A vast repertoire of Dscam binding specificities arises from modular interactions of variable Ig domains.
ABSTRACT Dscam encodes a family of cell surface proteins required for establishing neural circuits in Drosophila. Alternative splicing of Drosophila Dscam can generate 19,008 distinct extracellular domains containing different combinations of three variable immunoglobulin domains. To test the binding properties of many Dscam isoforms, we developed a high-throughput ELISA-based binding assay. We provide evidence that 95% (>18,000) of Dscam isoforms exhibit striking isoform-specific homophilic binding. We demonstrate that each of the three variable domains binds to the same variable domain in an opposing isoform and identify the structural elements that mediate this self-binding of each domain. These studies demonstrate that self-binding domains can assemble in different combinations to generate an enormous family of homophilic binding proteins. We propose that this vast repertoire of Dscam recognition molecules is sufficient to provide each neuron with a unique identity and homotypic binding specificity, thereby allowing neuronal processes to distinguish between self and nonself.
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ABSTRACT: The explosive growth in the number of protein sequences gives rise to the possibility of using the natural variation in sequences of homologous proteins to find residues that control different protein phenotypes. Because in many cases different phenotypes are each controlled by a group of residues, the mutations that separate one version of a phenotype from another will be correlated. Here we incorporate biological knowledge about protein phenotypes and their variability in the sequence alignment of interest into algorithms that detect correlated mutations, improving their ability to detect the residues that control those phenotypes. We demonstrate the power of this approach using simulations and recent experimental data. Applying these principles to the protein families encoded by Dscam and Protocadherin allows us to make testable predictions about the residues that dictate the specificity of molecular interactions.PLoS ONE 11/2014; 9(11):e107723. · 3.53 Impact Factor
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ABSTRACT: The complex, branched morphology of dendrites is a cardinal feature of neurons and has been used as a criterion for cell type identification since the beginning of neurobiology. Regulated dendritic outgrowth and branching during development form the basis of receptive fields for neurons and are essential for the wiring of the nervous system. The cellular and molecular mechanisms of dendritic morphogenesis have been an intensely studied area. In this review, we summarize the major experimental systems that have contributed to our understandings of dendritic development as well as the intrinsic and extrinsic mechanisms that instruct the neurons to form cell type-specific dendritic arbors. Expected final online publication date for the Annual Review of Physiology Volume 77 is February 10, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.Annual Review of Physiology 10/2014; 77(1). · 14.70 Impact Factor
Article: Fruit Flies in Biomedical Research.[Show abstract] [Hide abstract]
ABSTRACT: Many scientists complain that the current funding situation is dire. Indeed, there has been an overall decline in support in funding for research from the National Institutes of Health and the National Science Foundation. Within the Drosophila field, some of us question how long this funding crunch will last as it demotivates principal investigators and perhaps more importantly affects the longterm career choice of many young scientists. Yet numerous very interesting biological processes and avenues remain to be investigated in Drosophila, and probing questions can be answered fast and efficiently in flies to reveal new biological phenomena. Moreover, Drosophila is an excellent model organism for studies that have translational impact for genetic disease and for other medical implications such as vector-borne illnesses. We would like to promote a better collaboration between Drosophila geneticists/biologists and human geneticists/bioinformaticians/clinicians, as it would benefit both fields and significantly impact the research on human diseases. Copyright © 2015, The Genetics Society of America.Genetics 01/2015; · 4.87 Impact Factor
Cell, Volume 130
A Vast Repertoire of Dscam Binding
Specificities Arises from Modular
Interactions of Variable Ig Domains
Woj M. Wojtowicz, Wei Wu, Ingemar Andre, Bin Qian, David Baker,
and S. Lawrence Zipursky
Figure S1. Comparison of Binding Assays to Assess Dscam Binding Specificity
The binding properties of Dscam isoforms containing Ig2.7, Ig3.27 and highly-related Ig7 domains are
shown in four different binding assays. The table outlines the properties of each binding assay to indicate
1) sensitivity (as determined by whether heterophilic binding can be detected between isoforms 7.27.25
and 7.27.26), 2) whether the assay is readily quantitative, 3) whether the assay requires protein
purification, and 4) the number of binding experiments that can be conducted per day in our hands.
Representative binding data for these isoforms in each assay are shown (Matthews et al., 2007;
Wojtowicz et al., 2004)(J.J. Flanagan and S.L.Z, unpublished observations). F.L., full length.
Figure S2. Detection of Homophilic Binding Requires Clustering of Dscam Molecules.
(A) Immunoprecipitation assay to assess Dscam homophilic binding. Purified Dscam-Fc (bound to
protein G sepharose) was used as receptor protein to pull-down purified Dscam-His ligand protein. Pull-
downs were performed 1) in the absence (lanes 2-5) and presence (lanes 6-9) of Dscam-Fc receptor and 2)
in the absence (lanes 2-3, 6-7) and presence (lanes 4-5, 8-9) of α-His IgG-HRP which was used for
clustering Dscam-His ligand protein (Note that α-His IgG did not bind to protein G sepharose). (B)
ELISA-based binding assay to assess Dscam homophilic binding. Mouse α-Fc IgG was adsorbed to
ELISA plates and used to capture Dscam-Fc receptor protein from cell culture medium following
transient transfection. Dscam-AP ligand containing culture medium was added with or without α-AP IgG
for clustering Dscam-AP. Binding of Dscam-AP ligand to Dscam-Fc receptor was assessed by monitoring
AP activity following addition of AP substrate. This is a variation on the ELISA-based assay used
throughout this paper. AP activity was only linear over a 25-fold range while HRP activity was linear
over a 70-fold range. Additionally, more sensitive reagents are commercially available for HRP detection.
Therefore, the ELISA-based binding assay was optimized such that Dscam-AP is used as receptor and
Dscam-Fc is used as ligand with α-Fc IgG-HRP for clustering and detection of binding (see Figure 1 and
Figure S3. Swapping Only Surface Exposed Ig2 A’ β-Strand Residues Generates Promiscuous
Variable Domains. (A-B) Spacefill models of wild type variable Ig2 domains and A’ β-strand surface
swapped Ig2 domains are shown. Each variable Ig2 domain is represented by a different color. The
sequence alignment shows the A’ β-strand sequences (residues 105-114) for these Ig2 domains and the
surface exposed residues swapped. The binding properties of isoforms containing wild type and strand-
swapped Ig2 domains were tested using the ELISA-based binding assay. Binding is indicated as fold over
background by the number in each block. The average results of duplicate experiments are shown. (A)
Swapping residues between Ig2.4 and Ig2.7. (B) Swapping residues between Ig2.3 and Ig2.4. Potential
specificity residues were predicted by modeling different presumptive interfaces on the Ig2.1 interfaces in
the Ig1-4 crystal structure (Meijers et al., 2007).
Figure S4. The Ig2 Specificity Element can be Generalized to All Ig2 Variable Domains Tested.
(A) Spacefill models of wild type variable Ig2 domains (top row) and A’ β-strand swapped Ig2 domains
(bottom two rows). Each variable Ig2 domain is represented by a different color. Spacefill models of
swapping A’ β-strand from Ig2.1 (red) into Ig2.2-Ig2.12 (other colors) (Middle row). Spacefill models of
converse swaps of predicted A’ β-strand sequences of Ig2.4, Ig2.7 and Ig2.10 into Ig2.1 (Bottom row).
The boxed sequences on the right show an alignment of A’ β-strand sequences (residues 105-114) for all
12 variable Ig2 domains. (B) The binding properties of isoforms containing wild type and strand-swapped
Ig2 domains were tested using the ELISA-based binding assay. Binding is indicated as fold over
background (i.e. the number in each block). The unrelated control isoform 1.30.30 (not shown) was used
to provide a value for background binding. The average results of duplicate experiments are shown.
Specificity residues were predicted by modeling different presumptive interfaces on the Ig2.1 interfaces in
the Ig1-4 crystal structure (Meijers et al., 2007).
Figure S5. The Ig3 Specificity Element can be Generalized to Highly Diverse Ig3 Variable
(A) The A-A’ segment was swapped between pairs of Ig3 domains from distantly related regions of the
dendrograms (blue circles). (B-D) Spacefill models of wild type variable Ig3 domains and A-A’ segment
swapped Ig3 domains are shown. The sequence alignment shows the A-A’ segment sequences (residues
214-224) for these Ig3 domains and the residues swapped. The binding properties of isoforms containing
wild type and segment-swapped Ig3 domains were tested using the ELISA-based binding assay. Binding
is indicated as fold over background by a color scale and the number in each block. The unrelated control
isoform 1.30.30 (denoted “C”) was used to provide a value for background binding. The average results
of duplicate experiments are shown. (D) Note that heterophilic binding between Ig3.31 and Ig3.34 is
considerable though weaker for homophilic binding for each and, therefore, the color scale is changed to
aid visualization of specificity swaps. Despite strong heterophilic binding, the trend seen with the swaps
is the same as in B and C. Specificity residues were predicted by modeling different presumptive
interfaces on the Ig3.34 interface in the Ig1-4 crystal structure (Meijers et al., 2007).