JP2020036579A - Microbial population identification method - Google Patents

Abstract

To provide a microbial population identification method capable of identifying a microbial population involved in the change of the amount of a particular substance in a biological treatment method using microorganisms.SOLUTION: A microbial population identification method characterized in identifying a microbial population involved in the change of the amount of a particular substance, comprises the following steps of: a specimen creation step of creating specimens by resampling from datasets obtained by measuring the rate of change of the amount of a particular substance and the contents of microbial populations in which microorganisms are classified in a microbial samples containing the particular substance and the microorganisms; a first selection step of selecting a microbial population corresponding to the independent variable selected based on the regression coefficient by performing a penal regression analysis the regression coefficient of which is reducible to 0, with the contents of the microbial populations as independent variables and the corresponding change rate of the amount of the particular substance as the dependent variable for the specimens created by the resampling; and an identification step of identifying the selected microbial population as the microbial population involved in the change of the amount of the particular substance.SELECTED DRAWING: Figure 5

Description

  The present invention relates to a method for specifying a group of microorganisms that specifies a group of microorganisms involved in a change in the amount of a specific substance.

  Activated sludge method (biological wastewater treatment using microorganisms) and the like are used to remove COD components from coke oven wastewater (amsible water). Microbial community analysis is commonly performed to understand and optimize biological wastewater treatment processes such as activated sludge.

  Conventionally, for screening main microorganisms involved in water treatment, studies using a culture method using an agar medium or the like have been attempted for many years. However, less than 1% of the microorganisms can be cultured, and screening of major microorganisms was almost impossible.

  On the other hand, an analysis for microorganisms that are known to affect wastewater treatment in advance has been attempted. Patent Literature 1 discloses a primer set for detecting the presence of a filamentous bacterium that causes solid-liquid separation failure among filamentous bacteria appearing in a biological treatment method. According to such a primer set, it is said that the presence of Sphaerotilus natans which causes a solid-liquid separation failure can be detected, and the occurrence of the solid-liquid separation failure can be suppressed.

Japanese Patent No. 5337643

  With the method described in Patent Document 1, it is possible to detect the presence of several types of microorganisms whose functions are already known. It is difficult to determine which wastewater components each microorganism is responsible for treating.

  If the major microorganisms responsible for the treatment in a biological wastewater treatment process (such as the activated sludge method) can be identified, the environment in which those microorganisms take precedence can be optimized to optimize the water treatment process. In recent years, the development of a next-generation sequencer, which is a gene analyzer, has enabled comprehensive analysis of microorganisms in a water treatment process. However, identification of major microorganisms was difficult for the following reasons.

1) Since an enormous number of microorganisms of thousands or more are mixed and fluctuated, it is almost impossible to estimate a priority species of abundance (relative ratio) from raw data or a graph.
2) The role of most microorganisms (decomposition ability, etc.) is unknown. Many microorganisms are not involved in water treatment, such as the decomposition of dead microorganisms.
3) Various pollutants such as organic substances, nitrogen compounds, and sulfur compounds are mixed in the wastewater, and the microorganisms involved in each removal are different. It is more complicated because some microorganisms are involved in the treatment of multiple pollutants.
As described above, it is necessary to process complicated and enormous data, and it has been impossible to estimate a microorganism species correlated with water treatment data. For example, it is often attempted in the art to estimate the microorganism species correlated with the water treatment data by ordinary regression analysis. However, when the number of microorganism species is 100 times the number of water treatment data (= the number of collected samples). Since it is more than twice as large, it is impossible to estimate by ordinary regression analysis.

  The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to provide a method for identifying a group of microorganisms that can identify a group of microorganisms related to a change in the amount of a specific substance in a biological treatment method using microorganisms. And

The present inventor has conducted intensive studies to solve the above problems, and as a result of performing penalized regression analysis on a sample created by resampling, it is possible to specify a group of microorganisms involved in biological treatment using microorganisms. And found that the present invention was completed.
The present invention employs the following configuration as means for solving the above-mentioned problems.

(1) A method for identifying a group of microorganisms, comprising the following steps, wherein the group of microorganisms is involved in a change in the amount of a specific substance:
A specimen for preparing a specimen by resampling from a data set obtained by measuring the rate of change of the amount of the specific substance in the microorganism sample containing the specific substance and the microorganism, and the content of the microorganism group in which the microorganism is classified Creation process,
For the sample created by the re-sampling, the content of the microorganism group as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, a regression analysis with penalties that can reduce the regression coefficient to 0, Performing, the first selection step of selecting a microorganism group corresponding to the independent variable selected based on the regression coefficient,
A specifying step of specifying the selected microorganism group as a microorganism group related to a change in the amount of the specific substance.
(2) In the data set, the content of the microorganism group is determined by measuring the content of the microorganism group at the same time and / or before the same time as the measurement reference time of the rate of change in the amount of the specific substance. The method for identifying a microorganism group according to the above (1), which is obtained.
(3) The second selection in which the reliability is calculated from the frequency of selection of the microorganisms selected in the first selection step in a sample created by resampling, and the microorganisms are further selected based on the reliability. The method for identifying a microorganism group according to the above (1) or (2), comprising a step.
(4) Further, a regression analysis is performed using the content of the microorganism group selected in the first selection step or the second selection step as an independent variable and the corresponding change rate of the amount of the specific substance as a dependent variable. The microorganism group is further selected based on the p value, or, further, the content of the microorganism group selected in the first selection step or the second selection step as an independent variable, the corresponding amount of the specific substance The above (1) to (3), including a fifth selection step of calculating the Akaike information criterion (AIC) using the rate of change as a dependent variable and further selecting a microorganism group based on the obtained AIC value. )).
(5) The selection based on the value of the Akaike information criterion
Selecting a microorganism group as a combination of independent variables that minimizes the value of the AIC,
Selecting a group of microorganisms containing more than a majority by a combination of independent variables from the smallest AIC value to the m-th one, or a combination of the m-th independent variables determined by the histogram of the AIC for the higher number. The method for identifying a group of microorganisms according to the above (4), wherein a group of microorganisms contained in more than a majority is selected (where m is an integer of 1 or more).
(6) Further, the content of the microorganism group selected in the first selection step, the second selection step, or the fifth selection step is used as an independent variable, and the rate of change of the amount of the corresponding specific substance is dependent. The microorganism group according to any one of (1) to (5), further including a third selection step of performing a regression analysis as a variable to further select a microorganism group exhibiting either a positive correlation or a negative correlation. How to identify.
(7) Further, the content of the microorganism group selected in the first selection step or the second selection step is used as an independent variable, and the rate of change in the amount of the specific substance is used as a dependent variable, and a regularization term is used. The microorganism according to any one of (1) to (3), further comprising a fourth selection step of performing a principal component regression analysis to further select a group of microorganisms exhibiting at least one of a positive correlation and a negative correlation. How to identify groups.
(8) The method for identifying a microorganism group according to (7), wherein a one-step principal component regression model based on sparse regularization is used for the principal component regression analysis.
(9) The method for identifying a microorganism group according to any one of (1) to (8), wherein a regression analysis method with an L1 regularization term is used for the regression with penalties.
(10) The method for identifying a microorganism group according to any one of (1) to (9), further comprising the following steps:
In the microorganism sample, a speed acquisition step of acquiring a value of a change rate of the amount of the specific substance,
Decoding step of decoding the base sequence of the microorganism contained in the microorganism sample,
From the decoded base sequence, classify the microorganisms contained in the microorganism sample into microorganism groups, a ratio determining step of determining the relative content of the microorganism group in the microorganism sample,
An amount determination step of determining the content of the microorganism group in the microorganism sample from the determined relative content ratio of the microorganism group.
(11) The method for identifying a microorganism group according to (10), wherein a sequencer is used for decoding the base sequence.
(12) The microorganism is a microorganism used for biological wastewater treatment,
The microorganism sample is treated water in a treatment tank where the wastewater treatment is performed,
The change rate is calculated from the amount of the specific substance measured for the treated water,
The sample prepared by the re-sampling includes any one of the above (1) to (11) including data on the rate of change of the amount of the specific substance at two or more times in the same treatment tank and the content of the microorganism group. A method for identifying a microorganism group according to one of the above.
(13) The identification of the microorganism group according to any one of (1) to (12), wherein the specific substance is any one or more selected from the group consisting of ammonia, phenol, thiocyanate, and thiosulfate. Method.
(14) the microorganism is at least one selected from the group consisting of a microorganism that oxidizes ammonia to produce nitrite, a microorganism that decomposes phenol, a microorganism that decomposes thiocyanate, and a microorganism that decomposes thiosulfate; The method for identifying a microorganism group according to any one of (1) to (13).

  According to the method for specifying a microorganism group of the present invention, it is possible to specify a microorganism group related to a change in the amount of a specific substance.

FIG. 1 is a flowchart showing an embodiment of a method for identifying a microorganism group according to the present invention. FIG. 4 is a schematic diagram illustrating an example of a data set obtained by measuring a change rate of an amount of a specific substance and a content of a microorganism group according to an embodiment of the present invention. FIG. 4 is a schematic diagram illustrating an example of creation of a Bootstrap specimen according to the embodiment of the present invention. It is a schematic diagram explaining the concept of solution of a Lasso estimation value. FIG. 7 is a schematic diagram showing an example of a result of performing Lasso analysis on a Bootstrap sample according to the embodiment of the present invention. It is a mimetic diagram explaining an example of the 2nd selection process concerning an embodiment of the present invention. FIG. 2 is a schematic diagram illustrating a configuration of a biological treatment apparatus used in an example. It is a figure which shows the result of the nitrous acid production rate in the biological treatment apparatus acquired in the Example. It is a figure which shows the result of the thiocyan removal rate in the biological treatment apparatus acquired in the Example. It is a figure which shows the result of the relative ratio of each OTU with respect to all 3752 OTU detected by the biological treatment apparatus acquired in the Example. It is a figure which shows the average value of the measured value of the nitrite production | generation rate in a biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the average value of the measured value of the thiocyan removal rate in the biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the result of the nitrous acid production rate in the biological treatment apparatus acquired in the Example. It is a figure which shows the result of the thiocyan removal rate in the biological treatment apparatus acquired in the Example. It is a figure which shows the result of the thiosulfuric acid removal rate in the biological treatment apparatus acquired in the Example. It is a figure obtained in the example which shows the result of the phenol removal rate in the biological treatment apparatus. It is a figure which shows the average value of the measured value of the nitrite production | generation rate in a biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the average value of the measured value of the thiocyan removal rate in the biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the average value of the measured value of the thiosulfuric acid removal rate in a biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the average value of the measured value of the phenol removal rate in a biological treatment apparatus, the predicted value by regression analysis, and the predicted value by cross-validation acquired in the Example. It is a figure which shows the measured value of the nitrite production | generation rate in the biological treatment apparatus acquired in the Example, and the predicted value by cross-validation. It is a figure which shows the actual value of the thiocyan removal rate in the biological treatment apparatus acquired in the Example, and the predicted value by cross-validation. It is a figure which shows the measured value of the thiosulfuric acid removal rate in the biological treatment apparatus acquired in the Example, and the predicted value by cross-validation. It is a figure which shows the measured value of the phenol removal rate in the biological treatment apparatus acquired in the Example, and the predicted value by cross-validation.

≪Method of identifying microorganisms≫
Hereinafter, the method for identifying a microorganism group according to the embodiment will be described with reference to examples as appropriate. In addition, the method for specifying a microorganism group of the present invention is not limited to the following embodiments.

As shown in FIG. 1, the method for specifying a microorganism group according to the embodiment includes a speed acquisition step, a decoding step, a ratio determination step, a quantity determination step, a sample preparation step, a selection step having a first selection step, and a specification step. As the steps that the selection step may further include, a second selection step, a third selection step, a fourth selection step, and a fifth selection step are exemplified.
Through these speed acquisition step, decoding step, ratio determination step, and quantity determination step, data used in the sample preparation step is acquired. The acquisition of the data set obtained by measuring the change rate of the amount of the specific substance and the content of the microorganism group may be performed in parallel or independently.
Hereinafter, each step will be described in detail.

(Speed acquisition process)
The speed acquisition step is a step of acquiring a value of a change rate of the amount of the specific substance in the microorganism sample.
In the present embodiment, a case will be described in which the microorganism sample is treated water in a treatment tank in which biological wastewater treatment is performed (hereinafter sometimes simply referred to as “treated water”).

  Examples of the treated water include coke wastewater discharged from a coke plant. In biological wastewater treatment, treated water contains microorganisms used for biological wastewater treatment and specific substances to be treated by the microorganisms. The specific substance in the processing tank is processed by a specific microorganism group into which the microorganism has been classified, and the amount thereof varies. It can be assumed that the more microorganisms that process the specific substance are present in the processing tank, the higher the processing speed of the specific substance is.

  The specific substance is not particularly limited as long as it can be directly or indirectly treated by the microorganism. For example, the specific substance may include any one or more selected from the group consisting of ammonia, phenol, thiocyanate, and thiosulfuric acid.

  When the treated water is batch-processed in the treatment tank, the value of the change rate of the amount of the specific substance contained in the treated water can be obtained, for example, by calculating the amount of change of the specific substance in the treated water within a predetermined time. The amount of the specific substance may be represented by a concentration of the specific substance per treated water. The amount of the specific substance in the treated water may be determined for a treated water sample obtained by sampling from the treatment tank.

  Although it is desirable that the treated water in the treatment tank is always completely mixed, the amount of the specific substance contained in the treated water in the treatment tank is not always uniform in the treatment tank. In some cases, for example, when the microorganisms are immobilized on a carrier, the arrangement of the microorganisms in the treatment tank is biased. From the viewpoint of identifying the microorganisms involved in the change in the amount of specific substances, the amount of specific substances contained in the treated water in the treatment tank should be determined for the treated water near the microorganisms in the treatment tank. Is preferred.

When the treated water is continuously treated in the treatment tank, the value of the rate of change of the amount of the specific substance contained in the treated water in the treatment tank is, for example, the residence time of the treated water in the treatment tank, the water treatment time, the treatment tank. It can be obtained from the concentration of the specific substance contained in the water to be treated flowing into the water and the concentration of the specific substance contained in the treated water treated in the treatment tank.
The amount of the specific substance contained in the water to be treated is preferably obtained from the water to be treated before flowing into the treatment tank. In the case of an embodiment described later, such water to be treated includes treated water near 24 in the treatment tank of FIG.
On the other hand, the amount of the specific substance contained in the treated water treated in the treatment tank may be a value that has undergone biological treatment with microorganisms. It is preferable to obtain it. In the case of an embodiment described later, such treated water includes the treated water 20b and the treated water 25 in FIG. However, the treated water in the treatment tank is always completely mixed as described above, and in such a case, it can be determined for the treated water of 20a.

(Decryption process)
The decoding step is a step of decoding the base sequence of the microorganism contained in the microorganism sample.
In biological wastewater treatment, treated water contains microorganisms used for biological wastewater treatment and specific substances to be treated by the microorganisms.

Microorganisms targeted for biological wastewater treatment in this embodiment include, for example, microorganisms that oxidize ammonia to produce nitrite, microorganisms that degrade phenol, microorganisms that degrade thiocyan, and microorganisms that degrade thiosulfate. One or more selected from the group consisting of:
Examples of microorganisms that oxidize ammonia to produce nitrite include ammonia-oxidizing bacteria and ammonia-oxidizing archaea. Examples of microorganisms that decompose thiosulfate include Thiobacillus Thioparus. However, even with the same specific substance, a variety of microorganisms are involved in the change in the amount, so that only a small number of the specified microorganism groups are used. In addition, the number of microorganisms involved in the change in the amount of many specific substances has not been identified.

Usually, the microorganisms contained in the treated water in the treatment tank include not only one kind but a plurality of microorganism groups. As used herein, the term “microorganism group” refers to a population of microorganisms classified based on a certain trait or genotype, and includes, but is not limited to, genus, species, and subspecies. The genotype simply includes the base sequence of the genome of the microorganism.
The number of microorganisms contained in the treated water in the treatment tank may be, for example, 100 to 1,000,000, or 1,000 to 10,000.

  The base sequence of the microorganisms contained in the treated water in the treatment tank may be obtained from a treated water sample obtained by sampling from the treatment tank. The base sequence of the microorganism may be the base sequence of DNA of the microorganism, may be the base sequence of RNA of the microorganism, or may be the base sequence of a reverse transcript such as cDNA of the microorganism. When the treated water contains a carrier on which the microorganisms are immobilized or adhered, or when the microorganisms come into contact with a carrier on which the microorganisms are immobilized or adhered, the microorganisms contained in the treated water are microorganisms immobilized or adhered to the carrier. There may be.

In the decoding step of the present embodiment, for example, first, DNA is extracted from the carrier. The extracted DNA contains DNA of microorganisms contained in the treated water in the treatment tank. The base sequence of the obtained DNA may be decoded comprehensively or only a part thereof. As a case where only a part is decoded, for example, the base sequence of DNA of a specific gene may be decoded. Alternatively, the base sequence of the reverse transcript (cDNA) of RNA extracted from the carrier may be decoded.
Usually, when trying to phylogenetically classify microorganisms contained in treated water, the full length or a part of the base sequence of the ribosomal RNA gene (rRNA gene or rDNA) of the microorganism is decoded. When the specific substance to be processed is a single substance, the base sequence of the functional gene may be decoded. As an example, in the case of a microorganism that oxidizes ammonia to produce nitrite, the base sequence of the ammonia monooxygenase gene may be decoded. In the present embodiment, a case where a part of the base sequence of the 16S ribosomal RNA gene is decoded will be described.

  In some cases, the species of the microorganism can be identified from the decoded base sequence, but in the method for identifying a microorganism of the present embodiment, the identification of the species is not essential. For example, a base sequence of one read is regarded as one microorganism, the base sequence is classified based on the identity and homology of the base sequence, and the taxonomic group of the classified base sequence is treated as a microorganism group of the microorganism. Is also good. Normally, base sequence data having a homology of 97% or more are grouped into one group of clusters, and the most frequently occurring sequence in each cluster sequence is represented as a representative OTU (Operative Taxonomic Unit) sequence. One OTU is treated as one group of microorganisms. Incidentally, the homology may be 97% or more, or may be, for example, 80% or more, 90% or more, 95% or more, or 98% or more. . In the present embodiment, the microorganism group is classified based on the OTU.

Extraction and decoding of DNA can be performed by a known method. A sequencer can be used for decoding the nucleotide sequence.
The method for identifying a microorganism group according to the present embodiment is suitably used for data including a great deal of microorganism information. Therefore, it is preferable to use a next-generation sequencer for decoding the base sequence. As a next-generation sequencer, a sequencer utilizing a sequential DNA synthesis reaction by a DNA polymerase or the like is typical. Sequencer platforms used for decoding the base sequence include 454, Illumina, SOLiD, Ion torrent, and PacBio.

(Ratio determination process)
The ratio determining step is a step of classifying the microorganisms contained in the microorganism sample into microorganism groups from the base sequence decoded in the decoding step, and determining a relative content ratio of the microorganism group in the microorganism sample. .
For example, it is assumed that the treated water sample contains 30 base sequence reads classified into taxonomic group A and 20 base sequence reads classified into taxonomic group B. In this case, it can be considered that the treated water sample contains 30 parts of microorganisms classified into taxon A and 20 parts of microorganisms classified into taxon B.

(Quantity determination process)
The amount determination step is a step of determining the content of the microorganism group in the microorganism sample from the relative content ratio of the microorganism group determined in the ratio determination step. For example, the content of the microorganisms contained in the treated water in the treatment tank, the value of the number of microorganisms contained in the treated water sample corresponding to the treated water sample obtained the relative content ratio, the relative content ratio It can be determined by multiplying by the number of microorganisms.
The value of the number of microorganisms does not need to be the value of the number of microorganisms itself, but may be any value that reflects the value of the number of microorganisms between treated water samples. It is preferable that the microorganisms classified into all the above-mentioned taxa can be detected in common. For example, the number of microorganisms contained in the treated water sample may be determined from the amount of DNA contained in the treated water sample. Further, the number of microorganisms contained in the treated water sample may be obtained from the number of common genes contained in the treated water sample. When it is determined that there is no difference in the number of microorganisms between samples, for example, the relative ratio of the microorganisms determined in the ratio determination step in the previous period may be used as the content of the microorganisms.
Further, a value obtained by standardizing the content of the microorganism group determined in the above-mentioned amount determination step can be used as the standardized microorganism amount.

Through the above speed acquisition step, decoding step, ratio determination step, and amount determination step, the rate of change of the amount of the specific substance used in the sample preparation step, and the data of the content of the microorganism group in which the microorganisms are classified are Is obtained.
These data are acquired, for example, for each treated water sample. The rate of change in the amount of the specific substance and the data on the content of the microorganism group may be obtained from the same treated water sample. Or, within a range in which the correlation between the content of each microorganism group in the treatment tank and the change in the amount of the specific substance treated in the treatment tank can be assumed, the rate of change in the amount of the specific substance and the content of the microorganism group The data may be obtained from separate treated water samples. As an example of using a separate treated water sample, a treated water sample used for data acquisition of the content of the microorganism group is collected from the treatment tank, and several days later, the amount of the specific substance is removed from the same treatment tank. In some cases, a sample of the treated water used to determine the rate of change is taken.
In addition, the treated water sample may be derived from the same treatment tank, but the correlation between the content of each microorganism group in the treatment tank and the change in the amount of the specific substance treated in the treatment tank is assumed. To the extent possible, they may be derived from different treatment tanks.

  By combining the data on the rate of change in the amount of the specific substance obtained from the treated water sample and the data on the content of the microorganisms within a range in which these correlations can be assumed, the rate of change in the amount of the specific substance, and A data set obtained by measuring the content of the microorganism group into which the microorganism has been classified is created. This data set can be used as an initial sample for creating a sample by resampling in the embodiment. It is preferable that the sample prepared by the re-sampling includes data on the rate of change of the amount of the specific substance at two or more times in the same treatment tank and the content of the microorganism group.

(Sample preparation process)
In the sample preparation step, the rate of change in the amount of the specific substance in the microorganism sample containing the specific substance and the microorganism, and a data set obtained by measuring the content of the microorganism group in which the microorganism is classified, by re-sampling. This is the step of creating a specimen.

FIG. 2 shows data obtained by measuring the rate of change of the amount of the specific substance contained in the treated water in the treatment tank and the content of the microorganisms contained in the treated water in the treatment tank in the present embodiment. It is a schematic diagram which shows an example of a set.
In FIG. 2, t represents the number of data sets in which the rate of change of the specific substance and the content of the microorganism group have been obtained from the treated water, and numbers are assigned in order from the first to the nth. Here, the case where the specific substance is nitrous acid is shown.
Denovo represents the type of microorganism group, and p represents the number of microorganism groups. The circles in FIG. 2 represent data on the content of each microorganism group. For example, when p = 3752, the treated water contains 3752 kinds of microorganism groups, and the data set obtained by each measurement contains data on the content of 3752 microorganism groups. The squares in FIG. 2 represent data on the rate of change of the amount of nitrous acid contained in the treated water. Each data set includes data on the rate of change of the amount of nitrite. For example, if the data set is obtained from treated water once a day, if n = 23, it means that there are 23 data sets of treated water acquired during 23 days.

  The data acquisition frequency can be set arbitrarily, for example, once a day, once every three days, or once every seven days. In order to identify microorganisms with high accuracy, it is better to acquire data at a high frequency.However, if the acquisition frequency is too short, accidental fluctuation results may also be acquired. May be provided. In addition, the acquisition frequency may be appropriately set in consideration of the residence time of the microorganisms in the treatment tank.

  In the data set, the content of the microorganism group may be determined by using data at the same time as the measurement reference time of the change rate of the amount of the specific substance and / or data of the content at a time earlier than the same time. Good.

The example shown in the above table shows a case in which data is acquired at an acquisition frequency of every seven days (N represents an integer of 0 or more) from the Nth day of operation start. The rate of change of the amount of the specific substance can be calculated by, for example, dividing a value obtained by subtracting the concentration of the specific substance contained in the treated water from the concentration of the inflowing treated water by the water treatment time.
When a data set is created using the data at the same time point, for example, when a data set is created using the data at the second time point, the amount of the specific substance calculated from the substance amount on the operation (N + 14) day is calculated. A case where the data of the change speed and the data of the content value of the microorganism group acquired on the (N + 14) th day of operation can be exemplified.
When a data set is created using the content data at a time point earlier than the same time point, for example, when a data set is created using the change rate data of the amount of the specific substance at the second time point, the operation A case where the data of the change rate of the amount of the specific substance calculated from the substance amount on the (N + 14) day and the data of the value of the content of the microorganism group acquired on the (N + 7) day of the operation can be exemplified.

In addition, the data set can include data on the content of the microorganism group at a plurality of different time points. When a data set is created using the data on the content of the microorganisms at the same time point and a time point earlier than the same time point, for example, a data set is created using the change rate data of the amount of the specific substance at the second time point In this case, in addition to the data on the rate of change in the amount of the specific substance calculated from the substance amount on the operation (N + 14) day and the data on the content of the microorganisms obtained on the operation (N + 14) day, the operation A case where the data of the value of the content of the microorganism group acquired on the (N + 7) day is used can be exemplified.
The point in time before the same point in time can be, in addition to the data before the temporary point as exemplified above, data before any point in time, such as two points before, three points in time, etc. , Can be used in combination. Further, in the above example, time points at equal intervals every seven days are illustrated, but the time intervals at each time point may be the same or different.
In this way, by including data on the content of microbial communities at multiple different time points in the data set, data on microbial communities that have a certain time lag in the correlation between the rate of change of the amount of specific substances and the content Can be widely included in the analysis, and the microorganism group can be specified with higher accuracy.

  In FIG. 2, when data on the content of the microorganisms at a plurality of different time points is included, when data on the content of the microorganisms at the first time and the second time are additionally included, for example, 3752 Data for two time points is included for each type of microorganism group, and p = 3752 × 2.

As a technique for resampling, the Bootstrap method, the Jacknife method, or the like can be used. In the present embodiment, a case will be described in which the Bootstrap method is used as a technique for resampling.
FIG. 3 is a schematic diagram illustrating an example of a case where a Bootstrap sample is created from the data set of the acquired data illustrated in FIG. 2 by the Bootstrap method in the present embodiment. In FIG. 3, from the n data sets (left in the figure), n ′ data sets are resampled at random (right in the figure), and a plurality of sets of the n ′ data sets (group B) Create a bootstrap sample of ()). The values of n and n 'are usually the same, but may be different. The data sets to be sampled may overlap. For example, in the example shown in the first set in FIG. 3, t = 1 is sampled in duplicate. In FIG. 3, B represents a set number. For example, if B = 1000, 1000 sets of Bootstrap samples are created. The number of sets is preferably 100 or more, more preferably 1000 or more. More preferably, it is desirable to create a Bootstrap specimen while gradually increasing the number of groups, and to increase the number of groups until the number of microorganism groups selected in the first selection step described below does not change.

(First selection process)
The first selection step, for the sample created by the re-sampling, the content of the microorganism group as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, performing a penalized regression analysis, This is a step of selecting a microorganism group corresponding to the independent variable selected based on the regression coefficient.
In the regression analysis with penalties according to the present embodiment, regression analysis with penalties that can reduce the regression coefficient to 0 is performed for the purpose of giving a penalty that makes the value of the estimated coefficient smaller. Examples of the regression analysis with penalties that can reduce the regression coefficient to 0 include Lasso (Least Absolute Shrinkage and Selection Operator), Elastic net, and SCAD (Smoothly Clipped Absolute equation with a regression equation that includes a regression equation with a regression equation that includes a regression equation). Can be used.

In the present embodiment, a case where the above-mentioned Lasso method is used as the regression analysis with penalties will be described.
For example, when the specific substance is nitrous acid, the function of the following formula (A) is expressed.

  In the Lasso method, the coefficient can be reduced to zero. Therefore, selection (selection) of a variable (microorganism group) and estimation of a constant term and a coefficient can be performed simultaneously. As a result, regression analysis can be performed even on data having a large number of independent variables, which has not been practically analyzed conventionally.

  Lasso is a method of minimizing the function represented by the following equation (I) with respect to the parameter β. The second term of the following equation (I) is a regularization term of the L1 norm.

  The above equation (I) can be rewritten as a conditional minimization problem of the L1 norm for the coefficient β shown in the following equation (II).

  FIG. 4 shows this diagrammatically. FIG. 4 is a schematic diagram for explaining the concept of solving a Lasso estimation value. The portion where the square restricted area shown by hatching and the contour line are in contact is the solution. The following is an algorithm for determining the estimated Lasso value.

  In the case of a linear regression model, minimization of the following equation (III) is considered. Here, the independent variable is averaged to 0, the variance is normalized to 1, and the dependent variable is averaged to 0.

Next, β _j = 0 for an arbitrary j, and the following equation (IV) is calculated with j = 1, 2,..., P, 1, 2,.

  λ is fixed and updated with the following equation (V), and S (·, ·) is represented by the following equation (VI).

In the regression analysis with penalties according to this embodiment, the ratio (p / n) of the number p of the microorganism groups to the number n of the data sets may be 10 ≦ p / n ≦ 10000, and 100 ≦ p / n ≦ 1000. It may be.
In the regression analysis with penalties according to the present embodiment, the number p of microorganism groups per function may be 100 or more and 1,000,000 or less, or may be 1000 or more and 10,000 or less.

FIG. 5 is a schematic diagram showing the results of performing Lasso analysis on the Bootstrap samples of the B group. FIG. 5 shows that three Denovo (microorganism groups) corresponding to the variables whose coefficients did not become 0 were selected from the first set of Bootstrap samples in FIG.
Similarly, it shows that two Denovo (microorganisms) were selected from the Bootstrap specimen of the B group. As shown here, when each Bootstrap sample is subjected to Lasso analysis, the type of selected Denovo is not always the same, and the number of selected Denovo is not necessarily the same.
In the example specifically shown in Example 1 to be described later, 27 kinds of microorganisms could be selected from 3752 kinds of microorganisms.
In the first selection step, a group of microorganisms that greatly contributes to the change in the amount of the specific substance is selected.

(Second selection process)
The second selection step is a step of calculating reliability from a selection frequency in a sample created by resampling of the microorganism group selected in the first selection step, and further selecting a microorganism group based on the reliability. is there.

  In the first selection step, samples prepared by re-sampling are prepared and analyzed in group B. If the number of selections of each microorganism group in the first selection step is U times, in the first selection step of each microorganism group, Is represented by U / B, and the value can be used as the reliability.

FIG. 6 is a schematic diagram illustrating the second selection step. First, the degree of reliability that each microorganism group has been selected is calculated. As shown in FIG. 6, the results of analyzing the Bootstrap samples of group B are totaled, and the number of times selected in the first selection step is totaled. In the example shown in FIG. 6, Denovo1 is selected 988 times and Denovo2 is selected 675 times in the first selection step. Here, when 1000 sets of Bootstrap samples are used, the reliability of Denovo1 is 988/1000 = 0.988, and the reliability of Denovo2 is 675/1000 = 0.675.
Based on the obtained reliability, the microorganism group selected in the first selection step is further selected. It can be estimated that the greater the reliability of the microorganism group, the greater the contribution to the change in the amount of the specific substance. The reference value of the reliability in the second selection step may be appropriately selected. For example, the reliability is 0.3 or more, 0.4 or more, 0.5 or more, 0.6 or more, 0.7 or more, 0.8 or more. More than or a group of 0.9 or more microorganisms may be further selected. In the example of thiocyan removal specifically shown in Example 1 described below, five more microorganism groups could be selected from 27 kinds of microorganism groups based on a reliability of 0.6 or more.
In the second selection step, a group of microorganisms that contribute more to the change in the amount of the specific substance is selected.

(Third selection process)
The third selection step, the first selection step, the second selection step or the content of the microorganisms selected in the fifth selection step described later as an independent variable, the corresponding change rate of the amount of the specific substance the specific substance. This is a step of performing a regression analysis as a dependent variable and further selecting a microorganism group showing either a positive correlation or a negative correlation.
In the present embodiment, a case will be described in which the analysis is performed using the microorganisms selected in the second selection step among the first selection step, the second selection step, and the fifth selection step. In the third selection step, the number of independent variables has already been narrowed down after the first and second selection steps.Therefore, the regression analysis in the third selection step is not limited to regression analysis with penalties. A method of regression analysis without a penalty term by the maximum likelihood method or the like may be adopted. Further, for example, a p-value for the analyzed Denovo may be obtained. Based on the obtained p value, a group of microorganisms can be further selected from the group of microorganisms selected in the third selection step. For example, selecting a microorganism group having a p value of less than 0.05 can be exemplified. The p-value in this step is the reliability of the estimated value of the regression coefficient in the used regression analysis.
The regression analysis of the third selection step is performed, and the regression coefficient of each independent variable is calculated. Then, a group of microorganisms whose regression coefficient indicates either positive or negative is selected. In the example of thiocyan removal specifically shown in Example 1 described later, three more microorganism groups could be selected from the five microorganism groups as the microorganisms having a positive regression coefficient.
In the third selection step, a group of microorganisms that contributes more to the change in the amount of the specific substance and correlates positively or negatively with the change in the amount of the specific substance is selected.

(Fourth selection process)
In the fourth selection step, the content of the microorganism group selected in the first selection step or the second selection step as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, a regularization term This is a step of performing a principal component regression analysis provided with, and further selecting a group of microorganisms exhibiting at least either a positive correlation or a negative correlation.
In the present embodiment, a case will be described in which the analysis is performed using the microorganisms selected in the first selection step in the first selection step or the second selection step. As shown in FIG. 1, a microorganism group is further selected by a fourth selection step instead of the second selection step, the third selection step, and the fifth selection step.
First, a principal component regression analysis including a regularization term in the fourth selection step is performed, and a regression coefficient of each set principal component axis is calculated. Then, for any one or more of the principal component axes, a microorganism group in which the value obtained by multiplying the regression coefficient by the value of the OTU principal component in each principal component axis indicates at least either positive or negative is selected. . In principal component regression provided with a regularization term, the value of the principal component of OTU on each principal component axis may become zero. This OTU can be determined as a group of microorganisms not related to the change in the amount of the specific substance, and is excluded from the selection. The number of principal component axes to be set is arbitrary, but it is preferable that the number of principal component axes be about 1 to 5 so as not to make the statistical processing too complicated.

It is considered that each principal component axis set in the principal component regression analysis reflects some factor such as an external factor or an internal factor that affects the change of the specific substance. By performing the principal component regression analysis, the analysis can be performed for each principal component axis, and the relationship between the selected microorganism groups can be estimated.
Examples of the factors include the pH and temperature of the treated water, the amount of the specific substance to be treated, and the interaction between the microorganisms.

  The principal component regression (PCR) analysis preferably uses a one-step principal component regression model based on sparse regularization. This model is called a sparse principal component regression (SPCR) model, and analysis is performed based on the contents of a previously reported report (Kawano et al., Comput. Stat. Data Anal. 89, (2015) 192-203). It is possible to implement.

The outline of the SPCR will be described. Assuming that data y ₁ ,..., Y _n on the dependent variables are obtained in addition to the data x ₁ ,..., X _n , the dependent variables are represented by a data matrix X = (x ₁ ,. _n ) Consider a case in which it depends on the main component of ^T. In the SPCR, a minimization problem (sparse regularization) represented by the following equation (VII) using the main component B ^T x is considered. Here, [gamma] 0 is the intercept, γ = (γ1, ..., γk) T is the regression coefficient vector, lambda _beta and lambda _gamma is the regularization parameter which takes a positive value, a value in between w and ζ from 0 to 1 Represents a tuning parameter to be taken.

Principal component regression (PCR) is a two-step method that constructs a regression model after performing principal component analysis to reduce the number of independent variables to some extent. In the principal component regression, the principal component score becomes a new independent variable, but the selection of this new independent variable (principal component score) is obtained only from the principal component analysis and is not matched to the dependent variable.
In contrast to PCR, SPCR uses a weighted sum of a loss function related to principal component analysis and a loss function of regression error as an overall loss function, and introduces an appropriate sparse regularization to obtain a principal component score by a one-step method. Is a regression model in which is an independent variable.
By employing SPCR, it is possible to automatically extract the principal component score that contributes to the dependent variable, and it is possible to perform analysis with higher accuracy.

Hereinafter, a case where SPCR is performed in the fourth selection step will be described. First, SPCR is performed using the content of the microorganism group selected in the first selection step as an independent variable and the corresponding change rate of the amount of the specific substance as a dependent variable. In such a process, the low axis related to the change in the amount of the specific substance is statistically automatically omitted. The obtained regression coefficient of each principal component axis is calculated, and the value obtained by multiplying the regression coefficient and the value of the principal component of OTU in each principal component axis is at least positive for at least one of the principal component axes. Or, a microorganism group showing either negative is selected.
In the example of thiocyan removal specifically shown in Example 3 to be described later, 27 kinds of microorganisms could be selected from 34 kinds of microorganisms as the microorganisms having the above-mentioned value of positive or negative.

  It should be noted that as the value obtained by multiplying the regression coefficient and the value of the principal component of OTU on each principal component axis is positively larger, the positive contribution to the change in the amount of the specific substance is positive. This is considered to be a group of microorganisms that negatively contributes to the change of the microorganism. Therefore, the microorganism group can be further selected based on a value obtained by multiplying the regression coefficient by the value of the main component of OTU on each principal component axis.

  Further, it is possible to infer the relationship between the selected microorganism groups. Explaining the estimation of the relationship between the microbial groups using the result of Example 3 as an example, for example, referring to the data of thiocyanide removal shown in Table 13, three principal component axes are obtained. In the chemical wastewater treatment process, it can be said that three factors are largely affecting thiocyanide removal.

For example, it can be inferred that a group of microorganisms whose principal component values are obtained on the same principal component axis (the values of the principal components are not zero) may be affected by the same factor.
Among the microorganisms whose principal component values are obtained on the same principal component axis, those obtained by multiplying the regression coefficient by the value of the main component of OTU on each principal component axis have the same positive or negative value. It can be inferred that factors may be affected in the same way. On the other hand, the value obtained by multiplying the regression coefficient and the value of the principal component of OTU on each principal component axis in the group of microorganisms whose principal component values are obtained on the same principal component axis is opposite between positive and negative. It can be inferred that some may be adversely affected by the same factors.
Alternatively, among the microbial groups whose values are obtained on the same principal component axis, those whose values obtained by multiplying the regression coefficient by the value of the principal component of OTU on each principal component axis are positive or negative are symbiotic relationships. It can be inferred that it is easy to proliferate together. On the other hand, the value obtained by multiplying the regression coefficient and the value of the principal component of OTU on each principal component axis in the group of microorganisms whose principal component values are obtained on the same principal component axis is opposite between positive and negative. It can be inferred that some of them are in a competitive relationship and are difficult to proliferate together.

  The values of the principal components are obtained on different principal component axes, and it can be inferred that the groups of microorganisms for which the values of the principal components are not obtained on the same principal component axis may be affected by different factors. In this case, it can be inferred that the factors to be controlled may be different in each microorganism group.

  As a method of specifying what factors each principal component axis reflects, for example, the change rate of the content of the selected microorganism group or the amount of the specific substance and the assumption of the temperature and pH of the treated water, etc. Calculating a correlation with a factor to be performed and finding a factor having a high correlation. The accuracy of specifying the above factors may be increased by examining other microbial groups selected on the same principal component axis and confirming that they show the same correlation tendency.

In the case where the fourth selection step is performed after the first selection step, since the selection based on the reliability in the second selection step is not performed, a reasonable data interpretation without intention of artificial selection can be performed. there is a possibility.
When the fourth selection step is performed after the first selection step, a group of microorganisms calculated to be highly involved in the change of the specific substance even if the reliability is low is also selected.

(Fifth selection process)
The fifth selection step includes the following fifth (A) selection step or fifth (B) selection step.
Fifth (A) selection step, the content of the microorganism group selected in the first selection step or the second selection step as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, This is a step of performing regression analysis and further selecting a microorganism group based on the p-value.
Fifth (B) selection step, the content of the microorganisms selected in the first selection step or the second selection step as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, This is a step of calculating the Akaike information criterion and further selecting a microorganism group based on the obtained reference value (AIC).
In the present embodiment, a case will be described in which the analysis is performed using the microorganism group selected in the second selection step in the first selection step or the second selection step. In addition, by performing the third selection step after the fifth selection step, a group of microorganisms can be further selected (in the order of the fifth selection step to the third selection step in FIG. 1).

Hereinafter, the fifth (A) selection step will be described. In the present embodiment, a case will be described in which the analysis is performed using the microorganism group selected in the second selection step in the first selection step or the second selection step.
In the fifth (A) selection step of the present embodiment, the regression analysis is performed using the content of the microorganism group selected in the second selection step as an independent variable and the corresponding change rate of the amount of the specific substance as a dependent variable. Then, a microorganism group having a p value of less than 0.05 is selected. The p-value in this step is the reliability of the estimated value of the regression coefficient in the used regression analysis.
In the example specifically shown in Example 5 described later, five kinds of microorganism groups could be selected from the six kinds of microorganism groups.
In the fifth (A) selection step, a group of microorganisms that contribute more to the change in the amount of the specific substance is selected.

  Unlike the third selection step, the fifth (A) selection step does not further select a microorganism group showing either a positive correlation or a negative correlation. Based on the regression coefficient obtained in the above, a group of microorganisms exhibiting either a positive correlation or a negative correlation can be further selected from the microorganisms selected in the fifth selection step.

Hereinafter, the fifth (B) selection step will be described. In the present embodiment, a case will be described in which the analysis is performed using the microorganism group selected in the second selection step in the first selection step or the second selection step.
In the fifth (B) selection step of the present embodiment, first, the content of the microorganism group selected in the second selection step is set as an independent variable x, and the corresponding change rate of the amount of the specific substance is set as a dependent variable Y (Refer to the following equation (VIII)).

A linear regression model is constructed for the combination of the microorganism groups selected in the second selection step, and the constructed model is evaluated by Akaike information criterion AIC (Akaike information criterion). As the Akaike information criterion AIC, formulas of various known AICs or improved ones thereof may be appropriately used. For example, a formula represented by the following formula (IX) can be used. As for the Akaike information criterion AIC, for example, a previous report ("Information criterion" written by Sadanori Konishi, Genshiro Kitagawa, published by Asakura Shoten on September 25, 2004) can be referred to.

Then, a microorganism group is selected based on the obtained AIC value.
In the combination of the explanatory variables used, the smaller the value of the obtained AIC is, the more the combination is suitable for predicting the dependent variable, and based on this idea, the selection method based on the AIC is determined. It can be performed as appropriate.

For example, the following selection methods can be exemplified.
Method 1: A microorganism group is selected as a combination of independent variables that minimizes the value of AIC.
Method 2: A group of microorganisms containing more than a majority is selected by a combination of independent variables from the smallest AIC value to the m-th one. Here, m is an integer of 1 or more, for example, 3 or more.
Method 3: A group of microorganisms contained in more than the majority by the combination of the independent variables up to the m-th determined by the AIC histogram is selected.

  Regarding Method 1, in the example specifically shown in Example 7 described later, five microbial groups excluding denovovo2647_1 could be selected as a combination of independent variables that minimizes AIC (236.63 in this example).

  Regarding method 2, in the example specifically shown in Example 7 described later, the combination of independent variables from the smallest AIC to the m = th (236.63, 238.25, 241.33 in this example) Among them, denovovo2647_1 is used only when the AIC is 238.25, so that it is possible to select five microbial groups excluding denovovo2647_1 that has not been selected in more than a majority.

  The AIC histogram in the method 3 is such that two or more peaks are formed in a histogram in which the range from the maximum value to the minimum value of the calculated AIC is divided into sections and the number of combinations corresponding to each section is the vertical axis. An example is shown in which a section is selected, and combinations up to the m-th combination included in the peaks of the number of peaks from the smallest AIC value to the arbitrary number (however, the total number of peaks minus one) are used. When two peaks are obtained, the number of selected peaks is 1, for example, and the number of combinations included in the first peak is m.

  In the fifth (B) selection step, a group of microorganisms that contribute more to the change in the amount of the specific substance is selected.

  In the method for identifying a microorganism group of the present embodiment, the content of the microorganism group selected in the first selection step or the second selection step is used as an independent variable, and the rate of change in the amount of the corresponding specific substance is dependent. As a variable, a regression analysis is performed to further select a group of microorganisms based on the p value, or, as an independent variable, the content of the group of microorganisms selected in the first selection step or the second selection step as an independent variable. A fifth selection step of calculating the Akaike information criterion using the change rate of the amount of the specific substance as a dependent variable and further selecting a microorganism group based on the obtained reference amount value (AIC) is included. Since the data of the highly reliable microorganism group already selected in the first selection step or the second selection step is used, it is possible to accurately specify a microorganism group that greatly contributes to the change rate of the amount of the specific substance.

(Specific process)
The specifying step of the present embodiment is a step of specifying the microorganism group selected in the selecting step as a microorganism group related to a change in the amount of the specific substance.
The selection step includes a first selection step, and may further include any one or more steps selected from the group consisting of second to fifth selection steps. The combination of each selection step after the first selection step can be arbitrarily selected so that a microorganism group can be selected.
As an example, as shown in FIG.
First selection process → Second selection process,
First selection step → second selection step → third selection step,
First selection step → third selection step (not shown),
First selection step → Second selection step → Third selection step → Fifth selection step,
First selection process → third selection process → fifth selection process (not shown),
First selection process → fourth selection process,
First selection process → Second selection process → Fourth selection process,
First selection process → Fifth selection process,
First selection process → Second selection process → Fifth selection process,
First selection process → Fifth selection process → Third selection process,
The first selection step → the second selection step → the fifth selection step → the third selection step, and the like.

The selection step preferably includes a third selection step or a fourth selection step. The microorganisms selected in the third selection step, or the fourth selection step, among those that greatly contribute to the change in the amount of the specific substance, are microorganism groups positively or negatively correlated with the change in the amount of the specific substance. It can be determined that there is.
As a microorganism group positively correlated with the change in the amount of the specific substance, for example, when the rate of change in the amount of the specific substance is used as the rate of change in the amount of the specific substance, in the direction of increasing the specific substance in the treatment tank, A group of contributing microorganisms. Conversely, when the rate of change in the amount of the specific substance is used as the rate of change in the amount of the specific substance, it contributes in the direction of reducing the specific substance in the treatment tank.
As a microorganism group negatively correlated with the change in the amount of the specific substance, for example, when the rate of change in the amount of the specific substance is used as the rate of change in the amount of the specific substance, in the direction of decreasing the specific substance in the treatment tank, A group of contributing microorganisms. Conversely, when the rate of change in the amount of the specific substance is used as the rate of change in the amount of the specific substance, the rate contributes to increasing the amount of the specific substance in the treatment tank.

As described above, in the method for identifying a microorganism group of the present embodiment, the rate of change in the amount of the specific substance in the microorganism sample containing the specific substance and the microorganism, and the measurement of the content of the microorganism group in which the microorganism is classified From the data set obtained by, to create a sample by resampling, for the sample created by the resampling, the content of the microorganism group as an independent variable, the corresponding change rate of the amount of the specific substance amount Perform a regression analysis with penalties as the dependent variable. In other words, sample preparation by re-sampling and regression analysis with penalties for it are performed.
Conventionally, when analyzing microorganisms related to biological wastewater treatment, sufficient accuracy could not be obtained in penalized regression analysis because the types of microorganisms are diverse and the number of data is small. In the method for identifying a microorganism group according to the present embodiment, since the number of data can be significantly increased statistically by creating a sample by resampling, a regression analysis with penalties is performed using the increased sample. By doing so, a group of microorganisms related to the rate of change in the amount of the specific substance can be specified with high accuracy.

In the method for identifying a microorganism group of the present embodiment, the reliability of the microorganism group selected in the first selection step is calculated from the selection frequency in a sample created by resampling, and the microorganism group is determined based on the reliability. And a second selection step of further selecting. By preparing a sample by resampling and statistically increasing the number of data significantly, the reliability of selection can be accurately derived.
Therefore, a group of microorganisms related to the change rate of the amount of the specific substance can be specified with higher accuracy.

  In the method for identifying a microorganism group of the present embodiment, the content of the microorganism group selected in the first selection step, the second selection step or the fifth selection step as an independent variable, the corresponding specific substance The method includes a third selection step of performing a regression analysis using the rate of change in the amount as a dependent variable, and further selecting a microorganism group showing either a positive correlation or a negative correlation. Already selected in the first selection step, the second selection step or the fifth selection step, to use the data of the highly reliable microorganism group, a microorganism group that is positively or negatively correlated with the change rate of the amount of the specific substance, It can be specified accurately.

  In the method for identifying a microorganism group of the present embodiment, the content of the microorganism group selected in the first selection step or the second selection step is used as an independent variable, and the rate of change in the amount of the corresponding specific substance is dependent. A fourth selection step of performing a principal component regression analysis including a regularization term as a variable to further select a microorganism group exhibiting at least one of a positive correlation and a negative correlation. Since data of highly reliable microorganisms already selected in the first selection step or the second selection step is used, a microorganism group positively or negatively correlated with the rate of change in the amount of the specific substance can be accurately specified.

  In the method for identifying a microorganism group of the present embodiment, the content of the microorganism group selected in the first selection step or the second selection step is used as an independent variable, and the rate of change in the amount of the corresponding specific substance is dependent. As a variable, perform a regression analysis, further select a microorganism group based on the p-value, or, as an independent variable the content of the microorganism group selected in the first selection step or the second selection step, as Akaike information A fifth selection step of calculating a quantity standard (AIC) and further selecting a microorganism group based on the obtained AIC value is included. Since data of highly reliable microorganisms already selected in the first selection step or the second selection step is used, a microorganism group positively or negatively correlated with the rate of change in the amount of the specific substance can be accurately specified.

  Heretofore, most microorganisms involved in changes in the amount of a specific substance have been unknown. For this reason, it was difficult to understand the function of the microorganism even if the base sequence was decoded and phylogenetically classified, but according to the present embodiment, a group of microorganisms correlated with the change in the amount of the specific substance was identified. It can be specified with high accuracy.

≪Biological wastewater treatment≫
The method for specifying microorganisms of the present invention can be applied to, for example, a biological wastewater treatment method. In one embodiment, a microorganism group specified by the method for specifying a microorganism group of the present invention is detected, and the treatment condition of the treated water is controlled based on the increase or decrease of the microorganism group.
Here, the method for specifying a group of microorganisms of the present invention includes the method described in the section “Method for specifying a group of microorganisms”, and a detailed description thereof will be omitted.

First, a microorganism group related to a change in the amount of a specific substance is specified by the method for specifying a microorganism group of the present invention. The presence of the specified microorganism group in the treated water can be detected, for example, by detecting a nucleic acid having the sequence from the treated water based on the base sequence obtained in the decoding step. Further, the amount of the specified microorganisms in the treated water can be determined by the same operation as in the ratio determining step and the amount determining step.
For example, when the specified microorganism group is specified as contributing to the decomposition reaction of a desired substance in the treated water, the treatment conditions may be controlled to conditions suitable for the growth of the specified microorganism group.
For example, when it is specified that the specified microorganism group suppresses a decomposition reaction of a desired substance in the treated water, the treatment condition may be controlled to a condition that is not suitable for the growth of the specified microorganism group.
The treatment conditions include, for example, the temperature of treated water, pH, dissolved oxygen concentration, salt concentration, hydraulic retention time (HRT), sludge retention time (SRT), introduction of a microorganism-immobilizing carrier, growth promotion or inhibition of a substance. Addition, a stirring speed in the processing tank, and a combination of processing tanks having different processing conditions are mentioned.

  According to the biological wastewater treatment method of the embodiment, by detecting the microorganism group specified by the method for identifying the microorganism group of the present invention, by controlling the treatment conditions of the treated water based on the increase or decrease of the microorganism group. In addition, it is possible to improve the treatment efficiency of a specific substance of treated water.

As another embodiment of the biological wastewater treatment method to which the method for identifying a microorganism group of the present invention is applied, a microorganism resource containing at least one of the microorganism groups identified by the method for identifying a microorganism group of the present invention is transplanted. To treat the treated water.
Examples of the microbial resources include activated sludge, microbial preparations, microbial carriers, microbial strains, soil, sediment, seawater, river water, lake water, and the like.

According to the biological wastewater treatment method according to the present embodiment, by the method for identifying microorganisms of the present invention, by actively transplanting the identified microorganisms into the treated water, the treatment efficiency of the specific substance of the treated water Can be improved.
For example, when a new biological wastewater treatment device is installed, it is preferable to transplant at least one microorganism group specified by the microorganism group identification method of the present invention, and preferably to transplant a microorganism resource having a large amount of the microorganism group. The required processing performance can be obtained in a short time.
For example, when the microorganisms in the existing biological wastewater treatment device are killed due to temporary influx of toxins and the treatment performance is deteriorated, at least one of the microorganisms specified by the method for specifying the microorganisms of the present invention is used. By transplanting a microbial resource that contains, and preferably has a large amount of, those microbial groups, it is possible to recover the required processing performance in a short time.

  Hereinafter, the present invention will be specifically described with reference to test examples. However, the present invention is not limited to these.

[Example 1]
(1) Operation of biological wastewater treatment process, water quality analysis, decomposition rate calculation and collection of microbial samples Table 2 shows the solvents obtained by mixing industrial water and natural seawater at a volume ratio of 2: 3. Solutes were dissolved at the concentrations shown in Table 2 to prepare artificial wastewater (water to be treated).

  As shown in FIG. 7, an integrated biological treatment having a structure in which a biological treatment area 20a and a sedimentation area 20b are separated from each other by a partition wall 23 in one tank and communicate with each other below the partition wall 23. An apparatus 20 (processing tank) is prepared, and a sponge carrier 21 [fluid carrier (AQ-1 manufactured by Kanto INOAC)] having a size of 10 mm × 10 mm × 10 mm is placed in a biological treatment area 20 a of the biological treatment apparatus 20 in a volume ratio of 20. % (V / v).

  The above-mentioned water to be treated 24 flows into the biological treatment apparatus 20 of the first embodiment thus prepared, and activated sludge is introduced as a seed source of microorganisms, and the microorganisms are adapted to fix the microorganisms on the sponge carrier 21. At the time of the (first-stage treatment), the water to be treated 24 was introduced such that the hydraulic residence time was 24 hours. In addition, air aeration 22 was performed on the water 24 to be treated in each biological treatment apparatus 20 to form an aerobic fluidized bed, and the microorganisms were adapted.

  Immediately after the start of the biological treatment, the removal of thiocyanate ions was observed. However, the tendency of the pH to gradually decrease was observed, and the removal of thiocyanate ions was unstable. From the 69th day onward, the treatment was continued while adjusting the pH to about 7.5 using a 5 wt% -sodium hydroxide aqueous solution, and at the stage where the thiocyanate ion removal rate was 98% or more and stabilized, the microorganisms were treated (No. (One-stage processing) has been completed. At the end of the microbial adaptation treatment (first-stage treatment), nitrite ions had increased.

After the completion of the microorganism accommodating treatment (the first-stage treatment), the thiocyanate ion concentration and the nitrite ion concentration of the treated water in the biological treatment region 20a of each biological treatment device 20 are measured, and the thiocyanate ion and the nitrite ion are measured. Was monitored. Monitoring was performed approximately every 7 days.
In addition, while measuring the pH of the treated water in the biological treatment region 20a of each biological treatment device 20 and monitoring the pH value, the hydraulic retention time in the region becomes 18 hours from 90 days after the start of operation. As described above, the inflow amount of the water 24 is increased (second stage treatment), and the inflow amount of the water 24 is set so that the hydraulic residence time in the region becomes 12 hours from the 111th day after the start of operation. Further increase (third stage treatment), and further increase the inflow of the water 24 to be treated so that the hydraulic residence time in the region becomes 8 hours from the 118th day after the start of operation (fourth stage treatment) Finally, the operation was continued until the 175th day.

  During this period, the reduction tendency of the production of nitrite ions began to be observed while maintaining the removal rate of thiocyanate ions at a high value by reducing the hydraulic residence time in the region to 18 hours in the second stage treatment. In addition, by reducing the hydraulic retention time in the region to 12 hours in the third stage treatment, the generation of nitrite ions is almost completely suppressed while maintaining the thiocyanate ion removal rate at a high value. Further, even when the hydraulic retention time in the region is reduced to 8 hours in the fourth stage treatment, the removal rate of thiocyanate ions can be maintained at a high value while suppressing the generation of nitrite ions. It was confirmed.

  In the biological treatment in Example 1, the rate of nitrite generation and the rate of thiocyan removal per day with respect to the number of operating days were calculated according to equations (1) and (2).

  FIG. 8 shows the nitrite generation rate and FIG. 9 shows the thiocyan removal rate per operating days.

(2) DNA extraction, base sequence decoding, and determination of microorganism group DNA extraction from sponge carrier 21 to which microorganisms are attached in biological treatment region 20a of biological treatment device 20 and next-generation sequencing microflora analysis are outsourced (J-Bio21). ).
At each time corresponding to the measurement time of the rate of change in the amount of the specific substance, the sponge carrier to which the microorganisms are attached is collected, and the collected sponge carrier is divided into four parts, and then the Extract Soil DNA Plus ver. DNA was extracted and purified using 2 (J-Bio21).
The DNA concentration of the purified DNA solution was measured using PicoGreen dsDNA Assay Kit (Invitrogen).
Using the primers shown in Table 3, PCR amplification was performed on the V4 and V5 regions of the 16S rRNA gene of eubacteria.

The nucleotide sequence was determined by analyzing the PCR product using a next-generation sequencer (MiSeq).
The obtained base sequence was subjected to the following analysis using a QIIME (Quantitative Insights Into Microbiological Economy) pipeline. First, the quality of the data and the chimeras were checked, and only the sequence data meeting the criteria were filtered.
For sequence data that satisfies the criterion, sequence data having high similarity (having a homology of 97% or more) are grouped into one group of clusters, and the most frequently occurring sequence in each cluster sequence is represented by a representative OTU (OTU; Operational). Taxonomic Unit;
(Operational classification unit) sequence, and the subsequent analysis was performed using the representative sequence. That is, the detected presence and amount of each OTU was treated as indicating the presence and amount of one microorganism group.
As a result, a total of 3,752 OTU was detected from the microorganism samples of Example 1 and Example 2 described later. These OTUs were variously detected in each sample, and variously detected in only one sample.
Further, the relative ratio of each OTU to all OTUs was calculated from the number of detections of each OTU.
FIG. 10 is a graph showing the relative ratio of each OTU to all the detected 3752 OTUs.

  For each representative OTU sequence, a homology search was performed against Greengene's 16S rRNA gene database to estimate the phylogenetic classification.

(3) Determination of the amount of each microorganism group The number of eubacteria genes adhering to the sponge was quantified by the QP-PCR method (J-Bio21), which is one of the real-time PCR methods.
After appropriately diluting the DNA solution purified in the above (2), a reaction solution was prepared using the primers and QProbe shown in Table 4, and the number of genes was quantified by Rotor-Gene Q (QIAGEN).

  In the above (2), the relative ratio of each OTU to all OTUs was calculated. However, the total amount of microorganisms attached to the sponge varies depending on the collection date. Therefore, in order to accurately grasp the fluctuation of each OTU during the operation period of the biological wastewater treatment process, the amount of each OTU is determined by multiplying the relative ratio of each OTU by the number of the genuine bacteria attached to the sponge quantified. did.

(4) Preparation of Bootstrap sample, narrowing down of main water treatment microorganisms by Lasso method, regression analysis The main microorganisms involved in nitrite generation and thiocyanogen removal were estimated by statistical analysis.
As shown in the above (3), the relative ratio of each OTU was multiplied by the number of eubacteria genes attached to the sponge that was quantified, and statistically analyzed using the value of each OTU corrected by multiplying it by nitrite. The major OTUs involved in formation and thiocyanate removal were estimated.
As the value of the amount of each OTU in the data set used for the estimation, a value obtained by collecting at each time corresponding to the measurement reference time of the data of nitrite generation and thiocyanate removal rate was used (for example, operation (N + 7)). The data of the nitrite generation rate or the thiocyanate removal rate calculated from the substance amount on the day and the data of the amount of each OTU obtained on the operation (N + 7) day were used.)
However, while the data on nitrite generation and thiocyanate removal rates was 23, the total number of OTUs obtained using the next-generation sequencer was more than 100 times that of the data, and the correlation was analyzed by ordinary regression analysis. Was impossible.
Therefore, the present inventors decided to perform analysis using the Lasso method, which can simultaneously perform estimation and variable selection. Here, 1000 sets of bootstrap samples were prepared, and the number of data sets obtained by the measurement was pseudo-increased by applying the Lasso method to those samples, and the microorganism group was estimated by regression analysis. Successful. As a result, it was possible to narrow down the microorganism group affecting nitrite generation and thiocyan removal rate to 27 OTU.

Next, out of the 27 OTUs narrowed down above, OTUs with higher reliability with respect to the effect on nitrite generation and thiocyanate removal rate were further narrowed down. By further narrowing down the OTUs, a method for quantifying each OTU can be established, and the time and cost of periodically quantifying the OTUs can be reduced.
Usually, it is difficult to calculate the reliability by the regression analysis using the Lasso method alone. However, this time, 1000 sets of Bootstrap samples were prepared, and the reliability of the estimated microbial population was successfully obtained by applying the Lasso method to those samples.
Of the 27 OTUs estimated this time, 6 OTUs showed high reliability (0.6 or more) for nitrite generation, and 5 OTUs showed high reliability for thiocyanate removal.

As described above, a Bootstrap specimen was prepared and analyzed by the Lasso method, so that OTU that affects nitrite generation and thiocyanate removal rate could be extracted with high reliability. However, the OTU that contributes negatively has been nitrous acid removal and thiocyanogenesis, and is a group of microorganisms that have performed the opposite reaction to nitrite production and thiocyanogen removal.
Therefore, a regression analysis (maximum likelihood method) was again performed on the 6 or 5 OTUs selected as described above to calculate regression coefficients and p-values for nitrite generation and thiocyanate removal rates. As a result, OTUs positively correlated with nitrite generation and thiocyanate removal rate were 30 OTUs, respectively, and it was concluded that these were the main microbial groups involved in nitrite generation and thiocyanate removal, respectively.

The results are shown in Tables 5 and 6. Tables 5 and 6 show an OTU in which a bootstrap sample was prepared, the OTU of which the influence on the nitrite generation and the thiocyanate removal rate was estimated by the Lasso method, its reliability, and a high reliability (0.6 or more). Shows the regression coefficient and p-value calculated by performing a regression analysis (maximum likelihood method) on.
When it is necessary to further narrow down, based on the p-value of the regression analysis, for example, it is sufficient to narrow down to only OTU less than 0.05.

(5) Prediction by creating a regression equation A regression analysis was performed using the OTU (reliability 0.6 or more) selected in (4) as an independent variable to predict each water quality. In this case, the regression coefficient was also performed including the negative OTU.
As a result, the experimental value and the predicted value agreed with very high accuracy. In addition, further verification was performed by the cross-validation method, and it was also found that the results coincided with high accuracy.
11 and 12 show measured values of nitrite generation and thiocyanate removal rates, predicted values by regression analysis, and predicted values obtained by excluding one sample by cross-validation, respectively. In the figure, the dashed line indicates the actual measurement value, the solid line indicates the predicted value obtained by regression analysis, and the broken line indicates the predicted value obtained by cross-validation except for one sample.
As a result, it was shown that a water treatment microorganism group can be selected with high accuracy, and that water quality can be predicted by using the selected microorganism group as an independent variable.

[Example 2]
(1) Operation of biological wastewater treatment process, analysis of water quality, calculation of decomposition rate, and collection of microorganism sample Table 7 shows the solvent obtained by mixing industrial water and natural seawater at a volume ratio of 2: 3. Solutes were dissolved at the concentrations shown in Table 7 to prepare artificial drainage (water to be treated). In Example 2, in addition to the solute of Example 1, phenol and thiosulfate ions, which are main COD components contained in coke oven wastewater, were added.

As shown in FIG. 7, an integrated biological treatment having a structure in which a biological treatment area 20a and a sedimentation area 20b are separated from each other by a partition wall 23 in one tank and communicate with each other below the partition wall 23. An apparatus 20 was prepared. In addition, a sponge carrier [fluid carrier (AQ-1 manufactured by Kanto INOAC)] having a size of 10 mm × 10 mm × 10 mm and a high concentration of activated sludge as a microbial seeding source are put into a plastic bottle, and the mixture is hand-kneaded overnight. Microorganisms were attached to the sponge carrier by immersing the lid.
The sponge carrier 21 and the activated sludge thus prepared are put into the biological treatment region 20a of the biological treatment device 20 so that the volume ratio of the sponge carrier 21 becomes 20% (v / v). Was prepared.

  In the biological treatment apparatus 20 of the second embodiment prepared as described above, the above-mentioned water to be treated 24 flows into the biological treatment apparatus 20, and at the time of the microorganism adaptation treatment (first-stage treatment) for fixing microorganisms to the sponge carrier 21, the water to be treated is Twenty-four hydraulic residence times were allowed to flow in for 24 hours. In addition, air aeration 22 was performed on the water 24 to be treated in each biological treatment apparatus 20 to form an aerobic fluidized bed, and the microorganisms were adapted. The treatment was performed while adjusting the pH to around 7.5 using a 5 wt% -sodium hydroxide aqueous solution.

Immediately after the start of the biological treatment, the removal of thiocyanate ions was observed. After that, the removal rate of thiocyanate ions was stabilized at 99% or more. finished. During this microbial adaptation treatment (first-stage treatment), 22% or more of the ammonia in the water to be treated was oxidized to nitrite ions.
The microbial adaptation treatment (first-stage treatment) could be completed 71 days earlier than in Example 1 in which the microorganisms were not fixed on the sponge carrier in advance. This is because the pH was adjusted and because the sponge carrier 21 was rubbed well in advance, covered overnight, and immersed.

After the completion of the microorganism acclimatization treatment (the first stage treatment), the thiocyanate ion concentration and the nitrite ion concentration of the treated water in the biological treatment region 20a of the biological treatment device 20 are measured, and the thiocyanate ion and the nitrite ion concentration are measured. Monitoring was performed.
In addition, while measuring the pH of the treated water in the biological treatment area 20a of the biological treatment apparatus 20 and monitoring the pH value, the hydraulic retention time in the area is set to 18 hours from the 19th day after the start of operation. The inflow of the water 24 to be treated is increased (second stage treatment), and the inflow of the water 24 to be treated is further increased from the 39th day after the start of operation so that the hydraulic residence time in the region becomes 12 hours. The amount of inflow of the water to be treated 24 was further increased so that the hydraulic residence time in the region became 8 hours from the 46th day after the start of operation (third stage treatment). Thereafter, the inflow of the water 24 to be treated was reduced so that the hydraulic retention time in the region became 10 hours from the 74th day (fifth stage treatment), and the hydraulic retention time in the region from the 96th day. Was reduced to 24 hours (the sixth-stage treatment), and finally the operation was continued until the 164th day.

During this period, the reduction tendency of the production of nitrite ions began to be observed while maintaining the removal rate of thiocyanate ions at a high value by reducing the hydraulic residence time in the region to 18 hours in the second stage treatment. By further reducing the hydraulic residence time in the region to 12 hours, the generation of nitrite ions could be further suppressed while maintaining the thiocyanate ion removal rate at a high value.
However, when the hydraulic retention time in the region is further reduced to 8 hours in the fourth stage treatment, while the generation of nitrite ions is almost completely suppressed, the removal rate of thiocyanate ions is maintained for a while. Decreased. This is because microorganisms that decompose phenol and thiosulfuric acid, which were not contained in the water to be treated in Example 1, lived on the surface of the sponge carrier 21, and microorganisms that remove thiocyanate ions lived on the surface of the sponge carrier 21. It is considered that the number of fields was reduced, and as a result, the removal rate decreased.
As described above, since the thiocyanate ion removal rate has risen beyond the target value, in the fifth stage treatment, the hydraulic residence time is reduced by the conditions of the fourth stage treatment (the hydraulic residence time is 12 hours). The biological treatment was carried out after returning to 10 hours close to ()). As a result, the generation of nitrite ions could be almost completely suppressed while maintaining the thiocyanate ion removal rate at 94% or more.
Therefore, in the sixth stage treatment, the hydraulic retention time was extended to 24 hours, and while maintaining the thiocyanate ion removal rate at a high value, more surprisingly, Over the period, the generation of nitrite ions could be almost completely suppressed.

  In the biological treatment in Example 2, the nitrite generation rate, thiocyan removal rate, thiosulfuric acid removal rate, and phenol removal rate per day with respect to the number of operating days were determined by the above formulas (1), (2) and It was calculated according to equation (3) and equation (4) below.

  In the biological treatment in Example 2, the nitrite generation rate, the thiocyanate removal rate, the thiosulfuric acid removal rate, and the phenol removal rate are shown in FIG. 13, FIG. 14, Thiosulfuric acid removal rate, and FIG. This is shown in FIG.

(2) DNA extraction, base sequence decoding, and determination of microorganism group DNA extraction, base sequence decoding, and microorganism group determination were performed in the same manner as in (2) of Example 1 above.

(3) Determination of the amount of each microorganism group DNA extraction, nucleotide sequence decoding, and microorganism group determination were performed in the same manner as in (3) of Example 1 above.

(4) Preparation of Bootstrap specimen and narrowing down of main water treatment microorganisms by Lasso In the same manner as in (4) of Example 1, the main microorganisms involved in nitrite generation, thiocyanic acid removal, thiosulfuric acid removal and phenol removal were identified. Estimated by statistical analysis.
As a result of the analysis by the Lasso method, it was possible to narrow down the microorganisms that affect nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal rates to 28 OTU, 34 OTU, 36 OTU and 36 OTU, respectively.

  Of the OTUs estimated this time, the OTU that showed high reliability (0.6 or more) for nitrite generation was 3 OTU, the OTU that showed high reliability for thiocyanide removal was 5 OTU, and the OTU that showed high reliability was thiosulfate removal. The OTU that showed high reliability was 5 OTU, and the OTU that showed high reliability for phenol removal was 6 OTU.

As described above, by preparing a bootstrap sample and analyzing it by the Lasso method, it was possible to extract OTU which affects nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal rate with high reliability. OTU that contributed to the reaction was nitrite removal, thiocyanate formation, thiosulfate formation and phenol formation, and the microorganisms that had the opposite reactions to nitrite formation, thiocyanate removal, thiosulfate removal and phenol removal, respectively. Become.
Therefore, by performing a regression analysis on the highly reliable OTU selected as described above, the regression coefficients and p-values for the nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal rates were calculated. It was a regression coefficient. For this reason, it was concluded that the highly reliable OTU selected as described above was a major microorganism group related to nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal.
The results are shown in Tables 8 to 11. Table 8, Table 9, Table 10 and Table 11 prepared Bootstrap specimens and estimated the effects on nitrous acid generation, thiocyanate removal, thiosulfate removal and phenol removal rates by the Lasso method and their reliability, and high The regression coefficient and p-value calculated by performing regression analysis (maximum likelihood method) on the OTU showing the reliability (0.6 or more) are shown.
When it is necessary to further narrow down, based on the p-value of the regression analysis, for example, it is sufficient to narrow down to only OTU less than 0.05.

(5) Prediction by Creating Regression Equation As a result of performing regression analysis in the same manner as in (5) of Example 1 and predicting each water quality, the experimental value and the predicted value matched with extremely high accuracy. In addition, further verification was performed by the cross-validation method, and it was found that the values coincided with high accuracy.
17, 18, 19, and 20 show the measured values of the nitrite generation rate, the thiocyanate removal rate, the thiosulfate removal rate, and the phenol removal rate, the predicted values by regression analysis, and the predictions except one sample by cross-validation. Each value is shown. In the figure, the dashed line indicates the actual measurement value, the solid line indicates the predicted value obtained by regression analysis, and the broken line indicates the predicted value obtained by cross-validation except for one sample.
As a result, it was shown that a water treatment microorganism group can be selected with high accuracy, and that water quality can be predicted by using the selected microorganism group as an independent variable.

[Example 3]
Using the results of (1) to (3) of Example 2 above, in (4) of Example 2 described above, the following (4 ′) was used for the preparation of Bootstrap specimens and the group of microorganisms narrowed down by the Lasso method. The analysis by SPCR shown was performed.
In the above Example 2, as a result of the analysis by the Lasso method, the microbial groups affecting the nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal rates were narrowed down to 28 OTU, 34 OTU, 36 OTU and 36 OTU, respectively.

(4 ′) Narrowing down the main water treatment microorganisms by SPCR method for the narrowed down microorganisms, estimating the relationship between the microorganism species, and estimating the water treatment speed from the narrowed down microorganisms. Then, the analysis was performed using a sparse principal component regression model (SPCR) that can estimate the relationship between the microorganism species at the same time as narrowing down the main water treatment microorganism groups.
As a result of the analysis by the SPCR method, the numbers of the main components selected for the nitrite generation, the thiocyanate removal, the thiosulfate removal, and the phenol removal rate were 2, 3, 3, and 1, respectively. It can be inferred that OTUs with the same principal component axis are interrelated or affected by the same environmental factors.
Table 12 shows the main component axes for nitrite generation, Table 13 shows the main component axes for thiocyanate removal, Table 14 shows the main component axes for thiosulfuric acid removal, and Table 15 shows the main component axes for phenol removal. An OTU with a principal component value of 0 in each axis can be determined to be an OTU that does not contribute to the generation or processing of each substance, and can be narrowed down to an OTU in which the principal component value in each axis is not 0. it can. Therefore, as a result of the analysis by the SPCR method, the microorganism groups affecting the nitrite generation, thiocyanate removal, thiosulfate removal and phenol removal rates could be narrowed down to 28 OTU, 27 OTU, 32 OTU and 35 OTU, respectively.

  21, 22, 23, and 24 show measured values of nitrite generation rate, thiocyan removal rate, thiosulfuric acid removal rate, and phenol removal rate, and predicted values verified by removing one sample by cross-validation, respectively. In the figure, the dashed-dotted line is the actual measurement value, and the solid line is the predicted value verified by removing one sample by cross-validation.

The intercept for nitrous acid production was 32.70551, the regression coefficient for the first principal component axis was 3.3777741, and the regression coefficient for the second principal component axis was 4.634266. It can be seen that if the value obtained by multiplying the value of the main component of OTU on each principal component axis by the regression coefficient is positive, it has a positive effect on nitrite generation, and if negative, it has a negative effect. The predicted R ² value calculated by cross validation method is 0.575, changes in the measured and predicted values is as shown in FIG. 21, the predicted values and the measured values were consistent with high accuracy.
Creating Bootstrap specimens was carried out in Example 2, the predicted ^{R 2} value of the values obtained by the calculation and regression in Lasso method is 0.709, creating a Bootstrap specimens was carried out in Example 3, calculated in Lasso method and predicted ^{R 2} value of the values obtained by the principal component regression analysis (SPCR) method was 0.575.

The intercept for thiocyan removal is 361.94, the regression coefficient for the first principal component axis is 3.955, the regression coefficient for the second principal component axis is -22.30, and the regression coefficient for the third principal component axis is -75.96. , the predicted ^{R 2} value calculated by cross-validation method was 0.708. It can be seen that if the value obtained by multiplying the value of the principal component of OTU on each principal component axis by the regression coefficient is positive, it has a positive effect on thiocyan removal, and if negative, it has a negative effect. The transition between the measured value and the predicted value was as shown in FIG. 22, and the measured value and the predicted value coincided with high accuracy.
Creating Bootstrap specimens was carried out in Example 2, the predicted ^{R 2} value of the values obtained by the calculation and regression in Lasso method is 0.676, creating a Bootstrap specimens was carried out in Example 3, calculated in Lasso method and since the predicted R ² values obtained by principal component regression analysis (SPCR) method is 0.708, it is understood that further improved microbial identification accuracy by adopting the SPCR method.

The intercept for thiosulfuric acid removal is 399.47, the regression coefficient for the first principal axis is 0.398, the regression coefficient for the second principal axis is 22.99, the regression coefficient for the third principal axis is 60.04, predicted ^{R 2} value calculated by cross-validation method was 0.721. It can be seen that if the value obtained by multiplying the value of the main component of OTU on each principal component axis by the regression coefficient is positive, the removal of thiosulfuric acid has a positive effect. The transition between the measured value and the predicted value is as shown in FIG. 23, and the measured value and the predicted value match with high accuracy.
Creating Bootstrap specimens was carried out in Example 2, the predicted ^{R 2} value of the values obtained by the calculation and regression in Lasso method is 0.674, creating a Bootstrap specimens was carried out in Example 3, calculated in Lasso method and since the predicted R ² values obtained by principal component regression analysis (SPCR) method is 0.721, it is understood that further improved microbial identification accuracy by adopting the SPCR method.

Sections related to the phenol removal 204.8045, regression coefficients 25.152, predicted ^{R 2} value calculated by cross-validation method was 0.672. It can be seen that if the value obtained by multiplying the value of the main component of OTU on each principal component axis by the regression coefficient is positive, phenol removal has a positive effect, and if negative, it has a negative effect. The transition between the measured value and the predicted value is as shown in FIG. 24, and the measured value and the predicted value match with high accuracy.
Creating Bootstrap specimens was carried out in Example 2, the predicted ^{R 2} value of the values obtained by the calculation and regression in Lasso method is 0.612, creating a Bootstrap specimens was carried out in Example 3, calculated in Lasso method and since the predicted R ² values obtained by principal component regression analysis (SPCR) method is 0.672, it is understood that further improved microbial identification accuracy by adopting the SPCR method.

[Example 4]
Using the data of the thiocyan removal rate in (1) to (3) of the first embodiment, in (4) of the first embodiment, the value of the amount of each OTU in the data set used for the estimation is used as the value of the thiocyan removal rate. Data obtained by sampling each time corresponding to the measurement reference time and data obtained by sampling one time before the time corresponding to the measurement reference time were used (for example, on the (N + 14) th day of operation). Of the thiocyanate removal rate calculated from the thiocyanine amount of the above and the data of the amount value of each OTU obtained on the operation (N + 7) day (before the temporary point) and the operation (N + 14) day. Other than that, the microorganism group was narrowed down to those having a reliability of 0.6 or more by the preparation of a Bootstrap specimen and the Lasso method in the same manner as in (4) of Example 1 described above.
Table 16 shows the results.

The OTU which showed high reliability (0.6 or more) for thiocyan removal was 5 OTU. The OTU of “— 1” in the table is data obtained by sampling one time before the time corresponding to the measurement reference time.
Then, based on the results obtained, in the same manner as in Example 1 (5) to calculate the predicted R ² value by cross-validation. Calculated prediction R ² value is 0.903, very high prediction accuracy was observed.
Incidentally, in the case of not using the value of the quantity of one time before the OTU to the measurement reference time point of thiocyanate removal rate, because the predicted R ² value was 0.765, one time than the measurement reference time point of thiocyanate removal rate It can be seen that by including the data on the content of the previous microorganism group in the analysis, the microorganism identification accuracy could be further improved.

[Example 5]
In the above Example 4, a bootstrap sample was prepared and the microorganism group was determined to have a reliability of 0. 0 using the Lasso method in the same manner as in Example 4 except that the data on the thiocyan removal rate obtained in Example 2 was used. I narrowed down to 6 or more.
The OTU which showed high reliability (0.6 or more) for thiocyan removal was 6 OTU. The OTU of “— 1” in the table is data obtained by sampling one time before the time corresponding to the measurement reference time.
Next, a regression analysis (maximum likelihood method) was performed again on the selected 6 OTUs in the same manner as in (4) of Example 1 to calculate a regression coefficient and a p-value for the thiocyan removal rate.
Table 17 shows the results.

As a result, all of the selected 6OTUs showed a positive relationship with the thiocyanate removal rate, and it was concluded that each of the 6OTUs was a major microorganism group related to thiocyanate removal.
Further, based on the p-value of the regression analysis, the selected 6 OTUs were narrowed down to OTUs having a p-value of less than 0.05. As a result, it was possible to narrow down the microorganism group affecting the thiocyanate removal rate to 5 OTU.
It was then calculated predicted R ² value by cross validation method in the same manner as in Example 1 (5). Predicted ^{R 2} value calculated using the data of 5OTU which narrowed down finally was 0.711.
Incidentally, in the case of not using the value of the quantity of one time before the OTU to the measurement reference time point of thiocyanate removal rate, because the predicted R ² value was 0.677, one time than the measurement reference time point of thiocyanate removal rate It can be seen that by including the data on the content of the preceding microorganism group in the analysis, the microorganism identification accuracy can be further improved.
Moreover, since the predicted R ² value data calculated using the 6OTU not subjected to narrowing at p value was 0.622, by performing the narrowing based on the p value of the regression analysis, the microorganism identification accuracy It can be seen that further improvement was achieved.

[Example 6]
In the above-mentioned Example 4, except that the data of the phenol removal rate obtained in the above-mentioned Example 2 was used, a microorganism group was determined to have a reliability of 0. 0 by the preparation of a Bootstrap sample and the Lasso method in the same manner as in the above-mentioned Example 4. I narrowed down to 6 or more.
The OTU which showed high reliability (0.6 or more) for phenol removal was 6 OTU. The OTU of “— 1” in the table is data obtained by sampling one time before the time corresponding to the measurement reference time.
Next, a regression analysis (maximum likelihood method) was performed again on the selected 60TU as in (4) of Example 1 to calculate a regression coefficient and a p-value for the phenol removal rate.
The results are shown in Table 18.

As a result, all of the selected 6OTUs showed a positive relationship with the phenol removal rate, and it was concluded that each of the 6OTUs was a major microorganism group related to phenol removal.
Further, based on the p-value of the regression analysis, the selected 6 OTUs were narrowed down to OTUs having a p-value of less than 0.05. As a result, the group of microorganisms affecting the phenol removal rate could be narrowed down to 5 OTU.
It was then calculated predicted R ² value by cross validation method in the same manner as in Example 1 (5). Predicted ^{R 2} value calculated using the data of 5OTU which narrowed down finally was 0.732.
Incidentally, in the case of not using the value of the quantity of one time before the OTU to the measurement reference time point of phenol removal rate, because the predicted R ² value was 0.613, one time than the measurement reference time point of phenol removal rate It can be seen that by including the data on the content of the preceding microorganism group in the analysis, the microorganism identification accuracy can be further improved.
Moreover, since the predicted R ² value data calculated using the 6OTU not subjected to narrowing at p value was 0.648, by performing the narrowing based on the p value of the regression analysis, the microorganism identification accuracy It can be seen that further improvement was achieved.

[Example 7]
Microorganisms were narrowed down to 6 OTU having a reliability of 0.6 or more by the preparation of a Bootstrap specimen and the Lasso method as in Example 6 above.
Next, the results of the narrowing down were narrowed down by the AIC.
Table 19 shows the patterns of combinations of variable selection by AIC.

In the table, AIC values are shown in the upper row. In addition, “〇” in the table indicates that the data on the content of the microorganism group was used to calculate the AIC value, and “×” in the table indicates that the data on the content of the microorganism group was not used. It represents that.
In this example, the one with the smallest AIC value was adopted (the highest model was adopted) based on the obtained AIC value.
As a result, denovo 2647_1 was excluded, and the microorganism group affecting the phenol removal rate could be narrowed down to 50 OTU.
It was then calculated predicted R ² value by cross validation method in the same manner as in Example 1 (5). Predicted ^{R 2} value calculated using the data of 5OTU which narrowed down finally was 0.732.
Incidentally, in the case of not using the value of the quantity of one time before the OTU to the measurement reference time point of phenol removal rate, because the predicted R ² value was 0.613, one time than the measurement reference time point of phenol removal rate It can be seen that by including the data on the content of the preceding microorganism group in the analysis, the microorganism identification accuracy can be further improved.
Further, the predicted R ² value calculated using the data of 6OTU you did not refine in AIC so was 0.648, by performing the narrowing based on the value of the AIC, can further improve the microbial identification accuracy You can see that

  Each configuration and each combination in each embodiment is an example, and addition, omission, substitution, and other changes of the configuration are possible without departing from the gist of the present invention. The present invention is not limited by each embodiment, but is limited only by the scope of the claims.

20: biological treatment apparatus, 20a: biological treatment area, 20b: sedimentation area, 21: sponge carrier,
22 ... air aeration, 23 ... partition walls, 24 ... treated water, 25 ... treated water treated by biological treatment equipment

Claims (14)

A method for identifying a group of microorganisms, comprising identifying the group of microorganisms involved in a change in the amount of a specific substance, comprising the following steps:
A specimen for preparing a specimen by resampling from a data set obtained by measuring the rate of change of the amount of the specific substance in the microorganism sample containing the specific substance and the microorganism, and the content of the microorganism group in which the microorganism is classified Creation process,
For the sample created by the re-sampling, the content of the microorganism group as an independent variable, the corresponding change rate of the amount of the specific substance as a dependent variable, a regression analysis with penalties that can reduce the regression coefficient to 0, Performing, the first selection step of selecting a microorganism group corresponding to the independent variable selected based on the regression coefficient,
A specifying step of specifying the selected microorganism group as a microorganism group related to a change in the amount of the specific substance.   In the data set, the content of the microorganism group was obtained by measuring the content of the microorganism group at the same time and / or before the same time as the measurement reference time of the rate of change in the amount of the specific substance. The method for identifying a microorganism group according to claim 1, which is a microorganism.   The method further includes a second selection step of calculating reliability from a selection frequency of a sample of the microorganism group selected in the first selection step in a sample created by resampling, and further selecting a microorganism group based on the reliability. The method for identifying a microorganism group according to claim 1 or 2. Further, the content of the microorganisms selected in the first selection step or the second selection step as an independent variable, the change rate of the corresponding amount of the specific substance as a dependent variable, regression analysis, p-value Further selecting a microorganism group based on, or, further, the content of the microorganism group selected in the first selection step or the second selection step as an independent variable, the corresponding change rate of the amount of the specific substance the change rate of the specific substance The method according to any one of claims 1 to 3, further comprising a fifth selection step of calculating an Akaike information criterion (AIC) as a dependent variable and further selecting a microorganism group based on the obtained AIC value. The method for identifying a microorganism group described in the above. The selection based on the value of the Akaike information criterion,
Selecting a microorganism group as a combination of independent variables that minimizes the value of the AIC,
Selecting a group of microorganisms containing more than a majority by a combination of independent variables from the smallest AIC value to the m-th one, or a combination of the m-th independent variables determined by the histogram of the AIC for the higher number. The method for identifying a group of microorganisms according to claim 4, wherein a group of microorganisms contained in a majority is selected in (m is an integer of 1 or more).   Further, the first selection step, the second selection step, or the content of the microorganisms selected in the fifth selection step as an independent variable, as a dependent variable the rate of change of the amount of the specific substance corresponding, The method for identifying a group of microorganisms according to any one of claims 1 to 5, further comprising a third selection step of performing a regression analysis to further select a group of microorganisms showing either a positive correlation or a negative correlation.   Furthermore, a regularization term, wherein the content of the microorganism group selected in the first selection step or the second selection step is used as an independent variable, and the corresponding change rate of the amount of the specific substance is used as a dependent variable, The method for identifying a group of microorganisms according to any one of claims 1 to 3, further comprising a fourth selection step of performing a component regression analysis to further select a group of microorganisms exhibiting at least one of a positive correlation and a negative correlation.   The method according to claim 7, wherein a one-step principal component regression model based on sparse regularization is used for the principal component regression analysis.   The method for identifying a microorganism group according to any one of claims 1 to 8, wherein a regression analysis method with an L1 regularization term is used for the regression with penalties. The method for identifying a microorganism group according to any one of claims 1 to 9, further comprising the following steps:
In the microorganism sample, a speed acquisition step of acquiring a value of a change rate of the amount of the specific substance,
Decoding step of decoding the base sequence of the microorganism contained in the microorganism sample,
From the decoded base sequence, classify the microorganisms contained in the microorganism sample into microorganism groups, a ratio determining step of determining the relative content of the microorganism group in the microorganism sample,
An amount determination step of determining the content of the microorganism group in the microorganism sample from the determined relative content ratio of the microorganism group.   The method according to claim 10, wherein a sequencer is used for decoding the base sequence. The microorganism is a microorganism used for biological wastewater treatment,
The microorganism sample is treated water in a treatment tank where the wastewater treatment is performed,
The change rate is calculated from the amount of the specific substance measured for the treated water,
The specimen prepared by the re-sampling includes the rate of change of the amount of the specific substance at two or more times in the same treatment tank and data on the content of the microorganism group according to any one of claims 1 to 11. A method for identifying the microorganism group described in the above.   The method for identifying a microorganism group according to any one of claims 1 to 12, wherein the specific substance is at least one selected from the group consisting of ammonia, phenol, thiocyanate, and thiosulfuric acid.   2. The microorganism according to claim 1, wherein the microorganism is at least one selected from the group consisting of a microorganism that oxidizes ammonia to produce nitrite, a microorganism that decomposes phenol, a microorganism that decomposes thiocyanate, and a microorganism that decomposes thiosulfate. 14. The method for identifying a microorganism group according to any one of items 13 to 13.