Community level quantification and evaluation method based on inter-bacterial interaction and flora structure

Abstract

The invention discloses a community level quantification and evaluation method based on inter-bacterial interaction and a flora structure, which comprises the following steps: firstly, acquiring microbial community information, constructing a biological association network, and screening out highly related inter-bacterial interaction coefficients through significance analysis; secondly, calculating an average value of highly correlated inter-bacterium interaction coefficients of each species; taking the relative abundance table of the species as a weight, and multiplying the weight with the average inter-bacteria interaction coefficient matrix of the species; and finally, obtaining the total association strength of the community through two-step summation, and defining the total association strength as a new microbial community evaluation index as a community collaboration index. The larger the index is, the higher the total positive correlation strength of the community is, and the better the stability and functionality of the microbial community are. The community level quantification and evaluation method provided by the invention comprehensively covers inter-bacterial interaction and flora structure information of the target community, can realize quantification of multi-dimensional microbial community data to a low-dimensional community level, and can intuitively and accurately evaluate the overall functionality and stability of the microbial community.

Description

Technical Field

[0001] The present invention relates to a community level quantification and evaluation method, in particular to a community level quantification and evaluation method based on interbacterial interaction and bacterial community structure, and belongs to the field of bioinformatics and biotechnology. Background Art

[0002] The structure and function of microbial communities are directly related to the health and stability of ecosystems. Assessing the health and functional status of microbial communities is of great significance to ecological protection, environmental governance, and agricultural production. Sequencing technology requires the analysis of microbial communities to process massive amounts of data, but the complex multidimensionality of microbial communities makes it impossible to directly bring them into big data models. Traditional dimensionality reduction methods, such as non-metric multidimensional scaling analysis and principal component analysis, lack a clear biological explanation for their results, and therefore cannot parse the pros and cons of communities. Other microbial community evaluation indicators, such as diversity indices, usually only focus on the number and uniformity of species. Among them, the most commonly used α diversity cannot fully capture the interactions between species and the influence of species functions on the overall community; while β diversity cannot achieve the quantification and evaluation of the community. Existing evaluation methods or evaluation indicators cannot simultaneously capture the synergistic effects, competitive relationships within the community, and the response of the community to the external environment, and therefore cannot accurately reflect the overall health of the microbial community.

[0003] Microbial community assessment methods based on network analysis can identify positive or negative interactions by constructing association networks between species, and then evaluate the quality of the community through topological coefficients that meet biological interpretability, such as network density, number of nodes, and number of edges. However, these indicators do not fully consider the different effects of species abundance on community function. High-abundance species have a higher degree of impact on the community, while low-abundance species have a relatively low impact on the community. Therefore, relying solely on network topology to evaluate microbial communities actually ignores the contribution of important species to community function and stability, and also makes it impossible to accurately reflect the overall functionality and stability of the microbial community. Summary of the invention

[0004] Objective of the invention: The objective of the present invention is to provide a method for accurately evaluating the stability and functionality of microbial communities.

[0005] Technical solution: The present invention provides a community-level quantification and evaluation method based on interbacterial interactions and bacterial community structure, comprising the following steps: The first step is to obtain microbial community information and construct a biological association network, calculate the inter-species interaction coefficients, perform significance analysis on the network construction results, and screen out highly correlated inter-species interaction coefficients; The second step is to calculate the average of the highly correlated bacterial interaction coefficients of each species to obtain the species average bacterial interaction coefficient matrix; The third step is to use the relative abundance table of species as a weight and multiply it with the average bacterial interaction coefficient matrix of species to obtain the positive and negative association strengths of species. In the fourth step, the positive association strength and negative association strength of all species in the microbial community are summed up respectively to obtain the positive association strength and negative association strength of the microbial community, and then the sum is continued to obtain the total association strength of the community.

[0006] In the first step, screening highly correlated interbacterial interaction coefficients can avoid multicollinearity. The average interbacterial interaction coefficient of the species obtained in the second step can indicate the ecological niche of the species in the co-occurrence network. In the third step, species abundance is incorporated into the evaluation process, combining interbacterial interactions with bacterial community structure, and fully considering the different effects of species abundance on community function, thereby accurately indicating the contribution of different species to the total association strength of the community. In the fourth step, the positive and negative association strengths of the species are summed up to first obtain the positive and negative association strengths of the community, and then the sum is obtained to obtain the total association strength of the community, realizing community-level quantification.

[0007] The total community association strength obtained by the above community-level quantification and evaluation method can be regarded as a new microbial community evaluation indicator, which the inventors named the community synergy index (CSI). The larger the CSI index, the stronger the total positive association strength of the community, and the better the stability and functionality of the microbial community.

[0008] Preferably, in the first step, the method for constructing the biological association network includes the Spiec-Easi method, the Pearson correlation coefficient or the Spearman correlation coefficient. The Spiec-Easi method is the sparse inverse covariance estimation and statistical inference of ecological association.

[0009] Preferably, in the first step, the significance analysis method includes the iDIRECT method or the random matrix theory RMT method. The iDIRECT method can remove indirect relationships in network analysis. The random matrix theory RMT method can remove non-significant relationships in a network obtained based on correlation analysis.

[0010] Preferably, in the first step, the microbial community information can be obtained through open data sources or sequencing technology.

[0011] Preferably, the sequencing technology is 16S rRNA amplicon sequencing. The sequencing technology may also be other sequencing schemes known in the art.

[0012] Preferably, the 16S rRNA amplicon sequencing is used to determine the V3-V4 region of 16S rRNA, because the primer universality of 16S V4 is relatively the highest among all variable regions, and it is also the detection region widely recognized and used in current research.

[0013] Preferably, in the first step, the microbial community information includes species type and abundance.

[0014] Preferably, in the first step, the species in the microbial community include 16S rRNA-based taxonomic operational units (OTUs) and amplicon sequence variants (ASVs), as well as metagenomic assembled genomes (MAGs) from metagenomics. The species in the microbial community include but are not limited to the aforementioned three sources.

[0015] Preferably, in the first step, the microbial community is a biological treatment system.

[0016] Preferably, the biological treatment system is a sewage biological treatment system.

[0017] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the community-level quantification and evaluation method provided by the present invention comprehensively covers the interbacterial interactions and community structure information of the target community, can realize the quantification of multi-dimensional microbial community data to a low-dimensional community level, and can intuitively and accurately evaluate the overall functionality and stability of the microbial community. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the process of the community level quantification and evaluation method provided by the present invention; Figure 2 is the CSI index range of the key bacterial flora for SMX degradation in different biochemical pools in the embodiments of the present invention; Figure 3 This is the correlation between the CSI index and water quality characteristics and community information in the embodiment of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below in conjunction with the accompanying drawings. Example

[0020] This embodiment provides a community-level quantification and evaluation method based on interbacterial interactions and community structure. The microbial community was collected from the sewage biological treatment system, and its process parameters, water samples and sludge samples were collected from various biochemical pools in 177 sewage treatment plants across the country, including anaerobic pools, anoxic pools and aerobic pools, with a total of 1068 water samples and sludge samples.

[0021] The community-level quantification and evaluation method based on interbacterial interactions and bacterial community structure is as follows Figure 1As shown, the specific steps include: The first step is to obtain microbial community information and construct a biological association network, calculate the interbacterial interaction coefficients between species, conduct a significance analysis on the network construction results, and screen out highly correlated interbacterial interaction coefficients.

[0022] Microbial community information was derived from sequencing and literature research. The sludge samples were sequenced for 16S rRNA amplicon to determine the bacterial 16S rRNA V3-V4 region. The 16S primers used for sequencing were: 341F (CCTAYGGGRBGCASCAG); 806R (GGACTACNNGGGTATCTAAT). The species in the microbial community were classified based on the 16S rRNA operational units (OTUs). The sulfamethoxazole (SMX) degradation community composed of functional species and key species was determined through literature research and other methods.

[0023] Spiec-Easi was used to calculate the biological association network of the target water plant. iDIRECT was used to remove indirect relationships in the network analysis and screen out highly correlated bacterial interaction coefficients.

[0024] In the second step, the average value of the highly correlated interbacterial interaction coefficients of each species was calculated to obtain the species average interbacterial interaction coefficient matrix.

[0025] In the third step, the species relative abundance table was used as a weight to multiply the species average interbacterial interaction coefficient matrix to obtain the positive and negative association intensities of the species.

[0026] In the fourth step, the positive association intensity and negative association intensity of all species in the microbial community are summed up respectively to obtain the positive association intensity and negative association intensity of the microbial community. The sum is continued to obtain the total association intensity of the community, that is, the community synergy index CSI.

[0027] like Figure 2 As shown, the distribution of CSI indexes in different biochemical pools is visualized. There is no significant difference in the CSI index of the target communities in all biochemical pools, indicating that the variable range of CSI remains consistent in different habitats.

[0028] like Figure 3As shown in the figure, the CSI index was correlated with water quality characteristics and operating parameters, conventional evaluation indicators of microbial communities, and sulfamethoxazole concentration, including pH, dissolved oxygen (DO), temperature (T), sludge age (SRT), hydraulic retention time (HRT), total organic carbon (TOC), α diversity, β diversity, total nitrogen (TN), NH4+, NO3-, NO2-, and total phosphorus (TP). The CSI index was extremely significantly correlated with α diversity and β diversity (p<0.001), and was extremely significantly positively correlated with SMX concentration (p<0.001). The correlation results showed that the CSI index was extremely significantly correlated with the microbial community, proving that the CSI index can represent and quantify the microbial community to a certain extent. And because the functional species were taken into account, the CSI index of the target microbial community was highly positively correlated with the SMX concentration, indicating that the higher the CSI index, the stronger the potential degradation capacity of SMX of the target community. Therefore, the CSI index can be used as a quantitative and evaluation indicator of the target community, and it can intuitively and accurately reflect the overall functionality and stability of the microbial community.

Claims

1. A community-level quantification and evaluation method based on interbacterial interactions and bacterial community structure, It is characterized in that The following steps are involved: The first step is to obtain microbial community information and construct a biological association network, calculate the inter-species interaction coefficients, perform significance analysis on the network construction results, and screen out highly correlated inter-species interaction coefficients; The second step is to calculate the average of the highly correlated bacterial interaction coefficients of each species to obtain the species average bacterial interaction coefficient matrix; The third step is to use the relative abundance table of species as a weight and multiply it with the average bacterial interaction coefficient matrix of species to obtain the positive and negative association strengths of species. In the fourth step, the positive association strength and negative association strength of all species in the microbial community are summed up respectively to obtain the positive association strength and negative association strength of the microbial community, and then the sum is continued to obtain the total association strength of the community.

2. The community level quantification and assessment method according to claim 1, It is characterized in that In the first step, the biological association network is constructed by a method including the Spiec-Easi method, the Pearson correlation coefficient or the Spearman correlation coefficient.

3. The community level quantification and assessment method according to claim 1, It is characterized in that In the first step, the significance analysis method includes the iDIRECT method or the random matrix theory RMT method.

4. The community level quantification and assessment method according to claim 1, It is characterized in that In the first step, the microbial community information can be obtained through open data sources or sequencing technology.

5. The community level quantification and assessment method according to claim 4, It is characterized in that The sequencing technology is 16SrRNA amplicon sequencing.

6. The community level quantification and assessment method according to claim 5, It is characterized in that The 16S rRNA amplicon sequencing measures the V3-V4 region of 16S rRNA.

7. The community level quantification and assessment method according to claim 1, It is characterized in that In the first step, the microbial community information includes species types and abundances.

8. The community level quantification and assessment method according to claim 7, It is characterized in that The species include taxonomic operational units based on 16S rRNA and amplicon sequence variants, as well as metagenomic assembled genomes from metagenomics.

9. The community level quantification and assessment method according to claim 1, It is characterized in that In the first step, the microbial community is a biological treatment system.

10. The community level quantification and assessment method according to claim 9, It is characterized in that The biological treatment system is a sewage biological treatment system.

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