HGCA και ACT: Εργαλεία για τη μελέτη της γονιδιακής συνέκφρασης στον άνθρωπο και το πρότυπο φυτό Arabidopsis thaliana

Γονίδια με παρόμοια πρότυπα έκφρασης τείνουν να συμμετέχουν σε σχετικές βιολογικές διεργασίες. Ο πιο αποτελεσματικός τρόπος για την μελέτη της γονιδιακής συνέκφρασης βασίζεται στην ανάλυση μεταγραφωμικών δεδομένων του συνόλου των πιο αντιπροσωπευτικών δειγμάτων από κάθε ιστό ή είδος κυττάρου. Η συνέκφραση γονιδίων που αποκαλύπτεται από μία ποικιλία πειραμάτων μεταγραφωμικής, που υπάρχουν διαθέσιμα σε δημόσια καταθετήρια, μπορεί να περιέχει πληροφορίες που υπερβαίνουν τον αρχικό σκοπό του κάθε πειράματος και αυτό μπορεί να αποτελέσει ένα πολύτιμο εργαλείο πρόβλεψης για τη λειτουργία γονιδίων και τη συμμετοχή τους σε βιολογικά μονοπάτια. Δημιουργήθηκαν 3 εργαλεία συνέκφρασης: το Arabidopsis Coexpression Tool (ACT) που μελετάει τη γονιδιακή συνέκφραση στο πρότυπο φυτό Arabidopsis thaliana και είναι βασισμένο σε 3500 δείγματα μικροσυστοιχιών από 3 διαφορετικές βάσεις δεδομένων, το Human Gene Coexpression Analysis (HGCA) 1.5 που μελετάει τη γονιδιακή συνέκφραση στον άνθρωπο και είναι βασισμένο σε 1959 δείγματα μικροσυστοιχιών από την GEO και το HGCA2, πάλι για τον άνθρωπο, που είναι βασισμένο σε 3500 δείγματα RNA-seq από την GTEx. Η επιλογή των δειγμάτων σε κάθε περίπτωση έγινε με λεπτομερή τρόπο, χρησιμοποιήθηκαν καινοτόμοι αλγόριθμοι επεξεργασίας των δεδομένων και η ομαδοποίηση των γονιδίων έγινε με ιεραρχική ομαδοποίηση. Η ανάπτυξη των ιστοτόπων έγινε χρησιμοποιώντας μοντέρνες τεχνολογίες. Εισάγοντας ένα γονίδιο-οδηγό, τα εργαλεία παρουσιάζουν ένα φυλογενετικό υποδέντρο του οποίου τα φύλλα αποτελούν γονίδια συνεκφρασμένα με το γονίδιο εισόδου. Ο χρήστης μπορεί επιπλέον να πραγματοποιήσει ποικίλες αναλύσεις εμπλουτισμού βιολογικών όρων πάνω στα γονίδια του υποδέντρου. Μελετώντας τη λίστα συνεκφρασμένων γονιδίων και τα αποτελέσματα υπερεκπροσώπησης ανακαλύπτονται λειτουργικοί συνεργάτες ή προσδίδονται ρόλοι σε μη χαρακτηρισμένα γονίδια, κοιτώντας τους γείτονές τους στο υποδέντρο συνέκφρασης.Genes with similar expression patterns tend to participate in related biological processes. The most efficient way to study gene coexpression is based on the transcriptomic data analysis of the subset of samples which contain the best representatives of each tissue or cell type. The coexpression of genes revealed from a variety of transcriptomic experiments stored in public repositories, may contain information far beyond the original scope of each constituent experiment, and can be a valuable predictive tool for gene function and pathway membership. Three tools were developed: Arabidopsis Coexpression Tool (ACT) studies gene coexpression in model plant Arabidopsis thaliana, which is based on 3500 microarray samples from three different public repositories, Human Gene Coexpression Analysis (HGCA) 1.5 studies gene coexpression in human, which is based on 1959 microarray samples from GEO and HGCA2, again for human, based on 3500 RNA-seq samples from GTEx. Meticulous sample selection was performed in each case, novel data processing algorithms were used and genes were grouped using hierarchical clustering. The websites were developed using modern libraries. By typing a driver-gene, the tools output a coexpression subtree whose leaves contain genes coexpressed with the input gene. The user can perform a variety of biological term enrichment analyses upon the list of gene of the coexpression subtree. By studying the list of coexpressed genes and the enrichment analysis results, gene functional partners to the gene of interest can be discovered or a function can be assigned to a gene of unknown role by examining its coexpressed genes in neighbouring leaves

Arabidopsis Coexpression Tool:a tool for gene coexpression analysis in Arabidopsis thaliana

Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint

Approaches in Gene Coexpression Analysis in Eukaryotes

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs

The Stem Cell Expression Profile of Odontogenic Tumors and Cysts: A Systematic Review and Meta-Analysis

Background: Stem cells have been associated with self-renewing and plasticity and have been investigated in various odontogenic lesions in association with their pathogenesis and biological behavior. We aim to provide a systematic review of stem cell markers’ expression in odontogenic tumors and cysts. Methods: The literature was searched through the MEDLINE/PubMed, EMBASE via OVID, Web of Science, and CINHAL via EBSCO databases for original studies evaluating stem cell markers’ expression in different odontogenic tumors/cysts, or an odontogenic disease group and a control group. The studies’ risk of bias (RoB) was assessed via a Joanna Briggs Institute Critical Appraisal Tool. Meta-analysis was conducted for markers evaluated in the same pair of odontogenic tumors/cysts in at least two studies. Results: 29 studies reported the expression of stem cell markers, e.g., SOX2, OCT4, NANOG, CD44, ALDH1, BMI1, and CD105, in various odontogenic lesions, through immunohistochemistry/immunofluorescence, polymerase chain reaction, flow cytometry, microarrays, and RNA-sequencing. Low, moderate, and high RoBs were observed in seven, nine, and thirteen studies, respectively. Meta-analysis revealed a remarkable discriminative ability of SOX2 for ameloblastic carcinomas or odontogenic keratocysts over ameloblastomas. Conclusion: Stem cells might be linked to the pathogenesis and clinical behavior of odontogenic pathologies and represent a potential target for future individualized therapies