Gut Health: The value of connections
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
-
Gut microbiome health and dysbiosis: A clinical primerPharmacotherapy 42:849–857.https://doi.org/10.1002/phar.2731
-
Trophic interactions and the drivers of microbial community assemblyCurrent Biology 30:R1176–R1188.https://doi.org/10.1016/j.cub.2020.08.007
-
The global human gut microbiome: genes, lifestyles, and dietTrends in Molecular Medicine 29:789–801.https://doi.org/10.1016/j.molmed.2023.07.002
-
Evidence for a multi-level trophic organization of the human gut microbiomePLOS Computational Biology 15:e1007524.https://doi.org/10.1371/journal.pcbi.1007524
Article and author information
Author details
Publication history
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
-
- 872
- views
-
- 69
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.
-
- Computational and Systems Biology
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.