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

Mohammadi S, Saberidokht B, Subramaniam S, Grama A - BMC Syst Biol (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. mohammadi@purdue.edu.

ABSTRACT

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.

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Distribution of tissue-selectivity p-values in different tissue groups. a Brain tissues, b Blood cells, c Ganglion tissues, d Testis tissues. Each plot resembles the same bi-modal distribution as the gene-tissue membership density, with blood cells and brain tissues presenting the most clear separation of tissue-selective genes. The critical points of each distribution function, where the derivative of pdf function is approximately zero, is marked on each plot. These points provide optimal cutoff points for the tissue-selectivity p-values as they mark the points of shift in the underlying distribution function
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Fig6: Distribution of tissue-selectivity p-values in different tissue groups. a Brain tissues, b Blood cells, c Ganglion tissues, d Testis tissues. Each plot resembles the same bi-modal distribution as the gene-tissue membership density, with blood cells and brain tissues presenting the most clear separation of tissue-selective genes. The critical points of each distribution function, where the derivative of pdf function is approximately zero, is marked on each plot. These points provide optimal cutoff points for the tissue-selectivity p-values as they mark the points of shift in the underlying distribution function

Mentions: We start with all expressed non-housekeeping genes in each tissue group, i.e., genes that are expressed in at least one of the tissue members. Next, in order to identify the subset of expressed genes that are selectively expressed in each group, we use the tissue-selectivity p-value of each gene. In this formulation, a gene is identified as selectively expressed if it is expressed in a significantly higher number of tissues in the given group than randomly selected tissue subsets of the same size (see “Materials and methods” section for details). Figure 6 illustrates the distribution of tissue-selectivity p-values of expressed genes with respect to four major groups in Fig. 4. Each of these plots exhibit a bi-modal characteristic similar to the membership distribution function in Fig. 5. This can be explained by the fact that membership distribution is a mixture distribution, with the underlying components being the same distribution for the subset of genes that are expressed in different tissue groups. We use critical points of the p-value distributions to threshold for tissue-selective genes. The motivation behind our choice is that these points provide shifts in the underlying distribution, from tissue-selective to ubiquitous genes. Given the bi-modal characteristics of these distributions, they all have three critical points, the first of which we use as our cutoff point. This provides highest precision for declared tissue-selective genes, but lower recall than the other two choices.Fig. 6


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

Mohammadi S, Saberidokht B, Subramaniam S, Grama A - BMC Syst Biol (2015)

Distribution of tissue-selectivity p-values in different tissue groups. a Brain tissues, b Blood cells, c Ganglion tissues, d Testis tissues. Each plot resembles the same bi-modal distribution as the gene-tissue membership density, with blood cells and brain tissues presenting the most clear separation of tissue-selective genes. The critical points of each distribution function, where the derivative of pdf function is approximately zero, is marked on each plot. These points provide optimal cutoff points for the tissue-selectivity p-values as they mark the points of shift in the underlying distribution function
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4696342&req=5

Fig6: Distribution of tissue-selectivity p-values in different tissue groups. a Brain tissues, b Blood cells, c Ganglion tissues, d Testis tissues. Each plot resembles the same bi-modal distribution as the gene-tissue membership density, with blood cells and brain tissues presenting the most clear separation of tissue-selective genes. The critical points of each distribution function, where the derivative of pdf function is approximately zero, is marked on each plot. These points provide optimal cutoff points for the tissue-selectivity p-values as they mark the points of shift in the underlying distribution function
Mentions: We start with all expressed non-housekeeping genes in each tissue group, i.e., genes that are expressed in at least one of the tissue members. Next, in order to identify the subset of expressed genes that are selectively expressed in each group, we use the tissue-selectivity p-value of each gene. In this formulation, a gene is identified as selectively expressed if it is expressed in a significantly higher number of tissues in the given group than randomly selected tissue subsets of the same size (see “Materials and methods” section for details). Figure 6 illustrates the distribution of tissue-selectivity p-values of expressed genes with respect to four major groups in Fig. 4. Each of these plots exhibit a bi-modal characteristic similar to the membership distribution function in Fig. 5. This can be explained by the fact that membership distribution is a mixture distribution, with the underlying components being the same distribution for the subset of genes that are expressed in different tissue groups. We use critical points of the p-value distributions to threshold for tissue-selective genes. The motivation behind our choice is that these points provide shifts in the underlying distribution, from tissue-selective to ubiquitous genes. Given the bi-modal characteristics of these distributions, they all have three critical points, the first of which we use as our cutoff point. This provides highest precision for declared tissue-selective genes, but lower recall than the other two choices.Fig. 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, Purdue University, West Lafayette, 47907, USA. mohammadi@purdue.edu.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus