Biology subjects

Barre, E.

Publications and source records attributed to Barre, E..

3 recordsLinked to original sources

bioRxiv2026

CRISPR/Cas9-mediated deletion of Shp1 and Shp2 reveals distinct roles in human megakaryopoiesis and proplatelet formation

The non-receptor protein-tyrosine phosphatases Shp1 (PTPN6) and Shp2 (PTPN11) play critical roles in hematopoietic signaling networks, yet their specific functions in human megakaryopoiesis and thrombopoiesis remain incompletely understood. While Shp2 is recognized in murine models as a positive regulator of thrombopoietin (Tpo)-mediated signaling through the Ras/MAPK and PI3K/AKT pathways, Shp1 has been implicated in RhoA-dependent cytoskeletal remodeling. However, the extent to which these roles translate to human megakaryocyte (MK) development and platelet production is not known. In this study, we systematically investigated the contributions of Shp1 and Shp2 to human MK development and function using CRISPR/Cas9-mediated gene deletion of PTPN6 and PTPN11 in CD34+ hematopoietic stem and progenitor cells (HSPCs), combined with pharmacological inhibition of Shp2 using the structurally-distinct allosteric inhibitors SHP099 and RMC-4550. Efficient gene editing of PTPN6 and PTPN11 resulted in efficient ablation of Shp1 and Shp2 in CD34+ HSPC-derived MKs. Genetic deletion or pharmacological inhibition of Shp2 markedly impaired MK proliferation, polyploidization, maturation, and proplatelet formation, whereas loss of Shp1 expression did not. Further, Shp2 inhibition significantly reduced platelet production in a 3-dimensional human bone marrow tissue model. Deletion and inhibition of Shp2 abrogated Tpo-induced ERK1/2 and AKT phosphorylation, confirming its essential role in Mpl receptor signaling. These findings demonstrate the distinct functional roles of Shp1 and Shp2 in MKs and establish Shp2 as a critical positive regulator of Mpl- mediated megakaryopoiesis and thrombopoiesis. Key PointsO_LIEfficient deletion of Shp1 and Shp2 in human CD34 progenitor cell-derived MKs using CRISPR/Cas9. C_LIO_LILoss of Shp2 expression impairs thrombopoietin-induced human MK maturation, proplatelet formation and Mpl signaling. C_LI

cell biology↗

bioRxiv2025

Synergistic effects of deleting the tyrosine phosphatases Shp1 and Shp2 on megakaryopoiesis and thrombopoiesis in mice

The Src homology 2 (SH2) domain-containing non-transmembrane protein-tyrosine phosphatases 1 and 2 (Shp1 and Shp2) have been implicated in regulating signaling from a variety of receptors and cell types, including the thrombopoietin (Tpo) receptor Mpl in megakaryocytes (MKs) and platelets. We previously showed that deletion of Shp1 and Shp2 in the MK/platelet lineage in mice using the Pf4-Cre transgene/loxP system impairs megakaryopoiesis and thrombopoiesis. However, we also observed unexpected phenotypes including a motheaten-like phenotype in Shp1-deficient mice and severe myelofibrosis in mice lacking both phosphatases. To determine whether these were lineage-specific effects, we utilized the Gp1ba-Cre transgenic mouse to delete loxP-flanked Shp1 and Shp2 in mice. Bone marrow-derived MKs from these mice expressed approximately 20-25% of Shp1 and Shp2, whereas platelets contain 5-10% of each phosphatase compared with controls. Minor MK/platelet defects were observed in mice lacking either Shp1 or Shp2 alone, however mice lacking both Shp1 and Shp2 exhibited macrothrombocytopenia, mild bleeding following tail injury, and impaired GPVI-mediated platelet aggregation and Syk phosphorylation, associated with reduction GPVI and integrin 2 subunit expression. Reduced Shp1 and Shp2 expression resulting in a significant reduction in ploidy, a block in MK maturation and proplatelet-producing MKs. Tpo-mediated Ras/MAPK signaling was reduced in Shp1/2-deficient MKs. Treatment of MKs with structurally distinct Shp2 allosteric inhibitors recapitulated key aspects of the Shp2-deficient phenotype, including aberrant megakaryopoiesis and reduced Mpl signaling. Our study highlights the synergistic functions of Shp1 and Shp2 in the MK/platelet lineage, and identifies Shp2 as a potential therapeutic target in myeloproliferative neoplasms. Key PointsO_LIDeletion of Shp1 and Shp2 in the MK/platelet lineage in mice results in macrothrombocytopenia and minor effects on platelet function. C_LIO_LIDefects can be partially explained by reduced Mpl signaling and aberrant megakaryopoiesis in the absence of Shp2 activity. C_LI

cell biology↗

bioRxiv2021

Regulus, a transcriptional regulatory networks inference tool based on Semantic Web technologies

MotivationTranscriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory networks inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory networks can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based logical constraints to determine the global consistency of the candidate TF-gene relations and qualify them as activations or inhibitions. ResultsWe introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF endpoint. A dedicated SPARQL query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using a logical consistency check translated from biological knowledge, also allowing to qualify them as activation or inhibition. Regulus compares favorably to the closest network inference method, provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Altogether, Regulus is devoted to transcriptional network inference in settings where samples are scarce and cell populations are closely related. Regulus is available at https://gitlab.com/teamDyliss/regulus

bioinformatics↗