Indiana University-Purdue University Indianapolis (IUPUI) / Triple-negative breast cancers (TNBCs) are negative for the expression of estrogen (ER), progesterone (PR), and HER-2 receptors. TNBC accounts for 15% of all breast cancers and results in disproportionally higher mortality compared to ER & HER2-positive tumours. Moreover, there is a paucity of therapies for this subtype of breast cancer resulting primarily from an inadequate understanding of the transcriptional differences that differentiate TNBC from normal breast. To this end, we embarked on a comprehensive examination of the transcriptomes of TNBCs and normal breast tissues using next-generation whole transcriptome sequencing (RNA-Seq). By comparing RNA-seq data from these tissues, we report the presence of differentially expressed coding and non-coding genes, novel transcribed regions, and mutations not previously reported in breast cancer. From these data we have identified two major themes. First, BRCA1 mutations are well known to be associated with development of TNBC. From these data we have identified many genes that work in concert with BRCA1 that are dysregulated suggesting a role of BRCA1 associated genes with sporadic TNBC. In addition, we observe a mutational profile in genes also associated with BRCA1 and DNA repair that lend more evidence to its role. Second, we demonstrate that using microdissected normal epithelium maybe an optimal comparator when searching for novel therapeutic targets for TNBC. Previous studies have used other controls such as reduction mammoplasties, adjacent normal tissue, or other breast cancer subtypes, which may be sub-optimal and have lead to identifying ineffective therapeutic targets. Our data suggests that the comparison of microdissected ductal epithelium to TNBC can identify potential therapeutic targets that may lead to be better clinical efficacy. In summation, with these data, we provide a detailed transcriptional landscape of TNBC and normal breast that we believe will lead to a better understanding of this complex disease.

Les myopathies héréditaires représentent un large groupe de pathologies neuromusculaires progressives affectant l’intégrité générale, structurelle et fonctionnelle du muscle squelettique. Elles engendrent une myriade de symptômes, ternissant qualité de vie et autonomie, et pouvant même s’avérer mortelles. La pose d’un diagnostic juste peut être difficile, entravée notamment par une faible prévalence de certaines myopathies, l’importante hétérogénéité clinique existante, et le chevauchement symptomatique des diverses formes. Malgré les avancées récentes faites dans le domaine des techniques de séquençage qui contribuent grandement au dépistage, au moins 25% des individus atteints de myopathies demeurent sans diagnostic génétique. Suivant l’investigation clinique d’un patient (Z46) atteint d’une myopathie distale à apparition tardive, l’analyse par séquençage ARN (RNA-seq) a révélé un variant non-sens homozygote de signification inconnue (VUS) à la fin de l’exon 5 du gène MLIP. Les niveaux d’expression génique altérés de « Protéine musculaire interagissant avec LMNA » (MLIP) et son partenaire « Lamine de type A » (LMNA) ont poussé à approfondir l’investigation. Davantage d’altérations -omiques furent identifiées par les techniques de RT-PCR, qPCR et WB, renforçant l’effet pathogénique du variant. Consolidant tous les résultats, le séquençage de longues lectures (LRS) a révélé un mécanisme d’épissage alternatif compensatoire de MLIP, qui tend à contourner et minimiser la production de transcrits arborant l’exon 5 muté. La présente étude vise à : 1) apporter un diagnostic génétique définitif au patient Z46, posant le variant MLIP comme causatif de la myopathie distale; 2) démontrer le pouvoir diagnostique du RNA-seq dans la résolution de ce cas complexe par l’identification et l’élucidation du VUS; 3) témoigner de l’étendue de la caractérisation transcriptomique offerte par les longues lectures du LRS. Couplé à cela, la modélisation du variant pathogénique par CRISPR/Cas9 dans une lignée cellulaire de myoblastes humains immortalisés permettra l’évaluation des impacts morpho-fonctionnels; conférant un supplément d’informations relatives aux fonctions musculaires normales et pathologiques de MLIP, faiblement caractérisées jusqu’à présent. / Hereditary myopathies represent a large group of progressive neuromuscular disorders affecting the general, structural and functional integrity of skeletal muscle. They cause a myriad of symptoms, impairing quality of life and autonomy, and can even be fatal. Making an accurate diagnosis can be difficult, hampered in particular by the low prevalence of certain myopathies, the significant clinical heterogeneity that exists, and the symptomatic overlap of the various forms. Despite recent advances in sequencing techniques that greatly assist in screening, at least 25% of individuals with myopathies remain without a genetic diagnosis. Following the clinical investigation of a patient (Z46) with a late-onset distal myopathy, RNA-sequencing (RNA-seq) analysis revealed a homozygous nonsense variant of unknown significance (VUS) at the end of exon 5 of the MLIP gene. Altered gene expression levels of ‘’Muscular LMNA-Interacting Protein’’ (MLIP) and its partner ‘’A-type Lamin’’ (LMNA) prompted further investigation. More -omic alterations were identified by RT-PCR, qPCR and WB technics, reinforcing the pathogenic effect of the variant. Consolidating all results, Long-Read Sequencing (LRS) revealed a compensatory alternative splicing mechanism of MLIP, which tends to bypass and minimize the production of transcripts carrying the mutated exon 5. The present study aims to: 1) provide a formal genetic diagnosis for patient Z46, positing the MLIP variant as causative of the distal myopathy; 2) demonstrate the diagnostic power of RNA-seq in resolving this complex case through identification and elucidation of the VUS; 3) testify to the breadth of transcriptomic characterization afforded by the long reads of LRS. Coupled with this, CRISPR/Cas9 modeling of the pathogenic variant in an immortalized human myoblast cell line will allow assessment of morpho-functional impacts; conferring additional information related to the normal and pathological muscles functions of MLIP, poorly characterized thus far.

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Metastatic bone cancers cause severe symptoms including pain that compromises patient functional status, quality of life, and survival. Current treatment strategies have limited efficacy and dose-limiting side effects. Cancer-induced bone pain (CIBP) is a unique pain state that shares features with but is distinct from the pathology of neuropathic and inflammatory pain. This dissertation investigates how CIBP is generated and maintained by the direct effects of cancer cells on their metastatic microenvironment and the peripheral nervous system, including unique signaling properties and gene expression changes. In particular, we found that genetic knockdown of the functional subunit xCT of the system xC- cystine/glutamate antiporter can reduce CIBP, further elucidating this as a therapeutic of interest. We found that the neuroprotective voltage-gated calcium channel inhibitors progesterone and pregabalin markedly reduce mechanical hypersensitivity and excitability in sensory neurons of the dorsal root ganglion (DRG) in male rat models of neuropathic pain, but that these effects and less pronounced in females. In cancer pain, these sex differences are reversed, with females but not males demonstrating a delay in time-to-onset of mechanical hypersensitivity. We also analyzed gene expression at the DRG by RNA-Sequencing of rat models of CIBP. Our findings uncovered differential gene expression between CIBP and sham controls and between ipsilateral and contralateral DRGs in CIBP model rats. These studies have identified several promising avenues for therapeutic research for CIBP. / Dissertation / Doctor of Philosophy (PhD) / The tools we have right now to manage severe and chronic pain are insufficient. Patients with advanced cancers including bone cancer can suffer from very severe pain. This pain is generated in a number of ways including by the tumour itself releasing chemicals that activate pain-sensing nerves, by the destruction of the bone in and around the tumour, and by the sensitization of the nervous system, which can make pain worse and longer lasting. We have taken three approaches to researching cancer pain and to investigating new treatments. We have found that by reducing the amount of glutamate that cancer cells can release into their environment, we can reduce cancer pain in mice. We also found that treating rats with pregabalin and progesterone can change nerve signaling and reduce neuropathic pain, but that this effect is most pronounced in male rats with neuopathic pain and smaller in female rats with neuropathic pain, and even smaller in rats with cancer pain. We also analyzed expression of all the protein-coding genes in dorsal root ganglia from rats with cancer pain and found that there are many differences from rats without pain. Some of these differences may be promising new research targets. Going forward this research has provided important evidence necessary for next steps to develop new therapies and research strategies for cancer pain.

Droplet-based microfluidics is a powerful tool permitting massive-scale single-cell analysis in pico-/nano-liter water-in-oil droplets. It has been integrated into various library preparation techniques to accomplish high-throughput scRNA-seq, scDNA-seq, scATAC-seq, scChIP-seq, as well as scMulti-omics-seq. These advanced technologies have been providing unique and novel insights into both normal differentiation and disease development at single-cell level. In this thesis, we develop four new droplet-based tools for single-cell omics profiling. First, the developed Drop-BS is the first droplet-based platform to construct single-cell bisulfite sequencing libraries for DNA methylome profiling and allows production of BS library of 2,000-10,000 single cells within 2 d. We applied the technology to separately profile mixed cell lines, mouse brain tissues, and human brain tissues to reveal cell type heterogeneity. Second, the new Drop-ChIP platform only requires two steps of droplet generation to achieve multiple steps of reactions in droplets such as single-cell lysis, chromatin fragmentation, ChIP, and barcoding. Third, we aim to establish a droplet-based platform to accomplish high-throughput full-length RNA-seq (Drop-full-seq), which both current tube-based and droplet-based methods cannot realize. Last, we constructed an in-house droplet-based tool to assist single-cell ATAC-seq library preparation (Drop-ATAC), which provided a low-cost and facile protocol to conduct scATAC-seq in laboratories without the expensive instrument. / Doctor of Philosophy / Microfluidics is a collection of techniques to manipulate fluids in the micrometer scale. One of microfluidic techniques is called "droplet-based microfluidics". It can manipulate (i.e., generate, merge, sort, split, etc) pico-/nano-liter of water-in-oil droplets. First, since the water phase is separated by the continuous oil phase, these droplets are discrete and individual reactors. Second, droplet-based microfluidics can achieve highly parallel manipulation of thousands to millions of droplets. These two advantages make droplet-based microfluidics an ideal tool to perform single-cell assays. Over the past 10 years, various droplet-based platforms have been developed to study single-cell transcriptome, genome, epigenome, as well as multi-ome. To expand droplet-based tools for single-cell analysis, we aim to develop four novel platforms in this thesis. First, Drop-BS, by integrating droplet generation and droplet fusion techniques, can achieve high-throughput single-cell bisulfite sequencing library preparation. It can generate 10,000 single-cell BS libraries within 2 days which is difficult to achieve for conventional library preparation in tubes/microwells. Second, we developed a novel and facile Drop-ChIP platform to prepare single-cell ChIP-seq library. It is easy to operate since it only requires two steps of droplet generation. It also generates higher quality of data compared to previous work. In addition, we are working on the development and characterization of the other two droplet-based tools to achieve full-length single-cell RNA-seq and single-cell ATAC-seq.

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.

Next-generation sequencing has generated enormous amount of DNA and RNA sequences that potentially carry volumes of genetic information, e.g. protein-coding genes. The thesis is divided into three main parts describing i) GeneMarkS-2, ii) GeneMarkS-T, and iii) MetaGeneTack. In prokaryotic genomes, ab initio gene finders can predict genes with high accuracy. However, the error rate is not negligible and largely species-specific. Most errors in gene prediction are made in genes located in genomic regions with atypical GC composition, e.g. genes in pathogenicity islands. We describe a new algorithm GeneMarkS-2 that uses local GC-specific heuristic models for scoring individual ORFs in the first step of analysis. Predicted atypical genes are retained and serve as ‘external’ evidence in subsequent runs of self-training. GeneMarkS-2 also controls the quality of training process by effectively selecting optimal orders of the Markov chain models as well as duration parameters in the hidden semi-Markov model. GeneMarkS-2 has shown significantly improved accuracy compared with other state-of-the-art gene prediction tools. Massive parallel sequencing of RNA transcripts by the next generation technology (RNA-Seq) provides large amount of RNA reads that can be assembled to full transcriptome. We have developed a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions in RNA transcripts. Unsupervised estimation of parameters of the algorithm makes unnecessary several steps in the conventional gene prediction protocols, most importantly the manually curated preparation of training sets. We have demonstrated that the GeneMarkS-T self-training is robust with respect to the presence of errors in assembled transcripts and the accuracy of GeneMarkS-T in identifying protein-coding regions and, particularly, in predicting gene starts compares favorably to other existing methods. Frameshift prediction (FS) is important for analysis and biological interpretation of metagenomic sequences. Reads in metagenomic samples are prone to sequencing errors. Insertion and deletion errors that change the coding frame impair the accurate identification of protein coding genes. Accurate frameshift prediction requires sufficient amount of data to estimate parameters of species-specific statistical models of protein-coding and non-coding regions. However, this data is not available; all we have is metagenomic sequences of unknown origin. The challenge of ab initio FS detection is, therefore, twofold: (i) to find a way to infer necessary model parameters and (ii) to identify positions of frameshifts (if any). We describe a new tool, MetaGeneTack, which uses a heuristic method to estimate parameters of sequence models used in the FS detection algorithm. It was shown on several test sets that the performance of MetaGeneTack FS detection is comparable or better than the one of earlier developed program FragGeneScan.

Deux phénomènes liés au TFIIH ont été étudiés : l'influence des mutations spécifiques dans la sous-unité XPD de TFIIH sur la réponse transcriptionnelle de certains gènes après l'irradiation UV, et l'interaction entre le TFIIH et la transcription des gènes de classe III. Une analyse détaillée de la dynamique du transcriptome a été effectuée pour la réponse des cellules humaines mutantes XP-D/CS à l'UV. Il a été démontré que la dysrégulation sélective observée de l'expression des gènes était liée à l'incapacité pour la ré-initiation transcriptionnelle et à l'hétérochromatinisation suivante, où l'histonedésacétylase SIRT1 a été identifiée comme le principal facteur. Son inhibition a permis de recouvrer l'expression normale d'un nombre substantiel des gènes affectés. Une étude de la participation pangénomique du coeur de TFIIH dans latranscription a découvert son association avec les gènes actifs de classe III. Cette association a été démontrée être indépendante de Pol II. Le coeur de TFIIH a été montré participer directement à la transcription effectuée in vitro par Pol III.