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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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the National Research Foundation (NRF) Singapore through an NRF Fellowship awarded to M.J.F (NRF-NRFF2012-054) and NTU start-up funds awarded to M.J.F. This 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 awarded to Daniel Tenen as lead PI with M.J.F as co-investigator (MOE2014-T3-1-006). This research is supported by a National Research Foundation Competitive Research Programme grant awarded to V.T. as lead PI and M.J.F. as co-PI (NRF-CRP17-2017-02). This research is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative. This research is supported by a Ministry of Education Tier II grant awarded to M.J.F (T2EP30120-0020).

Recent progress on murine and human cardiac organoids have provided understanding to the developmental processes of the heart. However, there is still an unfulfilled need for improved modelling of cardiovascular diseases using human cardiac organoids. Herein, we report successful generation of intrinsically formed human chambered cardiac organoids (CCO) and highlight its utility in modelling disease. Single cell transcriptomic profiling of CCOs showed appropriate cardiovascular cell type composition exhibiting improved maturation. Functionally, CCOs recapitulated clinical cardiac hypertrophy by exhibiting thickened chamber walls, reduced ejection fractions, increased myofibrillar disarray and tachycardia. Therefore, CCOs improve current capabilities of disease modelling as an in vitro model bridging the gap to in vivo models, with the ability to assess functional parameters that previously can only be achieved in animal systems.

The mechanisms underlying gene repression and silencers are poorly understood. Here we investigate the hypothesis that H3K27me3-rich regions of the genome, defined from clusters of H3K27me3 peaks, may be used to identify silencers that can regulate gene expression via proximity or looping. We find that H3K27me3-rich regions are associated with chromatin interactions and interact preferentially with each other. H3K27me3-rich regions component removal at interaction anchors by CRISPR leads to upregulation of interacting target genes, altered H3K27me3 and H3K27ac levels at interacting regions, and altered chromatin interactions. Chromatin interactions did not change at regions with high H3K27me3, but regions with low H3K27me3 and high H3K27ac levels showed changes in chromatin interactions. Cells with H3K27me3-rich regions knockout also show changes in phenotype associated with cell identity, and altered xenograft tumor growth. Finally, we observe that H3K27me3-rich regions-associated genes and long-range chromatin interactions are susceptible to H3K27me3 depletion. Our results characterize H3K27me3-rich regions and their mechanisms of functioning via looping., Mechanisms underlying gene repression and silencers remain poorly understood. Here the authors investigate the role of H3K27me3-rich regions in the genome, as defined from clusters of H3K27me3 peaks, in regulating gene expression via looping.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is very limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (CHINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. CHINN is able to predict CTCF- and RNA polymerase II-associated chromatin interactions between open chromatin regions. CHINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. We applied CHINN to 84 chronic lymphocytic leukemia (CLL) samples and detected systematic differences in the chromatin interactome between IGVH-mutated and IGVH-unmutated CLL samples.

The repression of telomerase activity during cellular differentiation promotes replicative aging and functions as a physiological barrier for tumorigenesis in long-lived mammals, including humans. However, the underlying mechanisms remain largely unclear. Here we describe how miR-615-3p represses hTERT expression. mir-615-3p is located in an intron of the HOXC5 gene, a member of the highly conserved homeobox family of transcription factors controlling embryogenesis and development. Unexpectedly, we found that HoxC5 also represses hTERT expression by disrupting the long-range interaction between hTERT promoter and its distal enhancer. The 3′UTR of hTERT and its upstream enhancer region are well conserved in long-lived primates. Both mir-615-3p and HOXC5 are activated upon differentiation, which constitute a feed-forward loop that coordinates transcriptional and post-transcriptional repression of hTERT during cellular differentiation. Deregulation of HOXC5 and mir-615-3p expression may contribute to the activation of hTERT in human cancers., The expression of telomerase catalytic subunit hTERT is frequently upregulated in many cancers. Here, the authors show HoxC5 and miR-615-3p can negatively regulate hTERT to impede tumorigenesis by targeting the newly evolved cis-regulatory genomic elements of hTERT.