Potential Therapeutical Contributions of the Endocannabinoid System towards Aging and Alzheimer's Disease

International audienceAging can lead to decline in cognition, notably due to neurodegenerative processes overwhelming the brain over time. As people live longer, numerous concerns are rightfully raised toward long-term slowly incapacitating diseases with no cure, such as Alzheimer's disease. Since the early 2000's, the role of neuroinflammation has been scrutinized for its potential role in the development of diverse neurodegenerative diseases notably because of its slow onset and chronic nature in aging. Despite the lack of success yet, treatment of chronic neuroinflammation could help alleviate process implicated in neurodegenerative disease. A growing number of studies including our own have aimed at the endocannabinoid system and unfolded unique effects of this system on neuroinflammation, neurogenesis and hallmarks of Alzheimer's disease and made it a reasonable target in the context of normal and pathological brain aging. Alzheimer's disease (AD) is the most common neurodegenerative disease and accounts for the majority of diagnosed dementia after age 60. It is estimated to currently affect between 20 and 30 million people worldwide with an incidence of the disease between 3 and 30% over the age of 60 [1]. As life expectancy increases, the prevalence of AD and its burden on healthcare is very likely to increase dramatically in the next few decades. Currently available drugs do not reverse or stop the progression of the disease, but only relieve certain cognitive symptoms and thus provide no cure for this growing health and economic concern. Alzheimer's diseas

TRAF5-mediated Tax ubiquitination modulates IKK phosphorylation but not binding to NEMO

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Exclusion from the Golgi and very low levels of HTLV-2 Tax ubiquitination do not prevent IKK-gamma/NEMO relocalization and NF-κB activation

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Predominant role of Tax sumoylation in Tax-induced NF-kB activation in T cells

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Low levels of HTLV-2 Tax conjugation to ubiquitin and SUMO do not impede Tax-mediated activation of NF-κB

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Monitoring Greenland ice sheet buoyancy-driven calving discharge using glacial earthquakes

Since the 2000s, Greenland ice sheet mass loss has been accelerating, followed by increasing numbers of glacial earthquakes (GEs) at near-grounded glaciers. GEs are caused by calving of km-scale icebergs which capsize against the terminus. Seismic record inversion allows a reconstruction of the history of GE sources which captures capsize dynamics through iceberg-to-terminus contact. When compared with a catalog of contact forces from an iceberg capsize model, seismic force history accurately computes calving volumes while the earthquake magnitude fails to uniquely characterize iceberg size, giving errors up to 1 km ³ . Calving determined from GEs recorded ateight glaciers in 1993–2013 accounts for up to 21% of the associated discharge and 6% of the Greenland mass loss. The proportion of discharge attributed to capsizing calving may be underestimated by at least 10% as numerous events could not be identified by standard seismic detections (Olsen and Nettles, 2018). While calving production tends to stabilize in East Greenland, Western glaciers have released more and larger icebergs since 2010 and have become major contributors to Greenland dynamic discharge. Production of GEs and calving behavior are controlled by glacier geometry with bigger icebergs being produced when the terminus advances in deepening water. We illustrate how GEs can help in partitioning and monitoring Greenland mass loss and characterizing capsize dynamics

Identification of glucocorticoid-related molecular signature by whole blood methylome analysis

Objective Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing's syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. Design We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing's syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. Methods Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. Results Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing's syndrome correction. In Cushing's syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. Conclusions Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays

Reinforced Concretes of Tomorrow: Corrosion Behaviour according to Exposure Classes

Reinforced concrete is the most widely used building material but its durability in terms of concrete cover performance and corrosion of steel rebar is still a key point to be studied. To address this topic, within the frame of the national project PERFDUB, two series of eleven reinforced concrete specimens (with metric dimensions) were cast with innovative concrete mixes representative of the French experience, two shapes of rebar and two concrete covers. Then, these specimens were exposed in two natural exposure sites, one in Epernon for carbonation (XC4) and a second one in La Rochelle in the Atlantic Ocean in a tidal zone for chloride ions (XS3m). Their corrosion was carried out using non-destructive testing. In addition, in order to follow the corrosion evolution more accurately in a continuous way, two series of three specimens were casted with embedded sensors and were exposed in two other outdoor sites in Marne-la-Vallée (XC4) and in Eqiom facility (XS3e). The first results of this 20-year project in terms of corrosion of these reinforced concrete specimens obtained with laboratory and field equipment and with monitoring are presented in this paper

Whole blood methylome-derived features to discriminate endocrine hypertension

Background: Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. Results: Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods—Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine—predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. Conclusions: The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder