» Articles » PMID: 30455862

A Two-level Model for the Role of Complex and Young Genes in the Formation of Organism Complexity and New Insights into the Relationship Between Evolution and Development

Overview
Journal Evodevo
Publisher Biomed Central
Specialty Biology
Date 2018 Nov 21
PMID 30455862
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Background: How genome complexity affects organismal phenotypic complexity is a fundamental question in evolutionary developmental biology. Previous studies proposed various contributing factors of genome complexity and tried to find the connection between genomic complexity and organism complexity. However, a general model to answer this question is lacking. Here, we introduce a 'two-level' model for the realization of genome complexity at phenotypic level.

Results: Five representative species across Protostomia and Deuterostomia were involved in this study. The intrinsic gene properties contributing to genome complexity were classified into two generalized groups: the complexity and age degree of both protein-coding and noncoding genes. We found that young genes tend to be simpler; however, the mid-age genes, rather than the oldest genes, show the highest proportion of high complexity. Complex genes tend to be utilized preferentially in each stage of embryonic development, with maximum representation during the late stage of organogenesis. This trend is mainly attributed to mid-age complex genes. In contrast, young genes tend to be expressed in specific spatiotemporal states. An obvious correlation between the time point of the change in over- and under-representation and the order of gene age was observed, which supports the funnel-like model of the conservation pattern of development. In addition, we found some probable causes for the seemingly contradictory 'funnel-like' or 'hourglass' model.

Conclusions: These results indicate that complex and young genes contribute to organismal complexity at two different levels: Complex genes contribute to the complexity of individual proteomes in certain states, whereas young genes contribute to the diversity of proteomes in different spatiotemporal states. This conclusion is valid across the five species investigated, indicating it is a conserved model across Protostomia and Deuterostomia. The results in this study also support 'funnel-like model' from a new viewpoint and explain why there are different evo-devo relation models.

Citing Articles

The haplotype-resolved telomere-to-telomere carnation () genome reveals the correlation between genome architecture and gene expression.

Lan L, Leng L, Liu W, Ren Y, Reeve W, Fu X Hortic Res. 2024; 11(1):uhad244.

PMID: 38225981 PMC: 10788775. DOI: 10.1093/hr/uhad244.


Diversity in Expression Biases of Lineage-Specific Genes During Development and Anhydrobiosis Among Tardigrade Species.

Metivier J, Chain F Evol Bioinform Online. 2022; 18:11769343221140277.

PMID: 36578471 PMC: 9791283. DOI: 10.1177/11769343221140277.


Approaches for the Identification of Intrinsically Disordered Protein Domains.

Wang H, Yang Z, Yang D Methods Mol Biol. 2022; 2581:403-412.

PMID: 36413333 DOI: 10.1007/978-1-0716-2784-6_28.


Gene Expression Profile Provides Novel Insights of Fasting-Refeeding Response in Zebrafish Skeletal Muscle.

Sugasawa T, Komine R, Manevich L, Tamai S, Takekoshi K, Kanki Y Nutrients. 2022; 14(11).

PMID: 35684038 PMC: 9182819. DOI: 10.3390/nu14112239.


The Distinct Properties of the Consecutive Disordered Regions Inside or Outside Protein Domains and Their Functional Significance.

Wang H, Zhong H, Gao C, Zang J, Yang D Int J Mol Sci. 2021; 22(19).

PMID: 34639018 PMC: 8508753. DOI: 10.3390/ijms221910677.


References
1.
Anders S, Pyl P, Huber W . HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014; 31(2):166-9. PMC: 4287950. DOI: 10.1093/bioinformatics/btu638. View

2.
Guo H, Ingolia N, Weissman J, Bartel D . Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010; 466(7308):835-40. PMC: 2990499. DOI: 10.1038/nature09267. View

3.
Hamburger V, HAMILTON H . A series of normal stages in the development of the chick embryo. J Morphol. 2014; 88(1):49-92. View

4.
Punta M, Coggill P, Eberhardt R, Mistry J, Tate J, Boursnell C . The Pfam protein families database. Nucleic Acids Res. 2011; 40(Database issue):D290-301. PMC: 3245129. DOI: 10.1093/nar/gkr1065. View

5.
Moyers B, Zhang J . Phylostratigraphic bias creates spurious patterns of genome evolution. Mol Biol Evol. 2014; 32(1):258-67. PMC: 4271527. DOI: 10.1093/molbev/msu286. View