On theoretical models of gene expression evolution with random genetic drift and natural selection

Thermal reactionomes reveal divergent responses to thermal extremes in warm and cool-climate ant species

Background

The distributions of species and their responses to climate change are in part determined by their thermal tolerances. However, little is known about how thermal tolerance evolves. To test whether evolutionary extension of thermal limits is accomplished through enhanced cellular stress response (enhanced response), constitutively elevated expression of protective genes (genetic assimilation) or a shift from damage resistance to passive mechanisms of thermal stability (tolerance), we conducted an analysis of the reactionome: the reaction norm for all genes in an organism’s transcriptome measured across an experimental gradient. We characterized thermal reactionomes of two common ant species in the eastern U.S, the northern cool-climate Aphaenogaster picea and the southern warm-climate Aphaenogaster carolinensis, across 12 temperatures that spanned their entire thermal breadth.

Results

We found that at least 2 % of all genes changed expression with temperature. The majority of upregulation was specific to exposure to low temperatures. The cool-adapted A. picea induced expression of more genes in response to extreme temperatures than did A. carolinensis, consistent with the enhanced response hypothesis. In contrast, under high temperatures the warm-adapted A. carolinensis downregulated many of the genes upregulated in A. picea, and required more extreme temperatures to induce down-regulation in gene expression, consistent with the tolerance hypothesis. We found no evidence for a trade-off between constitutive and inducible gene expression as predicted by the genetic assimilation hypothesis.

Conclusions

These results suggest that increases in upper thermal limits may require an evolutionary shift in response mechanism away from damage repair toward tolerance and prevention.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2466-z) contains supplementary material, which is available to authorized users.

The evolution and adaptive potential of transcriptional variation in sticklebacks--signatures of selection and widespread heritability

Evidence implicating differential gene expression as a significant driver of evolutionary novelty continues to accumulate, but our understanding of the underlying sources of variation in expression, both environmental and genetic, is wanting. Heritability in particular may be underestimated when inferred from genetic mapping studies, the predominant "genetical genomics" approach to the study of expression variation. Such uncertainty represents a fundamental limitation to testing for adaptive evolution at the transcriptomic level. By studying the inheritance of expression levels in 10,495 genes (10,527 splice variants) in a threespine stickleback pedigree consisting of 563 individuals, half of which were subjected to a thermal treatment, we show that 74-98% of transcripts exhibit significant additive genetic variance. Dominance variance is also prevalent (41-99% of transcripts), and genetic sources of variation seem to play a more significant role in expression variance in the liver than a key environmental variable, temperature. Among-population comparisons suggest that the majority of differential expression in the liver is likely due to neutral divergence; however, we also show that signatures of directional selection may be more prevalent than those of stabilizing selection. This predominantly aligns with the neutral model of evolution for gene expression but also suggests that natural selection may still act on transcriptional variation in the wild. As genetic variation both within- and among-populations ultimately defines adaptive potential, these results indicate that broad adaptive potential may be found within the transcriptome.

Evolution of scaling emergence in large-scale spatial epidemic spreading

Background

Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified.

Methodology/Principal Findings

In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence.

Conclusions/Significance

The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

Self-organization of developing embryo using scale-invariant approach

BACKGROUND

Self-organization is a fundamental feature of living organisms at all hierarchical levels from molecule to organ. It has also been documented in developing embryos.

METHODS

In this study, a scale-invariant power law (SIPL) method has been used to study self-organization in developing embryos. The SIPL coefficient was calculated using a centro-axial skew symmetrical matrix (CSSM) generated by entering the components of the Cartesian coordinates; for each component, one CSSM was generated. A basic square matrix (BSM) was constructed and the determinant was calculated in order to estimate the SIPL coefficient. This was applied to developing C. elegans during early stages of embryogenesis. The power law property of the method was evaluated using the straight line and Koch curve and the results were consistent with fractal dimensions (fd). Diffusion-limited aggregation (DLA) was used to validate the SIPL method.

RESULTS AND CONCLUSION

The fractal dimensions of both the straight line and Koch curve showed consistency with the SIPL coefficients, which indicated the power law behavior of the SIPL method. The results showed that the ABp sublineage had a higher SIPL coefficient than EMS, indicating that ABp is more organized than EMS. The fd determined using DLA was higher in ABp than in EMS and its value was consistent with type 1 cluster formation, while that in EMS was consistent with type 2.

Analysis and simulation of gene expression profiles in pure and mixed cell populations

For analysis and interpretation of data obtained from experimental readouts of gene expression, such as microarrays and RNA-sequencing, log transformation is routinely applied. This is because expression data, like many biological parameters, are strongly skewed. We show here that gene expression levels in multicellular organisms often deviate from simple (log) normal distributions and instead exhibit shouldered or bimodal distributions. Based on a mathematical model and numerical simulations, we demonstrate that many observed distributions can be explained as mixtures of bimodal two-component lognormal models. This is due to the fact that after log-transformation, the resulting distributions display reductions in the first peak rather than increasing overlaps over a wide range of parameter values. By comparing the theoretical results with biological datasets, our findings suggest that the distributions are generally bimodal for single cell types and get obscured by the different cell types that are present in tissue samples. Our analysis thus provides an initial explanation for the various types of expression level distributions that are found for different datasets. This will be important for the interpretation of next-generation sequencing data such as transcriptomics by mRNA-sequencing and ChIP-sequencing of epigenetic marks.

Measuring gene expression divergence: the distance to keep

Background

Gene expression divergence is a phenotypic trait reflecting evolution of gene regulation and characterizing dissimilarity between species and between cells and tissues within the same species. Several distance measures, such as Euclidean and correlation-based distances have been proposed for measuring expression divergence.

Results

We show that different distance measures identify different trends in gene expression patterns. When comparing orthologous genes in eight rat and human tissues, the Euclidean distance identified genes uniformly expressed in all tissues near the expression background as genes with the most conserved expression pattern. In contrast, correlation-based distance and generalized-average distance identified genes with concerted changes among homologous tissues as those most conserved. On the other hand, correlation-based distance, Euclidean distance and generalized-average distance highlight quite well the relatively high similarity of gene expression patterns in homologous tissues between species, compared to non-homologous tissues within species.

Conclusions

Different trends exist in the high-dimensional numeric data, and to highlight a particular trend an appropriate distance measure needs to be chosen. The choice of the distance measure for measuring expression divergence can be dictated by the expression patterns that are of interest in a particular study.

Reviewers

This article was reviewed by Mikhail Gelfand, Eugene Koonin and Subhajyoti De (nominated by Sarah Teichmann).