Transforming growth factor-β signaling pathway-associated genes SMAD2 and TGFBR2 are implicated in metabolic syndrome in a Taiwanese population

Lin, Eugene; Kuo, Po-Hsiu; Liu, Yu Li; Yang, Albert C.; Tsai, Shih Jen

doi:10.1038/s41598-017-14025-4

Cited by 37 publications

(29 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, we performed quality control procedures with each individual, including kinship, sample quality, and population stratification ( 23 ). Then, plate-wise genotyping biases were checked.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

et al. 2018

Self Cite

View full text Add to dashboard Cite

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

show abstract

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…We analyzed the categorical data using the chi-square test. Differences for continuous variables were compared using the Student's t -test ( 37 ). Genotype frequencies were evaluated for Hardy-Weinberg equilibrium using a χ 2 goodness-of-fit test.…”

Section: Methodsmentioning

confidence: 99%

Combination of G72 Genetic Variation and G72 Protein Level to Detect Schizophrenia: Machine Learning Approaches

Lin

Lai

et al. 2018

Front. Psychiatry

Self Cite

View full text Add to dashboard Cite

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.

show abstract

“…The vascular remodeling regulator VEGFR2 and its Tyr-1175 phosphorylation are decreased in βCKO islets, which may indicate reduced ability to support vascular function (34,35). There are lower steady-state levels of TGFβR2, its signaling effector Smad3, and β-catenin, which regulate insulin secretion in addition to other roles in β-cell function (36)(37)(38)(39). Synuclein A inhibits insulin secretion by binding to K ATP channels and secretory granules (40), while PDGFR is a cilia-dependent microtubule modulator (27,41), and both are increased in βCKO islets.…”

Section: Inactivation Of β-Cell Cilia Leads To Altered Islet Compositmentioning

confidence: 99%

Primary cilia control glucose homeostasis via islet paracrine interactions

Hughes

Cho

Conway

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Pancreatic islets regulate glucose homeostasis through coordinated actions of hormone-secreting cells. What underlies the function of the islet as a unit is the close approximation and communication among heterogeneous cell populations, but the structural mediators of islet cellular cross talk remain incompletely characterized. We generated mice specifically lacking β-cell primary cilia, a cellular organelle that has been implicated in regulating insulin secretion, and found that the β-cell cilia are required for glucose sensing, calcium influx, insulin secretion, and cross regulation of α- and δ-cells. Protein expression profiling in islets confirms perturbation in these cellular processes and reveals additional targets of cilia-dependent signaling. At the organism level, the deletion of β-cell cilia disrupts circulating hormone levels, impairs glucose homeostasis and fuel usage, and leads to the development of diabetes. Together, these findings demonstrate that primary cilia not only orchestrate β-cell–intrinsic activity but also mediate cross talk both within the islet and from islets to other metabolic tissues, thus providing a unique role of cilia in nutrient metabolism and insight into the pathophysiology of diabetes.

show abstract

Transforming growth factor-β signaling pathway-associated genes SMAD2 and TGFBR2 are implicated in metabolic syndrome in a Taiwanese population

Cited by 37 publications

References 46 publications

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

Combination of G72 Genetic Variation and G72 Protein Level to Detect Schizophrenia: Machine Learning Approaches

Primary cilia control glucose homeostasis via islet paracrine interactions

Contact Info

Product

Resources

About