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Asymmetric divisions that yield daughter cells of different sizes are frequent during early embryogenesis, but the importance of such a physical difference for successful development remains poorly understood. Here, we investigated this question using the first division ofCaenorhabditis elegansembryos, which yields a large AB cell and a small P1cell. We equalized AB and P1sizes using acute genetic inactivation or optogenetic manipulation of the spindle positioning protein LIN-5. We uncovered that only some embryos tolerated equalization, and that there was a size asymmetry threshold for viability. Cell lineage analysis of equalized embryos revealed an array of defects, including faster cell cycle progression in P1descendants, as well as defects in cell positioning, division orientation, and cell fate. Moreover, equalized embryos were more susceptible to external compression. Overall, we conclude that unequal first cleavage is essential for invariably successful embryonic development ofC. elegans.

Asymmetric divisions that yield daughter cells of different sizes are frequent during early embryogenesis, but the importance of such a physical difference for successful development remains poorly understood. Here, we investigated this question using the first division ofC. elegansembryos, which yields a large AB cell and a small P1cell. We equalized AB and P1sizes using acute genetic inactivation or optogenetic manipulation of the spindle positioning protein LIN-5. We uncovered that only some embryos tolerated equalization, and that there was a size asymmetry threshold for viability. Cell lineage analysis of equalized embryos revealed an array of defects, including faster cell cycle progression in P1descendants, as well as defects in cell positioning, division orientation and cell fate. Moreover, equalized embryos were more susceptible to external compression. Overall, we conclude that unequal first cleavage is essential for invariably successful embryonic development ofC. elegans.

Loss of gene function is common throughout evolution, even though it often leads to reduced fitness. In this study, we systematically evaluated how an organism adapts after deleting genes that are important for growth under oxidative stress. By evolving, sequencing, and phenotyping over 200 yeast lineages, we found that gene loss can enhance an organism's capacity to evolve and adapt. Although gene loss often led to an immediate decrease in fitness, many mutants rapidly acquired suppressor mutations that restored fitness. Depending on the strain's genotype, some ultimately even attained higher fitness levels than similarly adapted wild-type cells. Further, cells with deletions in different modules of the genetic network followed distinct and predictable mutational trajectories. Finally, losing highly connected genes increased evolvability by facilitating the emergence of a more diverse array of phenotypes after adaptation. Together, our findings show that loss of specific parts of a genetic network can facilitate adaptation by opening alternative evolutionary paths. ispartof: MOLECULAR BIOLOGY AND EVOLUTION vol:37 issue:10 pages:2989-3002 ispartof: location:United States status: published

Mutations in theclk-1gene impair mitochondrial ubiquinone biosynthesis and extend the lifespan ofC. elegans.We demonstrate here that this life extension is linked to the repression of cytoplasmic protein translation.Clk-1mutations inhibit polyribosome formation similarly todaf-2mutations that dampen insulin signaling. Comparisons of total versus polysomal RNAs inclk-1mutants reveal a reduction in the translational efficiencies of mRNAs coding for elements of the translation machinery and an increase in those coding for the oxidative phosphorylation and autophagy pathways. Knocking down the transcription initiation factor TAF-4, a protein that becomes sequestered in the cytoplasm during early embryogenesis to induce transcriptional silencing, ameliorates theclk-1inhibition of polyribosome formation. These results underscore a prominent role for the repression of cytoplasmic protein translation in eukaryotic lifespan extension, and suggest that mutations impairing mitochondrial function are able to exploit this repression similarly to reductions of insulin signaling. Moreover, this report reveals an unexpected role for TAF-4 as a repressor of polyribosome formation when ubiquinone biosynthesis is compromised.

A central challenge in genetics is to predict phenotypic variation from individual genome sequences. Here we construct and evaluate phenotypic predictions for 19 strains of Saccharomyces cerevisiae. We use conservation-based methods to predict the impact of protein-coding variation within genes on protein function. We then rank strains using a prediction score that measures the total sum of function-altering changes in different sets of genes reported to influence over 100 phenotypes in genome-wide loss-of-function screens. We evaluate our predictions by comparing them with the observed growth rate and efficiency of 15 strains tested across 20 conditions in quantitative experiments. The median predictive performance, as measured by ROC AUC, was 0.76, and predictions were more accurate when the genes reported to influence a trait were highly connected in a functional gene network.

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts., Author Summary Organisms can evolve resistance to specific stress factors, which allows them to thrive in environments where non-adapted organisms fail to grow. However, the molecular mechanisms that underlie adaptation to complex stress factors that interfere with basic cellular processes are poorly understood. In this study, we reveal how yeast populations adapt to high ethanol concentrations, an ecologically and industrially relevant stress that is still poorly understood. We exposed six independent populations of genetically identical yeast cells to gradually increasing ethanol levels, and we monitored the changes in their DNA sequence over a two-year period. Together with novel computational analyses, we could identify the mutational dynamics and molecular mechanisms underlying increased ethanol resistance. Our results show how adaptation to high ethanol is complex and can be reached through different mutational pathways. Together, our study offers a detailed picture of how populations adapt to a complex continuous stress and identifies several mutations that increase ethanol resistance, which opens new routes to obtain superior biofuel yeast strains.

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.

The nematode Caenorhabditis elegans (C. elegans) is a widely used model organism in biological investigations. Due to its well-known and invariant cell lineage tree, it can be used to study the effects of mutations and various disease processes. Effective and efficient analysis of the wealth of time-lapse fluorescence microscopy image data acquired in such studies requires automation of the cell segmentation and tracking tasks involved. This is hampered by many factors, including autofluorescence effects, low and uneven contrast throughout the images, high noise levels, large numbers of possibly simultaneous cell divisions, and touching or clustering cells. In this paper, we present a new algorithm for segmentation and tracking of cells in C. elegans embryogenesis image data. It is based on the model evolution framework for image segmentation and uses a novel multi-object tracking scheme based on energy minimization via graph cuts. Preliminary experiments on publicly available test data demonstrate the potential of the algorithm compared to existing approaches.