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Genome Biol,
2011]
High-throughput phenotyping approaches (phenomics) are being combined with genome-wide genetic screens to identify alterations in phenotype that result from gene inactivation. Here we highlight promising technologies for 'phenome-scale' analyses in multicellular organisms.
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Neuron,
2002]
Cyclic GMP-dependent protein kinase (PKG) has been implicated in the regulation of diverse aspects of vertebrate and insect behavior, yet the mechanisms underlying these effects are poorly understood. In this issue of Neuron, Fujiwara et al. and L'Etoile et al. address the neural basis for PKG function in C. elegans and demonstrate the power of behavioral genetic analysis in simple systems in the elucidation of neuronal signaling mechanisms in vivo.
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J Biosci,
2009]
Understanding how the environment impacts development is of central interest in developmental and evolutionary biology. On the one hand, we would like to understand how the environment induces phenotypic changes (the study of phenotypic plasticity). On the other hand, we may ask how a development system maintains a stable and precise phenotypic output despite the presence of environmental variation. We study such developmental robustness to environmental variation using vulval cell fate patterning in the nematode Caenorhabditis elegans as a study system. Here we review both mechanistic and evolutionary aspects of these studies, focusing on recently obtained experimental results. First, we present evidence indicating that vulval formation is under stabilizing selection. Second, we discuss quantitative data on the precision and variability in the output of the vulval developmental system in different environments and different genetic backgrounds. Third, we illustrate how environmental and genetic variation modulate the cellular and molecular processes underlying the formation of the vulva. Fourth, we discuss the evolutionary significance of environmental sensitivity of this developmental system.
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Methods,
2016]
With the development of bio-imaging techniques, an increasing number of studies apply these techniques to generate a myriad of image data. Its applications range from quantification of cellular, tissue, organismal and behavioral phenotypes of model organisms, to human facial phenotypes. The bio-imaging approaches to automatically detect, quantify, and profile phenotypic changes related to specific biological questions open new doors to studying phenotype-genotype associations and to precisely evaluating molecular changes associated with quantitative phenotypes. Here, we review major applications of bioimage-based quantitative phenotype analysis. Specifically, we describe the biological questions and experimental needs addressable by these analyses, computational techniques and tools that are available in these contexts, and the new perspectives on phenotype-genotype association uncovered by such analyses.
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Biochim Biophys Acta,
2018]
As a master regulator of transcription by RNA polymerase (Pol) III, Maf1 represses the synthesis of highly abundant non-coding RNAs as anabolic signals dissipate, as the quality or quantity of nutrients decreases, and under a wide range of cellular and environmental stress conditions. Thus, Maf1 responds to changes in cell physiology to conserve metabolic energy and to help maintain appropriate levels of tRNAs and other essential non-coding RNAs. Studies in different model organisms and cell-based systems show that perturbations of Maf1 can also impact cell physiology and metabolism. These effects are mediated by changes in Pol III transcription and/or by effects of Maf1 on the expression of select Pol II-transcribed genes. Maf1 phenotypes can vary between different systems and are sometimes conflicting as in comparisons between Maf1 KO mice and cultured mammalian cells. These studies are reviewed in an effort to better appreciate the relationship between Maf1 function and cell physiology. This article is part of a Special Issue entitled: SI: Regulation of tRNA synthesis and modification in physiological conditions and disease edited by Dr. Boguta Magdalena.
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Trends in Genetics,
2004]
The complex relationship between the genotype and the phenotype constrains and biases phenotypic evolution. Indeed, random mutation can have non-random (anisotropic) effects on the phenotype. In this review, we propose an operational definition of the'phenotypic neighborhood' of a given genotype, as obtained after induced mutagenesis or in mutation accumulation lines, with examples of anisotropic distributions of phenotypes reached when exploring the vicinity of a genotype. We also compare the phenotypic neighborhood for a given developmental process among species, focusing on nematode vulva development. Finally, we compare the phenotypic neighborhood assessed by mutagenesis with the phenotypic spectrum of wild isolates of the same species and make inferences about the action of selection and/or drift on the same developmental process in
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Worm,
2012]
Model systems, including C. elegans, have been successfully studied to understand the genetic control of development. A genotype's phenotype determines its evolutionary fitness in natural environments, which are typically harsh, heterogeneous and dynamic. Phenotypic plasticity, the process by which one genome can produce different phenotypes in response to the environment, allows genotypes to better match their phenotype to their environment. Phenotypic plasticity is rife among nematodes, seen both as differences among life-cycles stages, perhaps best exemplified by parasitic nematodes, as well as developmental choices, such as shown by the C. elegans dauer/non-dauer developmental choice. Understanding the genetic basis of phenotypically plastic traits will probably explain the function of many genes whose function still remains unclear. Understanding the adaptive benefits of phenotypically plastic traits requires that we understand how plasticity differs among genotypes, and the effects of this in diverse, different environments.
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Lab Chip,
2010]
This paper reviews the technologies that have been invented in the last few years on high-throughput phenotyping, imaging, screening, and related techniques using microfluidics. The review focuses on the technical challenges and how microfluidics can help to solve these existing problems, specifically discussing the applications of microfluidics to multicellular model organisms. The challenges facing this field include handling multicellular organisms in an efficient manner, controlling the microenvironment and precise manipulation of the local conditions to allow the phenotyping, screening, and imaging of the small animals. Not only does microfluidics have the proper length scale for manipulating these biological entities, but automation has also been demonstrated with these systems, and more importantly the ability to deliver stimuli or alter biophysical/biochemical conditions to the biological entities with good spatial and temporal controls. In addition, integration with and interfacing to other hardware/software allows quantitative approaches. We include several successful examples of microfluidics solving these high-throughput problems. The paper also highlights other applications that can be developed in the future.
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Front Mol Biosci,
2022]
Sulfation is poorly understood in most invertebrates and a potential role of sulfation in the regulation of developmental and physiological processes of these organisms remains unclear. Also, animal model system approaches did not identify many sulfation-associated mechanisms, whereas phosphorylation and ubiquitination are regularly found in unbiased genetic and pharmacological studies. However, recent work in the two nematodes <i>Caenorhabditis elegans</i> and <i>Pristionchus pacificus</i> found a role of sulfatases and sulfotransferases in the regulation of development and phenotypic plasticity. Here, we summarize the current knowledge about the role of sulfation in nematodes and highlight future research opportunities made possible by the advanced experimental toolkit available in these organisms.
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J Exp Biol,
2007]
Probably all heritable traits, including disease susceptibility, are affected by interactions between mutations in multiple genes. We understand little, however, about how genes interact to produce phenotypes, and there is little power to detect interactions between genes in human population studies. An alternative approach towards understanding how mutations combine to produce phenotypes is to construct systematic genetic interaction networks in model organisms. Here I describe the methods that are being used to map genetic interactions in yeast and C. elegans, and the insights that these networks provide for human disease. I also discuss the mechanistic interpretation of genetic interaction networks, how genetic interactions can be used to understand gene function, and methods that have been developed to predict genetic interactions on a genome-wide scale.