A Multi-Omics Approach to Earth’s Microbiomes Project Demonstrates Microbial and Metabolite Diversity

Contrasting sharply with the little knowledge of the microbial world’s underlying structure is the rising awareness of its significance and variety. Despite recent developments in DNA sequencing, the establishment of general conclusions regarding microbial life on Earth is hampered by the absence of standardized techniques and shared analytical frameworks. Researchers at the University of California San Diego, La Jolla, CA, USA, intended to present a multi-omics profiling of Earth’s microbiomes that reveals microbial and metabolite diversity.

A multi-omics meta-analysis of a recent, varied set of microbial community samples were gathered for the Earth Microbiome Project. The description is focused on the relationships and co-occurrences of microbially-related metabolites and microbial taxa across environments. Amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, as well as untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry), were included. The identification of pooled microbial and metabolite features is made easier by standardized protocols and analytical techniques for characterizing microbial communities, including the evaluation of molecular diversity using untargeted metabolomics. This enables us to explore diversity at an extraordinary scale.

Initial work on standardized protocols for 16S ribosomal RNA (rRNA) sequencing of bacterial and archaeal communities gave researchers a better understanding of how communities form in the environment and supported distinct axes of separation of microbes along gradients of host association and salinity. Modern state-of-the-art techniques include multi-omics approaches, including metagenomics, transcriptomics, proteomics, and/or metabolomics. More recent work focusing on shotgun metagenomics data has started to give further information about functional potential across habitats.

Different secondary metabolites produced by microbes serve important roles in communication and defense and can improve human health and environmental sustainability. While metagenome mining and transcriptomics are effective methods for identifying microbial communities’ functional characteristics

The flexible technique known as liquid chromatography with untargeted tandem mass spectrometry (LC-MS/MS) can identify tens of thousands of metabolites in biological samples. Despite previously poor metabolite annotation rates when used on non-model animals, current computational developments have made it possible to classify compounds using their fragmentation spectra consistently.

Microbial secondary metabolites from several microbial communities from the Earth Microbiome Project have been quantified using LC-MS/MS. Here, the “microbial community” includes individuals from the domains of Bacteria and Archaea in order to prevent terminology ambiguity. A previously unreported group of about 900 samples from the scientific community, especially for multi-omics analysis, were gathered to build on the first investigation of the EMP archive, which concentrated on profiling bacterial and archaeal 16S rRNA1.

Additional standardized techniques for shotgun metagenomic sequencing and untargeted metabolomics to the scalable architecture of the EMP in order to catalog the world’s microbiota.

For the majority of samples, successful generation of data for shotgun metagenomics, untargeted metabolomics (LC-MS/MS and gas chromatography coupled with mass spectrometry (GC-MS)), bacterial and archaeal 16S rRNA, eukaryotic 18S rRNA, internal transcribed spacer (ITS) one of the fungal ITS region, and bacterial full-length rRNA operon. 

As a result, this study offers a wealth of information for answering open issues and acts as a standard for gathering more information.

The second question to investigate is whether metabolite diversity among habitats (also known as beta-diversity) is driven by either turnover (i.e., feature replacement) or nestedness (i.e., feature gain/loss resulting in variations in richness). Turnover should be the first thing considered. It noted the similarity between the datasets for microbially associated metabolite and microbiological taxon in the clustering of samples by the environment. A significant association between sample-to-sample distances based on metabolites and microbial taxa has been observed. It’s interesting to see that when contrasting samples based on microbial taxa vs. metabolites, salinity had a bigger influence. Quantification of nestedness was shown in the absence of full turnover in metabolites and microbial species across environments, which was indicated by the overlap of clusters representing various habitats in the ordinations. The concept of nestedness, which might shed light on the dynamics of community formation, indicates the degree to which characteristics in one environment are nested subsets of another environment.

The idea that “everything is everywhere, but the environment selects” is specifically investigated. Although their relative abundances will change significantly between various settings, the distributions of most of the major types of metabolites are predicted to be universal. Consequently, their relative abundances will have tremendous power in differentiating across habitats, whereas the presence or absence of metabolites alone may exhibit profiles that are rather homogeneous among samples. The idea that metabolite alpha- and beta-diversity will highly correlate with microbial diversity is thus explored by this alone.

Microbes and metabolites vary in the environments

The idea is that certain metabolites, microbial taxa, or microbial functional products (enzymes) may be used to categorize samples across settings in light of the significant correlations between metabolites, microorganisms, and the environment. The ability to classify samples according to their habitat is important because these qualities may be utilized as indicators, which can be helpful for identifying specific environmental conditions, identifying environmental change, or forecasting a variety of other properties. The machine-learning approach was used to analyze the metabolites, taxonomy, and functions of microbes that are associated with microbes.

Correlations with amplicon sequence data and GC–MS data

To begin to explore the additional data generated for EMP500 samples, including GC–MS and amplicon sequence data (that is, bacterial and archaeal 16S and full-length rRNA operon, eukaryotic 18S, fungal ITS), comparing sample–sample distances (that is, beta-diversity) between each pair of datasets was performed. Beyond providing insight into how certain community data are related, strong correlations between datasets may indicate similarity in the structuring of features among samples or habitats. Important findings further support a strong relationship between microbially related metabolites and microbial taxa (LC-MS/MS).

The next paragraphs describe some of the study’s drawbacks and limitations as well as how this methodology improves knowledge of the dynamics of microbial communities and functional diversity.

In order to further generalize these findings to those habitats, it is advised that future research concentrate on an additional sample of these habitats. Similar to the geographical expansion of sampling, the scope of inference was also expanded because many significant settings and locations were left out. Furthermore, it has been noted that the inherent design of the EMP (i.e., crowd-sourced samples from experts in respective fields) prevented explicit exploration of causation with respect to the environment in the analysis, and as a result, these findings are based primarily on observations, correlations among feature sets, and associated metadata.

Conclusion of Multi-Omics approach to EMP profiling

This strategy demonstrates how cutting-edge computational annotation techniques provide a robust toolset for analyzing untargeted metabolomics data. It is hoped that concurrent developments in metagenomic sequencing, genome assembly, and genome mining will enhance the identification and categorization of functional products produced by bacteria and offer more context for these discoveries. This work will be a crucial resource for further collaborative investigations because it adhered to defined procedures made available on GitHub and made this dataset freely accessible in Qiita and GNPS. Similarly, improving computational and equipment approaches for metabolomics will deepen the range of compounds examined in microbiome investigations.

Article Source: Reference Paper

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