MicroRNAs miR-203-3p, miR-664-3p and miR-708-5p are associated with median strain lifespan in mice

This is the final version of the article. Available from Springer Nature via the DOI in this record.MicroRNAs (miRNAs) are small non-coding RNA species that have been shown to have roles in multiple processes that occur in higher eukaryotes. They act by binding to specific sequences in the 3' untranslated region of their target genes and causing the transcripts to be degraded by the RNA-induced silencing complex (RISC). MicroRNAs have previously been reported to demonstrate altered expression in several aging phenotypes such as cellular senescence and age itself. Here, we have measured the expression levels of 521 small regulatory microRNAs (miRNAs) in spleen tissue from young and old animals of 6 mouse strains with different median strain lifespans by quantitative real-time PCR. Expression levels of 3 microRNAs were robustly associated with strain lifespan, after correction for multiple statistical testing (miR-203-3p [β-coefficient = -0.6447, p = 4.8 × 10(-11)], miR-664-3p [β-coefficient = 0.5552, p = 5.1 × 10(-8)] and miR-708-5p [β-coefficient = 0.4986, p = 1.6 × 10(-6)]). Pathway analysis of binding sites for these three microRNAs revealed enrichment of target genes involved in key aging and longevity pathways including mTOR, FOXO and MAPK, most of which also demonstrated associations with longevity. Our results suggests that miR-203-3p, miR-664-3p and miR-708-5p may be implicated in pathways determining lifespan in mammals.This work was funded by the Wellcome Trust (grant number WT097835MF to D. Melzer and L.W. Harries), and the NIH-NIA (grant number AG038070 to The Jackson Laboratory)

Allegro: Analyzing expression and sequence in concert to discover regulatory programs

A major goal of system biology is the characterization of transcription factors and microRNAs (miRNAs) and the transcriptional programs they regulate. We present Allegro, a method for de-novo discovery of cis-regulatory transcriptional programs through joint analysis of genome-wide expression data and promoter or 3′ UTR sequences. The algorithm uses a novel log-likelihood-based, non-parametric model to describe the expression pattern shared by a group of co-regulated genes. We show that Allegro is more accurate and sensitive than existing techniques, and can simultaneously analyze multiple expression datasets with more than 100 conditions. We apply Allegro on datasets from several species and report on the transcriptional modules it uncovers. Our analysis reveals a novel motif over-represented in the promoters of genes highly expressed in murine oocytes, and several new motifs related to fly development. Finally, using stem-cell expression profiles, we identify three miRNA families with pivotal roles in human embryogenesis

Analysis of a nanoparticle‑enriched fraction of plasma reveals miRNA candidates for down syndrome pathogenesis

Down syndrome (DS) is caused by the presence of part or all of a third copy of chromosome 21. DS is associated with several phenotypes, including intellectual disability, congenital heart disease, childhood leukemia and immune defects. Specific microRNAs (miRNAs/miR) have been described to be associated with DS, although none of them so far have been unequivocally linked to the pathology. The present study focuses to the best of our knowledge for the first time on the miRNAs contained in nanosized RNA carriers circulating in the blood. Fractions enriched in nanosized RNA-carriers were separated from the plasma of young participants with DS and their non-trisomic siblings and miRNAs were extracted. A microarray-based analysis on a small cohort of samples led to the identification of the three most abundant miRNAs, namely miR-16-5p, miR-99b-5p and miR-144-3p. These miRNAs were then profiled for 15 pairs of DS and non‑trisomic sibling couples by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results identified a clear differential expression trend of these miRNAs in DS with respect to their non-trisomic siblings and gene ontology analysis pointed to their potential role in a number of typical DS features, including ‘nervous system development’, ‘neuronal cell body’ and certain forms of ‘leukemia’. Finally, these expression levels were associated with certain typical quantitative and qualitative clinical features of DS. These results contribute to the efforts in defining the DS‑associated pathogenic mechanisms and emphasize the importance of properly stratifying the miRNA fluid vehicles in order to probe biomolecules that are otherwise hidden and/or not accessible to (standard) analysis

Recent Applications of RNA Sequencing in Food and Agriculture

RNA sequencing (RNA-Seq) is the leading, routine, high-throughput, and cost-effective next-generation sequencing (NGS) approach for mapping and quantifying transcriptomes, and determining the transcriptional structure. The transcriptome is a complete collection of transcripts found in a cell or tissue or organism at a given time point or specific developmental or environmental or physiological condition. The emergence and evolution of RNA-Seq chemistries have changed the landscape and the pace of transcriptome research in life sciences over a decade. This chapter introduces RNA-Seq and surveys its recent food and agriculture applications, ranging from differential gene expression, variants calling and detection, allele-specific expression, alternative splicing, alternative polyadenylation site usage, microRNA profiling, circular RNAs, single-cell RNA-Seq, metatranscriptomics, and systems biology. A few popular RNA-Seq databases and analysis tools are also presented for each application. We began to witness the broader impacts of RNA-Seq in addressing complex biological questions in food and agriculture

Integrating omics data from phenotypically-related genodermatoses. A Cytoscape approach using biological networks

The ongoing advance of high-throughput sequencing technologies is bringing to the biomedical research community the opportunity to disclose relatively uncharted and poorly addressed domains in genetic disorders. Specifically, this project aims to shed new light on the molecular mechanisms of three rare skin diseases: Recessive Dystrophic Epidermolysis Bullosa (RDEB), Kindler Syndrome (KS) and Xeroderma pigmentosum type C (XPC). To accomplish this, biological network construction is leveraged herein, by providing a convenient approach to integrate and downstream analyze molecular omics data obtained from the comparison of these three genodermatoses (RDEB, KS & XPC) against healthy control samples. Concretely, microRNAs, RNAs and protein datasets are conjointly combined in the form of graphs whose structure and arrangement can be analyzed. On this basis, and upon computational procedures, the representation of high-throughput omics data across networks serves for both a topological and functional characterization of the molecular entities embedded within the graphs. Cytoscape software harbors the toolkits needed to exploit the massive omics information presented in this work, closely operating with online ontologies containing crucial annotations on the molecular entities under the network conglomerates. Cytoscape platform is going to carry out the bioinformatics computational endeavours, conducting then to new insights where common mechanisms and candidate biomarkers shared by the three genodermatoses will be highlighted. In this manner, STRING, BiNGO and ClueGO (Cytoscape plug-ins) will assist in the finding of enriched functions (such as “cell adhesions” and “epidermal growth factor signaling”), whereas the topological analysis will rely on STRING and NetworkAnalyzer, following the principles of graph theory to identify candidate molecules like TFAP2A and L1CAM. With the aid of manual curations, these two approaches will stand for a narrowing-down strategy from which biological interpretations are obtained.Ingeniería Biomédic

The Role of Tumor Suppressor DEAR1 in the Acquisition of Mammary Stem/Progenitor Cell Properties

Breast cancer is the most commonly diagnosed cancer in women in America. Ductal carcinoma in situ (DCIS), one of the earliest pre-invasive forms of invasive ductal carcinoma (IDC), has a 30-50% risk of progressing to IDC. Understanding the mechanisms regulating progression from DCIS to IDC would help identify biomarkers to stratify patients at higher risk of progression or metastasis. Cumulative literature suggests the earliest phase of dissemination from the primary tumor is driven by the epithelial-mesenchymal transition (EMT) program. DEAR1 is a tumor suppressor gene which is mutated, undergoes loss of heterozygosity in breast cancer, and is downregulated in DCIS lesions and IDC. DEAR1 regulates acinar morphogenesis and cell polarity and is a negative regulator of TGF-β-driven EMT through inhibition of SMAD3. Studies have now demonstrated that induction of EMT promotes acquisition of stem/progenitor cell properties, further adding to the repertoire of cellular regulation by EMT mediators. I demonstrate that loss of DEAR1 in human mammary epithelial cells (HMECs) and DCIS cells results in a mammosphere phenotype independent of the canonical TGF-β pathway, suggesting that DEAR1 regulates stem/progenitor cell properties. DEAR1-knockdown (KD) HMEC mammospheres express high levels of stem/progenitor cell marker aldehyde dehydrogenase (ALDH1) and display a basal-like phenotype through repression of CD24 and EpCAM expression. There is significant upregulation of master EMT and stem cell regulators, including SNAI2, in DEAR1-KD HMECs and I show that DEAR1 binds to and promotes polyubiquitination of SNAI2. I reveal a novel DEAR1-SNAI2 axis that partially regulates stem/progenitor cell properties in HMECs and demonstrate a significant association between loss of DEAR1 in basal-like/triple-negative breast cancers (TNBC), early-age of onset, and risk of shorter time to metastasis in TNBC. Additionally, I identify a possible mechanism governing DEAR1 regulation in mammary epithelial cells through miRNAs miR-10b and miR-196b. Results herein demonstrate that DEAR1 promotes stem/progenitor cell properties partially through TGF-β-mediated EMT and also through SNAI2 independently of TGF-β-SMAD3 signaling. I hope to use this understanding of DEAR1 and its regulation of cell polarity, EMT, and stemness to stratify high risk patients who would benefit from more aggressive or targeted therapy

Invention of 3Mint for feature grouping and scoring in multi-omics

Advanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), microRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at https://github.com/malikyousef/3Mint/

Changes in RNA regulatory processes during mammalian ageing

Ageing is defined as a system-wide, gradual loss in overall organ and tissue function across the lifespan of an organism, and in humans is the single largest risk factor for most chronic diseases. Thanks to ongoing improvements in healthcare, human life expectancy is steadily rising, but the proportion of life spent free of chronic disease (known as healthspan) is not extending concurrently in our increasingly aged population. Socio-economic costs are growing, both in terms of healthcare spending and quality of life. A central goal of ageing research therefore is to find methods of extending healthspan. However, ageing is a complex, heterogeneous process and the underlying mechanisms of ageing and determinants of lifespan/healthspan are still not well understood. RNA regulators of gene expression are important factors in the ageing process, and I hypothesise that they may have potential to affect healthspan, or act as biomarkers of ageing. In this thesis, I have examined some of these RNA regulatory factors and their associations with ageing and lifespan in mammals. In order to do this, I assessed the expression patterns of RNA regulatory factors in two mouse models and a human cohort. In one mouse model, I found that both mRNA splicing regulatory factors and microRNAs are associated with strain-specific longevity during normal ageing, and that it is possible that these regulators play a causal role in determining strain lifespan. In the second mouse model, I showed these splicing factors to be associated with dietary restriction (a known treatment for extension of lifespan) and provided evidence that they could be mechanistically involved in the lifespan response to dietary restriction. I also showed that expression levels of these splicing factors were associated with cognitive decline and reduction in physical ability in humans. These results indicate that correct RNA regulation is a key component of the ageing process and suggests that the factors that govern these processes may represent useful future targets for healthpan intervention in ageing people.Velux Foundatio