Stochastic gene expression: from single molecules to the proteome

Benjamin B Kaufmann^{1, 2} and Alexander van Oudenaarden^1,

¹Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
²Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA

Available online 20 February 2007.

Protein production involves a series of stochastic chemical steps. One consequence of this fact is that the copy number of any given protein varies substantially from cell to cell, even within isogenic populations. Recent experiments have measured this variation for thousands of different proteins, revealing a linear relationship between variance and mean level of expression for much of the proteome. This simple relationship is frequently thought to arise from the random production and degradation of mRNAs, but several lines of evidence suggest that infrequent gene activation events also bear responsibility. In support of the latter hypothesis, single-molecule experiments have demonstrated that mRNA transcripts are often produced in large bursts. Moreover, the temporal pattern of these bursts appears to be correlated for chromosomally proximal genes, suggesting the existence of an upstream player.

The evolution of gene regulation by transcription factors and microRNAs

Summary

#!. Gene regulation in multicellular eukaryotes is complex, with many layers of regulation. Two fundamental mechanisms of gene regulation involve transcription factors and microRNAs, a large class of small, non-coding RNAs.
#2. It is widely believed that phenotypic evolution is closely linked to the evolution of gene regulation. To begin to understand the evolution of gene regulatory networks, it is important to first understand how the individual regulators and their regulatory interactions evolve.
#3. A combination of computational and experimental work has made it possible to begin to compare the evolution of transcriptional regulation with post-transcriptional regulation that is carried out by microRNAs.
For both transcription factors and microRNAs, the regulators themselves seem to be well conserved over large evolutionary distances, whereas their targets seem to have evolved much more quickly, indicating that large-scale rewiring of regulatory networks has taken place in the course of evolution.
#4. In animal evolution, the acquisition of new microRNA families seems to have been much more rapid than the acquisition of new transcription-factor families. Several authors have proposed that new microRNA families have had important roles in the development of novel tissue types and organs.
#5. Ultimately, a comprehensive picture of gene-regulation evolution will require a unification of different regulatory mechanisms. As an initial step in this direction, we suggest a simple model that describes the transcription of new microRNA genes. A corollary of this model is that many microRNAs that are expressed at low levels and in specific spatio-temporal domains might have little biological function in regulating target genes in trans.

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Next big thing

News

Nature 444, 130-131 (9 November 2006) | doi:10.1038/444130a; Published online 8 November 2006

It’s the junk that makes us human!

Erika Check

‘Non-coding’ DNA may organize brain cell connections.

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GGtools

This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor. Illustration with a HapMap+expression dataset is provided.

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Outlier sums for differential gene expression analysis

Tibshirani and Hastie

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).

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Relationships between p63 Binding, DNA Sequence, Transcription Activity, and Biological Function in Human Cells

From Church, GIngeras, and Struhl

Using tiled microarrays covering the entire human genome, we identify 5800 target sites for p63, a p53 homolog essential for stratified epithelial development. p63 targets are enriched for genes involved in cell adhesion, proliferation, death, and signaling pathways. The quality of the derived DNA sequence motif for p63 targets correlates with binding strength binding in vivo, but only a small minority of motifs in the genome is bound by p63. Conversely, many p63 targets have motif scores expected for random genomic regions. Thus, p63 binding in vivo is highly selective and often requires additional factors beyond the simple protein-DNA interaction. There is a significant, but complex, relationship between p63 target sites and p63-responsive genes, with ΔNp63 isoforms being linked to transcriptional activation. Many p63 binding regions are evolutionarily conserved and/or associated with sequence motifs for other transcription factors, suggesting that a substantial portion of p63 sites is biologically relevant.

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Affy vs. NimbleGen vs. PCR from Gerstein group

Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome

Genomic tiling microarrays have become a popular tool for interrogating the transcriptional activity of large regions of the genome in an unbiased fashion. There are several key parameters associated with each tiling experiment (e.g., experimental protocols and genomic tiling density). Here, we assess the role of these parameters as they are manifest in different tiling-array platforms used for transcription mapping. First, we analyze how a number of published tiling-array experiments agree with established gene annotation on human chromosome 22. We observe that the transcription detected from high-density arrays correlates substantially better with annotation than that from other array types. Next, we analyze the transcription-mapping performance of the two main high-density oligonucleotide array platforms in the ENCODE regions of the human genome. We hybridize identical biological samples and develop several ways of scoring the arrays and segmenting the genome into transcribed and nontranscribed regions, with the aim of making the platforms most comparable to each other. Finally, we develop a platform comparison approach based on agreement with known annotation. Overall, we find that the performance improves with more data points per locus, coupled with statistical scoring approaches that properly take advantage of this, where this larger number of data points arises from higher genomic tiling density and the use of replicate arrays and mismatches. While we do find significant differences in the performance of the two high-density platforms, we also find that they complement each other to some extent. Finally, our experiments reveal a significant amount of novel transcription outside of known genes, and an appreciable sample of this was validated by independent experiments.

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Global variation in copy number in the human genome

Nature 444, 444-454 (23 November 2006) | doi:10.1038/nature05329; Received 13 June 2006; Accepted 10 October 2006 

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

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Genomic signatures to guide the use of chemotherapeutics

Nature Medicine – 12, 1294 – 1300 (2006)
Published online: 22 October 2006; | doi:10.1038/nm1491

Anil Potti1, 2, Holly K Dressman1, 3, Andrea Bild1, 3, Richard F Riedel1, 2, Gina Chan4, Robyn Sayer4, Janiel Cragun4, Hope Cottrill4, Michael J Kelley2, Rebecca Petersen5, David Harpole5, Jeffrey Marks5, Andrew Berchuck1, 6, Geoffrey S Ginsburg1, 2, Phillip Febbo1, 2, 3, Johnathan Lancaster4 & Joseph R Nevins1, 2, 3

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

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