Associations among SNPs in two addictive genes, food addiction, and antioxidant markers in recreationally active young women.

Introduction: Introduction: food addiction is associated with genetic polymorphisms and decreased antioxidant intake. Objectives: this study determined the associations among food addiction, dopamine receptor 2 (DRD2) and toll-interleukin 1 receptor (TIR) domain-containing adaptor protein (TIRAP rs625413) gene polymorphisms, antioxidant capacities, and zinc levels among recreationally active Turkish women. Methods: the Yale Food Addiction Scale was used to evaluate the food addiction status. Serum antioxidant capacities and zinc levels were evaluated by blood analyses. Deoxyribonucleic acid (DNA) extraction was performed using peripheral blood leukocytes, and the polymorphism status of the DRD2 Taq 1A and TIRAP genes was investigated using a commercial kit. Results: the frequencies of the heterozygous genotypes of DRD2 Taq 1A and TIRAP were 23.1 % and 31.4 %, respectively, and the frequency of risk allele homozygous genotypes was 3.2 %. Most participants (94.4 %) had a nonpolymorphic/wild (CC) genotype in both genes; however, 11.5 % of the participants had a food addiction. The differences between serum antioxidant capacities, zinc levels, and body mass indices of those with and without food addiction were statistically significant. However, there were no differences in the serum zinc and antioxidant levels among the different genotypes. Conclusion: food addiction in young Turkish women was not associated with DRD2 Taq 1A or TIRAP polymorphisms but was associated with serum antioxidant capacities and zinc levels. Further studies on different loci of the same genes or genotypes of different genes with larger sample sizes are warranted.

Introducción: Introducción: la adicción a la comida está asociada con polimorfismos genéticos y disminución de la ingesta de antioxidantes. Objetivos: este estudio determinó las asociaciones entre la adicción a la comida, los polimorfismos del gen de la proteína adaptadora que contiene el dominio del receptor de dopamina 2 (DRD2) y del receptor de interleucina 1 (TIR) (TIRAP rs625413), las capacidades antioxidantes y los niveles de zinc entre mujeres turcas recreativamente activas. Métodos: se utilizó la escala de adicción a la comida de Yale para evaluar el estado de adicción a la comida. Las capacidades antioxidantes séricas y los niveles de zinc se evaluaron mediante análisis de sangre. La extracción de ácido desoxirribonucleico (ADN) se realizó a partir de leucocitos de sangre periférica y el estado de polimorfismo de los genes DRD2 Taq 1A y TIRAP se investigó con un kit comercial. Resultados: las frecuencias de los genotipos heterocigotos de DRD2 Taq 1A y TIRAP fueron 23,1 % y 31,4 %, respectivamente, y la frecuencia de genotipos homocigotos de alelos de riesgo fue de 3,2 %. La mayoría de las participantes (94,4 %) tenían un genotipo no polimórfico/salvaje (CC) en ambos genes; sin embargo, el 11,5 % de las participantes tenía adicción a la comida. Las diferencias entre las capacidades antioxidantes séricas, los niveles de zinc y los índices de masa corporal de aquellas con y sin adicción a la comida fueron estadísticamente significativas. Sin embargo, no hubo diferencias en los niveles séricos de zinc y antioxidantes entre los diferentes genotipos. Conclusión: la adicción a la comida en mujeres jóvenes turcas no se asoció con los polimorfismos DRD2 Taq 1A o TIRAP, pero se asoció con las capacidades séricas antioxidantes y los niveles de zinc. Se justifican más estudios sobre diferentes loci de los mismos genes o genotipos de diferentes genes con tamaños de muestra más grandes.

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.