Download Recent progress in understanding transcription factor binding

B RIEFINGS IN FUNC TIONAL GENOMICS . VOL 14. NO 1. 1^2
doi:10.1093/bfgp/elu050
Editorial
Recent progress in understanding
transcription factor binding specificity
Gene expression levels can vary greatly from gene to
gene and between individuals. To understand how
these differences arise, and be able to predict and
manipulate them, we need to dissect the molecular
mechanisms by which the regulatory programs
embedded in the genome are interpreted by the cellular machinery. This issue of Briefings in Functional
Genomics provides an overview of the available
approaches for quantifying the nucleotide binding
specificity of trans-acting factors, a prerequisite for
understanding and predicting gene regulatory network function.
Transcription factors typically belong to a structural family containing many other proteins with a
similar amino acid sequence. Even when the difference in nucleotide sequence preference between
such proteins is subtle, their target genes, and thus
the processes that they control, can be quite distinct.
In a pioneering microfluidics study, Maerkl and
Quake (1) showed that it is possible to predict
such functional differences using a quantitative,
purely sequence-based, thermodynamic model.
Comprehensive and accurate quantification of the
DNA binding specificity of all transcription factors
encoded in the genome may therefore transform our
ability to make functional predictions about the
regulatory network of the cell.
A first review by the Noyes laboratory (2) describes the bacterial one-hybrid (B1H) approach, in
which protein–DNA interaction strength is measured via expression of a reporter gene. This technology has been used to perform in-depth analyses of
the variation in DNA binding specificity within the
homeodomain and zinc finger families. A unique
advantage of the B1H approach is the ease with
which a large number of different protein sequences
can be assayed in parallel.
Methodologies that profile the DNA binding
specificity of a transcription factor in vitro have also
been put to excellent use in recent years. Siggers
and colleagues (3) review the widely used protein
binding microarray (PBM) technology. They highlight recent PBM studies of protein variants and
multi-protein complexes.
Affinity-based selection of random pools of DNA
molecules followed by massively parallel sequencing
(HT-SELEX or SELEX-seq) has become a powerful
alternative to microarray-based approaches. A paper
from the Stormo lab (4) describes a new variant of
this technology and puts it in context.
Purely computational strategies have also been
explored. Havranek, Bradley and colleagues
(5) review how supercomputers can be used to simulate how amino-acid side chains interact with DNA
nucleotides, and thereby predict binding free energies. They focus on the use of accurate potentials,
backbone flexibility and the role of water molecules
at the protein–DNA interface.
Genomic targeting by transcription factors is notoriously context dependent. Even the best models
of in vitro protein–DNA interaction have limited
power to predict in vivo occupancy. Competitive
interactions with nucleosomes are an important
aspect of this context dependence. Morozov and
coworkers (6) summarize our current understanding
of how nucleosomes alter transcription factor binding in vivo.
Epigenetic modifications of DNA are often associated with gene expression changes. However, the
mechanisms underlying these associations mostly
remain obscure. Rohs, Bussemaker and coworkers
(7) review our current understanding of how cytosine methylation affects protein–DNA interaction.
They propose that subtle methylation-induced
ß The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]
2
Editorial
changes in the geometry of DNA can modulate the
interaction with proteins via the minor groove in a
highly context-specific manner.
Control of local chromatin state and transcription rates by DNA binding factors is only one
side of the coin of gene expression regulation.
Equally important are the sequence-specific RNA
binding factors that interpret the signals embedded
in RNA transcripts, which control splicing, transport
and turnover. The complexity of this posttranscriptional regulatory system rivals that of the transcriptional machinery. Morris, Hughes and colleagues
(8) review the use of high-throughput technologies
for quantifying the sequence specificity of RNA binding proteins, paying particular attention to the role of
secondary structure.
In conclusion, thanks to the efforts of various
groups, binding specificity information is now available for a significant fraction of DNA and RNA
binding factors in human and other eukaryotes.
Nevertheless, we have only scratched the surface of
what we need to know to predict expression from
sequence. Much more experimental and computational work will be required to extend coverage to
all transcription factors and increase the accuracy and
precision of the sequence-to-affinity models that can
currently be derived from the data to a level where
functional differences between closely related family
members can be resolved. It has also become clear
that transcription factors can greatly influence each
other’s interaction with DNA when they bind as a
complex. Extending these insights beyond the small
number of specific cases where such complex
interactions have been studied in detail will require
many more years of experimental and computational
effort, but we are off to a good start.
Harmen J. Bussemaker
Department of Biological Sciences, Columbia University, 1212
Amsterdam Ave, MC 2441, NewYork, NY 10027, USA
[email protected]
References
1.
2.
3.
4.
5.
6.
7.
8.
Maerkl SJ, Quake SR. A systems approach to measuring the
binding energy landscapes of transcription factors. Science
2007;315(5809):233–7.
Xu DJ, Noyes MB. Understanding DNA-binding specificity by bacteria hybrid selection. Brief Funct Genomics 2015;
14:3–16.
Andrilenas KK, Penvose A, Siggers T. Using proteinbinding microarrays to study transcription factor specificity:
homologs, isoforms and complexes. Brief Funct Genomics
2015;14:17–29.
Stormo GD, Zuo Z, Chang YK. Spec-seq: determining
protein-DNA-binding specificity by sequencing. Brief
Funct Genomics 2015;14:30–8.
Joyce AP, Zhang C, Bradley P, Havranek JJ. Structurebased modeling of protein: DNA specificity. Brief Funct
Genomics 2015;14:39–49.
Chereji RV, Morozov AV. Functional roles of nucleosome stability and dynamics. Brief Funct Genomics 2015;14:
50–60.
Dantas Machado AC, Zhou T, Rao S, et al. Evolving
insights
on how cytosine
methylation affects
protein-DNA binding. Brief Funct Genomics 2015;14:61–73.
Cook KB, Hughes TR, Morris QD. High-throughput
characterization of protein-RNA interactions. Brief Funct
Genomics 2015;14:74–89.