, 1225 (2012);
et al.Zhengzheng S. Liang
Molecular Determinants of Scouting Behavior in Honey Bees
This copy is for your personal, non-commercial use only.
clicking here.colleagues, clients, or customers by
, you can order high-quality copies for your
If you wish to distribute this article to others
The following resources related to this article are available online at
here.following the guidelines
can be obtained by
Permission to republish or repurpose articles or portions of articles
Updated information and services,
): June 16, 2012 www.sciencemag.org (this information is current as of
version of this article at:
including high-resolution figures, can be found in the online
can be found at:
Supporting Online Material
, 2 of which can be accessed free:
cites 16 articles
This article appears in the following
registered trademark of AAAS.
is aScience 2012 by the American Association for the Advancement of Science; all rights reserved. The title
Copyright American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005.
(print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the Science
on June 16, 2012
survival are no better than those for the current
offspring, a Bruce effect is not an effective strat-
egy. Investigating whether the reproductive sup-
effect across mammals must await further data
from natural populations.
References and Notes
1. H. M. Bruce, Nature 184, 105 (1959).
2. H. M. Bruce, J. Reprod. Fertil. 1, 96 (1960).
3. A. S. Parkes, H. M. Bruce, Science 134, 1049 (1961).
4. L. Bartos, J. Bartosova, J. Pluhacek, J. Sindelarova,
Behav. Ecol. Sociobiol. 65, 1567 (2011).
5. S. D. Becker, J. L. Hurst, Proc. R. Soc. Lond. B Biol. Sci.
276, 1723 (2009).
6. N. Pillay, A. A. Kinahan, Behaviour 146, 139 (2009).
7. J. B. Labov, Am. Nat. 118, 361 (1981).
8. H. M. de la Maza, J. O. Wolff, A. Lindsey, Behav. Ecol.
Sociobiol. 45, 107 (1999).
9. S. J. Mahady, J. O. Wolff, Behav. Ecol. Sociobiol. 52,
10. F. F. Mallory, F. V. Clulow, Can. J. Zool. 55, 1 (1977).
11. B. C. R. Bertram, J. Zool. 177, 463 (1975).
12. J. Berger, Nature 303, 59 (1983).
13. G. Agoramoorthy, S. M. Mohnot, Hum. Evol. 3, 279
14. G. Agoramoorthy, S. M. Mohnot, V. Sommer, A. Srivastava,
Hum. Evol. 3, 297 (1988).
15. F. Colmenares, M. Gomendio, Folia Primatol. (Basel) 50,
16. M. E. Pereira, Am. J. Primatol. 4, 93 (1983).
17. U. Mori, R. I. M. Dunbar, Z. Tierpsychol. 67, 215 (1985).
18. F. H. Bronson, A. Coquelin, Eds., The Modulation of
Reproduction by Priming Pheromones in House Mice:
Speculations on Adaptive Function (Plenum Press,
New York, 1980), pp. 243–265.
19. J. O. Wolff, Bioscience 53, 421 (2003).
20. C. Darwin, The Descent of Man, and Selection in Relation
to Sex (Murray, London, 1971).
21. P. L. Schwagmeyer, Am. Nat. 114, 932 (1979).
22. S. B. Hrdy, Ethol. Sociobiol. 1, 13 (1979).
24. R. I. M. Dunbar, Reproductive Decisions: An Economic
Analysis of Gelada Baboon Social Strategies (Princeton
University Press, Princeton, 1984).
25. T. J. Bergman, N. Snyder-Mackler, S. C. Alberts,
Am. J. Phys. Anthropol. 144, 88 (2011).
26. J. C. Beehner, N. Nguyen, E. O. Wango, S. C. Alberts,
J. Altmann, Horm. Behav. 49, 688 (2006).
27. C. J. Dominic, J. Reprod. Fertil. 11, 415 (1966).
28. S. R. Milligan, J. Reprod. Fertil. 46, 97 (1976).
29. S. D. Becker, J. L. Hurst, in Chemical Signals in
Vertebrates, J. L. Hurst, Ed. (Springer, New York, 2008),
vol. 11, pp. 141–150.
30. J. C. Beehner, D. A. Onderdonk, S. C. Alberts, J. Altmann,
Behav. Ecol. 17, 741 (2006).
31. S. K. Wasser, D. P. Barash, Q. Rev. Biol. 58, 513 (1983).
32. W. J. Hamilton III, J. B. Bulger, Behav. Ecol. Sociobiol.
26, 357 (1990).
33. D. L. Cheney et al., Int. J. Primatol. 25, 401
Acknowledgments: Supported by the Wildlife Conservation
Society (SSF grant 67250), the National Geographic Society
(grant 8100-06), the Leakey Foundation, the National
Science Foundation (grants BCS-0715179 and BCS-0824592),
and the University of Michigan. Thanks to the Ethiopian
Wildlife Conservation Authority, the wardens and staff of
the Simien Mountains National Park, and the participants
of the University of Michigan Gelada Research project,
particularly A. Le Roux, N. Snyder-Mackler, D. Pappano, C. Wilton,
J. Jarvey, S. Liberman, A. Spencer, V. Wilson, N. Sands, M. Zakalik,
H. Gelaye, E. Jejaw, and A. Fanta for help in the field and
T. Parr for help in the laboratory. All protocols were noninvasive
and were approved in Ethiopia (Ethiopian Wildlife Conservation
Authority) and the United States (University of Michigan
University Committee on Use and Care of Animals, protocols
09554 and 0001011). The authors declare no competing
financial interests. All authors contributed extensively to the
work presented in this paper. Data available in the SOM.
Supporting Online Material
Materials and Methods
Data Files 1 to 3
6 September 2011; accepted 26 January 2012
Published online 23 February 2012;
Molecular Determinants of Scouting
Behavior in Honey Bees
Zhengzheng S. Liang,1Trang Nguyen,2Heather R. Mattila,3Sandra L. Rodriguez-Zas,1,4
Thomas D. Seeley,5Gene E. Robinson1,2,6*
Little is known about the molecular basis of differences in behavior among individuals. Here we
report consistent novelty-seeking behavior, across different contexts, among honey bees in their
tendency to scout for food sources and nest sites, and we reveal some of the molecular
underpinnings of this behavior relative to foragers that do not scout. Food scouts showed extensive
differences in brain gene expression relative to other foragers, including differences related to
catecholamine, glutamate, and g-aminobutyric acid signaling. Octopamine and glutamate
treatments increased the likelihood of scouting, whereas dopamine antagonist treatment decreased
it. These findings demonstrate intriguing similarities in human and insect novelty seeking and
suggest that this trait, which presumably evolved independently in these two lineages, may
be subserved by conserved molecular components.
Scouting behavior in the honey bee, Apis mel-
lifera, provides an excellent opportunity to ex-
plore this issue for two reasons. First, there are
striking individual differences in this behavior—
n important challenge in behavioral bi-
ology is to elucidate the molecular basis
of individual differences in behavior.
some bees act as scouts and others never do so.
Second, scouting is performed in two distinct
contexts: scouting for new food sources or new
nest sites, which suggests an underlying tendency
to seek something new. Novelty-seeking behavior
has been studied in vertebrates, including hu-
mans (1, 2), but not in insects.
Food scouts, who make up 5 to 25% of a
colony’s foraging force, search independently
for new food sources and continue to do so even
when plentiful sources have been found (3–5).
Non-scouts do not search for novel food sources
and instead rely on information from scouts
(communicated via “dance language”) to guide
their foraging. By constantly discovering new
flower patches, food scouts help ensure a high
influx of food to their colony, despite the ephem-
eral nature of each patch (5).
Nest scouts make up <5% of the population
of a swarm, which is a fragment of a colony that
has left its natal nest to start a new colony. Nest
scouts search independently for potential nesting
cavities and collectively choose the best one,
whereas non-scout swarm members rely on in-
formation from scouts to guide them to their
new home (6). Nest scouting also is a crucial
behavior; a colony’s survival depends on its nest
scouts finding suitably protective living quarters.
To determine the consistency of novelty seek-
ing in individual bees across the two behavioral
contexts, we determined whether nest scouts are
prone to also act as food scouts. We identified
and marked nest scouts in both artificial and
natural swarms (6). We then identified food scouts
with the standard “hive-moving” assay (5, 7),
after installing each swarm in a beehive and
moving it at night (when bees don’t forage) to a
new location outside the bees’ original home
range. This assay identifies food scouts as the
first bees to return to their hive in the morning;
under these circumstances, each successful for-
ager must have located a food source on her
own. There was a robust tendency of nest scouts
to seek novel resources across different contexts,
but it did not translate into every nest scout show-
ing food-scouting behavior. In nine trials in-
volving eight different colonies over 2 years, nest
scouts were on average 3.4 times more likely to
become food scouts than were bees that did not
search for nest sites during swarming (Fig. 1A).
These results demonstrate that some bees show
consistent novelty seeking across diverse behav-
To explore the molecular basis of novelty
seeking in bees, we developed a behavioral as-
say for food scouts (Fig. 1B) that tests novelty
1Neuroscience Program, University of Illinois at Urbana–
Champaign, Urbana, IL, USA.2Institute of Genomic Biology,
University of Illinois at Urbana–Champaign, Urbana, IL, USA.
3Department of Biological Sciences, Wellesley College,
Wellesley, MA, USA.4Department of Animal Sciences, Univer-
sity of Illinois at Urbana–Champaign, Urbana, IL, USA.5De-
partment of Neurobiology and Behavior, Cornell University,
Ithaca, NY, USA.6Department of Entomology, University of
Illinois at Urbana–Champaign, Urbana, IL, USA.
*To whom correspondence should be addressed. E-mail:
VOL 3359 MARCH 2012
on June 16, 2012
seeking more strongly than did previous scout
assays (3, 5, 7). A large screened outdoor en-
closure provided experimental control of food
sources under otherwise naturalistic conditions.
Foragers from a glass-walled observation hive
were trained to a training feeder that initially was
the only food source available to them. After 2
to 3 days of training, a novel feeder with differ-
ent visual and odor cues was placed at another
location in the enclosure. The foraging bees thus
had two possible food sources, familiar and
novel; some bees discovered the novel feeder
and switched to it. This procedure was repeated
on several consecutive days, and each time the
novel feeder was given new visual and odor
cues and placed in a new location. Only bees
that switched to two or more different novel
feeders, after being seen at least once at the
training feeder, were collected as scouts. These
rigorous criteria minimized the possibility of iden-
tifying scouts on the basis of an accidental dis-
covery of a novel feeder. The proportion of scout
bees identified with this assay (31.2, T 9.7% SD,
n = 182 bees, six trials) is roughly consistent
with what has been observed under more natural
conditions (3–5), suggesting that accidental dis-
coveries of novel feeders were not a major source
of error. Bees that met our criteria for identify-
ing food scouts were collected to compare their
brain gene expression with that of control non-
scouts (foragers that were never observed to
switch to a novel feeder).
Whole-genome microarray analysis revealed
a large neurogenomic signature for scouting be-
havior in the bee brain. Sixteen percent (1219
out of 7539) of the transcripts on the microarray
showed significant (false discovery rate <0.05)
differences in mRNA abundance between scouts
and non-scouts (table S3, A and B, and table
S4). Among the differentially expressed genes
were several related to catecholamine, glutamate,
are involved in regulating novelty seeking and
reward in vertebrates (1, 2, 8). For example, the
down-regulation of a dopamine receptor gene in
honey bee scouts parallels results for a similar
gene in individual mammals that are prone to
novelty seeking (9). These signaling systems
also are implicated in personality differences be-
tween humans that are related to novelty seeking
chain reaction analysis confirmed the microrar-
ray results for five genes related to catechola-
mine, glutamate, and GABA signaling (Fig. 2A
and fig. S1, A and B): D1-type dopamine recep-
tor DopR1, glutamate transporters Eaat-2 and
Vglut, AMPA-type glutamate receptor Glu-RI,
and GABA transporter Gat-a. Three addition-
al catecholamine receptor genes also were dif-
ferentially expressed but were undetected in
(b-adrenergic type octopamine receptor), and
S1 and S2).
Linear discriminant analysis (LDA) showed
a strong separation between scouts and non-scouts
based on the expression values for 10 neural sig-
naling genes related to catecholamine, glutamate,
and GABA signaling (Fig. 2B). In addition, we
used these 10 genes to show that scouts identified
with either the new feeder–discovery assay or the
hive-moving assay showed strong similarities in
brain gene expression to each other (fig. S2).
The association between scouting and cate-
cholamine, glutamate, and GABA signaling path-
ways could reflect effects of this behavior on
brain gene expression or effects of individual dif-
ferences in these pathways on scouting, or both.
We used the transcriptomic results as the basis for
designing experiments to test causal relation-
We collected non-scouts and provided them with
a chronic (25 to 30 hours) oral neurochemical
cages (20 bees per cage) in their hive before
moving it overnight to a location outside the
colony’s original home range.
Behavioral observations the following morn-
ing (14 hours after stopping the treatment) re-
vealed that glutamate [monosodium glutamate
(MSG)] caused a significant increase in scout-
ing (Fig. 3A), whereas the vesicular glutamate
transport blocker Chicago Sky Blue signifi-
cantly attenuated the MSG effects (Fig. 3B).
Octopamine caused a weaker, but still signif-
icant, increase in scouting (Fig. 3A). These re-
sults are consistent with predictions based on
microarray analysis. In contrast, dopamine antag-
onists caused a significant decrease in scouting
(Fig. 3C), which was contrary to microarray-based
prediction. Effects were not seen in all trials (figs.
S3 to S5), suggesting that factors such as food
availability, colony conditions, worker genotype,
or other unknown variables also affect the prob-
ability of becoming a scout. The treatments did
Fig. 1. (A) Consistent novelty-seeking behavior across different contexts. Nest scouts were significantly
more likely to later act as food scouts than were non-scout swarm members. The graph shows the
probabilities of food scouting for nine trials: four natural swarms and five artificial swarms, with eight
different colonies (Fisher’s exact test, 2-tailed test; *P < 0.05, **P < 0.01, ***P < 0.001), and the
overall mean probabilities [least-square means and standard errors; mixed-model analysis of variance
(ANOVA), 2-tailed test]. (B) Feeder-discovery assay for identifying food scouts. Additional details are in
the text and supporting online material.
9 MARCH 2012VOL 335
on June 16, 2012
not cause excess mortality (table S6), aberrant
locomotion, hyperactivity, or a general increase
in foraging activity (fig. S6), and they were dose-
dependent (fig. S7), which suggests that there
were specific treatment effects on scouting behav-
ior. GABA or a GABA receptor agonist (TACA)
did not affect the probability of scouting (fig.
S8), so the role of this neurotransmitter in bee
scouting remains unclear.
Multiple neurotransmitter systems appear to
be involved in the regulation of scouting in honey
bees, but it is not known how they interact at the
circuit level. Glutamatergic and dopaminergic neu-
rons are both found in the vertical lobes of
the mushroom bodies, a part of the insect brain
involved in reward learning (14, 15). DopR1 and
Eaat-2 gene expression is colocalized to the same
type of interneurons that provide sensory input
into these lobes (16, 17). These findings, together
with our own, suggest the vertical lobes of the
mushroom bodies as one possible neuroanatom-
ical locus for novelty-seeking behavior in honey
bees, although other brain regions are probably
involved as well.
Our results demonstrate intriguing parallels
between honey bees and humans in novelty-
seeking behavior. Although the molecular mech-
anisms that produce this behavioral variation are
similar, it is unknown whether both species in-
herited them from a common ancestor or evolved
them independently. Given the phylogenetic sep-
aration of bees and humans, we believe it is
likely that these mechanisms represent part of a
basic tool kit that has been used repeatedly in
the evolution of behavior. Further support for
this view comes from the finding that individual
differences in food-searching behavior in nem-
atodes (Caenorhabditis elegans) are caused, in
part, by noncoding polymorphisms in tyramine
receptor 3, which encodes a receptor for a cate-
cholamine closely related to octopamine and do-
It is common to look to animal models to
generate insights that may be applicable to hu-
man behavior. Our findings highlight the poten-
tial of the converse—using insights from human
research to further elucidate the molecular basis
of animal behavior. Animal studies, informed by
inferences from human research, might in turn
help identify evolutionarily conserved molecular
mechanisms underlying consistent differences in
various behaviors among humans, thus helping
us better understand how and why these behav-
ioral differences exist.
References and Notes
1. M. T. Bardo, R. L. Donohew, N. G. Harrington,
Behav. Brain Res. 77, 23 (1996).
2. A. D. Fidler et al., Proc. Biol. Sci. 274, 1685 (2007).
3. T. zu Oettingen-Spielberg, Z. Vgl. Physiol. 31, 454
4. M. Lindauer, Z. Vgl. Physiol. 34, 299 (1952).
5. T. D. Seeley, Behav. Ecol. Sociobiol. 12, 253
6. T. D. Seeley, Honeybee Democracy (Princeton Univ. Press,
Princeton, NJ, 2010).
Fig. 3. Glutamate or octopamine treatment increased the probability of scouting, whereas dopamine
antagonist treatment decreased it (*P < 0.05, ***P < 0.0001). (A) Oral administration of MSG to non-
scouts in sugar syrup (20 mg/ml) caused a significant effect in 7 out of 12 trials (with 11 colonies) over
2 years, an overall 73% increase in scouting probability as compared to sucrose-fed–only control bees
(P < 0.0001, mixed-model ANOVA, 2-tailed test). Octopamine (OA) treatment (4 mg/ml) caused a
significant effect in 3 out of 10 trials (in nine colonies) over 2 years, an overall 37% increase in
scouting probability (P < 0.05). Statistical tests were performed on square root–transformed data; the
graph represents the untransformed mean T SE of 12 trials for MSG (with 11 colonies) and 10 trials for
octopamine (with 9 colonies); results of individual trials are shown in figs. S3 and S4. (B) The glutamate
vesicular transporter blocker Chicago Sky Blue (CSB) (4 mg/ml) blocked the effect of MSG on scouting
(P < 0.05, least-square mean T SE for four previously MSG-responsive colonies; results of individual
trials are shown in fig. S3). (C) Non-scout foragers treated with dopamine antagonists (DAA) (either the
D1-receptor antagonist SCH-23390, the “pan-receptor” antagonist Flupenthixol, or both) showed an
overall 44% decrease in scouting probability in seven trials over three colonies (P < 0.05, the graph
represents least-square mean T estimated error; mixed-model ANOVA, 2-tailed test; results of individual
trials are shown in fig. S5). The probability of scouting was calculated from the proportion of foragers in
each treatment group that exhibited scouting behavior, based on a precise count of foragers when
releasing them from treatment cages.
Fig. 2. Transcriptomic analyses of individual differences in novelty-seeking between food scouts (S) and
non-scouts (NS) (n = 20 bees per group). (A) Selected microarray results highlight differences in brain
expression for 10 dopamine, octopamine, glutamate, or GABA signaling genes related to novelty
seeking, motivation, and reward in vertebrates. DopR2 and OctR1 did not show significant differences
in expression (in the latter case, probably because of very low expression levels). GABA transporter 1A
gene (Gat-a) expression was one of the best correlates of scouting behavior (permutation t test, P <
0.05). (B) Results of LDA for genes shown in (A) demonstrate clear separation between most scouts and
non-scouts based on differences in brain gene expression (standardized expression values: mean = 0,
SD = 1). This plot of LD1 versus LD2 accounted for 82% of the variation in brain gene expression
across scouts and non-scouts (n = 20 bees per group). S1, S2, S3 and N1, N2, N3: scouts and non-
scouts, respectively, from three different colonies.
VOL 335 9 MARCH 2012
on June 16, 2012
7. C. Dreller, Behav. Ecol. Sociobiol. 43, 191 (1998).
8. J. B. Becker, R. L. Meisel, Handbook of Neurochemistry
and Molecular Neurobiology: Behavioral Neurochemistry,
Neuroendocrinology and Molecular Neurobiology
(Springer, New York, 2007).
9. D. Viggiano, D. Vallone, H. Welzl, A. G. Sadile,
Behav. Genet. 32, 315 (2002).
10. R. P. Ebstein et al., Nat. Genet. 12, 78 (1996).
11. J. Benjamin et al., Nat. Genet. 12, 81 (1996).
12. J. A. Mustard et al., Brain Res. Mol. Brain Res. 113, 67
13. P. D. Evans, B. Maqueira, Invert. Neurosci. 5, 111
14. F. W. Schürmann, K. Elekes, M. Geffard, Cell Tissue Res.
256, 399 (1989).
15. G. Bicker, S. Schäfer, O. P. Ottersen, J. Storm-Mathisen,
J. Neurosci. 8, 2108 (1988).
16. R. Kucharski, E. E. Ball, D. C. Hayward, R. Maleszka, Gene
242, 399 (2000).
17. P. T. Kurshan, I. S. Hamilton, J. A. Mustard, A. R. Mercer,
J. Comp. Neurol. 466, 91 (2003).
18. A. Bendesky, M. Tsunozaki, M. V. Rockman, L. Kruglyak,
C. I. Bargmann, Nature 472, 313 (2011).
Acknowledgments: Special thanks to M. K. Carr-Markell and
J. Recchia-Rife for extensive help in the field. We also thank
the following: C. Nye and K. Pruiett (bee management);
A. Brockmann, P. Date, J. Dotterer, L. Felley, M. Girard,
S. Kantarovich, H. S. Pollock, and M. Wray (field assistance);
T. Newman (molecular studies); S. Aref and A. Toth (statistics);
E. Hadley (graphics); and A. M. Bell, D. F. Clayton, R. C. Fuller,
J. S. Rhodes, C. W. Whitfield, and members of the Robinson
laboratory (review of the manuscript). Supported by NSF
Frontiers in Biological Research grant EF 0425852 (B. L. Schatz,
PI, BeeSpace Project); NIH Director’s Pioneer Award
1DP1OD006416 (G.E.R); and the Illinois Sociogenomics
Initiative (G.E.R). Microarray data meet Minimum Information
About Microarray Experiment (MIAME) standards and are
available at ArrayExpress (www.ebi.ac.uk/arrayexpress,
#E-MTAB-491). Z.S.L. and G.E.R. conceived the project, designed
the experiments and wrote the paper; Z.S.L. performed
sample collection, molecular and field experiments, and
analyses; T.N. and S.L.R.-Z. performed microarray experiments
and statistical analyses, respectively; and H.R.M. and T.D.S.
contributed to protocol development and sample collection
and co-wrote the paper.
Supporting Online Material
Materials and Methods
Figs. S1 to S8
Tables S1 to S6
14 September 2011; accepted 1 February 2012
Atomic View of a Toxic Amyloid
Arthur Laganowsky,1* Cong Liu,1Michael R. Sawaya,1Julian P. Whitelegge,2Jiyong Park,1
Minglei Zhao,1Anna Pensalfini,3Angela B. Soriaga,1Meytal Landau,1Poh K. Teng,1Duilio Cascio,1
Charles Glabe,3David Eisenberg1†
Amyloid diseases, including Alzheimer’s, Parkinson’s, and the prion conditions, are each associated
with a particular protein in fibrillar form. These amyloid fibrils were long suspected to be the
disease agents, but evidence suggests that smaller, often transient and polymorphic oligomers
are the toxic entities. Here, we identify a segment of the amyloid-forming protein aB crystallin,
which forms an oligomeric complex exhibiting properties of other amyloid oligomers: b-sheet–rich
structure, cytotoxicity, and recognition by an oligomer-specific antibody. The x-ray–derived
atomic structure of the oligomer reveals a cylindrical barrel, formed from six antiparallel protein
strands, that we term a cylindrin. The cylindrin structure is compatible with a sequence segment
from the b-amyloid protein of Alzheimer’s disease. Cylindrins offer models for the hitherto elusive
structures of amyloid oligomers.
protein fibrils that have long been taken as the
defining feature of these disorders but instead
are lower molecular weight entities, often termed
small amyloid oligomers (1–7). These oligomers
are not generally stable aggregates; they appear
as transient speciesduringtheconversionof their
monomeric precursors to more massive, stable
fibrils, and sometimes they appear as an ensem-
ble of sizes and shapes. This polymorphic and
time-dependent nature of small amyloid oligo-
mers has made it difficult to pin down their as-
tudies from many laboratories have sug-
related conditions are not the associated
sembly pathways, their stoichiometries, their
atomic-level structures, their relationship to fi-
brils, and their pathological actions (1, 8–10).
What has emerged is a consensus, minimal def-
inition of small amyloid oligomers: They are
of proteins also known to form amyloid fibrils;
the oligomers exhibit greater cytotoxicity than ei-
protein; in many cases, the oligomer is recog-
nized by a “conformational” antibody (A11) that
binds oligomers but not fibrils, regardless of the
sequence of the constituent protein (5). This sug-
gests that oligomers display common conforma-
tion features that differ from those of fibrils (11).
In seeking to better define small amyloid
oligomers, we chose to work with aB crystallin
(ABC). This protein is a chaperone (12–14) that
forms amyloid fibrils (15), but the fibrils form
more slowly than those of the b-amyloid peptide
(Ab) or islet amyloid polypeptide (IAPP), so that
the oligomeric state may be trapped before the
of ABC that forms a relatively stable small oligo-
mer, which satisfies the definition of a small amy-
loid oligomer given in the preceding paragraph.
We identified the oligomer-forming segment
of ABC by inspection of its three-dimensional
(3D) structure (16) and by applying the Rosetta-
Profile algorithm to its sequence. This algorithm
identifies sequence segments that form the steric-
that two segments of high amyloidogenic pro-
(where D indicates Asp; E, Glu; G, Gly; I, Ile;
K, Lys; and V, Val), share the same Gly residue
95 at the C terminus of the first segment and the
N terminus of the second; moreover, the entire
11-residue segment KVKVLGDVIEV forms a
with Gly at its center. As predicted, the sec-
ond six-residue segment GDVIEV, termed G6V
(Table 1 defines the structures described in this
report), forms fibrils and microcrystals (fig. S1).
The microcrystals enabled us to determine the
atomic structure of G6V (fig. S2), which proved
to be a standard class 2 steric zipper (19), es-
sentially an amyloid-like protofilament.
K11V) formed both amyloid fibrils and oligo-
mers. Upon shaking at elevated temperature,
K11V forms fibrils similar to those of the parent
(15) and similar to those of a tandem repeat of
B and C; and table S1). The fibrils range from 20
to 100 nm in diameter as viewed by electron mi-
displayed rings at 4.8 and 12 Å resolutions, con-
sistent with the signature cross-b pattern of amy-
loid fibrils (fig. S1C). The amyloid fibrils of
producing apple-green bifringerance under polar-
the fibril-specific, conformation-dependent anti-
body OC (Fig. 1E) (20). Together these results
prove that the segments G6V, K11V, and K11V-
TR are all capable of converting to the amyloid
state (21, 22), as is their parent protein, ABC.
K11V, K11V-TR, and a sequence variant with
Leu replacing Val at position 2 (K11VV2L) all
form stable small oligomers intermediate in size
1Department of Biological Chemistry and Department of
Chemistry and Biochemistry, University of California Los
Angeles (UCLA), Howard Hughes Medical Institute (HHMI),
UCLA-DOE Institute for Genomics and Proteomics, Los Angeles,
CA 90095, USA.2The Neuropsychiatric Institute (NPI)–Semel
Institute for Neuroscience and Human Behavior, UCLA, Los
Angeles, CA 90024, USA.3Department of Molecular Biol-
ogy and Biochemistry, University of California, Irvine, CA
*Present address: Department of Chemistry, Chemistry Re-
search Laboratory, University of Oxford, Oxford, UK.
†To whom correspondence should be addressed. E-mail:
9 MARCH 2012 VOL 335
on June 16, 2012