Integration of CLIP experiments of RNAbinding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data

Background: Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein. Results: In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down. Conclusions: The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction

RNA sequencing-based transcriptome profiling of cardiac tissue Implicados novela putative disease mechanisms in FLNC-associated arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) encompasses a group of inherited cardiomyopathies including arrhythmogenic right ventricular cardiomyopathy (ARVC) whose molecular disease mechanism is associated with dysregulation of the canonical WNT signalling pathway. Recent evidence indicates that ARVC and ACM caused by pathogenic variants in the FLNC gene encoding filamin C, a major cardiac structural protein, may have different molecular mechanisms of pathogenesis. We sought to identify dysregulated biological pathways in FLNC-associated ACM. RNA was extracted from seven paraffin-embedded left ventricular tissue samples from deceased ACM patients carrying FLNC variants and sequenced. Transcript levels of 623 genes were upregulated and 486 genes were reduced in ACM in comparison to control samples. The cell adhesion pathway and ILK signalling were among the prominent dysregulated pathways in ACM. Consistent with these findings, transcript levels of cell adhesion genes JAM2, NEO1, VCAM1 and PTPRC were upregulated in ACM samples. Moreover, several actin-associated genes, including FLNC, VCL, PARVB and MYL7, were suppressed, suggesting dysregulation of the actin cytoskeleton. Analysis of the transcriptome for biological pathways predicted activation of inflammation and apoptosis and suppression of oxidative phosphorylation and MTORC1 signalling in ACM. Our data suggests dysregulated cell adhesion and ILK signalling as novel putative pathogenic mechanisms of ACM caused by FLNC variants which are distinct from the postulated disease mechanism of classic ARVC caused by desmosomal gene mutations. This knowledge could help in the design of future gene therapy strategies which would target specific components of these pathways and potentially lead to novel treatments for ACM

EventPointer 3.0: flexible and accurate splicing analysis that includes studying the differential usage of protein-domains

Alternative splicing (AS) plays a key role in cancer: all its hallmarks have been associated with different mechanisms of abnormal AS. The improvement of the human transcriptome annotation and the availability of fast and accurate software to estimate isoform concentrations has boosted the analysis of transcriptome profiling from RNA-seq. The statistical analysis of AS is a challenging problem not yet fully solved. We have included in EventPointer (EP), a Bioconductor package, a novel statistical method that can use the bootstrap of the pseudoaligners. We compared it with other state-of-the-art algorithms to analyze AS. Its performance is outstanding for shallow sequencing conditions. The statistical framework is very flexible since it is based on design and contrast matrices. EP now includes a convenient tool to find the primers to validate the discoveries using PCR. We also added a statistical module to study alteration in protein domain related to AS. Applying it to 9514 patients from TCGA and TARGET in 19 different tumor types resulted in two conclusions: i) aberrant alternative splicing alters the relative presence of Protein domains and, ii) the number of enriched domains is strongly correlated with the age of the patients

Guía para la introducción de la perspectiva de género en la docencia de la Universidad Pablo de Olavide

Universidad Pablo de Olavid

Interpretable precision medicine for acute myeloid leukemia

Precision medicine (PM) is a branch of medicine that defines a disease at a higher resolution using genetic and other technologies to enable more specific targeting of its subgroups. Because of its uses in clinical treatment and diagnostics, this field exemplifies the modern era of medicine. PM looks for not just the right drug, but also the right dosage and treatment regimen. PM encounters a variety of challenges, which will be explored in this dissertation. Large-scale sensitivity screens and whole-exome sequencing experiments (WES) have fostered a new wave of targeted treatments based on finding associations between drug sensitivity and response biomarkers. These experiments with the aid of state-of-the-art artificial intelligence (AI) algorithms are opening new therapeutic opportunities for diseases with unmet clinical needs. It has been proved that AI is capable of predicting novel personalized treatments based on complex genotypic and phenotypic patterns in tumors. The scientific community should make an effort to make these algorithms to be interpretable to humans so that the results could be easily approved by the medical regulators. The purpose of this thesis is to apply AI algorithms for precision oncology that are highly accurate, while guaranteeing that the predictions are interpretable by humans. This work is divided in three main sections. The first section comprises a new methodology to increase the predictive power of the discovery of novel treatments in large-scale screenings by exploiting that some biomarkers tend to appear in many treatments. This fact is called hub effect in gene essentiality (HUGE). Content of this section was published in [1]. The second section contains a novel interpretable AI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events and proposes a treatment guideline. Content of this section was published in [2]. Finally, the third section includes a detailed comparison of different recently published algorithms that attempt to overcome the barriers proposed by today's precision medicine. This study also includes two novel algorithms specifically designed to solve the challenges of applicability to clinical practice: Optimal Decision Tree (ODT) and Multinomial Lasso. The characterization of Interpretable Artificial Intelligence as approach with strong potential for use in clinical practice is one of the study's most significant achievements. We presen tunique methods for PM that are highly interpretable, and we summarize the needs that could be considered for constructing interpretable AI. We are confident that this method will transform the way PM is addressed, bridging the gap between AI and clinical practice

Integration of CLIP experiments of RNAbinding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data

Background: Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein. Results: In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down. Conclusions: The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction

DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC)

The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects

e-Galènica. Departament de Farmàcia i Tecnologia Farmacèutica

Podeu consultar la Setena trobada de professorat de Ciències de la Salut completa a: http://hdl.handle.net/2445/4335