Inferring Diploid 3D Chromatin Structures from Hi-C Data

The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuracy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not

Linear Nanostructures: Linear Assembly of Filamentous Bacteriophage via Leucine Zipper Interactions

Nanotechnology seeks to precisely assemble architectures on the nanometer scale. It is possible\ud to create complex nanostructures with the use of self-assembling biological building blocks,\ud which can be engineered to form specific interconnects. The filamentous bacteriophage M13 is\ud easily modified, and can display peptides on all five different types of coat proteins. An effective\ud system that directs ordered linear assembly of M13 would expand the possible applications of\ud this phage. Our aim is to characterize a linear assembly system that was designed by Sweeney et\ud al. In their model, each end of the phage displays a different, complementary leucine zipper\ud domain. The two complementary leucine zipper sequences heterodimerize to line up the phage in\ud linear structures. The Johnson Lab received a series of display plasmids from the Georgiou lab\ud and identified a mutation in one of the leucine zipper sequences. A previous student, Marina\ud Zambrotta, subcloned and corrected sequences containing the mutation. The corrected sequence\ud has now been recloned into the original context to generate a corrected display vector and was\ud used to produce fusion-displaying progeny phage. However, the expression of the display\ud constructs appears to have a strong negative effect on phage yields. Current effort is being\ud directed at optimizing improving phage yields, confirming display levels, and characterizing the\ud ability of these genetically engineered phage to assemble into linear arrays. These experiments\ud should shed further light on the properties of the system and make it a reliable and attractive\ud candidate for further work

Inferring whole-genome 3D chromatin structures from diploid Hi-C data

Thesis (Ph.D.)--University of Washington, 2023The three-dimensional organization of the genome plays an important part in regulating numerous basic cellular functions, including gene regulation, differentiation, the cell cycle, DNA replication, and DNA repair. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Accordingly, many methods have been developed to infer 3D structures from Hi-C data. However, many challenges remain. Importantly, although many methods exist to infer the 3D structure of haploid genomes, accurately inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data does not typically distinguish between homologous chromosomes. Inference is also complicated in the setting of low-coverage or high-resolution data, which can lead to poor performance and high computational costs. This work describes two methods for inferring 3D diploid chromatin structures from Hi-C data. The first approach extends a previously published haploid method and enables diploid inference via the addition of two constraints. We demonstrate the accuracy of this method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the inactive homolog exhibits a bipartite structure, whereas the active homolog does not. Our second method addresses the difficulties presented by low-coverage or high-resolution data via multiscale optimization, an optimization strategy that solves a large optimization problem by building upon the solutions to smaller versions of the problem. Similar approaches have been successfully employed in the context of haploid structural inference methods. However, because many organisms of interest are diploid, we sought to develop a multiscale optimization approach that infers the structure of diploid genomes. We use simulations to show that integrating multiscale optimization into our first method significantly improves the accuracy of inferred structures

Inferring Diploid 3D Chromatin Structures from Hi-C Data

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Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

Abstract Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF’s RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted