Skip to main content

Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

Abstract

Background

Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed.

Results

In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis.

Conclusion

The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.

Peer Review reports

Background

The demand for aquatic products has grown with the development of the global economy and the general improvement in living standards As an essential food and economic source, half of the global consumption of aquatic products comes from aquaculture. To meet the growing demand, the scale and output of aquaculture have to be increased to provide safe and healthy aquatic products. Eriocheir sinensis, also known as Chinese mitten crab and river crab, is an essential aquatic product because of its delicious and nutritious flesh. Every 100 g of edible crab contains about 14% protein, 5.9% fat, 7% carbohydrate, and various vitamins, chitin, niacin, trace elements and other nutrients, among which chitin account for 25-30% of the dry weight of crab shell [1]. Its nutritional composition is higher than that of most fish, which determines its high nutritional and economic values [2]. In the artificial culture of E. sinensis, artificial feed is usually used due to its easy management, low price and low pollution to the environment. Therefore, the types and proportions of different nutrients in the artificial feed need to be optimized for the sustainable development of the E. sinensis culture industry. In recent years, many researchers have tried to reveal the demand for various nutrients in the growth process of E. sinensis through the trial and error experiments based on nutritive physiology [3]. This helped find a more perfect and reasonable feed composition to improve the efficiency of E. sinensis breeding. For example, Sun Jinhui et al. found that the E. sinensis had obvious growth advantages when fed with formula feed and a natural diet containing 38% and 35% protein, respectively [4]. Sunxinjin et al. found that the proper addition of vitamin A in the feed promoted the growth of juvenile crab and improved feed efficiency, but the excessive addition (>160,000 IU/kg feed) inhibited the growth to a certain extent [5]. In addition, vitamin E effectively improved the anti-stress ability and immunity of crabs. For diseased crabs, an appropriate amount of vitamin E supplementation can not only reduced the mortality of shrimps and crabs, but also promoted their early recovery [6]. However, due to the complexity of the feed composition, finding the real optimal feed proportion based on the trial-and-error method is a difficult task.

With the continuous improvement of genome sequencing technology, it has become a research hotspot to study the metabolic process of organisms from a systematic perspective using omics Big Data, which promotes the rapid development of a genome-scale metabolic network model (GEM). A large number of high-throughput omics data and annotation information provide a broad database for the developing GEM [7]. The reconstruction of a GEM starts from omics sequencing data, combined with genome annotation, metabolism related database and experimental data, to reconstruct the metabolic network by intergrating gene-protein-reaction. It used in quantitative studies on the metabolic process of organisms from the perspective of systematics [8,9,10]. GEMs have been applied in analysising and predcting of gene expression and function, discovery of drug targets, guidance of metabolic engineering and aquaculture productions [11,12,13]. It has also been successfully applied to predict the nutritional requirements of several microorganisms, such as Escherichia coli and Bacillus subtills [14, 15].

In this study, a whole body GEM for growing juvenile E. sinensis was reconstructed based on the transcriptome sequencing data for five mixed organs and the KEGG database. The biomass equation was constructed based on the nutritional component detection of E. sinensis hepatopancreas, which gave GEM the ability to synthesize biomass. With flux balance analysis, the growth and the optimal nutritional requirement of E. sinensis were simulated and suggestions on feed improvement were proposed. This study made the analysis of nutritional requirement for E. sinensis take the first step from the traditional in vivo experiment to the in silico experiment. It also provided a theoretical basis for guiding the breeding industry and promoted the development of crab production.

Methods

Transcriptome sequencing

The healthy juvenile Chinese mitten crabs were taken from Tianjin Xieyuan aquaculture Co., Ltd. The average body length, body width and weight of crabs were 24.28 ± 1.54 mm, 26.97 ± 1.65 mm and 8.41 ± 1.47 g, respectively. The crabs were cultured in a plastic incubator (70 × 40 × 50 cm) at 20 ± 1°C with natural light. The crabs were fed compound feed twice a day. The daily feeding amount was about 5% of the body weight. The siphon method was used to clean up the residual bait and inject new water. The daily water change is more than 50%. As all metabolic activities in the non-molting stage of juvenile crab are aimed to prepare for molting. Therefore, in addition to the hepatopancreas, which is the metabolic and nutrient storage tissue, four other tissues which are closely related to molting, including gill, muscle, thoracic ganglion and eyestalk, were also selected as the sample for sequencing. While collecting the samples, the crabs were put on the ice plate for low-temperature anesthesia. The five tissues of crabs were quickly collected and put in liquid nitrogen immediately, and then, preserved in a −80°C refrigerator for preservation. Each sample contains the mixture of five tissues in a crab. All experimental procedures were conducted according to institutional guidelines for the care and use of laboratory animals in Tianjin Normal University and conformed to the National laboratory animal management regulations (Publication No. 2, 1988) approved by the National Science and Technology Commission.

Total RNA from mixed E. sinensis tissues was sequenced using the Illumina high-throughput sequencing technology by Novogene Inc. The standard procedure is the same with that in our previous work [16] (Supplementary file 1). The sequenced unigenes were subsequently aligned against the NR and Nt databases using BLAST searching with an E-value < 1*E-5 and the PFAM database using the HUMMER package with an E-value < 0.01. The GO annotation of unigenes was obtained using the Blast2GO program. The KO and pathway annotations were obtained by KAAS (KEGG Automatic Annotation Server). The RNA-seq data have been submitted to the Gene Expression Omnibus (GEO) database, and the accession number is GSE182818.

Preliminary reconstruction of GEM

The reconstruction of the GEM followed the standard protocol proposed by Thiele and Palsson [17]. The reconstruction process comprised of preliminary reconstruction, refinement of GEM, addition of biomass reaction, conversion of the network into a mathematical model and network evaluation. The workflow of GEM reconstruction is shown in Fig. 1.

Fig. 1
figure 1

Workflow of GEM reconstruction

In the preliminary reconstruction, the related information of metabolic reactions was downloaded from the KEGG database [18], including the corresponding relationship between reaction and enzyme, pathway, subsystem, and KO. Using KO ID as a bridge, the reaction information containing KO ID in the KEGG database was matched with unigenes containing KO ID in transcriptome data, and the preliminary reconstructed GEM of E. sinensis obtained, which was composed of unigenes, enzymes, reactions, main reaction equations, pathways, and subsystems. Visual Basic for Applications (VBA) was used for the data assembly and following network refinement.

Refinement of GEM

The preliminary reconstructed GEM were refined in the following aspects: (1) network balance of the reaction equactions were firstly checked and balanced with the principle of element and charge conservation; (2) the lacking information about main reactions, pathways and subsystem of some reactions were supplemented; (3) the redundant reactions such as step reaction, general reaction, incomplete reaction and macromolecular reaction were further processed to simplify the network and ensure the reasonable distribution of metabolic flux; (4) the metabolites with unclear chirality were standarlized; (5) gaps in the network were filled in the pathway scale and global scale with all the reactions in the KEGG database used for gap filling; (6) transfer reactions were added to the GEM according to the characteristics of species to fit the actual situation of biological metabolism and simulate the metabolic process more accurately; (7) the reactions in GEM were divided into two compartments: cytosol and extracellular. The detail of the refinement procedures were supplied in Supplementary file 1.

Addition of biomass reaction

Biomass reaction reflects the growth of cells or organisms, which usually represents the composition of cells. The biomass reaction of a species is expressed by linear equation according to the proportion of all the components in the cell [19]. The hepatopancreas of E. sinensis is responsible for the metabolism and storage of nutrients. It is closely related to the growth and reproduction of crustaceans. Therefore, the hepatopancreas of E. sinensis was used for detecting of nutritional components. The hepatopancreas sample used for nutritional component detection was from the same crabs used for transcriptome sequencing. The biomass constituents in the sample were tested experimentally by the Beijing Institute of Nutritional Resources using the standard method specified in “GB28050-2011 national food safety standard general principles for nutrition labeling of prepackaged food” [20]. The contents of 20 amino acids, 22 fatty acids, 9 trace elements and 2 saccharides in the sample were tested. The biomass reaction equation of E. sinensis was constructed based on the nutrient composition of hepatopancreas. The unit of detection results was converted into mmol gDW-1 to obtain the coefficient of biomass components. DW in the reaction flux unit represented the dry weight of cells. The proportion of each substance in the DW of cells was obtained by subtracting the weight of water from the total amount of each nutrient.

Conversion of the network into a mathematical model

The GEM of E. sinensis was transformed into a mathematical model for the following simulation. MATLAB 2018b software from Mathworks (https://matlab.mathworks.com/) and COBRA toolbox v.3.0 [21] were used for model transformation and subsequent simulation. Before the simulation, the flux constraints of reactions should be limited. The flux of reversible reactions was set to be (-1000, 1000) mmol gDW-1 h-1, and that for irreversible reactions is (0, 1000) mmol gDW-1 h-1. For the nutrients that could be synthesized by the model, the flux of their transport and exchange reactions was set to be (0, 1000) mmol gDW-1 h-1. The flux of the transport and exchange reactions for the nutrients that needed to be absorbed from the feeds or environments was set to be (-5, 1000) mmol gDW-1 h-1, except for trace elements set as (-1, 1000) mmol gDW-1 h-1.

Network evaluation

Synthesis of biomass and biomass precursors

After the refinement of the network, the GEM needed to be further evaluated for the accurate simulation of the metabolic process for some important precursors and synthesis of biomass. In this study, the synthesis of all the precursors of the biomass reaction was detected. As the trace elements and saccharides were all obtained from the feed, the precursors needing evaluation were actually all the amino acids and fatty acids. Ten essential amino acids (lysine, arginine, methionine, tryptophan, phenylalanine, leucine, histidine, valine, threonine, and isoleucine) [22] and 10 nonessential amino acids (alanine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, proline, serine and tyrosine) in E. sinensis were checked first. Then, the synthesis of all the fatty acids was checked, especially for 5 essential fatty acids, including linoleic acid (18:2n-6), linolenic acid (18n: 3n-3), eicosapentaenoic acid (20:5n-3, EPA), docosahexaenoic acid (22:6n-3, DHA), and arachidonic acid (20:4n-6, ARA) [23, 24]. Flux balance analysis (FBA) was used for the investigation, which is a constraint-based simulation optimization method commonly used in GEM simulation [25, 26]. The FBA assumes that the system is in a quasi steady state. Under this assumption, certain constraints and objective functions were set, and the optimal objective function value was calculated to study the flux distribution in cells under corresponding conditions. The objective function could be set according to the purpose of the study. The exchange reaction of each precursor was set to be the objective function, respectively. If the analog value for a certain precursor was 0, it reflected some defect existing in the metabolic route for this substance in the network. Then, taking the precursor as the starting point, its synthesis pathway was backtracked to see where the breakpoint appeared. After finding out the breakpoint, gap-filling reactions were added to complete the synthesis pathway. Subsequently, the synthesis of this precursor was simulated again. If it still could not be synthesized, the correction process was repeated until the precursor could be synthesized successfully. Similarly, in the investigation of biomass synthesis, the biomass reaction was set to be the objective function and its maximal flux was evaluated by the same method [27].

Testing basic properties of the model

For evaluating the quality of the model, the following 10 basic properties of icrab4665 were tested according to the Cobratoolbox tutorial (https://opencobra.github.io/cobratoolbox/stable/tutorials/tutorialModelSanityChecks.html): (1) leak test 1: whether the closed model could produce any exchanged metabolite from nothing; (2) leak test 2: if something leaked when demand reactions for each metabolite in the model were added; (3) if the model produced energy from water; (4) if the model produced matter when the ATP demand was reversed; (5) if the model had flux through the h[c] demand; (6) if the model produced too much atp demand from glucose; (7) if duplicated reactions occurred ; (8) if empty columns were present; (9) if demand reactions had a lb >= 0; and (10) whether single-gene deletion ran.

Analysis of optimal nutrition requirement

In the simulation of optimal nutrition requirement, the upper and lower limits of the biomass reaction were set as 1 gDW h-1. The biomass reaction was set to be the objective function. The FBA was used to simulate the flux distribution in the metabolic network for 1 gDW biomass synthesis. The flux of the exchange reaction for each nutrient was the optimal nutrient absorption required for the cell to accumulate 1 g of dry weight cell.

Specific growth rate

The growth rate of E. sinensis was evaluated as a specific growth rate (SGR) described in Ref. [28].

$$SGR/\%\cdot {\mathrm{d}}^{-1}=\left[\frac{\mathit{\ln}\left({m}_2\right)-\ln \left({m}_1\right)}{t_2-{t}_1}\right]\ast 100\%$$

where m1 and m2 represent the initial and final weight, respectively. t1 and t2 represent the initial and final time, respectively.

During the breeding, the actual feed for juvenile crab should be 3% - 5% weight/day and the crab we used in this study is averaged 8.41 g. Therefore, the feed for a crab per day should be 0.2523 (8.41 × 3%) to 0.4205 (8.41 × 5%) g. Assuming that all the feed can be absorbed and used for growth, if the biomass synthesis rate is 1.0 gDW d−1, the feed requirement rate is 1.0 g/gDW−1d−1. So the nutrients absorbing rate used in the simulation was the rate for 1 g feed per day. Therefore, if the simulated biomass synthesis rate was x gDW d-1, then the biomass synthesis rate for 1-day amount feed should be 0.2523 x to 0.4205 x gDW d-1. Considering this was the dry weight excluding 51.6% water, the fresh biomass synthesis rate should be 0.2523 x /(1-51.6%) to 0.4205 x /(1-51.6%) g d-1. Therefore the final SGR formula should be:

$${SGR}_{min}/\%\cdot {\mathrm{d}}^{-1}=\left[\mathit{\ln}\left(8.41+\frac{0.2523x}{1-51.6\%}\right)-\mathit{\ln}8.41\right]\ast 100\%$$
$${SGR}_{max}/\%\cdot {\mathrm{d}}^{-1}=\left[\mathit{\ln}\left(8.41+\frac{0.4205x}{1-51.6\%}\right)-\mathit{\ln}8.41\right]\ast 100\%$$

where x is the biomass synthesis rate simulated with icrab4556.

Results and discussion

Transcriptome sequencing

Total RNA from five mixed tissues, including gill, muscle, thoracic ganglion, eyestalk, and hepatopancreas, were extracted to obtain the E. sinensis transcriptome data. The RNA samples had no genomic contamination, protein and impurity contamination, and no color abnormality. The final quality testing result was level A, which was the highest level for eukaryotic transcriptome RNA. Total RNA was used as the input material for the RNA sample preparations and sequenced with the Illumina HiSeqTM2500. A total of 98,358,250 clean reads were obtained with Q20 at 96.02% and Q30 at 90.6%. The GC content of the clean reads was 51.68%. From these clean reads, 449,372 transcripts (mean length 590 nt) were assembled with Trinity software and then, 246,232 unigenes (the longest transcripts of each gene, mean length 851 nt) were constructed. Finally, 10,108 (4.1%), 29,836 (12.11%), and 67,612 (27.45%) unigenes were matched against NR, NT, and PFAM databases, respectively. Also, 68,664 unigenes (27.88% of total unigenes) were annotated to the GO database, including 50,113 assigned to the biological process category, 55,907 assigned to the molecular function category and 38,402 assigned to the cellular component category. Moreover, 25,157 unigenes were assigned to KO IDs.

GEM reconstruction of E. sinensis

The transcriptome sequencing results of E. sinensis comprised 246,232 unigenes, including 25,157 unigenes containing KO ID, and 5,846 nonrepetitive KO ID were obtained. The KEGG database contained information of 10,827 reactions, including 6,037 reactions with KO ID. Using KO ID as a bridge, the reaction information from the KEGG database was matched with unigenes in transcriptome data, and a preliminary reconstructed GEM of E. sinensis was obtained, which included 1759 metabolic reactions, 1838 metabolites and 4706 unigenes.

The preliminary reconstructed GEM was artificially refined to improve the quality of the network. Through the balance of reaction equation, 59 unbalanced reactions were revised. According to the KEGG pathway diagram and reactant pair information, combined with the characteristics of reaction and reactant, 129 reactions in the network were supplemented with main reactions. The improvement in the main reaction information was of great significance for the subsequent gap filling step of the network. Further, 142 reactions had missing pathways, and 171 reactions had missing subsystem information in the preliminary reconstructed GEM. According to the information provided by the KEGG database, 119 reactions were supplemented with concrete pathways and subsystems. The other reactions were all classified into “metabolic pathway,”

By integrating and deleting multi-step reactions, general reactions, incomplete reactions and macromolecular reactions, 63 redundant reactions in the network were processed. After the treatment of chirality conformations, 11 new redundant reactions were deleted. In the gap filling step, the gaps were identified and filled in the pathway and global scales. A total of 166 and 52 reactions were added to the network to fill the gaps in the pathway and global scales, respectively. Also, 225 WCCs were found in the network before gap filling, while the number decreased to 142 after gap filling on the pathway scale and 110 after gap filling on the global scale. It indicated that the gap filling step significantly increased the connectivity of the network.

According to the nutritional requirements of E. sinensis, 160 transfer reactions were added, including 80 exchange reactions and 80 transport reactions. The network was divided into two cellular compartments: intracellular and extracellular, and the metabolites were also divided into two categories: intracellular metabolites and extracellular metabolites. After compartmentalization, there are three types of reactions were found in the network model: (1) the reactants and products of the reaction were all intracellular metabolites, indicating that the reaction was an intracellular reaction; (2) the reactants and products of the reaction were all extracellular metabolites, indicating that the reaction was an exchange reaction; and (3) both intracellular and extracellular metabolites were involved in the reaction, which indicated that the reaction was a transport reaction. If an exchange or transport reaction is set to be irreversible, it means that the transported substance can not be obtained from the feed. If the crab itself cannot synthesize a particular substance, it will lead to the lack of the substance and may eventually lead to the development of diseases or even death. In the simulation, it might show as the failure of biomass synthesis. For example, if the transport reaction of an essential amino acid was set to be irreversible, the amino acid would not be available, and hence the flux of biomass synthesis reaction would be 0. However, if it was the change of an exchange or transport reaction for a nonessential amino acid, the biomass synthesis would not be affected. Therefore, as the nonessential amino acids do not needed to be imported from the feed, the reversibility of transport and exchange reactions for nonessential amino-acids were all set as irreversible.

The refined GEM contained 2055 reactions and 4665 unigenes. Based on the current data, including RNA-seq and KEGG, this network was still incomplete. Many parts of the network were still disconnected, which showed that the network still contained 110 WCCs after correction. These missing parts were largely due to the lack of understanding of E. sinensis. This was also the reason why the model still needed to be modified by adding the biomass equation and a series of simulations. Another reason was the limitations of the data itself. The network was constructed based on a limited pool of tissues, and the metabolic process of some substances did not exist in these tissues.

Most of the reactions in the GEM belong to the basic metabolism. Discontinuous growth is an important feature of E. sinensis. E. sinensis goes through more than 10 cycles of ecdysis throughout its life, and each ecdysis cycle is accompanied by a sharp increase in body size, weight, and development. Ecdysone is an important substance affecting the ecdysis process of E. sinensis. Ecdysone is synthesized and transformed into active 20-hydroxyecdysone in the Y organ. 20-hydroxyecdysone exists in many tissues and has the function of inducing ecdysis. On the other hand, 20-hydroxyecdysone is transformed to 20,26-dihydroxyecdysone catalyzed by 26-hydroxylase, thus loses its activity. The formation and inactivation of 20-hydroxyecdysone regulate the ecdysis and growth of E. sinensis. The GEM includes the reaction from 20-hydroxyecdysone to 20,26-dihydroxyecdysone (R08143), which is a characteristic reaction of E. sinensis as an animal grows in stages through ecdysis.

Addition of biomass reaction

The biomass includes about 51.6% water, 10.1% protein, 35% fat, 1% carbohydrate, 1.5% ash, some trace elements and other nutrients. In the nutritional component detection, the content of 20 amino acids, 22 fatty acids, 9 trace elements and 2 saccharides in cells were detected. The unit of each component was converted into mmol gDW-1, and then the content value was used as its coefficient in the biomass equation. The coefficient maintenance substances such as ATP and NADPH were also added to the biomass reaction. Finally, the biomass reaction was composed of the metabolites from the nutritional component detection and the maintenance substances (Supplementary file 2). The precursor composition of the biomass reaction plays an important role in the necessity simulation experiment. If a biomass precursor is not considered in the biomass reaction, the synthesis reaction and pathway of this substance may not be required for growth. Subsequently, the genes, reactions and substances related to this are not necessary. Therefore, the existence of a metabolite in the biomass reaction may affect the necessity of its related reaction, genes and other metabolites. On the contrary, the coefficient of a biomass precursor is very important for the accuracy of the predicted growth rate prediction. The biomass reaction produces the dry weight cells by adding the contents of each precursor. When one wishes to accurately predict the optimal growth rate, the content of each precursor plays an important role.

Network evaluation

Synthesis of biomass and biomass precursors

Taking biomass reaction as the objective function, the maximum value of the biomass synthesis rate was calculated to evaluate the network, taking the upper and lower limits of 2,055 reactions as constraints with the FBA algorithm. After the first calculation, the maximum flux value of biomass reaction was zero, which indicated that there are some defects in the biomass synthesis route of the network.

To identify the defects, the synthesis of each precursor for biomass synthesis was checked with the GEM, including 20 amino acids, 22 fatty acids, 9 trace elements and 2 saccharides. As all the trace elements and saccharides were obtained from the feed, the lower bound of their exchange reactions was set to be -1 mmol gDW-1 h-1 for trace elements and -5 mmol gDW-1 h-1 for saccharides. The precursors actually requiring verification of the synthetic ability were amino acids and fatty acids. Of the amino acids, 8 amino acids (alanine, asparagine, aspartic acid, glutamine, glutamic acid, glycine, proline, and serine) could be synthesized using the model, and the other 12 (lysine, arginine, methionine, tryptophan, phenylalanine, leucine, histidine, valine, threonine, isoleucine, cysteine, and tyrosine) could not be synthesized. Of the 12 amino acids that could not be synthesized, 10 were essential amino acids that must be obtained from the feed except cysteine and tyrosine. Therefore, the upstream and downstream metabolic flow of cysteine and tyrosine were further carefully checked to find the defects in their synthesis routes [29]. Actually, tyrosine and cysteine are semi-essential amino acids for most animals, which are also known as conditional amino acids, referring to the amino acid that can be synthesized in cells, but the synthetic amount cannot meet the growth needs. It still needs to be absorbed from the environment. Therefore, the lower limit of the exchange reactions for tyrosine and cysteine was modified to -3 mmol gDW-1 h-1. In the metabolic flux checking of cysteine synthesis route, it was found that the flux of the metabolic processes from sulfate to sulfite and then to hydrogen sulfide was 0. To complete these processes, the exchange and transport reactions of sulfate were first added to the network to absorb sulfate from the environment as a sulfur source. Then, the reactions from adenylyl sulfate to sulfite (R08553) and from sulfite to hydrogen sulfide (R0858) were added. With these reactions supplemented, the synthesis pathway of cysteine was complete and the maximum synthesis flux of cysteine was 8 mmol gDW-1 h-1. In the re-calculation of the biomass synthesizing rate, its maximum flux was 2.6639 gDW h-1, which indicated that the GEM could synthesize biomass.

In addition, studies on the necessity of fatty acids in E. sinensis are few. At present, linoleic acid, linolenic acid, EPA, DHA and APA are considered as necessary fatty acids [23, 24]. The model cannot synthesize these five fatty acids, which is consistent with the literature. According to the transcriptome sequencing results, the pathway involved in the synthesis of unsaturated fatty acids in the metabolic system of E. sinensis is extremely incomplete, and no unsaturated fatty acids can be synthesized in cells. In fact, the feed of E. sinensis is mostly compound feed, and hence may contain a variety of fatty acids or their precursors. However, no clear evidence is available on the determination of all kinds and contents of fatty acids in the feed. Therefore, the fatty acids that were required for biomass synthesis but could nbot be synthesized using the model were all set to be imported from the feed.

Testing basic properties of model

In the testing of leak metabolites, the model still produced glucose, fructose, H2O, PPi and H3PO4 when it was closed. The exchange reaction of the leak metabolites was set as the objective function separately in the closed model, and the flux distribution of the network was calculated with the FBA to find out the reason for the leakage of these metabolites. The reactions with the nonzero flux were carefully checked, revealing that the leakage of metabolites was caused by the material imbalance in the network. Although the material of each reaction in the network was balanced, the combination of some specific reactions caused the material imbalance of the whole network. The leakage of glucose and fructose was caused by R02110: (C12H20O10)n <=> (C6H10O5)n and R02109: (C6H10O5)n + H2O <=> (C12H20O10)n + C6H12O6. These two reactions both involved amylose and starch with an uncertain number of monomers, leading to material imbalance when the “n” represented different numbers in the two reactions. Similarly, the leakage of H2O, PPi and H3PO4 was caused by seven RNA-involved reactions (R00437–R00443) with uncertain phosphate groups , resulting in material imbalance. When these reactions were all deleted by setting their both upper and lower bounds as zero, there was no leakage of exchanged metabolites occurred in the model. However, still, leakage of metabolites was observed when demand reactions for each metabolite in the model were added. This was not difficult to understand for a less well studied species. Still, many uncertain issues were present in the metabolic process of E. sinensis. Therefore, it was difficult to guarantee no leakage of metabolites in the network.

In the testing of other basic properties tests, the model did not produce energy from water and did not produce matter when ATP demand was reversed. The model had no flux through the h[c] demand and did not produce too much ATP demand from glucose. No duplicated reactions and empty columns were found in the model. No demand reaction could have a flux when demand reactions were in the backward direction. Finally, single-gene deletion ran smoothly. The final testing results are shown in Fig. 2.

Fig. 2
figure 2

Results of basic properties testing for the model

The finally reconstructed GEM contained 4665 unigenes, 2060 reactions and 1891 metabolites (Supplementary file 3), named as icrab4665. The features of icrab4665 are shown in Table 1. The stoichiometric matrix of icrab4665 formed by the model transformation with the COBRA toolbox on the MATLAB platform is shown in Fig. 3.

Table 1 Features of icrab4665
Fig. 3
figure 3

Visualization of stoichiometric matrix. ‘nz’ is the number of nodes in the matrix

Analysis of the nutritional requirement of E. sinensis

Feed plays a key role in crab culture, and the nutritional needs of crabs are the theoretical basis for the design of formulated feed [30, 31]. In this study, the theoretical requirement of nutrients for the accumulating 1 g cell dry weight was calculated based on the GEM of E. sinensis. The simulation results were compared with the reported essential amino acids, essential fatty acids and mineral elements to propose constructive suggestions for the feed design.

Demand analysis of essential amino acids

The protein requirement of E. sinensis can be divided into essential amino acids and nonessential amino acids. E. sinensis need 10 kinds of essential amino acids [22]. The lack of essential amino acids leads to malnutrition, molting obstruction, slow growth and development, and even death. Studies have shown that the requirement of essential amino acids is closely related to their composition of essential amino acids in cells. Therefore, some researchers have analyzed the composition of amino acids in E. sinensis in different growth stages to estimate the requirement of these amino acids [32,33,34,35].

The requirement of essential amino acids was simulated with icrab4665 to identify the optimal composition of amino acids in E. sinensis feeds. The simulation results were further compared with the literature to check the difference between the theoretical optimal value and the content in current feeds. The unit of the content of amino acids was first transformed into mmol gDW−1 h−1 and then compared with the simulation results Table 2.

Table 2 Requirements of essential amino acid by E. sinensis in literature and simulation results

The simulation results were lower than the demand for essential amino acids provided in the literature. This might be because the requirement of essential amino acids provided in the literature was the total amount of amino acids to maintain the growth, development, metabolism, and accumulation in the body, while the network simulation results indicated the amount of essential amino acids absorbed by cells to accumulate 1 g of cell dry weight. Some amino acids were preferentially deposited in tissues, while some amino acids might have a more active metabolism, so the amount of essential amino acids deposited in the body was not completely consistent with its requirement. According to the work of Cui et al. [36], the protein deposition rate was calculated as the amount of protein deposition divided by the total protein requirement. Therefore, the deposition rates of the essential amino acids could be obtained by dividing the accumulation requirement of essential amino acids expressed as the simulation results by the total requirement of essential amino acids provided in the literature Table 2, (Supplementary file 4). The deposition rates of arginine, methionine, isoleucine, and phenylalanine were much lower than those of the other amino acids, which indicated that these amino acids might play a more active role in the metabolism of E. sinensis. Therefore, the amount of these amino acids in the feed should be proportionally higher than that of other amino acids to fulfill the requirement of E. sinensis. In addition, the abnormally large flux of threonine in the simulation results might be due to the lack of key rate-limiting reaction in threonine synthesis or metabolism route in the metabolic network.

Demand analysis of essential fatty acids

Crustaceans cannot synthesize polyunsaturated fatty acids from monounsaturated fatty acids. Therefore, it is necessary to provide specific n-3 and n-6 unsaturated fatty acids (UFAs) from the feed to meet their needs for essential fatty acids. The lack of essential fatty acids can inhibit the molting and growth of crustaceans, and even lead to the occurrence of diseases in severe cases. Some crustaceans cannot reproduce smoothly due to the lack of high UFAs [37]. Although several enzymes responsible for the desaturating and elongating of the polyunsaturated fatty acids have been identified in E. sinensis, the characterization of these desaturases and elongases has shown that these enzymes are not directly involved in the synthesizing of UFAs [38,39,40]. Therefore, the demands of UFAs in E. sinensis are mainly provided by the diets. Generally speaking, the essential fatty acids of crustaceans are linoleic acid, linolenic acid, EPA and DHA. Therefore, we compared the simulated requirements of these four UFAs to their contents in the literature. The results are shown in Table 3.

Table 3 Requirements of essential fatty acids by E. sinensis in literature and simulation results

The simulation results showed that the theoretical absorption amount of linoleic acid, EPA, and DHA by E. sinensis was higher than that reported by Wen et al. Actually, the required amounts of essential fatty acids reported in various examples in the literature are quite different. The types and requirements of lipids in the feed are also variable in different growth stages of E. sinensis. Even for the same growth stage, the conclusion about the optimal requirement for the fat content in the feed is not consistent [41]. Wen et al. reported that the optimal requirements of linoleic acid, linolenic acid, EPA, and DHA in the juvenile E. sinensis were 2.79%, 0.95%, 0.28%, and 0.53%, respectively [23]. Qi et al. found that the proper requirement of linoleic acid was 1.24% for the juvenile E. sinensis with weight 0.22 ± 0.01 g, and the suitable linoleic/linolenic ratio was 0.56-0.74 [42]. Wang et al. [43] showed that when 0.3% of EPA, and DHA were added to the diet, the weight gain and utilization of feed in juvenile crabs were significantly increased. Zhao [44] found that adding appropriate amounts of DHA and DHA/EPA mixture had obvious promoting effects on the growth of juvenile crabs. The suitable DHA content in the juvenile crab feed was about 0.2%, and the suitable DHA/EPA ratio was 2 - 3. Xu et al. [45] found that EPA and DHA had a significant effect on the weight gain of juvenile crabs, and the growth of juvenile crab slowed down when their relative content was lower than 0.03% and 5%, respectively. However, this study was published earlier, and the conclusion that DHA in the feed should not be less than 5% was clearly contradicted in later studies. In addition, Wang et al. [24] reported that arachidonic acid (20:4n-6, ARA) was also an essential for E. sinensis. The ARA content in the feed they reported was suitable for adult crabs weighing about 150 - 200 g, and the measurement and prediction in this study were based on juvenile crabs. Therefore, we did not compare the results of the ARA demand with it. In summary, linoleic acid, EPA and DHA were essential fatty acids to promote the growth of juvenile crabs, which was consistent with the simulation results. According to the simulation results, increasing the amount of linoleic acid, EPA, and DHA in the feed should be considered to promote the growth of E. sinensis.

The simulation results of linolenic acid were different for various types of linolenic acid. The α-linolenic acid was accumulated at the rate of 4.567 mmol gDW-1 h-1 and γ-linolenic acid was absorbed at the rate of 0.0024 mmol gDW-1 h-1. The metabolic flux analysis of α-linolenic acid demonstrated that the flux of α-linolenic acid accumulateion R07859 (from lecithin to α-linolenic acid) was 4.606 mmol gDW-1 h-1, but with no consumption reaction for α-linolenic acid, which resulted in the final accumulation of α-linolenic acid in the simulation result. In addition, the result of nutritional component analysis also showed α-linolenic acid accumulation in E. sinensis cells.

Requirement analysis for mineral elements

Mineral elements are indispensable nutrients of E. sinensis for maintaining the growth, development, health and reproduction. Unlike terrestrial animals, aquatic animals can absorb minerals from the aqueous environment relying on organs such as the mumps and intestines. Therefore, it is not necessary to supply mineral elements in the feed. However, in the whole life cycle of E. sinensis, the environment of its growth and development is mostly fresh water. The inorganic salt content in fresh water is relatively deficient. Therefore, E. sinensis, unlike other aquatic animals, tends to require mineral supplementation from the feed to meet its growth needs. The optimal requirements of mineral elements were simulated with icrab4665 and compared with the literature Table 4. The demands for calcium and magnesium in the literature were expressed by the content of calcium carbonate and L-aspartate magnesium in the feed. Therefore, the molar amounts of calcium and magnesium were calculated using the molar mass of calcium carbonate and L-aspartate magnesium in unit conversion. The demands for zinc, selenium and copper in literature were expressed as the content of each element in feed, so the molar amounts were calculated using the molar mass of these elements.

Table 4 Requirements of mineral elements by E. sinensis in literature and simulation

The requirements of magnesium, zinc, selenium and copper in the literature were quite close to the simulation results, which reflected the high quality of icrab4665. According to the simulation results, the feed addition of zinc and selenium was slightly increased. The requirement of calcium is complex for crabs, because that the calcium content in water and the different growth stages of crabs will all influence the calcium requirements in feed. For example, crabs in molting stage need large amounts of calcium to satisfy the renewal of shell and body growth. Therefore, the demand for dietary calcium for E. sinensis should be considered in combination with many factors, which may be the reason for the big difference in the calcium requirement between the literature and the simulation result.

Evaluation of the growth rate simulated with icrab4665

In order to further evaluate the growth rate of E. sinensis simulated with icrab4665, we set the maximum absorption rate of nutrients according to the values in the feed in the literature mentioned above (with 1g feed absorbtion per day) and simulated the maximum synthesis rate of biomass. The absorption rate of substances not mentioned in the literature remained unchanged. The final biomass synthesis rate was 0.1203 gDW d-1. The final SGR for simulation results was 0.75% - 1.24%. Hu et al. investigated the growth rate of juvenile crabs in the indoor tank for three different families and found that their SGR varied from 0.997% - 1.454% [28], which was in good agreement with our simulation results. The small differences between simulation results and the literature might be due to the differences in culture conditions, temperature or water quality.

In the intensive culture mode, a large amount of nitrogen, phosphorus, and other nutrients in the feed with an unreasonable high nutrition ratio are directly discharged into the water without being absorbed by E. sinensis, which is an important component of water pollution [51]. In the evaluation of growth rate, although we set the maximum absorption rate of nutrients according to the literature, not all nutrients could be absorbed because the feed composition in the literature was not optimal. The calculated nutrient absorptions of many nutrients were much less than their content in the feed (Supplementary file 5). The unabsorbed part will re-entered the water with the excreta of E. sinensis, resulting in the enrichment of nutrients such as nitrogen and phosphorus in water to cause water pollution. Therefore, an important way to reduce water pollution is to optimize the nutrient content in the feed, make full use of the nutrition in the feed and reduce the excretion of excess nutrients.

To further determine the key genes, reactions and pathways that play a role in growth, the flux comparison method can be used [52]. Firstly, the biomass synthesis rate were set to be a larger value and a smaller value, for example, 2.6636 gDW h-1 (the maximum value) and 1.0 gDW h-1, and the flux distribution in the GEM were calculated, respectively. Then, the flux of each reaction in the two calculations was compared to determine the key reactions and pathways. After comparison, the flux of 194 reactions were found changed when the biomass synthesize rate changes. Except the transport and exchange reactions, the flux-changed reactions were mostly distributed in carbohydrate metabolism (37 reactions), nucleic acid metabolism (13 reactions) and amino acid metabolism (20 reactions). The top 10 reactions with largest flux changes were distributed in galactose metabolism, pyruvate metabolism, pyrimidine metabolism, synthesis and degradation of ketone bodies, and several amino acid metabolism pathways. The flux-changed reactions and their corresponding unigenes, pathways and subsystems were listed in Supplementary file 6.

Conclusion

Optimization of feed is crucial for cultivating aquatic products, and traditional methods usually pass trial and error to pick out suitable additions. In this study, we reconstructed the largest GEM of E. sinensis icrab4665. The GEM was used to conduct the in silico prediction on the optimal proportion of feed suitable for the growth of E. sinensis and put forward reasonable suggestions for optimizing of feed. Based on the existing data, the simulation results of the model were not completely consistent with the experimental results. This difference was not only the reason for the completeness of the model, but also affected by the experimental results. For example, the literature values for amino acids might also be influenced by the experimental conditions or other factors, and this limited the analysis of the deposition rate. Further experiments, for example, isotope labeling experiments, may need to be applied to clarify the exact amount of metabolized and deposited amino acids. It can also be helpful for the study of the metabolic flow of amino acids. Referring to the metabolic process of amino acids in other animals, the active metabolized amino acids in a cell may be transported to other tissues with the blood to synthesize proteins with specific functions as needed. They may also be used to synthesize proteins in the hepatopancreas for storage, or become an important component of cells, so as to make E. sinensis more nutritious. It would be more accurate to analyze the metabolic flow of amino acids through the combination of in silico prediction and in vivo experiments.

In addition, the prediction results also need to be further verified by feeding experiments. The impact of nutrients proportion on the cost of the feed is not easy to be estimated. Based on the simulated optimal nutrients proportion, the proportion of some nutrients needs to be increased, while relatively, the proportion of other nutrients will be reduced. Therefore, the final change of feed cost should be determined according to the market value of these nutrients. However, a reasonable feed ratio is beneficial to the growth of E. sinensis. Therefore, even if the cost of the feed remains unchanged or even increases after optimization, considering that the value of adult crab is much higher than that of feed, it is likely to be cost-effective in terms of cost performance. Besides, since the cost of pure nutrients is relatively high, from the perspective of cost, a feed can be prepared with specific common feed components according to the nutritional needs simulated by the model, so as to reduce the feeding cost of E. sinensis. The feeding experiments should be carried out in follow-up studies to verify the accuracy of the model prediction, and provide new data for further improvement of the model.

One important thing to be noted is that the growth of E. sinensis belongs to metamorphosis development, therefore, the difference in metabolic processes between its different growth stages is extremely large. Therefore, the life stage of E. sinensis used in generating transcriptome data will affects the GEM a lot. The growth of E.sinensis includes five stages: fertilized egg, daphnia larva, megalopa larva, juvenile and adult, in which juvenile is the longest stage with the fastest growth rate as well. In the process of E.sinensis breeding, the growth of juvenile stage directly affects the size and quality of the adult crabs. Therefore, the E.sinensis in juvenile stage was chosen to reconstruct the GEM in this study and the GEM was only applicable to the feed formulation of E. sinensis for the juvenile stage. On the conotrary, the limited pool of tissues may also influence the comparison of simulated amino acids, fatty acids, and mineral composition with the data from the literature, as these are very lifecycle-dependent. During the juvenile stage, the crab maintains the same shape and grows through molting until adulthood. Therefore, the size of the juvenile crab used for RNA-seq has no major effect on the GEM.

Although the model still has many deficiencies to be improved, this study promoted the exploration of feed optimization for aquatic crustaceans from in vivo to in silico. The results might contribute to the improving feed efficiency and be beneficial for improving the aquaculture production of E. sinensis.

Availability of data and materials

The RNA-seq data is availability from the Gene Expression Omnibus (GEO) database wih the accession number GSE182818. The GEM and nutrient requirements analysis results are availability from the supplementary files.

Abbreviations

GEM:

genome-scale metabolic network model

PCR:

polymerase chain reaction

GEO:

Gene Expression Omnibus

VBA:

Visual Basic for Applications

WCCs:

weakly connected components

FBA:

Flux balance analysis

SGR:

specific growth rate

UFAs:

unsaturated fatty acids

References

  1. Jones M, Kujundzic M, John S, Bismarck A. Crab vs. Mushroom: A Review of Crustacean and Fungal Chitin in Wound Treatment. Mar Drugs. 2020;18(1):64.

    CAS  PubMed Central  Google Scholar 

  2. Chen ZQ. The Gender Differences on Edible Parts Nutrients and Metabolism of Chinese Mitten Crab. Master. Nanjing: Nanjing agricultural university; 2016.

  3. Sun JQ, Xu GY, Gao H, Yan BL. Advances in Nutritional Physiology of Eriocheirsinesis. Fish Sci Technol Inform. 2018;45(04):197–202.

    Google Scholar 

  4. Sun JH, Sun JF, Sun XL, Bai DQ, Qiao XT. Effects of diets with different protein contents on growth performance and digestive enzyme activities of Eriocheir sinensis. China Feed. 2012;23:25–7.

    Google Scholar 

  5. Sun XJ, Wang Y, Chen LQ, Li EC, Wu P. A Dietary vitamin A requirement of juvenile Chinese mitten crab, Eriocheir sinensis. J Shanghai Ocean Univ. 2008;18:539–45.

    Google Scholar 

  6. Chen YL, Liu WS, Wang XD, Li EC, Qiao F, Qin JG, et al. Effect of dietary lipid source and vitamin E on growth, non-specific immune response and resistance to Aeromonas hydrophila challenge of Chinese mitten crab Eriocheir sinensis. Aquacult Res. 2018;49(5):2023–32.

    CAS  Google Scholar 

  7. Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 2019;47(D1):D649–59.

    CAS  PubMed  Google Scholar 

  8. Ma HW, Zeng AP. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics. 2003;19(2):270–7.

    CAS  PubMed  Google Scholar 

  9. Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol Syst Biol. 2011;7.

  10. Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED. Metabolic network structure determines key aspects of functionality and regulation. Nature. 2002;420(6912):190–3.

    CAS  PubMed  Google Scholar 

  11. Liu C, Wang M, Wei X, Wu L, Xu J, Dai X, et al. An ATAC-seq atlas of chromatin accessibility in mouse tissues. Sci Data. 2019;6(1):65.

    PubMed  PubMed Central  Google Scholar 

  12. Hou X, Yang H, Chen X, Wang J, Wang C. RNA interference of mTOR gene delays molting process in Eriocheir sinensis. Comp Biochem Physiol B Biochem Mol Biol. 2021;256:110651.

    CAS  PubMed  Google Scholar 

  13. Kumar P, Kiran S, Saha S, Su Z, Paulsen T, Chatrath A, et al. ATAC-seq identifies thousands of extrachromosomal circular DNA in cancer and cell lines. Sci Adv. 2020;6(20):eaba2489.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol. 2007;3:121.

    PubMed  PubMed Central  Google Scholar 

  15. Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R. Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data. J Biol Chem. 2007;282(39):28791–9.

    CAS  PubMed  Google Scholar 

  16. Mykles DL. Ecdysteroid metabolism in crustaceans. J Steroid Biochem Mol Biol. 2011;127(3-5):196–203.

    CAS  PubMed  Google Scholar 

  17. Thiele I, Palsson BO. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93–121.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2020;49(D1):D545–51.

    PubMed Central  Google Scholar 

  19. Thiele I, Palsson BO. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93–121.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Liu D, Ge Y: "GB28050-2011General principles for nutrition labeling of prepackaged food in national food safety standard"Interpretation and food nutrition label common problems analysis. Scie Technol Food Industry. 2013;18:24–7.

  21. Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc. 2019;14(3):639–702.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Chen LQ, Li EC. Research status and progress on nutritional requirements of Chinese mitten crab, Eriocheir sinensis. Feed Industry. 2009;30(10):1–6.

    Google Scholar 

  23. Wen XB, Chen LQ, Zhou ZL, Ai C. Nutritional requirements of essential fatty acids for juvenile crab Eriocheir sinensis. In: The 7th National Symposium on young scholars of marine and limnological Sciences, vol. 2000; 2000. p. 2.

    Google Scholar 

  24. Niwa R, Niwa YS. The Fruit Fly Drosophila melanogaster as a Model System to Study Cholesterol Metabolism and Homeostasis. Cholesterol. 2011;2011:176802.

    PubMed  PubMed Central  Google Scholar 

  25. Orth JD, Thiele I, Palsson BO. What is flux balance analysis? Nat Biotechnol. 2010;28(3):245–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Toyoshima M, Toya Y, Shimizu H. Flux balance analysis of cyanobacteria reveals selective use of photosynthetic electron transport components under different spectral light conditions. Photosynth Res. 2020;143(1):31–43.

    CAS  PubMed  Google Scholar 

  27. Wang H, Ma HW, Zhao XM. Progress in genome-scale metabolic network: a review. Chin J Biotechnol. 2010;26(10):1340–8.

    Google Scholar 

  28. Hu QB, Li XD, Jiang YS, Si YG, Zheng Y, Sun N. Comparative Growth Traints in Different Families of Juvenile Chinese Mitten Handed Crab, Eriocheir sinensis, Cultured in Net Cages Disposed in Ricefields and in an Indoor Tank. Fish Sci. 2016;35(5):547–51.

    Google Scholar 

  29. Han BB. Genome-scale metabolic network reconstruction of Bacillus subtilis 168. P.H.D. Tianjin: Tianjin University; 2013.

  30. Zhou CX, Luo L. Research Progress on nutritional requirements of Chinese mitten crab. Shandong Fisheries. 2006;02:39–41.

    Google Scholar 

  31. Li XG, Zhou G, Zhang TQ, Zhou J, Lin H, Qi JF, et al. Effects of different diets on general nutritional composition of Chinese mitten crab. Jiangsu Academy Agric Sci. 2011;39(04):410–2.

    Google Scholar 

  32. Xu XZ. Summary and future research direction of (Eriocheir Sineensis) series compound feed of Eriocheir sinensis. Jiangxi Fishery Sci Technol. 1998;04:20-25+27.

    Google Scholar 

  33. Ye JY, Wang HY, Guo JL, Chen JM, Pan X, Shen BQ. Lysine,methionine and arginine requirements of junvenile Chinese mitten crab (Eriocheir sinensis). J Fish China. 2010;34(10):1541–8.

    CAS  Google Scholar 

  34. Yang X, Ye JY, Zhou ZJ, Zhang YX, Wu CL, Ming JH. Study on the optimal levels of dietary leucine and isoleucine for juvenile Chinese mitten crabs, Eriocheir sinensis. Acta Hydrobiologica Sinica. 2014;38(06):1062–70.

    CAS  Google Scholar 

  35. Wang W, Ye JY, Yang X, Zhang YX, Liu P. Threonine requirement of juvenile Chinese mitten crab. Animal Nutr. 2015;27(02):476–84.

    CAS  Google Scholar 

  36. Cui YY, Zhang NN, Ma QQ, Chen Q, Shen ZH, Du ZY, et al. Effects of four commonly used plant protein sources on growth performance,amino acids retention and antioxidant enzyme activities in juvenile Chinese mitten crab, Eriochier sinensis. Acta Hydrobiologica Sinica. 2017;41(01):146–54.

    Google Scholar 

  37. Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: An extended review and a software tool. PLoS One. 2017;12(12):e0190152.

    PubMed  PubMed Central  Google Scholar 

  38. Yang Z, Guo Z, Ji L, Zeng Q, Wang Y, Yang X, et al. Cloning and tissue distribution of a fatty acyl Delta6-desaturase-like gene and effects of dietary lipid levels on its expression in the hepatopancreas of Chinese mitten crab (Eriocheir sinensis). Comp Biochem Physiol B Biochem Mol Biol. 2013;165(2):99–105.

    CAS  PubMed  Google Scholar 

  39. Yang ZG, Shi QY, Cheng YX, Yao QQ, Que YQ, Yang Q, et al. Full-length cDNA cloning and expression analysis of the fatty acid elongase gene from Chinese mitten crab (Eriocheir sinensis). J Fish China. 2016;23(1):53–63.

    CAS  Google Scholar 

  40. Shi QY, Yang ZG, Yao QQ, Cheng YX, Yang Q, Wei BH. Full-length cDNA cloning of ELOVL6 and its tentative study in Chinese mitten crab (Eriocheir sinensis). J Fish China. 2016;40:344–55.

    Google Scholar 

  41. Wang LQ, Hu W, Li HY, Lu WX, Jiang H. The effects of dietary lipid levels on the growth response and feed conversion effeciency of juvenile crab Eriocheir sinensis. J Shanghai Fish Univ. 2003;01:19–23.

    Google Scholar 

  42. Roegner ME, Watson RD. De novo transcriptome assembly and functional annotation for Y-organs of the blue crab (Callinectes sapidus), and analysis of differentially expressed genes during pre-molt. Gen Comp Endocrinol. 2020;298:113567.

    CAS  PubMed  Google Scholar 

  43. Wang LQ, Hu W, Li HY, Jiang H. The effects of dietary DHA and EPA levels on the growth response and feed conversion effeciency of juvenile crab Eriocheir sinensis. Fishery Modern. 2003;06:39-40+33.

    Google Scholar 

  44. Zhao YT: Effects of dietary DHA levels and DHA/EPA ratios on growth and lipid composition of juvenile Chinese mitten crab Eriocheir sinensis. Master. Shanghai: Shanghai Ocean University; 2013.

  45. Xu XZ, He ZX, Fu PF. Effects of different Fat sources on the growth of Juvenile Crab. Feed Industry. 1997;05:17–9.

    Google Scholar 

  46. Qian GY, Zhu QH. Effects of calcium and phosphorus in formulated diet on growth and feeding coefficient of Chinese mitten crab, Eriocheir sinensis. J Fish Sci China. 2000;03:110–2.

    Google Scholar 

  47. Tian WJ, Wei JJ, Li EC, Yu N, Wu QQ, Chen LQ, et al. Animal Nutr. 2015;27(01):305–12.

    CAS  Google Scholar 

  48. Li WW, Gong YN, Jin XK, He L, Jiang H, Ren F, et al. The effect of dietary zinc supplementation on the growth, hepatopancreas fatty acid composition and gene expression in the Chinese mitten crab, Eriocheir sinensis (H. Milne-Edwards) (Decapoda: Grapsidae). Aquacult Res. 2010;41(11):e828–37.

    CAS  Google Scholar 

  49. Tian WJ, Li EC, Chen LQ, Sun LM, Chen YL, Li M, et al. Growth, body composition and anti-oxidative status of juvenile Chinese mitten crabs, Eriocheir sinensis fed different dietary selenium levels. J Fish Sci China. 2014;21(01):92–100.

    CAS  Google Scholar 

  50. Sun SM, Qin JG, Yu N, Ge XP, Jiang HB, Chen LQ. Effect of dietary copper on the growth performance, non-specific immunity and resistance to Aeromonas hydrophila of juvenile Chinese mitten crab, Eriocheir sinensis. Fish Shellfish Immun. 2013;34(5):1195–201.

    CAS  Google Scholar 

  51. Cao L. Study on pollution characteristics of typical crab pond culture growth cycle in Xinghua City. Yangzhou: Yangzhou University; 2021.

  52. Hao T, Han B, Ma H, Fu J, Wang H, Wang Z, et al. In silico metabolic engineering of Bacillus subtilis for improved production of riboflavin, Egl-237, (R,R)-2,3-butanediol and isobutanol. Mol Biosyst. 2013;9(8):2034–44.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable

Funding

This work was supported by the Key Research and Development Program of China (2018YFD0901301), National Natural Science Foundation of China (31770904), Tianjin Development Program for Innovation and Entrepreneurship team (ITTFRS2017007), Program for Innovative Research Team in University of Tianjin (TD13-5076). The Natural Science Foundation of Tianjin (19JCYBJC29700) and Doctoral Foundation of Tianjin Normal University (52XB1915).

Author information

Authors and Affiliations

Authors

Contributions

JL generated the data. YG and LZ analyzed the data and wrote the manuscript. JY tested the quality of the model. JL and BW organized the data. LJ, JS and TH designed the experiment and revised the manuscript. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Tong Hao or Jinsheng Sun.

Ethics declarations

Ethics approval and consent to participate

All experimental procedures were conducted according to institutional guidelines for the care and use of laboratory animals in Tianjin Normal University and conformed to the National laboratory animal management regulations (Publication No. 2, 1988) approved by the National Science and Technology Commission.

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:  Supplementary file 1.

 Detail procedure of transcriptome sequencing and refinement of the GEM.

Additional file 2: Supplementary file 2.

The results of nutritional composition detection of E. sinensis.

Additional file 3: Supplementary file 3.

The reconstructed model icrab4665.

Additional file 4: Supplementary file 4.

 The calculation process of Table 2.

Additional file 5: Supplementary file 5.

The nutrient requirement in literature and simulation results when setting limits according to literature.

Additional file 6: Supplementary file 6.

The list of reactions with flux changed when the biomass synthesis rate changes.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, J., Gou, Y., Yang, J. et al. Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis. BMC Genomics 23, 475 (2022). https://doi.org/10.1186/s12864-022-08698-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-022-08698-z

Keywords

  • Genome-scale metabolic network1
  • network analysis2
  • biomass3
  • nutrient requirement4
  • Eriocheir sinensis5