Topological features of a gene co-expression network predict patterns of natural diversity in environmental response

The ensemble of gene regulatory networks at mutation-selection balance

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a model of GRN evolution analogous to work from quasi-species theory, which is itself essentially the mutation-selection balance model from classical population genetics extended to multiple loci. With this GRN model, we prove that-across a broad spectrum of selection pressures-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium is related to the topology of the genotype network, in particular, via a specific network centrality measure termed the eigenvector centrality. Finally, we determine the structural characteristics of GRNs that are favoured in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species theory-and thus to classical populations genetics-providing a mechanistic explanation for the observed distribution of GRNs evolving in response to various evolutionary forces, and shows how complex fitness landscapes can emerge from simple evolutionary rules.

Patterns of pan-genome occupancy and gene coexpression under water-deficit in Brachypodium distachyon

Natural populations are characterized by abundant genetic diversity driven by a range of different types of mutation. The tractability of sequencing complete genomes has allowed new insights into the variable composition of genomes, summarized as a species pan-genome. These analyses demonstrate that many genes are absent from the first reference genomes, whose analysis dominated the initial years of the genomic era. Our field now turns towards understanding the functional consequence of these highly variable genomes. Here, we analysed weighted gene coexpression networks from leaf transcriptome data for drought response in the purple false brome Brachypodium distachyon and the differential expression of genes putatively involved in adaptation to this stressor. We specifically asked whether genes with variable "occupancy" in the pan-genome - genes which are either present in all studied genotypes or missing in some genotypes - show different distributions among coexpression modules. Coexpression analysis united genes expressed in drought-stressed plants into nine modules covering 72 hub genes (87 hub isoforms), and genes expressed under controlled water conditions into 13 modules, covering 190 hub genes (251 hub isoforms). We find that low occupancy pan-genes are under-represented among several modules, while other modules are over-enriched for low-occupancy pan-genes. We also provide new insight into the regulation of drought response in B. distachyon, specifically identifying one module with an apparent role in primary metabolism that is strongly responsive to drought. Our work shows the power of integrating pan-genomic analysis with transcriptomic data using factorial experiments to understand the functional genomics of environmental response.

Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava

Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.

Co-expression Networks in Predicting Transcriptional Gene Regulation

Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.

Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this, we used transcriptomes that were generated from genetically identical individual plants that were grown under the same conditions and for the same amount of time. Twelve time points were used to cover the 24-h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcriptional regulators GI, PIF4, and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia)

Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.

Exposure to environmental radionuclides is associated with altered metabolic and immunity pathways in a wild rodent

Wildlife inhabiting environments contaminated by radionuclides face putative detrimental effects of exposure to ionizing radiation, with biomarkers such as an increase in DNA damage and/or oxidative stress commonly associated with radiation exposure. To examine the effects of exposure to radiation on gene expression in wildlife, we conducted a de novo RNA sequencing study of liver and spleen tissues from a rodent, the bank vole Myodes glareolus. Bank voles were collected from the Chernobyl Exclusion Zone (CEZ), where animals were exposed to elevated levels of radionuclides, and from uncontaminated areas near Kyiv, Ukraine. Counter to expectations, we did not observe a strong DNA damage response in animals exposed to radionuclides, although some signs of oxidative stress were identified. Rather, exposure to environmental radionuclides was associated with upregulation of genes involved in lipid metabolism and fatty acid oxidation in the livers - an apparent shift in energy metabolism. Moreover, using stable isotope analysis, we identified that fur from bank voles inhabiting the CEZ had enriched isotope values of nitrogen: such an increase is consistent with increased fatty acid metabolism, but also could arise from a difference in diet or habitat between the CEZ and elsewhere. In livers and spleens, voles inhabiting the CEZ were characterized by immunosuppression, such as impaired antigen processing, and activation of leucocytes involved in inflammatory responses. In conclusion, exposure to low dose environmental radiation impacts pathways associated with immunity and lipid metabolism, potentially as a stress-induced coping mechanism.

Evolutionary and ecological functional genomics, from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.

Gene regulatory divergence between locally adapted ecotypes in their native habitats

Local adaptation is a key driver of ecological specialization and the formation of new species. Despite its importance, the evolution of gene regulatory divergence among locally adapted populations is poorly understood, especially how that divergence manifests in nature. Here, we evaluate gene expression divergence and allele-specific gene expression responses for locally adapted coastal perennial and inland annual accessions of the yellow monkeyflower, Mimulus guttatus, in a field reciprocal transplant experiment. Overall, 6765 (73%) of surveyed genes were differentially expressed between coastal and inland habitats, while 7213 (77%) were differentially expressed between the coastal perennial and inland annual accessions. Cis-regulatory variation was pervasive, affecting 79% (5532) of differentially expressed genes. We detected trans effects for 52% (3611) of differentially expressed genes. Expression plasticity of alleles across habitats (G × E interactions) appears to be relatively common (affecting 18% of transcripts) and could minimize fitness trade-offs at loci that contribute to local adaptation. We also found evidence that at least one chromosomal inversion may act as supergene by holding together haplotypes of differentially expressed genes, but this pattern depends on habitat context. Our results highlight multiple key patterns regarding the relationship between gene expression and the evolution of locally adapted populations.

Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana

Local adaptation can occur due to individual genetic variants that increase the fitness of individuals in their home environments but decrease fitness in other environments [genetic trade-offs (GTs)] or genetic variants that increase fitness in one environment but have no effect in other environments [conditional neutrality (CN)]. Here, we show that GT quantitative trait loci (QTLs) for fitness between Italian and Swedish Arabidopsis thaliana exhibit strong population genomic signatures of local adaptation, including elevated levels of allele frequency differentiation, correlations to climatic variables, and recent sweeps. Highly divergent genes between Italy and Sweden populations show evidence of more recent selection in Sweden than Italy, and the biological annotations of these genes suggest interesting mechanisms underlying local adaptation.

Evidence for adaptation to different climates in the model species Arabidopsis thaliana is seen in reciprocal transplant experiments, but the genetic basis of this adaptation remains poorly understood. Field-based quantitative trait locus (QTL) studies provide direct but low-resolution evidence for the genetic basis of local adaptation. Using high-resolution population genomic approaches, we examine local adaptation along previously identified genetic trade-off (GT) and conditionally neutral (CN) QTLs for fitness between locally adapted Italian and Swedish A. thaliana populations [Ågren J, et al. (2013) Proc Natl Acad Sci USA 110:21077–21082]. We find that genomic regions enriched in high F_ST SNPs colocalize with GT QTL peaks. Many of these high F_ST regions also colocalize with regions enriched for SNPs significantly correlated to climate in Eurasia and evidence of recent selective sweeps in Sweden. Examining unfolded site frequency spectra across genes containing high F_ST SNPs suggests GTs may be due to more recent adaptation in Sweden than Italy. Finally, we collapse a list of thousands of genes spanning GT QTLs to 42 genes that likely underlie the observed GTs and explore potential biological processes driving these trade-offs, from protein phosphorylation, to seed dormancy and longevity. Our analyses link population genomic analyses and field-based QTL studies of local adaptation, and emphasize that GTs play an important role in the process of local adaptation.