Neuroinflammation alters cellular proteostasis by producing endoplasmic reticulum stress, autophagy activation and disrupting ERAD activation

Proteostasis alteration and neuroinflammation are typical features of normal aging. We have previously shown that neuroinflammation alters cellular proteostasis through immunoproteasome induction, leading to a transient decrease of proteasome activity. Here, we further investigated the role of acute lipopolysaccharide (LPS)-induced hippocampal neuroinflammation in cellular proteostasis. In particular, we focused on macroautophagy (hereinafter called autophagy) and endoplasmic reticulum-associated protein degradation (ERAD). We demonstrate that LPS injection induced autophagy activation that was dependent, at least in part, on glycogen synthase kinase (GSK)-3β activity but independent of mammalian target of rapamycin (mTOR) inhibition. Neuroinflammation also produced endoplasmic reticulum (ER) stress leading to canonical unfolded protein response (UPR) activation with a rapid activating transcription factor (ATF) 6α attenuation that resulted in a time-dependent down-regulation of ERAD markers. In this regard, the time-dependent accumulation of unspliced X-box binding protein (XBP) 1, likely because of decreased inositol-requiring enzyme (IRE) 1α-mediated splicing activity, might underlie in vivo ATF6α attenuation. Importantly, lactacystin-induced activation of ERAD was abolished in both the acute neuroinflammation model and in aged rats. Therefore, we provide a cellular pathway through which neuroinflammation might sensitize cells to neurodegeneration under stress situations, being relevant in normal aging and other disorders where neuroinflammation is a characteristic featureUnión Europea PI12/00445Unión Europea ERDF PI12/0044

Influence of ERβ selective agonism during the neonatal period on the sexual differentiation of the rat hypothalamic-pituitary-gonadal (HPG) axis

<p>Abstract</p> <p>Background</p> <p>It is well established that sexual differentiation of the rodent hypothalamic-pituitary-gonadal (HPG) axis is principally orchestrated by estrogen during the perinatal period. Here we sought to better characterize the mechanistic role the beta form of the estrogen receptor (ERβ) plays in this process.</p> <p>Methods</p> <p>To achieve this, we exposed neonatal female rats to three doses (0.5, 1 and 2 mg/kg) of the ERβ selective agonist diarylpropionitrile (DPN) using estradiol benzoate (EB) as a positive control. Measures included day of vaginal opening, estrous cycle quality, GnRH and Fos co-localization following ovariectomy and hormone priming, circulating luteinizing hormone (LH) levels and quantification of hypothalamic kisspeptin immunoreactivity. A second set of females was then neonatally exposed to DPN, the ERα agonist propyl-pyrazole-triol (PPT), DPN+PPT, or EB to compare the impact of ERα and ERβ selective agonism on kisspeptin gene expression in pre- and post-pubescent females.</p> <p>Results</p> <p>All three DPN doses significantly advanced the day of vaginal opening and induced premature anestrus. GnRH and Fos co-labeling, a marker of GnRH activation, following ovariectomy and hormone priming was reduced by approximately half at all doses; the magnitude of which was not as large as with EB or what we have previously observed with the ERα agonist PPT. LH levels were also correspondingly lower, compared to control females. No impact of DPN was observed on the density of kisspeptin immunoreactive (-ir) fibers or cell bodies in the arcuate (ARC) nucleus, and kisspeptin-ir was only significantly reduced by the middle (1 mg/kg) DPN dose in the preoptic region. The second experiment revealed that EB, PPT and the combination of DPN+PPT significantly abrogated preoptic Kiss1 expression at both ages but ARC expression was only reduced by EB.</p> <p>Conclusion</p> <p>Our results indicate that selective agonism of ERβ is not sufficient to completely achieve male-typical HPG organization observed with EB or an ERα agonist.</p

Identification of Single Amino Acid Changes in the Rift Valley Fever Virus Polymerase Core Domain Contributing to Virus Attenuation In Vivo

Rift Valley fever (RVF) is an arboviral zoonotic disease affecting many African countries with the potential to spread to other geographical areas. RVF affects sheep, goats, cattle and camels, causing a high rate of abortions and death of newborn lambs. Also, humans can be infected, developing a usually self-limiting disease that can turn into a more severe illness in a low percentage of cases. Although different veterinary vaccines are available in endemic areas in Africa, to date no human vaccine has been licensed. In previous works, we described the selection and characterization of a favipiravir-mutagenized RVFV variant, termed 40Fp8, with potential as a RVF vaccine candidate due to the strong attenuation shown in immunocompromised animal models. Compared to the parental South African 56/74 viral strain, 40Fp8 displayed 7 amino acid substitutions in the L-protein, three of them located in the central region corresponding to the catalytic core of the RNAdependent RNA polymerase (RdRp). In this work, by means of a reverse genetics system, we have analyzed the effect on virulence of these amino acid changes, alone or combined, both in vitro and in vivo. We found that the simultaneous introduction of two changes (G924S and A1303T) in the heterologous ZH548-RVFV Egyptian strain conferred attenuated phenotypes to the rescued viruses as shown in infected mice without affecting virus immunogenicity. Our results suggest that both changes induce resistance to favipiravir likely associated to some fitness cost that could be the basis for the observed attenuation in vivo. Conversely, the third change, I1050V, appears to be a compensatory mutation increasing viral fitness. Altogether, these results provide relevant information for the safety improvement of novel live attenuated RVFV vaccines.info:eu-repo/semantics/publishedVersio

Plan de negocio: Aradron, drones para la agricultura

El trabajo realizado tiene como objetivo la creación de una empresa dedicada a la prestación de servicios destinados a la agricultura a través de drones. En el desarrollo del mismo se analizará el entorno y se realizará un estudio de mercado, aportando también un análisis Porter y DAFO. Para tomar decisiones adecuadas, se recogerá información del mercado a través de una encuesta y gracias a ella se elaborará un detallado plan de marketing. También diseñaremos una metodología que permita gestionar nuestros recursos de una manera eficiente. Y por último, realizaremos un estudio económico financiero que nos permitirá determinar la viabilidad del proyecto

Tumors defective in homologous recombination rely on oxidative metabolism: relevance to treatments with PARP inhibitors

Mitochondrial metabolism and the generation of reactive oxygen species (ROS) contribute to the acquisition of DNA mutations and genomic instability in cancer. How genomic instability influences the metabolic capacity of cancer cells is nevertheless poorly understood. Here, we show that homologous recombination-defective (HRD) cancers rely on oxidative metabolism to supply NAD+ and ATP for poly(ADP-ribose) polymerase (PARP)-dependent DNA repair mechanisms. Studies in breast and ovarian cancer HRD models depict a metabolic shift that includes enhanced expression of the oxidative phosphorylation (OXPHOS) pathway and its key components and a decline in the glycolytic Warburg phenotype. Hence, HRD cells are more sensitive to metformin and NAD+ concentration changes. On the other hand, shifting from an OXPHOS to a highly glycolytic metabolism interferes with the sensitivity to PARP inhibitors (PARPi) in these HRD cells. This feature is associated with a weak response to PARP inhibition in patient-derived xenografts, emerging as a new mechanism to determine PARPi sensitivity. This study shows a mechanistic link between two major cancer hallmarks, which in turn suggests novel possibilities for specifically treating HRD cancers with OXPHOS inhibitors

Inhibition of Notch pathway arrests PTEN-deficient advanced prostate cancer by triggering p27-driven cellular senescence

Activation of NOTCH signalling is associated with advanced prostate cancer and treatment resistance in prostate cancer patients. However, the mechanism that drives NOTCH activation in prostate cancer remains still elusive. Moreover, preclinical evidence of the therapeutic efficacy of NOTCH inhibitors in prostate cancer is lacking. Here, we provide evidence that PTEN loss in prostate tumours upregulates the expression of ADAM17, thereby activating NOTCH signalling. Using prostate conditional inactivation of both Pten and Notch1 along with preclinical trials carried out in Pten-null prostate conditional mouse models, we demonstrate that Pten-deficient prostate tumours are addicted to the NOTCH signalling. Importantly, we find that pharmacological inhibition of γ-secretase promotes growth arrest in both Pten-null and Pten/Trp53-null prostate tumours by triggering cellular senescence. Altogether, our findings describe a novel pro-tumorigenic network that links PTEN loss to ADAM17 and NOTCH signalling, thus providing the rational for the use of γ-secretase inhibitors in advanced prostate cancer patients

Cervical determinants of anal HPV infection and high-grade anal lesions in women: a collaborative pooled analysis

Cervical cancer screening might contribute to the prevention of anal cancer in women. We aimed to investigate if routine cervical cancer screening results-namely high-risk human papillomavirus (HPV) infection and cytohistopathology-predict anal HPV16 infection, anal high-grade squamous intraepithelial lesions (HSIL) and, hence, anal cancer.International Agency for Research on Cance

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches