Compost N2O emission path identification and environmental risk quantification method based on multiple omics

Abstract

The invention belongs to the technical field of biological information, and particularly relates to a compost N2O emission path identification and environmental risk quantification method based on multiple omics. The method comprises the following steps: extracting and sequencing total DNA (Deoxyribonucleic Acid) and total RNA (Ribonucleic Acid) of a collected compost sample; by analyzing metagenome data, reconstructing an N2O-generated microbial genome and performing function annotation on the N2O-generated microbial genome; according to a functional gene set annotated by a genome, dividing four generation ways of nitrification, nitrifying bacteria denitrification, heterotrophic denitrification and reduction of dissimilatory nitrate into ammonium; and analyzing gene expression dynamics in the genome by combining with a metatranscriptome and carrying out risk quantification. Compared with targeted gene detection technologies such as PCR (polymerase chain reaction) and the like, the method has the advantages that the generation way and the emission potential of N2O in a composting system are accurately recognized by fusing the metagenome and the metatranscriptome, the limitation of a traditional method on the gene coverage degree is broken through, and the method has important application value on accurate management and control of organic solid waste engineering greenhouse gases.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological information, and particularly relates to a method for identifying a compost N2O emission path and quantifying environmental risks based on multiomics. BACKGROUND

[0002] With the increasing demand for the resourceization of organic waste, composting technology, as an ecological engineering method for converting organic solid waste into agricultural fertilizer, has become an important way for sustainable waste management. However, the problem of a large amount of emitted greenhouse gas nitrous oxide (N2O) in the composting process is increasingly prominent. According to the IPCC assessment, the global warming potential of N2O on a 100-year scale is 273 times that of CO2, and its photolysis product is equivalent to 6% of the destruction of stratospheric ozone by chlorofluorocarbons. In the composting system, N2O is mainly produced by the following microbial metabolic pathways: nitrification, denitrification mediated by nitrifying bacteria, heterotrophic denitrification, and dissimilatory nitrate reduction to ammonium (DNRA). These processes are highly heterogeneous in space and time, making process optimization aimed at reducing N2O emission face significant uncertainty.

[0003] The current mainstream N2O emission path identification technology has limitations: the isotope site preference method (SP) can trace the source of N2O through isotope fingerprinting, but its benchmark database is mostly established in pure culture systems, which is difficult to cover the complex environment of multiple concurrent paths in composting, and some pathway data is scarce, which is easy to overestimate or underestimate the contribution. The inhibitor method quantifies the contribution of each pathway by selectively inhibiting specific gas production pathways, but the type and dosage of inhibitors are greatly affected by environmental conditions, which is easy to introduce experimental errors. Molecular biology methods (such as qPCR) can measure functional gene abundance, but it is difficult to analyze the gene interaction effects within microorganisms. For example, Chinese patent application CN 117230160 A discloses a method for evaluating the greenhouse gas N2O release potential of river sediments, which only evaluates the N2O release potential through the nirK / nirS to nosZ gene abundance ratio, only focuses on the denitrification pathway, and uses the gene abundance of nirK / nirS to represent the N2O production potential of the denitrification pathway, which overestimates the role of denitrification in N2O production, because in the composting system, there are also a large number of microorganisms that can only reduce nitrite to NO and cannot further generate N2O, thereby making the evaluation result deviate from the true situation.

[0004] In contrast, omics technologies have successfully revealed the functional microorganisms and their metabolic potential related to N2O emission in ecosystems such as rice fields and peatlands: metagenomics can not only elucidate the structure of dominant microbial communities, but also reconstruct microbial genomes from complex environmental samples, and analyze the N2O production pathways of microorganisms at the genomic level; metatranscriptomics can characterize the gene expression levels of microorganisms, and further analyze the N2O production potential of microorganisms at the gene expression level. Therefore, it is urgent to develop a multi-omics-based method for identifying N2O emission pathways and quantifying environmental risks in composting, in order to systematically analyze the N2O emission mechanisms and environmental risks of various microbial metabolic pathways during composting, and provide reliable support for the innovation and optimization of green and low-carbon composting processes. SUMMARY

[0005] The purpose of the present application is to solve the problem that the information that can be obtained by identifying microbial N2O emission pathways in the prior art is limited, and to provide a method for accurately identifying N2O-producing microorganisms, dividing their pathways, and further analyzing the risks of each pathway during composting by jointly applying metagenomics and metatranscriptomics.

[0006] The above-mentioned purpose of the present application is achieved by the following technical solutions:

[0007] Step one: total DNA and total RNA extraction and sequencing of the collected compost samples;

[0008] Step two: reconstructing metagenome assembled genomes (MAGs) of N2O-producing microorganisms by analyzing metagenomic data and performing functional annotation on them;

[0009] Step three: dividing the four types of production pathways, namely nitrification, denitrification of nitrifying bacteria, heterotrophic denitrification, and dissimilatory nitrate reduction to ammonium (DNRA), according to the functional gene set annotated by the genome;

[0010] Step four: combining metatranscriptomics to analyze the expression dynamics of key production genes in the four types of pathways and quantifying the risks.

[0011] Further, in the step two, the raw data obtained from metagenomic and metatranscriptomic sequencing are quality filtered to remove low-quality sequences using BBDuk tool. Then, the metatranscriptomic data are further filtered to remove non-coding RNAs using SortMeRNA software. The quality-controlled metagenomic reads are assembled using MEGAHIT software to obtain contigs with a length of no less than 500 bp. Then, the MetaBAT2 and MaxBin2 algorithms of the MetaWRAP pipeline are used for binning, and the Bin_refinement and Reassemble_bins modules are used for optimization to obtain primary MAGs. Meanwhile, VAMB is used for deep binning of contigs with a length of no less than 50 kb. The dRep tool is used to cluster the MAGs output by MetaWRAP and VAMB, with an ANI threshold of 99%. Then, CheckM2 software is used to evaluate the contamination and completeness of the MAGs after removing redundancy, and high-quality MAGs with a completeness of no less than 70% and a contamination of no more than 10% are retained. The ORF is identified using Prodigal software, and then functional annotation is performed using METABOLIC software and kofamScan software, respectively. The metabolic integrity threshold of METABOLIC software is set to 0.75, and the e-value threshold of kofamScan software is set to 10 -5 Then, according to the gene annotation results, the N2O-producing microbial genomes are screened, and MAGs encoding at least one N2O synthesis gene (hao and norB) and not containing N2O reduction gene (nosZ) are retained.

[0012] Further, in the step three, the MAGs can be classified according to the annotated functional gene set for the N2O production pathway: MAGs with both amoABC gene cluster and hao gene are nitrification pathway; MAGs with both amoABC gene cluster, norB gene, nirK or nirS gene are nitrifier denitrification pathway; MAGs with both nirK or nirS gene and norB gene, and without amoABC gene cluster are heterotrophic denitrification pathway; MAGs with both nrfA gene and norB gene are DNRA pathway.

[0013] Further, in step four, the ORFs are aligned to the KEGG database using DIAMOND software to obtain the KO number corresponding to the ORFs in each MAG, and then the corresponding sequences of N2O producing genes (hao and norB) in the MAGs are extracted according to the KO number using the SeqKit tool. The reads coverage of the target contigs is calculated using the contig mode of CoverM software, and the parameter setting is --min-read-percent-identity 0.99, --min-read-aligned-percent 0.7. Then the expression of the producing genes of the MAGs in the same pathway is summed up, and the proportion in all pathways is calculated, that is, the risk of the pathway in the compost N2O emission can be evaluated.

[0014] Advantages

[0015] (1) Improve research efficiency and reliability: By integrating multiple software and optimizing the binning strategy (MetaWRAP and VAMB double algorithm joint binning), combined with strict quality control standards (integrity ≥ 70%, pollution ≤ 10%), the coverage and accuracy of microbial data are significantly improved, laying a solid and high-quality data foundation for subsequent accurate functional annotation and pathway division.

[0016] (2) Improve the accuracy of pathway division: Based on the key functional gene set, the MAGs are strictly classified, and the MAGs containing N2O reduction genes (nosZ) are removed, focusing on net N2O producers, realizing the clear definition of four main N2O production pathways of nitrification, nitrifying bacteria denitrification, heterotrophic denitrification and DNRA, and improving the specificity of pathway attribution and the ecological significance of the results.

[0017] (3) Improve research depth and resolution: By using CoverM and other tools, combined with macro-transcriptome accurate quantification of key N2O synthesis gene (hao and norB) expression, the dynamic characteristics of four types of N2O production pathways are revealed from the aspects of gene potential and actual expression, providing direct, objective and quantitative basis for environmental risk assessment of different pathways in the composting process. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flow chart of the present application based on multi-omics data of compost N2O emission path analysis and environmental risk quantification method.

[0019] Figure 2 The functional annotation results and emission pathway classification of the reconstructed N2O producing microbial metagenome assembly genomes (MAGs) in the compost samples of cow manure, chicken manure, sheep manure and pig manure.

[0020] Figure 3To determine the relative contribution of different pathways to N2O emission risk in cow manure, chicken manure, sheep manure, and pig manure compost samples. DETAILED DESCRIPTION

[0021] The present application will be described in detail below with reference to examples, which should not be understood as limiting the present application. Features of software / programs, control methods, algorithms, etc. that are not explicitly described in the technical solution are considered to be conventional technical means known in the prior art. Based on the examples in the present application, all other examples obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0022] Example 1

[0023] In this example, samples collected during the composting process of cow manure, chicken manure, sheep manure, and pig manure were analyzed, and the specific steps were as follows:

[0024] Step one, sample nucleic acid extraction and sequencing: total DNA and total RNA were extracted from 0.5 g of compost sample using PowerSoil DNA extraction kit (Mo Bio Laboratories) and RNAprep Pure kit (Tiangen), respectively. The quality and concentration of DNA were detected using NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific) and 1% agarose gel electrophoresis. The RNA sample was amplified by 16S rDNA PCR using non-barcode primers to confirm the absence of DNA residues. For metagenomic sequencing, 1.0 μg of DNA was used to construct the library using Ultra TM DNA library preparation kit (New England Biolabs); after purification by AMPure XP system, the library was quality controlled by Agilent 2100 Bioanalyzer, and sequenced by 150 bp double-end sequencing on NovaSeq 6000 platform (Illumina). For macro-transcriptome analysis, 1.0 μg of RNA was fragmented to 250-300 bp using Covaris LE220 ultrasonic disrupter, and then the library was constructed using TruSeq RNA sample preparation kit (Illumina); the macro-transcriptome sequencing parameters were consistent with those of the metagenomic library.

[0025] Step 2: Metagenomic Assembly, Reconstruction, and Functional Annotation of Metagenomes from N2O-Generating Microorganisms: The raw metagenomic and metatranscriptome sequencing data were quality-filtered using the BBDuk tool (parameters: k=28, ktrim=r, hdist=1, mink=12, minlength=70, qtrim=rl, tbo=t, trimq=20, tpe=t) to remove low-quality sequences. The quality-controlled metatranscriptome data was further filtered using SortMeRNA software to remove non-coding RNAs. The quality-controlled metagenomic reads were assembled using MEGAHIT software to obtain contigs of at least 500 bp in length. Binning was performed using the MetaBAT2 and MaxBin2 algorithms in the MetaWRAP workflow, and primary MAGs were obtained after optimization using the Bin_refinement and Reassemble_bins modules. Simultaneously, VAMB was used for deep binning of contigs of at least 50 kb in length. The dRep tool was used to perform redundancy removal and clustering on MAGs output by MetaWRAP and VAMB (parameters: -sa 0.99, -nc 0.1). The CheckM2 software was used to evaluate the contamination and integrity of the MAGs after redundancy removal, retaining high-quality MAGs with integrity ≥70% and contamination ≤10%. ORF identification was performed using Prodigal software, followed by testing with METABOLIC software (metabolic integrity threshold set to 0.75) and kofamScan software (e-value threshold set to 10). -5 Functional annotation was performed. Based on the gene annotation results, the genomes of N2O-producing microorganisms were screened, and MAGs encoding at least one N2O synthesis gene (hao and norB) and not containing an N2O reduction gene (nosZ) were retained.

[0026] Step 3: Classification of N2O Production Pathways: Based on the functional gene sets annotated by MAGs, N2O production pathways are classified into four categories: nitrification, nitrifying bacterial denitrification, heterotrophic denitrification, and DNRA. The specific classification criteria are as follows:

[0027] Nitrification pathway: MAGs contain both the amoABC gene cluster and the hao gene.

[0028] Nitrifying bacteria denitrification pathway: MAGs simultaneously possess the amoABC gene cluster, norB gene, and nirK or nirS gene.

[0029] Heterotrophic denitrification pathway: MAGs contain the nirK or nirS gene and the norB gene, but lack the amoABC gene cluster.

[0030] DNRA pathway: MAGs contain the nrfA gene and the norB gene.

[0031] Step 4: Dynamic Analysis and Risk Quantification of Key Gene Expression: Using DIAMOND software, ORFs were aligned to the KEGG database to obtain the KO number corresponding to the ORFs in each MAG. Using the SeqKit tool, the corresponding sequences of N2O-producing genes (hao and norB) in the MAGs were extracted based on the KO number. The read coverage of the target contigs was statistically analyzed using the contig mode of CoverM software (parameters: --min-read-percent-identity 0.99, --min-read-aligned-percent 0.7) as a representation of gene expression levels. The expression levels of N2O-producing genes in MAGs from each pathway were summed separately, and the proportion of the total expression level of each pathway to the total expression level of all pathways was calculated to assess the relative risk of that pathway in N2O emissions from composting.

Claims

1. A method for identifying N2O emission pathways and quantifying environmental risks in composting based on multi-omics, characterized in that, The main steps include: Step 1: Extract and sequence total DNA and total RNA from the collected compost samples; Step 2: By analyzing metagenomic data, reconstruct the metagenomic assembly genomes (MAGs) of N2O-producing microorganisms and perform functional annotation; Step 3: Based on the functional gene set annotated in the genome, classify the production pathways into four categories: nitrification, nitrifying bacterial denitrification, heterotrophic denitrification, and dissimilar nitrate reduction to ammonium (DNRA). Step 4: Combine metatranscriptomics analysis to analyze the expression dynamics of key gene-producing genes in the genome in the four pathways and perform risk quantification.

2. The method according to claim 1, characterized in that, Step two involves the process of reconstructing the metagenomic assembly genomes (MAGs) of N2O-producing microorganisms, including: Data quality control: The BBDuk tool was used to filter the raw metagenomic and metatranscriptome sequencing reads to remove low-quality sequences; the SortMeRNA software was used to remove non-coding RNA from the metatranscriptome data. Sequence assembly: The quality-controlled metagenomic reads were assembled using MEGAHIT software to obtain contigs with a length of not less than 500 bp; Binning and Refinement: The MetaBAT2 and MaxBin2 dual algorithms of the MetaWRAP process are used for binning. After optimization by the Bin_refinement and Reassemble_bins modules, primary MAGs are obtained. At the same time, VAMB is used to perform deep binning on contigs with a length of not less than 50kb. Redundancy removal: The dRep tool was used to perform redundancy removal clustering on the MAGs output by MetaWRAP and VAMB, with an ANI threshold of 99%. Quality screening: The contamination and integrity of MAGs after redundancy removal were evaluated using CheckM2 software, and high-quality MAGs with integrity ≥70% and contamination ≤10% were retained.

3. The method according to claim 1, characterized in that, In step two, the annotation methods for functional genes include: Open reading frame (ORF) prediction: ORF identification was performed using Prodigal software; Functional gene annotation: Functional annotation was performed using METABOLIC and kofamScan software, respectively.

4. The method according to claim 1, characterized in that, In step two, the genomic screening criteria for N2O-producing microorganisms are: encoding at least one N2O synthesis gene (hao and norB) and not containing an N2O reduction gene (nosZ).

5. The method according to claim 1, characterized in that, In step three, the classification of MAGs into four generation pathways is based on the following criteria: Nitrification pathway: Both the amoABC gene cluster and the hao gene are present. Nitrifying bacteria denitrification pathway: coexistence of amoABC gene cluster, norB gene, nirK or nirS gene; Heterotrophic denitrification pathway: contains nirK or nirS genes and norB genes, but lacks the amoABC gene cluster; DNRA pathway: nrfA and norB genes are present.

6. The method according to claim 1, characterized in that, Step four, the key analysis of gene expression dynamics, includes: Gene annotation mapping: Using DIAMOND software, ORFs are aligned to the KEGG database to obtain the KO number corresponding to the ORFs in each MAGs; Sequence extraction of key genes: Using the SeqKit tool, the corresponding sequences of N2O generating genes (hao and norB) in MAGs were extracted based on the KO number; Transcription abundance quantification: Read coverage of target contigs was statistically analyzed using CoverM software.

7. The method according to claim 6, characterized in that, When quantifying the transcriptional abundance of N2O-generating genes, the contig mode of the CoverM software was used with the parameters set to --min-read-percent-identity 0.99 and --min-read-aligned-percent 0.

7.

8. The method according to claim 1, characterized in that, In step four, by summing the expression levels of genes that produce MAGs along the same pathway and calculating their proportion in all pathways, the risk of that pathway in N2O emissions from composting can be assessed.

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