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Find video protocols related to scientific articles indexed in Pubmed.
Multiple genomic changes associated with reorganization of gene regulation and adaptation in yeast.
Mol. Biol. Evol.
PUBLISHED: 04-14-2013
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Frequently during evolution, new phenotypes evolved due to novelty in gene regulation, such as that caused by genome rewiring. This has been demonstrated by comparing common regulatory sequences among species and by identifying single regulatory mutations that are associated with new phenotypes. However, while a single mutation changes a single element, gene regulation is accomplished by a regulatory network involving multiple interactive elements. Therefore, to better understand regulatory evolution, we have studied how mutations contributed to the adaptation of cells to a regulatory challenge. We created a synthetic genome rewiring in yeast cells, challenged their gene regulation, and studied their adaptation. HIS3, an essential enzyme for histidine biosynthesis, was placed exclusively under a GAL promoter, which is induced by galactose and strongly repressed in glucose. Such rewired cells were faced with significant regulatory challenges in a repressive glucose medium. We identified several independent mutations in elements of the GAL system associated with the rapid adaptation of cells, such as the repressor GAL80 and the binding sites of the activator GAL4. Consistent with the extraordinarily high rate of cell adaptation, new regulation emerged during adaptation via multiple trajectories, including those involving mutations in elements of the GAL system. The new regulation of HIS3 tuned its expression according to histidine requirements with or without these significant mutations, indicating that additional factors participated in this regulation and that the regulatory network could reorganize in multiple ways to accommodate different mutations. This study, therefore, stresses network plasticity as an important property for regulatory adaptation and evolution.
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Evidence-based annotation of gene function in Shewanella oneidensis MR-1 using genome-wide fitness profiling across 121 conditions.
PLoS Genet.
PUBLISHED: 08-09-2011
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Most genes in bacteria are experimentally uncharacterized and cannot be annotated with a specific function. Given the great diversity of bacteria and the ease of genome sequencing, high-throughput approaches to identify gene function experimentally are needed. Here, we use pools of tagged transposon mutants in the metal-reducing bacterium Shewanella oneidensis MR-1 to probe the mutant fitness of 3,355 genes in 121 diverse conditions including different growth substrates, alternative electron acceptors, stresses, and motility. We find that 2,350 genes have a pattern of fitness that is significantly different from random and 1,230 of these genes (37% of our total assayed genes) have enough signal to show strong biological correlations. We find that genes in all functional categories have phenotypes, including hundreds of hypotheticals, and that potentially redundant genes (over 50% amino acid identity to another gene in the genome) are also likely to have distinct phenotypes. Using fitness patterns, we were able to propose specific molecular functions for 40 genes or operons that lacked specific annotations or had incomplete annotations. In one example, we demonstrate that the previously hypothetical gene SO_3749 encodes a functional acetylornithine deacetylase, thus filling a missing step in S. oneidensis metabolism. Additionally, we demonstrate that the orphan histidine kinase SO_2742 and orphan response regulator SO_2648 form a signal transduction pathway that activates expression of acetyl-CoA synthase and is required for S. oneidensis to grow on acetate as a carbon source. Lastly, we demonstrate that gene expression and mutant fitness are poorly correlated and that mutant fitness generates more confident predictions of gene function than does gene expression. The approach described here can be applied generally to create large-scale gene-phenotype maps for evidence-based annotation of gene function in prokaryotes.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.