1H-NMR-Based Metabolomic Profiling of CSF in Early Amyotrophic Lateral Sclerosis

Background: Pathophysiological mechanisms involved in amyotrophic lateral sclerosis (ALS) are complex and none has identified reliable markers useful in routine patient evaluation. The aim of this study was to analyze the CSF of patients with ALS by 1 H NMR (Nuclear Magnetic Resonance) spectroscopy in order to identify biomarkers in the early stages of the disease, and to evaluate the biochemical factors involved in ALS. Methodology: CSF samples were collected from patients with ALS at the time of diagnosis and from patients without neurodegenerative diseases. One and two-dimensional 1 H NMR analyses were performed and metabolites were quantified by the ERETIC method. We compared the concentrations of CSF metabolites between both groups. Finally, we performed principal component (PCA) and discriminant analyses. Principal Findings: Fifty CSF samples from ALS patients and 44 from controls were analyzed. We quantified 17 metabolites including amino-acids, organic acids, and ketone bodies. Quantitative analysis revealed significantly lower acetate concentrations (p = 0.0002) in ALS patients compared to controls. Concentration of acetone trended higher (p = 0.015), and those of pyruvate (p = 0.002) and ascorbate (p = 0.003) were higher in the ALS group. PCA demonstrated that the pattern of analyzed metabolites discriminated between groups. Discriminant analysis using an algorithm of 17 metabolites reveale

Relationship of Weather Types on the Seasonal and Spatial Variability of Rainfall, Runoff, and Sediment Yield in the Western Mediterranean Basin

Rainfall is the key factor to understand soil erosion processes, mechanisms, and rates. Most research was conducted to determine rainfall characteristics and their relationship with soil erosion (erosivity) but there is little information about how atmospheric patterns control soil losses, and this is important to enable sustainable environmental planning and risk prevention. We investigated the temporal and spatial variability of the relationships of rainfall, runoff, and sediment yield with atmospheric patterns (weather types, WTs) in the western Mediterranean basin. For this purpose, we analyzed a large database of rainfall events collected between 1985 and 2015 in 46 experimental plots and catchments with the aim to: (i) evaluate seasonal differences in the contribution of rainfall, runoff, and sediment yield produced by the WTs; and (ii) to analyze the seasonal efficiency of the different WTs (relation frequency and magnitude) related to rainfall, runoff, and sediment yield. The results indicate two different temporal patterns: the first weather type exhibits (during the cold period: autumn and winter) westerly flows that produce the highest rainfall, runoff, and sediment yield values throughout the territory; the second weather type exhibits easterly flows that predominate during the warm period (spring and summer) and it is located on the Mediterranean coast of the Iberian Peninsula. However, the cyclonic situations present high frequency throughout the whole year with a large influence extended around the western Mediterranean basin. Contrary, the anticyclonic situations, despite of its high frequency, do not contribute significantly to the total rainfall, runoff, and sediment (showing the lowest efficiency) because of atmospheric stability that currently characterize this atmospheric pattern. Our approach helps to better understand the relationship of WTs on the seasonal and spatial variability of rainfall, runoff and sediment yield with a regional scale based on the large dataset and number of soil erosion experimental stations.Spanish Government (Ministry of Economy and Competitiveness, MINECO) and FEDER Projects: CGL2014 52135-C3-3-R, ESP2017-89463-C3-3-R, CGL2014-59946-R, CGL2015-65569-R, CGL2015-64284-C2-2-R, CGL2015-64284-C2-1-R, CGL2016-78075-P, GL2008-02879/BTE, LEDDRA 243857, RECARE-FP7, CGL2017-83866-C3-1-R, and PCIN-2017-061/AEI. Dhais Peña-Angulo received a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Ana Lucia acknowledge the "Brigitte-Schlieben-Lange-Programm". The “Geoenvironmental Processes and Global Change” (E02_17R) was financed by the Aragón Government and the European Social Fund. José Andrés López-Tarazón acknowledges the Secretariat for Universities and Research of the Department of the Economy and Knowledge of the Autonomous Government of Catalonia for supporting the Consolidated Research Group 2014 SGR 645 (RIUS- Fluvial Dynamics Research Group). Artemi Cerdà thank the funding of the OCDE TAD/CRP JA00088807. José Martínez-Fernandez acknowledges the project Unidad de Excelencia CLU-2018-04 co-funded by FEDER and Castilla y León Government. Ane Zabaleta is supported by the Hydro-Environmental Processes consolidated research group (IT1029-16, Basque Government). This paper has the benefit of the Lab and Field Data Pool created within the framework of the COST action CONNECTEUR (ES1306)

Fission Yeast MAP Kinase Sty1 Is Recruited to Stress-induced Genes

The stress-induced expression of many fission yeast genes is dependent upon the Sty1 mitogen-activated protein kinase (MAPK) and Atf1 transcription factor. Atf1 is phosphorylated by Sty1 yet this phosphorylation is not required for stress-induced gene expression, suggesting another mechanism exists whereby Sty1 activates transcription. Here we show that Sty1 associates with Atf1-dependent genes and is recruited to both their promoters and coding regions. This occurs in response to various stress conditions coincident with the kinetics of the activation of Sty1. Association with promoters is not a consequence of increased nuclear accumulation of Sty1 nor does it require the phosphorylation of Atf1. However, recruitment is completely abolished in a mutant lacking Sty1 kinase activity. Both Atf1 and its binding partner Pcr1 are required for association of Sty1 with Atf1-dependent promoters, suggesting that this heterodimer must be intact for optimal recruitment of the MAPK. However, many Atf1-dependent genes are still expressed in a pcr1Δ mutant but with significantly delayed kinetics, thus providing an explanation for the relatively mild stress sensitivity displayed by pcr1Δ. Consistent with this delay, Sty1 and Atf1 cannot be detected at these promoters in this condition, suggesting that their association with chromatin is weak or transient in the absence of Pcr1

SCORE2-diabetes : 10-year cardiovascular risk estimation in type 2 diabetes in Europe

Aims To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe. Methods and results SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m(2), and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m(2), and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively. Conclusion SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen

Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir