Biology subjects

Rajapakse, I.

Publications and source records attributed to Rajapakse, I..

4 recordsLinked to original sources

bioRxiv2018

Dynamic Network Analysis of the 4D Nucleome

MotivationFor many biological systems, it is essential to capture simultaneously the function, structure, and dynamics in order to form a comprehensive understanding of underlying phenomena. The dynamical interaction between 3D genome spatial structure and transcriptional activity creates a genomic signature that we refer to as the four-dimensional organization of the nucleus, or 4D Nucleome (4DN). The study of 4DN requires assessment of genome-wide structure and gene expression as well as development of new approaches for data analysis.\n\nResultsWe propose a dynamic multilayer network approach to study the co-evolution of form and function in the 4D Nucleome. We model the dynamic biological system as a temporal network with node dynamics, where the network topology is captured by chromosome conformation (Hi-C), and the function of a node is measured by RNA sequencing (RNA-seq). Network-based approaches such as von Neumann graph entropy, network centrality, and multilayer network theory are applied to reveal universal patterns of the dynamic genome. Our model integrates knowledge of genome structure and gene expression along with temporal evolution and leads to a description of genome behavior on a system wide level. We illustrate the benefits of our model via a real biological dataset on MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage. We show that our methods enable better predictions on form-function relationships and refine our understanding on how cell dynamics change during cellular reprogramming.\n\nAvailability: The software is available upon request.\n\nContactindikar@umich.edu\n\nSupplementary informationSee Supplementary Material.

bioinformatics↗

bioRxiv2017

Parental allele-specific genome architecture and transcription during the cell cycle

A normal human somatic cell inherits two haploid genomes. Individual chromosomes of each pair have distinct parental origins and parental alleles are known to unequally contribute to cellular function. We integrated chromosome conformation (form) and gene transcription (function) analyses to dissect the dynamics of the maternal and paternal genomes in lymphoblastoid cells during the cell cycle. We found a distinct set of homologous alleles with very different activity often located close to boundaries of euchromatin and heterochromatin domains. We also identified a set of allele-biased topologically associating domains (TADs) that were small sized and had higher gene density. Thousands of genes show allelically biased expression (ABE) with false discovery rate < 0.05, and 98% of them have no allelic switching during G1, S, and G2/M phases. A subset of ABE genes are preferentially localized near TAD boundaries, enriched with chromatin organization transcription factor binding sites, and contained higher number of sequence variants in CCCTC-binding factor sites. Our results extend previous findings of sequence variation as a basis for unequal functional parental genomes. Investigation of haplotype-resolved form-function dynamics may further our understanding of phenotypic traits, genetic diseases, vulnerability to complex disorders, and the development of cancers.

genomics↗

bioRxiv2017

An Algorithm for Cellular Reprogramming

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newtons laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.\n\nSignificance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

bioinformatics↗

bioRxiv2017

Genome Architecture Leads a Bifurcation in Cell Identity

Genome architecture is important in transcriptional regulation and study of its features is a critical part of fully understanding cell identity. Altering cell identity is possible through overexpression of transcription factors (TFs); for example, fibroblasts can be reprogrammed into muscle cells by introducing MYOD1. How TFs dynamically orchestrate genome architecture and transcription as a cell adopts a new identity during reprogramming is not well understood. Here we show that MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage undergoes a critical transition, which we refer to as a bifurcation point, where cell identity definitively changes. By integrating knowledge of genome-wide dynamical architecture and transcription, we found significant chromatin reorganization prior to transcriptional changes that marked activation of the myogenic program. We also found that the local architectural and transcriptional dynamics of endogenous MYOD1 and MYOG reflected the global genomic bifurcation event. These TFs additionally participate in entrainment of biological rhythms. Understanding the system-level genome dynamics underlying a cell fate decision is a step toward devising more sophisticated reprogramming strategies that could be used in cell therapies.

cell biology↗