Paired-End Tags For Unravelling Genomic Elements And Chromantin Interactions

Ph.DDOCTOR OF PHILOSOPH

MicroRNA-138 is a Prognostic Biomarker for Triple-Negative Breast Cancer and Promotes Tumorigenesis via TUSC2 repression

Breast cancer manifests as a spectrum of subtypes with distinct molecular signatures, and different responses to treatment. Of these subtypes, triple-negative breast cancer (TNBC) has the worst prognoses and limited therapeutic options. Here we report aberrant expression of microRNA-138 (miR-138) in TNBC. Increased miR-138 expression is highly specific to this subtype, correlates with poor prognosis in patients, and is functionally relevant to cancer progression. Our findings establish miR-138 as a specific diagnostic and prognostic biomarker for TNBC. OncomiR-138 is pro-survival; sequence-specific miR-138 inhibition blocks proliferation, promotes apoptosis and inhibits tumour growth in-vivo. miR-138 directly targets a suite of pro-apoptotic and tumour suppressive genes, including tumour suppressor candidate 2 (TUSC2). miR-138 silences TUSC2 by binding to a unique 5\u27-UTR target-site, which overlaps with the translation start-site of the transcript. Over-expression of TUSC2 mimics the phenotype of miR-138 knockdown and functional rescue experiments confirm that TUSC2 is a direct downstream target of miR-138. Our report of miR-138 as an oncogenic driver in TNBC, positions it as a viable target for oligonucleotide therapeutics and we envision the potential value of using antimiR-138 as an adjuvant therapy to alleviate this therapeutically intractable cancer

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples

Multiplex sequencing of paired-end ditags (MS-PET): a strategy for the ultra-high-throughput analysis of transcriptomes and genomes

The paired-end ditagging (PET) technique has been shown to be efficient and accurate for large-scale transcriptome and genome analysis. However, as with other DNA tag-based sequencing strategies, it is constrained by the current efficiency of Sanger technology. A recently developed multiplex sequencing method (454-sequencing™) using picolitre-scale reactions has achieved a remarkable advance in efficiency, but suffers from short-read lengths, and a lack of paired-end information. To further enhance the efficiency of PET analysis and at the same time overcome the drawbacks of the new sequencing method, we coupled multiplex sequencing with paired-end ditagging (MS-PET) using modified PET procedures to simultaneously sequence 200 000 to 300 000 dimerized PET (diPET) templates, with an output of nearly half-a-million PET sequences in a single 4 h machine run. We demonstrate the utility and robustness of MS-PET by analyzing the transcriptome of human breast carcinoma cells, and by mapping p53 binding sites in the genome of human colorectal carcinoma cells. This combined sequencing strategy achieved an approximate 100-fold efficiency increase over the current standard for PET analysis, and furthermore enables the short-read-length multiplex sequencing procedure to acquire paired-end information from large DNA fragments

ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing

ChIA-PET Tool can be used to process long-range chromatin interaction data. Results are visualized on a user-friendly genome browser

The use of multiple displacement amplification to amplify complex DNA libraries

Complex libraries for genomic DNA and cDNA sequencing analyses are typically amplified using bacterial propagation. To reduce biases, large numbers of colonies are plated and scraped from solid-surface agar. This process is time consuming, tedious and limits scaling up. At the same time, multiple displacement amplification (MDA) has been recently developed as a method for in vitro amplification of DNA. However, MDA has no selection function for the removal of ligation multimers. We developed a novel method of briefly introducing ligation reactions into bacteria to select single insert DNA clones followed by MDA to amplify. We applied these methods to a Gene Identification Signatures with Paired-End diTags (GIS-PET) library, which is a complex transcriptome library created by pairing short tags from the 5′ and 3′ ends of cDNA fragments together, and demonstrated that this selection and amplification strategy is unbiased and efficient

Next-Generation Sequencing of Apoptotic DNA Breakpoints Reveals Association with Actively Transcribed Genes and Gene Translocations

DNA fragmentation is a well-recognized hallmark of apoptosis. However, the precise DNA sequences cleaved during apoptosis triggered by distinct mechanisms remain unclear. We used next-generation sequencing of DNA fragments generated in Actinomycin D-treated human HL-60 leukemic cells to generate a high-throughput, global map of apoptotic DNA breakpoints. These data highlighted that DNA breaks are non-random and show a significant association with active genes and open chromatin regions. We noted that transcription factor binding sites were also enriched within a fraction of the apoptotic breakpoints. Interestingly, extensive apoptotic cleavage was noted within genes that are frequently translocated in human cancers. We speculate that the non-random fragmentation of DNA during apoptosis may contribute to gene translocations and the development of human cancers

Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation

Higher-order chromosomal organization for transcription regulation is poorly understood in eukaryotes. Using genome-wide Chromatin Interaction Analysis with Paired-End-Tag sequencing (ChIAPET), we mapped long-range chromatin interactions associated with RNA polymerase II in human cells and uncovered widespread promoter-centered intragenic, extragenic, and intergenic interactions. These interactions further aggregated into higher-order clusters, wherein proximal and distal genes were engaged through promoter-promoter interactions. Most genes with promoter-promoter interactions were active and transcribed cooperatively, and some interacting promoters could influence each other implying combinatorial complexity of transcriptional controls. Comparative analyses of different cell lines showed that cell-specific chromatin interactions could provide structural frameworks for cell-specific transcription, and suggested significant enrichment of enhancer-promoter interactions for cell-specific functions. Furthermore, genetically-identified disease-associated noncoding elements were found to be spatially engaged with corresponding genes through long-range interactions. Overall, our study provides insights into transcription regulation by three-dimensional chromatin interactions for both housekeeping and cell-specific genes in human cells

Roles, Functions, and Mechanisms of Long Non-coding RNAs in Cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer. They are involved in chromatin remodeling, as well as transcriptional and post-transcriptional regulation, through a variety of chromatin-based mechanisms and via cross-talk with other RNA species. lncRNAs can function as decoys, scaffolds, and enhancer RNAs. This review summarizes the characteristics of lncRNAs, including their roles, functions, and working mechanisms, describes methods for identifying and annotating lncRNAs, and discusses future opportunities for lncRNA-based therapies using antisense oligonucleotides.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Published versio

MYC overexpression leads to increased chromatin interactions at superenhancers and MYC binding sites

The MYC oncogene encodes for the MYC protein and is frequently dysregulated across multiple cancer cell types, making it an attractive target for cancer therapy. MYC overexpression leads to MYC binding at active enhancers, resulting in a global transcriptional amplification of active genes. Since superenhancers are frequently dysregulated in cancer, we hypothesized that MYC preferentially invades into superenhancers and alters the cancer genome organization. To that end, we performed ChIP-seq, RNA-seq, 4C-seq and SIQHiC (Spike-in Quantitative Hi-C) on the U2OS osteosarcoma cell line with tetracycline-inducible MYC. MYC overexpression in U2OS cells modulated histone acetylation and increased MYC binding at superenhancers. SIQHiC analysis revealed increased global chromatin contact frequency, particularly at chromatin interactions connecting MYC binding sites at promoters and enhancers. Immunofluorescence staining showed that MYC molecules formed punctate foci at these transcriptionally active domains after MYC overexpression. These results demonstrate the accumulation of overexpressed MYC at promoter-enhancer hubs and suggest that MYC invades into enhancers through spatial proximity. At the same time, the increased protein-protein interactions may strengthen these chromatin interactions to increase chromatin contact frequency. CTCF siRNA knockdown in MYC overexpressed U2OS cells demonstrated that removal of architectural proteins can disperse MYC and abrogate the increase in chromatin contacts. By elucidating the chromatin landscape of MYC driven cancers, we can potentially target MYC associated chromatin interactions for cancer therapy.Ministry of Education (MOE)Accepted versionThis research is supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 grant awarded to Daniel Tenen (MOE2014-T3-1-006). This research is supported by the NRF Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative and a Singapore Ministry of Education Academic Research Fund Tier 2 grant awarded to M.J.F. (MOET2EP30120-0009)