Dissertations / Theses on the topic 'OMIEC'

The aim of this thesis was to synthesize a functionalized polar conjugated polythiophene that could be (3D-) printed into form-stable structures for bio-interfacing. The material design rationale aimed for a water-processable polymer that had the capability of electronic and ionic conduction, by using a thiophene backbone and oligoethylene side chains. Functionalization of the oligoethylene side chains with carboxylate groups created a polyanion, which allowed for a bio-inspired approach to combine printability and form-stability through formation of complex coacervates. The synthesis of the conjugated monomer and polymer was optimized to provide a more sustainable and material efficient synthesis route. Combined structural analysis with 1H-NMR, FT-IR and UV-vis revealed successful synthesis of the target polymer. Spectro electrochemistry revealed that the polymer was optically and electrochemically active in both the protected and deprotected form. The obtained material is processable from water, and initial tests revealed that crosslinking can be achieved through formation of acid dimers, ionic crosslinks with Ca2+ ions and complex coacervation with a polycation.

Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet

La fibrillation atriale (FA) est un problème de santé publique majeur dans le monde entier. Le remodelage électrique, structurel et neuronal est sous-jacent à la myopathie atriale. La pharmacothérapie actuelle est souvent inefficace en raison du manque de connaissance de la pathophysiologie de la FA.Pour comprendre comment se réalise le remodelage atrial, une approche Omique qui explore le transcriptome, l'épigénome (méthylome et microOme) et le génome de patients atteints de FA a été réalisée. Parallèlement, le phénotype de vieux rats spontanément hypertendus (SHRs) a été caractérisé et une étude pharmacologique avec la décitabine (5-Aza-2'-deoxycitidine) a été menée. Les patients atteint de FA présentent un profil transcriptomique et d'expression de miRNA alteré dans l'oreillete gauche (OG), soulignant le rôle important d'un processus de "œmorphogénèse de la structure anatomique". L'expression réduite de Pitx2 était inversement corrélée à la taille de l'OG et ne pouvait pas être expliquée ni par le facteur de transcription ni par la surexpression de Smyd2, une cible de miR-519b. Les SHRs, similairement aux observations chez l'homme, ont développé des arythmies dépendantes de l'âge associées au remodelage atrial et ventriculaire gauche. La FA a été trouvée associée à l'hyperméthylation du promoteur de Pitx2 à la fois chez l'homme et chez les SHRs. L'agent hyperméthylant décitabine a amélioré le profil arhytmique de l'ECG et les activités SOD, et la réduction de la fibrose dans le ventricule gauche des SHRs. En utillisant une approche NGS basée sur un panel personnalisé de 55 gènes candidats à la myopathie atriale dans une cohorte de 94 patients atteints de FA, 11 nouvelles variantes faux-sens potentiellement pathogènes impliqués dans le remodelage structurel ont été identifiés. Des études fonctionnelles de ces variants ont débuté. Trois patients sont également des porteurs de variantes dans les gènes connus de FA. Les résultats actuels suggèrent que 1) la régulation épigénétique peut jouer un rôle dans la pathophysiologie de la FA 2) les agents hypométhylants doivent être considérés comme une nouvelle thérapie de la FA 3) une approche Omique peut aider à découvrir de nouveaux mécanismes sous-jacents à la myopathie atriale
Atrial fibrillation (AF) is a major public health care problem worldwide. Electrical, structural, and neural remodeling underlie atrial myopathy. Current pharmacotherapy is often ineffective due to the lack of knowledge of AF pathophysiology. To understand how atrial remodeling occurs, an Omic approach that explore the transcriptome, epigenome (methylome and microOme) and genome of AF patients was performed. In parallel, ageing spontaneously hypertensive rats (SHRs) were phenotypically characterised and a pharmacological study with decitabine (5-Aza-2’-deoxycitidine) was conducted. AF patients presented an altered transcriptomic and microRNA expression profile in the left atria (LA), emphasizing the important role of an "anatomical structure morphogenesis" process. The Pitx2 reduced expression was inversely correlated with LA size, and could not be explained by transcriptor factor. Smyd2 is a target of miR-519b-3p. SHRs, similar to what is observed in humans, developed age-dependent arrhythmias associated with left atrial and ventricular remodeling. AF was found to be associated with Pitx2 promoter hypermethylation both in humans and in SHRs. The hypomethylating agent decitabine improved ECG arrhythmic profiles and superoxide dismutase activities, and reduced fibrosis in the left ventricle of SHRs. Using a next-generation sequencing approach based on a custom panel of 55 atrial myopathy candidate genes in a cohort of 94 AF patients, 11 novel potentially pathogenic missense variants involved in structural remodeling were identified. Functional studies of these variants have started. Three patients were also carriers of variants in known AF-causing genes. The present results suggest that 1) epigenetic regulation may play a role in the pathophysiology of AF 2) hypomethylating agents have to be considered as a new AF therapy 3) an Omic approach may help to uncover new mechanisms underlying atrial myopathy

Thesis (PhD(Agric) Viticulture and Oenology. Wine Biotechnology))--University of Stellenbosch, 2009.
The main goal of this project was to elucidate the underlying genetic factors responsible for the different fermentation phenotypes and physiological adaptations of industrial wine yeast strains. To address this problem an ‘omic’ approach was pursued: Five industrial wine yeast strains, namely VIN13, EC1118, BM45, 285 and DV10, were subjected to transcriptional, proteomic and exometabolomic profiling during alcoholic fermentation in simulated wine-making conditions. The aim was to evaluate and integrate the various layers of data in order to obtain a clearer picture of the genetic regulation and metabolism of wine yeast strains under anaerobic fermentative conditions. The five strains were also characterized in terms of their adhesion/flocculation phenotypes, tolerance to various stresses and survival under conditions of nutrient starvation. Transcriptional profiles for the entire yeast genome were obtained for three crucial stages during fermentation, namely the exponential growth phase (day 2), early stationary phase (day 5) and late stationary phase (day 14). Analysis of changes in gene expression profiles during the course of fermentation provided valuable insights into the genetic changes that occur as the yeast adapt to changing conditions during fermentation. Comparison of differentially expressed transcripts between strains also enabled the identification of genetic factors responsible for differences in the metabolism of these strains, and paved the way for genetic engineering of strains with directed modifications in key areas. In particular, the integration of exo-metabolite profiles and gene expression data for the strains enabled the construction of statistical models with a strong predictive capability which was validated experimentally. Proteomic analysis enabled correlations to be made between relative transcript abundance and protein levels for approximately 450 gene and protein pairs per analysis. The alignment of transcriptome and proteome data was very accurate for interstrain comparisons. For intrastrain comparisons, there was almost no correlation between trends in protein and transcript levels, except in certain functional categories such as metabolism. The data also provide interesting insights into molecular evolutionary mechanisms that underlie the phenotypic diversity of wine yeast strains. Overall, the systems biology approach to the study of yeast metabolism during alcoholic fermentation opened up new avenues for hypothesis-driven research and targeted engineering strategies for the genetic enhancement/ modification of wine yeast for commercial applications.

The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.

Asthma is a respiratory syndrome associated with airflow limitation, bronchial hyperresponsiveness and inflammation of the airways in the lungs. Despite the ongoing research efforts, the outstanding heterogeneity displayed by the multiple forms in which this condition presents often hampers the attempts to determine and classify the phenotypic and endotypic biological structures at play, even when considering a limited assembly of asthmatic subjects. To increase our understanding of the molecular mechanisms and functional pathways that govern asthma from a systems medicine perspective, a computational workflow focused on the identification of personalized transcriptomic modules from the U-BIOPRED study cohorts, by the use of the novel MODifieR integrated R package, was designed and applied. A feature selection of candidate asthma biomarkers was implemented, accompanied by the detection of differentially expressed genes across sample categories, the production of patient-specific gene modules and the subsequent construction of a set of core disease modules of asthma, which were validated with genomic data and analyzed for pathway and disease enrichment. The results indicate that the approach utilized is able to reveal the presence of components and signaling routes known to be crucially involved in asthma pathogenesis, while simultaneously uncovering candidate genes closely linked to the latter. The present project establishes a valuable pipeline for the module-driven study of asthma and other related conditions, which can provide new potential targets for therapeutic intervention and contribute to the development of individualized treatment strategies.

We develop statistical tools for analyzing different types of phenotypic data in genome-wide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases as a mixture of multinomial logistic functions of cases and controls, according to disease prevalence. In a typical GWAS, where thousands of single-nucleotide polymorphisms (SNPs) are available for testing, score-based test statistics are ideal in this case. Other tests of associations such as Wald’s and likelihood ratio tests are known to be asymptotically equivalent to the score test, however their performance under small sample sizes can vary significantly. In order to allow the test comparison to be performed under the proposed case-base-control (CBC) design, we provide an estimation procedure using the maximum likelihood (ML) method along with the expectation-maximization (EM) algorithm. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for lack of controls. In the second part of the thesis, we consider a joint analysis of both genome-wide SNPs as well as multiple phenotypes, with a focus on the challenges they present in the estimation of SNP heritability. The current standard for performing this task is fit-ting a variance component model, despite its tendency to produce boundary estimates when small sample sizes are used. We propose a Bayesian covariance component model (BCCM) that takes into account genetic correlation among phenotypes and genetic correlation among individuals. The use of Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP heritability estimates over univariate and ML-based methods, thus explaining why recent progress in eQTL identification has been limited. I published this work as an article in the European Journal of Human genetics. In the third part of the thesis, we study the prospects of using the proposed BCCM for phenotype prediction. Results from real data show consistency in accuracy between ML based methods and the proposed Bayesian method, when effect sizes are estimated using their posterior mode. It is also noted that an initial imputation step relatively increases the predictive accuracy.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community.
Ph. D.

Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research. Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS). Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process. Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform.
Ph. D.

Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomics. Studies in these fields have taken different designs and directions from genomewide associations to studying the complex interplay between genetics and structural connectivity of a wide range of brain-related diseases. Nevertheless, such combinations of heterogeneous, high dimensional and inter-related data has introduced new challenges which cannot be handled with traditional statistical methods. In this thesis, we proposed analysis pipelines and methodologies to study the causal relationship between neuroimaging features, including tumour characteristics and connectomics, genetics and clinical factors in brain-related diseases. In doing so, we adopted two longitudinal study designs and modelled the association between Alzheimer's disease progression and genetic factors, utilising local and global brain connectivity networks. In addition to that, we performed a multi-stage radiogenomic analysis in glioblastoma using non-parametric statistical methods. To address some limitations in the methods, we adopted the Structural Equation Model and developed a mathematical model to examine the inter-correlation between neuroimaging and multi-omic characteristics of brain-related diseases. Our findings have successfully identified risk genes that were previously reported in the literature of Alzheimer's and glioblastoma diseases, and discovered potential risk variants which associate with disease progression. More specifically, we found some loci in the genes CDH18, ANTXR2 and IGF1, located in Chromosomes 5, 4 and 12, to have effect on the brain connectivity over time in Alzheimer's disease. We also found that the expression of APP, HFE, PLAU and BLMH have significant effects on the structural connectivity of local areas in the brain, these are the left Heschl gyrus, right anterior cingulate gyrus, left fusiform gyrus and left Heschl gyrus, respectively. These potential association patterns could be useful for early disease diagnosis, treatment and neurodegeneration prediction. More importantly, we identified gaps in the imaging genetics methodologies, we proposed a mathematical model accounting for these limitations and evaluated the model which produced promising results. Our proposed flexible model, BiGen, addresses the gaps in the existing tools by combining neuroimaging, genetics, environmental, and phenotype information to a single complex analysis, accounting for the heterogeneity, inter-correlation, and non-linearity of the variables. Moreover, BiGen adopts an important assumption which is hardly met in the literature of imaging genetics, and that is, all the four variables are assumed to be latent constructs, that means they can not be observed directly from the data, and are measured through observed indicators. This is an important assumption in both neuroimaging, behavioural and genetic studies, and it is one of the reasons why BiGen is flexible and can easily be extended to include more indicators and latent constructs in the context of brain-related diseases.

L’estrés per calor (EC) genera pèrdues significatives en l’industria lletera caprina quan l’índex de temperatura-humitat (THI) és > 75. Les tecnologies ‘òmiques’ ofereixen una perspectiva holística de com les cabres combaten l’EC i poden permetre trobar biomarcadors. Amb aquest objectiu, es van portar a terme 3 experiments (Exp.) utilitzant cabres lleteres Murciano-Granadines i una càmbra climàtica amb caixes metabòliques. Les cabres lleteres (n = 32) es van alimentar amb una ració total mesclada i es van munyir ⨯1 diàriament sota dos condicions climàtiques. Aquestes foren: TN (termo-neutralitat, THI = 59-65) i EC (dia, THI = 86; nit, THI = 77) amb fotoperíode (llum-foscor) constant (12-12 h). Es van controlar a diari diferents variables fisiològiques i productives, setmanalment la composició de la llet i a l’inici i al final de tots els períodes Exp, el pes corporal. En l’Exp.1, es van estudiar els canvis transcriptòmics en sang de 2 grups de 4 cabres (n = 8) sota TN o EC durant 35 dies. Els microarrays en mostres de sang van revelar que l’EC va augmentar l’expressió de 55 gens i en va reduir la de 88 gens. L’anàlisi Dynamic Impact Approach va indicar 31 rutes biològiques afectades per EC. Els efectes van ésser negatius per a la migració transendotelial dels leucòcits, l’adhesió cel·lular, el llinatge de cèl·lules hematopoiètiques, i la senyalització de Ca i PPAR, mentre que es va activar el metabolisme dels nucleòtids. En conclusió, l’EC va afectar negativament a la producció de llet i va alterar la funcionalitat de les cèl·lules immunitàries, fet que podria resultar en un sistema immunitari amb menor capacitat per combatre malalties. En l’Exp.2, es van avaluar candidats de biomarcadors d’EC en orina mitjançant 1H RMN (Resonància Magnètica Nuclear de protons). Les cabres (n = 16) es van sotmetre a les condicions de TN i EC segons un disseny crossover durant 35 d. Es va emprar un anàlisi de mínims quadrats discriminatoris parcials (PLS-DA) amb validació creuada que va permetre separar els grups TN i EC. Els metabòlits discriminants entre grups van ser derivats tòxics de la fenilalanina (Phe) (OH-fenilacetat, OH-fenilacetilglicina, fenilglioxilat i hipurat) que van augmentar en EC, respecte a TN. Una major excreció d’aquests compostos en orina va indicar que l’EC indueix un sobrecreixement de la microbiota gastrointestinal nociva segrestant els aminoàcids aromàtics de la ració. En consequència, l’EC hauria de disminuir la síntesi de neurotransmissors i hormones tiroidees, abaixant el rendiment i la composició de la llet. En conclusió, la minvada lletera de les cabres EC es va reflexar en el seu metaboloma per la presència de compostos tòxics derivats del tracte gastrointestinal en l’orina. Es van identificar els derivats de Phe i l’hipurat com biomarcadores d’EC en orina de cabres lleteres. En l’Exp.3, les cabres lleteres (n = 8) es van sotmetre a les condicions de TN i EC durant 15 dies i es van avaluar biomarcadors candidats en llet mitjançant 1H RMN. El dia 12, se les va exposar a lipopolisacàrid d’E. coli (LPS) o salí (CON) en una de les mamelles i es varen prendre mostres de llet a les 0, 4, 6, 12 y 24 h. El citrat en llet va augmentar en EC indicant un canvi en la funció mitocondrial dels macròfags (i.e. transport de citrat de la mitocòndria al citosol para produir mediadors inflamatoris). El metaboloma en llet de les mamelles TN-LPS va quedar menys afectat i va recuperar el seu nivell basal en un període més curt que en les mamelles EC-LPS. Els metabòlits discriminants van ésser la colina, els N-acetil-carbohidrats, L-lactat, ß-hidroxibutirat i la fosfocolina. Per tant, el perfil metabolòmic de la llet es va veure molt afectat per les condicions ambientals i de salut de la mamella. El citrat i la colina van indicar l’aparició d’estadis oxidatius e inflamatoris en la glàndula mamària sota EC o LPS, respectivament, pel que es proposen com a biomarcadors en llet.
Heat stress (HS) causes significant losses in the dairy goat industry when temperature-humidity index (THI) is >75. ‘Omic’ technologies offer a holistic approach to figure out how goats cope with HS and to find biomarkers. With this aim, 3 experiments (Exp.) were carried out using Murciano-Granadina dairy goats and a climatic chamber with metabolic boxes. Lactating does (n = 32) were fed a total mixed ration, freely watered and milked ⨯1 daily under different climatic conditions. They were: TN (thermal neutral, THI = 59-65) and HS (day, THI = 86; night, THI = 77). Photoperiod (light-dark) was constant (12-12 h). Physiological and performance traits were recorded daily, milk composition sampled weekly and BW at the start and the end of each Exp. period. In Exp.1, changes in blood transcriptome of 2 groups of 4 does (n = 8), under TN or HS were studied for 35 d. In addition to performance impairment, microarrays of blood samples at d 35, revealed that HS up-regulated 55 genes and down-regulated 88. Dynamic Impact Approach analysis showed 31 biological pathways affected by HS. Effects were negative in these related with leukocyte transendothelial migration, cell adhesion, hematopoietic cell lineage, Ca and PPAR signaling, whereas were positive on those activating nucleotide metabolism. In conclusion, HS worsened milk performances and altered the functionality of immune cells, which may result in a less competent immune system for fending-off diseases. In Exp.2, HS candidate biomarkers in urine were assessed by 1H NMR (proton Nuclear Magnetic Resonance)-based metabolomics. Does (n = 16) were submitted to the TN and HS conditions in a crossover design lasting 35 d. Partial least square-discriminant analysis with cross validation were used to separate between TN and HS clusters. Discriminating metabolites were Phenilalanine (Phe) derivative toxic compounds (OH-phenylacetate, OH-phenylacetylglycine, phenylglyoxylate and hippurate), which increased in HS vs. TN does. Increased urinary excretion of these compounds indicated a harmful gastrointestinal microbiota overgrowth by HS, which sequestrated dietary aromatic amino acids. Consequently, HS does should have decreased the synthesis of neurotransmitters and thyroid hormones, impairing milk yield and composition. In conclusion, lactational impairment of HS does was reflected in their metabolome by the presence of gut-derived toxic compounds in urine. Phe derivatives and hippurate were identified as key urinary biomarkers of HS dairy goats. In Exp.3, lactating dairy goats (n = 8) were submitted to the TN and HS conditions for 15 d and milk candidate biomarkers assessed by 1H NMR. On d 12, does were challenged with E. coli lipopolysaccharide (LPS) or saline (CON) by udder-half and milk samples collected post-challenge (h 0, 4, 6, 12 and 24). Treatments were: TN (CON and LPS) and HS (CON and LPS). Milk citrate increased in HS revealing a shift in macrophages’ function (i.e., transporting mitochondrial citrate to cytosol to produce inflammatory mediators). Differences between TN and HS in response to LPS over time where observed by PLS-DA. Milk metabolome in TN-LPS udder halves was less affected and restored earlier than in HS-LPS halves. Most discriminating metabolites were choline, N-acetylcarbohydrates, L-lactate, ß-hydroxybutyrate (BHBA) and phosphocholine. Overall, milk metabolomic profiles were markedly affected by ambient and udder health conditions. Citrate and choline indicated the occurrence of oxidative and inflammatory stages in HS and LPS stressed mammary glands, respectively, and were proposed as key biomarkers in milk.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 151-167).
Medulloblastoma (MB) is a chiefly pediatric cancer of the cerebellum that has been studied extensively using genomic, epigenomic, and transcriptomic data. It comprises at least four molecularly distinct subgroups: WNT, SHH, Group 3, and Group 4. Despite the detailed characterization of MB, many disease-driving events remain to be elucidated and therapeutic targets to be nominated. In this thesis, we describe three studies that contribute to a better understanding of this devastating disease: First, we describe a study that aims to fully describe the genomic landscape in the largest medulloblastoma cohort to date, using 491 sequenced MB tumors and 1,256 epigenetically analyzed cases. This work describes subgroup-specific driver alterations including previously unappreciated actionable targets; and, based on epigenetic data, identifies further heterogeneity within Group 3 and Group 4 tumors. Second, we focus on the proteomes and phospho-proteomes of 45 medulloblastoma samples.
We identified distinct pathways associated with two subsets of SHH tumors that showed robustly distinct proteomes, but similar transcriptomes, and found post-translational modifications of MYC that are associated with poor outcomes in Group 3 tumors. We also found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. This study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies. Third, we characterize the metabolomic space of MB on largely the same 45 tumors as used in the proteome-focused study. Here, we present preliminary insights from derived from integrative network and other analyses. We find that MB consensus subgroups are preserved in metabolic space, and that certain classes of metabolites are elevated in MYC-activated MB.
We also show that, similar to other cancers, a previously described gain-of-function mutation in IDH1 may cause elevated 2-hydroxyglutarate levels in MB. The work described in this thesis significantly enhances previous knowledge of medulloblastoma and its subgroups, and provides insights that may aid in the development of medulloblastoma therapies in the near future.
by Tobias Ehrenberger.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering

Environmental toxicology is undergoing a paradigm shift due to the new concerning environmental reality. Nowadays, to manage subtler and chronic effects of chemicals, either single or mixtures, is an imperative need, especially at low and environmentally relevant concentrations. Not least important is to deal with emerging contaminants (ECs), whose harmful effects in ecosystems and toxicity mechanisms are still unknown. Therefore, new strategies for assessing the toxicity of pollutants with greater environmental relevance must be developed, what requires the application of integrative approaches combining tools from different disciplines. Omic technologies allow the holistic measurement of effects at low levels of biological organization in high throughput platforms, and provide mechanistic data which may become essential in the development and application of more efficient and effective testing strategies. Overall, this thesis aimed to prove the importance of integrating omic and conventional toxicological approaches in order to obtain significant information that helps to unravel any new toxicity mechanism triggered by ECs on the aquatic environment using Daphnia magna as model species. The ECs studied included suspected lipid disruptors (endocrine disrupting compounds, EDCs) and ECs that are known to affect the central nervous system (i.e. neuroactive pharmaceuticals and other chemicals). Different integrative approaches have been developed to assess the toxicity of these compounds by linking effects on reproduction and behavior (individual organism responses), with gene expression changes and its subsequent metabolomic (and thus lipidomic) disruption in D. magna. The ability of EDCs and neuroactive pharmaceuticals to affect reproduction and disrupt lipid homeostasis, as well as the molecular signaling pathways that modulate this disruption, has been addressed throughout the thesis. Within the chapter 2, microarray transcriptomic analysis of D. magna adult females exposed to some EDCs during reproduction was performed, together with the effects on their lipidome by a lipidomic analysis using UHPLC-TOF MS. Common transcriptional mechanisms were identified as energy-related categories, molting and reproduction, and different lipid functional categories. The obtained results allowed to link reproductive effects with changes in lipid profiles and disrupted transference of lipids to eggs in D. magna females. Lipidomic effects of neuroactive pharmaceuticals at environmental concentrations and the driven molecular mechanisms behind them were studied in chapter 3. The hypothesis that serotonin may be involved in regulating lipid dynamics and fecundity responses in D. magna was confirmed by the analysis of the lipidome of genetically tryptophan hydrolase gene knockout clones. Finally, in chapter 4 a targeted metabolomic approached was developed to analyze neurotransmitters in D. magna samples and employed in the study of the effects of neuroactive pharmaceuticals that affected Daphnias’ cognitive behavior. Metabolomic results were linked to the associated transcriptional disruption studied through RNAseq, probing the suitability of these organisms for environmental neurotoxicity studies. Overall, the results obtained throughout this thesis allowed to link transcriptomic signaling pathways with metabolomic effects (lipidomic and neurotransmitter profiles) and with apical responses (reproduction and behavior).
La toxicología ambiental está experimentando un cambio de paradigma debido a la preocupante nueva realidad medioambiental. Hoy en día, gestionar los efectos más sutiles y crónicos de los compuestos químicos, ya sean individualizados o en mezclas, es una necesidad imperiosa, especialmente en concentraciones bajas y relevantes para el medio ambiente. No menos importante es ocuparse de los contaminantes emergentes (EC), cuyos efectos nocivos en los ecosistemas y sus mecanismos de toxicidad aún se desconocen. Por lo tanto, deben elaborarse nuevas estrategias con mayor relevancia ambiental para evaluar la toxicidad de los contaminantes, lo que requiere la aplicación de enfoques integradores que combinen herramientas de distintas disciplinas. Las tecnologías ómicas permiten medidas holística de efectos producidos a bajos niveles de organización biológica en plataformas de alto rendimiento, y proporcionan datos mecanicistas que pueden resultar esenciales para el desarrollo y la aplicación de estrategias de ensayo más eficientes y eficaces. En general, el objetivo de esta tesis ha sido demostrar la importancia de integrar enfoques toxicológicos ómicos con ensayos toxicológicos convencionales a fin de obtener información significativa que ayude a desentrañar cualquier nuevo mecanismo de toxicidad desencadenado por ECs en el medio acuático utilizando Daphnia magna como especie modelo. Los contaminantes estudiados en esta tesis incluyeron aquellos sospechosos de ser disruptores de lípidos (compuestos disruptores endocrinos, EDC) y ECs que se sabe que afectan al sistema nervioso central (es decir, fármacos neuroactivos y otros productos químicos). Se han desarrollado diferentes enfoques integradores para evaluar la toxicidad de estos compuestos vinculando los efectos sobre la reproducción y el comportamiento (respuestas individuales del organismo), con los cambios en la expresión de los genes y su posterior alteración metabolómica (y por tanto lipidómica) en D. magna. A lo largo de esta tesis se ha abordado la capacidad de los EDCs y de los fármacos neuroactivos de afectar a la reproducción y perturbar la homeostasis lipídica, así como a las vías de señalización molecular que modulan esta perturbación. En el capítulo 2, se realizó un análisis transcriptómico mediante microarrays de hembras adultas de D. magna expuestas a algunos EDCs durante su etapa reporductora, y se estudiaron los efectos producidos en su lipidoma mediante un análisis lipidómico utilizando UHPLC-TOF MS. Se identificaron mecanismos transcripcionales comunes descritos con categorías funcionales relacionadas con la energía, la muda y la reproducción, así como diferentes categorías funcionales de lípidos. Los resultados obtenidos permitieron vincular los efectos reproductivos con cambios en los perfiles de lípidos, así como con una alterada transferencia de lípidos de las hembras de D. magna a sus huevos. En el capítulo 3 se estudiaron los efectos lipidómicos de productos farmacéuticos neuroactivos en concentraciones ambientalmente relevantes y los mecanismos moleculares asociados a ellos. La hipótesis de que la serotonina puede participar en la regulación de la dinámica de los lípidos y las respuestas de la fecundidad en D. magna se confirmó mediante el análisis del lipidoma de clones con el gen triptófano hidrolasa silenciado. Por último, en el capítulo 4 se desarrolló un enfoque metabolómico dirigido para analizar neurotransmisores en D. magna y se empleó en el estudio de los efectos de fármacos neuroactivos que afectaban a su comportamiento cognitivo. Los resultados metabólicos se vincularon a la alteración transcripcional asociada estudiada a través del RNAseq, probando la idoneidad de estos organismos para estudios de neurotoxicidad ambiental. En general, los resultados obtenidos a lo largo de esta tesis permitieron vincular las vías de señalización transcriptómica con efectos metabolómicos (perfiles lipidómicos y de neurotransmisores) y con respuestas apicales (reproducción y comportamiento).

Due to the resurgence of human African trypanosomiasis (HAT), the world health organization (WHO) and various non-governmental organisations NGO’s have implemented strategies that have led to a significant drop in disease incidence, with only ~3,500 new HAT cases recorded in 2014. However the causative agent, T. brucei is still responsible for a heavy socio-economic burden, with T. brucei infections in cattle representing an estimated billion dollar loss annually. Of the two human infective T. brucei subspecies, T.b. rhodesiense is responsible for less than 3% of all HAT cases and is primarily considered a zoonosis. It causes acute disease comparative to the T.b. gambiense subspecies and two strains of differing phenotypes have been used to establish experimental infections, which reproduce the clinical manifestation observed in natural infections. This project utilized technological advances in order to understand the genetic mechanisms driving the phenotypic differences observed through the use of genomic, transcriptomic and metabolomic analysis. Firstly this work discusses the feasibility of sequencing directly from field samples by using Whatman FTA™ card and sequence capture in combination, and benchmarking the data against available whole genome sequence data. The resulting data showed both successful enrichment and lack of allelic drop out effect. This methodology was subsequently applied to multiple other strains and used to look at deleterious variants potentially giving rise to these phenotypic differences. Gross differences in the abundance of bloodstream forms in these strains were also observed, which indicated the phenotype of these strains may result from the regulation of differentiation. Transcriptomic and metabolomic data was also used to identify differential regulation driving these differences in virulence, and showed that only a small subset of genes were differentially regulated. Amongst these several candidate genes, which had previously been associated with drug resistance, were identified. Genomic and transcriptomic data also indicated that iron regulation is one of the key mechanisms driving this phenotypic change, with a high density of deleterious SNPs located in iron transport, and the greater than 10 fold increase in the expression of the transferrin receptor found in the transcriptomic data. However further analysis ideally on a larger set of strains, or SNPs derived from the entire genome rather than a subset, would be necessary to ascertain whether this is true.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

L'importance des symbioses dans l'évolution du vivant est désormais admise et les associations symbiotiques sont observées dans une grande diversité d'habitats. Notre étude porte sur une symbiose au sein d'un écosystème réduit, les sources hydrothermales de l'océan profond. Bathymodiolus azoricus est un bivalve hydrothermal vivant le long de la ride Médio-Atlantique, qui héberge dans des cellules branchiales spécialisées, deux types de γ-protéobactéries différentes : des méthanotrophes (MOX) et des sulfo-oxydantes (SOX). Ces dernières sont capables d'oxyder les composés réduits présents dans le fluide hydrothermal fournissant ainsi énergie et/ou source de carbone à leur hôte. Cette double endosymbiose est plastique ainsi, l'abondance relative du type de symbionte hébergé (SOX vs. MOX) varie en fonction des concentrations en composés réduits présent dans le milieu (H2S, CH4). L'objectif de ce travail de thèse est d'identifier les acteurs moléculaires impliqués dans l'acquisition, le maintien et la régulation des bactéries symbiotiques. Pour ce faire, une analyse OMICs globale (protéomique -nano LC-MS/MS- et transcriptomique -micro-array-) a été mise en ¿uvre sur des individus symbiotiques issus de population naturelle (site hydrothermal Lucky Strike, -1700m) et sur des individus ayant expérimentalement perdu ou maintenu leurs symbiotes. Suite à cette approche globale et exploratoire, une approche plus spécifique a été menée sur des familles de protéines impliquées dans des processus immunitaire et/ou d'interactions hôte/symbiotes. Cette thèse apporte un éclaircissement sur les mécanismes régissant les relations et la communication hôte/symbiote
Hydrothermal vents are located on the mid-ocean ridges, and are characterized by challenging physico-chemical conditions. Despite these conditions dense hydrothermal communities develop down around hydrothermal fluid emissions. The presence of marine invertebrates relies on their capacity to cope with these challenging factors, and, for those forming most of the biomass, on their ability to live in symbiosis with chemoautotrophic bacteria. Bathymodiolus azoricus is one of these symbiotic species that harbors two types of γ-proteobacteria, a sulfide-oxidizing bacterium (SOX) (using the oxidation of H2S as the source of energy and CO2 as source of carbon) and a methane-oxidizing bacterium (MOX) (that uses the oxidation of CH4 as both a source of energy and carbon). These bacteria are located in specific epithelial cells in the gill tissue of the mussel. The proportion and number of these symbiont types (SOX vs. MOX) in B.azoricus can change in response to environmental conditions, and especially on the relative concentration of reduced compounds. The aim of our study is to understand the molecular mechanisms of acquisition, regulation and maintenance of the symbiotic charge in B .azoricus gills. We therefore, performed a global OMICs analysis (proteomics –nano LC-MS/MS and transcriptomics- micro-array) on mussels from natural population (Lucky Strike, -1700m) and on mussels that experimentally loose or maintain their symbiotic rate. This exploratory approach was followed by a more specific approach on family of proteins involved in immunity process and/or in host/symbiont interactions. This PhD provides hypotheses on the mechanisms governing the relationship and communication between host and symbionts

To paraphrase Stan Ulam, a Polish mathematician who became a leading figure in the Manhattan Project, in this dissertation I focus not only on how computer science can help biologists, but also on how biology can inspire computer scientists. On one hand, computer science provides powerful abstraction tools for metabolic networks. Cell metabolism is the set of chemical reactions taking place in a cell, with the aim of maintaining the living state of the cell. Due to the intrinsic complexity of metabolic networks, predicting the phenotypic traits resulting from a given genotype and metabolic structure is a challenging task. To this end, mathematical models of metabolic networks, called genome-scale metabolic models, contain all known metabolic reactions in an organism and can be analyzed with computational methods. In this dissertation, I propose a set of methods to investigate models of metabolic networks. These include multi-objective optimization, sensitivity, robustness and identifiability analysis, and are applied to a set of genome-scale models. Then, I augment the framework to predict metabolic adaptation to a changing environment. The adaptation of a microorganism to new environmental conditions involves shifts in its biochemical network and in the gene expression level. However, gene expression profiles do not provide a comprehensive understanding of the cellular behavior. Examples are the cases in which similar profiles may cause different phenotypic outcomes, while different profiles may give rise to similar behaviors. In fact, my idea is to study the metabolic response to diverse environmental conditions by predicting and analyzing changes in the internal molecular environment and in the underlying multi-omic networks. I also adapt statistical and mathematical methods (including principal component analysis and hypervolume) to evaluate short term metabolic evolution and perform comparative analysis of metabolic conditions. On the other hand, my vision is that a biomolecular system can be cast as a ?biological computer?, therefore providing insights into computational processes. I therefore study how computation can be performed in a biological system by proposing a map between a biological organism and the von Neumann architecture, where metabolism executes reactions mapped to instructions of a Turing machine. A Boolean string represents the genetic knockout strategy and also the executable program stored in the ?memory? of the organism. I use this framework to investigate scenarios of communication among cells, gene duplication, and lateral gene transfer. Remarkably, this mapping allows estimating the computational capability of an organism, taking into account also transmission events and communication outcomes.

Dato il recente avvento delle tecnologie NGS, in grado di sequenziare interi genomi umani in tempi e costi ridotti, la capacità di estrarre informazioni dai dati ha un ruolo fondamentale per lo sviluppo della ricerca. Attualmente i problemi computazionali connessi a tali analisi rientrano nel topic dei Big Data, con databases contenenti svariati tipi di dati sperimentali di dimensione sempre più ampia. Questo lavoro di tesi si occupa dell'implementazione e del benchmarking dell'algoritmo QDANet PRO, sviluppato dal gruppo di Biofisica dell'Università di Bologna: il metodo consente l'elaborazione di dati ad alta dimensionalità per l'estrazione di una Signature a bassa dimensionalità di features con un'elevata performance di classificazione, mediante una pipeline d'analisi che comprende algoritmi di dimensionality reduction. Il metodo è generalizzabile anche all'analisi di dati non biologici, ma caratterizzati comunque da un elevato volume e complessità, fattori tipici dei Big Data. L'algoritmo QDANet PRO, valutando la performance di tutte le possibili coppie di features, ne stima il potere discriminante utilizzando un Naive Bayes Quadratic Classifier per poi determinarne il ranking. Una volta selezionata una soglia di performance, viene costruito un network delle features, da cui vengono determinate le componenti connesse. Ogni sottografo viene analizzato separatamente e ridotto mediante metodi basati sulla teoria dei networks fino all'estrapolazione della Signature finale. Il metodo, già precedentemente testato su alcuni datasets disponibili al gruppo di ricerca con riscontri positivi, è stato messo a confronto con i risultati ottenuti su databases omici disponibili in letteratura, i quali costituiscono un riferimento nel settore, e con algoritmi già esistenti che svolgono simili compiti. Per la riduzione dei tempi computazionali l'algoritmo è stato implementato in linguaggio C++ su HPC, con la parallelizzazione mediante librerie OpenMP delle parti più critiche.

Dissertation presented to obtain a Master degree in Biotechnology
In complex biological systems, it is unlikely that all relevant cellular functions can be fully described either by a mechanistic (parametric) or by a statistic (nonparametric) modelling approach. Quite often, hybrid semiparametric models are the most appropriate to handle such problems. Hybrid semiparametric systems make simultaneous use of the parametric and nonparametric systems analysis paradigms to solve complex problems. The main advantage of the semiparametric over the parametric or nonparametric frameworks lies in that it broadens the knowledge base that can be used to solve a particular problem, thus avoiding reductionism. In this M.Sc. thesis, a hybrid modelling method was adopted to describe in silico Escherichia coli cells. The method consists in a modified projection to latent structures model that explores elementary flux modes (EFMs) as metabolic network principal components. It maximizes the covariance between measured fluxome and any input “omic” dataset. Additionally this method provides the ranking of EFMs in increasing order of explained flux variance and the identification of correlations between EFMs weighting factors and input variables. When applied to a subset of E. coli transcriptome, metabolome, proteome and envirome (and combinations thereof) datasets from different E. coli strains (both wild-type and single gene knockout strains) the model is able, in general, to make accurate flux predictions. More particularly, the results show that envirome and the combination of envirome and transcriptome are the most appropriate datasets to make an accurate flux prediction (with 88.5% and 85.2% of explained flux variance in the validation partition, respectively). Furthermore, the correlations between EFMs weighting factors and input variables are consistent with previously described regulatory patterns, supporting the idea that the regulation of metabolic functions is conserved among distinct envirome and genotype variants, denoting a high level of modularity of cellular functions.

This thesis is centrally concerned with the ‘democratic debate’ and assessing the prospects for democratic transition in contemporary China. The first part of the thesis (including Chapters 1 and 2) reviews the (primarily) Western academic literature on democracy and democratisation. It is argued that while this literature is useful-up to a point-in understanding how the debate of democratisation is unfolding in China, and the processes that are generating political reforms and other changes that are conducive to democracy, it has wholly neglected the specificity of the Chinese case. The third chapter of the thesis duly embarks on a discussion of both the history of debate and discussion in China historically, arguing that this debate and discussion has to be understood in the context of Chinese history and culture specifically. This chapter identifies two strands of thought about democracy among academic commentators in China: first those who foresee a swift transition to democracy and the ‘gradualists’, who are primarily concerned with how problems of attendant social and political instability will impact on the prospects for democratisation. The second half of the thesis assesses the impact of Chinese economic reforms since the late 1970s, along with contemporary globalization and China’s growing integration into the global economy on the trajectory of political change in China. It explores important political changes within the regime, the emerging civil society forces, focusing specifically on changing state-society relations evidenced in growing village autonomy, changes in press media, and in other areas. The thesis combines the technique of discourse analysis (‘reading’ and analysing the changing discourse among state and civil society actors, including official political documents and speeches; and media -television and newspapers- and NGO sources) with an assessment of institutional changes within the party (elite), changes in power structures (the limited diffusion of power to civil society through electoral reform and changes in media operation and control), and changing state-society relations.

Les Céphalopodes comme la seiche Sepia officinalis, malgré une durée de vie courte, sont retrouvés dans les océans depuis des millions d’années. Chez ces animaux atypiques, peu de pathologies ont été observées révélant la présence d’un système immunitaire efficace mais peu étudié qui repose sur des processus innés. L’étude du système immunitaire de la seiche a été menée au niveau du corps blanc, organe hématopoïétique ; des cellules circulantes, les hémocytes et au niveau de la peau, première barrière avec le milieu extérieur. Au niveau du corps blanc, l’étude transcriptomique et protéomique a mis en évidence la présence de facteurs en lien avec l’hématopoïèse dont des membres de la voie de signalisation JAK-STAT. Des facteurs immunitaires ont également été identifiés révélant une possible multifonctionnalité du corps blanc. La réponse immunitaire face à Vibrio splendidus a pu être appréhendées à partir d’une analyse transcriptomique comparative sur les hémocytes. Toutefois cette dernière n’ayant pas permis d’identifier clairement des peptides antimicrobiens, une analyse in silico originale a été développée permettant de sélectionner cinq peptides candidats dont trois ont révélé une activité antibactérienne ciblée contre des bactéries du genre Vibrio. Enfin, une étude au niveau de la peau et de son mucus a été initiée. Cette étude par des approches -omiques a permis l’identification de facteurs en lien avec la reconnaissance des pathogènes et la réponse immunitaire. Par ailleurs, douze souches ont pu être isolées et identifiées au niveau du microbiome cutané. L’ensemble de ces résultats représente un apport majeur concernant le système immunitaire chez la seiche permettant d’initier des études fonctionnelles lors d’une infection ou en fin de vie. Ces études permettraient de comprendre le mode d’action des facteurs immunitaires identifiés, l’implication de chaque entité dans la réponse immunitaire ou dans la mise en place et la maintenance du microbiome
Cephalopods such as the cuttlefish Sepia officinalis, despite their short lifespan, have been found in the oceans for millions of years. In these atypical animals, few pathologies have been observed, revealing the presence of an effective but little studied immune system based on innate processes. The study of the cuttlefish's immune system has been carried out on the white body, a haematopoietic organ; on the circulating cells, the haemocytes; and on the skin, the first barrier with the external environment. At the white body level, the transcriptomic and proteomic study highlighted the presence of factors linked to haematopoiesis, including members of the JAK-STAT signalling pathway. Immune factors have also been identified, revealing a possible multifunctionality of the white body. The immune response to Vibrio splendidus could be apprehended from a comparative transcriptomic analysis of haemocytes. However, as the latter did not allow the clear identification of antimicrobial peptides, an original in silico analysis was developed to select five candidate peptides, three of which revealed a targeted antibacterial activity against bacteria of the Vibrio genus. Finally, a study of the skin and its mucus was initiated. This study using -omic approaches enabled the identification of factors related to pathogen recognition and immune response. In addition, twelve strains were isolated and identified at the level of the skin microbiome. All these results represent a major contribution concerning the immune system in cuttlefish, making it possible to initiate functional studies during an infection or at the end of life. These studies would make it possible to understand the mode of action of the identified immune factors, the involvement of each entity in the immune response or in the establishment and maintenance of the microbiome

L’aspect novateur de ce projet réside dans l’étude des acylations au niveau de la lysine K27 de l’histone H3, classiquement étudiée sous forme méthylée ou acétylée. Nous avons réalisé ce travail sur des cellules germinales méiotiques et post-méiotiques de souris. La spermiogénèse, qui implique un programme d’expression spécifique ainsi qu’une régulation fine de la transcription, est un processus particulièrement adapté à la compréhension du rôle des nouvelles modifications d’histones. Ces travaux regroupent l’utilisation de quatre approches omiques différentes, à savoir la protéomique, la métabolomique, la transcriptomique et le séquençage ChIP-seq afin de décrypter la régulation des acylations sur H3K27.Dans la première partie de ce projet, nous avons exploré la dynamique d’acétylation et de crotonylation sur les lysines d’histones au cours des processus de sporulation de la levure et de spermatogénèse de la souris, ce qui nous a permis de mettre en évidence un site d’intérêt H3K27 crotonylé. Son accumulation sur le variant d’histone H3.3 et sa stoechiométrie importante par rapport à la forme acétylée H3K27ac dans les cellules germinales post-méiotiques de souris nous ont conduits à étudier la distribution génomique de cette marque, par des analyses de ChIP-seq. L’analyse comparative de H3K27ac et H3K27cr a révélé une synergie entre la présence de ces deux marques à la fois au niveau des promoteurs et des enhancers distants, ce qui suggére une possible alternance des deux marques afin de réguler la transcription. Au niveau des promoteurs, nous avons observé une augmentation de ces modifications entre les stades méiotiques et post-méiotiques en amont des gènes caractéristiques de la spermiogénèse. D’autre part, la présence simultanée des deux marques coïncide avec la co-localisation de plusieurs régulateurs de la transcription spécifiques de ce processus (SLY, SOX30) et de protéines de liaison à la chromatine (BRD4, BORIS et CTCF), tandis qu’une sélectivité de fixation est observée lorsque H3K27ac et H3K27cr sont identifiées seules aux promoteurs. De façon intéressante, nous observons des résultats similaires au niveau des enhancers ainsi que des super-enhancers, confirmant que la régulation de la transcription est modulée par la présence alternative de ces deux acylations.La deuxième partie de ma thèse a porté sur l’étude de la propionylation et de la butyrylation de H3K27 au cours de la sporulation de la levure et de la spermatogénèse de la souris. Cependant, cette partie s’est avérée pleine de surprises car les analyses MS/MS en cellules HCD et la comparaison avec les peptides synthétiques correspondants n’ont pas permis de valider une propionylation et une butyrylation sur H3K27. Il s’est avéré qu’il s’agissait de structures strictement isobares avec ces modifications connues, mais de nature différente, puisque plus hydrophile que ces acylations. Plusieurs hypothèses ont été testées afin de déterminer la composition de ces modifications, mais au moment de la finalisation de ce manuscrit, nous n’avons pas encore trouvé le fin mot de l’histoire.Mes travaux de thèse rappellent les obervations de Goudarzi et al., à savoir une dynamique entre acétylation et acylation sur les résidus lysines à l’origine de la fixation différentielle de facteurs de régulation ou de protéines se liant à la chromatine et responsables de la régulation de la transcription. Ils ont également mis en lumière un rôle importante de H3K27cr, au niveau des enhancers en combinaison avec H3K27ac, lesquels ne sont classiquement pas étudiés dans les études fonctionnelles portant sur la compréhension du rôle de nouvelles acylations
The innovative aspect of this project lies in the study of acylations at lysine 27 from histone H3 (H3K27), conventionally studied in a methylated or an acetylated form. We performed this work on meiotic and post-meiotic mouse germ cells. Spermiogenesis, which involves a specific expression program as well as a fine regulation of transcription, is a process that is particularly well suited to understanding the roles of new histone modifications. This work combines the use of four different omics approaches, namely proteomics, metabolomics, transcriptomics and ChIP- sequencing to decipher the regulation of acylations on H3K27.In the first part of this project, we explored the dynamics of acetylation and crotonylation on histone lysines during the processes of yeast sporulation and mouse spermatogenesis, which allowed us to highlight in particular crotonylated H3K27. Its accumulation on the histone variant H3.3 and its important stoichiometry compared to the acetylated form H3K27ac in mouse post-meiotic germ cells led us to study the genomic distribution of this mark by ChIP-seq analysis. The comparative analysis of H3K27ac and H3K27cr revealed a synergy between the presence of these acylations at both promoters and distal enhancers, suggesting a possible alternation of the two marks to regulate transcription. At the promoter level, we observed an increase of these modifications between the meiotic and post-meiotic stages upstream of the genes characteristic of spermiogenesis. In addition, the simultaneous presence of the two marks coincides with the co-localization of several transcriptional regulators specific for this process (SLY, SOX30) and of chromatin-binding proteins (BRD4, BORIS and CTCF), whereas a binding selectivity is observed when H3K27ac and H3K27cr are identified alone at promoters. Interestingly, we observe similar results at enhancers as well as super-enhancers, confirming that the regulation of transcription is modulated by the alternative presence of these two acylations.The second part of my thesis focused on the study of the possible propionylation and butyrylation of H3K27 during yeast sporulation and mouse spermatogenesis. However, this part proved to be full of surprises because the MS/MS analyses and the comparison with the corresponding synthetic peptides did not make it possible to validate a propionylation and a butyrylation on H3K27. It turned out that the modifications observed on H3K27 from mouse histones were strictly isobaric with these known modifications, but of a different nature, since they are more hydrophilic. Several hypotheses were tested in order to determine the structure of these modifications, but at the time of finalizing this manuscript, we have not found out what it is all about.My PhD work contributes further to the idea of a dynamics between acetylation and acylations on lysine residues at the origin of the differential binding of chromatin-binding proteins responsible for regulating transcription. It also highlighted an important role of H3K27crat enhancers which are not classically considered in studies aiming at understanding the roles of new acylations

Orientadores: Márcia Miguel Castro Ferreira, Alvicler Magalhães
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química
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Resumo: Neste trabalho são apresentadas duas novas metodologias multivariadas. Na primeira, é desenvolvida uma ferramenta denominada bucketing otimizado para a correção dos desalinhamentos dos espectros de RMN 1H. A análise de componentes principais em intervalos (iPCA) é utilizada para explorar espectros de RMN 1H e 13C. Para a diminuição de ruído destes últimos é utilizada a análise de componentes principais em múltiplas escalas (MSPCA). Os modelos iPCA são construídos para as classes de amostras, metropolitanas e não metropolitanas, em conjunto e separadas, atuando complementarmente na detecção de amostras não conformes. Neste contexto, os padrões espectrais apontaram amostras, previamente reprovadas pelos parâmetros físico-químicos próprios do campo de biocombustíveis. Adicionalmente, os modelos reprovaram amostras com padrões espectrais distintos, não reprovadas pelos parâmetros citados. De modo geral, o desempenho dos modelos utilizando os espectros de RMN 1H foi satisfatório. Uma exceção foi a detecção de amostras fora da especificação para o teor de biodiesel, onde as distinções nos espectros não permitiram a discriminação de amostras com teores próximo ao limite. Contudo, ao se estender um pouco a faixa sugerida na legislação, os modelos mostraram boa melhoria. Os modelos a partir dos espectros de RMN 13C obtiveram desempenho inferior àqueles citados acima. No segundo estudo é apresentado um novo método denominado escalamento de diferenças individuais multinível (ML-INDSCAL), para analisar a variação intra-individual em dados das ciências ômicas, focando em mudanças nas covariâncias dentro dos grupos experimentais e evidenciando as relações entre as variáveis (BVRs). Como somente a variação intra-individual é usada para revelar as BVRs associadas às mudanças dinâmicas, as interpretações sobre o fenômeno no qual os efeitos se baseiam são melhoradas. Um conjunto de dados simulado é explorado para demonstrar a força do método. O método é também aplicado a um conjunto real de dados de um estudo de expressões genéticas em células expressando a proteína viral R (Vpr) na forma nativa e com as mutações R80A e F72A/R73A. O procedimento jack-knife é explorado na validação dos modelos ML-INDSCAL. O método ML-INDSCAL é o primeiro da literatura que combina a exploração da estrutura multinível do conjunto de dados e a investigação de BVRs e pode fornecer valiosas contribuições no campo de seleção de características
Abstract: In this work, two new multivariate methodologies are presented. In the first approach, a tool named optimized bucketing is developed to correct 1H NMR spectra misalignments. The interval principal component analysis (iPCA) is used in order to explore 1H and 13C NMR spectra. The multiscale principal component analysis (MSPCA) is used for denoising of 13C NMR spectra. The iPCA models are built for two classes of samples, metropolitan and non-metropolitan, together and isolated, complementarily providing out-of-specification samples detections. In this context, the spectral profiles pointed out samples out of specification, in accordance to their previously known physical-chemical parameters from the field of biofuels. Additionally, the models were able to identify samples with distinct spectral profiles, but not rejected by the cited parameters. In general, the iPCA models using 1H NMR spectra presented good performances. An exception involves the detection of out-of-specification samples for biodiesel content, where the distinction on spectra profiles did not allow discrimination of samples when the biodiesel content was close to the allowed limit. Nevertheless, a small extension in the range, adopted by the Brazilian legislation, was enough to produce an improvement. The models from the 13C NMR spectra achieved worse performance than those cited above. In the second study is presented a novel method named multilevel individual differences scaling (ML-INDSCAL) to analyze within-individual variation in omic data, focusing on the changing covariances within groups and evidencing the between variables relationships (BVRs). Since only the within-individual variation is used to reveal the BVRs associated to dynamic changes, the interpretations about the real phenomena underlying the treatment are improved. A simulated data set is explored to demonstrate the strength of the method. Also, the method is applied to a real data set from a study of expression profiles in cell lines expressing wild-type and two mutated (R80A and F72A/R73A strains) Vpr. A version of the jack-knife procedure is explored in order to validate the ML-INDSCAL models. The ML-INDSCAL is the first method in literature that combines the exploration of the multilevel structure and the BVRs investigation and it can provide valuable insights on the feature selection field
Doutorado
Físico-Química
Doutor em Ciências

La spermatogenèse chez les mammifères est une fonction biologique complexe incluant des processus de prolifération cellulaire, de méiose et de différenciation uniques visant à la production des gamètes mâles au sein du testicule. Si l’épithélium séminifère est bien décrit sur le plan de son organisation et de la morphologie des cellules qui le composent, les processus par lesquels les cellules germinales diploïdes indifférenciées entrent en méiose pour donner ensuite des cellules haploïdes subissant par la suite de nombreuses transformations morphologiques, ne sont pas totalement décryptés. Ils reposent sur l’expression coordonnée et séquentielle de gènes dont les produits spécifiques de chaque stade de développement des cellules germinales sont essentiels aux étapes clés de la spermatogenèse. La transcriptomique depuis les années 1990 et la protéomique depuis les années 2000 ont contribué à l’amélioration de la connaissance de ces mécanismes. Une étude protéomique visant à caractériser par des approches systématiques et différentielles les protéomes des cellules de Sertoli et de la lignée germinale, et d’autre part une étude récente, réalisée dans notre unité, qui a permis de caractériser et de quantifier le transcriptome des cellules testiculaires isolées de rat en utilisant le séquençage de novo des transcrits (RNA-Seq), ont été à la base de mes travaux de thèse. Cette dernière étude a mis en évidence l’accumulation de longs ARNs non codants (lncRNAs) et de transcrits testiculaires non annotés (TUTs) aux stades méiotique et post- méiotique de la spermatogenèse chez le rat. Dans ce contexte, mon travail a consisté à valider le potentiel codant de nombreux gènes exprimés dans les cellules germinales par une approche dite PIT (Proteomics Informed by Transcriptomics) couplant protéomique Shotgun et RNA-Seq. Dans ce type d’approche, les séquences protéiques déduites des transcrits des différents types cellulaires, assemblés par RNA-Seq, sont intégrées dans une base personnalisée de séquences protéiques utilisée pour interroger les données de spectrométrie de masse obtenues à partir de protéines de cellules méiotiques et post-Méiotiques. L’approche PIT a permis de montrer que 69 TUTs ou lncRNA (correspondant à 44 loci) codent pour des protéines dans les cellules méiotiques et post méiotiques. L’expression post-Méiotique de deux nouveaux transcrits, l’un codant pour la protéine VAMP9, une protéine de la famille SNARE, et l’autre pour une nouvelle énolase T-ENOL a pu être confirmée. L’expression post-Méiotique de T-ENOL a été confirmée par immunohistochimie à l’aide d’un anticorps polyclonal produit contre la protéine recombinante. Cette approche nous a également permis d’identifier de nouvelles isoformes de protéines connues spécifiques de chaque stade de la spermatogenèse. Les cellules germinales et les cellules de Sertoli entretiennent le dialogue nécessaire au bon déroulement de la spermatogenèse. Une autre partie de mon travail a consisté à identifier des protéines membranaires des cellules germinales et des corps résiduels, susceptibles d’intervenir dans le dialogue entre les cellules de Sertoli et les cellules germinales, par une approche protéomique de quantification relative ICPL. Cette approche a permis d’établir une liste de 166 protéines différentiellement exprimées entre les spermatocytes pachytène, les spermatides rondes et les corps résiduels, qui sont susceptibles de jouer un rôle dans la spermiogénèse. Grâce aux annotations de le Gene Ontology, j’ai pu établir une liste de 8 protéines ayant un rôle supposé dans la transduction du signal, la reconnaissance cellulaire ou bien la différenciation. Par ailleurs, j’ai pu établir par protéomique Shotgun un premier protéome des cellules de Sertoli, des cellules germinales et des corps résiduels chez le rat
Spermatogenesis in mammals is a complex biological function including cellular processes such as proliferation, meiosis and differentiation, aiming to the production of male gametes in the testis. If the seminiferous epithelium is well described in terms of organization and cellular morphology of cells that compose it, the processes by which undifferentiated diploid germ cells enter meiosis and give haploid cells that undergo many morphological transformations, are not fully decrypted. These processes rely on the coordinated and sequential expression of genes, including specific products for each stage of germ cell development These gene products are essential at each key stage of spermatogenesis. Transcriptomics since the 1990s, and proteomics since the 2000s have contributed to the improved. understanding of these mechanisms. A long term proteomic study aiming at characterizing the proteomes of Sertoli cells and germ cells, and a recent study that characterized and quantified the transcriptome of isolated rat testicular cells at high resolution using de novo sequencing of transcripts (RNA-Seq), have been the basis of my thesis work. The latter study showed the accumulation of long non-Coding RNAs (lncRNAs) and testicular unannotated transcripts (TUTs) at meiotic and post-Meiotic stages of spermatogenesis in the rat. In this context, my thesis work aimed at validating the coding potential of many genes expressed in germ cells using RNA-Seq combined with shotgun proteomics, a so-Called PIT (Proteomics Informed by transcriptomics) approach. In this approach, the protein sequences translated from the transcripts assembled by RNA-Seq in the different testicular cell types are integrated into a custom database of protein sequences used to query mass spectrometry data obtained from proteins of meiotic and post-Meiotic cells. The PIT approach showed that 69 TUTs or lncRNA (corresponding to 44 loci) code for proteins in meiotic cells and post meiotic cells, and we confirmed experimentally the meiotic and post-Meiotic expression for two new transcripts encoding for VAMP9, a protein of the SNARE family, and a new testicular enolase T-ENOL. The post-Meiotic expression of T-ENOL protein was confirmed by immunohistochemistry using a polyclonal antibody raised against the recombinant protein. This approach also allowed us to identify new isoforms of known proteins, specific to each stage of spermatogenesis. Germ cells and Sertoli cells maintain a dialogue which is necessary to the success of spermatogenesis and spermiogenesis. Another part of my work aimed at identifying membrane proteins, in germ cells and residual bodies, that may be involved in the dialogue between Sertoli cells and germ cells, using a ICPL relative quantification proteomic approach. The ICPL analysis enabled us to establish a list of 166 proteins whose expression is differential between pachytene spermatocytes, round spermatids and residual bodies. Their differential expression suggests that these proteins may play a role in spermiogenesis. Thanks to the Gene Ontology annotations, a list of 8 proteins with a putative role in signal transduction, cell recognition or differentiation, thus potentially involved in the dialogue between Sertoli and germ cells was drawn. In addition, I provided a first proteome of rat Sertoli cells, germ cells and residual bodies obtained by shotgun proteomics

L’identification des mutations constitutionnelles à l’origine d’une prédisposition génétique au cancer est essentielle à la prise en charge médicale des patients et de leurs familles. Depuis l’implémentation des technologies de séquençage à haut-débit dans les laboratoires diagnostiques, le principal défi n’est plus la détection des variations génétiques mais leur interprétation et leur classification. La question de l’interprétation de la variation est particulièrement cruciale lorsqu’elle conditionne la stratégie thérapeutique. Ainsi, il est essentiel de disposer de tests simples adaptables en routine diagnostique pour faciliter l’interprétation des variations génétiques. Dans ce contexte, nous avons utilisé un test fonctionnel développé par notre équipe pour classer des variations dans le gène TP53 à l’origine du syndrome de Li-Fraumeni et pour appréhender la corrélation génotype - phénotype chez les patients LFS. Dans un deuxième temps, nous avons évalué la pertinence d’une approche multi-omique (RNA-Seq et métabolomique) pour discriminer les cellules sauvages des cellules avec mutation hétérozygote du gène TP53 ou des gènes BRCA impliqués dans la prédisposition génétique aux cancers du sein et de l’ovaire. Sur la base des données de transcriptome, un modèle mathématique a été développé pour détecter les variants correspondant à des mutations délétères. Nous avons ensuite sélectionné les biomarqueurs les plus discriminants pour les intégrer dans un test fonctionnel de RT-MLPA dédié à la voie p53. Nous avons enfin adapté cet essai pour qu’il soit réalisable sur une simple prise de sang, sans immortalisation des lymphocytes du patient
The identification of the constitutional mutation responsible for a genetic predisposition to cancer is essential to the clinical management of the patient and its relatives. With the implementation of high-throughput sequencing to the diagnostic routine of these pathologies, the challenge no longer lies within the detection of alterations but in their biological and clinical interpretation. While specific treatments are emerging, simple functional assays to help with the interpretation of the detected variants are needed. In this context, we used a functional test developed by our team to classify variations in the TP53 gene responsible for Li-Fraumeni syndrome and to understand the genotype-phenotype correlation in LFS patients. On the other hand, we assessed the relevance of a multi-omic approach (RNA-Seq and metabolomics) to discriminate wild-type cells from cells with a deleterious heterozygous mutation in TP53 or in the BRCA genes implicated in genetic predisposition to breast and ovarian cancers. Based on the transcriptomic data, a mathematical model has been developed to detect variants corresponding to deleterious mutations. Then we selected the most discriminating biomarkers and integrated them into a RT-MLPA functional assay dedicated to the p53 pathway. We finally adapted this test to be feasible on a simple blood test, without immortalization of the patient's lymphocytes

Les tumeurs sont entourées d'un microenvironnement complexe comprenant des cellules tumorales, des fibroblastes et une diversité de cellules immunitaires. Avec le développement actuel des immunothérapies, la compréhension de la composition du microenvironnement tumoral est d'une importance critique pour effectuer un pronostic sur la progression tumorale et sa réponse au traitement. Cependant, nous manquons d'approches quantitatives fiables et validées pour caractériser le microenvironnement tumoral, facilitant ainsi le choix de la meilleure thérapie. Une partie de ce défi consiste à quantifier la composition cellulaire d'un échantillon tumoral (appelé problème de déconvolution dans ce contexte), en utilisant son profil omique de masse (le profil quantitatif global de certains types de molécules, tels que l'ARNm ou les marqueurs épigénétiques). La plupart des méthodes existantes utilisent des signatures prédéfinies de types cellulaires et ensuite extrapolent cette information à des nouveaux contextes. Cela peut introduire un biais dans la quantification de microenvironnement tumoral dans les situations où le contexte étudié est significativement différent de la référence. Sous certaines conditions, il est possible de séparer des mélanges de signaux complexes, en utilisant des méthodes de séparation de sources et de réduction des dimensions, sans définitions de sources préexistantes. Si une telle approche (déconvolution non supervisée) peut être appliquée à des profils omiques de masse de tumeurs, cela permettrait d'éviter les biais contextuels mentionnés précédemment et fournirait un aperçu des signatures cellulaires spécifiques au contexte. Dans ce travail, j'ai développé une nouvelle méthode appelée DeconICA (Déconvolution de données omiques de masse par l'analyse en composantes immunitaires), basée sur la méthodologie de séparation aveugle de source. DeconICA a pour but l'interprétation et la quantification des signaux biologiques, façonnant les profils omiques d'échantillons tumoraux ou de tissus normaux, en mettant l'accent sur les signaux liés au système immunitaire et la découverte de nouvelles signatures. Afin de rendre mon travail plus accessible, j'ai implémenté la méthode DeconICA en tant que librairie R. En appliquant ce logiciel aux jeux de données de référence, j'ai démontré qu'il est possible de quantifier les cellules immunitaires avec une précision comparable aux méthodes de pointe publiées, sans définir a priori des gènes spécifiques au type cellulaire. DeconICA peut fonctionner avec des techniques de factorisation matricielle telles que l'analyse indépendante des composants (ICA) ou la factorisation matricielle non négative (NMF). Enfin, j'ai appliqué DeconICA à un grand volume de données : plus de 100 jeux de données, contenant au total plus de 28 000 échantillons de 40 types de tumeurs, générés par différentes technologies et traités indépendamment. Cette analyse a démontré que les signaux immunitaires basés sur l'ICA sont reproductibles entre les différents jeux de données. D'autre part, nous avons montré que les trois principaux types de cellules immunitaires, à savoir les lymphocytes T, les lymphocytes B et les cellules myéloïdes, peuvent y être identifiés et quantifiés. Enfin, les métagènes dérivés de l'ICA, c'est-à-dire les valeurs de projection associées à une source, ont été utilisés comme des signatures spécifiques permettant d'étudier les caractéristiques des cellules immunitaires dans différents types de tumeurs. L'analyse a révélé une grande diversité de phénotypes cellulaires identifiés ainsi que la plasticité des cellules immunitaires, qu'elle soit dépendante ou indépendante du type de tumeur. Ces résultats pourraient être utilisés pour identifier des cibles médicamenteuses ou des biomarqueurs pour l'immunothérapie du cancer
Tumors are engulfed in a complex microenvironment (TME) including tumor cells, fibroblasts, and a diversity of immune cells. Currently, a new generation of cancer therapies based on modulation of the immune system response is in active clinical development with first promising results. Therefore, understanding the composition of TME in each tumor case is critically important to make a prognosis on the tumor progression and its response to treatment. However, we lack reliable and validated quantitative approaches to characterize the TME in order to facilitate the choice of the best existing therapy. One part of this challenge is to be able to quantify the cellular composition of a tumor sample (called deconvolution problem in this context), using its bulk omics profile (global quantitative profiling of certain types of molecules, such as mRNA or epigenetic markers). In recent years, there was a remarkable explosion in the number of methods approaching this problem in several different ways. Most of them use pre-defined molecular signatures of specific cell types and extrapolate this information to previously unseen contexts. This can bias the TME quantification in those situations where the context under study is significantly different from the reference. In theory, under certain assumptions, it is possible to separate complex signal mixtures, using classical and advanced methods of source separation and dimension reduction, without pre-existing source definitions. If such an approach (unsupervised deconvolution) is feasible to apply for bulk omic profiles of tumor samples, then this would make it possible to avoid the above mentioned contextual biases and provide insights into the context-specific signatures of cell types. In this work, I developed a new method called DeconICA (Deconvolution of bulk omics datasets through Immune Component Analysis), based on the blind source separation methodology. DeconICA has an aim to decipher and quantify the biological signals shaping omics profiles of tumor samples or normal tissues. A particular focus of my study was on the immune system-related signals and discovering new signatures of immune cell types. In order to make my work more accessible, I implemented the DeconICA method as an R package named "DeconICA". By applying this software to the standard benchmark datasets, I demonstrated that DeconICA is able to quantify immune cells with accuracy comparable to published state-of-the-art methods but without a priori defining a cell type-specific signature genes. The implementation can work with existing deconvolution methods based on matrix factorization techniques such as Independent Component Analysis (ICA) or Non-Negative Matrix Factorization (NMF). Finally, I applied DeconICA to a big corpus of data containing more than 100 transcriptomic datasets composed of, in total, over 28000 samples of 40 tumor types generated by different technologies and processed independently. This analysis demonstrated that ICA-based immune signals are reproducible between datasets and three major immune cell types: T-cells, B-cells and Myeloid cells can be reliably identified and quantified. Additionally, I used the ICA-derived metagenes as context-specific signatures in order to study the characteristics of immune cells in different tumor types. The analysis revealed a large diversity and plasticity of immune cells dependent and independent on tumor type. Some conclusions of the study can be helpful in identification of new drug targets or biomarkers for immunotherapy of cancer

In this work, a comprehensive review on automatic analysis of Proteomics and Genomics images is presented. Special emphasis is given to a particularly complex image produced by a technique called Two-Dimensional Gel Electrophoresis (2-DE), with thousands of spots (or blobs). Automatic methods for the detection, segmentation and matching of blob like features are discussed and proposed. In particular, a very robust procedure was achieved for processing 2-DE images, consisting mainly of two steps: a) A very trustworthy new approach for the automatic detection and segmentation of spots, based on the Watershed Transform, without any foreknowledge of spot shape or size, and without user intervention; b) A new method for spot matching, based on image registration, that performs well for either global or local distortions. The results of the proposed methods are compared to state-of-the-art academic and commercial products.

Yim, Aldrin Kay Yuen.
Thesis M.Phil. Chinese University of Hong Kong 2015.
Includes bibliographical references (leaves 135-140).
Abstracts also in Chinese.
Title from PDF title page (viewed on 06, December, 2016).

碩士
長庚大學
中醫學系天然藥物
100
Steroids are ubiquitous and abundant compounds in nature. Steroids are produced by eukaryotes where they have a variety of chemical structures and play important physiological roles. Many bacteria are capable of transforming and completely degrading steroids under oxic conditions. The microbial metabolism of steroids has gained considerable interest due to its potential applications in industrial and environmental biotechnology. The oxic degradation pathways of steroids by aerobic bacteria were established, and some of the involved enzymes were well characterized. The key players in these pathways are oxygenases which utilize dioxygen as a co-substrate. Steroidobacter denitrificans able to grow anaerobically on testosterone or estradiol was adopted as the model organism in this study. Our investigations revealed unique and interesting biochemical reactions and enzymes. We added [2,3,4-13C3]testosterone as the tracer in in vivo assays to explore the testosterone-derived intermediates. Steroid products purified from the in vivo assays were then identified using NMR spectroscopy and UPLC-Mass spectrometry. In this investigation, we applied OMICs approachs to identify the genes、enzymes and intermediates involved in the anoxic testosterone catabolism. According to our present data, a novel testosterone catabolic pathway is proposed.

abstract: This thesis research focuses on phylogenetic and functional studies of microbial communities in deep-sea water, an untapped reservoir of high metabolic and genetic diversity of microorganisms. The presence of photosynthetic cyanobacteria and diatoms is an interesting and unexpected discovery during a 16S ribosomal rRNA-based community structure analyses for microbial communities in the deep-sea water of the Pacific Ocean. Both RT-PCR and qRT-PCR approaches were employed to detect expression of the genes involved in photosynthesis of photoautotrophic organisms. Positive results were obtained and further proved the functional activity of these detected photosynthetic microbes in the deep-sea. Metagenomic and metatranscriptomic data was obtained, integrated, and analyzed from deep-sea microbial communities, including both prokaryotes and eukaryotes, from four different deep-sea sites ranging from the mesopelagic to the pelagic ocean. The RNA/DNA ratio was employed as an index to show the strength of metabolic activity of deep-sea microbes. These taxonomic and functional analyses of deep-sea microbial communities revealed a `defensive' life style of microbial communities living in the deep-sea water. Pseudoalteromonas sp.WG07 was subjected to transcriptomic analysis by application of RNA-Seq technology through the transcriptomic annotation using the genomes of closely related surface-water strain Pseudoalteromonas haloplanktis TAC125 and sediment strain Pseudoalteromonas sp. SM9913. The transcriptome survey and related functional analysis of WG07 revealed unique features different from TAC125 and SM9913 and provided clues as to how it adapted to its environmental niche. Also, a comparative transcriptomic analysis of WG07 revealed transcriptome changes between its exponential and stationary growing phases.
Dissertation/Thesis
Ph.D. Civil and Environmental Engineering 2013

Cyanobacteria are photosynthetic organisms with the potential to sustainably produce carbon-based end products by fixing carbon dioxide from the atmosphere. Optimizing the growth or biochemical production in cyanobacteria is an ongoing challenge in metabolic engineering. Rational design of metabolic pathways requires a deep understanding of regulatory mechanisms. Hence, a deeper understanding of photosynthetic regulation of the influence of the environment on metabolic fluxes provides exciting possibilities for enhancing the photosynthetic Calvin-Benson-Bassham cycle. One approach to study metabolic processes is to use omic-level techniques, such as proteomics and fluxomics, to characterize varying phenotypes that result from different environmental conditions or different genetic perturbations.

This dissertation examines the influence of light intensity on enzymatic abundances and the resulting Calvin-Benson-Bassham cycle fluxes using a combined proteomic and fluxomic approach in the model cyanobacteria Synechocystis sp. PCC 6803. The correlation between light intensity and enzymatic abundances is evaluated to determine which reactions are more regulated by enzymatic abundance. Additionally, carbon enrichment data from isotopic labelling experiments strongly suggest metabolite channeling as a flexible and light-dependent regulatory mechanism present in cyanobacteria. We propose and substantiate biological mechanisms that explains the formation of metabolite channels under specific redox conditions.

The same multi-omic approach was used to examine genetically modified cyanobacteria. Specifically, genetically engineered and conditionally growth-enhanced Synechocystis strains overexpressing the central Calvin-Benson-Bassham cycle enzymes FBP/SBPase or transketolase were evaluated. We examined the effect of the heterologous expression of each of these enzymes on the Calvin-Benson-Bassham cycle, as well as on adjacent central metabolic pathways. Using both proteomics and fluxomics, we demonstrate distinct increases in Calvin-Benson-Bassham cycle efficiency as a result of lowered oxidative pentose phosphate pathway activity. This work demonstrates the utility of a multi-omic approach in characterizing the differing phenotypes arising from environmental and genetic changes.

Advances in instrumentation and sample preparation techniques enable evermore in-depth molecular profiling to catalyze exciting research into complex biological processes. Current platforms survey biomolecular classes with varying depth. While sequencing is near comprehensive, and even enabled at single cell resolution, challenges remain in global metabolite surveys primarily due to the increased chemical diversity relative to other “omics” data types. At the same time, metabolism and the interaction of diverse biomolecules are increasingly recognized as vitally important components of many disease processes. Presented here is work describing the development and use of molecular interaction subnetworks for the functional interpretation of multi-omic data. Metabolic pathway-centric subnetworks for functional inference with protein or gene derived global profiling data were created from the integration of disparate network models: Protein- protein interaction (PPI) networks and metabolic models. The subnetworks were shown to increase mapping between metabolic pathways and the proteome, and the subnetwork- derived analysis shows dramatic improvement over primary enzymes alone with direct metabolomic experimental measurements for validation of pathway findings. We illustrate the functional utility of integrating PPI data with metabolic models by finding network modules previously but independently implicated in disease. Specifically, the analysis reveals abundance increases in known oncogenes in response to changes in breast cancer metabolism. Additionally, we reveal cellular mechanisms related to metabolic stress observed in patient sera following viral SARS-CoV-2 infection, and metabolic changes in a model of heart disease, where the characteristic muscle fibers make in-depth proteomic profiling difficult. Functional network models were additionally used to compare the response of varying cell lines in response to viral infection, showing significant context- specific differences. All of these findings demonstrate the importance of functional models to help interpret multi-omic data. The implications of revealing the connections between metabolism and protein subnetwork rewiring may be profound; for example, suggesting metabolic pathway activity may be as important a biomarker as mutation status in cancer. This research points to a means of practically inferring metabolic state from proteomic data. We further describe the release of our open-source software to accelerate integrative multi-omic analysis in the broader research community.
2023-06-16T00:00:00Z

Despite decades of effort, the mechanistic underpinnings of many cancers remain unsolved It has increasingly become appreciated that cancers can be more readily classified by their transcriptional identities rather than by genomics alone. A fuller understanding of the mechanistic connections between the aberrant genomics leading to the transcriptional dysregulation of tumors is key to both improving our knowledge of cancer biology as well as developing more precise and effective therapeutics. This thesis explores the development and application of a network based multi-omic master regulator framework designed to elucidate these pathways. In Chapter 2 we apply this analysis across 20 tumor types from the Cancer Genome Atlas and in doing so identify 407 key master regulators responsible for canalizing a high percentage of the driver genetics present across these samples. Further evaluation of these key regulators revealed a highly modular structure, indicating that the regulators work in coordinated groups to implement a variety of key cancer hallmarks. Genetic and pharmacological validation assays confirmed the predicted interactions and biological phenotypes. Chapter 3 focuses on the application of this analytical framework specifically on gastroesophageal tumors. Using a more fine-grained approach we find 15 distinct subtypes across a cohort of these heterogenous tumors. These subtypes align well with previously identified features of these cancers but also reveal novel genomic associations and key master regulators that can serve as potential avenues for therapeutic treatment.

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC.
2020-01-25

Tese de doutoramento em Engenharia Quimica e Biológica
The increasing resistance of pathogenic bacteria to antibiotics in recent years has become a major problem in controlling infections in animals and humans. Salmonella enterica, which causes human food poisoning worldwide, is one of the most problematic bacteria with significant incidence in poultry. The development of alternatives to antibiotics has led to the resurgence of interest in (bacterio)phages, discovered before the antibiotics but which have succumbed to the efficiency of the later. The goal of the work described in this thesis was the biological, genomic and proteomic characterization of a broad host range Salmonella bacteriophage (PVPSE1) with high potential for therapy. It was also aimed at characterizing the population dynamics of the phage‐bacteria system by developing mathematic models that describe host‐phage interaction intended to predict the outcome of a therapeutical use of this phage and also the optimization of phage production. This phage has been proven to be efficient against Salmonella isolated from different sources and also shown the ability to lyse non‐pathogenic E. coli strains. Prior to the characterization steps a new method of phage detection based on the plaque assay was developed in order to enlarge the minimal size plaques formed by PVP‐SE1 which was rendering phage detection and enumeration very difficult. The method was based on the addition of glycerol and antibiotics at sub‐inhibitory concentrations which enabled the improvement of phage plaques size and contrast without changing its efficiency of plating. The phage genome sequencing was determined and a bioinformatic analysis was accomplished. This genomic characterization did not reveal any factor or gene responsible for lysogeny, pathogenesis or other which could exclude the use of this myovirus in vivo. Some of the phage proteins identified during this characterization represent an added value for biotechnological applications. From these, the PVP‐SE1 lysozyme is particularly interesting, not only for its lytic ability against pathogenic bacteria but also for the presence of a peptidoglycan binding domain, an unusual feature among phage lysozymes infecting Gram‐negative bacteria. The identification of the phage tail fibers, which are responsible for bacteria recognition, in such a broad host range Salmonella phage will lead to further investigation in their use to construct a diagnostic tool. Moreover, PVPSE1 was found to be phylogenetically unique, likely leading to the creation of a new phage genus. The mathematical model developed was able to explain the complicate hostphage interaction and allowed a good agreement between the predictions and the experimental data. The model has shown the importance of using a distribution of the latent period in simulations but more importantly it has shown that the bacterial physiological state and bacterial growth rate exerts a major influence in phage production. Due to the ability of the phage to lyse a non‐pathogenic host the possibility of producing the phage in the non‐pathogenic E. coli BL21 was studied. When produced in this alternative host the phage did not modify, maintaining its lytic ability and spectrum among the Salmonella strains. This new approach enables the production of a safer phage product by avoiding the risk of introducing phage resistant pathogenic bacteria in the final product thus diminishing costs of necessary purification techniques. In conclusion, it is presented here the biological, genomic, proteomic and population dynamics characterization of phage PVP‐SE1 which showed to be an added value as a biocontrol agent and as a diagnostic tool for the problematic pathogenic Salmonella. This characterization will be necessary and valuable in the development of a commercial product (therapeutic, diagnostic or other) based on this phage.
O aumento da resistência de bactérias patogénicas aos antibióticos nos últimos anos representa um dos maiores problemas no controlo de infecções em animais e humanos. A Salmonella enterica é uma das bactérias mais problemáticas com grande incidência na produção aviária causando intoxicações alimentares com impacto global em humanos. O desenvolvimento de alternativas aos antibióticos provocou um ressurgimento dos (bacterió)fagos que tinham sido descobertos antes dos antibióticos, mas que rapidamente sucumbiram à eficiência daqueles. O trabalho descrito nesta dissertação teve como objectivo a caracterização biológica, genómica e proteómica de um fago de Salmonella (PVP‐SE1) que apresenta um largo espectro lítico e um grande potencial para terapia. Pretendeuse ainda caracterizar a dinâmica de populações existente entre o fago e a bactéria através do desenvolvimento de modelos matemáticos que descrevessem a interacção entre o fago e a bactéria de forma a prever o resultado do uso terapêutico deste fago e que permitisse a optimização da sua produção. O fago estudado mostrou‐se eficiente contra isolados de Salmonella de diferentes origens e também eficiente contra estirpes de E. coli não patogénicas. A detecção e enumeração deste fago são dificultadas pelas características das suas placas fágicas que apresentam pequena dimensão e baixo contraste. Assim foi imprescindível o desenvolvimento de um novo método que permitisse uma melhor observação das placas fágicas. Essa tarefa foi conseguida através da adição de glicerol e de antibióticos, a uma concentração sub‐inibitória, ao meio de cultura o que permitiu uma melhor visualização das placas fágicas pelo consequente aumento do seu tamanho e contraste sem contudo alterar a eficiência de plaqueamento. O gemoma do fago foi sequenciado e a sequência anotada recorrendo a ferramentas bioinformáticas. A caracterização genómica não revelou a presença de factores ou genes responsáveis por conversão lisogénica, patogénese ou outros que possam excluir o uso in vivo deste myovirus. Algumas das proteínas identificadas aquando da caracterização do fago podem representar uma maisvalia para a biotecnologia. De entre essas proteínas, a lisozima é particularmente interessante não apenas pela sua capacidade lítica contra bactérias patogénicas mas também pela presença de um domínio de ligação ao peptidoglicano que representa uma característica rara nas lisozimas de fagos que infectam bactérias Gram‐negativas. A identificação das fibras da cauda, responsáveis pelo reconhecimento das bactérias a infectar, num fago com tão largo espectro para a Salmonella conduzirá certamente a futuras investigações no seu uso para a construção de uma ferramenta de diagnóstico. De salientar ainda que o fago é filogeneticamente único e provavelmente dará origem à criação de um novo género na classificação dos fagos. O desenvolvimento do modelo matemático apresentado nesta dissertação permite explicar as complicadas interacções existentes entre as populações de fagos e de bactérias permitindo uma boa aproximação entre as simulações e os dados experimentais. A previsão do comportamento resultante do encontro de bactérias e fagos é de extrema importância na aplicação dos fagos como agentes terapêuticos e poderá desempenhar um papel preponderante na produção e optimização de fagos. O modelo revelou a importância de utilizar uma distribuição dos valores do tempo de latência nas simulações mas principalmente mostrou que o estado fisiológico da célula e a taxa de crescimento da bactéria influenciam grandemente a produção de fagos. Devido à sua capacidade para infectar uma bactéria não patogénica foi estudada a possibilidade de produzir o fago na bactéria E. coli BL21. Quando replicado neste hospedeiro alternativo o fago não se alterou, mantendo a sua capacidade e o seu espectro lítico contra as estirpes de Salmonella. Esta nova abordagem permite a produção de um produto fágico mais seguro pela eliminação do risco de introdução de uma bactéria patogénica resistente ao fago no produto final diminuindo assim os custos inerentes aos processos de purificação. Em conclusão, apresenta‐se aqui uma caracterização biológica, genómica, proteómica e de dinâmica de populações de um fago que se provou ser uma maisvalia como agente de controlo e como ferramenta de diagnóstico do agente patogénico Salmonella. Esta caracterização será necessária e valiosa no desenvolvimento de um produto comercial (terapêutico, de diagnóstico ou outro) baseado neste fago tão interessante.
Fundação para a Ciência e a Tecnologia (FCT) SFRH/BD/32278/2006
European Social Fund
Ministério da Ciência Tecnologia e Ensino Superior

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy associated with molecular heterogeneity, locoregional spread, resistance to therapy and relapse after initial treatment. Increasing evidence suggests that master developmental pathways with key roles in adult tissue homeostasis, including Hippo and Wnt/β-catenin signaling, are dysregulated in the initiation and progression of HNSCC. However, a comprehensive investigation into the crosstalk between these pathways is currently lacking, and may prove crucial to the discovery of novel targets for HNSCC therapy. More recent evidence points to the tumor microenvironment, mainly comprising cancer-associated fibroblasts (CAFs), as capable of influencing tumor cell behavior and promoting invasive HNSCC phenotypes. Nonetheless, current methods to screen for CAF markers in tumors are restricted to targeted immunostaining experiments with limited success and robustness across tissue types. The Cancer Genome Atlas network has generated multi-tiered molecular profiles for over 10,000 tumors spanning more than two dozen different cancer types, providing an unprecedented opportunity for the application and development of integrative methods aimed at the in silico interrogation of experimentally-derived signatures. These multi-omic profiles further enable one to link genomic anomalies, including somatic mutations and DNA copy number alterations, with phenotypic effects driven by pathogenic pathway activity. Effectively querying this vast amount of information to help elucidate subsets of functionally and clinically-relevant oncogenic drivers, however, remains an ongoing challenge. To address these issues, I first investigate the effects of oncogenic pathway perturbation in HNSCC using experimental models coupled with in vitro genome-wide transcriptional profiling. Next, I describe a new computational approach for the unbiased identification of CAF markers in HNSCC solely using bulk tumor RNA-sequencing information. Lastly, I have developed Candidate Driver Analysis or CaDrA - a statistical framework that allows one to query genetic and epigenetic alterations for candidate drivers of signature activity within a given disease context. Collectively, this work offers new perspectives on the molecular cues underlying HNSCC development, while simultaneously highlighting the power of integrative genomics methods capable of accelerating the discovery of novel targets for cancer diagnosis and therapy.

Modern healthcare research demands collaboration across disciplines to build preventive measures and innovate predictive capabilities for curing diseases. Along with the emergence of cutting-edge computational and statistical methodologies, data generation and analysis has become cheaper in the last ten years. However, the complexity of big data due to its variety, volume, and velocity creates new challenges for biologists, physicians, bioinformaticians, statisticians, and computer scientists. Combining data from complex multiple profiles is useful to better understand cellular functions and pathways that regulates cell function to provide insights that could not have been obtained using the individual profiles alone. However, current normalization and artifact correction methods are platform and data type specific, and may require both the training and test sets for any application (e.g. biomarker development). This often leads to over-fitting and reduces the reproducibility of genomic findings across studies. In addition, many bias correction and integration approaches require renormalization or reanalysis if additional samples are later introduced. The motivation behind this research was to develop and evaluate strategies for addressing data integration issues across data types and profiling platforms, which should improve healthcare-informatics research and its application in personalized medicine. We have demonstrated a comprehensive and coordinated framework for data standardization across tissue types and profiling platforms. This allows easy integration of data from multiple data generating consortiums. The main goal of this research was to identify regions of genetic-epigenetic co-ordination that are independent of tissue type and consistent across epigenomics profiling data platforms. We developed multi-‘omic’ therapeutic biomarkers for epigenetic drug efficacy by combining our biomarker regions with drug perturbation data generated in our previous studies. We used an adaptive Bayesian factor analysis approach to develop biomarkers for multiple HDACs simultaneously, allowing for predictions of comparative efficacy between the drugs. We showed that this approach leads to different predictions across breast cancer subtypes compared to profiling the drugs separately. We extended this approach on patient samples from multiple public data resources containing epigenetic profiling data from cancer and normal tissues (The Cancer Genome Atlas, TCGA; NIH Roadmap epigenomics data).

Tese de Doutoramento em Genética Molecular, Comparativa e Tecnológica
A resistência aos antimicrobianos representa uma ameaça séria e actual à saúde pública mundial e estirpes multirresistentes de Salmonella enterica subsp. enterica serotipo Typhimurium e fagotipo DT104B com resistência adicional às quinolonas têm sido responsáveis por surtos globais e elevada mortalidade. Devido ao elevado impacto das infecções por Salmonella não tifóide e à vital importância dos antibióticos da classe das fluoroquinolonas, a resistência às fluoroquinolonas em Salmonella não tifóide é considerada uma situação de elevada preocupação a nível internacional. Apesar de ser do conhecimento geral que a resistência às fluoroquinolonas é multifactorial, ainda se desconhecem muitos dos elementos que contribuem para este fenótipo. A proteómica tem progredido significativamente no que respeita à caracterização de proteínas envolvidas em mecanismos de resistência, tendo contribuído amplamente para o conhecimento actual dos efeitos das redes metabólicas na antibiorresistência assim como na identificação de novos alvos. De modo a contribuir com novas perspectivas sobre os mecanismos de resistência envolvidos, três estirpes clínicas clonalmente relacionadas de Salmonella Typhimurium DT104B com fenótipos de resistência distintos, Se6, Se20 e Se6-M, foram exaustivamente estudadas através de diferentes abordagens proteómicas, complementadas com métodos genómicos e transcriptómicos. Numa análise preliminar, o proteoma total da estirpe Se20, que adquiriu resistência às quinolonas in vivo após tratamento do paciente com ciprofloxacina, tal como o proteoma total da estirpe de referência SL1344, foram determinados por 2-DE~MALDI-TOF MS. Um total de 178 e 202 spots proteicos (representando 143 e 166 produtos de genes específicos) foram identificados, respectivamente, nas estirpes Se20 e SL1344, fornecendo uma visão global da expressão proteica destas estirpes em condições normais de crescimento. Subsequentemente, com o intuito de detectar proteínas diferencialmente expressas relacionadas com mecanismos de resistência, foram realizadas várias análises subproteómicas comparativas, combinando identificação por 2-DE~LC-MS/MS e shotgun LC-MS/MS para análise do subproteoma intracelular e membranar, respectivamente. Numa primeira instância, a estirpe Se20, seleccionada in vivo, foi comparada à estirpe Se6-M, um mutante altamente resistente seleccionado in vitro a partir da estirpe parental da Se20 (Se6) por passagens sucessivas em concentrações crescentes de ciprofloxacina. Um total de 50 e 7 proteínas (32+2 mais abundantes em Se20 e 18+5 mais abundantes em Se6-M) foram identificadas nos subproteomas intracelular e membranar, respectivamente. Posteriormente, cada estirpe resistente, Se20 e Se6-M, foi comparada individualmente ao nível subproteómico com a estirpe parental, Se6, e também sob stress com ciprofloxacina. No total, foram identificadas 14 proteínas diferencialmente expressas ao comparar as estirpes Se6 e Se20 e 91 proteínas ao comparar a estirpe Se20 com Se20+CIP. Um total de 35 proteínas diferencialmente expressas foram identificadas na comparação das estirpes Se6 e Se6-M e 82 foram identificadas entre Se6-M e Se6-M+CIP. Em conjunto, ambas as condições de stress revelaram um total de 125 proteínas relacionadas com a resposta à ciprofloxacina, das quais apenas 45 são comuns a ambas as estirpes. Das restantes, 45 e 38 foram identificadas exclusivamente nas estirpes Se20 e Se6-M, respectivamente, salientando a importância de incluir tanto estirpes adaptadas em laboratório como estirpes adaptadas clinicamente neste tipo de abordagens comparativas. O papel das proteínas identificadas como determinantes de resistência é discutido nos contextos do influxo reduzido de antibióticos através da diminuição da expressão de porinas e de alterações na organização e integridade da membrana externa através de modificações de LPS, lipoproteínas ou peptidoglicano. Adicionalmente, é também discutido o papel de importadores ABC, de reguladores metabólicos e dos mecanismos bacterianos de resposta ao stress, quer como determinantes de resistência ou alvos para o desenvolvimento de novos agentes antimicrobianos. O elevado número de proteínas identificadas neste estudo constitui informação valiosa sobre a expressão diferencial de proteínas envolvidas em mecanismos de resistência, fornecendo novas evidências sobre a natureza dos distúrbios fisiológicos causados pelo antibiótico. Tal pode conduzir à formulação de novas hipóteses no que respeita não só ao mecanismo de acção das fluoroquinolonas, mas também a proteínas-alvo secundárias implicadas em mecanismos adaptativos e compensatórios. Os resultados obtidos salientam ainda que o uso coordenado de técnicas proteómicas e bioinfomáticas de alto rendimento, complementadas com diferentes métodos genómicos e transcriptómicos, possibilitam uma melhor detecção dos múltiplos mecanismos envolvidos na aquisição de resistência. As vias envolvidas na aquisição de resistência, realçadas nas diversas abordagens realizadas neste estudo, podem ser úteis não só para prolongar o uso dos antibióticos actuais, como também para o desenvolvimento de novos antibióticos e estratégias alternativas para combater a emergência e disseminação de resistência no agente patogénico de origem alimentar de elevado impacto na saúde humana, Salmonella Typhimurium DT104B.
Antimicrobial resistance is a worldwide public health threat and Salmonella enterica subsp. enterica serotype Typhimurium phage type DT104B multiresistant strains with additional quinolone resistance have been responsible for global outbreaks and high mortality. Due to the worldwide high human health impact of nontyphoidal Salmonella infections and vital importance of the fluoroquinolone class of antibiotics, fluoroquinolone-resistance in nontyphoidal Salmonella is considered a situation of serious and international concern. Fluoroquinolone resistance is known to be multifactorial but is still far from a complete understanding. Proteomics achieved significant progress in the characterization of proteins involved in resistance mechanisms and have greatly contributed to the understanding of the effect of metabolic networks on antibiotic resistance and to identify new drug targets. Hence, in order to give new insights about the resistance mechanisms involved, three clonally related Salmonella Typhimurium DT104B clinical strains with different antimicrobial resistance phenotypes, Se6, Se20 and Se6-M, were thoroughly studied through different proteomic approaches complemented with genomic and transcriptomic methods. In a preliminary analysis, the complete proteome of the Se20 strain, which acquired quinolone resistance in vivo after patient treatment with ciprofloxacin, was determined by 2-DE~MALDI-TOF MS together with the total proteome of reference strain SL1344, in order to obtain an overview of global protein expression under normal growth conditions. A total of 178 and 202 protein spots (representing 143 and 166 unique gene products) were positively identified, respectively, in Se20 and SL1344, providing a snapshot of the major proteins involved in the basic cellular physiology of these strains. Subsequently, in order to specifically observe mechanism-related differential protein expression, a set of different comparative subproteomic analyses were performed by combining 2-DE~LC-MS/MS and shotgun LC-MS/MS identification approaches for intracellular and membrane subproteomes, respectively. First, the in vivo selected Se20 strain was compared to Se6-M, an in vitro selected highly resistant mutant, obtained from the parental strain of Se20 (Se6) by laboratory evolution with increasing ciprofloxacin concentrations. A total of 50 and 7 unique proteins (32+2 more abundant in Se20 and 18+5 more abundant in Se6-M) were identified in the intracellular and membrane subproteomes respectively. Afterwards, each of the quinolone-resistant strains, Se20 and Se6-M, were individually compared at the subproteomic level with their parental strain Se6 and also under ciprofloxacin stress. In total, 14 differentially abundant proteins were identified when comparing Se6 with Se20 and 91 were identified between Se20 and Se20+CIP. A total of 35 differentially abundant proteins were identified when comparing Se6 with Se6‑M and 82 were identified between Se6‑M and Se6‑M+CIP. Both stress conditions together revealed a total of 125 unique antibiotic-stress response proteins, of which only 45 were common to the two strains. Of the remaining, 45 and 38 stress response proteins were exclusively identified in Se20 and Se6-M respectively, highlighting the importance of including both laboratoryand clinically-adapted strains in these comprehensive comparative approaches. The roles of the identified proteins as resistance determinants is discussed in the contexts of reduced diffusion-mediated antibiotic uptake through porin downregulation and alterations in bacterial outer membrane organization and integrity through LPS, lipoprotein and peptidoglycan modifications. Additionally, the roles of identified ABC importers, global regulators of bacterial metabolism and bacterial stress response mechanisms as either determinants of antimicrobial resistance or targets for the development of new antimicrobial drugs is also discussed. The great number of proteins identified in the different comparative approaches provide valuable information about mechanism-related differential protein expression, giving new evidences on the nature of the physiological disturbance caused by the antibiotic, which might lead to new testable hypotheses on the mode of action of fluoroquinolone drugs and also secondary target proteins implicated in adaptation and compensatory mechanisms. Also, the results obtained emphasize that an improved detection of the multiple and superposing mechanisms that are usually involved in resistance acquisition can be achieved through the coordinated use of high-throughput proteomics and bioinformatics techniques complemented with different genomics and transcriptomics methods. By highlighting pathways involved in the acquisition of resistance, the comprehensive approaches performed in this study may be helpful not only to extend the useful life of current antimicrobials but also to develop new drugs and strategies to combat the emergence and spread of resistance in the high human health impact foodborne pathogen Salmonella Typhimurium DT104B.
QRENPOPH framework by the Portuguese Foundation for Science and Technology (FCT) and co-funded by the European Social Fund and the Ministry of Education and Science (MEC).

The thesis on "Social services in rural southwestern hinterland of Brno" describes the issue of social services (health, education and social care) in rural areas. The work is divided into theoretical and practical part. The theoretical part describes the basic social concepts, discusses the allocation, availability and deployment of social services, demographic aging, rural and urban schools. The theoretical part uses Czech and foreign literature, relevant data from the Czech Statistical Office, the Regional Information Service and others. In the practical part was conducted demographic analysis examined the segment of territory between Modřice and Rosice. They were identified and inventoried existing social services in various towns and villages. A created age pyramid confirms that the Czech population gets older. The research carried out shows that the current capacity of kindergartens, elementary schools and social facilities is insufficient.