RESULTS AND CONCLUSIONS: We screened 122 long-term metabolites and 108 short-term metabolites by analytical comparisons and analyzed their correlations with 12 cytokines. Glycerophospholipid metabolism (GPL) was identified as a significant short-term metabolic pathway suggesting that the activation of GPL might be linked to the self-replication of SFTSV, whereas pentose phosphate pathway and alanine, aspartate, and glutamate metabolism were indicated as significant long-term metabolic pathways playing a role in combating long-standing oxidative stress in the patients. Furthermore, our study suggests a new perspective that α-ketoglutarate could serve as a dietary supplement to protect recovering SFTS patients.

New strategies to study in depth the metabolic mechanism of astaxanthin biosynthesis in Phaffia rhodozyma.

Astaxanthin, a ketone carotenoid known for its high antioxidant activity, holds significant potential for application in nutraceuticals, aquaculture, and cosmetics. The increasing market demand necessitates a higher production of astaxanthin using Phaffia rhodozyma. Despite extensive research efforts focused on optimizing fermentation conditions, employing mutagenesis treatments, and utilizing genetic engineering technologies to enhance astaxanthin yield in P. rhodozyma, progress in this area remains limited. This review provides a comprehensive summary of the current understanding of rough metabolic pathways, regulatory mechanisms, and preliminary strategies for enhancing astaxanthin yield. However, further investigation is required to fully comprehend the intricate and essential metabolic regulation mechanism underlying astaxanthin synthesis. Specifically, the specific functions of key genes, such as crtYB, crtS, and crtI, need to be explored in detail. Additionally, a thorough understanding of the action mechanism of bifunctional enzymes and alternative splicing products is imperative. Lastly, the regulation of metabolic flux must be thoroughly investigated to reveal the complete pathway of astaxanthin synthesis. To obtain an in-depth mechanism and improve the yield of astaxanthin, this review proposes some frontier methods, including: omics, genome editing, protein structure-activity analysis, and synthetic biology. Moreover, it further elucidates the feasibility of new strategies using these advanced methods in various effectively combined ways to resolve these problems mentioned above. This review provides theory and method for studying the metabolic pathway of astaxanthin in P. rhodozyma and the industrial improvement of astaxanthin, and provides new insights into the flexible combined use of multiple modern advanced biotechnologies.

The improbability of detecting trade-offs and some practical solutions.

Trade-offs are a fundamental concept in evolutionary biology because they are thought to explain much of nature's biological diversity, from variation in life-histories to differences in metabolism. Despite the predicted importance of trade-offs, they are notoriously difficult to detect. Here we contribute to the existing rich theoretical literature on trade-offs by examining how the shape of the distribution of resources or metabolites acquired in an allocation pathway influences the strength of trade-offs between traits. We further explore how variation in resource distribution interacts with two aspects of pathway complexity (i.e., the number of branches and hierarchical structure) affects tradeoffs. We simulate variation in the shape of the distribution of a resource by sampling 106 individuals from a beta distribution with varying parameters to alter the resource shape. In a simple "Y-model" allocation of resources to two traits, any variation in a resource leads to slopes less than -1, with left skewed and symmetrical distributions leading to negative relationships between traits, and highly right skewed distributions associated with positive relationships between traits. Adding more branches further weakens negative and positive relationships between traits, and the hierarchical structure of pathways typically weakens relationships between traits, although in some contexts hierarchical complexity can strengthen positive relationships between traits. Our results further illuminate how variation in the acquisition and allocation of resources, and particularly the shape of a resource distribution and how it interacts with pathway complexity, makes its challenging to detect trade-offs. We offer several practical suggestions on how to detect trade-offs given these challenges.

The metabolic landscape of the bacteria Rhodococcus erythropolis used in the biodesulfurization of petroleum products: an emphasis on 2-hydroxybiphenyl.

Microorganisms produce diverse classes of metabolites under various physiological conditions. Many bacterial strains have been reported to carry out the process of desulfurization in a cost-effective manner by converting dibenzothiophene (DBT) into 2-hydroxybiphenyl (2-HBP) and then using the 2-HBP as a carbon source for growth and development. Key rate-limiting factors and an increased concentration of 2HBP (400 µM) affect the biodesulfurization activity of bacteria through the produced metabolites. Thus, this study was designed to explore the nature of the metabolites produced by Rhodococcus erythropolis in the presence of DBT and 2HBP supplemented with a culture medium. A total of 330 metabolites were detected, and the key metabolites identified were 11Z-eicosaenoyl-EA, 1-carboxyethylisoleucine, 1(3)-glyceryl-PGF2alpha, taurine, 2-hydroxynicotinic acid, 4,4-dimethyl-14alpha-hydroxymethyl-5alpha-cholest-8-en-3beta-ol, and 10-nitrooleic acid. The supplementation of DBT and DBT-2HBP resulted in the differential regulation of these metabolites, either through downregulation or overexpression. Furthermore, at high concentrations of 2-HBP, 1-carboxyethylisoleucine, taurine, 2-hydroxynicotinic acid, and nicotinic acid were upregulated. This work proposes that the identified metabolites may play a role in bacteria-mediated desulphurization and could be beneficial in developing a cost-effective method of desulphurization for refining petroleum.