10.5061/DRYAD.XSJ3TX9BD
Wojtowicz, Woj
0000-0001-7418-3282
Stanford University School of Medicine
Vielmetter, Jost
California Institute of Technology
Fernandes, Ricardo
0000-0001-5343-3334
Stanford University
Siepe, Dirk
Stanford University School of Medicine
Eastman, Catharine
Stanford University School of Medicine
Chisholm, Gregory
California Institute of Technology
Cox, Sarah
Genomics Institute of the Novartis Research Foundation
Klock, Heath
Genomics Institute of the Novartis Research Foundation
Anderson, Paul
Genomics Institute of the Novartis Research Foundation
Rue, Sarah
Genomics Institute of the Novartis Research Foundation
Miller, Jessica
California Institute of Technology
Glaser, Scott
Ontario Genomics Institute
Bragstad, Melisa
Genomics Institute of the Novartis Research Foundation
Vance, Julie
Novartis (Switzerland)
Lam, Annie
California Institute of Technology
Lesley, Scott
Genomics Institute of the Novartis Research Foundation
Zinn, Kai
California Institute of Technology
Garcia, Christopher
Genomics Institute of the Novartis Research Foundation
A human IgSF cell-surface interactome reveals a complex network of
protein-protein interactions
Dryad
dataset
2020
FOS: Biological sciences
Hutton Parker Foundation
https://ror.org/04gm9mk98
National Institutes of Health
https://ror.org/01cwqze88
HHMI-31760
G. Harold & Leila Y. Mathers Foundation
https://ror.org/02a7hjv13
STN182
National Cancer Institute
https://ror.org/040gcmg81
NIH R37 NS28182
Beckman Institute, California Institute of Technology
http://dx.doi.org/10.13039/100015381
2020-09-09T00:00:00Z
2020-09-09T00:00:00Z
en
https://doi.org/10.1016/j.cell.2020.07.025
206558 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Cell-surface protein-protein interactions (PPIs) mediate cell-cell
communication, recognition and responses. We executed an interactome
screen of 564 human cell-surface and secreted proteins, most of which are
immunoglobulin superfamily (IgSF) proteins, using a high-throughput,
automated ELISA-based screening platform employing a pooled-protein
strategy to test all 318,096 PPI combinations. Screen results, augmented
by phylogenetic homology analysis, revealed ~380 previously unreported
PPIs. We validated a subset using surface plasmon resonance and cell
binding assays. Observed PPIs reveal a large and complex network of
interactions both within and across biological systems. We identified new
PPIs for receptors with well-characterized ligands, and binding partners
for ‘orphan’ receptors. New PPIs include proteins expressed on multiple
cell types, and involved in diverse processes including immune and nervous
system development and function, differentiation/proliferation,
metabolism, vascularization, and reproduction. These PPIs provide a
resource for further biological investigation into their functional
relevance, and may offer new therapeutic drug targets.
High-throughput, automated ELISA-based protein-protein interaction (PPI)
screen of secreted proteins and the extracellular domain (ECD) region of
cell-surface single-transmembrane proteins. 564 secreted and ECD proteins
were produced in two different multimerized versions, 'bait' and
'prey', and every combination was tested for binding (i.e., 564
x 564 = 318,096). The read-out of the assay is colorimetric and plates are
read at O.D. 650 nm. Experiments were performed in triplicate. Background
was determined by averaging the binding signal from all 564
'prey' proteins on each 'bait'. The signal of each
protein was divided by the background to determine fold-over-background
binding (F.O.B.). Both raw and processed data are included in Wojtowicz et
al_DataS4. Multiple sequence alignment (MSA) of the 564 secreted and ECD
proteins included in the screen was performed using MUltiple Sequence
Comparison by Log- Expectation
(MUSCLE) (https://www.ebi.ac.uk/Tools/msa/muscle/) and Multiple Alignment
using Fast Fourier Transform (MAFFT)
(https://mafft.cbrc.jp/alignment/server/) online resources and analyzed
using both first iteration and second iteration parameters (MUSCLE) and
the default parameters (MAFFT) (https://www.ebi.ac.uk/Tools/msa/muscle/).
MSA files were submitted to the Interactive Tree of Life (iTOL)
(https://itol.embl.de/), an agglomerative hierarchical clustering
algorithm, to build a cluster hierarchy and generate phylogenetic
trees. Screen data were analyzed alongside the phylogenetic trees to
predict additional PPIs between subfamily members that may have been
missed in the screen (false negatives). MSA data files are provided as
follows: Wojtowicz et al_DataS6 (MUSCLE, first iteration), Wojtowicz et
al_DataS7 (MUSCLE, second iteration) and Wojtowicz et al_DataS8 (MAFFT).
MSA sequence files can be used to generate and visualize phylogenetic
trees. We use the Interactive Tree of Life (iTOL) (https://itol.embl.de/),
an agglomerative hierarchical clustering algorithm, to build a cluster
hierarchy and generate the phylogenetic trees.