A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts.

Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.

A Venomics Approach Coupled to High-Throughput Toxin Production Strategies Identifies the First Venom-Derived Melanocortin Receptor Agonists.

Animal venoms are rich in hundreds of toxins with extraordinary biological activities. Their exploitation is difficult due to their complexity and the small quantities of venom available from most venomous species. We developed a Venomics approach combining transcriptomic and proteomic characterization of 191 species and identified 20,206 venom toxin sequences. Two complementary production strategies based on solid-phase synthesis and recombinant expression in Escherichia coli generated a physical bank of 3597 toxins. Screened on hMC4R, this bank gave an incredible hit rate of 8%. Here, we focus on two novel toxins: N-TRTX-Preg1a, exhibiting an inhibitory cystine knot (ICK) motif, and N-BUTX-Ptr1a, a short scorpion-CSαß structure. Neither N-TRTX-Preg1a nor N-BUTX-Ptr1a affects ion channels, the known targets of their toxin scaffolds, but binds to four melanocortin receptors with low micromolar affinities and activates the hMC1R/Gs pathway. Phylogenetically, these two toxins form new groups within their respective families and represent novel hMC1R agonists, structurally unrelated to the natural agonists.

Predicción de riesgo de cáncer de mama en mujeres sanas de población española basado en el estudio de variantes genéticas / Prediction of cancer risk based on study of genetic variants in healthy women in the Spanish population

Objetivo: Estratificación de la población general con base en las variantes genotípicas para seleccionar a aquellas mujeres de alto riesgo a desarrollar un cáncer de mama que puedan ser candidatas a un seguimiento individualizado. Material y métodos: Se ha realizado un estudio caso-control en 856 mujeres con cáncer de mama y 839 mujeres controles de la población general pareadas por edad, analizando la asociación entre el riesgo a desarrollar cáncer de mama y un grupo de variantes basado en 76 polimorfismos de un cambio de base (SNP) de susceptibilidad. Resultados: Se han establecido 2curvas de casos y controles con base en las odds ratio (OR) genotípicas que diferencian las 2poblaciones con significación estadística (p = 2,293×10-15). Asimismo, se ha estratificado la población de casos y controles e identificado un 14% de la población que se encontraría en el grupo de alto riesgo con una OR > 2 (> 25% probabilidades de desarrollar un cáncer de mama). Este grupo sería candidato a un seguimiento individualizado. Conclusiones: El Polygenic Risk Score es un predictor del riesgo del cáncer de mama independiente que puede ayudar a seleccionar mujeres con alto riesgo para establecer medidas de seguimiento y tratamiento individualizado en función del riesgo genético

Objective: To stratify the general population based on genotypic variants in order to select women at high risk of breast cancer who could be candidates for individualized follow-up. Material and methods: We performed a case-control study in 856 women with breast cancer and 839 aged-matched control women from the general population. We analysed the association between the risk of developing breast cancer and a group of variants based on 76 susceptibility single nucleotide polymorphisms. Results: Two case-control curves were established based on genotypic odds ratios (OR) that differentiated the 2populations with statistical significance (P=2.293×10-15). Stratification of the case-control population showed that 14% of the population would be at high risk, with an OR>2 (> 25% probability of developing breast cancer). Persons in this group would be candidates for individualized follow-up. Conclusions: The Polygenic Risk Score is an independent predictor of breast cancer risk that may help to select women at high risk, with a view to establishing individualised follow-up and treatment according to genetic risk