Nuovi metodi computazionali per lo studio degli RNA circolari e caratterizzazione del circRNAoma nella leucemia linfoblastica acuta a cellule T

Circular RNAs (circRNAs), a type of endogenous RNAs with covalently closed-loop structures generated by backsplicing, have become a new research hotspot in biomedicine. Their diversity, stability, evolutionary conservation, and cell type- or tissue-specific expression patterns indicated that circRNAs could play important biological functions, as is emerging from recent functional investigations. Growing evidence has shown that their dysregulation is implicated in the pathogenesis of diseases and cancer through diverse mechanisms, such as transcription and splicing regulation, microRNA (miRNA) sponging activity, coding potential, modulation of RNA translation efficiency and of protein activity. In the oncohematology field, the study of circRNAs can facilitate a deeper understanding of their biological functions, of their leukemogenic potential and provide new perspectives on circRNA-based clinical applications. When this thesis work started, data about circRNA expression and roles in Acute Lymphoblastic Leukemia (ALL) were scattered, and almost nothing was known in T-cell ALL (T-ALL). T-ALL is an aggressive, highly proliferative malignancy caused by the accumulation of genetic lesions that affect the development of T-cells. The still unsatisfactory cure rate of this malignancy incites studies to improve patient stratification, identify new disease mechanisms and potential targets for innovative therapies. The aim of this PhD project was to reveal new insight about T-ALL with the study of circRNA expression, of the biological features underlying their dysregulation and by elucidating their functional impact on the pathogenesis of the disease. Furthermore, novel computational and statistical approaches have been improved and developed, endowing the scientific community with useful and robust bioinformatics tools to study circRNAs. The first highlight of this thesis is the elucidation of the T-ALL circRNAome. Analysis of RNA-seq data of 25 pediatric patients representative of five T-ALL molecular subtypes, compared to normal thymocytes from healthy donors, disclosed a dramatic circRNAs dysregulation in T-ALL samples, with a majority of circRNAs overexpressed in malignant cells. Moreover, circRNA signatures of T-ALL molecular subgroups have been disclosed. Second, the first data about circRNA oncogenic roles in T-ALL have been provided, showing that circZNF609 overexpression can contribute to T-ALL cell viability in vitro. Moreover, we studied circFBXW7, which is expressed at heterogenous levels in T-ALL patients. Silencing experiments in T-ALL cell lines revealed a significant effect of the circFBXW7 depletion on cell proliferation and apoptosis, indicating a tumor suppressor role in T-ALL. Observation of a marked circRNA overexpression in T-ALL incited the study of the mechanisms underlying dysregulated circRNA biogenesis. Particularly, we focused on the RNA binding protein Quaking (QKI), depleted in T-ALL, whose link with back splicing regulation was reported in the literature. A study cohort of 85 T-ALL samples was classified according to QKI expression into three groups (low, normal, or high QKI level) whose comparative analysis revealed a striking effect of QKI depletion in the T-ALL circRNAome. The QKI knockdown in Jurkat cells defined that one-third of abundant circRNAs in T-ALL are dynamically regulated by QKI. In parallel, three new computational methods have been developed to improve the analysis of circRNAs from RNA-seq data. CircIMPACT is a bioinformatics tool to evaluate the implication of circRNAs in the gene expression changes observed upon circRNA expression variation. CirComPara2 is a cutting-edge pipeline for sensitive and robust circRNA identification and quantification. Finally, we devised DECMiMo, a novel statistical approach to improve circRNA differential expression analysis.Circular RNAs (circRNAs), a type of endogenous RNAs with covalently closed-loop structures generated by backsplicing, have become a new research hotspot in biomedicine. Their diversity, stability, evolutionary conservation, and cell type- or tissue-specific expression patterns indicated that circRNAs could play important biological functions, as is emerging from recent functional investigations. Growing evidence has shown that their dysregulation is implicated in the pathogenesis of diseases and cancer through diverse mechanisms, such as transcription and splicing regulation, microRNA (miRNA) sponging activity, coding potential, modulation of RNA translation efficiency and of protein activity. In the oncohematology field, the study of circRNAs can facilitate a deeper understanding of their biological functions, of their leukemogenic potential and provide new perspectives on circRNA-based clinical applications. When this thesis work started, data about circRNA expression and roles in Acute Lymphoblastic Leukemia (ALL) were scattered, and almost nothing was known in T-cell ALL (T-ALL). T-ALL is an aggressive, highly proliferative malignancy caused by the accumulation of genetic lesions that affect the development of T-cells. The still unsatisfactory cure rate of this malignancy incites studies to improve patient stratification, identify new disease mechanisms and potential targets for innovative therapies. The aim of this PhD project was to reveal new insight about T-ALL with the study of circRNA expression, of the biological features underlying their dysregulation and by elucidating their functional impact on the pathogenesis of the disease. Furthermore, novel computational and statistical approaches have been improved and developed, endowing the scientific community with useful and robust bioinformatics tools to study circRNAs. The first highlight of this thesis is the elucidation of the T-ALL circRNAome. Analysis of RNA-seq data of 25 pediatric patients representative of five T-ALL molecular subtypes, compared to normal thymocytes from healthy donors, disclosed a dramatic circRNAs dysregulation in T-ALL samples, with a majority of circRNAs overexpressed in malignant cells. Moreover, circRNA signatures of T-ALL molecular subgroups have been disclosed. Second, the first data about circRNA oncogenic roles in T-ALL have been provided, showing that circZNF609 overexpression can contribute to T-ALL cell viability in vitro. Moreover, we studied circFBXW7, which is expressed at heterogenous levels in T-ALL patients. Silencing experiments in T-ALL cell lines revealed a significant effect of the circFBXW7 depletion on cell proliferation and apoptosis, indicating a tumor suppressor role in T-ALL. Observation of a marked circRNA overexpression in T-ALL incited the study of the mechanisms underlying dysregulated circRNA biogenesis. Particularly, we focused on the RNA binding protein Quaking (QKI), depleted in T-ALL, whose link with back splicing regulation was reported in the literature. A study cohort of 85 T-ALL samples was classified according to QKI expression into three groups (low, normal, or high QKI level) whose comparative analysis revealed a striking effect of QKI depletion in the T-ALL circRNAome. The QKI knockdown in Jurkat cells defined that one-third of abundant circRNAs in T-ALL are dynamically regulated by QKI. In parallel, three new computational methods have been developed to improve the analysis of circRNAs from RNA-seq data. CircIMPACT is a bioinformatics tool to evaluate the implication of circRNAs in the gene expression changes observed upon circRNA expression variation. CirComPara2 is a cutting-edge pipeline for sensitive and robust circRNA identification and quantification. Finally, we devised DECMiMo, a novel statistical approach to improve circRNA differential expression analysis

Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model

Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

CircIMPACT: An R Package to Explore Circular RNA Impact on Gene Expression and Pathways

Circular RNAs (circRNAs) are transcripts generated by back-splicing. CircRNAs might regulate cellular processes by different mechanisms, including interaction with miRNAs and RNA-binding proteins. CircRNAs are pleiotropic molecules whose dysregulation has been linked to human diseases and can drive cancer by impacting gene expression and signaling pathways. The detection of circRNAs aberrantly expressed in disease conditions calls for the investigation of their functions. Here, we propose CircIMPACT, a bioinformatics tool for the integrative analysis of circRNA and gene expression data to facilitate the identification and visualization of the genes whose expression varies according to circRNA expression changes. This tool can highlight regulatory axes potentially governed by circRNAs, which can be prioritized for further experimental study. The usefulness of CircIMPACT is exemplified by a case study analysis of bladder cancer RNA-seq data. The link between circHIPK3 and heparanase (HPSE) expression, due to the circHIPK3-miR558-HPSE regulatory axis previously determined by experimental studies on cell lines, was successfully detected. CircIMPACT is freely available at GitHub

Does the Integration of Pre-Coded Information with Narratives Improve in-Hospital Falls’ Surveillance?

To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy were considered in the analysis. Each record reported detailed pre-coded information about patient and fall’s characteristics, together with a narrative description of the fall. At first, multiple correspondence analysis (MCA) was performed on pre-coded information only. Then, it was re-run on the pre-coded data augmented with a variable representing the output analysis of the narrative records. This second analysis required a pre-processing of the narratives followed by text mining. Finally, a Hierarchical Clustering on the two MCA was carried out to identify distinct fall patterns. The dataset included 202 falls’ records. Three clusters corresponding to three distinct profiles of falls were identified through the Hierarchical Clustering performed using only pre-coded information. Hierarchical Clustering with the topic variable provided overlapping results. The present findings showed that the cluster analysis is effective in characterizing fall patterns; however, they do not sustain the hypothesis that the analysis of free-text information improves our understanding of such phenomenon

Measuring Caloric Intake at the Population Level (NOTION): Protocol for an Experimental Study

Background: The monitoring of caloric intake is an important challenge for the maintenance of individual and public health. The instruments used so far for dietary monitoring (eg, food frequency questionnaires, food diaries, and telephone interviews) are inexpensive and easy to implement but show important inaccuracies. Alternative methods based on wearable devices and wrist accelerometers have been proposed, yet they have limited accuracy in predicting caloric intake because analytics are usually not well suited to manage the massive sets of data generated from these types of devices. Objective: This study aims to develop an algorithm using recent advances in machine learning methodology, which provides a precise and stable estimate of caloric intake. Methods: The study will capture four individual eating activities outside the home over 2 months. Twenty healthy Italian adults will be recruited from the University of Padova in Padova, Italy, with email, flyers, and website announcements. The eligibility requirements include age 18 to 66 years and no eating disorder history. Each participant will be randomized to one of two menus to be eaten on weekdays in a predefined cafeteria in Padova (northeastern Italy). Flows of raw data will be accessed and downloaded from the wearable devices given to study participants and associated with anthropometric and demographic characteristics of the user (with their written permission). These massive data flows will provide a detailed picture of real-life conditions and will be analyzed through an up-to-date machine learning approach with the aim to accurately predict the caloric contribution of individual eating activities. Gold standard evaluation of the energy content of eaten foods will be obtained using calorimetric assessments made at the Laboratory of Dietetics and Nutraceutical Research of the University of Padova. Results: The study will last 14 months from July 2017 with a final report by November 2018. Data collection will occur from October to December 2017. From this study, we expect to obtain a series of relevant data that, opportunely filtered, could allow the construction of a prototype algorithm able to estimate caloric intake through the recognition of food type and the number of bites. The algorithm should work in real time, be embedded in a wearable device, and able to match bite-related movements and the corresponding caloric intake with high accuracy. Conclusions: Building an automatic calculation method for caloric intake, independent on the black-box processing of the wearable devices marketed so far, has great potential both for clinical nutrition (eg, for assessing cardiovascular compliance or for the prevention of coronary heart disease through proper dietary control) and public health nutrition as a low-cost monitoring tool for eating habits of different segments of the population

Systematic Review and Meta-Analysis of Surgical Treatment for Isolated Local Recurrence of Pancreatic Cancer

Purpose: To perform a systematic review and meta-analysis on the outcome of surgical treatment for isolated local recurrence of pancreatic cancer. Methods: A systematic review and meta-analysis based on Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines was conducted in PubMed, Scopus, and Web of Science. Results: Six studies concerning 431 patients with recurrent pancreatic cancer met the inclusion criteria and were included in the analysis: 176 underwent redo surgery, and 255 received non-surgical treatments. Overall survival and post-recurrence survival were significantly longer in the re-resected group (ratio of means (ROM) 1.99; 95% confidence interval (CI), 1.54\u20132.56, I2 = 75.89%, p = 0.006, and ROM = 2.05; 95% CI, 1.48\u20132.83, I2 = 76.39%, p = 0.002, respectively) with a median overall survival benefit of 28.7 months (mean difference (MD) 28.7; 95% CI, 10.3\u201347.0, I2 = 89.27%, p < 0.001) and median survival benefit of 15.2 months after re-resection (MD 15.2; 95% CI, 8.6\u201321.8, I2 = 58.22%, p = 0.048). Conclusion: Resection of isolated pancreatic cancer recurrences is safe and feasible and may offer a survival benefit. Selection of patients and assessment of time and site of recurrence are mandatory

CRAFT: a bioinformatics software for custom prediction of circular RNA functions

Circular RNAs (circRNAs), transcripts generated by backsplicing, are particularly stable and pleiotropic molecules, whose dysregulation drives human diseases and cancer by modulating gene expression and signaling pathways. CircRNAs can regulate cellular processes by different mechanisms, including interaction with microRNAs (miRNAs) and RNA-binding proteins (RBP), and encoding specific peptides. The prediction of circRNA functions is instrumental to interpret their impact in diseases, and to prioritize circRNAs for functional investigation. Currently, circRNA functional predictions are provided by web databases that do not allow custom analyses, while self-standing circRNA prediction tools are mostly limited to predict only one type of function, mainly focusing on the miRNA sponge activity of circRNAs. To solve these issues, we developed CRAFT (CircRNA Function prediction Tool), a freely available computational pipeline that predicts circRNA sequence and molecular interactions with miRNAs and RBP, along with their coding potential. Analysis of a set of circRNAs with known functions has been used to appraise CRAFT predictions and to optimize its setting. CRAFT provides a comprehensive graphical visualization of the results, links to several knowledge databases, and extensive functional enrichment analysis. Moreover, it originally combines the predictions for different circRNAs. CRAFT is a useful tool to help the user explore the potential regulatory networks involving the circRNAs of interest and generate hypotheses about the cooperation of circRNAs into the modulation of biological processes

CircFBXW7 in patients with T-cell ALL: depletion sustains MYC and NOTCH activation and leukemia cell viability

Abstract Circular RNAs (circRNAs) are emerging as new players in leukemogenic mechanisms. In patients with T-cell Acute Lymphoblastic Leukemia (T-ALL), the recent report of a remarkable dysregulation of circRNAs incited further functional investigation. Here we focus on circFBXW7, highly expressed in T-cells, with a notably high abundance of the circular compared to linear transcript of FBXW7. Two T-ALL patient cohorts profiled with RNA-seq were analyzed in comparison with five populations of developing thymocytes as normal counterpart, quantifying circRNA and gene expression. CircFBXW7 expression was very heterogeneous in T-ALL patients allowing their stratification in two groups with low and high expression of this circRNA, not correlated with FBXW7 mutation status and T-ALL molecular subgroups. With a loss-of-function study in T-ALL in vitro, we demonstrate that circFBXW7 depletion increases leukemic cell viability and proliferation. Microarray profiling highlighted the effect of the circFBXW7 silencing on gene expression, with activation of pro-proliferative pathways, supporting a tumor suppressor role of circFBXW7 in T-ALL. Further, MYC and intracellular NOTCH1 protein levels, as well as expression of MYC target and NOTCH signaling genes were elevated after circFBXW7 depletion, suggesting an inhibitory role of circFBXW7 in these oncogenic axes. Plus, low circFBXW7 levels were associated with a particular gene expression profile in T-ALL patients, which was remarkably mirrored by the effects of circFBXW7 loss-of-function in vitro. CircFBXW7 depletion notably emerges as a new factor enhancing a proliferative phenotype and the activation of the MYC signaling pathway, key players in this aggressive malignancy

CircRNAs Dysregulated in Juvenile Myelomonocytic Leukemia: CircMCTP1 Stands Out

Juvenile myelomonocytic leukemia (JMML), a rare myelodysplastic/myeloproliferative neoplasm of early childhood, is characterized by clonal growth of RAS signaling addicted stem cells. JMML subtypes are defined by specific RAS pathway mutations and display distinct gene, microRNA (miRNA) and long non-coding RNA expression profiles. Here we zoom in on circular RNAs (circRNAs), molecules that, when abnormally expressed, may participate in malignant deviation of cellular processes. CirComPara software was used to annotate and quantify circRNAs in RNA-seq data of a "discovery cohort" comprising 19 JMML patients and 3 healthy donors (HD). In an independent set of 12 JMML patients and 6 HD, expression of 27 circRNAs was analyzed by qRT-PCR. CircRNA-miRNA-gene networks were reconstructed using circRNA function prediction and gene expression data. We identified 119 circRNAs dysregulated in JMML and 59 genes showing an imbalance of the circular and linear products. Our data indicated also circRNA expression differences among molecular subgroups of JMML. Validation of a set of deregulated circRNAs in an independent cohort of JMML patients confirmed the down-regulation of circOXNAD1 and circATM, and a marked up-regulation of circLYN, circAFF2, and circMCTP1. A new finding in JMML links up-regulated circMCTP1 with known tumor suppressor miRNAs. This and other predicted interactions with miRNAs connect dysregulated circRNAs to regulatory networks. In conclusion, this study provides insight into the circRNAome of JMML and paves the path to elucidate new molecular disease mechanisms putting forward circMCTP1 up-regulation as a robust example

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

: The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation