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DOI: 10.1126/science.1112665
, 1387 (2005); 309Science
et al.Jason Gans,
Bacterial Diversity and High Metal Toxicity in Soil
Computational Improvements Reveal Great
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of the W2mefDRh4 cloned lines (Fig. 2C). In
contrast, PfRh4 was expressed in sialic acid–
independent lines W2mef/N and W2mefD175,
as well as in 7G8, HB3, and 3D7. Western
analysis was performed during the course of the
48-hour asexual blood stage life cycle of
W2mefD175 parasites. PfRh4 was detected in
mature schizonts (Fig. 2D), concomitant with
apical organelle development and expression of
other ligands involved in the invasion process
(1, 16).
W2mDRh4 parasites were grown on nor-
mal or neuraminidase-treated erythrocytes to
determine if they could switch to sialic acid–
independent invasion. W2mefDEBA181 par-
asites (17) were generated in the same way
as W2mefDRh4, and as expected, this line
was able to switch to sialic acid–independent
invasion (fig. S4A). Although W2mDRh4
parasites grew normally on untreated eryth-
rocytes (Fig. 3), they were unable to switch to
sialic acid–independent invasion even after
extended culture on neuraminidase-treated
erythrocytes (Fig. 3 and fig. S3C), suggest-
ing that invasion was completely blocked at
the first generation. Therefore, transcriptional
activation of the PfRh4 gene and expression
of the PfRh4 protein are required for switch-
ing of W2mef from sialic acid–dependent to
–independent invasion.
We constructed two independent trans-
genic parasite lines that expressed PfRh4 as a
chimeric protein with green fluorescent pro-
tein (GFP) to determine if subcellular local-
ization of PfRh4 is consistent with a role in
merozoite invasion; the results were identical
(figs. S1C and S5). The W2mef-Rh4GFP par-
asites could switch invasion pathways and
invaded neuraminidase-treated erythrocytes
efficiently, indicating that activation and func-
tion of PfRh4 were preserved (Fig. 4C). The
GFP-tagged PfRh4 protein showed the ex-
pected increase in molecular weight (Fig.
4A). Segmenting schizonts and merozoites
of W2mef-Rh4GFP1N/2N displayed fluores-
cence apical to the nucleus (Fig. 4B, fig.
S5D). PfRh4 colocalized well with PfRh2a/b
in segmenting schizonts, and the overlap con-
densed into a single apical dot in free mero-
zoites in which PfRh2a and b are present in
the neck of the rhoptries (11, 12)(Fig.4D).
PfRh4 was more apical than RAP1, a protein
located within the body of the rhoptries (18).
Therefore, PfRh4 is located at the apical tip
of free merozoites, consistent with a direct
function in invasion of erythrocytes.
We tested several sialic acid–dependent
strains for growth on neuraminidase-treated
erythrocytes to determine if the ability to switch
invasion pathways and use different recep-
tors for invasion is a general property of P.
falciparum. Cloned lines of CSL2 (fig. S1D)
were sialic acid–dependent but adapted to
sialic acid–independent invasion in a simi-
lar way to W2mef (Fig. 4C) in association
with elevated expression of PfRh4 protein
(Fig. 4E).
We have shown that activation of sialic acid–
independent invasion is regulated by differen-
tial gene expression and silencing of PfRh4.
Activation of PfRh4 occurs at a low level, and
these variant parasites can be selected by
growth on erythrocytes lacking sialic acid or
by genetic ablation of the EBA175 gene. Si-
lencing of the active PfRh4 gene occurs over
time when parasites are returned to normal
erythrocytes, showing that the switch in inva-
sion pathways can occur in both directions in
the presence of functional EBA175. The acti-
vation of PfRh4 in response to loss of EBA175
function suggests that the PfRh and ebl protein
families show some overlap with respect to
their function in invasion. The ability to switch
receptor usage for invasion from sialic acid–
dependent to –independent pathways repre-
sents a previously unknown strategy to evade
host receptor polymorphisms and immune
mechanisms and has important implications
for the design of vaccines against malaria
parasites.
References and Notes
1. J. W. Barnwell, M. R. Galinski, in Malaria: Parasite
Biology, Pathogenesis and Protection,I.W.Sherman,
Ed. (American Society for Microbiology, Washington,
DC, 1998), pp. 93–120.
2. V. C. Okoye, V. Bennett, Science 227, 169 (1985).
3. J. Baum, M. Pinder, D. J. Conway, Infect. Immun. 71,
1856 (2003).
4. S. A. Dolan, L. H. Miller, T. E. Wellems, J. Clin. Invest.
86, 618 (1990).
5. M. B. Reed et al., Proc. Natl. Acad. Sci. U.S.A. 97,
7509 (2000).
6. M. T. Duraisingh, A. G. Maier, T. Triglia, A. F. Cowman,
Proc. Natl. Acad. Sci. U.S.A. 100, 4796 (2003).
7. K. G. Le Roch et al., Science 301, 1503 (2003).
8. O. Kaneko, J. Mu, T. Tsuboi, X. Su, M. Torii, Mol.
Biochem. Parasitol. 121, 275 (2002).
9. M. J. Gardner et al., Nature 419, 498 (2002).
10. J. C. Rayner, M. R. Galinski, P. Ingravallo, J. W. Barnwell,
Proc. Natl. Acad. Sci. U.S.A. 97, 9648 (2000).
11.J.C.Rayner,E.Vargas-Serrato,C.S.Huber,M.R.
Galinski, J. W. Barnwell, J. Exp. Med. 194, 1571
(2001).
12. M. T. Duraisingh et al., EMBO J. 22, 1047 (2003).
13. P. R. Preiser, W. Jarra, T. Capiod, G. Snounou, Nature
398, 618 (1999).
14. M. R. Galinski, C. C. Medina, P. Ingravallo, J. W.
Barnwell, Cell 69, 1213 (1992).
15. T. Triglia, J. K. Thompson, A. F. Cowman, Mol.
Biochem. Parasitol. 116, 55 (2001).
16. A. F. Cowman et al., FEBS Lett. 476, 84 (2000).
17. T. W. Gilberger et al., J. Biol. Chem. 278, 14480
(2003).
18. G. R. Bushell, L. T. Ingram, C. A. Fardoulys, J. A.
Cooper, Mol. Biochem. Parasitol. 28, 105 (1988).
19. We thank T. Pappenfuss and M. Ritchie for help with
statistical analysis, R. Good for technical assistance, and
the Red Cross Blood Service for supply of human
erythrocytes and serum. J.S. is supported by a
Melbourne Research Scholarship and by the Australian
Government’s Cooperative Research Centre’s Program.
A.F.C. is supported by an International Research
Scholarship from the Howard Hughes Medical Institute.
This work is supported by the National Health and Med-
ical Research Council of Australia and the Wellcome
Trust. The microarray data have been deposited with
The Gene Expression Omnibus (www.ncbi.nih.gov/
projects/geo/) (Experiment ID: GSE2878).
Supporting Online Material
www.sciencemag.org/cgi/content/full/309/5739/1384/
DC1
Materials and Methods
SOM Text
Figs. S1 to S5
References
24 May 2005; accepted 5 July 2005
10.1126/science.1115257
Computational Improvements
Reveal Great Bacterial Diversity
and High Metal Toxicity in Soil
Jason Gans, Murray Wolinsky, John Dunbar
The complexity of soil bacterial communities has thus far confounded effec-
tive measurement. However, with improved analytical methods, we show that
the abundance distribution and total diversity can be deciphered. Reanalysis of
reassociation kinetics for bacterial community DNA from pristine and metal-
polluted soils showed that a power law best described the abundance dis-
tributions. More than one million distinct genomes occurred in the pristine soil,
exceeding previous estimates by two orders of magnitude. Metal pollution re-
duced diversity more than 99.9%, revealing the highly toxic effect of metal
contamination, especially for rare taxa.
For any complex system, the number and rel-
ative abundance of parts is fundamental to a
quantitative description of the system. Quan-
tification provides a framework to compare
equilibrium and dynamic properties and, for
biological communities, to evaluate perturba-
tions such as pollution, global climate change,
and foreign species encroachment. To quantify
plant and animal communities, ecologists survey
the number and relative abundance of species
(i.e., species-abundance distributions) (1, 2).
However, effective measurement of bacterial
species-abundance distributions has eluded
Bioscience Division, Los Alamos National Laboratory,
Los Alamos, NM 87501, USA.
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microbiologists owing to the overwhelming com-
plexity of bacterial communities.
Surveys of bacterial communities are typ-
ically attempted by counting small subunit
rRNA (16S rRNA) gene sequences. Aside from
the technical difficulties and biases (3), the sur-
vey size required for accurate analysis of soil
communities is impractically large. Accurately
estimating diversity in a community with a log-
normal species-abundance distribution requires
sampling about 80% of the species (4, 5). For a
typical gram of soil containing a billion bac-
terial cells, a survey of at least 10
6
16S rRNA
gene sequences, three orders of magnitude
larger than current survey efforts, would be
required to sample 80% of diversity in a com-
munity with 10,000 species.
Measuring total genetic diversity overcomes
the limitations of surveys (6). By using two
simplifying assumptions, genetic diversity can
be translated into species diversity. Genetic
diversity can be inferred from DNA reassocia-
tion kinetics of pooled genomic DNA from a
bacterial community. The length of time for
reassociation is proportional to the number and
relative abundance of distinct sequence frag-
ments (7). In 1990, landmark reassociation
studies with bacterial community DNA pro-
vided the basis for the now widely accepted
paradigm of B10,000 bacterial species per gram
soil[ (6). However, genetic diversity has been
grossly underestimated as a result of the use
of an analytical approach that implicitly as-
sumes all bacterial species in a sample are
equally abundant.
Using previously published data for
community DNA from pristine and metal-
contaminated soils (8), we demonstrated an
approach that enables quantitative compari-
son of different species-abundance models.
The original reassociation study was per-
formed to assess the effects of heavy metal
pollution in soil caused by the repeated ap-
plication of sewage sludge (8). The authors
purified bacterial cells from soil samples,
extracted DNA from the bacterial cells, exten-
sively purified the DNA by repeated hydroxy-
apatite chromatography, and then monitored
DNA reassociation in sealed cuvettes by op-
tical absorbance (8).
When all sequences are equally abundant,
the reassociation of DNA, monitored spectro-
scopically, follows pseudo-second-order reac-
tion kinetics (9). For samples containing DNA
fractions that differ in relative abundance, a
modified version of the basic equation for re-
association kinetics was developed that allows
n fractions (abundance classes) with differ-
ent reassociation rates but does not enable
direct comparison of different abundance mod-
els (10), i.e.
EC^
EC
0
^
0
P
nj1
i00
f
i
ð1 þ k
i
EC
0
^tÞ
g
P
nj1
i00
f
i
ð1Þ
where EC^ is the concentration of single-stranded
DNA, EC
0
^ is the initial concentration of single-
stranded DNA, t is time, g (the Bretardation fac-
tor[) is a heuristic DNA-sequence-independent
constant (9), k
i
and f
i
are the reassociation rate
and relative abundance of the ith DNA frac-
tion, and n is the number of fractions.
We recast this equation in terms of the total
number of species (S
t
)inacommunityandthe
Fig. 1. (A)SoilDNAreassoci-
ation data with best-fitting
Zipf- and model-free–based
Cot curves. The best fitting
delta function for the high-
metal soil is shown for com-
parison. The samples and the
E. coli DNA used as a control
exhibited equal optical purity
(measured as percent hyper-
chromicity) (21), which indi-
cates that the differences in
sample diversity were not
experimental artifacts arising
from DNA impurities. For sam-
ples with equi-abundant spe-
cies (i.e., delta function), the central, linear portion of the DNA reassociation curve is
expected to span a factor of two in log(C
0
t), whereas models that allow unequal spe-
cies abundance accommodate the expanded interval observed in experimental data
sets. (B) Model-dependent c
v
2
computed for the noncontaminated, low-metal, and
high-metal soil samples.
Fig. 2. Zipf (solid line) and model-free
(dotted line) species-abundance plots nor-
malized to S
t
for (A) the noncontami-
nated data set, (B) the low-metal data
set, and (C) the high-metal data set.
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relative abundance of each species to allow
direct comparison of different abundance models.
Using a number of substitutions and approx-
imations (11), we obtained
EC^
EC
0
^
0
X
V
0
NPðNÞdN
bNÀð1 þ bNEC
0
^tÞ
g
ð2Þ
where N is the number of individuals, P(N)dN
is the normalized species-abundance distri-
bution, bNÀ is the average number of in-
dividuals per species, and b is the ratio of the
reassociation rate of a reference genome (e.g.,
Escherichia coli) and the total number of in-
dividuals (N
t
) in a sample.
The evaluation of Eq. 2 requires values
for b and g (11) and an a priori form for the
species-abundance distribution. In the absence
of a strong justification for a particular distri-
bution, we adopted a variety drawn from macro-
ecology (11). To provide a relatively unbiased,
heuristic estimate of P(N), we also compared a
piece-wise linear approximation
PðNÞdN 0
"
N
0
ðD j 1Þ
X
nj1
i00
p
i
D
i
#
j1
X
nj1
i00
p
i
q
N j N
0
D
i
j q
N j N
0
D
iþ1
dN
ð3Þ
This Bmodel-free[ approximation has the form
of a histogram with geometric bar widths. There
are n bars, with heights p
i
and widths D
i
N
0
,
where N
0
is the location of the left edge of the
first bar and q is the Heaviside step function.
This yields nþ2 free parameters to be de-
termined by fitting Eq. 3 to experimental Cot
curves. The model-free approximation provides
a more flexible shape that does not require sym-
metry or continuity like the standard abundance
models and consequently provides a useful base-
line for assessing the fit of standard models.
Using this framework, we reanalyzed the
three published (8) reassociation data sets for
bacterial communities. Two observations were
noteworthy. First, we were able to describe the
general shape of the abundance distribution. Sec-
ond, we were able to estimate improved bound-
aries for the total amount of genetic diversity.
Empirical data and simulations both dem-
onstrate that DNA reassociation kinetics can
accurately identify different abundance patterns,
although the resolving power depends on the
completeness of the reassociation curve. For
example, a delta function (a distribution in
which all components, e.g., genes, are equally
abundant) provided the best fit (as expected)
for experimental, single-species E. coli DNA
reassociation curves. For contrast, we simulated
DNA reassociation for a theoretical bacterial
community with 5000 species following a
lognormal abundance distribution (11). After
adding Gaussian noise to the reassociation
curve equal to the noise seen in the soil DNA
data sets, we fit the curve with a variety of
abundance models and compared the fits using
c
v
2
(i.e., reduced c
2
, which accounts for differ-
ences in the number of free parameters between
models). Even with a reassociation curve only
50% complete, c
v
2
values clearly identified the
underlying distribution as lognormal while
other models were easily excluded (fig. S1).
For the soil bacterial communities (8), the
value of c
v
2
obtained from fitting each model
ruled out the delta, top hat, geometric, and neu-
tral models for the species-abundance distribu-
tion (Fig. 1). The lognormal distribution was
also discounted because it consistently produced
larger c
v
2
values than the zipf distribution (11).
The remaining models were qualitatively similar.
For all three soil DNA reassociation curves, the
model-free curve provided the best fit (Fig. 1),
followed closely by the zipf and log-Laplace
models (which were statistically indistinguish-
able). The fluctuation in the model-free DNA
reassociation curve for the noncontaminated
soil (Fig. 1) reflected a deviation in the shape
of the species-abundance distribution (Fig. 2),
not a significant increase in species diversity
compared with the zipf model.
The zipf and log-Laplace distributions
shared the same power-law form describing
the most abundant (large N) bacterial species.
The power-law envelope defined by the zipf
distribution had the form P(N ) È N
z
,where
z was approximately –2 (z 0 –1.96 T 0.02,
–2.11 T 0.01, and –2.08 T 0.03 for the non-
contaminated, low-metal, and high-metal
data sets, respectively). Power laws have de-
Fig. 3. Rank abundance plots of each
model for the noncontaminated soil.
Species are ranked in order from most
abundant to least abundant.
Fig. 4. The total number of
species (S
t
) computed for the
noncontaminated, low-metal,
and high-metal soil samples.
model free
zipf
log-Laplace
lognormal
neutral
geometric
top hat
delta
S
t
10
8
HighLowNone
10
7
10
6
10
5
10
4
10
3
10
2
10
1
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scribed the abundance distribution of artificial
life forms (z 0 –2, most commonly) (12),
marine phages (z 0 –1.64 and –1.73) (13, 14),
and plant communities (15)andmayarise
from a variety of mechanisms (12, 16, 17).
Alternatively, a log-Laplace distribution, which
would appear as a power law when measured
by DNA reassociation, may arise from an
ensemble of lognormals (18, 19) that individ-
ually describe the abundance distribution of
different functional groups (e.g., denitrifiers,
iron reducers, and sulfate reducers).
The zipf and log-Laplace differed math-
ematically in describing the rare species
(Fig. 3). This difference in the two functions
was not apparent in the values of c
v
2
as a
result of the incompleteness of the curves
and the magnitude of the measurement error,
which masks small changes in the shape. The
ambiguous shape of the distribution for rare
species demonstrates that a portion of the com-
munity is veiled. Although a reasonable esti-
mate can be obtained of the minimum number
of species in the community (including the
veiled fraction), additional work is required
to obtain a fully accurate description of the
entire species-abundance distribution.
Although the shape of the abundance dis-
tribution is of fundamental importance, the
total diversity is often of greatest interest in
environmental assessment and regulatory pol-
icy. For each soil, the model-free, zipf, and
log-Laplace estimates of S
t
agreed within a
factor of two (Fig. 4). Given the qualitative
and quantitative similarity of these distribu-
tions, we averaged the three to obtain an esti-
mate for each soil. Thus, the noncontaminated,
low-metal, and high-metal soils respectively
contained about 8.3 10
6
, 6.4 10
4
, and
7.9 10
3
species among approximately 10
10
cells Eor 10 g of soil; this represents the quan-
tity of DNA used in the reassociation exper-
iments (11)^. Our estimates of S
t
were larger
by a factor of 4 to 500 than the original es-
timates of 1.6 10
4
,6.4 10
3
,and2.0 10
3
species.
On the basis of our estimates, metal pol-
lution reduced diversity more than 99.9%. In-
terestingly, total bacterial biomass remained
unchanged at about 2 10
9
cells per gram
of soil despite metal exposure (8). Our abun-
dance models were consistent with this ob-
servation and indicated that the major effect
of metal exposure was the elimination of rare
taxa (Fig. 2). In the pristine soil, taxa with
abundance values G10
5
cells per gram ac-
counted for 99.9% of the diversity, and genetic
diversity from this fraction of the community
appears to have been purged by high metal
pollution. The functional importance of these
rare taxa for soil nutrient cycling and ecosys-
tem resilience is unknown.
To assess the overall error for S
t
,we
calculated the net impact of all error sources,
including measurement error, Cot curve com-
pleteness, calibration rate, and hybridization
of mismatched DNA (11). The relatively minor
effects of the first two factors were included in
the error estimates for S
t
showninFig.4and
could be reduced further (fig. S3). Given that
all error sources were random and uncorrelated,
the total error for S
t
, calculated by standard
propagation of errors (20), was a factor of
8.2. As this error range affects S
t
but is not
expected to influence the relative differences
between the soils, we are confident of the rel-
ative impact of metal pollution.
Comparing the ability of numerous species-
abundance distributions to reproduce experi-
mental DNA reassociation data showed that
the soil bacterial communities were naturally
best represented by the model-free approxi-
mation, followed closely by the zipf (i.e.,
power law) distribution. Hence, the original
study substantially underestimated the species
diversity of pristine soil bacterial communities.
Moreover, heavy metal pollution reduced bac-
terial diversity not by a factor of 8, as previ-
ously suggested, but by a factor of about 1000,
with rare species impacted the most. Although
the minimum number of species in the soils
can be estimated, the exact shape of the abun-
dance distribution for rare species remains
ambiguous and is an area for additional work.
Overall, the improved analytical approach
demonstrates that rigorous DNA reassocia-
tion studies can address otherwise intrac-
table problems in microbial ecology, such
as monitoring environmental perturbations
and mapping diversity geographically.
References and Notes
1. M. Tokeshi, Adv. Ecol. Res. 24, 111 (1993).
2. J. Chave, H. C. Muller-Landau, S. A. Levin, Am. Nat.
159, 1 (2002).
3. R. Narang, J. Dunbar, Microb. Ecol. 47, 396 (2003).
4. R. G. Hughes, Am. Nat. 128, 879 (1986).
5. F. W. Preston, Ecology 43, 185 (1962).
6. V. Torsvik, J. Goksøyr, F. L. Daae, Appl. Environ.
Microbiol. 56, 782 (1990).
7. R. J. Britten, D. E. Khone, Science 161, 529 (1968).
8. R. A. Sandaa et al., FEMS Microbiol. Ecol. 30, 237 (1999).
9. M. J. Smith, R. J. Britten, E. H. Davidson, Proc. Natl.
Acad. Sci. USA 72, 4805 (1975).
10. W. R. Pearson, E. H. Davidson, R. J. Britten, Nucleic
Acids Res. 4, 1727 (1977).
11. Materials and methods are available as supporting
material on Science Online.
12. C. Adami, R. Seki, R. Yirdaw, in Artificial Life VI: Pro-
ceedings of the Sixth International Conference on
Artificial Life, C. Adami, R. Belew, H. Kitano, C. Taylor,
Eds. (MIT Press, Cambridge, MA, 1998), pp. 221–227.
13. Exponent values were converted from the originally
published species rank-abundance representation to
the continuous abundance distribution representa-
tion used in this paper.
14. M. Breitbart et al., Proc. Natl. Acad. Sci. USA 99,
14250 (2002).
15. J. B. Wilson et al., J. Ecol. 84, 527 (1996).
16. S. Frontier, in Oceanography and Marine Biology: An
Annual Review, M. Barnes, Ed. (Aberdeen Univ. Press,
Aberdeen, Scotland, 1985), pp. 253–312.
17. R. V. Sole
´
, D. Alonso, A. McKane, Physica A (Amster-
dam) 286, 337 (2000).
18. A. P. Allen, B. L. Li, E. L. Charnov, Ecol. Lett. 4, 1 (2001).
19. S. Kotz, T. J. Kozubowski, K. Podgorski, The Laplace
Distribution and Generalizations. A Revisit With
Applications to Communications, Economics, Engi-
neering, and Finance (Birkhauser, Boston, 2001).
20. P. R. Bevington, D. K. Robinson, Data Reduction and
Error Analysis for the Physical Sciences (McGraw Hill,
New York, NY, 3rd ed., 2003).
21. F. L. Daae, V. Torsvik, personal communication.
22. This work was supported by Los Alamos National
Laboratory Directed Research and Development grant
20020008ER. The authors gratefully acknowledge
L. Ovreas and F.-L. Daae for helpful discussions about
their experimental work and two anonymous re-
viewers for extremely helpful manuscript comments.
Supporting Online Material
www.sciencemag.org/cgi/content/full/309/5739/1387/
DC1
Materials and Methods
Figs. S1 to S3
Table S1
References
23 March 2005; accepted 26 July 2005
10.1126/science.1112665
Circadian Clock Control by
SUMOylation of BMAL1
Luca Cardone,
1
Jun Hirayama,
1
Francesca Giordano,
1
Teruya Tamaru,
2
Jorma J. Palvimo,
3
*
Paolo Sassone-Corsi
1
.
The molecular machinery that governs circadian rhythmicity is based on clock
proteins organized in regulatory feedback loops. Although posttranslational mod-
ification of clock proteins is likely to finely control their circadian functions, only
limited information is available to date. Here, we show that BMAL1, an essen-
tial transcription factor component of the clock mechanism, is SUMOylated on a
highly conserved lysine residue (Lys
259
) in vivo. BMAL1 shows a circadian pattern
of SUMOylation that parallels its activation in the mouse liver. SUMOylation
of BMAL1 requires and is induced by CLOCK, the heterodimerization partner
of BMAL1. Ectopic expression of a SUMO-deficient BMAL1 demonstrates that
SUMOylation plays an important role in BMAL1 circadian expression and clock
rhythmicity. This reveals an additional level of regulation within the core mech-
anism of the circadian clock.
SUMOylation—the covalent linking of small
ubiquitin-related modifier protein (SUMO) to
lysine residues—is a reversible posttranslation-
al modification controlled by an enzymatic
pathway analogous to the ubiquitin pathway
(1–3). The addition of SUMO on target pro-
R EPORTS
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