Materials and Methods
Molecular cloning and generation of stable cell
lines
All plasmids used in this study were constructed by PCR and Gibson
isothermal assembly. The expression ORFs, hereafter named bait-BirA,
were constructed by fusing BioID2 to the C-terminal of each model
protein (with a glycine-serine linker added between) and a 3XFLAG tag at
C-terminal to simplify the immuno detection. ORFs were inserted into
pcDNA5/FRT (Invitrogen), which allows targeted integration of the
transgenes into the host genome. Gibson assembly primers were designed
by SnapGene software and used to amplify the corresponding fragments and
vectors with long overlapping overhangs, which were then assembled using
Gibson Assembly Master Mix (NEB). To obtain secretable BioID2 (without
any bait protein), Gibson assembly was employed to fuse the signal
peptide of SERPINC1 gene to the N-terminal of BirA (hereafter referred
to as Signal-BirA). Assembly products were transformed to the chemically
competent e. coli , and recombinant plasmids were verified by
restriction digestion and sequencing. For all experiments, Flp‐In 293
cells (Invitrogen) were cultured in DMEM media supplemented with fetal
bovine serum (10 %) and antibiotics (penicillin, 100 U mL−1 and
streptomycin, 100 μg mL−1) and maintained at 37 °C under 5 % CO2. For
generating stable cell lines, Flp-In 293 cells were seeded in 6 well
plates at a density of 0.5×106 cells per well the day
before transfection. Cells were then co-transfected with each pcDNA5/FRT
vector containing expression cassette and pOG44 plasmid using
Lipofectamine® 2000 according to the manufacturer’s
directions. After recovery from transfection, cells were grown in DMEM
containing 10% FBS, 1% PenStrep, and 150 μg/mL Hygromycin B to select
hygromycin-resistant cells. Individual resistant colonies were isolated,
pooled, and seeded in 24-well plates for further scaling up and screened
for expression of the fusion proteins by Western Blotting.
Immunofluorescence
Recombinant HEK293 cells expressing BioID2 fusions were grown in
complete medium supplemented with 50 uM biotin on coverslips until 70%
confluent. Cells were then fixed in PBS containing 4% PFA for 10 min at
room temperature. Blocking was performed by incubating fixed cells with
1% BSA and 5% normal goat serum in PBST. Anti-flag mouse monoclonal
antibody-Dylight 650 conjugate (Thermofisher), targeting the bait-BirA,
and streptavidin-DyLight 594 conjugate (Thermofisher), targeting the
biotinylated proteins, were diluted at 1:300 and 1:1000 in blocking
buffer, respectively and incubated with fixed cells for 30 minutes at
room temperature. Cells were then washed, counterstained with DAPI,
mounted on the slide using antifade vectashield mountant, and imaged
using Leica SP8 Confocal with Lightning Deconvolution. Colocalization
quantification was performed for the deconvolved images using Fiji’s
(ImageJ 1.52p) Coloc_2 analysis tool between the 650 (anti-flag) and
594 (Streptavidin) channels
(Schindelin, Rueden, Hiner,
& Eliceiri, 2015). This tool generates a comprehensive report for
evaluating pixel intensity colocalization of two channels by various
methods such as Pearson’s Coefficient (range: -1.0 to 1.0), Manders’
Colocalization Coefficients (MCC, range: 0 to 1.0), and Li’s Intensity
Correlation Quotient (IQC, range: -0.5 to 0.5)
(Li, 2004; Manders,
Verbeek, & Aten, 1993). Background pixel intensity was subtracted
using Fiji’s rolling ball algorithm and a region of interest (ROI).
Thresholds were determined using Coloc_2’s bisection method, which is
further used to adjust for background. Above threshold metrics were
reported.
Western blotting
To validate the secretion of bait-BirA proteins, supernatants of
cultures expressing fusion proteins were collected, centrifuged to
remove cell debris, and protein content was concentrated using Amicon
Ultra 4 mL 10 KD filter unit (MilliporeSigma) and quantified using
Bradford assay (Expedeon). 20 ug of total protein was loaded on SDS-PAGE
gel for electrophoresis and resolved proteins were transblotted to
nitrocellulose membrane using Trans-Blot Turbo Transfer System from
Bio-RAD. The membrane was blocked with 5% skim milk in TBST and probed
with HRP-conjugated anti-flag mouse monoclonal antibody (Thermofisher)
diluted at 1:10000 in the blocking buffer. The membrane was washed, and
Clarity Western ECL Substrate was added. Proteins’ bonds were visualized
using G:Box Gel Image Analysis Systems (SYNGENE). For staining of
intracellular biotinylated proteins, cells were grown in complete medium
supplemented with 50 μM biotin, lysed by RIPA buffer, and protein
content was quantified using Bradford assay. 20 ug of total protein was
loaded and resolved and transblotted as described earlier. The membrane
was blocked by 3% BSA in TBST and probed with HRP-conjugated
streptavidin diluted in blocking buffer at 1:2000 ratio. For visualizing
the proteins’ bands, the same Clarity Western ECL Substrate was used.
ELISA
Aliquots of clarified supernatants from esiRNA transfected cultures were
taken out of −80°C and thawn on the ice and immediately subjected to
ELISA. Flag levels were quantified from secreted bait-BirA fusion
proteins by competitive anti-flag ELISA using the Flag-Tag Detection
ELISA Kit (Cayman Chemicals). All measurements were performed according
to the manufacture instructions in triplicate. The effect of PDIA’s
knockdown on secretion of the model proteins was measured in comparison
with the negative control of each cell line transfected with EGFP esiRNA
as negative control (see below).
RNAi knockdown experiment
esiRNA targeting ERP44 was ordered from Sigma. HEK293 cell expressing
SERPINC1-BirA were seeded at 0.6X105 cells/well in
24-well plates with complete medium and reverse transfected with 72 ng
of ERP44 specific esiRNA or EGFP esiRNA as a negative control or KIF11
esiRNA as positive control using Lipofectamine RNAiMAX (Invitrogen). All
transfections were performed according to the manufacturer’s guidelines.
Targeted gene knockdown by esiRNA was allowed to occur for 48 and 72 h,
culture supernatants were harvested, clarified by low-speed
centrifugation, then aliquoted and stored at −80°C for further
experiments.
Mass Spectrometry
Cells were grown in 245 mm plates (one plate per biological replicate in
triplicate) to approximately 70% confluence in complete media and then
incubated for 24 h with 50 μM biotin. Cells were harvested and washed
twice in cold PBS, lysed with vigorous shaking (20 Hz) in 8M urea, 50mM
ammonium bicarbonate lysis buffer, extracted proteins were centrifuged
at 14,000 x g to remove cellular debris and quantified by BCA assay
(Thermo Scientific) as per manufacturer recommendations. Affinity
purification of biotinylated proteins was carried out in a Bravo
AssayMap platform (Agilent) using AssayMap streptavidin cartridges
(Agilent), and the bound proteins were subjected to on-cartridge
digestion with mass spec grade Trypsin/Lys-C Rapid digestion enzyme
(Promega, Madison, WI) at 70ºC for 2h. Digested peptides were then
desalted in the Bravo platform using AssayMap C18 cartridges and the
organic solvent was removed in a SpeedVac concentrator prior to LC-MS/MS
analysis. Dried peptides were reconstituted with 2% acetonitrile, 0.1%
formic acid, and analyzed by LC-MS/MS using a Proxeon EASY nanoLC system
(Thermo Fisher Scientific) coupled to a Q-Exactive Plus mass
spectrometer (Thermo Fisher Scientific). Peptides were separated using
an analytical C18 Acclaim PepMap column 0.075 x 500 mm, 2µm particles
(Thermo Scientific) in a 93-min linear gradient of 2-28% solvent B at a
flow rate of 300nL/min. The mass spectrometer was operated in positive
data-dependent acquisition mode. MS1 spectra were measured with a
resolution of 70,000, an AGC target of 1e6 and a mass range from 350 to
1700 m/z. Up to 12 MS2 spectra per duty cycle were triggered, fragmented
by HCD, and acquired with a resolution of 17,500 and an AGC target of
5e4, an isolation window of 1.6 m/z and a normalized collision energy of
25. Dynamic exclusion was enabled with a duration of 20 sec.
MS data Analysis
All mass spectra were analyzed with MaxQuant software
(Tyanova et al., 2016)
version 1.5.5.1. MS/MS spectra were searched against the Homo sapiens
Uniprot protein sequence database (version January 2018) and GPM cRAP
sequences (commonly known protein contaminants). Precursor mass
tolerance was set to 20ppm and 4.5ppm for the first search where initial
mass recalibration was completed and for the main search, respectively.
Product ions were searched with a mass tolerance 0.5 Da. The maximum
precursor ion charge state used for searching was 7.
Carbamidomethylation of cysteines was searched as a fixed modification,
while oxidation of methionines and acetylation of protein N-terminal
were searched as variable modifications. Enzyme was set to trypsin in a
specific mode and a maximum of two missed cleavages was allowed for
searching. The target-decoy-based false discovery rate (FDR) filter for
spectrum and protein identification was set to 1%. Enrichment of
proteins in streptavidin affinity purifications were calculated as the
ratio of intensity. To remove the systematic biases introduced during
various steps of sample processing and data generation, dataset were
normalized using the LOESS method
(Smyth, 2005) integrated
into Normalyzer (Chawade,
Alexandersson, & Levander, 2014). Perseus software
(Tyanova & Cox, 2018) was
employed for data preparation, filtering, and computation of
differential protein abundance. The DEP package
(Zhang et al., 2018) was
used to explore whether missing values in the dataset are biased to
lower intense proteins. Left-censored imputation was performed using
random draws from shifted distribution. A Student’s t‐test with a
multi‐sample permutation‐based correction for an FDR of 0.05 was
employed to identify differentially expressed proteins using log2
transformed data.
Detection of significant
interactions
The threshold for significant interactions was determined using the
known secretory pathway components as a gold standard. We set the
cutoffs for FDR at 0.1 and removed all interactors with negative fold
changes, as this optimizes the enrichment of known secretory pathway
components among the significant interactors. The enrichment for two
independent secretory pathway-related gene sets also peaked around the
cutoffs set through the gene set of known secretory pathway components,
suggesting the optimal cutoffs are robust to the gold standards chosen.
Aggregation of interactions and estimation of interaction
synergy
To further generalize the interactions between individual SecPs and
their interactors, we aggregated proteins based on several features.
Given the interactions between the SecPs and their interactors, we can
predict the important structural features implicated in the interactions
between the SecPs and a given SecM. The interactions between the
BirA-fused samples and the secretory pathway interactors can be pooled
according to shared properties of the SecPs to reveal interdependencies
between components of the secretory pathway and their products. These
common properties include shared structural motifs
(Blatch & Lässle, 1999),
known sites of PTM from Uniprot and phosphosite
(Blatch & Lässle, 1999;
Hornbeck et al., 2015). The secretory pathway interactors can also be
aggregated into curated biological pathways (mSigDB
(Liberzon et al., 2011),
KEGG (Kanehisa, Furumichi,
Tanabe, Sato, & Morishima, 2017), Reactome
(Croft et al., 2014)) and
subcellular localizations
(Thul et al., 2017).
Bayesian modeling
framework
To quantify such associations of each interacting protein with protein
features, we calculate the effective total frequency
(𝛿f,g) of interactions between each feature-gene pair
(f,g) by going through every SecP in our data and counting the number of
times this feature occurs in a SecP p (fp) .