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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-...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247791/ https://www.ncbi.nlm.nih.gov/pubmed/28163897 http://dx.doi.org/10.12688/f1000research.9708.1 |
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author | Thomas, Lina D. Vyshenska, Dariia Shulzhenko, Natalia Yambartsev, Anatoly Morgun, Andrey |
author_facet | Thomas, Lina D. Vyshenska, Dariia Shulzhenko, Natalia Yambartsev, Anatoly Morgun, Andrey |
author_sort | Thomas, Lina D. |
collection | PubMed |
description | 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 two mus musculus datasets: 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. Using in vitro knockdown 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. Conclusion: Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype. |
format | Online Article Text |
id | pubmed-5247791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-52477912017-02-02 Differentially correlated genes in co-expression networks control phenotype transitions Thomas, Lina D. Vyshenska, Dariia Shulzhenko, Natalia Yambartsev, Anatoly Morgun, Andrey F1000Res Research Article 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 two mus musculus datasets: 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. Using in vitro knockdown 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. Conclusion: Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype. F1000Research 2016-11-22 /pmc/articles/PMC5247791/ /pubmed/28163897 http://dx.doi.org/10.12688/f1000research.9708.1 Text en Copyright: © 2016 Thomas LD et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Thomas, Lina D. Vyshenska, Dariia Shulzhenko, Natalia Yambartsev, Anatoly Morgun, Andrey Differentially correlated genes in co-expression networks control phenotype transitions |
title | Differentially correlated genes in co-expression networks control phenotype transitions |
title_full | Differentially correlated genes in co-expression networks control phenotype transitions |
title_fullStr | Differentially correlated genes in co-expression networks control phenotype transitions |
title_full_unstemmed | Differentially correlated genes in co-expression networks control phenotype transitions |
title_short | Differentially correlated genes in co-expression networks control phenotype transitions |
title_sort | differentially correlated genes in co-expression networks control phenotype transitions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247791/ https://www.ncbi.nlm.nih.gov/pubmed/28163897 http://dx.doi.org/10.12688/f1000research.9708.1 |
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