Correlation between gene expression profiles and protein-protein interactions within and across genomes
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MOTIVATION: Function annotation of an unclassified protein on the basis of its interaction partners is well documented in the literature. Reliable predictions of interactions from other data sources such as gene expression measurements would provide a useful route to function annotation. We investigate the global relationship of protein-protein interactions with gene expression. This relationship is studied in four evolutionarily diverse species, for which substantial information regarding their interactions and expression is available: human, mouse, yeast and Escherichia coli. RESULTS: In E.coli the expression of interacting pairs is highly correlated in comparison to random pairs, while in the other three species, the correlation of expression of interacting pairs is only slightly stronger than that of random pairs. To strengthen the correlation, we developed a protocol to integrate ortholog information into the interaction and expression datasets. In all four genomes, the likelihood of predicting protein interactions from highly correlated expression data is increased using our protocol. In yeast, for example, the likelihood of predicting a true interaction, when the correlation is > 0.9, increases from 1.4 to 9.4. The improvement demonstrates that protein interactions are reflected in gene expression and the correlation between the two is strengthened by evolution information. The results establish that co-expression of interacting protein pairs is more conserved than that of random ones.
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Literature broadly has reported common co-regulation of genes in protein complexes at both transcription and translational levels.
“This is a unique finding since literature broadly has reported common co-regulation of genes in protein complexes at both transcription and translational levels96–102.”
From EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery (2026) · cites this paper as [100]
The Introduction (page not supplied) describes co-expression of genes encoding interacting proteins and discusses protein complexes, supporting the transcription-level portion of the claim. The paper does not establish co-regulation at the translational level; its Across species/Single species section cautions that gene expression does not always reflect protein abundance. (LLM, conf=0.86)
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- Authors
- Bhardwaj, N., & Lu, H.
- Affiliations
- University of Illinois Chicago (US)
- Corresponding author
- Bhardwaj, N.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-03-29
- Volume
- 21
- Issue
- 11
- Pages
- 2730-2738
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2133747068
- Cited by
- 186
- References
- 35
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 6 · 2022: 4 · 2021: 4 · 2020: 6 · 2019: 7 · 2018: 9 · 2017: 9 · 2016: 10 · 2015: 6 · 2014: 6 · 2013: 19 · 2012: 11
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computer applications in the biosciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- protein-protein interaction, interaction prediction, orthologs, functional annotation, human, mice, yeast, Escherichia coli
- MeSH terms
- Animals, Chromosome Mapping, Gene Expression Regulation, Humans, Phylogeny, Species Specificity, Statistics as Topic, Evolution, Molecular, Oligonucleotide Array Sequence Analysis, Proteome, Gene Expression Profiling, Protein Interaction Mapping, Mice
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