Evolution of Epistatic Networks and the Genetic Basis of Innate Behaviors

Inbreeding-Driven Innate Behavioral Changes in Drosophila melanogaster

Drosophila melanogaster has long been used to demonstrate the effect of inbreeding, particularly in relation to reproductive fitness and stress tolerance. In comparison, less attention has been given to exploring the influence of inbreeding on the innate behavior of D. melanogaster. In this study, multiple replicates of six different types of crosses were set in pair conformation of the laboratory-maintained wild-type D. melanogaster. This resulted in progeny with six different levels of inbreeding coefficients. Larvae and adult flies of varied inbreeding coefficients were subjected to different behavioral assays. In addition to the expected inbreeding depression in the-egg to-adult viability, noticeable aberrations were observed in the crawling and phototaxis behaviors of larvae. Negative geotactic behavior as well as positive phototactic behavior of the flies were also found to be adversely affected with increasing levels of inbreeding. Interestingly, positively phototactic inbred flies demonstrated improved learning compared to outbred flies, potentially the consequence of purging. Flies with higher levels of inbreeding exhibited a delay in the manifestation of aggression and courtship. In summary, our findings demonstrate that inbreeding influences the innate behaviors in D. melanogaster, which in turn may affect the overall biological fitness of the flies.

The genetic architecture of behavioral canalization

Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as 'behavioral canalization'. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution

Motivation

The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables' disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject.

Results

The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject's disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene-disease association studies to identify genetic architecture in the context of epistasis.

Availability and implementation

The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach.

Contact

ting.hu@queensu.ca.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Adding the neuro to cognition: from food storing to nest building

Typically, investigations of animal cognition couple careful experimental manipulations with examination of the animal's behavioural responses. Sometimes those questions have included attempts to describe the neural underpinnings of the behavioural outputs. Over the past 25 years, behaviours that involve spatial learning and memory (such as navigation and food storing) has been one context in which such dual or correlated investigations have been both accessible and productive. Here I review some of that work and where it has led. Because of the wealth of data and insights gained from that work and song learning before it, it seems that it might also be useful to try to add some neurobiology to other systems in animal cognition. I finish then, with a description of recent work on the cognition and neurobiology of avian nest building. It is still relatively early days but asking questions about the cognition of nest building has already shown both neural correlates of nest building and that learning and memory play a much greater role in this behaviour than previously considered. While it is not yet clear how putting these components together will be synergistic, the examples of song learning and food storing provide encouragement. Perhaps this might be true for other behaviours too?

A statistical framework for recovering pseudo-dynamic networks from static data

MOTIVATION

The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases.

RESULTS

We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations.

AVAILABILITY AND IMPLEMENTATION

DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Genomic Physics of COVID-19 Pathogenesis and Spread

Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.

Stereotyped, automatized and habitual behaviours: are they similar constructs under the control of the same cerebral areas?

Comprehensive knowledge about higher executive functions of motor control has been covered in the last decades. Critical goals have been targeted through many different technological approaches. An abundant flow of new results greatly progressed our ability to respond at better-posited answers to look more than ever at the challenging neural system functioning. Behaviour is the observable result of the invisible, as complex cerebral functioning. Many pathological states are approached after symptomatology categorisation of behavioural impairments is achieved. Motor, non-motor and psychiatric signs are greatly shared by many neurological/psychiatric disorders. Together with the cerebral cortex, the basal ganglia contribute to the expression of behaviour promoting the correct action schemas and the selection of appropriate sub-goals based on the evaluation of action outcomes. The present review focus on the basic classification of higher motor control functioning, taking into account the recent advances in basal ganglia structural knowledge and the computational model of basal ganglia functioning. We discuss about the basal ganglia capability in executing ordered motor patterns in which any single movement is linked to each other into an action, and many actions are ordered into each other, giving them a syntactic value to the final behaviour. The stereotypic, automatized and habitual behaviour's constructs and controls are the expression of successive stages of rule internalization and categorisation aimed in producing the perfect spatial-temporal control of motor command.