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\begin{document}

\title{In situ detection of protein interactions for recombinant therapeutic
enzymes}



\author[1]{Mojtaba Samoudi}%
\author[1]{Chih-Chung Kuo}%
\author[1]{Caressa  Robinson}%
\author[2]{Km Shams-Ud-Doha}%
\author[1]{Song-Min Schinn}%
\author[3]{Stefan Kol}%
\author[4]{Linus Weiss}%
\author[5]{Sara Petersen Bjørn}%
\author[5]{Bjørn Voldborg}%
\author[2]{Alexandre Rosa Campos}%
\author[6]{Nathan Lewis}%
\affil[1]{University of California San Diego}%
\affil[2]{Sanford Burnham Prebys Medical Discovery Institute}%
\affil[3]{Technical University of Denmark}%
\affil[4]{Eberhard Karls University Tübingen}%
\affil[5]{DTU Biosustain}%
\affil[6]{University of California, San Diego}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Despite their therapeutic potential, many protein drugs remain
inaccessible to patients since they are difficult to secrete. Each
recombinant protein has unique physicochemical properties and requires
different machinery for proper folding, assembly, and post-translational
modifications (PTMs). Here we aimed to identify the machinery supporting
recombinant protein secretion by measuring the protein-protein
interaction (PPI) networks of four different recombinant proteins
(SERPINA1, SERPINC1, SERPING1 and SeAP) with various PTMs and structural
motifs using the proximity-dependent biotin identification (BioID)
method. We identified PPIs associated with specific features of the
secreted proteins using a Bayesian statistical model, and found proteins
involved in protein folding, disulfide bond formation and
N-glycosylation were positively correlated with the corresponding
features of the four model proteins. Among others, oxidative folding
enzymes showed the strongest association with disulfide bond formation,
supporting their critical roles in proper folding and maintaining the ER
stability. Knock down of ERP44, a measured interactor with the highest
fold change, led to the decreased secretion of SERPINC1, which relies on
its extensive disulfide bonds. Proximity-dependent labeling successfully
identified the transient interactions supporting synthesis of secreted
recombinant proteins and refined our understanding of key molecular
mechanisms of the secretory pathway during recombinant protein
production.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

Therapeutic proteins are increasingly important for treating diverse
diseases, including cancers, autoimmunity/inflammation, infectious
diseases, and genetic disorders. For example, the plasma protein
therapeutics market is expected to grow by \$36 billion (USD) by 2024.
Mammalian cells are the dominant production system due to their ability
to perform PTMs that are required for drug safety and function (Jenkins,
Murphy, \& Tyther, 2008; Matasci, Hacker, Baldi, \& Wurm, 2008).
However, the complexities associated with the mammalian secretory
machinery remains a bottleneck in recombinant protein production
(Gutierrez et al., 2020). The secretory pathway machinery includes
\textgreater{}575 gene products tasked with the synthesis, folding,
PTMs, quality control, and trafficking of secreted proteins (SecPs)
(Feizi, Gatto, Uhlen, \& Nielsen, 2017; Lund et al., 2017; Novick,
Ferro, \& Schekman, 1981; Reynaud \& Simpson, 2002). The precision and
efficiency of the mammalian secretory pathway results from the
coordinated effort of these secretory machinery components (SecMs)
including chaperones, modifying enzymes (e.g., protein disulfide
isomerases and glycosyltransferases), and transporters within the
secretory pathway. Overexpression of heterologous proteins in this
tightly regulated and complex system could impact its functionality and
homeostasis, resulting in adaptive responses that can impair both
protein quantity and quality (Hussain, Maldonado-Agurto, \& Dickson,
2014; Young, Yuraszeck, \& Robinson, 2011). More importantly,
variability in the structures and modifications of recombinant proteins
could necessitate a customized secretion machinery to handle this
diversity, but the secretory machinery of recombinant protein producing
cells has not been adapted to facilitate the high titer secretion
desired for most recombinant proteins. A previous study also showed
human protein secretory pathway genes are expressed in a tissue-specific
pattern to support the diversity of secreted proteins and their
modifications (Feizi et al., 2017), suggesting that expression of
several SecMs is regulated to support client SecPs in the secretory
pathway. Unfortunately, the SecMs needed to support any given secreted
protein remain unknown. Thus, there is a need to elucidate the SecMs
that support the expression of different recombinant proteins with
specific features. This can guide mammalian synthetic biology efforts to
engineer enhanced cells capable of expressing proteins of different
kinds in a client-specific manner.

PPI networks are invaluable tools for deciphering the molecular basis of
biological processes. New proximity dependent labeling methods such as
BioID (Kim et al., 2014; Roux, Kim, Raida, \& Burke, 2012) and APEX
(Rhee et al., 2013) can identify weak and transient interactions in
living cells, along with stable interactions. Furthermore, BioID offers
a high-throughput approach for systematic detection of intracellular
PPIs occurring in various cellular compartments and has been used to
characterize PPI networks and subcellular organization (Varnait\selectlanguage{polish}ė \selectlanguage{english}\&
MacNeill, 2016). BioID relies on expressing a protein of interest fused
to a promiscuous biotin ligase (BirA) that can biotinylate the proximal
interactors in nanometer-scale labeling radius (Kim et al., 2014). For
example, this approach has mapped protein interactions at human
centrosomes and cilia (Firat-Karalar \& Stearns, 2015; Gupta et al.,
2015), focal adhesions (Dong et al., 2016), nuclear pore (Kim et al.,
2014) and ER membrane-bound ribosomes (Hoffman, Chen, Zheng, \&
Nicchitta, 2019) . Here we used BioID2, an improved smaller biotin
ligase for BioID (Kim et al., 2016; Varnait\selectlanguage{polish}ė \selectlanguage{english}\& MacNeill, 2016), to
explore how the SecMs involved vary for different secreted therapeutic
proteins (Fig. 1). Specifically, BioID2 was employed to identify SecMs
that interact with three SERPIN-family proteins (SERPINA1: treatment for
Alpha-1-antitrypsin deficiency, SERPINC1: treatment for Hereditary
antithrombin deficiency, and SERPING1: treatment for acute attacks of
hereditary angioedema) and secreted embryonic alkaline phosphatase
(SeAP), which is a truncated form of Alkaline Phosphatase, Placental
Type (ALPP). These proteins vary in their PTMs (e.g., glycosylation,
disulfide bond and residue modifications) and have different amino acid
sequences that consequently form different local motifs. Using a
Bayesian statistical model, we identified the critical PPIs that are
positively correlated with each protein feature. Identification of these
PPIs will refine our understanding of how the secretory pathway
functions during the expression of the recombinant proteins and
introduce novel targets for secretory pathway engineering in a client
specific manner.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Molecular cloning and generation of stable cell
lines}

{\label{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 \emph{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 \selectlanguage{greek}μ\selectlanguage{english}g mL-1) and maintained at 37 \selectlanguage{ngerman}°C under 5\selectlanguage{english} \% CO2. For
generating stable cell lines, Flp-In 293 cells were seeded in 6 well
plates at a density of 0.5x10\textsuperscript{6} 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\textsuperscript{(r)} 2000 according to the manufacturer's
directions. After recovery from transfection, cells were grown in DMEM
containing 10\% FBS, 1\% PenStrep, and 150 \selectlanguage{greek}μ\selectlanguage{english}g/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.

\subsection*{Immunofluorescence}

{\label{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.

\subsection*{Western blotting}

{\label{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 \selectlanguage{greek}μ\selectlanguage{english}M 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.

\subsection*{ELISA}

{\label{elisa}}

Aliquots of clarified supernatants from esiRNA transfected cultures were
taken out of -80\selectlanguage{ngerman}°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).

\subsection*{RNAi knockdown experiment}

{\label{rnai-knockdown-experiment}}

esiRNA targeting ERP44 was ordered from Sigma. HEK293 cell expressing
SERPINC1-BirA were seeded at 0.6X10\textsuperscript{5} 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 \selectlanguage{english}-80degC for further
experiments.

\subsection*{Mass Spectrometry}

{\label{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 \selectlanguage{greek}μ\selectlanguage{english}M 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 70\selectlanguage{ngerman}ºC 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, 2\selectlanguage{greek}µ\selectlanguage{english}m 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.

\subsection*{MS data Analysis}

{\label{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.

\subsection*{Detection of significant
interactions}

{\label{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.

\subsection*{Aggregation of interactions and estimation of interaction
synergy}

{\label{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 \& L\selectlanguage{ngerman}ässle,
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).

\subsection*{Bayesian modeling
framework}

{\label{bayesian-modeling-framework}}

To quantify such associations of each interacting protein with protein
features, we calculate the effective total frequency
(\selectlanguage{english}?\textsubscript{f,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 (f\textsubscript{p}) .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

The number f\textsubscript{p} is added to the total frequency,
?\textsubscript{f,g}, only when p and g interact (when
?\textsubscript{interact}(p,g) = 1). This effective interaction
frequency, ?\textsubscript{f,g}, takes us closer to estimating
interaction affinity between a feature and an interactor. To further
account for SecP and feature promiscuity, we implement an estimate of
the tendency for f and g to interact. We modeled the interaction
frequency ?\textsubscript{f,g} with a Poisson distribution.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

The mean for the Poisson distribution is parameterized by the sum of
feature promiscuity (number of SecMs \emph{g} connected to a feature) \selectlanguage{greek}μ\selectlanguage{english}f
, interactor promiscuity (number of SecPs with a feature
\emph{f}interacting with a SecM) \selectlanguage{greek}μ\selectlanguage{english}g , the interaction synergy sf,g and
an intercept variable. The feature promiscuity
\selectlanguage{greek}\emph{μ\textsubscript{f}}\selectlanguage{english}quantifies the probability of a feature
\emph{f} to partake in an interaction with any secMs. Likewise, the
interactor promiscuity\selectlanguage{greek}\emph{μ\textsubscript{g}}\selectlanguage{english} measures the tendency
for a secM \emph{g} to interact with any features. The coefficient of
interest, the interaction synergy \emph{s} \textsubscript{f,g} between
\emph{f} and \emph{g} , quantifying the degree to which \emph{f} and
\emph{g} interact more than by random chance, quantifies the degree to
which f and g interact more than by random chance. In previous work,
this approach has correctly estimated epistasis intensity (Shen et al.,
2017). To better regularize the promiscuities, their Bayesian priors are
all normally distributed around 0, with their variances parameterized by
the hyper priors ?\emph{\textsubscript{f}} , ?\emph{\textsubscript{g}}
and ?\emph{\textsubscript{s}} which follow an exponential distribution.
The intercept \emph{a} \selectlanguage{english}is parameterized by a standard normal
distribution. We used the \emph{rethinking} R package (McElreath, 2020)
to construct the model and sample the coefficients.

\section*{Results}

{\label{results}}

\subsection*{BioID can successfully tag proteins colocalized with
secreted
proteins}

{\label{bioid-can-successfully-tag-proteins-colocalized-with-secreted-proteins}}

We first investigated if intracellular PPIs between each SecP and their
supporting SecMs can be measured using the BioID method. To do this,
each bait-BirA was expressed in HEK293 cells using the Flp-In system
(see materials and methods) for targeted integration of the transgenes
into the same genomic locus to ensure comparable transcription rates of
each transgene. Variations in mRNA level caused by random integration
can trigger adaptive response such as the unfolded protein response in
some cell lines which reciprocally alters the active PPIs network
involved in the secretion. We observed successful secretion of bait-BirA
proteins into culture supernatant, evaluated by Western blot (Fig. 2a).
Thus, the BirA fusion did not change the intracellular localization of
the model proteins, and it is expected that they enter the secretory
pathway where they are processed and packaged for secretion. We also
verified the biotinylation profile by western blot for each cell line in
the presence and absence of biotin. The biotinylation profile of the
bait-BirA cells is different when biotin is added to the culture with
substantial increased biotinylation of specific proteins, while no
obvious change is observed for WT (Fig. 2b), suggesting that BioID2
successfully tagged specific proteins within the cells. Colocalization
of the bait-BirA proteins and the biotinylated proteins was then studied
by multicolor co-immunofluorescence microscopy to test whether
biotinylated proteins are actual partners of the model proteins. The
results demonstrated successful labeling of the interactors by BirA
through colocalization of the biotin-labeled proteins and bait-BirA,
while WT did not show increased biotinylation under the same
experimental conditions (Fig. 3). To quantify the colocalizations, we
calculated different colocalization metrics (see methods) from the
images and compared to the WT (Table 1 and Fig. S1), and the results
confirmed the specificity of the BioID labeling system to tag the
proximal proteins (closer Li's ICQ value to 0.5 or Pearson's R value to
one demonstrates a dependent protein staining pattern between the red
and green channels).

\subsection*{WT cells revealed endogenous biotinylation
landscape}

{\label{wt-cells-revealed-endogenous-biotinylation-landscape}}

After successful tagging of the proximal proteins we aimed to identify
the interactions with each bait protein. For this, cells were lysed, and
biotinylated proteins were purified using streptavidin. Purified
proteins were digested by trypsin, and peptides were subjected to
LC-MS/MS, and the biotinylated proteins in samples were mapped using
MaxQuant (Tyanova, Temu, \& Cox, 2016). Differentially biotinylated
proteins were then identified in each sample compared to the WT using
Perseus (Tyanova \& Cox, 2018). When implemented in a WT control cell
line, we identified proteins that are biotinylated endogenously along
with bona fide interactors. These include proteins that bind biotin as a
cofactor such as carboxylases which is problematic with
streptavidin-based protein detection (Tytgat et al., 2015). Although the
extent to which the general endogenous biotinylation has not been
systematically quantified, the biotinylated proteins isolated from the
WT sample showed considerable overlap with interacting proteins detected
in other model protein samples, suggesting endogenous biotinylation may
be more pervasive than previously believed. Thus, using the interaction
partners detected from the WT sample as background, we filtered out
interactions detected in each model bait-BirA sample that were likely a
result of endogenous biotinylation. Among the top differentially
biotinylated proteins in bait-BirA samples, the bait proteins showed the
highest log-fold change (LFC) (Fig. 4 and TableS1). This observation is
expected because the bait protein is a potential substrate for BirA
located in the closest vicinity of the enzyme and is considered as
evidence to show the biotinylation system is working properly within the
cell.

\subsection*{Interactors are enriched for secretory pathway components
and co-secreted
proteins}

{\label{interactors-are-enriched-for-secretory-pathway-components-and-co-secreted-proteins}}

For each secreted protein we identified probable PPIs as interactors
having 3-fold or greater enrichment and an adjusted p-value \textless{}
0.1, in model proteins compared to WT control (Fig. S2 for all
thresholds and Fig. 5b for all significant interactions) based on
secretory pathway-related gold standards compiled from 3 independent
gene sets (see methods). We saw a significant enrichment for the
secretory pathway machinery, secretory-resident and co-secreted proteins
among probable PPIs across all model protein samples (Fig. 5a and Fig.
S3). The secretory machinery components are more enriched among the top
300 hits for all model proteins than other co-secreted proteins,
suggesting more frequent interactions between the secretory pathway
machinery and their products than the crosstalk between co-secreted
proteins. Probable PPIs detected in all model proteins (n=19) and hits
shared among all SERPIN gene products are significantly enriched for
proteins involved in protein folding (Fig. 5b). Indeed, molecular
chaperones are highly promiscuous when assisting protein folding due to
their inherent flexibility (Mayer, 2010). Apart from the shared
interactions, PPIs for each model protein differ substantially. Thus,
the question remained if these private interactions correspond to unique
properties of each model protein.

\subsection*{Private interactors reflect post-translational and
structural features of model
proteins}

{\label{private-interactors-reflect-post-translational-and-structural-features-of-model-proteins}}

PPIs in secretory pathway mediate the folding, modification and
transportation of secreted proteins (Bonifacino \& Glick, 2004; Calakos,
Bennett, Peterson, \& Scheller, 1994; Ikawa et al., 1997; Pearl \&
Prodromou, 2006; Watanabe et al., 2019). Incidentally, co-expression
analysis has linked certain PTMs across the secretome to the expression
of their responsible enzymes. For example, PDIs are consistently
upregulated in tissues secreting disulfide-rich proteins (Feizi et al.,
2017). As the bait-BirA proteins differ in structural composition and
PTMs (Fig. 6), we wondered if bait-BirA proteins with shared features
have higher affinity for specific interactors. More specifically, we
hypothesize that proteins requiring a specific PTM would preferentially
interact with the secretory machinery components responsible for the PTM
synthesis. To establish a more comprehensive connection around
product-specific interactions with the secretory pathway, we aggregated
various PTM and structural properties across all model proteins and
analyzed their associations with the corresponding secretory machinery
using a Bayesian modeling framework (see methods). Among the studied
PTMs, bait-BirA proteins with disulfide bonds and N-linked glycans
demonstrated higher affinity towards specific interactions (fig. 7a)
that are known to help secretion of proteins with the corresponding
PTMs. Thus, we analyzed the detected interactions associated with
glycosylation, disulfide bond addition, and protein folding.

\subsection*{Proteins with increased glycosylation are associated with
quality control
pathways}

{\label{proteins-with-increased-glycosylation-are-associated-with-quality-control-pathways}}

We detected significant interactions in the Calnexin/Calreticulin cycle
and related processes for more heavily glycosylated proteins (Fig. 7a).
For example, the glycosylated baits interacted with calreticulin (CALR),
a calcium-binding chaperone that promotes folding, oligomeric assembly,
and quality control of glycoproteins in the ER (Nauseef, McCormick, \&
Clark, 1995). They also interacted with UGGT1, which recognizes
glycoproteins with minor folding defects and reglucosylates single
N-glycans near the misfolded part of the protein. Reglucosylated
proteins are then recognized by CALR for recycling to the ER and
refolding or degradation (Ferris, Jaber, Molinari, Arvan, \& Kaufman,
2013). Two members of the PDI family, PDIA3 and ERp29, which form a
complex with calreticulin/calnexin, also showed association with
N-glycosylated baites (Fig. 7a) suggesting their role in glycoprotein
folding and quality control. Calnexin/Calreticulin-PDIA3 complexes
promote the oxidative folding of nascent polypeptides (Sakono, Seko,
Takeda, \& Ito, 2014) and ERp29 promotes isomerization of
peptidyl-prolyl bonds to attain the native polypeptide structure (Sakono
et al., 2014; Tannous, Pisoni, Hebert, \& Molinari, 2015). Proteins with
chaperone activity, such as HSPA5 (Fig. 7a), were also found to interact
with N-linked glycan-containing bait-BirA. HSPA5 is a component of the
glycoprotein quality-control (GQC) system which recognizes glycoproteins
with amino acid substitutions, and targets them for ER-associated
degradation (ERAD) (Ferris, Kodali, \& Kaufman, 2014). EDEM3, another
interactor associated with the N-glycan containing proteins (Fig. 7a),
is a glycosyltransferase involved in ERAD mediated degradation of
glycoproteins by catalyzing mannose trimming from Man8GlcNAc2 to
Man7GlcNAc2 in N-glycans (Ninagawa et al., 2014). Given that most of
these molecular chaperones and enzymes are involved in ERAD mediated
degradation of the misfolded glycoproteins these findings suggest the
quality control pathways are critical for synthesizing and secreting
proteins with N-linked glycans.

\subsection*{Disulfide bond formation is rate-limiting in protein
secretion}

{\label{disulfide-bond-formation-is-rate-limiting-in-protein-secretion}}

Several members of the PDI family including P4HB, PDIA3, PDIA4 and PDIA6
significantly interacted with model-BirAs containing more disulfide
bonds (Fig. 7a). These enzymes catalyze the formation, breakage and
rearrangement of disulfide bonds through the thioredoxin-like domains
(Kozlov, M\selectlanguage{ngerman}äättänen, Thomas, \& Gehring, 2010). The identification of
various PDIs highlights the importance of the oxidative folding enzymes
in protein folding and maintaining stability that can limit the
efficiency of protein secretion. The proteins with more disulfide bonds
also interact with major ER chaperones HSAP5 and DNAJB11, a co-chaperone
of HSAP5, that play a key role in protein folding and quality control in
the ER lumen (Ng, Watowich, \& Lamb, 1992; Yu, Haslam, \& Haslam, 2000),
highlighting their important role in secretion of the disulfide bond
enriched proteins. The PDI, ERp44 showed the strongest association (LFC
\textgreater{} 8, Fig. S4) with disulfide bond enriched proteins i.e.
SERPINC1 and SERPING1. ERp44 mediates the ER localization of the
oxidoreductase Ero1\selectlanguage{greek}α \selectlanguage{english}(an oxidoreductin that reoxidizes P4HB to enable
additional rounds of disulfide formation) through the formation of
reversible mixed disulfides (Anelli et al., 2003). Hence, the strong
association of ERp44 suggests the importance of the thiol-mediated
retention in disulfide bond formation, particularly when secretory is
loaded with the proteins with dominant disulfide bond. In addition,
ERO1LB, PRDX4 and SIL1 were ER-localized enzymes that were associated
with disulfide bond formation. ERO1LB efficiently reoxidizes P4HB
(Mezghrani et al., 2001), PRDX4 couples hydrogen peroxide catabolism
with oxidative protein folding by reducing hydrogen peroxide (Zito,
2013), and SIL1 can reverse HSAP5 cysteine oxidation which alters its
chaperone activity to cope with suboptimal folding conditions
(Siegenthaler, Pareja, Wang, \& Sevier, 2017). The identification of
these oxidoreductase enzymes highlights the importance of ER redox
homeostasis in disulfide bond formation and protecting cells from the
consequences of misfolded proteins.

To validate the importance of the ERp44 interaction, which showed the
highest fold change, on productivity of proteins with more disulfide
bonds we knocked down ERP44 in the cells expressing SERPINC1-BirA, using
an orthogonal RNAi approach, esiRNAs, to target gene knockdown (Kittler,
Heninger, Franke, Habermann, \& Buchholz, 2005). HEK293 cells expressing
SERPINC1-BirA were transfected with esiRNA against ERP44, with EGFP and
KIF11 as negative/positive controls, respectively. The supernatants of
esiRNA experiments collected 48 and 72 h post-transfection for measuring
the secretion of SERPINC1-BirA by ELISA. All quantifications were
repeated in duplicate. The results showed ERP44 knockdown in
SERPINC1-BirA cells led to the 44\% and 41\% reduction in the secretion
of SERPINC1 at day two and three post-transfection, respectively,
supporting the importance of the Thiol-mediated retention on secretion
of the disulfide bond enriched protein.

\subsection*{Identified PPIs are associated with structural motifs on
bait
proteins}

{\label{identified-ppis-are-associated-with-structural-motifs-on-bait-proteins}}

In addition to PTMs, the Bayesian modeling framework found associations
between SecP structural features and the SecMs (Fig. 7b). For example,
model proteins depleted in the asx motif (Golovin \& Henrick, 2008)
showed higher tendency to interact with DNAJB1, a molecular chaperone of
the HSP70 family. The asx motif impacts N-glycan occupancy of
Asn-X-Thr/Ser sites, depending on the ability of the peptide to adopt an
Asx-turn motif (Imperiali, Shannon, \& Rickert, 1992; Imperiali,
Shannon, Unno, \& Rickert, 1992). As another example, NUCB1, a
chaperone-like amyloid binding protein that prevents protein aggregation
(Bonito-Oliva, Barbash, Sakmar, \& Graham, 2017), interacted more
strongly with our proteins with more ST-turns (Fig. 7b). ST turns occur
frequently at the N-termini of \selectlanguage{greek}α-\selectlanguage{english}helices (Doig, Stapley, Macarthur, \&
Thornton, 2008) and are regarded as helix capping features which
stabilize \selectlanguage{greek}α-\selectlanguage{english}helices in proteins (Aurora \& Rosee, 1998). Thus, the
enriched interaction of the NUCB1 with St-turn suggests that it can help
stabilize folding of protein with a predominant \selectlanguage{greek}α-\selectlanguage{english}helical secondary
structure.

\section*{Discussion}

{\label{discussion}}

BioID has been used to profile the proteome of different cellular
compartments and molecular complex systems (Varnait\selectlanguage{polish}ė \selectlanguage{english}\& MacNeill, 2016).
However, this is the first time that BioID has been used to identify
proteome of the secretory pathway during the recombinant protein
expression. Numerous PPIs guiding the folding, modifications, and
trafficking of the secreted and membrane proteins through the secretory
pathway are transient (Nyfeler, Michnick, \& Hauri, 2005; Schreiber,
Haran, \& Zhou, 2009) and therefore cannot be captured by conventional
methods such as co-immunoprecipitation. Consistent with previous studies
(Sears, May, \& Roux, 2019), these results showed the BioID can detect
weak and transient interactions \emph{in situ,} and therefore it is a
powerful approach to study luminal processes involved in protein
secretion. We found that disulfide bridge formation enzymes showed the
most significant association with bait proteins enriched in disulfide
bonds, supporting their critical roles in protein secretion and
maintaining ER stability. A previous study on difficult to express (DTE)
monoclonal antibodies showed less recognition by PDI impairs disulfide
bridge formation within the antibody light chain (LC) which can
initiates the intracellular degradation by the ubiquitin proteasome
system via ERAD (Mathias et al., 2020). Thus, insufficient interaction
between the secreted proteins with enriched disulfide bond and PDIs can
limit secretion efficiency and serve as a rate-limiting step for protein
production. In another study, the tissue specific analysis of SecMs
expression showed a positive correlation between the expression of P4HB
and PDIA4 and liver tissue where numerous disulfide bond enriched
proteins are secreted by hepatocytes (Feizi et al., 2017). These
observations are clear evidence that suggests the tissue-specific
fine-tuning of the PDI family expression level in response to the
enrichment of the disulfide sites. Together, these results showed PDIs
are actively involved in adaptive responses and secretion of proteins
with dominant disulfide bonds which are crucial for restoring ER
stability, and therefore yielding the recombinant proteins. Given the
associations between the SecMs and the features of the model proteins,
we also hypothesized that SecMs preferentially interacting with
bait-BirA proteins that carry certain structural features may be
essential for the secretion of those proteins. While evidence linking
SecMs to the structural motifs is lacking, many molecular chaperones
selectively interact with certain sequence and structural elements to
favor the particular folding pathways (Gidalevitz, Stevens, \& Argon,
2013). For example, chaperones of the HSP70 family evolved to bind
extended \selectlanguage{greek}β \selectlanguage{english}strand peptides; interestingly, the associations identified
between chaperones and asx motif and ST turn represent a novel
association for further study.

While we show BioID works well for studying the synthesis of secreted
proteins, we acknowledge that biotin-based methods have some limitations
as well. Biotin is actively imported into the cytoplasm of cells and can
freely diffuse to the nucleus, but it may not be as accessible in the
secretory pathway, thus reducing labeling efficacy in that compartment
(Kim \& Roux, 2016). Here we showed this challenge is not an
insurmountable issue, in that the BioID2 construct successfully
identified many expected luminal interactions. BioID2 requires less
biotin supplementation, and exhibits enhanced labeling of proximate
protein (Kim et al., 2016) allowing for BioID to be introduced to new
systems where biotinylation supplementation may not be easily
accomplished (Sears et al., 2019). More recently, two promiscuous
mutants of biotin ligase, TurboID and miniTurbo, have been developed to
catalyze proximity labeling even with much greater efficiency (Branon et
al., 2020) and therefore can be considered as an effective method when
proximal labeling of the endomembrane organelles is desired.

In summary, we demonstrate here an approach to identify the protein
interactions that synthesize and support secreted proteins, and thus
define the product-specific secretory pathway. The identification of
such machinery opens avenues for mammalian synthetic biology, wherein
biotherapeutic production hosts can be rationally engineered to improve
the titer and quality of diverse proteins in a client specific manner.

\section*{Acknowledgments}

{\label{acknowledgments}}

This work was supported by generous funding from the Novo Nordisk
Foundation provided to the Center for Biosustainability at the Technical
University of Denmark (NNF10CC1016517, BGV) and from NIGMS (R35
GM119850, NEL). The authors would also like to thank Tune Wulff for
support on pilot assays.

\section*{Conflict of Interest}

{\label{conflict-of-interest}}

The authors declare no competing interests.

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\section*{Tables}

{\label{tables}}

\textbf{Table 1. Colocalization metrics determined for each clone
between the 650 (anti-flag) and 594 (Streptavidin) channels in Fig.
S1.}There is little correlation reported for the WT, whereas there is
much higher correlation for clones containing BirA.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Cells} & \textbf{Pearson's R value} & \textbf{Li's ICQ value} &
\textbf{Manders' Coeff.
(tM\textsubscript{g},tM\textsubscript{r})}\tabularnewline
WT & 0.22 & 0.219 & 0.812, 0.518\tabularnewline
SERPINA1-BirA & 0.74 & 0.318 & 0.962, 0.888\tabularnewline
SERPING1-BirA & 0.73 & 0.260 & 0.999, 0.914\tabularnewline
SERPINC1-BirA & 0.88 & 0.392 & 0.991, 0.982\tabularnewline
SeAP-BirA & 0.93 & 0.392 & 0.952, 0.935\tabularnewline
Signal-BirA & 0.71 & 0.308 & 0.841, 0.791\tabularnewline
\bottomrule
\end{longtable}



\section*{Figure Legends}

{\label{figure-legends}}

\textbf{Figure 1. Flowchart of the BioID2 application to detect in situ
interactions supporting therapeutic proteins secretion.}

\textbf{Figure 2. Expression of bait-BirA proteins results in a
substantial increase in biotinylated proteins.} a) Successful secretion
of the bait-BirA proteins into the culture supernatant was evaluated by
Western blot using HRP-anti-flag antibody. b) The immunoblotting
biotinylation profiling of the model proteins and WT control in HEK293
cells with HRP-streptavidin. When the BirA domain was fused to the model
proteins, biotin addition led to the biotinylation of a subset of
proteins (B+) which are not seen in WT or absence of biotin. This
demonstrates that the BioID labeling system tags interactions as
secreted proteins are synthesized and trafficked through the secretory
pathway. A few endogenously biotinylated proteins appear in the absence
of biotin and in the WT.

\textbf{Figure 3. Bait-BirA fusion proteins are colocalized with
biotin-staining.} Co-Immunofluorescence demonstrated the intracellular
colocalization of the biotin-labeled proteins (stained with
Streptavidin-Dylight 594 and illustrated in green color) and bait-BirA
(stained with anti-flag monoclonal antibody-Dylight 650 and illustrated
in red color), while WT did not show increased biotinylation under the
same experimental conditions.

\textbf{Figure 4. Dozens of proteins show significantly increased
biotinylation after expression of bait-BirA proteins.} Volcano plot
showing the distribution of the quantified biotinylated proteins by MS
according to p-value and fold change. As depicted the bait-protein
significantly showed the highest fold change compared to WT almost in
all cases, indicating the capability of the BioID labeling system to tag
the in vivo interactors within the live cells. SeAP is a truncated form
of Alkaline phosphatase, placental type (ALPP).

\textbf{Figure 5. Interacting proteins are enriched for secretory
pathway machinery.} (a) To determine if significant interactions enrich
for secretory pathway-related genes, we performed an iterative
enrichment analysis in which we included the most significant
interactions first and iteratively added interactors with lower fold
changes. The y-axis indicates the overall coverage of 3 secretory
pathway-related gene sets and the x-axis the significance cutoffs (rank
ordered by fold change). The coverage of the gene set (top) along with
their corresponding hypergeometric enrichment p-values (bottom) are
shown. The top 300 hits for each secretable BirA sample (Fig. S3 for all
hits) showed significant enrichment of the secretory pathway components
and co-secreted proteins among the most significant hits for all samples
except Signal-BirA (which is a lone secreted BirA and not a mammalian
secreted protein). (b) Quantified interactions between interactors
(x-axis) and the model proteins (y-axis), where the shadowed entries
indicate significant interactions. The features of the model proteins,
detailed in Fig. 6, are summarized for each model protein on the left
and the interactors are labeled on top based on their secretory pathway
attributes.

\textbf{Figure 6. The bait proteins show diversity in their PTM and
structural content.} Dots and lines represent known PTM sites and
structural motifs in panel (a) and (b) respectively. The hills and
valleys indicate protein tertiary features. Note that solvent
accessibility and structural motifs are only available for regions
covered by the PDB structure, whereas predicted features such as protein
hydrophobicity and disorder are available for the entire protein.

\textbf{Figure 7. Detected interactors correlate with protein
features.}Interactors were associated with specific (a) PTMs and (b)
structural features of model proteins. The heatmap shows the
standardized interaction affinities estimated between certain
interactors and PTMs or structural features across all model proteins
(see methods). Only interactors having significant associations with
model protein features are shown.\selectlanguage{english}
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