Phenotypic Robustness Research Articles

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence

In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition.

Unveiling pepper immunity's robustness to temperature shifts: insights for empowering future crops.

Boosting plant immunity is an effective alternative to pesticides. However, environmental variations, accentuated by climate change, can compromise immunity. The robustness of a trait corresponds to the absence (or low level) of variation in that trait in the face of an environmental change. Here, we examined two types of robustness, robustness of immunity mean and robustness of immunity variation, and proposed nine quantitative robustness estimators. We characterized the immunity of a set of accessions representative of the natural diversity of pepper (Capsicum annuum L.), to two major pathogens: the oomycete Phytophthora capsici Leon. and potato virus Y. For each pathogen, we measured the immunity of accessions in two contrasting environments in terms of temperature. For each type of robustness and each pathogen, the impact of temperature change on immunity varied between accessions. The robustness estimators proved to be complementary and differed in terms of heritability and ability to discriminate accessions. A positive and significant correlation was observed between immunity and robustness. There was no significant relationship between the robustness of immunity to the two pathogens, but some accessions showed high immunity and robustness against both pathogens. These results justify the need to consider both immunity and robustness to environmental variations in order to select varieties adapted to current and future climate conditions. Phenotypic robustness should also be considered when assessing the "value of sustainable cultivation and use" of future plant varieties, particularly during the application process for protection rights granted from the European Community Plant Variety Office.

Structural perturbation of chromatin domains with multiple developmental regulators can severely impact gene regulation and development.

Chromatin domain boundaries delimited by CTCF motifs can restrict the range of enhancer action. However, disruption of domain structure often results in mild gene dysregulation and thus predicting the impact of boundary rearrangements on animal development remains challenging. Here, we tested whether structural perturbation of a chromatin domain with multiple developmental regulators can result in more acute gene dysregulation and severe developmental phenotypes. We targeted clusters of CTCF motifs in a domain of the mouse genome containing three FGF ligand genes - Fgf3, Fgf4, and Fgf15 - that regulate several developmental processes. Deletion of the 23.9kb cluster that defines the centromeric boundary of this domain resulted in ectopic interactions of the FGF genes with enhancers located across the deleted boundary that are active in the developing brain. This caused strong induction of FGF expression and perinatal lethality with encephalocele and orofacial cleft phenotypes. Heterozygous boundary deletion was sufficient to cause these fully penetrant phenotypes, and strikingly, loss of a single CTCF motif within the cluster also recapitulated ectopic FGF expression and caused encephalocele. However, such phenotypic sensitivity to perturbation of domain structure did not extend to all CTCF clusters of this domain, nor to all developmental processes controlled by these three FGF genes - for example, the ability to undergo lineage specification in the blastocyst and pre-implantation development were not affected. By tracing the impact of different chromosomal rearrangements throughout mouse development, we start to uncover the determinants of phenotypic robustness and sensitivity to perturbation of chromatin boundaries. Our data show how small sequence variants at certain domain boundaries can have a surprisingly outsized effect and must be considered as potential sources of gene dysregulation during development and disease.

Cell coupling compensates for changes in single-cell Her6 dynamics and provides phenotypic robustness.

This paper investigates the effect of altering the protein expression dynamics of the bHLH transcription factor Her6 at the single-cell level in the embryonic zebrafish telencephalon. Using a homozygote endogenous Her6:Venus reporter and 4D single-cell tracking, we show that Her6 oscillates in neural telencephalic progenitors and that the fusion of protein destabilisation (PEST) domain alters its expression dynamics, causing most cells to downregulate Her6 prematurely. However, counterintuitively, oscillatory cells increase, with some expressing Her6 at high levels, resulting in increased heterogeneity of Her6 expression in the population. These tissue-level changes appear to be an emergent property of coupling between single-cells, as revealed by experimentally disrupting Notch signalling and by computationally modelling alterations in Her6 protein stability. Despite the profound differences in the single-cell Her6 dynamics, the size of the telencephalon is only transiently altered and differentiation markers do not exhibit significant differences early on; however, a small increase is observed at later developmental stages. Our study suggests that cell coupling provides a compensation strategy, whereby an almost normal phenotype is maintained even though single-cell gene expression dynamics are abnormal, granting phenotypic robustness.

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins's biomorphs.

Biomorphs, Richard Dawkins's iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems.

In preprints: quantitative decoupling between regulatory modules safeguards phenotypic robustness.

In preprints: quantitative decoupling between regulatory modules safeguards phenotypic robustness.

Validation of reaction norm breeding values for robustness in Australian sheep

BackgroundThere can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data.ResultsUsing threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory.ConclusionsSlope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Performance and robustness analysis reveals phenotypic trade-offs in yeast.

To design strains that can function efficiently in complex industrial settings, it is crucial to consider their robustness, that is, the stability of their performance when faced with perturbations. In the present study, we cultivated 24 Saccharomyces cerevisiae strains under conditions that simulated perturbations encountered during lignocellulosic bioethanol production, and assessed the performance and robustness of multiple phenotypes simultaneously. The observed negative correlations confirmed a trade-off between performance and robustness of ethanol yield, biomass yield, and cell dry weight. Conversely, the specific growth rate performance positively correlated with the robustness, presumably because of evolutionary selection for robust, fast-growing cells. The Ethanol Red strain exhibited both high performance and robustness, making it a good candidate for bioproduction in the tested perturbation space. Our results experimentally map the robustness-performance trade-offs, previously demonstrated mainly by single-phenotype and computational studies.

Macrophage phenotype transitions in a stochastic gene-regulatory network model

Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.

The non-deterministic genotype-phenotype map of RNA secondary structure.

Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic (ND) phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of ND GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the ND map. This framework is validated by demonstrating close correspondence between the ND quantities and sample averages of their deterministic counterparts. When using the ND framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.

Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve.

Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organized as bricklayer's graphs, so-called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype maps for RNA secondary structure and the hydrophobic-polar (HP) model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

Enhancer-promoter interactions can bypass CTCF-mediated boundaries and contribute to phenotypic robustness.

How enhancers activate their distal target promoters remains incompletely understood. Here we dissect how CTCF-mediated loops facilitate and restrict such regulatory interactions. Using an allelic series of mouse mutants, we show that CTCF is neither required for the interaction of the Sox2 gene with distal enhancers, nor for its expression. Insertion of various combinations of CTCF motifs, between Sox2 and its distal enhancers, generated boundaries with varying degrees of insulation that directly correlated with reduced transcriptional output. However, in both epiblast and neural tissues, enhancer contacts and transcriptional induction could not be fully abolished, and insertions failed to disrupt implantation and neurogenesis. In contrast, Sox2 expression was undetectable in the anterior foregut of mutants carrying the strongest boundaries, and these animals fully phenocopied loss of SOX2 in this tissue. We propose that enhancer clusters with a high density of regulatory activity can better overcome physical barriers to maintain faithful gene expression and phenotypic robustness.

Cytokinins

The extraordinary variety that characterizes the living world in terms of forms and structures is the result of natural selection that allows an organism to be in perfect harmony with its environmental niche. Once a specific shape is acquired, many different factors act together to guarantee phenotypic robustness and developmental stability of the organism. Among these factors, hormones play a key role in the regulation and coordination of growth - they control the activity of a single cell, the progression to tissue organization, the development of specific organs, ending with the development of the entire body. In plants, hormones acquire yet another important role - plants, due to their sessile nature, along with the quest for robust development, rely on plastic development to adapt growth to a changing environment. Plant hormones play a crucial role in sensing and responding to different environmental stimuli, translating these inputs into specific developmental changes that adapt the plant body to the environment. Here, we will focus on cytokinins - a unique class of plant hormones - giving clues on their metabolism, on how they are perceived by cells and how cells change their activity in response to it. Most of the data presented have been derived by studies conducted on Arabidopsis thaliana, a plant used as a model system in plant science.

Developmental phenomics suggests that H3K4 monomethylation confers multi-level phenotypic robustness

How histone modifications affect animal development remains difficult to ascertain. Despite the prevalence of histone 3 lysine 4 monomethylation (H3K4me1) on enhancers, hypomethylation appears to have minor effects on phenotype and viability. Here, we genetically reduce H3K4me1 deposition in Drosophila melanogaster and find that hypomethylation reduces transcription factor enrichment in nuclear microenvironments, disrupts gene expression, and reduces phenotypic robustness. Using a developmental phenomics approach, we find changes in morphology, metabolism, behavior, and offspring production. However, many phenotypic changes are only detected when hypomethylated flies develop outside of standard laboratory environments or with specific genetic backgrounds. Therefore, quantitative phenomics measurements can unravel how pleiotropic modulators of gene expression affect developmental robustness under conditions resembling the natural environments of a species.

Insights on the bacterial composition of Parmigiano Reggiano Natural Whey Starter by a culture-dependent and 16S rRNA metabarcoding portrait

Natural whey starters (NWS) are undefined bacterial communities produced daily from whey of the previous cheese-making round, by application of high temperature. As a result, in any dairy plant, NWS are continuously evolving, undefined mixtures of several strains and/or species of lactic acid bacteria, whose composition and performance strongly depend on the selective pressure acting during incubation. While NWS is critical to assure consistency to cheese-making process, little is known about the composition, functional features, and plant-to-plant fluctuations. Here, we integrated 16S rRNA metabarcoding and culture-dependent methods to profile bacterial communities of 10 NWS sampled in the production area of Parmigiano Reggiano cheese. 16S rRNA metabarcoding analysis revealed two main NWS community types, namely NWS type-H and NWS type-D. Lactobacillus helveticus was more abundant in NWS type-H, whilst Lactobacillus delbrueckii/St. thermophilus in NWS type-D, respectively. Based on the prediction of metagenome functions, NWS type-H samples were enriched in functional pathways related to galactose catabolism and purine metabolism, while NWS type-D in pathways related to aromatic and branched chain amino acid biosynthesis, which are flavor compound precursors. Culture-dependent approaches revealed low cultivability of individual colonies as axenic cultures and high genetic diversity in the pool of cultivable survivors. Co-culturing experiments showed that fermentative performance decreases by reducing the bacterial complexity of inoculum, suggesting that biotic interactions and cross-feeding relationships could take place in NWS communities, assuring phenotypic robustness. Even though our data cannot directly predict these ecological interactions, this study provides the basis for experiments targeted at understanding how selective regime affects composition, bacterial interaction, and fermentative performance in NWS.

Mammalian adipogenesis regulator (Areg) cells use retinoic acid signalling to be non‐ and anti‐adipogenic in age‐dependent manner

Adipose stem and precursor cells (ASPCs) give rise to adipocytes and determine the composition and plasticity of adipose tissue. Recently, several studies have demonstrated that ASPCs partition into at least three distinct cell subpopulations, including the enigmatic CD142+ cells. An outstanding challenge is to functionally characterise this population, as discrepant properties, from adipogenic to non‐ and anti‐adipogenic, have been reported for these cells. To resolve these phenotypic ambiguities, we characterised mammalian subcutaneous CD142+ ASPCs across various experimental conditions, demonstrating that CD142+ ASPCs exhibit high molecular and phenotypic robustness. Specifically, we find these cells to be firmly non‐ and anti‐adipogenic both in vitro and in vivo, with their inhibitory signals also impacting adipogenic human cells. However, these CD142+ ASPC‐specific properties exhibit surprising temporal phenotypic alterations, and emerge only in an age‐dependent manner. Finally, using multi‐omic and functional assays, we show that the inhibitory nature of these adipogenesis‐regulatory CD142+ ASPCs (Aregs) is driven by specifically expressed secretory factors that cooperate with the retinoic acid signalling pathway to transform the adipogenic state of CD142− ASPCs into a non‐adipogenic, Areg‐like state.

Heterotypic transcriptional condensates formed by prion-like paralogous proteins canalize flowering transition in tomato

BackgroundParalogs that arise from gene duplications during genome evolution enable genetic redundancy and phenotypic robustness. Variation in the coding or regulatory sequence of paralogous transcriptional regulators diversifies their functions and relationships, which provides developmental robustness against genetic or environmental perturbation. The fate transition of plant shoot stem cells for flowering and reproductive success requires a robust transcriptional control. However, how paralogs function and interact to achieve such robustness is unknown.ResultsHere, we explore the genetic relationship and protein behavior of ALOG family transcriptional factors with diverse transcriptional abundance in shoot meristems. A mutant spectrum covers single and higher-order mutant combinations of five ALOG paralogs and creates a continuum of flowering transition defects, showing gradually enhanced precocious flowering, along with inflorescence simplification from wild-type-like to progressively fewer flowers until solitary flower with sterile floral organs. Therefore, these paralogs play unequal roles and act together to achieve a robust genetic canalization. All five proteins contain prion-like intrinsically disordered regions (IDRs) and undergo phase separation. Accumulated mutations following gene duplications lead to IDR variations among ALOG paralogs, resulting in divergent phase separation and transcriptional regulation capabilities. Remarkably, they retain the ancestral abilities to assemble into a heterotypic condensate that prevents precocious activation of the floral identity gene ANANTHA.ConclusionsOur study reveals a novel genetic canalization mechanism enabled by heterotypic transcriptional condensates formed by paralogous protein interactions and phase separation, uncovering the molecular link between gene duplication caused IDR variation and robust transcriptional control of stem cell fate transition.

Genome instability drives epistatic adaptation in the human pathogen Leishmania

How genome instability is harnessed for fitness gain despite its potential deleterious effects is largely elusive. An ideal system to address this important open question is provided by the protozoan pathogen Leishmania, which exploits frequent variations in chromosome and gene copy number to regulate expression levels. Using ecological genomics and experimental evolution approaches, we provide evidence that Leishmania adaptation relies on epistatic interactions between functionally associated gene copy number variations in pathways driving fitness gain in a given environment. We further uncover posttranscriptional regulation as a key mechanism that compensates for deleterious gene dosage effects and provides phenotypic robustness to genetically heterogenous parasite populations. Finally, we correlate dynamic variations in small nucleolar RNA (snoRNA) gene dosage with changes in ribosomal RNA 2'-O-methylation and pseudouridylation, suggesting translational control as an additional layer of parasite adaptation. Leishmania genome instability is thus harnessed for fitness gain by genome-dependent variations in gene expression and genome-independent compensatory mechanisms. This allows for polyclonal adaptation and maintenance of genetic heterogeneity despite strong selective pressure. The epistatic adaptation described here needs to be considered in Leishmania epidemiology and biomarker discovery and may be relevant to other fast-evolving eukaryotic cells that exploit genome instability for adaptation, such as fungal pathogens or cancer.