Gut Health: The value of connections

High proportions of gut bacteria that produce their own food can be an indicator for poor gut health.
  1. Vanessa Rossetto Marcelino  Is a corresponding author
  1. Melbourne Integrative Genomics, School of Biosciences and Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Australia

The human gut is home to over 30 trillion microbes that form a complex ecosystem (Sender et al., 2016). Each person has a unique and dynamic set of microorganisms in their gut, and researchers have long tried to identify and untangle the reasons for this remarkable variation. The list of factors determining which microbes colonize an individual’s gut is extensive, ranging from diet to contact with pets and farm animals, geographical location, ethnicity, history of medications, and various other individual and lifestyle characteristics (Parizadeh and Arrieta, 2023).

The composition of the gut microbiome has also been linked to a range of health conditions, with loss of species diversity being a common hallmark of disturbed microbiomes (Bidell et al., 2022). These associations have fuelled the idea that the gut microbiome can be used as a non-invasive biomarker of health status, or to improve and maintain human health by introducing beneficial bacteria and removing pathogens from the gut.

However, it is still largely unclear whether changes in the microbiome are the cause or consequence of disease. The challenges in teasing apart the many intricate factors shaping microbiome composition constitute a major roadblock to translating the vast body of microbiome research into clinical practices. Now, in eLife, Iva Veseli (University of Chicago), Jessika Füssel, A. Murat Eren and colleagues report that the extent to which bacteria can synthetize their own food is a significant trait determining the composition of unhealthy gut microbiomes (Veseli et al., 2023).

The team – who are based at various research institutes in the United States, Denmark and Germany – analysed gut microbiomes associated with inflammatory bowel disease (IBD) and other gastrointestinal conditions. The diversity of microbes in these communities is typically low due to antibiotics, diarrhoea and other features linked to a stressed gut environment. Unlike most previous studies that looked at taxonomic or species composition, Veseli et al. investigated the genome content of bacteria, focusing on their capacity to produce and metabolize essential nutrients, such as amino acids, carbohydrates and vitamins.

They found that stressed gut environments contained bacteria whose genomes encoded complete pathways to biosynthesise essential nutrients – i.e., they show high metabolic independence. In contrast, bacterial genomes from healthy individuals contained seemingly incomplete metabolic pathways, suggesting that they rely more extensively on nutrients produced by their peers to survive, also known as cross-feeding (Figure 1).

Bacteria living in stressed and healthy gut environments have distinct metabolic potentials.

The stressed gut microbiome (left) is predominantly colonized by a low diversity of bacteria whose genomes encode pathways for synthesising a range of essential metabolites, represented by the coloured shapes. These ‘metabolically independent’ bacteria are expected to generate their own food. Conversely, gut microbiomes associated with healthy individuals (right) are enriched in bacteria that seem genetically incapable of synthesising all the nutrients they need, suggesting that they rely more extensively on nutrients produced by other bacteria.

Image credit: Figure created with BioRender.com.

Next, Veseli et al. asked whether the overall metabolic independence of gut bacteria could be used as a biomarker of health status. First, the team developed an open-source software platform to systematically quantify metabolic independence from high-throughput sequencing data. They applied their newly developed approach to over 300 deeply sequenced stool samples from individuals with IBD and healthy controls. They then showed that, with the help of machine learning, it is indeed possible to accurately identify individuals with IBD based entirely on the estimated self-sufficiency of their microbiome.

To expand the scope of their findings beyond IBD, Veseli et al. showed that a short dose of antibiotics taken by healthy volunteers leads to a sharp increase in the proportions of self-sufficient gut bacteria, followed by a gradual recovery of bacteria that seem to rely on cross-feeding. These results support the claim that high metabolic independence is a hallmark of poorly diverse, stressed gut ecosystems, which can be used as a biomarker of gut health status. Since it is based on mechanisms rather than the taxonomic identity of microbiome members, the approach proposed by Veseli et al. is likely to be more robust to the ethnicity, geographic location and lifestyle factors that have obscured associations between microbiomes and health status in the past (Sze and Schloss, 2016; Gaulke and Sharpton, 2018).

The implications of this study bring a new perspective to the microbiome field. Bacteria typically labelled as pathogens for their association with unhealthy microbiomes might not be causative disease agents as previosuly assumed. Instead, they might simply be the only ones capable of surviving in a poorly diverse gut. The study also adds key evidence to the growing awareness of the relationships between microbial cross-feeding and microbiome composition, paving the way to test interesting questions in future research (Wang et al., 2019; Marcelino et al., 2023; Gralka et al., 2020; Watson et al., 2023). For example, what are the roles of bacteria with high metabolic independence in re-establishing a healthy gut microbiome after disruption? If self-sufficient bacteria are at the bottom of the microbial food chain, one can wonder whether these presumed villains will become heroes in restoring the gut ecosystem. These new perspectives bring us one step closer to fully benefit from the diagnostic and therapeutic potential of the human gut microbiome.

References

Article and author information

Author details

  1. Vanessa Rossetto Marcelino

    Vanessa Rossetto Marcelino is in the Melbourne Integrative Genomics, School of Biosciences and Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia

    For correspondence
    vmarcelino@unimelb.edu.au
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1755-0597

Publication history

  1. Version of Record published:

Copyright

© 2023, Rossetto Marcelino

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 812
    views
  • 61
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Vanessa Rossetto Marcelino
(2023)
Gut Health: The value of connections
eLife 12:e92319.
https://doi.org/10.7554/eLife.92319

Further reading

    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics
    Johanna KS Tiemann, Magdalena Szczuka ... Pierre Poulain
    Research Article

    The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD — data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal's choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.