Matthias Kormaksson

methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation,cellular specification and cancer development. Here, we describe an R package, methylKit, thatrapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation andhydroxymethylation sequencing experiments. methylKit includes functions for clustering, samplequality visualization, differential methylation analysis and annotation features, thus automatingand simplifying many of the steps for discerning statistically significant bases or regions of DNAmethylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statisticallysignificant regions of differential methylation and stratify tumor subtypes. methylKit is availableat http://code.google.com/p/methylkit.

Base-pair resolution DNA methylation sequencing reveals profoundly divergent epigenetic landscapes in acute myeloid leukemia

We have developed an enhanced form of reduced representation bisulfite sequencing with extended genomic coverage, which resulted in greater capture of DNA methylation information of regions lying outside of traditional CpG islands. Applying this method to primary human bone marrow specimens from patients with Acute Myelogeneous Leukemia (AML), we demonstrated that genetically distinct AML subtypes display diametrically opposed DNA methylation patterns. As compared to normal controls, we observed widespread hypermethylation in IDH mutant AMLs, preferentially targeting promoter regions and CpG islands neighboring the transcription start sites of genes. In contrast, AMLs harboring translocations affecting the MLL gene displayed extensive loss of methylation of an almost mutually exclusive set of CpGs, which instead affected introns and distal intergenic CpG islands and shores. When analyzed in conjunction with gene expression profiles, it became apparent that these specific patterns of DNA methylation result in differing roles in gene expression regulation. However, despite this subtype-specific DNA methylation patterning, a much smaller set of CpG sites are consistently affected in both AML subtypes. Most CpG sites in this common core of aberrantly methylated CpGs were hypermethylated in both AML subtypes. Therefore, aberrant DNA methylation patterns in AML do not occur in a stereotypical manner but rather are highly specific and associated with specific driving genetic lesions.

Mechanism-Based Epigenetic Chemosensitization Therapy of Diffuse Large B-Cell Lymphoma

UNLABELLED Although aberrant DNA methylation patterning is a hallmark of cancer, the relevance of targeting DNA methyltransferases (DNMT) remains unclear for most tumors. In diffuse large B-cell lymphoma (DLBCL) we observed that chemoresistance is associated with aberrant DNA methylation programming. Prolonged exposure to low-dose DNMT inhibitors (DNMTI) reprogrammed chemoresistant cells to become doxorubicin sensitive without major toxicity in vivo. Nine genes were recurrently hypermethylated in chemoresistant DLBCL. Of these, SMAD1 was a critical contributor, and reactivation was required for chemosensitization. A phase I clinical study was conducted evaluating azacitidine priming followed by standard chemoimmunotherapy in high-risk patients newly diagnosed with DLBCL. The combination was well tolerated and yielded a high rate of complete remission. Pre- and post-azacitidine treatment biopsies confirmed SMAD1 demethylation and chemosensitization, delineating a personalized strategy for the clinical use of DNMTIs. SIGNIFICANCE The problem of chemoresistant DLBCL remains the most urgent challenge in the clinical management of patients with this disease. We describe a mechanism-based approach toward the rational translation of DNMTIs for the treatment of high-risk DLBCL.

DNA methyltransferase 1 and DNA methylation patterning contribute to germinal center B-cell differentiation.

The phenotype of germinal center (GC) B cells includes the unique ability to tolerate rapid proliferation and the mutagenic actions of activation induced cytosine deaminase (AICDA). Given the importance of epigenetic patterning in determining cellular phenotypes, we examined DNA methylation and the role of DNA methyltransferases in the formation of GCs. DNA methylation profiling revealed a marked shift in DNA methylation patterning in GC B cells versus resting/naive B cells. This shift included significant differential methylation of 235 genes, with concordant inverse changes in gene expression affecting most notably genes of the NFkB and MAP kinase signaling pathways. GC B cells were predominantly hypomethylated compared with naive B cells and AICDA binding sites were highly overrepresented among hypomethylated loci. GC B cells also exhibited greater DNA methylation heterogeneity than naive B cells. Among DNA methyltransferases (DNMTs), only DNMT1 was significantly up-regulated in GC B cells. Dnmt1 hypomorphic mice displayed deficient GC formation and treatment of mice with the DNA methyltransferase inhibitor decitabine resulted in failure to form GCs after immune stimulation. Notably, the GC B cells of Dnmt1 hypomorphic animals showed evidence of increased DNA damage, suggesting dual roles for DNMT1 in DNA methylation and double strand DNA break repair.

Aberration in DNA methylation in B-cell lymphomas has a complex origin and increases with disease severity.

Despite mounting evidence that epigenetic abnormalities play a key role in cancer biology, their contributions to the malignant phenotype remain poorly understood. Here we studied genome-wide DNA methylation in normal B-cell populations and subtypes of B-cell non-Hodgkin lymphoma: follicular lymphoma and diffuse large B-cell lymphomas. These lymphomas display striking and progressive intra-tumor heterogeneity and also inter-patient heterogeneity in their cytosine methylation patterns. Epigenetic heterogeneity is initiated in normal germinal center B-cells, increases markedly with disease aggressiveness, and is associated with unfavorable clinical outcome. Moreover, patterns of abnormal methylation vary depending upon chromosomal regions, gene density and the status of neighboring genes. DNA methylation abnormalities arise via two distinct processes: i) lymphomagenic transcriptional regulators perturb promoter DNA methylation in a target gene-specific manner, and ii) aberrant epigenetic states tend to spread to neighboring promoters in the absence of CTCF insulator binding sites.

Variability in DNA methylation defines novel epigenetic subgroups of DLBCL associated with different clinical outcomes

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive form of non-Hodgkin lymphoma with variable biology and clinical behavior. The current classification does not fully explain the biological and clinical heterogeneity of DLBCLs. In this study, we carried out genomewide DNA methylation profiling of 140 DLBCL samples and 10 normal germinal center B cells using the HpaII tiny fragment enrichment by ligation-mediated polymerase chain reaction assay and hybridization to a custom Roche NimbleGen promoter array. We defined methylation disruption as a main epigenetic event in DLBCLs and designed a method for measuring the methylation variability of individual cases. We then used a novel approach for unsupervised hierarchical clustering based on the extent of DNA methylation variability. This approach identified 6 clusters (A-F). The extent of methylation variability was associated with survival outcomes, with significant differences in overall and progression-free survival. The novel clusters are characterized by disruption of specific biological pathways such as cytokine-mediated signaling, ephrin signaling, and pathways associated with apoptosis and cell-cycle regulation. In a subset of patients, we profiled gene expression and genomic variation to investigate their interplay with methylation changes. This study is the first to identify novel epigenetic clusters of DLBCLs and their aberrantly methylated genes, molecular associations, and survival.

Effects of inbreeding and genetic modification on Aedes aegypti larval competition and adult energy reserves

BackgroundGenetic modification of mosquitoes offers a promising strategy for the prevention and control of mosquito-borne diseases. For such a strategy to be effective, it is critically important that engineered strains are competitive enough to serve their intended function in population replacement or reduction of wild mosquitoes in nature. Thus far, fitness evaluations of genetically modified strains have not addressed the effects of competition among the aquatic stages and its consequences for adult fitness. We therefore tested the competitive success of combinations of wild, inbred and transgenic (created in the inbred background) immature stages of the dengue vector Aedes aegypti in the presence of optimal and sub-optimal larval diets.ResultsThe wild strain of Ae. aegypti demonstrated greater performance (based on a composite index of survival, development rate and size) than the inbred strain, which in turn demonstrated greater performance than the genetically modified strain. Moreover, increasing competition through lowering the amount of diet available per larva affected fitness disproportionately: transgenic larvae had a reduced index of performance (95-119%) compared to inbred (50-88%) and wild type larvae (38-54%). In terms of teneral energy reserves (glycogen, lipid and sugar), adult wild type mosquitoes had more reserves directly available for flight, dispersal and basic metabolic functions than transgenic and inbred mosquitoes.ConclusionsOur study provides a detailed assessment of inter- and intra-strain competition across aquatic stages of wild type, inbred, and transgenic mosquitoes and the impact of these conditions on adult energy reserves. Although it is not clear what competitive level is adequate for success of transgenic strains in nature, strong gene drive mechanisms are likely to be necessary in order to overcome competitive disadvantages in the larval stage that carryover to affect adult fitness.

Integrative Model-based clustering of microarray methylation and expression data

In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.

Bus Travel Time Predictions Using Additive Models

Many factors can affect the predictability of public bus services such as traffic, weather, day of week, and hour of day. However, the exact nature of such relationships between travel times and predictor variables is, in most situations, not known. In this paper we develop a framework that allows for flexible modeling of bus travel times through the use of Additive Models. The proposed class of models provides a principled statistical framework that is highly flexible in terms of model building. The experimental results demonstrate uniformly superior performance of our best model as compared to previous prediction methods when applied to a very large GPS data set obtained from buses operating in the city of Rio de Janeiro.

USapiens: A System for Urban Trajectory Data Analytics

In the past few years a growing number of cities have started monitoring the position of public transportation vehicles using GPS devices. In this paper, we focus on a particularly important urban dataset: GPS bus data. Buses are valuable sensors and information associated with buses can provide unprecedented insight into many different aspects of citys life, from human behavior to mobility patterns. But analyzing these large urban datasets presents many challenges. Urban datasets are complex, containing location and temporal components in addition that they are commonly released in their raw format. Furthermore, urban datasets may have noisy and missing data, locations gathered in a low sampling rate and not mapped to the underlying road network, among other issues which makes it difficult for citizens, administrators and developers to get insights. In this paper, we present a system, called USapiens, for analyzing large urban trajectory data. We first describe the architecture of the proposed system for pre-processing and analyzing urban trajectory data. We then detail five use cases we build using very large GPS dataset obtained from buses operating in the city of Rio de Janeiro to get insights into various aspects of public transportation in the city.