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

Overview of attention for article published in BMC Systems Biology, December 2015
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Title
Scope and limitations of yeast as a model organism for studying human tissue-specific pathways
Published in
BMC Systems Biology, December 2015
DOI 10.1186/s12918-015-0253-0
Pubmed ID
Authors

Shahin Mohammadi, Baharak Saberidokht, Shankar Subramaniam, Ananth Grama

Abstract

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. 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. 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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 286 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Unknown 283 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 64 22%
Student > Master 39 14%
Student > Ph. D. Student 37 13%
Researcher 21 7%
Student > Doctoral Student 11 4%
Other 24 8%
Unknown 90 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 103 36%
Agricultural and Biological Sciences 41 14%
Chemistry 11 4%
Medicine and Dentistry 8 3%
Engineering 8 3%
Other 24 8%
Unknown 91 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 February 2016.
All research outputs
#15,352,477
of 22,836,570 outputs
Outputs from BMC Systems Biology
#644
of 1,142 outputs
Outputs of similar age
#230,425
of 392,772 outputs
Outputs of similar age from BMC Systems Biology
#30
of 47 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 392,772 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.