Biology subjects

Boninsegna, L.

Publications and source records attributed to Boninsegna, L..

2 recordsLinked to original sources

bioRxiv2021

Integrative Genome Modeling Platform reveals essentialityof rare contact events in 3D genome organizations

A multitude of sequencing-based and microscopy technologies provide the means to unravel the relationship between the three-dimensional (3D) organization of genomes and key regulatory processes of genome function. However, it remains a major challenge to systematically integrate all available data sources to characterize the nuclear organization of genomes across different spatial scales. Here, we develop a multi-modal data integration approach to produce genome structures that are highly predictive for nuclear locations of genes and nuclear bodies, local chromatin compaction, and spatial segregation of functionally related chromatin. By performing a quantitative assessment of the predictive power of genome structures generated from different data combinations, we demonstrate that multimodal data integration can compensate for systematic errors and missing values in some of the data and thus, greatly increases accuracy and coverage of genome structure models. We also show that alternative combinations of different orthogonal data sources can converge to models with similar predictive power. Moreover, our study reveals the key contributions of low-frequency inter-chromosomal contacts (e.g., "rare" contact events) to accurately predicting the global nuclear architecture, including the positioning of genes and chromosomes. Overall, our results highlight the benefits of multi-modal data integration for genome structure analysis, available through the Integrative Genome structure Modeling (IGM) software package that we introduce here.

biophysics↗

bioRxiv2021

Mapping the nuclear microenvironment of genes at agenome-wide scale

The nuclear folding of chromosomes relative to nuclear bodies is an integral part of gene function. Here, we demonstrate that population-based modeling--from ensemble Hi-C data--can provide a detailed description of the nuclear microenvironment of genes and its role on gene function. We define the microenvironment by the subnuclear positions of genomic regions with respect to nuclear bodies, local chromatin compaction, and preferences in chromatin compartmentalization. These structural descriptors are determined in single cell models on a genome-wide scale, thereby revealing the structural variability between cells. We demonstrate that the structural microenvironment of a genomic region is linked to its functional potential in gene transcription, replication and chromatin compartmentalization. Some chromatin regions are distinguished by their strong preferences to a single microenvironment, due to associations to specific nuclear bodies in most cells. Other chromatin shows high structural variability, which is a strong indicator of functional heterogeneity. Moreover, we identify specialized nuclear microenvironments, which distinguish chromatin in different functional states and reveal a key role of nuclear speckles in chromosome organization. We demonstrate that our method produces highly predictive 3-dimensional genome structures, which accurately reproduce data from TSA-seq, DamID, GPSeq and super-resolution imaging. Thus, our method considerably expands the range of Hi-C data analysis and is widely applicable.

genomics↗