1,720,986 research outputs found
Development of computational pipelines for transcriptome and miRNome characterization from RNA-seq data applied to swine adipose tissue
Full text linkHigh 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
Study of differentially expressed short-RNAs in swine backfat between fat and lean animals and target prediction of genes regulating fat traits.
No full textmiRNome of italian large white pig subcutaneous fat tissue: new miRNAs, isomiRs and moRNAs
No full textSmall RNAs in Circulating Exosomes of Cancer Patients: A Minireview
Full text linkExtracellular 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
Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model
Full text linkFinding 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
CirComPara: a multi-method comparative bioinformatics pipeline to detect and study circRNAs from RNA-seq data.
Full text linkCircular 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
Transcriptional profiling of subcutaneous adipose tissue in Italian Large White pigs divergent for backfat thickness
Full text linkFat deposition is a widely studied trait in pigs because of its implications with animal growth efficiency, technological and nutritional characteristics of meat products, but the global framework of the biological and molecular processes regulating fat deposition in pigs is still incomplete. This study describes the backfat tissue transcription profile in Italian Large White pigs and reports genes differentially expressed between fat and lean animals according to RNA-seq data. The backfat transcription profile was characterised by the expression of 23 483 genes, of which 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 highly expressed (e.g. ACP5, BCL2A1, CCR1, CD163, CD1A, EGR2, ENPP1, GPNMB, INHBB, LYZ, MSR1, OLR1, PIK3AP1, PLIN2, SPP1, SLC11A1, STC1) and 14 lower expressed (e.g. ADSSL1, CDO1, DNAJB1, HSPA1A, HSPA1B, HSPA2, HSPB8, IGFBP5, OLFML3) in fat pigs. The main functional categories enriched in differentially expressed genes were immune system process, response to stimulus, cell activation and skeletal system development, for the overexpressed genes, and unfolded protein binding and stress response, for the underexpressed genes, which included five heat shock proteins. 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. Our results provide the opportunity to identify biomarkers of carcass fat traits to improve the pig production chain and to identify genetic factors that regulate the observed differential expression
A paclitaxel-hyaluronan conjugate (ONCOFID-P-BTM) in patients with BCG-unresponsive carcinoma in situ of the bladder: a dynamic assessment of the tumor microenvironment
Full text linkBackground The intravesical instillation of the paclitaxel-hyaluronan conjugate ONCOFID-P-B (TM) in patients with bacillus Calmette-Guerin (BCG)-unresponsive bladder carcinoma in situ (CIS; NCT04798703 phase I study), induced 75 and 40% of complete response (CR) after 12 weeks of intensive phase and 12 months of maintenance phase, respectively. The aim of this study was to provide a detailed description of the tumor microenvironment (TME) of ONCOFID-P-B (TM)-treated BCG-unresponsive bladder CIS patients enrolled in the NCT04798703 phase I study, in order to identify predictive biomarkers of response.Methods The composition and spatial interactions of tumor-infiltrating immune cells and the expression of the most relevant hyaluronic acid (HA) receptors on cancer cells, were analyzed in biopsies from the 20 patients enrolled in the NCT04798703 phase I study collected before starting ONCOFID-P-B (TM) therapy (baseline), and after the intensive and the maintenance phases. Clinical data were correlated with cell densities, cell distribution and cell interactions. Associations between immune populations or HA receptors expression and outcome were analyzed using univariate Cox regression and log-rank analysis.Results In baseline biopsies, patients achieving CR after the intensive phase had a lower density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL), but also fewer interactions between CTL and macrophages or T-regulatory cells, as compared to non-responders (NR). NR expressed higher levels of the HA receptors CD44v6, ICAM-1 and RHAMM. The intra-tumoral macrophage density was positively correlated with the expression of the pro-metastatic and aggressive variant CD44v6, and the combined score of intra-tumoral macrophage density and CD44v6 expression had an AUC of 0.85 (95% CI 0.68-1.00) for patient response prediction.Conclusions The clinical response to ONCOFID-P-B (TM) in bladder CIS likely relies on several components of the TME, and the combined evaluation of intra-tumoral macrophages density and CD44v6 expression is a potentially new predictive biomarker for patient response. Overall, our data allow to advance a potential rationale for combinatorial treatments targeting the immune infiltrate such as immune checkpoint inhibitors, to make bladder CIS more responsive to ONCOFID-P-B (TM) treatment
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