Biology subjects

Hill, M. C.

Publications and source records attributed to Hill, M. C..

6 recordsLinked to original sources

bioRxiv2025

NanoLoop: A deep learning framework leveraging Nanopore sequencing for chromatin loop prediction

Chromatin loops play a crucial role in gene regulation and cellular function, providing key insights into understanding the three-dimensional structure of the genome and its impact on cellular homeostasis. Nanopore sequencing technology, with its advantages in simultaneously detecting sequences and methylation patterns, brings new opportunities for studying three-dimensional genome structures. We introduce NanoLoop, the first algorithmic framework attempting to predict genome-wide chromatin interactions using Nanopore data. In experiments across four human lymphoblastoid cell lines, NanoLoop demonstrated excellent predictive performance and cross-cell line generalization capabilities. We also discovered four distinct methylation patterns at loop anchors that influence histone modification levels and determine various loop types. NanoLoop further predicted previously uncharacterized long-range chromatin loops, highlighting DNA methylations role in three-dimensional genome regulation and providing new insights into the complex regulatory relationships between epigenetic modifications and three-dimensional genome organization.

bioinformatics↗

bioRxiv2023

Fusion Neural Network (FusNet) for predicting protein-mediated loops

The organization of the three-dimensional (3D) genome is a complex, and requires a plethora of proteins to ensure the proper formation and regulation of chromatin loops as well as higher order structures. Studying protein-mediated loop regulation can help unravel the intricate interplay between these loops and their crucial roles in modulating gene expression across different cellular contexts. However, current targeted chromatin conformation capture experiments face limitations in capturing protein-mediated loops across various cell types, and existing computational methods fail to predict diverse protein-mediated loops. To address these issues, we propose a fusion neural network (FusNet) designed for predicting protein-mediated loops. FusNet leverages genome sequence information, open chromatin, and ChIP-seq data to efficiently represent and analyze the positions of loop anchors. To extract informative features and reduce the complexity of FusNet, we constructed a convolutional neural network, which compresses the dimensionality of the features while also preserving the most significant ones. To enhance the accuracy and generalization capacity of FusNet, we built a fusion layer by stacking the prediction of fundamental models with a meta-model. FusNet demonstrated its effectiveness in predicting protein-mediated loops, exhibiting high consistency with Hi-C data. Moreover, we find that the loops output from FusNet are highly associated with regulatory functions. Through association analysis with genetic risk variants, FusNet further revealed its potential for unraveling disease-related mechanisms. In conclusion, our study offers a novel computational approach for predicting various protein-mediated chromatin loops, which could substantially enhance research on the functional significance of protein-mediated loop structures in diverse cellular contexts. Significance StatementThe intricate spatial organization of the three-dimensional (3D) genome involves functional proteins critically contributing to chromatin loop formation and regulation. Understanding these protein-mediated loops is vital for elucidating their influence on 3D genome architecture and gene regulation across different cellular types and disease-related contexts. In this study, we propose a Fusion Neural Network (FusNet) for predicting protein-mediated loops. FusNet can concurrently capture and analyze multiple protein-mediated loops in various cell types to advance our understanding of the multitude of protein-mediated loop structures and their functional significance. Importantly, through association analysis with risk variants, FusNet manifests potential in revealing disease-related mechanisms.

bioinformatics↗

bioRxiv2021

Defining the Transcriptional and Epigenetic Basis of Organotypic Endothelial Diversity in the Developing and Adult Mouse

Significant phenotypic differences exist between the vascular endothelium of different organs, including cell-cell junctions, paracellular fluid transport, shape, and mural cell coverage. These organ-specific morphological features ultimately manifest as different functional capacities, as demonstrated by the dramatic differences in capillary permeability between the leaky vessels of the liver compared to the almost impermeable vasculature found in the brain. While these morphological and functional differences have been long appreciated, the molecular basis of endothelial organ specialization remains unclear. To determine the epigenetic and transcriptional mechanisms driving this functional heterogeneity, we profiled accessible chromatin, as well as gene expression, in six different organs, across three distinct time points, during murine development and in adulthood. After identifying both common, and organ-specific DNA motif usage and transcriptional signatures, we then focused our studies on the endothelium of the central nervous system. Using single cell RNA-seq, we identified key gene regulatory networks governing brain blood vessel maturation, including TCF/LEF and FOX transcription factors. Critically, these unique regulatory regions and gene expression signatures are evolutionarily conserved in humans. Collectively, this work provides a valuable resource for identifying the transcriptional regulators controlling organ-specific endothelial specialization and provides novel insight into the gene regulatory networks governing the maturation and maintenance of the cerebrovasculature.

developmental biology↗

bioRxiv2020

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model

Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and time-consuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell types which lack long-range contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. Whats more, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type-specific enhancer mediated loops, and promoter-promoter interactions, with a modest feature input requirement. Comparable to experimentally generated H3K27ac HiChIP data, we found that LoopPredictor was able to identify functional enhancer loops. Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs were highly conserved. LoopPredictor enables the dissection of cell type-specific long-range gene regulation, and can accelerate the identification of distal disease-associated risk variants.

bioinformatics↗

bioRxiv2019

NFIA regulates granule recruitment and exocytosis in the adult pancreas

After food ingestion, pancreatic cells secrete zymogen and hormone-containing granules to precisely control digestion and blood glucose levels. Identifying regulators of this process is paramount to combatting multiple pancreatic diseases. Here we show that pancreatic deletion of the transcription factor nuclear factor IA (NFIA) leads to hyperglycemia, hypoinsulinemia, and hypolipidemia. Surprisingly, insulin and digestive enzymes are produced in the absence of NFIA, however, they are not secreted properly and instead accumulate inside pancreatic cells. In NFIA-deficient mice we saw a reduction of insulin granules in the ready releasable pool and the first-phase insulin response was impaired. We found that NFIA binds to and activates Rab39b, a Rab GTPase critical for exocytosis. Re-expression of Rab39b in NFIA knockout islets restored glucose-stimulated insulin secretion. In sum, the NFIA-Rab39b axis regulates pancreatic physiology through granule recruitment and docking, linking NFIA to a new process with potential effects in diabetes, pancreatitis, and lipid disorders.

cell biology↗

bioRxiv2019

Detailed Regulatory Interaction Map of the Human Heart Facilitates Gene Discovery for Cardiovascular Disease

Most disease-associated variants identified by population based genetic studies are non-coding, which compromises finding causative genes and mechanisms. Presumably they interact through looping with nearby genes to modulate transcription. Hi-C provides the most complete and unbiased method for genome-wide identification of potential regulatory interactions, but finding chromatin loops in Hi-C data remains difficult and tissue specific data are limited. We have generated Hi-C data from primary cardiac tissue and developed a method, peakHiC, for sensitive and quantitative loop calling to uncover the human heart regulatory interactome. We identify complex CTCF-dependent and -independent contact networks, with loops between coding and non-coding gene promoters, shared enhancers and repressive sites. Across the genome, enhancer interaction strength correlates with gene transcriptional output and loop dynamics follows CTCF, cohesin and H3K27Ac occupancy levels. Finally, we demonstrate that intersection of the human heart regulatory interactome with cardiovascular disease variants facilitates prioritizing disease-causative genes.

genomics↗