Soluble NgR Fusion Protein Modulates the Proliferation of Neural Progenitor Cells via the Notch Pathway

NogoA, myelin-associated glycoprotein (MAG) and oligodendrocyte myelin glycoprotein are CNS myelin molecules that bind to the neuronal Nogo-66 receptor (NgR) and inhibit axon growth. The NgR antagonist, soluble NgR1-Fc protein (sNgR-Fc), facilitates axon regeneration by neutralizing the inhibitory effects of myelin proteins in experimental models of CNS injury. Here we aim to investigate the effect of sNgR-Fc on the proliferation of neural progenitor cells (NPCs). The hippocampus cells of embryonic rats were isolated and cultured in vitro. The expression of nestin, βIII-Tubulin, GFAP and Nogo-A on these cells was observed using immunocytochemistry. In order to investigate the effect on proliferation of NPCs, sNgR-Fc, MAG-Fc chimera and Notch1 blocker were added respectively. The total cell number for the proliferated NPCs was counted. BrdU was applied and the rate of proliferating cells was examined. The level of Notch1 was analyzed using Western blotting. We identified that NogoA is expressed in NPCs. sNgR-Fc significantly enhanced the proliferation of NPCs in vitro as indicated by BrdU labeling and total cell count. This proliferation effect was abolished by the administration of MAG suggesting specificity. In addition, we demonstrate that sNgR-Fc is a potent activator for Notch1 and Notch1 antagonist reversed the effect of sNgR-Fc on NPC proliferation. Our results suggest that sNgR-Fc may modulate Nogo activity to induce NPC proliferation via the Notch pathway

Deletion of the BDNF Truncated Receptor TrkB.T1 Delays Disease Onset in a Mouse Model of Amyotrophic Lateral Sclerosis

Brain Derived Neurotrophic Factor (BDNF) exerts strong pro-survival effects on developing and injured motoneurons. However, in clinical trials, BDNF has failed to benefit patients with amyotrophic lateral sclerosis (ALS). To date, the cause of this failure remains unclear. Motoneurons express the TrkB kinase receptor but also high levels of the truncated TrkB.T1 receptor isoform. Thus, we investigated whether the presence of this receptor may affect the response of diseased motoneurons to endogenous BDNF. We deleted TrkB.T1 in the hSOD1G93A ALS mouse model and evaluated the impact of this mutation on motoneuron death, muscle weakness and disease progression. We found that TrkB.T1 deletion significantly slowed the onset of motor neuron degeneration. Moreover, it delayed the development of muscle weakness by 33 days. Although the life span of the animals was not affected we observed an overall improvement in the neurological score at the late stage of the disease. To investigate the effectiveness of strategies aimed at bypassing the TrkB.T1 limit to BDNF signaling we treated SOD1 mutant mice with the adenosine A2A receptor agonist CGS21680, which can activate motoneuron TrkB receptor signaling independent of neurotrophins. We found that CGS21680 treatment slowed the onset of motor neuron degeneration and muscle weakness similarly to TrkB.T1 removal. Together, our data provide evidence that endogenous TrkB.T1 limits motoneuron responsiveness to BDNF in vivo and suggest that new strategies such as Trk receptor transactivation may be used for therapeutic intervention in ALS or other neurodegenerative disorders

Sortilin Participates in Light-dependent Photoreceptor Degeneration in Vivo

Both proNGF and the neurotrophin receptor p75 (p75NTR) are known to regulate photoreceptor cell death caused by exposure of albino mice to intense illumination. ProNGF-induced apoptosis requires the participation of sortilin as a necessary p75NTR co-receptor, suggesting that sortilin may participate in the photoreceptor degeneration triggered by intense lighting. We report here that light-exposed albino mice showed sortilin, p75NTR, and proNGF expression in the outer nuclear layer, the retinal layer where photoreceptor cell bodies are located. In addition, cone progenitor-derived 661W cells subjected to intense illumination expressed sortilin and p75NTR and released proNGF into the culture medium. Pharmacological blockade of sortilin with either neurotensin or the “pro” domain of proNGF (pro-peptide) favored the survival of 661W cells subjected to intense light. In vivo, the pro-peptide attenuated retinal cell death in light-exposed albino mice. We propose that an auto/paracrine proapoptotic mechanism based on the interaction of proNGF with the receptor complex p75NTR/sortilin participates in intense light-dependent photoreceptor cell death. We therefore propose sortilin as a putative target for intervention in hereditary retinal dystrophies

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Transfer learning in sentiment classification with deep neural networks

Cross-domain sentiment classifiers aim to predict the polarity (i.e. sentiment orientation) of target text documents, by reusing a knowledge model learnt from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Deep neural networks have recently reached the state-of-the-art in many NLP tasks, including in-domain sentiment classification, but few of them involve transfer learning and cross-domain sentiment solu- tions. This paper moves forward the investigation started in a previous work [1], where an unsupervised deep approach for text mining, called Paragraph Vector (PV), achieved cross-domain accuracy equivalent to a method based on Markov Chain (MC), developed ad hoc for cross-domain sentiment classification. In this work, Gated Recurrent Unit (GRU) is included into the previous investigation, showing that mem- ory units are beneficial for cross-domain when enough training data are available. Moreover, the knowledge models learnt from the source domain are tuned on small samples of target instances to foster transfer learning. PV is almost unaffected by fine-tuning, because it is already able to capture word semantics without supervision. On the other hand, fine tuning boosts the cross-domain performance of GRU. The smaller is the training set used, the greater is the improvement of accurac