Biology subjects

Lim, H.

Publications and source records attributed to Lim, H..

4 recordsLinked to original sources

bioRxiv2018

Risk factors associated with Parkinson’s disease: An 11-year population-based South Korean study

ObjectiveTo validate various known risk factors of Parkinsonism and to establish basic information to formulate public health policy by using a 10-year follow-up cohort model.\n\nMethodsThis population based nation-wide study was performed using the National Health Insurance Database of reimbursement claims of the Health Insurance Review and Assessment Service of South Korea data on regular health check-ups in 2003 and 2004, with 10 years follow-up.\n\nResultsWe identified 7,746 patients with Parkinsonism. Old age, hypertension, diabetes, depression, anxiety, taking statin medication, high body mass index, non-smoking, non-alcohol drinking, and low socioeconomic status were each associated with an increase in the risk of Parkinsonism (fully adjusted Cox proportional hazards model: hazard ratio (HR) 1.259, 95% confidence interval (CI) 1.194-1.328 for hypertension, HR 1.255, 95% CI 1.186-1.329 for diabetes, HR 1.554, 95% CI 1.664-1.965 for depression, HR 1.808, 95% CI 1.462-1.652 for anxiety, and HR 1.157, 95% CI 1.072-1.250 for taking statin medication).\n\nConclusionsIn our study, old age, depression, anxiety, and a non-smoker status were found to be risk factors of Parkinsonism, in agreement with previous studies. However, sex, hypertension, diabetes, taking statin medication, non-drinking of Alcohol, and lower socioeconomic status have not been described as risk factors in previous studies and need further verification in future studies.

epidemiology↗

bioRxiv2017

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

Existing1drug discovery process follows a reductionist model of \"one-drug-one-gene-one-disease,\" which is not adequate to tackle complex diseases that involve multiple malfunctioned genes. The availability of big omics data offers new opportunities to transform the drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for the prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework that is based on Random Walk with Restart and can assess the reliability of a specific prediction. In the benchmark study, tREMAP clearly outperforms the single rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications that have yet had an effective treatment. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes whose malfunction is responsible for many diseases including cancer, and kill triple negative breast cancer (TNBC) cells effectively at a low concentration (IC50 = 0.87 M). The TNBC is a deadly disease that currently does not have effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery, and has a great potential toward developing a targeted therapy for the effective treatment of TNBC.

bioinformatics↗

bioRxiv2017

Committed hemopoietic progenitors, not stem cells, are the principal responders to Hox gene transduction

As hemopoietic stem cells differentiate, their proliferative lifespan shortens by unknown mechanisms. Homeobox cluster (Hox) genes have been implicated by their enhancement of self-renewal when transduced into hemopoietic cells, but gene deletions have been inconclusive because of functional redundancy. Here we enforced HOXB4 expression in purified precursor stages, and compared responses of early stages expressing the endogenous genes with later stages that did not. Contrary to the prevalent view that transduced Hox genes enhance the self-renewal of hemopoietic stem cells, stem cells or their multipotent progeny expressing the endogenous genes showed little response. Instead, immortalization, extensive self-renewal and acquired reconstituting potential occurred in committed erythroid and myeloid progenitors where the endogenous genes were shutting down. The results change our understanding of the stages affected by exogenous HOX proteins and point to shutdown of the endogenous genes as a principal determinant of the shortened clonal lifespans of committed progenitor cells.

cell biology↗

bioRxiv2016

Ploidy tug-of-war: evolutionary and genetic environments influence the rate of ploidy drive in a human fungal pathogen

Variation in baseline ploidy is seen throughout the tree of life, yet the factors that determine why one ploidy level is selected over another remain poorly understood. Experimental evolution studies using asexual fungal microbes with manipulated ploidy levels intriguingly reveals a propensity to return to the historical baseline ploidy, a phenomenon that we term ploidy drive. We evolved haploid, diploid, and polyploid strains of the human fungal pathogen Candida albicans under three different nutrient limitation environments to test whether these conditions, hypothesized to select for low ploidy levels, could counteract ploidy drive. Strains generally maintained or acquired smaller genome sizes in minimal medium and under phosphorus depletion compared to in a complete medium, while mostly maintained or acquired increased genome sizes under nitrogen depletion. Surprisingly, improvements in fitness often ran counter to changes in total nuclear genome size; in a number of scenarios lines that maintained their original genome size often increased in fitness more than lines that converged towards diploidy. Combined, this work demonstrates a role for both the environment and genotype in determination of the rate of ploidy drive, and highlights questions that remain about the force(s) that cause genome size variation.

evolutionary biology↗