Development of computational pipelines for transcriptome and miRNome characterization from RNA-seq data applied to swine adipose tissue

High throughput technologies for DNA sequencing are used more and more frequently for gene expression profiling studies (RNA-seq). With respect to other techniques such as microarrays, RNA-seq has higher sensitivity in retrieving the expressed molecules and presents the advantageous feature of allowing the detection of unknown or uncharacterized transcripts. RNA-seq data processing involves several computational steps (input preprocessing for quality evaluation and cleaning; read alignment to reference genome; transcript identification, quantification, and annotation; differential expression assessment) that have to be performed in sequential order, thus resulting in a computational pipeline. Each single RNA-seq experiment can produce large amounts of data that require the use of efficient computational methods to obtain transcriptome qualitative and quantitative characterization. There are different methods that implement each conceptual pipeline step, and new ones are continuously proposed. However, because of the variety of biological questions and study designs to which RNA-seq experiments can be applied to, there is not a commonly adopted implementation of the processing workflow. In this thesis, we developed a computational pipeline for the analysis of RNA-seq data focused on the linear transcriptome, extended an existing pipeline that analyzes RNA-seq data of microRNAs (miRNAs) and miRNA-like small RNAs, and started to develop a computational pipeline for the detection and quantification of circular RNAs. The main objectives of the first two pipelines were the profiling of the set of the transcripts (transcriptome) and small RNAs (miRNome) expressed in the considered samples, by the identification of known and new RNAs. They allowed as well to investigate RNA sequence variations (such as miRNA isomiRs), transcripts and small RNAs expression levels, and to compare expression profiles between different sample groups. The pig (Sus scrofa) is a model organism for human diseases, and very important per se for the meat industry. Fat and backfat tissues are subject of very active research since fat attributes and deposition traits are in strong connection with technological aspects and quality of pig products. However, the global framework of the biological and molecular processes regulating backfat deposition in pig is still incomplete. We applied our pipelines to RNA-seq data of polyadenylated and of small RNAs from pig subcutaneous adipose tissue samples from 20 Italian Large White (ILW) individuals. Selected animals were reared under very standard conditions but presented, for fat traits, extreme and divergent phenotypes (FAT and LEAN pigs) and genetic merits. The backfat transcription profile was characterized by the expression of 23,483 genes, of which only 54.1% were represented by known genes. Of 63,418 expressed transcripts, about 80% were non-previously annotated isoforms. By comparing the expression level of FAT vs. LEAN pigs, we detected 86 robust differentially expressed transcripts, 72 more expressed in fat pigs (including ACP5, BCL2A1, CCR1, CD163, CD1A, EGR2, ENPP1, GPNMB, INHBB, LYZ, MSR1, OLR1, PIK3AP1, PLIN2, SPP1, SLC11A1, STC1) and 14 less expressed (including ADSSL1, CDO1, DNAJB1, HSPA1A, HSPA1B, HSPA2, HSPB8, IGFBP5, OLFML3). Overexpressed genes were implied particularly in immune system processes, response to stimulus, cell activation and skeletal system development. Underexpressed genes included five heat shock proteins and were involved in unfolded protein binding and stress response functional categories. Adipose tissue alterations and impaired stress response are linked to inflammation and, in turn, to adipose tissue secretory activity, similar to what is observed in human obesity. MiRNAs play important roles in cell differentiation and physiology acting as post-transcriptional regulators of gene expression by silencing targeted transcripts. The pig backfat miRNome showed the expression of hundreds of small RNAs, including putative new miRNAs, new miRNA isoforms (isomiRs), and new moRNAs, likely produced from the terminal regions of non-canonically processed hairpin precursors. From a first study on two samples, we detected 222 known miRNAs, 68 new miRNAs and 17 moRNAs expressed from known hairpins, and 312 new miRNAs expressed from 253 new hairpins. The expression of five small RNAs, including moRNA ssc-moR-21-5p and a miRNA from a new hairpin, was validated by a qRT-PCR assay, thus confirming the robustness of our results. A second study on 18 samples identified a largely overlapping miRNome in terms of expressed elements and variations, and was important to identify differentially expressed miRNAs and moRNAs in FAT and LEAN subjects. We predicted putative regulatory interactions between small RNAs and transcripts by sequence analysis, using custom target predictions on reconstructed transcript sequences and miRNA isomiRs. We then integrated target prediction results with combined analysis of miRNA and transcript expression data, to eventually select miRNA-transcript relations most supported by negative correlation of expression profiles. Further, the predicted network of miRNA-transcript interactions was enriched by information on transcript differential expression, functional annotations and coding potential predictions, and transcript overlap with pig QTL genomic regions. In this way we were able to focus on a restricted and possibly most significant number of interactions that need to be experimentally investigated. Additional considerations are coming from the study of the possible impact of specific differentially expressed miRNAs to genes belonging to the pathways most germane to adipose tissue features. The applicative results of these studies enlarged the knowledge of transcripts and small RNAs expressed in the pig adipose tissue, as well as small RNA-transcripts regulatory interactions, providing information helpful for a better understanding of ILW pig backfat and future studies on gene expression regulation in this tissue. Moreover, the methods presented here are currently undergoing further development and extension, and have applications well over and above those presented in this thesis

CircRNAs are here to stay: A perspective on the MLL recombinome

Chromosomal translocations harbored by cancer genomes are important oncogenic drivers. In MLL rearranged acute leukemia (MLLre) MLL/KMT2A fuses with over 90 partner genes. Mechanistic studies provided clues of MLL fusion protein leukemogenic potential, but models failed to fully recapitulate the disease. Recently, expression of oncogenic fusion circular RNAs (f-circ) by MLL-AF9 fusion was proven. This discovery, together with emerging data on the importance and diversity of circRNAs formed the incentive to study the circRNAs of the MLL recombinome. Through interactions with other RNAs, such as microRNAs, and with proteins, circRNAs regulate cellular processes also related to cancer development. CircRNAs can translate into functional peptides too. MLL and most of the 90 MLL translocation partners do express circRNAs and exploration of our RNA-seq dataset of sorted blood cell populations provided new data on alternative circular isoform generation and expression variability of circRNAs of the MLL recombinome. Further, we provided evidence that rearrangements of MLL and three of the main translocation partner genes can impact circRNA expression, supported also by preliminary observations in leukemic cells. The emerging picture underpins the view that circRNAs are worthwhile to be considered when studying MLLre leukemias and provides a new perspective on the impact of chromosomal translocations in cancer cells at large

Functional relevance of circRNA aberrant expression in pediatric acute leukemia with KMT2A::AFF1 fusion

: Circular RNAs (circRNAs) are emerging molecular players in leukemogenesis and promising therapeutic targets. In KMT2A::AFF1 (MLL::AF4)-rearranged leukemia, an aggressive disease compared with other pediatric B-cell precursor (BCP) acute lymphoblastic leukemia (ALL), data about circRNAs are limited. Here, we disclose the circRNA landscape of infant patients with KMT2A::AFF1 translocated BCP-ALL showing dysregulated, mostly ectopically expressed, circRNAs in leukemia cells. Most of these circRNAs, apart from circHIPK3 and circZNF609, previously associated with oncogenic behavior in ALL, are still uncharacterized. An in vitro loss-of-function screening identified an oncogenic role of circFKBP5, circKLHL2, circNR3C1, and circPAN3 in KMT2A::AFF1 ALL, whose silencing affected cell proliferation and apoptosis. Further study in an extended cohort disclosed a significantly correlated expression of these oncogenic circRNAs and their putative involvement in common regulatory networks. Moreover, it showed that circAFF1 upregulation occurs in a subset of cases with HOXA KMT2A::AFF1 ALL. Collectively, functional analyses and patient data reveal oncogenic circRNA upregulation as a relevant mechanism that sustains the malignant cell phenotype in KMT2A::AFF1 ALL

CirComPara: A Multi‐Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA‐seq Data

Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNAseq data using appropriate computational methods to identify the sequence reads spanning backsplice junctions that do not colinearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here,we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level. We applied our method to RNAseqdata of monocyte and macrophage samples in relation to haploinsufficiency of the RNAbinding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies

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/)

Small RNAs in Circulating Exosomes of Cancer Patients: A Minireview

Extracellular vesicles (EVs) secreted from many cell types play important roles in intercellular communication, both as paracrine and endocrine factors, as they can circulate in biological fluids, including plasma. Amid EVs, exosomes are actively secreted vesicles that contain proteins, lipids, soluble factors, and nucleic acids, including microRNAs (miRNAs) and other classes of small RNAs (sRNA). miRNAs are prominent post‐transcriptional regulators of gene expression and epigenetic silencers of transcription. We concisely review the roles of miRNAs in cell‐fate determination and development and their regulatory activity on almost all the processes and pathways controlling tumor formation and progression. Next, we consider the evidence linking exosomes to tumor progression, particularly to the setting‐up of permissive pre‐metastatic niches. The study of exosomes in patients with different survival and therapy response can inform on the possible correlations between exosomal cargo and disease features. Moreover, the exploration of circulating exosomes as possible sources of non‐invasive biomarkers could give new implements for anti‐cancer therapy and metastasis prevention. Since the characterization of sRNAs in exosomes of cancer patients sparks opportunities to better understand their roles in cancer, we briefly present current experimental and computational protocols for sRNAs analysis in circulating exosomes by RNA‐seq

EXPANDING THE KNOWLEDGE OF BLOOD TRANSCRIPTOME: CIRCULAR RNAS IN HEMATOPOIESIS

open3openA Bonizzato, E Gaffo, G te Kronnie, S BortoluzziBonizzato, A; Gaffo, E; TE KRONNIE, Geertruij; Bortoluzzi,

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

CirComPara: A Multi‐Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA‐seq Data

Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNAseq data using appropriate computational methods to identify the sequence reads spanning backsplice junctions that do not colinearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here,we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level. We applied our method to RNAseqdata of monocyte and macrophage samples in relation to haploinsufficiency of the RNAbinding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies