Artificial multicellular system and application thereof

Abstract

The invention discloses an artificial multicellular system and application thereof, and belongs to the technical field of microorganisms. According to the method, the optimal inoculation ratio of the returning soil to the fresh cow dung is determined to be 1: 1, secondary screening of a bacterial system is carried out under the condition, an artificial multicellular system is obtained, the final accumulated total methane yield reaches 560.39 mL / (gVS) and the methane yield is 36.20 mL / (gVS.d) when the artificial multicellular system is used for fermentation, and the methane yield is improved by 34.17% compared with that of a primary generation. Compared with a single-environment inoculum, the yield of methane in a TF group is increased by 68.76% compared with T-P1 (primary-generation only returning-to-field soil) and is increased by 58.01% compared with F-P1 (primary-generation only fresh cow dung). According to the method, the lignocellulose hydrolysis efficiency and the methane conversion capacity are effectively improved, and a new way is provided for lignocellulose waste treatment and biological energy source production.

Description

Technical Field

[0001] The present invention belongs to the field of microbial technology, and in particular relates to an artificial multicellular system and applications thereof. Background Art

[0002] Lignocellulose is one of the most abundant renewable resources on Earth. It mainly consists of cellulose, hemicellulose, and lignin. It is widely present in plant cell walls and has a high degree of structural stability. It is an important source of raw materials for bioenergy. However, the energy conversion efficiency of lignocellulose is low, mainly due to the strong binding of cellulose and lignin, which makes it difficult for microorganisms and enzymes to adsorb and degrade it, resulting in a slow hydrolysis rate and difficulty in achieving a dynamic balance of microbial synergy. The variability of the functions of natural microbial communities further reduces the conversion efficiency. In addition, the environmental sensitivity and stability of methanogens are poor, which restricts the efficiency of anaerobic digestion and low methane production. These problems limit the efficient use of lignocellulose in the field of bioenergy.

[0003] The construction of artificial multicellular systems provides an innovative solution to break through these technical bottlenecks. Artificial multicellular systems are a biotechnology method that achieves specific functions by artificially constructing and optimizing microbial communities. Compared with natural communities, artificial multicellular systems have the characteristics of controllable components. Suitable microbial species can be selected or designed according to functional requirements, and the target conversion efficiency can be significantly improved by regulating the proportion and function of different microorganisms. At present, common ways to construct artificial multicellular systems include "top-down" and "bottom-up" methods. The "top-down" method starts from the macroscopic level and induces the natural microbial community to evolve and reorganize in a specific functional direction by applying specific environmental pressure to it; the "bottom-up" method starts from the microscopic level and designs and constructs synthetic microbial communities with specific functions based on the genomic information and metabolic network of a single microorganism.

[0004] However, existing methods still face many challenges. Although composite bacterial systems enriched from natural environments through a "top-down" approach can effectively enhance the hydrolysis of lignocellulose, there is little research on methane conversion. The "bottom-up" compounding method is prone to compounding failure or inhibition of methane production due to the poor survival ability and environmental adaptability of methanogens. In addition, it is also difficult to screen methanogenic bacterial systems that can anaerobically degrade lignocellulose in a single environment, mainly due to the complexity of lignocellulose, niche restrictions and the influence of environmental factors. Summary of the Invention

[0005] To address these challenges, the present invention proposes an artificial multicellular system for converting lignocellulose to methane based on a multi-environmental approach. By introducing exogenous bacterial strains for enhancement, this system can provide a richer variety of microorganisms and optimized growth conditions within complex environments, significantly improving the efficiency of selecting methanogens. This system is suitable for processing lignocellulose-rich crop straw, promoting the efficient conversion of agricultural waste into renewable energy, and has important implications for promoting a circular economy and green development.

[0006] The present invention provides a method for screening an artificial multicellular system, comprising the following steps:

[0007] (1) Straw-returned soil and fresh cow dung are mixed in a mass ratio of 0.5-1.5:0.5-1.5, and then inoculated into a fermentation medium. The mass ratio of the total mass of the returned soil and fresh cow dung to the fermentation medium is 2-4:6-8;

[0008] (2) Culture at 36-38°C with shaking for 15-17 days;

[0009] (3) The inoculation ratio was 11-13%, and the fermentation time after each subculture was 15-17 days;

[0010] (4) After passage 5 or 6, an artificial multicellular system is obtained.

[0011] The fermentation medium comprises the following components: peptone: 2 g / L, yeast extract: 0.4 g / L, sodium chloride: 2 g / L, sodium bicarbonate: 5 g / L, dipotassium hydrogen phosphate: 0.4 g / L, ammonium chloride: 1.2-1.4 g / L, cysteine: 0.5 g / L, sodium sulfide: 0.25 g / L, and lignocellulose: 0.2%. Ultrapure water is added to prepare the medium, wherein 9-11 mL of trace element solution and 9-11 mL of vitamin solution are added to each liter of the medium.

[0012] The trace element solution comprises the following components: 0.5 g / L manganese sulfate monohydrate, 0.1 g / L ferrous sulfate heptahydrate, 0.04 g / L nickel chloride hexahydrate, 0.048 g / L cobalt chloride hexahydrate, 0.13 g / L zinc chloride, 0.01 g / L copper sulfate pentahydrate, 0.01 g / L potassium aluminum sulfate dodecahydrate, 0.01 g / L boric acid, and 0.025 g / L sodium molybdate dihydrate, which are prepared by adding ultrapure water;

[0013] The components of the vitamin solution are as follows: vitamin B7 0.002 g / L, vitamin B5 0.005 g / L, vitamin B12 0.0001 g / L, p-aminobenzoic acid 0.005 g / L, lipoic acid 0.005 g / L, niacin 0.005 g / L, vitamin B1 0.005 g / L, vitamin B2 0.005 g / L, vitamin B6 0.01 g / L, and fulvic acid 0.002 g / L, and ultrapure water is added to prepare the solution.

[0014] In one embodiment of the present invention, the mass ratio of returning straw to soil to fresh cow dung in step (1) is 1:1.

[0015] In one embodiment of the present invention, the mass ratio of the total mass of the returned soil and fresh cow dung to the culture medium in step (1) is 3:7.

[0016] In one embodiment of the present invention, the shaking culture time in step (2) is 16 days.

[0017] In one embodiment of the present invention, the inoculation ratio in step (3) is 12%.

[0018] In one embodiment of the present invention, the fermentation time after each passage in step (3) is 16 days.

[0019] In one embodiment of the present invention, the amount of ammonium chloride added to the fermentation medium is 1.3 g / L.

[0020] In one embodiment of the present invention, the amount of trace element solution and vitamin solution added to the fermentation medium is both 10 mL.

[0021] The present invention also provides an artificial multicellular system obtained by screening using the above screening method.

[0022] The present invention also provides the application of the above screening method in screening artificial multicellular systems.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention determined that the optimal inoculum ratio of returned soil to fresh cow dung was 1:1. Under this condition, rescreening of bacterial strains ultimately resulted in a cumulative total methane production of 560.39 mL / (gVS) and a methane yield of 36.20 mL / (gVS·d), a 34.17% increase compared to the initial generation. Compared to single-environment inoculum, the methane production of the TF group increased by 68.76% compared to T-P1 (initial generation with only returned soil) and by 58.01% compared to F-P1 (initial generation with only fresh cow dung). This method effectively improves the efficiency of lignocellulose hydrolysis and methane conversion capacity, providing a new approach for lignocellulose waste treatment and bioenergy production. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the result of determining the dosage ratio between the multiple environmental inocula in Example 1.

[0026] Figure 2 The methane production and yield during the passage of the TF group in Example 2.

[0027] Figure 3 This is the change in lignocellulose degradation rate during the passage process of the TF group in Example 2.

[0028] Figure 4 The results of the single-factor experiments in Example 3 are shown in Figure 3, where (A) is the single-factor experimental result of the inoculation ratio; (B) is the single-factor experimental result of the carbon-nitrogen ratio; and (C) is the single-factor experimental result of the fermentation time (inoculation ratio 30%, carbon-nitrogen ratio 4.4).

[0029] Figure 5 Figure 3 shows the effect of the interaction between inoculum size, carbon-nitrogen ratio and fermentation time on the methanogenesis rate of anaerobic degradation of lignocellulose, including (A) surface plot of inoculum size and nitrogen source ratio; (B) height line plot of inoculum size and nitrogen source ratio; (C) surface plot of inoculum size and fermentation time; (D) height line plot of inoculum size and fermentation time; (E) surface plot of nitrogen source ratio and fermentation time; and (F) height line plot of nitrogen source ratio and fermentation time. DETAILED DESCRIPTION

[0030] Example 1 Determination of the Multi-environment Inoculum Dosage Ratio

[0031] (1) Determination of inoculum of multivariate environmental samples

[0032] Through preliminary experiments with a large number of environmental sample combinations (e.g., rumen fluid + cow dung, returned soil + marine sediment, and rumen fluid + returned soil), we determined that a mixture of returned soil and fresh cow dung exhibits stable cell culture and efficient anaerobic degradation of lignocellulose to methane. Based on this, we further investigated the effects of the addition ratios of various components in the multi-sample (straw returned soil and fresh cow dung) on ​​the lignocellulose to methane conversion process.

[0033] Table 1

[0034]

[0035] (2) Preparation of culture medium and experimental group setting

[0036] Peptone cellulose (PCS) medium was used, and its composition was as follows:

[0037] Peptone: 2 g / L; Yeast extract: 0.4 g / L; Sodium chloride: 2 g / L; Sodium bicarbonate: 5 g / L; Dipotassium hydrogen phosphate: 0.4 g / L; Ammonium chloride: 1.5 g / L; Cysteine: 0.5 g / L; Sodium sulfide: 0.25 g / L; Lignocellulose: 0.2%. Add ultrapure water and, per liter of culture medium, add 10 mL of trace element solution and 10 mL of vitamin solution. Sterilize the culture medium by moist heat at 121°C for 20 minutes before use.

[0038] The components of the trace element solution are as follows: 0.5 g / L manganese sulfate monohydrate; 0.1 g / L ferrous sulfate heptahydrate; 0.04 g / L nickel chloride hexahydrate; 0.048 g / L cobalt chloride hexahydrate; 0.13 g / L zinc chloride; 0.01 g / L copper sulfate pentahydrate; 0.01 g / L potassium aluminum sulfate dodecahydrate; 0.01 g / L boric acid; and 0.025 g / L sodium molybdate dihydrate, and ultrapure water is added to prepare the solution.

[0039] The components of the vitamin solution are as follows: vitamin B7 0.002 g / L; vitamin B5 0.005 g / L; vitamin B12 0.0001 g / L; p-aminobenzoic acid 0.005 g / L; lipoic acid 0.005 g / L; niacin 0.005 g / L; vitamin B1 0.005 g / L; vitamin B2 0.005 g / L; vitamin B6 0.01 g / L; fulvic acid 0.002 g / L, and ultrapure water is added to prepare the solution.

[0040] The optimal inoculation ratio of the two environmental samples was determined by gradient experiments. The inoculation ratio (calculated by mass ratio) was set at 1:0, 1:1, 1:2, 2:1, and 0:1. The absolute addition mass ratio (fresh cow dung: straw returned to soil) was 15g:0g, 7.5g:7.5g, 5g:10g, 10g:5g, and 0g:15g. The absolute inoculation amount of fresh cow dung was 8.36×10 10 CUF / g (all microorganisms), the absolute inoculum amount of straw-returned soil was 7.45×10 9 CUF / g (all microorganisms). Three parallel experiments were set up for each gradient, and subculture was performed simultaneously. Use a 100mL 32-necked cillin bottle with a working volume of 50mL and add 35mL of sterilized peptone cellulose (PCS) medium. After inoculation, seal with a rubber stopper and press with an aluminum cap to ensure that the bottle is sealed. Place the culture bottle in a 37°C constant temperature shaker at 120rpm for incubation.

[0041] (3) Analysis of experimental results

[0042] At each passage, 15 mL of bacterial solution was added to 35 mL of new culture medium. After each passage, when methane production stagnated (i.e., the difference in methane production between two consecutive passages was less than 50 mL / (gVS) within 1 day), the culture conditions were kept unchanged. By the fifth passage, the mixed bacterial strain had reached a stable state. Figure 1 As shown, the methane production and yield of each experimental group in the fifth generation showed that the best gas production was achieved when the two inoculum ratio was 1:1. Specific data are as follows: In the TF group (returned soil + fresh cow dung) with a 1:1 ratio, the cumulative methane production was 515.25 mL / (gVS) and the methane yield was 30.31 mL / (gVS·d), representing a methane yield increase of 5.46%-48.94% compared to the other compositions.

[0043] (4) Conclusion

[0044] Gradient experiments were conducted to determine the optimal inoculation ratio, ensuring the scientific and reliable experimental data. Using methane production and yield as core indicators, the advantages of a 1:1 ratio were clearly demonstrated. This provided optimized inoculation conditions for the subsequent construction of artificial multicellular systems, significantly improving the efficiency of lignocellulose methane conversion.

[0045] Example 2 A screening method for an artificial multicellular system for converting lignocellulose to methane based on enhanced exogenous strains

[0046] This example provides a screening method for an artificial multicellular system for converting lignocellulose to methane based on enhanced exogenous strains. The specific steps are as follows:

[0047] (1) Multi-environmental sample inoculum

[0048] According to the experimental results of Example 1, the returned soil and fresh cow dung were mixed in a mass ratio of 1:1 as inoculum, and inoculated at a mass ratio of 30% (the mass ratio of the total mass of the returned soil and fresh cow dung to the culture medium was 3:7).

[0049] (2) Preparation of culture medium and experimental group setting

[0050] The preparation method of the culture medium was the same as that in Example 1. In the experimental group setting, in order to explore the difference in treatment effect between the multi-environment inoculum and the single-environment inoculum, the inoculum contained only the returned soil (the amount of returned soil added was equal to the total amount of returned soil and fresh cow dung added in the TF group) and the control group contained only fresh cow dung (the amount of fresh cow dung added was equal to the total amount of returned soil and fresh cow dung added in the TF group), and three parallel groups were set up in each group.

[0051] (3) Analysis of experimental results

[0052] During the subculture process, the liquid in the culture flask was observed to gradually become clear from turbid. The entire methanogenesis process exhibits an S-shaped trend: After inoculation, methane production is relatively low in the initial stage, during which the microbial community within the system primarily grows and expands. Subsequently, the anaerobic cellulose-degrading microorganisms within the system hydrolyze the lignocellulose into smaller sugar molecules (such as glucose and xylose). These monosaccharides are further converted by the anaerobic microorganisms into volatile fatty acids (VFAs), alcohols (such as ethanol), and gases (such as CO2 and CH4). This process is the core of anaerobic digestion, in which volatile fatty acids are key intermediates in the subsequent production of CH4. In the final stage of anaerobic digestion, methanogens convert VFAs and H2 into CO2 and CH4. When the composite bacterial system within the system reaches its maximum processing capacity, the methanogenesis process gradually ceases.

[0053] like Figure 2 As shown, P1-P6 represent the primary to sixth generations, respectively. The TF group maintained high methane production at the sixth generation, reaching a total methane yield of 560.39 mL / (gVS) and a methane yield of 36.20 mL / (gVS·d), a 34.17% increase compared to the primary generation. Compared to single-environment inoculum, the TF group increased methane production by 68.76% compared to T-P1 (primary generation containing only field soil) and by 58.01% compared to F-P1 (primary generation containing only fresh cow dung).

[0054] like Figure 3 The data show the percentage weight loss of the substrates (hemicellulose, cellulose, lignin) treated by the TF group at three different passage culture stages: the fourth generation (P4, 17d), the fifth generation (P5, 13d), and the sixth generation (P6, 17d). The P6 treatment performed best in the degradation of lignin, and also performed well in the degradation of hemicellulose. The P5 treatment was the best in the degradation of cellulose, but was not as good as the P6 treatment in the degradation of hemicellulose and lignin. The P4 treatment was not optimal in the degradation of all three components, especially in the degradation of cellulose and lignin. These results indicate that different passage processes have a significant effect on the degradation efficiency of different components of lignocellulose, and the P6 treatment showed a higher degradation efficiency overall.

[0055] (4) Conclusion

[0056] Based on the results of Example 1, a 1:1 ratio of returned soil to fresh cow dung mixed inoculum significantly increased methane production. By setting up a single-environment inoculum control group, the superiority of the multi-environment inoculum was confirmed. The methane production and yield of the TF group were significantly higher than those of the single-inoculum group, verifying the efficient degradation ability of the composite bacterial system. This method provides a reliable bacterial system screening and optimization scheme for efficient methane conversion of lignocellulose.

[0057] Example 3 Enrichment method of artificial multicellular system for enhancing lignocellulose conversion to methane by optimizing foreign strains based on Box-Behnken response surface methodology

[0058] 1. Single factor test results

[0059] In the anaerobic degradation of lignocellulose to produce methane, the inoculum ratio, carbon-nitrogen ratio (C / N), and fermentation time are three key factors that significantly influence methane yield. This example used single-factor experiments to investigate the mechanisms of action of these three factors on methane yield and to determine the central point of a response surface design.

[0060] (1) Effect of inoculation ratio on methane yield

[0061] The inoculation ratio refers to the volume ratio of the inoculated bacterial solution to the new generation culture system during the subculture process, reflecting the initial concentration of microorganisms in the fermentation system. The reasonable setting of the inoculation ratio is crucial to maintain the efficient anaerobic degradation process of lignocellulose. The experimental results show that ( Figure 4 A) The inoculation ratio significantly affects methane yield. At an inoculation ratio of 15%, the highest methane yield, 35.18 mL / (gVS·d), was achieved. When the inoculation ratio was below 10%, methane yield increased significantly with increasing inoculation ratios. This suggests that at low inoculation ratios, the number of methanogens is insufficient to fully degrade lignocellulose, limiting methane production. When the inoculation ratio exceeded 10%, the growth in methane yield leveled off or even decreased slightly. This may be due to a relatively insufficient substrate supply in the fermentation system caused by the high inoculation ratio, which in turn limited the metabolic activity of methanogens. Overall, there was no significant difference in the effects of inoculation ratios of 10%, 15%, and 30% on the rate of methanogenesis from anaerobic lignocellulose degradation. Therefore, selecting an inoculation ratio of 10% as the central point in the response surface design ensures a sufficient number of microorganisms while avoiding substrate limitation.

[0062] (2) Effect of carbon-nitrogen ratio on methane yield

[0063] The carbon-nitrogen ratio is the ratio of carbon source to nitrogen source in the fermentation substrate, which has an important influence on the growth and metabolic activity of microorganisms. During the anaerobic degradation of lignocellulose, the carbon-nitrogen ratio not only affects the growth rate of methanogens, but also affects the types and yields of their metabolites. The experimental results show that ( Figure 4 B) The effect of the carbon-nitrogen ratio on methane yield exhibits a distinct single-peak curve. At a carbon-nitrogen ratio of 4.4, methane yield reaches its maximum value of 34.69 mL / (gVS·d). A high carbon-nitrogen ratio leads to a low pH and acid accumulation, while a low carbon-nitrogen ratio leads to a high pH and ammonia inhibition. Therefore, a carbon-nitrogen ratio of 4.4 was selected as the central point in the response surface design.

[0064] (3) Effect of fermentation time on methane yield

[0065] Fermentation time is one of the important factors affecting the efficiency of anaerobic degradation of lignocellulose. If the fermentation time is too short, lignocellulose cannot be fully degraded and the methane yield is low; if the fermentation time is too long, it may lead to substrate depletion or accumulation of metabolites, inhibiting the methane production process. The experimental results show that ( Figure 4 C), the effect of fermentation time on methane yield shows a trend of first increasing and then decreasing. This is because the total amount and activity of microorganisms in the system are low in the early stage of fermentation, and it takes a certain amount of time to reach a higher level, resulting in low methane production and low methane yield in the early stage. In the later stage of fermentation, lignocellulose is fully degraded and reaches the peak that can be processed by microorganisms. Prolonging the fermentation time does not effectively increase methane production, but instead leads to a decrease in methane yield. When the fermentation time is 15 days, the methane yield reaches a maximum value of 34.25mL / (gVS·d). This shows that a fermentation time of 15 days is sufficient to fully degrade lignocellulose, while avoiding the problems of substrate depletion or metabolite accumulation caused by too long fermentation time. Therefore, selecting a fermentation time of 15 days as the center point of the response surface experimental design can not only achieve efficient degradation of lignocellulose but also promote stable methane production.

[0066] 2. Response surface experimental design and results

[0067] (1) Quadratic regression equation analysis

[0068] The experimental design was performed using Design Expert software (Table 2). The data were fitted with quadratic regression to obtain a quadratic regression equation for the methane yield of anaerobic degradation of lignocellulose versus the values ​​of the factors coded as self-inoculation ratio X1, carbon-nitrogen ratio X2, and fermentation time X3. The response value methane yield Y was input to obtain the response surface regression equation:

[0069] Y=-163.8975+6.68265X1+14.90773X2+14.71335X3+0.225X1X2-0.1021X1X3-0.

[0070] 14X2X3-0.23917X1 2 -1.55305X2 2 -0.383777X3 2

[0071] From the linear coefficients of the regression equation, it can be seen that the coefficients of factors X1 (inoculation ratio), X2 (carbon-nitrogen ratio), and X3 (fermentation time) are positive, indicating that they have a positive effect on the methane production rate; the absolute value of the coefficient X2 (carbon-nitrogen ratio)>X3 (fermentation time)>X1 (inoculation ratio), indicating that the carbon-nitrogen ratio has the most significant effect on the methane production rate; the square coefficients of the equation are all negative, indicating that when these three factors are fixed, the rice straw degradation rate can reach the maximum. The absolute value of the interaction coefficient is compared, and the interaction between the factors is further analyzed through the contour map and response surface map between the two factors, which vividly and objectively shows the influence of the two factors on the methane production rate. The relationship between each specific experimental factor and the response surface is a quadratic paraboloid relationship, not a linear relationship. The correlation coefficient R 2 =0.9209 (Table 2), indicating that the model can explain 92.09% of the variation in methane yield. The model can be used for the analysis and prediction of methane yield.

[0072] Table 2 Box-Behnken experiment matrix

[0073]

[0074]

[0075] (2) Fitting statistical analysis

[0076] In this analysis (Table 3), the standard deviation (Std.Dev.) value was 2.23, which is used to measure the degree of data dispersion and reflects the fluctuation of the observed values ​​around the mean. The smaller the value, the more concentrated the data. The mean (Mean) was 23.37, representing the average level of the data. The coefficient of variation (CV%) was 9.56, which is the percentage of the standard deviation to the mean and can measure the relative dispersion of the data, making it easier to compare the dispersion between data with different means. The adjusted coefficient of determination (AdjustedR2) was 0.9578, which takes into account the effect of the number of independent variables on the goodness of fit. 2 The problem of artificially high value due to adding independent variables is that the closer to 1, the better the fitting effect. The predicted coefficient of determination (Predicted R2) is 0.7788, which is used to evaluate the model's predictive ability for new data. It is similar to the Adjusted R 2 The difference between the two values ​​(0.9578) is less than 0.2, indicating good consistency and a reliable model prediction capability. The Adeq Precision value is 19.1031, which measures the signal-to-noise ratio. A value greater than 4 indicates sufficient signal. A higher ratio indicates a stronger model's ability to distinguish between signal and noise, resulting in a more effective model. This model can be used for methane yield analysis and prediction.

[0077] Table 3 Fit statistics

[0078]

[0079] (3) Variance analysis and significance test of regression model

[0080] Analysis of variance and significance tests for the quadratic regression model (Table 4) revealed an F-value of 41.30 and a p-value less than 0.0001, indicating a significant model. This indicates a significant regression relationship between the dependent variable, methane yield, and the independent variables (inoculation ratio, carbon-nitrogen ratio, fermentation time, their interaction term, and the quadratic term). Among the main effects, inoculation ratio (X1) had an F-value of 73.88 and a p-value less than 0.0001, indicating a highly significant effect on methane yield. The carbon-nitrogen ratio (X2) had an F-value of 15.01 and a p-value of 0.0061, indicating a significant effect. The fermentation time (X3) had an F-value of 97.93 and a p-value less than 0.0001, indicating a highly significant effect. Among the interaction terms, the F value of X1X2 is 4.91, and the p value is 0.0623; the F value of X1X3 is 5.22, and the p value is 0.0562; the F value of X2X3 is 1.90, and the p value is 0.2104, all of which are greater than 0.05, indicating that the interaction between inoculation ratio and carbon-nitrogen ratio, inoculation ratio and fermentation time, and carbon-nitrogen ratio and fermentation time has no significant effect on methane yield. 2 The F values ​​for the three factors were 30.16, 47.67, and 77.66, respectively, with p-values ​​of 0.0009, 0.0002, and less than 0.0001, respectively, indicating significant or extremely significant effects on methane yield. The F value for the lack-of-fit term was 3.44, with a p-value of 0.1318, >0.05, indicating that the lack-of-fit term was insignificant for the absolute error. Therefore, the regression model was valid and free of any lack-of-fit factors. Overall, the model was significant. The main effects of inoculum ratio, carbon-nitrogen ratio, and fermentation time, as well as their quadratic terms, had significant effects on methane yield, while the interaction term was insignificant.

[0081] Table 4 Variance analysis table of regression equation

[0082]

[0083]

[0084] (4) Response surface optimization analysis

[0085] The dimensionality reduction analysis of Design-Expert 10 software was used to obtain the response surface plot and contour plot to determine the interaction between the factors. Figure 5 The contour plot of inoculation ratio and carbon-nitrogen ratio on methane yield in A is approximately circular, and the response surface plot ( Figure 5The parabolic curve in B) indicates a weak interaction between the two factors. The X1X2 test (p = 0.0623 > 0.05) indicates a nonsignificant interaction between the two factors. When the inoculum ratio remains constant, methane production initially increases and then decreases with increasing carbon-nitrogen ratios. The composite bacterial consortium is most effective at degrading rice straw at a carbon-nitrogen ratio of approximately 4.8. When the carbon-nitrogen ratio remains constant, the parabola peaks as the inoculum ratio increases to approximately 12%-14%. Overall, the interaction between the inoculum ratio and the carbon-nitrogen ratio significantly influences the methane production rate.

[0086] Figure 5 The contour plot of the inoculation ratio and fermentation time on the methanogenesis rate in C is elliptical, and the response surface plot ( Figure 5 The parabolic curve in D) indicates a weak interaction between the two factors. The X1X3 test (p = 0.0562 > 0.05) indicates a nonsignificant interaction between X1X3. ​​When the inoculation ratio remains constant, the methanogenesis rate increases and then decreases with increasing fermentation time, reaching a peak at 16-18 days. When the fermentation time remains constant, the parabola peaks when the inoculation ratio increases to 12.5%-13.5%; as the inoculation ratio continues to increase, the methanogenesis rate begins to decline. Overall, the interaction between the inoculation ratio and fermentation time has a significant impact on the methanogenesis rate.

[0087] Figure 5 The contour plot of carbon-nitrogen ratio and fermentation time on methane production rate in E is elliptical, and the response surface diagram ( Figure 5 The parabolic surface in F) indicates a weak interaction between the two factors. The X2X3 test (p = 0.2104 > 0.05) indicates a nonsignificant X2X3 interaction. When the carbon-nitrogen ratio remains constant, the methanogenesis rate increases with fermentation time and then decreases, reaching a peak around 16 days. When the fermentation time remains constant, the methanogenesis rate increases with increasing carbon-nitrogen ratio and then decreases, reaching a peak around 4.6.

[0088] (5) Determination and verification of optimal conditions

[0089] Using response surface analysis, the optimal conditions for methanogenesis were established as follows: an inoculum ratio of 12.183%, a carbon-nitrogen ratio of 4.796, and a fermentation time of 16.357 days. At these conditions, the theoretical maximum methanogenesis rate reached 37.599 mL / g VS·d. The optimal conditions established by the response surface analysis were difficult to control in practice, so the optimized degradation conditions were adjusted to an inoculum ratio of 12.0%, a carbon-nitrogen ratio of 4.8, and a fermentation time of 16 days (each passage lasting 16 days). Three parallel experiments were conducted under these adjusted optimal conditions, resulting in a methanogenesis rate of 37.13 ± 2.35 mL / g VS·d (corresponding to the sixth passage). The actual values ​​matched the response values ​​well, confirming the feasibility of using this model for simulating methanogenesis rates.

[0090] The carbon-nitrogen ratio is determined by the amount of ammonium chloride added. A carbon-nitrogen ratio of 4.8 corresponds to the following medium composition: peptone: 2 g / L; yeast extract: 0.4 g / L; sodium chloride: 2 g / L; sodium bicarbonate: 5 g / L; dipotassium hydrogen phosphate: 0.4 g / L; ammonium chloride: 1.3 g / L; cysteine: 0.5 g / L; sodium sulfide: 0.25 g / L; and lignocellulose: 0.2%. Ultrapure water is added to the medium, with 10 mL of trace element solution and 10 mL of vitamin solution added per liter. The medium is sterilized by moist heat at 121°C for 20 minutes before use.

[0091] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for screening an artificial multicellular system, characterized in that: The method comprises the following steps: (1) Straw-returned soil and fresh cow dung are mixed in a mass ratio of 0.5-1.5:0.5-1.5, and then inoculated into a fermentation medium. The mass ratio of the total mass of the returned soil and fresh cow dung to the fermentation medium is 2-4:6-8; (2) Culture at 36-38°C with shaking for 15-17 days; (3) The inoculation ratio was 11-13%, and the fermentation time after each subculture was 15-17 days; (4) After passage 5 or 6, an artificial multicellular system is obtained; The fermentation medium comprises the following components: 2 g / L peptone, 0.4 g / L yeast extract powder, 2 g / L sodium chloride, 5 g / L sodium bicarbonate, 0.4 g / L dipotassium hydrogen phosphate, 1.2-1.4 g / L ammonium chloride, 0.5 g / L cysteine, 0.25 g / L sodium sulfide, and 0.2% lignocellulose, and ultrapure water is added to prepare the medium. 9-11 mL of trace element solution and 9-11 mL of vitamin solution are added to each liter of the medium. The trace element solution comprises the following components: 0.5 g / L manganese sulfate monohydrate, 0.1 g / L ferrous sulfate heptahydrate, 0.04 g / L nickel chloride hexahydrate, 0.048 g / L cobalt chloride hexahydrate, 0.13 g / L zinc chloride, 0.01 g / L copper sulfate pentahydrate, 0.01 g / L potassium aluminum sulfate dodecahydrate, 0.01 g / L boric acid, and 0.025 g / L sodium molybdate dihydrate, which are prepared by adding ultrapure water; The components of the vitamin solution are as follows: vitamin B7 0.002 g / L, vitamin B5 0.005 g / L, vitamin B12 0.0001 g / L, p-aminobenzoic acid 0.005 g / L, lipoic acid 0.005 g / L, niacin 0.005 g / L, vitamin B1 0.005 g / L, vitamin B2 0.005 g / L, vitamin B6 0.01 g / L, and fulvic acid 0.002 g / L, and ultrapure water is added to prepare the solution.

2. The screening method according to claim 1, wherein In the step (1), the mass ratio of returning straw to soil to fresh cow dung is 1:

1.

3. The screening method according to claim 2, characterized in that In the step (1), the mass ratio of the total mass of the returned soil and fresh cow dung to the mass of the culture medium is 3:

7.

4. The screening method according to claim 3, wherein The shaking culture time in step (2) is 16 days.

5. The screening method according to claim 4, characterized in that The inoculation ratio in step (3) is 12%.

6. The screening method according to claim 5, characterized in that The fermentation time after each passage in step (3) is 16 days.

7. The screening method according to claim 6, characterized in that The amount of ammonium chloride added to the fermentation medium is 1.3 g / L.

8. The screening method according to claim 7, characterized in that The amount of trace element solution and vitamin solution added to the fermentation medium is both 10 mL.

9. An artificial multicellular system obtained by screening using the screening method according to any one of claims 1 to 8.

10. Use of the screening method according to any one of claims 1 to 8 in screening artificial multicellular systems.

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