Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites

Development of Biclustering Techniques for Gene Expression Data Modeling and Mining

The next-generation sequencing technologies can generate large-scale biological data with higher resolution, better accuracy, and lower technical variation than the arraybased counterparts. RNA sequencing (RNA-Seq) can generate genome-scale gene expression data in biological samples at a given moment, facilitating a better understanding of cell functions at genetic and cellular levels. The abundance of gene expression datasets provides an opportunity to identify genes with similar expression patterns across multiple conditions, i.e., co-expression gene modules (CEMs). Genomescale identification of CEMs can be modeled and solved by biclustering, a twodimensional data mining technique that allows clustering of rows and columns in a gene expression matrix, simultaneously. Compared with traditional clustering that targets global patterns, biclustering can predict local patterns. This unique feature makes biclustering very useful when applied to big gene expression data since genes that participate in a cellular process are only active in specific conditions, thus are usually coexpressed under a subset of all conditions. The combination of biclustering and large-scale gene expression data holds promising potential for condition-specific functional pathway/network analysis. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-Seq data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, especially for scRNA-Seq data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. QUBIC2, a novel biclustering algorithm, is designed for large-scale bulk RNA-Seq and single-cell RNA-seq (scRNA-Seq) data analysis. Critical novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression; (ii) adopted the Gaussian mixture distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes; (iii) utilized a Dual strategy to expand the core biclusters, aiming to save dropouts from the background; and (iv) developed a statistical framework to evaluate the significances of all the identified biclusters. Method validation on comprehensive data sets suggests that QUBIC2 had superior performance in functional modules detection and cell type classification. The applications of temporal and spatial data demonstrated that QUBIC2 could derive meaningful biological information from scRNA-Seq data. Also presented in this dissertation is QUBICR. This R package is characterized by an 82% average improved efficiency compared to the source C code of QUBIC. It provides a set of comprehensive functions to facilitate biclustering-based biological studies, including the discretization of expression data, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization of any identified biclusters, and co-expression networks elucidation. In the end, a systematical summary is provided regarding the primary applications of biclustering for biological data and more advanced applications for biomedical data. It will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency

Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks

Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer’s disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named “FastGGM”

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

Integrative transcriptome and phenome analysis reveals unique regulatory cascades controlling the intraerythrocytic asexual and sexual development of human malaria parasites

The Plasmodium falciparum parasite, the major causative agent of malaria on the African continent, has evolved numerous cellular adaptations to effectively propagate its species. The parasite can proliferate asexually, producing mass amounts of progeny to subsist in the human host or differentiate into sexual forms (gametocytes) that, once mature, can transmit to a feeding Anopheles mosquito. Key to our ability to effectively develop chemical candidates that interfere with either of these processes is the identification and understanding of critical factors that regulate parasite development. This is particularly true for the development of antimalarials that can be used in malaria elimination strategies by targeting both parasite proliferation and transmission. We therefore hypothesized that parasite proliferation and differentiation use divergent mechanisms for gene expression that could be observed through a thorough investigation of the functional genome of these different parasite forms. This doctoral study therefore set out to increase our knowledge base on three crucial aspects of parasite development: 1) the atypical cell cycle that allows the rapid proliferation of asexual parasites; 2) the full molecular profile of gametocytogenesis enabling the cellular differentiation that allows the parasite to transmit; and 3) the metabolic differences between these proliferating and differentiating parasites that results from their strategy-specific mechanisms of developmental control. The atypical cell cycle of the parasite, associated with the massive cell number expansion in asexual development, is notoriously difficult to study. Here, we contributed a novel system by developing a cell cycle synchronization tool that reversibly blocks the development of asexual parasites at the G1/S transition. This results in an inescapable arrest of the cell cycle that is completely and functionally reversible; parasites re-initiate cell cycle progression and continue to S phase within 6 h. This system provided the opportunity to characterize cell cycle phases in the parasite and additionally evaluate molecular mechanisms associated with cell cycle arrest or re-initiation. During cell cycle arrest, the parasite enters a quiescent state reminiscent of a mitogen-activated restriction point. This arrest is unique and solely attributed to the removal of the specific mitogens within this system, polyamines. These analyses indicate the close interaction between transcriptional regulation and signal transduction cascades in the progression through the parasite’s cell cycle and for the first time highlight aspects of controlled cell cycle regulation in Plasmodium. In contrast to proliferation, the process of sexual differentiation only started receiving attention in the past few years. As such, we lack fundamental understanding of the mechanisms driving the unique gametocyte differentiation of P. falciparum parasites. This study contributes a detailed analysis of gametocyte differentiation that revealed distinct developmental transitions demarcating the start of gametocytogenesis, intermediate gametocyte development and finally maturation to produce the transmissible mature gametocytes. The study provides evidence for coordinated regulation of gene expression on a transcriptional level. We propose a model for regulation of gametocytogenesis in malaria parasites that involves active repression of gene sets mediated through epigenetics and RNA destabilization as well as active transcription of gene sets through successive ApiAP2 transcription factor activity. This data provides the most detailed framework of coordinated gene regulation events underlying development of P. falciparum gametocytes to date, a unique resource for the malaria community. The comprehensive and complex transcriptional regulation described for the proliferation and differentiation of the parasite led us to evaluate the functional consequence thereof. A whole cell phenotype microarray system was evaluated for its ability to measure the metabolic processes that define asexual and sexual stage metabolism as a functional consequence of changed gene expression profiles during proliferation and differentiation. The study provided metabolic profiles detailing carbon and nitrogen metabolism in asexual parasites, mature and immature gametocyte stages. The data highlighted dipeptide metabolism as a distinguishing feature in mature gametocytes and showed the presence of a low, delayed metabolic state concurrent with reduced transcriptional activity observed in this stage. These results show that gene expression changes associated with differentiation compared to proliferation translate to an observable metabolic phenotype and that transcriptional regulation shapes the molecular landscape underlying crucial events that enable the parasite’s intraerythrocytic asexual and sexual development.Thesis (PhD)--University of Pretoria, 2019.BiochemistryPhDUnrestricte

The integration of 'omic' disciplines and systems biology in cattle breeding

Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists - and they will also be discussed

Determinantes moleculares de la tolerancia a los antibióticos en la cepa de alto riesgo Pseudomonas aeruginosa AG1 mediante un enfoque multi-ómico: del genoma a la red transcriptómica en respuesta a la ciprofloxacina

La resistencia a los antibióticos es una amenaza importante para la salud pública porque compromete la administración de una terapia antibiótica adecuada. Pseudomonas aeruginosa es un patógeno oportunista que causa infecciones entre huéspedes inmunodeprimidos. P. aeruginosa AG1 (PaeAG1) es una cepa costarricense con resistencia a múltiples antibióticos como los β-lactámicos (incluidos los carbapenémicos), aminoglucósidos y fluoroquinolonas. PaeAG1 se identificó como el primer aislamiento de P. aeruginosa llevando los genes VIM-2 e IMP-18 que codifican las enzimas metalo-β-lactamasas (MBL). Según la Organización Mundial de la Salud (OMS), esta cepa se considera crítica, siendo clasificada en el grupo de Prioridad 1 por su resistencia a los carbapenémicos. PaeAG1 tiene características particulares a niveles genómicos y fenómicos, muchas de ellas relacionadas con la resistencia a los antibióticos. Debido a esto fue de interés estudiar los determinantes moleculares de la tolerancia a los antibióticos en PaeAG1 utilizando un enfoque multi-ómico. Primero, el ensamblaje del genoma fue el paso inicial para comprender la arquitectura genómica de esta cepa de alto riesgo. Del estudio con 13 enfoques diferentes, la selección del mejor ensamblaje reveló que el genoma de PaeAG1 tiene 57 islas genómicas que albergan seis profagos y dos integrones completos con los genes de las MBL. Además, se encontraron 250 genes de virulencia y 60 genes asociados a la resistencia a los antibióticos. Segundo, un enfoque genómico comparativo fue implementado para definir y actualizar la relación filogenética entre los genomas completos de P. aeruginosa, el contenido de islas genómicas en otras cepas, y la arquitectura de las regiones genómicas alrededor de los dos integrones portadores de MBL. Para el caso del IMP-18, el integrón que lo contiene y la arquitectura alrededor nunca habían sido reportados en la literatura. Luego, estudiamos el perfil proteómico de PaeAG1 después de la exposición a antibióticos usando electroforesis en gel bidimensional con un protocolo de análisis de imágenes y aprendizaje automático (inteligencia artificial). Los perfiles proteómicos mostraron que ciprofloxacina (CIP) induce un patrón proteico similar al control sin antibióticos, en contraste con otros antibióticos que se agruparon por separado. En cuarto lugar, para estudiar la respuesta central a múltiples perturbaciones en P. aeruginosa, es decir, el perturboma central, un enfoque de aprendizaje automático fue implementado. Utilizando datos transcriptómicos públicos, evaluamos seis enfoques para clasificar y seleccionar genes. La anotación molecular de 46 genes de la respuesta central reveló funciones biológicas relacionadas con la reparación del daño del ADN, metabolismo y la respiración aeróbica en el contexto de la tolerancia al estrés. Finalmente, para evaluar los efectos de la ciprofloxacina en PaeAG1, realizamos una comparación de curvas de crecimiento, análisis de expresión diferencial usando RNA-Seq y análisis de redes. El análisis transcriptómico mostró una expresión diferencial de 518 genes en el tiempo después del tratamiento con ciprofloxacina, incluyendo genes de fagos residentes que se regularon positivamente. Este último caso se validó a nivel fenómico utilizando ensayos de placa de fagos y que explicó las observaciones fenotípicas en la reducción de las curvas de crecimiento. En conjunto, utilizando un enfoque multiómico (a niveles genómico, genómico comparativo, perturbómico, transcriptómico, proteómico y fenómico), proporcionamos nuevos conocimientos sobre los determinantes genómicos y transcriptómicos asociados con la tolerancia a antibióticos en PaeAG1. Estos resultados no solo explican en parte la condición de alto riesgo de esta cepa que le permite conquistar ambientes nosocomiales y su perfil de multirresistencia, sino que esta información eventualmente podrá ser usada como parte de las estrategias para combatir a este patógeno.UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Interdisciplinarias::Doctorado Académico en Ciencia