The gut microbiome
There may be some truth in the idiom, “trust your gut feeling”. Something in the gut
can literally influence mood—the microbiota, a community of microorganisms
including bacteria, fungi, archaea, and protozoans, as well as some viruses. The gut
secretes 95% of our serotonin (Suhrid Banskota et al., 2018), a chemical that
stabilizes mood and creates feelings of happiness. Fecal transplants of gut
microbiota derived from patients with a major depressive disorder are also found to
induce depressive-like behaviour in mice, and it is suggested that an imbalanced
microbiome composition is a reason for depression in humans (Zheng et al., 2016). It
hints that the microbiome might play a more pivotal role in the body than one would
normally think.
There are approximately as many microbial cells as human cells in our body, the
colon (the gut) being the most densely populated with them (Ron Sender et al.,
2016). Microbiome composition varies from individual to individual, but it is
estimated up to 1000 species of microbes reside in a person’s gut. Some species are
commensal, coexisting with us without benefiting or harming us, while we are in a
mutualistic relationship with some others. For instance, members of the genus
Bifidobacterium aid digestion and fend off harmful bacteria. Unsurprisingly, there
are also harmful species such as Actinomyces viscosus and Actinomyces naeslundii in
the oral cavity, secreting acids that lead to tooth decay; other pathogenic species can
cause diseases. A healthy gut microbiome is a delicate balance between good and
bad microbes. Any imbalance, or dysbiosis, could bring about sweeping changes to
our body, as that microscopic world does not merely maintain gut health, but
performs more important tasks like regulating the immune system.
A machine mapping out the gut
The composition of the gut could be determined by DNA sequencing, targeting the
16S rRNA gene for the identification of bacteria and archaea. However,
understanding how the gut microbiome works is more troublesome. To discern the
activities in the gut, researchers have been using both direct and indirect inference
methods. The direct method is to experimentally verify the interactions by putting
microbial cultures together and observing their activity. The indirect method is to
infer interactions from the genome sequencing results of the culture. Yet, these
methods are not without their downsides, the former being slow and susceptible to
missing interactions, the latter possibly overestimating interactions as it includes
both active and inactive ones.
A group of researchers led by Akshit Goyal (2021) have adopted a third method,
using a model the team has developed in 2019. It suggested that the microbiome is
akin to macroscopic ecosystems, with complex interspecies interactions. Species are
located on different trophic levels, the lowest level feeding on the nutrients in the
gut, the second feeding on the metabolites it produces, the third feeding on what
comes out of the second, so on and so forth. Together, they form a sophisticated
cross-feeding network. At the end of the cross-feeding process, the unconsumed
substances can be found in the faecal metabolome, the chemicals in the faeces. With
the aid of the model, the group then developed a machine learning algorithm,
named GutCP (gut cross-feeding predictor), to map out the cross-feeding
interactions.
GutCP is required to predict the cross-feeding interactions in microbiomes such that
they match the actual metabolome and microbiome composition, given a set of data
of known cross-feeding interactions obtained from direct inference methods. It is
also assumed that certain polysaccharides, commonly found in the human diet, are
present in the gut in the beginning. The algorithm is allowed to add a random, new,
undiscovered cross-feeding interaction link for nutrient consumption or nutrient
production on various trophic levels. By trial and error, the algorithm preserves links
that lead to an improvement in prediction and rejects those that do not, until the
improvements become insignificant.
Results
To validate the predictions, the researchers tested the system with samples from 41
individuals, involving 221 metabolites and 72 microbial species. Using three quarters
of the samples for training and the remaining quarter for testing, the procedure was
done 4 times. Then, by performing 100 GutCP runs on each of those samples, it is
proved that more runs did not significantly affect the predictions. On average,
logarithmic error and average correlation showed an improvement of 64% and 20%
respectively after GutCP added new interactions. Overall, an encouraging 65% of the
predictions are also generated by other genome-based prediction methods.
By using GutCP, the levels of more metabolite and microbial species can be
predicted. Moreover, it managed to predict the levels of some metabolites more
accurately without adding links directly related to them. Instead, it was achieved by
adding links that led to a change in the abundance of microbes that produce them.
This is a result hardly obtained with other models.
Limitations
Despite the encouraging results the algorithm presents, there are some limitations
of using machine learning to crack the gut’s code. For example, GutCP aims to
predict the cross-feeding interactions in many samples, hence is weaker at analysing
the interactions involving rarer species and metabolites. Therefore, it will require
more data in order to further improve its predictive abilities. Additionally, as the
strains of microbes in humans usually differ from person to person, they could be
capable of different interactions, making the analysis of individual cases difficult.
While machine learning could provide a holistic look into the general mechanisms of
the gut microbiome, examining the activity of specific microbiome profiles in great
detail is not its forte yet.
Applications of knowledge about the gut microbiome
Why study the gut? The answer is simple—the gut is conjectured to be home to
many of our bodies’ secrets. For instance, as mentioned before, the gut has a huge
influence on the immune system. Even commensal bacteria in the microbiome that
do not directly benefit us can compete with pathogens for nutrients or generate
metabolites that limit their growth. Germ-free mice are found to have abnormal
immune systems, one feature being having decreased levels of antimicrobial
peptides and antibodies. However, colonizing them with commensal bacteria allows
their immune systems to normalise in a few weeks (Soraya Mezouar et al., 2018).
The gut could tell us about our diseases as well. A study on the microbiomes of
COVID-19 patients revealed that gut microbiota composition is correlated to disease
severity. It suggests that the gut microbiome could be changing the levels of
signalling molecules in the blood plasma such as cytokines, chemokines and
inflammation markers. This influences the intensity of host immune responses,
triggering overaggressive inflammatory responses in some and causing widespread
damage in the body (Yeoh YK et al., 2021). In light of these insights, the Faculty of
Medicine of The Chinese University of Hong Kong has even developed a probiotics
formula to tackle dysbiosis in the gut microbiome and boost immunity against
COVID-19. Hence, understanding the gut’s connection with the rest of the human
body may prove to be useful in finding cures for a variety of diseases ranging from
inflammatory bowel disease to COVID-19 [1], as manipulating the microbiome becomes
a viable solution once those links are established.
Conclusion
In conclusion, GutCP has discovered previously unknown cross-feeding interactions
in the human gut microbiome with its machine learning algorithm, yet still lacks the
ability to provide a complete map of such interactions and analyse individual cases
with great accuracy. Through developing novel methods, scientists have been able to
understand the composition of the microbiome and its workings more accurately. As
it is clear that the microbiome is closely intertwined with human health, scientists
will likely continue to venture into this unknown realm of microscopic flora, and
attempt to stave off illnesses with its power.
[1] CU Medicine Develops a Probiotic Formula to Target Imbalance in Gut Microbiota in COVID-19 (2020, 11 Jun) https://www.cpr.cuhk.edu.hk/en/press/cu-medicine-develops-a-probiotic-formula-to-target-imbalance-in-gut-microbiota-in-covid-19/
References
Suhrid Banskota et al. (2018). Serotonin in the gut: Blessing or a curse. https://doi.org/10.1016/j.biochi.2018.06.008
Zheng et al. (2016, Apr). Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. https://www.researchgate.net/publication/301277603_Gut_microbiome_remodeling_induces_depressive-like_behaviors_through_a_pathway_mediated_by_the_host's_metabolism
Ron Sender et al. (2016, Aug 19). Revised Estimates for the Number of Human and Bacteria Cells in the Body. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991899/
Tong Wang et al. (2019, Dec 19). Evidence for a multi-level trophic organization of the human gut microbiome https://doi.org/10.1371/journal.pcbi.1007524
Goyal et al. (2021) Ecology-guided prediction of cross-feeding interactions in the human gut microbiome. https://doi.org/10.1038/s41467-021-21586-6
Soraya Mezouar et al. (2018) Microbiome and the immune system: From a healthy steady-state to allergy associated disruption. https://doi.org/10.1016/j.humic.2018.10.001
Yeoh YK et al. (2021) Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. https://gut.bmj.com/content/70/4/698.info
CU Medicine Develops a Probiotic Formula to Target Imbalance in Gut Microbiota in COVID-19 (2020, 11 Jun) https://www.cpr.cuhk.edu.hk/en/press/cu-medicine-develops-a-probiotic-formula-to-target-imbalance-in-gut-microbiota-in-covid-19/
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