Genetic Control of Wiring Specificity
in the Fly Olfactory System
Weizhe Hong*,1and Liqun Luo†
*Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, and
†Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, California 94305
ABSTRACT Precise connections established between pre- and postsynaptic partners during development are essential for the proper
function of the nervous system. The olfactory system detects a wide variety of odorants and processes the information in a precisely
connected neural circuit. A common feature of the olfactory systems from insects to mammals is that the olfactory receptor neurons
(ORNs) expressing the same odorant receptor make one-to-one connections with a single class of second-order olfactory projection
neurons (PNs). This represents one of the most striking examples of targeting specificity in developmental neurobiology. Recent studies
have uncovered central roles of transmembrane and secreted proteins in organizing this one-to-one connection specificity in the
olfactory system. Here, we review recent advances in the understanding of how this wiring specificity is genetically controlled and focus
on the mechanisms by which transmembrane and secreted proteins regulate different stages of the Drosophila olfactory circuit
assembly in a coordinated manner. We also discuss how combinatorial coding, redundancy, and error-correcting ability could con-
tribute to constructing a complex neural circuit in general.
The Larry Sandler Memorial Lecture was established in rec-
ognition of Dr. Larry Sandler’s many contributions to Dro-
sophila genetics and his dedication to the training of
Drosophila biologists. The recipient of the Larry Sandler
Award for the most outstanding Ph.D. dissertation submitted
presents the Larry Sandler Memorial Lecture at the Annual
Genetics Society of America Drosophila Research Conference.
As the 2013 Larry Sandler Award winner, Weizhe Hong was
invited to write the following Review article on the subject of
system. Oneofthekeystepsinestablishinga functionalcircuit
is to ensure that neurons make precise connections with ap-
propriate synaptic partners. Deciphering the molecular mech-
anisms of how such connections are established and how
neural circuits are assembled will contribute to our general
HE precise assembly of neural circuits during develop-
ment is required for the proper function of the nervous
model to study the mechanisms underlying wiring specificity.
In this review, we focus our discussion on recent advances in
the understanding of the Drosophila olfactory system and
highlight the role of transmembrane and secreted molecules
in providing fine control over the target selection process. For
comprehensive reviews of olfactory system development and
function in other organisms, see Hildebrand and Shepherd
1997; Wilson and Mainen 2006; Vosshall and Stocker 2007;
Su et al. 2009; and Sakano 2010.
Organization of the Drosophila Olfactory System
From insects to mammals, the olfactory system displays re-
markable similarities with respect to circuit organization. In
Drosophila, each of the ?1300 adult olfactory receptor neu-
rons (ORNs) expresses only 1 or 2 of 60 olfactory receptors
(ORs) or ionotropic receptors (IRs) (Clyne et al. 1999; Gao
and Chess 1999; Vosshall et al. 1999; Goldman et al. 2005;
Benton et al. 2009; Silbering et al. 2011). The axons of ORNs
expressing a common OR or IR converge onto one specific
glomerulus in the antennal lobe, although their cell bodies
are dispersed in the olfactory epithelia (Gao et al. 2000;
Vosshall et al. 2000; Couto et al. 2005; Fishilevich and Vosshall
2005; Silbering et al. 2011). The antennal lobe consists of
Copyright © 2014 by the Genetics Society of America
Manuscript received September 20, 2013; accepted for publication November 7, 2013
1Corresponding author: Division of Biology and Biological Engineering, California
Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125.
Genetics, Vol. 196, 17–29 January 2014
50 glomeruli, which can be uniquely identified by their ste-
reotypical size, shape, and relative position (Laissue et al.
Olfactory information is relayed to higher brain centers by
the second-order olfactory projection neurons (PNs), which
send their dendrites to the antennal lobe and make synaptic
connections with ORN axons. The majority of PNs send their
dendrites to individual glomeruli in a stereotyped manner
within the antennal lobe and project their axons to higher
brain centers, including the mushroom body calyx and the
lateral horn (Laissue et al. 1999; Jefferis et al. 2001; Marin
et al. 2002; Wong et al. 2002). Thus, a given PN makes
synaptic connections with axons of only one ORN class (Fig-
ure 1). This one-to-one organizational principle is shared
from insects to mammals.
In most other sensory systems, neurons are interconnected
in a spatially continuous manner along particular axes in space.
For example, the visual centers in the brain are spatially
organized by a continuous two-dimensional representation
of photoreceptors. In contrast, the peripheral sensory organs
in the olfactory system show much less spatial order, and the
connections between olfactory neurons in the central nervous
system are organized in a structurally discrete manner
(reviewed in Luo and Flanagan 2007). The class-specific
convergence of ORN axons and PN dendrites onto a single
glomerulus and their precise one-to-one matching are
among the most striking examples of targeting specificity in
developmental neurobiology and provide a unique opportunity
to study how these connections are established in discrete
Development of PN–ORN Connections
The organization of specific connections between ORNs and
PNs emerges from sequential developmental events, which
can be roughly divided into three phases (Figure 2) (Jefferis
et al. 2004). In the first phase, PNs send dendritic processes
to the antennal lobe at the beginning of puparium formation
and elaborate diffuse dendritic processes at stereotypical posi-
tions in the proto-antennal lobe (Figure 2A). This phase occurs
before the ORN axons arrive at the antennal lobe. In the
second phase, the ORNs send axons from the antenna and
maxillary palps to the antennal lobe, where the incoming
axons defasciculate and primarily form axon bundles along
two main trajectories surrounding the antennal lobe. ORN
axons further converge onto subregions near the final target
area. In the third phase, ORN axons recognize the earlier-
arriving PN dendrites in the local vicinity and establish specific
synaptic connections with their cognate PN partners. Mean-
while, both dendrites and axons are refined to form discrete
glomeruli so that the processes of neighboring classes of PNs
and ORNs do not overlap.
When ?50 classes of PN dendrites and ORN axons reach
the proto-antennal lobe, they are faced with a complex envi-
ronment. How each class of neurons uniquely responds to this
complex environment and establishes class-specific wiring
patterns relies on differentially expressed transmembrane
and secreted proteins, which serve as receptors and ligands
to mediate interactions between different PNs, different ORNs,
and extracellular cues from the antennal lobe. In the following
sections, we collectively term transmembrane and secreted
proteins as “cell-surface molecules” and review recent advances
in the understanding of these molecules in regulating different
wiring stages in a coordinated manner.
PN Dendrite Targeting
Mosaic analysis with a repressible cell marker (MARCM)
studies demonstrated that PN dendrite target choice is specified
by cell lineage and birth timing within the lineages (Jefferis
et al. 2001). A number of transcription factors have been iden-
tified to link lineage and birth timing with dendrite targeting
specificity of the 50 PN classes (Komiyama et al. 2003; Zhu
et al. 2006b; Komiyama and Luo 2007; Spletter et al. 2007).
Furthermore, genetic screens for genes regulating PN dendrite
targeting isolated mutants involved in multiple biological reg-
ulatory processes, including chromatin remodeling (Tea et al.
2010; Tea and Luo 2011), microRNA processing (Berdnik et al.
2008), protein translation (Chihara et al. 2007), glycosylation
(Sekine et al. 2013), and sumoylation (Berdnik et al. 2012).
Thus, it is clear that a variety of processes contribute to the
specification of PN target choice, presumably by regulating the
expression of cell-surface molecules, the key effectors for cell–
Repulsive and attractive forces establish dendritic fields
As PNs elaborate dendrites in the proto-antennal lobe,
dendrites of the same class are restricted to a subregion
within the antennal lobe (Jefferis et al. 2004). Meanwhile,
dendrites of the same class expand and fully cover this sub-
region. Expanding dendritic fields to cover a specific region
Figure 1 Organization of the olfactory neural circuit. The olfactory sys-
tems from insects to mammals display remarkable similarities with respect
to their circuit organization. Individual classes of ORN axons make one-to-
one connections with individual classes of second-order PN dendrites
within one of ?50 discrete glomeruli in the antennal lobe. This specific
one-to-one connection is referred to as PN–ORN synaptic partner match-
ing. This illustration is modified from Jefferis and Hummel (2006).
W. Hong and L. Luo
of a certain size is likely important for the further development
of the antennal lobe, providing enough space and flexibility to
interact with ORN axons and eventually establishing the
morphology of individual glomeruli.
The cell adhesion molecule N-cadherin (Ncad) has been
shown to restrict dendrites to the appropriate glomerular
space. Loss of Ncad causes dendrites to spread beyond the
developing antennal lobe or invade neighboring glomeruli
(Zhu and Luo 2004). This phenotype was observed at an
early developmental stage, suggesting that Ncad functions in
initial confinement of dendrites to the appropriate glomerular
space. Ncad likely mediates adhesion between PN dendrites
(possibly between dendrites from the same classes) and in
this capacity dendrite–dendrite adhesion would restrict den-
drites of the same classes to a single glomerulus.
Dscam, a member of the Ig-domain superfamily of trans-
membrane proteins, was found to promote the elaboration of
PN dendrites to occupy individual glomerular space (Zhu et al.
2006a). Loss of Dscam in PNs leads to a drastic reduction of
their dendritic field size, suggesting that Dscam plays a re-
pulsive role between sister dendrites of the same PN class.
This suggests that Dscam functions in PN dendrites to ex-
pand and occupy the local space, while also preventing them
from collapsing onto each other. Both Dscam and Ncad do
not appear to play instructive roles for class-specific target-
ing, as they equally affect all PN classes examined (Zhu and
Luo 2004; Zhu et al. 2006a).
Molecular gradients determine coarse positions
How is glomerulus-specific targeting of PNs achieved? It is
likely that a set of cell-surface receptors, differentially expressed
in the various PN classes, enables them to uniquely respond to
extracellular cues. A transmembrane semaphorin, Sema-1a,
was found to directly regulate PN dendrite targeting specificity
gradient along the dorsolateral–ventromedial axis of the
Figure 2 Assembly of the Drosophila olfactory circuit. The adult olfactory circuit starts to be assembled at the beginning of the pupal stage. (A) PN
dendrite targeting. PNs are born in the embryonic and larval stages and specified by factors involving chromatin remodeling, transcription, microRNA
processing, protein translation, glycosylation, and sumoylation. They start to extend their dendrites at the larval–pupal transition, which creates the
proto-antennal lobe. Sema-1a cell-autonomously regulates PN dendrite targeting along the dorsolateral–ventromedial axis. Sema-2a/-2b proteins form
countergradients to the Sema-1a gradient along the same axis and serve as the extracellular cues that direct this targeting. Subsequently, the differential
Caps expression instructs the segregation of PN dendrites into discrete glomeruli. (B) ORN axon targeting. ORNs are born in the early pupa. Pioneering
ORN axons arrive at the antennal lobe at 18 hr after puparium formation and choose different trajectories surrounding the antennal lobe. ORN axons
require PN-independent mechanisms to converge to appropriate target regions, including ORN–ORN interactions mediated by Sema-1a and ORN target
interactions mediated by Hh. By that time, PN dendrites already coarsely pattern the antennal lobe. (C) The independent PN and ORN targeting is
coordinated by the one-to-one class-specific matching between ORN axons and PN dendrites. Ten-m and Ten-a, which are highly expressed in select PN
and ORN matching pairs, instruct synaptic matching specificity between PNs and ORNs through homophilic attraction.
developing antennal lobe, and the removal of Sema-1a in
dorsolateral PNs cause their dendrites to mistarget to ven-
tromedial glomeruli (Figure 2A). This suggests that the lev-
els of Sema-1a in PN dendrites instruct their coarse target
positions along this Sema-1a-specific axis. Interestingly, the
function of Sema-1a in regulating dendrite targeting is cell
autonomous and requires its cytoplasmic domain, suggest-
ing that it functions as a receptor. Its putative ligand is likely
also distributed in a gradient along the same dorsolateral–
Two secreted semaphorins, Sema-2a and Sema-2b, direct
PN dendrite targeting along the dorsolateral–ventromedial
axis and are candidate extracellular ligands for Sema-1a
(Sweeney et al. 2011). Sema-2a/-2b proteins form counter-
gradients to the Sema-1a gradient along the same axis, and
loss of Sema-2a/-2b also causes ventromedial mistargeting
of PN dendrites that normally target to dorsolateral glomeruli,
similar to what was observed in Sema-1a loss-of-function
mutants. Thus, the novel interaction between secreted and
membrane-bound semaphorins generates a coarse map of
PN dendrites along one axis (Figure 2A). Interestingly,
Sema-2a is predominantly expressed by ORN axons of the
larval antennal lobe, which undergoes degeneration during
metamorphosis (Sweeney et al. 2011). The degenerating
larval ORNs occupy a ventromedial position with respect
to the developing adult antennal lobe, suggesting that se-
cretion of Sema-2a by the larval axons could participate in
patterning a developing adult neural circuit.
Discrete determinants constrain glomerular targeting
After the initial coarse targeting of PN dendrites along one
axis, neighboring PN dendrites need to be segregated and
confined to class-specific glomeruli. The leucine-rich repeat
(LRR)-containing transmembrane protein Capricious (Caps)
plays an essential role in this class-specific targeting process
(Hong et al. 2009). Unlike semaphorins, which are distrib-
uted in continuous gradients, Caps is differentially expressed
in a subset of PN classes that innervate intercalated glomeruli
(Figure 2A and Figure 3A). The differential Caps expression
instructs the segregation of Caps+and Caps2dendrites into
class-specific, discrete glomeruli. Loss of Caps in Caps+PNs
causes their dendrites to invade glomeruli occupied by Caps2
PNs (Figure 3B), whereas misexpressing Caps in Caps2PNs
causes their dendrites to make ectopic innervation of glomer-
uli occupied by Caps+PNs (Figure 3C). The function of Caps
in PN dendrite targeting is likely mediated by PN–PN inter-
actions and is independent of presynaptic ORNs. Furthermore,
Caps does not mediate PN dendrite targeting through homo-
philic interactions. Identification of the putative heterophilic
ligand(s) will provide new insight into this discrete targeting
Caps provides only a single discrete cell-surface identity
code for the various PN classes. Since the antennal lobe
contains ?50 different classes of PNs, other cell-surface mol-
ecules functioning together with Caps are likely required to
further distinguish within the groups of Caps+or Caps2
PNs. Indeed, a closely related LRR transmembrane protein
Tartan is expressed in a distinct subset of PNs that partially
overlap with Caps-expressing PNs (Hong et al. 2009). Tartan
and Caps play partially redundant roles in regulating PN
dendrite targeting. Additional cell-surface molecules are likely
involved in determining the unique identity of each of the
?50 PN classes.
ORN Axon Targeting
ORNs are born in the olfactory sensilla that derive from an
undifferentiated epithelium, the antennal disc. The sensillar
types are initially specified by two transcription factors, Atonal
Figure 3 Discrete expression of Caps instructs PN dendrite targeting into
class-specific glomeruli. (A) Schematic showing differential expression of
Caps in a subset of PNs that innervate intercalated glomeruli (in gray) in
the antennal lobe (e.g., VC1 is Caps+and DL1 is Caps2). (B) Loss of Caps
in VC1 PNs (Caps+) causes their dendrites to invade glomeruli innervated
by Caps2PNs. (C) Misexpression of Caps in DL1 PNs (Caps2) causes their
dendrites to invade glomeruli innervated by Caps+PNs. Asterisks indicate
the normal and ectopic targets of dendrites. This schematic is from Hong
et al. 2009 and de Wit et al. 2011.
W. Hong and L. Luo
and Amos (Gupta and Rodrigues 1997; Goulding et al.
2000). In each sensillum, a common sensory organ precursor
gives rise to ORNs and nonneuronal supporting cells (Endo
et al. 2007). In contrast to the mouse olfactory system, Dro-
sophila ORNs target their axons to the antennal lobe before
the onset of their OR gene expression, and these two processes
do not depend on each other. Both processes, however, are
regulated by the common upstream Notch signaling pathway
(Endo et al. 2007). ORNs with high Notch activity (Notch ON)
and low Notch activity (Notch OFF) differentiate into distinct
classes within each sensillum and target axons to distinct
Initial trajectory choice
When ORN axons reach the brain, they defasciculate and
form two main trajectories to circumnavigate the developing
antennal lobe: one goes ventromedially and the other
dorsolaterally. Selecting the proper trajectory is essential for
targeting specificity of certain ORN classes (Joo et al. 2013).
A key determinant of this trajectory choice is the secreted
semaphorin Sema-2b. Notch signaling restricts Sema-2b ex-
pression to ORN axons that take the ventromedial but not
dorsolateral trajectory. Sema-2b functions cell autonomously
in ORNs for trajectory choice in response to antennal lobe-
produced Sema-2a and Sema-2b, which also regulate PN den-
drite targeting along the ventromedial–dorsolateral axis just
before ORNs arrive (see above). Sema-2b further mediates
axon–axon interactions that consolidate trajectory choice
and promote the formation of the ventromedial axon bundle
(Figure 2B). Semaphorins thus demonstrate how the same
molecules can coordinate multiple stages of neural circuit
assembly along a common developmental axis.
Axon ingrowth and coarse targeting
In addition to Sema-2a/-2b, Hedgehog (Hh) is another
central brain-derived cue that regulates axon targeting of
ORNs (Chou et al. 2010b). Peripheral Hh signaling in the
antennal disc divides ORNs into two distinct subsets: one
subset expresses low levels of the Patched (Ptc) receptor, and
the other expresses high levels of Ptc. Different Ptc levels in
ORNs determine the responsiveness of ORN axons to brain-
derived Hh. Only low-Ptc ORNs respond to brain-derived Hh
for target selection (Figure 2B). Thus, the peripheral and
central Hh signaling serves as a two-step mechanism to co-
ordinate ORN cell body positions in the periphery and their
axonal targets in the brain (Chou et al. 2010b).
The infiltration of ORN axons into the antennal lobe
requires Ncad. When Ncad is removed from ORNs, their
axons reach the vicinity of the antennal lobe, but the initial
axonal convergence into the protoglomeruli is disrupted
(Hummel and Zipursky 2004). This in turn affects subse-
quent steps of axon targeting and results in severe disorga-
nization of the adult antennal lobe. Several additional
molecules have been shown to be required for the initial
coarse targeting of ORN axons to specific glomeruli. For
example, Dscam and two downstream effectors of Dscam,
the SH2/SH3 adaptor Dock and the serine/threonine kinase
Pak, are broadly expressed in the developing antennal lobe,
and their loss-of-function mutants display ectopic targeting
of ORN axons (Ang et al. 2003; Hummel et al. 2003). The
Robo receptors are also involved in the coarse targeting of
ORN axons, as removal of Robo receptors results in wide-
spread mistargeting phenotypes (Jhaveri et al. 2004). The
mechanisms by which Dscam and Robo receptors regulate
ORN axon targeting remain to be further investigated.
Axon–axon repulsion for sequential targeting
ORN axon–axon interactions not only regulate trajectory
choice but also play a direct role in their proper targeting
of specific glomeruli. For example, maxillary palp ORN axons
enter the antennal lobe later than antennal ORN axons, and
their target choice is constrained by early-arriving antennal
axons. Sema-1a was found to mediate this interaction
(Sweeney et al. 2007). Here, as opposed to its function as
a receptor in PNs, Sema-1a functions as a repulsive ligand
on antennal ORN axons to prevent late-arriving maxillary palp
axons from invading regions already occupied by the antennal
axons (Figure 2B) (Sweeney et al. 2007). PlexinA likely serves
as the receptor for Sema-1a in ORN axon targeting.
Involvement of glial cells
Evidence suggests that glial cells are also involved in
patterning the antennal lobe. The transient interhemispheric
fibrous ring (TIFR) is a glial structure located between the
antennal lobes, and its glial processes are closely associated
with ORN axons (Simon et al. 1998). The Derailed (Drl) re-
ceptor tyrosine kinase is expressed in these glial cell processes
near the antennal lobe, and its ligand, Wnt5, is expressed in
ORN axons (Yao et al. 2007; Sakurai et al. 2009). Drl acts in
glial cells to modulate the Wnt5 signaling, and this ORN–glia
interaction contributes to the precise targeting of ORNs to
Evidence for axon–dendrite recognition
How do ORN axons make final connections with early-
arriving PN dendrites within a particular glomerulus? Three
scenarios can be envisioned: (1) ORN axons may initially
form connections with many classes of PNs in the vicinity,
after which the incorrect connections are pruned by sensory
activity; (2) ORN axons and PN dendrites may indepen-
dently target to precise locations in the antennal lobe where
the final connections are made; and (3) ORNs and PNs may
target to a rough target area and the mutual interaction
between the two determines connection specificity.
The first hypothesis is unlikely because the olfactory recep-
tors that produce sensory activity are not expressed until specific
PN–ORN connections are formed (Jefferis et al. 2004). The
second and the third hypotheses could not be distinguished
until the following observation. When a Dscam transgene is
overexpressed in a subset of PNs, it causes PN dendrites to
shift to a neighboring location (Zhu et al. 2006a). This den-
dritic position shift occurs early in development, before the
PN dendrites contact the ORN axons. Interestingly, the cog-
nate partner ORN axons subsequently arrive and follow the
shifted PN dendrites to the new position, suggesting that
the PN–ORN connection specificity is maintained despite the
shift of the relative position of PNs. Therefore, late-arriving
ORN axons could recognize cues on correct partner PN den-
drites to finalize PN–ORN connections. This provides the
first evidence supporting an essential role of axon–dendrite
recognition in determining their final connectivity.
Based on the above observations, two unbiased genetic
screens were performed to search for molecules controlling
PN–ORN matching (Hong et al. 2012). These screens identi-
fied two evolutionarily conserved EGF repeat-containing
transmembrane Teneurins, Ten-m and Ten-a, as synaptic
partner matching molecules (Hong et al. 2012). Ten-m and
Ten-a are highly expressed in select PN and ORN matching
pairs (Figure 4A). Loss- and gain-of-function experiments in-
volving Teneurins cause class-specific ectopic connections be-
tween ORNs and PNs (Figure 4B). Specifically, increasing
Teneurin levels in Teneurin-low PNs causes their dendrites
to lose endogenous connections with Teneurin-low ORNs and
mismatch with Teneurin-high ORNs, whereas overexpressing
Teneurins in PNs that already express high levels of Teneurins
does not disrupt the proper PN–ORN connections. Moreover,
Teneurins mediate homophilic interactions in vitro and pro-
mote trans-cellular PN–ORN attraction via homophilic inter-
actions in vivo. These findings suggest that Teneurins instruct
synaptic matching specificity between PNs and ORNs through
homophilic attraction, by matching Ten-m and Ten-a levels in
PN and ORN partners (Figure 2C).
Teneurins are unlikely to be the only molecules regulating
PN–ORN matching. Since the antennal lobe contains ?50 pairs
of PNs and ORNs, additional cell-surface molecules most likely
work together with Teneurins to further distinguish matching
specificity among different Teneurin-high classes or among
different Teneurin-low classes, so that each of the ?50 PN–
ORN connections can be precisely established.
Refinement, Maintenance, and Synaptogenesis
After converging onto individual glomeruli and making the
proper connections, PN dendrites and ORN axons are restricted
into single glomeruli with no overlap between neighboring
classes. It is likely that in addition to restricting dendritic fields
in an early developmental stage, N-cadherin continues to
contribute to the refinement of dendrites to single glomeruli
through interactions between dendrites from the same classes.
Once the adult antennal lobes are formed and proper
axon–dendrite connections are established, the organization
of the PN dendrites and ORN axons appears stable and in-
sensitive to perturbations. Selective cell ablation or different
olfactory experience leads to minimal changes in glomerular
organization (Devaud et al. 2003; Berdnik et al. 2006).
Synaptogenesis occurs at a late stage of antennal lobe
development. Ultrastructural analysis using electron micros-
copy suggests that the formation of presynaptic specializa-
tion occurs ?48–72 hr after puparium formation, although
synaptobrevin-GFP, a synaptic marker, is already accumu-
lated in the developing axons before this period (Devaud
et al. 2003). This suggests that synapse formation follows
after the establishment of connections between PN den-
drites and ORN axons. Teneurins, the instructive molecules
involved in matching proper PN and ORN partners, were
found to be required for synaptogenesis in the Drosophila
neuromuscular junction (Mosca et al. 2012); it will be
Figure 4 Teneurins instruct class-specific matching between PN den-
drites and ORN axons. (A) Ten-m and Ten-a are highly expressed in select
matching pairs of ORN and PN classes. Shown is a developing antennal
lobe at 48 hr after puparium formation stained by antibodies against Ten-m,
Ten-a, and a neuropil marker, N-cadherin. Solid lines encircle the DA1
glomerulus (Ten-m low, Ten-a high); dashed lines encircle the VA1d/
VA1lm glomeruli (Ten-m high, Ten-a low). (B) High-level expression of
Ten-m or Ten-a promotes homophilic attraction between specific pairs of
PN dendrites and ORN axons to form proper and stable connections (blue,
Ten-m high; orange, Ten-a high). The differential expressions of Ten-m
and Ten-a in select PN–ORN pairs instruct the one-to-one class-specific
matching. Loss of ten-a in DA1 PNs (Ten-a high) causes dendrites to
mismatch with Or47b ORNs (Ten-a low). Misexpression of Ten-m in
DA1 PNs (Ten-m low) causes dendrites to mismatch with Or88a and
Or47b ORNs (Ten-m high). Misexpression of Ten-a in VA1d PNs (Ten-a low)
causes dendrites to mismatch with Or23a and Or67d ORNs (Ten-a high). This
schematic is from Hong et al. (2012).
W. Hong and L. Luo
interesting to test whether they also contribute to the for-
mation of synapses in the olfactory system.
In addition to the processes of ?1300 ORNs and ?200 PNs,
the antennal lobe also contains neurites from ?200 local
interneurons (LNs) (Das et al. 2008; Lai et al. 2008; Okada
et al. 2009; Chou et al. 2010a). LNs make connections with
both ORN axons and PN dendrites and play important roles
in ORN-to-PN information transformation (Wilson 2013). In
contrast to the one-to-one PN–ORN connectivity, LNs show
a remarkable diversity and variability in their glomerular
innervation patterns (Chou et al. 2010a). Some LN classes
extend neurites throughout the antennal lobe, whereas other
LN classes restrict dendrites only to a subset of glomeruli.
Among LN classes that show restricted patterns, some inner-
vate continuous glomerular regions and others innervate the
antennal lobe in a patchy configuration. Surprisingly, certain
LN classes display a striking variability in their glomerular
innervation pattern across different animals.
Arborization of LN dendrites requires Dscam. Similar to
its function in PN dendrites, Dscam plays a repulsive role
between sister dendrites of the same LNs, causing the
dendrites to expand and preventing them from collapsing
onto each other (Zhu et al. 2006a). In addition, the estab-
lishment of LN arborization also requires interactions with
ORN axons during development (Chou et al. 2010a). The
molecular mechanism underlying the variability of the LN
dendritic arborization, however, is still largely unknown. It
would be interesting to further characterize the developmental
process of LNs and to examine whether PN dendrites contrib-
ute to the glomeruli-specific arborization of LNs, and whether
LNs contribute to the establishment of PN–ORN connectivity.
Interestingly, LN dendrites and ORN axons appear to occupy
distinct subcompartments within a glomerulus (Hummel and
Zipursky 2004). Dendrites of at least some LNs predominantly
occupy only a small region in the central part of each glo-
merulus, and these dendrites are surrounded by synapses
formed by ORN axons in the peripheral areas of a glomeru-
lus. In contrast to the lack of overlap between LN dendrites
and ORN synapses, PN dendrites extend throughout an entire
glomerulus and overlap extensively with ORN axons (Hummel
and Zipursky 2004). This subcompartmental specialization
within a glomerulus would be an intriguing topic for further
Principles and Open Questions
A working model for olfactory circuit assembly
The advances summarized above suggest that a series of
coordinated mechanisms regulate three major stages in the
assembly of the fly olfactory system (Figure 2):
1. PN dendrite patterning precedes ORN axon targeting and
relies on adhesive and repulsive molecules to establish their
dendritic field. Their glomerulus-specific targeting is con-
trolled by global gradients, including Sema-1a and Sema-
2a/-2b, as well as local discrete determinants such as
Capricious and Tartan that are distributed in a “salt-and-
pepper” fashion on dendrites projecting to different glo-
meruli (Figure 2A).
2. Prior to arriving at their coarse target in the antennal
lobe, ORN axons first make critical developmental trajec-
tory choices through both axon-derived and central-brain-
derived cues such as Sema-2a and Sema-2b. The initial
targeting of axons into appropriate antennal lobe regions
involves several cellular and molecular mechanisms that
include axon–axon interactions mediated by Sema-1a and
PlexinA as well as axon–target interactions mediated by
Hh and its receptor Ptc (Figure 2B).
3. The final one-to-one matching between ORN axons and
PN dendrites is coordinated by Teneurin-mediated homo-
philic attractive interactions between axons and dendrites
(Figure 2C). These mechanisms act in a sequential and
coordinated manner, which eventually leads to the precise
one-to-one connectivity between 50 PN and ORN classes
in 50 glomeruli.
Given the number of ORN and PN classes that need to be
precisely wired in the olfactory circuit, the molecules identified
so far are most likely incomplete. Future identification and
mechanistic studies of additional players might reveal new
principles that have not been uncovered thus far (discussed
Continuous vs. discrete neural maps
Spatial representation of the external world in neural maps
is important for the nervous system to effectively process
and integrate sensory information (McLaughlin and O’Leary
2005; Luo and Flanagan 2007). In many sensory systems,
such as the visual and auditory systems, neurons connect
nearby spatial/tonic inputs to nearby target regions in the
brain and form a spatially continuous neural map. In the
olfactory system, however, synaptic connections are organized
into spatially separated structural units, thereby forming a dis-
crete neural map.
The formation of continuous neural maps is typically
mediated by graded expression of cell-surface molecules,
which segregate axons or dendrites along the concentration
gradient at the target. By contrast, studies of olfactory circuit
development suggest that the formation of a discrete neural
map usually arises from a sequential strategy. The first step
involves an initial, coarse projection of neurites to broad
target zones through the action of molecular gradients,
a mechanism similar to the formation of continuous neural
maps. This initial coarse targeting is followed by a local,
precise targeting to distinct structural units via local discrete
determinants. For example, an initial coarse map of PN
dendrites is established by global gradients of Sema-1a and
Sema-2a/-2b, followed by local binary choices specified by
class-specific expression of Capricious. This sequential strategy
for neural map formation is also seen in the targeting process
of the mammalian ORN axons to the olfactory bulb (Imai
et al. 2010; Sakano 2010) and thus may represent a general
solution to the problem of constructing a discrete map.
Multiple types of cellular interactions
Recent advances have highlighted several types of inter-
actions between neurons in the developing antennal lobe,
including interactions between axons from different ORN
classes (Sema-1a/PlexinA and Sema-1b/PlexinB), repulsive
interactions between dendrites from the same PN classes
(Dscam), attractive interactions between dendrites from the
same PN classes (Ncad), interactions between dendrites from
different PN classes (Caps/Trn), and attractive interactions
between axons and dendrites that target the same glomeruli
(Teneurins), as well as the interactions between ORNs and
glia (Wnt5/Drl). It should be noted that these previous works
largely focused on part of the antennal lobe where reagents
are available to genetically label and manipulate specific
classes of neurons. It remains to be tested whether these
previously identified mechanisms are generalizable to other
classes of PNs and ORNs or whether new mechanisms are
Other conceptually different types of interactions may also
contribute to wiring specificity of the olfactory circuit. For
example, axon–dendrite repulsions between nonpartner PNs
and ORNs that innervate neighboring glomeruli could serve
as a mechanism to avoid inappropriate PN–ORN connections
and ensure the proper matching. In addition, dendrite–dendrite
repulsions between neighboring PN classes may help confine
dendrites of a single class of PNs into a single glomerulus
and prevent them from entering neighboring glomeruli. Fi-
nally, LNs may interact with PNs to contribute to patterning
of the antennal lobe or may directly regulate wiring speci-
ficity. These hypotheses remain to be tested.
Combinatorial Code in Constructing a Complex
Identifying individual genes and examining how each of
them functions in select neurons is a common experimental
approach to reveal the mechanisms underlying wiring spec-
ificity. In a complex system involving interactions among
thousands of neurons, this bottom-up approach sometimes
has limitations in understanding system-level features that
could be critical in gaining a complete picture of the target
selection process. In the following section, we make an
attempt to consider this problem in a top–down approach
and speculate how combinatorial coding, redundancy, and
error-correcting ability could contribute to establishing identi-
ties and wiring patterns of different classes of neurons.
One neuron, many molecules
To specify targeting of different neuronal classes, the nervous
system could utilize two alternative strategies: each neuron
could be encoded (1) by expressing one unique molecule or
(2) by expressing a unique combination of molecules. Several
studies suggest that the olfactory system adopts the latter
strategy; namely, a single neuron expresses a combination of
molecules, and each molecule is expressed in multiple
neurons. For example, both Caps and Tartan are expressed
in multiple classes of PNs; some PNs express both whereas
others express either one of them (Hong et al. 2009). This
distinct and partially overlapping pattern of expression forms
a combinatorial code to regulate PN dendrite targeting. Sim-
ilarly, both Ten-m and Ten-a are expressed in distinct and
partially overlapping subsets of PNs and ORNs; some PN–
ORN pairs express both of them whereas others express only
either one of them (Hong et al. 2012). In this capacity,
Teneurins form part of a combinatorial code for PN–ORN
matching. Both cases exemplify how combinatorial action of
multiple molecules assigns identities to individual PN and
ORN classes. The use of a combinatorial code could greatly
reduce the number of molecules required to uniquely iden-
tify each neuronal class (Figure 5, A and B, Figure 6A, and
Table 1, comparing models I and II).
Different ways of using a combinatorial code
We next discuss how combinatorial actions of multiple
molecules determine the identities and wiring patterns of
different neuronal classes. To simplify our discussion, we take
only the presence or the absence of a molecule into account
and propose three possible models (models II–IV, Figure 5
and Table 1). In all three models, each class of neurons
expresses a combination of molecules. But these models have
different minimum Hamming distance (Hamming 1950).
Here, Hamming distance is defined as the molecular differ-
ence between any two classes of neurons. In model II, at least
one molecule is different between any two classes; in model
III, at least two molecules are different; and in model IV, at
least three molecules are different. Although these models are
simplified from the real target selection process, this simpli-
fication allows us to focus on a few important system-level
properties, discuss how these properties may contribute to
wiring specificity, and evaluate which models are more con-
sistent with experimental observations.
Coding capacity and redundancy
The three models have different levels of coding capacity,
redundancy, and robustness (Figure 6). Here, coding capac-
ity is defined as the maximum number of identities a given
number of molecules could theoretically encode, redun-
dancy is defined as the minimum number of molecules a neu-
ronal class needs to change to become identical to another
class (i.e., minimum Hamming distance), and robustness is
defined as the ability of the entire system to resist molecular
changes through redundancy (Table 1). If we assume when
one class becomes identical to another class following per-
turbation they mistarget to the places of each other, we can
measure coding robustness in three properties: percentage
of classes unaffected by perturbation, average ectopic targets
W. Hong and L. Luo
a class may mistarget following perturbation, and total mis-
targeting events following perturbation (Table 2).
Among these three models, model II has the highest
coding capacity, but is also the least robust, because altering
one molecule may affect a large number of neurons expressing
this molecule and render them indistinguishable from other
classes that do not express this molecule (Figure 5B and Fig-
ure 6, B and C). This does not seem to be what was observed
in vivo. All single-gene loss-of-function mutants of the cell-
surface molecules studied thus far tend to cause weak phe-
notypes; dendrites or axons tend to still occupy their original
glomeruli and only a small fraction of neurites innervate
ectopic targets (Komiyama et al. 2007; Hong et al. 2009;
Chou et al. 2010b; Sweeney et al. 2011). Loss of cell-surface
proteins tends to produce phenotypes that are weaker than
those produced by mutating nuclear factors (Tea and Luo
2011) or protein modification enzymes (Sekine et al. 2013).
One possibility is that cell-surface molecules are partially re-
dundant with each other, and mutating transcription factors
or protein modification enzymes causes misregulation of mul-
tiple cell-surface molecules and leads to stronger defects.
Indeed, several cell-surface molecules previously identi-
fied play partially redundant roles in regulating targeting of
axons and dendrites (Hong et al. 2009; Sweeney et al.
2011). Thus, a redundant coding system illustrated in model
III, in which two or more molecules are different between
any two classes of neurons, is more consistent with what was
observed in vivo (Figure 5C). Compared to model II, model
Figure 5 Four hypothetical models of encoding neuronal identities. (A–D) Illustrated are examples of four hypothetical models (illustrated for specific
cases in Table 1) by which one molecule or a unique combination of multiple molecules determines the identity of different neuronal classes. (A) Model I.
Each class of neurons expresses only one of the molecules. (B–D) Models II–IV. Each class of neurons expresses a unique combination of molecules. (B)
Model II. At least one molecule is different between any two classes of neurons. This model has a low robustness. For example, removing molecule C
from neuron 3 makes this neuron identical to neuron 9. (C) Model III. At least two molecules are different between any two classes of neurons. Model III
has a medium redundancy that increases the robustness of the wiring specificity. For example, removing molecule C from neuron 5 does not make this
neuron identical to any other neuronal classes. Removing molecule C from a subset of class 5 neurons may change this subset such that they have the
same Hamming distance from the remaining unaltered class 5 neurons as to the classes of neurons that possess the closest identity (i.e., classes with one
molecule difference, marked by yellow dots), which could lead to a partial mistargeting. Simultaneous manipulation of two or more molecules (e.g.,
removing both molecules A and C from neuron 5 or removing C and misexpressing B in neuron 5) may produce stronger phenotypes. If misexpression of
a molecule overrides the action of one other molecule, misexpressing molecule C in neuron 4 may cause it to mistarget to places where neurons 1, 2, 6,
and 9 are located. (D) Model IV. At least three molecules are different between any two classes of neurons. This model has high redundancy but low
III keeps the entire system intact when removing one mol-
ecule and produces fewer average ectopic targets and fewer
total mistargeting events when removing more molecules
(Figure 6, B and C). This is reminiscent of what was observed
in the loss of function of Tartan, Sema-2a, or Sema-2b alone
(Hong et al. 2009; Sweeney et al. 2011). Thus, model III
would not only achieve a marked reduction of molecules re-
quired but also increase the redundancy that makes wiring
specificity more robust (Figure 6, A–C).
Note that during evolution there is less selection pressure
on any gene that serves completely redundant functions,
unless the gene is required elsewhere for the fitness of the
organism. This may limit the degree of redundancy even
though redundancy increases robustness.
Wiring specificity could be impaired by two types of errors
that may occur spontaneously or artificially. Adding/removing
a molecule in all the neurons within a class or in all classes is
considered type A. In this case, all neurons from a single class
still possess a uniform combination of molecules. This occurs
in a whole-animal mutant of a gene or in complete removal of
a gene from a neuronal class. By contrast, adding/removing
a molecule in a subset of neurons within a class is considered
type B. In this situation, a single class of neurons is divided
into two subpopulations of neurons that possess different
combinations of molecules. This could occur when genes are
mutated in a mosaic manner.
Model III (d $ 2) offers redundancy for type A errors;
a perturbed class still possesses a unique identity that can be
distinguished from that of all other classes. But model III
may not tolerate the type B errors, which may cause the
perturbed subpopulation to have the same Hamming dis-
tance to the unchanged subpopulation of the same class as
to other classes with the closest identity (yellow dots in
Figure 5C), so that the system is unable to determine which
class the perturbed neurons belong to and correct the error.
Since PNs and ORNs are separately specified, PN–ORN
matching seems more susceptible to type B errors than PN
dendrite targeting. However, misexpression screens of the
same set of cell-surface molecules identified many fewer
molecules affecting PN–ORN matching than those affecting
PN dendrite targeting (Hong et al. 2009, 2012; W. Hong and
L. Luo, unpublished results), suggesting the PN–ORN match-
ing might possess a higher robustness, especially with re-
spect to type B errors.
Model IV (d $ 3) has the highest redundancy among all
three models and offers correcting ability for type B errors
based on relative Hamming distance. A neuron with one
molecule removed has a shorter Hamming distance to its
original class than to other classes with the closest identity.
This error-correcting ability could contribute to the specific-
ity of PN–ORN matching. Although the higher redundancy
greatly limits the number of identities certain molecules are
able to encode, as indicated by the Hamming bound (Hamming
1950) (Table 1, Figure 6A), this reduced capacity may not
pose a problem for PN–ORN matching as matching occurs at
the last stage of the wiring process after both PNs and ORNs
arrive at the local target area where they only encounter
a limited number of possible partners.
Figure 6 Coding capacity and robust-
ness. (A) Relationship between maximum
coding capacity and number of molecules
in different models, calculated based on
Table 1. The curve of model IV shows the
Hamming bound, an upper bound of
maximum coding capacity (Table 1). (B
and C) Robustness of models II and III,
measured by three properties calculated
based on Table 2. (B) Percentage of clas-
ses unaffected after removal of a given
number of molecules. (C) Left, average
ectopic targets a class may mistarget fol-
lowing removal of a given number of mol-
ecules; right, total mistargeting events
following removal of a given number
of molecules. The properties in B and
C were calculated using representative
cases of models II and III in which six
and seven molecules are used to encode
64 classes of neurons, respectively. In
these two cases, the total numbers of
encoded classes are the same, allowing
a direct comparison of ectopic targets
and mistargeting events between mod-
els II and III.
W. Hong and L. Luo
One hypothesis, which is the most consistent with our
observations so far, is that model III (d $ 2) is more broadly
used in targeting of the same type of neurons in an initial
stage (e.g., PN dendrite targeting) and model IV (d $ 3) is
used locally to match multiple types of neurons in a final
stage (e.g., PN–ORN matching). To what extent and how
broadly the error-correcting ability is involved in controlling
wiring specificity of neural circuits is an intriguing avenue to
explore in the future.
How to reverse-engineer a redundant system
Although redundancy enhances wiring robustness, it presents
a greater challenge for geneticists to identify molecules and
examine their loss-of-function phenotypes, as it is more dif-
ficult to perturb a redundant system using genetic experi-
ments. How can we reverse-engineer a redundant system?
First, it appears to be easier to cause a noticeable phenotype
with misexpression compared with loss of function, partly
because misexpression of a molecule may increase its ex-
pression to a level much higher than its endogenous level
and this may create a stronger force that partially overrides
other molecules (Hong et al. 2009, 2012). Second, simulta-
neous manipulation of two molecules could also potentially
create a larger change of identity code and cause a stronger
phenotype. Indeed, in PN dendrite targeting, loss of Caps or
Trn alone causes only partial or no mistargeting of a single
PN VC1, whereas removing both Caps and Trn causes VC1
dendrites to completely mistarget to an ectopic glomerulus
(Hong et al. 2009). Third, when a class contains multiple
neurons, removing or misexpressing a molecule from a single
neuron or a subset of neurons using MARCM (type B errors)
may produce a partial but noticeable mistargeting pheno-
type (Komiyama et al. 2007; Hong et al. 2009).
Although several molecules have been identified as part
of the combinatorial code to determine targeting and connec-
tion specificity, they are clearly not enough for all ?50 pairs of
PNs and ORNs. To overcome redundancy in future identifica-
tion of additional wiring specificity molecules, enhancer screens
in a sensitized background in which one molecule is removed
and/or misexpression screens (Figure 5C and Figure 6, B
Table 2 Definition of properties related to robustness
No. maximum identities encoded
No. molecules removed
No. neuronal classes affected following the removal of l molecules
Percentage of unaffected classes following the removal of l molecules
Ectopic targets of a particular class following the removal of l molecules
Average ectopic targets per affected class following the removal of l molecules
Total mistargeting events following the removal of l molecules
aOnly type A errors are considered.
bn; m; and d are parameters defined by different models in Table 1.
Table 1 Comparison of different models encoding neuronal identities
in a neuron
capacity (N) Robustness
I. Each neuron class expresses a unique moleculeSpecific
II. Each neuron class expresses a unique
combination of molecules; at least one
is different between two classes
III. Each neuron class expresses a unique
combination of molecules; at least two
are different between two classes
IV. Each neuron class expresses a unique
combination of molecules; at least three
or more are different between two classes
where t ¼?d 21
aDefined as the Hamming distance between two classes.
bUpper bound for the maximum coding capacity of model IV. It is not reachable in some cases (Hamming 1950).
and C) should be considered in addition to classic loss-of-
function screens. Identification of combinatorial code in
each of the ?50 PN and ORN classes remains a future chal-
lenge. It would also be important to discover how multiple
molecules work together in the same neuron to respond to
external cues and whether they regulate independent or
common downstream signaling pathways.
It should be emphasized that all four models are drasti-
cally simplified in the sense that only the presence or the
absence of a molecule is considered. In reality, molecules
could be expressed at different levels, and identities could be
specified in a spatial and/or temporal manner; all these
contribute to the specificity. Moreover, the real biological
system is unlikely to be designed via a top–down mechanism
such that all classes use the same strategy. During evolution,
different molecules and mechanisms were recruited gradually
in an ad hoc basis. Nevertheless, the discussion of these sim-
plified hypothetical models could guide our thinking about
how the system could work, how we should design genetic
experiments that allow us to perturb the specificity, and how
we should interpret results and ultimately uncover the code.
The convergence of ORN axons and PN dendrites onto single
glomeruli and the precise one-to-one matching between ORNs
and PNs are remarkable examples of targeting specificity in
neural development. Recent studies have uncovered several key
mechanisms that control different aspects of this target speci-
ficity. These mechanisms not only enhance the understanding of
olfactory system assembly but also provide general insights into
the principles by which complex neural circuits are assembled.
As discussed above, many questions still remain to be
addressed. Combining sophisticated genetic manipulations,
we could identify additional cell-surface molecules that
work together with the ones that have been identified and
uncover the combinatorial code for each of the ?50 pairs of
PNs and ORNs. All of these joint endeavors will help create
a more comprehensive picture of the olfactory circuit wiring
process and help us better understand the principles govern-
ing wiring specificity in general.
The authors thank A. Ward, W. Joo, X. Gao, J. Charalel, F.
Ding, B. Wu, E. Wu, and Y. Hong for commenting on the
manuscript. W. Hong is a Helen Hay Whitney Fellow. L. Luo
is an investigator of the Howard Hughes Medical Institute.
Research on the olfactory circuit wiring in the Luo labora-
tory has been supported by National Institutes of Health
grant R01 DC-005982.
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Histone deacetylase Rpd3
Molecular architecture of
An olfactory sen-
Early events in olfactory
Communicating editor: J. Rine