INSIGHTS | PERSPECTIVES
1066 6 MARCH 2015 • VOL 347 ISSUE 6226 sciencemag.org SCIENCE
successful. These pioneering initiatives are
beginning to link pharmaceutical companies, academia, and disease experts across
the “gene to bedside” spectrum in the locations where these diseases have the greatest
impact. The MMV provides free, open access to a range of compounds with activity
against a range of pathogens for independent researchers to screen, with users requested to publish their data in the public
domain, thus continuing the drug development research cycle (17).
The impact of bacterial AMR in low-income countries is severe and likely to
worsen. New antimicrobial agents may
provide some respite against AMR and
infections caused by such drug-resistant
pathogens. However, introducing novel
broad-range antimicrobials into the current melee of antimicrobial use and misuse in lower-income countries would only
have a short-term limited impact on infections caused by potentially life-threatening
pathogens. Restricting the use of the same
classes of antimicrobial compounds in animals and humans has to be an immediate
priority, including a direct ban of any new
antimicrobials developed for treating infections in humans. Lastly, new antimicrobial
agents should only be administered to those
who really need them. This means that the
current capacity to perform microbial diagnostics and downstream antimicrobial
susceptibility testing needs to be greatly
improved, alongside the development of rational prescribing practice. ■
REFERENCES AND NOTES
1. Department of Health & Department for Environment
Food & Rural Affairs, UK Five Year Antimicrobial Resistance
Strategy 2013 to 2018 (2013).
2. Centers for Disease Control and Prevention, Antibiotic
Resistance Threats in the United States, 2013; www.
3. World Health Organization, “Antimicrobial resistance:
Global report on surveillance” (2014).
4. M. Perros, Science 347, 1062 (2015).
5. T.R. Walsh et al., Lancet Infect. Dis. 11,355(2011).
6. H. Chang The et al ., EMBO Mol. Med. 10.15252/
7. S. P. Stone et al ., BMJ 344, e3005 (2012).
8. K. E. Holt etal ., Proc.Natl.Acad.Sci.U.S.A. 110, 17522
9. A. Mutreja et al., Nature 477, 462 (2011).
10. P. Roumagnac et al ., Science 314, 1301 (2006).
11. S. Kariuki et al ., J. Clin. Microbiol.48, 2171 (2010).
12. C. M. Parry et al ., PLOS Negl. Trop. Dis. 5, e1163 (2011).
13. K. D. Koirala et al ., Antimicrob. Agents Chemother. 56, 2761
14. M. N. Robertson etal .,Parasitology 141, 148 (2014).
15. See www.gatesfoundation.org/What-We-Do/
16. See www.mmv.org.
17. K. Ingram-Sieber et al ., PLOS Negl. Trop. Dis. 8, e2610
S. B. is a Sir Henry Dale Fellow, jointly funded by the Wellcome
Trust and the Royal Society (100087/Z/12/Z).
Mammalian proteins are expressed at ~103 to 108 molecules per cell (1). Differences between cell types, between normal and disease states, and betweenindividuals are largely defined by changes in the
abundance of proteins, which are in turn
determined by rates of transcription, messenger RNA (mRNA) degradation, translation, and protein degradation. If the rates
for one of these steps differ much more
than the rates of the other three, that step
would be dominant in defining the variation in protein expression. Over the past
decade, system-wide studies have claimed
that in animals, differences in translation
rates predominate (2–5). On page 1112 of
this issue, Jovanovic et al. (6), as well as
recent studies by Battle et al. (7) and Li et
al. (1), challenge this conclusion, suggesting that transcriptional control makes the
Earlier studies used mass spectrometry,
DNA microarrays, and mRNA sequenc-
ing (mRNA-Seq) to measure protein and
mRNA levels for thousands of genes (2–5),
and also to measure the rates of mRNA
degradation, translation, and/or protein
degradation (through labeling with stable
isotopes) (4, 5). Some studies examined a
single cell type at steady state (5), whereas
others analyzed the differences between
tissue types (4), between tumors (2), or be-
tween inbred mouse strains (3). Each study
found a moderate to low correlation be-
tween protein and mRNA abundance data
(coefficient of determination R2 ≤ 0.4). This
was taken to suggest that no more than
40% of the variance in protein levels is
explained by variance in the rates of tran-
scription and mRNA degradation and, by
implication, that the remaining variance in
protein expression (≥60%) is explained by
translation and protein degradation (2–5).
By employing degradation rate data for
mRNAs and proteins in addition to abun-
dance data, it was further estimated that
transcription explains 34% of the variance
in protein abundance, mRNA degradation
6%, translation 55%, and protein degrada-
tion 5% (5) (see the figure).
The high-throughput methods used in
these studies, however, show substantial stochastic variation between replica data and
also suffer systematic, reproducible biases (1,
4–6, 8, 9). For example, label-free mass spectrometry can underestimate amounts of all
lower-abundance proteins by as much as a
factor of 10 (1, 8), and mRNA-Seq data are
biased by guanine-cytosine base pair content
by a factor of up to 3 (9). Because each type
of error has different causes and because
RNA and protein techniques differ greatly,
the errors should be uncorrelated. Thus,
the correlation of protein versus mRNA as
measured will be lower than that between
error-free data. The papers by Jovanovic et
al., Battle et al., and Li et al. used careful statistical efforts to estimate and/or reduce the
impact of errors and thereby find the higher
correlation expected between true protein
and true mRNA levels.
Jovanovic et al. examined mouse bone
marrow dendritic cells at steady state and
during response to bacterial lipopolysaccharide (LPS) (6). They used a Bayesian
model to estimate the true rates of translation and protein destruction from noisy
mass spectrometry data. In addition, three
independent estimates of protein abundance were made from three samples, each
digested with a different protease. These
three differently biased estimates were
then used in separate parts of the analy-
the central dogma
By Jingyi Jessica Li1 and Mark D. Biggin2
Transcription, not translation, chiefly determines protein
abundance in mammals
1Department of Statistics and Department of Human Genetics,
University of California, Los Angeles, CA 90095, USA. 2Genomics
Division, La wrence Berkeley National Laboratory, Berkeley, CA
94720, USA. E-mail: email@example.com; firstname.lastname@example.org
“Before gene expression
can be correctly modeled,
an accurate accounting of
molecular abundances and
expression rate constants