GENOME-WIDE DNA METHYLATION PROFILING OF OBESE INSULIN RESISTANT CHILDREN

Introduzione: L\u2019insulino resistenza si presenta quando la risposta delle cellule all\u2019insulina \ue8 diminuita causando un drammatico innalzamento dei livelli di zucchero nel sangue. I diversi fattori di rischio per l\u2019insulino resistenza includono uno stile di vita sedentario, obesit\ue0, storia familiare di diabete e invecchiamento. Negli ultimi anni, il diabete di tipo 2, l\u2019insulino resistenza e l\u2019obesit\ue0 sono considerevolmente aumentate nella popolazione contribuendo all\u2019incremento in morbidit\ue0 e mortalit\ue0 nel mondo. I molti meccanismi proposti per spiegare il funzionamento dell\u2019insulino resistenza includono varianti genetiche, deregolazioni trascrittomiche e modificazioni epigenetiche, come per esempio la metilazione del DNA. Scopo: L\u2019obiettivo di questa tesi comprende l\u2019utilizzo di metodologie bioinformatiche applicate allo studio della metilazione del DNA lungo tutto il genoma usando l\u2019Infinium Human Methylation EPIC array (~850k CpGs) per studiare la componente epigenetica dell\u2019insulino resistenza in una coorte di 186 soggetti pediatrici obesi, uniformemente divisi in due gruppi (insulino resistenti/insulino sensibili). Risultati: L\u2019analisi bioinformatica della metilazione a livello genomico, suggerisce una forte modulazione della composizione in termini di tipi cellulari nei soggetti insulino resistenti, suggerendo un possibile ruolo dell\u2019infiammazione nella malattia. Inoltre, l\u2019analisi della metilazione differenziale su singoli CpG o regioni, accompagnata da un\u2019analisi di \u201cgene set enrichment\u201d, mette in evidenza diverse vie collegate al metabolismo di carboidrati e grassi. In aggiunta, associando i probes differenzialmente metilati con risultati di studi riportati in letteratura, emergono ulteriori fattori che si potrebbero considerare durante lo studio di questa condizione. Conclusioni: In conclusione, abbiamo utilizzato diversi approcci bioinformatici applicandoli ad una numerosa coorte di individui per studiare la metilazione del DNA a livello genomico nel contesto dell\u2019insulino resistenza, con risultati che supportano l\u2019ipotesi che la metilazione sia pi\uf9 legata a cambiamenti globali piuttosto che cambiamenti localizzati in pochi loci.Background: Insulin resistance occurs when the response of cells to insulin is decreased hence causing blood sugar levels to rise dramatically. Among others, the most common risk factors for insulin resistance include sedentary lifestyle, obesity, family history of diabetes and advanced age. In the last few decades, type 2 diabetes, insulin resistance, and obesity have increased dramatically in the general population contributing to an increase in morbidity and mortality around the world. Among others, the main mechanisms proposed for the action of insulin resistance include genetic variants, transcriptomic dysregulations, and epigenetic changes such as DNA methylation. Aim: The aim of this thesis is to employ bioinformatic methods to genome-wide DNA methylation using the Infinium Human Methylation EPIC array (~850k CpGs) to study the epigenetic component of insulin resistance in a cohort of 186 obese pediatric individuals equally divided in two groups (insulin resistant/insulin sensitive). Results: Bioinformatic analysis of the genome-wide methylation data, suggests a strong modulation of cell type composition in insulin resistant subjects proposing a role of inflammation in this disease. Furthermore, differential methylation of single CpG or regions, coupled with gene set enrichment analysis highlighted several pathways involved with carbohydrates and fat metabolism. Additionally, associating differentially methylated probes with previously reported studies highlights additional factors that may be useful to consider when studying this condition. Conclusions: In conclusion, we employed different bioinformatics strategies applied to a large cohort of individuals to study genome-wide DNA methylation in IR, with results supporting the hypothesis that methylation is more related to general methylation landscape changes rather than methylation variations in a few loci

Methylation profile study of CD14+ monocytesof multiple sclerosis-affected individuals.

Methylation is one of the most studied epigenetic mechanisms known to affect gene expression. It refers to the covalent binding of a methyl group to the fifth position of cytosine residues in the CpG dinucleotide context in mammals. In our study we analysed 26 CD14+ monocyte samples coming from relapsing remitting-multiple sclerosis (MS) patients anc controls. DNA libraries were prepared by SeqCap Epi Enrichment System (Roche) with enzymatic fragmentation and bisulfite conversion of 26 DNAs (pool 1) and then sequenced by Illumina Next-Generation Sequencing platform. The aim was to estimate the epigenetic profile and investigate differentially methylated regions between cases and controls. Our preliminary results showed an unexpected epigenetic pattern (~2.5 million CpGs after QC steps) lacking many methylation signals, suggesting that the enzymatic fragmentation disrupted somehow most of methylated cytosines. To evaluate whether the method of DNA fragmentation had an impact on the observed results, eight samples (pool 2) belonging to pool 1 were then analysed using mechanical fragmentation of DNA as a second and independent method. Pool 2 samples showed the expected methylation profile, with many loci either fully methylated or non-methylated. Methylation profiles from samples common to pool 1 and pool 2 were then compared to one another. Through bioinformatic and statistical tools the data were processed to infer any correlations between the methylation signals (β values) of the two pools and then to recover as many lost methylation signals as possible from the pool 1 samples, using the pool 2 samples as reference. Preliminary results showed that most fully methylated loci in pool 2 showed a lower β value in pool 1 samples, while for hypomethylated loci the two pools show a concordance of ~99%. Moreover, differentially methylated loci between MS cases and controls show a signal of differential methylation (nominal pvalue threshold 1%) for 1.359 CpG loci, a part of them map on the DIP2C gene. Further analyses need to be done to investigate the impact of enzymatic fragmentation on methylation estimation and to get the epigenetic profiles on the dataset of 26 MS samples. In addition, miRNA expression from this dataset will be integrated with methylation signals

Pocket-sized genomics and transcriptomics analyses: a look at the newborn BioVRPi project

BioVRPi is a newborn project, started in January 2021, that focuses on Raspberry Pi (RPi) employment in bioinformatics, with particular regards on genomics. In the previous years, some research groups have already reported several examples of applications for RPi, including bioinformatic basic training and proteomics. Our project aims to develop and offer a low-cost, stable, and tested bioinformatic environment for students and researchers involved in genomics and transcriptomics fields. Raspberry Pi is a small single-board low-cost computer that was developed by the Raspberry Pi Foundation since 2012. Its original purpose aimed to facilitate computer science basic teaching in developing countries, but the growing worldwide interest has permitted its constant progress and development. Thanks to its features, RPi can suit several disciplines in need for computational supports and reach almost every, if not all, research group in the world. We tested RPi capabilities on real case studies, relatively to Genome-Wide Association Studies (GWAS) for complex traits in Homo sapiens data and in transcriptomic analyses (RNA-seq) on the Strongyloides stercoralis human parasite samples, using two RPi-4 devices equipped with different amount of RAM (8GB for genomics and 2 GB for transcriptome analyses, respectively), and running a 64-bit Operating System. The analyses leveraged on state-of-art bioinformatic toolset, such as Plink and Plink1.9, SAMtools, Bowtie 2, R, and different R packages, all compiled from source code. Moreover, the GWAS was run according to the golden standard protocols and results from the different platforms were compared. The results showed that RPi are effective devices that can efficiently handle whole GWAS and RNA-seq analyses. Benchmarking showed that the computational time taken by RPi was of the same order of magnitude when compared to the ones from a commonly used bioinformatic computer. At last, BioVRPi project shows how to implement new strategies for bioinformatic analyses, in order to provide a having-fun environment to learn and explore new alternatives in bioinformatic data analysis

Analyzing BioRad-Illumina Single Cell RNA-Seq data with open source tools

Single cell RNA-Seq is a powerful technique that is becoming more popular since it enables to sequence the transcriptome of each cell within a population of different cell types in a single experiment. Currently, there are a few different technologies, like BioRad-Illumina ddSeq and 10X Chromium

Studiare la regione HLA negli esomi

La regione HLA (6p21.3, ~4Mb) contiene un gruppo di geni altamente polimorfici coinvolti nella risposta immunitaria

Dissection of HLA-C gene region to investigate its association with complex traits

The genomic region of HLA gene cluster (6p21.3) contains several highly polymorphic genes involved in the immune response and therefore, they have been previously associated to several immune and inflammatory disorders. For the present study we investigated more than 3500 known alleles of HLA-C gene (from IPD-IMGT/HLA database, genomic sequences) that are grouped into 14 serogroups (e.g., C*01:02:01:01). All the sequences have been aligned against the human genome reference sequence (both versions; hg19 and hg38). Overall, the HLA-C gene (length ~3000 bp) contains more than 1500 SNPs. We used a clustering approach to understand how the alleles are evolutionarily connected. According to the clustering analysis we observed that sequences of the same serogroup cluster together more often than sequences of other serogroups, even if with several exceptions. This confirms that alleles of the same serogroup share strong identity in their sequence. Interestingly, as general rule, we observed that the main tree presents two branches, containing each a similar structure (relative distance between serogroups). Of note, the sequences of C*07 and C*17 serogroups belonged to one of the two branches only, whereas the sequences of C*06 and C*12 serogroups were observed in the alternative branch of the tree. We are now studying what are the main features that characterize the two branches. Moreover, the study will go on by investigating the association of the HLA-C serogroup SNP-based alleles with kidney related disease (INCIPE study) and Alzheimer’s disease (NIAGADS database) in large cohorts of individuals

Identification of miRNAs of Strongyloides stercoralis L1 and iL3 larvae isolated from human stool

Strongyloidiasis is a neglected tropical disease caused by the soil-transmitted nematode by Strongyloides stercoralis, that affects approximately 600 million people worldwide. In immunosuppressed individuals disseminated strongyloidiasis can rapidly lead to fatal outcomes. There is no gold standard for diagnosing strongyloidiasis, and infections are frequently misdiagnosed. A better understanding of the molecular biology of this parasite can be useful for example for the discovery of potential new biomarkers. Interestingly, recent evidence showed the presence of small RNAs in Strongyloididae, but no data was provided for S. stercoralis. In this study, we present the first identification of miRNAs of both L1 and iL3 larval stages of S. stercoralis. For our purpose, the aims were: (i) to analyse the miRNome of L1 and iL3 S. stercoralis and to identify potential miRNAs of this nematode, (ii) to obtain the mRNAs profiles in these two larval stages and (iii) to predict potential miRNA target sites in mRNA sequences. Total RNA was isolated from L1 and iL3 collected from the stool of 5 infected individuals. For the miRNAs analysis, we used miRDeep2 software and a pipeline of bio-informatic tools to construct a catalog of a total of 385 sequences. Among these, 53% were common to S. ratti, 19% to S. papillosus, 1% to Caenorhabditis elegans and 44% were novel. Using a differential analysis between the larval stages, we observed 6 suggestive modulated miRNAs (STR-MIR-34A-3P, STR-MIR-8397-3P, STR-MIR-34B-3P and STR-MIR-34C-3P expressed more in iL3, and STR-MIR-7880H-5P and STR-MIR-7880M-5P expressed more in L1). Along with this analysis, we obtained also the mRNAs profiles in the same samples of larvae. Multiple testing found 81 statistically significant mRNAs of the total 1553 obtained (FDR < 0.05; 32 genes expressed more in L1 than iL3; 49 genes expressed more in L3 than iL1). Finally, we found 33 predicted mRNA targets of the modulated miRNAs, providing relevant data for a further validation to better understand the role of these small molecules in the larval stages and their valuein clinical diagnostics

Physical activity prevents cartilage degradation: a metabolomics study pinpoints the involvement of vitamin B6

Osteoarthritis (OA) is predominantly characterized by the progressive degradation of articular cartilage, the connective tissue produced by chondrocytes, due to an imbalance between anabolic and catabolic processes. In addition, physical activity (PA) is recognized as an important tool for counteracting OA. To evaluate PA effects on the chondrocyte lineage, we analyzed the expression of SOX9, COL2A1, and COMP in circulating progenitor cells following a half marathon (HM) performance. Therefore, we studied in-depth the involvement of metabolites affecting chondrocyte lineage, and we compared the metabolomic profile associated with PA by analyzing runners' sera before and after HM performance. Interestingly, this study highlighted that metabolites involved in vitamin B6 salvage, such as pyridoxal 5'-phosphate and pyridoxamine 5'-phosphate, were highly modulated. To evaluate the effects of vitamin B6 in cartilage cells, we treated differentiated mesenchymal stem cells and the SW1353 chondrosarcoma cell line with vitamin B6 in the presence of IL1\u3b2, the inflammatory cytokine involved in OA. Our study describes, for the first time, the modulation of the vitamin B6 salvage pathway following PA and suggests a protective role of PA in OA through modulation of this pathway

Orthogonal Proteogenomic Analysis Identifies the Druggable PA2G4-MYC Axis in 3q26 AML

The overexpression of the ecotropic viral integration site-1 gene (EVI1/MECOM) marks the most lethal acute myeloid leukemia (AML) subgroup carrying chromosome 3q26 abnormalities. By taking advantage of the intersectionality of high-throughput cell-based and gene expression screens selective and pan-histone deacetylase inhibitors (HDACis) emerge as potent repressors of EVI1. To understand the mechanism driving on-target anti-leukemia activity of this compound class, here we dissect the expression dynamics of the bone marrow leukemia cells of patients treated with HDACi and reconstitute the EVI1 chromatin-associated co-transcriptional complex merging on the role of proliferation-associated 2G4 (PA2G4) protein. PA2G4 overexpression rescues AML cells from the inhibitory effects of HDACis, while genetic and small molecule inhibition of PA2G4 abrogates EVI1 in 3q26 AML cells, including in patient-derived leukemia xenografts. This study positions PA2G4 at the crosstalk of the EVI1 leukemogenic signal for developing new therapeutics and urges the use of HDACis-based combination therapies in patients with 3q26 AML

ScrInHeat: a computational approach for genomic screening of pathogenic factors in poorly studied bacteria

Background: The detection and identification of pathogenic factors (PFs) scattered across a genome can provide useful insights about the pathogenic potential of bacterial strains. Although very important, this process is often carried out in little depth, also due to a lack of tools that predict/identify potential pathogenic factors in poorly characterized species. We developed a collection of computational tools named ScrInHeat that allows to screen for PFs providing standardized annotation and visualization. Methods: ScrInHeat works through the following steps: building of PFs databases, screening of genomic assemblies/sets of proteins, integration of results, and generation of Gene Ontology (GO) annotated heatmaps for a simple results visualization. Its performance was evaluated through comparison against 3 freely available databases/tools with similar purpose: VFDB, PATRIC and ABRicate. Performances were compared using NCBI genomic assemblies of a well- and a poorly-characterized species, respectively Escherichia coli and Achromobacter xylosoxidans. Results: ScrInHeat was able to build reliable PFs databases when compared to the well-curated ones: although some PFs were not identified (62% content overlap), all the sequences within ScrInHeat database were truly related to pathogenicity. Moreover, when compared to the currently available A. xylosoxidans database, ScrInHeat could identify 148 additional PFs. ScrInHeat also offered cellular component, molecular function and biological process GO annotations while reducing the manual effort needed to filter and display results in the form of a heatmap. Conclusions: ScrInHeat proved to be a fast and versatile tool to identify PFs and readily visualize and interpret results with the support of GO annotations. Overall, it represents a new useful starting tool to study poorly characterized bacteria and aid microbiologists by prompting further characterization of putative PFs