Shh production and Gli signaling is activated in vivo in lung, enhancing the Th2 response during a murine model of allergic asthma

The pathophysiology of allergic asthma is driven by T-helper 2 (Th2) immune responses following aeroallergen inhalation. The mechanisms that initiate, potentiate and regulate airways allergy are incompletely characterized. We have previously shown that Hedgehog (Hh) signaling to T-cells, via downstream Gli transcription factors, enhances T-cell conversion to a Th2 phenotype. Here, we show for the first time that Gli-dependent transcription is activated in T-cells in vivo during murine allergic airways disease (AAD) a model for the immunopathology of asthma; and that genetic repression of Gli signaling in Tcells decreases the differentiation and/or recruitment of Th2 cells to the lung. We report that T-cells are not the only cells capable of expressing activated Gli during AAD. A substantial proportion of eosinophils and lung epithelial cells, both central mediators of the immunopathology of asthma, are also able to undergo Hh/Gli signaling. Finally, we show that Shh increases Il4 expression in eosinophils. We therefore propose that Hh signaling during AAD is complex, involving multiple cell types, signaling in an auto- or paracrine fashion. Improved understanding of the role of this major morphogenetic pathway in asthma may give rise to new drug targets for this chronic condition

Targeting class I histone deacetylases by the novel small molecule inhibitor 4SC-202 blocks oncogenic hedgehog-GLI signaling and overcomes smoothened inhibitor resistance

Aberrant activation of Hedgehog (HH)/GLI signaling is causally involved in numerous human malignancies, including basal cell carcinoma (BCC) and medulloblastoma. HH pathway antagonists targeting smoothened (SMO), an essential effector of canonical HH/GLI signaling, show significant clinical success in BCC patients and have recently been approved for the treatment of advanced and metastatic BCC. However, rapid and frequent development of drug resistance to SMO inhibitors (SMOi) together with severe side effects caused by prolonged SMOi treatment call for alternative treatment strategies targeting HH/GLI signaling downstream of SMO. In this study, we report that 4SC-202, a novel clinically validated inhibitor of class I histone deacetylases (HDACs), efficiently blocks HH/GLI signaling. Notably, 4SC-202 treatment abrogates GLI activation and HH target gene expression in both SMOi-sensitive and -resistant cells. Mechanistically, we propose that the inhibition of HDACs 1/2/3 is crucial for targeting oncogenic HH/GLI signaling, and that class I HDAC inhibitors either in combination with SMOi or as second-line therapy may improve the treatment options for HH-associated malignancies with SMOi resistance

Genomic characterization of Gli-activator targets in sonic hedgehog-mediated neural patterning

Sonic hedgehog (Shh) acts as a morphogen to mediate the specification of distinct cell identities in the ventral neural tube through a Gli-mediated (Gli1-3) transcriptional network. Identifying Gli targets in a systematic fashion is central to the understanding of the action of Shh. We examined this issue in differentiating neural progenitors in mouse. An epitope-tagged Gli-activator protein was used to directly isolate cis-regulatory sequences by chromatin immunoprecipitation (ChIP). ChIP products were then used to screen custom genomic tiling arrays of putative Hedgehog (Hh) targets predicted from transcriptional profiling studies, surveying 50-150 kb of non-transcribed sequence for each candidate. In addition to identifying expected Gli-target sites, the data predicted a number of unreported direct targets of Shh action. Transgenic analysis of binding regions in Nkx2.2, Nkx2.1 (Titf1) and Rab34 established these as direct Hh targets. These data also facilitated the generation of an algorithm that improved in silico predictions of Hh target genes. Together, these approaches provide significant new insights into both tissue-specific and general transcriptional targets in a crucial Shh-mediated patterning process

The Role of GLI-1 in Endocrine Resistant Breast Cancer

Estrogen receptor positive (ER+) and estrogen receptor negative (ER-) are two major types of breast cancer. For women with ER+ positive breast cancer, patients are treated with the antiestrogenic compounds, tamoxifen or faslodex for five years, immediately after surgical resection of tumors. Unfortunately, 30-40% of these patients will develop resistance to endocrine therapy. Our recent study has shown that the Hedgehog (Hhg) signaling pathway plays a significant role in endocrine resistance and that the aberrantly activated transcription factor, GLI-1, is vital to the development of resistance. However, not much is known about the GLI-1 target genes that might contribute to endocrine resistance. Our goal is to determine novel target genes of GLI-1 and determine how these genes promote endocrine therapy resistance.PelotoniaA five-year embargo was granted for this item.Academic Major: Biomedical Scienc

Alcune considerazioni sul rapporto tra semantica e metafisica nella teoria degli eventi di Kim

La teoria degli eventi che Kim delinea \ue8 considerata una delle pi\uf9 influenti teorie metafisiche de- gli eventi. In questo lavoro si presenta tale teoria e si esamina la sua plausibilit\ue0. In particolare, si indaga la tesi semantica di Kim secon- do cui due nominali per eventi sono coreferenziali solo se le espres- sioni predicative che essi contengono stanno per la stessa propriet\ue0. Inoltre, si esamina i) se gli eventi concepiti alla Kim debbano essere distinti dai fatti e ii) quali sono i motivi per cui tale teoria d\ue0 luogo ad una implausibile moltiplicazione degli eventi

Synergistic inhibition of the Hedgehog pathway by newly designed Smo and Gli antagonists bearing the isoflavone scaffold

Aberrant activation of the Hedgehog (Hh) pathway is responsible for the onset and progression of several malignancies. Small molecules able to block the pathway at the upstream receptor Smoothened (Smo) or the downstream effector Gli1 have thus emerged recently as valuable anticancer agents. Here, we have designed, synthesized, and tested new Hh inhibitors taking advantage by the highly versatile and privileged isoflavone scaffold. The introduction of specific substitutions on the isoflavone's ring B allowed the identification of molecules targeting preferentially Smo or Gli1. Biological assays coupled with molecular modeling corroborated the design strategy, and provided new insights into the mechanism of action of these molecules. The combined administration of two different isoflavones behaving as Smo and Gli antagonists, respectively, in primary medulloblastoma (MB) cells highlighted the synergistic effects of these agents, thus paving the way to further and innovative strategies for the pharmacological inhibition of Hh signaling

Empirical Bounds on Linear Regions of Deep Rectifier Networks

We can compare the expressiveness of neural networks that use rectified linear units (ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive and the known analytical bounds are identical for networks with same dimensions. In this work, we approximate the number of linear regions through empirical bounds based on features of the trained network and probabilistic inference. Our first contribution is a method to sample the activation patterns defined by ReLUs using universal hash functions. This method is based on a Mixed-Integer Linear Programming (MILP) formulation of the network and an algorithm for probabilistic lower bounds of MILP solution sets that we call MIPBound, which is considerably faster than exact counting and reaches values in similar orders of magnitude. Our second contribution is a tighter activation-based bound for the maximum number of linear regions, which is particularly stronger in networks with narrow layers. Combined, these bounds yield a fast proxy for the number of linear regions of a deep neural network.Comment: AAAI 202