Phenotype Of Organism Research Articles

A versatile transposon-based technology to generate loss- and gain-of-function phenotypes in the mouse liver

BackgroundUnderstanding the contribution of gene function in distinct organ systems to the pathogenesis of human diseases in biomedical research requires modifying gene expression through the generation of gain- and loss-of-function phenotypes in model organisms, for instance, the mouse. However, methods to modify both germline and somatic genomes have important limitations that prevent easy, strong, and stable expression of transgenes. For instance, while the liver is remarkably easy to target, nucleic acids introduced to modify the genome of hepatocytes are rapidly lost, or the transgene expression they mediate becomes inhibited due to the action of effector pathways for the elimination of exogenous DNA. Novel methods are required to overcome these challenges, and here we develop a somatic gene delivery technology enabling long-lasting high-level transgene expression in the entire hepatocyte population of mice.ResultsWe exploit the fumarylacetoacetate hydrolase (Fah) gene correction-induced regeneration in Fah-deficient livers, to demonstrate that such approach stabilizes luciferase expression more than 5000-fold above the level detected in WT animals, following plasmid DNA introduction complemented by transposon-mediated chromosomal gene transfer. Building on this advancement, we created a versatile technology platform for performing gene function analysis in vivo in the mouse liver. Our technology allows the tag-free expression of proteins of interest and silencing of any arbitrary gene in the mouse genome. This was achieved by applying the HADHA/B endogenous bidirectional promoter capable of driving well-balanced bidirectional expression and by optimizing in vivo intronic artificial microRNA-based gene silencing. We demonstrated the particular usefulness of the technology in cancer research by creating a p53-silenced and hRas G12V-overexpressing tumor model.ConclusionsWe developed a versatile technology platform for in vivo somatic genome editing in the mouse liver, which meets multiple requirements for long-lasting high-level transgene expression. We believe that this technology will contribute to the development of a more accurate new generation of tools for gene function analysis in mice.

The GRN concept as a guide for evolutionary developmental biology.

Organismal phenotypes result largely from inherited developmental programs, usually executed during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and help determine the evolutionary trajectories of species. Modern evolutionary biology must, therefore, account for these mechanisms in both theory and in practice. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown in tandem with advances in "omic" technologies and experimental techniques. However, while the GRN concept is widely utilized, it is often less clear what practical implications it has for conducting research in evolutionary developmental biology. In this Perspective, we attempt to provide clarity by discussing how experiments and projects can be designed in light of the GRN concept. We first map familiar biological notions onto the more abstract components of GRN models. We then review how diverse functional genomic approaches can be directed toward the goal of constructing such models and discuss current methods for functionally testing evolutionary hypotheses that arise from them. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design. Taken together, the practical implications that we draw from the GRN concept provide a set of guideposts for studies aiming at unraveling the molecular basis of phenotypic diversity.

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development

BackgroundOntologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.ResultsHere we present the Xenopus phenotype ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.ConclusionsThe XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype–phenotype data that can be directly related to other uPheno compliant resources.

Gene Body Methylation in Plants: Mechanisms, Functions, and Important Implications for Understanding Evolutionary Processes.

Gene body methylation (gbM) is an epigenetic mark where gene exons are methylated in the CG context only, as opposed to CHG and CHH contexts (where H stands for A, C, or T). CG methylation is transmitted transgenerationally in plants, opening the possibility that gbM may be shaped by adaptation. This presupposes, however, that gbM has a function that affects phenotype, which has been a topic of debate in the literature. Here, we review our current knowledge of gbM in plants. We start by presenting the well-elucidated mechanisms of plant gbM establishment and maintenance. We then review more controversial topics: the evolution of gbM and the potential selective pressures that act on it. Finally, we discuss the potential functions of gbM that may affect organismal phenotypes: gene expression stabilization and upregulation, inhibition of aberrant transcription (reverse and internal), prevention of aberrant intron retention, and protection against TE insertions. To bolster the review of these topics, we include novel analyses to assess the effect of gbM on transcripts. Overall, a growing body of literature finds that gbM correlates with levels and patterns of gene expression. It is not clear, however, if this is a causal relationship. Altogether, functional work suggests that the effects of gbM, if any, must be relatively small, but there is nonetheless evidence that it is shaped by natural selection. We conclude by discussing the potential adaptive character of gbM and its implications for an updated view of the mechanisms of adaptation in plants.

CRISPR somatic genome engineering and cancer modeling in the mouse pancreas and liver.

Genetically engineered mouse models (GEMMs) transformed the study of organismal disease phenotypes but are limited by their lengthy generation in embryonic stem cells. Here, we describe methods for rapid and scalable genome engineering in somatic cells of the liver and pancreas through delivery of CRISPR components into living mice. We introduce the spectrum of genetic tools, delineate viral and nonviral CRISPR delivery strategies and describe a series of applications, ranging from gene editing and cancer modeling to chromosome engineering or CRISPR multiplexing and its spatio-temporal control. Beyond experimental design and execution, the protocol describes quantification of genetic and functional editing outcomes, including sequencing approaches, data analysis and interpretation. Compared to traditional knockout mice, somatic GEMMs face an increased risk for mouse-to-mouse variability because of the higher experimental demands of the procedures. The robust protocols described here will help unleash the full potential of somatic genome manipulation. Depending on the delivery method and envisaged application, the protocol takes 3-5 weeks.

The evolution, evolvability and engineering of gene regulatory DNA.

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using suchmodels to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatorysequences andaddressing fundamental questions in regulatory evolution.

Comparative analysis of genome code complexity and manufacturability with engineering benchmarks

When knowledge has advanced to a state that includes a predictive understanding of the relationship between genome sequence and organism phenotype it will be possible for future engineers to design and produce synthetic organisms. However, the possibility of synthetic biology does not necessarily guarantee its feasibility, in much the same way that the possibility of a brute force attack fails to ensure the timely breaking of robust encryption. The size and range of natural genomes, from a few million base pairs for bacteria to over 100 billion base pairs for some plants, suggests it is necessary to evaluate the practical limits of designing genomes of similar complexity. This analysis characterizes the complexity of natural genomes, compares them to existing engineering benchmarks, and shows that existing large software programs are on similar scale with the genome of complex natural organisms.

The link between body size, colouration and thermoregulation and their integration into ecogeographical rules: a critical appraisal in light of climate change

Biological rules describe general morphological, structural and genetic patterns within and across species. Within these, ecogeographical rules correlate phenotypic variation of organisms with biogeography. The latter have been developed over the last 150 years, and recently have gained renewed attention due to climate change, as researchers try to predict how species will respond to different environmental conditions based on certain phenotypic features. However, there is no agreement whether such rules hold true in our rapidly changing world. Among the ecogeographical rules, six have focused on the contributions of the coloured integument and body size to the thermal balance of individuals. They are therefore particularly relevant for predicting how species' phenotypes will respond to future climatic scenarios. Here, we provide an overview and discuss these ecogeographical rules, particularly in light of thermoregulation, and probe the extent to which these rules apply, and the taxonomic levels at which they are relevant. The predictions of these rules often contradict each other, and indeed we argue that use of the word ‘rule' itself hinders their careful investigation. Moreover, disrupted climate patterns and global warming alter the environmental conditions under which these rules were once formulated. Thus, these rules may be outdated in our current rapidly changing environment. We conclude by proposing a revised concept of ecogeographical rules, where the micro‐environment should have a stronger influence on the phenotype of organisms than its geographical location, and we suggest precautions researchers should take when testing them.

Efficient CRISPR Mutagenesis in Sturgeon Demonstrates Its Utility in Large, Slow-Maturing Vertebrates.

In the last decade, the CRISPR/Cas9 bacterial virus defense system has been adapted as a user-friendly, efficient, and precise method for targeted mutagenesis in eukaryotes. Though CRISPR/Cas9 has proven effective in a diverse range of organisms, it is still most often used to create mutant lines in lab-reared genetic model systems. However, one major advantage of CRISPR/Cas9 mutagenesis over previous gene targeting approaches is that its high efficiency allows the immediate generation of near-null mosaic mutants. This feature could potentially allow genotype to be linked to phenotype in organisms with life histories that preclude the establishment of purebred genetic lines; a group that includes the vast majority of vertebrate species. Of particular interest to scholars of early vertebrate evolution are several long-lived and slow-maturing fishes that diverged from two dominant modern lineages, teleosts and tetrapods, in the Ordovician, or before. These early-diverging or “basal” vertebrates include the jawless cyclostomes, cartilaginous fishes, and various non-teleost ray-finned fishes. In addition to occupying critical phylogenetic positions, these groups possess combinations of derived and ancestral features not seen in conventional model vertebrates, and thus provide an opportunity for understanding the genetic bases of such traits. Here we report successful use of CRISPR/Cas9 mutagenesis in one such non-teleost fish, sterlet Acipenser ruthenus, a small species of sturgeon. We introduced mutations into the genes Tyrosinase, which is needed for melanin production, and Sonic hedgehog, a pleiotropic developmental regulator with diverse roles in early embryonic patterning and organogenesis. We observed disruption of both loci and the production of consistent phenotypes, including both near-null mutants’ various hypomorphs. Based on these results, and previous work in lamprey and amphibians, we discuss how CRISPR/Cas9 F0 mutagenesis may be successfully adapted to other long-lived, slow-maturing aquatic vertebrates and identify the ease of obtaining and injecting eggs and/or zygotes as the main challenges.

Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.

Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts missing performance data accurately at the level of the individual from simple morphological measures. This model performed exceptionally well, even for performance traits that were missing values for >90% of the sampled individuals. Furthermore, incorporating phylogeny did not improve model fit, indicating that the phenotypic data alone preserved sufficient information to predict the performance based on morphological information. This approach can both significantly increase our understanding of performance evolution and act as a bridge to incorporate performance into future work on phenomics.

Bisphenol a affects neurodevelopmental gene expression, cognitive function, and neuromuscular synaptic morphology in Drosophila melanogaster

Bisphenol A (BPA) is an environmentally prevalent endocrine disrupting chemical that can impact human health and may be an environmental risk factor for neurodevelopmental disorders. BPA has been associated with behavioral impairment in children and a variety of neurodevelopmental phenotypes in model organisms. We used Drosophila melanogaster to explore the consequences of developmental BPA exposure on gene expression, cognitive function, and synapse development. Our transcriptome analysis indicated neurodevelopmentally relevant genes were predominantly downregulated by BPA. Among the misregulated genes were those with roles in learning, memory, and synapse development, as well as orthologs of human genes associated with neurodevelopmental and neuropsychiatric disorders. To examine how gene expression data corresponded to behavioral and cellular phenotypes, we first used a predator-response behavioral paradigm and found that BPA disrupts visual perception. Further analysis using conditioned courtship suppression showed that BPA impairs associative learning. Finally, we examined synapse morphology within the larval neuromuscular junction and found that BPA significantly increased the number of axonal branches. Given that our findings align with studies of BPA in mammalian model organisms, this data indicates that BPA impairs neurodevelopmental pathways that are functionally conserved from invertebrates to mammals. Further, because Drosophila do not possess classic estrogen receptors or estrogen, this research suggests that BPA can impact neurodevelopment by molecular mechanisms distinct from its role as an estrogen mimic.

Unraveling the tree of life: A grand challenge for Biology

Building the Tree of Life is an ongoing activity of scientists around the world, one that combines information from both the genotype and phenotype of organisms. I review recent trends in this effort and describe a number of models, including the multispecies coalescent model, as means to achieve this end.

Phenotypic systems biology for organisms: Concepts, methods and case studies.

Design principles of phenotypes in organisms are fundamental issues in physical biology. So far, understanding “systems” of living organisms have been chiefly promoted by understanding the underlying biomolecules such as genes and proteins, and their intra- and inter-relationships and regulations. After a long period of sophistication, biophysics and molecular biology have established a general framework for understanding ‘molecular systems’ in organisms without regard to species, so that the findings of fly studies can be applied to mouse studies. However, little attention has been paid to exploring “phenotypic systems” in organisms, and thus its general framework remains poorly understood. Here I review concepts, methods, and case studies using butterfly and moth wing patterns to explore phenotypes as systems. First, I present a unifying framework for phenotypic traits as systems, termed multi-component systems. Second, I describe how to define components of phenotypic systems, and also show how to quantify interactions among phenotypic parts. Subsequently, I introduce the concept of the macro-evolutionary process, which illustrates how to generate complex traits. In this point, I also introduce mathematical methods, “phylogenetic comparative methods”, which provide stochastic processes along molecular phylogeny as bifurcated paths to quantify trait evolution. Finally, I would like to propose two key concepts, macro-evolutionary pathways and genotype-phenotype loop (GP loop), which must be needed for the next directions. I hope these efforts on phenotypic biology will become one major target in biophysics and create the next generations of textbooks. This review article is an extended version of the Japanese article, Biological Physics in Phenotypic Systems of Living Organisms, published in SEIBUTSU-BUTSURI Vol. 61, p. 31–35 (2021).

Layers, folds, and semi-neuronal information processing

What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation.

Systematic Conformation-to-Phenotype Mapping via Limited Deep-Sequencing of Proteins

Non-native conformations drive protein misfolding diseases, complicate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well-suited for elucidating them and their phenotypic effects. Especially intractable are the transient conformations populated by intrinsically disordered proteins. We describe an approach to systematically discover, stabilize, and purify native and non-native conformations, generated in vitro or in vivo, and directly link conformations to molecular, organismal, or evolutionary phenotypes. This approach involves high-throughput disulfide scanning (HTDS) of the entire protein. To reveal which disulfides trap which chromatographically resolvable conformers, we devised a deep-sequencing method for double-Cys variant libraries of proteins that precisely and simultaneously locates both Cys residues within each polypeptide. HTDS of the abundant E. coli periplasmic chaperone HdeA revealed distinct classes of disordered hydrophobic conformers with variable cytotoxicity depending on where the backbone was cross-linked. HTDS can bridge conformational and phenotypic landscapes for many proteins that function in disulfide-permissive environments.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits.

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Hotspots of root-exuded amino acids are created within a rhizosphere-on-a-chip.

The rhizosphere is a challenging ecosystem to study from a systems biology perspective due to its diverse chemical, physical, and biological characteristics. In the past decade, microfluidic platforms (e.g. plant-on-a-chip) have created an alternative way to study whole rhizosphere organisms, like plants and microorganisms, under reduced-complexity conditions. However, in reducing the complexity of the environment, it is possible to inadvertently alter organism phenotype, which biases laboratory data compared to in situ experiments. To build back some of the complexity of the rhizosphere in a fully-defined, parameterized approach we have developed a rhizosphere-on-a-chip platform that mimics the physical structure of soil. We demonstrate, through computational simulation, how this synthetic soil structure can influence the emergence of molecular "hotspots" and "hotmoments" that arise naturally from the plant's exudation of labile carbon compounds. We establish the amenability of the rhizosphere-on-a-chip for long-term culture of Brachypodium distachyon, and experimentally validate the presence of exudate hotspots within the rhizosphere-on-a-chip pore spaces using liquid microjunction surface sampling probe mass spectrometry.

Phenopype: A phenotyping pipeline for Python

Abstract Digital images are an intuitive way to capture, store and analyse organismal phenotypes. Many biologists are taking images to collect high‐dimensional phenotypic information from specimens to investigate complex ecological, evolutionary and developmental phenomena, such as relationships between trait diversity and ecosystem function, multivariate natural selection or developmental plasticity. As a consequence, images are being collected at ever‐increasing rates, but extraction of the contained phenotypic information poses a veritable analytical bottleneck. phenopype is a high‐throughput phenotyping pipeline for the programming language Python that aims at alleviating this bottleneck. The package facilitates immediate extraction of high‐dimensional phenotypic data from digital images with low levels of background noise and complexity. At the core, phenopype provides functions for rapid signal processing‐based image preprocessing and segmentation, data extraction, as well as visualization and data export. This functionality is provided by wrapping low‐level computer vision libraries (such as OpenCV) into accessible functions to facilitate scientific image analysis. In addition, phenopype provides a project management ecosystem to streamline data collection and to increase reproducibility. phenopype offers two different workflows that support users during different stages of scientific image analysis. The low‐throughput workflow uses regular Python syntax and has greater flexibility at the cost of reproducibility, which is suitable for prototyping during the initial stages of a research project. The high‐throughput workflow allows users to specify and store image‐specific settings for analysis in human‐readable YAML format, and then execute all functions in one step by means of an interactive parser. This approach facilitates rapid program‐user interactions during batch processing, and greatly increases scientific reproducibility. Overall, phenopype intends to make the features of powerful but technically involved low‐level CV libraries available to biologists with little or no Python coding experience. Therefore, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection. Furthermore, image annotations produced by phenopype can be used as training data, thus presenting a stepping stone towards the application of deep learning architectures.

Designing PCR Primers for the Amplification-Refractory Mutation System.

The recent development in genetic research indicates that there exists intraspecific genetic variability in many organism groups. These variations, which result in a variety of genotypes and phenotypes within a population, are called polymorphism. Mutations in different ways can alter the organism's phenotype and affect its fitness, for example, by altering disease susceptibility or resistance. Therefore, the detection of point mutations in different genes of a population is of particular importance. The amplification-refractory mutation system technique is a PCR-based method to detect single nucleotide polymorphisms in the genome. The high repeatability, low cost, high accessibility, and no need for sophisticated technology are the main advantages of the ARMS-PCR technique, compared with other available methods such as PCR-RFLP. This chapter describes the design and analysis method of primers for the ARMS-PCR technique.

Modular Evolution of the Drosophila Metabolome.

Comparative phylogenetic studies offer a powerful approach to study the evolution of complex traits. Although much effort has been devoted to the evolution of the genome and to organismal phenotypes, until now relatively little work has been done on the evolution of the metabolome, despite the fact that it is composed of the basic structural and functional building blocks of all organisms. Here we explore variation in metabolite levels across 50 My of evolution in the genus Drosophila, employing a common garden design to measure the metabolome within and among 11 species of Drosophila. We find that both sex and age have dramatic and evolutionarily conserved effects on the metabolome. We also find substantial evidence that many metabolite pairs covary after phylogenetic correction, and that such metabolome coevolution is modular. Some of these modules are enriched for specific biochemical pathways and show different evolutionary trajectories, with some showing signs of stabilizing selection. Both observations suggest that functional relationships may ultimately cause such modularity. These coevolutionary patterns also differ between sexes and are affected by age. We explore the relevance of modular evolution to fitness by associating modules with lifespan variation measured in the same common garden. We find several modules associated with lifespan, particularly in the metabolome of older flies. Oxaloacetate levels in older females appear to coevolve with lifespan, and a lifespan-associated module in older females suggests that metabolic associations could underlie 50 My of lifespan evolution.