In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach

<p>Abstract</p> <p>Background</p> <p>The transcription factor OCT4 is highly expressed in pluripotent embryonic stem cells which are derived from the inner cell mass of mammalian blastocysts. Pluripotency and self renewal are controlled by a transcription regulatory network governed by the transcription factors OCT4, SOX2 and NANOG. Recent studies on reprogramming somatic cells to induced pluripotent stem cells highlight OCT4 as a key regulator of pluripotency.</p> <p>Results</p> <p>We have carried out an integrated analysis of high-throughput data (ChIP-on-chip and RNAi experiments along with promoter sequence analysis of putative target genes) and identified a core OCT4 regulatory network in human embryonic stem cells consisting of 33 target genes. Enrichment analysis with these target genes revealed that this integrative analysis increases the functional information content by factors of 1.3 – 4.7 compared to the individual studies. In order to identify potential regulatory co-factors of OCT4, we performed a <it>de novo </it>motif analysis. In addition to known validated OCT4 motifs we obtained binding sites similar to motifs recognized by further regulators of pluripotency and development; e.g. the heterodimer of the transcription factors C-MYC and MAX, a prerequisite for C-MYC transcriptional activity that leads to cell growth and proliferation.</p> <p>Conclusion</p> <p>Our analysis shows how heterogeneous functional information can be integrated in order to reconstruct gene regulatory networks. As a test case we identified a core OCT4-regulated network that is important for the analysis of stem cell characteristics and cellular differentiation. Functional information is largely enriched using different experimental results. The <it>de novo </it>motif discovery identified well-known regulators closely connected to the OCT4 network as well as potential new regulators of pluripotency and differentiation. These results provide the basis for further targeted functional studies.</p

An accessible GenePattern notebook for the copy number variation analysis of Illumina Infinium DNA methylation arrays [version 1; peer review: 2 approved]

Illumina Infinium DNA methylation arrays are a cost-effective technology to measure DNA methylation at CpG sites genome-wide and across cohorts of normal and cancer samples. While copy number alterations are commonly inferred from array-CGH, SNP arrays, or whole-genome DNA sequencing, Illumina Infinium DNA methylation arrays have been shown to detect copy number alterations at comparable sensitivity. Here we present an accessible, interactive GenePattern notebook for the analysis of copy number variation using Illumina Infinium DNA methylation arrays. The notebook provides a graphical user interface to a workflow using the R/Bioconductor packages minfi and conumee. The environment allows analysis to be performed without the installation of the R software environment, the packages and dependencies, and without the need to write or manipulate code

Kdm6a deficiency restricted to mouse hematopoietic cells causes an age- and sex-dependent myelodysplastic syndrome-like phenotype

Kdm6a/Utx, a gene on the X chromosome, encodes a histone H3K27me3 demethylase that has an orthologue on the Y chromosome (Uty) (Zheng et al. 2018). We previously identified inactivating mutations of Kdm6a in approximately 50% of mouse acute promyelocytic leukemia samples; however, somatic mutations of KDM6A are more rare in human AML samples, ranging in frequency from 2-15% in different series of patients, where their role in pathogenesis is not yet clear. In this study, we show that female Kdm6aflox/flox mice (with allele inactivation initiated by Vav1-Cre in hematopoietic stem and progenitor cells (HSPCs) have a sex-specific phenotype that emerges with aging, with features resembling a myelodysplastic syndrome (MDS). Female Kdm6a-knockout (KO) mice have an age-dependent expansion of their HSPCs with aberrant self-renewal, but they did not differentiate normally into downstream progeny. These mice became mildly anemic and thrombocytopenic, but did not develop overt leukemia, or die from these cytopenias. ChIP-seq and ATAC-seq studies showed only minor changes in H3K27me3, H3K27ac, H3K4me, H3K4me3 and chromatin accessibility between Kdm6a-WT and Kdm6a-KO mice. Utilizing scRNA-seq, Kdm6a loss was linked to the transcriptional repression of genes that mediate hematopoietic cell fate determination. These data demonstrate that Kdm6a plays an important role in normal hematopoiesis, and that its inactivation may contribute to AML pathogenesis

Simultaneous deletion of Tet1 and Tet3 increases transcriptome variability in early embryogenesis

Dioxygenases of the TET (Ten-Eleven Translocation) family produce oxidized methylcytosines, intermediates in DNA demethylation, as well as new epigenetic marks. Here we show data suggesting that TET proteins maintain the consistency of gene transcription. Embryos lacking Tet1 and Tet3 (Tet1/3 DKO) displayed a strong loss of 5-hydroxymethylcytosine (5hmC) and a concurrent increase in 5-methylcytosine (5mC) at the eight-cell stage. Single cells from eight-cell embryos and individual embryonic day 3.5 blastocysts showed unexpectedly variable gene expression compared with controls, and this variability correlated in blastocysts with variably increased 5mC/5hmC in gene bodies and repetitive elements. Despite the variability, genes encoding regulators of cholesterol biosynthesis were reproducibly down-regulated in Tet1/3 DKO blastocysts, resulting in a characteristic phenotype of holoprosencephaly in the few embryos that survived to later stages. Thus, TET enzymes and DNA cytosine modifications could directly or indirectly modulate transcriptional noise, resulting in the selective susceptibility of certain intracellular pathways to regulation by TET proteins.J.K. was supported by a postdoctoral fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. W.A.P. was supported by the National Science Foundation predoctoral graduate research fellowship while this work was being performed, and subsequently by a postdoctoral fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. L.C. was the recipient of a Feodor-Lynen fellowship from the Alexander von Humboldt foundation. M.L. is supported by the Max Planck Society within its International Max Planck Research School for Computational Biology and Scientific Computing program (IMPRS-CBSC). A.T. was the recipient of an Irvington postdoctoral fellowship from the Cancer Research Institute. This work was supported by NIH R01 Grants AI044432 and HD065812 (to A.R.) and a Director’s New Innovator Award (DP2-OD-008646-01) (to S.K.).This is the author accepted manuscript. The final version is available from The National Academy of Sciences via http://dx.doi.org/10.1073/pnas.151051011

Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease

Inhibition of dual-specificity tyrosine phosphorylation-regulated kinase 2 perturbs 26S proteasome-addicted neoplastic progression

Dependence on the 26S proteasome is an Achilles' heel for triple-negative breast cancer (TNBC) and multiple myeloma (MM). The therapeutic proteasome inhibitor, bortezomib, successfully targets MM but often leads to drug-resistant disease relapse and fails in breast cancer. Here we show that a 26S proteasome-regulating kinase, DYRK2, is a therapeutic target for both MM and TNBC. Genome editing or small-molecule mediated inhibition of DYRK2 significantly reduces 26S proteasome activity, bypasses bortezomib resistance, and dramatically delays in vivo tumor growth in MM and TNBC thereby promoting survival. We further characterized the ability of LDN192960, a potent and selective DYRK2-inhibitor, to alleviate tumor burden in vivo. The drug docks into the active site of DYRK2 and partially inhibits all 3 core peptidase activities of the proteasome. Our results suggest that targeting 26S proteasome regulators will pave the way for therapeutic strategies in MM and TNBC

TET proteins regulate the lineage specification and TCR-mediated expansion of iNKT cells

TET proteins oxidize 5-methylcytosine in DNA to 5-hydroxymethylcytosine and other oxidation products. We found that simultaneous deletion of Tet2 and Tet3 in mouse CD4+CD8+ double-positive thymocytes resulted in dysregulated development and proliferation of invariant natural killer T cells (iNKT cells). Tet2-Tet3 double-knockout (DKO) iNKT cells displayed pronounced skewing toward the NKT17 lineage, with increased DNA methylation and impaired expression of genes encoding the key lineage-specifying factors T-bet and ThPOK. Transfer of purified Tet2-Tet3 DKO iNKT cells into immunocompetent recipient mice resulted in an uncontrolled expansion that was dependent on the nonclassical major histocompatibility complex (MHC) protein CD1d, which presents lipid antigens to iNKT cells. Our data indicate that TET proteins regulate iNKT cell fate by ensuring their proper development and maturation and by suppressing aberrant proliferation mediated by the T cell antigen receptor (TCR)

Second-generation molecular subgrouping of medulloblastoma: an international meta-analysis of Group 3 and Group 4 subtypes

In 2012, an international consensus paper reported that medulloblastoma comprises four molecular subgroups (WNT, SHH, Group 3, and Group 4), each associated with distinct genomic features and clinical behavior. Independently, multiple recent reports have defined further intra-subgroup heterogeneity in the form of biologically and clinically relevant subtypes. However, owing to differences in patient cohorts and analytical methods, estimates of subtype number and definition have been inconsistent, especially within Group 3 and Group 4. Herein, we aimed to reconcile the definition of Group 3/Group 4 MB subtypes through the analysis of a series of 1501 medulloblastomas with DNA-methylation profiling data, including 852 with matched transcriptome data. Using multiple complementary bioinformatic approaches, we compared the concordance of subtype calls between published cohorts and analytical methods, including assessments of class-definition confidence and reproducibility. While the lowest complexity solutions continued to support the original consensus subgroups of Group 3 and Group 4, our analysis most strongly supported a definition comprising eight robust Group 3/Group 4 subtypes (types I–VIII). Subtype II was consistently identified across all component studies, while all others were supported by multiple class-definition methods. Regardless of analytical technique, increasing cohort size did not further increase the number of identified Group 3/Group 4 subtypes. Summarizing the molecular and clinico-pathological features of these eight subtypes indicated enrichment of specific driver gene alterations and cytogenetic events amongst subtypes, and identified highly disparate survival outcomes, further supporting their biological and clinical relevance. Collectively, this study provides continued support for consensus Groups 3 and 4 while enabling robust derivation of, and categorical accounting for, the extensive intertumoral heterogeneity within Groups 3 and 4, revealed by recent high-resolution subclassification approaches. Furthermore, these findings provide a basis for application of emerging methods (e.g., proteomics/single-cell approaches) which may additionally inform medulloblastoma subclassification. Outputs from this study will help shape definition of the next generation of medulloblastoma clinical protocols and facilitate the application of enhanced molecularly guided risk stratification to improve outcomes and quality of life for patients and their families

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology