Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Speciation Rates01:07

Speciation Rates

Speciation can proceed at markedly different rates, and evolutionary biologists commonly describe these differences through the models of gradualism and punctuated equilibrium. Both patterns explain how new species arise, but they differ in the tempo and continuity of evolutionary change. In both cases, evolutionary change arises from heritable variation within populations, with natural selection often shaping traits that improve survival and reproduction under specific environmental conditions.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Recombinant attenuated Salmonella vaccine promotes CD8<sup>+</sup> T cell-dependent antitumor immunity via IFN-γ-induced IRF-1-mediated upregulation of IL-7.

Cell & bioscience·2025
Same author

Machine Learning-Based Prediction of Gout Using Polygenic Risk Scores and Clinical Variables: A Korean Cohort Study.

Lifestyle genomics·2025
Same author

A subset of Polycomb-targeted transcription factor genes become hypermethylated yet upregulated in colorectal cancer.

Computational and structural biotechnology journal·2025
Same author

Transcription factors overcome the repressive impact of Polycomb-associated methylation in tumors.

bioRxiv : the preprint server for biology·2025
Same author

Quercetin-3-Methyl Ether Induces Early Apoptosis to Overcome HRV1B Immune Evasion, Suppress Viral Replication, and Mitigate Inflammatory Pathogenesis.

Biomolecules & therapeutics·2025
Same author

Lactoferrin-Derived Peptide Chimera Induces Caspase-Independent Cell Death in Multiple Myeloma.

Cells·2025

Related Experiment Video

Updated: Jun 10, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Expression breadth and expression abundance behave differently in correlations with evolutionary rates.

Seung Gu Park1, Sun Shim Choi

  • 1Department of Medical Biotechnology, College of Biomedical Science, and Institute of Bioscience & Biotechnology, Kangwon National University, Chunchon 200-701, Korea.

BMC Evolutionary Biology
|August 10, 2010
PubMed
Summary

Gene expression breadth, not abundance, is key to protein evolution rates in multicellular organisms. This study clarifies evolutionary principles by analyzing gene expression data and evolutionary rates.

More Related Videos

Combined Nucleotide and Protein Extractions in Caenorhabditis elegans
10:37

Combined Nucleotide and Protein Extractions in Caenorhabditis elegans

Published on: March 17, 2019

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Related Experiment Videos

Last Updated: Jun 10, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Combined Nucleotide and Protein Extractions in Caenorhabditis elegans
10:37

Combined Nucleotide and Protein Extractions in Caenorhabditis elegans

Published on: March 17, 2019

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Area of Science:

  • Molecular Evolution
  • Evolutionary Systems Biology
  • Genomics

Background:

  • Understanding protein evolutionary rates is crucial in molecular evolution.
  • Gene expression abundance is often cited as a key factor, particularly in unicellular organisms.
  • Expression breadth is also important for multicellular organisms.

Purpose of the Study:

  • To investigate the relationship between gene expression variables (abundance and breadth) and protein evolutionary rates.
  • To determine whether expression abundance or breadth is more influential in dictating evolutionary rates in multicellular organisms.

Main Methods:

  • Analysis of two genome-scale expression datasets (microarrays and ESTs).
  • Kendall's rank correlation tests to assess relationships between expression abundance (EA) and expression breadth (EB).
  • Novel random shuffling and Fixed Group Analysis (FGA) to validate findings and control for variables.

Main Results:

  • A significant positive correlation was found between expression abundance and expression breadth.
  • Analyses consistently indicated a stronger link between expression breadth and evolutionary rates compared to expression abundance.
  • Random shuffling and FGA confirmed the robustness of the findings.

Conclusions:

  • Gene expression breadth is a more significant determinant of protein evolutionary rates than expression abundance in multicellular organisms.
  • These findings refine our understanding of the principles governing protein evolution.