O câncer de mama é um dos que mais afeta mulheres em todo o mundo e em 2016 estima-se que haverá aproximadamente 58 mil novos casos no Brasil. É uma doença caracteristicamente heterogênea, o que dificulta o diagnóstico, prognóstico e a abordagem terapêutica utilizada. Os biomarcadores já existentes para câncer de mama não são suficientes para explicar a ocorrência de metástase, que é a principal causa de morte entre os pacientes. Neste sentido, os RNAs longos não codificadores (lncRNAs) vêm se estabelecendo como importantes moléculas regulatórias em diversos processos biológicos e alguns têm sido associados com o desenvolvimento e a progressão do câncer de mama. Apesar disso, muito ainda precisa ser elucidado a respeito dessa nova classe de ncRNAs e seu envolvimento na metástase. Desta forma, determinar uma assinatura de lncRNAs em tumores metastáticos de mama pareados com seus respectivos tumores primários pode sugerir novas moléculas envolvidas no mecanismo de metástase. Neste estudo, foram analisadas amostras de cinco tumores primários de mama e seus correspondentes metastáticos em Sistema Nervoso Central (n=2) e em linfonodos (n=3). O RNA total de cada amostra foi extraído e avaliado quanto à sua qualidade para obtenção do transcriptoma pelo método de RNA-Seq. Para o tratamento dos dados foram usados os aplicativos: Tophat para o alinhamento das reads, o Cufflinks para a montagem e quantificação dos transcritos e o pacote DESeq2 para análise de expressão diferencial. O resultado da análise demonstrou que os lncRNAs com íntrons retidos são os mais abundantes e que as amostras de tumores primários e metastáticos compartilham a maioria dos lncRNAs expressos, tornando-os qualitativamente bastante semelhantes entre si. Dentre os lncRNAs diferencialmente expressos, doze são comuns entre os tumores primários, independente dos subtipos moleculares das amostras, e não há transcritos comuns às amostras metastáticas. A análise de agrupamento hierárquico definiu três assinaturas de expressão de lncRNAs para as amostras de tumores primários, metástase em cérebro e em linfonodos, podendo indicar lncRNAs potencialmente envolvidos no mecanismo de metástase. / Breast cancer is the principall cancer that affects women in the world and it is expect to Brazil, 58.000 new cases in 2016. It is a heterogeneous disease, what makes diagnosis, prognosis and therapeutical approach more difficult. Existing biomarkers for breast cancer are not sufficient to explain metastasis occurrence, which is the main cause of mortality. In this way, long non-coding RNAs (lncRNAs) have been establishing as important regulatory molecules in several biological processes and some of them have been associated to breast cancer development and progression. Nevertheless, many aspects about this new ncRNA class and its involvement in metastasis need to be elucidated. Thus, determining a lncRNA signature of metastatic tumors paired with their related primary tumors may indicate new molecules involved in the mecanism of metastasis. In the present study, Five samples of primary breast tumors and their related metastasis in central nervous system (n=2) and in lymph nodes (n=3) were analyzed. Total RNA of each sample was extracted and had its quality evaluated for transcriptome obtainment by RNA-Seq method. Data processing was performed utilizing the applications: Tophat, for read alignment; Cufflinks, for transcripts assembly and quantification; and DESeq2 package, for differential expression analysis. The results revealed that lncRNAs containing retained introns are the most abundant transcripts and that primary and metastatic samples share most of the expressed lncRNAs, what makes them qualitatively similar to each other. Differential expression analysis showed that twelve lncRNAs are common among primary tumors, apart from samples mollecular subtypes, and metastatic tumors do not have any transcript in common. Hierarchical clustering analysis defined three lncRNA expression signatures, for primary tumors, brain and lymph node metastatis, what may indicate lncRNAs potentially involved in metastasis mechanism.

[EN] Transcriptomics is one of the most important and relevant areas of bioinformatics. It allows detecting the genes that are expressed at a particular moment in time to explore the relation between genotype and phenotype. Transcriptomic analysis has been historically performed using microarrays until 2008 when high-throughput RNA sequencing (RNA-Seq) was launched on the market, replacing the old technique. However, despite the clear advantages over microarrays, it was necessary to understand factors such as the quality of the data, reproducibility and replicability of the analyses and potential biases. The first section of the thesis covers these studies. First, an R package called NOISeq was developed and published in the public repository "Bioconductor", which includes a set of tools to better understand the quality of RNA-Seq data, minimise the impact of noise in any posterior analyses and implements two new methodologies (NOISeq and NOISeqBio) to overcome the difficulties of comparing two different groups of samples (differential expression). Second, I show our contribution to the Sequencing Quality Control (SEQC) project, a continuation of the Microarray Quality Control (MAQC) project led by the US Food and Drug Administration (FDA, United States) that aims to assess the reproducibility and replicability of any RNA-Seq analysis. One of the most effective approaches to understand the different factors that influence the regulation of gene expression, such as the synergic effect of transcription factors, methylation events and chromatin accessibility, is the integration of transcriptomic with other omics data. To this aim, a file that contains the chromosomal position where the events take place is required. For this reason, in the second chapter, we present a new and easy to customise tool (RGmatch) to associate chromosomal positions to the exons, transcripts or genes that could regulate the events. Another aspect of great interest is the study of non-coding genes, especially long non-coding RNAs (lncRNAs). Not long ago, these regions were thought not to play a relevant role and were only considered as transcriptional noise. However, they represent a high percentage of the human genes and it was recently shown that they actually play an important role in gene regulation. Due to these motivations, in the last chapter we focus, first, in trying to find a methodology to find out the generic functions of every lncRNA using publicly available data and, second, we develop a new tool (spongeScan) to predict the lncRNAs that could be involved in the sequestration of micro-RNAs (miRNAs) and therefore altering their regulation task. / [ES] La transcriptómica es una de las áreas más importantes y destacadas en bioinformática, ya que permite ver qué genes están expresados en un momento dado para poder explorar la relación existente entre genotipo y fenotipo. El análisis transcriptómico se ha realizado históricamente mediante el uso de microarrays hasta que, en el año 2008, la secuenciación masiva de ARN (RNA-Seq) fue lanzada al mercado y comenzó a desplazar poco a poco su uso. Sin embargo, a pesar de las ventajas evidentes frente a los microarrays, resultaba necesario entender factores como la calidad de los datos, reproducibilidad y replicabilidad de los análisis así como los potenciales sesgos. La primera parte de la tesis aborda precisamente estos estudios. En primer lugar, se desarrolla un paquete de R llamado NOISeq, publicado en el repositorio público "Bioconductor", el cual incluye un conjunto de herramientas para entender la calidad de datos de RNA-Seq, herramientas de procesado para minimizar el impacto del ruido en posteriores análisis y dos nuevas metodologías (NOISeq y NOISeqBio) para abordar la problemática de la comparación entre dos grupos (expresión diferencial). Por otro lado, presento nuestra contribución al proyecto Sequencing Quality Control (SEQC), una continuación del proyecto Microarray Quality Control (MAQC) liderado por la US Food and Drug Administration (FDA) que pretende evaluar precisamente la reproducibilidad y replicabilidad de los análisis realizados sobre datos de RNA-Seq. Una de las estrategias más efectivas para entender los diferentes factores que influyen en la regulación de la expresión génica, como puede ser el efecto sinérgico de los factores de transcripción, eventos de metilación y accesibilidad de la cromatina, es la integración de la transcriptómica con otros datos ómicos. Para ello se necesita generar un fichero que indique las posiciones cromosómicas donde se producen estos eventos. Por este motivo, en el segundo capítulo de la tesis presentamos una nueva herramienta (RGmatch) altamente customizable que permite asociar estas posiciones cromosómicas a los posibles genes, transcritos o exones a los que podría estar regulando cada uno de estos eventos. Otro de los aspectos de gran interés en este campo es el estudio de los genes no codificantes, especialmente los ARN largos no codificantes (lncRNAs). Hasta no hace mucho, se pensaba que estos genes no jugaban ningún papel fundamental y se consideraban como simple ruido transcripcional. Sin embargo, suponen un alto porcentaje de los genes del ser humano y se ha demostrado que juegan un papel crucial en la regulación de otros genes. Por este motivo, en el último capítulo nos centramos, en un primer lugar, en intentar obtener una metodología que permita averiguar las funciones generales de cada lncRNA haciendo uso de datos ya publicados y, en segundo lugar, generamos una nueva herramienta (spongeScan) que permite predecir qué lncRNAs podrían estar secuestrando determinados micro-RNAs (miRNAs), alterando así la regulación llevada a cabo por estos últimos. / [CA] La transcriptòmica és una de les àrees més importants i destacades en bioinformàtica, ja que permet veure quins gens s'expressen en un moment donat per a poder explorar la relació existent entre genotip i fenotip. L'anàlisi transcriptòmic s'ha fet històricament per mitjà de l'ús de microarrays fins l'any 2008 quan la tècnica de seqüenciació massiva d'ARN (RNA-Seq) es va fer pública i va començar a desplaçar a poc a poc el seu ús. No obstant això, a pesar dels avantatges evidents enfront dels microarrays, resultava necessari entendre factors com la qualitat de les dades, reproducibilitat i replicabilitat dels anàlisis, així com els possibles caires introduïts. La primera part de la tesi aborda precisament estos estudis. En primer lloc, es va programar un paquet de R anomenat NOISeq publicat al repositori públic "Bioconductor", el qual inclou un conjunt d'eines per a entendre la qualitat de les dades de RNA-Seq, eines de processat per a minimitzar l'impact del soroll en anàlisis posteriors i dos noves metodologies (NOISeq i NOISeqBio) per a abordar la problemàtica de la comparació entre dos grups (expressió diferencial). D'altra banda, presente la nostra contribució al projecte Sequencing Quality Control (SEQC), una continuació del projecte Microarray Quality Control (MAQC) liderat per la US Food and Drug Administration (FDA) que pretén avaluar precisament la reproducibilitat i replicabilitat dels anàlisis realitzats sobre dades de RNA-Seq. Una de les estratègies més efectives per a entendre els diferents factors que influïxen a la regulació de l'expressió gènica, com pot ser l'efecte sinèrgic dels factors de transcripció, esdeveniments de metilació i accessibilitat de la cromatina, és la integració de la transcriptómica amb altres dades ómiques. Per això es necessita generar un fitxer que indique les posicions cromosòmiques on es produïxen aquests esdeveniments. Per aquest motiu, en el segon capítol de la tesi presentem una nova eina (RGmatch) altament customizable que permet associar aquestes posicions cromosòmiques als possibles gens, transcrits o exons als que podria estar regulant cada un d'aquests esdeveniments regulatoris. Altre dels aspectes de gran interés en aquest camp és l'estudi dels genes no codificants, especialment dels ARN llargs no codificants (lncRNAs). Fins no fa molt, encara es pensava que aquests gens no jugaven cap paper fonamental i es consideraven com a simple soroll transcripcional. No obstant això, suposen un alt percentatge dels gens de l'ésser humà i s'ha demostrat que juguen un paper crucial en la regulació d'altres gens. Per aquest motiu, en l'últim capítol ens centrem, en un primer lloc, en intentar obtenir una metodologia que permeta esbrinar les funcions generals de cada lncRNA fent ús de dades ja publicades i, en segon lloc, presentem una nova eina (spongeScan) que permet predeir quins lncRNAs podríen estar segrestant determinats micro-RNAs (miRNAs), alterant així la regulació duta a terme per aquests últims. / Furió Tarí, P. (2020). Development of bioinformatic tools for massive sequencing analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/152485 / TESIS

No description available.

Ces dernières années, les avancées technologiques ont révélé qu’une majeure partie de notre génome est transcrit en ARNs non codants, qui sont impliqués dans de nombreuses pathologies. Dès lors, nous avons voulu, lors de cette thèse, mieux comprendre le rôle des ARNs non codants dans le cancer du sein.Dans un premier temps, nous avons étudié les micros ARNs non codants (miRs) dans un modèle cellulaire de cancer mammaire. Nous avons identifié deux miRs qui sont exprimés dans les lignées mammaires normales, mais pas dans les lignées cancéreuses. La surexpression de miR-137 entraine une diminution de la prolifération et de la migration des cellules cancéreuses. De plus, nous avons identifié que miR-137 cible directement la protéine histone déméthylase KDM5B et diminue son expression génique et protéique. Ensuite, nous avons cherché si d’autres histones déméthylases de la famille KDM5 pouvaient être régulées par les miRs. Nous avons découvert que KDM5C, qui est surexprimée dans les lignées mammaires cancéreuses, est une cible directe de miR-138. La surexpression de miR-138 dans les lignées tumorales diminue l’expression de KDM5C et entraine une chute de la prolifération cellulaire. Globalement, ces résultats révèlent que les miRs peuvent réguler les protéines épigénétiques KDM5 et contrôler la prolifération cellulaire.La deuxième partie de cette thèse est consacrée à un nouveau sujet de recherche pour la communauté scientifique :l’étude des longs ARNs non codants (lncRNAs). Des études pionnières révèlent que quelques lncRNAs sont impliqués dans les cancers du sein. Des milliers de lncRNAs existent, mais très peu ont été caractérisés. Dès lors, nous avons décidé d’évaluer globalement leur profil d’expression dans une large cohorte de cancers du sein. Nous avons identifié 215 lncRNAs dérégulés dans les tumeurs. Nos résultats révèlent que l’expression des lncRNAs permet de classer les cancers du sein en différents sous-types. Des analyses bioinformatiques ont permis de prédire leurs fonctions et leurs implications dans différentes voies moléculaires clés du cancer du sein telles que les voies PI3K/AKT/mTOR et MAPK. Nous avons également découvert que 210 lncRNAs sont des marqueurs pronostics indépendants du risque de rechute. Enfin, nous avons choisi deux lncRNAs que nous avons étudiés expérimentalement. Nous avons montré que lnc-KIN-2 contrôle la prolifération cellulaire en régulant l’expression des gènes GATA3 et ESR1. Ensuite, nous avons découvert que CYTOR, un lncRNA surexprimé dans les tumeurs mammaires, régule des gènes de la voie EGFR/mTOR et est requis pour la prolifération et la migration cellulaire ainsi que pour le maintien du cytosquelette et de la morphologie normale de la cellule.En démontrant l’importance biologique des ARNs non codants dans le développement des tumeurs mammaires, ces résultats laissent entrevoir la mise en lumière de nouveaux mécanismes par lesquels ces ARNs particuliers, qui ne codent pas des protéines, contribuent au processus de cancérogénèse et pourraient devenir des nouvelles cibles thérapeutiques. / Doctorat en Sciences biomédicales et pharmaceutiques (Médecine) / info:eu-repo/semantics/nonPublished

No description available.

Le cancer du sein représente à l’échelle mondiale le deuxième cancer le plus fréquent et la première des tumeurs malignes de la femme. Actuellement, seuls certains biomarqueurs (RO, RP, récepteur HER2, index Ki67) et la signature transcriptomique PAM50 sont pris en compte dans la classification morphologique et l’orientation thérapeutique. Les analyses transcriptomiques à haut débit ont révélé que plus de 80% du génome humain est transcrit en ARN. Parmi les ARNs non codants, les transcrits dont la longueur est supérieure à 200 nt sont arbitrairement qualifiés de longs ARNs non codants (lncRNAs). Les lncRNAs jouent un rôle crucial dans le maintien de l’homéostasie cellulaire et présentent des profils d’expression anormaux dans diverses pathologies, dont le cancer. L’objectif principal de mon projet de thèse a consisté à analyser l’expression des lncRNAs, leur fonctionnalité et leur rôle dans l’oncogénèse mammaire. La première partie s’est focalisée sur l’étude des gènes ANRIL (ainsi que 10 gènes de la même voie de signalisation) et MALAT1, deux lncRNAs dont les mécanismes d’action et la signification clinique au cours de la cancérogénèse mammaire sont encore controversés. ANRIL et MALAT1 sont respectivement surexprimés dans 20% et 14% des tumeurs de notre série, confirmant leurs rôles pro-oncogénique dans la cancérogénèse mammaire. La surexpression de MALAT1 se traduit en RNA-FISH par la présence de volumineux speckles intranucléaires. La complexité de leur dérégulation est liée à la présence d’isoformes et de réseaux d’interactions avec les mRNAs et les miRNAs. Concernant les sous-unités appartenant aux complexes Polycomb PRC2 et PRC1qui interagissent avec ANRIL, EZH2 (PRC2) est normalement ciblé par 3 miRNAs onco-suppresseurs (miR-26A1, miR-125B et miR-214) qui sont sous-exprimés dans notre série de CCIs. Les 2 oncomiRs miR-181B1 and miR-181A2 qui ciblent et inactivent CBX7 (PRC1), apparaissent surexprimés dans notre série, en relation avec l’activation de l’oncogène HMGA1. Concernant MALAT1, le complexe de biogénèse des miRNAs Drosha-DGCR8-Microprocesseur régule l’expression d’un variant d’épissage ∆-MALAT1 et ce dernier est impliqué dans l’activation de la voie PI3K/Akt. Des corrélations significatives sont observées entre MALAT1 et des gènes impliqués dans l’épissage alternatif, le cycle cellulaire, l’apoptose, la réparation de l’ADN et la migration cellulaire. Les profils transcriptomiques aberrants de ces 2 lncRNAs semblent caractéristiques des carcinomes mammaires. Ainsi, ANRIL (i) présente une association positive inattendue avec le cluster p16-CDKN2A/p15-CDKN2B/p14-ARF dans notre série, alors que cette association apparait négative dans les carcinomes de prostate et (ii) inactive épigénétiquement les miRNAs onco-suppresseurs miR-99a/miR-449a dans les carcinomes gastriques et non dans notre série de CCIs. D’un point de vue clinique, deux signatures pronostiques indépendantes ont pu être identifiées, l’une intégrant les 2 partenaires protéiques d’ANRIL appartenant aux complexes Polycomb (surexpression d’EZH2 / sous-expression de CBX7), et l’autre représentée par la sous-expression de Δ-MALAT1, observée dans 20% des tumeurs de notre série. La présence de variants d’épissage alternatifs, de réseaux d’interactions multiples et d’une spécificité d’organe devra être prise en compte lors de l’évaluation des thérapies épigénétiques ciblant ANRIL (inhibiteurs des bromodomaines et des oncoMIRs) ou MALAT1 (ASOs) dans les cancers du sein. La deuxième partie du projet de thèse a consisté en l’analyse du transcriptome non codant des carcinomes mammaires par une stratégie pangénomique, afin d’identifier de nouveaux types de lncRNAs, tels les nouveaux lncRNAs antisens, circulaires et associés à des séquences ultra-conservées ou induisant des résistances médicamenteuses. / Breast cancer is the second most common cancer and the first malignancy of women. Currently, only few biomarkers (ER, PR, receptor HER2, index Ki67) and transcriptomic signature PAM50 are included in the morphological classification and therapeutic orientation. Transcriptome genome-wide analyses unexpectedly revealed that over 80% of the DNA is transcribed into RNA. Among these noncoding RNAs, transcripts longer than 200 nt are arbitrarily qualified as long noncoding RNAs (lncRNAs). LncRNAs play a crucial role in maintenance of cellular homeostasis and present abnormal expression patterns in various diseases, including cancer. The main objective of my project was to analyze expression of lncRNAs, their functionality and their roles in breast oncogenesis. The first part focused on the study of ANRIL and MALAT1 genes, two lncRNAs whose mechanisms of action and clinical significance in breast carcinogenesis are still controversial. ANRIL and MALAT1 respectively overexpressed in 20% and 14% of tumors in our series, confirming their pro-oncogenic roles in mammary carcinogenesis. MALAT1 overexpression results in RNA-FISH by presence of huge intranuclear speckles. Complexity of their deregulation is associated with presence of various isoforms and interaction networks with miRNAs, mRNAs and other lncRNAs. Concerning PRC2/PRC1 polycomb sub-units interacting with ANRIL, EZH2 (PRC2) is normally targeted by 3 onco-suppressor miRNAs (miR-26A1, miR-125B and miR-214) that are under-expressed in our series of CCIs. The 2 oncomiRs miR-181B1 and miR-181A2 that normally target and inactivate CBX7 (PRC1) appear overexpressed in our series of CCIs, resulting from activation of the oncogene HMGA1. Concerning MALAT1, the miRNAs biogenesis complex Drosha-DGCR8-Microprocessor regulates expression levels of the splicing variant Δ-MALAT1 and the latter is involved in activation of PI3K/Akt pathway. Significant correlations were observed between MALAT1 and genes involved in alternative splicing, cell cycle, apoptosis, DNA repair and migration. Aberrant transcriptomic profiles of these two lncRNAs seem characteristics of mammary carcinomas. Thus, ANRIL (i) presents an unexpected positive association with the p16-CDKN2A/p15-CDKN2B/p14-ARF cluster in our series of CCIs, whereas this association appears negative in prostate carcinomas and (ii) epigenetically inactivates onco-suppressor miRNAs miR99a/miR-449a in gastric carcinomas, but not in our series. From a clinic point of view, two independent prognostic signatures were identified, one incorporating two protein partners of ANRIL belonging to the polycomb complexes (EZH2 overexpression / CBX7 under-expression) and the other represented by under-expression of the variant Δ-MALAT1 observed in 20% of tumors in our series. The presence of alternative splice variants, multiple interactions with mRNAs and miRNAs and organ specificity should be considered when evaluating epigenetic antitumoral drugs designed to target ANRIL (bromodomains and oncoMIRs inhibitors) and MALAT1 (ASOs) in breast cancers. The second part of the project involved analysis of non-coding transcriptome of mammary carcinomas to identify new types of lncRNAs, including new antisens lncRNAs, circular lncRNAs, induced lncRNAs, noncoding ultraconserved transcripts and lncRNAs associated with resistance to systemic treatments. The preliminary analysis performed on a small cohort of breast cancers (n=8) will allow the implementation of the main (n=40) which will enhance robustness of identified signatures.

Orientador: Guilherme Targino Valente / Resumo: A intensa utilização de combustíveis fósseis gerapreocupações constantes devido aos impactos de sua combustão ao meio ambiente. Os biocombustíveis são uma alternativa viável aos combustíveis fósseis por apresentarem vantagens como serem menos agressivos ao meio ambiente. O bioetanol é um dos biocombustíveis mais utilizados no mundo e sua produção pode ser feita pela fermentação realizada pela levedura Saccharomyces cerevisiae. No entanto, altas concentrações de etanol inibem diversos mecanismos biológicos da levedura, causando a diminuição da produtividade. A partir de resultados prévios, observou-se que o ciclo celular é uma das vias mais afetadas pelo etanol e, além disso, constatou-se a presença de lncRNAs regulando esta via emduas linhagens de S. cerevisiae, a BY4742 e SEY6210. Utilizando operadores Booleanos, um modelo lógico discreto foi desenvolvido para o ciclo celular no qual os nós do sistema assumem até quatro valores discretos que representam a quantidade ou o graude ativaçãodesses nós. O modelo desenvolvido apresentou boa performance preditiva, acertando 87.27% dos 109 fenótipos obtidosda literatura, tornando possível a simulação de novos elementos. Experimentos prévios demonstraram que as leveduras de baixatolerância ao etanol conseguem retomar o crescimento mais rápido do que as de alta tolerância. Nesse trabalho, simulações feitas com dados de expressão diferencial via RNA-Seq permitiu inferir que isso ocorre porque as linhagens de baixa tolerância sofrem arre... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The intense use of fossil fuels raised concern about the future due to their negative environmental impact. Bio-fuels are alternatives to the fossil fuels due to be biodegradable and less environmentally harmful. The bio-ethanol is one of the most popular bio-fuel. It can be produced by fermentation using the yeast Saccharomyces cereviae. However, high ethanol concentration inhibits the yeast decreasing the ethanol yield. Previous data of our groups showed the cell cycle is one of most affected pathways during ethanol stress. Moreover, it was found lncRNAs regulating this pathway in the BY4742 and SEY6210 strains. Using Boolean operators the discrete logical model of the cell cycle was developed. The nodes may get up to four discrete values to represent theirs abundance of activation degree. This model correctly modeled around 87.27% of correct predictions based on 109 phenotypes from the literature, hence, this model is desirable to predict cell cycle behavior after addition of new elements. According to previous data of our group, the lower tolerant strains recover the normal growth faster than higher tolerant strains after stress relief. The simulations here presented by adding RNASeq information into the model, showed a cell cycle arrest at final phase of the cell cycle (M phase) in lower tolerant strains whereas in the higher tolerant ones this arrest occurs at the first phase (G1 phase) during the ethanol treatment. The simulations also indicated that in SEY6210 (low to... (Complete abstract click electronic access below) / Mestre

In a cell, the DNA undergoes transcription to form mature transcripts, some of which in turn undergo translation to form proteins. Although over 85% of the human genome is transcribed, it comprises only about 2% protein-coding genes, the rest being noncoding. One of the non-coding gene elements, called long non-coding RNAs (lncRNAs), are emerging as key players in various regulatory roles in the human genome. The generally accepted theory posits lncRNAs to be over 200 nucleotides long and to be able to grow over 10 kilobases, bearing a similarity with mRNAs. The majority of lncRNAs undergo alternative splicing and are weakly polyadenylated in combination with complex secondary structures. Among the annotated lncRNAs, so far it has been only a meagre portion for which functional roles have been detected, while functions of the vast majority remain to be discovered. Observed functional roles include thus far gene expression regulation through various mechanisms at transcriptional and post-transcriptional levels. With the advent of next-generation sequencing (NGS) and advances in RNA sequencing technology (RNA-Seq), it is easier to reconstruct the transcriptome by extracting information about the splicing machinery. RNA-Seq has helped consortia like GENCODE, ENCODE, and others to curate their annotation catalogues. In this PhD thesis, certain aspects of the human lncRNA transcriptome will be explored, such as the challenges in lncRNA annotation. Those challenges stem from the lack of signals that are common in mRNAs and make them easier to detect, for instance signals of ORFs and transcription start sites. Concurrently, owing to a lack of understanding of the connection between sequence and function, lncRNAs have been typically annotated based upon their location in relation to mRNAs and their functions have been predicted through a guilt-by-association approach. In the first part of the PhD research work, the splice junctions in the lncRNA transcriptome were mapped in an attempt to explore the isoform diversity of lncRNAs by using sequencing data from B-cell lymphoma. In this phase of the research work, multiple junction-spanning reads from the sequencing data with a very large read depth were found to represent the splice junctions. Using GENCODE v19 as a reference it was found that the human transcriptome harbours a large number of rare exons and introns that have remained unannotated. Concomitantly, it can be inferred that the current human transcriptome annotation is confined to a very well-defined set of splice variants. However, although the isoforms are well-defined, the same cannot be said about their biological functions and it remains to be explored why the processing machinery of lncRNAs is restricted to a set of very few splice sites. In the human genome, small regulatory RNAs like miRNAs and small nucleolar RNAs (snoRNAs) overlap with lncRNAs in their genomic loci. To further understand the human transcriptome, in the second part of the PhD research work, a study was undertaken in an attempt to distinguish the miRNA and snoRNA hosting lncRNAs from the lncRNAs that did not have any overlaps with the smaller RNAs. To this end, machine learning techniques were implemented on curated datasets employing features inspired by a few of the prevalent features used in published lncRNA detection tools encompassing not just sequence information, but also secondary structure and conservation information. Classification was attempted through supervised as well as unsupervised learning approaches; random forests for the former, PCA and k-means for the latter. In the end, the three RNA classes could not be separated with certitude, especially when the hosted RNA was not supplied to the classifier, however, this lack of detectable association can be confirmed to be of biological interest. It suggests that the function of host genes is not closely tied to the function of the hosted genes at least in this case. Nevertheless, understanding the dynamics of snoRNA and miRNA host genes can improve the knowledge of functional evolution of lncRNAs, as the fact that the smaller RNA genes are conserved makes it comparably easier to trace the host lncRNAs over much larger evolutionary timescales than most other lncRNAs. With the accelerated availability of sequencing techniques it can be expected that expanded investigation into conservation patterns and host gene functions will be possible in the near future.

Long non-coding RNAs (lncRNAs) are key regulators of gene expression, playing crucial roles in biological processes across many species, tissues and diseases. The liver is a highly responsive organ in which large changes in gene expression are perpetuated by a myriad of internal and external stimuli; as such, the liver makes an ideal system in which to study lncRNAs. Global patterns of expression, maturation and localization were established for both lncRNA and protein-coding gene (PCG) transcripts across five subcellular compartments in male and female mouse liver, both with and without exposure to TCPOBOP, a direct agonist of the nuclear receptor CAR. In contrast to PCGs, lncRNAs showed very strong enrichment for tight chromatin binding, which increased the sensitivity for lncRNA detection and facilitated discovery of many novel sex-biased and xenobiotic-responsive lncRNAs. These findings helped identify candidate regulatory lncRNAs based on their co-localization within topologically associating domains, or their transcription divergent or antisense to PCGs associated with pathways linked to liver physiology and disease. The liver cell type-specific expression of lncRNAs and PCGs was assessed by single nucleus RNA-seq (snRNA-seq). Liver sexual dimorphism was largely restricted to hepatocyte populations, where many sex-biased genes exhibited zonated expression. Changes in lncRNA and PCG expression following exposure to endogenous hormones (growth hormone) and exogenous chemicals (TCPOBOP) was assessed, identifying cell cluster-specific perturbations to native sex-bias and hepatocyte zonation-dependent gene expression, and highlighting the interconnectedness between liver sexual dimorphism and zonation of the hepatic lobule at the single nuclei level. Finally, an in vivo method for epigenetic reprogramming of lncRNAs using a dual adeno-associated virus delivery system was utilized to knockdown two TCPOBOP-inducible lncRNAs in mouse liver. The knockdown phenotype of one of these lncRNAs, established by snRNA-seq, suggests it plays a functional role in regulating cholesterol metabolism and transport, triglyceride catabolism, and pyruvate metabolism in mouse liver. Together, these studies characterize hepatic lncRNA expression patterns, on both the sub-cellular and single cell levels, and present a strategy for interrogating the roles of specific lncRNAs in liver tissue in vivo. / 2023-01-18T00:00:00Z

The objective of this study was to unveil a novel area of gene regulation in breast cancer and breast cancer prevention through the study of a recent discovered class of genetic regulators named long non-coding RNAs (lncRNAs). LncRNAs are RNA molecules longer than 200 nucleotides that are not translated into proteins, but regulate the transcription of genes involved in different cellular processes, including differentiation, cancer initiation and progression. The link between lncRNAs and cancer is well documented in the literature. More recently, their relevance in the transcription field is beginning to be explored and their roles have been found to vary from guiding proteins to the genome to scaffolding proteins complexes needed for the transcription of a specific gene. Initial transcriptome analysis of normal breast of parous and nulliparous postmenopausal women revealed that several lncRNAs are differentially expressed in the parous breast. This observation provided evidence of a potential role of lncRNAs in the regulation of transcription and their function in pregnancy’s preventive effect in reducing the lifetime risk of developing breast cancer. Specifically, RNA sequencing of healthy postmenopausal breast tissue biopsies from eight parous and eight nulliparous women using Illumina platform was performed. The sequencing results showed that there are 42 lncRNAs differentially expressed between parous and nulliparous breast tissue. These data led to the hypothesis that these novel lncRNAs may be drivers in the process of development that occurs in the mammary gland during pregnancy, providing protection against breast cancer. After analysis of these 42 lncRNAs using bioinformatics tools, review of the scientific literature, and real-time PCR analysis, two lncRNAs (lncBHLHE41 and lncEPCAM) were selected to be tested in vitro, using different molecular techniques in human epithelial breast cell lines to determine their relevance in breast cancer. This project provided novel information on lncRNAs induced by pregnancy in the breast tissue, and identified two lncRNAs as potential key regulators in breast differentiation and cancer progression. The manipulation of these lncRNAs led to evidence of their function in vitro and, using xenograft studies, we determined their relevance in vivo. Although treatment for cancer using lncRNAs as targets is in its infancy at the clinic, the advancement in knowledge and technology to study their relevance in disease could lead to the development of therapeutics for breast cancer and breast cancer prevention in the near future. / Biochemistry