Method for determining interaction relationship of microbial flora in solid state fermentation of composite microbial system and application thereof

Abstract

The present application belongs to the technical field of microbial solid-state fermentation, and relates to a determination method for the mutual action relationship of a microbial flora in a compound microbial system and application thereof. The determination method comprises the following steps: performing solid-state fermentation on each single bacterium in the compound microbial system, and extracting the maximum thermal power and total heat production of each single bacterium; performing solid-state fermentation on the compound microbial system, and extracting the measured maximum thermal power and measured total heat production of the compound microbial system; based on the inoculation proportion of each single bacterium in the compound microbial system and the thermal spectrum data of the single bacterium, the theoretical maximum thermal power and theoretical total heat production are calculated; the mutual action relationship of the microbial flora is determined according to the difference between the maximum thermal power and total heat production of the compound microbial system and the theoretical maximum thermal power and theoretical total heat production. The determination system capable of distinguishing different cooperation modes is established by combining the total heat production and maximum thermal power parameters, the thermal spectrum data is monitored in real time throughout the whole process, the dynamic tracking of the mutual action of the microbial flora is realized, and engineering guidance is provided for precisely regulating the fermentation process and directionally optimizing the function of the microbial flora.

Description

Technical Field

[0001] This invention belongs to the field of microbial solid-state fermentation technology, and relates to a method for determining the interaction relationship of microbial communities in solid-state fermentation of complex microbial systems and its application. Background Technology

[0002] In the field of microbial solid-state fermentation, especially in the development and application of complex microbial systems, a deep understanding of the interactions between microbial species, such as synergy, competition, and antagonism, is crucial for achieving precise control and improving fermentation efficiency and stability. However, existing techniques for analyzing microbial interactions still have significant limitations and cannot meet the actual needs of industrial solid-state fermentation. Community structure analysis based on high-throughput sequencing relies on sample detection at the fermentation endpoint or a specific time point. It is a static "post-hoc" analysis that cannot capture the temporal evolution of microbial interactions as fermentation progresses, making it difficult to reveal the transformation patterns of interaction relationships at different stages, resulting in a serious lag in fermentation regulation.

[0003] Inferring microbial interactions based on genome sequence association only reflects the probability of species "coexistence" or "repulsion," and cannot directly correlate with the metabolic activity and functional output of the microbial community during actual operation. It is easy to misjudge the nature of the interaction by ignoring the influence of environmental conditions on the function of the microbial community.

[0004] High-throughput sequencing and bioinformatics analysis are complex, time-consuming, and costly, making them unsuitable as online monitoring tools for industrial production and unable to provide rapid feedback for real-time intervention in fermentation processes. The constructed interaction networks are typically densely packed with nodes and complex structures, requiring in-depth interpretation by professionals to extract effective information, which hinders frontline technicians from quickly applying them to strain compatibility and process optimization.

[0005] Solid-state fermentation is accompanied by significant thermodynamic changes, which are a macroscopic manifestation of the growth and metabolism of the microbial community. However, existing technologies lack the means to directly correlate microbial interactions with macroscopic thermodynamic parameters that can be detected online (such as heat production rate and total enthalpy), and cannot use easily monitored temperature / thermal spectrum signals to quantify and analyze the type and intensity of interactions. Summary of the Invention

[0006] The purpose of this invention is to provide a method for determining the interaction relationships of microbial communities in solid-state fermentation of composite microbial systems and its application. By combining two key parameters in the thermogram—total heat production (H) and maximum thermal power (Pmax)—a discrimination system capable of distinguishing different cooperative modes is established. This method monitors thermogram data in real time throughout the entire process, overcoming the limitation of traditional endpoint detection methods that cannot capture dynamic interactions, thus achieving full-process dynamic tracking of microbial community interactions. It not only determines "whether there is cooperation" like traditional techniques, but also delves deeper into the question of "how there is cooperation," subdividing synergistic effects into "intensity-total double-increase type" and "efficiency optimization type," providing directly quantifiable engineering guidance for precise control of the fermentation process and targeted optimization of microbial community function.

[0007] Based on this, according to a first aspect of the present invention, the present invention provides a method for determining the microbial community interaction relationship in solid-state fermentation of a complex microbial system, comprising the following steps: S1. Acquisition of single-strain thermal spectrum data: Each single strain in the compound bacterial system is subjected to solid-state fermentation, and the fermentation process is monitored in real time to obtain the maximum thermal power Pmax and total heat production H of each single strain. S2. Acquisition of thermal spectrum data of the compound bacterial system: The compound bacterial system is subjected to solid-state fermentation, the fermentation process is monitored in real time, and the measured maximum thermal power Pmax and the measured total heat production H of the compound bacterial system are extracted. S3. Construction of theoretical thermal spectrum curve: Based on the inoculation ratio of each single bacterium in the composite bacterial system and the thermal spectrum data of the single bacterium, the theoretical thermal power-time curve of the composite bacterial system is constructed, and the theoretical maximum thermal power Pmax and the theoretical total heat production H are calculated. S4. Interaction relationship determination: Based on the difference between the maximum thermal power Pmax and total heat production H of the composite bacterial system extracted in the composite bacterial system thermal spectrum data acquisition step and the theoretical maximum thermal power Pmax and theoretical total heat production H calculated in the theoretical thermal spectrum curve construction step, the interaction relationship of the bacterial community is determined.

[0008] In some embodiments of the present invention, the interaction relationship determination step determines the interaction relationship in the following way: When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax > the theoretical maximum heat power Pmax, the interaction relationship of the microbial community is determined to be "intensity-total double increase type synergy"; When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax < the theoretical maximum heat power Pmax, the microbial community interaction relationship is determined to be "efficiency-optimized synergy". When the measured total heat production H is less than the theoretical total heat production H, the interaction between the microbial community is determined to be "antagonistic".

[0009] In some embodiments of the present invention, the formulas for calculating the theoretical maximum thermal power Pmax and the theoretical total heat production H in the theoretical thermal spectrum curve construction step are as follows: Theoretical maximum thermal power Pmax = Σ[Ri×Pmax-i]; Theoretical total heat production H = Σ[Ri×Hi]; Where Ri is the inoculation ratio of strain i in the composite bacterial system, Pmax-i is the measured maximum thermal power of strain i, and Hi is the measured total heat production of strain i.

[0010] In some embodiments of the present invention, the composite bacterial system includes Pediococcus lactis and Bacillus.

[0011] In some embodiments of the present invention, in step S2, the total amount of inoculation for solid-state fermentation of the compound microbial system is the same as the amount of inoculation for solid-state fermentation of the single microorganism. When there are two types of single microorganisms in the compound microbial system, the ratio of single microorganism inoculation is 1:9 to 9:1.

[0012] In some embodiments of the present invention, in the single-strain thermal spectrum data acquisition step, the concentration of the solid-state fermentation inoculum is 1×10⁻⁶. 8 ~1×10 9 CFU / mL, inoculum size is 0.3~0.8mL / 4mL fermentation substrate.

[0013] In some embodiments of the present invention, the substrate for solid-state fermentation is wheat flour, corn flour, soybean meal or a mixture thereof, and the substrate is sterilized at 115-125°C for 15-25 minutes.

[0014] According to a second aspect of the present invention, the present invention also provides a solid-state fermentation complex microbial system, wherein the interaction relationship of the complex microbial system is determined by the above-described determination method, the complex microbial system includes Pediococcus lactis and Bacillus, and when the inoculation amount of Pediococcus lactis is greater than the inoculation amount of Bacillus, the interaction relationship is a "double-increase synergistic" interaction. When the inoculation amount of *Pediococcus lactis* is less than or equal to the inoculation amount of *Bacillus*, the interaction relationship is "efficiency-optimized synergy".

[0015] According to a third aspect of the present invention, the present invention also provides an application of the determination method in the optimization of solid-state fermentation process, including: adjusting the inoculation ratio of strains in the compound microbial system based on the interaction relationship determination result to improve fermentation efficiency or optimize product quality.

[0016] According to a fourth aspect of the present invention, the present invention also provides a microbial community interaction relationship discrimination system constructed by the determination method, comprising: Data acquisition module: Acquires thermal spectrum data of single bacteria and complex bacterial strains through a micro calorimeter. The thermal spectrum data includes total heat production H and maximum thermal power Pmax. Theoretical curve calculation module: Based on the inoculation ratio of single bacteria in the compound bacterial system and the thermal spectrum data of single bacteria, calculate the theoretical thermal power curve, theoretical total heat production H and maximum thermal power Pmax of the compound bacterial system; Interaction determination module: Compares the measured total heat production H and maximum heat power Pmax of the compound bacterial system with the theoretical total heat production H and maximum heat power Pmax, and outputs the interaction relationship type.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: By combining two key parameters—total heat production (H) and maximum thermal power (Pmax)—in the thermogram, a discrimination system capable of distinguishing different cooperative modes is established. This method monitors thermogram data in real time throughout the entire process, overcoming the limitation of traditional endpoint detection in capturing dynamic interactions and achieving full-process dynamic tracking of microbial community interactions. It not only determines "whether cooperation occurs" like traditional techniques but also delves deeper into the question of "how cooperation occurs," subdividing synergistic effects into "intensity-total double-increase type" and "efficiency optimization type," providing directly quantifiable engineering guidance for precise control of the fermentation process and targeted optimization of microbial community function. Attached Figure Description

[0018] Figure 1 The graphs show the thermal power and total heat production of single and mixed cultures in the embodiments of this application; PA (black) represents a single strain of *Pediococcus lactis* (P); BV (red) represents a single strain of *Bacillus* (B); P5B5 (blue) represents a P:B complex strain of 5:5; P9B1 (green) represents a P:B complex strain of 9:1; P1B9 (purple) represents a P:B complex strain of 1:9. Detailed Implementation

[0019] The following specific embodiments further illustrate the technical solution of the present invention. These specific embodiments do not represent a limitation on the scope of protection of the present invention. Non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the scope of protection of the present invention.

[0020] As described in the background section, existing technologies for analyzing interactions in solid-state fermentation complex microbial systems largely rely on methods such as microbial community sequencing and targeted detection of metabolites. While these methods can achieve qualitative analysis of interactions, they suffer from drawbacks such as long detection cycles, high costs, and process delays. Furthermore, they cannot quantify the type and intensity of interactions between microorganisms using easily monitored temperature and thermal spectrum signals, making it difficult to support real-time dynamic control of the fermentation process.

[0021] Based on this, the present invention attempts to establish a quantitative correlation between microbial interactions and the thermodynamic parameters of the fermentation system by taking thermodynamic characteristics as the core entry point, in order to overcome the limitations of the existing technology.

[0022] During the experimental exploration, we first optimized the processing logic of the thermal spectrum data. Experiments have verified that the thermal spectrum curve of solid-state fermentation is a continuous time-series curve. If the thermal power value at a single time point (such as time t) is used for linear superposition calculation, a massive number of discrete data points need to be processed. This not only greatly increases the computational complexity, but also easily introduces noise interference due to local environmental fluctuations during the fermentation process (such as short-term temperature anomalies or local unevenness of the substrate), resulting in distortion of the interaction analysis results.

[0023] Furthermore, this invention systematically verifies the applicability of the core determination parameters: If only "total heat production (H)" is used as the criterion, it essentially only reflects the total energy accumulated by the substrate during fermentation and cannot distinguish between interaction modes of "low metabolic peak but long duration" and "high metabolic peak but short duration". For example, in "efficiency-optimized synergy", although the microbial community achieves a significant increase in total heat production by extending the metabolic cycle, the metabolic burst force is not enhanced. If only H is used for judgment, it is easy to misjudge it as "no significant interaction" and cannot accurately reflect the true type of microbial community interaction. If only "maximum thermal power Pmax" is used as the criterion, it focuses on reflecting the most vigorous stage of bacterial community metabolism and cannot reflect the thoroughness of substrate utilization. For example, in a complex bacterial system with "antagonism but high peak values", the metabolic burst of a single strain may mask the limited substrate utilization of other strains. In this case, using only Pmax to determine the interaction may easily lead to misjudgment as "synergistic effect" and cannot objectively reflect the actual intensity of bacterial community interaction.

[0024] Based on the above verification results, this invention creatively proposes a technical solution that uses a joint determination of "total heat production H" and "maximum thermal power Pmax": Total heat production H can quantitatively characterize the overall amount of substrate utilization and reflect the final impact of microbial community interaction on fermentation efficiency; maximum heat power Pmax can intuitively reflect the explosive power of microbial community metabolism and reflect the growth and metabolic activity of microbial community during the interaction process; the two together form a "total amount-peak value" dual-dimensional constraint, which can accurately distinguish three types of core interaction modes: "intensity-total amount double increase synergy", "efficiency optimization synergy", and "antagonistic effect".

[0025] Taking the core embodiment of this invention as an example, the measured total heat production H of the P5B5 group composite bacterial system is significantly higher than the theoretical value, but the measured maximum heat power Pmax is lower than the theoretical value. If only H is used for judgment, it will be misjudged as "intensity-total double increase type synergy"; however, by using the dual-parameter joint judgment method of this invention, it can be accurately identified as "efficiency optimization type synergy". This fully demonstrates that a single parameter cannot achieve accurate judgment of the interaction type. The joint judgment logic of total heat production H combined with maximum heat power Pmax simplifies the processing dimensions of thermal spectrum data and achieves the accuracy and practicality of interaction analysis through dual-parameter complementarity, ultimately becoming the core technical judgment criterion of this invention.

[0026] Unless otherwise specified, all reagents, raw materials, equipment, software, etc. used in this application are available from commercial or public sources.

[0027] Pediococcus acidilactici and Bacillus subtilis were extracted and isolated from high-temperature Daqu (a type of Chinese liquor). The extraction method was as follows: Pediococcus lactis: Take 10 g of Daqu sample and add it to 90 mL of sterile physiological saline. Incubate at 30 °C with a stirring rate of 180 r·min. -1 Incubate with shaking for 30 min. Then incubate the suspension at 1000 r·min. -1 Centrifuge at low speed for 3 min, and collect the supernatant and precipitate separately. Spread 100 μL of the supernatant onto a modified MRS agar plate and incubate anaerobically at 30 °C for 2–3 days. After incubation, pick well-grown single colonies from the plate and purify them using the streak method on fresh modified MRS plates. Once the purified colonies have grown to a uniform morphology, pick single colonies and inoculate them into liquid MRS medium for further culture. Store the purified strain in cryovials containing 25% glycerol at -80 °C for long-term storage.

[0028] Bacillus: Add 10 g of Daqu sample to 90 mL of sterile physiological saline and incubate at 37 °C with a flow rate of 180 r·min. -1 Incubate with shaking for 30 min. Then incubate the suspension at 1000 r·min. -1 Centrifuge at low speed for 3 min, and collect the supernatant and precipitate separately. Spread 100 μL of the supernatant onto a modified LB agar plate and incubate aerobically at 37 °C for 2–3 days. After incubation, pick well-grown single colonies from the plate and purify them using the streak method on fresh modified LB agar plates. Once the purified colonies have grown to a uniform morphology, pick single colonies and inoculate them into liquid LB agar for expansion. Store the purified strain in cryovials containing 25% glycerol at -80 °C for long-term storage.

[0029] Genomic DNA was extracted from each isolated strain using a DNA extraction kit (Sangon Biotech (Shanghai) Co., Ltd., Shanghai, China). Using the extracted genomic DNA as a template, the 16S rRNA gene was amplified using universal bacterial primers 27F (5'-AGAGTTTGATCCTGGCTCAG-3') and 1492R (5'-GGTTACCTTGTTACGACTT-3'). After passing PCR amplification by 1.0% agarose gel electrophoresis, the amplified products were sent to Sangon Biotech (Shanghai) Co., Ltd. for sequencing. The obtained 16S rRNA gene sequences were assembled and compared with known sequences in the NCBI database using the BLAST program (https: / / blast.ncbi.nlm.nih.gov / ) to determine the taxonomic position of the strains.

[0030] Daqu extract: Add 50 g of Daqu to 100 mL of deionized water, place it in a 100 mL centrifuge tube, fix the centrifuge tube and shake at 2000 rpm for 15 min, sonicate in an ice bath for 20 min, filter with gauze to obtain crude extract, centrifuge at 1000 rpm for 10 min, and take the supernatant for later use.

[0031] MRS liquid culture medium composition: peptone 10.0 g·L -1 8.0 g·L beef extract -1 20.0 g·L glucose -1 4.0 g·L yeast extract -1 Dipotassium hydrogen phosphate 2.0 g·L -1 Sodium acetate 5.0 g·L -1 Diammonium hydrogen citrate 2.0 g·L -1 Magnesium sulfate 0.2 g·L -1 Manganese sulfate 0.04 g·L -1 Acetic acid 5.0 mL / L, lactic acid 10.0 mL / L, Tween 80 1.0 g·L -1 The final pH was 3.2 at 37°C.

[0032] Modified LB medium: 10 g / L peptone -1 Sodium chloride 10 g·L -1 Yeast extract 5 g·L -1 40 mL / L of Daqu extract (for solid culture medium, add 20 g / L of agar). -1 The final pH at 37°C was 5.5.

[0033] Solid-state fermentation substrate: wheat flour (containing 8% mother koji, which is conventional high-temperature koji pulverized through an 80-mesh sieve). Fill each 4mL ampoule with 1g of wheat flour, autoclave at 121℃ for 20 minutes, and cool before use.

[0034] Wheat juice culture medium: After crushing wheat, mix it with water at a material-to-liquid ratio of 1:4 (w / v), add heat-resistant α-amylase and incubate at 90℃ for 1 h, then continue heating to boiling for 30 min, then add water to the original volume, immediately cool to 60℃, add saccharifying enzyme and incubate at 60℃ for 4 h, filter through 4 layers of gauze, and add ammonium sulfate 6 g·L⁻¹. -1 Magnesium sulfate 1.2 g·L -1 Dipotassium hydrogen phosphate 2.4 g·L -1 After sterilization at 115℃ for 20 minutes, place in a refrigerator at 4℃.

[0035] This application provides a method for determining the microbial community interaction relationships in solid-state fermentation of a complex microbial system, comprising the following steps: S1. Acquisition of single-strain thermal spectrum data: Each single strain in the composite strain is subjected to solid-state fermentation, and the fermentation process is monitored in real time to obtain the real-time maximum thermal power Pmax and total heat production H of each single strain; S2. Acquisition of thermal spectrum data of the compound bacterial system: The compound bacterial system is subjected to solid-state fermentation, the fermentation process is monitored in real time, and the measured maximum thermal power Pmax and the measured total heat production H of the compound bacterial system are extracted. S3. Construction of theoretical thermal spectrum curve: Based on the inoculation ratio of each single bacterium in the composite bacterial system and the thermal spectrum data of the single bacterium, the theoretical thermal power-time curve of the composite bacterial system is constructed, and the theoretical maximum thermal power Pmax and the theoretical total heat production H are calculated. S4. Interaction relationship determination: Based on the difference between the maximum thermal power Pmax and total heat production H of the composite bacterial system extracted in the composite bacterial system thermal spectrum data acquisition step and the theoretical maximum thermal power Pmax and theoretical total heat production H calculated in the theoretical thermal spectrum curve construction step, the interaction relationship of the bacterial community is determined.

[0036] By standardizing the fermentation environment conditions for single and complex microorganisms, the comparability of thermographic data is ensured. This method monitors thermographic data in real time throughout the entire process, overcoming the limitation of traditional endpoint detection in capturing dynamic interactions and achieving full-process dynamic tracking of microbial community interactions. By combining the two key parameters, total heat production (H) and maximum thermal power (Pmax), in the thermographic spectrum, a discrimination system capable of distinguishing different cooperative modes is established. This method not only determines "whether there is cooperation" like traditional techniques, but also delves into the question of "how there is cooperation," subdividing synergistic effects into "intensity-total double increase type" and "efficiency optimization type," providing directly quantifiable engineering guidance for precise control of the fermentation process and targeted optimization of microbial community function.

[0037] Furthermore, in the interaction determination step, the interaction relationship is determined in the following way: When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax > the theoretical maximum heat power Pmax, the interaction relationship of the microbial community is determined to be "intensity-total double increase type synergy"; When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax < the theoretical maximum heat power Pmax, the microbial community interaction relationship is determined to be "efficiency-optimized synergy". When the measured total heat production H is less than the theoretical total heat production H, the interaction between the microbial community is determined to be "antagonistic".

[0038] By providing clear and quantifiable criteria for determining interaction relationships, different types of microbial community interactions can be quickly distinguished without relying on complex bioinformatics analysis. It can not only determine "whether an interaction exists" but also accurately identify "interaction patterns," providing direct guidance for targeted optimization of microbial strain compatibility and solving the problems of ambiguous interaction determination and weak engineering guidance in existing technologies.

[0039] Furthermore, in the theoretical thermal spectrum curve construction step, the formulas for calculating the theoretical maximum thermal power Pmax and the theoretical total heat production H are as follows: Theoretical maximum thermal power Pmax = Σ[Ri×Pmax-i]; Theoretical total heat production H = Σ[Ri×Hi]; Where Ri is the inoculation ratio of strain i in the composite bacterial system, Pmax-i is the measured maximum thermal power of strain i, and Hi is the measured total heat production of strain i.

[0040] By weighting and superimposing single-strain thermal spectrum parameters based on inoculation ratio, the heat production pattern of bacterial communities under non-interaction conditions is simulated, providing a reliable reference benchmark for subsequent comparison with measured data and determination of interaction relationships, and avoiding misjudgment of interaction due to ambiguity in theoretical models.

[0041] Furthermore, the complex bacterial system includes Pediococcus lactis and Bacillus.

[0042] The applicable strain range of the method is clearly defined, covering commonly used functional strains in the field of solid-state fermentation, thereby improving the versatility of the method.

[0043] Furthermore, in step S2, the total inoculation amount for solid-state fermentation of the compound microbial system is the same as that for solid-state fermentation of a single microorganism. When there are two types of single microorganisms in the compound microbial system, the ratio of single microorganism inoculation is 1:9 to 9:1.

[0044] By controlling the total inoculation amount to be consistent, the interference of differences in inoculation amount on the thermal spectrum data is eliminated, ensuring the validity of the comparison between measured and theoretical data; the range of inoculation ratios for the two single bacteria is limited to cover common strain compatibility ratios, providing more reference dimensions for strain ratio optimization.

[0045] Furthermore, in the single-strain thermal spectrum data acquisition step, the concentration of the single-strain solid-state fermentation inoculum is 1×10⁻⁶. 8 ~1×10 9 CFU / mL, inoculum size is 0.3~0.8mL / 4mL fermentation substrate.

[0046] Limiting the concentration and amount range of single-strain inoculation ensures that single-strain fermentation can stably generate detectable thermal spectrum signals, avoiding weak thermal power signals and unreliable data due to too low an inoculation amount, or excessive substrate consumption and distortion of thermal spectrum curves due to too high an inoculation amount.

[0047] Furthermore, the substrate for solid-state fermentation is wheat flour, corn flour, soybean meal, or a mixture thereof, and the substrate is sterilized at 115-125°C for 15-25 minutes.

[0048] Sterilization eliminates unwanted microorganisms in the substrate, preventing their metabolic heat from interfering with the interaction determination and fermentation effect of the target microbial community. At the same time, it ensures the stability of the substrate's nutrient composition, providing a good environment for the synergistic metabolism of the microbial community and guaranteeing the stability and repeatability of the fermentation process.

[0049] Furthermore, this application provides a solid-state fermentation complex microbial system. The interaction relationship of the complex microbial system is determined by the above-mentioned determination method. The complex microbial system includes Pediococcus lactis and Bacillus. When the inoculation amount of Pediococcus lactis is greater than the inoculation amount of Bacillus, the interaction relationship is "intensity-total amount double-increase synergistic". When the inoculum size of *Pediococcus lactis* is less than or equal to that of *Bacillus*, the interaction relationship is "efficiency-optimized synergy".

[0050] Clarifying the interaction patterns and inoculation ratios between specific strain combinations of Pediococcus lactis and Bacillus provides direct guidance for the industrial application of such complex microbial systems. By precisely defining the correspondence between inoculation ratios and interaction types, the ratios can be adjusted in a targeted manner according to actual production needs, thereby improving fermentation efficiency and product quality.

[0051] Furthermore, this application provides an application of the above-mentioned determination method in the optimization of solid-state fermentation process, including: adjusting the inoculation ratio of the strains in the compound microbial system based on the interaction relationship determination result to improve fermentation efficiency or optimize product quality.

[0052] By combining interaction determination methods with industrial production needs, the limitations of existing technologies that "can only analyze interactions but cannot guide applications" can be overcome. By adjusting the inoculation ratio to optimize the microbial community interaction mode, fermentation efficiency can be improved in a targeted manner, such as shortening the fermentation cycle, or product quality, such as increasing the yield of the target product and reducing the content of by-products. This provides a scientific basis for the precise control of solid-state fermentation processes and reduces the trial-and-error costs of industrial production.

[0053] Furthermore, this application provides a microbial community interaction relationship discrimination system constructed by the determination method, comprising: Data acquisition module: Acquires thermal spectrum data of single bacteria and complex bacterial strains through a micro calorimeter. The thermal spectrum data includes total heat production H and maximum thermal power Pmax. Theoretical curve calculation module: Based on the inoculation ratio of single bacteria in the compound bacterial system and the thermal spectrum data of single bacteria, calculate the theoretical thermal power curve, theoretical total heat production H and maximum thermal power Pmax of the compound bacterial system; Interaction determination module: Compares the measured total heat production H and maximum heat power Pmax of the compound bacterial system with the theoretical total heat production H and maximum heat power Pmax, and outputs the interaction relationship type.

[0054] The method is modularized and systematized to achieve automated analysis of microbial community interactions. The data acquisition module ensures accurate acquisition of thermal spectrum data, the theoretical curve calculation module guarantees rapid solution of theoretical parameters, and the interaction determination module realizes automatic output of results. No in-depth interpretation by professionals is required, which lowers the threshold for using the method and meets the needs of online monitoring and rapid decision-making in industrial production.

[0055] Example A method for determining the microbial community interactions in solid-state fermentation of a complex microbial system includes the following steps: S1. Acquisition of single-strain thermal spectrum data: Pediococcus lactis (Group P): Single colonies of Pediococcus lactis were picked and inoculated into MRS liquid medium, and incubated at 37°C for 24 hours. After incubation, the bacterial cells were collected by centrifugation, resuspended in sterile physiological saline, and the bacterial concentration was adjusted to 1×10⁻⁶. 9 CFU / mL was used to obtain a suspension of Pediococcus lactis for later use; Bacillus (Group B): Single colonies of Bacillus were picked and inoculated into YPD liquid medium, and cultured at 37°C with shaking at 180 rpm for 18 h. Subsequent centrifugation and resuspension were performed in the same manner as for Pediococcus lactis. The final bacterial concentration was adjusted to 1×10⁻⁶. 9 CFU / mL was used to obtain a Bacillus suspension for later use.

[0056] 0.5 mL of Pediococcus lactis suspension and 0.5 mL of Bacillus spp. suspension were separately inoculated into 4 mL ampoules containing solid fermentation substrate.

[0057] After inoculation, the ampoule is sealed and placed in the detection channel of the TAM IV thermal conductivity multichannel micro calorimeter. The temperature is kept constant at 40°C, and the data acquisition interval is 10 minutes. The thermal power-time curve (i.e., thermogram) is continuously monitored and recorded for at least 24 hours.

[0058] The instrument's built-in software was used to record the thermal power-time curves of individual bacteria, and key parameters were extracted. Real-time thermal power values ​​P-single(t), maximum thermal power, and total heat production H of Pyorrhizococcus lactis and Bacillus.

[0059] The thermogram of *Pediococcus lactis* (Group P) showed a single peak, reaching its maximum thermal power (Pmax = 400.5 μW) about 2.5 hours after inoculation. The fermentation cycle was short, basically ending within about 10 hours, with a total heat production H of 5.8 J / g. This indicates that *Pediococcus lactis* can rapidly initiate metabolism on the substrate, and the duration is short.

[0060] The thermogram of Bacillus (Group B) showed a narrow and high peak, reaching its maximum thermal power (Pmax=1691.4μW) about 5 hours after inoculation. The fermentation cycle was relatively short, basically ending within about 10 hours, with a total heat production H of 9.1J / g. This is consistent with the characteristics of Bacillus, which grows slowly and can secrete a variety of enzyme systems to degrade complex substrates (such as proteins and starch), and has relatively sustained heat production.

[0061] S2. Acquisition of thermal spectrum data of complex bacterial strains: Preparation of compound bacterial strains: Three compound bacterial strain experimental groups were set up, with three replicates in each group, and the total inoculum volume for each group was 0.5 mL. P9B1 group: 0.9 mL of Pleurotus ostreatus suspension and 0.1 mL of Bacillus suspension were mixed, the mixed cells were collected by centrifugation, dissolved in 0.5 mL of wheat juice culture medium, and then inoculated into 4 mL ampoules containing solid fermentation substrate; P5B5 group: Mix 0.5 mL of Pyocorae suspension and 0.5 mL of Bacillus suspension, collect the mixed cells by centrifugation, dissolve in 0.5 mL of wheat juice culture medium, and inoculate into 4 mL ampoules containing solid fermentation substrate; Group P1B9: 0.1 mL of Pediococcus lactis suspension and 0.9 mL of Bacillus suspension were mixed, the mixed cells were collected by centrifugation, dissolved in 0.5 mL of wheat juice culture medium, and then inoculated into 4 mL ampoules containing solid fermentation substrate.

[0062] After inoculation, the ampoule is sealed and placed in the detection channel of the TAM IV thermal conductivity multichannel micro calorimeter. The temperature is kept constant at 40°C, and the data acquisition interval is 10 minutes. The thermal power-time curve (i.e., thermogram) is continuously monitored and recorded for at least 24 hours.

[0063] Record the measured heat power-time curves of each group of compound bacterial strains and extract the parameters: measured maximum heat power Pmax and measured total heat production H.

[0064] S3. Construction of theoretical thermal spectrum curves: Based on the thermal spectrum data of each single bacterium and the inoculation ratio, the theoretical thermal power-time curve of the composite bacterial strain was constructed, and the theoretical parameters were calculated: Real-time theoretical thermal power: Ptheoretical(t) = [RP × P1 - single(t)] + [RB × P2 - single(t)]; Where Ptheoretical(t) is the theoretical thermal power value (μW) at time t. RP represents the inoculation ratio of Pediococcus lactis, and RB represents the inoculation ratio of Bacillus subtilis (RP=0.9; RB=0.1 for P9B1 group; RP=0.5; RB=0.5 for P5B5 group; RP=0.1; RB=0.9 for P1B9 group). P1-single (t) and P2-single (t) are the real-time thermal power values ​​of a single strain of Pediococcus lactis and a single strain of Bacillus spp., respectively, measured at time t.

[0065] Theoretical maximum thermal power: Theoretical Pmax = RP × Pmax - P + RB × Pmax - B; Wherein, RP is the inoculation ratio of Pediococcus lactis, RB is the inoculation ratio of Bacillus spp. (P9B1 group RP=0.9; RB=0.1; P5B5 group RP=0.5; RB=0.5; P1B9 group RP=0.1; RB=0.9); Pmax-P is the measured Pmax of Pediococcus lactis, and Pmax-B is the measured Pmax of Bacillus spp.

[0066] Theoretical total heat production: Theoretical H = RP × H - P + RB × HB; Wherein, RP is the inoculation ratio of Pediococcus lactis, RB is the inoculation ratio of Bacillus spp. (P9B1 group RP=0.9; RB=0.1; P5B5 group RP=0.5; RB=0.5; P1B9 group RP=0.1; RB=0.9); HP is the measured H of Pediococcus lactis, and HB is the measured H of Bacillus spp.

[0067] Thermal power and total heat production of single and mixed cultures, as follows: Figure 1 As shown in the figure on the left, the thermal power-time curve characterizes instantaneous metabolic activity, and the results are as follows: Single-strain characteristics: PA (black): Low peak value (approximately 400 μW), early start-up, and rapid decay, indicating mild and short-cycle metabolism.

[0068] BV (red): Extremely high peak value (approximately 1700 μW), delayed start-up, rapid rise and fall, representing explosive, strong metabolic activity.

[0069] Characteristics of the compound bacteria: P9B1 (green): The peak value is significantly higher than PA and also higher than the theoretical superposition value, reflecting an enhanced metabolic burst.

[0070] P5B5 (blue) / P1B9 (purple): The peak value is lower than that of BV single bacteria, but the metabolic duration is longer, reflecting a lower peak value but a longer cycle.

[0071] Interaction manifestations: P9B1: Both peak value and total heat production are higher than theoretical values, belonging to the intensity-total double increase type of synergy; P5B5 / P1B9: Peak value is lower than theoretical values ​​but total heat production is higher, belonging to the efficiency optimization type of synergy.

[0072] The right figure shows the total heat production-time curve, which characterizes the cumulative substrate utilization.

[0073] Single-strain characteristics: PA (black): Final total heat production is approximately 5.8 J, accumulating slowly.

[0074] BV (red): The final total heat production is about 9.1 J, which accumulates rapidly in the early stage and then tends to level off.

[0075] Characteristics of the compound bacteria: The total heat production of all composite strains (blue / green / purple) was much higher than the theoretical superposition value of single strains (P9B1 approximately 33.8 J, P5B5 approximately 31.3 J, P1B9 approximately 37.4 J), proving that the substrate was utilized more thoroughly.

[0076] P1B9 (purple) / P9B1 (green) had the fastest accumulation rate, followed by P5B5 (blue), both significantly better than single bacteria.

[0077] Interactions: The total heat production of the complex bacterial system far exceeded that of the linear superposition of single bacteria, proving that there is nutritional complementarity / enzyme synergy among the bacterial communities, which improves substrate utilization efficiency.

[0078] The theoretical and actual thermal parameters for single-strain and mixed-strain cultures are shown in Table 1 below: Table 1. Theoretical and actual thermal parameters of single and complex bacteria.

[0079] S4. Determining Interaction Relationships: By comparing the measured parameters with the theoretical parameters, the interaction relationships of the microbial community can be determined. Specifically: (1) In group P9B1 (Pediococcus lactis: Bacillus = 9:1), the measured total heat production H was greater than the theoretical total heat production H, and the measured maximum heat power Pmax was greater than the theoretical maximum heat power Pmax. Under this ratio, Pediococcus lactis had an absolute advantage. The simultaneous increase of the two thermodynamic parameters indicated a typical synergistic effect. The mechanism can be inferred to be: Pediococcus lactis multiplies rapidly in the early stage of fermentation, producing lactic acid and lowering the pH of the system, which creates favorable conditions for Bacillus, which prefers a slightly acidic environment. At the same time, the proteases and amylases secreted by Bacillus degrade macromolecules into small peptides, amino acids and sugars. These products can be utilized more efficiently by Pediococcus lactis, thus forming a positive metabolic cycle, which significantly improves the metabolic efficiency and energy output of the entire bacterial system. Therefore, if the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax > the theoretical maximum heat power Pmax, the interaction relationship of the bacterial community is determined to be "intensity-total double-increase synergy".

[0080] (2) Group P5B5 (Pediococcus lactis: Bacillus spp. = 1:1) and Group P1B9 (Pediococcus lactis: Bacillus spp. = 1:9). Both groups showed the same trend: the measured total heat production H was greater than the theoretical total heat production H, but the measured maximum heat power Pmax was less than the theoretical maximum heat power Pmax. The increase in total heat production proved the existence of a cooperative relationship, ultimately resulting in more substrate being degraded and utilized. However, the decrease in maximum heat power revealed a fundamentally different quality of cooperation compared to Group P9B1. This was not a simple mutual stimulation, but rather an optimization and restructuring of a metabolic strategy. This is an efficiency-optimized synergy that "trades peak intensity for process durability and thorough substrate utilization." Therefore, if the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax < the theoretical maximum heat power Pmax, the microbial community interaction is determined to be an "efficiency-optimized synergy"; this pattern is usually accompanied by a more stable and longer-lasting fermentation process.

[0081] (3) If the measured total heat production H < the theoretical total heat production H, then the interaction relationship of the microbial community is determined to be "antagonistic".

[0082] Total heat production (H) serves as a qualitative indicator of the interaction, determining the fundamental question of whether cooperation exists. A measured total heat production (H) greater than the theoretical total heat production (H) is a necessary condition for determining the existence of positive interaction. Maximum heat power (Pmax) acts as a modifier of the interaction mode. Based on a qualitative assessment of cooperation, the direction of change in maximum heat power (Pmax) further distinguishes different types of cooperation, revealing the different biological mechanisms and metabolic strategies behind them.

[0083] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for determining the microbial community interactions in solid-state fermentation of a complex microbial system, characterized in that, Includes the following steps: S1. Acquisition of single-strain thermal spectrum data: Each single strain in the compound bacterial system is subjected to solid-state fermentation, and the fermentation process is monitored in real time to obtain the maximum thermal power Pmax and total heat production H of each single strain. S2. Acquisition of thermal spectrum data of the compound bacterial system: The compound bacterial system is subjected to solid-state fermentation, the fermentation process is monitored in real time, and the measured maximum thermal power Pmax and the measured total heat production H of the compound bacterial system are extracted. S3. Construction of theoretical thermal spectrum curve: Based on the inoculation ratio of each single bacterium in the composite bacterial system and the thermal spectrum data of the single bacterium, the theoretical thermal power-time curve of the composite bacterial system is constructed, and the theoretical maximum thermal power Pmax and the theoretical total heat production H are calculated. S4. Interaction relationship determination: Based on the difference between the maximum thermal power Pmax and total heat production H of the composite bacterial system extracted in the composite bacterial system thermal spectrum data acquisition step and the theoretical maximum thermal power Pmax and theoretical total heat production H calculated in the theoretical thermal spectrum curve construction step, the interaction relationship of the bacterial community is determined.

2. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 1, characterized in that, In the interaction relationship determination step, the interaction relationship is determined in the following way: When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax > the theoretical maximum heat power Pmax, the interaction relationship of the microbial community is determined to be "intensity-total double increase type synergy"; When the measured total heat production H > the theoretical total heat production H, and the measured maximum heat power Pmax < the theoretical maximum heat power Pmax, the microbial community interaction relationship is determined to be "efficiency-optimized synergy"; When the measured total heat production H is less than the theoretical total heat production H, the interaction between the microbial community is determined to be "antagonistic".

3. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 1, characterized in that, In the theoretical thermal spectrum curve construction step, the formulas for calculating the theoretical maximum thermal power Pmax and the theoretical total heat production H are as follows: Theoretical maximum thermal power Pmax = Σ[Ri×Pmax-i]; Theoretical total heat production H = Σ[Ri×Hi]; Where Ri is the inoculation ratio of strain i in the composite bacterial system, Pmax-i is the measured maximum thermal power of strain i, and H_i is the measured total heat production of strain i.

4. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 1, characterized in that, The complex bacterial system includes Pediococcus lactis and Bacillus.

5. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 1, characterized in that, In step S2, the total amount of inoculated material for solid-state fermentation of the compound microbial system is the same as the amount of inoculated material for solid-state fermentation of the single microorganism. When there are two types of single microorganisms in the compound microbial system, the ratio of single microorganism inoculation is 1:9 to 9:

1.

6. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 1, characterized in that, In the single-strain thermal spectrum data acquisition step, the concentration of the solid-state fermentation inoculum is 1×10⁻⁶. 8 ~1×10 9 CFU / mL, inoculum size is 0.3~0.8mL / 4mL fermentation substrate.

7. The method for determining the microbial community interaction relationship in solid-state fermentation of a compound microbial system as described in claim 6, characterized in that, The substrate for solid-state fermentation is wheat flour, corn flour, soybean meal, or a mixture thereof, and the substrate is sterilized at 115-125°C for 15-25 minutes.

8. A solid-state fermentation complex microbial system, characterized in that, The interaction relationship of the complex bacterial system is determined by the determination method described in any one of claims 1 to 7. The complex bacterial system includes Pediococcus lactis and Bacillus. When the inoculation amount of Pediococcus lactis is greater than the inoculation amount of Bacillus, the interaction relationship is "intensity-total amount double-increase synergistic". When the inoculation amount of *Pediococcus lactis* is less than or equal to the inoculation amount of *Bacillus*, the interaction relationship is "efficiency-optimized synergy".

9. The application of the determination method according to any one of claims 1 to 8 in a solid-state fermentation process, characterized in that, The applications include: adjusting the inoculation ratio of strains in the compound microbial system based on the interaction relationship determination results to improve fermentation efficiency or optimize product quality.

10. A microbial community interaction discrimination system constructed according to the determination method of any one of claims 1 to 8, characterized in that, include: Data acquisition module: Acquires thermal spectrum data of single bacteria and complex bacterial strains through a micro calorimeter. The thermal spectrum data includes total heat production H and maximum thermal power Pmax. Theoretical curve calculation module: Based on the inoculation ratio of single bacteria in the compound bacterial system and the thermal spectrum data of single bacteria, calculate the theoretical thermal power curve, theoretical total heat production H and maximum thermal power Pmax of the compound bacterial system; Interaction determination module: Compares the measured total heat production H and maximum heat power Pmax of the compound bacterial system with the theoretical total heat production H and maximum heat power Pmax, and outputs the interaction relationship type.

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