668 research outputs found
NETWORK ANALYTICS FOR THE MIRNA REGULOME AND MIRNA-DISEASE INTERACTIONS
miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics
2012 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
URC Schedule and Abstract Book for the Fourth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics
Date: November 17-18, 2012Plenary speaker: Christine E. Heitsch, Associate Professor of Mathematics at Georgia Institute of TechnologyFeatured speaker: John W. Glasser, Center for Disease Contro
Following the trail of cellular signatures : computational methods for the analysis of molecular high-throughput profiles
Over the last three decades, high-throughput techniques, such as next-generation sequencing, microarrays, or mass spectrometry, have revolutionized biomedical research by enabling scientists to generate detailed molecular profiles of biological samples on a large scale. These profiles are usually complex, high-dimensional, and often prone to technical noise, which makes a manual inspection practically impossible. Hence, powerful computational methods are required that enable the analysis and exploration of these data sets and thereby help researchers to gain novel insights into the underlying biology. In this thesis, we present a comprehensive collection of algorithms, tools, and databases for the integrative analysis of molecular high-throughput profiles. We developed these tools with two primary goals in mind. The detection of deregulated biological processes in complex diseases, like cancer, and the identification of driving factors within those processes. Our first contribution in this context are several major extensions of the GeneTrail web service that make it one of the most comprehensive toolboxes for the analysis of deregulated biological processes and signaling pathways. GeneTrail offers a collection of powerful enrichment and network analysis algorithms that can be used to examine genomic, epigenomic, transcriptomic, miRNomic, and proteomic data sets. In addition to approaches for the analysis of individual -omics types, our framework also provides functionality for the integrative analysis of multi-omics data sets, the investigation of time-resolved expression profiles, and the exploration of single-cell experiments. Besides the analysis of deregulated biological processes, we also focus on the identification of driving factors within those processes, in particular, miRNAs and transcriptional regulators. For miRNAs, we created the miRNA pathway dictionary database miRPathDB, which compiles links between miRNAs, target genes, and target pathways. Furthermore, it provides a variety of tools that help to study associations between them. For the analysis of transcriptional regulators, we developed REGGAE, a novel algorithm for the identification of key regulators that have a significant impact on deregulated genes, e.g., genes that show large expression differences in a comparison between disease and control samples. To analyze the influence of transcriptional regulators on deregulated biological processes,, we also created the RegulatorTrail web service. In addition to REGGAE, this tool suite compiles a range of powerful algorithms that can be used to identify key regulators in transcriptomic, proteomic, and epigenomic data sets. Moreover, we evaluate the capabilities of our tool suite through several case studies that highlight the versatility and potential of our framework. In particular, we used our tools to conducted a detailed analysis of a Wilms' tumor data set. Here, we could identify a circuitry of regulatory mechanisms, including new potential biomarkers, that might contribute to the blastemal subtype's increased malignancy, which could potentially lead to new therapeutic strategies for Wilms' tumors. In summary, we present and evaluate a comprehensive framework of powerful algorithms, tools, and databases to analyze molecular high-throughput profiles. The provided methods are of broad interest to the scientific community and can help to elucidate complex pathogenic mechanisms.Heutzutage werden molekulare Hochdurchsatzmessverfahren, wie Hochdurchsatzsequenzierung, Microarrays, oder Massenspektrometrie, regelmĂ€Ăig angewendet, um Zellen im groĂen Stil und auf verschiedenen molekularen Ebenen zu charakterisieren. Die dabei generierten DatensĂ€tze sind in der Regel hochdimensional und oft verrauscht. Daher werden leistungsfĂ€hige computergestĂŒtzte Anwendungen benötigt, um deren Analyse zu ermöglichen. In dieser Arbeit prĂ€sentieren wir eine Reihe von effektiven Algorithmen, Programmen, und Datenbaken fĂŒr die Analyse von molekularen HochdurchsetzdatensĂ€tzen. Diese AnsĂ€tze wurden entwickelt, um deregulierte biologische Prozesse zu untersuchen und in diesen wichtige SchlĂŒsselmolekĂŒle zu identifizieren. ZusĂ€tzlich wurden eine Reihe von Analysen durchgefĂŒhrt um die verschiedenen Methoden zu evaluieren. Zu diesem Zweck haben wir insbesondere eine Wilmstumor Studie durchgefĂŒhrt, in der wir verschiedene regulatorische Mechanismen und dazugehörige Biomarker identifizieren konnten, die fĂŒr die erhöhte MalignitĂ€t von Wilmstumoren mit blastemreichen Subtyp verantwortlich sein könnten. Diese Erkenntnisse könnten in der Zukunft zu einer verbesserten Behandlung dieser Tumore fĂŒhren. Diese Ergebnisse zeigen eindrucksvoll, dass unsere AnsĂ€tze in der Lage sind, verschiedene molekulare Hochdurchsatzmessungen auszuwerten und dabei helfen können pathogene Mechanismen im Zusammenhang mit Krebs oder anderen komplexen Krankheiten aufzuklĂ€ren
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Discovery of tissue specific network properties associated with cancer driver genes
Tese de Mestrado em BioquĂmica, Faculdade de CiĂȘncias, Universidade de Lisboa, 2022Using the notion of disease modules, network medicine has effectively identified diseaseassociated genes in recent years. In biological networks, genes linked to a particular illness tend to
interact closely [1]. These networks allow both physical and functional connections between
biomolecules to be identified, resulting in a map of cell components and processes that constitute
biological systems [2]. Not all disease-associated genes, however, have a major impact on disease
phenotype.
The discovery of important genes able to produce or change disease phenotype paves the path
to new therapies and a personalized medicine strategy. Recent research has found that biological
network topological features per se may accurately predict perturbation effects in a dynamical model of
the system with a 65-80% accuracy [3, 4].
Biological networks differ depending on whatever tissue or cell type is being studied. As a
result, each gene's topological features and ability to impact the system may alter [5].
The main goal of this thesis is to discover network topological parameters associated with
influential cancer driver genes using context specific networks. In order to achieve this, we evaluated
local network features around each driver gene across multiple tissue specific networks, including
tissues that are affected in the disease and others where the gene perturbation has no significant effect.
We aimed to identify topological parameters and its characteristics contributing to the cancer driver
geneâs influential role.
The results of this dissertation point out that several topological parameters can be used to
determine cancer âdriverâ genes. We found that these genes have higher values of topological
parameters, such as Degree or Closeness, in tissues where they tend to cause cancer. We also found that
this difference is present in oncogenes and tumor suppressor genes. Another factor that we found to
influence the value of topological parameters is the number of tissues in which these genes cause the
disease. There is an increasing trend of topological parameter values with the increase of the number of
tissues in which they cause cancer. Together, these results support the significant association of
topological parameters like the Degree with the influential role of a driver gene in cancer.Usando a noção de mĂłdulos de doença, a medicina de redes identificou eficazmente nos Ășltimos
anos genes associados a doenças. Nas redes biológicas, os genes ligados a uma determinada doença
tendem a interagir proximamente [1] . Essas redes permitem que conexĂ”es fĂsicas e funcionais entre
biomoléculas sejam identificadas, resultando num mapa de componentes celulares e processos que
constituem sistemas biolĂłgicos [2]. Nem todos os genes associados Ă doença, no entanto, tĂȘm um grande
impacto no fenótipo da doença.
A descoberta de genes importantes capazes de produzir ou alterar o fenótipo da doença abre
caminho para novas terapias e uma estratégia de medicina personalizada. Pesquisas recentes
descobriram que as caracterĂsticas topolĂłgicas da rede biolĂłgica podem prever com precisĂŁo os efeitos
de perturbação num modelo dinùmico do sistema com uma precisão de 65-80% [3, 4].
As redes biológicas diferem dependendo do tipo de tecido ou célula estudado. Como resultado,
as caracterĂsticas topolĂłgicas de cada gene e a capacidade de impactar o sistema podem ser alteradas
[5].
O principal objetivo desta dissertação é descobrir parùmetros topológicos de rede associados a
genes promotores de cancro usando redes especĂficas de tecido. Para conseguir isso, avaliamos as
caracterĂsticas da rede local em torno de cada gene promotor em vĂĄrias redes especĂficas de tecidos,
incluindo tecidos afetados pela doença e outros onde a perturbação do gene não tem efeito significativo.
Deste modo, podemos identificar parĂąmetros topolĂłgicos e as caracterĂsticas que contribuem para o
papel influente dos genes promotores do cancro.
Para atingir os nossos objetivos, começåmos por construir e otimizar as nossas redes especĂficas
de tecidos. Cada rede especĂfica de tecido foi construĂda usando quatro bases de dados diferentes de
interaçÔes proteĂna-proteĂna, vias de sinalização e fatores de transcrição. TentĂĄmos quatro mĂ©todos
diferentes de construir as redes, incluindo o uso do filtro de nĂveis de expressĂŁo gĂ©nica acima de 0,1 e 5
transcritos por milhĂŁo em cada tecido. ConstruĂmos tambĂ©m uma matriz associando os genes promotores
de cancro (retirados de uma base de dados online de genes promotores de cancro) aos tecidos onde
provocam a doença. Cada gene promotor foi inserido em seis categorias diferentes de acordo com o
nĂșmero de tecidos onde provocam cancro, sendo a categoria seis aquela que inclui os genes que
provocam a doença em seis ou mais tecidos. Começåmos por comparar os valores dos parùmetros
topológicos dos genes em tecidos onde estes provocam a doença versus os seus valores em tecidos onde
não a provocam. Esses valores também foram comparados com uma lista de genes associados ao cancro
(retirados de uma base de dados online de genes associados a doenças), mas não promotores de cancro,
e uma lista de genes não associados a nenhuma doença. Este estudo foi feito sobre os quatro diferentes
mĂ©todos de construção de rede. ContinuĂĄmos o estudo observando como os parĂąmetros topolĂłgicos mostraram diferenças ao nĂvel do tecido. AnalisĂĄmos em cada tecido os valores dos parĂąmetros
topológicos dos genes promotores que causam a doença num determinado tecido versus os valores dos
genes que não causam doença naquele tecido. Depois de comparar os valores dos parùmetros
topolĂłgicos usando todos os genes promotores juntos num grupo global, querĂamos verificar se a
diferença entre os valores destes nos tecidos onde causam cancro versus os valores nos tecidos onde não
provocam a doença, tambĂ©m estava presente dentro das categorias do nĂșmero de tecidos onde os genes
promotores causam cancro e como esses valores aumentam ou diminuem ao longo dessas categorias.
Avaliamos em seguida o impacto combinado dos valores dos parĂąmetros topolĂłgicos (selecionando o
parĂąmetro topolĂłgico âDegreeâ) de genes promotores de cancro em tecidos onde causam doença versus
onde nĂŁo causam e tambĂ©m a diferença entre estes ao longo das seis diferentes categorias de nĂșmero de
tecidos onde provocam cancro, usando um Modelo Linear Generalizado (GLM) para avaliar a interação
desses fatores.
Da base de dados de onde retiramos a lista de genes promotores de cancro, também retiramos
uma lista de oncogenes e genes supressores de tumor que usåmos para avaliar também as diferenças dos
valores dos seus parĂąmetros topolĂłgicos nos tecidos onde causam cancro versus os tecidos onde nĂŁo
causam. A fim de avaliar outras variåveis que possam ter impacto para além dos parùmetros topológicos
e que possam tambĂ©m diferir dependendo do nĂșmero de tecidos onde os genes âdriversâ causam a
doença, usamos os dados da base de dados de onde retiramos os genes promotores que incluĂam
informaçÔes sobre o nĂșmero de interaçÔes que cada gene promotor estabelece com diferentes miRNA e
sobre o nĂșmero de complexos proteicos que estes genes integram. TambĂ©m avaliamos o impacto da
expressĂŁo gĂ©nica nas diferentes categorias de nĂșmero de tecidos. Por fim, enriquecemos funcionalmente
os genes promotores de cancro, usando dois métodos diferentes. No primeiro método usamos os genes
que tinham uma diferença topológica maior (para este estudo usamos apenas o parùmetro topológico
âDegreeâ) entre os tecidos onde causam ou nĂŁo cancro. Classificamos cada gene como positivo,
negativo e nĂŁo significativo com base na diferença entre o valor mĂ©dio do âDegreeâ nos tecidos onde
causam cancro versus o valor nos tecidos onde não causam. O segundo método foi o enriquecimento
dos diferentes genes promotores de cancro de acordo com o nĂșmero de tecidos que causam cancro.
Fizemos esse estudo usando as diferentes categorias de nĂșmero de tecidos.
Globalmente, os nossos resultados sugerem que os valores dos parĂąmetros topolĂłgicos (por
exemplo, âDegreeâ e âClosenessâ) tendem a ser maiores nos tecidos em que os genes promoteres de
cancro provocam a doença ( âTissue Driversâ), seguidos pelos valores dos genes de cancro que sĂŁo nĂŁo
promotores de cancro mas estĂŁo associados ao desenvolvimento da doença (âDisease Genesâ), os
valores dos genes promotores de cancro nos tecidos onde nĂŁo causam cancro (âNonTissueDriversâ) e
por Ășltimo, com os menores valores de parĂąmetros topolĂłgicos, os genes que nĂŁo estĂŁo associados a
qualquer doença. A diferença entre os valores dos parĂąmetros topolĂłgicos nos âTissueDriversâ versus
âNonTissueDriversâ Ă© estatisticamente significativa na maioria dos parĂąmetros topolĂłgicos testados e
nos diferentes mĂ©todos de rede utilizados, exceto no mĂ©todo âJustHuRiTPM5Zminmaxâ (usando apenas a base de dados Huri). Quando analisĂĄmos em cada tecido os valores dos parĂąmetros topolĂłgicos,
pudemos ver que os valores de âDegreeâ tendem a ser maiores nos genes promotores de cancro que
causam cancro naquele tecido em comparação com os genes promotores que não provocam cancro nesse
tecido. Essa diferença é estatisticamente significativa em muitos dos tecidos analisados.
Em relação a como os valores dos parùmetros topológicos se comportam ao longo das diferentes
categorias associadas ao nĂșmero de tecidos em que os genes promotores causam cancro, descobrimos
que nos genes promotores de cancro que causam doença em apenas em um e dois tecidos, o valor do
âDegreeâ nos tecidos onde causam cancro Ă© menor que o valor apresentado nos tecidos onde nĂŁo causam
cancro. Observamos a tendĂȘncia inversa nos genes promotores que causam cancro em seis ou mais
tecidos (o valor do âDegreeâ Ă© maior nos tecidos onde causam cancro). Observamos tambĂ©m que o valor
do âDegreeâ aumenta gradativamente ao longo do nĂșmero da categoria de tecidos, atingindo o valor
mais alto na categoria seis (constituĂda por genes promotores que provocam cancro em seis ou mais
tecidos).
No modelo linear generalizado (GLM), pudemos ver o efeito combinado da variĂĄvel tipo de
tecido (onde o gene promotor provoca ou não cancro, mostrando uma diferença estatisticamente
significativa entre estas duas situaçÔes) e da variĂĄvel nĂșmero de tecidos onde os genes promotores
provocam cancro (mostrando também uma valor estatisticamente significativo entre as diferentes
categorias). A interação entre esses dois fatores também foi estatisticamente significativa.
TambĂ©m pudemos observar valores de âDegreeâ estatisticamente diferentes entre os genes
promotores supressores de tumor nos tecidos que causam cancro (com valores mais altos) e os valores
nos tecidos onde não causam. Vimos também a mesma diferença nos Oncogenes, mas com menor
significĂąncia. Os valores do âDegreeâ nos genes Supressores de Tumores foram inferiores aos valores
do âDegreeâ apresentados pelos Oncogenes.
Pudemos igualmente ver uma clara tendĂȘncia de correlação entre o aumento do nĂșmero de
tecidos com o aumento do nĂșmero de complexos que os genes promotores de cancro integram. O mesmo
comportamento foi observado em relação ao nĂșmero de miRNAs com os quais os genes promotores
interagem.
Em relação Ă expressĂŁo do mRNA ao longo das categorias de nĂșmero de tecidos, pudemos ver
uma diferença estatisticamente significativa nas categorias dois e trĂȘs entre os valores dos genes
promotores(em relação ao parĂąmetro topolĂłgico âDegreeâ) nos tecidos onde causam cancro versus onde
nĂŁo causam.
Finalmente, no estudo de enriquecimento de funçÔes pudemos ver que os processos biológicos,
funçÔes moleculares e componentes celulares que obtivemos enriquecidos usando o método das
diferentes categorias de nĂșmero de tecidos estĂŁo muito mais relacionados com os processos de cancro
baseados na literatura (âhallmarks of cancerâ). NĂŁo conseguimos encontrar uma divisĂŁo muito clara
entre funçÔes biolĂłgicas enriquecidas que tiveram uma diferença de z-score do âDegreeâ acima de 1 e aqueles com diferença abaixo de -1. NĂŁo encontramos nenhum processo de enriquecimento funcional
relevante em nenhum desses dois grupos de genes e que de alguma forma os pudesse distinguir entre si.
Os resultados desta dissertação apontam para que vårios parùmetros topológicos possam estar
associados a genes promotores de cancro. VerificĂĄmos que estes genes tĂȘm valores de parĂąmetros
topolĂłgicos, como o Degree ou Closeness, mais elevados nos tecidos onde tendencionalmente provocam
cancro. Verificåmos também que esta diferença estå presente nos oncogenes e nos genes supressores de
tumor. Outro fator que verificamos influenciar o valor dos parĂąmetros topolĂłgicos, Ă© o nĂșmero de
tecidos em que estes genes provocam a doença. HĂĄ uma tendĂȘncia crescente do valor topolĂłgico com
um nĂșmero de tecidos em que provocam cancro
Extracellular Vesicles and Their Importance in Human Health
Extracellular vesicle is a wide term that involves many different types of vesicles. Almost all the cell types studied secrete vesicles to the extracellular environment related to cell - cell communication. Extracellular vesicles have been found in different biological fluids, such as blood, milk, saliva, tears, urine, and cerebrospinal fluid. These vesicles transport different molecules, including mRNA, proteins, and lipids, some of them cell type specific that make them ideal biomarkers in both health and disease conditions. However, their contribution to different conditions is not well understood. The aim of this book is to provide an overview of the extracellular vesicles in the human body, how they are internalized, and their participation in several diseases
The Chondrocyte Channelome: A Novel Ion Channel Candidate in the Pathogenesis of Pectus Deformities
Costal cartilage is a type of rod-like hyaline cartilage connecting the ribs to the sternum. The chest wall deformities pectus excavatum (PE) and pectus carinatum (PC) involve displacement of the sternum causing a depression or protrusion of the chest. There is little knowledge about costal cartilage and pectus deformities with much of its understanding based on assumptions from articular cartilage. Chondrocytes are subjected to a constantly changing environment with fluctuations in pH and osmolarity. Ion channels detect these changes and in turn regulate proliferation, differentiation, and extracellular matrix production. Using ion channel qPCR arrays, we produced expression profiles for normal, fetal, PE-affected, and PC-affected costal chondrocytes as well as articular chondrocytes. Costal and articular chondrocytes had many commonly expressed ion channels with certain channels specific to each cartilage type. The discrepancy in ion channel expression is likely to be a reflection of the functional differences between the two cartilage types. Additionally, fetal costal chondrocytes had several other distinct ion channels possibly due to the differentiation status of the cells. In PC and PE chondrocytes, ACCN1 (ASIC2) and KCNN2 (SK2) were consistently down-regulated compared to normal costal chondrocytes. However, Western blot analysis found deceases only in ASIC2 protein levels. ASIC2 is a proton-gated ion channel involved in cell response to extracellular pH changes. Calcium monitoring revealed a delay in the formation calcium transients in PC cells when challenged with low pH which may be caused by aberrant signaling from ASIC channels. Immunofluorescent analysis of connexins found that Cx43 was present in chondrocytes with phosphorylated Cx43 localizing in and around the nucleus. Analysis of ATP release found that release is likely a connexin-mediated process, though external acidosis did not induce ATP release. Analysis of microRNAs found upregulation and down-regulation of several microRNAs in PC versus control cells, though further studies are needed to identify a possible microRNA signature for pectus deformities. Overall, we have generated a comprehensive ion channel profile for the costal chondrocytes, as well as identified a possible contributing factor for pectus deformities
Towards More Predictive, Physiological and Animal-free In Vitro Models: Advances in Cell and Tissue Culture 2020 Conference Proceedings
Experimental systems that faithfully replicate human physiology at cellular, tissue and organ level are crucial to the development of efficacious and safe therapies with high success rates and low cost. The development of such systems is challenging and requires skills, expertise and inputs from a diverse range of experts, such as biologists, physicists, engineers, clinicians and regulatory bodies. Kirkstall Limited, a biotechnology company based in York, UK, organised the annual conference, Advances in Cell and Tissue Culture (ACTC), which brought together people having a variety of expertise and interests, to present and discuss the latest developments in the field of cell and tissue culture and in vitro modelling. The conference has also been influential in engaging animal welfare organisations in the promotion of research, collaborative projects and funding opportunities. This report describes the proceedings of the latest ACTC conference, which was held virtually on 30th September and 1st October 2020, and included sessions on in vitro models in the following areas: advanced skin and respiratory models, neurological disease, cancer research, advanced models including 3-D, fluid flow and co-cultures, diabetes and other age-related disorders, and animal-free research. The roundtable session on the second day was very interactive and drew huge interest, with intriguing discussion taking place among all participants on the theme of replacement of animal models of disease
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