Scope and limitations of yeast as a model organism for studying human tissue-specific pathways. 2015

Shahin Mohammadi, and Baharak Saberidokht, and Shankar Subramaniam, and Ananth Grama
Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. mohammadi@purdue.edu.

BACKGROUND Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue. RESULTS We present a novel framework to systematically quantify the suitability of yeast as a model organism for different human tissues. To this end, we develop a computational method for dissecting the global human interactome into tissue-specific cellular networks. By individually aligning these networks with the yeast interactome, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. Finally, we couple our framework with a novel statistical model to assess the conservation of tissue-specific pathways and infer the overall similarity of each tissue with yeast. We further study each of these subspaces in detail, and shed light on their unique biological roles in the human tissues. CONCLUSIONS Our framework provides a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. While tissue-selective genes are significantly associated with the onset and development of a number of tissue-specific pathologies, we show that the human-specific subset has even higher association. Consequently, they provide excellent candidates as drug targets for therapeutic interventions.

UI MeSH Term Description Entries
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012441 Saccharomyces cerevisiae A species of the genus SACCHAROMYCES, family Saccharomycetaceae, order Saccharomycetales, known as "baker's" or "brewer's" yeast. The dried form is used as a dietary supplement. Baker's Yeast,Brewer's Yeast,Candida robusta,S. cerevisiae,Saccharomyces capensis,Saccharomyces italicus,Saccharomyces oviformis,Saccharomyces uvarum var. melibiosus,Yeast, Baker's,Yeast, Brewer's,Baker Yeast,S cerevisiae,Baker's Yeasts,Yeast, Baker
D013045 Species Specificity The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species. Species Specificities,Specificities, Species,Specificity, Species
D049490 Systems Biology Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems. Biology, Systems
D053263 Gene Regulatory Networks Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations. Gene Circuits,Gene Modules,Gene Networks,Transcriptional Networks,Gene Module,Circuit, Gene,Circuits, Gene,Gene Circuit,Gene Network,Gene Regulatory Network,Module, Gene,Modules, Gene,Network, Gene,Network, Gene Regulatory,Network, Transcriptional,Networks, Gene,Networks, Gene Regulatory,Networks, Transcriptional,Regulatory Network, Gene,Regulatory Networks, Gene,Transcriptional Network
D053858 Metabolic Networks and Pathways Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites. Metabolic Networks,Metabolic Pathways,Metabolic Network,Metabolic Pathway,Network, Metabolic,Networks, Metabolic,Pathway, Metabolic,Pathways, Metabolic
D060066 Protein Interaction Maps Graphs representing sets of measurable, non-covalent physical contacts with specific PROTEINS in living organisms or in cells. Protein-Protein Interaction Map,Protein-Protein Interaction Network,Protein Interaction Networks,Interaction Map, Protein,Interaction Map, Protein-Protein,Interaction Network, Protein,Interaction Network, Protein-Protein,Map, Protein Interaction,Map, Protein-Protein Interaction,Network, Protein Interaction,Network, Protein-Protein Interaction,Protein Interaction Map,Protein Interaction Network,Protein Protein Interaction Map,Protein Protein Interaction Network,Protein-Protein Interaction Maps,Protein-Protein Interaction Networks
D019295 Computational Biology A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets. Bioinformatics,Molecular Biology, Computational,Bio-Informatics,Biology, Computational,Computational Molecular Biology,Bio Informatics,Bio-Informatic,Bioinformatic,Biologies, Computational Molecular,Biology, Computational Molecular,Computational Molecular Biologies,Molecular Biologies, Computational

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