Biology subjects

Park, J.-H.

Publications and source records attributed to Park, J.-H..

3 recordsLinked to original sources

bioRxiv2018

Diagnosis and Prognosis Using Machine Learning Trained on BrainMorphometry and White Matter Connectomes

Accurate, reliable prediction of risk for Alzheimers disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping--morphometry and structural connectomics--and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimers Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid {beta}, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.\n\nHighlightsO_LIWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.\nC_LIO_LIIn predicting the progression from MCI to AD, the morphometry model showed the best performance.\nC_LIO_LITwo independent clinical datasets were used in this study: one for model building, the other for generalizability testing.\nC_LI

neuroscience↗

bioRxiv2018

Alpha-synuclein-induced mitochondrial dysfunction is mediated via a sirtuin 3-dependent pathway.

The sirtuins are highly conserved nicotinamide adenine dinucleotide (NAD+)-dependent enzymes that play a broad role in cellular metabolism and aging. Mitochondrial sirtuin 3 (SIRT3) is downregulated in aging and age-associated diseases such as cancer and neurodegeneration and plays a major role in maintaining mitochondrial function and preventing oxidative stress. Mitochondria dysfunction is central to the pathogenesis of Parkinson disease with mutations in mitochondrial-associated proteins such as PINK1 and parkin causing familial Parkinson disease. Here, we demonstrate that the presence of alpha-synuclein oligomers in mitochondria induce a corresponding decrease in mitochondrial SIRT3 activity and decreased mitochondrial biogenesis. We show that SIRT3 downregulation in the presence of alpha-synuclein accumulation is accompanied by increased phosphorylation of AMP-activated protein kinase (AMPK) and cAMP-response element binding protein (CREB), increased phosphorylation of dynamin-related protein 1(DRP1) and decreased levels of optic atrophy 1 (OPA1), which are indicative of impaired mitochondrial dynamics. Treatment with the AMPK agonist 5-aminoimidazole-4-carboxamide-1-{beta}-d-ribofuranoside (AICAR) restores SIRT3 expression and improves alpha-synuclein-induced mitochondrial dysfunction by decreasing oligomer formation. The accumulation of alpha-synuclein oligomers in mitochondria corresponds with SIRT3 downregulation not only in an experimental cellular model, but also in vivo in a rodent model of Parkinson's disease, and importantly in human post mortem brains with neuropathologically confirmed Lewy body disease (LBD). Taken together our findings suggest that pharmacologically increasing SIRT3 levels will counteract syn-induced mitochondrial dysfunction by normalizing mitochondrial bioenergetics. These data support a protective role for SIRT3 in Parkinson disease-associated pathways and reveals significant mechanistic insight into the interplay of SIRT3 and syn.

neuroscience↗

bioRxiv2017

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

Summary-level statistics from genome-wide association studies are now widely used to estimate heritability and co-heritability of traits using the popular linkage-disequilibrium-score (LD-score) regression method. We develop a likelihood-based approach for analyzing summary-level statistics and external LD information to estimate common variants effect-size distributions, characterized by proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of summary-level results across 32 GWAS reveals that while all traits are highly polygenic, there is wide diversity in the degrees of polygenicity. The effect-size distributions for susceptibility SNPs could be adequately modeled by a single normal distribution for traits related to mental health and ability and by a mixture of two normal distributions for all other traits. Among quantitative traits, we predict the sample sizes needed to identify SNPs which explain 80% of GWAS heritability to be between 300K-500K for some of the early growth traits, between 1-2 million for some anthropometric and cholesterol traits and multiple millions for body mass index and some others. The corresponding predictions for disease traits are between 200K-400K for inflammatory bowel diseases, close to one million for a variety of adult onset chronic diseases and between 1-2 million for psychiatric diseases.

genetics↗