Differentially correlated genes in co-expression networks control phenotype transitions.
BACKGROUND
Co-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer).


METHODS
Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for twomus musculusdatasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks.    Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as "bottlenecks" rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Usingin vitroknockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth.


CONCLUSIONS
Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.
Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil.
Lina D
LD
Thomas
College of Pharmacy, Oregon State University, Corvallis, USA.
Dariia
D
Vyshenska
College of Veterinary Medicine, Oregon State University, Corvallis, USA.
Natalia
N
Shulzhenko
Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brazil.
Anatoly
A
Yambartsev
College of Pharmacy, Oregon State University, Corvallis, USA.
Andrey
A
Morgun
biological state transition; co-expression networks; differential co-expression analysis