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SETDB1 is a key regulator of lineage-specific genes and endogenous retroviral elements (ERVs) through its deposition of repressive H3K9me3 mark. Apart from its H3K9me3 regulatory role, SETDB1 has seldom been studied in terms of its other potential regulatory roles. To investigate this, a genomic survey of SETDB1 binding in mouse embryonic stem cells across multiple libraries was conducted, leading to the unexpected discovery of regions bereft of common repressive histone marks (H3K9me3, H3K27me3). These regions were enriched with the CTCF motif that is often associated with the topological regulator Cohesin. Further profiling of these non-H3K9me3 regions led to the discovery of a cluster of non-repeat loci that were co-bound by SETDB1 and Cohesin. These regions, which we named DiSCs (Domains involving SETDB1 and Cohesin) were seen to be proximal to the gene promoters involved in embryonic stem cell pluripotency and lineage development. Importantly, it was found that SETDB1-Cohesin co-regulate target gene expression and genome topology at these DiSCs. Depletion of SETDB1 led to localized dysregulation of Cohesin binding thereby locally disrupting topological structures. Dysregulated gene expression trends revealed the importance of this cluster in ES cell maintenance as well as at gene ‘islands’ that drive differentiation to other lineages. The ‘unearthing’ of the DiSCs thus unravels a unique topological and transcriptional axis of control regulated chiefly by SETDB1.

SETDB1 is a key regulator of lineage-specific genes and endogenous retroviral elements (ERVs) through its deposition of repressive H3K9me3 mark. Apart from its H3K9me3 regulatory role, SETDB1 has seldom been studied in terms of its other potential regulatory roles. To investigate this, a genomic survey of SETDB1 binding in mouse embryonic stem cells across multiple libraries was conducted, leading to the unexpected discovery of regions bereft of common repressive histone marks (H3K9me3, H3K27me3). These regions were enriched with the CTCF motif that is often associated with the topological regulator Cohesin. Further profiling of these non-H3K9me3 regions led to the discovery of a cluster of non-repeat loci that were co-bound by SETDB1 and Cohesin. These regions, which we named DiSCs (domains involving SETDB1 and Cohesin) were seen to be proximal to the gene promoters involved in embryonic stem cell pluripotency and lineage development. Importantly, it was found that SETDB1-Cohesin co-regulate target gene expression and genome topology at these DiSCs. Depletion of SETDB1 led to localized dysregulation of Cohesin binding thereby locally disrupting topological structures. Dysregulated gene expression trends revealed the importance of this cluster in ES cell maintenance as well as at gene ‘islands’ that drive differentiation to other lineages. The ‘unearthing’ of the DiSCs thus unravels a unique topological and transcriptional axis of control regulated chiefly by SETDB1.

The molecular cues for CENPA positioning in epigenetically regulated centromeres is elusive. Candida albicans has small regional non-repetitive centromeres that do not harbor conventional heterochromatin. Deletion of native centromere leads to activation of a neocentromere... The diploid budding yeast Candida albicans harbors unique CENPA-rich 3- to 5-kb regions that form the centromere (CEN) core on each of its eight chromosomes. The epigenetic nature of these CENs does not permit the stabilization of a functional kinetochore on an exogenously introduced CEN plasmid. The flexible nature of such centromeric chromatin is exemplified by the reversible silencing of a transgene upon its integration into the CENPA-bound region. The lack of a conventional heterochromatin machinery and the absence of defined boundaries of CENPA chromatin makes the process of CEN specification in this organism elusive. Additionally, upon native CEN deletion, C. albicans can efficiently activate neocentromeres proximal to the native CEN locus, hinting at the importance of CEN-proximal regions. In this study, we examine this CEN-proximity effect and identify factors for CEN specification in C. albicans. We exploit a counterselection assay to isolate cells that can silence a transgene when integrated into the CEN-flanking regions. We show that the frequency of reversible silencing of the transgene decreases from the central core of CEN7 to its peripheral regions. Using publicly available C. albicans high-throughput chromosome conformation capture data, we identify a 25-kb region centering on the CENPA-bound core that acts as CEN-flanking compact chromatin (CFCC). Cis- and trans-chromosomal interactions associated with the CFCC spatially segregates it from bulk chromatin. We further show that neocentromere activation on chromosome 7 occurs within this specified region. Hence, this study identifies a specialized CEN-proximal domain that specifies and restricts the centromeric activity to a unique region.

Centromeres of Candida albicans form on unique and different DNA sequences but a closely related species, Candida tropicalis, possesses homogenized inverted repeat (HIR)-associated centromeres. To investigate the mechanism of centromere type transition, we improved the fragmented genome assembly and constructed a chromosome-level genome assembly of C. tropicalis by employing PacBio sequencing, chromosome conformation capture sequencing (3C-seq), chromoblot, and genetic analysis of engineered aneuploid strains. Further, we analyzed the 3D genome organization using 3C-seq data, which revealed spatial proximity among the centromeres as well as telomeres of seven chromosomes in C. tropicalis. Intriguingly, we observed evidence of inter-centromeric translocations in the common ancestor of C. albicans and C. tropicalis. Identification of putative centromeres in closely related Candida sojae, Candida viswanathii and Candida parapsilosis indicates loss of ancestral HIR-associated centromeres and establishment of evolutionary new centromeres (ENCs) in C. albicans. We propose that spatial proximity of the homologous centromere DNA sequences facilitated karyotype rearrangements and centromere type transitions in human pathogenic yeasts of the CUG-Ser1 clade. Ministry of Education (MOE) Nanyang Technological University Published version We thank all the members of KS laboratory and AS laboratory for stimulating discussions and critical reading of the manuscript. We acknowledge S Sun and J Heitman for helping with SMRT-seq of C. tropicalis at the PacBio sequencing facility at Duke University. We also thank AIS Khalil for helping with CNAtra software for CNV analysis. Illumina sequencing experiments for the C. sojae genome were performed at Clevergene Biocorp, Bangalore, India. We also thank B Suma for confocal microscopy, JNCASR. KG acknowledges Shyama Prasad Mukherjee Fellowship from Council of Scientific and Industrial Research (CSIR), Govt. of India [07/733 (0181)/2013-EMR-I] and financial assistance from JNCASR. This project is supported by a grant (BT/PR27490/Med/29/1323/2018) from the Department of Biotechnology (DBT), Govt. of India to KS. KS acknowledges TATA innovation fellowship (BT/HRD/35/01/03/2017) and Department of Biotechnology grant in Life Science Research, Education and Training at JNCASR (BT/INF/22/SP27679/2018). Intramural funding from JNCASR is acknowledged. This work is also supported by Nanyang Technological University’s Nanyang Assistant Professorship grant and Singapore Ministry of Education Academic Research Fund Tier 1 grant [RG39/18] to AS.

Background The widespread concern about genetic drift and cross-contamination of cell lines calls for a pressing need for their authentication. The current genetic techniques for authentication are time-consuming and require specific documentary standard and laboratory protocols. Given the fact that whole-genome sequencing (WGS) data are readily available, read depth (RD)-based computational analyses has allowed the estimation of genetic profiles of cell lines. Results We propose WGS-derived aneuploidy profiling as a prototype of digital karyotyping for authentication of cancer cell lines. Here, we describe a Python-based software AStra for de novo estimation of the genome-wide aneuploidy profile, the copy number of every genomic loci, from raw WGS reads. We demonstrated that aneuploidy profile offers a unique signature that can distinguish the clonal variants (strains) of a cell line. We evaluated our approach using simulated data and variety of cancer cell lines. We further showed that cell lines exhibit distinct aneuploidy patterns which corroborate well with the experimental observations. Conclusions AStra is a simple, user-friendly, and free tool that provides the elementary information about the chromosomal aneuploidy for cell line authentication. AStra provides an analytical and visualization platform for rapid and easy comparison between different cell lines/strains. We recommend AStra for rapid first-pass quality assessment of scientific data that employ cancer cell lines. AStra is an open source software and is available at https://github.com/AISKhalil/AStra.

Background: Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map. Results: We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel ‘entire-fragment’ counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD’s explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias. Conclusions: HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra. Ministry of Education (MOE) Nanyang Technological University Published version This work was supported by the Nanyang Technological University’s Nanyang Assistant Professorship grant and Singapore Ministry of Education Academic Research Fund Tier 1 grants (RG46/16 and RG39/18) to AS. AC is supported by the Nanyang Technological University start-up grant. The funding bodies were not involved in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

MotivationHyperploidy and segmental aneuploidy are hallmarks of cancer cells due to chromosome segregation errors and genomic instability. In such situations, accurate aneuploidy profiling of cancer data is critical for calibration of copy number (CN)-detection tools. Additionally, cancer cell populations suffer from different levels of clonal heterogeneity and aneuploidy alterations over time. The degree of heterogeneity adversely affects the segregation of the depth of coverage (DOC) signal into integral CN states. This, in turn, strongly influences the reliability of this data for ploidy profiling and copy number variation (CNV) analysis.ResultsWe developed AStra framework for aneuploidy profiling of cancer data and assessing their suitability for copy number analysis without any prior knowledge of the input sequencing data. AStra estimates the best-fit aneuploidy profile as the spectrum with most genomic segments around integral CN states. We employ this spectrum to extract the CN-associated features such as the homogeneity score (HS), whole-genome ploidy level, and CN correction factor. The HS measures the percentage of genomic regions around CN states. It is used as a reliability assessment of sequencing data for downstream aneuploidy profiling and CNV analysis. We evaluated the accuracy of AStra using 31 low-coverage datasets from 20 cancer cell lines. AStra successfully identified the aneuploidy spectrum of complex cell lines with HS greater than 75%. Benchmarking against nQuire tool showed that AStra is superior in detecting the ploidy level using both low- and high-coverage data. Furthermore, AStra accurately estimated the ploidy of 26/27 strains of MCF7 (hyperploid) cell line which exhibit varied levels of aneuploidy spectrum and heterogeneity. Remarkably, we found that HS is strongly correlated with the doubling time of these strains.Availability and implementationAStra is an open source software implemented in Python and is available at https://github.com/AISKhalil/AStra