Publications

A major unanswered question in neuroscience is whether there exists genomic variability between individual neurons of the brain, contributing to functional diversity or to an unexplained burden of neurological disease. To address this question, we developed a method to amplify genomes of single neurons from human brains. Because recent reports suggest frequent LINE-1 (L1) retrotransposition in human brains, we performed genome-wide L1 insertion profiling of 300 single neurons from cerebral cortex and caudate nucleus of three normal individuals, recovering >80% of germline insertions from single neurons. While we find somatic L1 insertions, we estimate <0.6 unique somatic insertions per neuron, and most neurons lack detectable somatic insertions, suggesting that L1 is not a major generator of neuronal diversity in cortex and caudate. We then genotyped single cortical cells to characterize the mosaicism of a somatic AKT3 mutation identified in a child with hemimegalencephaly. Single-neuron sequencing allows systematic assessment of genomic diversity in the human brain.

Transposable elements (TEs) are abundant in the human genome, and some are capable of generating new insertions through RNA intermediates. In cancer, the disruption of cellular mechanisms that normally suppress TE activity may facilitate mutagenic retrotranspositions. We performed single-nucleotide resolution analysis of TE insertions in 43 high-coverage whole-genome sequencing data sets from five cancer types. We identified 194 high-confidence somatic TE insertions, as well as thousands of polymorphic TE insertions in matched normal genomes. Somatic insertions were present in epithelial tumors but not in blood or brain cancers. Somatic L1 insertions tend to occur in genes that are commonly mutated in cancer, disrupt the expression of the target genes, and are biased toward regions of cancer-specific DNA hypomethylation, highlighting their potential impact in tumorigenesis.

Phenotypes of diseases, including prognosis, are likely to have complex etiologies and be derived from interactive mechanisms, including genetic and protein interactions. Many computational methods have been used to predict survival outcomes without explicitly identifying interactive effects, such as the genetic basis for transcriptional variations. We have therefore proposed a classification method based on the interaction between genotype and transcriptional expression features (CORE-F). This method considers the overall "genetic architecture," referring to genetically based transcriptional alterations that influence prognosis. In comparing the performance of CORE-F with the ensemble tree, the best-performing method predicting patient survival, we found that CORE-F outperformed the ensemble tree (mean AUC, 0.85 vs. 0.72). Moreover, the trained associations in the CORE-F successfully identified the genetic mechanisms underlying survival outcomes at the interaction-network level.

Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.

DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10× coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = 2.90 x 10⁻⁴) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.

Extracellular matrix (ECM) proteins are secreted to the exterior of the cell, and function as mediators between resident cells and the external environment. These proteins not only support cellular structure but also participate in diverse processes, including growth, hormonal response, homeostasis, and disease progression. Despite their importance, current knowledge of the number and functions of ECM proteins is limited. Here, we propose a computational method to predict ECM proteins. Specific features, such as ECM domain score and repetitive residues, were utilized for prediction. Based on previously employed and newly generated features, discriminatory characteristics for ECM protein categorization were determined, which significantly improved the performance of Random Forest and support vector machine (SVM) classification. We additionally predicted novel ECM proteins from non-annotated human proteins, validated with gene ontology and earlier literature. Our novel prediction method is available at biosoft.kaist.ac.kr/ecm.

Many approaches to discovering significant pathways in gene expression profiles have been developed to facilitate biological interpretation and hypothesis generation. In this work, the authors propose a pathway identification scheme integrating the activity of pathway member genes with that of target genes of transcription factors (TFs) in the same pathway by the weighted Z-method. The authors evaluated the integrative scoring scheme in gene expression profiles of essential thrombocythemia patients with JAK2V617F mutation status, primary breast tumour samples with the status of metastasis occurrence, two independent lung cancer expression profiles with their prognosis, and found that our approach identified cancer-type-specific pathways better than gene set enrichment analysis (GSEA) and Tian's method using the original pathways [pathways that have TFs from database] and the extended pathways (including target genes of TFs of the original pathways). The success of our scheme implicates that adding information of transcriptional regulation is better way of utilising mRNA measurements for estimating differential activities of pathways from gene expression profiles more exactly.

BACKGROUND: Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples. RESULTS: Through the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation. CONCLUSION: We showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.

BACKGROUND: The Janus kinase-signal transducer and activator of transcription (JAK/STAT) pathway is one of the most important targets for myeloproliferative disorder (MPD). Although several efforts toward modeling the pathway using systems biology have been successful, the pathway was not fully investigated in regard to understanding pathological context and to model receptor kinetics and mutation effects. RESULTS: We have performed modeling and simulation studies of the JAK/STAT pathway, including the kinetics of two associated receptors (the erythropoietin receptor and thrombopoietin receptor) with the wild type and a recently reported mutation (JAK2V617F) of the JAK2 protein. CONCLUSION: We found that the different kinetics of those two receptors might be important factors that affect the sensitivity of JAK/STAT signaling to the mutation effect. In addition, our simulation results support clinically observed pathological differences between the two subtypes of MPD with respect to the JAK2V617F mutation.