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There are currently a large number of in vitro culture and molecular biology techniques for human microbiome research, which can be used to detect and analyze the composition of microbial communities, species diversity, and the impact on human cell pathways.
However, in the cross-field of microbial ecology and epidemiology, the design and methods of population-scale microbiome research are still in the early stages of development, and human variability, environmental exposure, and experimental reproducibility need to be fully considered.
The existing microbiome research technology has many similarities with the technology used in human gene expression and genome-wide association research.
Human-related microbial populations are usually analyzed based on their composition, for example, by sequencing 16S rRNA genes to generate phylogenetic or taxonomic profiles.
16S rRNA gene research mainly focuses on bacteria.
Although highly sensitive, they are easily affected by DNA extraction and PCR amplification.
Metagenomic sequencing can further describe the functional genetic potential of the entire community, and cannot reflect which part of this genetic potential is transcribed or translated in a specific environment.
The metatranscriptome uses all the RNAs of the community for reverse transcription to build a library and sequence, and the subsequent basic analysis process is similar to that of the metagenomics.
The macrometabolome is to extract the total metabolites of the community for mass spectrometry detection.
Macrotranscriptomics, metabolomics, and metaproteomics technologies can link nucleotide sequence maps to biologically active products.
Through microbial rRNA gene sequencing, metagenomics, macrotranscriptomics and non-targeted metabolomics and other methods, combined with the core strategy of "whole microbiome association analysis (MWAS)", it can accurately decode the composition spectrum, functional spectrum and Expression profile, excavate key biomarkers, and then clarify the complex interaction mechanism and causality between "bacteria-host-environment (ecosystem)".
1 Microbial strains are the basic epidemiological unit of microbiome classification.
Many sequencing technologies that do not rely on culture can only describe microbial communities at the genus or species level, but not all strains in a genus/species have the same function Characteristics, especially in terms of pathogenicity.
For example, E.
coli has neutral, enterohaemorrhagic E.
coli and probiotics.
Variation between these strains has an important impact on human health.
The intestinal symbiotic bacteria Bacteroides vulgatus, which is traditionally regarded as harmless, also shows strong intraspecific genomic variability.
Amplicon sequencing can only distinguish between groups that are higher than the nucleotide similarity threshold (such as 97%), and its ability to identify strains is limited, because the key functional differences of strains may come from other than amplified genes.
area.
Similarly, the assembly strategy of metagenomic sequencing data may also deliberately avoid nucleotide-level variation.
Metagenomic sequencing analysis to accurately identify strains usually relies on two methods of single nucleotide variation (SNVs, which require more than 10 times the sequencing depth) or identification of variable regions.
Metagenomic sequencing can only accurately identify single dominant strains in any species in complex communities, and requires extremely high sequencing depth to distinguish secondary strains.
Analysis strategies of strains and functions for human microbiome research 2 Metatranscriptomics characterizes the environmental specificity, dynamics, and molecular biological activity of microbial communities.
Taxonomic analysis is often accompanied by functional analysis that matches the composition of the community and its genes and/or pathways.
Metagenomic sequencing can only generate information about the potential functions of the community, and what kind of abundance organisms may perform what biological processes (genes are not necessarily transcribed under current conditions).
Macro transcription and protein are analyzed based on the functional activity level, paying more attention to the activity state of the microbial community, which can be used to study the response process of the community such as environmental stress and drug treatment from time to time.
The metatranscriptome can also supplement biological information such as the detection of RNA viruses in metagenomic research and the quantification of rare functional genes.
A typical macrotranscriptome research, such as single-microbial RNA-seq, includes the positioning and assembly of transcripts, annotation of function and classification information, data standardization, and differential expression analysis.
However, the application of macrotranscriptome sequencing to the study of the human microbiome on an epidemiological scale still has certain difficulties.
The microbiome samples used for the macrotranscriptome must retain RNA when collected, and are more sensitive to storage conditions and time, and there are certain technical requirements for RNA extraction.
The generated metatranscriptome data usually requires matching metagenomic data to explain, otherwise the change in DNA copy number cannot be distinguished from the change in transcription activity.
For the amplicon-based macrotranscriptome, the copy number variation of 16S rRNA genes, the difference in ribosomal transcription rate, and the abundance of 16S rRNA transcription have not been definitively explained.
The downstream biological information analysis of the macrotranscriptome expression profile must further explain the changes in the classification composition and technical deviations related to the RNA-seq experiment.
The normalized differential gene expression analysis can also use the existing analysis tools of RNA-seq.
3 Microbiota-related metabolomics is a new method to characterize biological activity.
Non-targeted metabolomics may be one of the most effective methods that can explain the mechanism of biological activity.
Nuclear magnetic resonance (NMR) and mass spectrometry techniques can detect stool, skin, Circulating metabolism and small molecular substances in human-related microbial communities.
In the human microecology, more than 10% of small molecules may be of microbial origin or modified by microbes.
Therefore, human microbiome research needs to associate specific microbial strains with metabolic small molecules.
At present, the application of related research designs on population scale is still very limited, and it remains to be seen which microbiota-related metabolites are suitable for predicting or regulating population health.
Although 16S rRNA sequencing can easily study the composition of the microbial community, the current understanding of the microbial gene pool is not complete, which limits the understanding of the impact of related metabolites.
Metagenomic sequencing provides a deeper understanding of existing genes, but the functions of most of these genes are still unknown.
Metabolites are the closest to phenotypic omics, which can be used as phenotypic data for functional verification of microbial communities.
Special metabolites of microbial communities may also be molecular markers for diseases, drug treatments, and environmental governance.
If you want to further understand the functional potential of disease-related microbial flora, you can conduct a combined analysis of microbiome + metabolomics to explore the impact of microbes on the biochemistry and function of the organism while understanding the diversity of microorganisms.
The combined use of 16S rDNA sequencing, metagenomic sequencing, and metabonomics can overcome the limitations of single omics research to a certain extent.
Many advances have been made in the study of the relationship between intestinal microbes and health diseases, showing good application prospects.
4 Statistical issues and practices in modern epidemiological microbiology research In all microbiome epidemiological research based on amplicons, metagenomic sequencing and other methods, the purpose is to determine whether and how the abundance of microbes and molecular characteristics is related to Sample characteristics (such as donor health, disease status or outcome, dietary intake, drugs, or environment) are correlated.
The data related to the microbial community is usually composed of counts with a composition structure, that is, the microbiome data is usually expressed as relative abundance.
When performing classical statistical inference, it is easy to produce false positive results.
In order to solve these problems, several analysis methods have been developed.
These methods all rely on the normalization of the data.
The main difference lies in the selection of different data transformations and statistical models when calculating the p-value.
Normalization is very important in the analysis of differential abundance and can reduce the impact of sequencing depth changes.
After inputting the raw counts (featureCounts), the metagenomic analysis uses a zero-expansion Gaussian mixture model to normalize the logarithmic counts and analyze the difference abundance to reduce bias.
After MaAsLin performs arcsine square root transformation on the microbiome data, it can be analyzed by a conventional linear model.
Some RNA-seq data differential expression analysis tools, such as edgeR, DESeq2 and limma, are also often used in microbiome research.
At the same time, non-parametric alternative methods such as LEfSe, Metastats and ANCOM are widely used.
Strains and molecular function analysis strategies for human microbiome research 5 Human microbiome multi-omics research examples Multi-omics integration and related human microbiome research has increasingly become the first choice for high-scoring research papers: (1) Published in Cell Host & in 2019 The article on Microbe (IF 15.
9) studied the method of "16S + metagenomic sequencing + metabonomics" and found that obesity and microbiome composition, functional changes caused by individual bacterial categories, and serum metabolites related to intestinal microbes Significant changes are related; the correlation between T2D and intestinal flora is not obvious; drugs (such as antihypertensive drugs and hypoglycemic drugs) and dietary supplements are significantly related to changes in intestinal flora.
In conclusion, this study distinguished the microbial components of two related metabolic diseases, obesity and D2T, and determined the diet and drug dosage that need to be considered in future studies.
(2) An article published on GeroScience (IF 6.
4) in 2020, researchers used 16S rRNA gene sequencing, metabonomic detection, immune factor detection and other technologies to analyze the microbiome, metabolome, immune factors and host of male Wistar rats A multi-omics integrated correlation study on the relevance of longevity revealed that the rat gut microbiome and serum metabolome changed significantly during the aging process.
There are important “correlated pairs” in the interaction of the intestinal flora, serum metabolites, and immune factors.
For example, key age-related metabolites such as 4-hydroxyproline, proline and lysine poly They are grouped together and are positively correlated with intestinal flora such as Bifidobacterium, Lactobacillus, and Akkermansia.
It provides new insights for understanding the interaction mechanism among the gut microbiome, serum metabolome, and immune factors in the aging process.
(3) An article published on mSystems (IF 6.
6) in 2020, through the "16S + metagenomics + target metabolome" to detect the stool and urine samples of athletes and people who do not exercise regularly, and compare the metabolic phenotypes between the two groups of samples And the difference between intestinal microbes. It was found that the two groups of samples showed unique differences in metabolic phenotype and microbial diversity.
Microbial diversity corresponds to a high degree of adherence to healthy eating habits and physical exercise, and is associated with a series of different microbial-derived metabolites.
At present, the research on the microbiome is becoming more and more complex, and there have been a variety of methods based on sequence, molecule and culture to study the microbiome on a population scale.
In order to link microbial data with human health, the problems of experimental design, data analysis, and epidemiological statistics must be considered.
For example, transforming population research data into molecular mechanism research to find out the characteristics of the flora that affect human health; data acquisition, standardization and quantitative analysis in the integration of multi-omics studies of flora; understanding experimental design factors and bioinformatics data Tools of analysis and statistics and their limitations.
In general, 16S and metagenomics are based on DNA analysis to explore the species composition and potential functional characteristics of the community.
The experimental analysis is less difficult and easier to implement.
It can be used as a basic exploration and is the most widely used in the study of microbial communities.
The multi-omics integrated association research is the combination of multiple high-throughput detection research strategies, such as (meta) genome sequencing, (macro) transcriptome sequencing, proteome quantitative detection and metabonomic analysis, etc.
, which are applied to the same scientific problem.
For multi-omics sequencing data, in addition to screening the microbial marker species or genes (Biomarker) in each group of samples through differential statistical analysis, it is also necessary to analyze the transcriptome, metabolome, proteome, or physical and chemical indicators obtained by testing, clinical indicators, etc.
Various types of data are correlated with microbiome data to obtain specific microbial species and their genes associated with changes in various indicators.
Through multi-omics integration and association research, we can comprehensively screen related Biomarkers at the microbial species/function level and the host genome/transcriptome/proteome/metabolome level at the same time, so as to deeply analyze the "microbiome-host-environmental influence factors" The interaction and its regulation mechanism.
References: [1] Mallick H, Ma S, Franzosa EA, et al.
Experimental design and quantitative analysis of microbial community multiomics.
Genome Biol 2017;18:228.
[2] Aron-Wisnewsky J, Warmbrunn MV, Nieuwdorp M, et al.
Metabolism and Metabolic Disorders and the Microbiome: The Intestinal Microbiota Associated With Obesity, Lipid Metabolism, and Metabolic Health: Pathophysiology and Therapeutic Strategies.
Gastroenterology 2020.
END Call for contributions "Medical Fang" is now officially open to fans! The content must be originally published and related to scientific research.
Once adopted, a generous reward (300-2000 yuan) will be given.
Please stamp for details.
"Medical side" has always been committed to serving "medical people", pushing the most cutting-edge and most valuable original clinical and scientific research articles to clinicians and scientific researchers.
The medical department has launched "Laboratory Basics", "SCI Writing Skills", "Document Intensive Reading and Analysis", "Easy Learning of Medical English", "National Natural Science Foundation of China", "Clinical Data Mining", "Gene Data Mining", "R Language Tutorial", "Medical Statistics", "Minimally Invasive Animal Experiment Training" and other special courses, if you need to know the detailed tweets of the courses, you can follow the "Medical Party" public account and click on "Exquisite Topics" to enter the Tencent classroom: https://medfun .
ke.
qq.
com NetEase Cloud Classroom: http://study.
163.
com/u/ykt1467466791112 Customer Service Tel: 15821255568 Customer Service WeChat: yixuefang1234
There are currently a large number of in vitro culture and molecular biology techniques for human microbiome research, which can be used to detect and analyze the composition of microbial communities, species diversity, and the impact on human cell pathways.
However, in the cross-field of microbial ecology and epidemiology, the design and methods of population-scale microbiome research are still in the early stages of development, and human variability, environmental exposure, and experimental reproducibility need to be fully considered.
The existing microbiome research technology has many similarities with the technology used in human gene expression and genome-wide association research.
Human-related microbial populations are usually analyzed based on their composition, for example, by sequencing 16S rRNA genes to generate phylogenetic or taxonomic profiles.
16S rRNA gene research mainly focuses on bacteria.
Although highly sensitive, they are easily affected by DNA extraction and PCR amplification.
Metagenomic sequencing can further describe the functional genetic potential of the entire community, and cannot reflect which part of this genetic potential is transcribed or translated in a specific environment.
The metatranscriptome uses all the RNAs of the community for reverse transcription to build a library and sequence, and the subsequent basic analysis process is similar to that of the metagenomics.
The macrometabolome is to extract the total metabolites of the community for mass spectrometry detection.
Macrotranscriptomics, metabolomics, and metaproteomics technologies can link nucleotide sequence maps to biologically active products.
Through microbial rRNA gene sequencing, metagenomics, macrotranscriptomics and non-targeted metabolomics and other methods, combined with the core strategy of "whole microbiome association analysis (MWAS)", it can accurately decode the composition spectrum, functional spectrum and Expression profile, excavate key biomarkers, and then clarify the complex interaction mechanism and causality between "bacteria-host-environment (ecosystem)".
1 Microbial strains are the basic epidemiological unit of microbiome classification.
Many sequencing technologies that do not rely on culture can only describe microbial communities at the genus or species level, but not all strains in a genus/species have the same function Characteristics, especially in terms of pathogenicity.
For example, E.
coli has neutral, enterohaemorrhagic E.
coli and probiotics.
Variation between these strains has an important impact on human health.
The intestinal symbiotic bacteria Bacteroides vulgatus, which is traditionally regarded as harmless, also shows strong intraspecific genomic variability.
Amplicon sequencing can only distinguish between groups that are higher than the nucleotide similarity threshold (such as 97%), and its ability to identify strains is limited, because the key functional differences of strains may come from other than amplified genes.
area.
Similarly, the assembly strategy of metagenomic sequencing data may also deliberately avoid nucleotide-level variation.
Metagenomic sequencing analysis to accurately identify strains usually relies on two methods of single nucleotide variation (SNVs, which require more than 10 times the sequencing depth) or identification of variable regions.
Metagenomic sequencing can only accurately identify single dominant strains in any species in complex communities, and requires extremely high sequencing depth to distinguish secondary strains.
Analysis strategies of strains and functions for human microbiome research 2 Metatranscriptomics characterizes the environmental specificity, dynamics, and molecular biological activity of microbial communities.
Taxonomic analysis is often accompanied by functional analysis that matches the composition of the community and its genes and/or pathways.
Metagenomic sequencing can only generate information about the potential functions of the community, and what kind of abundance organisms may perform what biological processes (genes are not necessarily transcribed under current conditions).
Macro transcription and protein are analyzed based on the functional activity level, paying more attention to the activity state of the microbial community, which can be used to study the response process of the community such as environmental stress and drug treatment from time to time.
The metatranscriptome can also supplement biological information such as the detection of RNA viruses in metagenomic research and the quantification of rare functional genes.
A typical macrotranscriptome research, such as single-microbial RNA-seq, includes the positioning and assembly of transcripts, annotation of function and classification information, data standardization, and differential expression analysis.
However, the application of macrotranscriptome sequencing to the study of the human microbiome on an epidemiological scale still has certain difficulties.
The microbiome samples used for the macrotranscriptome must retain RNA when collected, and are more sensitive to storage conditions and time, and there are certain technical requirements for RNA extraction.
The generated metatranscriptome data usually requires matching metagenomic data to explain, otherwise the change in DNA copy number cannot be distinguished from the change in transcription activity.
For the amplicon-based macrotranscriptome, the copy number variation of 16S rRNA genes, the difference in ribosomal transcription rate, and the abundance of 16S rRNA transcription have not been definitively explained.
The downstream biological information analysis of the macrotranscriptome expression profile must further explain the changes in the classification composition and technical deviations related to the RNA-seq experiment.
The normalized differential gene expression analysis can also use the existing analysis tools of RNA-seq.
3 Microbiota-related metabolomics is a new method to characterize biological activity.
Non-targeted metabolomics may be one of the most effective methods that can explain the mechanism of biological activity.
Nuclear magnetic resonance (NMR) and mass spectrometry techniques can detect stool, skin, Circulating metabolism and small molecular substances in human-related microbial communities.
In the human microecology, more than 10% of small molecules may be of microbial origin or modified by microbes.
Therefore, human microbiome research needs to associate specific microbial strains with metabolic small molecules.
At present, the application of related research designs on population scale is still very limited, and it remains to be seen which microbiota-related metabolites are suitable for predicting or regulating population health.
Although 16S rRNA sequencing can easily study the composition of the microbial community, the current understanding of the microbial gene pool is not complete, which limits the understanding of the impact of related metabolites.
Metagenomic sequencing provides a deeper understanding of existing genes, but the functions of most of these genes are still unknown.
Metabolites are the closest to phenotypic omics, which can be used as phenotypic data for functional verification of microbial communities.
Special metabolites of microbial communities may also be molecular markers for diseases, drug treatments, and environmental governance.
If you want to further understand the functional potential of disease-related microbial flora, you can conduct a combined analysis of microbiome + metabolomics to explore the impact of microbes on the biochemistry and function of the organism while understanding the diversity of microorganisms.
The combined use of 16S rDNA sequencing, metagenomic sequencing, and metabonomics can overcome the limitations of single omics research to a certain extent.
Many advances have been made in the study of the relationship between intestinal microbes and health diseases, showing good application prospects.
4 Statistical issues and practices in modern epidemiological microbiology research In all microbiome epidemiological research based on amplicons, metagenomic sequencing and other methods, the purpose is to determine whether and how the abundance of microbes and molecular characteristics is related to Sample characteristics (such as donor health, disease status or outcome, dietary intake, drugs, or environment) are correlated.
The data related to the microbial community is usually composed of counts with a composition structure, that is, the microbiome data is usually expressed as relative abundance.
When performing classical statistical inference, it is easy to produce false positive results.
In order to solve these problems, several analysis methods have been developed.
These methods all rely on the normalization of the data.
The main difference lies in the selection of different data transformations and statistical models when calculating the p-value.
Normalization is very important in the analysis of differential abundance and can reduce the impact of sequencing depth changes.
After inputting the raw counts (featureCounts), the metagenomic analysis uses a zero-expansion Gaussian mixture model to normalize the logarithmic counts and analyze the difference abundance to reduce bias.
After MaAsLin performs arcsine square root transformation on the microbiome data, it can be analyzed by a conventional linear model.
Some RNA-seq data differential expression analysis tools, such as edgeR, DESeq2 and limma, are also often used in microbiome research.
At the same time, non-parametric alternative methods such as LEfSe, Metastats and ANCOM are widely used.
Strains and molecular function analysis strategies for human microbiome research 5 Human microbiome multi-omics research examples Multi-omics integration and related human microbiome research has increasingly become the first choice for high-scoring research papers: (1) Published in Cell Host & in 2019 The article on Microbe (IF 15.
9) studied the method of "16S + metagenomic sequencing + metabonomics" and found that obesity and microbiome composition, functional changes caused by individual bacterial categories, and serum metabolites related to intestinal microbes Significant changes are related; the correlation between T2D and intestinal flora is not obvious; drugs (such as antihypertensive drugs and hypoglycemic drugs) and dietary supplements are significantly related to changes in intestinal flora.
In conclusion, this study distinguished the microbial components of two related metabolic diseases, obesity and D2T, and determined the diet and drug dosage that need to be considered in future studies.
(2) An article published on GeroScience (IF 6.
4) in 2020, researchers used 16S rRNA gene sequencing, metabonomic detection, immune factor detection and other technologies to analyze the microbiome, metabolome, immune factors and host of male Wistar rats A multi-omics integrated correlation study on the relevance of longevity revealed that the rat gut microbiome and serum metabolome changed significantly during the aging process.
There are important “correlated pairs” in the interaction of the intestinal flora, serum metabolites, and immune factors.
For example, key age-related metabolites such as 4-hydroxyproline, proline and lysine poly They are grouped together and are positively correlated with intestinal flora such as Bifidobacterium, Lactobacillus, and Akkermansia.
It provides new insights for understanding the interaction mechanism among the gut microbiome, serum metabolome, and immune factors in the aging process.
(3) An article published on mSystems (IF 6.
6) in 2020, through the "16S + metagenomics + target metabolome" to detect the stool and urine samples of athletes and people who do not exercise regularly, and compare the metabolic phenotypes between the two groups of samples And the difference between intestinal microbes. It was found that the two groups of samples showed unique differences in metabolic phenotype and microbial diversity.
Microbial diversity corresponds to a high degree of adherence to healthy eating habits and physical exercise, and is associated with a series of different microbial-derived metabolites.
At present, the research on the microbiome is becoming more and more complex, and there have been a variety of methods based on sequence, molecule and culture to study the microbiome on a population scale.
In order to link microbial data with human health, the problems of experimental design, data analysis, and epidemiological statistics must be considered.
For example, transforming population research data into molecular mechanism research to find out the characteristics of the flora that affect human health; data acquisition, standardization and quantitative analysis in the integration of multi-omics studies of flora; understanding experimental design factors and bioinformatics data Tools of analysis and statistics and their limitations.
In general, 16S and metagenomics are based on DNA analysis to explore the species composition and potential functional characteristics of the community.
The experimental analysis is less difficult and easier to implement.
It can be used as a basic exploration and is the most widely used in the study of microbial communities.
The multi-omics integrated association research is the combination of multiple high-throughput detection research strategies, such as (meta) genome sequencing, (macro) transcriptome sequencing, proteome quantitative detection and metabonomic analysis, etc.
, which are applied to the same scientific problem.
For multi-omics sequencing data, in addition to screening the microbial marker species or genes (Biomarker) in each group of samples through differential statistical analysis, it is also necessary to analyze the transcriptome, metabolome, proteome, or physical and chemical indicators obtained by testing, clinical indicators, etc.
Various types of data are correlated with microbiome data to obtain specific microbial species and their genes associated with changes in various indicators.
Through multi-omics integration and association research, we can comprehensively screen related Biomarkers at the microbial species/function level and the host genome/transcriptome/proteome/metabolome level at the same time, so as to deeply analyze the "microbiome-host-environmental influence factors" The interaction and its regulation mechanism.
References: [1] Mallick H, Ma S, Franzosa EA, et al.
Experimental design and quantitative analysis of microbial community multiomics.
Genome Biol 2017;18:228.
[2] Aron-Wisnewsky J, Warmbrunn MV, Nieuwdorp M, et al.
Metabolism and Metabolic Disorders and the Microbiome: The Intestinal Microbiota Associated With Obesity, Lipid Metabolism, and Metabolic Health: Pathophysiology and Therapeutic Strategies.
Gastroenterology 2020.
END Call for contributions "Medical Fang" is now officially open to fans! The content must be originally published and related to scientific research.
Once adopted, a generous reward (300-2000 yuan) will be given.
Please stamp for details.
"Medical side" has always been committed to serving "medical people", pushing the most cutting-edge and most valuable original clinical and scientific research articles to clinicians and scientific researchers.
The medical department has launched "Laboratory Basics", "SCI Writing Skills", "Document Intensive Reading and Analysis", "Easy Learning of Medical English", "National Natural Science Foundation of China", "Clinical Data Mining", "Gene Data Mining", "R Language Tutorial", "Medical Statistics", "Minimally Invasive Animal Experiment Training" and other special courses, if you need to know the detailed tweets of the courses, you can follow the "Medical Party" public account and click on "Exquisite Topics" to enter the Tencent classroom: https://medfun .
ke.
qq.
com NetEase Cloud Classroom: http://study.
163.
com/u/ykt1467466791112 Customer Service Tel: 15821255568 Customer Service WeChat: yixuefang1234
