Gut Health: The value of connections
High proportions of gut bacteria that produce their own food can be an indicator for poor gut health.
- 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).
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
Vanessa Rossetto Marcelino
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
-
- 850
- views
-
- 65
- 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)
Gut Health: The value of connections
eLife 12:e92319.
https://doi.org/10.7554/eLife.92319
Further reading
-
- Cell Biology
- Computational and Systems Biology
Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.
-
- Computational and Systems Biology
Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.
