gene relevance based on multiple evidences in complex networks

Abstract Motivation Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers). Results We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios. Availability and implementation The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd. Supplementary information Supplementary data are available at Bioinformatics online

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes’ relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy

Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods’ results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations

Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates

Although sequencing of ribosome footprints has uncovered aspects of mRNA translation, the determinants of ribosome flux remain incompletely understood. Combining ribosome footprint data with measurements of protein synthesis rates, we inferred transcriptome-wide rates of translation initiation and elongation in yeast strains with varying translation capacity. We found that the translation elongation rate varies up to ~20-fold among transcripts and is significantly correlated with the rate of translation initiation. Our data indicate that the amino acid composition of the synthesized proteins impacts the rate of translation elongation to the same extent as measures of codon and transfer RNA (tRNA) adaptation. Elongation is slow on transcripts encoding ribosomal proteins, which have a lower protein output compared with other transcripts with similar ribosome densities.Although protein synthesis dynamics has been studied both with theoretical models and by profiling ribosome footprints, the determinants of ribosome flux along open reading frames (ORFs) are not fully understood. Combining measurements of protein synthesis rate with ribosome footprinting data, we here inferred translation initiation and elongation rates for over a 1,000 ORFs in exponentially growing wild-type yeast cells. We found that the amino acid composition of synthesized proteins is as important a determinant of translation elongation rate as parameters related to codon and transfer RNA (tRNA) adaptation. We did not find evidence of ribosome collisions curbing the protein output of yeast transcripts, either in high translation conditions associated with exponential growth, or in strains in which deletion of individual ribosomal protein (RP) genes leads to globally increased or decreased translation. Slow translation elongation is characteristic of RP-encoding transcripts, which have markedly lower protein output compared with other transcripts with equally high ribosome densities

TRAWLING: a Transcriptome Reference Aware of spLIciNG events

Alternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events. In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user. Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes. Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis

Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host–Gut Microbiota Cross-Talk

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by “omics” technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism

Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host–Gut Microbiota Cross-Talk

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by “omics” technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism

The role of elasticity on adhesion and clustering of neurons on soft surfaces

Abstract The question of whether material stiffness enhances cell adhesion and clustering is still open to debate. Results from the literature are seemingly contradictory, with some reports illustrating that adhesion increases with surface stiffness and others suggesting that the performance of a system of cells is curbed by high values of elasticity. To address the role of elasticity as a regulator in neuronal cell adhesion and clustering, we investigated the topological characteristics of networks of neurons on polydimethylsiloxane (PDMS) surfaces - with values of elasticity (E) varying in the 0.55–2.65 MPa range. Results illustrate that, as elasticity increases, the number of neurons adhering on the surface decreases. Notably, the small-world coefficient – a topological measure of networks – also decreases. Numerical simulations and functional multi-calcium imaging experiments further indicated that the activity of neuronal cells on soft surfaces improves for decreasing E. Experimental findings are supported by a mathematical model, that explains adhesion and clustering of cells on soft materials as a function of few parameters - including the Young’s modulus and roughness of the material. Overall, results indicate that – in the considered elasticity interval – increasing the compliance of a material improves adhesion, improves clustering, and enhances communication of neurons