Networking the nucleus

Dynamic Sensor Selection for Biomarker Discovery

Advances in methods of biological data collection are driving the rapid growth of comprehensive datasets across clinical and research settings. These datasets provide the opportunity to monitor biological systems in greater depth and at finer time steps than was achievable in the past. Classically, biomarkers are used to represent and track key aspects of a biological system. Biomarkers retain utility even with the availability of large datasets, since monitoring and interpreting changes in a vast number of molecules remains impractical. However, given the large number of molecules in these datasets, a major challenge is identifying the best biomarkers for a particular setting. Here, we apply principles of observability theory to establish a general methodology for biomarker selection. We demonstrate that observability measures effectively identify biologically meaningful sensors in a range of time series transcriptomics data. Motivated by the practical considerations of biological systems, we introduce the method of dynamic sensor selection (DSS) to maximize observability over time, thus enabling observability over regimes where system dynamics themselves are subject to change. This observability framework is flexible, capable of modeling gene expression dynamics and using auxiliary data, including chromosome conformation, to select biomarkers. Additionally, we demonstrate the applicability of this approach beyond genomics by evaluating the observability of neural activity. These applications demonstrate the utility of observability-guided biomarker selection for across a wide range of biological systems, from agriculture and biomanufacturing to neural applications and beyond.

The corrected gene proximity map for analyzing the 3D genome organization using Hi-C data

BACKGROUND

Genome-wide ligation-based assays such as Hi-C provide us with an unprecedented opportunity to investigate the spatial organization of the genome. Results of a typical Hi-C experiment are often summarized in a chromosomal contact map, a matrix whose elements reflect the co-location frequencies of genomic loci. To elucidate the complex structural and functional interactions between those genomic loci, networks offer a natural and powerful framework.

RESULTS

We propose a novel graph-theoretical framework, the Corrected Gene Proximity (CGP) map to study the effect of the 3D spatial organization of genes in transcriptional regulation. The starting point of the CGP map is a weighted network, the gene proximity map, whose weights are based on the contact frequencies between genes extracted from genome-wide Hi-C data. We derive a null model for the network based on the signal contributed by the 1D genomic distance and use it to "correct" the gene proximity for cell type 3D specific arrangements. The CGP map, therefore, provides a network framework for the 3D structure of the genome on a global scale. On human cell lines, we show that the CGP map can detect and quantify gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies. Analyzing the expression pattern of metabolic pathways of two hematopoietic cell lines, we find that the relative positioning of the genes, as captured and quantified by the CGP, is highly correlated with their expression change. We further show that the CGP map can be used to form an inter-chromosomal proximity map that allows large-scale abnormalities, such as chromosomal translocations, to be identified.

CONCLUSIONS

The Corrected Gene Proximity map is a map of the 3D structure of the genome on a global scale. It allows the simultaneous analysis of intra- and inter- chromosomal interactions and of gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies, thus revealing hidden associations between global spatial positioning and gene expression. The flexible graph-based formalism of the CGP map can be easily generalized to study any existing Hi-C datasets.

MOCHI enables discovery of heterogeneous interactome modules in 3D nucleome

The composition of the cell nucleus is highly heterogeneous, with different constituents forming complex interactomes. However, the global patterns of these interwoven heterogeneous interactomes remain poorly understood. Here we focus on two different interactomes, chromatin interaction network and gene regulatory network, as a proof of principle to identify heterogeneous interactome modules (HIMs), each of which represents a cluster of gene loci that is in spatial contact more frequently than expected and that is regulated by the same group of transcription factors. HIM integrates transcription factor binding and 3D genome structure to reflect “transcriptional niche” in the nucleus. We develop a new algorithm, MOCHI, to facilitate the discovery of HIMs based on network motif clustering in heterogeneous interactomes. By applying MOCHI to five different cell types, we found that HIMs have strong spatial preference within the nucleus and show distinct functional properties. Through integrative analysis, this work shows the utility of MOCHI to identify HIMs, which may provide new perspectives on the interplay between transcriptional regulation and 3D genome organization.

Transient crosslinking kinetics optimize gene cluster interactions

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through "flexible" clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, "rigid" clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions.

Entering the Next Dimension: Plant Genomes in 3D

After linear sequences of genomes and epigenomic landscape data, the 3D organization of chromatin in the nucleus is the next level to be explored. Different organisms present a general hierarchical organization, with chromosome territories at the top. Chromatin interaction maps, obtained by chromosome conformation capture (3C)-based methodologies, for eight plant species reveal commonalities, but also differences, among them and with animals. The smallest structures, found in high-resolution maps of the Arabidopsis genome, are single genes. Epigenetic marks (histone modification and DNA methylation), transcriptional activity, and chromatin interaction appear to be correlated, and whether structure is the cause or consequence of the function of interacting regions is being actively investigated.

MrTADFinder: A network modularity based approach to identify topologically associating domains in multiple resolutions

Genome-wide proximity ligation based assays such as Hi-C have revealed that eukaryotic genomes are organized into structural units called topologically associating domains (TADs). From a visual examination of the chromosomal contact map, however, it is clear that the organization of the domains is not simple or obvious. Instead, TADs exhibit various length scales and, in many cases, a nested arrangement. Here, by exploiting the resemblance between TADs in a chromosomal contact map and densely connected modules in a network, we formulate TAD identification as a network optimization problem and propose an algorithm, MrTADFinder, to identify TADs from intra-chromosomal contact maps. MrTADFinder is based on the network-science concept of modularity. A key component of it is deriving an appropriate background model for contacts in a random chain, by numerically solving a set of matrix equations. The background model preserves the observed coverage of each genomic bin as well as the distance dependence of the contact frequency for any pair of bins exhibited by the empirical map. Also, by introducing a tunable resolution parameter, MrTADFinder provides a self-consistent approach for identifying TADs at different length scales, hence the acronym "Mr" standing for Multiple Resolutions. We then apply MrTADFinder to various Hi-C datasets. The identified domain boundaries are marked by characteristic signatures in chromatin marks and transcription factors (TF) that are consistent with earlier work. Moreover, by calling TADs at different length scales, we observe that boundary signatures change with resolution, with different chromatin features having different characteristic length scales. Furthermore, we report an enrichment of HOT (high-occupancy target) regions near TAD boundaries and investigate the role of different TFs in determining boundaries at various resolutions. To further explore the interplay between TADs and epigenetic marks, as tumor mutational burden is known to be coupled to chromatin structure, we examine how somatic mutations are distributed across boundaries and find a clear stepwise pattern. Overall, MrTADFinder provides a novel computational framework to explore the multi-scale structures in Hi-C contact maps.

The accommodation of the roughly 2m of DNA in the nuclei of mammalian cells results in an intricate structure, in which the topologically associating domains (TADs) formed by densely interacting genomic regions emerge as a fundamental structural unit. Identification of TADs is essential for understanding the role of 3D genome organization in gene regulation. By viewing the chromosomal contact map as a network, TADs correspond to the densely connected regions in the network. Motivated by this mapping, we propose a novel method, MrTADFinder, to identify TADs based on the concept of modularity in network science. Using MrTADFinder, we identify domains at various resolutions, and further explore the interplay between domains and other chromatin features like transcription factors binding and histone modifications at different resolutions. Overall, MrTADFinder provides a new computational framework to investigate the multiple length scales that are built inside the organization of the genome.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

The International Nucleome Consortium

The eukaryotic genome adopts in the cell nucleus a 3-dimensional configuration that varies with cell types, developmental stages and environmental condition as well as between normal and pathological states. Understanding genome function will therefore require the elucidation of the structure-function relationship of the cell nucleus as a complex, dynamic biological system, referred to as the nucleome. This exciting and timely task calls for a multi-faceted, interdisciplinary and multi-national effort. We propose the establishment of an International Nucleome Consortium to coordinate this effort worldwide.

SCFSlimb ubiquitin ligase suppresses condensin II-mediated nuclear reorganization by degrading Cap-H2

Condensin complexes play vital roles in chromosome condensation during mitosis and meiosis. Condensin II uniquely localizes to chromatin throughout the cell cycle and, in addition to its mitotic duties, modulates chromosome organization and gene expression during interphase. Mitotic condensin activity is regulated by phosphorylation, but mechanisms that regulate condensin II during interphase are unclear. Here, we report that condensin II is inactivated when its subunit Cap-H2 is targeted for degradation by the SCF(Slimb) ubiquitin ligase complex and that disruption of this process dramatically changed interphase chromatin organization. Inhibition of SCF(Slimb) function reorganized interphase chromosomes into dense, compact domains and disrupted homologue pairing in both cultured Drosophila cells and in vivo, but these effects were rescued by condensin II inactivation. Furthermore, Cap-H2 stabilization distorted nuclear envelopes and dispersed Cid/CENP-A on interphase chromosomes. Therefore, SCF(Slimb)-mediated down-regulation of condensin II is required to maintain proper organization and morphology of the interphase nucleus.

A complex network framework for unbiased statistical analyses of DNA-DNA contact maps

Experimental techniques for the investigation of three-dimensional (3D) genome organization are being developed at a fast pace. Currently, the associated computational methods are mostly specific to the individual experimental approach. Here we present a general statistical framework that is widely applicable to the analysis of genomic contact maps, irrespective of the data acquisition and normalization processes. Within this framework DNA-DNA contact data are represented as a complex network, for which a broad number of directly applicable methods already exist. In such a network representation, DNA segments and contacts between them are denoted as nodes and edges, respectively. Furthermore, we present a robust method for generating randomized contact networks that explicitly take into account the inherent 3D nature of the genome and serve as realistic null-models for unbiased statistical analyses. By integrating a variety of large-scale genome-wide datasets we demonstrate that meiotic crossover sites display enriched genomic contacts and that cohesin-bound genes are significantly colocalized in the yeast nucleus. We anticipate that the complex network framework in conjunction with the randomization of DNA-DNA contact networks will become a widely used tool in the study of nuclear architecture.

On emerging nuclear order

Although the nonrandom nature of interphase chromosome arrangement is widely accepted, how nuclear organization relates to genomic function remains unclear. Nuclear subcompartments may play a role by offering rich microenvironments that regulate chromatin state and ensure optimal transcriptional efficiency. Technological advances now provide genome-wide and four-dimensional analyses, permitting global characterizations of nuclear order. These approaches will help uncover how seemingly separate nuclear processes may be coupled and aid in the effort to understand the role of nuclear organization in development and disease.

Dynamic as well as stable protein interactions contribute to genome function and maintenance

The cell nucleus is responsible for the storage, expression, propagation, and maintenance of the genetic material it contains. Highly organized macromolecular complexes are required for these processes to occur faithfully in an extremely crowded nuclear environment. In addition to chromosome territories, the nucleus is characterized by the presence of nuclear substructures, such as the nuclear envelope, the nucleolus, and other nuclear bodies. Other smaller structural entities assemble on chromatin in response to required functions including RNA transcription, DNA replication, and DNA repair. Experiments in living cells over the last decade have revealed that many DNA binding proteins have very short residence times on chromatin. These observations have led to a model in which the assembly of nuclear macromolecular complexes is based on the transient binding of their components. While indeed most nuclear proteins are highly dynamic, we found after an extensive survey of the FRAP literature that an important subset of nuclear proteins shows either very slow turnover or complete immobility. These examples provide compelling evidence for the establishment of stable protein complexes in the nucleus over significant fractions of the cell cycle. Stable interactions in the nucleus may, therefore, contribute to the maintenance of genome integrity. Based on our compilation of FRAP data, we propose an extension of the existing model for nuclear organization which now incorporates stable interactions. Our new “induced stability” model suggests that self-organization, self-assembly, and assisted assembly contribute to nuclear architecture and function.

The human connectome: a complex network

The human brain is a complex network. An important first step toward understanding the function of such a network is to map its elements and connections, to create a comprehensive structural description of the network architecture. This paper reviews current empirical efforts toward generating a network map of the human brain, the human connectome, and explores how the connectome can provide new insights into the organization of the brain's structural connections and their role in shaping functional dynamics. Network studies of structural connectivity obtained from noninvasive neuroimaging have revealed a number of highly nonrandom network attributes, including high clustering and modularity combined with high efficiency and short path length. The combination of these attributes simultaneously promotes high specialization and high integration within a modular small-world architecture. Structural and functional networks share some of the same characteristics, although their relationship is complex and nonlinear. Future studies of the human connectome will greatly expand our knowledge of network topology and dynamics in the healthy, developing, aging, and diseased brain.

Nuclear architecture in developmental biology and cell specialisation

Epigenetic changes, including DNA methylation patterns, histone modifications and histone variants, as well as chromatin remodelling play a fundamental role in the regulation of pre‐ and postimplantation mammalian development. Recent studies have indicated that nuclear architecture provides an additional level of regulation, which needs to be explored in order to understand how a fertilised egg is able to develop into a full organism. Studies of 3D preserved nuclei of IVF preimplantation embryos from different mammalian species, such as mouse, rabbit and cow, have demonstrated that nuclear architecture undergoes major changes during early development. Both similarities and species‐specific differences were observed. Nuclear transfer experiments demonstrated changes of nuclear phenotypes, which to some extent reflect changes seen in IVF preimplantation embryos albeit with a different timing compared with IVF embryos. The dynamics of nuclear architecture is further substantiated by major changes during postmitotic terminal cell differentiation. Recent breakthroughs of 3D fluorescence microscopy with resolution beyond the conventional Abbe limit in combination with 3D electron microscopy provide the potential to explore the topography of nuclear structure with unprecedented resolution and detail.