Loss of SOX10 function contributes to the phenotype of human Merlin-null schwannoma cells.

Loss of the Merlin tumour suppressor causes abnormal de-differentiation and proliferation of Schwann cells and formation of schwannoma tumours in patients with neurofibromatosis type 2. Within the mature peripheral nerve the normal development, differentiation and maintenance of myelinating and non-myelinating Schwann cells is regulated by a network of transcription factors that include SOX10, OCT6 (now known as POU3F1), NFATC4 and KROX20 (also known as Egr2). We have examined for the first time how their regulation of Schwann cell development is disrupted in primary human schwannoma cells. We find that induction of both KROX20 and OCT6 is impaired, whereas enforced expression of KROX20 drives both myelin gene expression and cell cycle arrest in Merlin-null cells. Importantly, we show that human schwannoma cells have reduced expression of SOX10 protein and messenger RNA. Analysis of mouse SOX10-null Schwann cells shows they display many of the characteristics of human schwannoma cells, including increased expression of platelet derived growth factor receptor beta (PDGFRB) messenger RNA and protein, enhanced proliferation, increased focal adhesions and schwannoma-like morphology. Correspondingly, reintroduction of SOX10 into human Merlin-null cells restores the ability of these cells to induce KROX20 and myelin protein zero (MPZ), localizes NFATC4 to the nucleus, reduces cell proliferation and suppresses PDGFRB expression. Thus, we propose that loss of the SOX10 protein, which is vital for normal Schwann cell development, is also key to the pathology of Merlin-null schwannoma tumours

Matrix stiffness mechanosensing modulates the expression and distribution of transcription factors in Schwann cells

After peripheral nerve injury, mature Schwann cells (SCs) de-differentiate and undergo cell reprogramming to convert into a specialized cell repair phenotype that promotes nerve regeneration. Reprogramming of SCs into the repair phenotype is tightly controlled at the genome level and includes downregulation of pro-myelinating genes and activation of nerve repair-associated genes. Nerve injuries induce not only biochemical but also mechanical changes in the tissue architecture which impact SCs. Recently, we showed that SCs mechanically sense the stiffness of the extracellular matrix and that SC mechanosensitivity modulates their morphology and migratory behavior. Here, we explore the expression levels of key transcription factors and myelin-associated genes in SCs, and the outgrowth of primary dorsal root ganglion (DRG) neurites, in response to changes in the stiffness of generated matrices. The selected stiffness range matches the physiological conditions of both utilized cell types as determined in our previous investigations. We find that stiffer matrices induce upregulation of the expression of transcription factors Sox2, Oct6, and Krox20, and concomitantly reduce the expression of the repair-associated transcription factor c-Jun, suggesting a link between SC substrate mechanosensing and gene expression regulation. Likewise, DRG neurite outgrowth correlates with substrate stiffness. The remarkable intrinsic physiological plasticity of SCs, and the mechanosensitivity of SCs and neurites, may be exploited in the design of bioengineered scaffolds that promote nerve regeneration upon injury

Deciphering the regulatory landscapte of fetal and adult γδ T-cell development at single-cell resolution

γδ T cells with distinct properties develop in the embryonic and adult thymus and have been identified as critical players in a broad range of infections, antitumor surveillance, autoimmune diseases, and tissue homeostasis. Despite their potential value for immunotherapy, differentiation of γδ T cells in the thymus is incompletely understood. Here, we establish a high‐resolution map of γδ T‐cell differentiation from the fetal and adult thymus using single‐cell RNA sequencing. We reveal novel sub‐types of immature and mature γδ T cells and identify an unpolarized thymic population which is expanded in the blood and lymph nodes. Our detailed comparative analysis reveals remarkable similarities between the gene networks active during fetal and adult γδ T‐cell differentiation. By performing a combined single‐cell analysis of Sox13, Maf, and Rorc knockout mice, we demonstrate sequential activation of these factors during IL ‐17‐producing γδ T‐cell (γδT17) differentiation. These findings substantially expand our understanding of γδ T‐cell ontogeny in fetal and adult life. Our experimental and computational strategy provides a blueprint for comparing immune cell differentiation across developmental stages

Impaired Spleen Formation Perturbs Morphogenesis of the Gastric Lobe of the Pancreas

Despite the extensive use of the mouse as a model for studies of pancreas development and disease, the development of the gastric pancreatic lobe has been largely overlooked. In this study we use optical projection tomography to provide a detailed three-dimensional and quantitative description of pancreatic growth dynamics in the mouse. Hereby, we describe the epithelial and mesenchymal events leading to the formation of the gastric lobe of the pancreas. We show that this structure forms by perpendicular growth from the dorsal pancreatic epithelium into a distinct lateral domain of the dorsal pancreatic mesenchyme. Our data support a role for spleen organogenesis in the establishment of this mesenchymal domain and in mice displaying perturbed spleen development, including Dh +/−, Bapx1−/− and Sox11−/−, gastric lobe development is disturbed. We further show that the expression profile of markers for multipotent progenitors is delayed in the gastric lobe as compared to the splenic and duodenal pancreatic lobes. Altogether, this study provides new information regarding the developmental dynamics underlying the formation of the gastric lobe of the pancreas and recognizes lobular heterogeneities regarding the time course of pancreatic cellular differentiation. Collectively, these data are likely to constitute important elements in future interpretations of the developing and/or diseased pancreas

Transcription factor profiling identifies Sox9 as regulator of proliferation and differentiation in corneal epithelial stem/progenitor cells

Understanding transcription factor (TF) regulation of limbal epithelial stem/progenitor cells (LEPCs) may aid in using non-ocular cells to regenerate the corneal surface. This study aimed to identify and characterize TF genes expressed specifically in LEPCs isolated from human donor eyes by laser capture microdissection. Using a profiling approach, preferential limbal expression was found for SoxE and SoxF genes, particularly for Sox9, which showed predominantly cytoplasmic localization in basal LEPCs and nuclear localization in suprabasal and corneal epithelial cells, indicating nucleocytoplasmic translocation and activation during LEPC proliferation and differentiation. Increased nuclear localization of Sox9 was also observed in activated LEPCs following clonal expansion and corneal epithelial wound healing. Knockdown of SOX9 expression in cultured LEPCs by RNAi led to reduced expression of progenitor cell markers, e.g. keratin 15, and increased expression of differentiation markers, e.g. keratin 3. Furthermore, SOX9 silencing significantly suppressed the proliferative capacity of LEPCs and reduced levels of glycogen synthase kinase 3 beta (GSK-3ß), a negative regulator of Wnt/ß-catenin signaling. Sox9 expression, in turn, was significantly suppressed by treatment of LEPCs with exogenous GSK-3ß inhibitors and enhanced by small molecule inhibitors of Wnt signaling. Our results suggest that Sox9 and Wnt/ß-catenin signaling cooperate in mutually repressive interactions to achieve a balance between quiescence, proliferation and differentiation of LEPCs in the limbal niche. Future molecular dissection of Sox9-Wnt interaction and mechanisms of nucleocytoplasmic shuttling of Sox9 may aid in improving the regenerative potential of LEPCs and the reprogramming of non-ocular cells for corneal surface regeneration

Generation of mouse ES cell lines engineered for the forced induction of transcription factors

Here we report the generation and characterization of 84 mouse ES cell lines with doxycycline-controllable transcription factors (TFs) which, together with the previous 53 lines, cover 7–10% of all TFs encoded in the mouse genome. Global gene expression profiles of all 137 lines after the induction of TFs for 48 hrs can associate each TF with the direction of ES cell differentiation, regulatory pathways, and mouse phenotypes. These cell lines and microarray data provide building blocks for a variety of future biomedical research applications as a community resource

Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets