Previsão e análise da estrutura e dinâmica de redes biológicas

Increasing knowledge about the biological processes that govern the dynamics of living organisms has fostered a better understanding of the origin of many diseases as well as the identification of potential therapeutic targets. Biological systems can be modeled through biological networks, allowing to apply and explore methods of graph theory in their investigation and characterization. This work had as main motivation the inference of patterns and rules that underlie the organization of biological networks. Through the integration of different types of data, such as gene expression, interaction between proteins and other biomedical concepts, computational methods have been developed so that they can be used to predict and study diseases. The first contribution, was the characterization a subsystem of the human protein interactome through the topological properties of the networks that model it. As a second contribution, an unsupervised method using biological criteria and network topology was used to improve the understanding of the genetic mechanisms and risk factors of a disease through co-expression networks. As a third contribution, a methodology was developed to remove noise (denoise) in protein networks, to obtain more accurate models, using the network topology. As a fourth contribution, a supervised methodology was proposed to model the protein interactome dynamics, using exclusively the topology of protein interactions networks that are part of the dynamic model of the system. The proposed methodologies contribute to the creation of more precise, static and dynamic biological models through the identification and use of topological patterns of protein interaction networks, which can be used to predict and study diseases.O conhecimento crescente sobre os processos biológicos que regem a dinâmica dos organismos vivos tem potenciado uma melhor compreensão da origem de muitas doenças, assim como a identificação de potenciais alvos terapêuticos. Os sistemas biológicos podem ser modelados através de redes biológicas, permitindo aplicar e explorar métodos da teoria de grafos na sua investigação e caracterização. Este trabalho teve como principal motivação a inferência de padrões e de regras que estão subjacentes à organização de redes biológicas. Através da integração de diferentes tipos de dados, como a expressão de genes, interação entre proteínas e outros conceitos biomédicos, foram desenvolvidos métodos computacionais, para que possam ser usados na previsão e no estudo de doenças. Como primeira contribuição, foi proposto um método de caracterização de um subsistema do interactoma de proteínas humano através das propriedades topológicas das redes que o modelam. Como segunda contribuição, foi utilizado um método não supervisionado que utiliza critérios biológicos e topologia de redes para, através de redes de co-expressão, melhorar a compreensão dos mecanismos genéticos e dos fatores de risco de uma doença. Como terceira contribuição, foi desenvolvida uma metodologia para remover ruído (denoise) em redes de proteínas, para obter modelos mais precisos, utilizando a topologia das redes. Como quarta contribuição, propôs-se uma metodologia supervisionada para modelar a dinâmica do interactoma de proteínas, usando exclusivamente a topologia das redes de interação de proteínas que fazem parte do modelo dinâmico do sistema. As metodologias propostas contribuem para a criação de modelos biológicos, estáticos e dinâmicos, mais precisos, através da identificação e uso de padrões topológicos das redes de interação de proteínas, que podem ser usados na previsão e no estudo doenças.Programa Doutoral em Engenharia Informátic

A peptide-based interaction screen on disease-related mutations

Zahlreiche pathogene „missense“-Mutation, die verhindern, dass Proteine korrekt gefaltet werden, befinden sich in geordneten Regionen von Proteinen. Andere krankheitsrelevante Mutationen befinden sich in ungeordneten Regionen und beeinflussen somit nur begrenzt die Funktionalität, zum Beispiel durch Veränderungen kurzer linearer Sequenzmotive, die Protein-Protein Interaktionen vermitteln. In dieser Arbeit wird ein peptidbasierter Interaktionsscreen präsentiert mit dem sich Veränderungen im Interaktom identifizieren lassen. Synthetische Peptide von wild-typ und zugehörigen mutierten Proteinregionen ermöglichen die gleichzeitige Untersuchung von mehr als hundert Mutationen mittels Massenspektrometrie. Mehr als ein Drittel aller getesteten Mutationen hatten veränderte Interaktionen zur Folge. Darunter befanden sich auch drei Prolin zu Leucin Mutationen in zytosolischen Regionen von Transmembranproteinen, die zusammen mit dem benachbarten Leucin einem Dileucinmotiv ergeben und dadurch verstärkt mit Clathrin interagieren. Dieses Motiv wurde bereits mit Clathrin-vermittelter Endozytose in Verbindung gebracht. Die hinzugewonnene Endozytose könnte Krankheitsmechanismen erklären, da die Mislokalisation der betroffenen Transmembranproteine zum effektiven Verlust derer Funktion führen würde. Diese Hypothese wurde hier von verschiedenen in vitro und in vivo Experimenten bezüglich der P485L Mutation im Glukose Transporter-1 (GLUT1), die das GLUT1-Defizit-Syndrom hervorruft, bestätigt. Weitere Evidenz wurde außerdem für die Funktionalität anderer mutationsbedingter Dileucinmotive gewonnen. Die systematische Analyse von pathogenen Mutationen hat gezeigt, dass Dileucinmotive signifikant und spezifisch in ungeordneten zytosolischen Regionen von Transmembranproteinen überrepräsentiert sind. Dieser Peptidescreen macht das Potenzial unvoreingenommener Analysen zur Aufklärung von Krankheitsmechanismen deutlich, die von Veränderungen in Protein-Protein Interaktionen hervorgerufen werden.Many disease-associated missense mutations prevent proteins from folding correctly and lead to loss-of-function. These mutations are often found in ordered regions of proteins. Another class of disease-related missense mutations can be found in disordered regions. These are thought to impair only specific parts of a protein’s functions. Those mutations could modify short linear motifs that mediate protein-protein interactions. Here, we designed a peptide-based interaction screen to identify interactions that are affected by mutations in disordered regions. We used synthetic peptides corresponding to the wild type and mutated protein regions spotted on cellulose membrane to pull-down interaction partners. This setup allows for the screening of more than hundred mutations at a time via mass spectrometry. Here, we focused on mutations implicated in neurological diseases. More than one-third of tested variant pairs show differential interactions. Three disease-related proline to leucine mutations in cytosolic tails of transmembrane proteins lead to gain of a dileucine sequence. Several dileucine-containing peptide motifs are involved in clathrin-mediated endocytosis (CME). Also in the presented screen, the newly created motifs mediate interaction with the CME machinery. This could explain the disease mechanisms since mislocalization of the affected transmembrane proteins would lead to their loss of function. This hypothesis has been corroborated for glucose transporter-1 (GLUT1) P485L, causing GLUT1 deficiency syndrome. We were able to provide functional evidence also for additional gained dileucine motifs. A systematic analysis of pathogenic mutations revealed dileucine motifs to be overrepresented in structurally disordered cytosolic regions of transmembrane proteins. The data gained with the peptide screen highlights the power of differential interactome mapping as a generic approach to unravel disease mechanisms caused by changes in protein-protein interactions

Integrating Functional Genomics with Systems Biology to Discover Drivers and Therapeutic Targets of Human Malignancies

Genome-wide RNAi screening has emerged as a powerful tool for loss-of-function studies that may lead to therapeutic target discovery for human malignancies in the era of personalized medicine. However, due to high false-positive and false-negative rates arising from noise of high-throughput measurements and off-target effects, powerful computational tools and additional knowledge are much needed to analyze and complement it. Availability of high-throughput genomic data including gene expression profiles, copy number variations from large-sampled primary patients and cell lines allows us to tackle underlying drivers causally associated with tumorigenesis or drug-resistance. In my dissertation, I have developed a framework to integrate functional RNAi screens with systems biology of cancer genomics to tailor potential therapeutics for reversal of drug-resistance or treatment of aggressive tumors. I developed a series of algorithms and tools to deconvolute, QC and post-analyze high-throughput shRNA screening data by next-generation sequencing technology (shSeq), particularly a novel Bayesian hierarchical modeling approach to integrate multiple shRNAs targeting the same gene, which outperforms existing methods. In parallel, I developed a systems biology algorithm, NetBID2, to infer disease drivers from high-throughput genomic data by reverse-engineering network and Bayesian inference, which is able to detect hidden drivers that traditional methods fail to find. Integrating NetBID2 with functional RNAi screens, I have identified known and novel driver-type therapeutic targets in various disease contexts. For example, I discovered that AKT1 is a driver for glucocorticoid (GC) resistance, a problem in the treatment of T-ALL. The inhibition of AKT1 was validated to reverse GC-resistance. Additionally, upon silencing predicted master regulators of GC resistance with shRNA screens, 13 out of 16 were validated to significantly overcome resistance. In breast cancer, I discovered that STAT3 is required for transformation of HER2+ breast cancer, an aggressive breast tumor subtype. The suppression of STAT3 was confirmed in vitro and in vivo to be an effective therapy for HER2+ breast cancer. Moreover, my analysis revealed that STAT3 silencing only works in ER- cases. Using my framework, I have also identified potential therapeutic targets for ABC or GCB-type DLBCL and subtype-based breast cancer that are currently being validated

A Computational and Experimental Study of the Regulatory Mechanisms of the Complement System

The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. Our study also revealed differential regulatory effects of C4BP. While C4BP delays but does not decrease the classical complement activation, it attenuates but does not significantly delay the lectin pathway activation. We also found that the major inhibitory role of C4BP is to facilitate the decay of C3 convertase. In summary, the present work elucidates the regulatory mechanisms of the complement system and demonstrates how the bio-pathway machinery maintains the balance between activation and inhibition. The insights we have gained could contribute to the development of therapies targeting the complement system.Singapore. Ministry of Education (Grant T208B3109)Singapore. Agency for Science, Technology and Research (BMRC 08/1/21/19/574)Singapore-MIT Alliance (Computational and Systems Biology Flagship Project)Swedish Research Counci

Suuremahuliste andmete kasutamine geenidevaheliste seoste leidmiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Geenid määravad ära, millistest RNA ja valgu molekulidest elusorganism koosneb. Ainult geenide tuvastamisest ei piisa, et aru saada kuidas organism toimib, millal ja kuidas erinevad geenide produktid avalduvad ja mida need teevad. Elusorganismi olemuse mõistmiseks ja bioloogiliste protsesside mõjutamiseks on vajalik aru saada geenide ja valkude omavahelistest seostest. Suure läbilaskevõimega tehnoloogiad võimaldavad hõlpsasti mõõta bioloogiliste protsesside erinevaid tahke. See omakorda on toonud kaasa andmemahtude üha kiireneva kasvutrendi ning vajaduse uute meetodite järele, mis aitaks toorandmeid analüüsida, andmeid omavahel kombineerida ning tulemusi visualiseerida. Samuti on kasvanud vajadus arvutuslike meetoditega katsetada, kas olemasolevad andmemudelid kirjeldavad bioloogilist uurimisobjekti piisavalt täpselt. Käesolevas uurimistöös on näidatud erinevaid bioinformaatilisi meetodeid, kuidas suuremahuliste ning eritüübiliste eksperimentaalsete andmete kombineerimist saab rakendada geenidevaheliste seoste leidmiseks. Suuremahulistele andmetele on integreerimise ja omavahel võrreldavaks tegemisega võimalik anda lisaväärtust. Töö käigus koondati kokku ja tehti avalikkusele ligipääsetavaks embrüonaalsete tüvirakkude regulatsiooni käsitlevate publikatsioonide lisafailides avaldatud info ESCDb andmebaasi näol. Neid andmeid kasutades on teadlaskonnal võimalik leida geenide vahelisi seoseid, mida eraldiseisvaid andmeid analüüsides ei ole võimalik välja selgitada. Andmebaasi kogutud info kombineerimisel arvutusliku mudeldamisega õnnestus leida käesoleva töö raames uus regulaator embrüonaalsetes tüvirakkudes — IL11. Lisaks võimaldas erinevate andmetüüpide kombineerimine leida embrüonaalsete tüvirakkude keskse regulaatori — OCT4 geeni alternatiivsed märklaudgeenide moodulid. Kasutades DNA konserveerumisinfot koos regulatoorsete motiivide analüüsiga leiti kolm uut rasvatüvirakkude diferentseerumise regulaatorvalku. Samuti käsitletakse töös automaatset grupeerimis- ja visualiseerimismetoodikat VisHiC, mis aitab esile tõsta huvitavaid geenigruppe, mida teiste meetoditega edasi uurida. Töös on näidatud erinevaid suuremahuliste andmestike integreerimise viise, mis võimaldavad leida selliseid geenidevahelisi seoseid, mida ei oleks võimalik leida kui analüüsiksime üht andmestikku korraga.In order to understand the basic principles of how organisms function, and to be able to affect the biological processes, we need to understand relationships between genes and proteins. Modern high-throughput technology enables to study different sides of biological processes in a rapid manner. This, however, has led to a steady growth of amount of data available. The need for more sophisticated methods for analysing raw data, for combining different data sources, and to visualise the results, has emerged. Additionally, computational modeling is required to test if our understanding of biological processes is supported by the available data. A variety of bioinformatics methods are used to demonstrate how to combine different type of high-throughput data for identifying relationships between genes. Furthermore, it was shown that through combining various data types from different sources adds value to already published data. In the thesis, data from publications about embryonic stem cell regulation were collected together and made available through Embryonic Stem Cell Database (ESCDb). Complementary data in the database allows researchers to find relationships between genes that would not be possible when analysing only one dataset at a time. One of the main findings of this study illustrates how using computational modelling on data from the ESCDb allowed to find a novel pluripotency regulator — IL11. Additionally, integration of different data types led to identification of alternative gene regulatory modules of core pluripotency regulator OCT4. Similarly, combination of conservation data and regulatory motif analysis led to identification of three new regulators of adipocyte differentiation. This thesis also covers innovative methodology, VisHiC, for automatic identification and visualisation of functionally related gene sets. This methodology allows to find relevant gene sets for further characterisation from large high-throughput datasets. This doctoral thesis demonstrates that integration of different high-throughput datasets enables establishing gene-gene relationships that would not be possible when looking at a single data type in isolation

Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

Characterization of YisK, a Cell Shape Modifier and Enzyme in Bacillus subtilis

Bacterial growth and division requires the careful coordination of peptidoglycan (PG) synthesis and PG hydrolysis, allowing the insertion of new cell wall material at sites of active growth. In many rod-shaped bacteria, the bacterial actin homolog MreB is thought to coordinate this balance of synthesis and hydrolysis, particularly during cell elongation, and the current model is that MreB-like proteins act as a scaffold, directing the PG synthesis machinery to sites of active growth. Despite their importance, very little is known about how MreB-like proteins in prokaryotes are regulated. Using a Bacillus subtilis misexpression screen, we identified yisK and yodL, which cause a loss of cell shape and viability when misexpressed. Suppressors resistant to YisK’s killing activity primarily occur in mbl (the structural gene for an MreB paralog in B. subtilis), while suppressors resistant to YodL’s activity primarily occur in MreB. Consistent with the idea that YisK targets Mbl activity and YodL targets MreB activity, deletion of mbl confers resistance to YisK, while deletion of MreB confers resistance to YodL. In an mbl deletion background, YisK expressing cells also become 20% shorter, suggesting that YisK activity affects at least one other target integral to cell shape. Using a bacterial 2-hybrid assay, we detected an interaction between YisK and FtsE (the ATPase of the ABC Transporter FtsEX). Interestingly, published data indicates that FtsEX, which is important for regulating the activity of the D,L-endopeptidase CwlO, appears to act in the same pathway as Mbl, and both ftsE and cwlO mutants exhibit short-cell phenotypes. Our data suggest that ftsE is required for YisK-dependent cell shortening, but not cell widening. YisK shows ~40% amino acid identity to an FAH from Mycobacterium abcsessus, and we have obtained a preliminary crystal structure for YisK, with a dicarboxylic acid, most likely L-tartrate, bound in the active site. Surprisingly, introducing mutations in YisK’s active site has no effect on its ability to perturb cell shape. Our current model is that YisK is an enzyme, possibly involved in the dicarboxylate pathway, that utilizes interactions with Mbl and possibly FtsE to localize its enzymatic activity to specific regions within the cell

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina