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Blood platelets are produced by large bone marrow (BM) precursor cells, megakaryocytes (MKs), which extend cytoplasmic protrusions (proplatelets) into BM sinusoids. The molecular cues that control MK polarization towards sinusoids and limit transendothelial crossing to proplatelets remain unknown. Here, we show that the small GTPases Cdc42 and RhoA act as a regulatory circuit downstream of the MK-specific mechanoreceptor GPIb to coordinate polarized transendothelial platelet biogenesis. Functional deficiency of either GPIb or Cdc42 impairs transendothelial proplatelet formation. In the absence of RhoA, increased Cdc42 activity and MK hyperpolarization triggers GPIb-dependent transmigration of entire MKs into BM sinusoids. These findings position Cdc42 (go-signal) and RhoA (stop-signal) at the centre of a molecular checkpoint downstream of GPIb that controls transendothelial platelet biogenesis. Our results may open new avenues for the treatment of platelet production disorders and help to explain the thrombocytopenia in patients with Bernard–Soulier syndrome, a bleeding disorder caused by defects in GPIb-IX-V.
A recent study by Peng and Yang in Scientific Reports using confocal-microscopy based automated quantification of anti-synapsin labeled microglomeruli in the mushroom bodies of honeybee brains reports potentially incorrect numbers of microglomerular densities. Whereas several previous studies using visually supervised or automated counts from confocal images and analyses of serial 3D electron-microscopy data reported consistent numbers of synaptic complexes per volume, Peng and Yang revealed extremely low numbers differing by a factor of 18 or more from those obtained in visually supervised counts, and by a factor 22–180 from numbers in two other studies using automated counts. This extreme discrepancy is especially disturbing as close comparison of raw confocal images of anti-synapsin labeled whole-mount brain preparations are highly similar across these studies. We conclude that these discrepancies may reside in potential misapplication of confocal imaging followed by erroneous use of automated image analysis software. Consequently, the reported microglomerular densities during maturation and after manipulation by insecticides require validation by application of appropriate confocal imaging methods and analyses tools that rely on skilled observers. We suggest several improvements towards more reliable or standardized automated or semi-automated synapse counts in whole mount preparations of insect brains.
Background:
Intrauterine exposure to gestational diabetes mellitus (GDM) confers a lifelong increased risk for metabolic and other complex disorders to the offspring. GDM-induced epigenetic modifications modulating gene regulation and persisting into later life are generally assumed to mediate these elevated disease susceptibilities. To identify candidate genes for fetal programming, we compared genome-wide methylation patterns of fetal cord bloods (FCBs) from GDM and control pregnancies.
Methods and results:
Using Illumina’s 450K methylation arrays and following correction for multiple testing, 65 CpG sites (52 associated with genes) displayed significant methylation differences between GDM and control samples. Four candidate genes, ATP5A1, MFAP4, PRKCH, and SLC17A4, from our methylation screen and one, HIF3A, from the literature were validated by bisulfite pyrosequencing. The effects remained significant after adjustment for the confounding factors maternal BMI, gestational week, and fetal sex in a multivariate regression model. In general, GDM effects on FCB methylation were more pronounced in women with insulin-dependent GDM who had a more severe metabolic phenotype than women with dietetically treated GDM.
Conclusions:
Our study supports an association between maternal GDM and the epigenetic status of the exposed offspring. Consistent with a multifactorial disease model, the observed FCB methylation changes are of small effect size but affect multiple genes/loci. The identified genes are primary candidates for transmitting GDM effects to the next generation. They also may provide useful biomarkers for the diagnosis, prognosis, and treatment of adverse prenatal exposures.
The availability of pollen in agricultural landscapes is essential for the successful growth and reproduction of honey bee colonies (Apis mellifera L.). The quantity and diversity of collected pollen can influence the growth and health of honey bee colonies, but little is known about the influence of landscape structure on pollen diet. In a field experiment, we rotated 16 honey bee colonies across 16 agricultural landscapes, used traps to collect samples of collected pollen and observed intra-colonial dance communication to gain information about foraging distances. DNA metabarcoding was applied to analyze mixed pollen samples. Neither the amount of collected pollen nor pollen diversity was related to landscape diversity. However, we found a strong seasonal variation in the amount and diversity of collected pollen in all sites independent of landscape diversity. The observed increase in foraging distances with decreasing landscape diversity suggests that honey bees compensated for lower landscape diversity by increasing their pollen foraging range in order to maintain pollen amount and diversity. Our results underscore the importance of a diverse pollen diet for honey bee colonies. Agri-environmental schemes aiming to support pollinators should focus on possible spatial and temporal gaps in pollen availability and diversity in agricultural landscapes.
OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased number of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.