Reducing Mass Confusion over the Microbiome

Abstract

As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.

Figure 1:

Timeline of some major technological advances in (A) Microbiome science^,- and (B) Mass Spectrometry adapted from Palmblad et al. Red font corresponds to advances in metabolomics research.

Figure 2:

Host-microbe vs. microbiome approaches based on sample type. (A) Spatial interrogation of host-microbe systems often involve whole organisms, such as plants, insects such as leaf-cutting ants, the Hawaiian bobtail squid and their associated microorganisms.^,, The samples can be directly collected from the native environment or lab cultivated. (B) Larger organisms that serve as model systems for humans, such as mice, facilitate measurements of organs that can be cryosectioned for spatial or extracted for bulk analysis of microbial metabolites in that organ. Examples of this include swiss rolled murine intestines and spleen infected with pathogenic bacteria.^, (C) Human tissue samples that have been donated for scientific research or biopsies from living patients can be used for either spatial or bulk analysis to interrogate the interface of the host and microbial communities. There are limited examples of this due to the complexities and consent required to collect these samples.^, (D) Microbial cultures, whether monoculture or complex community, can be analyzed spatially to gain metabolomic information about heterogeneity throughout the culture. These samples can also be extracted and analyzed for presence/absence of metabolites without spatial information.^,, (E) Some samples are only compatible with bulk analysis of metabolites due to their complexity. For example, human microbial communities can often only be interrogated via surface sampling using a swab, then cultured and extracted. Biofluids are also incompatible with spatial analysis. This is where a majority of known methods to study microbial communities lie.^,, A benefit of this type of non-invasive sampling is that it can be compatible with longitudinal study design.

Figure 3:

Column chart of genomic and metabolomic statistics from open-access sources by year (2016, 2019, 2023). All 2023 data was accessed on May 16, 2023. 2016 and 2019 statistics are based on the number of entries at 12:01 am on January 1st of that calendar year, unless otherwise stated. The number of genomes sequenced was obtained using NCBI and recording the number of complete microbial genome assemblies only. Putative BGCs were found using the AntiSMASH database with 2016 and 2019 statistics acquired from publications for versions 1.0 and 2.0.^- Verified BGCs represent the number of experimentally characterized gene-metabolite connections, downloaded from MIBiG versions 1.3 and 2.0. The number of microbial metabolites was obtained from a download of the entire NPAtlas database. A search of the MassIVE database was performed by filtering either the “Title” or “Keywords” fields for “microb” and manually assessing the results for only microbial metabolomics datasets. It is important to note there is likely redundancy in these datasets, as there can be multiple adducts and MS/MS spectra for any analyte as well as multiple instances across different acquisitions.