High-order interactions distort the functional landscape of microbial consortia

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Dynamical effects of hypergraph links in a network of fractional-order complex systems

In recent times, the fractional-order dynamical networks have gained lots of interest across various scientific communities because it admits some important properties like infinite memory, genetic characteristics, and more degrees of freedom than an integer-order system. Because of these potential applications, the study of the collective behaviors of fractional-order complex networks has been investigated in the literature. In this work, we investigate the influence of higher-order interactions in fractional-order complex systems. We consider both two-body and three-body diffusive interactions. To elucidate the role of higher-order interaction, we show how the network of oscillators is synchronized for different values of fractional-order. The stability of synchronization is studied with a master stability function analysis. Our results show that higher-order interactions among complex networks help the earlier synchronization of networks with a lesser value of first-order coupling strengths in fractional-order complex simplices. Besides that, the fractional-order also shows a notable impact on synchronization of complex simplices. For the lower value of fractional-order, the systems get synchronized earlier, with lesser coupling strengths in both two-body and three-body interactions. To show the generality in the outcome, two neuron models, namely, Hindmarsh-Rose and Morris-Leccar, and a nonlinear Rössler oscillator are considered for our analysis.

Analysis of Hypergraph Signals via High-Order Total Variation

Beyond pairwise relationships, interactions among groups of agents do exist in many real-world applications, but they are difficult to capture by conventional graph models. Generalized from graphs, hypergraphs have been introduced to describe such high-order group interactions. Inspired by graph signal processing (GSP) theory, an existing hypergraph signal processing (HGSP) method presented a spectral analysis framework relying on the orthogonal CP decomposition of adjacency tensors. However, such decomposition may not exist even for supersymmetric tensors. In this paper, we propose a high-order total variation (HOTV) form of a hypergraph signal (HGS) as its smoothness measure, which is a hyperedge-wise measure aggregating all signal values in each hyperedge instead of a pairwise one in most existing work. Further, we propose an HGS analysis framework based on the Tucker decomposition of the hypergraph Laplacian induced by the aforementioned HOTV. We construct an orthonormal basis from the HOTV, by which a new spectral transformation of the HGS is introduced. Then, we design hypergraph filters in both vertex and spectral domains correspondingly. Finally, we illustrate the advantages of the proposed framework by applications in label learning.

Design of synthetic human gut microbiome assembly and butyrate production

The capability to design microbiomes with predictable functions would enable new technologies for applications in health, agriculture, and bioprocessing. Towards this goal, we develop a model-guided approach to design synthetic human gut microbiomes for production of the health-relevant metabolite butyrate. Our data-driven model quantifies microbial interactions impacting growth and butyrate production separately, providing key insights into ecological mechanisms driving butyrate production. We use our model to explore a vast community design space using a design-test-learn cycle to identify high butyrate-producing communities. Our model can accurately predict community assembly and butyrate production across a wide range of species richness. Guided by the model, we identify constraints on butyrate production by high species richness and key molecular factors driving butyrate production, including hydrogen sulfide, environmental pH, and resource competition. In sum, our model-guided approach provides a flexible and generalizable framework for understanding and accurately predicting community assembly and metabolic functions.

Complex yeast-bacteria interactions affect the yield of industrial ethanol fermentation

Sugarcane ethanol fermentation represents a simple microbial community dominated by S. cerevisiae and co-occurring bacteria with a clearly defined functionality. In this study, we dissect the microbial interactions in sugarcane ethanol fermentation by combinatorically reconstituting every possible combination of species, comprising approximately 80% of the biodiversity in terms of relative abundance. Functional landscape analysis shows that higher-order interactions counterbalance the negative effect of pairwise interactions on ethanol yield. In addition, we find that Lactobacillus amylovorus improves the yeast growth rate and ethanol yield by cross-feeding acetaldehyde, as shown by flux balance analysis and laboratory experiments. Our results suggest that Lactobacillus amylovorus could be considered a beneficial bacterium with the potential to improve sugarcane ethanol fermentation yields by almost 3%. These data highlight the biotechnological importance of comprehensively studying microbial communities and could be extended to other microbial systems with relevance to human health and the environment.

From Microbial Dynamics to Functionality in the Rhizosphere: A Systematic Review of the Opportunities With Synthetic Microbial Communities

Synthetic microbial communities (SynComs) are a useful tool for a more realistic understanding of the outcomes of multiple biotic interactions where microbes, plants, and the environment are players in time and space of a multidimensional and complex system. Toward a more in-depth overview of the knowledge that has been achieved using SynComs in the rhizosphere, a systematic review of the literature on SynComs was performed to identify the overall rationale, design criteria, experimental procedures, and outcomes of in vitro or in planta tests using this strategy. After an extensive bibliography search and a specific selection process, a total of 30 articles were chosen for further analysis, grouping them by their reported SynCom size. The reported SynComs were constituted with a highly variable number of members, ranging from 3 to 190 strains, with a total of 1,393 bacterial isolates, where the three most represented phyla were Proteobacteria, Actinobacteria, and Firmicutes. Only four articles did not reference experiments with SynCom on plants, as they considered only microbial in vitro studies, whereas the others chose different plant models and plant-growth systems; some of them are described and reviewed in this article. Besides, a discussion on different approaches (bottom-up and top-down) to study the microbiome role in the rhizosphere is provided, highlighting how SynComs are an effective system to connect and fill some knowledge gaps and to have a better understanding of the mechanisms governing these multiple interactions. Although the SynCom approach is already helpful and has a promising future, more systematic and standardized studies are needed to harness its full potential.

Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI.

Toxicity drives facilitation between 4 bacterial species

Competition between microbes is extremely common, with many investing in mechanisms to harm other strains and species. Yet positive interactions between species have also been documented. What makes species help or harm each other is currently unclear. Here, we studied the interactions between 4 bacterial species capable of degrading metal working fluids (MWF), an industrial coolant and lubricant, which contains growth substrates as well as toxic biocides. We were surprised to find only positive or neutral interactions between the 4 species. Using mathematical modeling and further experiments, we show that positive interactions in this community were likely due to the toxicity of MWF, whereby each species' detoxification benefited the others by facilitating their survival, such that they could grow and degrade MWF better when together. The addition of nutrients, the reduction of toxicity, or the addition of more species instead resulted in competitive behavior. Our work provides support to the stress gradient hypothesis by showing how harsh, toxic environments can strongly favor facilitation between microbial species and mask underlying competitive interactions.

Massively parallel screening of synthetic microbial communities

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.

Microbial coexistence through chemical-mediated interactions

Many microbial functions happen within communities of interacting species. Explaining how species with disparate growth rates can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Here, we ask how microbes interacting through their chemical environment can achieve coexistence in a continuous growth setup (similar to an industrial bioreactor or gut microbiota) where external resources are being supplied. We formulate and experimentally constrain a model in which mediators of interactions (e.g. metabolites or waste-products) are explicitly incorporated. Our model highlights facilitation and self-restraint as interactions that contribute to coexistence, consistent with our intuition. When interactions are strong, we observe that coexistence is determined primarily by the topology of facilitation and inhibition influences not their strengths. Importantly, we show that consumption or degradation of chemical mediators moderates interaction strengths and promotes coexistence. Our results offer insights into how to build or restructure microbial communities of interest.

Changes in the genetic requirements for microbial interactions with increasing community complexity

Microbial community structure and function rely on complex interactions whose underlying molecular mechanisms are poorly understood. To investigate these interactions in a simple microbiome, we introduced E. coli into an experimental community based on a cheese rind and identified the differences in E. coli's genetic requirements for growth in interactive and non-interactive contexts using Random Barcode Transposon Sequencing (RB-TnSeq) and RNASeq. Genetic requirements varied among pairwise growth conditions and between pairwise and community conditions. Our analysis points to mechanisms by which growth conditions change as a result of increasing community complexity and suggests that growth within a community relies on a combination of pairwise and higher-order interactions. Our work provides a framework for using the model organism E. coli as a readout to investigate microbial interactions regardless of the genetic tractability of members of the studied ecosystem.