|
| |
|
|
|
|
||||
| Home Help Feedback Subscriptions Archive Search Table of Contents | ||||
First published online April 20, 2007
Journal of Experimental Biology 210, 1526-1547 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.005017
Review Article |
A new paradigm for developmental biology
ARC Centre for Functional and Applied Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia QLD 4072, Australia
e-mail: j.mattick{at}imb.uq.edu.au
Accepted 19 February 2007
 |
Summary |
|---|
 TOP Summary IntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
Key words: non-coding RNA, intron, regulation
|
Introduction |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
), as well as a brain that evolves in situ in
response to experience (Edelman,
1993). This is an extraordinary feat of genetic programming, which
in all likelihood, requires enormous amounts of information. This information
directs not just a human developmental program, or that of another species,
but the idiosyncrasies of the particular program that was inherited by the
individual from their parents and their ancestors, as exemplified by the shape
of our nose, mouth and ears and other identifying familial features.
How is this feat achieved, and where is this information embedded? In the
only well-studied case, the nematode worm Caenorhabditis elegans, it
is known that developmental ontogeny is precise and invariant, with each cell
in the adult being the result of a spatially and temporally ordered
progression of cell division, selected apoptosis (programmed cell death) and,
ultimately, differentiation into nerve, muscle, gut, germ and other
specialized cells (Ambros,
2001; Sternberg and Felix,
1997). Similar processes are observed in the development of
insects and mammals (Baehrecke,
2002; McCarthy,
2003), for example in the apoptosis that sculpts the eye ommatidia
in the former (Clark et al.,
2002) and separates the digits of the fore- and hindlimbs in the
latter (Zuzarte-Luis and Hurle,
2005). Thus, it is likely that the ontogeny of higher animals,
while vastly more complex and likely to be subject to individual (genomic)
variation, is also precisely programmed
(Clarke and Tickle, 1999).
Indeed, the almost exact identity of monozygotic twins in their physical
characteristics and idiosyncrasies, as well as a high degree of concordance in
their psychological characteristics (independent of environment), is clear
testimony to the precision and reproducibility of the genetic instructions
they share.
The genetic programming of development is usually considered to be directed
by proteins involved in morphogenetic signalling and various aspects of gene
regulation. These include homeodomain-containing proteins, chromatin-modifying
proteins, and transcription factors acting on cis-regulatory
elements, informed by those involved in cell surface receptor and signal
transduction systems. Together they form elaborate modular regulatory networks
(Arnone and Davidson, 1997;
Bantignies and Cavalli, 2006;
Levine and Davidson, 2005;
Levine and Tjian, 2003)
– notwithstanding the recent discovery of microRNAs (see below) that are
regarded as an interesting extension of the current paradigm
(Davidson, 2006) rather than
the vanguard of another entire layer of regulation. This protein-centric
perspective underpins most conceptions of the control of development, as
exemplified by elegant studies on sea urchin embryogenesis and fruitfly
development (Ben-Tabou de-Leon and
Davidson, 2006; Davidson,
2006; Levine and Davidson,
2005; Stathopoulos and
Levine, 2005). On the other hand, many proteins are shared in
common throughout the metazoa (Duboule and
Wilkins, 1998). Moreover, the genomes of C. elegans
(Stein et al., 2003), which
only has 1000 cells, and sea urchins
(Sodergren et al., 2006) have
essentially the same number of annotated protein-coding genes as those of
vertebrates, including humans (Aparicio et
al., 2002; International
Human Genome Sequencing Consortium, 2004a;
International Human Genome Sequencing
Consortium, 2004b; Goodstadt
and Ponting, 2006; Taft et
al., 2007).
All of these observations suggest that significant amounts of relevant
information must lie beyond protein-coding sequences, presumably in expanded
regulatory regions that control the expression of these proteins
(Kleinjan and van Heyningen,
2005; Taft et al.,
2007). It also seems likely, although firm conclusions are limited
by the poor cDNA library coverage in many species, that the proteome is
expanded in more developmentally complex species by the increased use of
alternative splicing (Graveley,
2001; Smith and Valcarcel,
2000; Stamm et al.,
2005). This in turn, however, mandates an increase in regulation,
assuming that cell- or tissue-specific alternative splicing is not random.
Thus evolutionary innovation and phenotypic divergence is achieved not only by
variations in the structure and function of proteins, but also and probably
more so, by those in the regulatory circuitry that controls their deployment
(Davidson, 2006;
Duboule and Wilkins, 1998;
Jacob, 1977;
Zuckerkandl and Cavalli,
2007).
|
Analogue components and digital information transfer in complex systems |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
In addition to sophisticated operational controls, complex entities
(whether aircraft or organisms) require extensive and detailed design plans
for their construction, information about which has also to be stored in the
system, along with the specifications of the components themselves. Random
changes to assembly plans may have more subtle effects than those that alter
component structure (particularly those that compromise component function),
creating design variations that often have less severe consequences, although
there will be exceptions in both directions. In biology these changes will
therefore often result in minor defects, quantitative trait variation or
alterations in disease susceptibility. Altered regulatory information has been
shown to underlie such variation in a number of cases where it has been
possible to map the causative nucleotide changes to completion in
well-structured pedigrees (Clark et al.,
2006; Clop et al.,
2006; Ishii et al.,
2006; Smit et al.,
2003; Van Laere et al.,
2003).
While it has long been recognized that genetic information is encoded
digitally in DNA, it has also been widely assumed that the cellular outputs of
this information, expressed via the intermediate of messenger RNA
(mRNA), are almost exclusively analogue components. That is, it has been
assumed that most genes are synonymous with proteins and that most genetic
information is transacted by proteins. This is essentially true for the
prokaryotes, whose genomes comprise densely packed protein-coding sequences,
although these genomes clearly also encode a limited number of small
regulatory RNAs that function in part by sequence-specific interactions with
other RNAs and DNA (Gottesman,
2005; Mattick and Makunin,
2006; Vogel and Sharma,
2005; Winkler,
2005). The situation is similar in unicellular eukaryotes such as
the yeasts Saccharomyces cerevisiae
(David et al., 2006;
Olivas et al., 1997) and
Schizosaccharomyces pombe
(Watanabe et al., 2002).
Interestingly, although of similar complexity, the former has more
protein-coding sequences than the latter, whereas the latter has many more
introns (Goffeau et al.,
1996; Wood et al.,
2002) and a more elaborate RNA signalling infrastructure, which
includes the basic components of the RNA interference (RNAi) pathway
(Martienssen et al., 2005).
This suggests that there may be some trade-off between protein- and RNA-based
forms of gene regulation in simple eukaryotes. In any case, at first
approximation it is reasonable to say that micro-organisms, particularly the
prokaryotes, are in fact largely analogue devices (the `bicycles' of biology)
and that proteins not only comprise the primary structural and catalytic
components of these cells but are also the main agents by which they are
regulated.
For the past 50 years it has been assumed that the same applies in more
complex organisms, i.e. that regulation, particularly developmental
regulation, is also largely analogue (protein-based) in multicellular
organisms (Davidson, 2006),
despite the fact that genome sequence analysis has shown that the numbers of
protein-coding genes do not scale strongly or consistently with morphological
complexity (Taft et al.,
2007) (Fig. 1).
This apparently quite reasonable assumption (at least initially) led logically
to two subsidiary assumptions: (i) that the increased regulatory
sophistication of more complex organisms is achieved through combinatoric
interactions of regulatory proteins intersecting with more complex regulatory
sequences in promoters and untranslated regions of mRNAs (etc.)
(Buchler et al., 2003;
Levine and Tjian, 2003); and
(ii) that the vast amounts of non-protein-coding sequences in more complex
organisms are, apart from a limited amount of cis-acting regulatory
sequences, evolutionary debris. The latter view has been reinforced by the
fact that many of these non-coding sequences are derived from transposons (DNA
sequences that can move within the genome to new positions), themselves widely
assumed to be non-functional, selfish DNA
(Doolittle and Sapienza, 1980;
Orgel and Crick, 1980) and to
be evolving `neutrally' (Waterston et
al., 2002). These assumptions have remained largely unquestioned
for many years and have become articles of faith, but they are not necessarily
correct.
|
|
Non-linear scaling of regulatory information in integrated systems |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
; Mattick and Gagen,
2005). This limit can only be relaxed and raised by changing the
physical basis and efficiency of the control architecture. In other domains,
this limit has been raised by superimposition of digital communication and
control systems, using symbolic or sequence-specific strings to store and
transmit information within the system. This allows both higher information
density and improved transmission accuracy, the latter to overcome the problem
of amplified noise (unintended crosstalk) inherent in analogue computation,
thereby achieving higher operational sophistication and complexity (see e.g.
Collen, 1994). Good examples
are the transition from analogue to digital computing
(Weinstein and Keim, 1965)
and the evolution of aircraft from purely mechanical devices to modern
passenger or military jets, wherein a large proportion of the information and
cost is entailed in the computing and software systems, including hundreds of
kilometers of optical fiber (Csete and
Doyle, 2002). Imagine what a bicycle engineer, or even an
aeronautical engineer, might have made of the latter when unexpectedly
confronted with it, a situation akin to the discovery of introns in the late
1970s (see below).
It should be noted that the power and precision of digital communication
and control systems has only been broadly established in the human
intellectual and technological experience during the past 20–30 years,
well after the central tenets of molecular biology were developed and after
introns had been discovered. The latter was undoubtedly the biggest surprise
(Williamson, 1977), and its
misinterpretation possibly the biggest mistake, in the history of molecular
biology. Although introns are transcribed, since they did not encode proteins
and it was inconceivable that so much non-coding RNA could be functional,
especially in an unexpected way, it was immediately and almost universally
assumed that introns are non-functional and that the intronic RNA is degraded
(rather than further processed) after splicing. The presence of introns in
eukaryotic genomes was then rationalized as the residue of the early assembly
of genes that had not yet been removed and that had utility in the evolution
of proteins by facilitating domain shuffling and alternative splicing
(Crick, 1979;
Gilbert, 1978;
Padgett et al., 1986).
Interestingly, while it has been widely appreciated for many years that DNA
itself is a digital storage medium, it was not generally considered that some
of its outputs may themselves be digital signals, communicated via
RNA1.
Analysis of prokaryotic genomes has shown that, as predicted
(Croft et al., 2003), the
numbers of genes encoding regulatory proteins scale almost quadratically with
gene number or genome size (Croft et al.,
2003; Gagen and Mattick,
2005; Mattick,
2004; Mattick and Gagen,
2005; van Nimwegen,
2003). In addition, extrapolation of these relationships show that
the point where the number of new regulatory genes is predicted to exceed the
number of new (non-regulatory) functional genes is close to the observed upper
size limit of bacterial genomes (Gagen
and Mattick, 2004; Gagen and
Mattick, 2005). This implies (albeit does not prove) that bacteria
have reached a complexity ceiling imposed by the accelerating cost of
protein-based regulation, possibly early in evolution. It also implies (i)
that the more complex eukaryotes must have solved the problem some other way,
most likely by the co-option of RNA as a sequence-specific regulatory molecule
[microRNAs (miRNAs) being a good example] and, more subtly, (ii) that the
combinatorics of regulatory factors per se cannot be used to enlarge
the regulatory space to get past this ceiling, as there is no a
priori reason to expect that prokaryotes could not have easily evolved
more complex promoters and recruited additional transcription factors, etc.
This in turn suggests that the complex gene regulatory regimes in the higher
organisms may operate through multiple layers of regulation and regulatory
decisions, rather than multiple (combinatoric) inputs at any given point.
In any case, and consistent with the non-linear scaling of regulatory
information, there is a strong relationship between the extent of
non-protein-coding DNA sequences in the genomes of higher organisms and their
relative complexity. Indeed this appears to be the only consistent
relationship between genome information content and complexity
(Taft et al., 2007)
(Fig. 1). These
non-protein-coding sequences occupy almost 99% of the human genome
(Frith et al., 2005), and it
has been inconceivable to many that they might all be functional as
cis-acting regulatory elements (although these have clearly expanded
in complex organisms). Again this view is implicitly predicated on the
assumption that most genetic information is transacted by proteins.
|
The major output of metazoan genomes is non-coding RNA |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
40% of the human genome) or as intergenic or
antisense transcripts (Frith et al.,
2005; Mattick and Makunin,
2006). Indeed it appears that the vast majority of all genomes,
from yeast to insects and mammals (wherein most studies have been done), are
transcribed, much on both strands
(Carninci et al., 2005;
Cheng et al., 2005;
David et al., 2006;
Manak et al., 2006). Both
cDNA (Carninci et al., 2005;
Katayama et al., 2005;
Okazaki et al., 2002) and
genome tiling array studies (Cheng et al.,
2005; Kampa et al.,
2004; Kapranov et al.,
2002; Kapranov et al.,
2005) of the transcriptome have revealed an extraordinarily
complex landscape of interleaved and overlapping transcripts, with distal
exons, elaborate splicing patterns and alternative polyadenylation sites, many
of which appear to have no protein-coding capacity
(Mattick and Makunin, 2006).
The most recent data show that at least 85% of the Drosophila genome
(Manak et al., 2006), 70% of
the mouse genome (Carninci et al.,
2005) and 93% of the ENCODE regions of the human genome (The
ENCODE Project Consortium, manuscript submitted for publication) have
experimentally documented transcripts. Moreover, there also appears to be a
large and mostly distinct population of non-polyadenylated transcripts located
in the nucleus and the cytoplasm, which (despite indications from some very
early studies) it was not appreciated existed, because of the widespread use
of oligo dT to purify mRNA and to construct cDNA libraries
(Cheng et al., 2005).
There are literally tens of thousands of long non-coding RNAs (ncRNAs) that
have been identified in mammals (Carninci
et al., 2005; Kampa et al.,
2004; Okazaki et al.,
2002), including many antisense transcripts
(Alfano et al., 2005;
Cocquet et al., 2005;
Katayama et al., 2005;
Korneev and O'Shea, 2005;
Pandorf et al., 2006;
Reis et al., 2004;
Tufarelli et al., 2003;
Werner, 2005;
Werner and Berdal, 2005) and
large numbers of smaller RNAs such as miRNAs
(Berezikov et al., 2006a;
Berezikov et al., 2006b) and
piRNAs (Aravin et al., 2006;
Girard et al., 2006;
Lau et al., 2006). Many of
these ncRNAs are expressed in a cell- or tissue-specific manner, suggesting
that they are developmentally regulated. Characterized long ncRNAs include
H19 (Barsyte-Lovejoy et al.,
2006; Brannan et al.,
1990; Wrana,
1994), 7H4 (Velleca
et al., 1994), bic
(Tam et al., 1997),
NTT (Liu et al.,
1997), BORG (Takeda
et al., 1998), Xist
(Brockdorff, 1998),
Tsix (Lee et al.,
1999), DD3
(Bussemakers et al., 1999),
Msx1 (Blin-Wakkach et al.,
2001), Air (Sleutels
et al., 2002), MALAT-1
(Ji et al., 2003),
adapt33 (Wang et al.,
2003), SCA8
(Mutsuddi et al., 2004),
MIAT (Ishii et al.,
2006), CTN (Prasanth
et al., 2005), NFAT
(Willingham et al., 2005),
PRINS (Sonkoly et al.,
2005), TUG1 (Young
et al., 2005), PINC
(Ginger et al., 2006),
SAF (Yan et al.,
2005), Evf-2 (Feng
et al., 2006), HSR1
(Shamovsky et al., 2006) and
HAR1 (Pollard et al.,
2006), most of which have been associated with specific cellular
or developmental functions and/or disease. However, most of the ncRNAs
discovered in genome-wide transcriptomic analyses or expressed from particular
genomic regions have not been studied in any detail, although high-throughput
cell-based and other screening strategies are beginning to be deployed to
ascertain their function (Mattick,
2005; Reis et al.,
2004; Willingham et al.,
2005). Moreover, the documented numbers of these RNAs are
conservative estimates: more are being regularly discovered as genomic
analyses of one sort or another delve deeper into the transcriptome. Recent
evidence suggests that deep sequencing has not remotely exhausted the
repertoire of either long ncRNAs (Carninci
et al., 2005) or short ncRNAs
(Berezikov et al., 2006a;
Berezikov et al., 2006b;
Cummins et al., 2006;
Ruby et al., 2006) and that
there may be hundreds of thousands of small RNAs expressed in humans (T. R.
Gingeras, personal communication; L. Croft, R. J. Taft and J.S.M., unpublished
data).
These observations confront and very largely contradict the traditional
protein-centric view of genetic information and genome organization
(Mattick and Makunin, 2006).
Either the bulk of the transcriptional output from the human genome and those
of other complex organisms is random `noise' (or, in the case of introns, the
residue of evolutionary baggage retained and accumulated within genes, as
widely assumed) or this transcription comprises a massive but hitherto hidden
layer of expression of systemic genetic information that is transacted by RNA
(Mattick, 1994;
Mattick, 2001;
Mattick, 2003;
Mattick, 2004). The former
has been described as a rather nihilistic view
(Werner, 2005), but is one
that is comfortable for the prevailing orthodoxy. On the other hand, the
latter is strongly supported by the observations that: (i) all well-studied
loci in insects and mammals express a large number of non-protein-coding
transcripts (e.g. Ashe et al.,
1997; Bae et al.,
2002; Holmes et al.,
2003; Jones and Flavell,
2005; Lemons and McGinnis,
2006; Lipshitz et al.,
1987; Sanchez-Herrero and
Akam, 1989; Sessa et al.,
2007); (ii) many of the experimentally detected ncRNAs are
differentially expressed (Carninci et al.,
2005; Cheng et al.,
2005; Ravasi et al.,
2006), apparently under the control of common transcription
factors (Barsyte-Lovejoy et al.,
2006; Cawley et al.,
2004); (iii) at least some have specific subcellular locations
(Ginger et al., 2006;
Prasanth et al., 2005); and
(iv) at least some have been shown to be functional
(Brannan et al., 1990;
Brockdorff, 1998;
Feng et al., 2006;
Ginger et al., 2006;
Prasanth et al., 2005;
Velleca et al., 1994;
Willingham et al., 2005;
Wrana, 1994;
Young et al., 2005).
Microarray analyses have shown that large numbers of ncRNAs are dynamically
regulated during the differentiation of embryonal stem cells, myoblasts,
neuronal cells and the gonadal ridge, as well as during T-cell and macrophage
activation (M. E. Dinger, K. C. Pang, I. Qureshi, M. Crowe, A. C. Perkins, S.
M. Grimmond, D. A. Hume, P. A. Koopman, G. E. O Muscat, S. Bruce, M. F. Mehler
and J.S.M., manuscript in preparation) and in cancer
(Lu et al., 2005;
Reis et al., 2004). In
addition, in situ hybridization analyses are revealing large numbers
of ncRNAs that are expressed in particular regions of the brain and in
particular subcellular locations (T. R. Mercer, M. E. Dinger, S. Sunkin, M. F.
Mehler and J.S.M., in preparation). Many of these ncRNAs are antisense or
intronic to genes encoding proteins important in neural development, function
and disease. It is also now evident that many of the complex genetic phenomena
in complex organisms, including transcriptional and post-transcriptional gene
silencing (Cogoni and Macino,
2000; Matzke et al.,
2001; Zamore and Haley,
2005), imprinting (Kelley and
Kuroda, 2000; Morison et al.,
2005; Nikaido et al.,
2003) and probably also transvection
(Mattick and Gagen, 2001) and
transinduction (Ashe et al.,
1997), are linked to RNA signalling
(Mattick, 2003;
Mattick and Gagen, 2001).
|
Digital–analogue conversion of RNA signals |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
). A good case in point is that of miRNAs, some of which are
widely distributed among species and highly conserved while others are
species-specific (Berezikov et al.,
2006a; Berezikov et al.,
2006b), with two documented cases of mutations in miRNA target
sites underpinning disease (Abelson et al.,
2005) or quantitative trait variation
(Clop et al., 2006). RNAs also
intrinsically possess much more precise specificity of interactions with other
RNAs and DNA than is usually possible by and between proteins, thus
potentially improving the precision of the control system and minimizing noise
from crosstalk, especially in complex regulatory networks. (The problem of
noise was a primary limitation of analogue computers and a primary driving
force in the transition to digital computing.) Thus it appears that evolution
may have discovered the power of digital communication and control systems a
billion years before we did (see below).
However, the sequence-specific interaction of a regulatory RNA with its
target is relatively sterile unless this interaction can be converted into a
meaningful analogue action. At its simplest level, this may comprise antisense
binding to block another interaction, and this primitive mechanism seems to be
a common feature of regulatory RNAs in prokaryotes. However, a more
sophisticated strategy is to embed secondary signals either in the RNA itself
or in the structure of the resulting RNA:RNA or RNA:DNA complex, to recruit
different types of complexes, which then undertake the type of analogue action
required upon receipt of the signal. Good examples are (i) the complexes of
RNA-modifying enzymes that act at a site adjacent to and determined by the
position of the sense:antisense interaction between small nucleolar RNAs
(snoRNAs) and their targets (Bachellerie
et al., 2002; Meier,
2005), and (ii) the RNA-induced silencing (RISC) complexes that
act on RNAs bound to small interfering RNAs (siRNAs) and miRNAs
(Tang, 2005). Thus, there are
two components to RNA signals: a sequence-specific interaction with the
intended target(s) and a secondary or tertiary structural component that acts
as a transducer to recruit generic infrastructural proteins to impart
different types of actions. Indeed, this two-stage principle also applies to
other classes of functional RNAs including snRNAs and tRNAs, which recognize
splice junctions in pre-mRNAs or codons in mRNAs and recruit the spliceosome
or ribosome, respectively. That is, RNAs function as adaptors, with a target
sequence-specific address code and separate structural motifs that specify the
type of consequent function and bind the appropriate proteins.
Such considerations suggest that a receptive infrastructure for RNA
signalling must have co-evolved with the RNA signals themselves and become
progressively more sophisticated as RNA regulatory and transport networks
gained currency during the evolution of the eukaryotes. Examples include the
proteins of the argonaute family and others associated with RNA interference
(Carmell et al., 2002), and
those containing RRM domains, KH domains, SR domains, SET domains,
pumilio-homology domains and double-stranded RNA-binding domains, which occur
in a wide range of developmental regulators with global functions
(Anantharaman et al., 2002;
Bernstein and Allis, 2005;
Saunders and Barber, 2003;
Wang et al., 2002). Indeed
many of the so-called nucleic acid binding proteins and chromatin-binding
proteins whose target specificity is uncertain or unknown may in fact
recognize different types of RNA signals. This possibility is supported by
evidence suggesting that regulatory proteins containing C2H2 zinc fingers
(Shi and Berg, 1995), Y-boxes
(Ladomery, 1997),
chromodomains (Akhtar et al.,
2000; Bernstein and Allis,
2005), tudor domains
(Maurer-Stroh et al., 2003)
and SET domains (Krajewski et al.,
2005), and others such as DNA methyl transferases
(Jeffery and Nakielny, 2004),
may recognize such RNA signals in one form or another.
|
The origin and evolution of RNA-based regulatory networks in complex organisms |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
;
Mattick and Gagen, 2001),
together with other factors such as the level of atmospheric oxygen
(Canfield et al., 2007). It
follows that the RNA-based regulatory systems underpinning the ability to
control more complex developmental trajectories must have been largely in
place prior to the metazoan radiation and have been a critical factor enabling
this evolutionary event (Mattick,
1994; Mattick,
2001; Mattick,
2004; Mattick and Gagen,
2001). Following the emergence of all modern animal phyla at that
time, often referred to as the Cambrian explosion
(Fig. 2), these new dynasties
of multicellular organisms settled down to `battle it' out in evolutionary
competition. This was achieved, firstly, by refining and introducing new
adaptations to body plans to improve their competitiveness for survival and
reproduction, and to enable the colonization of new ecological niches and new
domains such as the land and the air. The latter presented new physical and
physiological challenges, which required significant innovations in proteins
as well as in the regulatory architecture controlling developmental ontogeny
(Bejerano et al., 2004;
Kleinjan and van Heyningen,
2005; Mattick and Gagen,
2001). Recent data indicate that many regulatory RNAs, such as
miRNAs, emerged in the ancestors of the Bilateria
(Hertel et al., 2006;
Prochnik et al., 2007) and in
major transitions of metazoan evolution, including the advent of the
vertebrates and eutherian mammals (Hertel
et al., 2006). Secondly, there would have been considerable
evolutionary advantage, and therefore pressure, to enhance sensory and
cognitive capacities to recognize and respond to opportunities and threats and
to alter the environment in favour of better survival and reproduction. This
led to the evolution of learning and memory, an even greater mechanistic
challenge that almost certainly involved RNA editing as a means of dynamically
intersecting the environment with otherwise hardwired genetic information,
ultimately leading to the emergence of higher-order cognition
(Mehler and Mattick,
2007).
|
), its evolution as a regulatory molecule with associated
infrastructure and networks probably had its genesis in the invasion of
eukaryotic protein-coding genes by mobile self-splicing group II introns
(Cavalier-Smith, 1991;
Cousineau et al., 2000;
Lambowitz and Zimmerly, 2004;
Mattick, 1994;
Palmer and Logsdon, Jr,
1991). These sequences occur in prokaryotes
(Ferat and Michel, 1993;
Martinez-Abarca and Toro,
2000) but are restricted to non-protein-coding sequences by the
intimate coupling between transcription and translation
(Cavalier-Smith, 1991;
Mattick, 1994), thereby
restricting the target area for evolutionary experimentation. While RNA
regulation occurs in prokaryotes, it is not well developed, just as there is
little need for digital control systems in a bicycle. The need to find
solutions to the accelerating problem of increasing regulatory sophistication
required to underpin multicellular development – ultimately through the
co-option of RNA as a compact signalling molecule and later connecting these
signals to different types of actions through the co-evolution of different
types of RNA binding and effector proteins – might have been felt by
both prokaryotes and eukaryotes, but the latter may have had more opportunity
to do so, especially given the compartmentalization of their cells. This
latter feature probably arose due to the lifestyle of early eukaryotes as
phagocytic cellular predators, such as amoebae or macrophages
(Cavalier-Smith, 1991).
Importantly, the separation of transcription from translation by the
introduction of a nuclear membrane allowed introns to invade protein-coding
sequences, as their negative effects could be minimized as long as they were
(self) spliced out before export to the cytoplasm. In so doing, it also
created the raw material for a new round of molecular evolution of RNA signals
produced in parallel with protein-coding sequences
(Mattick, 1994)
(Fig. 2).
The subsequent evolution of the spliceosome occurred by the devolution of
the originally cis-acting catalytic sequences within introns to
trans-acting generic co-factors (spliceosomal RNAs) and the
recruitment of ancillary proteins. This reduced the internal sequence
constraints on the introns, allowing them more freedom to evolve and flexibly
explore new functional space (as RNA molecules). It also made their excision
from primary transcripts more efficient, perversely providing them with even
greater facility to expand and invade other genes
(Mattick, 1994). As these RNA
networks began to be established, proteins capable of recognizing subsets of
signals in these networks would have been selected for, increasing the
sophistication of the system. Moreover, it would be expected that increasing
numbers of genes would have evolved solely to express RNA as higher-order
regulators in this increasingly complex system. This will have occurred at
least in part by gene duplication followed by loss of protein-coding capacity,
as appears to have happened in Xist (the ncRNA controlling X
chromosome inactivation in female mammals)
(Duret et al., 2006) and in
many of the non-protein-coding genes that encode snoRNAs or miRNAs in their
introns (Cavaille et al.,
2001; Mattick and Makunin,
2005; Rodriguez et al.,
2004; Tycowski et al.,
1996; Ying and Lin,
2005). Interestingly, many ncRNAs are alternatively spliced
(Cocquet et al., 2005;
Pang et al., 2005),
suggesting that there is an operational distinction between RNA sourced from
exons and introns. The other major source of functional RNAs has almost
certainly been various other types of mobile (transposable) elements, many of
which are derived from small RNAs and have been a potent force in genome
evolution and genetic innovation (Brosius,
1999; Brosius,
2005; Waterston et al.,
2002).
|
The extent of the genome under evolutionary selection |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
46% in humans) is composed of transposon-derived sequences
(Lander et al., 2001;
Waterston et al., 2002),
often pejoratively referred to as repeats, and assumed to be non-functional
and therefore evolving `neutrally'
(Waterston et al., 2002). The
same assumption has often been made about introns, although it is now evident
that there are significant amounts of conserved sequences within them
(Dermitzakis et al., 2003;
Hare and Palumbi, 2003;
Sironi et al., 2005),
presumably reflecting either functional RNA products or important
cis-acting regulatory sequences. In any case, on the assumption that
ancient repeats (ARs) can be used as a yardstick of the background neutral
evolutionary rate, it has been estimated that 5% of the human genome is
under purifying selection in mammals
(Waterston et al., 2002), and
therefore functional, with the remainder largely considered to comprise
genetically inert, neutrally evolving evolutionary debris.
This is in direct contradiction to the suggestion that much of the
genome-wide transcription, which is developmentally regulated, is functional.
However, it is questionable whether the ARs that are used as yardsticks for
these estimations are really evolving neutrally. First, if ARs have no
functional relevance to the organism, they would be expected to evolve freely
and eventually to either acquire function or be deleted (M. Pheasant and
J.S.M., manuscript submitted for publication), as appears to have occurred
with a large fraction of ARs (Waterston
et al., 2002). That is, the more ancient the extant sequence, the
more likely it is to have acquired function. Second, in agreement with this
logic, there are increasing numbers of transposon-derived sequences of all
classes, both ancient and modern, including lineage-specific repeats such as
Alu elements that have been shown to have undergone functional
exaptation as gene promoters, regulatory elements, exons and microRNA
precursors (Bejerano et al.,
2006; Britten,
2006; Brosius,
1999; Dagan et al.,
2004; Ferrigno et al.,
2001; Hasler and Strub,
2006; Krull et al.,
2005; Landry et al.,
2001; Lev-Maor et al.,
2003; Lippman et al.,
2004; Matlik et al.,
2006; Nigumann et al.,
2002; Smalheiser and Torvik,
2005; Smalheiser and Torvik,
2006; Volff,
2006; Zhou et al.,
2002).
These observations throw increasing doubt on the widespread assumption that
such sequences are mostly parasitic, and remain as inert genomic passengers.
Transposable elements have also been found to underlie the birth of new genes
and regulatory networks (Brandt et al.,
2005; Cordaux et al.,
2006; Landry et al.,
2001; Zhou et al.,
2002) and to influence early development
(Peaston et al., 2004) and
phenotypic variation (Whitelaw and
Martin, 2001). It is also possible to identify AR sequences that
are clearly conserved, some of which are very ancient
(Nishihara et al., 2006),
such as recently discovered classes of ARs in humans sharing common ancestors
with those in marsupials (Kamal et al.,
2006) and fish (Ogiwara et
al., 2002; Xie et al.,
2006), including an example of the slowest evolving regions of the
human genome (Bejerano et al.,
2006). Moreover, some major classes of ARs show variable rates of
sequence conservation within them. One example is the class of so-called
`mammalian interspersed repeats' (MIRs), of which there are 300 000
copies in the human genome (Smit and
Riggs, 1995). These MIRs date back 130 million years and are
tRNA-derived SINEs (short interspersed elements) with a consensus length of
260 nt including a 70 nt central region and 15–25 nt more highly
conserved core (Silva et al.,
2003; Smit and Riggs,
1995). The fact that hundreds of thousands of such elements have
an internal sequence that is conserved more highly than the rest of the
element is prima facie evidence that this class of ARs (or at least
the conserved core within them) is not neutrally evolving and is likely under
selection, presumably for function and possibly as regulatory RNAs.
It is also clear that there are widely different rates of evolution of
different types of genomic sequences, particularly of gene regulatory
sequences, some of which are extraordinarily highly conserved blocks
(Bejerano et al., 2004), while
many others cover extended genomic regions and exhibit rapid turnover
(Fisher et al., 2006;
Frith et al., 2006;
Smith et al., 2004;
Taylor et al., 2006). The
latter includes the remarkable functional conservation of regulatory sequences
controlling ret gene expression in zebrafish and humans, although
there is little recognizable primary sequence conservation
(Fisher et al., 2006). The
cis-regulatory elements of the HoxA cluster have also been shown to
undergo accelerated evolution, presumably under positive selection during the
origin of amniotes and mammals (Wagner et
al., 2004). Moreover, it is evident that phenotypic
diversification may be due as much, if not more, to changes in regulatory
architecture than to the protein components
(Duboule and Wilkins, 1998;
Levine and Tjian, 2003;
Mattick and Gagen, 2001).
Indeed, regulatory sequences often exhibit considerable evolutionary
plasticity (depending on the number of their interacting targets; see below)
and relatively low conservation (Pang et
al., 2006) compared with proteins whose evolutionary flexibility
is limited by both analogue structure–function relationships and
multitasking, i.e. the differential use of the same components in multiple
contexts (Duboule and Wilkins,
1998; Mattick and Gagen,
2001).
There are also other regions of the genome under evolutionary constraints
that are not evident at the primary sequence level, including shuffled
cis-regulatory elements (Sanges
et al., 2006), gene deserts
(Ovcharenko et al., 2005),
transposon-free regions (Simons et al.,
2006), chromatin domains
(Bernstein et al., 2005;
Bernstein, B. E. et al.,
2006), regions under indel-purifying selection
(Lunter et al., 2006), the
distances between ultra-conserved elements
(Sun et al., 2006) and
regions predicted to contain common RNA secondary or tertiary structures
(Lescoute et al., 2005;
Washietl et al., 2005). Thus,
the proportion of functionally meaningful DNA in the human genome is
substantially greater than estimated from sequence conservation alone
(Smith et al., 2004).
Different rates of evolution also occur within and between different
classes of functional gene products, both RNAs and proteins. While most
protein-coding sequences are highly constrained and hence highly conserved,
some are much more flexible and others have diverged under positive selection
(Bustamante et al., 2005). The
estimated 5% of the human genome that is conserved with mouse does not include
35% of annotated protein-coding sequences and 17% of RefSeq annotated genes
(M. Pheasant and J.S.M., manuscript submitted for publication). Many miRNAs
are highly conserved (Pang et al.,
2006) but many are not, being lineage- or even species-specific
(Berezikov et al., 2006a;
Berezikov et al., 2006b).
There are also thousands of recently discovered small RNAs (piRNAs) expressed
in testis that are not conserved between rodents and humans, although similar
RNAs are produced from syntenically orthologous loci
(Aravin et al., 2006;
Girard et al., 2006;
Lau et al., 2006). SnoRNAs
have very divergent sequences and many are identifiable only by the loose
consensus and positioning of the C/D (RUGAUGA/CUGA)
(Shanab and Maxwell, 1992) or
H(ANANNA)/ACA boxes (Meier,
2005). It is also clear that many longer functional
non-protein-coding RNAs (ncRNAs), such as the Xist and Tsix
transcripts involved in X-chromosome dosage compensation, are evolving quickly
(Chureau et al., 2002;
Migeon et al., 2001;
Nesterova et al., 2001;
Pang et al., 2006). In other
cases, there is evidence of recent positive selection in ncRNAs, such as the
HAR1 transcript expressed in particular regions of the brain
(Pollard et al., 2006). While
functionally validated RNAs do not presently add up to a large fraction of the
genome, they do illustrate that lack of conservation does not necessarily
equate to lack of function (Pang et al.,
2006; Smith et al.,
2004). They also point to the likelihood that many functional
transcripts, particularly regulatory ncRNAs, are not highly conserved over
significant evolutionary distances.
Most of the mammalian genome appears to be evolving more quickly than
protein-coding sequences, and at a (regionally adjusted) rate similar to
ancient transposon-derived sequences. However, this is evidence simply that
the majority of the genome is under similar average selection pressures (M.
Pheasant and J. S. Mattick, manuscript submitted for publication), rather than
being non-functional and evolving neutrally, although the latter is the
favored explanation (Waterston et al.,
2002) being consistent with the orthodox view. Moreover, it has
been known for some time that the nucleotide substitution frequency varies
across the genome. This has often been interpreted as the result of regional
variation in the background mutation or fixation (related to recombination)
frequencies, rather than selection, as it was (again) inconceivable that the
vast intronic and intergenic sequences could be under selection, since that in
turn would impute function. Variation in substitution frequencies beyond that
which might be expected from random events is also observed at close range
within genomic regions, and the data are more consistent with the genome
comprising different types of genetic information that are evolving at
different rates under different selection pressures and different
structure–function constraints (M. Pheasant and J.S.M., manuscript
submitted for publication).
|
Functional constraints on the evolution of regulatory RNAs |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
)
– over 500 million years of evolution from worms to mammals. The exact
sequence of these small RNAs does not seem to matter that much: it is easy to
design them artificially against almost any sequence, and such siRNAs are now
commonly employed as experimental tools
(Chalk et al., 2005;
Truss et al., 2005). So why
have some been so frozen in evolution? The answer appears to be that those
miRNAs that were first cloned are common central regulators that have multiple
targets (John et al., 2004;
Lewis et al., 2005;
Lim et al., 2005), which
makes co-variation almost impossible in evolutionary terms. If the odds of a
miRNA and a target co-varying by compensatory mutations in the same generation
are 10–5, the odds of co-variation of an miRNA with 20
targets are 10–100. Most miRNAs that have been subsequently
identified through bioinformatics means have also invoked evolutionary
conservation as a filter (Berezikov et
al., 2005; Jones-Rhoades and
Bartel, 2004), thereby likely also restricting their discovery to
those that have multiple targets.
Clearly, the level of selection pressure on such sequences will be a
function of the number of interactions that must be maintained, rather than
the precise sequence itself. Those with one or few interacting partners will
be able to evolve relatively freely and also explore new connections in
regulatory networks, which themselves can evolve to explore new developmental
space, which (given a relatively stable proteome) may be the major route to
higher complexity and phenotypic variation. Thus, logic would suggest that
there may be many miRNAs that are not highly conserved over significant
evolutionary distances, for which there is some supporting evidence
(Berezikov et al., 2006a;
Berezikov et al., 2006b;
Lindow and Krogh, 2005).
There is also good reason to expect that some, and perhaps many, miRNAs will
have very restricted expression, as exemplified by the miRNA lsy-6,
which controls left/right neuronal asymmetry in C. elegans and is
expressed in only a few neurons (Johnston
and Hobert, 2003). Indeed, recent deep sequencing shows that the
rate of new miRNA discovery continues unabated, albeit with a logarithmic drop
as deeper sequencing finds those that are not so highly expressed or are only
expressed in a limited subset of cells. Many of these rarer miRNAs are less
conserved, being order- or species-specific
(Berezikov et al., 2006a;
Berezikov et al., 2006b;
Cummins et al., 2006).
Moreover, if conservation is dropped as a requirement for the bioinformatics
prediction, there are well over 1 million plausible miRNA precursor
(stem–loop) structures in the mammalian genome, with a large fraction
showing evidence of producing small RNA products in array-based assays (L.
Croft, R. Taft and J.S.M., unpublished observations).
Other newly discovered classes of putative small regulatory RNAs, such as
the 26–31 nt piRNAs (Aravin et al.,
2006; Girard et al.,
2006; Lau et al.,
2006) and 21 nt 21U-RNAs
(Ruby et al., 2006), show
little long range evolutionary conservation. Many longer ncRNAs exhibit
short-range sequence conservation only in small patches
(Pang et al., 2006), as
exemplified by the case of Xist in mammals, even though mutational
studies have suggested that most of the molecule is functional
(Nesterova et al., 2001).
Thus, it seems safe to predict that the sequence of many, if not most,
regulatory RNAs will not be highly conserved over significant evolutionary
distances, even in cases of conserved function, due to more relaxed
structure–function constraints (allowing rapid drift) and to selection
pressures for adaptive radiation by altering the endogenous regulatory
circuitry (network structure) underpinning developmental processes.
|
Endogenous feed-forward control of development by RNA networks |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
). This is not to deny that some RNAs may have interesting
(and as yet unappreciated) catalytic functions
(Salehi-Ashtiani et al.,
2006), or that secondary structural motifs or domains in RNA may
be important mediators of interactions with proteins. Nonetheless, if the
major function of the massive numbers of ncRNAs transcribed from animal, and
particularly mammalian, genomes is regulation, as is likely, the logical
extension is that the main (but not exclusive) role of such regulation is to
control differentiation and development, rather than (simply) the short-term
physiological responses of terminally differentiated cells. In summary, if
functional, these RNAs must be mainly regulatory and, if so, their major
regulatory function must be to direct development. This conclusion is well supported by what we currently know or suspect of RNA regulation at many different levels of gene control (see below), but has one very profound implication: that the enormous amount of information required to program development is endogenously embedded in these RNA networks and that most regulatory transactions during development are directed by RNA, albeit mediated by proteins and supplemented by external cues that are conveyed by proteins (see below).
These RNA (and protein) networks, initially laid down by transcription in
the female (and also possibly the male) gamete, create an epigenetic state
that is asymmetric in the fertilized embryo and that is asymmetrically
inherited by daughter cells. Thus each of the daughter cells has a defined
subsequent state and is on a pre-programmed pathway of division and
differentiation controlled by internal and external cues, the latter of which
probably becomes operative at the time of syncytial formation in insects and
morula formation in mammals. Thus, every cell in the developing organism
contains an epigenetic
memory2 of what its
pathway has been, and where it is headed. In computer science, this is akin to
what is termed a dynamical recurrent neural network
(Aussem et al., 1995;
Sudharsanan and Sundareshan,
1994), in which the current state of the network (in this case the
gene regulatory and expression network) is defined as the combination of part
history and current (external) inputs.
This information about the state of the network (and the embedded
trajectories) is enclosed in the structure of the chromatin (almost certainly
itself controlled by RNA signalling; see below), the protein repertoire (also
directed and regulated by RNA; see below) and, ultimately, the RNA networks
that are current in individual cells. These RNA networks have been described
as the cellular `soft wiring' or `ribotype'
(Herbert and Rich, 1999a;
Herbert and Rich, 1999b).
Thus, RNA transcription and processing may be thought of as a series of steps,
one or more of which have two mutually exclusive outcomes: a default outcome
and an alternative outcome that is controlled by appropriate regulatory
signals. These outcomes can be used either to regulate cellular responses
directly or to control other RNA processing events, the latter forming
networks wherein the processing of one RNA (either to produce more regulatory
RNAs or alternative splice variants of mRNAs) is sequentially contingent on
another (Herbert and Rich,
1999a; Herbert and Rich,
1999b).
While such networks would be clearly subject to natural selection
(Herbert and Rich, 1999a;
Herbert and Rich, 1999b;
Mattick, 1994), I suggest
that they now dominate the genomic programming of complex organisms and are
the primary drivers of development in an unfolding cascade of regulatory
interactions that gives each cell a unique identity and vectorial place in the
developmental trajectory. This therefore constitutes an endogenous
feed-forward regulation of differentiation and development, which is largely
predetermined by embedded unfolding RNA networks. Thus, the current behaviour
and trajectory of each cell are determined by the networks operative in the
preceding cell or state, until the terminal state is reached, at which point
the cell cycle is suspended and differentiation completed. This also suggests
that there are, in fact, 1014 different cells (i.e. cells with
a specific history and identity) in humans, leaving aside those that may have
clonally expanded during (e.g.) fat storage or immune responses.
|
Parallel expression of exonic sequences and efferent RNA signals |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
;
Bridgeman, 1995;
Elman, 1998;
Plunkett et al., 1997) and
would in theory, and possibly practice, permit much more complex communication
and control networks to operate in different cells and states during ontogeny
(Mattick, 1994;
Mattick, 2001;
Mattick and Gagen, 2001).
Almost all snoRNAs and a large proportion of miRNAs in animals are encoded
within introns (Baskerville and Bartel,
2005; Cai et al.,
2004; Mattick and Makunin,
2005; Rodriguez et al.,
2004; Ying and Lin,
2005). Moreover, many snoRNA and miRNA gene loci appear to be
polycistronic (Cavaille et al.,
2002; Huang et al.,
2004; Lau et al.,
2001; Runte et al.,
2001; Seitz et al.,
2004). Although introns are thought to be degraded after excision
from primary transcripts (Padgett et al.,
1986), there is good evidence that intronic RNAs may actually be
processed to smaller RNAs with significantly long half-lives and specific
subcellular locations (Clement et al.,
2001; Clement et al.,
1999). Recently, it was shown that ectopic expression of intronic
sequences derived from the CFTR gene causes specific changes in
transcription of various genes in HeLa cells, with different intron sequences
resulting in a distinctive pattern of effects on specific subsets of genes
(Hill et al., 2006). There is
also evidence that coding and noncoding regions contain sequences that match
others in the genome in functionally congruent networks
(Rigoutsos et al., 2006).
|
|
Layers of RNA-directed control of gene expression in development |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
Chromatin structure and epigenetic memory
The fine control of chromatin structure is one of the major hallmarks of
eukaryotes and of gene regulation in multicellular development
(Margueron et al., 2005).
Chromatin architecture is altered by DNA modification (methylation) and
histone modifications of various types (including compound patterns of
methylation, acetylation and phosphorylation at various residues)
(Lam et al., 2005;
Peterson and Laniel, 2004) in
different ways at many different loci in different cell lineages. This
involves proteins such as the polycomb group and trithorax group, which
mediate repressive and permissive effects, respectively (epigenetic memories),
on gene expression in development
(Bantignies and Cavalli, 2006;
Cernilogar and Orlando, 2005;
Lund and van Lohuizen, 2004).
As there are only a limited number of enzymes (DNA methyltransferases, histone
acetylases and deacetylases, etc.) that perform these modifications, there
must be some other signal that specifically directs these modifications to the
myriad of target loci around the genome. Indeed, in the absence of an army of
DNA sequence-specific binding proteins, the only logical alternative is RNA
signals.
While the details of this putative RNA signalling are unknown, there is a
great deal of evidence to support its existence
(Andersen and Panning, 2003;
Bernstein and Allis, 2005;
Lippman and Martienssen,
2004; Schmitt and Paro,
2006). This includes the observations that (i) DNA
methytransferase and some domains in chromatin remodelling enzymes and binding
effector proteins, such as SET, tudor domains and chromodomains, appear to
interact with RNA (Bernstein and Allis,
2005; Jeffery and Nakielny,
2004; Sanchez-Elsner et al.,
2006), (ii) many regulatory regions affecting chromatin structure
and the expression of adjacent protein-coding genes are themselves transcribed
in spatially and temporally regulated ways
(Bae et al., 2002;
Lipshitz et al., 1987;
Sanchez-Elsner et al., 2006),
and (iii) such non-coding transcripts play important roles in activation of
gene expression by targeting global protein regulators such as HP1, Ash1 and
the chromatin insulator protein CP190 to the cognate sequences in
cis-regulatory response elements, including polycomb- and
trithorax-response elements (PREs and TREs)
(Grimaud et al., 2006;
Lei and Corces, 2006b;
Maison et al., 2002;
Sanchez-Elsner et al., 2006;
Schmitt et al., 2005) (see
also below). It also includes the well-characterized roles of RNAs in DNA
methylation and transcriptional silencing in plants
(Aufsatz et al., 2002;
Mette et al., 2000;
Wassenegger, 2000) and in
animals (Bayne and Allshire,
2005; Imamura et al.,
2004; Jeffery and Nakielny,
2004; Morris et al.,
2004; Ting et al.,
2005; Tufarelli et al.,
2003; Weiss et al.,
1996), imprinting in mammals
(Sleutels et al., 2002),
heterochromatin formation in Drosophila
(Birchler et al., 2004;
Pal-Bhadra et al., 2004),
global activation or repression of sex chromosomes for dosage compensation in
insects and mammals (Andersen and Panning,
2003), RNA interference-mediated heterochromatin assembly and
chromosome dynamics in fission yeast
(Martienssen et al., 2005;
Verdel and Moazed, 2005),
meiosis (Cho et al., 2005;
Watanabe et al., 2001), and
programmed DNA elimination in Tetrahymena
(Mochizuki and Gorovsky,
2004). More recently it has been shown that a specialized set of
RNAi components, including members of the argonaute family, are required for
DNA methylation in plants (Qi et al.,
2006) and yeast (Irvine et
al., 2006), as well as transcriptional gene silencing and
associated alterations to chromatin structure involving polycomb recruitment
in Drosophila (Grimaud et al.,
2006) and in human cells (Kim
et al., 2006). This indicates that the RNAi machinery may regulate
higher-order nuclear organization to orchestrate gene expression during
development (Lei and Corces,
2006a). The nuclear organization of chromatin insulators is also
affected by the RNAi machinery (Lei and
Corces, 2006b).
The proteins of the polycomb group (PcG) and trithorax group (TrxG) are
important global regulators of transcriptional silencing and activation and
mediators of epigenetic memory in development, best characterized in homeotic
loci (Boyer et al., 2006;
Guenther et al., 2005;
Negre et al., 2006;
Ringrose and Paro, 2004;
Schwartz et al., 2006;
Schwartz and Pirrotta, 2007;
Squazzo et al., 2006). Both
PcG and TrxG are recruited to genomic elements (termed PREs and TREs,
respectively) that encompass hundreds of base pairs. These elements have a
very weak consensus in Drosophila and none have been identified yet
in mammals (Ringrose and Paro,
2004; Ringrose and Paro,
2007). Although five proteins associated with PcG or TrxG
complexes (GATA, PSQ, Zeste, PHO and PHO-like) have DNA-binding properties,
they bind to rather degenerate sequences and have not been demonstrated to
have a role in target recognition in vivo
(Ringrose and Paro, 2004).
Moreover many PREs/TREs are transcribed as ncRNAs
(Schmitt et al., 2005), and
Hox gene loci exhibit complex patterns of non-coding transcripts on
both strands (Carninci et al.,
2005; Engstrom et al.,
2006). The activation of the HoxA genes is also
accompanied by intergenic antisense ncRNA transcription
(Sessa et al., 2007). These
observations, together with recent data suggesting that such transcripts and
the RNAi pathway play a central role in PcG- and TrxG-mediated epigenetic
regulation (Bernstein, E. et al.,
2006; Grimaud et al.,
2006; Lei and Corces,
2006a; Sanchez-Elsner et al.,
2006; Schmitt et al.,
2005), suggest that the specificity of this process is controlled
by RNA. Thus, the locus- and stage-specific epigenetic modification of
chromatin by proteins with global functions may be viewed as the first
derivative of a genomically encoded developmental program that is elaborated
via unfolding RNA regulatory networks, informed by contextual cues
and modulated by environmental inputs.
Transcription
There is also increasing evidence that transcription itself is influenced,
directly or indirectly, by RNA signalling
(Goodrich and Kugel, 2006;
Kim et al., 2006). Not only
do certain classes of transcription factors either bind RNA or have high
affinity for nucleic acid structures involving RNA
(Ladomery, 1997;
Shi and Berg, 1995), but also
transcription has been shown to be both inhibited by single-stranded RNA
directed at transcription start sites
(Janowski et al., 2005) and
activated by double-stranded RNAs (dsRNAs) directed at promoter sequences
(Li et al., 2006). The latter
requires the Argonaute 2 (Ago2) protein and is associated with a loss of
lysine-9 methylation on histone 3 at dsRNA-target sites
(Li et al., 2006). The
ß-globin LCR (`locus control region'), which is considered to be the
archetypal long-distance transcriptional `enhancer', is itself specifically
transcribed in erythroid cells (Ashe et
al., 1997). Enhancers controlling expression of homeotic and other
genes are also specifically transcribed
(Feng et al., 2006;
Jones and Flavell, 2005;
Ronshaugen and Levine, 2004).
It has also been shown that transactivation of the steroid receptor, as well
as MyoD (which regulates skeletal myogenesis), requires the ncRNA called SRA
(Caretti et al., 2006;
Hube et al., 2006;
Lanz et al., 1999;
Lanz et al., 2002). The ncRNA
7SK is involved in the transcriptional activation of the proto-oncogene
c-myc (Krause,
1996), among other examples
(Goodrich and Kugel,
2006).
Splicing
There is an enormous amount of post-transcriptional processing of RNA, both
protein-coding and non-coding, much of which involves and is probably
regulated by other RNAs. The mechanism of control of alternative splicing, the
other major hallmark of developmentally complex organisms, is not known, but a
range of circumstantial evidence suggests that this process too is controlled
by RNA signals. This evidence includes: (i) that alternative splicing choices
are not well explained by what is known about proteins involved in splicing or
splicing regulation, despite speculations about combinatorial interactions
(Blencowe, 2006;
Caceres and Kornblihtt, 2002;
Pozzoli and Sironi, 2005;
Soller, 2006); (ii) that
alternative splice sites are generally more highly conserved than constitutive
splice sites (suggesting that a sequence-specific trans-acting
sequence is required to address the former)
(Sorek and Ast, 2003;
Sugnet et al., 2004;
Sugnet et al., 2006); and,
most convincingly, (iii) the well-established observation that synthetic RNA
derivatives directed against splice sites can easily alter splicing patterns
both in vivo and in vitro
(Garcia-Blanco, 2005;
Gendron et al., 2006;
Kole and Sazani, 2001;
Roberts et al., 2006;
Wilton and Fletcher, 2005).
If this can easily be achieved by artificial means, then it is not unlikely
that nature will employ a similar mechanism. It is not immediately obvious
where such regulatory RNAs may be sourced, as conserved splice sites do not
have obvious orthologous sequences elsewhere in the genome. However, it is
possible that antisense transcripts are the source of these signals
(Yan et al., 2005). It is
also possible that, given the high affinity of RNA:RNA interactions, the
antisense elements in putative trans-acting RNAs are short and
difficult to identify, as they are in reverse when trying to identify the
possible targets of orphan (non-rRNA directed) small nucleolar RNAs
(Cavaille et al., 2000) (see
below).
RNA modification and RNA editing
There is also considerable post-transcriptional modification and editing of
RNA in eukaryotes, especially complex eukaryotes. SnoRNAs range from 60 to 300
nucleotides in length and guide the site-specific modification of target RNAs
via short regions of base pairing. There are two major classes: (i)
the box C/D snoRNAs, which guide 2'-O-ribose-methylation, and (ii) the
box H/ACA snoRNAs, which guide pseudo-uridylation of target RNAs
(Bachellerie et al., 2002;
Henras et al., 2004;
Kiss et al., 2004;
Meier, 2005). The action of
snoRNAs was initially thought to be restricted to rRNA modification in the
nucleolus during ribosome biogenesis, but it is now evident that they can
target other RNAs, including small nuclear (spliceosomal) RNAs and mRNAs
(Bachellerie et al., 2002;
Henras et al., 2004;
Kishore and Stamm, 2006;
Kiss et al., 2004;
Meier, 2005). A subset of box
H/ACA snoRNAs is located in Cajal bodies (a class of small nuclear organelle),
and are sometimes called scaRNAs (small Cajal body RNAs)
(Meier, 2005), where they
modify telomerase RNA in a cell-cycle dependent manner
(Jady et al., 2004;
Jady et al., 2003). At least
some snoRNAs exhibit tissue-specific and developmental regulation and/or
imprinting (Cavaille et al.,
2000; Cavaille et al.,
2002; Cavaille et al.,
2001; Rogelj and Giese,
2004), which is indicative of a regulatory function. There are
also a number of so-called `orphan' snoRNAs without known targets
(Cavaille et al., 2000;
Cavaille et al., 2002;
Cavaille et al., 2001;
Huttenhofer et al., 2001;
Kiss et al., 2004;
Vitali et al., 2003), one of
which has recently been shown to be involved in the aberrant splicing of the
serotonin receptor 5-HT(2C)R gene in Prader–Willi syndrome patients
(Cavaille et al., 2000;
Kishore and Stamm, 2006).
RNAs may also be edited by enzymes termed ADARs (Adenosine
Deaminases Acting on RNAs), which catalyze the
deamination of adenosine to inosine to alter coding capacity, splicing
patterns or regulatory functions, and also by the APOBEC family of cytidine
deaminases, which catalyze C-U/C-T editing of both RNA and DNA
(Navaratnam and Sarwar,
2006). The targets of RNA editing include not only mRNAs but also
miRNAs and other ncRNAs whose functions are as yet unknown
(Athanasiadis et al., 2004;
Blow et al., 2004;
Blow et al., 2006;
Levanon et al., 2004;
Yang et al., 2006). RNA
editing appears to be the major mechanism by which environmental signals
overwrite encoded genetic information to modify gene function and regulation,
particularly in the nervous system, where it is well documented to modify
transcripts encoding proteins involved in fast neural transmission. These
include ion channels and ligand-gated receptors
(Bass, 2002;
Valente and Nishikura, 2005)
such as the serotonin receptor, which is regulated in the same region by
snoRNA-mediated RNA modification (Kishore
and Stamm, 2006). In humans, where RNA editing is considerably
more prevalent than in mouse (Athanasiadis
et al., 2004; Blow et al.,
2004; Levanon et al.,
2004), RNA editing alters many transcripts from genomic loci
encoding proteins involved in neural cell identity, maturation and function.
This implies a role for RNA editing not only in the regulation of neural
transmission but also of brain development
(Mehler and Mattick,
2007).
mRNA translation and stability
It is now well established that mRNA translation and mRNA stability are
controlled by miRNAs, primarily directed at sequences in the 3'
untranslated region (UTR). 3' UTRs have expanded greatly during metazoan
evolution and in humans occupy over 1% of the genome, accounting for almost as
much of the mRNA sequences as the protein-coding sequences themselves
(Frith et al., 2005), and
suggesting that extremely complex regulatory controls are embedded within
them. miRNAs have been shown to be centrally involved in gene regulation in
both plants and animals (Bartel,
2004; Carrington and Ambros,
2003; Mattick and Makunin,
2005; Pasquinelli et al.,
2005), including flowering in plants
(Chen, 2004) and many aspects
of development (Bernstein et al.,
2003; Giraldez et al.,
2005; Hornstein et al.,
2005; Kanellopoulou et al.,
2005; Ronshaugen et al.,
2005), cell growth and differentiation
(Baehrecke, 2003;
Brennecke et al., 2003;
Chen et al., 2004;
Hatfield et al., 2005;
Johnston and Hobert, 2003;
Kuwabara et al., 2004;
Naguibneva et al., 2006;
Wienholds et al., 2005) in
animals. miRNA regulation has also been shown to be perturbed in developmental
abnormalities including cancer (Croce and
Calin, 2005; Esquela-Kerscher
and Slack, 2006; Hammond,
2006) and possibly other diseases
(Abelson et al., 2005), as well
as in quantitative trait variation (Clop
et al., 2006). Some miRNAs have also been shown to regulate
Hox gene expression (Hornstein
et al., 2005; Mansfield et
al., 2004; Naguibneva et al.,
2006; Yekta et al.,
2004) and to exhibit expression patterns reminiscent of
hox genes in embryonic development
(Mansfield et al., 2004).
Moreover, as noted above, while there are 103 known miRNAs,
there may be far more expressed in mammals. It is also worth noting that
neither specific nor general biological functions have yet been ascribed to
the thousands of piRNAs that are expressed in mammalian testis, although they
are known to interact with the Piwi subfamily of Argonaute proteins, which are
required for germ cell maintenance and meiosis
(Aravin et al., 2006;
Girard et al., 2006;
Lau et al., 2006). The same
is true of the class of 21U-RNAs recently discovered in C. elegans
(Ruby et al., 2006).
RNA intersection in signalling cascades and other aspects of cell biology
While it is already clear that various proteins involved in gene regulation
have RNA binding domains or domains that intersect with complexes involving
RNA, there is also evidence that proteins involved in cellular signal
transduction cascades also bind RNA. This is exemplified by the RasGAP-binding
protein G3BP/rasputin, which contains both an RNA recognition motif (RRM) and
SH3 binding domains (Irvine et al.,
2004; Pazman et al.,
2000; Zekri et al.,
2005). There is also evidence that ncRNAs may be involved in
regulating nuclear factor trafficking
(Willingham et al., 2005),
and the large numbers of ncRNAs that appear to have a cytoplasmic location
(Cheng et al., 2005) suggest
that many other cellular functions are also regulated by such RNAs.
|
The role of proteins in development |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
The first class encompasses those whose role is to transmit contextual
signals from the cell and the external environment (other cells and
circulating signals) into the gene regulatory networks of the cell. It
includes secreted proteins (as well as other ligands such as steroid hormones)
that act locally or systemically and their receptors, for example the
patched-hedgehog (Murone et al.,
1999) and Wnt-frizzled systems
(Gordon and Nusse, 2006),
which may be positioned asymmetrically on different parts of the cell surface.
This class also includes internal protein kinase-mediated signal transduction
cascades. These signals are critical to the fidelity of the developmental
process, both as feedback controls to correct (inevitable) stochastic errors
in the endogenous RNA-directed program, and as important additional positional
information to supplement the endogenously specified developmental program.
For example, imagine a robot that has been given full instructions for the
specification and assembly of a motor vehicle but is denied any environmental
reference information (through vision, touch, etc.). It would be impossible to
design a program whose execution would be sufficiently precise as to preclude
the necessity for feedback controls or (particularly in the case of self
assembling multicellular systems) remove the enormous advantages of positional
information and cell–cell communication during growth and development.
However, as noted already, simply because environmental signalling is critical
to the process of ontogeny, this does not mean that this is where the majority
of the relevant information is embedded.
The second class of `regulatory' proteins important for development encompasses those that effect analogue functions to control gene expression at various levels. These proteins are directed to the appropriate site of action in many, if not most, cases by RNA signals, albeit also influenced (activated or repressed) by intersections with the protein-based signal transduction systems that usually operate via phosphorylation. These effector proteins include homeotic and other types of chromatin-modifying proteins, transcription factors, splicing factors, RNA editing enzymes, RNA modification enzymes, and Argonaute proteins and others in RISC complexes. Many of these proteins are themselves developmentally regulated at the transcriptional and post-transcriptional level, and are contributory variables in the complex matrix of RNA:DNA:protein interactions and the resultant regulatory networks.
|
Genetic signatures of RNA regulatory networks |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
).
Indeed, the entire world of miRNAs and their central role in regulating
differentiation and development lay hidden for many years despite intense
genetic scrutiny of fruitflies and mammals. It was only revealed by the
characterization of the small RNA products of the let-7 and
lin-4 loci, which control developmental timing in C. elegans
(Lee et al., 1993;
Reinhart et al., 2000), and
the intersection of these findings with the characterization of the
similar-sized small RNAs produced by RNAi
(Hammond et al., 2000;
Zamore et al., 2000), also
discovered in C. elegans (Fire
et al., 1998). This suggested that a similar mechanism may produce
(other) short regulatory RNAs (Grishok et
al., 2001; Lagos-Quintana et
al., 2001; Lau et al.,
2001; Lee and Ambros,
2001; Ruvkun,
2001).
There may also be a difference between protein-coding sequences and those
encoding regulatory sequences (whether acting in cis or in trans
via RNA) in terms of their functional sensitivity to point mutations,
which comprise the bulk of the natural and induced mutations in mammals. On
the other hand, many non-coding regulatory mutations have been known for a
long time in Drosophila, where many mutations have been obtained by
deletion or insertion. However, these have almost inevitably been interpreted
as affecting cis-regulatory DNA elements
(Duncan, 2002), despite the
fact that many of the regions concerned, such as bxd in the
bithorax complex (Lipshitz et
al., 1987; Petruk et al.,
2006), which includes PRE/TRE response elements
(Tillib et al., 1999), are
known to be transcribed into separately regulated ncRNAs (i.e. may represent
separate genetic units) and to be involved in the complex and still poorly
understood genetic phenomena of transvection and polycomb-mediated
developmental memory (Mattick and Gagen,
2001). Apart from a few cases of regulatory mutations affecting
quantitative traits that have been mapped to completion in well-structured
animal pedigrees, most screens in mammals (especially in humans) progress from
positional mapping to mutation screening of exons, with little prospect (due
to the enormous technical and statistical difficulties) of identifying
mutations that lie outside these limited regions in large intronic or
intergenic sequences. However, some such mutations are being identified,
including one in a novel ncRNA called MIAT, which appears to increase the risk
for myocardial infarction (Ishii et al.,
2006). I predict that as re-sequencing of target regions in
affected populations becomes feasible with new sequencing technologies, more
of these mutations/variations will be discovered and that many will affect
regulatory RNAs sourced from such regions. Indeed, apart from revealing the
true extent of the involvement of RNA in the developmental programming of
humans and other complex organisms, these discoveries will go to the heart of
what is perhaps the most interesting aspect of our biology, the genetic
factors controlling or influencing our individual physical, physiological and
psychological variation, including disease susceptibility.
|
Conclusion |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
; Mattick and Makunin,
2006). This evolved to breach the operational limits imposed by
solely protein-based regulatory systems, in the face of the nonlinear scaling
of regulatory requirements as living organisms explored higher organizational
and macro-functional complexity (Mattick,
2004). Indeed, it may well be that most of the human genome is
functional (M. Pheasant and J.S.M., manuscript submitted for publication),
including many sequences such as introns and other mobile element-derived
sequences that have been long considered as parasitic evolutionary debris
rather than the historic raw material for genetic innovation and the current
embodiment of higher levels of regulatory sophistication. Thus it appears that
the genome is largely composed of sequences encoding components of RNA
regulatory networks that co-evolved with a sophisticated protein
infrastructure to interact with RNAs and act on their instructions.
The advantages of RNA over protein as a regulatory molecule are its genomic
compactness, its high sequence specificity, and its mutability and associated
ease of re-configuration of interaction networks to explore phenotypic and
functional diversity. This leads to a new conception of how multicellular
development is regulated and where the relevant information is embedded, i.e.
that development is primarily driven by endogenous RNA regulatory networks,
which are contextually informed and whose instructions are functionally
executed by proteins. There is clearly a long way to go to understand and
parse these networks, with many surprises yet in store, including the likely
discovery of new classes and subclasses of small and large regulatory RNAs,
and many biological and mechanistic aspects to decipher. Whatever the details
may be, the irony is that what was dismissed as junk because it was not
understood may well comprise the majority of the information that underpinned
the emergence and now directs the development of complex organisms
(Mattick, 1994;
Mattick, 2004;
Mattick and Gagen, 2001),
including ultimately the brain (Mehler
and Mattick, 2007). It probably also contains a large fraction of
the information that determines the phenotypic differences between individuals
and the diversity of species.
|
Acknowledgments |
|---|
|
Footnotes |
|---|
1 On some early occasions it was suggested that RNA may act as a regulatory
molecule. The possibility was first mooted briefly by Jacob and Monod in 1961
(Jacob and Monod, 1961) but
lapsed when the archetypal gene regulatory factor, the lac repressor,
was subsequently shown to be a protein
(Gilbert and Muller-Hill,
1966). The existence of RNA regulatory networks was first
postulated by Britten and Davidson in
1969 (Britten and Davidson,
1969; Davidson et al.,
1977), in an attempt to explain the vastly greater complexity of
the RNA in the nucleus (then called `heterogenous nuclear RNA' or hnRNA)
compared to the cytoplasm where mRNA is located. Although this paper is of
historical importance for first proposing a major role for regulatory
mechanisms in the evolution of higher eukaryotes, the idea of RNA regulation
itself was not pursued, even following the discovery of introns, despite the
fact that this discovery provided an explanation (at least in part) of the
origin of hnRNA and an obvious potential source of the co-production of gene
regulatory signals from the excised intronic RNA
(Mattick, 1994;
Mattick and Gagen, 2001). 
2 This memory can be quite plastic and can be modulated by contextual cues
(cell signalling), set in a new direction by artificial translocation of the
cell to a new context, or (in some cases) recapitulated when required, such as
during the regeneration of fingertips, tails, limbs or rays in mammals,
lizards, axolotls and starfish.
|
References |
|---|
|
TOPSummaryIntroductionAnalogue components and digital...Non-linear scaling of regulatory...The major output of...Digital-analogue conversion of...The origin and evolution...The extent of the...Functional constraints on the...Endogenous feed-forward control...Parallel expression of exonic...Layers of RNA-directed control...The role of proteins...Genetic signatures of RNA...ConclusionReferences |
|---|
Abelson, J. F., Kwan, K. Y., O'Roak, B. J., Baek, D. Y.,
Stillman, A. A., Morgan, T. M., Mathews, C. A., Pauls, D. L., Rasin, M. R.,
Gunel, M. et al. (2005). Sequence variants in SLITRK1 are
associated with Tourette's syndrome. Science
310,317
-320.
Akhtar, A., Zink, D. and Becker, P. B. (2000). Chromodomains are protein–RNA interaction modules. Nature 407,405 -409.[CrossRef][Medline]
Alfano, G., Vitiello, C., Caccioppoli, C., Caramico, T., Carola,
A., Szego, M. J., McInnes, R. R., Auricchio, A. and Banfi, S.
(2005). Natural antisense transcripts associated with genes
involved in eye development. Hum. Mol. Genet.
14,913
-923.
Ambros, V. (2001). The temporal control of cell cycle and cell fate in Caenorhabditis elegans. Novartis Found. Symp. 237,203 -214; discussion 214-220.[Medline]
Anantharaman, V., Koonin, E. V. and Aravind, L.
(2002). Comparative genomics and evolution of proteins involved
in RNA metabolism. Nucleic Acids Res.
30,1427
-1464.
Andersen, A. A. and Panning, B. (2003). Epigenetic gene regulation by noncoding RNAs. Curr. Opin. Cell Biol. 15,281 -289.[CrossRef][Medline]
Andersen, R. A., Snyder, L. H., Bradley, D. C. and Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20,303 -330.[CrossRef][Medline]
Aparicio, S., Chapman, J., Stupka, E., Putnam, N., Chia, J. M.,
Dehal, P., Christoffels, A., Rash, S., Hoon, S., Smit, A. et al.
(2002). Whole-genome shotgun assembly and analysis of the genome
of Fugu rubripes. Science
297,1301
-1310.
Aravin, A., Gaidatzis, D., Pfeffer, S., Lagos-Quintana, M., Landgraf, P., Iovino, N., Morris, P., Brownstein, M. J., Kuramochi-Miyagawa, S., Nakano, T. et al. (2006). A novel class of small RNAs bind to MILI protein in mouse testes. Nature 442,203 -207.[Medline]
Arnone, M. I. and Davidson, E. H. (1997). The hardwiring of development: organization and function of genomic regulatory systems. Development 124,1851 -1864.[Abstract]
Ashe, H. L., Monks, J., Wijgerde, M., Fraser, P. and Proudfoot,
N. J. (1997). Intergenic transcription and transinduction of
the human beta-globin locus. Genes Dev.
11,2494
-2509.
Athanasiadis, A., Rich, A. and Maas, S. (2004). Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biol. 2, e391.[CrossRef][Medline]
Aufsatz, W., Mette, M. F., van der Winden, J., Matzke, A. J. and
Matzke, M. (2002). RNA-directed DNA methylation in
Arabidopsis. Proc. Natl. Acad. Sci. USA
99,16499
-16506.
Aussem, A., Murtagh, F. and Sarazin, M. (1995). Dynamical recurrent neural networks – towards environmental time series prediction. Int. J. Neural Syst. 6, 145-170.[CrossRef][Medline]
Bachellerie, J. P., Cavaille, J. and Huttenhofer, A. (2002). The expanding snoRNA world. Biochimie 84,775 -790.[Medline]
Bae, E., Calhoun, V. C., Levine, M., Lewis, E. B. and Drewell,
R. A. (2002). Characterization of the intergenic RNA profile
at abdominal-A and Abdominal-B in the Drosophila bithorax complex.
Proc. Natl. Acad. Sci. USA
99,16847
-16852.
Baehrecke, E. H. (2002). How death shapes life during development. Nat. Rev. Mol. Cell Biol. 3, 779-787.[CrossRef][Medline]
Baehrecke, E. H. (2003). miRNAs: micro managers of programmed cell death. Curr. Biol. 13,R473 -R475.[CrossRef][Medline]
Bantignies, F. and Cavalli, G. (2006). Cellular memory and dynamic regulation of polycomb group proteins. Curr. Opin. Cell Biol. 18,275 -283.[CrossRef][Medline]
Barsyte-Lovejoy, D., Lau, S. K., Boutros, P. C., Khosravi, F.,
Jurisica, I., Andrulis, I. L., Tsao, M. S. and Penn, L. Z.
(2006). The c-Myc oncogene directly induces the H19 noncoding RNA
by allele-specific binding to potentiate tumorigenesis. Cancer
Res. 66,5330
-5337.
Bartel, D. P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116,281 -297.[CrossRef][Medline]
Baskerville, S. and Bartel, D. P. (2005).
Microarray profiling of microRNAs reveals frequent coexpression with
neighboring miRNAs and host genes. RNA
11,241
-247.
Bass, B. L. (2002). RNA editing by adenosine deaminases that act on RNA. Annu. Rev. Biochem. 71,817 -846.[CrossRef][Medline]
Bayne, E. H. and Allshire, R. C. (2005). RNA-directed transcriptional gene silencing in mammals. Trends Genet. 21,370 -373.[CrossRef][Medline]
Bejerano, G., Pheasant, M., Makunin, I., Stephen, S., Kent, W.
J., Mattick, J. S. and Haussler, D. (2004). Ultraconserved
elements in the human genome. Science
304,1321
-1325.
Bejerano, G., Lowe, C. B., Ahituv, N., King, B., Siepel, A., Salama, S. R., Rubin, E. M., Kent, W. J. and Haussler, D. (2006). A distal enhancer and an ultraconserved exon are derived from a novel retroposon. Nature 441, 87-90.[CrossRef][Medline]
Ben-Tabou de-Leon, S. and Davidson, E. H.
(2006). Deciphering the underlying mechanism of specification and
differentiation: the sea urchin gene regulatory network. Sci.
STKE 2006,pe47
.
Berezikov, E., Guryev, V., van de Belt, J., Wienholds, E., Plasterk, R. H. and Cuppen, E. (2005). Phylogenetic shadowing and computational identification of human microRNA genes. Cell 120,21 -24.[CrossRef][Medline]
Berezikov, E., Thuemmler, F., van Laake, L. W., Kondova, I., Bontrop, R., Cuppen, E. and Plasterk, R. H. (2006a). Diversity of microRNAs in human and chimpanzee brain. Nat. Genet. 38,1375 -1377.[CrossRef][Medline]
Berezikov, E., van Tetering, G., Verheul, M., van de Belt, J.,
van Laake, L., Vos, J., Verloop, R., van de Wetering, M., Guryev, V., Takada,
S. et al. (2006b). Many novel mammalian microRNA candidates
identified by extensive cloning and RAKE analysis. Genome
Res. 16,1289
-1298.
Bernstein, B. E., Kamal, M., Lindblad-Toh, K., Bekiranov, S., Bailey, D. K., Huebert, D. J., McMahon, S., Karlsson, E. K., Kulbokas, E. J., 3rd, Gingeras, T. R. et al. (2005). Genomic maps and comparative analysis of histone modifications in human and mouse. Cell 120,169 -181.[CrossRef][Medline]
Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K. et al. (2006). A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125,315 -326.[CrossRef][Medline]
Bernstein, E. and Allis, C. D. (2005). RNA
meets chromatin. Genes Dev.
19,1635
-1655.
Bernstein, E., Kim, S. Y., Carmell, M. A., Murchison, E. P., Alcorn, H., Li, M. Z., Mills, A. A., Elledge, S. J., Anderson, K. V. and Hannon, G. J. (2003). Dicer is essential for mouse development. Nat. Genet. 35,215 -217.[CrossRef][Medline]
Bernstein, E., Duncan, E. M., Masui, O., Gil, J., Heard, E. and
Allis, C. D. (2006). Mouse polycomb proteins bind
differentially to methylated histone H3 and RNA and are enriched in
facultative heterochromatin. Mol. Cell. Biol.
26,2560
-2569.
Birchler, J. A., Kavi, H. H. and Fernandez, H. R. (2004). Heterochromatin: RNA points the way. Curr. Biol. 14,R759 -R761.[CrossRef][Medline]
Blencowe, B. J. (2006). Alternative splicing: new insights from global analyses. Cell 126, 37-47.[CrossRef][Medline]
Blin-Wakkach, C., Lezot, F., Ghoul-Mazgar, S., Hotton, D.,
Monteiro, S., Teillaud, C., Pibouin, L., Orestes-Cardoso, S., Papagerakis, P.,
Macdougall, M. et al. (2001). Endogenous Msx1 antisense
transcript: in vivo and in vitro evidences, structure, and potential
involvement in skeleton development in mammals. Proc. Natl. Acad.
Sci. USA 98,7336
-7341.
Blow, M., Futreal, P. A., Wooster, R. and Stratton, M. R.
(2004). A survey of RNA editing in human brain. Genome
Res. 14,2379
-2387.
Blow, M. J., Grocock, R. J., van Dongen, S., Enright, A. J., Dicks, E., Futreal, P. A., Wooster, R. and Stratton, M. R. (2006). RNA editing of human microRNAs. Genome Biol. 7,R27 .[CrossRef][Medline]
Boyer, L. A., Plath, K., Zeitlinger, J., Brambrink, T., Medeiros, L. A., Lee, T. I., Levine, S. S., Wernig, M., Tajonar, A., Ray, M. K. et al. (2006). Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature 441,349 -353.[CrossRef][Medline]
Brandt, J., Schrauth, S., Veith, A. M., Froschauer, A., Haneke, T., Schultheis, C., Gessler, M., Leimeister, C. and Volff, J. N. (2005). Transposable elements as a source of genetic innovation: expression and evolution of a family of retrotransposon-derived neogenes in mammals. Gene 345,101 -111.[CrossRef][Medline]
Brannan, C. I., Dees, E. C., Ingram, R. S. and Tilghman, S.
M. (1990). The product of the H19 gene may function as an
RNA. Mol. Cell. Biol.
10, 28-36.
Brennecke, J., Hipfner, D. R., Stark, A., Russell, R. B. and Cohen, S. M. (2003). bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113, 25-36.[CrossRef][Medline]
Bridgeman, B. (1995). A review of the role of efference copy in sensory and oculomotor control systems. Ann. Biomed. Eng. 23,409 -422.[Medline]
Britten, R. (2006). Transposable elements have
contributed to thousands of human proteins. Proc. Natl. Acad. Sci.
USA 103,1798
-1803.
Britten, R. J. and Davidson, E. H. (1969). Gene
regulation for higher cells: a theory. Science
165,349
-357.
Brockdorff, N. (1998). The role of Xist in X-inactivation. Curr. Opin. Genet. Dev. 8, 328-333.[CrossRef][Medline]
Brosius, J. (1999). RNAs from all categories generate retrosequences that may be exapted as novel genes or regulatory elements. Gene 238,115 -134.[CrossRef][Medline]
Brosius, J. (2005). Disparity, adaptation,
exaptation, bookkeeping, and contingency at the genome level.
Paleobiology 31,1
-16.
Buchler, N. E., Gerland, U. and Hwa, T. (2003).
On schemes of combinatorial transcription logic. Proc. Natl. Acad.
Sci. USA 100,5136
-5141.
Bussemakers, M. J., van Bokhoven, A., Verhaegh, G. W., Smit, F.
P., Karthaus, H. F., Schalken, J. A., Debruyne, F. M., Ru, N. and Isaacs, W.
B. (1999). DD3: a new prostate-specific gene, highly
overexpressed in prostate cancer. Cancer Res.
59,5975
-5979.
Bustamante, C. D., Fledel-Alon, A., Williamson, S., Nielsen, R., Hubisz, M. T., Glanowski, S., Tanenbaum, D. M., White, T. J., Sninsky, J. J., Hernandez, R. D. et al. (2005). Natural selection on protein-coding genes in the human genome. Nature 437,1153 -1157.[CrossRef][Medline]
Caceres, J. F. and Kornblihtt, A. R. (2002). Alternative splicing: multiple control mechanisms and involvement in human disease. Trends Genet. 18,186 -193.[CrossRef][Medline]
Cai, X., Hagedorn, C. H. and Cullen, B. R.
(2004). Human microRNAs are processed from capped, polyadenylated
transcripts that can also function as mRNAs. RNA
10,1957
-1966.
Canfield, D. E., Poulton, S. W. and Narbonne, G. M.
(2007). Late-Neoproterozoic deep-ocean oxygenation and the rise
of animal life. Science
315, 92-95.
Caretti, G., Schiltz, R. L., Dilworth, F. J., Di Padova, M., Zhao, P., Ogryzko, V., Fuller-Pace, F. V., Hoffman, E. P., Tapscott, S. J. and Sartorelli, V. (2006). The RNA helicases p68/p72 and the noncoding RNA SRA are coregulators of MyoD and skeletal muscle differentiation. Dev. Cell 11,547 -560.[CrossRef][Medline]
Carmell, M. A., Xuan, Z., Zhang, M. Q. and Hannon, G. J.
(2002). The Argonaute family: tentacles that reach into RNAi,
developmental control, stem cell maintenance, and tumorigenesis.
Genes Dev. 16,2733
-2742.
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M.
C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C. et al.
(2005). The transcriptional landscape of the mammalian genome.
Science 309,1559
-1563.
Carrington, J. C. and Ambros, V. (2003). Role
of microRNAs in plant and animal development. Science
301,336
-338.
Cavaille, J., Buiting, K., Kiefmann, M., Lalande, M., Brannan,
C. I., Horsthemke, B., Bachellerie, J. P., Brosius, J. and Huttenhofer, A.
(2000). Identification of brain-specific and imprinted small
nucleolar RNA genes exhibiting an unusual genomic organization.
Proc. Natl. Acad. Sci. USA
97,14311
-14316.
Cavaille, J., Vitali, P., Basyuk, E., Huttenhofer, A. and
Bachellerie, J. P. (2001). A novel brain-specific box C/D
small nucleolar RNA processed from tandemly repeated introns of a noncoding
RNA gene in rats. J. Biol. Chem.
276,26374
-26383.
Cavaille, J., Seitz, H., Paulsen, M., Ferguson-Smith, A. C. and
Bachellerie, J. P. (2002). Identification of
tandemly-repeated C/D snoRNA genes at the imprinted human 14q32 domain
reminiscent of those at the Prader-Willi/Angelman syndrome region.
Hum. Mol. Genet. 11,1527
-1538.
Cavalier-Smith, T. (1991). Intron phylogeny: a new hypothesis. Trends Genet. 7, 145-148.[Medline]
Cawley, S., Bekiranov, S., Ng, H. H., Kapranov, P., Sekinger, E. A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A. J. et al. (2004). Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116,499 -509.[CrossRef][Medline]
Cernilogar, F. M. and Orlando, V. (2005). Epigenome programming by Polycomb and Trithorax proteins. Biochem. Cell Biol. 83,322 -331.[CrossRef][Medline]
Chalk, A. M., Warfinge, R. E., Georgii-Hemming, P. and
Sonnhammer, E. L. (2005). siRNAdb: a database of siRNA
sequences. Nucleic Acids Res.
33,D131
-D134.
Chen, C. Z., Li, L., Lodish, H. F. and Bartel, D. P.
(2004). MicroRNAs modulate hematopoietic lineage differentiation.
Science 303,83
-86.
Chen, X. (2004). A microRNA as a translational
repressor of APETALA2 in Arabidopsis flower development.
Science 303,2022
-2025.
Cheng, J., Kapranov, P., Drenkow, J., Dike, S., Brubaker, S.,
Patel, S., Long, J., Stern, D., Tammana, H., Helt, G. et al.
(2005). Transcriptional maps of 10 human chromosomes at
5-nucleotide resolution. Science
308,1149
-1154.
Cho, Y. S., Iguchi, N., Yang, J., Handel, M. A. and Hecht, N.
B. (2005). Meiotic messenger RNA and noncoding RNA targets of
the RNA-binding protein Translin (TSN) in mouse testis. Biol.
Reprod. 73,840
-847.
Chureau, C., Prissette, M., Bourdet, A., Barbe, V., Cattolico,
L., Jones, L., Eggen, A., Avner, P. and Duret, L. (2002).
Comparative sequence analysis of the X-inactivation center region in mouse,
human, and bovine. Genome Res.
12,894
-908.
Clark, R. M., Wagler, T. N., Quijada, P. and Doebley, J. (2006). A distant upstream enhancer at the maize domestication gene tb1 has pleiotropic effects on plant and inflorescent architecture. Nat. Genet. 38,594 -597.[CrossRef][Medline]
Clark, S. W., Fee, B. E. and Cleveland, J. L.
(2002). Misexpression of the eyes absent family triggers the
apoptotic program. J. Biol. Chem.
277,3560
-3567.
Clarke, J. D. and Tickle, C. (1999). Fate maps old and new. Nat. Cell Biol. 1,E103 -E109.[CrossRef][Medline]
Clement, J. Q., Qian, L., Kaplinsky, N. and Wilkinson, M. F. (1999). The stability and fate of a spliced intron from vertebrate cells. RNA 5,206 -220.[Abstract]
Clement, J. Q., Maiti, S. and Wilkinson, M. F.
(2001). Localization and stability of introns spliced from the
Pem homeobox gene. J. Biol. Chem.
276,16919
-16930.
Clop, A., Marcq, F., Takeda, H., Pirottin, D., Tordoir, X., Bibe, B., Bouix, J., Caiment, F., Elsen, J. M., Eychenne, F. et al. (2006). A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nat. Genet. 38,813 -818.[CrossRef][Medline]
Cocquet, J., Pannetier, M., Fellous, M. and Veitia, R. A. (2005). Sense and antisense Foxl2 transcripts in mouse. Genomics 85,531 -541.[CrossRef][Medline]
Cogoni, C. and Macino, G. (2000). Post-transcriptional gene silencing across kingdoms. Curr. Opin. Genet. Dev. 10,638 -643.[CrossRef][Medline]
Collen, M. F. (1994). The origins of
informatics. J. Am. Med. Inform. Assoc.
1, 91-107.
Cordaux, R., Udit, S., Batzer, M. A. and Feschotte, C.
(2006). Birth of a chimeric primate gene by capture of the
transposase gene from a mobile element. Proc. Natl. Acad. Sci.
USA 103,8101
-8106.
Cousineau, B., Lawrence, S., Smith, D. and Belfort, M. (2000). Retrotransposition of a bacterial group II intron. Nature 404,1018 -1021.[CrossRef][Medline]
Crick, F. (1979). Split genes and RNA splicing.
Science 204,264
-271.
Croce, C. M. and Calin, G. A. (2005). miRNAs, cancer, and stem cell division. Cell 122, 6-7.[CrossRef][Medline]
Croft, L. J., Lercher, M. J., Gagen, M. J. and Mattick, J. S. (2003). Is prokaryotic complexity limited by accelerated growth in regulatory overhead? Genome Biology Preprint Depository http://genomebiology.com/qc/2003/5/1/p2.
Csete, M. E. and Doyle, J. C. (2002). Reverse
engineering of biological complexity. Science
295,1664
-1669.
Cummins, J. M., He, Y., Leary, R. J., Pagliarini, R., Diaz, L.
A., Jr, Sjoblom, T., Barad, O., Bentwich, Z., Szafranska, A. E., Labourier, E.
et al. (2006). The colorectal microRNAome. Proc.
Natl. Acad. Sci. USA 103,3687
-3692.
Dagan, T., Sorek, R., Sharon, E., Ast, G. and Graur, D.
(2004). AluGene: a database of Alu elements incorporated within
protein-coding genes. Nucleic Acids Res.
32,D489
-D492.
David, L., Huber, W., Granovskaia, M., Toedling, J., Palm, C.
J., Bofkin, L., Jones, T., Davis, R. W. and Steinmetz, L. M.
(2006). A high-resolution map of transcription in the yeast
genome. Proc. Natl. Acad. Sci. USA
103,5320
-5325.
Davidson, E. H. (2006). The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. New York: Academic Press.
Davidson, E. H., Klein, W. H. and Britten, R. J. (1977). Sequence organization in animal DNA and a speculation on hnRNA as a coordinate regulatory transcript. Dev. Biol. 55,69 -84.[CrossRef][Medline]
Dermitzakis, E. T., Reymond, A., Scamuffa, N., Ucla, C.,
Kirkness, E., Rossier, C. and Antonarakis, S. E. (2003).
Evolutionary discrimination of mammalian conserved non-genic sequences (CNGs).
Science 302,1033
-1035.
Doolittle, W. F. and Sapienza, C. (1980). Selfish genes, the phenotype paradigm and genome evolution. Nature 284,601 -603.[CrossRef][Medline]
Duboule, D. and Wilkins, A. S. (1998). The evolution of `bricolage'. Trends Genet. 14, 54-59.[CrossRef][Medline]
Duncan, I. W. (2002). Transvection effects in Drosophila. Annu. Rev. Genet. 36,521 -556.[CrossRef][Medline]
Duret, L., Chureau, C., Samain, S., Weissenbach, J. and Avner,
P. (2006). The Xist RNA gene evolved in eutherians
by pseudogenization of a protein-coding gene. Science
312,1653
-1655.
Edelman, G. M. (1993). Neural Darwinism: selection and reentrant signalling in higher brain function. Neuron 10,115 -125.[CrossRef][Medline]
Elman, J. L. (1998). Connectionism, artificial life, and dynamical systems: new approaches to old questions. In A Companion to Cognitive Science (ed. W. Bechtel and G. Graham), pp. 488-505. Oxford: Basil Blackwood.
Engstrom, P. G., Suzuki, H., Ninomiya, N., Akalin, A., Sessa, L., Lavorgna, G., Brozzi, A., Luzi, L., Tan, S. L., Yang, L. et al. (2006). Complex loci in human and mouse genomes. PLoS Genet. 2,e47 .[CrossRef][Medline]
Esquela-Kerscher, A. and Slack, F. J. (2006). Oncomirs – microRNAs with a role in cancer. Nat. Rev. Cancer 6,259 -269.[CrossRef][Medline]
Feng, J., Bi, C., Clark, B. S., Mady, R., Shah, P. and Kohtz, J.
D. (2006). The Evf-2 noncoding RNA is transcribed from the
Dlx-5/6 ultraconserved region and functions as a Dlx-2 transcriptional
coactivator. Genes Dev.
20,1470
-1484.
Ferat, J. L. and Michel, F. (1993). Group II self-splicing introns in bacteria. Nature 364,358 -361.[CrossRef][Medline]
Ferrigno, O., Virolle, T., Djabari, Z., Ortonne, J. P., White, R. J. and Aberdam, D. (2001). Transposable B2 SINE elements can provide mobile RNA polymerase II promoters. Nat. Genet. 28,77 -81.[CrossRef][Medline]
Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E. and Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans.Nature 391,806 -811.[CrossRef][Medline]
Fisher, S., Grice, E. A., Vinton, R. M., Bessling, S. L. and
McCallion, A. S. (2006). Conservation of RET regulatory
function from human to zebrafish without sequence similarity.
Science 312,276
-279.
Frith, M. C., Pheasant, M. and Mattick, J. S. (2005). The amazing complexity of the human transcriptome. Eur. J. Hum. Genet. 13,894 -897.[CrossRef][Medline]
Frith, M. C., Ponjavic, J., Fredman, D., Kai, C., Kawai, J.,
Carninci, P., Hayshizaki, Y. and Sandelin, A. (2006).
Evolutionary turnover of mammalian transcription start sites.
Genome Res. 16,713
-722.
Gagen, M. J. and Mattick, J. S. (2004). Inherent size constraints on prokaryote gene networks due to `accelerating' growth. arXiv Preprint Archive http://arXiv.org/abs/q-bio.MN/0312021.
Gagen, M. J. and Mattick, J. S. (2005). Inherent size constraints on prokaryote gene networks due to `accelerating' growth. Theory Biosci. 123,381 -411.[CrossRef][Medline]
Garcia-Blanco, M. A. (2005). Making antisense of splicing. Curr. Opin. Mol. Ther. 7, 476-482.[Medline]
Gendron, D., Carriero, S., Garneau, D., Villemaire, J., Klinck, R., Elela, S. A., Damha, M. J. and Chabot, B. (2006). Modulation of 5' splice site selection using tailed oligonucleotides carrying splicing signals. BMC Biotechnol. 6, 5.[CrossRef][Medline]
Gesteland, R. F., Cech, T. R. and Atkins, J. F. (2006). The RNA World. Cold Spring Harbor: Cold Spring Harbor Laboratory Press.
Gilbert, W. (1978). Why genes in pieces? Nature 271,501 .[CrossRef][Medline]
Gilbert, W. and Muller-Hill, B. (1966).
Isolation of the lac repressor. Proc. Natl. Acad. Sci.
USA 56,1891
-1898.
Ginger, M. R., Shore, A. N., Contreras, A., Rijnkels, M.,
Miller, J., Gonzalez-Rimbau, M. F. and Rosen, J. M. (2006). A
noncoding RNA is a potential marker of cell fate during mammary gland
development. Proc. Natl. Acad. Sci. USA
103,5781
-5786.
Giraldez, A. J., Cinalli, R. M., Glasner, M. E., Enright, A. J.,
Thomson, J. M., Baskerville, S., Hammond, S. M., Bartel, D. P. and Schier, A.
F. (2005). MicroRNAs regulate brain morphogenesis in
zebrafish. Science 308,833
-838.
Girard, A., Sachidanandam, R., Hannon, G. J. and Carmell, M. A. (2006). A germline-specific class of small RNAs binds mammalian Piwi proteins. Nature 442,199 -202.[Medline]
Goffeau, A., Barrell, B. G., Bussey, H., Davis, R. W., Dujon, B., Feldmann, H., Galibert, F., Hoheisel, J. D., Jacq, C., Johnston, M. et al. (1996). Life with 6000 genes. Science 274,563 -567.
Goodrich, J. A. and Kugel, J. F. (2006). Non-coding-RNA regulators of RNA polymerase II transcription. Nat. Rev. Mol. Cell Biol. 7,612 -616.[CrossRef][Medline]
Goodstadt, L. and Ponting, C. P. (2006). Phylogenetic reconstruction of orthology, paralogy, and conserved synteny for dog and human. PLoS Comput. Biol. 2, e133.[CrossRef][Medline]
Gordon, M. D. and Nusse, R. (2006). Wnt
signalling: multiple pathways, multiple receptors, and multiple transcription
factors. J. Biol. Chem.
281,22429
-22433.
Gottesman, S. (2005). Micros for microbes: non-coding regulatory RNAs in bacteria. Trends Genet. 21,399 -404.[CrossRef][Medline]
Graveley, B. R. (2001). Alternative splicing: increasing diversity in the proteomic world. Trends Genet. 17,100 -107.[CrossRef][Medline]
Gray, H. (1918). Anatomy of the Human Body. Philadelphia: Lea & Febiger.
Grimaud, C., Bantignies, F., Pal-Bhadra, M., Ghana, P., Bhadra, U. and Cavalli, G. (2006). RNAi components are required for nuclear clustering of Polycomb group response elements. Cell 124,957 -971.[CrossRef][Medline]
Grishok, A., Pasquinelli, A. E., Conte, D., Li, N., Parrish, S., Ha, I., Baillie, D. L., Fire, A., Ruvkun, G. and Mello, C. C. (2001). Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing. Cell 106, 23-34.[CrossRef][Medline]
Guenther, M. G., Jenner, R. G., Chevalier, B., Nakamura, T.,
Croce, C. M., Canaani, E. and Young, R. A. (2005). Global and
Hox-specific roles for the MLL1 methyltransferase. Proc. Natl.
Acad. Sci. USA 102,8603
-8608.
Hammond, S. M. (2006). MicroRNAs as oncogenes. Curr. Opin. Genet. Dev. 16, 4-9.[CrossRef][Medline]
Hammond, S. M., Bernstein, E., Beach, D. and Hannon, G. J. (2000). An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature 404,293 -296.[CrossRef][Medline]
Hare, M. P. and Palumbi, S. R. (2003). High
intron sequence conservation across three mammalian orders suggests functional
constraints. Mol. Biol. Evol.
20,969
-978.
Hasler, J. and Strub, K. (2006). Alu elements
as regulators of gene expression. Nucleic Acids Res.
34,5491
-5497.
Hatfield, S. D., Shcherbata, H. R., Fischer, K. A., Nakahara, K., Carthew, R. W. and Ruohola-Baker, H. (2005). Stem cell division is regulated by the microRNA pathway. Nature 435,974 -978.[CrossRef][Medline]
Henras, A. K., Dez, C. and Henry, Y. (2004). RNA structure and function in C/D and H/ACA s(no)RNPs. Curr. Opin. Struct. Biol. 14,335 -343.[CrossRef][Medline]
Herbert, A. and Rich, A. (1999a). RNA processing and the evolution of eukaryotes. Nat. Genet. 21,265 -269.[CrossRef][Medline]
Herbert, A. and Rich, A. (1999b). RNA processing in evolution: the logic of soft-wired genomes. Ann. N. Y. Acad. Sci. 870,119 -132.[CrossRef][Medline]
Hertel, J., Lindemeyer, M., Missal, K., Fried, C., Tanzer, A., Flamm, C., Hofacker, I. L. and Stadler, P. F. (2006). The expansion of the metazoan microRNA repertoire. BMC Genomics 7,25 .[CrossRef][Medline]
Hill, A. E., Hong, J. S., Wen, H., Teng, L., McPherson, D. T., McPherson, S. A., Levasseur, D. N. and Sorscher, E. J. (2006). Micro-RNA-like effects of complete intronic sequences. Front. Biosci. 11,1998 -2006.[Medline]
Holmes, R., Williamson, C., Peters, J., Denny, P. and Wells,
C. (2003). A comprehensive transcript map of the mouse Gnas
imprinted complex. Genome Res.
13,1410
-1415.
Hornstein, E., Mansfield, J. H., Yekta, S., Hu, J. K., Harfe, B. D., McManus, M. T., Baskerville, S., Bartel, D. P. and Tabin, C. J. (2005). The microRNA miR-196 acts upstream of Hoxb8 and Shh in limb development. Nature 438,671 -674.[CrossRef][Medline]
Huang, Z. P., Zhou, H., Liang, D. and Qu, L. H. (2004). Different expression strategy: multiple intronic gene clusters of box H/ACA snoRNA in Drosophila melanogaster. J. Mol. Biol. 341,669 -683.[CrossRef][Medline]
Hube, F., Guo, J., Chooniedass-Kothari, S., Cooper, C., Hamedani, M. K., Dibrov, A. A., Blanchard, A. A., Wang, X., Deng, G., Myal, Y. et al. (2006). Alternative splicing of the first intron of the steroid receptor RNA activator (SRA) participates in the generation of coding and noncoding RNA isoforms in breast cancer cell lines. DNA Cell Biol. 25,418 -428.[CrossRef][Medline]
Huttenhofer, A., Kiefmann, M., Meier-Ewert, S., O'Brien, J., Lehrach, H., Bachellerie, J. P. and Brosius, J. (2001). RNomics: an experimental approach that identifies 201 candidates for novel, small, non-messenger RNAs in mouse. EMBO J. 20,2943 -2953.[CrossRef][Medline]
Imamura, T., Yamamoto, S., Ohgane, J., Hattori, N., Tanaka, S. and Shiota, K. (2004). Non-coding RNA directed DNA demethylation of Sphk1 CpG island. Biochem. Biophys. Res. Commun. 322,593 -600.[CrossRef][Medline]
International Human Genome Sequencing Consortium (2004a). Finishing the euchromatic sequence of the human genome. Nature 431,931 -945.[CrossRef][Medline]
International Human Genome Sequencing Consortium (2004b). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432,695 -716.[CrossRef][Medline]
Irvine, D. V., Zaratiegui, M., Tolia, N. H., Goto, D. B.,
Chitwood, D. H., Vaughn, M. W., Joshua-Tor, L. and Martienssen, R. A.
(2006). Argonaute slicing is required for heterochromatic
silencing and spreading. Science
313,1134
-1137.
Irvine, K., Stirling, R., Hume, D. and Kennedy, D. (2004). Rasputin, more promiscuous than ever: a review of G3BP. Int. J. Dev. Biol. 48,1065 -1077.[CrossRef][Medline]
Ishii, N., Ozaki, K., Sato, H., Mizuno, H., Saito, S., Takahashi, A., Miyamoto, Y., Ikegawa, S., Kamatani, N., Hori, M. et al. (2006). Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J. Hum. Genet. 51,1087 -1099.[CrossRef][Medline]
Jacob, F. (1977). Evolution and tinkering.
Science 196,1161
-1166.
Jacob, F. and Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3,318 -356.[Medline]
Jady, B. E., Darzacq, X., Tucker, K. E., Matera, A. G., Bertrand, E. and Kiss, T. (2003). Modification of Sm small nuclear RNAs occurs in the nucleoplasmic Cajal body following import from the cytoplasm. EMBO J. 22,1878 -1888.[CrossRef][Medline]
Jady, B. E., Bertrand, E. and Kiss, T. (2004).
Human telomerase RNA and box H/ACA scaRNAs share a common Cajal body-specific
localization signal. J. Cell Biol.
164,647
-652.
Janowski, B. A., Huffman, K. E., Schwartz, J. C., Ram, R., Hardy, D., Shames, D. S., Minna, J. D. and Corey, D. R. (2005). Inhibiting gene expression at transcription start sites in chromosomal DNA with antigene RNAs. Nat. Chem. Biol. 1,216 -222.[CrossRef][Medline]
Jeffery, L. and Nakielny, S. (2004). Components
of the DNA methylation system of chromatin control are RNA-binding proteins.
J. Biol. Chem. 279,49479
-49487.
Ji, P., Diederichs, S., Wang, W., Boing, S., Metzger, R., Schneider, P. M., Tidow, N., Brandt, B., Buerger, H., Bulk, E. et al. (2003). MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer. Oncogene 22,6087 -6097.
John, B., Enright, A. J., Aravin, A., Tuschl, T., Sander, C. and Marks, D. S. (2004). Human microRNA targets. PLoS Biol. 2,e363 .[CrossRef][Medline]
Johnston, R. J. and Hobert, O. (2003). A microRNA controlling left/right neuronal asymmetry in Caenorhabditis elegans. Nature 426,845 -849.[CrossRef][Medline]
Jones, E. A. and Flavell, R. A. (2005). Distal
enhancer elements transcribe intergenic RNA in the IL-10 family gene cluster.
J. Immunol. 175,7437
-7446.
Jones-Rhoades, M. W. and Bartel, D. P. (2004). Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell 14,787 -799.[CrossRef][Medline]
Kamal, M., Xie, X. and Lander, E. S. (2006). A
large family of ancient repeat elements in the human genome is under strong
selection. Proc. Natl. Acad. Sci. USA
103,2740
-2745.
Kampa, D., Cheng, J., Kapranov, P., Yamanaka, M., Brubaker, S.,
Cawley, S., Drenkow, J., Piccolboni, A., Bekiranov, S., Helt, G. et al.
(2004). Novel RNAs identified from an in-depth analysis of the
transcriptome of human chromosomes 21 and 22. Genome
Res. 14,331
-342.
Kanellopoulou, C., Muljo, S. A., Kung, A. L., Ganesan, S.,
Drapkin, R., Jenuwein, T., Livingston, D. M. and Rajewsky, K.
(2005). Dicer-deficient mouse embryonic stem cells are defective
in differentiation and centromeric silencing. Genes
Dev. 19,489
-501.
Kapranov, P., Cawley, S. E., Drenkow, J., Bekiranov, S.,
Strausberg, R. L., Fodor, S. P. and Gingeras, T. R. (2002).
Large-scale transcriptional activity in chromosomes 21 and 22.
Science 296,916
-919.
Kapranov, P., Drenkow, J., Cheng, J., Long, J., Helt, G., Dike,
S. and Gingeras, T. R. (2005). Examples of the complex
architecture of the human transcriptome revealed by RACE and high-density
tiling arrays. Genome Res.
15,987
-997.
Katayama, S., Tomaru, Y., Kasukawa, T., Waki, K., Nakanishi, M.,
Nakamura, M., Nishida, H., Yap, C. C., Suzuki, M., Kawai, J. et al.
(2005). Antisense transcription in the mammalian transcriptome.
Science 309,1564
-1566.
Kelley, R. L. and Kuroda, M. I. (2000). Noncoding RNA genes in dosage compensation and imprinting. Cell 103,9 -12.[CrossRef][Medline]
Kim, D. H., Villeneuve, L. M., Morris, K. V. and Rossi, J. J. (2006). Argonaute-1 directs siRNA-mediated transcriptional gene silencing in human cells. Nat. Struct. Mol. Biol. 13,793 -797.[CrossRef][Medline]
Kishore, S. and Stamm, S. (2006). The snoRNA
HBII-52 regulates alternative splicing of the serotonin receptor 2C.
Science 311,230
-232.
Kiss, A. M., Jady, B. E., Bertrand, E. and Kiss, T.
(2004). Human box H/ACA pseudouridylation guide RNA machinery.
Mol. Cell. Biol. 24,5797
-5807.
Kleinjan, D. A. and van Heyningen, V. (2005). Long-range control of gene expression: emerging mechanisms and disruption in disease. Am. J. Hum. Genet. 76, 8-32.[CrossRef][Medline]
Kole, R. and Sazani, P. (2001). Antisense effects in the cell nucleus: modification of splicing. Curr. Opin. Mol. Ther. 3,229 -234.[Medline]
Korneev, S. and O'Shea, M. (2005). Natural antisense RNAs in the nervous system. Rev. Neurosci. 16,213 -222.[Medline]
Krajewski, W. A., Nakamura, T., Mazo, A. and Canaani, E.
(2005). A motif within SET-domain proteins binds single-stranded
nucleic acids and transcribed and supercoiled DNAs and can interfere with
assembly of nucleosomes. Mol. Cell. Biol.
25,1891
-1899.
Krause, M. O. (1996). Chromatin structure and function: the heretical path to an RNA transcription factor. Biochem. Cell Biol. 74,623 -632.[Medline]
Krull, M., Brosius, J. and Schmitz, J. (2005).
Alu-SINE exonization: en route to protein-coding function. Mol.
Biol. Evol. 22,1702
-1711.
Kuwabara, T., Hsieh, J., Nakashima, K., Taira, K. and Gage, F. H. (2004). A small modulatory dsRNA specifies the fate of adult neural stem cells. Cell 116,779 -793.[CrossRef][Medline]
Ladomery, M. (1997). Multifunctional proteins suggest connections between transcriptional and post-transcriptional processes. BioEssays 19,903 -909.[CrossRef][Medline]
Lagos-Quintana, M., Rauhut, R., Lendeckel, W. and Tuschl, T.
(2001). Identification of novel genes coding for small expressed
RNAs. Science 294,853
-858.
Lam, A. L., Pazin, D. E. and Sullivan, B. A. (2005). Control of gene expression and assembly of chromosomal subdomains by chromatin regulators with antagonistic functions. Chromosoma 114,242 -251.[CrossRef][Medline]
Lambowitz, A. M. and Zimmerly, S. (2004). Mobile group II introns. Annu. Rev. Genet. 38, 1-35.[CrossRef][Medline]
Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W. et al. (2001). Initial sequencing and analysis of the human genome. Nature 409,860 -921.[CrossRef][Medline]
Landry, J. R., Medstrand, P. and Mager, D. L. (2001). Repetitive elements in the 5' untranslated region of a human zinc-finger gene modulate transcription and translation efficiency. Genomics 76,110 -116.[CrossRef][Medline]
Lanz, R. B., McKenna, N. J., Onate, S. A., Albrecht, U., Wong, J., Tsai, S. Y., Tsai, M. J. and O'Malley, B. W. (1999). A steroid receptor coactivator, SRA, functions as an RNA and is present in an SRC-1 complex. Cell 97,17 -27.[CrossRef][Medline]
Lanz, R. B., Razani, B., Goldberg, A. D. and O'Malley, B. W.
(2002). Distinct RNA motifs are important for coactivation of
steroid hormone receptors by steroid receptor RNA activator (SRA).
Proc. Natl. Acad. Sci. USA
99,16081
-16086.
Lau, N. C., Lim, L. P., Weinstein, E. G. and Bartel, D. P.
(2001). An abundant class of tiny RNAs with probable regulatory
roles in Caenorhabditis elegans. Science
294,858
-862.
Lau, N. C., Seto, A. G., Kim, J., Kuramochi-Miyagawa, S.,
Nakano, T., Bartel, D. P. and Kingston, R. E. (2006).
Characterization of the piRNA complex from rat testes.
Science 313,363
-367.
Lee, J. T., Davidow, L. S. and Warshawsky, D. (1999). Tsix, a gene antisense to Xist at the X-inactivation centre. Nat. Genet. 21,400 -404.[CrossRef][Medline]
Lee, R. C. and Ambros, V. (2001). An extensive
class of small RNAs in Caenorhabditis elegans. Science
294,862
-864.
Lee, R. C., Feinbaum, R. L. and Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75,843 -854.[CrossRef][Medline]
Lei, E. P. and Corces, V. G. (2006a). A long-distance relationship between RNAi and Polycomb. Cell 124,886 -888.[CrossRef][Medline]
Lei, E. P. and Corces, V. G. (2006b). RNA interference machinery influences the nuclear organization of a chromatin insulator. Nat. Genet. 38,936 -941.[CrossRef][Medline]
Lemons, D. and McGinnis, W. (2006). Genomic
evolution of Hox gene clusters. Science
313,1918
-1922.
Lescoute, A., Leontis, N. B., Massire, C. and Westhof, E.
(2005). Recurrent structural RNA motifs, isostericity matrices
and sequence alignments. Nucleic Acids Res.
33,2395
-2409.
Lev-Maor, G., Sorek, R., Shomron, N. and Ast, G.
(2003). The birth of an alternatively spliced exon: 3'
splice-site selection in Alu exons. Science
300,1288
-1291.
Levanon, E. Y., Eisenberg, E., Yelin, R., Nemzer, S., Hallegger, M., Shemesh, R., Fligelman, Z. Y., Shoshan, A., Pollock, S. R., Sztybel, D. et al. (2004). Systematic identification of abundant A-to-I editing sites in the human transcriptome. Nat. Biotechnol. 22,1001 -1005.[CrossRef][Medline]
Levine, M. and Davidson, E. H. (2005). Gene
regulatory networks for development. Proc. Natl. Acad. Sci.
USA 102,4936
-4942.
Levine, M. and Tjian, R. (2003). Transcription regulation and animal diversity. Nature 424,147 -151.[CrossRef][Medline]
Lewis, B. P., Burge, C. B. and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120,15 -20.[CrossRef][Medline]
Li, L. C., Okino, S. T., Zhao, H., Pookot, D., Place, R. F.,
Urakami, S., Enokida, H. and Dahiya, R. (2006). Small dsRNAs
induce transcriptional activation in human cells. Proc. Natl. Acad.
Sci. USA 103,17337
-17342.
Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S. and Johnson, J. M. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433,769 -773.[CrossRef][Medline]
Lindow, M. and Krogh, A. (2005). Computational evidence for hundreds of non-conserved plant microRNAs. BMC Genomics 6,119 .[Medline]
Lippman, Z. and Martienssen, R. (2004). The role of RNA interference in heterochromatic silencing. Nature 431,364 -370.[CrossRef][Medline]
Lippman, Z., Gendrel, A. V., Black, M., Vaughn, M. W., Dedhia, N., McCombie, W. R., Lavine, K., Mittal, V., May, B., Kasschau, K. D. et al. (2004). Role of transposable elements in heterochromatin and epigenetic control. Nature 430,471 -476.[CrossRef][Medline]
Lipshitz, H. D., Peattie, D. A. and Hogness, D. S.
(1987). Novel transcripts from the Ultrabithorax domain of the
bithorax complex. Genes Dev.
1, 307-322.
Liu, A. Y., Torchia, B. S., Migeon, B. R. and Siliciano, R. F. (1997). The human NTT gene: identification of a novel 17-kb noncoding nuclear RNA expressed in activated CD4+ T cells. Genomics 39,171 -184.[CrossRef][Medline]
Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A. et al. (2005). MicroRNA expression profiles classify human cancers. Nature 435,834 -838.[CrossRef][Medline]
Lund, A. H. and van Lohuizen, M. (2004). Polycomb complexes and silencing mechanisms. Curr. Opin. Cell Biol. 16,239 -246.[CrossRef][Medline]
Lunter, G., Ponting, C. P. and Hein, J. (2006). Genome-wide identification of human functional DNA using a neutral indel model. PLoS Comput. Biol. 2, e5.[CrossRef][Medline]
Maison, C., Bailly, D., Peters, A. H., Quivy, J. P., Roche, D., Taddei, A., Lachner, M., Jenuwein, T. and Almouzni, G. (2002). Higher-order structure in pericentric heterochromatin involves a distinct pattern of histone modification and an RNA component. Nat. Genet. 30,329 -334.[CrossRef][Medline]
Manak, J. R., Dike, S., Sementchenko, V., Kapranov, P., Biemar, F., Long, J., Cheng, J., Bell, I., Ghosh, S., Piccolboni, A. et al. (2006). Biological function of unannotated transcription during the early development of Drosophila melanogaster. Nat. Genet. 38,1151 -1158.[CrossRef][Medline]
Mansfield, J. H., Harfe, B. D., Nissen, R., Obenauer, J., Srineel, J., Chaudhuri, A., Farzan-Kashani, R., Zuker, M., Pasquinelli, A. E., Ruvkun, G. et al. (2004). MicroRNA-responsive `sensor' transgenes uncover Hox-like and other developmentally regulated patterns of vertebrate microRNA expression. Nat. Genet. 36,1079 -1083.[CrossRef][Medline]
Margueron, R., Trojer, P. and Reinberg, D. (2005). The key to development: interpreting the histone code? Curr. Opin. Genet. Dev. 15,163 -176.[CrossRef][Medline]
Martienssen, R. A., Zaratiegui, M. and Goto, D. B. (2005). RNA interference and heterochromatin in the fission yeast Schizosaccharomyces pombe. Trends Genet. 21,450 -456.[CrossRef][Medline]
Martinez-Abarca, F. and Toro, N. (2000). Group II introns in the bacterial world. Mol. Microbiol. 38,917 -926.[CrossRef][Medline]
Matlik, K., Redik, K. and Speek, M. (2006). L1 antisense promoter drives tissue-specific transcription of human genes. J. Biomed. Biotechnol. 2006,71753 .[Medline]
Mattick, J. S. (1994). Introns: evolution and function. Curr. Opin. Genet. Dev. 4, 823-831.[CrossRef][Medline]
Mattick, J. S. (2001). Non-coding RNAs: the architects of eukaryotic complexity. EMBO Rep. 2, 986-991.[CrossRef][Medline]
Mattick, J. S. (2003). Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms. BioEssays 25,930 -939.[CrossRef][Medline]
Mattick, J. S. (2004). RNA regulation: a new genetics? Nat. Rev. Genet. 5, 316-323.[CrossRef][Medline]
Mattick, J. S. (2005). The functional genomics
of noncoding RNA. Science
309,1527
-1528.
Mattick, J. S. and Gagen, M. J. (2001). The
evolution of controlled multitasked gene networks: the role of introns and
other noncoding RNAs in the development of complex organisms. Mol.
Biol. Evol. 18,1611
-1630.
Mattick, J. S. and Gagen, M. J. (2005).
Accelerating networks. Science
307,856
-858.
Mattick, J. S. and Makunin, I. V. (2005). Small
regulatory RNAs in mammals. Hum. Mol. Genet.
14,R121
-R132.
Mattick, J. S. and Makunin, I. V. (2006).
Non-coding RNA. Hum. Mol. Genet.
15,R17
-R29.
Matzke, M., Matzke, A. J. and Kooter, J. M.
(2001). RNA: guiding gene silencing.
Science 293,1080
-1083.
Maurer-Stroh, S., Dickens, N. J., Hughes-Davies, L., Kouzarides, T., Eisenhaber, F. and Ponting, C. P. (2003). The Tudor domain `Royal Family': tudor, plant Agenet, chromo, PWWP and MBT domains. Trends Biochem. Sci. 28,69 -74.[CrossRef][Medline]
McCarthy, J. V. (2003). Apoptosis and development. Essays Biochem. 39, 11-24.[Medline]
Mehler, M. F. and Mattick, J. S. (2007). Non-protein-coding RNAs and RNA editing in brain development, functional diversification and neurological disease. Physiol. Rev. 87 (in press).
Meier, U. T. (2005). The many facets of H/ACA ribonucleoproteins. Chromosoma 114, 1-14.[CrossRef][Medline]
Mette, M. F., Aufsatz, W., van der Winden, J., Matzke, M. A. and Matzke, A. J. (2000). Transcriptional silencing and promoter methylation triggered by double-stranded RNA. EMBO J. 19,5194 -5201.[CrossRef][Medline]
Migeon, B. R., Chowdhury, A. K., Dunston, J. A. and McIntosh, I. (2001). Identification of TSIX, encoding an RNA antisense to human XIST, reveals differences from its murine counterpart: implications for X inactivation. Am. J. Hum. Genet. 69,951 -960.[CrossRef][Medline]
Mochizuki, K. and Gorovsky, M. A. (2004). Small RNAs in genome rearrangement in Tetrahymena. Curr. Opin. Genet. Dev. 14,181 -187.[CrossRef][Medline]
Morison, I. M., Ramsay, J. P. and Spencer, H. G. (2005). A census of mammalian imprinting. Trends Genet. 21,457 -465.[CrossRef][Medline]
Morris, K. V., Chan, S. W., Jacobsen, S. E. and Looney, D.
J. (2004). Small interfering RNA-induced transcriptional gene
silencing in human cells. Science
305,1289
-1292.
Murone, M., Rosenthal, A. and de Sauvage, F. J. (1999). Hedgehog signal transduction: from flies to vertebrates. Exp. Cell Res. 253,25 -33.[CrossRef][Medline]
Mutsuddi, M., Marshall, C. M., Benzow, K. A., Koob, M. D. and Rebay, I. (2004). The spinocerebellar ataxia 8 noncoding RNA causes neurodegeneration and associates with staufen in Drosophila. Curr. Biol. 14,302 -308.[CrossRef][Medline]
Naguibneva, I., Ameyar-Zazoua, M., Polesskaya, A., Ait-Si-Ali, S., Groisman, R., Souidi, M., Cuvellier, S. and Harel-Bellan, A. (2006). The microRNA miR-181 targets the homeobox protein Hox-A11 during mammalian myoblast differentiation. Nat. Cell Biol. 8,278 -284.[CrossRef][Medline]
Navaratnam, N. and Sarwar, R. (2006). An overview of cytidine deaminases. Int. J. Hematol. 83,195 -200.[CrossRef][Medline]
Negre, N., Hennetin, J., Sun, L. V., Lavrov, S., Bellis, M., White, K. P. and Cavalli, G. (2006). Chromosomal distribution of PcG proteins during Drosophila development. PLoS Biol. 4,e170 .[CrossRef][Medline]
Nesterova, T. B., Slobodyanyuk, S. Y., Elisaphenko, E. A.,
Shevchenko, A. I., Johnston, C., Pavlova, M. E., Rogozin, I. B., Kolesnikov,
N. N., Brockdorff, N. and Zakian, S. M. (2001).
Characterization of the genomic Xist locus in rodents reveals conservation of
overall gene structure and tandem repeats but rapid evolution of unique
sequence. Genome Res.
11,833
-849.
Nigumann, P., Redik, K., Matlik, K. and Speek, M. (2002). Many human genes are transcribed from the antisense promoter of L1 retrotransposon. Genomics 79,628 -634.[CrossRef][Medline]
Nikaido, I., Saito, C., Mizuno, Y., Meguro, M., Bono, H.,
Kadomura, M., Kono, T., Morris, G. A., Lyons, P. A., Oshimura, M. et al.
(2003). Discovery of imprinted transcripts in the mouse
transcriptome using large-scale expression profiling. Genome
Res. 13,1402
-1409.
Nishihara, H., Smit, A. F. and Okada, N.
(2006). Functional noncoding sequences derived from SINEs in the
mammalian genome. Genome Res.
16,864
-874.
Ogiwara, I., Miya, M., Ohshima, K. and Okada, N.
(2002). V-SINEs: a new superfamily of vertebrate SINEs that are
widespread in vertebrate genomes and retain a strongly conserved segment
within each repetitive unit. Genome Res.
12,316
-324.
Okazaki, Y., Furuno, M., Kasukawa, T., Adachi, J., Bono, H., Kondo, S., Nikaido, I., Osato, N., Saito, R., Suzuki, H. et al. (2002). Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420,563 -573.[CrossRef][Medline]
Olivas, W. M., Muhlrad, D. and Parker, R.
(1997). Analysis of the yeast genome: identification of new
non-coding and small ORF-containing RNAs. Nucleic Acids
Res. 25,4619
-4625.
Orgel, L. E. and Crick, F. H. (1980). Selfish DNA: the ultimate parasite. Nature 284,604 -607.[CrossRef][Medline]
Ovcharenko, I., Loots, G. G., Nobrega, M. A., Hardison, R. C.,
Miller, W. and Stubbs, L. (2005). Evolution and functional
classification of vertebrate gene deserts. Genome Res.
15,137
-145.
Padgett, R. A., Grabowski, P. J., Konarska, M. M., Seiler, S. and Sharp, P. A. (1986). Splicing of messenger RNA precursors. Annu. Rev. Biochem. 55,1119 -1150.[CrossRef][Medline]
Pal-Bhadra, M., Leibovitch, B. A., Gandhi, S. G., Rao, M.,
Bhadra, U., Birchler, J. A. and Elgin, S. C. (2004).
Heterochromatic silencing and HP1 localization in Drosophila are dependent on
the RNAi machinery. Science
303,669
-672.
Palmer, J. D. and Logsdon, J. M., Jr (1991). The recent origins of introns. Curr. Opin. Genet. Dev. 1, 470-477.[CrossRef][Medline]
Pandorf, C. E., Haddad, F., Roy, R. R., Qin, A. X., Edgerton, V.
R. and Baldwin, K. M. (2006). Dynamics of myosin heavy chain
gene regulation in slow skeletal muscle: role of natural antisense RNA.
J. Biol. Chem. 281,38330
-38342.
Pang, K. C., Stephen, S., Engstrom, P. G., Tajul-Arifin, K.,
Chen, W., Wahlestedt, C., Lenhard, B., Hayashizaki, Y. and Mattick, J. S.
(2005). RNAdb – a comprehensive mammalian noncoding RNA
database. Nucleic Acids Res.
33,D125
-D130.
Pang, K. C., Frith, M. C. and Mattick, J. S. (2006). Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function. Trends Genet. 22, 1-5.[CrossRef][Medline]
Pasquinelli, A. E., Hunter, S. and Bracht, J. (2005). MicroRNAs: a developing story. Curr. Opin. Genet. Dev. 15,200 -205.[CrossRef][Medline]
Pazman, C., Mayes, C. A., Fanto, M., Haynes, S. R. and Mlodzik, M. (2000). Rasputin, the Drosophila homologue of the RasGAP SH3 binding protein, functions in ras- and Rho-mediated signalling. Development 127,1715 -1725.[Abstract]
Peaston, A. E., Evsikov, A. V., Graber, J. H., de Vries, W. N., Holbrook, A. E., Solter, D. and Knowles, B. B. (2004). Retrotransposons regulate host genes in mouse oocytes and preimplantation embryos. Dev. Cell 7,597 -606.[CrossRef][Medline]
Peterson, C. L. and Laniel, M. A. (2004). Histones and histone modifications. Curr. Biol. 14,R546 -R551.[CrossRef][Medline]
Petruk, S., Sedkov, Y., Riley, K. M., Hodgson, J., Schweisguth, F., Hirose, S., Jaynes, J. B., Brock, H. W. and Mazo, A. (2006). Transcription of bxd noncoding RNAs promoted by trithorax represses Ubx in cis by transcriptional interference. Cell 127,1209 -1221.[CrossRef][Medline]
Plunkett, K., Karmiloff-Smith, A., Bates, E., Elman, J. L. and Johnson, M. H. (1997). Connectionism and developmental psychology. J. Child Psychol. Psychiatry 38, 53-80.[Medline]
Pollard, K. S., Salama, S. R., Lambert, N., Lambot, M. A., Coppens, S., Pedersen, J. S., Katzman, S., King, B., Onodera, C., Siepel, A. et al. (2006). An RNA gene expressed during cortical development evolved rapidly in humans. Nature 443,167 -172.[CrossRef][Medline]
Pozzoli, U. and Sironi, M. (2005). Silencers regulate both constitutive and alternative splicing events in mammals. Cell. Mol. Life Sci. 62,1579 -1604.[CrossRef][Medline]
Prasanth, K. V., Prasanth, S. G., Xuan, Z., Hearn, S., Freier, S. M., Bennett, C. F., Zhang, M. Q. and Spector, D. L. (2005). Regulating gene expression through RNA nuclear retention. Cell 123,249 -263.[CrossRef][Medline]
Prochnik, S. E., Rokhsar, D. S. and Aboobaker, A. A. (2007). Evidence for a microRNA expansion in the bilaterian ancestor. Dev. Genes Evol. 217, 73-77.[CrossRef][Medline]
Qi, Y., He, X., Wang, X. J., Kohany, O., Jurka, J. and Hannon, G. J. (2006). Distinct catalytic and non-catalytic roles of ARGONAUTE4 in RNA-directed DNA methylation. Nature 443,1008 -1012.[CrossRef][Medline]
Ravasi, T., Suzuki, H., Pang, K. C., Katayama, S., Furuno, M.,
Okunishi, R., Fukuda, S., Ru, K., Frith, M. C., Gongora, M. M. et al.
(2006). Experimental validation of the regulated expression of
large numbers of non-coding RNAs from the mouse genome. Genome
Res. 16,11
-19.
Reinhart, B. J., Slack, F. J., Basson, M., Pasquinelli, A. E., Bettinger, J. C., Rougvie, A. E., Horvitz, H. R. and Ruvkun, G. (2000). The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403,901 -906.[CrossRef][Medline]
Reis, E. M., Nakaya, H. I., Louro, R., Canavez, F. C., Flatschart, A. V., Almeida, G. T., Egidio, C. M., Paquola, A. C., Machado, A. A., Festa, F. et al. (2004). Antisense intronic non-coding RNA levels correlate to the degree of tumor differentiation in prostate cancer. Oncogene 23,6684 -6692.[CrossRef][Medline]
Rigoutsos, I., Huynh, T., Miranda, K., Tsirigos, A., McHardy, A.
and Platt, D. (2006). Short blocks from the noncoding parts
of the human genome have instances within nearly all known genes and relate to
biological processes. Proc. Natl. Acad. Sci. USA
103,6605
-6610.
Ringrose, L. and Paro, R. (2004). Epigenetic regulation of cellular memory by the Polycomb and Trithorax group proteins. Annu. Rev. Genet. 38,413 -443.[CrossRef][Medline]
Ringrose, L. and Paro, R. (2007).
Polycomb/Trithorax response elements and epigenetic memory of cell identity.
Development 134,223
-232.
Roberts, J., Palma, E., Sazani, P., Orum, H., Cho, M. and Kole, R. (2006). Efficient and persistent splice switching by systemically delivered LNA oligonucleotides in mice. Mol. Ther. 14,471 -475.[CrossRef][Medline]
Rodriguez, A., Griffiths-Jones, S., Ashurst, J. L. and Bradley,
A. (2004). Identification of mammalian microRNA host genes
and transcription units. Genome Res.
14,1902
-1910.
Rogelj, B. and Giese, K. P. (2004). Expression and function of brain specific small RNAs. Rev. Neurosci. 15,185 -198.[Medline]
Ronshaugen, M. and Levine, M. (2004). Visualization of trans-homolog enhancer-promoter interactions at the Abd-B Hox locus in the Drosophila embryo. Dev. Cell 7, 925-932.[CrossRef][Medline]
Ronshaugen, M., Biemar, F., Piel, J., Levine, M. and Lai, E.
C. (2005). The Drosophila microRNA iab-4 causes a dominant
homeotic transformation of halteres to wings. Genes
Dev. 19,2947
-2952.
Ruby, J. G., Jan, C., Player, C., Axtell, M. J., Lee, W., Nusbaum, C., Ge, H. and Bartel, D. P. (2006). Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127,1193 -1207.[CrossRef][Medline]
Runte, M., Huttenhofer, A., Gross, S., Kiefmann, M., Horsthemke,
B. and Buiting, K. (2001). The IC-SNURF-SNRPN transcript
serves as a host for multiple small nucleolar RNA species and as an antisense
RNA for UBE3A. Hum. Mol. Genet.
10,2687
-2700.
Ruvkun, G. (2001). Glimpses of a tiny RNA
world. Science 294,797
-799.
Salehi-Ashtiani, K., Luptak, A., Litovchick, A. and Szostak, J.
W. (2006). A genomewide search for ribozymes reveals an
HDV-like sequence in the human CPEB3 gene. Science
313,1788
-1792.
Sanchez-Elsner, T., Gou, D., Kremmer, E. and Sauer, F.
(2006). Noncoding RNAs of trithorax response elements recruit
Drosophila Ash1 to Ultrabithorax. Science
311,1118
-1123.
Sanchez-Herrero, E. and Akam, M. (1989). Spatially ordered transcription of regulatory DNA in the bithorax complex of Drosophila. Development 107,321 -329.[Abstract]
Sanges, R., Kalmar, E., Claudiani, P., D'Amato, M., Muller, F. and Stupka, E. (2006). Shuffling of cis-regulatory elements is a pervasive feature of the vertebrate lineage. Genome Biol. 7,R56 .[CrossRef][Medline]
Saunders, L. R. and Barber, G. N. (2003). The
dsRNA binding protein family: critical roles, diverse cellular functions.
FASEB J. 17,961
-983.
Schmitt, S. and Paro, R. (2006). RNA at the steering wheel. Genome Biol. 7, 218.[CrossRef][Medline]
Schmitt, S., Prestel, M. and Paro, R. (2005).
Intergenic transcription through a polycomb group response element counteracts
silencing. Genes Dev.
19,697
-708.
Schwartz, Y. B. and Pirrotta, V. (2007). Polycomb silencing mechanisms and the management of genomic programmes. Nat. Rev. Genet. 8,9 -22.[Medline]
Schwartz, Y. B., Kahn, T. G., Nix, D. A., Li, X. Y., Bourgon, R., Biggin, M. and Pirrotta, V. (2006). Genome-wide analysis of Polycomb targets in Drosophila melanogaster. Nat. Genet. 38,700 -705.[CrossRef][Medline]
Seitz, H., Royo, H., Bortolin, M. L., Lin, S. P.,
Ferguson-Smith, A. C. and Cavaille, J. (2004). A large
imprinted microRNA gene cluster at the mouse Dlk1-Gtl2 domain.
Genome Res. 14,1741
-1748.
Sessa, L., Breiling, A., Lavorgna, G., Silvestri, L., Casari, G.
and Orlando, V. (2007). Noncoding RNA synthesis and loss of
Polycomb group repression accompanies the colinear activation of the human
HOXA cluster. RNA 13,223
-239.
Shamovsky, I., Ivannikov, M., Kandel, E. S., Gershon, D. and Nudler, E. (2006). RNA-mediated response to heat shock in mammalian cells. Nature 440,556 -560.[CrossRef][Medline]
Shanab, G. M. and Maxwell, E. S. (1992). Determination of the nucleotide sequences in mouse U14 small nuclear RNA and 18S ribosomal RNA responsible for in vitro intermolecular base-pairing. Eur. J. Biochem. 206,391 -400.[Medline]
Shi, Y. and Berg, J. M. (1995). Specific
DNA-RNA hybrid binding by zinc finger proteins.
Science 268,282
-284.
Silva, J. C., Shabalina, S. A., Harris, D. G., Spouge, J. L. and Kondrashovi, A. S. (2003). Conserved fragments of transposable elements in intergenic regions: evidence for widespread recruitment of MIR- and L2-derived sequences within the mouse and human genomes. Genet. Res. 82,1 -18.[CrossRef][Medline]
Simons, C., Pheasant, M., Makunin, I. V. and Mattick, J. S.
(2006). Transposon-free regions in mammalian genomes.
Genome Res. 16,164
-172.
Sironi, M., Menozzi, G., Comi, G. P., Cagliani, R., Bresolin, N.
and Pozzoli, U. (2005). Analysis of intronic conserved
elements indicates that functional complexity might represent a major source
of negative selection on non-coding sequences. Hum. Mol.
Genet. 14,2533
-2546.
Sleutels, F., Zwart, R. and Barlow, D. P. (2002). The non-coding Air RNA is required for silencing autosomal imprinted genes. Nature 415,810 -813.[Medline]
Smalheiser, N. R. and Torvik, V. I. (2005). Mammalian microRNAs derived from genomic repeats. Trends Genet. 21,322 -326.[CrossRef][Medline]
Smalheiser, N. R. and Torvik, V. I. (2006). Alu elements within human mRNAs are probable microRNA targets. Trends Genet. 22,532 -536.[CrossRef][Medline]
Smit, A. F. and Riggs, A. D. (1995). MIRs are
classic, tRNA-derived SINEs that amplified before the mammalian radiation.
Nucleic Acids Res. 23,98
-102.
Smit, M., Segers, K., Carrascosa, L. G., Shay, T., Baraldi, F., Gyapay, G., Snowder, G., Georges, M., Cockett, N. and Charlier, C. (2003). Mosaicism of Solid Gold supports the causality of a noncoding A-to-G transition in the determinism of the callipyge phenotype. Genetics 163,453 -456.[Medline]
Smith, C. W. and Valcarcel, J. (2000). Alternative pre-mRNA splicing: the logic of combinatorial control. Trends Biochem. Sci. 25,381 -388.[CrossRef][Medline]
Smith, N. G., Brandstrom, M. and Ellegren, H. (2004). Evidence for turnover of functional noncoding DNA in mammalian genome evolution. Genomics 84,806 -813.[CrossRef][Medline]
Sodergren, E., Weinstock, G. M., Davidson, E. H., Cameron, R.
A., Gibbs, R. A., Angerer, R. C., Angerer, L. M., Arnone, M. I., Burgess, D.
R., Burke, R. D. et al. (2006). The genome of the sea urchin
Strongylocentrotus purpuratus. Science
314,941
-952.
Soller, M. (2006). Pre-messenger RNA processing and its regulation: a genomic perspective. Cell. Mol. Life Sci. 63,796 -819.[CrossRef][Medline]
Sonkoly, E., Bata-Csorgo, Z., Pivarcsi, A., Polyanka, H.,
Kenderessy-Szabo, A., Molnar, G., Szentpali, K., Bari, L., Megyeri, K., Mandi,
Y. et al. (2005). Identification and characterization of a
novel, psoriasis susceptibility-related noncoding RNA gene, PRINS.
J. Biol. Chem. 280,24159
-24167.
Sorek, R. and Ast, G. (2003). Intronic
sequences flanking alternatively spliced exons are conserved between human and
mouse. Genome Res. 13,1631
-1637.
Squazzo, S. L., O'Geen, H., Komashko, V. M., Krig, S. R., Jin,
V. X., Jang, S. W., Margueron, R., Reinberg, D., Green, R. and Farnham, P.
J. (2006). Suz12 binds to silenced regions of the genome in a
cell-type-specific manner. Genome Res.
16,890
-900.
Stamm, S., Ben-Ari, S., Rafalska, I., Tang, Y., Zhang, Z., Toiber, D., Thanaraj, T. A. and Soreq, H. (2005). Function of alternative splicing. Gene 344, 1-20.[CrossRef][Medline]
Stathopoulos, A. and Levine, M. (2005). Genomic regulatory networks and animal development. Dev. Cell 9, 449-462.[CrossRef][Medline]
Stein, L. D., Bao, Z., Blasiar, D., Blumenthal, T., Brent, M. R., Chen, N., Chinwalla, A., Clarke, L., Clee, C., Coghlan, A. et al. (2003). The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics. PLoS Biol. 1, E45.[Medline]
Sternberg, P. W. and Felix, M. A. (1997). Evolution of cell lineage. Curr. Opin. Genet. Dev. 7, 543-550.[CrossRef][Medline]
Sudharsanan, S. I. and Sundareshan, M. K. (1994). Supervised training of dynamical neural networks for associative memory design and identification of nonlinear maps. Int. J. Neural Syst. 5,165 -180.[CrossRef][Medline]
Sugnet, C. W., Kent, W. J., Ares, M., Jr and Haussler, D. (2004). Transcriptome and genome conservation of alternative splicing events in humans and mice. Pac. Symp. Biocomput. 2004,66 -77.
Sugnet, C. W., Srinivasan, K., Clark, T. A., O'Brien, G., Cline, M. S., Wang, H., Williams, A., Kulp, D., Blume, J. E., Haussler, D. et al. (2006). Unusual intron conservation near tissue-regulated exons found by splicing microarrays. PLoS Comput. Biol. 2, e4.[CrossRef][Medline]
Sun, H., Skogerbo, G. and Chen, R. (2006).
Conserved distances between vertebrate highly conserved elements.
Hum. Mol. Genet. 15,2911
-2922.
Taft, R. J., Pheasant, M. and Mattick, J. S. (2007). The relationship between non-protein-coding DNA and eukaryotic complexity. BioEssays 29,288 -299.[CrossRef][Medline]
Takeda, K., Ichijo, H., Fujii, M., Mochida, Y., Saitoh, M.,
Nishitoh, H., Sampath, T. K. and Miyazono, K. (1998).
Identification of a novel bone morphogenetic protein-responsive gene that may
function as a noncoding RNA. J. Biol. Chem.
273,17079
-17085.
Tam, W., Ben-Yehuda, D. and Hayward, W. S. (1997). bic, a novel gene activated by proviral insertions in avian leukosis virus-induced lymphomas, is likely to function through its noncoding RNA. Mol. Cell. Biol. 17,1490 -1502.[Abstract]
Tang, G. (2005). siRNA and miRNA: an insight into RISCs. Trends Biochem. Sci. 30,106 -114.[CrossRef][Medline]
Taylor, M. S., Kai, C., Kawai, J., Carninci, P., Hayashizaki, Y. and Semple, C. A. (2006). Heterotachy in mammalian promoter evolution. PLoS Genet. 2, e30.[CrossRef][Medline]
Tillib, S., Petruk, S., Sedkov, Y., Kuzin, A., Fujioka, M.,
Goto, T. and Mazo, A. (1999). Trithorax- and Polycomb-group
response elements within an Ultrabithorax transcription maintenance unit
consist of closely situated but separable sequences. Mol. Cell.
Biol. 19,5189
-5202.
Ting, A. H., Schuebel, K. E., Herman, J. G. and Baylin, S. B. (2005). Short double-stranded RNA induces transcriptional gene silencing in human cancer cells in the absence of DNA methylation. Nat. Genet. 37,906 -910.[CrossRef][Medline]
Truss, M., Swat, M., Kielbasa, S. M., Schafer, R., Herzel, H.
and Hagemeier, C. (2005). HuSiDa–the human siRNA
database: an open-access database for published functional siRNA sequences and
technical details of efficient transfer into recipient cells.
Nucleic Acids Res. 33,D108
-D111.
Tufarelli, C., Stanley, J. A., Garrick, D., Sharpe, J. A., Ayyub, H., Wood, W. G. and Higgs, D. R. (2003). Transcription of antisense RNA leading to gene silencing and methylation as a novel cause of human genetic disease. Nat. Genet. 34,157 -165.[CrossRef][Medline]
Tycowski, K. T., Shu, M. D. and Steitz, J. A. (1996). A mammalian gene with introns instead of exons generating stable RNA products. Nature 379,464 -466.[CrossRef][Medline]
Valente, L. and Nishikura, K. (2005). ADAR gene family and A-to-I RNA editing: diverse roles in posttranscriptional gene regulation. Prog. Nucleic Acid Res. Mol. Biol. 79,299 -338.[CrossRef][Medline]
Van Laere, A. S., Nguyen, M., Braunschweig, M., Nezer, C., Collette, C., Moreau, L., Archibald, A. L., Haley, C. S., Buys, N., Tally, M. et al. (2003). A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425,832 -836.[CrossRef][Medline]
van Nimwegen, E. (2003). Scaling laws in the functional content of genomes. Trends Genet. 19,479 -484.[CrossRef][Medline]
Velleca, M. A., Wallace, M. C. and Merlie, J. P.
(1994). A novel synapse-associated noncoding RNA. Mol.
Cell. Biol. 14,7095
-7104.
Verdel, A. and Moazed, D. (2005). RNAi-directed assembly of heterochromatin in fission yeast. FEBS Lett. 579,5872 -5878.[CrossRef][Medline]
Vitali, P., Royo, H., Seitz, H., Bachellerie, J. P.,
Huttenhofer, A. and Cavaille, J. (2003). Identification of 13
novel human modification guide RNAs. Nucleic Acids
Res. 31,6543
-6551.
Vogel, J. and Sharma, C. M. (2005). How to find small non-coding RNAs in bacteria. Biol. Chem. 386,1219 -1238.[CrossRef][Medline]
Volff, J. N. (2006). Turning junk into gold: domestication of transposable elements and the creation of new genes in eukaryotes. BioEssays 28,913 -922.[CrossRef][Medline]
Wagner, G. P., Fried, C., Prohaska, S. J. and Stadler, P. F.
(2004). Divergence of conserved non-coding sequences: rate
estimates and relative rate tests. Mol. Biol. Evol.
21,2116
-2121.
Wang, X., McLachlan, J., Zamore, P. D. and Hall, T. M. (2002). Modular recognition of RNA by a human pumilio-homology domain. Cell 110,501 -512.[CrossRef][Medline]
Wang, Y., Davies, K. J., Melendez, J. A. and Crawford, D. R. (2003). Characterization of adapt33, a stress-inducible riboregulator. Gene Expr. 11, 85-94.[Medline]
Washietl, S., Hofacker, I. L., Lukasser, M., Huttenhofer, A. and Stadler, P. F. (2005). Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nat. Biotechnol. 23,1383 -1390.[CrossRef][Medline]
Wassenegger, M. (2000). RNA-directed DNA methylation. Plant Mol. Biol. 43,203 -220.[CrossRef][Medline]
Watanabe, T., Miyashita, K., Saito, T. T., Yoneki, T., Kakihara,
Y., Nabeshima, K., Kishi, Y. A., Shimoda, C. and Nojima, H.
(2001). Comprehensive isolation of meiosis-specific genes
identifies novel proteins and unusual non-coding transcripts in
Schizosaccharomyces pombe. Nucleic Acids Res.
29,2327
-2337.
Watanabe, T., Miyashita, K., Saito, T. T., Nabeshima, K. and Nojima, H. (2002). Abundant poly(A)-bearing RNAs that lack open reading frames in Schizosaccharomyces pombe. DNA Res. 9,209 -215.[Abstract]
Waterston, R. H., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, J. F., Agarwal, P., Agarwala, R., Ainscough, R., Alexandersson, M., An, P. et al. (2002). Initial sequencing and comparative analysis of the mouse genome. Nature 420,520 -562.[CrossRef][Medline]
Weinstein, S. M. and Keim, A. (1965). Fundamentals of Digital Computers. New York: Holt, Rinehart and Winston.
Weiss, A., Keshet, I., Razin, A. and Cedar, H. (1996). DNA demethylation in vitro: involvement of RNA. Cell 86,709 -718.[CrossRef][Medline]
Werner, A. (2005). Natural antisense transcripts. RNA Biol. 2, 53-62.[Medline]
Werner, A. and Berdal, A. (2005). Natural
antisense transcripts: sound or silence? Physiol.
Genomics 23,125
-131.
Whitelaw, E. and Martin, D. I. (2001). Retrotransposons as epigenetic mediators of phenotypic variation in mammals. Nat. Genet. 27,361 -365.[CrossRef][Medline]
Wienholds, E., Kloosterman, W. P., Miska, E., Alvarez-Saavedra,
E., Berezikov, E., de Bruijn, E., Horvitz, H. R., Kauppinen, S. and Plasterk,
R. H. (2005). MicroRNA expression in zebrafish embryonic
development. Science
309,310
-311.
Williamson, B. (1977). DNA insertions and gene structure. Nature 270,295 -297.[CrossRef]
Willingham, A. T., Orth, A. P., Batalov, S., Peters, E. C., Wen,
B. G., Aza-Blanc, P., Hogenesch, J. B. and Schultz, P. G.
(2005). A strategy for probing the function of noncoding RNAs
finds a repressor of NFAT. Science
309,1570
-1573.
Wilton, S. D. and Fletcher, S. (2005). RNA splicing manipulation: strategies to modify gene expression for a variety of therapeutic outcomes. Curr. Gene Ther. 5, 467-483.[CrossRef][Medline]
Winkler, W. C. (2005). Riboswitches and the role of noncoding RNAs in bacterial metabolic control. Curr. Opin. Chem. Biol. 9,594 -602.[CrossRef][Medline]
Wood, V., Gwilliam, R., Rajandream, M. A., Lyne, M., Lyne, R., Stewart, A., Sgouros, J., Peat, N., Hayles, J., Baker, S. et al. (2002). The genome sequence of Schizosaccharomyces pombe.Nature 415,871 -880.[CrossRef][Medline]
Wrana, J. L. (1994). H19, a tumour suppressing RNA? BioEssays 16,89 -90.[CrossRef][Medline]
Xie, X., Kamal, M. and Lander, E. S. (2006). A
family of conserved noncoding elements derived from an ancient transposable
element. Proc. Natl. Acad. Sci. USA
103,11659
-11664.
Yan, M. D., Hong, C. C., Lai, G. M., Cheng, A. L., Lin, Y. W.
and Chuang, S. E. (2005). Identification and characterization
of a novel gene Saf transcribed from the opposite strand of Fas.
Hum. Mol. Genet. 14,1465
-1474.
Yang, W., Chendrimada, T. P., Wang, Q., Higuchi, M., Seeburg, P. H., Shiekhattar, R. and Nishikura, K. (2006). Modulation of microRNA processing and expression through RNA editing by ADAR deaminases. Nat. Struct. Mol. Biol. 13, 13-21.[CrossRef][Medline]
Yekta, S., Shih, I. H. and Bartel, D. P.
(2004). MicroRNA-directed cleavage of HOXB8 mRNA.
Science 304,594
-596.
Ying, S. Y. and Lin, S. L. (2005). Intronic microRNAs. Biochem. Biophys. Res. Commun. 326,515 -520.[CrossRef][Medline]
Young, T. L., Matsuda, T. and Cepko, C. L. (2005). The noncoding RNA taurine upregulated gene 1 is required for differentiation of the murine retina. Curr. Biol. 15,501 -512.[CrossRef][Medline]
Zamore, P. D. and Haley, B. (2005). Ribo-gnome:
the big world of small RNAs. Science
309,1519
-1524.
Zamore, P. D., Tuschl, T., Sharp, P. A. and Bartel, D. P. (2000). RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell 101,25 -33.[CrossRef][Medline]
Zekri, L., Chebli, K., Tourriere, H., Nielsen, F. C., Hansen, T.
V., Rami, A. and Tazi, J. (2005). Control of fetal growth and
neonatal survival by the RasGAP-associated endoribonuclease G3BP.
Mol. Cell. Biol. 25,8703
-8716.
Zhou, Y. H., Zheng, J. B., Gu, X., Saunders, G. F. and Yung, W.
K. (2002). Novel PAX6 binding sites in the human genome and
the role of repetitive elements in the evolution of gene regulation.
Genome Res. 12,1716
-1722.
Zuckerkandl, E. and Cavalli, G. (2007). Combinatorial epigenetics, `junk DNA', and the evolution of complex organisms. Gene 390,232 -242.[CrossRef][Medline]
Zuzarte-Luis, V. and Hurle, J. M. (2005). Programmed cell death in the embryonic vertebrate limb. Semin. Cell Dev. Biol. 16,261 -269.[CrossRef][Medline]
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg 
Reddit 
Technorati 
Twitter What's this?
Related articles in JEB:
This article has been cited by other articles:
|
|
 |
|
  M. E. Dinger, P. P. Amaral, T. R. Mercer, and J. S. Mattick Pervasive transcription of the eukaryotic genome: functional indices and conceptual implications Brief Funct Genomic Proteomic, November 1, 2009; 8(6): 407 - 423. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Childs, Z. Nikoloski, P. May, and D. Walther Identification and classification of ncRNA molecules using graph properties Nucleic Acids Res., May 1, 2009; 37(9): e66 - e66. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. G. Hawkins, S. Santoso, C. Adams, V. Anest, and K. V. Morris Promoter targeted small RNAs induce long-term transcriptional gene silencing in human cells Nucleic Acids Res., May 1, 2009; 37(9): 2984 - 2995. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. R. Mercer, M. E. Dinger, J. Mariani, K. S. Kosik, M. F. Mehler, and J. S. Mattick Noncoding RNAs in Long-Term Memory Formation Neuroscientist, October 1, 2008; 14(5): 434 - 445. [Abstract] [PDF]
|
|
|
|
 |
|
 M. E. Dinger, P. P. Amaral, T. R. Mercer, K. C. Pang, S. J. Bruce, B. B. Gardiner, M. E. Askarian-Amiri, K. Ru, G. Solda, C. Simons, et al. Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation Genome Res., September 1, 2008; 18(9): 1433 - 1445. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Tsirigos and I. Rigoutsos Human and mouse introns are linked to the same processes and functions through each genome's most frequent non-conserved motifs Nucleic Acids Res., June 1, 2008; 36(10): 3484 - 3493. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. E Dinger, T. R Mercer, and J. S Mattick RNAs as extracellular signaling molecules J. Mol. Endocrinol., April 1, 2008; 40(4): 151 - 159. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. P. Amaral, M. E. Dinger, T. R. Mercer, and J. S. Mattick The Eukaryotic Genome as an RNA Machine Science, March 28, 2008; 319(5871): 1787 - 1789. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Pheasant and J. S. Mattick Raising the estimate of functional human sequences Genome Res., September 1, 2007; 17(9): 1245 - 1253. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Carninci Constructing the landscape of the mammalian transcriptome J. Exp. Biol., May 1, 2007; 210(9): 1497 - 1506. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Quackenbush Extracting biology from high-dimensional biological data J. Exp. Biol., May 1, 2007; 210(9): 1507 - 1517. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
