Functional Mapping of Genes Modulating Plant Shade Avoidance Using Leaf Traits.

Shade avoidance syndrome (SAS) refers to a set of plant responses that increases light capture in dense stands. This process is crucial for plants in natural and agricultural environments as they compete for resources and avoid suboptimal conditions. Although the molecular, biochemical, and physiological mechanisms underlying the SAS response have been extensively studied, the genetic basis of developmental variation in leaves in regard to leaf area, petiole length, and leaf length (i.e., their allometric relationships) remains unresolved. In this study, with the recombinant inbred line (RIL) population, the developmental traits of leaves of Arabidopsis were investigated under two growth density conditions (high- and low-density plantings). The observed changes were then reconstructed digitally, and their allometric relationships were modelled. Taking the genome-wide association analysis, the SNP genotype and the dynamic phenotype of the leaf from both densities were combined to explore the allometry QTLs. Under different densities, leaf change phenotype was analyzed from two core ecological scenarios: (i) the allometric change of the leaf area with leaf length, and (ii) the change of the leaf length with petiole length. QTLs modulating these two scenarios were characterized as 'leaf shape QTLs' and 'leaf position QTLs'. With functional mapping, results showed a total of 30 and 24 significant SNPs for shapeQTLs and positionQTLs, respectively. By annotation, immune pathway genes, photosensory receptor genes, and phytohormone genes were identified to be involved in the SAS response. Interestingly, genes modulating the immune pathway and salt tolerance, i.e., systemic acquired resistance (SAR) regulatory proteins (MININ-1-related) and salt tolerance homologs (STH), were reported to mediate the SAS response. By dissecting and comparing QTL effects from low- and high-density conditions, our results elucidate the genetic control of leaf formation in the context of the SAS response. The mechanism with leaf development × density interaction can further aid the development of density-tolerant crop varieties for agricultural practices.

A behavioral model for mapping the genetic architecture of gut-microbiota networks.

The gut microbiota may play an important role in affecting human health. To explore the genetic mechanisms underlying microbiota-host relationships, many genome-wide association studies have begun to identify host genes that shape the microbial composition of the gut. It is becoming increasingly clear that the gut microbiota impacts host processes not only through the action of individual microbes but also their interaction networks. However, a systematic characterization of microbial interactions that occur in densely packed aggregates of the gut bacteria has proven to be extremely difficult. We develop a computational rule of thumb for addressing this issue by integrating ecological behavioral theory and genetic mapping theory. We introduce behavioral ecology theory to derive mathematical descriptors of how each microbe interacts with every other microbe through a web of cooperation and competition. We estimate the emergent properties of gut-microbiota networks reconstructed from these descriptors and map host-driven mutualism, antagonism, aggression, and altruism QTLs. We further integrate path analysis and mapping theory to detect and visualize how host genetic variants affect human diseases by perturbing the internal workings of the gut microbiota. As the proof of concept, we apply our model to analyze a published dataset of the gut microbiota, showing its usefulness and potential to gain new insight into how microbes are organized in human guts. The new model provides an analytical tool for revealing the "endophenotype" role of microbial networks in linking genotype to end-point phenotypes.

Corrigendum: A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation.

[This corrects the article DOI: 10.3389/fpls.2019.01557.].

Interrogation of Internal Workings in Microbial Community Assembly: Play a Game through a Behavioral Network?

Increasing evidence shows that the influence of microbiota on biogeochemical cycling, plant development, and human health is executed through a complex network of microbe-microbe interactions. However, characterizing how microbes interact and work together within closely packed and highly heterogeneous microbial ecosystems is extremely challenging. Here, we describe a rule-of-thumb framework for visualizing polymicrobial interactions and extracting general principles that underlie microbial communities. We integrate elements of metabolic ecology, behavioral ecology, and game theory to quantify the interactive strategies by which microbes at any taxonomic level compete for resources and cooperate symbiotically with each other to form and stabilize ecological communities. We show how the framework can chart an omnidirectional landscape of microbial cooperation and competition that may drive various natural processes. This framework can be implemented into genome-wide association studies to unravel the genetic mechanisms underlying microbial interaction networks and their evolutionary consequences along spatiotemporal gradients.IMPORTANCE Identifying general biological rules that underlie the complexity and heterogeneity of microbial communities has proven to be highly challenging. We present a rule-of-thumb framework for studying and characterizing how microbes interact with each other across different taxa to determine community behavior and dynamics. This framework is computationally simple but conceptually meaningful, and it can provide a starting point to generate novel biological hypotheses about microbial interactions and explore internal workings of microbial community assembly in depth.

Mapping Covariation Quantitative Trait Loci That Control Organ Growth and Whole-Plant Biomass.

Covariation between organ growth and biomass accumulation plays an important role in plants. Plant to capture optimal fitness in nature, which depend coordinate and interact for distinct organs such as leaves, stems, and roots. Although many studies have focused on plant growth or biomass allocation, detailed information on the genetic mechanism of coordinated variation is lacking. Here, we expand a new mapping model based on functional mapping to detect covariation quantitative trait loci (QTLs) that govern development of plant organs and whole biomass, which, via a series of hypothesis tests, allows quantification of how QTLs regulate covariation between organ growth and biomass accumulation. The model was implemented to analyze leaf number data and the whole dry weight of recombinant inbred lines (RILs) of Arabidopsis. Two key QTLs related to growth and biomass allocation that reside within biologically meaningful genes, CRA1 and HIPP25, are characterized. These two genes may control covariation between two traits. The new model will enable the elucidation of the genetic architecture underlying growth and biomass accumulation, which may enhance our understanding of fitness development in plants.

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation.

How one trait developmentally varies as a function of others shapes a spectrum of biological phenomena. Despite its importance to trait dissection, the understanding of whether and how genes mediate such developmental covariation is poorly understood. We integrate developmental allometry equations into the functional mapping framework to map specific QTLs that govern the correlated development of different traits. Based on evolutionary game theory, we assemble and contextualize these QTLs into an intricate but organized network coded by bidirectional, signed, and weighted QTL-QTL interactions. We use this approach to map shoot height-diameter allometry QTLs in an ornamental woody species, mei (Prunus mume). We detect "pioneering" QTLs (piQTLs) and "maintaining" QTLs (miQTLs) that determine how shoot height varies with diameter and how shoot diameter varies with height, respectively. The QTL networks inferred can visualize how each piQTL regulates others to promote height growth at a cost of diameter growth, how miQTL regulates others to benefit radial growth at a cost of height growth, and how piQTLs and miQTLs regulate each other to form a pleiotropic web of primary and secondary growth in trees. Our approach provides a unique gateway to explore the genetic architecture of developmental covariation, a widespread phenomenon in nature.