A method for identifying lignocellulose-degrading functional microorganisms under low temperature conditions

Abstract

The application discloses a method for identifying lignocellulose-degrading functional microorganisms under low-temperature conditions and belongs to the technical field of environmental microorganisms. In order to solve the problems of low screening efficiency and incomplete evaluation in the prior art, the application provides the method for identifying the lignocellulose-degrading functional microorganisms under the low-temperature conditions. The double technical means of Bayesian model quantitative sequencing and PCA verification are mutually verified, the possible deviation of a single screening method is effectively avoided, and the reliability and application adaptability of the screening result are improved. The method provided by the application realizes objective and quantitative identification of the lignocellulose-degrading functional microorganisms under the low-temperature conditions, overcomes the problems of single evaluation index and unstable screening result in the prior art, is suitable for resource utilization of straws in cold regions, and has good universality, repeatability and practical application value.

Description

Technical Field

[0001] This invention belongs to the field of environmental microbiology technology, and in particular relates to a method for identifying microorganisms that degrade lignocellulose under low-temperature conditions. Background Technology

[0002] Agricultural straw and other biomass resources are mainly composed of cellulose, hemicellulose, and lignin. Among them, lignin has a complex structure and high stability, which is a key factor limiting the efficient degradation and resource utilization of lignocellulose. Current straw treatment methods still mainly involve burning, landfilling, or simple return to the field, which easily leads to environmental pollution and resource waste. Biodegradation technology has received widespread attention due to its environmental friendliness and sustainability. However, existing lignocellulose-degrading microorganisms generally show better degradation effects under mesophilic or hyperthermic conditions. In low-temperature environments, they generally suffer from slow growth, reduced enzyme activity, and uneven degradation, making it difficult to meet the needs of agricultural waste treatment in cold or cool regions.

[0003] Furthermore, existing methods for screening and evaluating low-temperature degrading microorganisms mostly rely on single indicators such as straw degradation rate or the activity of a certain type of enzyme. These methods fail to comprehensively reflect the overall degradation capacity of microorganisms for different lignocellulose components, resulting in highly subjective and reproducible screening results, and a lack of systematic and objective identification methods. Therefore, those skilled in the art urgently need to establish an identification method suitable for low-temperature conditions that can provide multi-indicator, quantitative, and comprehensive evaluation of lignocellulose-degrading microorganisms. Summary of the Invention

[0004] To address the problems of low screening efficiency and incomplete evaluation in existing methods for screening and evaluating low-temperature degrading microorganisms, this invention provides a method for identifying lignocellulose-degrading functional microorganisms under low-temperature conditions.

[0005] One objective of this invention is to provide a method for identifying microorganisms that degrade lignocellulose under low-temperature conditions, the identification method comprising the following steps: S1: Soil samples were placed in sterile water and shaken for 24 h, then allowed to stand for 2 h. The supernatant was collected and the initial microbial community was separated. The initial microbial community was placed in MC medium for continuous subculture and domestication culture, with each generation cultured for 72 h. Microbial communities resistant to low-temperature straw degradation were screened and enriched. S2: Multidimensional functional indicators of the microbiome screened and enriched in S1 were detected. Bayesian model was used to quantify the correlation between the obtained multidimensional functional indicator data, and a correlation coefficient matrix was constructed. PCA method was used to verify cross-substrate adaptability and screen out microbiomes with better comprehensive performance that are resistant to low-temperature straw degradation.

[0006] In a preferred embodiment of the present invention, the inoculum amount for continuous subculture in S1 is 1%, the temperature for continuous subculture is ≤10℃, and the number of subcultures in continuous subculture is ≥20.

[0007] In a preferred embodiment of the present invention, the MC culture medium in S1 uses straw material containing lignocellulose as the main carbon source, and the MC culture medium comprises the following components: 6 g / L NaHCO3, 0.45 g / L K2HPO4, 0.45 g / L KH2PO4, 0.9 g / L (NH4)2SO4, 0.9 g / L NaCl, 0.09 g / L MgSO4·7H2O, 0.09 g / L CaCl2·2H2O, 1 g / L L-cysteine ​​hydrochloride, 0.25 g / L yeast extract, 0.5 g / L casein peptone, 1% (w / v) vitamin solution, and 1% (w / v) carbon substrate.

[0008] In a preferred embodiment of the present invention, the multi-dimensional functional index detection in S2 includes: cellulose degradation rate, hemicellulose degradation rate, lignin degradation rate, cellulose holoenzyme activity, endonuclease activity, exonuclease activity, enzyme efficiency, enzyme ratio, enzyme ratio deviation, enzyme stability, and average OD. 600 Value, lignin degradation rate, average carbohydrate degradation rate, and lignin / carbohydrate selectivity ratio.

[0009] In a preferred embodiment of the present invention, the Bayesian model in S2 includes model L, model C and model S; model L is a lignin quantification model, model C is a carbohydrate quantification model and model S is a selective quantification model.

[0010] In a preferred embodiment of the present invention, the model L formula is: lignin z_Eeff + z_BCI + z_StabE + (1 | strain) + (1 | straw); where lignin is the lignin degradation rate, used as the dependent variable; z_Eeff is the standardized enzyme efficiency; and z_BCI is the standardized average OD. 600 z_StabE is the standardized enzyme stability and is used as the independent variable; sd(Intercept) = 0.78 for strain and sd(Intercept) = 2.62 for straw; regression coefficients Intercept = 15.96 and sigma = 2.42.

[0011] In a preferred embodiment of the present invention, the formula for model C is: Carb z_Eratio + z_Eeff + z_BCI + (1 | strain) + (1 | straw); where Carb is the average carbohydrate degradation rate, used as the dependent variable; z_Eratio is the standardized enzyme ratio, z_Eeff is the standardized enzyme efficiency, and z_BCI is the standardized average OD. 600 The values ​​are used as independent variables; the sd(Intercept) of strain is 4.19, and the sd(Intercept) of straw is 2.11; the regression coefficients Intercept = 23.68 and sigma = 3.33.

[0012] In a preferred embodiment of the present invention, the formula for model S is: Sel_rs z_Eeff + z_Erdev + z_BCI + (1 | strain) + (1 | straw); where Sel_rs is the lignin / carbohydrate selectivity ratio, used as the dependent variable; z_Eeff is the standardized enzyme efficiency; z_Erdev is the standardized enzyme ratio deviation; and z_BCI is the standardized average OD. 600 The values ​​are used as independent variables; the sd(Intercept) of strain is 0.17, and the sd(Intercept) of straw is 0.89; the regression coefficients Intercept = 0.72 and sigma = 0.14.

[0013] In a preferred embodiment of the present invention, the specific steps of using the PCA method to verify cross-substrate compatibility in S2 are as follows: using the PCA method to perform dimensionality reduction processing on the multi-index degradation rate data and extracting the principal components with a cumulative contribution rate ≥90%, wherein the multi-index degradation rate data includes cellulose degradation rate data, hemicellulose degradation rate data, and lignin degradation rate data.

[0014] In a preferred embodiment of the present invention, the linear combination expression used in the PCA method is: PC1 principal component score: PC1 = 0.287 X CS-纤维素 +0.470 X CS-半纤维素 +0.467 X CS-木质素 +0.446 X RS-纤维素 +0.356 X RS-半纤维素 +0.392 X RS-木质素 ; PC2 principal component score: PC2 = 0.689 XCS-纤维素 +0.197 X CS-木质素 -0.268 X RS-纤维素 -0.638 X RS-半纤维素 ; PC3 principal component score: PC3 = 0.563 X CS-纤维素 -0.150 X CS-半纤维素 -0.216 X CS-木质素 +0.295 X RS-纤维素 +0.354 X RS-半纤维素 -0.633 X RS-木质素 .

[0015] Compared with the prior art, the beneficial effects of the present invention are: the present invention provides a method for identifying lignocellulose-degrading microorganisms under low temperature conditions, realizing the objective and quantitative identification of lignocellulose-degrading microorganisms under low temperature conditions, overcoming the problems of single evaluation indicators and unstable screening results in the prior art, and is applicable to the resource utilization of straw in cold regions, with good versatility, repeatability and practical application value.

[0016] This invention involves 20 generations of continuous domestication culture of microbial samples from the natural environment at ≤10℃, which gradually enriches and dominates microorganisms that can grow stably at low temperatures. This helps the candidate microorganisms obtained by the identification method provided by this invention to have stable and repeatable low-temperature adaptability, overcoming the problems of insufficient metabolic activity and unstable degradation efficiency of conventional lignocellulose degrading microorganisms in low-temperature environments.

[0017] This invention simultaneously detects multiple functional indicators such as straw degradation rate, cellulase holoenzyme activity, endonuclease activity, and exonuclease activity. It characterizes the lignocellulose degradation process from two levels: substrate degradation results and enzymatic function, comprehensively covering the key links in lignocellulose degradation and avoiding the one-sidedness and uncertainty caused by relying solely on a single degradation rate or a single enzyme activity indicator for evaluation.

[0018] This invention introduces Bayesian models and PCA methods to reduce and integrate multi-dimensional functional indicators, constructing a multi-dimensional comprehensive evaluation system. While highlighting the overall lignocellulose degradation capacity of candidate microbiomes, it also takes into account their adaptability differences in different straw materials and different lignocellulose components, making the screening results more objective, stable, and more in line with the application needs of actual straw treatment and biomass resource utilization under low temperature conditions.

[0019] This invention performs metagenomic analysis on the functional microbiomes obtained by the identification method provided by this invention, and verifies their degradation potential by analyzing the core functional genes and metabolic pathways related to lignocellulose degradation. This provides a clear basis for the screening results and further improves the reliability and credibility of the identification method provided by this invention in practical applications.

[0020] The identification and comprehensive evaluation methods provided by this invention do not depend on specific species, genera or sample sources. They are applicable to the identification and evaluation of lignocellulose-degrading microorganisms in different regions, different straw types and different low-temperature application scenarios, and have good versatility and promotional value. Attached Figure Description

[0021] Figure 1 The graph shows the straw degradation rate in Example 1; AS represents the soil treatment group in the coniferous forest area; BS represents the soil treatment group in the broadleaf forest area. Figure 2 This is a bar chart of the total enzyme activity in Example 1; Figure 3 This is a bar chart of exonuclease activity in Example 1; Figure 4 This is a bar chart of endonuclease activity in Example 1; Figure 5 This provides an annotation of the basic CAZy functions of the G7 microbiome in Example 5; Figure 6 The KEGG metabolic pathway annotation results are shown for the G7 microbiome in Example 5. Detailed Implementation

[0022] Those skilled in the art can refer to the content of this document and appropriately improve the process parameters to achieve the desired results. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments, and those skilled in the art can obviously make modifications or appropriate alterations and combinations to the methods and applications described herein without departing from the content and scope of this invention to implement and apply the technology of this invention.

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the materials, reagents, methods, and instruments used are all conventional materials, reagents, methods, and instruments in the art, and can be obtained commercially by those skilled in the art.

[0024] The culture medium and reagents used in the following examples have the following composition: MC medium: 6 g / L NaHCO3, 0.45 g / L K2HPO4, 0.45 g / L KH2PO4, 0.9 g / L (NH4)2SO4, 0.9 g / L NaCl, 0.09 g / L MgSO4·7H2O, 0.09 g / L CaCl2·2H2O, 1 g / L L-cysteine ​​hydrochloride, 0.25 g / L yeast extract, 0.5 g / L casein peptone, 1% (w / v) vitamin solution, 1% (w / v) carbon substrate; Fermentation medium: 5.95 g / L NaNO3, 0.522 g / L KCl, 1.497 g / L KH2PO4, 0.493 g / L MgSO4·7H2O, 5 g / L yeast extract, 2 g / L casein, 1 mL / L trace element stock solution, 1% (w / v) crushed corn bran; Vitamin solution: 2 mg / L folic acid, 10 mg / L pyridoxine hydrochloride, 5 mg / L riboflavin, 2 mg / L biotin, 5 mg / L thiamine, 5 mg / L niacin, 5 mg / L calcium pantothenate, 0.1 mg / L vitamin B. 12 5 mg / L p-aminobenzoic acid, 5 mg / L lipoic acid, 900 mg / L KH2PO4, 40 g / L sodium 2-mercaptoethanesulfonate; Trace element stock solution: 76 mM / L ZnSO4, 178 mM / L H3BO4, 25 mM / L MnCl2, 18 mM / L FeSO4, 7.1 mM / L CoCl2, 6.4 mM / L CuSO4, 6.2 mM / L NaMoO4, 174 mM / L EDTA.

[0025] Example 1: Preliminary screening of psychrophilic bacteria based on straw degradation and cellulase activity indicators This invention involves collecting soil samples from the broad-leaved forest area of ​​Xiaoshan Forest Farm, Xinqing Forestry Bureau, Heilongjiang Province. 5 g of soil samples from both the coniferous forest area (AS) and the broad-leaved forest area (BS) were collected and placed in 100 mL of sterile water. The samples were shaken for 24 h and allowed to stand for 2 h. The supernatant was then collected to separate the initial microbial community, which was used as the original strain resource for subsequent domestication and cultivation.

[0026] This invention uses 10℃ as the acclimatization and cultivation temperature, and straw materials (including but not limited to corn straw, rice straw, wheat straw, or other agricultural and forestry waste containing lignocellulose) as the sole carbon source. MC medium is prepared, and the initial microbial community is continuously passaged and acclimatized (1% inoculum) for 20 generations, with each generation cultured for 72 hours, to gradually screen and enrich microorganisms that are resistant to low temperatures and have straw degradation capabilities. After acclimatization and cultivation, a total of 40 groups of microorganisms that can stably grow under low-temperature conditions were obtained. Multi-dimensional functional indicators of these 40 groups of microorganisms were tested, and 10 groups of microorganisms with superior overall performance and resistance to low-temperature straw degradation were preliminarily screened.

[0027] The multi-dimensional functional indicators include: cellulose degradation rate, hemicellulose degradation rate, lignin degradation rate, total cellulose enzyme activity, endonuclease activity, exonuclease activity, enzyme efficiency, enzyme ratio, enzyme ratio deviation, enzyme stability, and average OD. 600 Value, lignin degradation rate, average carbohydrate degradation rate, and lignin / carbohydrate selectivity ratio.

[0028] The results are as follows Figure 1 As shown, the overall straw degradation capacity of the microbiome gradually increased with the number of domestication generations. Specifically, the AS group showed relatively stable straw degradation in the early generations, but its degradation capacity significantly improved from the 11th generation onwards, reaching its highest straw loss rate of approximately 28% in the 17th generation. The BS group showed a more significant improvement in straw degradation capacity, with the straw loss rate remaining stable at approximately 22.61%-24.04% during the 15th-17th generations, demonstrating better overall degradation performance than the AS group. These results indicate that continuous low-temperature domestication helps improve the microbiome's ability to degrade straw under low-temperature conditions, and cold-resistant straw-degrading microorganisms gradually accumulate and become dominant.

[0029] Cellulase activity was used to characterize the overall degradation capacity of cold-resistant microbial communities of lignocellulose in straw. Results are as follows: Figure 2 As shown, compared with the AS group, the BS group exhibited higher holoenzyme activity in most passage stages. Specifically, the BS group reached a high level of holoenzyme activity in the 14th-15th generation culture stage, while the AS group maintained high holoenzyme activity in the 17th-20th generation culture stage. This result indicates that after low-temperature acclimatization, cold-tolerant microbial communities can maintain strong straw decomposition potential at different passage stages.

[0030] The results are as follows Figure 3As shown, with the increase in the number of domestication passages, the activity of cellulase in the AS group generally showed an upward trend, and remained at a high level during the 16th-20th generation culture stage. The activity of cellulase in the BS group fluctuated somewhat in the early stage of domestication, but also maintained high activity during the 17th-20th generation culture stage. These results indicate that after multiple generations of low-temperature domestication culture, the psychrophilic microorganisms can stably secrete cellulase under low-temperature conditions, thereby enhancing their ability to decompose cellulose.

[0031] The results are as follows Figure 4 As shown, the exonuclease activity in the AS group reached its peak during the 17th generation; the exonuclease activity in the BS group was generally higher than that in the AS group, reaching its highest value during the 19th generation. The cellulose exonuclease activity of both groups of psychrophilic microorganisms was significantly higher during the 15th-19th generation stages than at other passage stages, indicating that after multiple generations of low-temperature acclimatization, the ability of psychrophilic microorganisms to secrete cellulose exonucleases is significantly enhanced, which is beneficial for improving the decomposition efficiency of lignocellulose.

[0032] In summary, among the 40 groups of microorganisms screened in this invention, 10 groups are cold-resistant microorganisms with good cold adaptability and the potential to decompose lignocellulose. They are named G1 (AS16), G2 (AS17), G3 (AS18), G4 (AS19), G5 (BS15), G6 (BS16), G7 (BS17), G8 (BS18), G9 (BS19) and G10 (BS20), respectively.

[0033] Example 2: Quantitative Ranking of Preliminary Screening Results of Psychrophilic Bacteria Based on Bayesian Model Based on the 10 groups of low-temperature resistant straw-degrading bacteria obtained in Example 1, three Gaussian distribution Bayesian models (Model L, Model C, and Model S) were constructed. All models employed the NUTS (No-U-Turn Sampler) sampling algorithm, with four chains sampling in parallel. Each chain had 4000 iterations (iter), a warmup period of 2000, and a thinning period of 1, resulting in a total of 8000 effective samples (total post-warmup draws). The latent scaling factor (Rhat) for all parameters in the three models was 1.00, meeting the convergence criteria and indicating stable sampling. The total effective sample size (Bulk_ESS) was ≥1800; the tail effective sample size (Tail_ESS) was ≥2400. By combining standardized independent variables with a two-layer random effect of microbiome and straw, precise quantitative analysis of microbiome-related target parameters was achieved. (I) In the model involved in this invention, the data indicators corresponding to each letter identifier are defined as follows: (1) Basic and degradation indicators: strain: Microbiome number, used to group and distinguish different candidate bacteria (G1-G10). Straw: Types of straw, including rice straw and corn straw; Cellulose: Cellulose degradation rate: refers to the percentage of cellulose components degraded by psychrophilic bacteria; hemicellulose: Hemicellulose degradation rate, refers to the percentage of hemicellulose components degraded by psychrophilic bacteria; lignin: Lignin degradation rate, refers to the percentage of lignin components in straw degraded by psychrophilic bacteria. The higher the value, the stronger the lignin degradation ability. Carb: Average carbohydrate degradation rate, calculated as (cellulose + hemicellulose) / 2, quantifies the overall degradation degree of non-lignin components by psychrophilic bacteria. The lower the value, the stronger the lignin targeting. Sel_rs: Lignin / carbohydrate selectivity ratio (core dependent variable), calculated as lignin / (Carb+∑) (adding ∑ to avoid a denominator of 0). A higher value indicates that the psychrophilic bacteria preferentially degrade lignin.

[0034] (2) Growth and enzyme indicators: BCI: Average OD 600 The value quantifies the growth capacity and biomass level of psychrophilic bacteria; a higher value indicates more vigorous growth. E_total: Mean total enzyme activity, which is completely consistent with act_mean_total (mean total enzyme activity). It quantifies the total level of lignin-degrading enzymes secreted by psychrophilic bacteria. The higher the value, the stronger the enzyme activity. E_eff: Enzyme efficiency, calculated as E_total / (BCI +∑), quantifies the total enzyme activity level corresponding to a unit of biomass. A higher value indicates higher enzyme secretion efficiency and lower culture cost. E_ratio: Enzyme ratio, calculated as act_mean_exo / (act_mean_endo +∑) (act_mean_exo is the average activity of exonucleases, and act_mean_endo is the average activity of endonucleases). It reflects the synergistic relationship of enzyme systems, and the synergistic efficiency is optimal when the value is close to the population median. E_ratio_dev: Enzyme ratio deviation, which refers to the absolute difference between the E_ratio of psychrophilic bacteria and the population median; Enz_stability: Enzyme stability, calculated as 1 - (act_cv_endo + act_cv_exo + act_cv_total) / 3 (act_cv_endo, act_cv_exo, and act_cv_total are the coefficients of variation of enzyme activity for endonuclease, exonuclease, and holoenzyme, respectively). The closer the value is to 1, the stronger the enzyme secretion stability and the more suitable it is for industrialization.

[0035] (3) Standardized derivative indicators: z_Eeff: Standardized enzyme efficiency, the result of Z-score standardization of E_eff; z_BCI: Standardized Mean OD 600 The value is the result of Z-score standardization of BCI; z_Eratio: Standardized enzyme ratio, the result of Z-score standardization of E_ratio; z_Erdev: Standardized enzyme ratio deviation, the result of Z-score standardization of E_ratio_dev; z_StabE: Standardized enzyme stability, the result of Z-score standardization of Enz_stability.

[0036] Z-score normalization formula: z = (x μ) / σ; In the formula: μ is the mean, and σ is the standard deviation.

[0037] (II) Bayesian Model Construction Scheme Model L (Lignin Quantification Model): Used to quantify and characterize the lignin degradation properties of psychrophilic bacteria; Formula: lignin z_Eeff + z_BCI + z_StabE + (1 ∣ strain) + (1 ∣ straw); Dependent variable: lignin (lignin degradation rate); Independent variables: z_Eeff (standardized enzyme efficiency), z_BCI (standardized mean OD) 600 z_StabE (normalized enzyme stability); Model C (Carbohydrate Quantification Model): Used to quantitatively analyze the degree of degradation of non-lignin components by psychrophilic bacteria; Formula: Carb z_Eratio + z_Eeff + z_BCI + (1 ∣ strain) + (1 ∣ straw); Dependent variable: Carb (average rate of carbohydrate degradation). Independent variables: z_Eratio (standardized enzyme ratio), z_Eeff (standardized enzyme efficiency), z_BCI (standardized mean OD) 600 value); Model S (Selective Quantification Model): Used to accurately quantify the targeting of psychrophilic bacteria in preferentially degrading lignin. Formula: Sel_rs z_Eeff + z_Erdev + z_BCI + (1 ∣ strain) + (1 ∣ straw); Dependent variable: Sel_rs (lignin / carbohydrate selectivity ratio); Independent variables: z_Eeff (standardized enzyme efficiency), z_Erdev (standardized enzyme ratio deviation), z_BCI (standardized mean OD) 600 value); Note: (1 | strain) and (1 | straw) are two-layer random effects, controlling for the effects of “microbiome individual differences” and “straw type differences” on the quantification results, respectively.

[0038] Through the collaborative operation of the above models L, C, and S, the core performance of 10 groups of low-temperature resistant straw-degrading bacteria was quantitatively characterized, and the quantitative results of the key dimensions of each microbiome were output, as shown in Table 1 below.

[0039] Table 1. Results of Bayesian model multidimensional quantitative screening of 10 candidate psychrophilic bacteria

[0040] Note: In the table above, L_eff_mean corresponds to the core output of model L, which directly reflects the core lignin degradation capability of the microbiome; C_eff_mean corresponds to the core output of model C, which quantifies the comprehensive degradation degree of cellulose and hemicellulose by psychrophilic bacteria; S_eff_mean corresponds to the core output of model S, which intuitively reflects the selective advantage of psychrophilic bacteria in preferentially degrading lignin; Stability_mean is quantified based on the enzyme stability index (Enz_stability), and the closer the value is to 1, the better the repeatability and stability of enzyme secretion by psychrophilic bacteria, and the stronger the adaptability for industrial applications.

[0041] Lignin is the core component in straw that is most difficult to degrade, and its degradation ability directly determines the practical application value of psychrophilic bacteria. To ensure the relevance and practicality of the screening results, this invention establishes a comprehensive screening criterion in the Bayesian model quantification ranking process, prioritizing the core quantitative index of lignin degradation (L_eff_mean) and taking into account the quantitative index of enzyme secretion stability (Stability_mean), and ranks 10 groups of candidate psychrophilic bacteria.

[0042] The results showed that G5, G9, and G7 ranked among the top three in overall performance, indicating that they are excellent psychrophilic bacteria with core application potential. Further evaluation of the lignin targeting selectivity quantification index (S_eff_mean) showed that the values ​​of this index for G5, G9, and G7 were also at the upper-middle level among all candidate psychrophilic bacteria, fully confirming that the above three psychrophilic bacteria not only have strong core lignin degradation capabilities, but also good lignin targeting degradation specificity. Their overall performance meets the actual application requirements of straw lignin degradation. This embodiment completed the core function quantification and initial screening steps.

[0043] Example 3: Final screening of psychrophilic bacteria based on PCA Based on the 10 groups of low-temperature resistant straw-degrading bacteria obtained in Example 1, in order to further objectively evaluate their comprehensive straw degradation ability, statistical analysis was performed on the cellulose degradation rate, hemicellulose degradation rate and lignin degradation rate of each microbial group in degrading corn straw and rice straw, and the above degradation rate detection data were standardized by Z-score. Z-score normalization formula: z = (x μ) / σ; In the formula: μ is the mean, and σ is the standard deviation.

[0044] Subsequently, a correlation coefficient matrix was constructed based on standardized data, and PCA was used to reduce the dimensionality of the multi-index degradation rate data. The top three principal components with a cumulative contribution rate of not less than 90% were extracted. The results are shown in Table 2, and their linear combination expression is as follows: PC1 principal component score: PC1 = 0.287 X CS 纤维素 +0.470 X CS 半纤维素 +0.467 X CS 木质素 +0.446 X RS 纤维素 +0.356 X RS 半纤维素 +0.392 X RS 木质素 ; PC2 principal component score: PC2 = 0.689 X CS 纤维素 +0.197 X CS 木质素 0.268 X RS 纤维素 0.638 X RS 半纤维素 ; PC3 principal component score: PC3 = 0.563 X CS 纤维素 0.150 X CS 半纤维素 0.216 X CS 木质素 +0.295 X RS 纤维素 +0.354 X RS 半纤维素 0.633 X RS 木质素 ; The linear combination coefficients of the principal components were determined based on specific experimental data. The coefficients may differ under different experimental conditions, but this does not affect the implementation of the comprehensive evaluation method. The principal component scores for each microbiome were calculated again, and the results are shown in Table 3.

[0045] Table 2. Principal component analysis of three types of lignocellulose in straw.

[0046] Table 3 Summary of principal component scores of the microbiome

[0047] The analysis results show that the first three principal components extracted can effectively characterize the main information of the original degradation rate data. Among them, PC1 shows a high and relatively balanced positive loading on the degradation rates of cellulose, hemicellulose, and lignin in corn straw and rice straw, which can be used to comprehensively reflect the overall degradation capacity of different microbial communities on different straw types and lignocellulose components; PC2 and PC3 reflect the performance differences of different microbial communities in terms of straw type and degradation components. The positive or negative value of the principal component score only indicates the deviation of the sample from the overall average level in the characteristic direction represented by the principal component, and is not directly equivalent to the absolute strength of degradation capacity. Therefore, negative score samples can still be included in the evaluation system, rather than being directly excluded. Based on the principal component scores of each microbial community, the comprehensive degradation capacity of the 10 groups of cold-resistant microorganisms was ranked. The results show that G7 has the highest score on PC1, indicating that it has a relatively balanced and excellent lignocellulose degradation capacity in both corn straw and rice straw systems; at the same time, G7 also has a high score on PC2, showing that it has good functional adaptability to different straw substrates.

[0048] This embodiment completed the cross-substrate compatibility verification. The results need to be combined with the results of Example 2 for comprehensive judgment. The comprehensive performance of 10 groups of low-temperature resistant straw-degrading microorganisms was compared, and G7 was finally determined to be the functional microorganism with the best comprehensive straw degradation performance in low-temperature environment, which will be used for subsequent degradation performance verification and application research.

[0049] Example 4: Verification of the superiority of the Bayesian model + PCA technology solution and comparison of the limitations of a single model To further verify the innovation and irreplaceability of the combined technical solution of Bayesian model quantization core function + PCA verification of cross-substrate adaptability in this invention, based on the measured data of Examples 1-3, and by comparing the screening effect of a single model, the advantages of the technical solution of this invention are clarified as follows: When using a Bayesian model alone, quantitative screening can be achieved through core indicators such as L_eff_mean (core lignin degradation capability), Stability_mean (enzyme stability), and S_eff_mean (lignin targeting capability). As shown in Table 1, the values ​​for G5 (L_eff_mean=17.34198, Stability_mean=23.92384, S_eff_mean=0.91422), G9 (L_eff_mean=17.25695, Stability_mean=17.86848, S_eff_mean=0.91380), and G7 (L_eff_mean=16.76294, Stability_mean=28.463) are relatively low. 90, S_eff_mean=0.91123) represents the dominant microbiome under this model. However, this model does not involve the degradation adaptability analysis of microbiome to different straw substrates (corn straw / rice straw) and different lignocellulose components (cellulose / hemicellulose / lignin), and cannot reflect the comprehensive adaptability of microbiome in complex practical application scenarios. When PCA is used alone, as shown in Table 3, G7's PC1 score reaches 2.84019, which is significantly higher than other candidate microbiomes. It can screen out microbiomes with excellent adaptability to multiple straw substrates. However, PCA only reduces the dimensionality based on the degradation rate index and does not include quantitative indicators of core industrial functions such as enzyme secretion efficiency and lignin targeting. It cannot reflect key characteristics such as "lignin preferential degradation" and "enzyme secretion stability", resulting in an incomplete screening dimension. This invention employs a synergistic approach of "quantifying core functional indicators using a Bayesian model and verifying cross-substrate adaptability using PCA." This approach not only relies on the Bayesian model to ensure the core degradation performance of the screened microbiome but also verifies its cross-substrate adaptability advantage through PCA. This effectively avoids the one-sidedness of screening with a single model and achieves dual assurance of core functional strength and practical application adaptability.

[0050] To further demonstrate the indivisibility of the technical solution, three sets of comparison scenarios were set up: (1) Relying solely on the Bayesian model as the screening standard: the optimal microbiome was G5, but the PC1 score of G5 was only 0.83765 (Table 3), which was significantly lower than the 2.84019 of G7, indicating that its adaptability to different straw substrates and lignocellulose components was insufficient and could not meet the needs of complex practical applications; (2) Relying solely on PCA as the screening standard: the optimal microbiome was G7, but the screening results did not include the core quantitative indicators such as lignin targeting (S_eff_mean) and enzyme stability (Stability_mean) in Table 1, which could not guarantee the preferential degradation ability of microbiome on lignin and enzyme secretion stability in industrial applications. There were key deficiencies in the screening dimensions, and it was impossible to stably screen out target microbiomes with multiple advantages.

[0051] Example 5: Metagenomic Function Confirmation of Final Screening Results of Psychrophilic Microbiome Based on Bayesian Model + PCA Metagenomic high-throughput sequencing was performed on the psychrophilic bacterium G7, which was screened using the method described in this invention. Based on the metagenomic functional annotation results, statistical analysis was performed on the functional genes in G7 related to lignocellulose degradation.

[0052] The results are as follows Figure 5 As shown, G7 is rich in genes encoding various carbohydrate-active enzymes related to the degradation of cellulose, hemicellulose and lignin, including functional gene families such as glycoside hydrolases, carbohydrate esterases and auxiliary activities.

[0053] Further analysis of the metabolic functional composition of G7 was conducted based on the KEGG metabolic pathway annotation results. The results are as follows: Figure 6 As shown, metabolic pathways related to carbohydrate metabolism, energy metabolism, and polysaccharide degradation have high gene abundance in G7, indicating that it has the genetic basis to construct a metabolic network related to lignocellulose degradation.

[0054] The metagenomic functional annotation analysis results above show that multiple functional genes and metabolic pathways related to lignocellulose degradation were detected in microbiome G7, which was screened by the comprehensive evaluation method based on Bayesian model + PCA described in this invention. This provides verification support for the effectiveness and reliability of the method of this invention in the identification of functional microorganisms that degrade lignocellulose at low temperatures from the perspective of functional gene composition and metabolic pathways.

[0055] The above comparative analysis shows that the Bayesian model quantification core function + PCA verification of cross-substrate adaptability of the present invention is an indivisible and complete technical solution. The absence of any link will lead to defects in the screening results, such as insufficient core function or lack of application adaptability. This cannot be achieved by replacing conventional technology or by a limited number of experiments, which fully demonstrates the inventiveness of the present invention.

[0056] The specific embodiments of the present invention disclosed above are merely illustrative of the invention. These embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for identifying microorganisms that degrade lignocellulose under low-temperature conditions, characterized in that, The identification method includes the following steps: S1: Soil samples were placed in sterile water and shaken for 24 h, then allowed to stand for 2 h. The supernatant was collected and the initial microbial community was separated. The initial microbial community was placed in MC medium for continuous subculture and domestication culture, with each generation cultured for 72 h. Microbial communities resistant to low-temperature straw degradation were screened and enriched. S2: Multidimensional functional indicators of the microbiome screened and enriched in S1 were detected. Bayesian model was used to quantify the correlation between the obtained multidimensional functional indicator data, and a correlation coefficient matrix was constructed. PCA method was used to verify cross-substrate adaptability and screen out microbiomes with better comprehensive performance that are resistant to low-temperature straw degradation.

2. The identification method according to claim 1, characterized in that, The inoculum size for continuous subculture and domestication culture described in S1 is 1%, the temperature for continuous subculture and domestication culture is ≤10℃, and the number of generations for continuous subculture and domestication culture is ≥20.

3. The identification method according to claim 1, characterized in that, The MC medium described in S1 uses straw material containing lignocellulose as the main carbon source. The MC medium comprises the following components: 6 g / L NaHCO3, 0.45 g / L K2HPO4, 0.45 g / L KH2PO4, 0.9 g / L (NH4)2SO4, 0.9 g / L NaCl, 0.09 g / L MgSO4·7H2O, 0.09 g / L CaCl2·2H2O, 1 g / L L-cysteine ​​hydrochloride, 0.25 g / L yeast extract, 0.5 g / L casein peptone, 1% (w / v) vitamin solution, and 1% (w / v) carbon substrate.

4. The identification method according to claim 1, characterized in that, The multi-dimensional functional indicators described in S2 include: cellulose degradation rate, hemicellulose degradation rate, lignin degradation rate, total cellulose enzyme activity, endonuclease activity, exonuclease activity, enzyme efficiency, enzyme ratio, enzyme ratio deviation, enzyme stability, and average OD. 600 Value, lignin degradation rate, average carbohydrate degradation rate, and lignin / carbohydrate selectivity ratio.

5. The identification method according to claim 1, characterized in that, The Bayesian model described in S2 includes model L, model C, and model S; model L is a lignin quantification model, model C is a carbohydrate quantification model, and model S is a selective quantification model.

6. The identification method according to claim 5, characterized in that, The formula for model L is: lignin z_Eeff + z_BCI + z_StabE + (1 | strain) + (1 | straw); where lignin is the lignin degradation rate, as the dependent variable; z_Eeff is the standardized enzyme efficiency; and z_BCI is the standardized average OD. 600 z_StabE is the standardized enzyme stability and is used as the independent variable; sd(Intercept) = 0.78 for strain and sd(Intercept) = 2.62 for straw; regression coefficients Intercept = 15.96 and sigma = 2.

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7. The identification method according to claim 5, characterized in that, The formula for model C is: Carb z_Eratio + z_Eeff + z_BCI + (1 | strain) + (1 | straw); where Carb is the average carbohydrate degradation rate, used as the dependent variable; z_Eratio is the standardized enzyme ratio, z_Eeff is the standardized enzyme efficiency, and z_BCI is the standardized average OD. 600 The values ​​are used as independent variables; the sd(Intercept) of strain is 4.19, and the sd(Intercept) of straw is 2.11; the regression coefficients Intercept = 23.68 and sigma = 3.

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8. The identification method according to claim 5, characterized in that, The formula for model S is: Sel_rs z_Eeff + z_Erdev + z_BCI + (1 | strain) + (1 | straw); where Sel_rs is the lignin / carbohydrate selectivity ratio, used as the dependent variable; z_Eeff is the standardized enzyme efficiency; z_Erdev is the standardized enzyme ratio deviation; and z_BCI is the standardized average OD. 600 The values ​​are used as independent variables; the sd(Intercept) of strain is 0.17, and the sd(Intercept) of straw is 0.89; the regression coefficients Intercept = 0.72 and sigma = 0.

14.

9. The identification method according to claim 1, characterized in that, The specific steps for cross-substrate compatibility verification using the PCA method described in S2 are as follows: the PCA method is used to perform dimensionality reduction processing on the multi-index degradation rate data, and the principal components with a cumulative contribution rate ≥90% are extracted. The multi-index degradation rate data includes cellulose degradation rate data, hemicellulose degradation rate data, and lignin degradation rate data.

10. The identification method according to claim 9, characterized in that, The linear combination expression used in the PCA method is: PC1 principal component score: PC1 = 0.287 X CS-纤维素 +0.470 X CS-半纤维素 +0.467 X CS-木质素 +0.446 X RS-纤维素 +0.356 X RS-半纤维素 +0.392 X RS-木质素 ; PC2 principal component score: PC2 = 0.689 X CS-纤维素 +0.197 X CS-木质素 -0.268 X RS-纤维素 -0.638 X RS-半纤维素 ; PC3 principal component score: PC3 = 0.563 X CS-纤维素 -0.150 X CS-半纤维素 -0.216 X CS-木质素 +0.295 X RS-纤维素 +0.354 X RS-半纤维素 -0.633 X RS-木质素 .

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