Consistency across multi-omics layers in a drug-perturbed gut microbial community
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Abstract
Multi-omics analyses are increasingly employed in microbiome studies to obtain a holistic view of molecular changes occurring within microbial communities exposed to different conditions. However, it is not always clear to what extent each omics data type contributes to our understanding of the community dynamics and whether they are concordant with each other. Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods complement each other in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.
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a baseline for integrating different data types in “in natura” settings.
I'm not sure this is entirely true since this study is from a synthetic community of species that are specifically picked and also have no strain diversity, so the dynamics are limited to species composition changes mostly and not dynamics of similar strains, or other factors such as phages, microbial eukaryotes, etc. The largest "natural" multi-omics studies I could think of that integrated different types of data were Woodcroft et al. from permafrost https://www.nature.com/articles/s41586-018-0338-1 and Herald et al. from wastewater: https://www.nature.com/articles/s41467-020-19006-2 (which includes metabolomics)
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we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions.
From the outset I find this quite surprising because there are a couple examples for low complexity enrichment communities or complex naturally occurring communities where this isn't the case that metagenomic/metatranscriptomic/metaproteomic data are concurrent with each other with what species are the most abundant corresponding to most "active" - is this because the synthetic community members were spiked in at the same abundance or the nature of these select synthetic communities exhibit "stable" behavior over time? I think some of these results are only applicable to synthetic communities for different multi-omics measurements being consistent with one another because of the …
we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions.
From the outset I find this quite surprising because there are a couple examples for low complexity enrichment communities or complex naturally occurring communities where this isn't the case that metagenomic/metatranscriptomic/metaproteomic data are concurrent with each other with what species are the most abundant corresponding to most "active" - is this because the synthetic community members were spiked in at the same abundance or the nature of these select synthetic communities exhibit "stable" behavior over time? I think some of these results are only applicable to synthetic communities for different multi-omics measurements being consistent with one another because of the inherent makeup of the community
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For metaproteomics data, we estimated species abundance
I don't think this is species abundance but rather the relative activity of that species based on protein intensity. I also think there are difficulties with calculating species "abundance" in this way because if I remember correctly you will sometimes have a redundant peptide (or a "core genome" protein) that can't be attributed back to a certain genome and those have to be tossed out, so this calculation is based on protein intensities for unique peptides attributed back to a particular genome, correct?
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Our results show generally high consistency between omics data types in relative species abundance estimations, and underline that metaproteomics can, in principle, provide robust species abundance estimates, at least for synthetic microbial communities, albeit with lower sensitivity.
I think I might be confused by the overall framing - is the take home that these different methods should be consistent so that you could use any one alone for surveying a community and know it should give you the same information? Or that if they don't coincide which method is the most reliable for the information that is needed? Some of these conclusions for these methods seem to only be applicable to stable, simple synthetic communities
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a baseline for integrating different data types in “in natura” settings.
I'm not sure this is entirely true since this study is from a synthetic community of species that are specifically picked and also have no strain diversity, so the dynamics are limited to species composition changes mostly and not dynamics of similar strains, or other factors such as phages, microbial eukaryotes, etc. The largest "natural" multi-omics studies I could think of that integrated different types of data were Woodcroft et al. from permafrost https://www.nature.com/articles/s41586-018-0338-1 and Herald et al. from wastewater: https://www.nature.com/articles/s41467-020-19006-2 (which includes metabolomics)
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Our results show generally high consistency between omics data types in relative species abundance estimations, and underline that metaproteomics can, in principle, provide robust species abundance estimates, at least for synthetic microbial communities, albeit with lower sensitivity.
I think I might be confused by the overall framing - is the take home that these different methods should be consistent so that you could use any one alone for surveying a community and know it should give you the same information? Or that if they don't coincide which method is the most reliable for the information that is needed? Some of these conclusions for these methods seem to only be applicable to stable, simple synthetic communities
-
For metaproteomics data, we estimated species abundance
I don't think this is species abundance but rather the relative activity of that species based on protein intensity. I also think there are difficulties with calculating species "abundance" in this way because if I remember correctly you will sometimes have a redundant peptide (or a "core genome" protein) that can't be attributed back to a certain genome and those have to be tossed out, so this calculation is based on protein intensities for unique peptides attributed back to a particular genome, correct?
-
we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions.
From the outset I find this quite surprising because there are a couple examples for low complexity enrichment communities or complex naturally occurring communities where this isn't the case that metagenomic/metatranscriptomic/metaproteomic data are concurrent with each other with what species are the most abundant corresponding to most "active" - is this because the synthetic community members were spiked in at the same abundance or the nature of these select synthetic communities exhibit "stable" behavior over time? I think some of these results are only applicable to synthetic communities for different multi-omics measurements being consistent with one another because of the …
we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions.
From the outset I find this quite surprising because there are a couple examples for low complexity enrichment communities or complex naturally occurring communities where this isn't the case that metagenomic/metatranscriptomic/metaproteomic data are concurrent with each other with what species are the most abundant corresponding to most "active" - is this because the synthetic community members were spiked in at the same abundance or the nature of these select synthetic communities exhibit "stable" behavior over time? I think some of these results are only applicable to synthetic communities for different multi-omics measurements being consistent with one another because of the inherent makeup of the community
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