scholarly journals Development and selective grain make plasticity 'take the lead' in adaptive evolution

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Miguel Brun-Usan ◽  
Alfredo Rago ◽  
Christoph Thies ◽  
Tobias Uller ◽  
Richard A. Watson
Keyword(s):  
Phenotypic Plasticity ◽  
Adaptive Evolution ◽  
Phenotypic Variation ◽  
Biological Evolution ◽  
Adaptive Plasticity ◽  
Genetic Evolution ◽  
Gene Regulation Network ◽  
Models Of Development ◽  
Regulation Network ◽  
Selection For

Abstract Background Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.

scholarly journals Development and Selective Grain Make Plasticity ‘Take the Lead’ in Adaptive Evolution

2021 ◽  
Author(s):  
MIGUEL BRUN USAN ◽  
Alfredo Rago ◽  
Christoph Thies ◽  
Tobias Uller ◽  
Richard A. Watson
Keyword(s):  
Phenotypic Plasticity ◽  
Adaptive Evolution ◽  
Phenotypic Variation ◽  
Biological Evolution ◽  
Adaptive Plasticity ◽  
Genetic Evolution ◽  
Gene Regulation Network ◽  
Models Of Development ◽  
Regulation Network ◽  
Selection For

Abstract Background: Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales . So how do biological systems come to exhibit such extraordinary capacity to evolve ? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results: To better understand the interaction between plasticity and genetic evolvability , we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First , we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa . This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions: Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.

scholarly journals Developmental models reveal the role of phenotypic plasticity in explaining genetic evolvability

2020 ◽  
Author(s):  
Miguel Brun-Usan ◽  
Alfredo Rago ◽  
Christoph Thies ◽  
Tobias Uller ◽  
Richard A. Watson
Keyword(s):  
Phenotypic Plasticity ◽  
Phenotypic Variation ◽  
Biological Systems ◽  
Rapid Evolution ◽  
Biological Evolution ◽  
Adaptive Plasticity ◽  
Genetic Evolution ◽  
Developmental Models ◽  
Gene Regulation Network ◽  
Selection For

AbstractBiological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation for natural selection to act on. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental change are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity is, in general, the most efficient and results in the rapid evolution of high genetic evolvability. Thus, without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, this shows how plasticity can influence adaptive evolution and helps explain the genetic evolvability observed in biological systems.

scholarly journals Identification of miRNAs and Their Response to Cold Stress in Astragalus Membranaceus

Biomolecules ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 182 ◽  
Author(s):  
Merhaba Abla ◽  
Huigai Sun ◽  
Zhuyun Li ◽  
Chunxiang Wei ◽  
Fei Gao ◽  
...  
Keyword(s):  
Cold Stress ◽  
Redox Homeostasis ◽  
Astragalus Membranaceus ◽  
Pcr Analysis ◽  
Small Rna Sequencing ◽  
Factors Affecting ◽  
Gene Regulation Network ◽  
Qrt Pcr ◽  
Yield And Quality ◽  
Regulation Network

Astragalus membranaceus is an important medicinal plant widely cultivated in East Asia. MicroRNAs (miRNAs) are endogenous regulatory molecules that play essential roles in plant growth, development, and the response to environmental stresses. Cold is one of the key environmental factors affecting the yield and quality of A. membranaceus, and miRNAs may mediate the gene regulation network under cold stress in A. membranaceus. To identify miRNAs and reveal their functions in cold stress response in A. membranaceus, small RNA sequencing was conducted followed by bioinformatics analysis, and quantitative real time PCR (qRT-PCR) analysis was performed to profile the expression of miRNAs under cold stress. A total of 168 conserved miRNAs belonging to 34 families and 14 putative non-conserved miRNAs were identified. Many miRNA targets were predicted and these targets were involved in diversified regulatory and metabolic pathways. By using qRT-PCR, 27 miRNAs were found to be responsive to cold stress, including 4 cold stress-induced and 17 cold-repressed conserved miRNAs, and 6 cold-induced non-conserved miRNAs. These cold-responsive miRNAs probably mediate the response to cold stress by regulating development, hormone signaling, defense, redox homeostasis, and secondary metabolism in A. membranaceus. These cold-corresponsive miRNAs may be used as the candidate genes in further molecular breeding for improving cold tolerance of A. membranaceus.

scholarly journals Effects of Heterogeneous Environment After Deforestation on Plant Phenotypic Plasticity of Three Shrubs Based on Leaf Traits and Biomass Allocation

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinniu Wang ◽  
Jing Gao ◽  
Yan Wu ◽  
Bo Xu ◽  
Fusun Shi ◽  
...  
Keyword(s):  
Phenotypic Plasticity ◽  
Carbon Content ◽  
Nitrogen Content ◽  
Leaf Area ◽  
Biomass Allocation ◽  
Principal Components ◽  
Leaf Traits ◽  
Adaptive Plasticity ◽  
Heterogeneous Environments ◽  
Heterogeneous Environment

Phenotypic plasticity among natural plant populations is a species-specific ecological phenomenon of paramount importance that depends on their life forms, development stages, as well as environmental factors. While this phenomenon is broadly understood, it has hardly been observed in nature. This study aimed at understanding phenotypic plasticity and ecological adaptability in three shrubs (Salix etosia, Rubus setchuenensis, and Hydrangea aspera) affected by potential environmental variables after deforesting in sparse Larix spp. forest and tall shrub mixed secondary forests. Soil organic carbon content, total nitrogen content, and available nitrogen content were greater outside the forests, contrary to other measured factors whose availability was higher in the forest interiors. In case of leaf traits and stoichiometric indicators, there were significant interactions of leaf area (LA), leaf dry matter (DW), specific leaf area (SLA), and leaf phosphorus content (LPC) between shrub species and heterogeneous environments (P < 0.05) but not for leaf C/N, N/P, and C/P. Principal components analysis (PCA) indicated that soil temperature, pH value, soil carbon content, soil nitrogen content, and MBC and MBN mainly constituted the first component. Summarized results indicated that TB and leaf C/P of S. etosia were significantly correlated with three principal components, but only marginal significant correlations existed between R/S and relevant components. SLA and R/S of R. setchuenensis had marginal significant relationships with independent variables. Both SLA and TB of H. aspera were significantly correlated with three principal components. Based on the pooled values of leaf functional traits and leaf stoichiometric indicators, R. setchuenensis (vining type) had better leaf traits plasticity to adapt to a heterogeneous environment. In descending order, the ranks of biomass allocation plasticity index of three shrubs were H. aspera (bunch type), R. setchuenensis (vining type), and S. etosia (erect type). The highest integrated plasticity values of leaf traits and biomass allocation was observed in H. aspera (bunch type), followed by R. setchuenensis, and by S. etosia with less adaptive plasticity in heterogeneous environments.

scholarly journals Deciphering gene regulation from gene expression dynamics using deep neural network

2018 ◽  
Author(s):  
Jingxiang Shen ◽  
Mariela D. Petkova ◽  
Yuhai Tu ◽  
Feng Liu ◽  
Chao Tang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Gene Regulation ◽  
Gene Network ◽  
Deep Neural Network ◽  
Biological Functions ◽  
Gene Regulation Network ◽  
Regulation Network ◽  
Gene Expression Dynamics

AbstractComplex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation – the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN “black box”. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants’ behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

The Dawn of Civilization

2018 ◽  
pp. 234-263
Author(s):  
Kevin N. Laland
Keyword(s):  
Cultural Evolution ◽  
Slow Rate ◽  
Large Scale ◽  
Cultural Practices ◽  
Morphological Evolution ◽  
Biological Evolution ◽  
Genetic Evolution ◽  
The Third ◽  
The World ◽  
Adaptive Challenges

This chapter traces the evolution of human civilization from nomadic hunter-gatherer societies to the advent of agriculture and its large-scale impacts on the world. It describes this history in three ages of adaptive evolution. First, there was the age in which biological evolution dominated, in which we adapted to the circumstances of life in a manner no different from every other creature. Second came the age when gene–culture coevolution was in the ascendency. Through cultural activities, our ancestors set challenges to which they adapted biologically. In doing so, they released the brake that the relatively slow rate of independent environmental change imposes on other species. The results are higher rates of morphological evolution in humans compared to other mammals, with human genetic evolution reported as accelerating more than a hundredfold over the last 40,000 years. Now we live in the third age, where cultural evolution dominates. Cultural practices provide humanity with adaptive challenges, but these are then solved through further cultural activity, before biological evolution gets moving.

scholarly journals Phenotypic Variation and Plasticity in the Colonizing Species Xanthium Strumarium L. (Noogoora Burr)

1981 ◽  
Vol 34 (6) ◽  
pp. 639 ◽  
Author(s):  
GF Moran ◽  
DR Marshall ◽  
WJ Müller
Keyword(s):  
Phenotypic Plasticity ◽  
Phenotypic Variation ◽  
Reproductive Strategies ◽  
Quantitative Traits ◽  
Genotypic Variation ◽  
Xanthium Strumarium ◽  
Quantitative Characters ◽  
Major Mode ◽  
Varying Environments ◽  
Colonizing Ability

Levels of genotypic (O'G 2) and environmentally induced (O'E2) variation for 15 quantitative characters were estimated in seven populations of the four naturalized races of X. strumarium in Australia. Estimates of O'G2 indicated that populations of X. strumarium were often genetically variable for quantitative traits. However, for the majority of the characters studied, O'E2 was a larger component of the total phenotypic variation than was O'G 2 , indicating that phenotypic plasticity is the major mode of adaptation of this species to variable and varying environments. Few significant differences were found among the races, or among populations within a race, in either O'G2 or O'E2. This suggests that marked differences in colonizing ability of the four races of X. strumarium are probably not .due to differences in phenotypic plasticity (individual buffering) or genotypic variation (populational buffering) but to differences in such factors as their reproductive strategies and photoperiodic requirements for flowering.

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