3D chromatin architecture and transcription regulation in cancer

Chromatin has distinct three-dimensional (3D) architectures important in key biological processes, such as cell cycle, replication, differentiation, and transcription regulation. In turn, aberrant 3D structures play a vital role in developing abnormalities and diseases such as cancer. This review discusses key 3D chromatin structures (topologically associating domain, lamina-associated domain, and enhancer–promoter interactions) and corresponding structural protein elements mediating 3D chromatin interactions [CCCTC-binding factor, polycomb group protein, cohesin, and Brother of the Regulator of Imprinted Sites (BORIS) protein] with a highlight of their associations with cancer. We also summarise the recent development of technologies and bioinformatics approaches to study the 3D chromatin interactions in gene expression regulation, including crosslinking and proximity ligation methods in the bulk cell population (ChIA-PET and HiChIP) or single-molecule resolution (ChIA-drop), and methods other than proximity ligation, such as GAM, SPRITE, and super-resolution microscopy techniques

Linear-time Algorithms for RNA Folding: Partition Function, Stochastic Sampling and RNA-RNA Interaction

RNAs play important roles in the central dogma of molecular biology, and are involved in multiple biology processes such as chromatin modification, transcriptional interference and translation initiation. The functions of RNAs, especially non-coding RNAs, are highly related to its secondary structures, therefore computational methods for RNA structure prediction are of great interest. In this dissertation, we propose linear-time algorithms for RNA folding partition function, stochastic sampling and RNA-RNA interaction, which can efficiently and accurately predict and analyze RNA secondary structure. The partition function-based methods are proposed to compute folding ensembles and estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore a slow calculation for long sequences. We design a linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities, which is shown to be orders of magnitude faster than classical folding systems such as Vienna RNAfold and CONTRAfold. More interestingly, the resulting base pairing probabilities are even better correlated with the ground truth structures. On the other hand, partition function and the estimated base-pairing probabilities provide compact representations of the exponentially large ensemble, but they cannot provide direct and intuitive descriptions, and cannot be directly used for accessibility prediction. Stochastic sampling algorithm, which samples secondary structures according to their probabilities in the Boltzmann ensemble, is widely used, e.g., for accessibility prediction. However, current sampling algorithms are unnecessarily complicated, repeatedly perform redundant work, and scale cubically with the sequence length. These issues prevent it from being used for full-length viral genomes such as SARS-CoV-2. To alleviate these problems, we first propose a hypergraph framework under which the sampling algorithm can be greatly simplified, then present a lazy-saving sampling strategy under this framework of which redundant work is eliminated. Finally, we propose LinearSampling, the first end-to-end linear-time stochastic sampling algorithm, which can be used to detect SARS-CoV-2 potential regions of diagnostics and treatment. Many RNAs function through RNA-RNA interactions. two-stand folding, which can directly predict the structures with consideration of RNA-RNA interaction, is also well-desired. Some existing tools, such as RNAhybrid and RNAplex, are not only less informative but also less accurate due to omitting the competing between intermolecular and intramolecular base pairs. Another group of tools such as RNAup focus on predicting the binding region rather than predicting two-strand co-folding structure. Other tools like RNAcofold are too slow due to cubic runtime complexity. To address these issues, we propose LinearCoFold and LinearCoPartition, which is able to predict two-strand folding structure, partition function and base pairing prob-abilities in linear runtime and space. Our new coffolding algorithms are orders of magnitude faster than the baseline RNAcofold, and perform better PPV and sensitivity on the RNA-RNA interaction dataset

Detection of 3D Genome Folding at Multiple Scales

Understanding 3D genome structure is crucial to learn how chromatin folds and how genes are regulated through the spatial organization of regulatory elements. Various technologies have been developed to investigate genome architecture. These technologies include ligation-based 3C Methodologies such as Hi-C and Micro-C, ligation-based pull-down methods like Proximity Ligation-Assisted ChIP-seq (PLAC Seq) and Paired-end tag sequencing (ChIA PET), and ligation-free methods like Split-Pool Recognition of Interactions by Tag Extension (SPRITE) and Genome Architecture Mapping (GAM). Although these technologies have provided great insight into chromatin organization, a systematic evaluation of these technologies is lacking. Among these technologies, Hi-C has been one of the most widely used methods to map genome-wide chromatin interactions for over a decade. To understand how the choice of experimental parameters determines the ability to detect and quantify the features of chromosome folding, we have first systematically evaluated two critical parameters in the Hi-C protocol: cross-linking and digestion of chromatin. We found that different protocols capture distinct 3D genome features with different efficiencies depending on the cell type (Chapter 2). Use of the updated Hi-C protocol with new parameters, which we call Hi-C 3.0, was subsequently evaluated and found to provide the best loop detection compared to all previous Hi-C protocols as well as better compartment quantification compared to Micro-C (Chapter 3). Finally, to understand how the aforementioned technologies (Hi-C, Micro-C, PLAC-Seq, ChIA-PET, SPRITE, GAM) that measure 3D organization could provide a comprehensive understanding of the genome structure, we have performed a comparison of these technologies. We found that each of these methods captures different aspects of the chromatin folding (Chapter 4). Collectively, these studies suggest that improving the 3D methodologies and integrative analyses of these methods will reveal unprecedented details of the genome structure and function

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page

Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state

Finding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes. I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator

Inference in systems biology: modelling approaches and applications

The main topic of this thesis is the study of biological regulatory systems using different computational modelling approaches in order to gain new insights into not yet completely understood biological processes. In "systems biology", mathematical models represent a powerful tool to study biological processes. Models are abstractions of reality always including some degree of simplification: an important ingredient of the modelling process, having a major role in suggesting the appropriate level of abstraction and simplification, is the purpose of the model, that is the question they have to answer. This thesis is focused on the analysis of how models of different complexity appropriately describe the available data to achieve a given purpose. Such analysis guides the choice of the most appropriate degree of simplification of the system under study that allows neglecting some aspects without compromising the results of the model. Three levels of detail for inference and modelling are analyzed in this thesis depending on the system under consideration. The first level is the network level, where molecules are nodes connected by edges and the interest is in the inference of the topology of connections at large scale. In the second level the network is interpreted as a mean to produce qualitative simulations and predictions which can be compared with experimental data. The third level of detail consist in a more mechanistic dynamic description of the system using ordinary differential equations but limiting the analysis to small subsystems. For each level of detail, appropriate approaches have been developed and applied to in silico and real data of different biological systems. Finally, different modelling appraches have been integrated to analyze insulin signalling pathway on different levels of simplification using a novel experimental dataset collected specifically for this purpos