Food and the Gut Microbiome: A New Perspective

“If more of us valued food and cheer and song above hoarded gold, it would be a merrier world.”

– J.R.R. Tolkien (The Hobbit)

Understanding Food As Information (FAI) requires a paradigm shift. It requires moving beyond reductionist lists of nutrients and caloric totals and embracing a biological systems approach. FAI posits that food functions as information the body processes, in many ways analogous to how we process information through language.

This week’s Study Spotlight provides compelling support in this direction. Instead of asking whether a specific nutrient affects health, the researchers asked a much bigger question:

How do dietary compounds interact with the gut microbiome at a systems level?

Using genome-scale metabolic modeling, they mapped interactions between:

818 gut microbial species
1,390 metabolites
312 dietary compounds

Their goal was to move beyond isolated nutrient studies and examine the full network of interactions between what we eat and the microbial communities that process it. They modeled dietary compounds as a structured “vocabulary” that the gut microbiome can “read.”

Through this lens, they revealed four important findings:

The system is highly structured—not random.
Microbes don’t metabolize dietary compounds arbitrarily. The network shows nested organization: species with smaller metabolic repertoires tend to process subsets of compounds handled by more metabolically versatile species.
Functional redundancy is high.
Many dietary compounds can be processed by multiple microbial species. This redundancy makes the microbiome resilient and stable. Think of your gut microbiome like a room full of multilingual translators listening to the same speech. If only one person in the room understands French and they leave, the message is lost. But if ten people understand French, the meaning survives even if a few step out.

That’s what is meant by “functional redundancy.” Many different microbial species can process the same dietary compounds, so if one species declines, others can step in and carry on the job. This overlap doesn’t make the system inefficient—it makes it resilient. Just as language stays stable because many people share the same words and grammar, your microbiome stays stable because multiple microbes can “understand” and act on the same food signals.

Some compounds are highly specialized.
Certain dietary molecules are metabolized by only a small number of microbes, opening the door to targeted dietary interventions.
Metabolic capacity is conserved within genera.
Species within the same genus share similar metabolic capabilities, suggesting evolutionary patterning rather than random drift. Think of a microbial genus like a family of closely related chefs trained in the same culinary school. They may have different names and slightly different styles, but they tend to know how to prepare many of the same dishes because they share a common training and recipe book.

In the same way, bacterial species within the same genus usually have very similar metabolic skills; they can process many of the same food compounds. That similarity isn’t random; it reflects shared evolutionary history. Just as siblings often inherit similar talents, these microbes inherit similar biochemical toolkits, suggesting that their abilities were shaped and preserved over time rather than scattered by chance.

In short, the gut microbiome does not merely extract calories: it interprets structured chemical patterns. These findings strengthen the case that food is not just calories/macronutrients, but a patterned message whose interpretation depends on the receiving network.

The Study:

The researchers leveraged a curated genome-scale metabolic model (AGREDA/AGORA-derived reconstructions) to build an interaction map between 818 gut microbial species and both metabolites and dietary compounds. These dietary compounds were a subset of metabolites emphasizing food-derived molecules.

They then analyzed this as a bipartite network/incidence matrix, a kind of dictionary that says: “this microbe has the metabolic machinery that includes this compound.” They found:

There’s approximately a fourfold variation across genera in the number of metabolites and dietary compounds represented in microbial metabolic networks, while species within the same genus are metabolically similar.
The degree to which metabolites are “widely usable” varies drastically. For example, some are used by almost all species, while others are used by only a very few.
Using a 17-day, high-frequency longitudinal human microbiome dataset (MCTS), researchers found that species with similar metabolic capacity tend to share the same ecological niche.
The microbiome’s capacity to process dietary compounds appears functionally stable.
They propose using this systems approach as a way to design targeted synbiotics (prebiotic + probiotic) to achieve specific results.

The Caveat:

This paper is remarkable because it operationalizes “food tokens” as machine-readable signals. In other words, the researchers moved from viewing food as individual nutrients to viewing it as discrete informational tokens. In this model, the gut microbiome functions as a kind of biochemical computer that recognizes subsets of those tokens.

The researchers were looking to understand which microbes metabolize which dietary compounds. That’s exactly the principle upon which Food As Information rests: the meaning of an input depends on the decoding by the receiver. What a message means depends upon who reads it.

They also revealed that the ‘nestedness’ result resembles an “error-correcting code.” The microbe–dietary compound incidence matrix is more nested than the microbe–metabolite matrix. This enables many microbes to share overlapping capacities for processing dietary compounds, driving high dietary-compound functional redundancy.

In FAI terms, this resembles robust coding. In communication systems, redundancy protects the message against noise or dropouts. In microbiome ecology, redundancy can protect function when taxa fluctuate. If “food signals” are transmitted through a noisy gut ecosystem, a nested/redundant decoding architecture makes the system more reliable. This suggests that natural selection has favored architectures that preserve function despite ecological fluctuation..

A key FAI idea is that foods differ not just by macronutrients but by informational richness and specificity. The current study shows a spectrum of ‘meaning’ whereby some dietary compound classes are widespread across taxa (interpretable as “core vocabulary” and others are restricted to a few taxa (interpretable as “rare keywords” that can steer specific microbial responders). They even give examples of dietary compounds that are metabolized by only a limited number of species. These compounds could be used to target specific microbes.

In many ways, this paper reframes the idea of “precision nutrition” to better reflect precision signaling. Why this matters is because, for example, instead of telling someone “eat X grams of fiber,” we would recommend a specific structure, e.g., cellulose, phlorizin, or specific polyphenols to shift that microbial module. The synbiotic framing is explicit: identify specialized compounds and pair them with linked beneficial species. That’s a paradigm shift because it treats dietary change as targeted message design: choose inputs with known semantics in a given biochemical “language.”

These findings and insights highlight why the same label does not always equate to the same message. Even when two foods share equivalent calories and macronutrients, their compound-level pattern can differ drastically, e.g., in terms of polyphenols, glycosides, lignans, etc., and your gut microbiome has an uneven ability to interpret those patterns.

FAI is the framework that explains why “nutritionally similar” foods can generate different downstream informational consequences: because the receiver’s decoding set interacts with the message’s fine structure.

The authors acknowledge limitations: they focused on dietary compounds, but real-life interventions are delivered through foods, which require accurate documentation of dietary compounds in foods. Another limitation is that genome-scale models tell you what a microbe could do under certain conditions, not what it actually does in vivo. The models predict metabolic potential, what microbes are equipped to do, not necessarily what they are doing at every moment. Nonetheless, the paper is insightful and exciting, but it is important to remember that the findings are at least partially speculative.

That said, this study suggests that our gut microbiome does not merely “absorb nutrients.” It interprets patterns of compounds within the context of ecological and evolutionary constraints. Dietary inputs act as signals that influence microbial composition, metabolite production, and downstream host physiology; ultimately, they decode messages that impart health and happiness or disability and disease.

Importantly, the study demonstrates:

Food-derived compounds operate within a network grammar.
The microbiome’s response is shaped by redundancy, specialization, and ecological organization.
Whole dietary patterns, not isolated nutrients, determine the informational landscape.

This research shows that what we eat interacts with microbial systems in patterned, networked ways. Our gut microbiome behaves like an interpreter of dietary signals. Some messages are broadly understood. Others are niche. And the message’s structure matters.

When we shift from thinking about food as a collection of nutrients to seeing it as information flowing through nested biological systems, we begin to understand why: