13 research outputs found

    APPLIED QUANTITATIVE PROTEOMICS ANALYSIS

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    En esta tesis se ha aplicado el estado del arte en análisis cuantitativo en proteómica. Los datos analizados en este trabajo, provenientes de tres proyectos distintos, fueron obtenidos usando tres de las técnicas más utilizadas en proteómica: cuantificación label-free, marcaje isobárico y SWATH. Los resultados obtenidos en los diferentes proyectos son también interpretados mediante múltiples herramientas bioinformáticas. La cuantificación label-free es utilizada aquí para obtener la combinación óptima de software y parámetros usando un conjunto de datos públicos. El marcaje isobárico, usando TMT, se emplea en el estudio de los diferentes perfiles de expresión proteica, obtenidos con dos modelos de hipoxia de diferente severidad en cerebros de rata. La técnica SWATH se busca en la búsqueda de biomarcadores de síndorme de ovario poliquístico en plasma. Por último, los elementos necesarios para la implantación de una plataforma de análisis proteómica , en términos de software y hardware, se describen en forma detallada. In this thesis, the state of the art in quantitative proteomics analysis has been applied. The data analyzed in this work, coming from three different projects, were acquired using three of the most used techniques in proteomics: label-free, isobaric labeling and SWATH. The results obtained in the different projects are also interpreted using multiple bioinformatics tools. The label-free quantization is used here to asses the optimal combination of software and parameters using a public data set. Isobaric labeling, using TMT, is employed to study the different profiles in protein expression when two hypoxic models, with different severity, are applied in rat brains. The SWATH technique is used in the search of biomarkers for polycystic ovary syndrome in plasma. Finally, the elements required for setting up a platform for proteomics analysis, both in terms of hardware and software, are comprehensively described.Tesis Univ. Jaén. Departamento de Biología Experimenta

    Knowledge extraction from biomedical data using machine learning

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    PhD ThesisThanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.European Union Seventh Framework Programme (FP7/2007- 2013) that funded part of this work under the “D-BOARD” project (grant agreement number 305815)

    McNair Scholars Research Journal Volume XIII

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    https://commons.stmarytx.edu/msrj/1012/thumbnail.jp

    McNair Scholars Research Journal Volume XIII

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    https://commons.stmarytx.edu/msrj/1012/thumbnail.jp

    Computational analysis of innate and adaptive immune responses

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    Both innate and adaptive immune processes rely on the activation of differentiated haematopoietic stem cell lineages to affect an appropriate response to pathogens. This thesis employs a largely network biology focused approach to better understand the specificity of immune cell responses in two distinct cases of pathogenic challenge. In the context of adaptive immunity, I studied the transcriptional responses of T cells during Graft-versus-Host Disease (GvHD). GvHD represents one of the major complications to arise following allogeneic hematopoietic stem cell transplantation and yet why only particular organs are damaged as a result of this pathology is still unclear. To investigate whether key GvHD transcriptional signatures seen in effector CD8+ T cells compared to naïve T cells are triggered in target organs or the secondary lymphoid organs, a module-based association test was developed to combine the output of gene clustering algorithms with that of differential expression analysis. This methodology significantly aided the identification of skin specific effector T cell transcriptional programs believed to drive murine GvHD pathogenesis at this site. Turning to the innate immune response, I investigated the transcriptional profiles of resting and activated macrophages in the setting of Tuberculosis (TB), the second leading cause of death from infectious disease worldwide. Regression-based analyses and clustering of macrophage expression data provided insight into the variations in gene expression profiles of naïve macrophages compared to those infected with Mycobacterium tuberculosis (MTB) or a vaccine strain of mycobacteria (BCG). The availability of genotype data as part of the macrophage dataset facilitated an expression quantitative trait loci (eQTL) study which highlighted a novel association between the cytoskeleton gene BCAR1 and TB risk, together with a previously undescribed trans-eQTL module specific to MTB infected macrophages. Potential genetic variants impacting expression of the aforementioned GvHD specific T cell transcriptional signatures were additionally investigated using external trans-eQTL datasets

    The application of gene-set analysis to identify the molecular genetic correlates of human cognitive abilities

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    Individual differences across seemingly disparate cognitive tests are not independent. This general factor of cognitive ability allows around half of the variation in a diverse battery of cognitive tests to be explained in terms of individual differences along a single dimension. An individual's position on this dimension, as ascertained using standardised tests of cognitive ability (intellectual quotient (IQ) tests), has been shown to be predictive of important life events ranging from educational and occupational success, to enjoying good health and longevity. Genetic differences have been shown to be associated with differences in cognitive ability and recent molecular genetic research has demonstrated that variants in linkage disequilibrium with common single nucleotide polymorphisms (SNPs) can explain around 50% of the variation in general cognitive ability. The goal of this thesis was to build on these findings by applying gene-set analysis methods to examine genome-wide association data sets to test guided hypotheses regarding the mechanisms and genetic architecture of human cognitive differences. Gene set analysis is a method that can lead to an increase in statistical power and help derive functional meaning from the results of genome wide association studies (GWAS). Existing GWAS data sets provided by the Cognitive Ageing Genetics in England and Scotland (CAGES) consortium, the Brisbane Adolescent Twin Study (BATS) and the Norwegian Cognitive NeuroGenetics (NCNG) cohort were used. The individuals in each of these groups have also completed a battery of cognitive tests enabling the extraction of a general factor of fluid cognitive ability and a measure of crystallised ability. In Chapter 3, the role of synaptic plasticity was examined using data derived from proteomic experiments on human and animal brain tissue which details the molecular constituents of the postsynaptic density and the associated components of the glutamatergic synapse. These components include: the a-amino-3-hydroxy-5-methyl-4-isoxazoiepropionic acid receptor complex (AMPA-RC), the A-methyl-D-aspartate receptor complex (NMDARC), and the metabotropic glutamate 5 receptor complex (mGlu5-RC). Using a competitive test of enrichment it was shown that the genes responsible for the proteins of the NMDA-RC were associated with fluid cognitive ability. This study (published as Hill et al., 2014) indicates that individual differences in synaptic plasticity may underlie some of the differences in fluid cognitive ability. In Chapter 4, rather than using traditionally defined linear pathways, the focus was on a gene set created by grouping genes according to their cellular function. Linear pathways, such as the glutamatergic system share proteins, a property which can be exploited by utilising horizontal pathway analysis, also termed functional gene group analysis. In a functional gene group analysis genes are grouped according to their cellular function such as ligand gated ion channels, neurotransmitter metabolism, and G protein relays. This chapter (published as Hill et al., 2014) examined the role that heterotrimeric G proteins play in cognitive abilities as previous work has indicated a role for them in individual differences in human cognitive ability. The analyses carried out in this chapter indicate that whilst heterotrimeric G proteins may be required to engage in cognitive tasks, genetic variation in the genes that code for these proteins is not associated with normal variation in cognitive ability. Chapter 5 examined the role of functional SNPs, defined as those that have been shown to be able to alter protein expression. Previous research has shown an association between genotype and methylation status and between genotype and gene expression in human cortical tissue. Using the results of previous research, gene sets were assembled which detailed SNPs known to alter methylation status and gene expression in the frontal cortex, the temporal cortex, the pons, and the cerebellum. In addition, the bioinformatics database dbQsnp was mined to assemble a SNP set detailing SNPs in known promoter regions. Finally, a gene set was made using published literature to capture SNPs affecting microRNA. Two complementary statistical methods were used to examine these sets for an association with general cognitive ability. The results of these analyses indicate that these gene sets are not more associated with cognitive ability beyond what would be expected by chance. Chapter 6 exploits the current knowledge of the molecular genetics of non-syndromic autosomal recessive intellectual disability (NS-ARID). The 40 genes associated with NSARID have a large deleterious effect on cognitive ability and appear to do so without the cognitive deficit being the product of obvious pathology. These 40 NS-ARID genes were examined as a gene set for an enriched association with cognitive abilities. Additionally, the biological systems that these genes are involved in were examined using an automated literature mining tool. These systems were then examined for an enriched association with general cognitive ability. When examining the 40 NS-ARID genes as a set there was no evidence that they were associated with cognitive abilities. The results of the literature search provided 180 additional gene sets based on the relationship between the 40 NS-ARID genes. These gene sets were examined for an enriched association with cognitive ability where the sodium ion transporter gene set (G0:0006814) was shown to be significantly enriched in the CAGES data set, but not BATS data set, for fluid ability. This could indicate that whilst the same genes are not involved in both intellectual disabilities and in cognitive abilities, the genes that can contain mutations resulting in intellectual disabilities are found in pathways that govern the normal range of cognitive ability. The results of this thesis indicate that common SNPs which tag causal variants are not randomly distributed across the genome but are clustered in genes that work together as part of a larger mechanism. In addition this work provides working examples of how multiple data sources that can be utilised to construct gene sets designed to explore the known relationship between genotype and cognitive ability and to utilise GWAS data sets to prioritise groups of genes

    Molecular targets and targeted therapies in pheochromocytoma

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    Pheochromocytoma and paraganglioma are tumors of chromaffin cells occurring within the adrenal medulla or the sympathetic nervous system, respectively. About 15% of these tumors are malignant, especially in patients with mutations in the subunit B of the succinate dehydrogenase, who have a 40% probability of developing distant metastases. For these malignancies surgery is currently the treatment of choice, but, especially for advanced forms, treatment is unsatisfactory and prognosis is very poor. Thus, novel treatment options for these patients are urgently in need. Recent advances in our understanding of the molecular pathology of pheochromocytoma and paraganglioma have led to the identification of key oncogenic events. Several molecular pathways have been suggested to play a role in these tumors, including the RTKs/Ras/MAPK, PI3K/Akt/mTOR, HIF, HSP90 and mithocondrial proteins involved in energy-producing pathways. This increased knowledge can be matched by the increased number of novel compounds, including tyrosine kinase inhibitors and other novel targeted therapies already in clinical trial for other cancers, targeting signaling pathways important for tumor proliferation, survival and metastatic dissemination. The overarching objective of this research project was to identify mechanism-based, molecularly targeted therapeutic approaches to modulate cancer cell growth and metastatic growth in pheochromocytoma, promoting the translational development of more effective therapeutic options for these tumors. The lack of sensitive animal models of pheochromocytoma has hindered the study of this tumor and in vivo evaluation of antitumor agents.To this end, two in vivo models for the evaluation of efficacy of several molecular targeted therapies were developed: an experimental metastasis model to 3 monitor tumor spreading and a subcutaneous model to monitor tumor growth and spontaneous metastasis. These models offer a platform for sensitive, non-invasive and real-time monitoring of pheochromocytoma primary growth and metastatic burden to follow the course of tumor progression and for testing relevant antitumor treatments in metastatic pheochromocytoma. I then use in vitro experiments and the in vivo models above described to test the efficacy of selective ATP-competitive inhibitors targeting both mTORC1 and mTORC2 complexes, pointing out an important role for the mTOR signaling pathway in the development of pheochromocytoma. Moreover, I investigated also the 90 kDa heat shock protein (Hsp90) as a potential therapeutic target for advanced pheochromocytoma, using both first and second generation Hsp90 inhibitors. As an alternative approach to identify potential drugs that can more rapidly be implemented into clinical trials in patients with metastatic pheochromocytoma or paraganglioma, I used a drug repurposing/repositioning approach. With this strategy, several molecules with potential bioactivity in pheochromocytoma cells were identified, including an example of a combination with synergistic effect

    Laboratory Directed Research and Development Program FY 2008 Annual Report

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    The Rise and Fall of the Bovine Corpus Luteum

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    This dissertation describes a study of the mechanisms regulating the genesis and subsequent involution of the temporary endocrine structure, the corpus luteum (CL), through the use of a bovine model. The CL is essential for maintaining a suitable uterine environment for embryo implantation and early development through secretion of the steroid hormone progesterone. The “Rise and Fall” of the CL occurs within each estrous cycle whereby the CL must form from the ruptured follicle, secrete sufficient progesterone for uterine maturation, and at the end of the cycle (or pregnancy) regress to allow new follicular development. During the rise of the CL, the composition and regulation of lipid droplets (LDs) were studied and it was determined that LDs are a common luteal cell structure formed by day 3 post-ovulation, and store both cholesteryl esters and triglycerides. Additionally, the LD-associated proteome was examined and established that steroidogenic enzymes are enriched in purified LD fractions. Demonstrating that luteal LDs may serve as critical mediators of steroidogenesis by storing steroid precursors in close association with steroidogenic enzymes. At the fall of the CL, alterations in the luteal transcriptome revealed changes consistent with early activation of cytokine signaling. One such cytokine, C-X-C motif chemokine ligand 8 (previously IL-8), was assessed for its ability to regulate luteal cell function. CXCL8 expression was determined to be induced in bovine luteal cells via p38 and JNK signaling and could induce bovine neutrophil migration. However, neutrophils had no effect on progesterone secretion unlike activated peripheral blood mononuclear cells which could inhibit luteal cell progesterone secretion. In total, the studies described herein indicate that both LDs and cytokines play important roles in CL development, function, and regression

    Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification

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    Concealed threat detection is at the heart of critical security systems designed to en- sure public safety. Currently, methods for threat identification and detection are primarily manual, but there is a recent vision to automate the process. Problematically, developing computer vision models capable of operating in a wide range of settings, such as the ones arising in threat detection, is a challenging task involving multiple (and often conflicting) objectives. Automated machine learning (AutoML) is a flourishing field which endeavours to dis- cover and optimise models and hyperparameters autonomously, providing an alternative to classic, effort-intensive hyperparameter search. However, existing approaches typ- ically show significant downsides, like their (1) high computational cost/greediness in resources, (2) limited (or absent) scalability to custom datasets, (3) inability to provide competitive alternatives to expert-designed and heuristic approaches and (4) common consideration of a single objective. Moreover, most existing studies focus on standard classification tasks and thus cannot address a plethora of problems in threat detection and, more broadly, in a wide variety of compelling computer vision scenarios. This thesis leverages state-of-the-art convolutional autoencoders and semantic seg- mentation (Chapter 2) to develop effective multi-objective AutoML strategies for neural architecture search. These strategies are designed for threat detection and provide in- sights into some quintessential computer vision problems. To this end, the thesis first introduces two new models, a practical Multi-Objective Neuroevolutionary approach for Convolutional Autoencoders (MONCAE, Chapter 3) and a Resource-Aware model for Multi-Objective Semantic Segmentation (RAMOSS, Chapter 4). Interestingly, these ap- proaches reached state-of-the-art results using a fraction of computational resources re- quired by competing systems (0.33 GPU days compared to 3150), yet allowing for mul- tiple objectives (e.g., performance and number of parameters) to be simultaneously op- timised. This drastic speed-up was possible through the coalescence of neuroevolution algorithms with a new heuristic technique termed Progressive Stratified Sampling. The presented methods are evaluated on a range of benchmark datasets and then applied to several threat detection problems, outperforming previous attempts in balancing multiple objectives. The final chapter of the thesis focuses on thread detection, exploiting these two mod- els and novel components. It presents first a new modification of specialised proxy scores to be embedded in RAMOSS, enabling us to further accelerate the AutoML process even more drastically while maintaining avant-garde performance (above 85% precision for SIXray). This approach rendered a new automatic evolutionary Multi-objEctive method for cOncealed Weapon detection (MEOW), which outperforms state-of-the-art models for threat detection in key datasets: a gold standard benchmark (SixRay) and a security- critical, proprietary dataset. Finally, the thesis shifts the focus from neural architecture search to identifying the most representative data samples. Specifically, the Multi-objectIve Core-set Discovery through evolutionAry algorithMs in computEr vision approach (MIRA-ME) showcases how the new neural architecture search techniques developed in previous chapters can be adapted to operate on data space. MIRA-ME offers supervised and unsupervised ways to select maximally informative, compact sets of images via dataset compression. This operation can offset the computational cost further (above 90% compression), with a minimal sacrifice in performance (less than 5% for MNIST and less than 13% for SIXray). Overall, this thesis proposes novel model- and data-centred approaches towards a more widespread use of AutoML as an optimal tool for architecture and coreset discov- ery. With the presented and future developments, the work suggests that AutoML can effectively operate in real-time and performance-critical settings such as in threat de- tection, even fostering interpretability by uncovering more parsimonious optimal models. More widely, these approaches have the potential to provide effective solutions to chal- lenging computer vision problems that nowadays are typically considered unfeasible for AutoML settings
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