Today one of the greatest aims within the area of bioinformatics is to gain a complete understanding of the functionality of genes and the systems behind gene regulation. Regulatory relationships among genes seem to be of a complex nature since transcriptional control is the result of complex networks interpreting a variety of inputs. It is therefore essential to develop analytical tools detecting complex genetic relationships. This project examines the possibility of the data mining technique artificial neural network (ANN) detecting regulatory relationships between genes. As an initial step for finding regulatory relationships with the help of ANN the goal of this project is to train an ANN to predict the expression of an individual gene. The genes predicted are the nuclear receptor PPAR-g and the insulin receptor. Predictions of the two target genes respectively were made using different datasets of gene expression data as input for the ANN. The results of the predictions of PPAR-g indicate that it is not possible to predict the expression of PPAR-g under the circumstances for this experiment. The results of the predictions of the insulin receptor indicate that it is not possible to discard using ANN for predicting the gene expression of an individual gene.

Metabolic syndrome is taking epidemic proportions, especially in developed countries. Each risk factor component of the syndrome independently increases the risk of developing coronary artery disease. The risk factors are obesity, dyslipidemia, hypertension, diabetes type 2, insulin resistance, and microalbuminuria. Nuclear receptors is a family of receptors that has recently received a lot of attention due to their possible involvement in metabolic syndrome. Putting the receptors into context with their co-factors and ligands may reveal therapeutic targets not found by studying the receptors alone. Therefore, in this thesis, interactions between genes in nuclear receptor pathways were analysed with the goal of investigating if these interactions can supply leads to biomarkers for metabolic syndrome. Metabolic syndrome donor gene expression data from the BioExpressä, database was analysed with the APRIORI algorithm (Agrawal et al. 1993) for generating and mining association rules. No association rules were found to function as biomarkers for metabolic syndrome, but the resulting rules show that the data mining technique successfully found associations between genes in signaling pathways.

In a modern systematic view of biology, cell functions arise from the interaction between molecular components. One of the challenging problems in systems biology with high-throughput measurements is discovering the important components involved in the development and progression of complex diseases, which may serve as biomarkers for accurate predictive modeling and as targets for therapeutic purposes. Due to the non-linearity and heterogeneity of these complex diseases, traditional biomarker identification approaches have had limited success at finding clinically useful biomarkers. In this dissertation we propose novel methods for biomarker identification that explicitly take into account the non-linearity and heterogeneity of complex diseases. We first focus on the methods to deal with non-linearity by taking into account the interactions among features with respect to the disease outcome of interest. We then focus on the methods for finding disease subtypes with their subtype-specific biomarkers for heterogeneous diseases, where we show how prior biological knowledge and simultaneous disease stratification and personalized biomarker identification can help achieve better performance. We develop novel computational methods for more accurate and robust biomarker identification including methods for estimating the interactive effects, a network-based feature ranking algorithm that takes into account the interactive effects between biomarkers, different approaches for finding distances between somatic mutation profiles for better disease stratification using prior knowledge, and a network-regularized bi-clique finding algorithm for simultaneous subtype and biomarker identification. Our experimental results show that our proposed methods perform better than the state-of-the-art methods for both problems.

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Orientador: Iscia Teresinha Lopes Cendes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-27T18:55:52Z (GMT). No. of bitstreams: 1 Borges_MuriloGuimaraes_M.pdf: 8164302 bytes, checksum: cc367cbf534276995150e7db644f6e94 (MD5) Previous issue date: 2015 / Resumo: Os avanços técnicos em sequenciamento alcançados em menos de uma década, atrelados ao desenvolvimento e barateamento do sequenciamento de alto desempenho, oferecem-nos a possibilidade de aplicação dessas tecnologias na medicina genômica. Nesse contexto, surge o sequenciamento do exoma humano, constituído das regiões codificantes do genoma, menor que 2% de sua totalidade. O sequenciamento do exoma (WES) se estabelece hoje como uma ferramenta custo-efetiva com a finalidade de identificar variantes de sequência relacionadas a várias doenças humanas. A análise através da bioinformática é essencial para lidar com o alto volume de dados gerados e realizar a ligação entre o experimento biológico e os dados obtidos. Objetivo: Aplicar e avaliar protocolos e aplicações disponíveis na análise dos dados gerados pelo sequenciamento de exomas humanos, bem como aplicar e aperfeiçoar protocolos e aplicações disponíveis para predizer variantes como potencialmente patológicas a partir de dados gerados pelo sequenciamento de exomas humanos. Materiais e métodos: Foram utilizadas as seguintes ferramentas: FastQC, Rqc, BWA, Picard, GATK e VEP. Estas foram então aplicadas às sequências do exoma humano possibilitando a identificação de variações nos perfis de qualidade das sequências, realinhamento local ao redor de inserções e deleções, recalibração da qualidade e posterior chamada das variantes potencialmente envolvidas nos fenótipos em estudo. No intuito de avaliar se a cobertura no exoma sofre variações mediante diferenças técnicas e étnicas, selecionamos amostras do Projeto 1000 Genomas. Resultados: A aplicação de nosso protocolo em 27 amostras WES resultou em gráficos de controle de qualidade pré e pós-alinhamento, que nos permitiram avaliar de modo global os perfis de qualidade destas sequências; realinhamento ao redor de inserções e deleções que ocorreu em mais de 15% da definição do exoma, realinhando mais de 79% das sequências; recalibração da qualidade que nos permitiu minimizar sua variação por ciclo da reação. Das sequências empregadas, 72% foram pareadas ao genoma, contudo 46% se estendem para fora da definição do exoma, com uma cobertura média de 59x para o exoma estendido e 66x para o exoma restrito. Temos que a cobertura para WES possui uma tendência a variar de acordo com a metodologia de captura empregada e ao grupo étnico de onde as amostras foram obtidas. Conclusão: A aplicação de um workflow para interrogação de variantes que considera a qualidade das sequências fornecidas pelo sequenciador, o alinhamento contra o genoma, realinhamento ao redor de regiões sabidamente conhecidas como portadoras de variações, recalibração da qualidade e anotação permitiu identificar variantes de sequência. Além disso, através da cobertura obtida pelo sequenciamento do exoma foi possível perceber diferenças técnicas e populacionais, refletindo que a complexidade do genoma pode interferir na reação de captura das sequências, influenciando na efetividade da técnica empregada / Abstract: The technical advances in sequencing made in less than a decade associated with the development and low costs of high throughput sequencing techniques allow their application in genomic medicine. Therefore, Whole Exome Sequencing (WES), which corresponds to less than 2% of the entire genome, emerges as a cost-effective tool that aims to identify variants related to human diseases. Bioinformatics is fundamental to process the big volume of data and link the obtained results with the biology. Objective: We aim to apply and evaluate protocols and applications designed for WES data analysis on human subjects. We also intend to apply and enhance protocols and applications designed to predict variants as potentially pathological from WES data. Materials and Methods: We used the following tools: FastQC, Rqc, BWA, Picard, GATK e VEP. We applied them to exome data, determining variation in quality profiles, local realignment, quality recalibration and variant calls. We also evaluated whether or not technical and population differences affect the depth profiles of samples from the 1000 Genomes Project. Results: We applied our protocol on 27 samples, resulting in pre and post-alignment quality control charts. Local realignment took place at more than 15% of the exome definition, extending to more than 79% of sequences. Quality recalibration minimized per cycle variation. In total, 72% of the sequences were paired against the genome, nevertheless 46% extended off-target. The mean coverage was 59X for the exome. We also detected that depth tends to vary based on technical and population differences between samples. Conclusion: We applied a variant-calling workflow that accounts for sequence quality, the alignment against the genome, local realignment, quality recalibration and annotation. In addition, we concluded that depth depends on technical and population differences, showing that genomic complexity may interfere with the capturing phase, affecting downstream analyses / Mestrado / Fisiopatologia Médica / Mestre em Ciências

Tissues are heterogeneous communities of cells that work together to achieve a higher-order function. Large-scale single cell RNA-sequencing (scRNA-seq) offers an unprecedented opportunity to systematically map the transcriptional programs underlying this diversity. However, extracting biological signal from noisy, high-dimensional scRNA-seq data requires carefully designed, statistically robust methodology that makes appropriate assumptions both for the data and for the biological question of interest. This thesis explores computational approaches to finding biological signal in scRNA-seq datasets. Chapter 2 focuses on preprocessing and cell-centric approaches to downstream analysis that have become a mainstay of analytical pipelines for scRNA-seq, and includes dissections of lineage diversity in high grade glioma and in the largest neural stem cell niche in the adult mouse brain. Notably, the former study suggests that heterogeneity in high grade glioma arises from at least two distinct biological processes: aberrant neural development and mesenchymal transformation. Chapter 3 presents a flexible approach for de novo discovery of gene expression programs without an a priori structure across cells, revealing subtle properties of a spatially sampled high grade glioma that would not have been apparent with previous approaches. Chapter 4 leverages our prior work and a unique tissue resource to build a unified reference map of human T cell functional states across tissues and ages. We discover and validate a novel pan-T cell activation marker and a previously undescribed kinetic intermediate in CD4+ T cell activation. Finally, ongoing work defines key programs of gene expression in tissue-associated T cells in infants and adults and predicts their candidate regulators.

<p>The following work describes the methodology and materials used to study three human protein complexes involved in the etiology and progression of cancer. The first, ubiquitin-specific protease 7 (USP7) is an isopeptidase that employs a unique auto-regulatory mechanism. The second is another ubiquitin-specific protease, USP28, which forms higher order states in solution. Lastly, the third case was a protein complex that utilizes an oxidation-sensitive dimeric protein, Keap1, and two components of an E3 ligase – Cul3-Rbx1. Each of these studies involved overcoming unique challenges for cryo-EM sample optimization. Not all yielded the quality of data that would result in high-resolution (< 6 Å) densities. Despite this, new information was discovered about each system.</p> <p>USP7 has a unique mechanism of intramolecular regulation that stems from a hypothesized tethered-rheostat, whereby the c-terminal distal domains activate the catalytic domain via a hypothetical wide degree of conformational movement. My cryo-EM work, done in collaboration with the Wen Jiang lab, is the first comprehensive structural data that provides structural evidence for the movement of the tethered-rheostat. The particle set showed a great degree of conformational heterogeneity, even after a strategy was employed with a chemically-modified ubiquitin substrate to ameliorate these issues. The data showed that during the ubiquitin-bound state, after the release of a hypothetical substrate, but prior to the release of mono-ubiquitin, the HUBL4-5 domains do not remain engaged with the catalytic domain. This information suggests a change to existing models of catalysis. </p> <p>Additionally, the structural model built from the cryo-EM density has revealed an interfacial region between domains that were previously not thought to interact. This interfacial region between the TRAF domain and HUBL1-3 represents a candidate location of binding for a mixed, non-competitive inhibitor of USP7 previously identified in the lab. Enzyme kinetics, DSF, and Glide molecular docking experiments all yielded data that corroborate this idea.</p> <p>Structural studies on USP28 have been difficult as the multi-domain enzyme adopts oligomers in solution and is generally not amenable to crystallographic analysis. Prior to the work described herein, the only structural data were a solution NMR structure describing a few alpha-helical motifs in the N-terminus. During my graduate studies, two articles were published of the USP28 catalytic domain crystallographic structure. Both corroborated the existence of a dimer. The USP28 catalytic domain migrates during analytical gel filtration assays with the apparent molecular weight of a tetramer. Furthermore, glutaraldehyde crosslinking experiments show the catalytic domain appears to adopt a tetrameric state, like the USP25 tetramer. The USP25 tetramer was published alongside the USP28 catalytic domain dimer, concluding that a USP28 tetrameric state was not observed. Upon cryo-EM data collection and single particle analysis, it was observed that the compositional heterogeneity of the dataset was too great for any meaningful reconstruction. Although, the dataset appeared to how the presence of the <i>E. coli</i> GroEL chaperone complex. Co-expression experiments confirmed that the GroEL chaperone complex migrates with USP28 throughout the purification and may be useful for purifying USPs for structural studies.</p> <p>Currently, our lab has a single-angle X-ray scattering (SAXS) model of the Keap1-Cul3 E3 ligase complex. But, the field does not fully agree on the molecular stoichiometry or the overall structure-function of this oxidation sensor – E3 ligase complex. It is hypothesized that Keap1 forms a dimer through its BTB domain, and a single Cul3 molecule then binds this dimer. The oxidation state of Keap1 cysteines appears to be critical to the interaction, but the field remains uncertain about which residues are responsible for the interaction with the Cul3-Rbx1 E3 ligase. To better understand this interaction and to obtain structural information to corroborate the SAXS model, recombinant Keap1 and Cul3-Rbx1 were purified and their interaction was tested by ITC, gel filtration assay, and a new technique called <i>mass photometry</i>. </p> <p>It was found that the Keap1 Cys151 residue is not the oxidation sensor critical to the interaction, contrary to what some in the field anticipated. Additionally, it was found that under oxidative conditions, WTKeap1 could not form a complex with Cul3-Rbx1. The complex was successfully purified and was measured by SDS-PAGE, gel filtration assay, and mass photometry, and then used for cryo-EM single particle analysis. Full data collection and analysis has not yet been completed. It is anticipated that like the data from mass photometry, analytical SEC, and cryo-EM single particle analysis will show the complex appears to show a 1:1 Keap1-Cul3 stoichiometry, as opposed to the anticipated 2:1 ratio.</p>

<p>  </p> <p>Proteins are the active biomolecules of the cell. They perform metabolic action, give the cell structure, protect the cell from antigens, give the cell motility, and much more. The function of proteins are intrinsically linked to their structures, so it is therefore necessary to characterize the structure of a protein to fully understand its function and operation. In this research the application of computational methods, primarily molecular dynamics, towards protein structure determination, refinement, and quality assessment were studied. I applied molecular dynamics techniques to four major projects; the determination of relative error of atomic models deposited with electron microscopy maps in the EMDB, solving and refining atomics structure models for the PhageG major capsid proteins, the elucidation of the structure the protein USP7 and the binding pose of a of a candidate therapeutic drug, and the determination of relative stability of candidate protein folds to distinguish near native models from not. Each year an increasing number of protein structures have been solved using electron microscopy (EM). The influx of solved structure has proven to be a boon to the community, but it is necessary to note that the quality EM maps vary substantially. To understand to what extent atomic structure models generated from EM matched their respective maps, two computational structure refinement methods were used to examine how much structures could be refined. The deviation from the starting structure by refinement, as well as the disagreement between refined models produced by the two computational methods, scaled inversely with both the global and local map resolutions. The results suggested that the observed discrepancy between the deposited maps and refined models is due to the lack of resolvable structural data present in EM maps at low to moderate resolutions, and therefore these annotations must be used with caution in further applications. I also successfully implemented molecular dynamics as a method for protein structure quality assessment. Proteins tend towards shapes which minimize their energy. Experimentally, the stability of a protein can be measured through several techniques, one such technique includes the controlled application of tension to proteins in an atomic force microscopy (AFM) framework.  This kind of tension-based approach is of interest as it probes the force required to unfold individual domains of a protein rather than a bulk characteristic like molting point or activity. It has been shown that key features observed in an AFM experiment can be well reproduced with molecular dynamics simulation, which has been applied to characterize the mechanisms of unfolding of proteins as well as ligand-protein interactions.  Steered molecular dynamics (SMD) was applied to pull and unfold proteins and determine the force required to unfold them. The relative force required to unfold different models with the same sequence was used to estimate relative model accuracy.  This follows from the hypothesis that the structural stability of a given model’s conformation would positively correlate with its accuracy, i.e. how close that model is to its native fold. It was found that near-native models could be successfully selected by comparing the forces required to unfold models, indicating that high unfolding forces indeed indicated high model stability, which in turn correlated with model accuracy. I also applied molecular dynamics-based approaches for refinement of protein structures that are determined from cryo-EM density maps. Computational approaches for protein structure refinement are often developed with the design aim of requiring a user input and experimental data. I modeled the atomic structure of the major capsid protein gp27 and the decoration protein gp26 of PhageG to a 6.1Å resolution electron microscopy map. PhageG modeling was done by mapping the sequences to a presumed homolog (Hk97), arranging the subunits into hexamers and trimmers as suggested by mass spectroscopy data, rigid docking to respective map segments, refinement against half maps using MDFF across a range of weights, and then finally refinement to the whole map using the optimized weight. I also modeled the atomic structure of the protein USP7 to an 8.2 Å resolution map. USP7 modeling was done by combining crystalized domains of the whole structure, rigidly docking the model to the EM map by hand, and then refining in a similar manner as PhageG, with the added approach of weight scaling to overcome local minima along the relaxation. The USP7 model was further validated by exhibiting a ligand-protein binding pose, determined by glide, which corresponded to enzymatic activity mutation assays. In summary I applied molecular dynamics, in conjunction with other computational methods, towards protein structure determination, refinement, and quality assessment.</p>

Motivation: SARS-CoV-2 is the virus responsible for the COVID-19 pandemic that currently impacts our world. SARS-CoV-2 is an enveloped, positive sense single stranded RNA virus and like other RNA viruses is known to form RNA secondary structure in its genome. In related viruses the secondary structures are responsible for fulfilling roles including proper expression of viral gene products and possibly regulation of viral genome replication. I hypothesize that SARS-CoV-2 may be capable of forming additional secondary structures beyond what is already known and that those secondary structures are identifiable on the basis of sequence conservation with related RNA viruses. Results: By repurposing and expanding an existing computational pipeline de- signed for the detection of structural RNAs in vertebrates, I identified 40 regions of the SARS-CoV-2 genome highly likely to form secondary structure. Partial re- identification of known secondary structures in the SARS-CoV-2 genome was achieved. To further explore the role these structures may fill, the 9 most conservatively pre- dicted structures were analyzed in wild viral samples collected from three Canadian provinces, and distinct patterns of mutation were observed. The 40 regions identi- fied by my modified pipeline were compared against three contemporary works and the differences between findings were quantified. Lastly, Variants of Concern for SARS-CoV-2 were analyzed for prevalent but poorly reported mutations that may influence RNA secondary structure. Code developed for this work is available at https://github.com/aziesel/MSc. / Graduate / 2023-04-06

Breast cancer is the most common type of cancer among women worldwide. Multiple studies have reported the role of tumor-immune interactions and mechanisms that the immune system uses to combat tumor cells. Therapies based on the immune response are evolving by time, but more research is required to understand and identify the patterns and relationships within the tumor microenvironment. This study aims to characterize immune cell expression patterns using a multiplex method and to investigate the way different subpopulations in breast cancer patients’ tissue samples are correlated with clinicopathological characteristics. The results of this study indicate that there must be an association within immune cell composition and clinicopathological characteristics (Estrogen Receptor Status (ER+/ER-), Progesterone Receptor (PR+/PR-), Grade (I,II,III), which is a way to characterize the cancer cells on how similar they look to normal ones, Menopause, Tumor size, Nodal status, HR status, HER2) but validation in larger patient population is required in order to evaluate the role of the immune infiltration as a predictive / prognostic biomarker in early breast cancer.