Transcriptome analysis of androgen-induced cellular senescence in prostate cancer cell lines

Prostate cancer (PCa) is initially sensitive to androgen deprivation therapy (ADT) but invariably develops into castration resistant PCa (CRPC) over time. Yet, CRPC remains dependent on androgen receptor (AR) signaling. Studies showed that androgen seems to have a biphasic role for PCa growth. The growth of human AR-positive both castration sensitive PCa (CSPC) and CRPC cell lines can be inhibited by supraphysiological androgen level (SAL).The bipolar androgen therapy (BAT) includes cycles of SAL under continuous ADT and is in clinical trial to treat CRPC patients. Interestingly, SAL induces cellular senescence in AR-positive PCa cells and ex vivo in PCa tumor patients’ samples. However, while AKT signaling plays a role in this pathway, the underlying mechanism of SAL induced senescence is not completely understood. This thesis shows for the first time that the cell cycle inhibitor p15INK4b is involved in SAL-induced cellular senescence in both CSPC LNCaP and CRPC C4-2 cells. Treatment with the AKT inhibitor (AKTi) potently inhibited SAL-induced expression of p15INK4b and cellular senescence in both cell lines. Transcriptome sequencing (RNA-seq) comparing the SAL-induced transcriptomes of LNCaP with C4-2 cells as well as of AKTi treated cells revealed landscapes for cell senescence. Interestingly, ANXA2 and lncRNA SAT1 are among identified genes. ANXA2 knockdown reduces the percentage of SAL induced senescent cells, which suggests the ANXA2 as a mediator of cellular senescence in PCa cells. Another identified gene is the lncRNA SAT1. SAL treatment of native patient tumor samples ex vivo results in up-regulation of lncRNA SAT1. This suggests that lncRNA SAT1 serves as a tumor suppressor with SAL. Further results also indicate that the lncRNA SAT1 is crucial for SAL-induced cancer cell senescence and being an upstream factor for p15INK4b. LncRNA SAT1 may have a positive feedback loop with AKT or be present in the AR-AKT complex

Differential Gene expression using a negative binomial model

Hlavním cílem této diplomové práce je analýza diferenciální exprese genů na základě negativního binomického modelu. Úvodní část je věnována teoretickému základu, pojednává o sekvenování RNA, sekvenování nové generace, výhodách a možném využití, formátu fastQ aj. Následující část už se zabývá samotnou praktickou částí, zde byl vybrán vhodný set genů, které budou později analyzovány a příslušná data byla stažena. Tato data byla zarovnána k lidskému genomu verze 37 Burrowsovou-Wheelerovou transformací s využitím bowtie mapovače, byly tak vytvořeny soubory ve formátu SAM. Toto soubory dat byly později setříděny pomocí nástroje SAMtools. Následně byly v programovém prostředí Matlab (verze R2013b) vytvořeny anotované objekty genů s využitím služby Ensembl´s BioMart. Dále byla určena genová exprese a byly odhadnuty faktory velikosti knihovny. Na závěr byly odhadnuty parametry negativního binomického rozložení a byla vyhodnocena diferenciální exprese genů.The main goal of this master thesis is to carry out the analysis of differential gene expression using a negative binomial model. The first part is devoted to theoretical basis, discusses the RNA sequencing, Next-Generation Sequencing (NGS), the benefits and applications, and FASTAQ format. The second part is the practical part, there was chosen a suitable data set of genes, that will be later analyzed, and the relevant data was downloaded. This data was aligned to the human genome version 37 by Burrows-Wheeler transform and the SAM formatted files were created using the Bowtie mapper. The SAM formatted files were sorted by SAMtools. In the following part of this work was created an annotation object of target genes using Ensembl´s BioMart service and Matlab (version R2013b). Next, digital gene expression was determined and library size factor was estimated. In the end the negative binomial distribution parameters were estimated and data was tested for a differential gene expression.

Quantification of Unmethylated Alu (QUAlu): a tool to assess global hypomethylation in routine clinical samples

Hypomethylation of DNA is a hallmark of cancer and its analysis as tumor biomarker has been proposed, but its determination in clinical settings is hampered by lack of standardized methodologies. Here, we present QUAlu (Quantification of Unmethylated Alu), a new technique to estimate the Percentage of UnMethylated Alu (PUMA) as a surrogate for global hypomethylation. QUAlu consists in the measurement by qPCR of Alu repeats after digestion of genomic DNA with isoschizomers with differential sensitivity to DNA methylation. QUAlu performance has been evaluated for reproducibility, trueness and specificity, and validated by deep sequencing. As a proof of use, QUAlu has been applied to a broad variety of pathological examination specimens covering five cancer types. Major findings of the preliminary application of QUAlu to clinical samples include: (1) all normal tissues displayed similar PUMA; (2) tumors showed variable PUMA with the highest levels in lung and colon and the lowest in thyroid cancer; (3) stools from colon cancer patients presented higher PUMA than those from control individuals; (4) lung squamous cell carcinomas showed higher PUMA than lung adenocarcinomas, and an increasing hypomethylation trend associated with smoking habits. In conclusion, QUAlu is a simple and robust method to determine Alu hypomethylation in human biospecimens and may be easily implemented in research and clinical settings.RB was supported by a FPI fellowship from Ministerio de Economía y Competitividad. AD-V was supported in part by a contract PTC2011-1091 from Ministerio de Economía y Competitividad. This work was supported by grants from FEDER, the Ministerio de Economía y Competitividad (SAF2011/23638 to MAP), the Instituto de Salud Carlos III (FIS PI11/02421 to JR, FIS PI11/01359 and FIS PI14/00240 to MR, FIS PI14/00308 to MJ, FIS PI12/00511 to MP), and Fundació Olga Torres (to MJ)

Quantification of unmethylated Alu (QUAlu): a tool to assess global hypomethylation in routine clinical samples

Hypomethylation of DNA is a hallmark of cancer and its analysis as tumor biomarker has been proposed, but its determination in clinical settings is hampered by lack of standardized methodologies. Here, we present QUAlu (Quantification of Unmethylated Alu), a new technique to estimate the Percentage of UnMethylated Alu (PUMA) as a surrogate for global hypomethylation. QUAlu consists in the measurement by qPCR of Alu repeats after digestion of genomic DNA with isoschizomers with differential sensitivity to DNA methylation. QUAlu performance has been evaluated for reproducibility, trueness and specificity, and validated by deep sequencing. As a proof of use, QUAlu has been applied to a broad variety of pathological examination specimens covering five cancer types. Major findings of the preliminary application of QUAlu to clinical samples include: (1) all normal tissues displayed similar PUMA; (2) tumors showed variable PUMA with the highest levels in lung and colon and the lowest in thyroid cancer; (3) stools from colon cancer patients presented higher PUMA than those from control individuals; (4) lung squamous cell carcinomas showed higher PUMA than lung adenocarcinomas, and an increasing hypomethylation trend associated with smoking habits. In conclusion, QUAlu is a simple and robust method to determine Alu hypomethylation in human biospecimens and may be easily implemented in research and clinical settings

Transcriptome Analyses of Tumor-Adjacent Somatic Tissues Reveal Genes Co-Expressed with Transposable Elements

Background: Despite the long-held assumption that transposons are normally only expressed in the germ-line, recent evidence shows that transcripts of transposable element (TE) sequences are frequently found in the somatic cells. However, the extent of variation in TE transcript levels across different tissues and different individuals are unknown, and the co-expression between TEs and host gene mRNAs have not been examined. Results: Here we report the variation in TE derived transcript levels across tissues and between individuals observed in the non-tumorous tissues collected for The Cancer Genome Atlas. We found core TE co-expression modules consisting mainly of transposons, showing correlated expression across broad classes of TEs. Despite this co-expression within tissues, there are individual TE loci that exhibit tissue-specific expression patterns, when compared across tissues. The core TE modules were negatively correlated with other gene modules that consisted of immune response genes in interferon signaling. KRAB Zinc Finger Proteins (KZFPs) were over-represented gene members of the TE modules, showing positive correlation across multiple tissues. But we did not find overlap between TE-KZFP pairs that are co-expressed and TE-KZFP pairs that are bound in published ChIP-seq studies. Conclusions: We find unexpected variation in TE derived transcripts, within and across non-tumorous tissues. We describe a broad view of the RNA state for non-tumorous tissues exhibiting higher level of TE transcripts. Tissues with higher level of TE transcripts have a broad range of TEs co-expressed, with high expression of a large number of KZFPs, and lower RNA levels of immune genes

Absolute Enrichment: Gene Set Enrichment Analysis for Homeostatic Systems

The Gene Set Enrichment Analysis (GSEA) identifies sets of genes that are differentially regulated in one direction. Many homeostatic systems will include one limb that is upregulated in response to a downregulation of another limb and vice versa. Such patterns are poorly captured by the standard formulation of GSEA. We describe a technique to identify groups of genes (which sometimes can be pathways) that include both up- and down-regulated components. This approach lends insights into the feedback mechanisms that may operate, especially when integrated with protein interaction databases

Bayesian Statistical Modeling of Spatially Resolved Transcriptomics Data

Spatially resolved transcriptomics (SRT) quantifies expression levels at different spatial locations, providing a new and powerful tool to investigate novel biological insights. As experimental technologies enhance both in capacity and efficiency, there arises a growing demand for the development of analytical methodologies. One question in SRT data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process, which could limit the models\u27 ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, in Chapter 2, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting SV genes than those kernel-based methods. When applied to two real SRT datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. Spatial domain identification is another direction in SRT analysis, which enables the transcriptomic characterization of tissue structures and further contributes to the evaluation of heterogeneity across different tissue locations. Current spatial domain analysis of SRT data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, in Chapter 3, we develop a multi-stage statistical method called iIMPACT. It includes a finite mixture model to identify and define spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and a negative binomial regression model to detect domain-specific spatially variable genes. Through multiple case studies, we demonstrated iIMPACT outperformed existing methods, confirmed by ground truth biological knowledge. These findings underscore the accuracy and interpretability of iIMPACT as a new clustering approach, providing valuable insights into the cellular spatial organization and landscape of functional genes within SRT data. Most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, in Chapter 4, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication