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Baiqu Metabolomics Information: "Second and Third Generation Metagenome + Metabolome" Combining Three Swords to Unravel the SV Mutation of Intestinal
Microbiota in Healthy Individuals 14.
919) published a paper entitled "Short- and long-read metagenomics expand individualized structural variations in gut microbiomes"
.
Researcher Wang Jun from the Institute of Microbiology, Chinese Academy of Sciences and Song Mozhi from the Institute of Zoology are the co-corresponding authors; Chen Liang and Zhao Na, assistant researchers from the Institute of Microbiology, Chinese Academy of Sciences, Cao Jiabao, a doctoral student, and Liu Xiaolin, a master student are the first authors; Shanghai Baiqu Researcher Liu Zhipeng and researcher Fan Yanqun from the founding team of the Metabolomics Technology Research Center are co-authors of the paper
.
This study established a new method for hybrid assembly of ONT third-generation sequencing and Illumina second-generation sequencing data (Fig.
1a), and detected more structural variations (SVs) including insertion mutations, deletion mutations, and gene inversions
.
At the same time, through the joint metagenomics and metabolomics analysis of a cross-sectional cohort of healthy people composed of 100 individuals and a longitudinal tracking cohort of 10 individuals, it was found that SVs were significantly different between individuals, but within the same body It is relatively stable, and it is also found that SVs not only affect the function of flora and metabolites, but also have a certain impact on human phenotype
.
The research team first used a known dataset (ZymoBIOMICS™ Microbial Community) to compare the hybrid assembly method of ONT and Illumina with several other assembly methods.
nucleotide identity, ANI) and coding density have better results
.
At the same time, through the analysis of the intestinal data of the two populations, it is found that the hybrid assembly method can improve the data quality
.
Compared with the second-generation metagenomic assembly results, it is found that although the hybrid assembly method has 17.
3% fewer contigs, the number of assembled sequences is increased by 5.
1%, and the N50 value has increased by more than 3 times.
.
After binning the contigs, reconstructed metagenome-assembled genomes (MAGs) representing a single strain were obtained, and 9,612 MAGs with an average N50 of 117kb were obtained by mixed assembly (20-83 per sample).
After deduplication, 692 MAGs were obtained (Figure 1b, 1c), of which 623 were in the UHGG database, and 208 MAGs were of higher quality.
At the same time, 67 new genomic bins were also found.
After deduplication with the new version of dRep Reduced by 2 MAGs
.
From a comprehensive consideration, 159 non-redundant MAGs contained 23S, 16S and 5S rRNA sequences, and 448 MAGs contained at least one of these types of rRNA
.
The Illumina-based assembly method yielded 616 MAGs with an N50 of about half of the mixed assembly, and only 9 MAGs contained three types of rRNA sequences, and 258 MAGs contained at least one rRNA sequence
.
Among all samples, Fusicatenibacter saccharivorans appeared most frequently, followed by Anaerostipes hadrus and gathobacter rectalis, and 189 bacteria appeared in at least 10 samples in the form of MAGs
.
In view of the characteristics of more SVs that can be found by ONT sequencing, various types of SVs were found through the alignment of MAGs
.
189 bacteria were compared with dRep, and 317,558 insertion mutations, 34,129 deletion mutations and 1,373 gene inversions were identified (Figure 1d).
1050~1150bp, Figure 1e) SVs fragments were analyzed, and it was found that mobile elements and extrachromosomal mobile genetic elements (eMGEs) were more in the two mutated short SVs fragments, so it was inferred that the short-sequence SVs may be related to bacteriophages.
Integration is related to other mobile elements; however, not all SVs have detectable mobile elements, other SVs may be caused by replication and recombination, and the specific mechanism needs to be further verified
.
Figure 1.
Verification results of the third-generation and second-generation hybrid assembly methods
Next, the detected SVs were further verified by re-matching the reference MAG or the SV-containing sequence in the MAG
.
After manual matching, it was found that more than 97% of the randomly selected SVs sets were consistent with the number of reads in multiple locations of the ONT, thus verifying the reliability of single-molecule sequencing to obtain specific SVs (Figure 2a).
qualitative
.
Analysis of SVs at the species level (MAGs) found that the total number of SVs was positively correlated with the number of MAGs and gene size in all samples
.
However, due to the uneven distribution of SVs in the bacterial genome, we further corrected the average SV number and genome size, and found that Firmicutes with the highest phylum-level diversity in the 1M genome had 20.
4 SVs, Verrucomicrobia, which belongs to Akkermensia, had 19.
5 SVs.
While Desulfobacteroita and Proteobacteria had the fewest SVs (Fig.
2b, 2c)
.
Analysis of 189 MAGs in the two populations found that there were 16.
7 SVs per Mb of genome between different individuals, while the median of SVs per Mb of genome at different time points in the same individual was 0
.
Therefore, SVs can be used to distinguish bacterial species and gut microbiota between individuals, while the stability of specific bacteria within 10 days within individuals (Fig.
2d) The results indirectly suggest that the strain differentiation or displacement within 3 years found in the LifeLines cohort cohort may be due to caused by the gradual accumulation of SV
.
3a), but no pathways related to gene inversion were found, and the top 30 pathways were not found.
There are 19 pathways related to polysaccharide degradation, sphingolipid metabolomics and other metabolomics-related pathways; some pathways related to environmental information processing (such as phosphotransferase system, PTS) and ABC transport were also found.
protein, etc.
)
.
To further investigate the impact of SVs on body functions, especially microbial metabolomics, metabolomic analysis of stool, serum, and urine samples from a cross-sectional cohort showed that SVs lead to changes in gene function that allow SVs to mutate The bacteria in the group were not associated with metabolites, while the bacteria in the mutant group without SVs were significantly associated with metabolites
.
Correlation analysis showed that 11 bacteria were significantly associated with metabolites in feces, serum and urine, involving 889 bacteria-metabolite association pairs affected by SV (Fig.
3b, 3c)
.
Association analysis of SVs and metabolomics found that 70 SVs affected the bacterial association with 74 fecal metabolites, 31 SVs affected the bacterial association with 66 urinary metabolites, and 2 SVs affected the bacterial association with 2 fecal metabolites.
serum metabolites were significantly associated
.
In previous studies, inositol has been found to be associated with deletion mutations in Anaerostipes hadrus, but in this study, it was found that both insertion and deletion mutations at the locus of the Bacteroides uniformis genome made the association between the bacteria and inositol in urine samples disappear
.
The presence of 12 SV-affected genes made the association between Fusicatenibacter saccharivorans and Neotrehalose in fecal samples insignificant (Fig.
3d); similarly, the presence of 33 SV-affected genes made the association between Agathobacter rectalis and F1P insignificant (Fig.
3e)
.
The results of functional analysis also indicated that SVs have an impact on bacterial and metabolite associations by affecting the functions of related genes
.
To further study the effect of SVs mutation on phenotype, two metabolites affected by SVs, F1P and neotrehalose in cross-sectional cohort samples, were selected for association analysis with fasting blood glucose, and found that both F1P and neotrehalose were significantly negatively correlated with fasting blood glucose, and F.
There was also a significant negative correlation between saccharivorans and fasting glucose, but in the subgroup of SVs, the association became insignificant (Fig.
3h); the presence of SVs also weakened the association of A.
rectalis with glucose (Fig.
3i)
.
Figure 3.
Results of functional studies related to SVs in gut microbes
Since phage infection of bacterial genomes and escape of viruses can lead to the production of SVs, ProphageHunter was used to analyze all MAGs, and 2247 progenitors with genome sizes ranging from 1236 bp to 91792 bp, dominated by long-tailed phage Siphoviridae and muscle-tailed phage Myoviridae phage (Fig.
4a)
.
Association analysis of prophage elements and bacterial genomes yielded 1,077 prophage-host pairs (Fig.
4b); of these, only 72 were in the MVP database; while NGS data detected only 1,815 prophages, of which 80.
77% detected in the mixed assembly; from the results we can see that the ONT-second generation mixed assembly data is more favorable for the discovery of prophages
.
In addition to prophages, there is also a CRISPR-Cas system for resisting viral superinfection in the flora gene.
The spacers of the loci in this system contain the characteristic sequences of specific viruses, which may be related to the insertion mutation or deletion mutation of the strain
.
Likewise, analysis of all MAGs found 150,058 CRISPR spacers, with an average of 1,665 ± 560 spacers per sample, most of the spacers were newly discovered, with only 17,600 (11.
73%) aggregated in the CRISPROpenDB database, 22,962 (15.
30%) ) appeared in the gut microbiota of Western populations; only 9542 spacers were found based on next-generation sequencing-based assembly methods
.
From this we can also see that the new metagenomic assembly method has a stronger ability to discover genetic elements (such as CRISPR spacers)
.
β-diversity analysis of prophage/CRISPR spacers found (Jaccard distance) that the individual differences in the cross-sectional cohort were significantly greater than the differences within the tracking cohorts
.
Population-level compositional analysis of prophages and CRISPR spacers showed strong covariation between the two; Procrustes analysis, which revealed correlations between prophage and viral community composition, showed that prophages and viruses were found among different individuals in the cross-sectional cohort.
composition was significantly correlated (Fig.
4c); analysis of the active viral sequences in the metagenomic data found that 47 of the 2247 identified prophage pools were potentially active, thus indicating the presence of a large number of inactive prophages in bacterial genes, Thereby maintaining the stability of SVs
.
element discovery
.
At the same time, through the analysis of cross-sectional cohort and longitudinal tracking cohort data, it is found that SVs have strong heterogeneity among different individuals and stability within individuals; through functional analysis and metabolomic analysis, it is found that SVs can affect the flora and metabolism.
associations between species and phenotypes
.
Article/Aqu Metabolomics
Microbiota in Healthy Individuals 14.
919) published a paper entitled "Short- and long-read metagenomics expand individualized structural variations in gut microbiomes"
.
.
This study established a new method for hybrid assembly of ONT third-generation sequencing and Illumina second-generation sequencing data (Fig.
1a), and detected more structural variations (SVs) including insertion mutations, deletion mutations, and gene inversions
.
At the same time, through the joint metagenomics and metabolomics analysis of a cross-sectional cohort of healthy people composed of 100 individuals and a longitudinal tracking cohort of 10 individuals, it was found that SVs were significantly different between individuals, but within the same body It is relatively stable, and it is also found that SVs not only affect the function of flora and metabolites, but also have a certain impact on human phenotype
.
The research team first used a known dataset (ZymoBIOMICS™ Microbial Community) to compare the hybrid assembly method of ONT and Illumina with several other assembly methods.
nucleotide identity, ANI) and coding density have better results
.
At the same time, through the analysis of the intestinal data of the two populations, it is found that the hybrid assembly method can improve the data quality
.
Compared with the second-generation metagenomic assembly results, it is found that although the hybrid assembly method has 17.
3% fewer contigs, the number of assembled sequences is increased by 5.
1%, and the N50 value has increased by more than 3 times.
.
After binning the contigs, reconstructed metagenome-assembled genomes (MAGs) representing a single strain were obtained, and 9,612 MAGs with an average N50 of 117kb were obtained by mixed assembly (20-83 per sample).
After deduplication, 692 MAGs were obtained (Figure 1b, 1c), of which 623 were in the UHGG database, and 208 MAGs were of higher quality.
At the same time, 67 new genomic bins were also found.
After deduplication with the new version of dRep Reduced by 2 MAGs
.
From a comprehensive consideration, 159 non-redundant MAGs contained 23S, 16S and 5S rRNA sequences, and 448 MAGs contained at least one of these types of rRNA
.
The Illumina-based assembly method yielded 616 MAGs with an N50 of about half of the mixed assembly, and only 9 MAGs contained three types of rRNA sequences, and 258 MAGs contained at least one rRNA sequence
.
Among all samples, Fusicatenibacter saccharivorans appeared most frequently, followed by Anaerostipes hadrus and gathobacter rectalis, and 189 bacteria appeared in at least 10 samples in the form of MAGs
.
In view of the characteristics of more SVs that can be found by ONT sequencing, various types of SVs were found through the alignment of MAGs
.
189 bacteria were compared with dRep, and 317,558 insertion mutations, 34,129 deletion mutations and 1,373 gene inversions were identified (Figure 1d).
1050~1150bp, Figure 1e) SVs fragments were analyzed, and it was found that mobile elements and extrachromosomal mobile genetic elements (eMGEs) were more in the two mutated short SVs fragments, so it was inferred that the short-sequence SVs may be related to bacteriophages.
Integration is related to other mobile elements; however, not all SVs have detectable mobile elements, other SVs may be caused by replication and recombination, and the specific mechanism needs to be further verified
.
Figure 1.
Verification results of the third-generation and second-generation hybrid assembly methods
.
After manual matching, it was found that more than 97% of the randomly selected SVs sets were consistent with the number of reads in multiple locations of the ONT, thus verifying the reliability of single-molecule sequencing to obtain specific SVs (Figure 2a).
qualitative
.
Analysis of SVs at the species level (MAGs) found that the total number of SVs was positively correlated with the number of MAGs and gene size in all samples
.
However, due to the uneven distribution of SVs in the bacterial genome, we further corrected the average SV number and genome size, and found that Firmicutes with the highest phylum-level diversity in the 1M genome had 20.
4 SVs, Verrucomicrobia, which belongs to Akkermensia, had 19.
5 SVs.
While Desulfobacteroita and Proteobacteria had the fewest SVs (Fig.
2b, 2c)
.
Analysis of 189 MAGs in the two populations found that there were 16.
7 SVs per Mb of genome between different individuals, while the median of SVs per Mb of genome at different time points in the same individual was 0
.
Therefore, SVs can be used to distinguish bacterial species and gut microbiota between individuals, while the stability of specific bacteria within 10 days within individuals (Fig.
2d) The results indirectly suggest that the strain differentiation or displacement within 3 years found in the LifeLines cohort cohort may be due to caused by the gradual accumulation of SV
.
Figure 2.
Variation results of human gut microbial structure
Next, functional enrichment analysis was performed on the genes related to SVs detected in the population, and 267 pathways were found to be related to insertion and deletion mutations (Fig. Variation results of human gut microbial structure
3a), but no pathways related to gene inversion were found, and the top 30 pathways were not found.
There are 19 pathways related to polysaccharide degradation, sphingolipid metabolomics and other metabolomics-related pathways; some pathways related to environmental information processing (such as phosphotransferase system, PTS) and ABC transport were also found.
protein, etc.
)
.
To further investigate the impact of SVs on body functions, especially microbial metabolomics, metabolomic analysis of stool, serum, and urine samples from a cross-sectional cohort showed that SVs lead to changes in gene function that allow SVs to mutate The bacteria in the group were not associated with metabolites, while the bacteria in the mutant group without SVs were significantly associated with metabolites
.
Correlation analysis showed that 11 bacteria were significantly associated with metabolites in feces, serum and urine, involving 889 bacteria-metabolite association pairs affected by SV (Fig.
3b, 3c)
.
Association analysis of SVs and metabolomics found that 70 SVs affected the bacterial association with 74 fecal metabolites, 31 SVs affected the bacterial association with 66 urinary metabolites, and 2 SVs affected the bacterial association with 2 fecal metabolites.
serum metabolites were significantly associated
.
In previous studies, inositol has been found to be associated with deletion mutations in Anaerostipes hadrus, but in this study, it was found that both insertion and deletion mutations at the locus of the Bacteroides uniformis genome made the association between the bacteria and inositol in urine samples disappear
.
The presence of 12 SV-affected genes made the association between Fusicatenibacter saccharivorans and Neotrehalose in fecal samples insignificant (Fig.
3d); similarly, the presence of 33 SV-affected genes made the association between Agathobacter rectalis and F1P insignificant (Fig.
3e)
.
The results of functional analysis also indicated that SVs have an impact on bacterial and metabolite associations by affecting the functions of related genes
.
To further study the effect of SVs mutation on phenotype, two metabolites affected by SVs, F1P and neotrehalose in cross-sectional cohort samples, were selected for association analysis with fasting blood glucose, and found that both F1P and neotrehalose were significantly negatively correlated with fasting blood glucose, and F.
There was also a significant negative correlation between saccharivorans and fasting glucose, but in the subgroup of SVs, the association became insignificant (Fig.
3h); the presence of SVs also weakened the association of A.
rectalis with glucose (Fig.
3i)
.
Figure 3.
Results of functional studies related to SVs in gut microbes
4a)
.
Association analysis of prophage elements and bacterial genomes yielded 1,077 prophage-host pairs (Fig.
4b); of these, only 72 were in the MVP database; while NGS data detected only 1,815 prophages, of which 80.
77% detected in the mixed assembly; from the results we can see that the ONT-second generation mixed assembly data is more favorable for the discovery of prophages
.
In addition to prophages, there is also a CRISPR-Cas system for resisting viral superinfection in the flora gene.
The spacers of the loci in this system contain the characteristic sequences of specific viruses, which may be related to the insertion mutation or deletion mutation of the strain
.
Likewise, analysis of all MAGs found 150,058 CRISPR spacers, with an average of 1,665 ± 560 spacers per sample, most of the spacers were newly discovered, with only 17,600 (11.
73%) aggregated in the CRISPROpenDB database, 22,962 (15.
30%) ) appeared in the gut microbiota of Western populations; only 9542 spacers were found based on next-generation sequencing-based assembly methods
.
From this we can also see that the new metagenomic assembly method has a stronger ability to discover genetic elements (such as CRISPR spacers)
.
β-diversity analysis of prophage/CRISPR spacers found (Jaccard distance) that the individual differences in the cross-sectional cohort were significantly greater than the differences within the tracking cohorts
.
Population-level compositional analysis of prophages and CRISPR spacers showed strong covariation between the two; Procrustes analysis, which revealed correlations between prophage and viral community composition, showed that prophages and viruses were found among different individuals in the cross-sectional cohort.
composition was significantly correlated (Fig.
4c); analysis of the active viral sequences in the metagenomic data found that 47 of the 2247 identified prophage pools were potentially active, thus indicating the presence of a large number of inactive prophages in bacterial genes, Thereby maintaining the stability of SVs
.
Figure 4.
Results of studies related to viruses and CRISPR in gut microbiota pools
In this study, a hybrid assembly method based on third-generation sequencing and second-generation sequencing was established, which not only improved the data quality, but also detected a large number of structural variations including insertional mutations and gene inversions, which is also beneficial to prophages and CRISPR spacers and other genes. Results of studies related to viruses and CRISPR in gut microbiota pools
element discovery
.
At the same time, through the analysis of cross-sectional cohort and longitudinal tracking cohort data, it is found that SVs have strong heterogeneity among different individuals and stability within individuals; through functional analysis and metabolomic analysis, it is found that SVs can affect the flora and metabolism.
associations between species and phenotypes
.
Article/Aqu Metabolomics
