Technical versus biological variability in a synthetic human gut community

Non-stochastic reassembly of a metabolically cohesive gut consortium shaped by N-acetyl-lactosamine-enriched fibers

Diet is one of the main factors shaping the human microbiome, yet our understanding of how specific dietary components influence microbial consortia assembly and subsequent stability in response to press disturbances - such as increasing resource availability (feeding rate) - is still incomplete. This study explores the reproducible re-assembly, metabolic interplay, and compositional stability within microbial consortia derived from pooled stool samples of three healthy infants. Using a single-step packed-bed reactor (PBR) system, we assessed the reassembly and metabolic output of consortia exposed to lactose, glucose, galacto-oligosaccharides (GOS), and humanized GOS (hGOS). Our findings reveal that complex carbohydrates, especially those containing low inclusion (~1.25 gL-1) components present in human milk, such as N-acetyl-lactosamine (LacNAc), promote taxonomic, and metabolic stability under varying feeding rates, as shown by diversity metrics and network analysis. Targeted metabolomics highlighted distinct metabolic responses to different carbohydrates: GOS was linked to increased lactate, lactose to propionate, sucrose to butyrate, and CO2, and the introduction of bile salts with GOS or hGOS resulted in butyrate reduction and increased hydrogen production. This study validates the use of single-step PBRs for reliably studying microbial consortium stability and functionality in response to nutritional press disturbances, offering insights into the dietary modulation of microbial consortia and their ecological dynamics.

Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms

Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Resource sharing of an infant gut microbiota synthetic community in combinations of human milk oligosaccharides

Quickly after birth, the gut microbiota is shaped via species acquisition and resource pressure. Breastmilk, and more specifically, human milk oligosaccharides are a determining factor in the formation of microbial communities and the interactions between bacteria. Prominent human milk oligosaccharide degraders have been rigorously characterized, but it is not known how the gut microbiota is shaped as a complex community. Here, we designed BIG-Syc, a synthetic community of 13 strains from the gut of vaginally born, breastfed infants. BIG-Syc replicated key compositional, metabolic, and proteomic characteristics of the gut microbiota of infants. Upon fermentation of a four and five human milk oligosaccharide mix, BIG-Syc demonstrated different compositional and proteomic profiles, with Bifidobacterium infantis and Bifidobacterium bifidum suppressing one another. The mix of five human milk oligosaccharides resulted in a more diverse composition with dominance of B. bifidum, whereas that with four human milk oligosaccharides supported the dominance of B. infantis, in four of six replicates. Reintroduction of bifidobacteria to BIG-Syc led to their engraftment and establishment of their niche. Based on proteomics and genome-scale metabolic models, we reconstructed the carbon source utilization and metabolite and gas production per strain. BIG-Syc demonstrated teamwork as cross-feeders utilized simpler carbohydrates, organic acids, and gases released from human milk oligosaccharide degraders. Collectively, our results showed that human milk oligosaccharides prompt resource-sharing for their complete degradation while leading to a different compositional and functional profile in the community. At the same time, BIG-Syc proved to be an accurate model for the representation of intra-microbe interactions.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.