Biology subjects

Dang, D.

Publications and source records attributed to Dang, D..

6 recordsLinked to original sources

bioRxiv2024

Uncovering topologically associating domains from three-dimensional genome maps with TADGATE

Topologically associating domains (TADs) emerge as indispensable units in three-dimensional (3D) genome organization, playing a critical role in gene regulation. However, accurately identifying TADs from sparse chromatin contact maps and exploring the structural and functional elements within TADs remain challenging. To this end, we develop a graph attention auto-encoder, TADGATE, to accurately identify TADs even from ultra-sparse contact maps and generate the imputed maps while preserving or enhancing the underlying topological structures. TADGATE can capture specific attention patterns, pointing to two types of units with different characteristics in TADs. Moreover, we find that the organization of TADs is closely associated with chromatin compartmentalization, and TAD boundaries in different compartmental environments exhibit distinct biological properties. We also utilize a two-layer Hidden Markov Model to functionally annotate the TADs and their internal regions, revealing the overall properties of TADs and the distribution of the structural and functional elements within TADs. At last, we apply TADGATE to highly sparse and noisy Hi-C contact maps from 21 human tissues or cell lines, enhancing the clarity of TAD structures, investigating the nature of conserved and cell type-specific boundaries, and unveiling the cell type-specific transcriptional regulatory mechanisms associated with topological domains.

bioinformatics↗

bioRxiv2022

Defining the separation landscape of topological domains for decoding consensus domain organization of 3D genome

Topologically associating domains (TADs) have emerged as basic structural and functional units of genome organization, and have been determined by many computational methods from Hi-C contact maps. However, the TADs obtained by different methods vary greatly, which makes the accurate determination of TADs a challenging issue and hinders subsequent biological analyses about their organization and functions. Obvious inconsistencies among the TADs identified by different methods indeed make the statistical and biological properties of TADs overly depend on the method we chose rather than on the data. To this end, we employ the consensus structural information captured by these methods to define the TAD separation landscape for decoding consensus domain organization of the 3D genome. We demonstrate that the TAD separation landscape could be used to compare domain boundaries across multiple cell types for discovering conserved and divergent topological structures, decipher three types of boundary regions with diverse biological features, and identify Consensus Topological Associating Domains (ConsTADs). We illustrate that these analyses could deepen our understanding of the relationships between the topological domains and chromatin states, gene expression, and DNA replication timing. In short, we provide an alternative solution to deal with the serious inconsistencies of TADs obtained via different methods by defining the TAD separation landscape and ConsTAD.

bioinformatics↗

bioRxiv2022

β-catenin programs a tissue-specific epigenetic vulnerability in aggressive adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare cancer in which tissue-specific differentiation is paradoxically associated with dismal outcomes. The differentiated ACC subtype CIMP-high is prevalent, incurable, and routinely fatal. CIMP-high ACC possess abnormal DNA methylation and frequent {beta}-catenin activating mutations. Here, we demonstrate that ACC differentiation is maintained by a balance between nuclear, tissue-specific {beta}-catenin-containing complexes and the epigenome. On chromatin, {beta}-catenin binds master adrenal transcription factor SF1 and hijacks the adrenocortical super-enhancer landscape to maintain differentiation. Off chromatin, {beta}-catenin binds histone methyltransferase EZH2, which is redistributed by the CIMP-high DNA methylation signature. SF1/{beta}-catenin and EZH2/{beta}-catenin complexes exist in normal adrenals and are selected for through all phases of ACC evolution. Pharmacologic EZH2 inhibition in CIMP-high ACC favors EZH2/{beta}-catenin assembly and purges SF1/{beta}-catenin from chromatin, erasing differentiation and restraining cancer growth in vitro and in vivo. Our studies illustrate how tissue-specific programs shape oncogene selection, surreptitiously encoding targetable therapeutic vulnerabilities.

cancer biology↗

bioRxiv2022

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance that Predict Disease Outcomes

Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate. Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species and immune cells (> 12,500 diverse datasets). This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates. These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics. One Sentence SummarySignatures of macrophage reactivity and tolerance (SMaRT) predict disease outcomes

systems biology↗

bioRxiv2021

SpinX: Time-resolved 3D Analysis of Mitotic Spindle Dynamics using Deep Learning Techniques and Mathematical Modelling

Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as object segmentation and tracking difficult. Here we present SpinX, a framework for reconstructing gaps between successive frames by combining Deep Learning and mathematical object modelling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbour-cell information, non-uniform illumination and variable marker intensities. The automation and continuity introduced allows precise 3-Dimensional tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

cell biology↗

bioRxiv2019

Computational approach to identifying universal macrophage biomarker

Macrophages are a type of white blood cell, of the immune system, that engulfs and digests cellular debris, cancer cells, and anything else that does not have the type of proteins specific to healthy body cells on its surface. Understanding gene expression dynamics in macrophages are crucial for studying human diseases. Recent advances in high-throughput technologies have enabled the collection of immense amounts of biological data. A reliable marker of macrophage is essential to study their function. Traditional approaches use a number of markers that may have tissue specific expression patterns. To identify universal biomarker of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify universal cell cycle genes. We performed BECC analysis on a seed gene CD14, a known macrophage marker. FCER1G and TYROBP were among the top candidates which were validated as strong candidates for universal biomarkers for macrophages in human and mouse tissues. To our knowledge, such a finding is first of its kind.\n\nCONTRIBUTIONS TO THE FIELDWe have developed a computational approach to identify universal biomarkers of different entities in a biological system. We applied this approach to study macrophages and identified universal biomarkers of this particular cell type. FCER1G and TYROBP were among the top candidates which were validated as strong candidates for universal biomarkers for macrophages in human and mouse tissues. The expression patterns of TYROBP and FCER1G are found to be more homogeneous compared to currently used biomarkers such as ITGAM, EMR1 (F4/80), and CD68. Further, we demonstrated that this homogeneity extends to all the tissues currently profiled in the public domain in multiple species including human and mouse. FCER1G and TYROBP expression patterns were also found to be extremely specific to macrophages found in various tissues. They are strongly co-expressed together. We believe that these two genes are the most reliable candidates of universal biomarker for macrophages.

immunology↗