CN116758976B - Identification method for quantitative contribution of functional microorganisms - Google Patents
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Abstract
The invention relates to a method for identifying quantitative contribution of functional microorganisms, which is used for solving the problem that the contribution of functional microorganisms at different stages in the existing sewage treatment system cannot be effectively identified. The identification method of the invention can be at least used for quantitatively contributing identification of functional microorganisms in a sewage biological treatment system, and comprises the following steps: s100: acquiring absolute abundance and functional gene quantity of different microorganisms in the sewage biological treatment process; s200: the correlation analysis of different genus and species microorganisms and different functional genes is carried out, a co-occurrence network is constructed, and key host microorganisms of the functional genes are determined; s300: constructing a stepwise regression quantitative statistical model by utilizing the functional conversion rates of different sewage biological treatment stages and the absolute abundance values of the functional genes of different stages, and calculating the weights of the functional gene variables in the functional conversion rate relation; s400: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to a method for identifying quantitative contribution of functional microorganisms.
Background
Biological sewage treatment technology is widely used in urban sewage treatment plants, rural decentralized sewage treatment facilities and industrial wastewater treatment units, for example, wherein a microbial population participating in nitrogen metabolism in a sewage treatment system plays a crucial role in sewage biological denitrification effect, denitrification characteristics and metabolic pathways of microorganisms are determined by self genes, and external environmental stimulation can influence the expression of nitrogen functional genes and the activity of related enzymes, so that different denitrification effects are generated. The prior art can not realize effective identification of the quantitative contribution of the microorganisms with denitrification function at different stages of the sewage treatment system.
Disclosure of Invention
In view of the above analysis, the present invention is directed to a method for identifying quantitative contributions of functional microorganisms, so as to solve the problem that the functional microorganism contributions at different stages in the existing sewage treatment system cannot be effectively identified.
The invention provides a method for identifying quantitative contributions of functional microorganisms, which can be at least used for identifying the quantitative contributions of the functional microorganisms in a sewage biological treatment system, and comprises the following steps:
s100: acquiring absolute abundance and functional gene quantity of different microorganisms in the sewage biological treatment process;
s200: the correlation analysis of different genus and species microorganisms and different functional genes is carried out, a co-occurrence network is constructed, and key host microorganisms of the functional genes are determined;
s300: constructing a stepwise regression quantitative statistical model by utilizing the functional conversion rates of different sewage biological treatment stages and the absolute abundance values of the functional genes of different stages, and calculating the weights of the functional gene variables in the functional conversion rate relation;
s400: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
Further, in the step S100, the absolute abundance and the number of functional genes of different microorganisms in the treatment process of the sewage biological treatment system are obtained by using a high-throughput absolute quantitative sequencing method.
Further, the high throughput absolute quantitative sequencing method comprises the following steps:
s101: extracting DNA in a microorganism sample in a sewage biological treatment system, and adding Spike-in DNA with known copy number into the extracted DNA;
s102: carrying out PCR amplification on the microorganism sample DNA and the Spike-in DNA together, constructing a 16SrDNA/ITS library, carrying out high-throughput second-generation sequencing, and drawing a standard curve of the Spike-in DNA sequence according to an internal standard sequence;
s103: and (3) calculating the absolute copy number of the species and the genes in the microbial sample according to the standard curve of the Spike-in DNA sequence obtained in the step S102.
Further, in the step S101, the number of the added Spike-in DNA sequences is not less than 6, and the concentration gradient is not less than 3.
Further, in the step S200, a non-parameter correlation method of Spearman grade correlation coefficient is adopted, an absolute copy number correlation analysis model of microorganisms at a genus level and an absolute copy number correlation analysis model of functional genes are established, a relationship group with significant levels of microorganisms and functional genes is screened out, a co-occurrence network analysis is performed on the relationship group with significant levels of microorganisms and functional genes, and key host microorganisms are determined.
Further, step S300 is described, in which according to the functional effects in different operation stages in the sewage biological treatment system, the functional conversion rates of different functional metabolic types are calculated, the functional genes directly participating in different functional metabolic types are set as candidate independent variables, the functional conversion rates are set as dependent variables, a stepwise regression quantitative statistical model is adopted to establish a quantitative response relationship between the functional conversion rates and the functional genes, and functional conversion rates and functional gene equations of different functional metabolic types are constructed, wherein the functional genes in the equations are final independent variables.
Further, the step S400 includes the steps of:
s401: calculating the weight of an independent variable in an equation according to conversion rates and functional gene equations of different functional metabolic types constructed in the step S300;
s402: obtaining key host microorganisms carrying independent variable genes in different form transformation equations according to the co-occurrence network analysis result of the step S200;
s403: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
Further, the identification method can be at least used for identifying the quantitative contribution of denitrifying microorganisms in a sewage biological treatment system, and comprises the following steps:
s100: acquiring absolute abundance of different microorganisms and nitrogen metabolism gene quantity of a sewage biological treatment system in the treatment process;
s200: analyzing the relativity of microorganisms of different genus and species and different nitrogen metabolism genes, constructing a co-occurrence network, and determining key host microorganisms of the nitrogen metabolism genes;
s300: constructing a stepwise regression quantitative statistical model by utilizing nitrogen conversion rates of different sewage biological treatment stages and absolute abundance values of nitrogen metabolism genes of different stages, and calculating weights of nitrogen metabolism gene variables in a nitrogen conversion rate relation;
s400: and determining the quantitative contribution value of the microorganism on denitrification in the sewage biological treatment system by combining the weights of the key host microorganism and the nitrogen metabolism genes.
Further, in the step S100, the absolute abundance of different microorganisms and the number of nitrogen metabolism genes in the treatment process of the sewage biological treatment system are obtained by adopting a high-throughput absolute quantitative sequencing technology.
Further, in the step S200, a non-parametric correlation method of Spearman grade correlation coefficient is used to establish an absolute copy number of the microorganism at the genus level and an absolute copy number correlation analysis model of the nitrogen metabolism gene, a relationship group with significant level is screened out, and co-occurrence network analysis is performed on the relationship group with significant level to determine the key host microorganism
When a non-parametric correlation model constructed using a non-linear Spearman's rank correlation coefficient is used, the condition setting for screening a set of relationships of microorganisms having significant levels to nitrogen metabolism genes should be atpAt a level of less than or equal to 0.01, and satisfies the absolute value SRCC of the correlation coefficient of more than or equal to 0.95 or less than or equal to-0.95.
Compared with the prior art, the invention has at least one of the following advantages: the invention can achieve stable and efficient denitrification effect under the condition of fluctuation of sewage water quality and water quantity at least based on key denitrification microorganism identification, denitrification function reinforcement and operation regulation and control, is a sewage treatment technology capable of effectively reducing the operation cost of sewage biological treatment, reducing operation maintenance difficulty and having wide application prospect.
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a flow chart of a method for identifying the quantitative contribution of a functional microorganism in an embodiment;
FIG. 2 is a graph of the labeling of internal standard Spike-in DNA sequences of an example of an embodiment;
FIG. 3 is a graph showing the difference in absolute copy number ratio of nitrogen functional genes of nitrogen metabolic pathways at different stages in an embodiment;
FIG. 4 is a graph showing the co-occurrence network of nitrogen metabolism genes and dominant bacteria at example stage I in the detailed description;
FIG. 5 is a graph showing the co-occurrence network of nitrogen metabolism genes and dominant bacteria in example stage II of the present embodiment.
Detailed Description
The following detailed description of preferred embodiments of the invention is made in connection with the accompanying drawings, which form a part hereof, and together with the description of the embodiments of the invention, are used to explain the principles of the invention and are not intended to limit the scope of the invention.
In describing embodiments of the present invention, it should be noted that, unless explicitly stated and limited otherwise, the term "coupled" should be interpreted broadly, for example, as being fixedly coupled, as being detachably coupled, as being integrally coupled, as being mechanically coupled, as being electrically coupled, as being directly coupled, as being indirectly coupled via an intermediate medium. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The terms "top," "bottom," "above … …," "below," and "on … …" are used throughout the description to refer to the relative positions of components of the device, such as the relative positions of the top and bottom substrates inside the device. It will be appreciated that the devices are versatile, irrespective of their orientation in space.
The working surface of the invention can be a plane or a curved surface, and can be inclined or horizontal. For convenience of explanation, the embodiments of the present invention are placed on a horizontal plane and used on the horizontal plane, and thus "up and down" and "up and down" are defined.
The modern molecular biology technology adopts a non-culture mode to carry out gene sequencing analysis on a microorganism sample, such as a fluorescent quantitative polymerase chain reaction (Polymerase chainreaction, PCR) technology, an amplicon sequencing technology, a metagenome sequencing technology and the like, so that the accuracy of biological analysis is improved, the species composition of a microbial community, the evolution relationship among species and the diversity characteristics are widened, the species relative abundance change of microorganisms of a single sample can be obtained, and the absolute abundance and quantity information of species in different microorganism samples are lacked. Therefore, depending on biological data obtained by sequencing by conventional molecular biological technology, only the "relative proportion" of the composition in the microbiota can be seen, but the absolute quantity of the "true" information is not contained, and the effective distinction of the quantitative contribution of functional microorganisms (such as microorganisms with denitrification function) at different stages of the sewage biological treatment system cannot be realized.
With the development of amplicon technology, absolute quantitative sequencing technology based on bacterial 16SrDNA region and fungal ITS can realize the quantification of absolute abundance and absolute number of species in different biological samples by adding a known standard curve, and provides technical support for the precise identification of functional microorganisms (especially microorganisms with pollutant removal and conversion functions, such as denitrifying microorganisms) in a sewage biological treatment system, so that the invention develops a method for identifying the contribution of the functional microorganisms in the sewage biological treatment system on the premise of a high-throughput absolute quantitative sequencing technical means, realizes the precise and rapid identification of key functional microorganisms, and can achieve the precise strengthening and precise regulation of certain functions (the functions are matched with the functions possessed by the functional microorganisms, such as the denitrifying microorganisms possess the denitrification functions) through the regulation and control of the key functional microorganisms, especially under extreme conditions (such as low temperature, impact load and the like).
In one embodiment of the invention, a method for identifying the quantitative contribution of functional microorganisms is disclosed, which can be used for identifying the quantitative contribution of functional microorganisms in a sewage biological treatment system, and comprises the following steps:
s100: acquiring absolute abundance of different microorganisms and functional gene quantity of a sewage biological treatment system in the treatment process;
s200: analyzing the relativity of microorganisms of different genus and species and different functional genes, constructing a co-occurrence network, and determining key host microorganisms (namely functional microorganisms) of the functional genes;
s300: constructing a stepwise regression quantitative statistical model by utilizing the functional conversion rates of different sewage biological treatment stages and the absolute abundance values of the functional genes of different stages, and calculating the weights of the functional gene variables in the functional conversion rate relation;
s400: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
The function may be a function that any microorganism such as denitrification, dephosphorization, desulfurization and the like can be realized in sewage, and the functional gene corresponding thereto is a gene having a function such as denitrification, dephosphorization, desulfurization and the like, and the functional microorganism corresponding thereto is a microorganism having a function such as denitrification, dephosphorization, desulfurization and the like, and similarly, the functional conversion rate corresponding thereto is a conversion rate of nitrogen, phosphorus, sulfur and the like.
The identification method for quantitatively contribution of the functional microorganisms can accurately and rapidly identify the key functional microorganisms with certain functions at different stages in the sewage biological treatment system, so that the accurate strengthening and fine regulation of certain functions can be rapidly achieved under extreme conditions.
Illustratively, the identification method of the present invention can be used at least for quantitative contribution identification of denitrifying microorganisms in a sewage biological treatment system, where the identification method comprises the steps of:
s100: acquiring absolute abundance of different microorganisms and nitrogen metabolism gene quantity of a sewage biological treatment system in the treatment process;
s200: analyzing the relativity of microorganisms of different genus and species and different nitrogen metabolism genes, constructing a co-occurrence network, and determining key host microorganisms of the nitrogen metabolism genes;
s300: constructing a stepwise regression quantitative statistical model by utilizing nitrogen conversion rates of different sewage biological treatment stages and absolute abundance values of nitrogen metabolism genes of different stages, and calculating weights of nitrogen metabolism gene variables in a nitrogen conversion rate relation;
s400: and determining the quantitative contribution value of the microorganism on denitrification in the sewage biological treatment system by combining the weights of the key host microorganism and the nitrogen metabolism genes.
The step S100:
preferably, the absolute abundance of different microorganisms and the number of functional genes in the sewage biological treatment system during the treatment process are obtained by using a high-throughput absolute quantitative sequencing method. Illustratively, the absolute abundance of different microorganisms and the number of nitrogen metabolism genes in the sewage biological treatment system during treatment are obtained by using a high throughput absolute quantitative sequencing method.
The high throughput absolute quantitative sequencing method comprises the following steps:
s101: extracting DNA in a microorganism sample in a sewage biological treatment system, and adding Spike-in DNA (internal standard sequence DNA) with known copy number into the extracted DNA;
s102: carrying out PCR amplification on the microorganism sample DNA and the internal standard sequence DNA together, constructing a 16SrDNA (bacteria)/ITS (archaea) library, carrying out high-throughput second-generation sequencing, and drawing a standard curve of Spike-in DNA sequence according to the internal standard sequence;
s103: and (3) calculating the absolute copy number of the species and the genes in the microbial sample according to the standard curve of the Spike-in DNA sequence obtained in the step S102.
To obtain a standard curve of Spike-in DNA sequence, the number of Spike-in DNA sequences to be added should not be less than 6, and the concentration gradient should not be less than 3 (e.g. 10 3 、10 4 、10 5 copy/ng DNA) to ensure that absolute copy numbers of species and functional genes in the microbial community in microbial film samples in the wastewater biological treatment system can be calculated based on the standard curve.
And (3) according to the genome and metabolic pathway database of the step S100, determining functional metabolic pathways at different stages in the sewage biological treatment system and absolute copy numbers of corresponding functional genes. Illustratively, according to the genome and metabolic pathway database of the step S100, determining the absolute copy numbers of the nitrogen metabolic pathways and the corresponding nitrogen functional genes at different stages in the sewage biological treatment system, wherein the nitrogen metabolic pathways mainly comprise: a nitrification pathway, a denitrification pathway, a catabolic nitrate reduction pathway, an assimilation nitrate reduction pathway, an ammonia nitrogen assimilation to organic nitrogen pathway, and the like.
The step S200:
a non-parametric correlation method (Spearman 'rank correlation coefficients, SRCC) of the Spearman's rank correlation coefficient was used to model the absolute copy number of the microorganism at the genus level and the absolute copy number of the functional gene, and a set of relationships with significant levels of the microorganism to the functional gene was screened for co-occurrence network analysis of the set of relationships with significant levels to determine the key host microorganism (i.e., where the microorganism with significant relationship to the functional gene was defined as the key host microorganism).
Illustratively, a nonparametric correlation method using Spearman's rank correlation coefficients is used to model absolute copy number at the genus level and absolute copy number of nitrogen metabolism genes for the analysis, a set of relationships with significant levels of microorganisms to nitrogen metabolism genes is screened for co-occurrence network analysis of a set of relationships with significant levels, and key host microorganisms are determined (i.e., where microorganisms with significant relationships to nitrogen metabolism genes are defined as key host microorganisms).
Preferably, a non-parametric correlation model is constructed using non-linear Spearman rank correlation coefficients, and the set of relationships and conditions for screening microorganisms with significant levels of functional genes are set forth inpAt the level of less than or equal to 0.01, and satisfies the correlation coefficient SRCC of more than or equal to 0.95 or less than or equal to minus 0.95, namely the absolute value of the correlation coefficient SRCC is more than or equal to 0.95. On the one hand, a non-parametric correlation model constructed by a non-linear Spearman rank correlation coefficient is closer to the actual situation; on the other hand, so arrangedp、The SRCC value is used for screening out core functional bacteria, so that the quantitative contribution is more favorable to be obtained, and the accuracy is ensured.
Illustratively, the condition setting for screening the set of relationships between microorganisms having significant levels and nitrogen metabolism genes is set to be based on the non-parametric correlation model constructed from the non-linear Spearman' S rank correlation coefficient in the step S200pAt a level of less than or equal to 0.01, and satisfies the absolute value SRCC of the correlation coefficient of more than or equal to 0.95 or less than or equal to-0.95.
The step 300:
according to functional effects in different operation stages in a sewage biological treatment system, calculating functional conversion rates of different functional metabolic types, setting functional genes directly participating in the different functional metabolic types as candidate independent variables, setting the functional conversion rates as dependent variables, establishing quantitative response relation between the functional conversion rates and the functional genes by adopting a stepwise regression quantitative statistical model, and constructing functional conversion rates and functional gene equations of the different functional metabolic types, wherein the functional genes in the equations are final independent variables.
The functional conversion rate may be obtained from a reactor of a wastewater biological treatment system.
Taking a sewage biological treatment system for denitrification treatment as an example, according to denitrification effects in different operation stages in the sewage biological treatment system, calculating nitrogen conversion rates of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen, setting nitrogen metabolism genes directly involved in conversion of the ammonia nitrogen, the nitrite nitrogen, the nitrate nitrogen and the total nitrogen as candidate independent variables, setting the nitrogen conversion rates as dependent variables, establishing a quantitative response relation between the nitrogen conversion rates and the nitrogen metabolism genes by adopting a stepwise regression quantitative statistical model, and constructing an equation of the ammonia nitrogen, the nitrite nitrogen, the nitrate nitrogen, the total nitrogen conversion rates and the nitrogen metabolism genes, wherein the nitrogen metabolism genes in the equation are final independent variables.
The step 400 includes the steps of:
s401: and calculating the weight of the independent variable in the equation according to the conversion rate and the functional gene equation constructed in the step S300. Illustratively, the weights of the independent variables in the equation are calculated from the coefficients of the independent variables in the conversion equations of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, and total nitrogen.
S402: and (3) obtaining key host microorganisms carrying independent variable genes in different form conversion equations according to the co-occurrence network analysis result of the step (S200), wherein the weight of the independent variable is the function contribution rate of the functional microorganisms participating in different forms. Illustratively, according to the co-occurrence network analysis result of S200, the key host microorganism carrying the independent variable genes in the conversion equation of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen is obtained, and the weight of the independent variable is the contribution rate of the denitrifying microorganism in participating in the conversion of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen.
S403: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
Application of the identification method of the present invention to the identification of quantitative contributions of denitrifying microorganisms in a biological treatment system of sewage, in particular, comprising the following steps:
s100: acquiring absolute abundance of different microorganisms and nitrogen metabolism gene quantity of a sewage biological treatment system in the treatment process;
specifically, the absolute abundance of different microorganisms and the number of nitrogen metabolism genes in the sewage biological treatment system during the treatment process are obtained by adopting a high-throughput absolute quantitative sequencing technology, and the step S100 comprises the following steps:
s101: extracting DNA in a microorganism sample in a sewage biological treatment system, and adding internal standard sequence DNA (Spike-in DNA) with known copy number into the extracted DNA;
s102: carrying out PCR amplification on the microorganism sample DNA and the internal standard sequence DNA together, constructing a 16SrDNA (bacteria)/ITS (archaea) library, carrying out high-throughput second-generation sequencing, and drawing a standard curve according to the internal standard sequence (shown in figure 1);
s103: and calculating the absolute copy number (copy/ng DNA or copy/g sample) of the species and the genes in the microbial sample through a standard curve of the Spike-in DNA sequence, and obtaining the absolute copy number of the species and the genes in the microbial sample. Further analysis, the changes in the relative abundance of nitrogen functional bacteria are shown in tables 1 and 2 to give the overall ratio profile for the different bacteria.
In table 1 and table 2, the genus bacteria have chinese names, and the genus bacteria have no exact chinese names, and are not shown in brackets.
TABLE 1 absolute copy number of level I bacteria in the stage I of a biological wastewater treatment System
TABLE 2 absolute copy number of stage II level bacteria in a biological wastewater treatment System
Changes in the relative abundance of nitrifying bacteria were first analyzed. In stage I, the main ammonia oxidizing bacteria in samples 2-IAOB) And nitrite oxidation finesBacteriaNOB) Is thatNitrosomonas(Nitromonas) andNitrospira(Nitrospira) with absolute copy number of 0.02% and 0.08%, respectively, in sample 3-INitrosomonasAndNitrospirathe absolute copy number of (C) was slightly higher than the ratio of 0.05% and 0.11%, respectively, in sample 3-IAOBAndNOBthe ratio of absolute copy numbers of (c) is less than 0.05%. In stage II, sample 3-IINitrosomonasAndNitrospirathe ratio of absolute copy number of (c) was increased to 0.13% and 2.03%, respectively. In another formAOB，NitrosospiraThe ratio of the absolute copy number of the (nitrosaminium) in the phase I is less than 0.01%, and the ratio of the absolute copy number in the samples 1-II, 2-II and 3-II in the phase II is respectively increased to 0.23%,0.02% and 2.33%, so that the (nitrosaminium) is the dominant autotrophic nitrifying bacteria.
And secondly, analyzing the change of the relative abundance of denitrifying bacteria. Wherein the dominant heterotrophic nitrifying bacteria of samples 1-I, 2-I and 3-I in stage I mainly compriseRhodanobacter(Luo He Bacillus),Acidovorax(acidophilic bacteria),Riemerella(Richbacillus) and its useRudaeaThe total ratio was 36.4%, 17.9% and 17.8%, respectively, whereinRhodanobacterThe ratio of absolute copy number in sample 2-I was 31.6%, which is a dominant denitrifying bacterium capable of utilizing NO under aerobic conditions 3 - -N or NO 2 - N performs the denitrification process as an electron acceptor.
AcidovoraxThe absolute copy number ratios in samples 2-I and 3-I were 12.54% and 14.54%, respectively, and were typical heterotrophic nitrifying bacteriaPseudoxanthomonas(Pseudomonas) andBrevundimonas(Brevibacterium) is a typical facultative anaerobic denitrifying bacterium with absolute copy numbers of 0.08% -2.81% and 0.03% -1.29%, respectively, wherePseudoxanthomonasAndBrevundimonasthe enrichment was higher in samples 2-I and 3-I.
In stage II, the heterotrophic nitrifying bacteria are markedly increased in species, includingRudaea、Comamonas(Comamonas sp),Chryseobacterium(Staphylococcus aureus),Dechloromonas(Dechloromonad)）、RhodanobacterAndAcidovoraxwhereinRudaeaThe ratio of absolute copy number in biological samples 2-II and 3-II increases significantly from 3.77% and 1.57% for stage I to 8.91% and 24.55% for stage II, respectively,Comamonasthe enrichment degree is obviously improved, the ratio of absolute copy number is 1.58% -23.09%,Dechloromonasthe absolute copy number duty cycle in phase I is less than 0.01%, while in phase II,Dechloromonasthe absolute copy number ratios in samples 1-II, samples 2-II and samples 3-II were significantly increased to 2.05%,0.49% and 1.26%, respectively, compared toComamonasHas obvious positive correlation with the relative abundance ofR 2 =0.973，p=0.0021), explaining low temperatureDechloromonasAndComamonasfor NH 4 + -N and NO 3 - The removal of N has a synergistic relationship.
The differences in the different nitrogen metabolic pathways were then analyzed. According to PIRUSt study method, 17 nitrogen functional genes were selected, involving a nitrification pathway, a denitrification pathway, a reduction of catabolized nitrate to ammonia nitrogen (DNRA) pathway, a Nitrate Assimilation (NA) pathway, and an ammonia nitrogen Assimilation (AON) pathway, as shown in FIG. 2.
At NH 4 + In the course of N conversion, the nitration pathway, DNRA pathway and AON pathway are involved, wherein functional genes encoding ammonia monooxygenase and hydroxylamine dehydrogenase,amoABCandhaothe relative abundance in phase II is generally increasing compared to phase I,amoABCandhaothe average relative abundance of the genes increased from 0.04% and 0.03% for phase I to 1.67% and 0.75% for phase II, which is comparable to that of phase IIAOBAndNOBthe trend of the relative abundance changes is closely related.
In NO 3 - In the course of N reduction, a number of nitrogen metabolic pathways are involved, which are key steps in nitrogen conversion, whereinnapAAndnarGgenes capable of encoding respiratory and periplasmic nitrate reductase, respectively, for NO 3 - Conversion of N to NO 2 - -N，napAAndnarGthe total relative abundance of the type of genes in phase I and phase II was 8.92% and 9.24%, respectively, the relative abundance of the two-phase genesNo significant difference, whereinnarGThe type gene is very sensitive to DO concentration, whereasnapAGenes of this type can complete the process of encoding respiratory nitrate reductase under aerobic conditions.
In the NA pathway, the number of molecules involved in the NA pathway,nirAthe average relative abundance of the genes increased from 0.39% for phase I to 1.94% for phase II, whilenirAThe gene can promote denitrification process under the condition of sufficient carbon source, which shows that the gene is carried in the stage InirAThe heterotrophic nitrifying bacteria of the gene has more obvious survival advantage.
S200: analyzing the relativity of microorganisms of different genus and species and different nitrogen metabolism genes, constructing a co-occurrence network, and determining key host microorganisms of the nitrogen metabolism genes;
specifically, a non-parametric correlation method (Spearmans 'rank correlation coefficients, SRCC) of Spearman's rank correlation coefficient is used to build an absolute copy number at genus level and an absolute copy number correlation analysis model of nitrogen metabolism functional genes of microorganisms, a set of relationships with significant levels of microorganisms and nitrogen metabolism genes is screened to perform co-occurrence network analysis on the set of relationships with significant levels, wherein the microorganisms with significant relationships with nitrogen metabolism genes are defined as key host microorganisms, and the analysis process is as follows:
the relationship between nitrifying and denitrifying bacteria and the Nitrogen Functional Gene (NFG) subtype was further elucidated by co-occurrence network analysis, as shown in fig. 3 and 4, 29 and 36 genera were screened out as key hosts in stage I and stage II, respectively, and linked to the denitrifying NFG subtype by 54 sides and 73 sides, respectively.
In the phase I of the process,Rhodanobacter(Luo He Bacillus),Aquicella、Ottowia(thiootobacter oxydans) andAeromonas(Aeromonas) 4 denitrifying NFG subtypesnarG，narI，nirKAndnorB) Closely related, critical hosts for the NFG subtype, whereinRhodanobacterIs a dominant heterotrophic nitrifying bacterium, and is a core denitrifying bacterium in stage I. In comparison with stage I, the various autotrophic nitrifying bacteria in stage II,Nitrosospira(nitrosaminium),Nitrosomonas(Nitromonas) andNitrospira(nitrifying spiral) and nitrifying NFG subtypeamoABCAndhaogenes), whereinNitrosospiraProven to be portable in network analysisnapAAndnirKa key host for the gene. In the aspect of the denitrifying bacteria, the bacteria,Hydrogenophaga(hydrogenphaga),Dechloromonas(Dechloromonad)Comamonas(Comamonas)nosZ、narG、nasAAndgltDthe genes show a positive correlation, indicating that these three heterotrophic nitrifying bacteria can pass through the nitrogen metabolic pathway.
S300: constructing a stepwise regression quantitative statistical model by utilizing nitrogen conversion rates of different sewage biological treatment stages and absolute abundance values of nitrogen metabolism genes of different stages, and calculating weights of nitrogen metabolism gene variables in a nitrogen conversion rate relation;
specifically, according to denitrification effects in different operation stages of a sewage biological treatment system, nitrogen conversion rates of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen are calculated, nitrogen metabolism genes directly involved in conversion of the ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen are set as candidate independent variables, the nitrogen conversion rates are set as dependent variables, a quantitative response relationship between the nitrogen conversion rates and the nitrogen metabolism genes is established by adopting a stepwise regression quantitative statistical model, and ammonia nitrogen (NH 4 + -N), nitrite nitrogen (NO 2 - -N), nitrate nitrogen (NO 3 - -N) and the Total Nitrogen (TN) conversion rate with nitrogen metabolism gene equation, where the nitrogen metabolism gene is the final argument; the analytical procedure was as follows:
select and combine with NH 4 + -N、NO 3 - -N、NO 2 - -the relative abundance ratio of the N and TN conversion directly related Nitrogen Functional Gene (NFG) subtype is used as a variable, a stepwise regression quantitative statistical model is established, the linear relation between the NFG and the nitrogen conversion rate is calculated, and a nitrogen conversion rate equation is obtained:
(one)
(II)
(III)
(IV)
Equation (one) is an ammonia nitrogen conversion rate equation, equation (two) is a nitrite nitrogen conversion rate equation, equation (three) is a nitrate nitrogen conversion rate equation, and equation (four) is a total nitrogen conversion rate equation.
S400: and determining the quantitative contribution value of the microorganism on denitrification in the sewage biological treatment system by combining the weights of the key host microorganism and the nitrogen metabolism genes.
Specifically, the weights of the independent variables in the equation are calculated from the coefficients of the independent variables in the conversion equation of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen. And (3) obtaining key host microorganisms carrying independent variable genes in ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen conversion equations according to the co-occurrence network analysis result of the step (S200), wherein the weight of the independent variable is the contribution rate of the denitrification microorganisms in the conversion of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen and total nitrogen.
Illustratively, the argument in equation (one)Weight=100% ×3.89/(3.89+1.54+0.024) =71.3), independent variable +_in equation (three)>Weight=100% ×5.38/(5.38+0.48+0.87) =79.9), independent variable +_in equation (four)>Weight=100% ×5.93/(5.93+2.65+1.07+0.372) =59.1%.
First, the weights are calculated, at stepwise regressionIn quantitative statistical model, NH 4 + The conversion of-N is mainly determined bynirA/glnAAndamoABC/haodetermining, according to the coefficient weights of equation (one), the "two-step" ammonia nitrogen is assimilated into NH of the organic nitrogen (AON) pathway and the nitrification pathway 4 + The contribution of the N conversion was 71.3% and 28.2%, respectively. NO (NO) 3 - In the N conversion formula, two nitrogen metabolism pathways are mainly determined, namely denitrification pathwaynapAB/nirK) And DNRA pathwaynapAB/nirB) For NO 3 - The contribution of the reduction of N was 79.9% and 7.1%, respectively, i.e. NO 3 - The reduction of N is mainly determined by two actions, aerobic denitrification and autotrophic denitrification, respectively.
Next, the critical host bacteria are identified, in combination with the co-occurrence network analysis of step S200,AcidovoraxandRudaeais thatnirAAndglnAthe key host of the gene plays a key role in denitrification. Based on the co-occurrence network analysis,Acidovoraxis thatnapABThe key host of the gene is that,Nitrosospirais thatnapABAndnirKkey host of genes, descriptionAcidovoraxAndNitrosospiracan realize low-temperature NO through aerobic denitrification and autotrophic denitrification 3 - -N removal effect.
Finally, the percent denitrification contribution of the key host bacteria was determined, as seen from the above analysis,AcidovoraxandRudaeathe genus of bacterianirAAndglnAthe key host bacteria of the gene realizes heterotrophic nitrification process through a two-step AON approach to complete NH 4 + Direct removal of N, at NH 4 + 71.3% of the N conversion contribution.AcidovoraxAndNitrosospirais thatnapABAndnirKthe key host of the gene realizes low-temperature NO through aerobic denitrification and autotrophic denitrification respectively 3 - N-removal effect, NO 3 - 79.9% of the N conversion contribution.
In the sewage biological treatment system in the example, when the pollution source in the treated sewage is NH 4 + Mainly (e.g. with a proportion of more than 50%)When the strain is added, the following steps are preferred: 71.3% is addednirAAndglnAthe key host bacteria of the gene (illustratively, 71.3% addedAcidovoraxAnd/orRudaea，71.3% can be addedAcidovorax，Or 71.3% can be addedRudaeaOr is added intoAcidovoraxAndRudaeatotal 71.3%) and 28.2% were addedamoABCAndhaocritical host bacteria of the genes, residual additionnapABAndnrfAa key host bacterium for the gene. When the pollution source in the treated sewage is NO 3 - In the case of a dominant (for example, more than 50%) strain is added preferably: 79.9% is addednapABAndnirKthe key host bacteria of the gene (illustratively, 79.9% addedAcidovoraxAnd/orNitrosospira，79.9% can be addedAcidovorax，Or 79.9% may be addedNitrosospiraOr is added intoAcidovoraxAndNitrosospiratotal 79.9%) and 7.1% was addednapABAndnirBcritical host bacteria of the genes, residual additionhaoAndnxrAa key host bacterium for the gene.
It should be noted that, in the practical application, two to four groups of independent variables (functional genome) adopted by the equation in the example can be strictly or approximately added into corresponding key host bacteria according to respective weights, when more than six groups of independent variables are adopted to build a stepwise regression quantitative statistical model, it is unnecessary to add bacteria according to the weights in the equation, and more than 80% of bacteria can well maintain good pollutant degradation effect according to the weights in the equation.
Under the conditions that the conventional bioreactor cannot normally operate due to fluctuation of low temperature and water quality and water quantity, the quantitative contribution obtained according to the invention is added with the key functional flora in a fixed proportion, so that the quick recovery and the quick start of the primary flora can be ensured. In the embedding immobilization technology in sewage treatment, key host flora is embedded in a fixed proportion, which is favorable for maintaining the stable state of biological communities and keeping good pollutant degradation effect.
The method can be applied to bioreactors (active sludge systems, membrane biological reaction systems and the like), and when rural domestic sewage, bath wastewater, kitchen waste nondegradable wastewater and low-concentration ammonia nitrogen industrial wastewater with fluctuating water quality and water quantity are treated, the problems of poor treatment capacity, substandard effluent quality, incapability of stable operation and the like often exist.
In the starting stage of the operation of the bioreactor, the rapid starting of the bioreactor can be ensured; in the low-temperature operation stage of the bioreactor, the high-efficiency denitrification and dephosphorization removal effect of the bioreactor can be ensured; under the condition of fluctuation of the quality and quantity of the inflow water of the bioreactor, the stable removal performance of pollutants of the bioreactor can be ensured.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. A method for identifying a quantitative contribution of a functional microorganism, at least for use in a biological treatment system for sewage, the method comprising the steps of:
s100: acquiring absolute abundance and functional gene quantity of different microorganisms in the sewage biological treatment process;
s200: establishing a correlation analysis model between microorganisms of different genus species and different functional genes, screening out a relation group with a significant level of microorganisms and the functional genes, performing co-occurrence network analysis on the relation group with the significant level, and determining key host microorganisms of the functional genes;
s300: constructing stepwise regression quantitative statistical models by utilizing functional conversion rates and absolute abundance values of functional genes in different sewage biological treatment stages, setting functional genes directly participating in different functional metabolic types as candidate independent variables, setting the functional conversion rates as dependent variables, establishing quantitative response relations between the functional conversion rates and the functional genes by adopting the stepwise regression quantitative statistical models, constructing functional conversion rates and functional gene equations of different functional metabolic types, taking the functional genes in the equations as final independent variables, and calculating weights of the functional gene variables in the functional conversion rate relation;
s400: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
2. The method according to claim 1, wherein the step S100 is performed by using a high throughput absolute quantitative sequencing method to obtain absolute abundance and number of functional genes of different microorganisms in the sewage biological treatment system.
3. The identification method according to claim 2, characterized in that the high throughput absolute quantitative sequencing method comprises the steps of:
s101: extracting DNA in a microorganism sample in a sewage biological treatment system, and adding Spike-in DNA with known copy number into the extracted DNA;
s102: carrying out PCR amplification on the microorganism sample DNA and the Spike-in DNA together, constructing a 16SrDNA/ITS library, carrying out high-throughput second-generation sequencing, and drawing a standard curve of the Spike-in DNA sequence according to an internal standard sequence;
s103: and (3) calculating the absolute copy number of the species and the genes in the microbial sample according to the standard curve of the Spike-in DNA sequence obtained in the step S102.
4. The method according to claim 3, wherein the number of Spike-in DNA sequences added in the step S101 is not less than 6, and the concentration gradient is not less than 3.
5. The method according to claim 1, wherein the step S200 is performed by establishing a model of analysis of absolute copy number of microorganisms at genus level and absolute copy number of functional genes by non-parametric correlation method of Spearman' S rank correlation coefficient, screening out a set of relationship between microorganisms and functional genes having significant levels, and performing co-occurrence network analysis of the set of relationship having significant levels to determine key host microorganisms.
6. The method according to claim 1, wherein the step S300 is performed by calculating the functional conversion rates of different functional metabolic types according to the functional effects in different operation stages in the sewage biological treatment system, setting the functional genes directly participating in the different functional metabolic types as candidate independent variables, setting the functional conversion rates as dependent variables, establishing quantitative response relationship between the functional conversion rates and the functional genes by using a stepwise regression quantitative statistical model, and constructing equations of the functional conversion rates and the functional genes of the different functional metabolic types, wherein the functional genes in the equations are final independent variables.
7. The identification method according to claim 3, wherein the step S400 comprises the steps of:
s401: calculating the weight of an independent variable in an equation according to conversion rates and functional gene equations of different functional metabolic types constructed in the step S300;
s402: obtaining key host microorganisms carrying independent variable genes in different form transformation equations according to the co-occurrence network analysis result of the step S200;
s403: and by combining the weights of the key host microorganisms and the functional genes, the quantitative contribution value of the microorganisms in the sewage biological treatment system on the functions is defined.
8. The identification method according to claim 1, characterized in that it can be used at least for quantitative contribution identification of denitrifying microorganisms in a sewage biological treatment system, comprising the steps of:
s100: acquiring absolute abundance of different microorganisms and nitrogen metabolism gene quantity of a sewage biological treatment system in the treatment process;
s200: analyzing the relativity of microorganisms of different genus species and different nitrogen metabolism genes, constructing a co-occurrence network, and determining key host microorganisms of the nitrogen metabolism genes;
s300: constructing a stepwise regression quantitative statistical model by utilizing nitrogen conversion rates of different sewage biological treatment stages and absolute abundance values of nitrogen metabolism genes of different stages, and calculating weights of nitrogen metabolism gene variables in a nitrogen conversion rate relation;
s400: and determining the quantitative contribution value of the microorganism on denitrification in the sewage biological treatment system by combining the weights of the key host microorganism and the nitrogen metabolism genes.
9. The method according to claim 8, wherein the step S100 is performed by using a high throughput absolute quantitative sequencing technique to obtain absolute abundance of different microorganisms and nitrogen metabolism gene number during the treatment of the sewage biological treatment system.
10. The identification method according to claim 8, wherein in the step S200, a non-parametric correlation method of Spearman' S rank correlation coefficient is used to establish an absolute copy number at genus level and an absolute copy number correlation analysis model of nitrogen metabolism genes, and a co-occurrence network analysis is performed on a relationship group having significant levels by screening a relationship group having significant levels of microorganisms and nitrogen metabolism genes to determine key host microorganisms;
when a non-parametric correlation model constructed using a non-linear Spearman's rank correlation coefficient is used, the condition setting for screening a set of relationships of microorganisms having significant levels to nitrogen metabolism genes should be at
pAt a level of less than or equal to 0.01, and satisfies the absolute value SRCC of the correlation coefficient of more than or equal to 0.95 or less than or equal to-0.95.
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| 黑龙江省表层冻土细菌群落结构组成和功能特征;关健飞;曹阳;;生态学报(第14期);全文 * |
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