Immune checkpoints include a series of receptor-ligand pairs that play a key role in the proliferation, activation, and immune regulatory responses of immune cells. Although immune checkpoint inhibitors (ICIs), such as programmed death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) have achieved good therapeutic effects in clinical practice, some patients still experience ineffective treatment and immune resistance. A large amount of evidence has shown that immune checkpoint proteins are related to cell metabolism during immune regulation. On the one hand, immune checkpoints connect to alter the metabolic reprogramming of tumor cells to compete for nutrients required by immune cells. On the other hand, immune checkpoints regulate the metabolic pathways of immune cells, such as phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) to affect the activation of immune cells. Based on a review of the literature, this article reviews the mechanisms by which PD-1, CTLA-4, T cell immunoreceptor with Ig and ITIM domains (TIGIT), T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), cluster of differentiation 47 (CD47), and indoleamine 2,3-dioxygenase 1 (IDO1) regulate cell metabolic reprogramming, and looks forward to whether targeting the ligand-receptor pairs of immune checkpoints in a "dual regulation" manner and inhibiting metabolic pathways can effectively solve the problem of tumor immune resistance.
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Humans ; Neoplasms/genetics* ; Metabolic Networks and Pathways/immunology* ; Animals ; Immune Checkpoint Inhibitors/pharmacology*

To explore the differentially expressed genes (DEGs) related to biosynthesis of active ingredients in wolfberry fruits of different varieties of Lycium barbarum L. and reveal the molecular mechanism of the differences of active ingredients, we utilized Illumina NovaSeq 6000 high-throughput sequencing technology to conduct transcriptome sequencing on the fruits of 'Ningqi No.1' and 'Ningqi No.7' during the green fruit stage, color turning stage and maturity stage. Subsequently, we compared the profiles of related gene expression in the fruits of the two varieties at different development stages. The results showed that a total of 811 818 178 clean reads were obtained, resulting in 121.76 Gb of valid data. There were 2 827, 2 552 and 2 311 DEGs obtained during the green fruit stage, color turning stage and maturity stage of 'Ningqi No. 1' and 'Ningqi No. 7', respectively, among which 2 153, 2 050 and 1 825 genes were annotated in six databases, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and clusters of orthologous groups of proteins (KOG). In GO database, 1 307, 865 and 624 DEGs of green fruit stage, color turning stage and maturity stage were found to be enriched in biological processes, cell components and molecular functions, respectively. In the KEGG database, the DEGs at three developmental stages were mainly concentrated in metabolic pathways, biosynthesis of secondary metabolites and plant-pathogen interaction. In KOG database, 1 775, 1 751 and 1 541 DEGs were annotated at three developmental stages, respectively. Searching the annotated genes against the PubMed database revealed 18, 26 and 24 DEGs related to the synthesis of active ingredients were mined at the green fruit stage, color turning stage and maturity stage, respectively. These genes are involved in carotenoid, flavonoid, terpenoid, alkaloid, vitamin metabolic pathways, etc. Seven DEGs were verified by RT-qPCR, which showed consistent results with transcriptome sequencing. This study provides preliminary evidences for the differences in the content of active ingredients in different Lycium barbarum L. varieties from the transcriptional level. These evidences may facilitate further exploring the key genes for active ingredients biosynthesis in Lycium barbarum L. and analyzing their expression regulation mechanism.
Flavonoids/metabolism* ; Fruit/genetics* ; Gene Expression Profiling/methods* ; Gene Expression Regulation, Plant ; Lycium/metabolism* ; Metabolic Networks and Pathways ; Transcriptome

This study aims to explore the effect of Jingfang Mixture on the protein expression of urticaria in mice and explain the mechanism of Jingfang Mixture in the treatment of urticaria. Twenty-seven male Kunming mice were randomly divided into a normal group, a model group and a Jingfang Mixture group according to body weight. Except for the normal group, mice in the model group and the Jingfang Mixture group were injected with the mixture of ovalbumin and Al(OH)_3 gel for the first immunization, and the second immunization was performed on the 10 th day to induce the urticaria model. Mice in the Jingfang Mixture group started to be administered on the 6 th day after the initial immunization, and was administered continuously for 21 days. The normal group and the model group were replaced with the same amount of purified water. Twenty-four hours after the last administration, an appropriate amount of skin was taken, and label-free quantitative proteomics technology was used to detect the differences in protein expression in skin tissue. The signaling pathways involved in the differential proteins was further analyzed. The results of proteomics indicated that seventy-six proteins were involved in the intervention of Jingfang Mixture on mice with urticaria, and the differential proteins were mainly enriched in biological process(BP), molecular function(MF), and cellular component(CC). Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis showed that the signaling pathways regulated by Jingfang Mixture mainly involved carbon metabolism, metabolic pathways, glucagon signaling pathway, glycolysis/gluconeogenesis, pentose phosphate pathway, hypoxia inducible factor-1(HIF-1) signaling pathway, purine metabolism, adherens junction, calcium signaling pathway, leukocyte transendothelial migration, and inflammatory mediator regulation of transient receptor potential(TRP) channels, which were involved in skin tissue energy metabolism and immune regulation. The findings of this study showed that the protective effect of Jingfang Mixture on mice with urticaria was closely related to the regulation of immune disorders, and the regulatory effect on immune system may be achieved through the regulation of energy metabolism by Jingfang Mixture.
Male ; Mice ; Animals ; Proteomics/methods* ; Metabolic Networks and Pathways ; Urticaria/genetics* ; Signal Transduction ; Technology

Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.
Genome ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Models, Biological ; Thermodynamics

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.
Algorithms ; Biosynthetic Pathways ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Systems Biology

Cancer development is a complicated process controlled by the interplay of multiple signaling pathways and restrained by oxygen and nutrient accessibility in the tumor microenvironment. High plasticity in using diverse nutrients to adapt to metabolic stress is one of the hallmarks of cancer cells. To respond to nutrient stress and to meet the requirements for rapid cell proliferation, cancer cells reprogram metabolic pathways to take up more glucose and coordinate the production of energy and intermediates for biosynthesis. Such actions involve gene expression and activity regulation by the moonlighting function of oncoproteins and metabolic enzymes. The signal - moonlighting protein - metabolism axis facilitates the adaptation of tumor cells under varying environment conditions and can be therapeutically targeted for cancer treatment.
Energy Metabolism ; Epigenesis, Genetic ; Humans ; Metabolic Networks and Pathways ; Neoplasms/genetics* ; Tumor Microenvironment

Based on observing the cytological characteristics of the flower buds of the functional male sterile line (S13) and the fertile line (F142) in eggplant, it was found that the disintegration period of the annular cell clusters in S13 anther was 2 days later than that of F142, and the cells of stomiun tissue and tapetum in F142 disintegrated on the blooming day, while it did not happen in S13. The comparative transcriptomic analysis showed that there were 1 436 differential expression genes (DEGs) (651 up-regulated and 785 down-regulated) in anthers of F142 and S13 at 8, 5 days before flowering and flowering day. The significance analysis of GO enrichment indicated that there were more unigene clusters involved in single cell biological process, metabolism process and cell process, and more catalytic activity and binding function were involved in molecular functions. Through KEGG annotation we found that the common DEGs were mainly enriched in the biosynthesis of secondary metabolites, metabolic pathway, protein processing in endoplasmic reticulum, biosynthesis of amino acids, carbon metabolism and plant hormone signal transduction. The fifteen genes co-expression modules were identified from 16 465 selected genes by weighted gene co-expression network analysis (WGCNA), three of which (Plum2, Royalblue and Bisque4 modules) were highly related to S13 during flower development. KEGG enrichment showed that the specific modules could be enriched in phenylpropanoid biosynthesis, photosynthesis, porphyrin and chlorophyll metabolism, α-linolenic acid metabolism, polysaccharide biosynthesis and metabolism, fatty acid degradation and the mutual transformation of pentose and glucuronic acid. These genes might play important roles during flower development of S13. It provided a reference for further study on the mechanism of anther dehiscence in eggplant.
Flowers/genetics* ; Gene Expression Profiling ; Gene Expression Regulation, Plant ; Humans ; Infertility, Male ; Male ; Metabolic Networks and Pathways/genetics* ; Solanum melongena/genetics* ; Transcriptome/genetics*

Genome-scale metabolic network model (GSMM) is an extremely important guiding tool in the targeted modification of industrial microbial strains, which helps researchers to quickly obtain industrial microbes with specific traits and has attracted increasing attention. Here we reviewe the development history of GSMM and summarized the construction method of GSMM. Furthermore, the development and application of GSMM in industrial microorganisms are elaborated by using four typical industrial microorganisms (Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae) as examples. In addition, prospects in the development trend of GSMM are proposed.
Corynebacterium glutamicum/genetics* ; Escherichia coli/genetics* ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics*

Microbial cell factories capable of producing valuable chemicals from renewable feedstocks provide a promising alternative towards sustainability. However, environmental stress remarkably affects the performance of microbial cell factories. By extending the chronological lifespan of microbial cells, the performance of microbial cell factories can be greatly improved. Firstly, an evaluation system for chronological lifespan and semi-chronological lifespan was established based on the changes in survival rates. Secondly, the addition of anti-aging drugs such as cysteine, carnosine, aminoguanidine and glucosamine increased the chronological lifespan of E. coli by 80%, 80%, 50% and 120%, respectively. Finally, we demonstrated that extending the chronological lifespan of E. coli increased the yield of metabolites produced by E. coli cell factories with endogenous (lactic acid and pyruvic acid) or exogenous (malic acid) metabolic pathway by 30.0%, 25.0%, and 27.0%, respectively. The strategy of extending chronological lifespan of E. coli provides a potential approach for enhancing the performance of microbial cell factories.
Escherichia coli/genetics* ; Lactic Acid ; Longevity ; Metabolic Engineering ; Metabolic Networks and Pathways

In 1990s, Bailey and Stephanopoulos put forward the concept of classic metabolic engineering, aiming to use DNA recombination technology to rewire metabolic network to achieve improved cell performance and increased target products. In the last 30 years since the birth of metabolic engineering, life science have flourished, and new disciplines such as genomics, systems biology and synthetic biology have emerged, injecting new connotations and vitality into the development of metabolic engineering. Classic metabolic engineering research has entered into an unprecedented stage of systems metabolic engineering. The application of synthetic biology tools and strategies, such as omics technology, genomic-scale metabolic model, parts assembly, circuits design, dynamic control, genome editing and many others, have greatly improved the design, build, and rewiring capabilities of complex metabolism. The intervention of machine learning and the combination of evolutionary engineering and metabolic engineering will further promote the development of systems metabolic engineering. This paper analyzes the development of metabolic engineering in the past 30 years and summarizes the novel theories, techniques, strategies, and applications of metabolic engineering that have emerged over the past 30 years.
Gene Editing ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Synthetic Biology ; Systems Biology