Indika Rajapakse

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.

The emergence of lineage-specific chromosomal topologies from coordinate gene regulation

Although the importance of chromosome organization during mitosis is clear, it remains to be determined whether the nucleus assumes other functionally relevant chromosomal topologies. We have previously shown that homologous chromosomes have a tendency to associate during hematopoiesis according to their distribution of coregulated genes, suggesting cell-specific nuclear organization. Here, using the mathematical approaches of distance matrices and coupled oscillators, we model the dynamic relationship between gene expression and chromosomal associations during the differentiation of a multipotential hematopoietic progenitor. Our analysis reveals dramatic changes in total genomic order: Commitment of the progenitor results in an initial increase in entropy at both the level of gene coregulation and chromosomal organization, which we suggest represents a phase transition, followed by a progressive decline in entropy during differentiation. The stabilization of a highly ordered state in the differentiated cell types results in lineage-specific chromosomal topologies and is related to the emergence of coherence—or self-organization—between chromosomal associations and coordinate gene regulation. We discuss how these observations may be generally relevant to cell fate decisions encountered by progenitor/stem cells.

Transcripts involved in calcium signaling and telencephalic neuronal fate are altered in induced pluripotent stem cells from bipolar disorder patients

Bipolar disorder (BP) is a chronic psychiatric condition characterized by dynamic, pathological mood fluctuations from mania to depression. To date, a major challenge in studying human neuropsychiatric conditions such as BP has been limited access to viable central nervous system tissue to examine disease progression. Patient-derived induced pluripotent stem cells (iPSCs) now offer an opportunity to analyze the full compliment of neural tissues and the prospect of identifying novel disease mechanisms. We have examined changes in gene expression as iPSC derived from well-characterized patients differentiate into neurons; there was little difference in the transcriptome of iPSC, but BP neurons were significantly different than controls in their transcriptional profile. Expression of transcripts for membrane bound receptors and ion channels was significantly increased in BP-derived neurons compared with controls, and we found that lithium pretreatment of BP neurons significantly altered their calcium transient and wave amplitude. The expression of transcription factors involved in the specification of telencephalic neuronal identity was also altered. Control neurons expressed transcripts that confer dorsal telencephalic fate, whereas BP neurons expressed genes involved in the differentiation of ventral (medial ganglionic eminence) regions. Cells were responsive to dorsal/ventral patterning cues, as addition of the Hedgehog (ventral) pathway activator purmorphamine or a dorsalizing agent (lithium) stimulated expression of NKX2-1 (ventral identity) or EMX2 (dorsal) in both groups. Cell-based models should have a significant impact on our understanding of the genesis and therefore treatment of BP; the iPSC cell lines themselves provide an important resource for comparison with other neurodevelopmental disorders.

Functional organization of the human 4D Nucleome

Significance We explored the human genome as a dynamical system. Using a data-guided mathematical framework and genome-wide assays, we interrogated the dynamical relationship between genome architecture (structure) and gene expression (function) and its impact on phenotype, which defines the 4D Nucleome. Structure and function entrained with remarkable persistence in genes that underlie wound healing processes and circadian rhythms. Using genome-wide intragene and intergene contact maps, we identified gene networks with high potential for coregulation and colocalization, consistent with expression via transcription factories. In an intriguing example, we found periodic movements of circadian genes in three dimensions that entrained with expression. This work can be broadly applied to identifying genomic signatures that define critical cell states during differentiation, reprogramming, and cancer. The 4D organization of the interphase nucleus, or the 4D Nucleome (4DN), reflects a dynamical interaction between 3D genome structure and function and its relationship to phenotype. We present initial analyses of the human 4DN, capturing genome-wide structure using chromosome conformation capture and 3D imaging, and function using RNA-sequencing. We introduce a quantitative index that measures underlying topological stability of a genomic region. Our results show that structural features of genomic regions correlate with function with surprising persistence over time. Furthermore, constructing genome-wide gene-level contact maps aided in identifying gene pairs with high potential for coregulation and colocalization in a manner consistent with expression via transcription factories. We additionally use 2D phase planes to visualize patterns in 4DN data. Finally, we evaluated gene pairs within a circadian gene module using 3D imaging, and found periodicity in the movement of clock circadian regulator and period circadian clock 2 relative to each other that followed a circadian rhythm and entrained with their expression.

Dynamics and control of state-dependent networks for probing genomic organization

A state-dependent dynamic network is a collection of elements that interact through a network, whose geometry evolves as the state of the elements changes over time. The genome is an intriguing example of a state-dependent network, where chromosomal geometry directly relates to genomic activity, which in turn strongly correlates with geometry. Here we examine various aspects of a genomic state-dependent dynamic network. In particular, we elaborate on one of the important ramifications of viewing genomic networks as being state-dependent, namely, their controllability during processes of genomic reorganization such as in cell differentiation.

Structures and Assumptions: Strategies to Harness Gene × Gene and Gene × Environment Interactions in GWAS

Genome-wide association studies, in which as many as a million single nucleotide polymorphisms (SNP) are measured on several thousand samples, are quickly becoming a common type of study for identifying genetic factors associated with many phenotypes. There is a strong assumption that interactions between SNPs or genes and interactions between genes and environmental factors substantially contribute to the genetic risk of a disease. Identification of such interactions could potentially lead to increased understanding about disease mechanisms; drug × gene interactions could have profound applications for personalized medicine; strong interaction effects could be beneficial for risk prediction models. In this paper we provide an overview of different approaches to model interactions, emphasizing approaches that make specific use of the structure of genetic data, and those that make specific modeling assumptions that may (or may not) be reasonable to make. We conclude that to identify interactions it is often necessary to do some selection of SNPs, for example, based on prior hypothesis or marginal significance, but that to identify SNPs that are marginally associated with a disease it may also be useful to consider larger numbers of interactions.

Multivariate Detection of Gene-Gene Interactions

Unraveling the nature of genetic interactions is crucial to obtaining a more complete picture of complex diseases. It is thought that gene‐gene interactions play an important role in the etiology of cancer, cardiovascular, and immune‐mediated disease. Interactions among genes are defined as phenotypic effects that differ from those observed for independent contributions of each gene, usually detected by univariate logistic regression methods. Using a multivariate extension of linkage disequilibrium (LD), we have developed a new method, based on distances between sample covariance matrices for groups of single nucleotide polymorphisms (SNPs), to test for interaction effects of two groups of genes associated with a disease phenotype. Since a disease‐associated interacting locus will often be in LD with more than one marker in the region, a method that examines a set of markers in a region collectively can offer greater power than traditional methods. Our method effectively identifies interaction effects in simulated data, as well as in data on the genetic contributions to the risk for graft‐versus‐host disease following hematopoietic stem cell transplantation.

SEWAL: an open-source platform for next-generation sequence analysis and visualization

Next-generation DNA sequencing platforms provide exciting new possibilities for in vitro genetic analysis of functional nucleic acids. However, the size of the resulting data sets presents computational and analytical challenges. We present an open-source software package that employs a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run (∼108 sequences). The algorithm results in quasilinear time processing of entire Illumina lanes (∼107 sequences) on a desktop computer in minutes. To facilitate visual analysis of sequencing data, the software produces three-dimensional scatter plots similar in concept to Sewall Wright and John Maynard Smith’s adaptive or fitness landscape. The software also contains functions that are particularly useful for doped selections such as mutation frequency analysis, information content calculation, multivariate statistical functions (including principal component analysis), sequence distance metrics, sequence searches and sequence comparisons across multiple Illumina data sets. Source code, executable files and links to sample data sets are available at http://www.sourceforge.net/projects/sewal.

Networking the nucleus

The nuclei of differentiating cells exhibit several fundamental principles of self‐organization. They are composed of many dynamical units connected physically and functionally to each other—a complex network—and the different parts of the system are mutually adapted and produce a characteristic end state. A unique cell‐specific signature emerges over time from complex interactions among constituent elements that delineate coordinate gene expression and chromosome topology. Each element itself consists of many interacting components, all dynamical in nature. Self‐organizing systems can be simplified while retaining complex information using approaches that examine the relationship between elements, such as spatial relationships and transcriptional information. These relationships can be represented using well‐defined networks. We hypothesize that during the process of differentiation, networks within the cell nucleus rewire according to simple rules, from which a higher level of order emerges. Studying the interaction within and among networks provides a useful framework for investigating the complex organization and dynamic function of the nucleus.

Functional consequence of mutation in rat cardiac troponin T is affected differently by myosin heavy chain isoforms

Cardiac troponin T (cTnT) is an essential component of the thin filament regulatory unit (RU) that regulates Ca2+ activation of tension in the heart muscle. Because there is coupling between the RU and myosin crossbridges, the functional outcome of cardiomyopathy‐related mutations in cTnT may be modified by the type of myosin heavy chain (MHC) isoform. Ca2+ activation of tension and ATPase activity were measured in muscle fibres from normal rat hearts containing α‐MHC isoform and propylthiouracil (PTU)‐treated rat hearts containing β‐MHC isoform. Muscle fibres from normal and PTU‐treated rat hearts were reconstituted with two different mutations in rat cTnT; the deletion of Glu162 (cTnTE162DEL) and the deletion of Lys211 (cTnTK211DEL). α‐MHC and β‐MHC isoforms had contrasting impact on tension‐dependent ATP consumption (tension cost) in cTnTE162DEL and cTnTK211DEL reconstituted muscle fibres. Significant increases in tension cost in α‐MHC‐containing muscle fibres corresponded to 17% (P < 0.01) and 23% (P < 0.001) when reconstituted with cTnTE162DEL and cTnTK211DEL, respectively. In contrast, tension cost decreased when these two cTnT mutants were reconstituted in muscle fibres containing β‐MHC; by approximately 24% (P < 0.05) when reconstituted with cTnTE162DEL and by approximately 17% (P= 0.09) when reconstituted with cTnTK211DEL. Such differences in tension cost were substantiated by the mechano‐dynamic analysis of cTnT mutant reconstituted muscle fibres from normal and PTU‐treated rat hearts. Our observation demonstrates that qualitative changes in MHC isoform alters the nature of cardiac myofilament dysfunction induced by mutations in cTnT.