Sex-specific evolution of a Drosophila sensory system via interacting cis- and trans-regulatory changes

The evolution of gene expression via cis-regulatory changes is well established as a major driver of phenotypic evolution. However, relatively little is known about the influence of enhancer architecture and intergenic interactions on regulatory evolution. We address this question by examining chemosensory system evolution in Drosophila. D. prolongata males show a massively increased number of chemosensory bristles compared to females and males of sibling species. This increase is driven by sex-specific transformation of ancestrally mechanosensory organs. Consistent with this phenotype, the Pox neuro transcription factor (Poxn), which specifies chemosensory bristle identity, shows expanded expression in D. prolongata males. Poxn expression is controlled by non-additive interactions among widely dispersed enhancers. Although some D. prolongata Poxn enhancers show increased activity, the additive component of this increase is slight, suggesting most changes in Poxn expression are due to epistatic interactions between Poxn enhancers and trans-regulatory factors. Indeed, the expansion of D. prolongata Poxn enhancer activity is only observed in cells that express doublesex (dsx), the gene that controls sexual differentiation in Drosophila and also shows increased expression in D. prolongata males due to cis-regulatory changes. Although expanded dsx expression may contribute to increased activity of D. prolongata Poxn enhancers, this interaction is not sufficient to explain the full expansion of Poxn expression, suggesting that cis-trans interactions between Poxn, dsx, and additional unknown genes are necessary to produce the derived D. prolongata phenotype. Overall, our results demonstrate the importance of epistatic gene interactions for evolution, particularly when pivotal genes have complex regulatory architecture.

Overexpression of Terpenoid Biosynthesis Genes From Garden Sage (Salvia officinalis) Modulates Rhizobia Interaction and Nodulation in Soybean

In legumes, many endogenous and environmental factors affect root nodule formation through several key genes, and the regulation details of the nodulation signaling pathway are yet to be fully understood. This study investigated the potential roles of terpenoids and terpene biosynthesis genes on root nodule formation in Glycine max. We characterized six terpenoid synthesis genes from Salvia officinalis by overexpressing SoTPS6, SoNEOD, SoLINS, SoSABS, SoGPS, and SoCINS in soybean hairy roots and evaluating root growth and nodulation, and the expression of strigolactone (SL) biosynthesis and early nodulation genes. Interestingly, overexpression of some of the terpenoid and terpene genes increased nodule numbers, nodule and root fresh weight, and root length, while others inhibited these phenotypes. These results suggest the potential effects of terpenoids and terpene synthesis genes on soybean root growth and nodulation. This study provides novel insights into epistatic interactions between terpenoids, root development, and nodulation in soybean root biology and open new avenues for soybean research.

Glycan Activation of Clec4b Induces Reactive Oxygen Species Protecting against Neutrophilia and Arthritis

Animal models for complex diseases are needed to position and analyze the function of interacting genes. Previous positional cloning identified Ncf1 and Clec4b to be major regulators of arthritis models in rats. Here, we investigate epistasis between Ncf1 and Clec4b, two major regulators of arthritis in rats. We find that Clec4b and Ncf1 exert an additive effect on arthritis given by their joint ability to regulate neutrophils. Both genes are highly expressed in neutrophils, together regulating neutrophil availability and their capacity to generate reactive oxygen species. Using a glycan array, we identify key ligands of Clec4b and demonstrate that Clec4b-specific stimulation triggers neutrophils into oxidative burst. Our observations highlight Clec4b as an important regulator of neutrophils and demonstrate how epistatic interactions affect the susceptibility to, and severity of, autoimmune arthritis.

On the sparsity of fitness functions and implications for learning

Fitness functions map biological sequences to a scalar property of interest. Accurate estimation of these functions yields biological insight and sets the foundation for model-based sequence design. However, the fitness datasets available to learn these functions are typically small relative to the large combinatorial space of sequences; characterizing how much data are needed for accurate estimation remains an open problem. There is a growing body of evidence demonstrating that empirical fitness functions display substantial sparsity when represented in terms of epistatic interactions. Moreover, the theory of Compressed Sensing provides scaling laws for the number of samples required to exactly recover a sparse function. Motivated by these results, we develop a framework to study the sparsity of fitness functions sampled from a generalization of the NK model, a widely used random field model of fitness functions. In particular, we present results that allow us to test the effect of the Generalized NK (GNK) model’s interpretable parameters—sequence length, alphabet size, and assumed interactions between sequence positions—on the sparsity of fitness functions sampled from the model and, consequently, the number of measurements required to exactly recover these functions. We validate our framework by demonstrating that GNK models with parameters set according to structural considerations can be used to accurately approximate the number of samples required to recover two empirical protein fitness functions and an RNA fitness function. In addition, we show that these GNK models identify important higher-order epistatic interactions in the empirical fitness functions using only structural information.

Epi-MEIF, a flexible and efficient method for detection of high order epistatic interactions from complex phenotypic traits

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for high-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The epiMEIF model is fitted on a group of potential causal SNPs and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify high order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture of complex phenotypes.

Synergistic epistasis of the deleterious effects of transposable elements

The replicative nature and generally deleterious effects of transposable elements (TEs) raise an outstanding question about how TE copy number is stably contained in host populations. Classic theoretical analyses predict that, when the decline in fitness due to each additional TE insertion is greater than linear, or when there is synergistic epistasis, selection against TEs can result in a stable equilibrium of TE copy number. While several mechanisms are predicted to yield synergistic deleterious effects of TEs, we lack empirical investigations of the presence of such epistatic interactions. Purifying selection with synergistic epistasis generates repulsion linkage between deleterious alleles. We investigated this population genetic signal in the likely ancestral Drosophila melanogaster population and found evidence supporting the presence of synergistic epistasis among TE insertions, especially TEs expected to exert large fitness impacts. Even though synergistic epistasis of TEs has been predicted to arise through ectopic recombination and TE-mediated epigenetic silencing mechanisms, we only found mixed support for the associated predictions. We observed signals of synergistic epistasis for a large number of TE families, which is consistent with the expectation that such epistatic interaction mainly happens among copies of the same family. Curiously, significant repulsion linkage was also found among TE insertions from different families, suggesting the possibility that synergism of TEs’ deleterious fitness effects could arise above the family level and through mechanisms similar to those of simple mutations. Our findings set the stage for investigating the prevalence and importance of epistatic interactions in the evolutionary dynamics of TEs.

Heterogeneity of the GFP fitness landscape and data-driven protein design

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Counterintuitively, mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

Quantitative-enhancer-FACS-seq (QeFS) reveals epistatic interactions among motifs within transcriptional enhancers in developing Drosophila tissue

Understanding the contributions of transcription factor DNA binding sites to transcriptional enhancers is a significant challenge. We developed Quantitative enhancer-FACS-Seq for highly parallel quantification of enhancer activities from a genomically integrated reporter in Drosophila melanogaster embryos. We investigate the contributions of the DNA binding motifs of four poorly characterized TFs to the activities of twelve embryonic mesodermal enhancers. We measure quantitative changes in enhancer activity and discover a range of epistatic interactions among the motifs, both synergistic and alleviating. We find that understanding the regulatory consequences of TF binding motifs requires that they be investigated in combination across enhancer contexts.

Quantitative Trait Locus Mapping Combined with RNA Sequencing Reveals the Molecular Basis of Seed Germination in Oilseed Rape

Rapid and uniform seed germination improves mechanized oilseed rape production in modern agricultural cultivation practices. However, the molecular basis of seed germination is still unclear in Brassica napus. A population of recombined inbred lines of B. napus from a cross between the lower germination rate variety ‘APL01’ and the higher germination rate variety ‘Holly’ was used to study the genetics of seed germination using quantitative trait locus (QTL) mapping. A total of five QTLs for germination energy (GE) and six QTLs for germination percentage (GP) were detected across three seed lots, respectively. In addition, six epistatic interactions between the QTLs for GE and nine epistatic interactions between the QTLs for GP were detected. qGE.C3 for GE and qGP.C3 for GP were co-mapped to the 28.5–30.5 cM interval on C3, which was considered to be a novel major QTL regulating seed germination. Transcriptome analysis revealed that the differences in sugar, protein, lipid, amino acid, and DNA metabolism and the TCA cycle, electron transfer, and signal transduction potentially determined the higher germination rate of ‘Holly’ seeds. These results contribute to our knowledge about the molecular basis of seed germination in rapeseed.