An evolutionarily young defense metabolite influences the root growth of plants via the ancient TOR signaling pathway.

To optimize fitness a plant should monitor its metabolism to appropriately control growth and defense. Primary metabolism can be measured by the universally conserved TOR (Target of Rapamycin) pathway to balance growth and development with the available energy and nutrients. Recent work suggests that plants may measure defense metabolites to potentially provide a strategy ensuring fast reallocation of resources to coordinate plant growth and defense. There is little understanding of mechanisms enabling defense metabolite signaling. To identify mechanisms of defense metabolite signaling, we used glucosinolates, an important class of plant defense metabolites. We report novel signaling properties specific to one distinct glucosinolate, 3-hydroxypropylglucosinolate across plants and fungi. This defense metabolite, or derived compounds, reversibly inhibits root growth and development. 3-hydroxypropylglucosinolate signaling functions via genes in the ancient TOR pathway. If this event is not unique, this raises the possibility that other evolutionarily new plant metabolites may link to ancient signaling pathways

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.Comment: The first two authors contributed equally. Accepted at IEEE VIS 202

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory performance, automatic sleep staging solutions are not widely adopted in a clinical context yet. We argue that most deep learning solutions for sleep scoring are limited in their real-world applicability as they are hard to train, deploy, and reproduce. Moreover, these solutions lack interpretability and transparency, which are often key to increase adoption rates. In this work, we revisit the problem of sleep stage classification using classical machine learning. Results show that state-of-the-art performance can be achieved with a conventional machine learning pipeline consisting of preprocessing, feature extraction, and a simple machine learning model. In particular, we analyze the performance of a linear model and a non-linear (gradient boosting) model. Our approach surpasses state-of-the-art (that uses the same data) on two public datasets: Sleep-EDF SC-20 (MF1 0.810) and Sleep-EDF ST (MF1 0.795), while achieving competitive results on Sleep-EDF SC-78 (MF1 0.775) and MASS SS3 (MF1 0.817). We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models. This observation opens the door to clinical adoption, as a representative feature vector allows to leverage both the interpretability and successful track record of traditional machine learning models.Comment: The first two authors contributed equally. Submitted to Biomedical Signal Processing and Contro

Regulation of Translation by TOR, eIF4E and eIF2 alpha in Plants:Current Knowledge, Challenges and Future Perspectives

An important step in eukaryotic gene expression is the synthesis of proteins from mRNA, a process classically divided into three stages, initiation, elongation, and termination. Translation is a precisely regulated and conserved process in eukaryotes. The presence of plant-specific translation initiation factors and the lack of well-known translational regulatory pathways in this kingdom nonetheless indicate how a globally conserved process can diversify among organisms. The control of protein translation is a central aspect of plant development and adaptation to environmental stress, but the mechanisms are still poorly understood. Here we discuss current knowledge of the principal mechanisms that regulate translation initiation in plants, with special attention to the singularities of this eukaryotic kingdom. In addition, we highlight the major recent breakthroughs in the field and the main challenges to address in the coming years

Evolution of the TOR Pathway

The TOR kinase is a major regulator of growth in eukaryotes. Many components of the TOR pathway are implicated in cancer and metabolic diseases in humans. Analysis of the evolution of TOR and its pathway may provide fundamental insight into the evolution of growth regulation in eukaryotes and provide a practical framework on which experimental evidence can be compared between species. Here we performed phylogenetic analyses on the components of the TOR pathway and determined their point of invention. We find that the two TOR complexes and a large part of the TOR pathway originated before the Last Eukaryotic Common Ancestor and form a core to which new inputs have been added during animal evolution. In addition, we provide insight into how duplications and sub-functionalization of the S6K, RSK, SGK and PKB kinases shaped the complexity of the TOR pathway. In yeast we identify novel AGC kinases that are orthologous to the S6 kinase. These results demonstrate how a vital signaling pathway can be both highly conserved and flexible in eukaryotes

Epigenomic and functional analyses reveal roles of epialleles in the loss of photoperiod sensitivity during domestication of allotetraploid cottons

Abstract Background Polyploidy is a pervasive evolutionary feature of all flowering plants and some animals, leading to genetic and epigenetic changes that affect gene expression and morphology. DNA methylation changes can produce meiotically stable epialleles, which are transmissible through selection and breeding. However, the relationship between DNA methylation and polyploid plant domestication remains elusive. Results We report comprehensive epigenomic and functional analyses, including ~12 million differentially methylated cytosines in domesticated allotetraploid cottons and their tetraploid and diploid relatives. Methylated genes evolve faster than unmethylated genes; DNA methylation changes between homoeologous loci are associated with homoeolog-expression bias in the allotetraploids. Significantly, methylation changes induced in the interspecific hybrids are largely maintained in the allotetraploids. Among 519 differentially methylated genes identified between wild and cultivated cottons, some contribute to domestication traits, including flowering time and seed dormancy. CONSTANS (CO) and CO-LIKE (COL) genes regulate photoperiodicity in Arabidopsis. COL2 is an epiallele in allotetraploid cottons. COL2A is hypermethylated and silenced, while COL2D is repressed in wild cottons but highly expressed due to methylation loss in all domesticated cottons tested. Inhibiting DNA methylation activates COL2 expression, and repressing COL2 in cultivated cotton delays flowering. Conclusions We uncover epigenomic signatures of domestication traits during cotton evolution. Demethylation of COL2 increases its expression, inducing photoperiodic flowering, which could have contributed to the suitability of cotton for cultivation worldwide. These resources should facilitate epigenetic engineering, breeding, and improvement of polyploid crops

Regulation of translation initiation under biotic and abiotic stresses

[EN] Plants have developed versatile strategies to deal with the great variety of challenging conditions they are exposed to. Among them, the regulation of translation is a common target to finely modulate gene expression both under biotic and abiotic stress situations. Upon environmental challenges, translation is regulated to reduce the consumption of energy and to selectively synthesize proteins involved in the proper establishment of the tolerance response. In the case of viral infections, the situation is more complex, as viruses have evolved unconventional mechanisms to regulate translation in order to ensure the production of the viral encoded proteins using the plant machinery. Although the final purpose is different, in some cases, both plants and viruses share common mechanisms to modulate translation. In others, the mechanisms leading to the control of translation are viral- or stress-specific. 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What determines cell size?

AbstractFirst paragraph (this article has no abstract) For well over 100 years, cell biologists have been wondering what determines the size of cells. In modern times, we know all of the molecules that control the cell cycle and cell division, but we still do not understand how cell size is determined. To check whether modern cell biology has made any inroads on this age-old question, BMC Biology asked several heavyweights in the field to tell us how they think cell size is controlled, drawing on a range of different cell types. The essays in this collection address two related questions - why does cell size matter, and how do cells control it