DoHwan Park

Domain landscapes of somatic mutations in cancer

BackgroundLarge-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.ResultsWe mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the “mutational landscapes” of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain “peaks” were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.ConclusionsUsing our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, themethod shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Mobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes

The purpose of this study was to assess effects of a mobile coaching system on glycated hemoglobin (HbA1c) levels in younger versus older patients over 1 year. Participants (n = 118) included adult patients with Type 2 diabetes cared for by community physicians. Intervention patients received mobile phone coaching and individualized web portal. Control patients received usual care. Patients were stratified into two age groups: younger (<55 years) and older (≥55 years). The intervention resulted in greater 12-month declines in HbA1c, compared with usual care, for patients in both age groups (p < .0001). Among older patients, HbA1c changed by −1.8% (95% confidence interval [CI] = [−2.4, −1.1]) in the intervention group and −0.3% (95% CI = [−0.9, +0.3]) in the control group. Among younger patients, HbA1c changed by −2.0% (95% CI = [−2.5, −1.5]) in the intervention group and −1.0% (95% CI = [−1.6, −0.4]) in the control group. The mobile health intervention was as effective at managing Type 2 diabetes in older adults as younger persons.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

BACKGROUND AND OBJECTIVE With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants. METHODS Disease mutations aggregated from multiple databases were mapped to domains, and were classified as either cancer- or non-cancer-related. The statistical method for identifying significantly disease-associated domain positions was applied to both sets of mutations and to randomly generated mutation sets for comparison. To leverage the known function of protein domain regions, the method optionally distributes significant scores to associated functional feature positions. RESULTS Most disease mutations are localized within protein domains and display a tendency to cluster at individual domain positions. The method identified significant disease mutation hotspots in both the cancer and non-cancer datasets. The domain significance scores (DS-scores) for cancer form a bimodal distribution with hotspots in oncogenes forming a second peak at higher DS-scores than non-cancer, and hotspots in tumor suppressors have scores more similar to non-cancers. In addition, on an independent mutation benchmarking set, the DS-score method identified mutations known to alter protein function with very high precision. CONCLUSION By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations.

BackgroundThe body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled with disease association studies. However, genomic association studies are limited by the molecular complexity of the phenotype being studied and the population size needed to have adequate statistical power. One way to circumvent this problem, which is critical for the study of rare diseases, is to study the molecular patterns emerging from functional studies of existing disease mutations. Current gene-centric analyses to study mutations in coding regions are limited by their inability to account for the functional modularity of the protein. Previous studies of the functional patterns of known human disease mutations have shown a significant tendency to cluster at protein domain positions, namely position-based domain hotspots of disease mutations. However, the limited number of known disease mutations remains the main factor hindering the advancement of mutation studies at a functional level. In this paper, we address this problem by incorporating mutations known to be disruptive of phenotypes in other species. Focusing on two evolutionarily distant organisms, human and yeast, we describe the first inter-species analysis of mutations of phenotypic relevance at the protein domain level.ResultsThe results of this analysis reveal that phenotypic mutations from yeast cluster at specific positions on protein domains, a characteristic previously revealed to be displayed by human disease mutations. We found over one hundred domain hotspots in yeast with approximately 50% in the exact same domain position as known human disease mutations.ConclusionsWe describe an analysis using protein domains as a framework for transferring functional information by studying domain hotspots in human and yeast and relating phenotypic changes in yeast to diseases in human. This first-of-a-kind study of phenotypically relevant yeast mutations in relation to human disease mutations demonstrates the utility of a multi-species analysis for advancing the understanding of the relationship between genetic mutations and phenotypic changes at the organismal level.

Frontline Science: Myeloid-derived suppressor cells (MDSCs) facilitate maternal–fetal tolerance in mice

During successful pregnancy, a woman is immunologically tolerant of her genetically and antigenically disparate fetus, a state known as maternal–fetal tolerance. How this state is maintained has puzzled investigators for more than half a century. Diverse, immune and nonimmune mechanisms have been proposed; however, these mechanisms appear to be unrelated and to act independently. A population of immune suppressive cells called myeloid‐derived suppressor cells (MDSCs) accumulates in pregnant mice and women. Given the profound immune suppressive function of MDSCs, it has been suggested that this cell population may facilitate successful pregnancy by contributing to maternal–fetal tolerance. We now report that myeloid cells with the characteristics of MDSCs not only accumulate in the circulation and uterus of female mice following mating but also suppress T cell activation and function in pregnant mice. Depletion of cells with the phenotype and function of MDSCs from gestation d 0.5 through d 7.5 resulted in implantation failure, increased T cell activation, and increased T cell infiltration into the uterus, whereas induction of MDSCs restored successful pregnancy and reduced T cell activation. MDSC‐mediated suppression during pregnancy was accompanied by the down‐regulation of L‐selectin on naïve T cells and a reduced ability of naïve T cells to enter lymph nodes and become activated. Because MDSCs regulate many of the immune and nonimmune mechanisms previously attributed to maternal–fetal tolerance, MDSCs may be a unifying mechanism promoting maternal–fetal tolerance, and their induction may facilitate successful pregnancy in women who spontaneously abort or miscarry because of dysfunctional maternal–fetal tolerance.

The histone methyltransferases Set5 and Set1 have overlapping functions in gene silencing and telomere maintenance

ABSTRACT Genes adjacent to telomeres are subject to transcriptional repression mediated by an integrated set of chromatin modifying and remodeling factors. The telomeres of Saccharomyces cerevisiae have served as a model for dissecting the function of diverse chromatin proteins in gene silencing, and their study has revealed overlapping roles for many chromatin proteins in either promoting or antagonizing gene repression. The H3K4 methyltransferase Set1, which is commonly linked to transcriptional activation, has been implicated in telomere silencing. Set5 is an H4 K5, K8, and K12 methyltransferase that functions with Set1 to promote repression at telomeres. Here, we analyzed the combined role for Set1 and Set5 in gene expression control at native yeast telomeres. Our data reveal that Set1 and Set5 promote a Sir protein-independent mechanism of repression that may primarily rely on regulation of H4K5ac and H4K8ac at telomeric regions. Furthermore, cells lacking both Set1 and Set5 have highly correlated transcriptomes to mutants in telomere maintenance pathways and display defects in telomere stability, linking their roles in silencing to protection of telomeres. Our data therefore provide insight into and clarify potential mechanisms by which Set1 contributes to telomere silencing and shed light on the function of Set5 at telomeres.

Input permutation method to detect active voxels in fMRI study

Correctly identifying voxels or regions of interest (ROI) that actively respond to a given stimulus is often an important objective/step in many functional magnetic resonance imaging (fMRI) studies. In this article, we study a nonparametric method to detect active voxels, which makes minimal assumption about the distribution of blood oxygen level-dependent (BOLD) signals. Our proposal has several interesting features. It uses time lagged correlation to take into account the delay in response to the stimulus, due to hemodynamic variations. We introduce an input permutation method (IPM), a type of block permutation method, to approximate the null distribution of the test statistic. Also, we propose to pool the permutation-derived statistics of preselected voxels for a better approximation to the null distribution. Finally, we control multiple testing error rate using the local false discovery rate (FDR) by Efron [Correlation and large-scale simultaneous hypothesis testing. J Am Stat Assoc 102 (2007) 93-103] and Park et al. [Estimation of empirical null using a mixture of normals and its use in local false discovery rate. Comput Stat Data Anal 55 (2011) 2421-2432] to select the active voxels.

Estimation of empirical null using a mixture of normals and its use in local false discovery rate

When high dimensional microarray data is given, it is of interest to select significant genes by controlling a given level of Type-I error. One popular way to control the level is the false discovery rate (FDR). This paper considers gene selection based on the local false discovery rate. In most of the previous studies, the null distribution of gene expression is commonly assumed to be a normal distribution. However, if the null distribution has heavier tail than that of normal, there may exist too many false discoveries leading to the failure of controlling the given level of FDR. We propose a novel procedure which enriches a class of null distribution based on a mixture of normals. We present simulation studies to show that our proposed procedure is less sensitive to variation of null distribution than local false discovery rate with a single normal for the null. We also provide real example of gene expression profiles of antigen-specific human CD8+ T-lymphocytes treated with cytokine Interleukin-2 (IL-2) and Interleukin-15 (IL-15) for comparison.

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are ‘gene-centric’ in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new ‘domain-centric’ method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots’ unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.

Repression of Middle Sporulation Genes in Saccharomyces cerevisiae by the Sum1-Rfm1-Hst1 Complex Is Maintained by Set1 and H3K4 Methylation

The conserved yeast histone methyltransferase Set1 targets H3 lysine 4 (H3K4) for mono, di, and trimethylation and is linked to active transcription due to the euchromatic distribution of these methyl marks and the recruitment of Set1 during transcription. However, loss of Set1 results in increased expression of multiple classes of genes, including genes adjacent to telomeres and middle sporulation genes, which are repressed under normal growth conditions because they function in meiotic progression and spore formation. The mechanisms underlying Set1-mediated gene repression are varied, and still unclear in some cases, although repression has been linked to both direct and indirect action of Set1, associated with noncoding transcription, and is often dependent on the H3K4me2 mark. We show that Set1, and particularly the H3K4me2 mark, are implicated in repression of a subset of middle sporulation genes during vegetative growth. In the absence of Set1, there is loss of the DNA-binding transcriptional regulator Sum1 and the associated histone deacetylase Hst1 from chromatin in a locus-specific manner. This is linked to increased H4K5ac at these loci and aberrant middle gene expression. These data indicate that, in addition to DNA sequence, histone modification status also contributes to proper localization of Sum1. Our results also show that the role for Set1 in middle gene expression control diverges as cells receive signals to undergo meiosis. Overall, this work dissects an unexplored role for Set1 in gene-specific repression, and provides important insights into a new mechanism associated with the control of gene expression linked to meiotic differentiation.