Changwon Yoo

Big data analysis using modern statistical and machine learning methods in medicine.

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Integrated Bioinformatics, Environmental Epidemiologic and Genomic Approaches to Identify Environmental and Molecular Links between Endometriosis and Breast Cancer.

We present a combined environmental epidemiologic, genomic, and bioinformatics approach to identify: exposure of environmental chemicals with estrogenic activity; epidemiologic association between endocrine disrupting chemical (EDC) and health effects, such as, breast cancer or endometriosis; and gene-EDC interactions and disease associations. Human exposure measurement and modeling confirmed estrogenic activity of three selected class of environmental chemicals, polychlorinated biphenyls (PCBs), bisphenols (BPs), and phthalates. Meta-analysis showed that PCBs exposure, not Bisphenol A (BPA) and phthalates, increased the summary odds ratio for breast cancer and endometriosis. Bioinformatics analysis of gene-EDC interactions and disease associations identified several hundred genes that were altered by exposure to PCBs, phthalate or BPA. EDCs-modified genes in breast neoplasms and endometriosis are part of steroid hormone signaling and inflammation pathways. All three EDCs–PCB 153, phthalates, and BPA influenced five common genes—CYP19A1, EGFR, ESR2, FOS, and IGF1—in breast cancer as well as in endometriosis. These genes are environmentally and estrogen responsive, altered in human breast and uterine tumors and endometriosis lesions, and part of Mitogen Activated Protein Kinase (MAPK) signaling pathways in cancer. Our findings suggest that breast cancer and endometriosis share some common environmental and molecular risk factors.

Redox signalling to nuclear regulatory proteins by reactive oxygen species contributes to oestrogen-induced growth of breast cancer cells

Background:17β-Oestradiol (E2)-induced reactive oxygen species (ROS) have been implicated in regulating the growth of breast cancer cells. However, the underlying mechanism of this is not clear. Here we show how ROS through a novel redox signalling pathway involving nuclear respiratory factor-1 (NRF-1) and p27 contribute to E2-induced growth of MCF-7 breast cancer cells.Methods:Chromatin immunoprecipitation, qPCR, mass spectrometry, redox western blot, colony formation, cell proliferation, ROS assay, and immunofluorescence microscopy were used to study the role of NRF-1.Results:The major novel finding of this study is the demonstration of oxidative modification of phosphatases PTEN and CDC25A by E2-generated ROS along with the subsequent activation of AKT and ERK pathways that culminated in the activation of NRF-1 leading to the upregulation of cell cycle genes. 17β-Oestradiol-induced ROS by influencing nuclear proteins p27 and Jab1 also contributed to the growth of MCF-7 cells.Conclusions:Taken together, our results present evidence in the support of E2-induced ROS-mediated AKT signalling leading to the activation of NRF-1-regulated cell cycle genes as well as the impairment of p27 activity, which is presumably necessary for the growth of MCF-7 cells. These observations are important because they provide a new paradigm by which oestrogen may contribute to the growth of breast cancer.

Alcohol-induced serotonergic modulation: the role of histone deacetylases.

Previous studies have demonstrated that alcohol use disorders (AUDs) are regulated by multiple mechanisms such as neurotransmitters and enzymes. The neurotransmitter, serotonin (5-hydroxytryptamine, 5-HT) may contribute to alcohol effects and serotonin receptors, including 5-HT3, play an important role in AUDs. Recent studies have also implicated histone deacetylases (HDACs) and acetyltransferases (HATS) in regulation of drug addiction, and HDAC inhibitors (HDACi) have been reported as transcriptional modulators of monoaminergic neurotransmission. Therefore, we hypothesize that HDACs may play a role in ethanol-induced serotonergic modulation. The effects of ethanol on serotonin and 5-HT3, and the role HDACs, HDAC activity and the HDACi, trichostatin A (TSA), play in alcohol-induced serotonergic effects were studied. Human SK-N-MC and neurons, were treated with ethanol (0.05, 0.1 and 0.2%), and/or TSA (50 nM), and 5-HT3 levels were assessed at 24-72 h. Gene expression was evaluated by qRT-PCR and protein by western blot and flow cytometry. Serotonin release was assessed by ELISA and HDAC activity by fluorometric assay. Our results show an increase in 5-HT3 gene after ethanol treatment. Further, ethanol significantly increased HDACs 1 and 3 genes accompanied by an increased in HDAC activity while TSA significantly inhibited HDACs. Studies with TSA show a significant upregulation of ethanol effects on 5-HT3, while surprisingly TSA inhibited ethanol-induced serotonin production. These results suggest that ethanol affects 5-HT3 and serotonin through mechanisms involving HDACs and HATs. In summary, our studies demonstrate some of the novel properties of HDAC inhibitors and contribute to the understanding of the mechanisms involve in alcohol-serotonergic modulation in the CNS.

Gene expression profile of endothelial cells exposed to estrogenic environmental compounds: Implications to pulmonary vascular lesions

AIMS The cardiovascular system is an important target of estrogenic compounds. Considering the recent studies that question previously reported cardio-protective effects of estrogen, there is a growing concern that estrogenic environmental compounds may contribute to the pathology of vascular lesion formation. MAIN METHODS Real-time quantitative PCR was used to monitor the expression of genes involved in vascularization. Using Bayesian network modeling, we determined a gene network that estrogenic chemicals modulate in human vascular endothelial cells. KEY FINDINGS We showed that planar and coplanar polychlorinated biphenyls (PCBs) induce the expression of different genes compared to estradiol. Non-planar PCB congener 153 induced NOTCH3 which is a new finding as well as CCL2 and IL8 similar to what has been reported by other non-planar PCBs in endothelial cells. Our gene network indicated that experimental treatments signal a network containing TGF-beta receptor and NOTCH3; molecules biologically relevant to signaling pulmonary vascular lesions. SIGNIFICANCE We report in the present study that exposure of vascular endothelial cells to environmentally relevant concentrations of estrogenic PCBs induce gene networks implicated in the process of inflammation and adhesion. Our data suggest that PCBs can promote vascular lesion formation by activating gene networks involved in endothelial cell adhesion, cell growth, and pro-inflammatory molecules which were different from natural estrogen. Since inflammation and adhesion are a hallmark in the pathology of endothelial cell dysfunction, reconstructing gene networks provide insight into the potential mechanisms that may contribute to the vascular risks associated with estrogenic environmental chemicals.

Estrogenic Endocrine Disrupting Chemicals Influencing NRF1 Regulated Gene Networks in the Development of Complex Human Brain Diseases

During the development of an individual from a single cell to prenatal stages to adolescence to adulthood and through the complete life span, humans are exposed to countless environmental and stochastic factors, including estrogenic endocrine disrupting chemicals. Brain cells and neural circuits are likely to be influenced by estrogenic endocrine disruptors (EEDs) because they strongly dependent on estrogens. In this review, we discuss both environmental, epidemiological, and experimental evidence on brain health with exposure to oral contraceptives, hormonal therapy, and EEDs such as bisphenol-A (BPA), polychlorinated biphenyls (PCBs), phthalates, and metalloestrogens, such as, arsenic, cadmium, and manganese. Also we discuss the brain health effects associated from exposure to EEDs including the promotion of neurodegeneration, protection against neurodegeneration, and involvement in various neurological deficits; changes in rearing behavior, locomotion, anxiety, learning difficulties, memory issues, and neuronal abnormalities. The effects of EEDs on the brain are varied during the entire life span and far-reaching with many different mechanisms. To understand endocrine disrupting chemicals mechanisms, we use bioinformatics, molecular, and epidemiologic approaches. Through those approaches, we learn how the effects of EEDs on the brain go beyond known mechanism to disrupt the circulatory and neural estrogen function and estrogen-mediated signaling. Effects on EEDs-modified estrogen and nuclear respiratory factor 1 (NRF1) signaling genes with exposure to natural estrogen, pharmacological estrogen-ethinyl estradiol, PCBs, phthalates, BPA, and metalloestrogens are presented here. Bioinformatics analysis of gene-EEDs interactions and brain disease associations identified hundreds of genes that were altered by exposure to estrogen, phthalate, PCBs, BPA or metalloestrogens. Many genes modified by EEDs are common targets of both 17 β-estradiol (E2) and NRF1. Some of these genes are involved with brain diseases, such as Alzheimer’s Disease (AD), Parkinson’s Disease, Huntington’s Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD—APOE, APP, ATP5A1, CALM1, CASP3, GSK3B, IL1B, MAPT, PSEN2 and TNF—underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1. These genes are also responsive to the following EEDs: ethinyl estradiol (APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1), BPA (APBB2, EIF2S1, ENO1, MAPT, and PAXIP1), dibutyl phthalate (DPYSL2, EIF2S1, and ENO1), diethylhexyl phthalate (DPYSL2 and MAPT). To validate findings from Comparative Toxicogenomics Database (CTD) curated data, we used Bayesian network (BN) analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely different from male human AD patients. AD-associated NRF1 target genes—APLP1, APP, GRIN1, GRIN2B, MAPT, PSEN2, PEN2, and IDE—are also regulated by E2. NRF1 regulates targets genes with diverse functions, including cell growth, apoptosis/autophagy, mitochondrial biogenesis, genomic instability, neurogenesis, neuroplasticity, synaptogenesis, and senescence. By activating or repressing the genes involved in cell proliferation, growth suppression, DNA damage/repair, apoptosis/autophagy, angiogenesis, estrogen signaling, neurogenesis, synaptogenesis, and senescence, and inducing a wide range of DNA damage, genomic instability and DNA methylation and transcriptional repression, NRF1 may act as a major regulator of EEDs-induced brain health deficits. In summary, estrogenic endocrine disrupting chemicals-modified genes in brain health deficits are part of both estrogen and NRF1 signaling pathways. Our findings suggest that in addition to estrogen signaling, EEDs influencing NRF1 regulated communities of genes across genomic and epigenomic multiple networks may contribute in the development of complex chronic human brain health disorders.

HIV infection and drugs of abuse: role of acute phase proteins

BackgroundHIV infection and drugs of abuse such as methamphetamine (METH), cocaine, and alcohol use have been identified as risk factors for triggering inflammation. Acute phase proteins such as C-reactive protein (CRP) and serum amyloid A (SAA) are the biomarkers of inflammation. Hence, the interactive effect of drugs of abuse with acute phase proteins in HIV-positive subjects was investigated.MethodsPlasma samples were utilized from 75 subjects with METH use, cocaine use, alcohol use, and HIV-positive alone and HIV-positive METH, cocaine, and alcohol users, and age-matched control subjects. The plasma CRP and SAA levels were measured by ELISA and western blot respectively and the CD4 counts were also measured.ResultsObserved results indicated that the CRP and SAA levels in HIV-positive subjects who are METH, cocaine and alcohol users were significantly higher when compared with either drugs of abuse or HIV-positive alone. The CD4 counts were also dramatically reduced in HIV-positive with drugs of abuse subjects compared with only HIV-positive subjects.ConclusionsThese results suggest that, in HIV-positive subjects, drugs of abuse increase the levels of CRP and SAA, which may impact on the HIV infection and disease progression.

Discovering gene–environment interactions in Glioblastoma through a comprehensive data integration bioinformatics method

Glioblastoma multiforme (GBM) is the most common and aggressive type of human brain tumor. Although considerable efforts to delineate the underlying pathophysiological pathways have been made during the last decades, only very limited progress on treatment have been achieved because molecular pathways that drive the aggressive nature of GBM are largely unknown. Recent studies have emphasized the importance of environmental factors and the role of gene-environment interactions (GEI) in the development of GBM. Factors such as small sample sizes and study costs have limited the conduct of GEI studies in brain tumors however. Additionally, advances in high-throughput microarrays have produced a wealth of information concerning molecular biology of glioma. In particular, microarrays have been used to obtain genetic and epigenetic changes between normal non-tumor tissue and glioma tissue. Due to the relative rarity of gliomas, microarray data for these tumors is often the product of small studies, and thus pooling this data becomes desirable. To address the challenge of small sample sizes and GEI study difficulties, we introduce a comprehensive bioinformatics method using genetic variations (copy number variations and small-scale variations) and environmental data integration that links with glioblastoma (GEG) to identify: (1) genes that interact with chemicals and have genetic variants linked to the development of GBM, (2) important pathways that may be influenced by environmental exposures (or endogenous chemicals), and (3) genes with variants in GBM that have been understudied in relation to GBM development. The first step in our GEG method identified genes responsive to environmental exposures using the Environmental Genome Project, Comparative Toxicology, and Seattle SNPs databases. These environmentally responsive genes were then compared to a curated list of genes containing copy number variation and/or mutations in GBM. This comparison produced a list of genes responsive to the environment and important to GBM that was then further analyzed using gene networking tools such as RSpider, Cytoscape, and DAVID. Using this GEG bioinformatics method we were able to identify 173 genes with the potential to be involved in GEI that may be important to the development of GBM. Sixty five of these environmentally responsive genes have not been reported as important to GBM development, despite several of them having substantial potential for response to chemicals and subsequent disease related actions. The main biological functions of these 173 genes include signaling by nerve growth factor, DNA repair, integrin cell surface interactions, biological oxidations, apoptosis, synaptic transmission, cell cycle checkpoints, and arachidonic acid metabolism. Importantly, some of these functions have been implicated in the development of several cancers, including glioma. In summary, our GEG bioinformatics approach revealed potential gene-environment interactions, and generated new data for hypothesis generation, in GBM.

HIV Subtypes B and C gp120 and Methamphetamine Interaction: Dopaminergic System Implicates Differential Neuronal Toxicity

HIV subtypes or clades differentially induce HIV-associated neurocognitive disorders (HAND) and substance abuse is known to accelerate HIV disease progression. The HIV-1 envelope protein gp120 plays a major role in binding and budding in the central nervous system (CNS) and impacts dopaminergic functions. However, the mechanisms utilized by HIV-1 clades to exert differential effects and the methamphetamine (METH)-associated dopaminergic dysfunction are poorly understood. We hypothesized that clade B and C gp120 structural sequences, modeling based analysis, dopaminergic effect, and METH potentiate neuronal toxicity in astrocytes. We evaluated the effect of clade B and C gp120 and/or METH on the DRD-2, DAT, CaMKs and CREBP transcription. Both the structural sequence and modeling studies demonstrated that clade B gp120 in V1-V4, α -2 and N-glycosylated sites are distinct from clade C gp120. The distinct structure and sequence variation of clade B gp120 differentially impact DRD-2, DAT, CaMK II and CaMK IV mRNA, protein and intracellular expression compared to clade C gp120. However, CREB transcription is upregulated by both clade B and C gp120, and METH co-treatment potentiated these effects. In conclusion, distinct structural sequences of HIV-1 clade B and C gp120 differentially regulate the dopaminergic pathway and METH potentiates neurotoxicity.