A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation
Open accessAbstract
Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin 'smoking gun' through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question "which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?" it yields the correct answer, "myostatin". Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven 'weighting' procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.
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Genes with varying co-expression partners under different conditions are more likely to be regulators that underlie phenotypic differences.
“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [10]
The Differential wiring and Wiring the regulatory network sections report that changes in co-expression between two cattle crosses helped identify regulators implicated in their phenotypic differences. But the analysis compared a preselected set of transcriptional regulators with differentially expressed genes; it does not establish that genes with changing co-expression partners generally are more likely to be regulators. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGenes that are differentially co-expressed between different sample groups are more likely to be regulators, and are therefore likely to explain differences between phenotypes.
“Genes that are differentially co-expressed between different sample groups are more likely to be regulators, and are therefore likely to explain differences between phenotypes [4, 8–10].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [10]
The Differential hubbing section reports that genes with the largest changes in significant co-expression connections were enriched for transcriptional regulators. But it cautions that this measure failed to identify myostatin, the regulator containing the known causal mutation. The Wiring the regulatory network section links a more targeted analysis of already identified regulators to phenotypic differences; it does not establish that differentially co-expressed genes generally explain those differences. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDifferential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples or between different tissues, cell types or species.
“Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples [4, 8–10] or between different tissues [11], cell types [5] or species [132, 133].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [10]
The Differential wiring section describes comparing gene co-expression between Piedmontese and Wagyu crosses, and the Animals and samples section identifies both as cattle crosses. This supports the use of differential co-expression to identify a gene underlying a difference, but not the different-species comparison to which this citation is attached. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIf intra-modular hub genes are TFs or targets of a TF, this TF is more likely to have a causal role in the phenotype under investigation.
“If intra-modular hub genes are TFs or targets of a TF, this TF is more likely to have a causal role in the phenotype under investigation [10].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [10]
In “Wiring the regulatory network,” the paper links a module containing transcriptional regulators to phenotypic change. It does not establish that a TF is more likely to be causal when intra-modular hub genes are that TF or its targets. The “Differential hubbing” section instead reports that hubbing analyses failed to identify the regulator with the known causal mutation in this dataset. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDifferentially connected genes appear to play a regulatory part in the difference in the phenotype observed between two groups.
“These genes appear to play a regulatory part in the difference in the phenotype observed between two groups (Figure 3D) [8–10].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [10]
Differential co-expression or connectivity methods are additional means for determining gene connectivity changes between conditions and can identify disruption or gain of function in pathways.
“Differential co-expression or connectivity methods36,37 are additional means for determining gene connectivity changes between conditions and can identify disruption or gain of function in pathways.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [36]
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References 50 · Cited by 221
Research works printed but not linked: 1
- Greenwood PL, Cafe LM, Hearnshaw H, Hennessy DW (2006) Consequences of fetal and pre-weaning growth for the characteristics of longissimus myofibres at 30 months of age in heifers sired by Piedmontese and Wagyu bulls. Australian Society of Animal Production 26th Biennial Conference.
- Authors
- Hudson, N. J., Reverter, A., & Dalrymple, B. P.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-05-01
- Volume
- 5
- Issue
- 5
- Pages
- e1000382
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2671163
- OpenAlex
- W2139329624
- Cited by
- 221
- References
- 50
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 11 · 2023: 7 · 2022: 14 · 2021: 15 · 2020: 15 · 2019: 10 · 2018: 16 · 2017: 18 · 2016: 20 · 2015: 26 · 2014: 15 · 2013: 16 · 2012: 12
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- transcription factor regulation, myostatin, Piedmontese cattle, causal mutation identification, co-expression network, microarray data integration, missense mutations, post-translational regulation, muscle growth, mRNA levels
- MeSH terms
- Algorithms, Animals, Cattle, Computer Simulation, Gene Expression Regulation, Male, Models, Genetic, Phosphorylation, RNA Processing, Post-Transcriptional, Transcription Factors, Transcription, Genetic, Cluster Analysis, Mutation, Missense, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Myostatin
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