Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus

Eukaryotic genomes are packaged into a 3-dimensional structure in the nucleus. Current methods for studying genome-wide structure are based on proximity ligation. However, this approach can fail to detect known structures, such as interactions with nuclear bodies, because these DNA regions can be too far apart to directly ligate. Accordingly, our overall understanding of genome organization remains incomplete. Here, we develop split-pool recognition of interactions by tag extension (SPRITE), a method that enables genome-wide detection of higher-order interactions within the nucleus. Using SPRITE, we recapitulate known structures identified by proximity ligation and identify additional interactions occurring across larger distances, including two hubs of inter-chromosomal interactions that are arranged around the nucleolus and nuclear speckles. We show that a substantial fraction of the genome exhibits preferential organization relative to these nuclear bodies. Our results generate a global model whereby nuclear bodies act as inter-chromosomal hubs that shape the overall packaging of DNA in the nucleus

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies

Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus

Eukaryotic genomes are packaged into a 3-dimensional structure in the nucleus. Current methods for studying genome-wide structure are based on proximity ligation. However, this approach can fail to detect known structures, such as interactions with nuclear bodies, because these DNA regions can be too far apart to directly ligate. Accordingly, our overall understanding of genome organization remains incomplete. Here, we develop split-pool recognition of interactions by tag extension (SPRITE), a method that enables genome-wide detection of higher-order interactions within the nucleus. Using SPRITE, we recapitulate known structures identified by proximity ligation and identify additional interactions occurring across larger distances, including two hubs of inter-chromosomal interactions that are arranged around the nucleolus and nuclear speckles. We show that a substantial fraction of the genome exhibits preferential organization relative to these nuclear bodies. Our results generate a global model whereby nuclear bodies act as inter-chromosomal hubs that shape the overall packaging of DNA in the nucleus

Identifying prognostic gene-signatures using a network-based approach

The main objective of this study is to develop a novel network-based methodology to identify prognostic signatures of genes that can predict recurrence in cancer. Feature selection algorithms were used widely for the identification of gene signatures in genome-wide association studies. But most of them do not discover the causal relationships between the features and need to compromise between accuracy and complexity. The network-based techniques take the molecular interactions between pairs of genes in to account and are thus a more efficient means of finding gene signatures, and they are also better in terms of its classification accuracy without compromising over complexity. Nevertheless, the network-based techniques currently being used have a few limitations each. Correlation-based coexpression networks do not provide predictive structure or causal relations among the genes. Bayesian networks cannot model feedback loops. Boolean networks can model small scale molecular networks, but not at the genome-scale. Thus the prediction logic induced implication networks are chosen to generate genome-wide coexpression networks, as they integrate formal logic and statistics and also overcome the limitations of other network-based techniques.;The first part of the study includes building of an implication network and identification of a set of genes that could form a prognostic signature. The data used consisted of 442 samples taken from 4 different sources. The data was split into training set UM/HLM (n=256) and two testing sets DFCI (n=82) and MSK (n=104). The training set was used for the generation of the implication network and eventually the identification of the prognostic signature. The test sets were used for validating the obtained signature. The implication networks were built by using the gene expression data associated with two disease states (metastasis or non-metastasis), defined by the period and status of post-operative survival. The gene interactions that differentiated the two disease states, the differential components, were identified. The major cancer hallmarks (E2F, EGF, EGFR, KRAS, MET, RB1, and TP53) were considered, and the genes that interacted with all the major hallmarks were identified from the differential components to form a 31-gene prognostic signature. A software package was created in R to automate this process which has C-code embedded into it. Next, the signature was fitted into a COX proportional hazard model and the nearest point to the perfect classification in the ROC curve was identified as the best scheme for patient stratification on the training set (log-rank p-value=1.97e-08), and two test sets DFCI (log-rank p-value=2.13e-05) and MSK (log-rank p-value=1.24e-04) in Kaplan-Meier analyses.;Prognostic validation was carried out on the test sets using methods such as Concordance Probability Estimate (CPE) and Gene Set Enrichment Analysis (GSEA). The accuracy of this signature was evaluated with CPE, which achieves 0.71 on the test set DFCI (log-rank p-value=5.3e-08) and 0.70 on test set MSK (log-rank p-value=2.1e-07). The hazard ratio of this 31-gene prognostic signature is 2.68 (95% CI: [1.88, 3.82]) on the DFCI dataset and 3.31 (95% CI: [2.11, 5.2]) on the MSK set. These results demonstrate that our 31-gene signature was significantly more accurate than previously published signatures on the same datasets. The false discovery rate (FDR) of this 31-gene signature is 0.21 as computed with GSEA, which showed that our 31 gene signature was comparable to other lung cancer prognostic signatures on the same datasets.;Topological validation was performed on the test sets for the identified signature to validate the computationally derived molecular interactions. The interactions from implication networks were compared with those from Bayesian networks implemented in Tetrad IV. Various curated databases and bioinformatics tools were used in the topological evaluation, including PRODISTIN, KEGG, PubMed, NCI-Nature pathways, MATISSE, STRING 8, Ingenuity Pathway Analysis, and Pathway Studio 6. The results showed that the implication networks generated all the curated interactions from various tools and databases, whereas Bayesian networks contained only a few of them. It can thus be concluded that implication networks are capable of generating many more gene or protein interactions when compared to the currently used network techniques such as Bayesian networks

Higher-Order RNA and DNA Hubs Shape Genome Organization in the Nucleus

Although the entire genome is present within the nucleus of every cell, distinct genes need to be accessed and expressed in different cellular conditions. Accordingly, the nucleus of each cell is a highly organized arrangement of DNA, RNA, and protein that is dynamically assembled and regulated in different cellular states. These dynamic nuclear structures are largely arranged around functionally related roles and often occur across multiple chromosomes. These include large nuclear bodies (i.e., nucleolus, nuclear speckle), smaller nuclear bodies (i.e., Cajal bodies and histone locus bodies), and gene-gene interactions (i.e., transcription compartments and loops). Yet, what molecular components are involved in establishing this dynamic organization have been largely unknown due to a lack of methods to measure the RNA and DNA components of nuclear bodies and their spatial arrangements in the nucleus. Here, we present Split-Pool Recognition of Interactions by Tag Extension (SPRITE), which enables genome-wide detection of higher-order interactions within the nucleus. In the second chapter, we introduce SPRITE and recapitulate known structures identified by proximity ligation and identify additional interactions occurring across larger distances, including two hubs of inter-chromosomal interactions that are arranged around the nucleolus and nuclear speckles. We show that a substantial fraction of the genome exhibits preferential organization relative to these nuclear bodies. Our results generate a global model whereby nuclear bodies act as inter-chromosomal hubs that shape the overall packaging of DNA in the nucleus. In the third chapter, we provide a detailed experimental protocol for performing SPRITE and an automated computational pipeline for analyzing SPRITE data. Finally, in the fourth chapter, we present a dramatically improved implementation of the SPRITE method that enables comprehensive mapping of all classes of RNA in the nucleus, from abundant RNAs encoded from DNA repeats to low abundance RNAs such as nascent pre-mRNAs and lncRNAs. We find that RNAs localize broadly across the nucleus, with individual RNAs localizing within discrete territories ranging from nuclear bodies to individual topologically associated domains. We uncover that nascent mRNAs interact in structures corresponding to nascent mRNA chromosome territories and compartments. Together, these results uncover a central and widespread role for non-coding RNA in demarcating 3D nuclear structures within the nucleus.</p

Systems biology approaches to a rational drug discovery paradigm

The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524

Simulation of Clinical PET Studies for the Assessment of Quantification Methods

On this PhD thesis we developed a methodology for evaluating the robustness of SUV measurements based on MC simulations and the generation of novel databases of simulated studies based on digital anthropomorphic phantoms. This methodology has been applied to different problems related to quantification that were not previously addressed. Two methods for estimating the extravasated dose were proposed andvalidated in different scenarios using MC simulations. We studied the impact of noise and low counting in the accuracy and repeatability of three commonly used SUV metrics (SUVmax, SUVmean and SUV50). The same model was used to study the effect of physiological muscular uptake variations on the quantification of FDG-PET studies. Finally, our MC models were applied to simulate 18F-fluorocholine (FCH) studies. The aim was to study the effect of spill-in counts from neighbouring regions on the quantification of small regions close to high activity extended sources

Cancer progression: a single cell perspective

Tumor tissues are constituted by a dynamic diversity of malignant and non-malignant cells, which shape a puzzling biological ecosystem affecting cancer biology and response to treatments. Over the course of the tumoral disease, cancer cells acquire genotypic and phenotypic changes, allowing them to improve cellular fitness and overcome environmental and treatment constraints. This progression is depicted by an evolutionary process in which single cells expand as a result of an interaction between single-cell changes and the lovelopments have made it possible to depict the development of cancer at the single-cell level, offering a novel method for understanding the biology of this complex disease. Here, we review those complex interactions from the perspective of single cells and introduce the concept of omics for single-cell studies. This review emphasizes the evolutionary dynamics that control cancer progression and the capacity of single cells to escape the local environment and colonize distant sites. We are assisting a rapid progression of studies carried out at the single-cell level, and we survey relevant single-cell technologies looking at multi-omics studies. These path for precision medicine in cancer

Statistical perspectives on dependencies between genomic markers

To study the genetic impact on a quantitative trait, molecular markers are used as predictor variables in a statistical model. This habilitation thesis elucidated challenges accompanied with such investigations. First, the usefulness of including different kinds of genetic effects, which can be additive or non-additive, was verified. Second, dependencies between markers caused by their proximity on the genome were studied in populations with family stratification. The resulting covariance matrix deserved special attention due to its multi-functionality in several fields of genomic evaluations