Fruit weight is controlled by <i>Cell Size Regulator</i> encoding a novel protein that is expressed in maturing tomato fruits

by Qi Mu, Zejun Huang, Manohar Chakrabarti, Eudald Illa-Berenguer, Xiaoxi Liu, Yanping Wang, Alexis Ramos, Esther van der Knaap

Increases in fruit weight of cultivated vegetables and fruits accompanied the domestication of these crops. Here we report on the positional cloning of a quantitative trait locus (QTL) controlling fruit weight in tomato. The derived allele of Cell Size Regulator (CSR-D) increases fruit weight predominantly through enlargement of the pericarp areas. The expanded pericarp tissues result from increased mesocarp cell size and not from increased number of cell layers. The effect of CSR on fruit weight and cell size is found across different genetic backgrounds implying a consistent impact of the locus on the trait. In fruits, CSR expression is undetectable early in development from floral meristems to the rapid cell proliferation stage after anthesis. Expression is low but detectable in growing fruit tissues and in or around vascular bundles coinciding with the cell enlargement stage of the fruit maturation process. CSR encodes an uncharacterized protein whose clade has expanded in the Solanaceae family. The mutant allele is predicted to encode a shorter protein due to a 1.4 kb deletion resulting in a 194 amino-acid truncation. Co-expression analyses and GO term enrichment analyses suggest association of CSR with cell differentiation in fruit tissues and vascular bundles. The derived allele arose in Solanum lycopersicum var cerasiforme and appears completely fixed in many cultivated tomato’s market classes. This finding suggests that the selection of this allele was critical to the full domestication of tomato from its intermediate ancestors.

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Cell Cycle-Related Kinase (CCRK) regulates ciliogenesis and Hedgehog signaling in mice

by Ashley Snouffer, Desmond Brown, Hankyu Lee, Jonathon Walsh, Floria Lupu, Ryan Norman, Karl Lechtreck, Hyuk Wan Ko, Jonathan Eggenschwiler

The Hedgehog (Hh) signaling pathway plays a key role in cell fate specification, proliferation, and survival during mammalian development. Cells require a small organelle, the primary cilium, to respond properly to Hh signals and the key regulators of Hh signal transduction exhibit dynamic localization to this organelle when the pathway is activated. Here, we investigate the role of Cell Cycle Related kinase (CCRK) in regulation of cilium-dependent Hh signaling in the mouse. Mice mutant for Ccrk exhibit a variety of developmental defects indicative of inappropriate regulation of this pathway. Cell biological, biochemical and genetic analyses indicate that CCRK is required to control the Hedgehog pathway at the level or downstream of Smoothened and upstream of the Gli transcription factors, Gli2 and Gli3. In vitro experiments indicate that Ccrk mutant cells show a greater deficit in response to signaling over long time periods than over short ones. Similar to Chlamydomonas mutants lacking the CCRK homolog, LF2, mouse Ccrk mutant cells show defective regulation of ciliary length and morphology. Ccrk mutant cells exhibit defects in intraflagellar transport (the transport mechanism used to assemble cilia), as well as slowed kinetics of ciliary enrichment of key Hh pathway regulators. Collectively, the data suggest that CCRK positively regulates the kinetics by which ciliary proteins such as Smoothened and Gli2 are imported into the cilium, and that the efficiency of ciliary recruitment allows for potent responses to Hedgehog signaling over long time periods.

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Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach

by Elsje Pienaar, Jansy Sarathy, Brendan Prideaux, Jillian Dietzold, Véronique Dartois, Denise E. Kirschner, Jennifer J. Linderman

Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.

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An optimal strategy for epilepsy surgery: Disruption of the rich-club?

by Marinho A. Lopes, Mark P. Richardson, Eugenio Abela, Christian Rummel, Kaspar Schindler, Marc Goodfellow, John R. Terry

Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.

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