Interaction of PACAP with Sonic hedgehog reveals complex regulation of the Hedgehog pathway by PKA

AbstractSonic hedgehog (Shh) signaling is essential for proliferation of cerebellar granule cell progenitors (cGCPs) and its aberrant activation causes a cerebellar cancer medulloblastoma. Pituitary adenylate cyclase activating polypeptide (PACAP) inhibits Shh-driven proliferation of cGCPs and acts as tumor suppressor in murine medulloblastoma. We show that PACAP blocks canonical Shh signaling by a mechanism that involves activation of protein kinase A (PKA) and inhibition of the translocation of the Shh-dependent transcription factor Gli2 into the primary cilium. PKA is shown to play an essential role in inhibiting gene transcription in the absence of Shh, but global PKA activity levels are found to be a poor predictor of the degree of Shh pathway activation. We propose that the core Shh pathway regulates a small compartmentalized pool of PKA in the vicinity of primary cilia. GPCRs that affect global PKA activity levels, such as the PACAP receptor, cooperate with the canonical Shh signal to regulate Gli protein phosphorylation by PKA. This interaction serves to fine-tune the transcriptional and physiological function of the Shh pathway

PACAP Promotes Matrix-Driven Adhesion of Cultured Adult Murine Neural Progenitors.

New neurons are born throughout the life of mammals in germinal zones of the brain known as neurogenic niches: the subventricular zone of the lateral ventricles and the subgranular zone of the dentate gyrus of the hippocampus. These niches contain a subpopulation of cells known as adult neural progenitor cells (aNPCs), which self-renew and give rise to new neurons and glia. aNPCs are regulated by many factors present in the niche, including the extracellular matrix (ECM). We show that the neuropeptide PACAP (pituitary adenylate cyclase-activating polypeptide) affects subventricular zone-derived aNPCs by increasing their surface adhesion. Gene array and reconstitution assays indicate that this effect can be attributed to the regulation of ECM components and ECM-modifying enzymes in aNPCs by PACAP. Our work suggests that PACAP regulates a bidirectional interaction between the aNPCs and their niche: PACAP modifies ECM production and remodeling, in turn the ECM regulates progenitor cell adherence. We speculate that PACAP may in this manner help restrict adult neural progenitors to the stem cell niche in vivo, with potential significance for aNPC function in physiological and pathological states

PACAP Promotes Matrix-Driven Adhesion of Cultured Adult Murine Neural Progenitors.

New neurons are born throughout the life of mammals in germinal zones of the brain known as neurogenic niches: the subventricular zone of the lateral ventricles and the subgranular zone of the dentate gyrus of the hippocampus. These niches contain a subpopulation of cells known as adult neural progenitor cells (aNPCs), which self-renew and give rise to new neurons and glia. aNPCs are regulated by many factors present in the niche, including the extracellular matrix (ECM). We show that the neuropeptide PACAP (pituitary adenylate cyclase-activating polypeptide) affects subventricular zone-derived aNPCs by increasing their surface adhesion. Gene array and reconstitution assays indicate that this effect can be attributed to the regulation of ECM components and ECM-modifying enzymes in aNPCs by PACAP. Our work suggests that PACAP regulates a bidirectional interaction between the aNPCs and their niche: PACAP modifies ECM production and remodeling, in turn the ECM regulates progenitor cell adherence. We speculate that PACAP may in this manner help restrict adult neural progenitors to the stem cell niche in vivo, with potential significance for aNPC function in physiological and pathological states

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response