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 >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
(VarnaitÄ— & 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; VarnaitÄ— & 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.