scholarly journals Deconstructing higher-order interactions in the microbiota: A theoretical examination

2019 ◽  
Author(s):  
Yitbarek Senay ◽  
Guittar John ◽  
Sarah A. Knutie ◽  
C. Brandon Ogbunugafor
Keyword(s):  
Gut Microbiota ◽  
Evolutionary Genetics ◽  
Infection Risk ◽  
Higher Order ◽  
Microbial Interactions ◽  
Order Effects ◽  
List Type ◽  
Linear Effect ◽  
Pairwise Interactions ◽  
Host Infection

AbstractThe animal gut is a complex ecosystem containing many interacting species. A major objective of microbiota research is to identity the scale at which gut taxa shape hosts. However, most studies focus solely on pairwise interactions and ignore higher-order interactions involving three or more component taxa. Higher-order interactions represent non-additive effects that cannot be predicted from first-order or pairwise interactions.Possible reasons as to why studies of higher higher-order interactions have been scarce is that many host-associated systems are experimentally intractable, gut microbiota are prohibitively species rich, and the influence of any given taxon on hosts is often context-dependent. Furthermore, quantifying emergent effects that represent higher-order interactions that are not simply the result of lower-order interactions, present a combinatorial challenge for which there are few well-developed statistical approaches in host-microbiota studies.In this perspective, our goal is to quantify the existence of emerging higher-order effects and characterize their prevalence in the microbiota. To do so, we adapt a method from evolutionary genetics used to quantify epistatic effects between mutations and use it to quantify the effects of higher-order microbial interactions on host infection risk.We illustrate this approach by applying it to an in silico dataset generated to resemble a population of hosts with gut-associated microbial communities. We assign each host a pathogen load, and then determine how emergent interactions between gut taxa influence this host trait.We find that the effect of higher-order interactions generally increases in magnitude with the number of species in the gut community. Based on the average magnitude of interaction for each order, we find that 9th order interactions have the largest non-linear effect on determining host infection risk.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota can be essential for understanding their effects on host infection risk. We conclude that insofar as higher-order interactions between taxa may profoundly shape important organismal phenotypes (such as susceptibility to infection), that they deserve greater attention in microbiome studies.

scholarly journals Growth tradeoffs produce complex microbial communities on a single limiting resource

2018 ◽  
Author(s):  
Michael Manhart ◽  
Eugene I. Shakhnovich
Keyword(s):  
Microbial Growth ◽  
Growth Traits ◽  
Fundamental Problem ◽  
Higher Order ◽  
Microbial Populations ◽  
Order Effects ◽  
Pairwise Interactions ◽  
Lag Times ◽  
The Relationship ◽  
Higher Order Effects

The relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology. Higher-order ecological effects beyond pairwise interactions may be key to complex ecosystems, but mechanisms to produce these effects remain poorly understood. Here we show that higher-order effects can arise from variation in multiple microbial growth traits, such as lag times and growth rates, on a single limiting resource with no other interactions. These effects produce a range of ecological phenomena: an unlimited number of strains can exhibit multi stability and neutral coexistence, potentially with a single keystone strain; strains that coexist in pairs do not coexist all together; and the champion of all pairwise competitions may not dominate in a mixed community. Since variation in multiple growth traits is ubiquitous in microbial populations due to pleiotropy and non-genetic variation, our results indicate these higher-order effects may also be widespread, especially in laboratory ecology and evolution experiments.

scholarly journals Interaction modifications lead to greater robustness than pairwise non-trophic effects in food webs

2018 ◽  
Author(s):  
J. Christopher D. Terry ◽  
Rebecca J. Morris ◽  
Michael B. Bonsall
Keyword(s):  
Theoretical Research ◽  
Order Effects ◽  
List Type ◽  
Dynamic Simulations ◽  
Dynamic Complexity ◽  
Trophic Effects ◽  
Trophic Networks ◽  
Pairwise Interactions ◽  
Species Vulnerability ◽  
Interaction Modifications

AbstractConsiderable emphasis has been placed recently on the importance of incorporating non-trophic effects in to our understanding of ecological networks. Interaction modifications are well established as generating strong non-trophic impacts by modulating the strength of inter-specific interactions.For simplicity and comparison with direct interactions within a network context, the consequences of interaction modifications have often been described as direct pairwise interactions. The consequences of this assumption have not been examined in non-equilibrium settings where unexpected consequences of interaction modifications are most likely.To test the distinct dynamic nature of these ‘higher-order’ effects we directly compare, using dynamic simulations, the robustness to extinctions under perturbation of systems where interaction modifications are either explicitly modelled or represented by corresponding equivalent pairwise non-trophic interactions.Full, multi-species representations of interaction modifications resulted in a greater robustness to extinctions compared to equivalent pairwise effects. Explanations for this increased stability despite apparent greater dynamic complexity can be found in additional routes for dynamic feedbacks. Furthermore, interaction modifications changed the relative vulnerability of species to extinction from those trophically connected close to the perturbed species towards those receiving a large number of modifications.Future empirical and theoretical research into non-trophic effects should distinguish interaction modifications from direct pairwise effects in order to maximise information about the system dynamics. Interaction modifications have the potential to shift expectations of species vulnerability based exclusively on trophic networks.

scholarly journals Deciphering microbial interactions in synthetic human gut microbiome communities

2017 ◽  
Author(s):  
Ophelia S. Venturelli ◽  
Alex C. Carr ◽  
Garth Fisher ◽  
Ryan H. Hsu ◽  
Rebecca Lau ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Community Assembly ◽  
Gut Microbiome ◽  
Community Dynamics ◽  
Microbial Interactions ◽  
Model Parameters ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Pairwise Interactions ◽  
Key Species

ABSTRACTThe human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

Time after time: support for memory accounts of temporal preparation.

2019 ◽  
Author(s):  
Joe Butler ◽  
Samuel Ngabo ◽  
Marcus Missal
Keyword(s):  
Response Times ◽  
Reaction Times ◽  
Evidence Base ◽  
Higher Order ◽  
Order Effects ◽  
Previous Trial ◽  
Adaptive Responses ◽  
Temporal Preparation ◽  
Subsequent Trial ◽  
Higher Order Effects

Complex biological systems build up temporal expectations to facilitate adaptive responses to environmental events, in order to minimise costs associated with incorrect responses, and maximise the benefits of correct responses. In the lab, this is clearly demonstrated in tasks which show faster response times when the period between warning (S1) and target stimulus (S2) on the previous trial was short and slower when the previous trial foreperiod was long. The mechanisms driving such higher order effects in temporal preparation paradigms are still under debate, with key theories proposing that either i) the foreperiod leads to automatic modulation of the arousal system which influences responses on the subsequent trial, or ii) that exposure to a foreperiod results in the creation of a memory trace which is used to guide responses on the subsequent trial. Here we provide data which extends the evidence base for the memory accounts, by showing that previous foreperiod exposures are cumulative with reaction times shortening after repeated exposures; whilst also demonstrate that the higher order effects associated with a foreperiod remain active for several trials.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals Responsible innovation, anticipation and responsiveness: case studies of algorithms in decision support in justice and security, and an exploration of potential, unintended, undesirable, higher-order effects

AI and Ethics ◽  
2021 ◽  
Author(s):  
Marc Steen ◽  
Tjerk Timan ◽  
Ibo van de Poel
Keyword(s):  
Decision Support ◽  
Case Studies ◽  
Personal Data ◽  
Higher Order ◽  
Popular Media ◽  
Responsible Innovation ◽  
Order Effects ◽  
Key Dimensions ◽  
Higher Order Effects

AbstractThe collection and use of personal data on citizens in the design and deployment of algorithms in the domain of justice and security is a sensitive topic. Values like fairness, autonomy, privacy, accuracy, transparency and property are at stake. Negative examples of algorithms that propagate or exacerbate biases, inequalities or injustices have received ample attention, both in academia and in popular media. To supplement this view, we will discuss two positive examples of Responsible Innovation (RI): the design and deployment of algorithms in decision support, with good intentions and careful approaches. We then explore potential, unintended, undesirable, higher-order effects of algorithms—effects that may occur despite good intentions and careful approaches. We do that by engaging with anticipation and responsiveness, two key dimensions of Responsible Innovation. We close the paper with proposing a framework and a series of tentative recommendations to promote anticipation and responsiveness in the design and deployment of algorithms in decision support in the domain of justice and security.

scholarly journals Antibiotics at birth and later antibiotic courses: effects on gut microbiota

2021 ◽  
Author(s):  
Sofia Ainonen ◽  
Mysore V Tejesvi ◽  
Md. Rayhan Mahmud ◽  
Niko Paalanne ◽  
Tytti Pokka ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Control Group ◽  
List Type ◽  
Antibiotic Exposure ◽  
Microbiota Composition ◽  
Long Term Effects ◽  
The Impact ◽  
Antibiotic Courses ◽  
Gut Microbiota Composition

Abstract Background Intrapartum antibiotic prophylaxis (IAP) is widely used, but the evidence of the long-term effects on the gut microbiota and subsequent health of children is limited. Here, we compared the impacts of perinatal antibiotic exposure and later courses of antibiotic courses on gut microbiota. Methods This was a prospective, controlled cohort study among 100 vaginally delivered infants with different perinatal antibiotic exposures: control (27), IAP (27), postnatal antibiotics (24), and IAP and postnatal antibiotics (22). At 1 year of age, we performed next-generation sequencing of the bacterial 16S ribosomal RNA gene of fecal samples. Results Exposure to the perinatal antibiotics had a clear impact on the gut microbiota. The abundance of the Bacteroidetes phylum was significantly higher in the control group, whereas the relative abundance of Escherichia coli was significantly lower in the control group. The impact of the perinatal antibiotics on the gut microbiota composition was greater than exposure to later courses of antibiotics (28% of participants). Conclusions Perinatal antibiotic exposure had a marked impact on the gut microbiota at the age of 1 year. The timing of the antibiotic exposure appears to be the critical factor for the changes observed in the gut microbiota. Impact Infants are commonly exposed to IAP and postnatal antibiotics, and later to courses of antibiotics during the first year of life. Perinatal antibiotics have been associated with an altered gut microbiota during the first months of life, whereas the evidence regarding the long-term impact is more limited. Perinatal antibiotic exposure had a marked impact on the infant’s gut microbiota at 1 year of age. Impact of the perinatal antibiotics on the gut microbiota composition was greater than that of the later courses of antibiotics at the age of 1 year.

Sign in / Sign up

Export Citation Format

Share Document