Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Guidelines for the functional annotation of microRNAs using the Gene Ontology.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).

Gene expression analysis: teaching students to do 30,000 experiments at once with microarray.

Genome scale experiments routinely produce large data sets that require computational analysis, yet there are few student-based labs that illustrate the design and execution of these experiments. In order for students to understand and participate in the genomic world, teaching labs must be available where students generate and analyze large data sets. We present a microarray-based gene expression analysis experiment that is tailored for undergraduate students. The methods in this article describe an expression analysis experiment that can also be applied to CGH and SNP experiments. Factors such as technical difficulty, duration, cost, and availability of materials and equipments are considered in the lab design. The microarray teaching lab is performed in two sessions. The first is an introductory wet bench exercise that allows students to master the basic technical skills. The second builds on the concepts and skills with students acquiring and analyzing the microarray data. This lab exercise familiarizes students with large-scale data experiments and introduces them to the initial analysis steps.

SNARE complex-mediated degranulation in mast cells.

Mast cell function and dysregulation is important in the development and progression of allergic and autoimmune disease. Identifying novel proteins involved in mast cell function and disease progression is the first step in the design of new therapeutic strategies. Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are a family of proteins demonstrated to mediate the transport and fusion of secretory vesicles to the membrane in mast cells, leading to the subsequent release of the vesicle cargo through an exocytotic mechanism. The functional role[s] of specific SNARE family member complexes in mast cell degranulation has not been fully elucidated. Here, we review recent and historical data on the expression, formation and localization of various SNARE proteins and their complexes in murine and human mast cells. We summarize the functional data identifying the key SNARE family members that appear to participate in mast cell degranulation. Furthermore, we discuss the utilization of RNA interference (RNAi) methods to validate SNARE function and the use of siRNA as a therapeutic approach to the treatment of inflammatory disease. These studies provide an overview of the specific SNARE proteins and complexes that serve as novel targets for the development of new therapies to treat allergic and autoimmune disease.

Biomarker discovery and compound evaluation using two-hybrid proteomic systems.

Many proteomic technologies require a heavy investment in expertise and technology, which place these approaches beyond many labs and small companies. However, proteomic approaches are ideal for pilot experiments, identifying relevant biomarkers and protein pathways for development or analysis of therapeutic compounds. The two-hybrid proteomic systems are available and affordable to most researchers, requiring little more than standard microbiological equipment. The screens rapidly generate data, identifying protein interactions that can be used to construct small local protein networks. Using data from large-scale projects, these small local protein networks can be used to identify the larger cellular pathways that are being affected by therapeutic compounds in the screen. The foundation for the two-hybrid proteomic systems are commercially available, as are high quality cDNA libraries. The straightforwardness of the two-hybrid proteomic system allows smaller groups to focus their resources on critical cellular pathways and molecular targets by taking advantage of a trusted molecular assay and an ever growing set of postgenomic era databases.