Copyright ? 2010 by the Genetics Society of America
A Genomewide RNA Interference Screen for Modifiers of Aggregates
Formation by Mutant Huntingtin in Drosophila
Sheng Zhang,*,1Richard Binari,*,†Rui Zhou* and Norbert Perrimon*,†,1
*Department of Genetics and†Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115
Manuscript received November 27, 2009
Accepted for publication January 18, 2010
Protein aggregates are a common pathological feature of most neurodegenerative diseases (NDs).
Understanding their formation and regulation will help clarify their controversial roles in disease
organism that has been applied extensively in modeling NDs and screening for toxicity modifiers. We
generated transgenic fly lines that express enhanced-GFP-tagged mutant Huntingtin (Htt) fragments with
different lengths of polyglutamine (polyQ) tract and showed that these Htt mutants develop protein
aggregates in a polyQ-length- and age-dependent manner in Drosophila. To identify central regulators of
then performed a genomewide RNA interference screen for regulators of mutant Htt aggregation and
isolated 126 genes involved in diverse cellular processes. Interestingly, although our screen focused only on
mutant Htt aggregation, several of the identified candidates were known previously as toxicity modifiers of
NDs. Moreover, modulating the in vivo activity of hsp110 (CG6603) or tra1, two hits from the screen, affects
neurodegeneration in a dose-dependent manner in a Drosophila model of Huntington’s disease. Thus,
other aggregates regulators isolated in our screen may identify additional genes involved in the protein-
folding pathway and neurotoxicity.
pathological feature of many neurodegenerative dis-
eases, such as Alzheimer’s, Parkinson’s, amyotrophic
lateral sclerosis (ALS, or Lou Gehrig’s disease), and
polyglutamine (polyQ) diseases (Rosen et al. 1993;
Spillantini et al. 1997; Sisodia 1998; Gusella and
MacDonald 2002; Caughey and Lansbury 2003;
Bruijn et al. 2004; Ross and Poirier 2005; Pasinelli
and Brown 2006). The close link between the diseases
and aggregates is especially prominent among polyQ
diseases, as best exemplified by Huntington’s disease
(HD). HD is caused by the expansion of a polyQ tract at
the N terminus of the Huntingtin (Htt) protein
(Huntington’s Disease Collaborative Research
Group 1993). In humans, the length of this polyQ tract
is normally within the range of 7–26, whereas in HD
patients it is invariably expanded to .35 (Andrew et al.
1993; Huntington’s Disease Collaborative Re-
search Group 1993; Snell et al. 1993). One particu-
larly intriguing pathological feature of HD is the
HE presence of protein aggregates in the brains of
patients has long been recognized as a common
presence of intracellular aggregates composed of
processed N-terminal Htt (Sisodia 1998; Gusella
and MacDonald 2002; Ross and Poirier 2005). The
intimate links between aggregates and HD are quite
striking. In particular, genetic analyses revealed the
existence of an inverse correlation between the length
of the polyQ tract and the age of onset of the disease,
whereas many in vivo and in vitro studies have
demonstrated that the mutant Htt containing longer
glutamine tracts has an increasing propensity to form
aggregates (Penney et al. 1997; Scherzinger et al.
1997). Similar relationships are also observed in eight
other polyQ diseases such as spinocerebellar ataxias
(Sisodia 1998; Gusella and MacDonald 2002; Ross
and Poirier 2005). Because of this intimate link,
aggregates have been suspected as the cause of neural
toxicity. However, other studies found no correlation
between the distribution of aggregates in the brain
and neurodegeneration and proposed that aggregates
are simply by-products of the disease process. Still other
studies have argued that aggregates play a beneficial
role bysequestering toxic specieswithinthecell(Sisodia
1998; Gusella and MacDonald 2002; Caughey and
Lansbury 2003; Ross and Poirier 2005). Notably, a
recent study following aggregates formation in cultured
neurons proposed that aggregatesformationreducesthe
levels of diffusive mutant Htt and protects against its
toxicity (Arrasate et al. 2004).
1Corresponding authors: University of Texas Health Science Center at
Houston, SRB–430H, 1825 Pressler St., Houston, TX 77030.
E-mail: firstname.lastname@example.org; and Department of Genetics,
Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115.
Genetics 184: 1165–1179 (April 2010)
Drosophila has been an excellent model system for
studying various neurodegenerative diseases (NDs)
because toxicity of these disease proteins can often be
well recapitulated in the fly (Bilen and Bonini 2005;
Marsh and Thompson 2006). Because of this, Dro-
sophila has been widely used to screen for toxicity
modifiers of different disease proteins, especially for
polyQ diseases, and adiverse group oftoxicity modifiers
with functions in different cellular processes have been
isolated, such as protein folding (e.g., dnaj1 and tpr2)
ubiquitin modification and protein degradation (e.g.,
smt3 and uba2) (Chan et al. 2002; Steffan et al. 2004),
et al. 2003; Kanuka et al. 2003; Shulman and Feany
2006). Notably, when tested in Drosophila, a human
chaperone Hsp70 and a Drosophila co-chaperone dhdj1
(also known as Hsp40 or dnaj1) can significantly
suppress polyQ toxicity and also alter the solubility of
aggregates, suggesting a possible correlation between
aggregates and neurotoxicity (Warrick et al. 1999;
Chan et al. 2000).
Although Drosophila has been successfully used to
regulatorsofaggregates formation hasbeenconducted,
probably due to a lack of a simpler assay to assess the
aggregates formed in vivo. To systematically identify
central regulators of protein aggregation in this model
organism, we focused on the well-studied HD protein
Htt. Earlier studies in different model systems have
already demonstrated that expression of mutant Htt or
other polyQ disease proteins induces polyQ-length-
2002; Bilen and Bonini 2005; Marsh and Thompson
2006). To evaluate the aggregates formation by mutated
Htt in Drosophila, we first generated transformant lines
for enhanced GFP (eGFP)-tagged Htt exon 1 fragments
containing different lengths of glutamine repeats and
analyzed their ability to form aggregates in Drosophila
tissues. We then established a cell-based quantitative
assay that allows automated measurement of aggregates
within cells by combining automated microscopy with
quantitative analysis. Using this automated protocol, we
performed a high-throughput genomewide RNA inter-
a diverse group of genes that can affect formation of
aggregates by mutant Htt.
MATERIAL AND METHODS
DNA plasmids: DNA containing the mutant Httex1-Qn-
eGFP variants (Q25, Q46, Q72, and Q103) were generously
provided by the Hereditary Disease Foundation [originally
from A. Kazantsev (Kazantsev et al. 1999)] and cloned into
the hygromycin-resistance pMK33 vector that contains the
copper-inducible metallothionein promoter. See File S1 for
details about the plasmids and cloning procedures.
RNAi screen: Using a MultiDrop liquid dispenser, stable
Httexon1-Qp47-eGFP cells were aliquoted uniformly into 384-
well double-stranded RNA (dsRNA)-containing plates at ?1 3
104pMK33-Httex1-Qp46eGFP cells per well in 20 ml of serum-
free medium. After incubation at room temperature for 1 hr,
Biosciences) was added. After 3 days of incubation, 25 ml of
to reach a final copper concentration of 400 mm to induce the
for another 2 days, cells were fixed using 4% formaldehyde at
room temperature for 20 min. For cell staining, the samples
were washed three times with PBST (13 PBS plus 0.1% Triton-
final concentration; Molecular Probe) and TRITC-conjugated
times with 13 PBST.
Image acquisition and data analysis: Using the 320 objec-
tives on an automated Nikon TE300 microscope, triple-
channel images from four different sites in each well were
captured: the FITC channel for the level of eGFP expression
and information on aggregates within the site; the DAPI
channel for cell nuclei; and the TRITC channel for overall
cell morphology. Images from the FITC and DAPI channels in
each site were quantified using MetaMorph software (Univer-
sal Imaging) to calculate the number, size, and signal intensity
of the aggregates and determine the cell number within the
site. For aggregates, images from the FITC channel were
segmented on the basis of a predetermined threshold param-
aggregates are highly enriched with the eGFP-tagged mutant
Htt (Httex1-Qp46-eGFP), the level of fluorescent signal from
the aggregates is significantly higher than that from the
cytoplasm and from background noise, thus allowing their
clear identification and quantization by the software (see
Figure 2B). To ensure the accuracy of this automated quanti-
zation, we also performed visual scoring and manual quanti-
zation for a few selected sites in each plate and confirmed that
the manual results were comparable to those from the
automated quantization. Similarly, cell numbers in the same
site were counted by analyzing the corresponding DAPI
channel images. In general, ?4000 cells were analyzed for
every dsRNA-treated sample. The information on aggregates
and cell number was then used to calculate the three
parameters for evaluating the effect of dsRNA treatment on
aggregates formation: the average number, size, and signal
the cell number in each site. Results from all four sites in the
same well were then averaged to access the aggregation status
in this dsRNA-treated well.
Primary screens: Two sets of 63 plates from the Drosophila
RNAi Screening Center and three sets of two kinase/phos-
phatase plates from M. Kulkarni (M. M. Kulkarni and N.
Perrimon, personal communication), containing 250 ng of
dsRNA per well, were screened. For each plate screened, the
average value and standard deviation (SD) on these three
evaluation parameters from the whole plate were also calcu-
lated. In the primary screen, those dsRNAs that decreased or
increased the aggregates formation by .23 SD from the
whole-plateaverage for anyofthethree evaluationparameters
were considered to have a significant effect on aggregates
formation and were selected as potential candidates. See File
S1 for detailed description of assays in secondary screens.
Drosophila stocks: The Httex1p-Q93 and Q48-myc/Flag
flies were generously provided by L. Thompson and J. L.
1166S. Zhang et al.
formation of aggregates in
Drosophila. (A) Structure
of the Htt exon1 (Httex1)
constructs used for the
studies. eGFP-tagged Httex1
with different lengths of
followed by its proline-rich
region (P) are under the
control of the UAS ele-
1993). (B–P) PolyQ-length-
mation of aggregates in
Drosophila. (B–K) Httex1-
Qn-eGFP were expressed
in the eye using the eye-
specific GMR-Gal4 driver.
morphology of adults of
different ages. (Bottom)
Images of the same eyes un-
der green fluorescent light,
with their age and geno-
type indicated below. Gen-
otypes: (B) GMR-Gal4/1
and (C–K) GMR-Gal4/1;
driver alone at day 2. Note
that there is no visible
eGFP signal in this eye
(bottom) in the absence
of the UAS-eGFP reporter.
(C and D) Adult eyes with
sion. Q25 is found mainly
in a diffuse cytoplasmic pat-
tern at both day 2 (C) and
day 30 (D). Note that the
center bright spots in both
eyes and in E are not aggre-
gates but rather an optical
the ‘‘deep pseudopupil,’’
imposition of evenly dif-
fuse GFP signals emitted
from the underlying regu-
larly arranged ommatidia
sion. At day 2 (E, left
eye), Httex1-Q46 is found
mainly in a diffuse cytoplas-
mic pattern. As the fly ages,
sporadic aggregates gradu-
ally accumulate (F, white arrows) and become prominent by day 30 (G). (H and I) Adult eyes with Httex1-Q72-eGFP expression.
At day 2 (H), Q72 is found mainly in a diffuse cytoplasmic pattern but sporadic aggregates are already visible (H, white arrows).
Aggregates become prominent by day 30 (I). (J and K) Adult eyes with Httex1-Q103-eGFP expression. Aggregates already become
prominent at day 2 (J) and persist at day 30 (K). Note also that there is a mild loss of pigment at the posterior of this eye at day 30
(K, white arrowhead), indicating the degeneration of underlying eye tissues at this stage. In all images, the eyes are oriented as
dorsal side is up and anterior is to the right. (L–O) Confocal images of aggregates formation in the brain by the Httex1-Q46-eGFP,
which was expressed in the CNS using the pan-neuronal Elav-Gal4 driver. Genotypes: Elav-Gal4/1; UAS-Httex1-Q46-eGFP/1. The
protein was distributed evenly, and no obvious aggregates were found in the brain at the younger age (L and N, day 2), whereas
RNAi Screen for Aggregation Regulators 1167
genetic testing is GMR-Gal4/1; UAS-Httex1p-Q93. The fol-
lowing fly hsp110 alleles showed dosage-dependent genetic
interaction with the HD93 flies: l(3)00082, l(3)S031820,
l(3)S064906, l(3)S004112, and l(3)S0134802. The following
tra1 alleles were tested and showed dosage-dependent genetic
interaction with the HD93 flies Nipped-ANC116and Nipped-ANC186.
See File S1 for a detailed description of genetic crosses and fly
stocks. Additional information can be found in File S1.
Htt mutants form aggregates in a polyQ-length- and
age-dependent manner in Drosophila tissues: Toscreen
for regulators of aggregates formation, we first needed
to select a mutant disease protein whose propensity to
form aggregates could be modulated by the cellular
genetic environment. Htt mutants are ideal candidates
for this purpose, given the unique feature of a correla-
tion between their propensity to form aggregates and
Gusella and MacDonald 2002). Accordingly, we
focused our study on the well-established Htt exon 1
(Httex1) constructs, which contain different lengths of
the polyQ tract: Httex1 with 25 glutamines (Q25)
representing wild-type Htt control and mutated Htt
containing an increasing length of the glutamine
repeats 46Q, 72Q, and 103Q (simplified from here on
1999). These Httex 1 proteins, which are tagged at their
C termini with eGFP, have been previously shown to
form visible polyQ length-dependent aggregates in
cultured mammalian cells (Kazantsev et al. 1999). We
cloned this set of eGFP–Httex1 constructs into the
pUAST vector and generated corresponding transgenic
fly lines, which allow the targeted expression of Httex1
transgenes in selected tissues using the Gal4 binary
expression system (Figure 1A) (Brand and Perrimon
We then examined the aggregation property of these
Httex1 proteins in several Drosophila tissues. Remark-
ably, when expressed in the eye using the eye-specific
GMR-Gal4 driver, the aggregation patterns of the eGFP-
tagged Httex1 proteins can be conveniently monitored
directly in adult fly eyes under fluorescence microscopy
(Figure 1, B–K). For example, while Httex1-Q25 main-
tains a diffuse cytoplasmic distribution over the life of
the fly (Figure 1, C and D), Httex1-Q103 exists almost
exclusively in prominent aggregates soon after it is
expressed and is already prominent in newly hatched
adults (Figure 1, J and K, and data not shown). Fur-
thermore, while the expression of both Httex1-Q46 and
-Q72 is diffuse in young flies, aggregates gradually
develop as the flies age, with Q72 forming aggregates
at a faster rate than Q46 (Figure 1, E–I).
We next analyzed aggregates formation by these
Httex1 proteins in the fly central nervous system
(CNS). When expressed in all neuronal cells using the
pan-neuronal Elav-Gal4 driver, these Httex1 proteins
showed aggregation patterns similar to those in the eye
(Figure1, L–O, and data not shown). Notably, again
among the tested Httex1 proteins, the age-dependent
nature of aggregates formation was most clearly exem-
plified in flies expressing Httex1-Q46. As shown by
confocal imaging in 2-day-old adult flies, Httex1-
Q46-eGFP protein was evenly distributed in the brain
(Figure1, L and N). However, by day 30, predominant
eGFP-positive aggregates were present throughout the
brain (Figure1, M and O). Thus, consistent with the
observations in mammalian cells, these Htt exon1
proteins show both age- and polyQ-length-dependent
patterns of aggregates formation in Drosophila tissues.
An important biochemical definition of protein
aggregates is their resistance to detergent SDS, which
can be detected on Western blot as SDS-insoluble large
protein complexes that are retained in the stacking gel.
To confirm that the observed bright eGFP-positive
puncta in Drosophila tissues are indeed protein aggre-
gates, we extracted whole proteins from adult fly heads
at different ages and performed Western analysis. As
shown in Figure 1P, SDS-resistant large protein com-
plexes were absent in 30-day-old control flies expressing
eGFP alone or eGFP-tagged wild-type Httex1-Q25; they
were present in both young (2-day-old) and aged
(30-day-old) Httex1-Q72 and Httex1-Q103 flies; most
tellingly, these complexes were absent in young flies
with blue dashed lines), mirroring the appearance of
eGFP-positive puncta in these animals. Thus, the de-
velopment of SDS-insoluble large protein complexes, a
correlates with the appearance of eGFP-positive puncta
in the fly tissues, indicating that these fluorescent puncta
are indeed protein aggregates.
In addition to aggregates formation, we also observed
the expected, polyQ-length-dependent toxic effect of
numerous prominent aggregates were present at day 30 (M and O). (N and O) Higher magnification view of regions (the olfactory
bulb and the a- and b-lobes of mushroom body) highlighted in L and M, respectively. Note that the images in the younger brain (L
and N) were exposed for a longer time to maximize the detection for aggregates. (P) Confirmation by Western blot of the de-
velopment of SDS-insoluble aggregates in Httex1-Qn-eGFP flies. Whole-protein extracts from adult fly heads were probed with
anti-eGFP antibody. (Bottom) Ages and the type of the expressed protein in the examined flies. Large protein complexes that were
retained in the stacking gel, as highlighted, were found in samples from Httex1-Q72, Q103 (lanes 5–7) and aged Q46 flies (lane 4,
30 days old), but were absent in young Q46 flies (lane 3, 2 days old) and other control flies (lane 1, flies expressing eGFP alone;
lane 2, Httex1-Q23-eGFP; both were 30 days old).
1168 S. Zhang et al.
polyQ tracts causes more severe degenerative pheno-
as well as progressive eye degeneration, declining
mobility, and shortened life span in adults (arrowhead
in Figure 1K and data not shown). Together, these
results demonstrated that both the formation of ag-
gregates and the toxicity of mutant Htt could be
recapitulated in Drosophila expressing eGFP-tagged
Htt-exon1-Qn reporters. Furthermore, among the
tested Httex1 proteins, the progressive nature of ag-
gregates development was most clearly exhibited by
Httex1-Q46-expressing animals, suggesting that Httex1
with an intermediate length of glutamine repeats (i.e.,
Q46) might be more susceptible to modulations by
other cellular factors and thus represents a good
candidate as a reporter in screens for regulators of
based high-throughput ge-
for modifiers of aggregates
formation in Drosophila
cells. (A) Structure of the
Httex1-eGFP reporter con-
struct used in the cell-
based assay, which is under
the control of the copper-
inducible (Cu11) metallo-
thionein (met) promoter.
(B) Confocal images of ag-
Httex1-Qp46-eGFP in Dro-
sophila S2 cells. Note that
only ?50% of the cells de-
gregates, whereas in the
remaining cells the Httex1-
Qp46 protein was present
identified by the promi-
nent eGFP signals (green),
overall cell morphology us-
and cell nuclei stained by
DAPI (blue). (C) Auto-
mated quantification of ag-
gregates and cell number.
Aggregates were revealed
by their prominent eGFP
signals and the cell nuclei
by DAPI staining (left).
Overlaying of computer-
simulated objects on the
basis of quantification anal-
yses (middle) with their
original images revealed
significant overlap, demon-
strating the accuracy of
this quantification method
(right). (D) Scattered plot
comparison of quantifica-
tion results for two dupli-
cate plates based on the
parameters of average ag-
size (middle), and inten-
sity (right). The circular
dashed lines indicate a ra-
dius of 23 SD for each pa-
rameter. Most dsRNAs tested are within the 23 SD range, with the position of CG6603 highlighted (red arrows). (E) Flow chart for
the genomewide RNAi screen to isolate modifiers of aggregates formation.
RNAi Screen for Aggregation Regulators 1169
An imaging-based, high-throughput assay for aggre-
gates formation in Drosophila cells: With the recent
advent of RNAi technology and the development of
genomewide dsRNA libraries, it is possible to quickly
and systematically evaluate all known genes in a model
organism for their effects on a studied question. In
Drosophila, RNAi-mediated gene knockdown is espe-
cially efficient in cultured cells, and genomewide RNAi
screens have been well developed and applied to
exploring multiple signaling (Caplen et al. 2000;
Clemens et al. 2000; Friedman and Perrimon 2007).
To establish a relevant cell-based assay suitable for an
RNAi screen for aggregation regulators in Drosophila,
we chose hemocyte-like S2 and neuronal-like BG2 cells
and generated stably transformed cell linesin which the
expression of the above eGFP-tagged Httex1 proteins is
controlled by the copper-inducible metallothionein pro-
moter (Figure 2A). Consistent with the in vivo results in
both of these two cell lines, these Httex1 proteins
showed behavior similar to polyQ-length-dependent
aggregation (Figure 2B and data not shown). Impor-
tantly, aggregates in these cells emit intense fluorescent
signals that are easily identifiable even when viewed at
low magnification (Figure 2C), making it feasible to use
the image-based approach to screen for aggregates
In adopting such an image-based assay for a high-
throughput screen, it is important that individual
aggregates and each cell can be clearly distinguished
to ensure reliable quantification. We found that, under
the cultured condition, neuronal-like BG2 cells more
reaching confluence, impeding the clear visualization
and quantification of both the aggregates and cell
number (data not shown), while such issues are much
less prominent in S2 cells, which are more prone to
spread out and form a single layer over the surface
before reaching confluence (Figure 2B). Moreover, in
aggregates develop in ?50% of the cells, which is ideal
for a modifier screen to identify both suppressors and
enhancers of mutant Htt aggregation (Figure 2B).
Accordingly, we chose this S2 cell line expressing
Httex1-Q46-eGFP for the ensuing RNAi screen.
To demonstrate the relevance and sensitivity of this
cell-based system for a modifier screen, we treated these
cells with a dsRNA against dhdj1, a known suppressor of
aggregates (Chan et al. 2000; Kazemi-Esfarjani and
Benzer 2000), and found that it significantly enhanced
with dsRNA targeting the eGFP tag in the Httex1-Q46
reporter completely abolished fluorescent signals (Fig-
a high-throughput screen, we further developed an
automated assay protocol. Using this protocol, sample
aggregates revealed by their prominent eGFP signals
and individual cells by DAPI nuclear staining. Details
about the aggregates (i.e., the size, total number, and
the imaged fields were automatically quantified using
the MetaMorph analytic software. Since the aggregates
are highly enriched with eGFP-tagged mutant Htt
(Httex1-Q46-eGFP), the level of fluorescent signal from
the aggregates is significantly higher than that from the
cytoplasm and from background noise, thus allowing
their clear identification and quantification. As seen in
Figure 2C, this automated quantification gave rise to
satisfactory accuracy in measuring both the aggregates
and the cell number (see legend for Figure 2C and
materials and methods for details).
dsRNAs tested show no effect on aggregates formation,
while dsRNA specifically directed against dhdj1 as a
positive control significantly enhanced aggregates for-
Figure 3.—Sample im-
ages from the RNAi screen.
Examples of images from
wells treated with a water
against eGFP (D), CG6603
(B, dhsp110), dhdj1 (also
known as dnaj1 or hsp40)
(C), lilli (E), and smt3 (F).
Aggregates were identified
by the prominent eGFP
signals (green), overall cell
stain (red), and cell nuclei
stained by DAPI (blue).
1170 S. Zhang et al.
mation, and dhdj1 was reproducibly categorized as a
strong suppressor (Figure 3C). Furthermore, compari-
son between duplicate experiments reveals that overall
both the assay and the quantification results are highly
reproducible (Figure 2D and data not shown).
Genomewide RNAi screens for regulators of aggre-
gates formation: Primary screens: After optimization of
this cell-based assay in a 384-well plate format, we per-
formed an RNAi screen in duplicate using a Drosophila
genomewide library of ?24,000 dsRNAs (Boutros et al.
2004), with the effect of each dsRNA on aggregates
formation quantified on the basis of three parameters:
the average number, size, and signal intensity of ag-
gregates (Figure 2D, and supporting information,
Figure S1). For each plate, the average value and SD
for these three parameters from whole-plate samples
were also calculated. For a dsRNA-treated sample in the
primary screen, if the value of any of the three
parameters was .2 3 SD of the whole-plate average, it
wasconsidered tohaveasignificanteffecton aggregates
formation and was selected as a potential candidate
(Figure 2, D and E; also see materials and methods
for screen details). On the basis of the above selection
criterion, we identified 644 candidate dsRNAs from the
primary screens that potentially enhance or suppress
categorization of aggrega-
tion regulators from the
screen. Pie chart represen-
tation of candidate genes
based upon their Gene On-
function or protein do-
mains shows the categories
of all 126 hits (A) and the
54 suppressors (B) and 72
enhancers (C) that can
of mutant Htt aggregates
hancer’’ is defined geneti-
causes reduced aggregates
mediated knockdown of
target gene expression in
the assay. Conversely, ‘‘sup-
pressor’’ is defined as the
gene that causes increased
aggregates formation when
its expression is knocked
RNAi Screen for Aggregation Regulators1171
Secondary assays: To eliminate false positives resulting
from indirect effects on aggregates formation, we
further evaluated the 644 candidate dsRNAs from the
primary screen in the following steps (Figure 2E and
Figure S2;seelegendsand materialsand methodsfor
details): (1) removed dsRNA amplicons that contain
21-bp overlaps with more than 5 other genes in the
genome, which are expected to cause significant non-
specific off-target RNAi effects (Kulkarni et al. 2006;
Ma et al. 2006); (2) removed dsRNA amplicons that are
known to function in general protein synthesis (e.g.,
cytoplasmic or mitochondrial ribosomal proteins),
which affects overall protein synthesis within the cell,
indirectly affecting the formation of aggregates that
depends upon the amount of available misfolded Htt
protein (Scherzinger et al. 1999); (3) removed dsRNA
amplicons that failed to reproduce their effect on
aggregation in an intermediate screen using the resyn-
thesized dsRNAs; (4) identified and removed candi-
dates that affect the activity of metallothionein promoter
components and the copper transporter); (5) validated
each remaining hit by synthesizing and retesting one or
two more dsRNAs targeting different regions of the
candidate gene. Genes that failed to repeat their effect
on modulating aggregation in these additional rounds
of testing were removed from consideration. In total,
126 genes passed both the primary screens and all
subsequent validation steps (Figure 2E and Figure S2).
Functional categorization of regulators of aggregates
formation identified by the screen: Ofthe126validated
regulators from the screen, 52 act as enhancers and 74
as suppressors of aggregates formation (Figure 4, A–C,
and Table 1; see Table S1 and Table S2 for a list of these
genes and associated information). Throughout this
study, ‘‘suppressor’’ is defined genetically as a candidate
that causes an increased formation of aggregates after
dsRNA-mediated knockdown of the corresponding
gene. And vice versa, ‘‘enhancer’’ is similarly defined
as a candidate that leads to a decreased formation of
aggregates in the assay. Examples of some of these
modifiers are shown in Figure 3. Interestingly, although
overall only 50% of fly genes are conserved in humans
(Rubin et al. 2000), 71% (90 of 126) of isolated
candidates have predicted human orthologs, with 25
of them encoding proteins that have been implicated in
human diseases (see Table S2).
On the basis of their predicted function, these 126
genes can be further categorized into different classes,
the largest category of which contains genes that
function mainly in protein biogenesis (Figure 4A and
Table 1). Interestingly, the major classes of suppressors
from a Caenorhabditis elegans screen in the muscle tissue
for modifiers of polyQ aggregation are also involved in
protein biogenesis, which has been proposed to repre-
sent the ‘‘protein homeostatic buffer’’ that can respond
to and prevent the aggregation of misfolded proteins
(Nollen et al. 2004; Morimoto 2008).
In addition, all 14 genes in the cytoskeleton/protein
trafficking group were isolated as enhancers of aggre-
gation, suggesting that formation of visible aggregates
might involve active transport to concentrate misfolded
proteins into a specific compartment within the cell
(Garcia-Mata et al. 1999), while interference with
cytoskeleton integrity or the protein transport machin-
ery might disrupt such a process.
In eukaryotes, multiple families of chaperones with
diverse cellular functions are present (Craig et al. 1994;
Whitesell and Lindquist 2005; Bukau et al. 2006).
Within the cell, chaperones are essential in facilitating
Aggregation modulators isolated from the screen
Functional classesNo. of modulatorsSuppressors Enhancers
Nonsense-mediated mRNA decay
Ubiquitin, SUMOylation and proteasome
Table 1 lists the functional classes for the aggregation modulators isolated from the screen (column 1), the
total number of modifiers in each functional class (column 2), as well as the number of modifiers in each en-
hancer and suppressor subset (columns 3 and 4). Candidates were categorized on the basis of the Gene On-
tology biological function or protein domains. ‘‘Suppressor’’ is defined genetically as those candidates that
cause an increased formation of aggregates after dsRNA-mediated knockdown of the corresponding candidate
gene, and vice versa. ‘‘Enhancer’’ is similarly defined as those candidates that cause a decreased formation of
aggregates in the assay (also see Figure 3 and Table S1 and Table S2 for more details).
1172 S. Zhang et al.
the proper folding of newly synthesized proteins and
the refolding of misfolded proteins to maintain them in
their soluble native state (Craig et al. 1994; Whitesell
and Lindquist 2005; Bukau et al. 2006). Since the
aggregates in this assay were derived from misfolded
mutantHtt, itis expectedthat chaperones playacentral
role in regulating aggregates formation. Indeed, an-
other major class of modifiers from the screen includes
several chaperones and their regulatory proteins, such
as the chaperone proteins CG6603 (a member of
Hsp110 subfamily chaperones; see below), hsp83
(Hsp90 family), and hsc70-5 (Hsp70 family); the co-
chaperone dhdj1 (Hsp40 family); chaperonin T-cp1; and
response to heat shock and other cellular stresses to
induce the expression of downstream chaperone pro-
teins (Craig et al. 1994). Knockdown of all these
chaperone genes enhanced aggregates formation (i.e.,
suppressors), while depletion of CG6603 gave rise to the
strongest enhancing effect of all the regulators isolated
(Figure 2D and Figure 3B; also see below). Surprisingly,
knockdown of both hsc70-3 and hsc70-4, two abundant
Hsp70 chaperones, caused reduction of aggregates
formation. Previous studies suggest that each chaper-
one has selective substrate preferences, and chaperones
further show specificity in modulating polyQ neurotox-
icity (Chan et al. 2000). In addition, some chaperones
are known to be involved in essential cellular processes.
For example, hsc70-4 in Drosophila is critical for
clathrin-dependent endocytosis (Chang et al. 2002,
2004). Thus, modulating the activities of these chaper-
ones might indirectly affect the aggregates formation,
and their observed differential effect on aggregation
could be due to their distinctive cellular functions and
substrate specificity. Moreover, a dozen modifiers, such
modification and protein degradation pathways, indi-
cating that clearance of misfolded proteins by the
cellular degradation machinery serves as an important
regulatory step in aggregates formation.
Notably, several components of the nonsense-
mediated mRNA decay (NMD) pathway were isolated,
which all acted as suppressors of aggregates formation.
NMD is a cellular surveillance machinery that promotes
the degradation of abnormal mRNAs containing pre-
mature termination codons (Valencia-Sanchez and
Maquat 2004). The potential mechanism for the NMD
pathway in modulating aggregation formation is not
clear. Interestingly, one central component of the NMD
complex, smg1, encodes a large protein with a PI3,4-
kinase domain, a Huntingtin, elongation factor 3, pro-
tein phosphatase 2A, and the yeast PI3-kinase TOR1
(HEAT) repeat domain, and, notably, a heat-shock
protein Hsp70-like domain (Gatfield et al. 2003). This
raises the possibility that the NMD pathway also has
some functional overlap with the chaperone machinery
and, as such, senses and modulates the formation of
aggregates through its crosstalk with chaperones. Alter-
Aggregation regulators previously identified as toxicity modifiers
Modifier nameFunctionModification of NDsReference
Chen et al. (2003)
Wu et al. (2005)
Chan et al. (2000);
Kazemi-Esfarjani and Benzer (2000)
Chan et al. (2000);
Warrick et al. (2000)
Fernandez-Funez et al. (2000)
Steffan et al. (2001)
Shulman et al. (2003)
Hsc70-4 Chaperone PolyQ (SCA3)
and AD (Tau)
Tra1(Nipped-A) Kinase/transcriptional regulation
Tor TOR signaling/autophagy
Nuclear pore component/protein trafficking
Rab1 Vesicle/protein trafficking
Steffan et al. (2004)
Chan et al. (2002)
Ravikumar et al. (2004)
Kanuka et al. (2003)
Fernandez-Funez et al. (2000)
Fernandez-Funez et al. (2000)
Cooper et al. 2006
Shulman et al. 2003
Table 2 lists the known toxicity modifiers of neurodegenerative diseases (NDs) that were also isolated as modifiers of aggregates
formation from the RNAi screen. PolyQ, polyglutamine diseases; HD, Huntington’s disease; PD, Parkinson’s disease; AD, Alz-
heimer’s disease; SCA, spinocerebellar ataxia type; SBMA, spinalbulbar muscular atrophy.
aPros26 and Nup44A were not themselves isolated as modifiers of aggregation, but several other components in either the
proteasome complex or the nuclear pore complex were isolated in the screen.
RNAi Screen for Aggregation Regulators 1173
natively, since NMD acts by promoting the decay of
abnormal mRNAs, another attractive possibility is that it
recognizes the expanded CAG repeats in the mRNA
transcripts of mutant Htt and promotes their degrada-
tion (Valencia-Sanchez and Maquat 2004).
A number of kinases and phosphatases were also
isolated from the screen, including a PI-3,4 kinase
PI3K68D, the a- and b-subunits of casein kinase II
(CKII), as well as the phosphatases CG1906 and spa-
ghetti. CG1906 encodes a phosphatase 2C-like protein,
while spaghetti encodes a tetratricopeptide repeat (TPR)-
containing protein with serine/threonine phosphatase
activity. The exact role of these signaling molecules in
Hsp90, another aggregates regulator isolated from the
screen, and normally forms a large chaperone complex
(Marhold et al. 2000). This raises the possibility that
spaghetti affects aggregates formation by regulating the
activity of chaperones within the cell. It is not clear if
these kinases and phosphatases function together and
kinases and phosphatases provide targets of choice in
drug design, it will be of interest to identify the exact
pathway(s) regulated by these signaling molecules to
decipher how they influence aggregates formation.
Another potential class of modifiers, including jra1,
raw, tao1, cka, and lic, is involved in Jun-N-terminal
kinase (JNK) signaling. The presence of aggregates
within the cell might trigger a cellular stress/defense
response, which is normally mediated by the JNK-
signaling pathway (Weston and Davis 2002).
We also noted that, although our screen focused on
identifying regulators of aggregates formation, a num-
ber of isolated candidates from the screen have been
previously identified as toxicity modifiers for polyQ or
other aggregates-associated diseases (Table 2). For
example, smt3 and uba 2, which encode SUMO protein
and SUMO-activating enzyme, respectively, have been
shown previously to modulate polyQ pathogenesis in
Drosophila (Chan et al. 2002; Steffan et al. 2004), and
both were isolated in our screen as aggregation en-
of the Sin3 chromatin-remodeling complex involved in
transcriptional repression and were previously character-
ized as modifiers of polyQ toxicity (Fernandez-Funez
et al. 2000; Steffan et al. 2001), were identified in our
screen as aggregation suppressors. In addition, tao1 has
beenreportedto beinvolvedin tauopathy and rab1 tobe
associated with a-synuclein-induced toxicity (Shulman
and Feany 2003; Cooper et al. 2006). It is possible that
the mechanisms by which these genes modulate neuro-
regulating aggregates formation. For example, it has
been suggested that through the SUMO modification
of Htt, smt3 and uba2 might modulate cellular toxicity
by affecting Htt’s transcriptional repression activity or
its subcellular localization (Chan et al. 2002; Steffan
et al. 2004). Nevertheless, the observation of an overlap
between these two groups of modifiers raises the possi-
bility that the effects of these modifiers on aggregates
Figure 5.—In vivo modification of the neuro-
degeneration phenotype associated with a Dro-
sophila HD model by Hsp110 and Tra1. In
7-day-old HD93 flies (genotype: GMR-Gal4/1;
UAS-Httex1p Q93/1) (Steffan et al. 2001), de-
generation is manifested externally by the loss
of pigmentation in the posterior of the eye
(B), as compared to the control of GMR-Gal4
eyes (A). By day 30, the degeneration has ex-
panded to encompass the entire eye (E). In a het-
erozygous hsp110 (dhsp110) mutant background,
the degeneration was accelerated and had spread
to the entire eye by day 7 (C). Such degeneration
was significantly suppressed by coexpression of
wild-type Drosophila Hsp110 (dHsp110), even
at day 30 (F). See also Figure S4 for additional
controls for the test. The eye degeneration phe-
notype of HD93 flies was also significantly ac-
celerated ina heterozygous
background (D). In all eye images, the anterior
side is up and the ventral side is to the left.
1174 S. Zhang et al.
formation also contribute to their role in modulating
Hsp110 as a potent suppressor of aggregates
formation and neurotoxicity: CG6603 was isolated as
the most potent aggregates suppressor from the screen:
RNAi-mediated depletion of CG6603 caused formation
of prominent aggregates in almost 100% of dsRNA-
treated cells (Figure 3B), and quantification analyses of
the three evaluation parameters showed that CG6603
is the most potent suppressor of aggregates formation
identified in our screen (Figure 2D), suggesting that
it might play an important role in regulating the
aggregation of mutant Htt protein. Because of this, we
further examined its role during development and in
The CG6603 gene in Drosophila has not yet been
extensively studied. Several mutant alleles of CG6603
exist, all of which cause early lethality when homozy-
gous. By mosaic clonal analyses, we further found that
patches of CG6603 mutant tissue failed to develop,
indicating that the gene encodes an essential cellular
function (data not shown). Further, overexpression of
wild-type CG6603 caused a toxicity effect in the targeted
tissues (Figure S3). Together, these results indicate that
an optimal level of CG6603 activity is essential for
normal animal development.
To study the role of CG6603 in neurodegeneration,
we next tested its genetic interaction with HD93
(Httex1p-93Q), a well-characterized Drosophila HD
model (Steffan et al. 2001). In HD93 flies, a mutant
Htt containing a polyQ tract of 93 residues is ectopically
expressed in the Drosophila eye, causing an age-
dependent degeneration. At day 7, the degeneration is
manifested exteriorly by the clear depigmentation in
the posterior region of the eye (compare Figure 5B with
5A); as the fly ages this degeneration gradually spreads
to the whole eye (compare Figure 5E with 5B). When
tested in HD93 flies, CG6603 showed a strong dosage-
dependent modifying effect on neurodegeneration: in
flies heterozygous for mutations in CG6603, which
halved the endogenous dosage of the corresponding
protein, eye degeneration was significantly accelerated
(Figure 5C). Furthermore, increasing the endogenous
level of wild-type CG6603 by ectopically expressing low
levels of CG6603 notably suppressed eye degeneration
in HD93 flies (Figure 5F; see Figure S4 for additional
controls for this assay).
CG6603 encodes a protein of 804 amino acids with
significant sequence homology to the Hsp110 subfam-
ily of chaperones, which is distinguished from other
Hsp70 family chaperones by its unusually large size
(Easton et al. 2000). Currently, alleles of CG6603 are
referred to as Hsc70Cb, solely due to its cytological
location at polytene band 70C. To avoid confusion with
the general Hsp70 proteins, we refer to it as dHsp110 in
this study. Consistent with our result that Hsp110 was
the most potent suppressor of aggregates formation by
mutant Htt, a previous study showed that mammalian
Hsp110 conferred cellular thermo-resistance in cul-
tured cells and was severalfold more efficient than
Hsc70 in preventing aggregation of denatured lucifer-
ase (Oh et al. 1997). Thus, together with the observa-
tion of its dosage-dependentgenetic interaction with
HD flies, Hsp110, a strong aggregation suppressor,
might also be a potent suppressor of mutant polyQ-
tra1 modulates the toxicity of mutant Htt in vivo: In
addition to dhsp110, we found that two mutant alleles of
tra1 (also called Nipped-A), another aggregation sup-
pressor identified from the screen, also showed a
dosage-dependent modifying effect on neurotoxicity
of mutant Htt: in a heterozygous tra1 mutant back-
ground, the eye degeneration of HD93 flies was signif-
icantly enhanced (Figure 5D), suggesting a role of tra1
in mutant Htt-induced neurotoxicity. Similar to hsp110,
knockdown of tra1 significantly increased aggregates
formation in the assay. tra1 is believed to function in the
Spt-Ada-Gcn5-Acetyltransferase complex to regulate
transcription. It encodes a large protein containing a
HEAT domain, a PI-3,4 kinase-like domain, and a TPR
repeat. The role of tra1 in regulating aggregates forma-
tion remains to be clarified.
A genomewide RNAi screen for regulators of
aggregates formation in Drosophila cells: In Drosoph-
ila, which has been extensively used to study polyQ and
other neurodegenerative diseases, most of the previous
studies focused on toxicity; no systematic analysis of
regulators of aggregates formation has been carried out
in this system. By developing a cell-based quantitative
assay and using a genomewide collection of dsRNAs in
Drosophila, we performed a high-throughput RNAi
screen and identified .100 aggregation regulators.
Clearly, more studies are needed to further validate
the identified candidates and investigate the exact
mechanisms by which these regulators modulate aggre-
gation. Nevertheless, this genomewide RNAi screen
should help complement the previous studies focusing
on toxicity in neurodegenerative diseases. For example,
by comparing hits from our screen with previously
identified toxicity modifiers in Drosophila, we found
an interesting overlap between these two groups of
genes. In addition, we showed that Drosophila hsp110
(CG6603) and tra1 can modify neurodegeneration of
HD flies in a dosage-dependent manner. Together,
these findings support that modulating aggregates
formation might have therapeutical implications in
treating neurodegenerative diseases.
Hsp110 chaperone as a potent suppressor of
aggregates formation and neurotoxicity: From the
screen, hsp110 (CG6603) stands out as the most potent
suppressor of aggregates formation. By further charac-
RNAi Screen for Aggregation Regulators 1175
terizing its mutant phenotypes, we found that an
optimal level of CG6603 activity is essential for normal
animal development because both loss of its endoge-
nous function and its high-level overexpression were
detrimental to the animal. Moreover, CG6603 modu-
lates neurodegeneration of HD flies in a dosage-
dependent manner. Together, these data suggest a
unique role for CG6603 in regulating normal animal
development, protein folding, and neurotoxicity.
Notably, in COS-7 cells, overexpression of the Hsp110
family member Hsp105a suppresses both the aggrega-
tion and the cellular toxicity of the mutated androgen
receptor, the causative gene for the polyQ disease
spinalbulbar muscular atrophy (Ishihara et al. 2003).
In addition, a Hsp110 homolog in C. elegans has recently
been identified as a strong suppressor of aggregates
formation by the ALS-associated mutant SOD1 (see
below), and more interestingly, in a mouse ALS model,
age(Wangetal.2009a,b). Together, theseresultssuggest
a conserved role of Hsp110 family chaperones in modu-
latingmisfolding andtoxicityofmutant disease proteins.
Despite being highly conserved from yeast to humans,
being one of the most abundant proteins in the mam-
malian brain and much more effective than Hsc70 in
preventing aggregation of denatured protein, there
of proteins, and their exact biochemical function
remains unclear (Oh et al. 1997, 1999; Easton et al.
2000; Shaner et al. 2005; Yam et al. 2005). It has been
suggested that Hsp110 cannot on its own refold a
denatured protein, but instead functions as a ‘‘holdase’’
by binding and maintaining the denatured protein in a
soluble folding-competent state and subsequently co-
operating with other Hsp70 chaperones for refolding
(Oh et al. 1997, 1999; Shaner et al. 2005; Yam et al.
2005). More recently, Hsp110 proteins were also sug-
gested as acting as nucleotide exchange factors for
general Hsp70 chaperones (Dragovic et al. 2006;
Raviol et al. 2006; Polier et al. 2008; Schuermann
et al. 2008). Interestingly, although multiple families of
chaperone proteins exist in eukaryotes, Hsp110 showed
the strongest effect as an aggregates suppressor in the
screen (Figure 2D and Figure 3B and data not shown).
Chaperones function in diverse cellular processes, but
each chaperone has a selective substrate preference
(Bukau et al. 2006). In addition, chaperones display
specificity in modulating polyQ neurotoxicity (Chan
et al. 2000). Thus, given the dosage-sensitive effect of
Hsp110 in normal Drosophila development and in
modulating Htt toxicity, it is possible that Hsp110 plays
a rate-limiting regulatory role for general Hsp70 chap-
chaperone for Hsp70 chaperones by stimulating their
ATPase activity (Craig et al. 1994; Bukau et al. 2006).
is more specific for the proper folding of mutant polyQ
proteins. The detailed mechanism of action of Hsp110
proteins remains to be clarified. Nevertheless, results
from our RNAi screen and genetic analyses, together
with previous studies, suggest that Hsp110 chaperones
play an important role in regulating the proper folding
and toxicity of mutant Htt.
Comparison with aggregation regulators isolated
from other large-scale RNAi screens in model organ-
isms: Recently,severallarge-scale RNAi-basedscreensin
C. elegans for aggregation regulators of different ND-
our candidates with those isolated from these studies.
A total of 186 suppressors were isolated from a
genomewide RNAi screen for genes regulating polyQ
by dsRNA promotes aggregates formation in muscles
expressed a threshold-length polyQ protein (Q35-YFP)
(Nollen et al. 2004). Comparison of the results of these
Hsc70-5, uba1, T-cp1, and rab1, as detailed in Table S3.
However, we also noted significant differences be-
tween the results of the two screens. For example, on
one hand, knockdown of the rme-8 gene, a DnaJ domain
chaperone protein, could enhance aggregates forma-
tion in C. elegans, whereas its closest homolog in
Drosophila, Rme-8, showed no effect in our study. On
the otherhand, the fly dhdj1 gene (also called hsp40 and
dnaj1), which encodes a DnaJ domain protein of the
Hsp40 co-chaperone family, showed a strong effect in
worm, dnaj-13, was not found in the C. elegans study. In
addition, many regulators from our study, such as
hsp110, JNK members, and the NMD pathway, were not
identified in the C. elegans screen, and vice versa. Most
of genes isolated from these two screens elicits different
responses in the two systems, such as rab1 and Tbp-1, as
downregulation of these genes’ expression led to an
increased aggregates formation in the C. elegans study
such genes, perhaps the most obvious examples are
those involved in protein biogenesis. In the C. elegans
screen, 40 identified suppressors were ribosomal pro-
formation of aggregates in the muscle (Nollen et al.
2004). In our study, 97 cytoplasmic and mitochondrial
ribosomal proteins were also isolated from the primary
screen. However, knockdown of these ribosomal pro-
teins in the assay significantly diminished aggregates
formation in Drosophila cells (see Table S3, Figure S2,
and File S1). Because formation of aggregates is sen-
et al. 1999), and ribosomal proteins are involved in
general protein synthesis, we suspect that the effect of
these ribosomal proteins in our assay is likely to be
indirect and thus removed from the follow-up studies.
1176 S. Zhang et al.
screens is still not clear, but likely due to many factors,
such as an intrinsic species difference between the fly
and the worm, different assay approaches (visual in-
spection of whole animals vs. computer imaging of
cultured cells), or differential sensitivity of the experi-
mental system (muscle tissue in C. elegans vs. Drosophila
S2 cell lines). However, one important element might
be that, compared with the cell-based assay that reflects
mainly an autonomous cellular effect, C. elegans as a
complicated multi-cellular organism can integrate the
sensing and responses to the aggregates-induced stress
within a specific tissue (e.g., the muscle) at the whole-
animal level, eliciting both autonomous and more
complicated nonautonomous responses (Prahlad
and Morimoto 2009). Indeed, one recent study sug-
heat-shock response in C. elegans (Prahlad et al. 2008).
Further investigations will be needed to address these
screens should complement each other because they
provide a valuable comparison that can broaden our
understanding of the regulation of aggregates forma-
tion in different experimental systems.
A major component of the Lewy bodies inclusions in
Parkinson’s disease is a-synuclein (a-Syn) (Spillantini
genomewide and the other targeting 868 candidates, 84
down, these hits increased aggregation of fluorescence-
tagged human a-Syn expressed in worm body-wall
muscles (Hamamichi et al. 2008; van Ham et al. 2008).
only limited overlaps: in both cases, a chaperone
protein—Hsp83 or Dnaj-1 (Hsp40), respectively (Table
S4 and Table S5).
Mutations in the human superoxide dismutase
(SOD1) gene are associated with ALS (Rosen et al.
1993; Bruijn et al. 2004; Pasinelli and Brown 2006). A
whole-genome RNAi screen in C. elegans identified 81
suppressors, which, when knocked down by RNAi in-
YFP-tagged misfolding-prone SOD1 (G85R) mutant
(Wang et al. 2009a). Interestingly, homologs of eight
of these hits were also identified in our study, including
hsf, hsp110, and rab11, with another four among the
candidates from our primary screen (Table S6). Again
we noted several cases where two homologous genes
elicited opposite responses in the two screens (e.g., uba2
and rab11; see Table S6). We hypothesize that, as in the
above polyQ study, similar factors might be accountable
for such opposite responses in this SOD1 screen as well.
Nevertheless, the observation of more overlapping hits
among polyQ, Htt (a polyQ disease gene), and SOD1
mutants than with a-Syn raises an intriguing possibility
their aggregation processes than with a-Syn.
Note: While the manuscript for this study was being
prepared, a RNAi screen similar to this study was
reported by Doumanis et al. (J. Doumanis, K. Wada,
Y. Kino, A. W. Moore and N. Nukina, 2009, RNAi
mutant Huntingtin aggregation. PLoS One 4: e7275)
who used a Httex1-62Q-eGFP reporter in Drosophila
BG2 cells and identified 21 regulators after testing 7200
Drosophila genes. Comparing the results from the two
screens revealed several similarities (Table S7). First, 3
identical genes were isolated from both screens: sec23,
deflated (defl), and CG4738 (Table S7). Importantly,
these 3 genes showed similar effects on aggregation in
both screens: knocking down of sec23 and CG4378
decreased aggregates formation, while reducing the
another 6 genes from the Doumanis et al. (2009) study
were among the original 644 candidates from our
primary screens, which were eventually removed from
our final hit list after failing to pass the second screens.
These 6 genes also showed similar effects on aggregates
formation in both studies (Table S7). Moreover, we
noted that, although Nup154, one of the hits from the
Doumanis et al. (2009) study, was not identified in our
screen, several other Nup family proteins, including
Nup62, Nup98, Nup170, and Nup 358, were isolated
from our screen. The remaining differences between
the results of the two studies are likely due to different
selection criteria as well as different RNAi libraries and
cell types used in the screens. It is highly likely that, due
tounavoidable variationsin theexperiments,extra false
positives are included and extra false negatives are
removed from both studies. Additional analyses are
warranted to examine the identified regulators from
these studies. Nevertheless, the significant overlap of
the hits from these two independent screens supports
the reproducibility of the image-based RNAi screen for
studying regulations of aggregates formation. Further
experimentswill be needed tovalidatethe invivo effects
of the identified candidates from these screens and
uncover the mechanisms underlying their regulations.
We thank the Hereditary Disease Foundation (HDF) for providing
mutant Htt plasmids; L. Thompson, J. L. Marsh, N. Bonini, and M.
Feany for providing advice and Drosophila stocks; the Drosophila
RNAi Screening Center for providing technical support; B. Mathey-
Prevot for advice and critical reading of the manuscript; M. Markstein
and R. Griffin for critical reading of the manuscript; members of the
Perrimon lab for assistance, in particular J. Phillips and P. Bradley for
Fellow of the Leukemia and Lymphoma Society; S.Z. gratefully
acknowledges support in the form of a fellowship from the Harvard
Center for Neurodegeneration and Repair as well as the Lieberman
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Drosophila as a model for human
Targeted gene expression as a
Protofibrils, pores, fibrils,
The J-domain pro-
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Communicating editor: O. Hobert
RNAi Screen for Aggregation Regulators1179
Supporting Information Supporting Information
A Genomewide RNA Interference Screen for Modifiers
of Aggregates Formation by Mutant Huntingtin in Drosophila of Aggregates Formation by Mutant Huntingtin in Drosophila
A Genomewide RNA Interference Screen for Modifiers
Sheng Zhang, Richard Binari, Rui Zhou and Norbert Perrimon Sheng Zhang, Richard Binari, Rui Zhou and Norbert Perrimon
Copyright © 2010 by the Genetics Society of America
S. Zhang et al. 2 SI
Materials and Methods
DNA fragments containing the mutant Httex1-Qn-eGFP variants (Q25, Q46, Q72 and Q103) were cloned from DNA constructs
generously provided by the Hereditary Disease Foundation (HDF) (originally from Dr. A. Kazantsev (KAZANTSEV et al. 1999)).
To clone into the hygromycin-resistance pMK33 vector which contains the copper-inducible metallothionein promoter, the
Httex1-Q25-eGFP and Httex1-Q103-eGFP fragments were digested with XhoI and SpeI restriction enzymes and inserted into the
same sites in the pMK33 vector, while the Httex1-Q46-eGFP as well as Httex1-Q72-eGFP were amplified by PCR and inserted
blunt-ended into EcoRV site in the pMK33 vector.
For generating transgenic flies, DNA containing the Httex1-Q25-eGFP and Httex1-Q103-eGFP fragments were digested with
XhoI and XbaI and inserted into the same sites in the pUAST vector (BRAND and PERRIMON 1993), and DNA containing the
Httex1-Q46-eGFP and Httex1-Q72-eGFP fragments were digested with KpnI and SpeI and inserted into the same sites in the
A cDNA encoding CG6603 (the fly Hsp110) was obtained from the Berkeley Drosophila Genome Project (clone ID
LD32979). The EcoRI/XhoI fragment containing full-length CG6603 was cloned into the EcoRI and XhoI sites of pUAST
Drosophila SL2 cells (Schneider’s Line S2 cells; http://www.flyrnai.org) were grown at 25°C in Schneider’s media (GIBCO)
with 5% heat inactivated fetal bovine serum (FBS; JRH Biosciences). Each of the mutant Httex1-Qn-eGFP constructs in pMK33
vector was transfected into SL2 cells using Effectene reagents (QIAGEN) and selected with 0.2mg/ml of Hygromycin
consecutively for 5 generations to establish stably-transformed SL2 lines. The resulting stable cell lines are maintained in
To eliminate genes that could indirectly affect aggregates formation, the 644 candidate genes from the primary screen were
further evaluated and tested using the following criteria (Figs. 2E , S2 and their legends for more details): (1) Remove dsRNAs
with significant off-target effects: we removed candidates with dsRNA amplicons that contain 21-bp overlaps with more than 5
other genes in the genome, as knockdown of the expression of these overlapping genes would be expected to cause a significant
non-specific off-target RNAi effects (KULKARNI et al. 2006; MA et al. 2006). (http://www.flyrnai.org/); (2) Remove candidates
that function in general protein synthesis: we studied the available information about the known functions of the candidate genes
isolated from the primary screen, mainly through checking the references in Flybase (http://flybase.bio.indiana.edu/) and the
PubMed in NCBI (National Center for Biotechnology Information ) (http://www.ncbi.nlm.nih.gov/). From these analyses, we
found that a large number of genes are involved in general protein synthesis, including 97 genes encoding cytoplasmic or
mitochondrial ribosomal proteins. Knockdown of all these genes in the assay significantly reduced aggregates formation, but
since the formation of aggregates depends upon the amount of available mis-folded Htt protein (SCHERZINGER et al. 1999), it is
highly likely that the observed reduction is not specific to aggregates formation, but simply reflects a general decrease in overall
protein synthesis within the cell. Accordingly, most of these ribosomal proteins were not pursued in the following validation
steps. (3) Re-test the dsRNAs from the primary screen: to ensure the reproducibility of the effects of these candidate dsRNAs on
aggregates formation, we performed an intermediate screen by re-synthesizing and re-testing the amplicons specific to genes
S. Zhang et al. 3 SI
retained from the primary screen up to this point. More specifically, DNA templates for the dsRNA amplicons used in the
primary screen were re-amplified, and the corresponding dsRNAs were re-synthesized and re-tested for 8 additional rounds.
Candidates that failed to repeat their effect on modulating aggregation in these additional rounds of testing were removed; (4)
Luciferase assay: since the expression of the Httex1-Q46-eGFP reporter in the screen was controlled by the inducible
metallothionein promoter, genes that regulate the activity of this promoter would also be identified in the primary and
intermediate screens. To eliminate such promoter-related false positives, we established stable cell lines in which the expression
of luciferase was controlled by the same metallothionein promoter, and performed luciferase-based assays to examine the effect
of candidate dsRNAs on the activity of the metallothionein promoter (see below). Using this luciferase-based secondary assay,
we eliminated a number of candidates that affect cellular copper uptake or the activity of the metallothionein promoter in the
pMK33 vector, which controls the expression of the Httex1-Qp46 reporter (e.g., COP complex components). (5) Validation with
2nd set of dsRNAs: to further ensure that the modulating effect observed in the primary screen was specific only for the candidate
genes, for each candidate that passed the above selections, one or two more sets of dsRNAs targeting different regions of this
candidate gene were synthesized and re-tested. Genes that failed to repeat their effect on modulating aggregation in these
additional rounds of testing were removed from consideration.
As with the primary screen, 384-well plates were used in all the secondary assays, with 5ul of 50ng/ul dsRNA samples or
water controls aliquoted into each well in the plate. As different to the primary screen, in each secondary assay plate, more than
100 evenly-positioned wells were aliquoted with 5ul of water as controls. For all the secondary assays, the effect of dsRNA
treatment on aggregates formation was evaluated on the same three evaluation parameters (i.e., the average number, size and
intensity of the aggregates), but instead of using the values from the whole plate as an evaluation standards, average values and
standard deviation (SD) from these more than 100 water control wells were used as evaluation standards for each plate.
Accordingly, in the secondary assays, those dsRNAs that decreased or increased aggregates formation by more than 2xSD of the
water controls on the plate were considered to have a significant effect on aggregates formation and were selected as hits.
126 hits passed all the above selection steps. Table S2 provides details regarding the amplicons used in this study, and
additional information is available at the DRSC website (http://www.flyrnai.org).
Luciferase-based assay on the metallothionein promoter
Two stable cell lines (RZ-1 and RZ-14) were generated, each carrying three transgenes encoding the Firefly luciferase, Renilla
luciferase and a hairpin (a Renilla luciferase hairpin in RZ-14, a firefly luciferase hairpin in RZ-1), all under the control of the
metallothionein promoter. About 20,000 cells were treated with ~200 ng dsRNA in 384-well plates and induced with 25 uM
CuSO4 72 hours after dsRNA treatment. Luciferase assay was performed after another 48 hours following the manufacture’s
recommendation (Promega). The firefly luciferase activity (in RZ-14) and the Renilla luciferase activity (in RZ-1) were
employed to access the effect of dsRNA treatment on the metallothionein promoter activity.
Drosophila stocks and genetic crosses
pUAST-dHsp110 (CG6603) DNA and pUAST-Httex1-Qn-eGFP DNA were injected into w1118 embryos and transformants
were selected following standard procedures. Around 20 independent transgenic lines for each of the constructs were established
and tested. Targeted expression of Httex1-Qn-eGFP (Q25, Q46, Q72 and Q103) or dHsp110 (CG6603) was achieved using the
binary UAS-Gal4 expression system (BRAND and PERRIMON 1993). A gmr-Gal4 driver was used for all eye-specific expression
(HAY et al. 1994).
Although CG6603 encodes the only Hsp110 ortholog in Drosophila, alleles of CG6603 refer to it as Hsc70Cb, solely due to
its cytological location at polytene band 70C. To avoid confusion with the general Hsp70 proteins, we renamed it it as dHsp110.
The following mutant alleles for Drosophila dhsp110 (CG6603) were tested: l(3)70Ca1 (From the Bloomington Stock Center,
stock # BL-4911), l(3)00082 (BL-11485), l(3)S148513 (from the Szeged Drosophila Stock Centre at University of Szeged, stock
S. Zhang et al. 4 SI
# 010975), l(3)S004112 (stock # 0100040), l(3)S031820 (stock # 0100228), l(3)S064906 (stock # 0100467), and
l(3)S0134802 (stock # 0100866). The following dhsp110 alleles showed dosage-dependent genetic interaction with the HD93
flies: l(3)00082, l(3)S031820, l(3)S064906, l(3)S004112 and l(3)S0134802. To test for genetic interactions, HD93 flies (Httex1p-
Q93, genotype gmr-Gal4/+; UAS-Httex1p-Q93. from Drs. L. Thompson and J.L. Marsh (STEFFAN et al. 2001)) were crossed to
the above dhsp110 mutant alleles or the UAS-dHsp110 transformants, and w1118 or UAS-LacZ transgenic flies were used as
cross controls. The resulting trans-heterozygous progeny were collected and aged for the same time as the progeny from the
w1118 and UAS-LacZ control crosses (genotype for the mutant dhsp110 crosses: gmr-Gal4/+; UAS-Httex1p-Q93/+; dhsp110-/+;
genotype for the UAS-dHsp110 cross: gmr-Gal4/+; UAS-Httex1p-Q93 /+; UAS-dHsp110/+; genotype for the w1118 control:
w1118; gmr-Gal4/+; UAS-Httex1p-Q93/+. genotype for the UAS-LacZ control: gmr-Gal4/+; UAS-Httex1p-Q93/+; UAS-
LacZ/+). Eye imaging was done using a Zeiss Stemi SV11 microscope. To generate mosaic mutant clones, three dhsp110 alleles,
l(3)00082, l(3)S031820 and l(3)S064906, were recombined onto an FRT80B chromosome, and mosaic mutant clones in adults
were generated according to standard procedures using the eyeless-Flipase and hs-Flipase drivers (XU and RUBIN 1993). In the
eye, mosaic clones homozygous for either of the three dhsp110 alleles were not viable.
BRAND, A. H., and N. PERRIMON, 1993 Targeted gene expression as a means of altering cell fates and generating dominant
phenotypes. Development 118: 401-415.
HAY, B. A., T. WOLFF and G. M. RUBIN, 1994 Expression of baculovirus P35 prevents cell death in Drosophila. Development
KAZANTSEV, A., E. PREISINGER, A. DRANOVSKY, D. GOLDGABER and D. HOUSMAN, 1999 Insoluble detergent-resistant aggregates
form between pathological and nonpathological lengths of polyglutamine in mammalian cells. Proc Natl Acad Sci U S
A 96: 11404-11409.
KULKARNI, M. M., M. BOOKER, S. J. SILVER, A. FRIEDMAN, P. HONG et al., 2006 Evidence of off-target effects associated with
long dsRNAs in Drosophila melanogaster cell-based assays. Nat Methods 3: 833-838.
MA, Y., A. CREANGA, L. LUM and P. A. BEACHY, 2006 Prevalence of off-target effects in Drosophila RNA interference screens.
Nature 443: 359-363.
SCHERZINGER, E., A. SITTLER, K. SCHWEIGER, V. HEISER, R. LURZ et al., 1999 Self-assembly of polyglutamine-containing
huntingtin fragments into amyloid-like fibrils: implications for Huntington's disease pathology. Proc Natl Acad Sci U S
A 96: 4604-4609.
STEFFAN, J. S., L. BODAI, J. PALLOS, M. POELMAN, A. MCCAMPBELL et al., 2001 Histone deacetylase inhibitors arrest
polyglutamine-dependent neurodegeneration in Drosophila. Nature 413: 739-743.
XU, T., and G. M. RUBIN, 1993 Analysis of genetic mosaics in developing and adult Drosophila tissues. Development 117: 1223-
S. Zhang et al. 5 SI
FIGURE S1.—Procedure for genome-wide RNAi screen on aggregation modulators. Httex1-Qp46 cells were mixed with the
dsRNAs in 384-well plates for 3 days to knock down target gene expression. Copper (CuSO4) was then added to induce reporter
expression and aggregates formation, and after two days, the cells were fixed and stained with DAPI and Tritc-labeled phalloidin
to reveal the cell nuclei and overall cell morphology, respectively. Images, from four sites in each well (equal to about 4,000
cells), were then collected to identify the eGFP aggregates. Information on both the aggregates and cell number in the imaged
fields were automatically quantified using the Metamorph analytic software (Fig. 2C, also see Materials and Methods for details).
This method allowed us to accurately quantify the effect of dsRNA treatment on the average number, size and intensity of
aggregates, which were normalized with cell numbers. For each plate, the average value and standard deviation (SD) for these
three parameters from the whole plate samples were also calculated. In the primary screen, for a dsRNA-treated sample, if the
value of any of the three parameters was beyond 2xSD of the whole plate average, it was considered to have a significant effect
on aggregates formation and was selected as a potential candidate.
S. Zhang et al. 6 SI
FIGURE S2.—Flow chart of the RNAi screening and validation steps for aggregation modulators of mutant Htt. In primary
screen, genome-wide Drosophila RNAi libraries containing >24,000 dsRNA were tested in duplicates and 644 dsRNA with
significant effect (over 2XSD of a plate average) on aggregates formation were isolated (see Fig. S1 and “Secondary screens”
below for details).
Out of these 644 dsRNA, sequences of 31 dsRNA turned out to have high off-target effect (targeting over five different genes)
and were removed from the ensuing studies. Curation of the remaining corresponding genes’ known functions revealed that many
are involved in general protein synthesis, including 97 ribosomal proteins, 8 SnRNP proteins, components of transcription
initiation complexes and translation initiation factors. 131 of such dsRNA were also excluded from further analyses.
Amplicons for the remaining 463 dsRNA were cherry-picked and their dsRNA were re-synthesized and re-tested in the same
aggregation assay. 262 of the re-synthesized dsRNA failed in the repeating experiments while the other 201 dsRNA showed
In a luciferase-based assay to identify false positives that act by regulating the activity of the metallothionein promoter
employed in the aggregation assay, 37 of the above 201 dsRNA showed significant effect, including those involved in general
transcriptional regulation or cellular endocytosis (e.g., Cop complex components such as alpha-COP, beta-Cop and zeta-Cop).
These 37 dsRNA were excluded from further consideration.
Lastly, to confirm the specificity of the dsRNA with their corresponding genes, one or two more set of dsRNA targeting
different regions of the remaining 164 candidates were synthesized and re-tested. In total, 126 genes passed all these validation
S. Zhang et al. 7 SI
FIGURE S3.—High level of dhsp110 (CG6603) expression disrupts the proper formation of adult Drosophila eye.
(A-D) Wild-type (wt) adult flies have well-patterned eye structure. (A) A wt adult eye imaged by scanning electronic
microscopy. Each eye is composed of about 800 ommatidia. (B) Well-organized internal structure of adult eye, which is
composed of lattice-like ommatidium units as revealed by tangential section. (C and D) (C) High magnification view of a single
ommatidium unit and (D) its cartoon representation. Each ommatidium is composed of 8 photoreceptor cells (PR) surrounded by
pigment cells. Only 7 PR cells are visible in each sectioned layer. (E and F) Images of adult fly eyes with high-level dhsp110
expression. Genotype: GMR-Gal4/+; UAS-dhsp110/+. Although these flies show normal external eye morphology (E, bright-
field imaging), their internal eye structure are severely disrupted (F, tangential section image), including a thickening of pigment
cells, loss of PR cells and abnormally formed rhabdomeres.
S. Zhang et al. 8 SI
FIGURE S4.—Modification of HD93 toxicity by dhsp110. Bright-filed images of adult fly eyes at age (A-D) day 1 or (E-H)
day 30. Control flies that express (A and E) lacZ gene or (B and F) dhsp110 alone did not show obvious loss of pigmentation as
S. Zhang et al. 9 SI
flies age. Flies that co-express Httex1p-Q93 with (C, G1 and G2) lacZ gene show a clear de-pigmentation of adult eyes as they
age, (D, H1 and H2) while such degeneration phenotype was significantly suppressed by the presence of dhsp110 gene.
Genotypes: (A and E) GMR-Gal4/+; UAS-LacZ/+. (B and F) GMR-Gal4/+; UAS-dhsp110/+. (C, G1 and G2) GMR-Gal4/ UAS-
LacZ; UAS-Httex1p Q93/+. (D, H1 and H2) GMR-Gal4/ UAS-dhsp110; UAS-Httex1p Q93 /+ (STEFFAN et al. 2001). In all eye
images, the anterior side is up and the ventral side is to the left.
S. Zhang et al. 10 SI
Tables S1-S7 are available for download as Excel files at http://www.genetics.org/cgi/content/full/genetics.109.112516/DC1
List of hits identified from the RNAi screen as regulators of aggregates formation
Please note that in this study, “Suppressor” is defined genetically as the candidates that cause an increased formation of
aggregates after dsRNA-mediated knockdown of the corresponding genes, whereas “Enhancer” is similarly defined as those that
cause a decreased formation of aggregates in the assay.
The columns in the Table S1 are as follows: (1) Gene symbol; (2) Modifier class “Enhancer” and “Suppressor”.; (3)
Functional categorization (based on the “GeneOntology (GO)” index biological function or protein domains.); (4) IDs of DRSC
amplicons (http://www.flyrnai.org/); (5) FBGN: ID of FlyBase Genome annotations; (6) Protein domain (from the Flybase:
http://flybase.bio.indiana.edu/); (7) Molecular function (curated from the Flybase)
List of hits identified from the RNAi screen as regulators of aggregates formation and their human homologues
The columns in the Table S2 are as follows: (1) Gene symbol; (2) Gene full name; (3) Functional categorization (based on the
“GeneOntology (GO)” index biological function or protein domains); (4) Modifier class (see Table S1 for definition of
“Enhancer” and “Suppressor”); (5) Gene ID by CG number (http://flybase.bio.indiana.edu/) (6) Human homologues by
“Database of Pairwise Orthologs” (http://inparanoid.cgb.ki.se/); (7) Human homologues (curated from the Homophila website
http://superfly.ucsd.edu/homophila); (8) Disease-related human orthologs (curated from the Homophila website
Please note that for consistence, the effects of the C. elegans modifiers and their Drosophila homologues on aggregates
formation are described according to Nollen et. al., (2004) as “Enhance” or “Suppress”, respectively. “Enhance” indicates that
the cognate dsRNA treatment increases aggregation formation, and vise versa, “Suppress” suggests that the cognate dsRNA
treatment reduces aggregation formation.
Importantly, in our study and in Table S1 and S2, the identified modifiers are listed as “Suppressor” and “Enhancer”.
“Suppressor” is defined genetically as the genes for which their cognate dsRNA treatment enhances aggregation formation, that
is, causing an increased formation of aggregates after dsRNA-mediated knockdown of the corresponding genes, whereas
“Enhancer” is similarly defined as those that lead to a decreased formation of aggregates in the assay. Accordingly, genes that
cause “Enhance” and “Suppress” effect in Tables S3-S6 correspond to the “Suppressor” and “Enhancer” in our study as listed in
Tables S1 and S2, respectively.
In Tables S3-S6, information on the C. elegans modifiers are directly from the corresponding studies. Drosophila
homologues (column E) of the C. elegans genes were identified manually by first downloading the protein sequences of the
worm modifiers from the NCBI website with the “cosmid nr.” or other information listed in respective studies, which were then
used to search the Drosophila database (http://flybase.bio.indiana.edu/) using the BLASTp program from the NCBI site. In
most cases, only the closet homologues were listed and compared with the hits from our study.
Drosophila homologues that were also identified in the primary screens in our study are labeled as "1st", those
identified as final candidates after passing all the secondary assays are marked as "F".
S. Zhang et al. 11 SI
Comparison of modifiers from this study in Drosophila and the Nollen et. al., study (2004) in C. elegans
Drosophila homologues that were also identified in our primary screens are labeled in column G as "1st", those identified as final
candidates after passing all the secondary assays are marked in column H as "F. The effects of the C. elegans modifiers and their
Drosophila homologues on aggregates formation are listed in column C and F, respectively (See the above Notes for more
The protein sequences for the worm modifiers were retrieved using the “cosmid nr.” listed in Nollen et. al., (2004) study. “-”
in column E indicates that no Drosophila homologue of the corresponding worm gene was identified from the search. Protein
sequences for a few worm modifiers could not be retrieved from the NCBI website using the “cosmid nr.” provided and were
indicated as “none” in column E.
Comparison of modifiers for mutant Htt aggregation from this study in Drosophila and for mutant a-Synuclein by the
Hamamichi et al. study in C. elegans (2008)
The effects of the genes on aggregates formation in corresponding assays are listed in columns C and G, respectively. Please see
“Notes” in front of the Table S3 for more details.
* For dnj-19, a DnaJ domain co-chaperone, its closest homologue in Drosophila is droj2 (FBGN0038145) with E value at
4.79546e-47. dnaJ-1 (FBgn0015657), another homolog of this gene (E value of 1.63364e-20), marked in column H as "1st" and
in column I as "F", was the only overlapping hit from these two studies
Comparison of modifiers for mutant Htt aggregation from this study in Drosophila and for mutant ? ?-Synuclein by the
van Ham et al. study in C. elegans (2008)
Information on the C. elegans modifiers are directly from the Table 1 and Table S1 in the van Ham et al. (2008) study. Please see
“Notes” in front of the Table S3 for more details.
The protein sequences of the worm modifiers were downloaded from the NCBI website with the “Cosmid no.” or “Gene”
provided in Table 1 and Table S1 in van Ham et al. (2008) study. “-” in column E indicates that no Drosophila homologues was
identified from the search. Protein sequences for a few worm modifiers could not be retrieved from the NCBI website using the
“Cosmid no.” or “Gene” provided in Table 1 and Table S1 in van Ham et al. (2009) study and were indicated with “?”.
The five Drosophila homologues that were isolated in the primary screens from our study are labeled in column H as "1st".
* For chaperone R151.7, its closest Drosophila homolog is trap1 (FBgn0026761) with E value at 2.73574e-150. hsp83
(FBgn0001233), another homolog of this gene (E value of 1.06342e-42), was also isolated as a final candidate in our study and is
marked as "F" in column I.
S. Zhang et al. 12 SI Download full-text
Comparison of modifiers for mutant Htt aggregation from this study in Drosophila and for mutant SOD from the Wang
et. al., study (2009) in C. elegans
Drosophila homologues that were identified in the primary screens from our study are labeled in column H as "1st", those
isolated as final candidates are marked as "F" in column I. Please see “Notes” in front of the Table S3 for more details.
“-” in column E indicates that no Drosophila homologue of the corresponding worm gene was identified.
“*” For dnj-19, a DnaJ domain co-chaperone, its closest homolog in Drosophila is droj2 (FBgn0038145) with E value at
4.79546e-47. Its homology with dnaJ-1 (FBgn0015657) is at a E value of 1.63364e-20.
Comparison of modifiers from Doumanis et. al., study (2009) with the candidates from this study
Please see “Notes” in front of the Table S3 for more details. The same modifiers that were also identified in our primary screens
are labeled in column D as "1st", those identified as final candidates after passing all the secondary assays are marked in column
F as "F".