Genetic Modification and Application in Cassava, Sweetpotato and Yams

Cassava (Manihot esculenta Crantz), sweetpotato (Ipomoea batatas) and yams (Dioscorea spp.) are important root and tuber crops grown for food, feed and various industrial applications. However, their genetic gain potentials are limited by breeding and genetic bottlenecks for improvement of many desired traits. This book chapter covers the applications and potential benefits of genetic modification in breeding selected outcrossing root and tuber crops. It assesses how improvement of selected root and tuber crops through genetic modification overcomes both the high heterozygosity and serious trait separation that occurs in conventional breeding, and contributes to timely achievement of improved target traits. It also assesses the ways genetic modification improves genetic gain in the root and tuber breeding programs, conclusions and perspectives. Conscious use of complementary techniques such as genetic modification in the root and tuber breeding programs can increase the selection gain by reducing the long breeding cycle and cost, as well as reliable exploitation of the heritable variation in the desired direction.

Heritable Variation, Genetic and Phenotypic Correlations for Tuber Traits and Host Plant Resistance to Late Blight for Potato Breeding in Scandinavian Testing Sites

Potato breeding aims to improve crop productivity, quality and resilience based on heritable characteristics. Estimating the trait heritability and correlations—both genetic and phenotypic—among characteristics in a target population of environments allows us to define the best breeding method that leads to selection gains. Breeding clones (47) and released cultivars (209) were grown using simple lattice designs at three testing sites in northern and southern Sweden to estimate the best linear unbiased predictors (BLUPs) derived from mixed linear models for characteristics such as tuber weight (total and according to sizes), host plant resistance to late blight (caused by the oomycete Phytophthora infestans) and tuber quality (starch percentage based on specific gravity measurements and reducing sugars). There was significant heritable variation for all the characteristics investigated. Tuber starch percentage and total tuber weight were the traits with the highest broad-sense heritability (H2), while the weight for the smallest size (<40 mm) had the highest H2 among the different tuber categories. These results show the potential for further improving these traits for Scandinavia through recombination and selection in segregating offspring. The genetic and phenotypic correlations among the tuber weight characteristics were significant (p ≤ 0.05) irrespective of their sizes, but none were significant (p > 0.05) with tuber starch percentage. Host plant resistance to late blight was negatively and significantly associated with tuber weight and starch percentage, thereby showing the strong effects of this disease on the productivity and quality of the potatoes.

Substrate-Specific Effects of Natural Genetic Variation on Proteasome Activity

The bulk of targeted cellular protein degradation is performed by the proteasome, a multi-subunit complex consisting of the 19S regulatory particle, which binds, unfolds, and translocates substrate proteins, and the 20S core particle, which degrades them. Protein homeostasis requires precise, dynamic control of proteasome activity. To what extent genetic variation creates differences in proteasome activity is almost entirely unknown. Using the ubiquitin-independent degrons of the ornithine decarboxylase and Rpn4 proteins, we developed reporters that provide high-throughput, quantitative measurements of proteasome activity in vivo in genetically diverse cell populations. We used these reporters to characterize the genetic basis of variation in proteasome activity in the yeast Saccharomyces cerevisiae. We found that proteasome activity is a complex, polygenic trait, shaped by variation throughout the genome. Genetic influences on proteasome activity were predominantly substrate-specific, suggesting that they primarily affect the function or activity of the 19S regulatory particle. Our results demonstrate that individual genetic differences create heritable variation in proteasome activity and suggest that genetic effects on proteasomal protein degradation may be an important source of variation in cellular and organismal traits.

Dimensionality and modularity of adaptive variation: Divergence in threespine stickleback from diverse environments

In nature, populations are subjected to a wide variety of environmental conditions that affect fitness and induce adaptive or plastic responses in traits, resulting in phenotypic divergence between populations. The dimensionality of that divergence, however, remains contentious. At the extremes, some contend that populations diverge along a single axis of trait covariance with greatest availability of heritable variation, even if this does not lead a population directly to its fitness optimum. Those at the other extreme argue that selection can push populations towards their fitness optima along multiple phenotype axes simultaneously, resulting in divergence in numerous dimensions. Here, we address this debate using populations of threespine stickleback (Gasterosteus aculeatus) in the Cook Inlet region of southern Alaska from lakes with contrasting ecological conditions. We calculated effective dimensionality of divergence in several trait suites (defensive, swimming, and trophic) thought to be under correlated selection pressures, as well as across all traits. We also tested for integration among the trait suites and between each trait suite and the environment. We found that populations in the Cook Inlet radiation exhibit dimensionality of phenotype high enough to preclude a single axis of divergence.

Cryptic individual-level variation in propensity for selfing in a hermaphroditic snail

For mating-system evolution, individual-level variation is as important as variation among populations. In self-compatible hermaphrodites, individuals may vary in their lifetime propensity for selfing, which consists of a fundamental, likely genetic and an environmental component. According to the reproductive assurance hypothesis explaining partial selfing, a key environmental factor is mate availability, which fluctuates with population density. We quantified individual variation in selfing propensity in a hermaphroditic snail by manipulating mate availability in the laboratory, recording mating behaviour, estimating selfing rates from progeny arrays, and measuring female lifetime fitness. Our results revealed four classes of individuals with different selfing propensities: pure outcrossers, pure selfers, and two types of plastic individuals. These classes only became apparent in the laboratory; the field population is outcrossing. All classes were present both under low and increased mate availability; this large among-individual variation in selfing propensities meant that effects of the pairing treatment on the frequency and extent of selfing were non-significant despite large effect sizes and sufficient statistical power. We believe that selfing propensities may have a genetic component and when selected on cause mean selfing rates to evolve. We propose that heritable variation in selfing propensities offers a reconciliation between the reproductive assurance hypothesis and its weak empirical support: distributions of selfing propensities vary temporally and spatially, thus obscuring the relationship between population density and realised selfing rates.

Heritable variation and lack of tradeoffs suggest adaptive capacity in Acropora cervicornis despite negative synergism under climate change scenarios

Knowledge of multi-stressor interactions and the potential for tradeoffs among tolerance traits is essential for developing intervention strategies for the conservation and restoration of reef ecosystems in a changing climate. Thermal extremes and acidification are two major co-occurring stresses predicted to limit the recovery of vital Caribbean reef-building corals. Here, we conducted an aquarium-based experiment to quantify the effects of increased water temperatures and p CO 2 individually and in concert on 12 genotypes of the endangered branching coral Acropora cervicornis, currently being reared and outplanted for large-scale coral restoration. Quantification of 12 host, symbiont and holobiont traits throughout the two-month-long experiment showed several synergistic negative effects, where the combined stress treatment often caused a greater reduction in physiological function than the individual stressors alone. However, we found significant genetic variation for most traits and positive trait correlations among treatments indicating an apparent lack of tradeoffs, suggesting that adaptive evolution will not be constrained. Our results suggest that it may be possible to incorporate climate-resistant coral genotypes into restoration and selective breeding programmes, potentially accelerating adaptation.

Natural Selection beyond Life? A Workshop Report

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of “heritable variation in fitness among individuals”. Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with “life-like” or “proto-biotic” systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection can be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems.

Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10-4.7 -10-4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.