Distinct N and C cross-feeding networks in a synthetic mouse gut consortium

The complex interactions between gut microbiome and host or pathogen colonization resistance cannot solely be understood from community composition. Missing are causal relationships such as metabolic interactions among species to better understand what shapes the microbiome. Here, we focused on metabolic niches generated and occupied by the Oligo-Mouse-Microbiota consortium, a synthetic community composed of 12 members that is increasingly used as a model for the mouse gut microbiome. Combining mono-cultures and spent medium experiments with untargeted metabolomics uncovered broad metabolic diversity in the consortium, constituting a dense cross-feeding network with more than 100 pairwise interactions. Quantitative analysis of the cross-feeding network revealed distinct C and N food webs that highlight the two Bacteroidetes consortium members B. caecimuris and M. intestinale as primary suppliers of carbon, and a more diverse group as nitrogen providers. Cross-fed metabolites were mainly carboxylic acids, amino acids, and the so far not reported nucleobases. In particular the dicarboxylic acids malate and fumarate provided a strong physiological benefit to consumers, presumably as anaerobic electron acceptors. Isotopic tracer experiments validated the fate of a subset of cross-fed metabolites, in particular the conversion of the most abundant cross-fed compound succinate to butyrate. Thus, we show that this consortium is tailored to produce the anti-inflammatory metabolite butyrate. Overall, we provide evidence for metabolic niches generated and occupied by OMM members that lays a metabolic foundation to facilitate understanding of the more complex in vivo behavior of this consortium in the mouse gut.

A Fast Lasso-Based Method for Inferring Higher-Order Interactions

Large-scale genotype-phenotype screens provide a wealth of data for identifying molecular alterations associated with a phenotype. Epistatic effects play an important role in such association studies. For example, siRNA perturbation screens can be used to identify combinatorial gene-silencing effects. In bacteria, epistasis has practical consequences in determining antimicrobial resistance as the genetic background of a strain plays an important role in determining resistance. Recently developed tools scale to human exome-wide screens for pairwise interactions, but none to date have included the possibility of three-way interactions. Expanding upon recent state-of-the art methods, we make a number of improvements to the performance on large-scale data, making consideration of three-way interactions possible. We demonstrate our proposed method, Pint, on both simulated and real data sets, including antibiotic resistance testing and siRNA perturbation screens. Pint outperforms known methods in simulated data, and identifies a number of biologically plausible gene effects in both the antibiotic and siRNA models. For example, we have identified a combination of known tumor suppressor genes that is predicted (using Pint) to cause a significant increase in cell proliferation.

Specific Urban Elements Identified for Tuberculosis Epidemic in Guangzhou by Using Geographical Detector

Background: The prevalence of tuberculosis (TB) in China has heavily affected people’s health for decades, which has been widely investigated for the rural regions and west parts. However, its spatial features in urban areas remain little understood. Thus, this study aims to identify its spatial differentiations and their influencing factors in highly urbanized region on a fine scale.Methods: Together with the TB cases in 2017 obtained from Guangzhou Institute of Tuberculosis Control and Prevention, in total 18 socioeconomic and environmental variables were included in this study. Two spatial analysis tools were respectively applied to select the relative appropriate spatial scale (global Moran’s I), and to identify specific urban factors (the Geographical detector) for this epidemic in the central four districts of Guangzhou.Results: The 2 km × 2 km grid was determined as the most appropriate spatial scale due to its relatively higher spatial autocorrelation (Moran’s I=0.33, Z=4.71). At this spatial level, the TB epidemic in the four central districts was obviously closely associated with most of socioeconomic factors (0.31<r<0.76) at the significance level of 0.01. By contrast, among environmental factors, only the concentration of fine particulate matter (PM2.5) correlated with this epidemic (r=0.21) at the significance level of 0.05. Similarly, according to the q-values derived from geographical detector analysis, socioeconomic factors posed stronger impacts (0.08<q<0.57) on the spatial differentiations of TB prevalence than those of environmental variables (0.06<q<0.27), Furthermore, 153 pairs of variables presented more powerful explanatory abilities for this epidemic’s spatial disparities due to their notable enhancements of q-values (7.3%<sq<311.6%) caused by the pairwise interactions.Conclusion: The spatial heterogeneity of TB prevalence was remarkably influenced by a series of specific urban elements and their pairwise interactions across the central region of Guangzhou. We accordingly suggest that more attentions should be paid to the areas with pairwise interactions of these specific urban elements in this city. This study would provide meaningful clues for local authorities making more targeted interventions on this disease in China’s municipal areas featured by both high urbanization and severe tuberculosis.

Interindividual cooperation mediated by partisanship complicates Madison’s cure for “mischiefs of faction”

Political theorists have long argued that enlarging the political sphere to include a greater diversity of interests would cure the ills of factions in a pluralistic society. While the scope of politics has expanded dramatically over the past 75 y, polarization is markedly worse. Motivated by this paradox, we take a bottom–up approach to explore how partisan individual-level dynamics in a diverse (multidimensional) issue space can shape collective-level factionalization via an emergent dimensionality reduction. We extend a model of cultural evolution grounded in evolutionary game theory, in which individuals accumulate benefits through pairwise interactions and imitate (or learn) the strategies of successful others. The degree of partisanship determines the likelihood of learning from individuals of the opposite party. This approach captures the coupling between individual behavior, partisan-mediated opinion dynamics, and an interaction network that changes endogenously according to the evolving interests of individuals. We find that while expanding the diversity of interests can indeed improve both individual and collective outcomes, increasingly high partisan bias promotes a reduction in issue dimensionality via party-based assortment that leads to increasing polarization. When party bias becomes extreme, it also boosts interindividual cooperation, thereby further entrenching extreme polarization and creating a tug-of-war between individual cooperation and societal cohesion. These dangers of extreme partisanship are highest when individuals’ interests and opinions are heavily shaped by peers and there is little independent exploration. Overall, our findings highlight the urgency to study polarization in a coupled, multilevel context.

The Evolutionary Dynamics of Hyperparasites

Evolutionary theory has typically focused on pairwise interactions, such as those between hosts and parasites, with relatively little work on more complex interactions including hyperparasites: parasites of parasites. Hyperparasites are common in nature, with the chestnut blight fungus virus CHV-1 a well-known natural example, but also notably include the phages of important human bacterial diseases. Theory on hyperparasitism has mostly focused on their impact on the evolution of virulence of their parasite host and relatively little is known about evolutionary trajectories of hyperparasites themselves. Our general modeling framework highlights the central role the that ability of a hyperparasite to be transmitted with its parasite plays in their evolution. Hyperparasites which transmit with their parasite hosts (hitchhike) will be selected for lower virulence, trending towards hypermutualism or hypercommensalism and select against causing a reduction in parasite virulence (hypovirulence). We examine the impact on the evolution of hyperparasite systems a of a wide range of host and parasite traits showing, for example, that high parasite virulence selects for higher hyperparasite virulence feeding back into selection for hypovirulence in the parasite. Our results have implications for hyperparasite research, both as biocontrol agents and for understanding of how hyperparasites shape community ecology and evolution.

Reconstruction of pairwise interactions using energy-based models*

Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics. Closely connected is the problem of inverse statistical mechanics, where the goal is to infer the parameters of such models given observed data. An open problem in this field is the question of how to train these models in the case where the data contain additional higher-order interactions that are not present in the pairwise model. In this work, we propose an approach based on energy-based models and pseudolikelihood maximization to address these complications: we show that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions. We show these improvements to hold consistently when compared to a standard approach using only the pairwise model and to an approach using only a neural network. This is in line with the general idea that simple interpretable models and complex black-box models are not necessarily a dichotomy: interpolating these two classes of models can allow to keep some advantages of both.

Stability criteria for the consumption and exchange of essential resources

Models of pairwise interactions have informed our understanding of when ecological communities will have stable equilibria. However, these models do not explicitly include the effect of the resource environment, which has the potential to refine or modify our understanding of when a group of interacting species will coexist. Recent consumer-resource models incorporating the exchange of resources alongside competition exemplify this: such models can lead to either stable or unstable equilibria, depending on the resource supply. On the other hand, these recent models focus on a simplified version of microbial metabolism where the depletion of resources always leads to consumer growth. Here, we model an arbitrarily large system of consumers governed by Liebig's law, where species require and deplete multiple resources, but each consumer's growth rate is only limited by a single one of these multiple resources. Consumed resources that do not lead to growth are leaked back into the environment, thereby tying the mismatch between depletion and growth to cross-feeding. For this set of dynamics, we show that feasible equilibria can be either stable or unstable, once again depending on the resource environment. We identify special consumption and production networks which protect the community from instability when resources are scarce. Using simulations, we demonstrate that the qualitative stability patterns we derive analytically apply to a broader class of network structures and resource inflow profiles, including cases in which species coexist on only one externally supplied resource. Our stability criteria bear some resemblance to classic stability results for pairwise interactions, but also demonstrate how environmental context can shape coexistence patterns when ecological mechanism is modeled directly.

Multitrophic higher-order interactions modulate species persistence

There is growing recognition that interactions between species pairs are modified in a multispecies context by the density of a third species. However, how these higher-order interactions (HOIs) affect species persistence remains poorly understood. To explore the effect of HOIs steaming from multiple trophic layers on plant persistence, we experimentally built a mutualistic system containing three plants and three pollinators species with two contrasting network structures. For both structures, we first estimated the statistically supported HOIs on plant species, in addition to the pairwise interactions among plants and plant-pollinators. Following a structuralist approach, we then assessed the effects of the supported HOIs on the persistence probability of each of the three competing plant species and their combinations. HOIs produced substantial effects on the strength and sign of per capita interactions between plant species to such an extent that predictions of species persistence differ from a non-HOIs scenario. Changes in network structure due to removing a plant-pollinator link further modulated the species persistence probabilities by reorganizing per capita interaction strengths of both pairwise interactions and HOIs. Our study provides empirical evidence of the joint importance of HOIs and network structure for determining the probability of species to persist within diverse communities.

Population growth of microcrustaceans in water from habitats with differing salinities

Inland salt marshes are a rare habitat in North America. Little is known about the invertebrates in these habitats and their ability to cope with the brackish conditions of the marsh. We studied the population growth of ostracods found in an inland salt marsh (Maple River salt marsh) and of copepods found in the wetland habitat immediately adjacent to the freshwater Kalamazoo River. By studying these species in water from both habitats, we aimed to find out if they performed differently in the two habitats. We also tested Daphnia pulex in water from the two habitats due to the history of Daphnia spp. as model organisms. We found that copepods performed better in water taken from the Maple River salt marsh, and the ostracods and D. pulex performed equally well in either water. This was unexpected, since ostracods are found in the salt marsh and copepods in the freshwater area. As a second experiment, we tested the invertebrates in pairwise interactions. In water from the Kalamazoo River, ostracods outperformed the other two species, but there was no difference between D. pulex and copepods. No species outperformed the other in salt marsh water. Our results show no local adaptation to salinity, suggesting that ostracods and copepods may be limited in their respective distributions by dispersal limitation or habitat suitability.