Dr Grzegorz Kudla: Chromosomes and Gene Expression
Post-transcriptional regulation
of gene expression
Summary
Sequence elements in coding regions of genes play an important, but often underestimated, role in the regulation of gene expression. We are particularly interested in synonymous mutations, which do not change protein sequence, but can influence mRNA and protein levels. Our goal is to understand the role of such mutations in eukaryotic cells. To achieve this, we construct combinatorial libraries of mutated genes, and measure gene expression and function using high-throughput assays. We also develop new methods to study the roles of protein-RNA and RNA-RNA interactions in gene regulation.
Figure 1: Escherichia coli cells expressing 24 synonymous variants of the GFP gene
Post-transcriptional regulation is mediated by cis-acting sequence elements located in RNA transcripts, and by trans-acting molecules that interact with those transcripts. In collaboration with other groups, we use deep sequencing-based methods to investigate the interactions of regulatory proteins with RNA in yeast and human cells. We also developed a new method for high-throughput mapping of RNA-RNA interactions, called CLASH. Our method is conceptually similar to the Chromosome Conformation Capture technique that has been widely used for the analysis of DNA structure. In collaboration with the Tollervey lab, we currently apply CLASH to the analysis of microRNA targets in human cells.
Key Publications
- Kudla G, Granneman S, Hahn D, Beggs JD, Tollervey D: Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc Natl Acad Sci U S A 108:10010-10015, 2011.
PubMed Abstract
- Plotkin JB, Kudla G: Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12:32-42, 2011.
PubMed Abstract
- Wlotzka W, Kudla G, Granneman S, Tollervey D: The nuclear RNA polymerase II surveillance system targets polymerase III transcripts. EMBO J 30:1790-1803, 2011.
PubMed Abstract
- Kudla G, Murray AW, Tollervey D, Plotkin JB: Coding-sequence determinants of gene expression in Escherichia coli. Science 324:255-258, 2009.
PubMed Abstract
- Granneman S, Kudla G, Petfalski E, Tollervey D: Identification of protein binding sites on U3 snoRNA and pre-rRNA by UV cross-linking and high-throughput analysis of cDNAs. Proc Natl Acad Sci U S A 106:9613-9618, 2009.
PubMed Abstract
- Kudla G, Lipinski L, Caffin F, Helwak A, Zylicz M: High guanine and cytosine content increases mRNA levels in mammalian cells. PLoS Biol 4:e180, 2006.
PubMed Abstract
Collaborations
Joshua Plotkin (University of Pennsylvania)
David Tollervey (University of Edinburgh)
Claudia Schneider (Newcastle University)
Torben Jensen (Aarhus University)
Purpose
In order to survive and grow, cells need to decode the information written in the genome. The "genetic code", which specifies how the information in the DNA is used to make different proteins, was solved in the 1960’s.
In contrast, the "regulatory code", which determines the amount of each protein produced, is still poorly understood.
If we can understand the regulatory code, we will be able to predict the effects of different DNA mutations on protein amounts in the cells. This information could be used, for example, to understand the mechanisms of various diseases, or to artificially manipulate the amounts of proteins produced by cells.
Approach, Progress and Future Work
Functional consequences of synonymous mutations
A major goal of research in biology is to understand how DNA sequence mediates the regulation of gene expression. We, and other groups, have shown that synonymous substitutions in the coding sequence of genes influence gene expression, fitness and evolution, and in humans, they are known to cause a number of diseases.
We use experimental and computational approaches to systematically investigate the effects of synonymous mutations in eukaryotic cells. The methods we use include yeast and mammalian cell culture, gene synthesis, high-throughput fluorescence assays, deep sequencing, and bioinformatic data analysis.
