Roberto Sacco1, Rui Martins1,2, Wojciech Garncarz1, Katharina L Willmann1, Ana Krolo1, Sylvia Knapp1,2, Keiryn L Bennett1, Kaan Boztug1,3,4. 1. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria. 2. Department of Medicine I, Laboratory of Infection Biology, Medical University of Vienna, 1090 Vienna, Austria. 3. Department of Paediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria. 4. Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases and CeRUD Vienna Center for Rare and Undiagnosed Diseases, 1090 Vienna, Austria.
Abstract
NF-κB signaling is a central pathway of immunity and integrates signal transduction upon a wide array of inflammatory stimuli. Noncanonical NF-κB signaling is activated by a small subset of TNF family receptors and characterized by NF-κB2/p52 transcriptional activity. The medical relevance of this pathway has recently re-emerged from the discovery of primary immunodeficiency patients that have loss-of-function mutations in the MAP3K14 gene encoding NIK. Nevertheless, knowledge of protein interactions that regulate noncanonical NF-κB signaling is sparse. Here we report a detailed state-of-the-art mass spectrometry-based protein-protein interaction network including the noncanonical NF-κB signaling nodes TRAF2, TRAF3, IKKα, NIK, and NF-κB2/p100. The value of the data set was confirmed by the identification of interactions already known to regulate this pathway. In addition, a remarkable number of novel interactors were identified. We provide validation of the novel NIK and IKKα interactor FKBP8, which may regulate processes downstream of noncanonical NF-κB signaling. To understand perturbed noncanonical NF-κB signaling in the context of misregulated NIK in disease, we also provide a differential interactome of NIK mutants that cause immunodeficiency. Altogether, this data set not only provides critical insight into how protein-protein interactions can regulate immune signaling but also offers a novel resource on noncanonical NF-κB signaling.
NF-κB signaling is a central pathway of immunity and integrates signal transduction upon a wide array of inflammatory stimuli. Noncanonical NF-κB signaling is activated by a small subset of TNF family receptors and characterized by NF-κB2/p52 transcriptional activity. The medical relevance of this pathway has recently re-emerged from the discovery of primary immunodeficiency patients that have loss-of-function mutations in the MAP3K14 gene encoding NIK. Nevertheless, knowledge of protein interactions that regulate noncanonical NF-κB signaling is sparse. Here we report a detailed state-of-the-art mass spectrometry-based protein-protein interaction network including the noncanonical NF-κB signaling nodes TRAF2, TRAF3, IKKα, NIK, and NF-κB2/p100. The value of the data set was confirmed by the identification of interactions already known to regulate this pathway. In addition, a remarkable number of novel interactors were identified. We provide validation of the novel NIK and IKKα interactor FKBP8, which may regulate processes downstream of noncanonical NF-κB signaling. To understand perturbed noncanonical NF-κB signaling in the context of misregulated NIK in disease, we also provide a differential interactome of NIK mutants that cause immunodeficiency. Altogether, this data set not only provides critical insight into how protein-protein interactions can regulate immune signaling but also offers a novel resource on noncanonical NF-κB signaling.
NF-κB signaling
is one of the most important signaling pathways
in immunity with two major branches, termed canonical and noncanonical.
These pathways are characterized by activation of different transcription
factor heterodimers that are members of the NF-κB family (REL-A/p65,
REL-B, c-REL, NF-κB1/p50, and NF-κB2/p52). Canonical NF-κB
signaling is activated by a wide array of inflammatory stimuli (e.g.,
cytokine receptors, pattern recognition receptors, antigen receptors).
Signaling occurs via an IKKα/IKKβ/IKKγ complex and,
further, via the formation of p65/p50 heterodimers.[1] In contrast, noncanonical NF-κB signaling is characterized
by the induction of proteolytic processing of NF-κB2/p100 to
p52[2] (Figure A). REL-B and p52 form a heterodimer that
translocates to the nucleus and transcriptionally activates a subset
of NF-κB target genes. These are primarily involved in homeostasis,
plus development and functioning of the adaptive immune system.[3] The noncanonical pathway is activated by a small
group of TNF family receptors including B-cell activating factor (BAFF),
lymphotoxin β receptor, and CD40.[2] The majority of these receptors, however, can also activate canonical
NF-κB signaling. Downstream of noncanonical receptor activation,
signals are relayed through the TNF receptor-associated factor (TRAF)
proteins TRAF2 and TRAF3 via regulation of the serine/threonine kinase
NF-κB inducing kinase (NIK, MAP3K14).[4] NIK is central to noncanonical NF-κB signaling. Under steady-state
conditions, a complex containing TRAF3 (a ubiquitin ligase that acts
on NIK and thus represents a negative regulator) and TRAF2 continuously
targets NIK for degradation.[5] Upon receptor
ligation, TRAF3 is degraded and NIK accumulates. This enables NIK
to activate phosphorylation of the downstream effectors, kinase IKKα
(IκB kinase α) and NF-κB2/p100.[6] NIK is the primary kinase that can induce NF-κB2/p100
proteolytic processing to produce the active p52 transcription factor
(reviewed in ref (2)). Therefore, NIK is essential for signaling via the noncanonical
NF-κB branch.[4] Signaling via either
branch of the NF-κB pathway is flexible, as crosstalk between
canonical and noncanonical NF-κB pathways is known to occur
in different situations and in various cell types (reviewed in ref (7)).
Figure 1
Noncanonical NF-κB
signaling pathway. (A) Schematic depiction
of the function and key proteins of noncanonical NF-κB signaling.
Proteins used in this study are marked by a red asterisk. (B) HEK293
Flp-In stable cell line system expressing SH-tagged NIKwt, NIKK429A/K430A, and NIKP565R upon doxycycline
induction. IB showing expression of NIK upon doxycycline treatment
and NF-κB2/p100 processing due to expression of NIKwt. This is a reduced depiction of the IB shown in Supplementary Figure S2B. Gray dotted lines indicate IB lanes
that are not adjacent to one another.
Noncanonical NF-κB
signaling pathway. (A) Schematic depiction
of the function and key proteins of noncanonical NF-κB signaling.
Proteins used in this study are marked by a red asterisk. (B) HEK293
Flp-In stable cell line system expressing SH-tagged NIKwt, NIKK429A/K430A, and NIKP565R upon doxycycline
induction. IB showing expression of NIK upon doxycycline treatment
and NF-κB2/p100 processing due to expression of NIKwt. This is a reduced depiction of the IB shown in Supplementary Figure S2B. Gray dotted lines indicate IB lanes
that are not adjacent to one another.Recently, the medical relevance of this pathway has been
elucidated
by our identification of a NIK deficiency. Here patients have a catalytically
inactive version of the kinase (NIKP565R).[8] Patients show a life-threatening combined immunodeficiency
that results in increased susceptibility to bacterial, viral, and
protozoan infections and subsequent need for hematopoietic stem-cell
transplantation. Lymphoid lineages are most affected by the mutation:
B-cell survival is reduced in the patients, resulting in low B-cell
numbers and hypogammaglobulinemia; T cells are functionally affected;
and NK cells are not only reduced but also have impaired cytotoxic
function. Together, this leads to a severe dysfunction of lymphoid
immunity. Other kinase-deficient mutants of NIK have only been characterized
in vitro.[9] In mice, the NIK mutant aly(10,11) has been a long-standing subject
of immunological research. This mutant preserves kinase activity but
abolishes interaction with effectors such as TRAF2 and IKKα.
Additionally, knockout animals[12] have been
described. Both models show disorganization of lymphatic tissue in
the peripheral nodes, Peyer’s patches, spleen, and thymus as
well as reduced B-cell numbers and immunoglobulin (Ig) serum levels
concomitant to humoral immunodeficiency.[10,12,13] This largely recapitulates the findings
in humans. Because noncanonical NF-κB signaling is required
for healthy lymphoid tissue survival, the pathway is also commonly
activated in cancers, for example, B-cell neoplasms[14] or multiple myelomas.[15,16] Fine-tuned
regulation of the noncanonical pathway and cross-talk with other immune
and cellular pathways is still not fully understood. This fact is
supported by the recent discovery of additional novel regulators.[17,18] For example, the TRAF3 interactor and deubiquitinase OTUD7B acts
via protein–protein interaction to regulate the pathway.[18] Because the NF-κB pathway is of such essential
interest, it has been extensively explored in large-scale proteomic
studies;[19] however, a detailed and technologically
up-to-date interactome analysis of noncanonical NF-κB signaling
has not yet been reported. Here we present the protein–protein
interaction network of the noncanonical NF-κB pathway using
NF-κB2/p100, IKKα, TRAF2, TRAF3, and NIK as nodes. Additionally,
we chose this well-studied pathway for expanding our knowledge on
a recently discovered primary immunodeficiency protein, NIK, because
the effect of a given newly identified disease-causing mutation is
rarely studied in the context of cellular protein machinery. Therefore,
as a proof-of-principle, we compare the interactome of wild-type NIK
to previously reported single gene-defect causing mutants, namely,
the recently identified novel kinase inactive mutant observed in human
patients (NIKP565R),[8] a second
catalytically inactive mutant (NIKK429A/K430A),[9] and the human aly equivalent
mutant (NIKG860R).[11]
Experimental
Procedures
Constructs and Cells
Human MAP3K14 and mutant versions thereof (NIKK429A/K430A, NIKP565R, NIKG860R), GFP control, TRAF3, TRAF2, IKKα, and NFKB2 cDNA were cloned into pTO-SII-HA-GW
vectors[20] for N-terminal streptavidin–hemagglutinin
(S-HA) tag fusion using gateway recombination as described previously.[20,21] The plasmids were transfected into HEK293 Flp-In-TREx cells (Life
Technologies, Carlsbad, CA) together with a Flp recombinase expression
plasmid pOG44 (Life Technologies). Recombinants were selected according
to the instructions supplied by the manufacturer and cell lines that
have doxycycline-dependent stable transgene expression were generated.
Cells were maintained in DMEM (PAA Laboratories, Germany) supplemented
with 10% fetal calf serum (FCS), penicillin (100 U/mL), and streptomycin
(100 μg/mL) (PAA Laboratories, Germany). The inducible expression
of the SH-tagged constructs upon treatment with 1 μg/mL doxycycline
(Sigma-Aldrich, Austria) for 24 h was verified by Western blotting.
To validate interactions, we cloned FKBP8 and FBXW7 cDNA into pCS2-6myc-GW expression vector[21] for N-terminal myc tag fusion expression using
gateway recombination as described above.
Tandem Affinity Purification
Tandem affinity purifications
(TAPs) were performed as previously described,[22] with the following modifications. In brief, confluent 15
cm dishes of HEK293 were incubated with 1 μg/mL doxycycline
for 24 h. Cells were lysed in buffer I (50 mM HEPES, pH 8.0, 150 mM
NaCl, 5 mM EDTA, 0.5% NP-40, 50 mM NaF, 1 mM Na3VO4, 1 mM PMSF) and complete protease inhibitor cocktail (Sigma-Aldrich,
Austria). Protein complexes (55–100 mg protein input) were
isolated using 400 μL of Strep-Tactin agarose bead slurry (2-1201-010,
IPA, Germany), followed by elution with biotin. A second purification
step with 200 μL of HA agarose bead slurry (A 2095, Sigma-Aldrich,
Austria) was performed, followed by elution with 100 mM formic acid.
Eluates were immediately neutralized with triethylammonium bicarbonate
(TEAB). After reducing cysteine bonds with 10 mM dithiothreitol (DTT)
and alkylation in 55 mM iodacetamide at room temperature, the samples
were digested overnight at 37 °C with 2.5 μg trypsin (Promega,
Austria). The peptides were purified using C18 microspin columns (3M,
USA) according to the protocol provided by the manufacturer, lyophilized,
redissolved in 5% formic acid. 5% v/v of the digest of the SH-GFP
control pulldown sample was used for C18 purification and subsequent
MS analysis. The fraction of digested peptide of the SH-tagged bait
proteins used was normalized to the SH-GFP control sample (as described
in ref (22)) using
anti-HA immunoblot data. Each SH-tagged protein was prepared as biological
replicates and analyzed as back-to-back technical replicates by liquid
chromatography mass spectrometry.
Liquid Chromatography Mass
Spectrometry
Mass spectrometry
was performed on a hybrid linear trap quadrupole (LTQ) Orbitrap Velos
mass spectrometer (ThermoFisher Scientific, Waltham, MA) running the
Xcalibur software (version 2.1.0). The instrument was coupled to an
Agilent 1200 HPLC nanoflow system with a dual pump, one precolumn,
and one analytical column (Agilent Biotechnologies, Palo Alto, CA)
via a nanoelectrospray ion source with a liquid junction (Proxeon,
Odense, Denmark). The peptide mixtures were automatically loaded from
the thermostated autosampler (4 °C) onto a trap column (Zorbax
300SB-C18 5 μm, 5 × 0.3 mm, Agilent Biotechnologies) with
the binary pump solvent that was comprised of 0.1% trifluoracetic
acid (TFA) in water at a flow rate of 45 μL/min. The peptides
were eluted by back-flushing from the trap column onto a 16 cm fused
silica analytical column with an inner diameter of 50 μm packed
with C18 reversed-phase material (ReproSil-Pur 120 C18-AQ, 3 μm,
Dr. Maisch, Ammerbuch-Entringen, DE). The solvents for peptide separation
were composed of 0.4% FA in water (solvent A) and 0.4% FA in 20% isopropanol,
70% methanol (solvent B). A multistep linear gradient elution of the
peptides was achieved by a 27 min gradient ranging from 3 to 30% solvent
B, followed by a 25 min gradient from 30 to 70% solvent B and, finally,
a 7 min gradient from 70 to 100% solvent B at a constant flow rate
of 100 nL/min. The analyses were performed in a data-dependent acquisition
mode. The top 15 most intense ions were selected for collision-induced
dissociation (CID). Normalized collision energy was 30%. Dynamic exclusion
of selected ions for MS2 fragmentation was 60 s, and a
single lock mass at m/z 445.120024
Si(CH3)2O)6[23] was used for internal mass calibration with a target loss mass abundance
of 0%. Maximal ion accumulation time allowed was 50 ms for MSn in the LTQ and 500 ms in the C-trap. Overfilling of the C-trap
was prevented by automatic gain control set to 106 ions
for a full Fourier transform mass spectrometer (FTMS) scan and 5 ×
105 ions for MSn mode. Intact peptides were
detected in the Orbitrap mass analyzer at resolution of 60 000.
Signal threshold for triggering MSMS fragmentation was set 2000 ion
counts.[24]
Data Processing and Bioinformatic
Analysis
Acquired
raw mass spectrometry data files were converted into Mascot generic
format (mgf) files using msconvert (ProteoWizard Library v2.1.2708).
The resultant peak lists were compared with the human SwissProt database
version v2013.01_20130110 (37 398 sequences including isoforms
obtained from varsplic.pl[25] and appended
known contaminants) with the search engines Mascot (v2.3.02, MatrixScience,
London, U.K., www.matrixscience.com) and Phenyx (v2.6,
GeneBio, Geneva, Switzerland).[26] Submission
to the search engines was performed using a Perl script that performs
an initial search with relatively broad mass tolerances (Mascot only)
on both precursor and fragment ions (±10 ppm and ±0.6 Da,
respectively). High-confidence peptide identifications are used to
recalibrate all precursor and fragment ion masses prior to a second
search with narrower mass tolerances (±4 ppm and ±0.3 Da).
One missed tryptic cleavage site was allowed. Carbamidomethyl cysteine
and oxidized methionine were set as variable modifications, respectively.
To validate the proteins, we processed Mascot and Phenyx output files
by parsers developed internally. Proteins with ≥2 unique peptides
above a score T1, or with a single peptide above a score T2, were
selected as unambiguous identifications. Additional peptides for these
validated proteins with score > T3 were also accepted. For Mascot
and Phenyx, T1, T2, and T3 were equal to 12, 45, and 10 and 5.5, 9.5,
and 3.5, respectively (p value <10–3). Following the selection criteria, proteins were grouped on the
basis of shared peptides, and only the group reporters are considered
in the final output of identified proteins. Spectral conflicts between
Mascot and Phenyx peptide identifications were discarded. The whole
procedure was repeated against a reversed database to assess the protein
group false discovery rate (FDR). Peptide and protein group identifications
were <0.1 and <1% FDR, respectively.All proteins identified
in the SH-GFP negative control experiments as well as additional proteins
from control experiments in our internal repository were removed as
nonspecifically interacting proteins. Only proteins that were identified
in both biological replicates and were absent in the GFP experiments
were further considered. Using the SAINT[27] algorithm and the Homo sapiens CRAPome[28] (version 1.1) database (www.crapome.org, accessed 25 August 2015), high confidence scores were calculated.
Spectral counts from technical replicates were added up, and biological
replicates (n = 2) were used as the input for the
analysis tools. The FC-A score was determined from internal negative
control experiments (including SH-GFP) with a default background estimation,
and the FC-B score was ascertained from CRAPome controls with a stringent
background estimation. For calculating the FC-B score, CRAPome controls
with matching experimental procedures were selected (i.e., HEK293
cells, S-HA tag, total cell lysate, 1D-LC–MS, Strep-Tactin
and HA agarose affinity resins). We used the SAINT algorithm to calculate
the probability score of the identified interactions via SAINT express[29] with default options. This analysis included
negative control experiments from our internal repository. SAINT and
CRAPome scores were plotted using Ggplot2 in R. Only interactions
with a SAINT score of 1 and a CRAPome FC-A score >2.5 were considered.
To retrieve preannotated interactions, the STRING database (version
9.1) was used, applying a default confidence score = 0.4. Networks
were generated using cytoscape (version 3.0.2). Gene set enrichment
analysis was performed by uploading complete, filtered noncanonical
NF-κB signaling network data to DAVID annotation database with
default settings.
Co-Immunoprecipitation and Immunoblot Analysis
Co-immunoprecipitation
(Co-IP) was performed using doxycycline-inducible HEK293 Flp-In cells
as previously described. Confluent 15 cm dishes of cells (containing
3 × 107 cells) were induced with 1 μg/μL
doxycycline for 24 h. For transfection experiments, cells were transfected
with pCS2-6myc-GW expression vector using X-tremeGENE reagent (Roche,
Vienna, Austria) and induced 12 h after transfection. After lysis
in buffer I (composition given above in the TAP section), 7 mg of
total protein together with 70 μL of HA–agarose bead
slurry (Sigma-Aldrich, Austria) were used for immunoprecipitation
(IP). For immunoblot (IB) analysis, protein was isolated with buffer
I. SDS-PAGE and polyvinylidene difluoride or nitrocellulose membranes
were prepared according to standard methods. Primary antibodies (1:1000
dilution) for IB analyses were: rabbit antihuman IKKα (2682),
NIK (4994), NF-κB2/p100/p52 (4882) (Cell Signaling, Frankfurt,
Germany), and rabbit antihuman FKBP8 (Pierce/ThermoFisher, Vienna,
Austria). Tagged recombinant proteins were detected with antihuman c-myc (BD Biosciences, Schwechat, Austria) at 1:1000 dilution
and horseradish peroxidase-coupled anti-HA (Sigma-Aldrich, Austria)
at 1:3000 dilution. Mouse antihuman GAPDH (Santa Cruz Biotechnology,
TX) at 1:1000 dilution was a loading control. Horseradish peroxidase-conjugated
goat antirabbit (Biorad, Vienna, Austria) and goat antimouse (BD Biosciences,
Schwechat, Austria) were secondary antibodies at 1:10 000 or
1:50 000 dilution. Signals were detected with a chemiluminescent
substrate (Amersham ECL Prime Western Blotting Detection Reagent,
GE Healthcare Life Sciences, Vienna, Austria) and Hyperfilm ECL (GE
Healthcare Life Sciences).
Results and Discussion
Experimental
Design
To understand the cellular context
of the noncanonical NF-κB signaling pathway (Figure A) and in particular the central
protein NIK, our aim was to map the protein interaction network of
NIK, IKKα, TRAF2, TRAF3, and NF-κB2/p100 by tandem affinity
purification mass spectrometry (TAP-MS). N-terminally tagged SH-fusion
proteins were expressed by stable recombinant human embryonic kidney
HEK293 Flp-In cells that enable doxycycline-dependent protein production.[20] Doxycycline induction of NIKwt revealed
that NF-κB2/p100 processing to p52 can be induced in HEK293
Flp-In cells by overexpression and subsequent accumulation of NIK
(Figure B). This was
not the case for the catalytically inactive mutants NIKP565R and NIKK429A/K430A. Thus, we were confident that all
components of noncanonical NF-κB signaling are expressed and
functional in the HEK293 Flp-In system, serving as a model for noncanonical
NF-κB signaling in lymphocytes.
Interactome Integrating
the Five SH-Tagged Proteins
From the five SH-tagged proteins,
numerous protein interactors were
obtained. The data were analyzed by SAINT and the CRAPome database
and filtering was applied according to the thresholds described in
the material and methods section (Supplementary Figure S1A,B). Only interacting proteins that were identified
in both biological replicates and that were not identified in the
SH-GFP experiments were considered for the final interactome. 109
high confidence interacting proteins linked by 145 interactions were
identified between all five SH-tagged baits (Figure , see Supplemetary Table S1 for all protein–protein interactions ranked by SAINT
probability and CRAPome score). From these, we found that 30 had been
previously reported with high confidence in the STRING database (Figure , Table S1), and 115 interactions were novel. Out of all of
the previously reported (“known”) interacting proteins
identified in our experiments, 85.7% had both a score of 1 in SAINT
and an FC-A score of >2.5 (Supplementary Figure S1A). This result not only confirmed that known interactions
were retrieved with high confidence but also allowed us to conclude
that our scoring system was valid. This supported the quality of the
interaction network and the potential of the interactome as a resource
for novel candidate pathway regulators. In line with this, several
functionally characterized regulators of canonical/noncanonical NF-κB
signaling were among the identified proteins, including cIAP1, cIAP2
(encoded by BIRC2 and BIRC3, respectively), REL-A/p65, REL-B, c-REL,
NF-κB1/p50, IKKβ, IKKγ, and FBXW7. These interactors
are discussed below.
Figure 2
Interaction network of the noncanonical NF-κB signaling
pathway.
(A) Proteins shown were identified by LC–MS. Edge (line) thickness
and edge transparency represent the CRAPome FC-A score. Red edges
indicate novel interactions, and gray edges indicate known interactions.
SH-tagged proteins, proteasomal machinery proteins, ribosomal proteins,
and mitochondrial-localizing proteins are colored yellow, dark-blue,
green, and red, respectively. Ubiquitination/deubiquitination machinery
proteins, kinases/phosphatases, and transcription factors are framed
in blue, red, and purple, respectively. Asterisks denote proteins
that cause primary immunodeficiency phenotypes in humans when mutated.
(B) Gene Ontology term enrichment analysis using DAVID functional
annotation clustering. Selected clusters are shown.
Interaction network of the noncanonical NF-κB signaling
pathway.
(A) Proteins shown were identified by LC–MS. Edge (line) thickness
and edge transparency represent the CRAPome FC-A score. Red edges
indicate novel interactions, and gray edges indicate known interactions.
SH-tagged proteins, proteasomal machinery proteins, ribosomal proteins,
and mitochondrial-localizing proteins are colored yellow, dark-blue,
green, and red, respectively. Ubiquitination/deubiquitination machinery
proteins, kinases/phosphatases, and transcription factors are framed
in blue, red, and purple, respectively. Asterisks denote proteins
that cause primary immunodeficiency phenotypes in humans when mutated.
(B) Gene Ontology term enrichment analysis using DAVID functional
annotation clustering. Selected clusters are shown.For instance, cIAP1 and 2 were copurified with
SH-TRAF2. These
proteins are known to form a complex with TRAF2 and TRAF3 that then
acts as a ubiquitin ligase competent complex controlling NIK degradation.[5] The association of SH-TRAF2 and SH-TRAF3 with
different components of the proteasomal machinery (Figure A) is consistent with the role
of these two proteins in the regulation of proteolytic processes in
NF-κB signaling.The most downstream member of noncanonical
NF-κB signaling
that was analyzed is NF-κB2/p100. The SH-tagged version of the
protein revealed an association with known NF-κB family transcription
factors that can heterodimerize with NF-κB2/p52 (i.e., REL-A/p65,
REL-B, c-REL, NF-κB1/p50).An only recently reported interaction
between NF-κB2/p100
and FBXW7[17] was also observed (Figure A). As a substrate-targeting
subunit of the SCF ubiquitin ligase complexes, FBXW7 was discovered
to constitutively target NF-κB2/p100 for proteasomal degradation.
Because the uncleaved nuclear p100 form of NF-κB2 is inhibitory
to noncanonical NF-κB signaling,[6] FBXW7 was shown to represent a novel positive regulator of noncanonical
NF-κB activity.[17] This interaction
was validated by co-IP via the HA-tag in doxycycline-inducible HEK293
cells (Figure A).
The data clearly indicated that our network encompasses, besides long-documented
interactions, also an only recently characterized novel regulator,
FBXW7, suggesting that additional candidates may also be real and
potentially relevant interactions. Interestingly, in the previous
study, NF-κB2/p100 was identified as an interactor of FBXW7
by immunopurification and mass spectrometry.[17] In our study, the reciprocal strategy resulted in the identification
of the interaction, thus strengthening previous observations. Additionally,
we observed a previously undescribed interaction between FBXW7 and
TRAF2, which could be confirmed using the co-IP and Western blot method
(Figure A). This provides
further evidence of the role of FBXW7 in NF-κB signaling, and
it is conceivable that control of NF-κB2/p100 by FBXW7 may indeed
involve TRAF complexes.
Figure 3
Validation of interacting proteins identified
by LC–MS.
(A) Co-IP using SH-GFP doxycycline-inducible control cell line, SH-TRAF2
and SH-NF-κB2/p100-inducible HEK293 Flp-In lines concomitantly
expressing myc-FBXW7. IP with anti-HA agarose beads confirmed the
association of SH-TRAF2 and SH-NF-κB2/p100 with myc-tagged FBXW7.
(B) Co-IP with SH-GFP doxycycline-inducible control cell line, SH-IKKα
and SH-NIK doxycycline-inducible HEK293 Flp-In lines. IP with anti-HA
agarose beads confirmed the association of SH-IKKα and SH-NIK
with endogenously expressed FKBP8. Gray dotted lines indicate lanes
that are not adjacent to one another.
Validation of interacting proteins identified
by LC–MS.
(A) Co-IP using SH-GFP doxycycline-inducible control cell line, SH-TRAF2
and SH-NF-κB2/p100-inducible HEK293 Flp-In lines concomitantly
expressing myc-FBXW7. IP with anti-HA agarose beads confirmed the
association of SH-TRAF2 and SH-NF-κB2/p100 with myc-tagged FBXW7.
(B) Co-IP with SH-GFP doxycycline-inducible control cell line, SH-IKKα
and SH-NIK doxycycline-inducible HEK293 Flp-In lines. IP with anti-HA
agarose beads confirmed the association of SH-IKKα and SH-NIK
with endogenously expressed FKBP8. Gray dotted lines indicate lanes
that are not adjacent to one another.IKKα and NIK are the kinases central to noncanonical
NF-κB
signaling. In our network study, well-known and studied interaction
partners were identified as interactors of SH-tagged kinases. IKKα
has been reported to form a trimeric IKKα/IKKβ/IKKγ
complex.[30] These findings are reflected
in our data (Figure A). In turn, the kinase NIK exhibits an IKK interaction domain,[31,32] and indeed the IKK complex was identified from the SH-NIK data.
The downstream effector NF-κB2/p100 is also reported to participate
in a NIK signaling complex,[33] as evident
from the data. How the NIK interactome in its entirety relates to
the function of the kinase in various biological processes remains
to be explored. Gene set enrichment analysis of the complete, filtered
noncanonical NF-κB signaling network showed an expected enrichment
of proteins involved in protein turnover, apoptosis regulation, and
immune signaling groups. In addition, there was enrichment for some
unexpected groups, such as proteins that are localized to the mitochondria
(Figure B).Because the ultimate aim of this study was to enable a systematic
view of noncanonical NF-κB signaling rather than an extensive
characterization of single interactions, only limited validation of
single interaction candidates was initiated. To assess the predictive
quality of our data set, however, we validated the identified interactions
of FKBP8. FKBP8 was a high-scoring interactor that copurified with
SH-NIK and was also observed as an interacting protein with SH-IKKα,
albeit with a subthreshold CRAPome FC-A confidence score (2.21). FKBP8
is a cochaperone of the immunophilin family and is classified under
the biological function of cell death and mitochondrial location.
One of the various biological roles of FKBP8 is also the regulation
of the antiapoptotic Bcl-2 protein.[34] Co-IP
of SH-NIK and SH-IKKα from doxycycline-inducible HEK293 cells
with endogenous FKBP8 confirmed its interaction with NIK and IKKα
(Figure B). Reciprocal
co-IP corroborated the association between FKBP8 and IKKα and
indicated a robust association between the proteins (Supplemetary Figure S2A). Noncanonical NF-κB signaling
activity, as measured by NF-κB2/p100 processing, was not enhanced
by the presence of FKBP8 (Supplementary Figure S2B). Rather than direct positive regulation of noncanonical
NF-κB signaling, this observation suggested an alternative role
for FKBP8 (Supplementary Figure S2B). FKBP8
is an atypical member of the immunophilin family (reviewed in ref (34)) and has been implicated
as a negative regulator of Bcl-2. Bcl-2 is a pro-survival factor that
protects cells by interfering with pro-apoptotic proteins at the mitochondrial
membrane. Mitochondrial membrane proteins were enriched in the noncanonical
NF-κB interactome network. Interestingly, an association of
NF-κB signaling with mitochondria has been previously reported;[35,36] however, 40% of filtered mitochondrial interactors stem from the
inner mitochondrial membrane, and thus these data could also partly
be explained by a postlysis association of mitochondrial proteins
with the baits (Table S1). Of note, FKBP8,
the protein chosen here for validation because of its role in survival
signaling, is a protein that localizes to the outer mitochondrial
membrane. This finding is consistent with the possibility of intracellular
association of noncanonical NF-κB signaling components with
mitochondria and potentially regulation of cell survival. Therefore,
FKBP8 could be involved in the control of cell survival by the noncanonical
NF-κB pathway.
Differential Interactome of NIK Mutants
To compare
the biological consequences of recently reported and long-known NIK mutations on the NIK interactome, the mutants NIKG860R (the amino acid change of the aly/aly mouse introduced into human NIK), NIKK429A/K430A (catalytically
inactive), and the recently identified in human patients, novel, catalytically
inactive mutant NIKP565R (Figure A) were expressed as N-terminal SH-tagged
proteins in the doxycycline-inducible HEK293 system. Using the filtering
procedure described in the material and methods section (Supplementary Figure S1B), a total of 89 interacting
proteins were identified (Figure B). The “core interactome” was composed
of 17 proteins that were common to all NIK variants (Figure B–D). TRAF3,[5] FKBP8, various subunits of the HSP90–CDC37
protein chaperone complex, and a group of SLC25 mitochondrial membrane
transporters (Figure D) constituted the “core interactome”. Overlaying the
shared NIK proteins with public interaction data from STRING revealed
interconnectivity between the HSP90 chaperone group and the cochaperone
FKBP8 (Figure D).
The data suggested that in this biological process, the HSP90–FKBP8
chaperone complex crosstalks with noncanonical NF-κB signaling.
The HSP90 complex has been implicated in the maturation of other kinases
of TNF-receptor associated pathways[37,38] and may represent
a novel NIK interaction complex. Also, the aforementioned inhibition
of Bcl-2 by FKBP8 is dependent on the HSP90 complex, and FKBP8 is
known to interact with this complex.[39] It
remains to be determined if the role of HSP90–CDC37 in kinase
maturation involves the interaction with FKBP8 or if the interaction
of FKBP8 with the NIK and IKKα kinases of the noncanonical NF-κB
pathway has a separate role.
Figure 4
Interactions of NIK wild-type and mutants. (A)
Schematic depiction
of the NIK protein indicating the domains and mutants relevant to
this study.[41] (B) Interaction comparison
of NIKwt, NIKG860R, NIKK29R/K430A, and NIKP565R. In the network, SH-tagged proteins and
interactors are colored yellow and blue, respectively. Core interactors
shared by all variants are represented within the red circle. (C)
Venn diagram depicting the number of interactors that overlap between
the different NIK variants. (D) Network depiction of NIK core interactors.
NIK is indicated as a red circle. Edges in red and gray depict interactions
identified in this study and interactions retrieved from the STRING
database, respectively. Interactors colored in yellow, green, and
red denote HSP90 complex members, ribosomal complex members, and SLC25
family members, respectively.
Interactions of NIK wild-type and mutants. (A)
Schematic depiction
of the NIK protein indicating the domains and mutants relevant to
this study.[41] (B) Interaction comparison
of NIKwt, NIKG860R, NIKK29R/K430A, and NIKP565R. In the network, SH-tagged proteins and
interactors are colored yellow and blue, respectively. Core interactors
shared by all variants are represented within the red circle. (C)
Venn diagram depicting the number of interactors that overlap between
the different NIK variants. (D) Network depiction of NIK core interactors.
NIK is indicated as a red circle. Edges in red and gray depict interactions
identified in this study and interactions retrieved from the STRING
database, respectively. Interactors colored in yellow, green, and
red denote HSP90 complex members, ribosomal complex members, and SLC25
family members, respectively.Interactions specific to, or abrogated in NIK mutants, were
also
observed. Notably, only SH-NIKwt and the catalytically
inactive mutants SH-NIKK429A/K430A and SH-NIKP565R copurified IKKα and its complex members IKKβ/IKKγ.
The NIKG860ARaly mutant is reported to
have the mutation within the IKKα interaction region[11] and thus did not copurify IKKα (Supplementary Figure S3). Conversely, NF-κB2/p100
was only identified with high confidence in the SH-NIKwt and SH-NIKG860R experiments. NF-κB2/p100 is a target
gene that is expressed upon NF-κB activation.[40] Thus, this observation may be a consequence of the increased
abundance of NF-κB2/p100 in NIKwt cells and subsequent
functional p52 signaling, as differential binding of p100 by catalytically
inactive NIK has previously not been observed.[33]
Impact of the Study
To enhance our
knowledge on the
noncanonical NF-κB signaling pathway from a systems biology
point of view, we have provided here a detailed resource describing
the interactome of the key nodes of noncanonical NF-κB signaling.
A number of well-known interactors were verified from the data. More
importantly, however, the resource provides a surprising number of
novel candidate interactions that hint at unexpected regulatory links
between pathway members and their interactors, such as the potential
functional interaction between FKBP8 and NIK/IKKα. NF-κB
signaling plays a central role in immunity and represents a convergence
point for integrating diverse immune stimuli. We provide, to our knowledge,
for the first time a refined interaction map of factors specific to
noncanonical NF-κB signaling. This map includes mutant variants
of the pathway-defining kinase NIK and provides information on the
interplay with canonical NF-κB signaling components. In vivo,
such interplay is crucial for immune cells to react appropriately
to innate and adaptive immune stimuli. Studies in a physiological
setting using immune cells will confirm whether the set of interacting
proteins we found in our inducible HEK293 system is comparable in
these cells. As we showed that overexpression of NIK induces processing
of p100 in our system, we are confident that this system is suitable
to study the pathway of interest. The purpose of this study was to
build an up-to-date data set of basal, unstimulated noncanonical NF-κB
signaling to provide a robust reference interactome. Further studies
could compare the interactomes in stimulated versus unstimulated cells
because induction of NF-κB may lead to a proteomic profile altered
to the baseline reference.Therefore, an extended network of
connections including canonical and noncanonical NF-κB signaling
proteins as presented here will provide a valuable platform for a
deeper understanding of the nature of the pathway. Furthermore, in
the light of the recently discovered rare genetic condition NIK deficiency,[8] our comparison of various NIK mutants will aid
in understanding this disorder further.Taken together, this
study has generated valuable novel interactome
data for noncanonical NF-κB signaling. The data provide a new,
unbiased and global overview on the pathway and identify unexpected
candidate interactions between canonical and noncanonical signaling
proteins and potential novel regulators.
Authors: Emmanuel Dejardin; Nathalie M Droin; Mireille Delhase; Elvira Haas; Yixue Cao; Constantin Makris; Zhi-Wei Li; Michael Karin; Carl F Ware; Douglas R Green Journal: Immunity Date: 2002-10 Impact factor: 31.745
Authors: Christina M Annunziata; R Eric Davis; Yulia Demchenko; William Bellamy; Ana Gabrea; Fenghuang Zhan; Georg Lenz; Ichiro Hanamura; George Wright; Wenming Xiao; Sandeep Dave; Elaine M Hurt; Bruce Tan; Hong Zhao; Owen Stephens; Madhumita Santra; David R Williams; Lenny Dang; Bart Barlogie; John D Shaughnessy; W Michael Kuehl; Louis M Staudt Journal: Cancer Cell Date: 2007-08 Impact factor: 31.743
Authors: Emmy Van Quickelberghe; Delphine De Sutter; Geert van Loo; Sven Eyckerman; Kris Gevaert Journal: Sci Data Date: 2018-12-18 Impact factor: 6.444