IDENTIFICATION AND EXPLORATION OF NOVEL MOLECULAR SIGNATURES IN BIOLOGICAL SYSTEMS THROUGH GENOMICS AND BIOINFORMATICS

The last two decades have witnessed rapid developments in –omics technologies which enable the study of biological and disease processes in a high throughput manner. Among the -omics approaches, genomics and the related bioinformatic methods have emerged as most popular applications able to accelerate science discoveries in basic research and drug discovery and therapeutics. Genomics is an interdisciplinary field of science focusing on the structure, function, evolution, mapping, and editing of genomes (Wikipedia, url: https://en.wikipedia.org/wiki/Genomics). Over the years, the field of genomics has undergone several revolutions. Prior to the advent of Next Generation Sequencing (NGS), genomics was limited to the characterization of single disease-associated genes (e.g. Huntington disease, cystic fibrosis, cancer) or to the study of small genomes (e.g. bacteria, viruses). As physical mapping with large-insert clones became possible, the subcloned fragments of large genomes could be sequenced as individual projects, and their finished sequences combined together to reconstruct the sequence of entire chromosomes. Using this approach and beginning from 1985, in 2003 the Human Genome Project was able to complete the sequence of the DNA in the human genome (I. H. G. S. Consortium et al., 2001; Venter et al., 2001), thus providing a basic platform for the development of new technologies. In the same period, other large genomes, including those of model organisms, were also decoded (M. G. S. Consortium et al., 2002; R. G. S. P. Consortium et al., 2004; Myers et al., 2000). Hybridization-based methods such as microarrays exploited the information gained from genome projects to develop rapid, high throughput assays to allow the measurement of genetic variation, gene expression and chromatin binding, which spread rapidly in all fields of research. Most recently, these methods were quickly replaced by NGS, which allows similar studies to be conducted with much higher sensitivity and in an unbiased whole-genome and –transcriptome fashion. As a result, sequencing has become an essential and obligatory tool and not only for biologists. In the early days of NGS, the initial focus of every genomic scientist was on the de-novo assembly of novel genomes for species that were never sequenced before. These efforts led to the completion of many novel genomic sequences which include even large genomes of mammals and plants. In the case of de-novo assembly, the genomic sequence is built from scratch without the use of an existing scaffold. Advances in sequencing technology have recently led to a dramatic increase in speed and throughput capacity, and a sharp reduction in costs. These improvements enabled the shift from de-novo to re-sequencing of entire genomes from additional individuals of species already sequenced. In the case of re-sequencing, short reads can be aligned to reference genomes as a substrate for variation discovery or gene expression analysis. Re-sequencing applications provide the scientific community with an unprecedented opportunity to address fundamental evolutionary questions, as well as to extend the use of sequencing to population genetic studies to infer ancient population history. The availability of new data types given by an always increasing number of NGS applications continues to engage and excite the computational biology community working on software development and on the analysis of new data types generated to solve complex biomedical problems. In this context, the main objective of my research was to explore different biological systems to identify new molecular signals through the development and implementation of genomic and bioinformatic methods. This objective was accomplished by participating to three different research projects where I applied genomic and bioinformatic solutions to different areas of biology: genome composition, organization and regulation, malaria biology, and cancer. The first chapter provides an introduction to the main technology and biology concepts explored in my research, while the following three chapters describe in details the research work conducted during my studies

Lack of Sik1 in Mouse Embryonic Stem Cells Impairs Cardiomyogenesis by Down-Regulating the Cyclin-Dependent Kinase Inhibitor p57kip2

Sik1 (salt inducible kinase 1) is a serine/threonine kinase that belongs to the stress- and energy-sensing AMP-activated protein kinase family. During murine embryogenesis, sik1 marks the monolayer of future myocardial cells that will populate first the primitive ventricle, and later the primitive atrium suggesting its involvement in cardiac cell differentiation and/or heart development. Despite that observation, the involvement of sik1 in cardiac differentiation is still unknown. We examined the sik1 function during cardiomyocyte differentiation using the ES-derived embryoid bodies. We produced a null embryonic stem cell using a gene-trap cell line carrying an insertion in the sik1 locus. In absence of the sik1 protein, the temporal appearance of cardiomyocytes is delayed. Expression profile analysis revealed sik1 as part of a genetic network that controls the cell cycle, where the cyclin-dependent kinase inhibitor p57Kip2 is directly involved. Collectively, we provided evidence that sik1-mediated effects are specific for cardiomyogenesis regulating cardiomyoblast cell cycle exit toward terminal differentiation

PRGdb 2.0 : towards a community-based database model for the analysis of R-genes in plants

The Plant Resistance Genes database (PRGdb; http://prgdb.org) is a comprehensive resource on resistance genes (R-genes), a major class of genes in plant genomes that convey disease resistance against pathogens. Initiated in 2009, the database has grown more than 6-fold to recently include annotation derived from recent plant genome sequencing projects. Release 2.0 currently hosts useful biological information on a set of 112 known and 104 310 putative R-genes present in 233 plant species and conferring resistance to 122 different pathogens. Moreover, the website has been completely redesigned with the implementation of Semantic MediaWiki technologies, which makes our repository freely accessed and easily edited by any scientists. To this purpose, we encourage plant biologist experts to join our annotation effort and share their knowledge on resistance-gene biology with the rest of the scientific community

AS601245, an Anti-Inflammatory JNK Inhibitor, and Clofibrate Have a Synergistic Effect in Inducing Cell Responses and in Affecting the Gene Expression Profile in CaCo-2 Colon Cancer Cells

PPARαs are nuclear receptors highly expressed in colon cells. They can be activated by the fibrates (clofibrate, ciprofibrate etc.) used to treat hyperlipidemia. Since PPARα transcriptional activity can be negatively regulated by JNK, the inhibition of JNK activity could increase the effectiveness of PPARα ligands. We analysed the effects of AS601245 (a JNK inhibitor) and clofibrate alone or in association, on proliferation, apoptosis, differentiation and the gene expression profile of CaCo-2 human colon cancer cells. Proliferation was inhibited in a dose-dependent way by clofibrate and AS601245. Combined treatment synergistically reduced cell proliferation, cyclin D1 and PCNA expression and induced apoptosis and differentiation. Reduction of cell proliferation, accompanied by the modulation of p21 expression was observed in HepG2 cells, also. Gene expression analysis revealed that some genes were highly modulated by the combined treatment and 28 genes containing PPRE were up-regulated, while clofibrate alone was ineffective. Moreover, STAT3 signalling was strongly reduced by combined treatment. After combined treatment, the binding of PPARα to PPRE increased and paralleled with the expression of the PPAR coactivator MED1. Results demonstrate that combined treatment increases the effectiveness of both compounds and suggest a positive interaction between PPARα ligands and anti-inflammatory agents in humans

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes

Decatransin, a novel natural product inhibiting protein translocation at the Sec61/SecY translocon

A new cyclic decadepsipeptide was isolated from Chaetosphaeria tulasneorum with potent bioactivity on mammalian and yeast cells. Chemogenomic profiling in S. cerevisiae indicated that the Sec61 translocon, the machinery for protein translocation and membrane insertion at the endoplasmic reticulum, is the target. The profiles were similar to those of cyclic heptadepsipeptides of a distinct chemotype (HUN-7293/cotransin) that had previously been shown to inhibit cotranslational translocation at the mammalian Sec61 translocon. Unbiased, genome-wide mutagenesis followed by full-genome sequencing in both fungal and mammalian cells identified dominant mutations in Sec61p/Sec61α1 to confer resistance. Most, but not all, of these mutations affected inhibition by both chemotypes, despite an absence of structural similarity. Biochemical analysis confirmed inhibition of protein translocation into the endoplasmic reticulum of both co- and posttranslationally translocated substrates by both chemotypes, demonstrating a mechanism independent of a translating ribosome. Most interestingly, both chemotypes were found to also inhibit SecYEG, the bacterial Sec61 homolog. We suggest "decatransin" as the name for this novel decadepsipeptide translocation inhibitor

HumMeth27QCReport: an R package for quality control and primary analysis of Illumina Infinium methylation data

<p>Abstract</p> <p>Background</p> <p>The study of the human DNA methylome has gained particular interest in the last few years. Researchers can nowadays investigate the potential role of DNA methylation in common disorders by taking advantage of new high-throughput technologies. Among these, Illumina Infinium assays can interrogate the methylation levels of hundreds of thousands of CpG sites, offering an ideal solution for genome-wide methylation profiling. However, like for other high-throughput technologies, the main bottleneck remains at the stage of data analysis rather than data production.</p> <p>Findings</p> <p>We have developed <it>HumMeth27QCReport</it>, an R package devoted to researchers wanting to quickly analyse their Illumina Infinium methylation arrays. This package automates quality control steps by generating a report including sample-independent and sample-dependent quality plots, and performs primary analysis of raw methylation calls by computing data normalization, statistics, and sample similarities. This package is available at CRAN repository, and can be integrated in any Galaxy instance through the implementation of ad-hoc scripts accessible at Galaxy Tool Shed.</p> <p>Conclusions</p> <p>Our package provides users of the Illumina Infinium Methylation assays with a simplified, automated, open-source quality control and primary analysis of their methylation data. Moreover, to enhance its use by experimental researchers, the tool is being distributed along with the scripts necessary for its implementation in the Galaxy workbench. Finally, although it was originally developed for HumanMethylation27, we proved its compatibility with data generated with the HumanMethylation450 Bead Chip.</p

PRGdb: a bioinformatics platform for plant resistance gene analysis

PRGdb is a web accessible open-source (http://www.prgdb.org) database that represents the first bioinformatic resource providing a comprehensive overview of resistance genes (R-genes) in plants. PRGdb holds more than 16 000 known and putative R-genes belonging to 192 plant species challenged by 115 different pathogens and linked with useful biological information. The complete database includes a set of 73 manually curated reference R-genes, 6308 putative R-genes collected from NCBI and 10463 computationally predicted putative R-genes. Thanks to a user-friendly interface, data can be examined using different query tools. A home-made prediction pipeline called Disease Resistance Analysis and Gene Orthology (DRAGO), based on reference R-gene sequence data, was developed to search for plant resistance genes in public datasets such as Unigene and Genbank. New putative R-gene classes containing unknown domain combinations were discovered and characterized. The development of the PRG platform represents an important starting point to conduct various experimental tasks. The inferred cross-link between genomic and phenotypic information allows access to a large body of information to find answers to several biological questions. The database structure also permits easy integration with other data types and opens up prospects for future implementations

CGG Repeat-Induced FMR1 Silencing Depends on the Expansion Size in Human iPSCs and Neurons Carrying Unmethylated Full Mutations

In fragile X syndrome (FXS), CGG repeat expansion greater than 200 triplets is believed to trigger FMR1 gene silencing and disease etiology. However, FXS siblings have been identified with more than 200 CGGs, termed unmethylated full mutation (UFM) carriers, without gene silencing and disease symptoms. Here, we show that hypomethylation of the FMR1 promoter is maintained in induced pluripotent stem cells (iPSCs) derived from two UFM individuals. However, a subset of iPSC clones with large CGG expansions carries silenced FMR1. Furthermore, we demonstrate de novo silencing upon expansion of the CGG repeat size. FMR1 does not undergo silencing during neuronal differentiation of UFM iPSCs, and expression of large unmethylated CGG repeats has phenotypic consequences resulting in&nbsp;neurodegenerative features. Our data suggest that UFM individuals do not lack the cell-intrinsic ability to silence FMR1 and that inter-individual variability in the CGG repeat size required for silencing exists in the FXS population