G-CSF increases mesenchymal precursor cell numbers in the bone marrow via an indirect mechanism involving osteoclast-mediated bone resorption

AbstractDuring the course of studies to investigate whether MPC circulate in response to G-CSF, the agent most frequently used to induce mobilization of hematopoietic progenitors, we observed that while G-CSF failed to increase the number of MPC in circulation (assayed in vitro as fibroblast colony-forming cells, CFU-F), G-CSF administration nevertheless resulted in a time-dependent increase in the absolute number of CFU-F within the BM, peaking at Day 7. Treatment of BM cells from G-CSF-treated mice with hydroxyurea did not alter CFU-F numbers, suggesting that the increase in their numbers in response to G-CSF administration is not due to proliferation of existing CFU-F. Given previous studies demonstrating that G-CSF potently induces bone turnover in mice, we hypothesized that the increase in CFU-F may be triggered by the bone resorption that occurs following G-CSF administration. In accord with this hypothesis, administration of an inhibitor of osteoclast differentiation, osteoprotegerin (OPG), prevented the increase of CFU-F numbers induced by G-CSF. In conclusion, these data indicate that the cytokine treatment routinely used to mobilize hematopoietic stem cells could provide a readily applicable method to induce in vivo expansion of MPC for clinical applications

Greater bone formation of Y2 knockout mice is associated with increased osteoprogenitor numbers and altered Y1 receptor expression

Germ line or hypothalamus-specific deletion of Y2 receptors in mice results in a doubling of trabecular bone volume. However, the specific mechanism by which deletion of Y2 receptors increases bone mass has not yet been identified. Here we show that cultured adherent bone marrow stromal cells from Y2(-/-) mice also demonstrate increased mineralization in vitro. Isolation of two populations of progenitor cell types, an immature mesenchymal stem cell population and a more highly differentiated population of progenitor cells, revealed a greater number of the progenitor cells within the bone of Y2(-/-) mice. Analysis of Y receptor transcripts in cultured stromal cells from wild-type mice revealed high levels of Y1 but not Y2, Y4, Y5, or y6 receptor mRNA. Interestingly, germ line Y2 receptor deletion causes Y1 receptor down-regulation in stromal cells and bone tissue possibly due to the lack of feedback inhibition of NPY release and subsequent overstimulation of Y1 receptors. Furthermore, deletion of Y1 receptors resulted in increased bone mineral density in mice. Together, these findings indicate that the greater number of mesenchymal progenitors and the altered Y1 receptor expression within bone cells in the absence of Y2 receptors are a likely mechanism for the greater bone mineralization in vivo and in vitro, opening up potential new treatment avenues for osteoporosis

Integrin-Alpha IIb Identifies Murine Lymph Node Lymphatic Endothelial Cells Responsive to RANKL

Microenvironment and activation signals likely imprint heterogeneity in the lymphatic endothelial cell (LEC) population. Particularly LECs of secondary lymphoid organs are exposed to different cell types and immune stimuli. However, our understanding of the nature of LEC activation signals and their cell source within the secondary lymphoid organ in the steady state remains incomplete. Here we show that integrin alpha 2b (ITGA2b), known to be carried by platelets, megakaryocytes and hematopoietic progenitors, is expressed by a lymph node subset of LECs, residing in medullary, cortical and subcapsular sinuses. In the subcapsular sinus, the floor but not the ceiling layer expresses the integrin, being excluded from ACKR4+LECs but overlapping with MAdCAM-1 expression. ITGA2b expression increases in response to immunization, raising the possibility that heterogeneous ITGA2b levels reflect variation in exposure to activation signals. We show that alterations of the level of receptor activator of NF-κB ligand (RANKL), by overexpression, neutralization or deletion from stromal marginal reticular cells, affected the proportion of ITGA2b+LECs. Lymph node LECs but not peripheral LECs express RANK. In addition, we found that lymphotoxin-β receptor signaling likewise regulated the proportion of ITGA2b+LECs. These findings demonstrate that stromal reticular cells activate LECs via RANKL and support the action of hematopoietic cell-derived lymphotoxin

Analyse et discussion des données recueillies chez 58 enfants présentant une sympomatologie d'asthme aux urgences

POITIERS-BU Médecine pharmacie (861942103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Unsupervised variable selection for kernel methods in systems biology

Kernel methods have proven to be useful and successful to analyse large-scale multi-omics datasets [Schölkopf et al., 2004]. However, as stated in [Hofmann et al., 2015, Mariette et al., 2017], these methods usually suffer from a lack of interpretability as the information of thousands descriptors is summarized in a few similarity measures, that can be strongly in uenced by a large number of irrelevant descriptors. To address this issue, feature selection is a widely used strategy: it consist in selecting the most promising features during or prior the analysis. However, most existing methods are proposed in a supervised framework [Tibshirani, 1996, Robnik-Sikonja and Kononenko, 2003, Lin and Tang, 2006]. In the unsupervised framework, the number of proposals is much less important, because there is no objective criterion or value on which to tune the quality of a given feature. Proposals thus aim at preserving at best the similarities between individuals like the SPEC approach [Zhao and Liu, 2007] or at recovering a latent cluster structure, like MCFS [Cai et al., 2010], NDFS [Li et al., 2012] and UDFS [Yang et al., 2011]. In this communication, we will present a feature selection algorithm that explicitly takes advantage of the kernel structure in an unsupervised fashion

Unsupervised variable selection for kernel methods in systems biology

National audienceKernel methods have proven to be useful and successful to analyse large-scale multi-omics datasets [Schölkopf et al., 2004]. However, as stated in [Hofmann et al., 2015, Mariette et al., 2017], these methods usually suffer from a lack of interpretability as the information of thousands descriptors is summarized in a few similarity measures, that can be strongly in uenced by a large number of irrelevant descriptors. To address this issue, feature selection is a widely used strategy: it consist in selecting the most promising features during or prior the analysis. However, most existing methods are proposed in a supervised framework [Tibshirani, 1996, Robnik-Sikonja and Kononenko, 2003, Lin and Tang, 2006]. In the unsupervised framework, the number of proposals is much less important, because there is no objective criterion or value on which to tune the quality of a given feature. Proposals thus aim at preserving at best the similarities between individuals like the SPEC approach [Zhao and Liu, 2007] or at recovering a latent cluster structure, like MCFS [Cai et al., 2010], NDFS [Li et al., 2012] and UDFS [Yang et al., 2011]. In this communication, we will present a feature selection algorithm that explicitly takes advantage of the kernel structure in an unsupervised fashion

Feature selection for kernel methods in systems biology

International audienceThe substantial development of high-throughput biotechnologies has rendered large-scale multiomics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a 1 penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.7, published on CRAN

Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation

Abstract Background Genotyping-by-sequencing (GBS) has emerged as a powerful and cost-effective approach for discovering and genotyping single-nucleotide polymorphisms. The GBS technique was largely used in crop species where its low sequence coverage is not a drawback for calling genotypes because inbred lines are almost homozygous. In contrast, only a few studies used the GBS technique in animal populations (with sizeable heterozygosity rates) and many of those that have been published did not consider the quality of the genotypes produced by the bioinformatic pipelines. To improve the sequence coverage of the fragments, an alternative GBS preparation protocol that includes selective primers during the PCR amplification step has been recently proposed. In this study, we compared this modified protocol with the conventional two-enzyme GBS protocol. We also described various procedures to maximize the selection of high quality genotypes and to increase the accuracy of imputation. Results The in silico digestions of the bovine genome showed that the combination of PstI and MspI is more suitable for sequencing bovine GBS libraries than the use of single digestions with PstI or ApeKI. The sequencing output of the GBS libraries generated a total of 123,666 variants with the selective-primer approach and 272,103 variants with the conventional approach. Validating our data with genotypes obtained from mass spectrometry and Illumina’s bovine SNP50 array, we found that the genotypes produced by the conventional GBS method were concordant with those produced by these alternative genotyping methods, whereas the selective-primer method failed to call heterozygotes with confidence. Our results indicate that high accuracy in genotype calling (>97%) can be obtained using low read-depth thresholds (3 to 5 reads) provided that markers are simultaneously filtered for genotype quality scores. We also show that factors such as the minimum call rate and the minor allele frequency positively influence the accuracy of imputation of missing GBS data. The highest accuracies (around 85%) of imputed GBS markers were obtained with the FIMPUTE program when GBS and SNP50 array genotypes were combined (80,190 to 100,297 markers) before imputation. Conclusions We discovered that the conventional two-enzyme GBS protocol could produce a large number of high-quality genotypes provided that appropriate filtration criteria were used. In contrast, the selective-primer approach resulted in a substantial proportion of miscalled genotypes and should be avoided for livestock genotyping studies. Overall, our study demonstrates that carefully adjusting the different filtering parameters applied to the GBS data is critical to maximize the selection of high quality genotypes and to increase the accuracy of imputation of missing data. The strategies and results presented here provide a framework to maximize the output of the GBS technique in animal populations and qualified the PstI/MspI GBS assay as a low-cost high-density genotyping platform. The conclusions reported here regarding read-depth and genotype quality filtering could benefit many GBS applications, notably genome-wide association studies, where there is a need to increase the density of markers genotyped across the target population while preserving the quality of genotypes

Tetraspanins: useful multifunction proteins for the possible design and development of small-molecule therapeutic tools

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