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New genes drive the evolution of gene interaction networks in the human and mouse genomes.

Zhang W, Landback P, Gschwend AR, Shen B, Long M - Genome Biol. (2015)

Bottom Line: These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions.We identify a few human lineage-specific hub genes that have evolved brain development-related functions.Our data cast new conceptual insights into the evolution of genetic networks.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, Soochow University, Suzhou, Jiangsu, 215006, China. wyzhang@uchicago.edu.

ABSTRACT

Background: The origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. The mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. Herein, we present a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes.

Results: We examine the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We find a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identify a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explore the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.

Conclusions: Our results unveil a remarkable network topological integration process of new genes: over 5000 new genes were integrated into the ancestral GGI networks of human and mouse; new genes gradually acquire increasing number of gene partners; some human-specific genes evolved into hub structure with critical phenotypic effects. Our data cast new conceptual insights into the evolution of genetic networks.

No MeSH data available.


Related in: MedlinePlus

Expression breadths of genes in regards to their PPI network connectivity and divergence times. a Average number of tissues with expression of genes with various PPI network connectivity based on RNA-seq expression level data. b Average number of tissues with expression of genes with various PPI network connectivity based on protein expression level data. The error bars show the standard error of the mean for each group of genes, and the solid line indicates the linear regression correlation between network connectivity of genes and their expression breadths. c Average number of tissues with expression of genes from different phylogenetic branches based on RNA-seq expression level data. d Average number of tissues with expression of genes from different phylogenetic branches based on protein expression level data. The dash line indicates the polynomial regression correlation between divergence times of genes and their expression breadths. Branch assignment is labeled near each data point. The age assignment for each branch follows Fig. 1
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Fig5: Expression breadths of genes in regards to their PPI network connectivity and divergence times. a Average number of tissues with expression of genes with various PPI network connectivity based on RNA-seq expression level data. b Average number of tissues with expression of genes with various PPI network connectivity based on protein expression level data. The error bars show the standard error of the mean for each group of genes, and the solid line indicates the linear regression correlation between network connectivity of genes and their expression breadths. c Average number of tissues with expression of genes from different phylogenetic branches based on RNA-seq expression level data. d Average number of tissues with expression of genes from different phylogenetic branches based on protein expression level data. The dash line indicates the polynomial regression correlation between divergence times of genes and their expression breadths. Branch assignment is labeled near each data point. The age assignment for each branch follows Fig. 1

Mentions: As most biological characteristics arise from the complex interactions between cell’s numerous components [4], the integration of new genes into the GGI network might indicate the emergence of novel functions for these new genes. Furthermore, the gradual evolution of more interactions in GGI networks might signal the process of new genes acquiring pleiotropic functions. This hypothesis could be indirectly confirmed by the strong correlation of connectivity of genes and their divergence times (Fig. 2a) and a strong linear correlation between the connectivity of genes and their expression breadths at both RNA expression level (Pearson linear correlation test, R2 = 0.9384, Fig. 5a) and protein expression level (Pearson linear correlation test, R2 = 0.9457, Fig. 5b). Thus it could hint that new genes gradually evolve broader expression patterns and therefore acquire pleiotropic functions, as they gradually evolve more linking partners (Fig. 2a), and genes with more linking partners tend to have broader expression patterns (Fig. 5a and b).Fig. 5


New genes drive the evolution of gene interaction networks in the human and mouse genomes.

Zhang W, Landback P, Gschwend AR, Shen B, Long M - Genome Biol. (2015)

Expression breadths of genes in regards to their PPI network connectivity and divergence times. a Average number of tissues with expression of genes with various PPI network connectivity based on RNA-seq expression level data. b Average number of tissues with expression of genes with various PPI network connectivity based on protein expression level data. The error bars show the standard error of the mean for each group of genes, and the solid line indicates the linear regression correlation between network connectivity of genes and their expression breadths. c Average number of tissues with expression of genes from different phylogenetic branches based on RNA-seq expression level data. d Average number of tissues with expression of genes from different phylogenetic branches based on protein expression level data. The dash line indicates the polynomial regression correlation between divergence times of genes and their expression breadths. Branch assignment is labeled near each data point. The age assignment for each branch follows Fig. 1
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4590697&req=5

Fig5: Expression breadths of genes in regards to their PPI network connectivity and divergence times. a Average number of tissues with expression of genes with various PPI network connectivity based on RNA-seq expression level data. b Average number of tissues with expression of genes with various PPI network connectivity based on protein expression level data. The error bars show the standard error of the mean for each group of genes, and the solid line indicates the linear regression correlation between network connectivity of genes and their expression breadths. c Average number of tissues with expression of genes from different phylogenetic branches based on RNA-seq expression level data. d Average number of tissues with expression of genes from different phylogenetic branches based on protein expression level data. The dash line indicates the polynomial regression correlation between divergence times of genes and their expression breadths. Branch assignment is labeled near each data point. The age assignment for each branch follows Fig. 1
Mentions: As most biological characteristics arise from the complex interactions between cell’s numerous components [4], the integration of new genes into the GGI network might indicate the emergence of novel functions for these new genes. Furthermore, the gradual evolution of more interactions in GGI networks might signal the process of new genes acquiring pleiotropic functions. This hypothesis could be indirectly confirmed by the strong correlation of connectivity of genes and their divergence times (Fig. 2a) and a strong linear correlation between the connectivity of genes and their expression breadths at both RNA expression level (Pearson linear correlation test, R2 = 0.9384, Fig. 5a) and protein expression level (Pearson linear correlation test, R2 = 0.9457, Fig. 5b). Thus it could hint that new genes gradually evolve broader expression patterns and therefore acquire pleiotropic functions, as they gradually evolve more linking partners (Fig. 2a), and genes with more linking partners tend to have broader expression patterns (Fig. 5a and b).Fig. 5

Bottom Line: These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions.We identify a few human lineage-specific hub genes that have evolved brain development-related functions.Our data cast new conceptual insights into the evolution of genetic networks.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, Soochow University, Suzhou, Jiangsu, 215006, China. wyzhang@uchicago.edu.

ABSTRACT

Background: The origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. The mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. Herein, we present a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes.

Results: We examine the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We find a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identify a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explore the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.

Conclusions: Our results unveil a remarkable network topological integration process of new genes: over 5000 new genes were integrated into the ancestral GGI networks of human and mouse; new genes gradually acquire increasing number of gene partners; some human-specific genes evolved into hub structure with critical phenotypic effects. Our data cast new conceptual insights into the evolution of genetic networks.

No MeSH data available.


Related in: MedlinePlus