Graph-based and constraint-based heterologous metabolic pathway design methods and application / 生物工程学报

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.

Development of metabolic models with multiple constraints: a review / 生物工程学报

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.

Analysis of differential genes and metabolic pathway related to functional male sterility in eggplant / 生物工程学报

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.

Construction of Escherichia coli cell factories / 生物工程学报

As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene editing, metabolic pathway assembly, and dynamic regulation have been used to design, construct, and optimize E. coli cell factories, which remarkably improved the efficiency for biotechnological production of chemicals. In this review, three key aspects for constructing E. coli cell factories, including pathway design, pathway assembly and regulation, and optimization of global cellular performance, are summarized. The technologies that have played important roles in metabolic engineering of E. coli, as well as their future applications, are discussed.

Metabolic regulation in constructing microbial cell factories / 生物工程学报

The regulation of the expression of genes involved in metabolic pathways, termed as metabolic regulation, is vital to construct efficient microbial cell factories. With the continuous breakthroughs in synthetic biology, the mining and artificial design of high-quality regulatory elements have substantially improved our ability to modify and regulate cellular metabolic networks and its activities. The research on metabolic regulation has also evolved from the static regulation of single genes to the intelligent and precise dynamic regulation at the systems level. This review briefly summarizes the advances of metabolic regulation technologies in the past 30 years.

Advances in the development of constraint-based genome-scale metabolic network models / 生物工程学报

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.

An evolving and flourishing metabolic engineering / 生物工程学报

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.

Current status and future perspectives of metabolic network models of industrial microorganisms / 生物工程学报

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.

Effect of key notes of TCA cycle on L-glutamate production / 生物工程学报

Glutamic acid is an important amino acid with wide range of applications and huge market demand. Therefore, by performing transcriptome sequencing and re-sequencing analysis on Corynebacterium glutamicum E01 and high glutamate-producing strain C. glutamicum G01, we identified and selected genes with significant differences in transcription and gene levels in the central metabolic pathway that may have greatly influenced glutamate synthesis and further increased glutamic acid yield. The oxaloacetate node and α-ketoglutarate node play an important role in glutamate synthesis. The oxaloacetate node and α-ketoglutarate node were studied to explore effect on glutamate production. Based on the integrated strain constructed from the above experimental results, the growth rate in a 5-L fermenter was slightly lower than that of the original strain, but the glutamic acid yield after 48 h reached (136.1±5.53) g/L, higher than the original strain (93.53±4.52) g/L, an increase by 45.5%; sugar-acid conversion rate reached 58.9%, an increase of 13.7% compared to 45.2% of the original strain. The application of the above experimental strategy improved the glutamic acid yield and the sugar-acid conversion rate, and provided a theoretical basis for the metabolic engineering of Corynebacterium glutamicum.

Draft genome sequence of Paraburkholderia tropica Ppe8 strain, a sugarcane endophytic diazotrophic bacterium

Abstract Paraburkholderia tropica (syn Burkholderia tropica) are nitrogen-fixing bacteria commonly found in sugarcane. The Paraburkholderia tropica strain Ppe8 is part of the sugarcane inoculant consortium that has a beneficial effect on yield. Here, we report a draft genome sequence of this strain elucidating the mechanisms involved in its interaction mainly with Poaceae. A genome size of approximately 8.75 Mb containing 7844 protein coding genes distributed in 526 subsystems was de novo assembled with ABySS and annotated by RAST. Genes related to the nitrogen fixation process, the secretion systems (I, II, III, IV, and VI), and related to a variety of metabolic traits, such as metabolism of carbohydrates, amino acids, vitamins, and proteins, were detected, suggesting a broad metabolic capacity and possible adaptation to plant association.

Genome sequence of Streptomyces gilvigriseus MUSC 26T isolated from mangrove forest

Abstract Streptomycetes remain as one of the important sources for bioactive products. Isolated from the mangrove forest, Streptomyces gilvigriseus MUSC 26T was previously characterised as a novel streptomycete. The high quality draft genome of MUSC 26T contained 5,213,277 bp with G + C content of 73.0%. Through genome mining, several gene clusters associated with secondary metabolites production were revealed in the genome of MUSC 26T. These findings call for further investigations into the potential exploitation of the strain for production of pharmaceutically important compounds.

Transcriptional profiling of genes involved in n-hexadecane compounds assimilation in the hydrocarbon degrading Dietzia cinnamea P4 strain

The petroleum-derived degrading Dietzia cinnamea strain P4 recently had its genome sequenced and annotated. This allowed employing the data on genes that are involved in the degradation of n-alkanes. To examine the physiological behavior of strain P4 in the presence of n-alkanes, the strain was grown under varying conditions of pH and temperature. D. cinnamea P4 was able to grow at pH 7.0-9.0 and at temperatures ranging from 35 ºC to 45 ºC. Experiments of gene expression by real-time quantitative RT-PCR throughout the complete growth cycle clearly indicated the induction of the regulatory gene alkU (TetR family) during early growth. During the logarithmic phase, a large increase in transcriptional levels of a lipid transporter gene was noted. Also, the expression of a gene that encodes the protein fused rubredoxin-alkane monooxygenase was enhanced. Both genes are probably under the influence of the AlkU regulator.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

Network models in the study of metabolism

The systematic study of the genetic fingerprint (genomics) and the biochemistry (metabolites) that goes with a specific cellular process requires the characterization of all the small molecules that form the profile of metabolites and the associated genes. The metabolome represents the collection of all the metabolites during certain process in an organism. The transcriptome represents the gene expression profile, all the messengers RNA in a defined condition. Then to understand the whole process, the studies of metabolites must be accompanied with studies of the gene expression, hence the metabolome must be accompanied by the transcriptome, so we can identify genes and metabolites whose synthesis is induced by a specific process, an infection or stress. Studies of metabolomics generate an enormous amount of data, then they need mathematical and computational tools to establish the correlations between the biochemical and genetic data, and to build up networks that represent the complex metabolic interactions that occur in each case, using tools like Graph and Networks Theory to elucidate the emergent properties inherent to the complex interactions of the metabolic maps. This paper describes the major mathematical tools that can be used for these studies, with emphasis on a semi-qualitative proposal known as the kinetic structural model.

Energy-responsive timekeeping.

An essential component of energy homeostasis lies in an organism's ability to coordinate daily patterns in activity, feeding, energy utilization and energy storage across the daily 24-h cycle. Most tissues of the body contain the molecular clock machinery required for circadian oscillation and rhythmic gene expression. Under normal circumstances, behavioural and physiological rhythms are orchestrated and synchronized by the suprachiasmatic nucleus (SCN) of the hypothalamus, considered to be the master circadian clock. However, metabolic processes are easily decoupled from the primarily light-driven SCN when food intake is desynchronized from normal diurnal patterns of activity. This dissociation from SCN based timing demonstrates that the circadian system is responsive to changes in energy supply and metabolic status. There has long been evidence for the existence of an anatomically distinct and autonomous food-entrainable oscillator (FEO) that can govern behavioural rhythms, when feeding becomes the dominant entraining stimulus. But now rapidly growing evidence suggests that core circadian clock genes are involved in reciprocal transcriptional feedback with genetic regulators of metabolism, and are directly responsive to cellular energy supply. This close interaction is likely to be critical for normal circadian regulation of metabolism, and may also underlie the disruption of proper metabolic rhythms observed in metabolic disorders, such as obesity and type-II diabetes.