Biology subjects

Kukalev, A.

Publications and source records attributed to Kukalev, A..

4 recordsLinked to original sources

bioRxiv2020

Multiplex-GAM: genome-wide identification of chromatin contacts yields insights not captured by Hi-C

Technologies for measuring 3D genome topology are increasingly important for studying mechanisms of gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of Genome Architecture Mapping (GAM), a ligation-free technique to map chromatin contacts genome-wide. We perform a detailed comparison of contacts obtained by multiplex-GAM and Hi-C using mouse embryonic stem (mES) cells. We find that both methods detect similar topologically associating domains (TADs). However, when examining the strongest contacts detected by either method, we find that only one third of these are shared. The strongest contacts specifically found in GAM often involve "active" regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain "inactive" regions. Our work shows that active genomic regions are involved in extensive complex contacts that currently go under-estimated in genome-wide ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

molecular biology↗

bioRxiv2020

Comparison of the Hi-C, GAM and SPRITE methods by use of polymer models of chromatin

Powerful technologies have been developed to probe chromatin 3D physical interactions genome-wide, such as Hi-C, GAM and SPRITE. Due to their intrinsic differences and without a benchmarking reference, it is currently difficult to assess how well each method represents the genome 3D structure and their relative performance. Here, we develop a computational approach to implement Hi-C, GAM and SPRITE in-silico to compare the three methods in a simplified, yet controlled framework against known polymer 3D structures. We test our approach on models of three 6-Mb genomic regions, around the Sox9 and the HoxD genes in mouse ES cells, and around the Epha4 gene in mouse CHLX-12 cells. The model-derived contact matrices consistently match Hi-C, GAM and SPRITE experiments. We show that in-silico Hi-C, GAM and SPRITE average data are overall faithful to the 3D structures of the polymer models. We find that the inherent variability of model single-molecule 3D conformations and experimental efficiency differently affect the contact data of the different methods. Similarly, the noise-to-signal levels vary with genomic distance differently in in-silico Hi-C, SPRITE and GAM. We benchmark the performance of each technology in bulk and in single-cell experiments, and identify the minimal number of cells required for replicates to return statistically consistent chromatin contact measures. Under the same experimental conditions, SPRITE requires the lowest number of cells, Hi-C is close to SPRITE, while GAM is the most reproducible method to capture interactions at large genomic distances.

biophysics↗

bioRxiv2020

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

Neurons and oligodendrocytes are terminally differentiated cells that sustain cascades of gene activation and repression to execute highly specialized functions, while retaining homeostatic control. To study long-range chromatin folding without disturbing the native tissue environment, we developed Genome Architecture Mapping in combination with immunoselection (immunoGAM), and applied it to three cell types from the adult murine brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendroglia (OLGs) from the cortex. We find cell-type specific 3D chromatin structures that relate with patterns of gene expression at multiple genomic scales, including extensive reorganization of topological domains (TADs) and chromatin compartments. We discover the loss of TAD insulation, or TAD melting, at long genes (>400 kb) when they are highly transcribed. We find many neuron-specific contacts which contain accessible chromatin regions enriched for putative binding sites for multiple neuronal transcription factors, and which connect cell-type specific genes that are associated with neurodegenerative disorders such as Parkinsons disease, or specialized functions such as synaptic plasticity and memory. Lastly, sensory receptor genes exhibit increased membership in heterochromatic compartments that establish strong contacts in brain cells. However, their silencing is compromised in a subpopulation of PGNs with molecular signatures of long-term potentiation. Overall, our work shows that the 3D organization of the genome is highly cell-type specific, and essential to better understand mechanisms of gene regulation in highly specialized tissues such as the brain.

molecular biology↗

bioRxiv2020

GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data

MotivationGenome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAMs ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. ResultsWe present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. AvailabilityGAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer: julia.markowski@mdc-berlin.de

bioinformatics↗