Abstract
Cancers represent complex autonomous systems, displaying
self‐sufficiency in growth signaling. Autonomous growth is fueled by a
cancer cell's ability to “secrete‐and‐sense” growth factors (GFs): a
poorly understood phenomenon. Using an integrated computational and
experimental approach, here we dissect the impact of a feedback‐coupled
GTPase circuit within the secretory pathway that imparts
secretion‐coupled autonomy. The circuit is assembled when the
Ras‐superfamily monomeric GTPase Arf1, and the heterotrimeric GTPase
Giαβγ and their corresponding GAPs and GEFs are coupled by GIV/Girdin,
a protein that is known to fuel aggressive traits in diverse cancers.
One forward and two key negative feedback loops within the circuit
create closed‐loop control, allow the two GTPases to coregulate each
other, and convert the expected switch‐like behavior of Arf1‐dependent
secretion into an unexpected dose–response alignment behavior of
sensing and secretion. Such behavior translates into cell survival that
is self‐sustained by stimulus‐proportionate secretion. Proteomic
studies and protein–protein interaction network analyses pinpoint GFs
(e.g., the epidermal GF) as key stimuli for such self‐sustenance.
Findings highlight how the enhanced coupling of two biological switches
in cancer cells is critical for multiscale feedback control to achieve
secretion‐coupled autonomy of growth factors.
Keywords: cellular autonomy, dose–response alignment (DoRA), epidermal
growth factor receptor (EGFR), G proteins, Golgi secretion
Subject Categories: Computational Biology, Signal Transduction
__________________________________________________________________
A combined computational modeling and experimental approach is used to
dissect a Golgi‐localized GTPase circuitry in eukaryotes and to
understand how it empowers cancer cells to achieve self‐sufficiency in
growth factor signaling.
graphic file with name MSB-19-e11127-g004.jpg
Introduction
Self‐sufficiency in growth signaling, a.k.a, growth signaling autonomy,
is the first of the six hallmarks of all cancers to have been clearly
defined (Hanahan & Weinberg, [46]2000). While most growth factors (GFs)
are made by one cell type to stimulate the proliferation of another,
many cancer cells synthesize GFs to which they are responsive, creating
a positive feedback signaling loop called autocrine stimulation (Fedi
et al, [47]1997). Serum‐free cell culture studies squarely implicate
such stimulation as key support for intracellular mechanisms that
impart autonomy (reviewed in Chigira et al, [48]1990). Autonomy in
cancer cells obviates dependence on extrinsic GFs, as illustrated in
the case of platelet‐derived GF (PDGF) and tumor GF α (TGFα) in
glioblastomas and sarcomas, respectively (Fedi et al, [49]1997). Beyond
cancers, “secrete‐and‐sense” circuits that allow cells to secrete and
sense the same signaling molecule are ubiquitous (Youk &
Lim, [50]2014); these autocrine secrete‐and‐sense mechanisms do not
just enable autonomy (Maire & Youk, [51]2015) but also generate diverse
social behaviors, and recur across species (Youk & Lim, [52]2014).
Autocrine secretion of GFs relies on an essential, efficient, and
accurate molecular machinery that constitutes a central paradigm of
modern cell biology, that is, the secretory pathway (Trombetta &
Parodi, [53]2003; Matlin & Caplan, [54]2017). This pathway consists of
various modules that are compartmentalized on the endoplasmic reticulum
(ER) and the Golgi apparatus, and are responsible for folding,
processing of the post‐translational modifications, and trafficking of
the proteins routed to the cell membrane (Kelly, [55]1985; Rothman &
Orci, [56]1992). Nearly all these aspects of the secretory pathway have
been found to be dysregulated in cancers, ranging from observed changes
in Golgi shape (“onco‐Golgi”; Petrosyan, [57]2015), or its function
(Zhang, [58]2021), which inspired the development of disruptors of this
ER‐Golgi secretory system as anti‐cancer agents (Wlodkowic
et al, [59]2009; Ohashi et al, [60]2012, [61]2016, [62]2017; Luchsinger
et al, [63]2018; Núñez‐Olvera et al, [64]2020).
Despite these insights, the core mechanisms of cell secretion that
impart cell autonomy remain poorly understood. To begin with, it is
still unknown whether or not secretion is proportional to GF
stimulation, and whether such secretion is sufficient to support cell
survival, perhaps via closed‐loop autocrine sensing and signaling (the
so‐called “secrete‐and‐sense” loop; Youk & Lim, [65]2014). A recent
study has shown that the secretory functions of the Golgi apparatus
require the unlikely coupling of two distinct species of GTPases at the
Golgi (Lo et al, [66]2015; Fig [67]1A): one is the small or monomeric
(m) GTPase Arf1 and the other is the heterotrimeric (t) GTPases Gi.
GTPases serve as molecular switches that gate signal transduction: “on”
when GTP‐bound (active) and “off” when GDP‐bound (inactive). The
“ADP‐ribosylation factor” (Arf1; Kahn & Gilman, [68]1986) mGTPase is
localized to the Golgi complex in mammalian cells and is essential for
the secretory pathway (Stearns et al, [69]1990); it associates with
Golgi membranes upon activation and is released from Golgi membranes
into the cytosol upon inactivation. Such cycles of association and
dissociation are regulated by Golgi‐associated, guanine nucleotide
exchange factors (GEFs), and GTPase activating proteins (GAPs).
Trimeric GTPases were detected in the Golgi over three decades ago
(Stow et al, [70]1991; Barr et al, [71]1992), and numerous studies have
provided clues that they may regulate membrane traffic and maintain the
structural integrity of the Golgi (reviewed in Cancino &
Luini, [72]2013). However, the concept of G protein activation at the
Golgi and the potential impact of such activation remained
controversial, primarily due to the lack of direct proof of G protein
activation. The study that reported the coupling of Arf1 mGTPase and
Giαβγ tGTPase provided direct evidence, the first of its kind, that the
two GTPases are coupled by a linker protein, Gα‐Interacting
vesicle‐associated protein (GIV; Lo et al, [73]2015). Activation of
Arf1 mGTPase facilitates the recruitment of GIV on the membrane via a
direct, nucleotide‐dependent interaction. Upon recruitment, GIV binds
and activates Gαi serving its role as a GEF for the tGTPase, Gi. Such
activation of Gi at the Golgi affects two fundamental functions of the
Golgi, that is, vesicle trafficking and the structural organization of
the Golgi stacks—both via modulation of Arf1 signaling. These findings
firmly established that Gαi is functionally active in the Golgi.
Figure 1. Study design and approach.
Figure 1
[74]Open in a new tab
1. Schematic shows a system of two species of GTPases, mGTPases (mG),
and heterotrimeric GTPases (tG), coupled by the linker protein,
GIV/Girdin, that is localized on the Golgi membranes within the
secretory pathway as the focus of this study. The circuit begins
when active Arf1‐GTP directly binds GIV's N‐term HOOK domain,
recruits GIV to Golgi membranes, and activates Gi (Lo et
al, [75]2015; arrow 1). The circuit is completed when GIV's
C‐terminus orchestrates two feedback loops (arrows 2 and 3), both
of which are essential for the inactivation of Arf1 (Lo et
al, [76]2015; Kalogriopoulos et al, [77]2019). See also Fig [78]EV1
for illustrations detailing the sequential steps within the dynamic
nature of the motif, and Movie [79]EV1 for the visualization of
these dynamic steps as a movie gif.
2. Schematic of the mathematical model that we used to study the role
of such coupling of GTPase (top panel) in autocrine
secretion‐supported cell survival and proliferation (bottom panel).
The modeling in the top panel is experimentally constrained, and
the modeling in the bottom panel is a predictive module. This model
is based on the nominal time scale of these events (left panel) and
has the typical behavior shown in the right panel.
Because tGTPases are known to primarily transduce extracellular signals
(“sensing”) into intracellular signals that shape cellular responses,
we asked how coupling of the two GTPases, one that guards cell
secretion (Arf1) and another that gates signal sensing (Gi), may impact
the cell's ability to secrete‐and‐sense. In systematically
interrogating this question, we viewed the experimentally validated
interactions and functions of the two GTPases and their GEFs and GAPs
as a circuit of coupled GTPases. Such coupling, whose structural basis
has been experimentally validated (Fig [80]1A‐right), forms a closed
loop that is comprised of one forward reaction and two negative
feedback loops (Figs [81]1A‐left and [82]EV1; Movie [83]EV1;
[84]Materials and Methods). The forward reaction is the recruitment of
GIV/Girdin by active Arf1 on Golgi membranes (arrow 1). GIV is a
multi‐modular cytosolic signal transducer that is a prototypical member
of the family of guanine nucleotide exchange modulators (GEM) of
tGTPases; GIV's GEM domain binds and activates the tGTPase Gαi, and
thereby, serves as a tGEF within this circuit. One negative feedback
loop involves the activation of the GAP for Arf1 (ArfGAP2/3) by GIV,
which terminates Arf1 signaling (arrow 2); the other is due to GIV's
role as a GEF to activate Gi and thus enhance Arf1GAP2/3, which also
lead to the termination of Arf1 signaling (arrow 3). This phenomenon of
co‐regulation between the two classes of GTPases maintains the Golgi
shape and function, two closely intertwined processes that are
regulated by Arf1. The triggers for and the consequence(s) of such
co‐regulation on signal sensing/response remained unknown.
Figure EV1. An endomembrane network motif of two species of GTPases regulates
membrane trafficking through the secretory pathway and regulates Golgi
functions.
Figure EV1
[85]Open in a new tab
1. Dynamics within the endomembrane GTPase system. Left to right
panels display the deconstructed arrows denoting key molecular
events/chemical reaction cascades within this system, in which, the
GIV‐GEM links the monomeric (m) and trimeric (t) GTPase systems and
enables the conversion of extracellular stimulus (ligand; left)
into membrane trafficking events (e.g., vesicle
uncoating/budding/fusion; right). The forward and feedback
reactions (arrows) are numbered 1–3. See Movie [86]EV1 for a gif of
the circuit.
2. Schematic summarizing the findings reported by Lo et al ([87]2015)
delineating how arrows 1–3 within the endomembrane GTPase system
regulate the finiteness of Arf1 signaling for efficient secretion.
Because coupling of two species of GTPase switches, Arf1 and Gi, with
feedback control is likely to generate complex, nonlinear, and
non‐intuitive emergent properties, we use cross‐disciplinary approaches
to dissect the role of the coupled GTPases within the secretory pathway
and explore its functional significance in eukaryotic cells. Using
computational biology approaches and explicit integration of
experimental biology and computational methods, we also assess the
impact of perturbing this motif, that is, uncoupling the GTPases. Our
findings show how coupling makes secretion responsive to GFs, in
particular the epidermal GF (EGF), and appears to impart
secretion‐coupled autonomy.
Results
An integrated computational and experimental approach to dissect a
Golgi‐localized GTPase circuit
We began by developing a mathematical model for this coupled circuit
(Fig [88]1B; see [89]Materials and Methods) and drawing clues from
protein–protein interaction (PPI) network analyses, to generate
testable hypotheses and validate them experimentally. The integrated
approach allowed us to connect across time scales of the emergent
behavior of the coupled GTPase circuit with cellular secretion, cell
survival, and ultimately, secretion‐coupled survival, that is,
autocrine autonomy.
The first part of the mathematical model is an experimentally
constrained module for the coupled GTPases switches (upper panel in
Fig [90]1B), where normalized Hill functions are used (Saucerman &
McCulloch, [91]2004; Cao et al, [92]2020; see [93]Materials and Methods
for details). This approach was chosen to capture the key timescales
and molecular players involved rather than focus on the specific
biochemical reactions. Additionally, this approach has fewer free
parameters than the traditional approach of building networks with
large numbers of reactions (Getz et al, [94]2019), leading to less
ambiguity in decision‐making for model development. The kinetic
parameters of the coupled GTPases module were subsequently tuned to fit
the time course data of GTPases in control cells and GIV‐depleted
cells.
The second part of the mathematical model is a predictive module for
cell secretion and secretion‐coupled cell survival (lower panel in
Fig [95]1B). The coupling of this predictive module with the above
experimentally constrained module is achieved by setting the secretion
rate as a function of mGAP. The following findings allow us to make
this coupling in the model: the finiteness of the Arf1
activation‐inactivation cycle was assumed to be a surrogate indicator
of successful anterograde cargo movement through the compartments
within the secretory pathway, that is, the ER–Golgi intermediate
compartment (ERGIC) to the Golgi, because Arf1 regulates membrane
traffic through a cycle of GTP binding and hydrolysis (Donaldson &
Jackson, [96]2011); GTP binding is a pre‐requisite for membrane
curvature and vesicle formation (Beck et al, [97]2008) from the donor
compartment, whereas GTP hydrolysis is a pre‐requisite for vesicle
uncoating (Tanigawa et al, [98]1993) and fusion with acceptor
compartment. Therefore, we set the secretion rate as a function of GTP
hydrolysis, a process regulated by mGAP. Except this setting for
secretion rate, the model for cell secretion and cell
survival/proliferation is similar to the model proposed by Hart
et al ([99]2014), where the kinetic parameters are from biologically
plausible ranges reported previously (Adler et al, [100]2018).
We chose two different cancer cell lines to conduct the experiments:
cervical (HeLa) and breast (MDA‐MB231) cancer cell lines. Our choice
was guided by two reasons: (i) HeLa cells not only represent the most
robust system to study Golgi structure (Ayala & Colanzi, [101]2016;
Wortzel et al, [102]2017) and function (Rauter et al, [103]2020) but
also provide continuity with prior work because all biophysical and
functional studies that led to the discovery of the coupled GTPases at
the Golgi were performed in this model and (ii) we and others have
shown that transcriptional upregulation or post‐transcriptional
activation (Dunkel et al, [104]2012; Bhandari et al, [105]2015; Sasaki
et al, [106]2015) of GIV (the “linker” between the two GTPases;
Fig [107]1A) supports several aggressive tumor cell properties (of
which, many were demonstrated in MDA‐MB231 cells (Jiang
et al, [108]2008; Lopez‐Sanchez et al, [109]2015; Wang
et al, [110]2015; Wang et al, [111]2017; Midde et al, [112]2018;
Rahman‐Zaman et al, [113]2018; Rohena et al, [114]2020)), including,
invasion, matrix degradation, proliferation, and survival
(Garcia‐Marcos et al, [115]2015; Aznar et al, [116]2016). Elevated
expression of GIV has also been reported in a variety of solid tumors
(Garcia‐Marcos et al, [117]2015; Getz et al, [118]2019), both in
primary tumors (Ghosh, [119]2015; Ghosh et al, [120]2016b) as well as
in circulating tumor cells (Barbazan et al, [121]2016; Dunkel
et al, [122]2018), and has been shown to correlate with tumor
aggressiveness and poor survival across cancers.
Finally, model and PPI network‐driven predictions of uncoupling the
GTPases or interrupting secrete‐and‐sense autonomy were experimentally
validated in the two cancer cell lines that lack GTPase coupling in the
absence of the GIV linker protein.
EGF activates Arf1 (mG*) at the Golgi and triggers the recruitment of a GEF
for trimeric Giαβγ
First, we sought to model the impact of coupling on m/tGTPase signaling
in response to the input signal (Fig [123]1A). Key events within the
circuit were measured experimentally using available tools and
experimental approaches (Arrows 1–3; [124]Materials and Methods). The
EGF was prioritized as an input signal because of prior evidence
documenting its role in the regulation of Golgi secretion
(Blagoveshchenskaya et al, [125]2008), its fragmentation during mitosis
(Shaul & Seger, [126]2006), and most importantly, in the activation of
Arf1 (Boulay et al, [127]2008; Haines et al, [128]2014, [129]2015).
We measured Arf1 activity in response to EGF using an established
pull‐down assay (Fig [130]2A and B) with the Glutathione S Transferase
(GST)‐tagged GAT domain of GGA3. This domain is known to selectively
bind the active GTP‐bound pool of Arf1 (Cohen & Donaldson, [131]2010).
The levels of Arf1·GTP were increased ~3‐fold within 5 min after ligand
stimulation, followed by a return toward baseline by 30 min, which we
assume reflects the level of Arf1 activity in cells at a steady state
(Fig [132]2B). These temporal dynamics were used to fit the parameters
for Arf1 activity in the computational model of the circuit (blue line
in Fig [133]2C; R^2 and normalized RMSE are 0.72 and 0.19 respectively;
see [134]Materials and Methods and Table [135]EV1 for model
parameters). Such fitting completed the characterization of the first
GTPase switch, that is, Arf1; in this case, the input is ligand
stimulus (EGF) and the output is Arf1‐GTP (OUTPUT #1; mG*).
Figure 2. EGF activates Arf1 (mG*) and triggers the recruitment of GIV‐GEM on
Golgi.
Figure 2
[136]Open in a new tab
1. Schematic showing the specific step being interrogated in panels
(B, C), that is, Arf1 activation under EGF stimulation.
2. Immunoblot shows GST‐GGA‐GAT domain bound Arf1 (active; top) and
total Arf1 (input lysates; bottom) from equal aliquots of lysates
of HeLa cells that were stimulated with EGF for the indicated time
points prior to lysis.
3. Graphs display the model fitting for Arf1 activation dynamics. The
experimentally determined Arf1 activation (in B) dynamics are
displayed as black dots with error bars, representing mean ± SEM
(n = 3 biological replicates), and numerical simulation is shown by
the blue continuous line.
4. Schematic showing the specific step being interrogated in panels
(E, F), that is, recruitment of GIV‐GEM on Golgi.
5. HeLa cells expressing Arf1‐HA were serum starved overnight (E, top)
and subsequently stimulated with EGF for 5 min (E, bottom) prior to
fixation with PFA. Fixed cells were stained for Arf1 (HA; green)
and GIV (red) and nuclei (DAPI; blue). Panels on the left show
overlay of all three stains and representative RGB plots of
sections through the Arf1‐stained pixels. Panels on the right
display the magnified 3D surface plots of the boxed regions in the
left panels. Scale bar = 10 μm.
6. Scatter plot shows the Mandler's overlap coefficient (MOC) for
Arf1‐HA and GIV colocalization in (E) that was calculated on 13–15
cells/experiment, n = 3 independent experiments. P‐values were
determined using Mann–Whitney t‐test: ***P = 0.0002.
Source data are available online for this figure.
A key consequence of Arf1 activity within the coupled GTPase circuit is
the first segment of the Gi activation pathway, that is, the
recruitment of GIV (Fig [137]2D), which is not only an effector of Arf1
but also the GEF of Gi (Lo et al, [138]2015). Previous studies showed
that an evolutionarily conserved region in the N‐terminal Hook domain
of GIV can directly and preferentially bind to the active GTP‐bound
conformation of Arf1 (Lo et al, [139]2015), revealing the structural
basis of the recruitment of GIV by active Arf1 (Fig [140]1A‐right). To
test whether GIV recruitment occurs in cells responding to EGF, we used
immunofluorescence microscopy to observe HA‐tagged Arf1 (green;
Fig [141]2E) and endogenous GIV (red; Fig [142]2E).
Membrane‐colocalization of Arf1 and GIV was significantly increased
within 5 min after EGF stimulation for serum‐starved cells, as
determined by quantification of the Arf1‐positive Golgi regions using a
Mander's overlap coefficient (MOC; Fig [143]2F). These results indicate
that EGF‐induced Arf1 activity triggers the recruitment of GIV at the
Golgi.
EGF triggers the activation of Gi (tG*) on Golgi membranes, and then
activates ArfGAP, terminating Arf1 signaling via feedback loops within the
closed‐loop system
We next evaluated the second segment of the Gi activation pathway, that
is, the ability of membrane‐recruited GIV to bind and activate the
tGTPase Gi at the Golgi (Fig [144]3A). To be more specific, we compared
the Gi activation level between control cells and GIV‐depleted cells.
The Gi activation level is measured by a well‐established Förster
resonance energy transfer (FRET)‐based assay (Gibson &
Gilman, [145]2006). In this assay, the α and βγ subunits of Gi were
tagged with YFP and CFP, respectively; if Gi is activated, that is, the
α and βγ subunits dissociate, YFP and CFP stay far from each other,
leading to low FRET (Fig [146]3B; See [147]Materials and Methods for
details). Besides, the GIV‐depleted cells were obtained using a short
hairpin RNA to target GIV (shGIV cells; Fig [148]3C; See [149]Materials
and Methods for details). When we conducted FRET assays in these cells,
we found that there was a significant drop in FRET (i.e., activation of
Gi and trimer dissociation after EGF stimulation) at the Golgi within
5 min after EGF stimulation in control cells. Activation of Gi
continued to peak by 15 min in control cells, reaching a plateau by
25–30 min (Figs [150]3D top and E, and [151]EV2A and B for FRET at the
PM). We noted that the temporal propagation of the input signal (EGF)
takes ~5 min to trigger events at the Golgi, which is considerably
delayed compared to most of the well‐defined EGF‐stimulated,
receptor‐proximal events (Fig [152]EV4A) which begin within ~2–5 s
(Reddy et al, [153]2016). This delay is consistent with the concept of
propagation delay in networks (Brent, [154]2009). On the other hand, in
shGIV cells, such tGTPase activation was abolished due to the
non‐changed FRET (Figs [155]3D bottom and E). Taken together, these
results demonstrate that Gi is activated at the Golgi upon EGF
stimulation and that such activation requires GIV.
Figure 3. EGF triggers the activation of Gi (tG*) on Golgi membranes,
activates ArfGAP, and terminates Arf1 signaling via a feedback loop.
Figure 3
[156]Open in a new tab
* A
Schematic showing the specific step being interrogated in (B–G),
that is, Gi activation.
* B
Schematic describing the mechanism of the FRET Gαi activity
reporter. Serum‐starved conditions are expected to have more
inactive trimeric Gi, and hence show high FRET (top). Upon ligand
stimulation, GIV‐dependent dissociation of trimers is expected,
with a resultant loss of intermolecular FRET.
* C
Equal aliquots (~45 μg) of whole cell lysates of control
(shControl; top) and GIV‐GEM depleted (shGIV; bottom) HeLa cells
were analyzed for GIV and tubulin (loading control) by
immunoblotting (IB).
* D
Control (sh Control; top) and GIV‐GEM depleted (shGIV; bottom) HeLa
cells were co‐transfected with Gαi1‐YFP, Gβ1‐CFP and Gγ2
(untagged), and live cells were analyzed by FRET imaging at steady
state, after being serum starved in 0.2% FBS overnight and then
after stimulation with 50 nM EGF. Representative freeze‐frame FRET
images are shown. FRET image panels display intensities of acceptor
emission due to efficient energy transfer in each pixel. The FRET
scale is shown in the inset. Golgi and PM regions of interest are
indicated with arrows. Scale bar = 10 μm. See also Fig [157]EV2A
and B for free‐frame images for additional time points in control
HeLa cells.
* E
ΔFRET/CFP at the Golgi (derived from D) as a function of time. The
data are represented as mean ± SEM. Interrupted lines display the
fitting results using exponential functions for shControl (blue)
and shGIV cells (red). Data represent five regions of interest
(ROIs) analyzed over the pixels corresponding to the Golgi of 3–5
cells from two independent biological experiments, that is, n = 8
biological replicates. P‐values, as determined against t0 using
Mann–Whitney are displayed.
* F
HeLa cells starved with 0.2% FBS overnight or stimulated
subsequently with 50 nM EGF were fixed and stained for active Gαi
(green; anti‐Gαi:GTP mAb) and Man II (red) and analyzed by confocal
microscopy. Activation of Gαi was detected exclusively after EGF
stimulation. When detected, active Gαi colocalizes with Man II
(yellow pixels in merge panel). See also Fig [158]EV2C and D for
additional time points and stimulus. Scale bar = 7.5 μm.
* G
Model fit for the fold change of active tGTPase (denoted as tG*).
Experiment data are the fold change of ΔFRET/CFP in (D) and is
shown as mean ± SEM (n = 8; 3–5 cells from two independent
biological experiments). Continuous lines display the model
simulation results after parameter fitting (See Table [159]EV1 for
parameters).
* H
Schematic shows the step being interrogated in (I–K), that is, the
termination of Arf1 signaling.
* I
Immunoblot shows bound Arf1 (active; top) and total Arf1 (input
lysates; bottom) from equal aliquots of lysates of control (sh
Control) and GIV‐depleted (shGIV) HeLa cells. Cells were stimulated
with EGF for the indicated time points prior to lysis. Bar graphs
in Fig [160]EV2E display the fold change in Arf1 activity
normalized to t0 min. “Low” and “high” indicate exposures.
* J, K
Model predictions of Arf1 activation dynamics (J) and Gαi
activation dynamics (K) when negative feedback do not exist. The
depletion of negative feedback in the model is achieved by deleting
either
[MATH: tG*→mGAP :MATH]
or
[MATH: tGEF→mGAP :MATH]
(interrupted green line). These two depletion ways have no
difference due to AND gate logic; please see also Fig [161]EV3 for
model predictions using OR logic. The red line in (J) was obtained
by setting the GIV amount to 10% of the control cell, matching the
low concentration of GIV in shGIV cells. As a reference, the
experimental data (i.e., error bars in black and red) and model fit
results (curves in blue and red) in Figs [162]2C and [163]3G are
also displayed here, which were plotted in a same way as in
Figs [164]2C and [165]3G.
Source data are available online for this figure.
Figure EV2. GEV‐GEM is required for Gi activation at the Golgi and for
maintaining the finiteness of Arf1 signaling upon EGF stimulation.
Figure EV2
[166]Open in a new tab
1. FRET‐based studies were carried out in sh Control cells as in
Fig [167]3B and C. Briefly, HeLa cells were co‐transfected with
Gαi1‐YFP, Gβ1‐CFP and Gγ2 (untagged), and live cells were analyzed
by FRET imaging at steady state, after being serum starved in 0.2%
FBS overnight and then after stimulation with 50 nM EGF.
Representative freeze‐frame FRET images are shown. FRET image
panels display intensities of acceptor emission due to efficient
energy transfer in each pixel. The FRET scale is shown in the
inset. Golgi and PM regions of interest are indicated with arrows.
Scale bar = 10 μm.
2. Bar graphs display the change in FRET at t5 min at the Golgi and
the PM regions of 3–5 cells, from four independent biological
replicates. Scale bar = 7.5 μm. Results are displayed as
mean ± SEM. Statistical significance was determined by student
t‐test and the P‐values are depicted as: **P < 0.01;
****P < 0.0001.
3. Schematic showing how a conformation‐specific anti‐Gαi·GTP antibody
detects GTP‐bound active Gαi in situ.
4. HeLa cells starved with 0.2% FBS overnight or stimulated
subsequently with 50 nM EGF or 250 μM LPA were fixed and stained
for active Gαi (green; anti‐Gαi:GTP mAb) and Man II (red) and
analyzed by confocal microscopy. Activation of Gαi was detected
exclusively after LPA/EGF stimulation. When detected, active Gαi
colocalizes with Man II (yellow pixels in merge panel). Negative
control (secondary antibody) staining was carried out on cells
stimulated with EGF, 15 min. Scale bar = 10 μm.
5. Control (sh Control) and GIV‐depleted (shGIV) HeLa cells that were
stimulated with EGF for the indicated time points prior to lysis
were assessed for Arf1 activity. Immunoblots are shown in
Fig [168]3I. Bar graphs display the fold change in Arf1 activity
normalized to t0 min that was observed in control (shControl) and
GIV‐depleted (shGIV) cells. Results are expressed as mean ± SEM;
n = 3 biological replicates; P‐values were determined using
Mann–Whitney t‐test compared to t0: *P < 0.05; **P < 0.01;
***P < 0.001. Immunoblots are representative of findings from at
least three independent repeats.
6. Line graph in red displays a model‐derived simulation of Arf1
activation dynamics (mG*) in cells without tGEF (shGIV). As a
reference, the results of model‐derived simulation fit to
experimental data in control cells are displayed in blue, and the
experimental data for Arf1 activation dynamics are shown as
mean ± SEM (n = 3 biological replicates).
Figure EV4. Coupled switches enable the alignment of endomembrane responses
(Arf1 and tG* activities) to the dose of an extracellular stimulus.
Figure EV4
[169]Open in a new tab
* A
Published dynamics of EGF‐stimulated events that are initiated at
the PM (blue, continuous) or experimentally determined dynamics of
events at the Golgi confirmed here (red, interrupted) are compared.
The interrupted line at 5 min provides a reference frame for the
observed peak Arf1 activity upon EGF stimulation.
* B, C
Dose responses of fractional activations of mGEF and active Arf1
(mG*) for the single switch (B; mG alone) and coupled switches (C;
mG and tG). We perform stochastic simulations in the presence of
noise in EGF (see [170]Materials and Methods for details). The mean
and the standard deviation (SD) of species are evaluated at steady
states. The dimensionless EGF concentrations in the simulations are
obtained through normalization, that is, dividing the EGF
concentration by 217.4 nM (=50 nM/0.23). In all simulations, noise
is introduced only in stimulus (i.e., EGF).
* D, E
The same plots as in Fig [171]4A and B but in the presence of three
different types of noise: noise in stimulus (shown in red), noise
in stimulus and connections simultaneously (shown in green), and
noise in stimulus and species simultaneously (shown in blue; see
[172]Materials and Methods for details). Data are shown as mean
values (dashed curves), with the shading showing the SD. The black
curves are fitting curves (r ^2 > 0.95) for red dashed curves (see
[173]Materials and Methods for the calculations of r ^2 and
[MATH: nHill :MATH]
).
* F, G
The fractional activations of tGTPase (tG*) as a function of EGF
(F) or tGEF (G). The plots are generated in a similar way as in (F,
G).
[MATH: tGEF¯ :MATH]
denotes the mean of tGEF. r ^2 > 0.95 for all fitting curves. The
EGF‐tG* relationship shows a Hill coefficient of 1.73, and the
tGEF→tG* switch (switch #2) shows a dose–response behavior close to
the saturation regime of an ultrasensitive curve (n [Hill
] = 3.82).
Because FRET studies require the overexpression of G protein subunits
at levels much higher than relevant in physiology, we sought to
validate our FRET‐based findings on endogenous Gi. To this end, we
performed confocal immunofluorescence microscopy using a bona fide
marker of the organelle, the Golgi‐localized α‐mannosidase II (Man II;
Zuber et al, [174]2000), and anti‐Gαi·GTP mAb, which selectively
recognizes the active (GTP‐bound) conformation of the G protein (Lane
et al, [175]2008). These signals colocalized not only in EGF‐stimulated
cells (Fig [176]3F) but also in cells exposed to other stimuli, for
example, 10% serum (containing a mixture of GFs) and lysophosphatidic
acid (LPA), a ligand for the GPCR, LPA‐receptor (LPAR; Fig [177]EV2C
and D). The findings confirmed that Gi is activated on Golgi membranes
after GF stimulation and suggested the prevalence of this event in
response to diverse stimuli.
We fitted the above experimental data by tuning the kinetic parameters.
We obtained a good fit for the fold change of Gi activation in both
control and shGIV cells (Fig [178]3G; R^2 and RMSE, 0.54 and 0.41 for
control cells;
[MATH: − :MATH]
0.44 and 0.71 for shGIV cells). The low level of GIV in shGIV cells was
mimicked by decreasing the levels of expression of GIV to 10% of that
in control cells (Fig [179]3C). Thus, the model matched the overall
trend of experimental data in both cells (see Table [180]EV1 for model
parameters).
We next evaluated the feedback loops, which are critical for the
“closed loop” architecture of the circuit, that is, the deactivation of
Arf1 (mG*) by ArfGAP2/3 (mGAP; Fig [181]3H). Two negative feedback
loops activate ArfGAP2/3 (arrows 2 and 3 in Fig [182]1A). Arrow 2
represents GIV's ability to bind and recruit ArfGAP2/3 to COPI vesicles
and the Golgi membranes; failure to do so results in elevated levels of
Arf1·GTP and stalled anterograde secretion in these cells (Lo
et al, [183]2015). Arrow 3 represents GIV's ability to activate Gi and
release “free” Gβγ; GIV's GEF function triggers this (Lo
et al, [184]2015) and “free” Gβγ is a co‐factor for ArfGAP2/3. Both
negative feedback loops depend on the forward reaction, arrow 1, which
involves the recruitment of GIV (tGEF; Fig [185]1A). Using the Arf1
activity after ligand stimulation as a readout, we next measured the
activity of ArfGAP2/3 in control and GIV‐depleted (i.e., shGIV) cells
responding to EGF (Fig [186]3I). We found that Arf1 activity peaked
within 5 min after EGF stimulation and rapidly reduced thereafter by
15 min in control cells but remained sustained until 15 min in
GIV‐depleted cells (Figs [187]3I, and [188]EV2E and F), suggesting that
EGF activates both mGEFs and mGAPs of Arf1. While activation of Arf1 is
brought on by mGEF(s) (the identity of which remains unknown) and
achieves similar levels of activation regardless of the presence or
absence of GIV, termination of Arf1 activity by mGAP (ArfGAP) requires
GIV.
Finally, we used the model which was fitted to the experimental data in
Figs [189]2C and [190]3G to make predictions. We conducted two
simulations: one in which we decreased the GIV level to simulate the
Arf1 activation dynamics in the shGIV cell line (red line in
Fig [191]3J), and one in which we deleted either arrows 2 or 3 to
simulate the Arf1 and Gi activation dynamics for the uncoupled GTPase
switches. Based on the experimental results before (Lo
et al, [192]2015), arrows 2 and 3 are modeled by an “AND gate”‐like
digital logical operation (Kime & Mano, [193]2003), that is, a HIGH
output (ArfGAP2/3 activity, and resultant termination of Arf1
signaling) results only if both the inputs to the AND gate (arrows 2
and 3) are HIGH. We also tested the “OR” logic for the negative
feedback (Fig [194]EV3) and found the model predictions to be
indistinguishable from those obtained with the AND gate. It is possible
that one of these logical modes of operation is more efficient than the
other under certain circumstances. For the first simulation, the
simulated Arf1 activation dynamics (red line in Fig [195]3J) captured
the sustained activation of Arf1 dynamics in shGIV cells, indicating
the ability of the model to capture the experimental data. For the
second simulation, the simulated Arf1 dynamics (green line in
Fig [196]3J) is the same as that in shGIV cells, suggesting the
equivalency of deleting GIV and uncoupling GTPase switches. The
simulated Gi dynamics (green line in Fig [197]3K) is similar to (maybe
even slightly higher than) that in control cells, which is consistent
with the fact that the feedback loops have no effect on Gi. Thus,
negative feedback within the “closed‐loop control” (CLC) exerts a
significant effect on the mGTPase (Arf1) and little or no effect on the
tGTPase (Gi).
Figure EV3. Simulations of Arf1 activation dynamics (mG*) and Gi activation
dynamics (tG*) when using OR logic.
Figure EV3
[198]Open in a new tab
* A
Comparisons of equations for mGAP when using different logics. The
AND and OR logic are modeled by
[MATH: fact2tGEFfact3tG* :MATH]
and
[MATH: fact2tGEF+fact3tG* :MATH]
, respectively. The constants 0.24 and 0.0017 ensure that the
steady‐state values of all species when using OR logic are the same
as those for AND logic. The differences between the two models are
underlined in red in the equations.
* B, C
Simulations of Arf1 (B) and Gi (C) activation dynamics for OR
logic. The OR logic also captures the experimental data reasonably
well but the AND logic is better informed experimentally. The
experimental data are the same as those in Fig [199]3J and K.
Coupled GTPases are predicted to enable high‐fidelity concordant response to
EGF
To gain insights into how coupling impacts information transduction, we
compared the dose–response alignment (DoRA) performance between the
coupled and uncoupled GTPase circuits. Typically, DoRA, referring to
the close match of the receptor occupancy and the downstream response
no matter what the stimuli level is (Andrews et al, [200]2016), is
believed to improve information transduction, since the downstream
molecules reflect the receptor occupancy faithfully. We regarded the
mGEF as an alternative to the receptor because it serves as the first
input to the coupled circuit via its ability to trigger the activation
of the mGTPase switch. Therefore, a close match of dose–response curves
of mGEF and mG* is equivalent to the linear relation between mGEF and
mG*. Using the model that has been fitted to the data in Figs [201]2C
and [202]3G, we simulated the steady‐state value of mG* and mGEF over a
wide range of stimuli and then plotted the fractional activation of mG*
for a given mGEF activity to observe the linearity. The misalignment in
the case of a single switch is evident; a single Arf1 switch displays
hyperresponsiveness, in that, max mG* is achieved even with minimal
mGEF activity (Fig [203]4A). In the case of coupled switches, similar
plots of fractional activation of mG* for a given mGEF activity show
DoRA with an unexpected linear relationship (Fig [204]4B). These
results also hold in the presence of noise, such as noise in EGF
stimulus and the intracellular noise [simulated within the
concentrations of the different species (nodes) and the connections
between them (arrows)] (see [205]Materials and Methods and
Fig [206]EV4). These results suggest that coupled switches exhibit
higher fidelity in information transduction than uncoupled switches.
Although unexpected for a GTPase switch, this finding is consistent
with what is generally expected in a closed loop with negative feedback
(Becskei & Serrano, [207]2000; Åström & Murray, [208]2021).
Figure 4. The predicted impacts of uncoupling the coupled switches.
Figure 4
[209]Open in a new tab
* A, B
Fractional activations of mGEF vs. active Arf1 (mG*) for the single
switch (A; mG alone) and coupled switches (B; mG and tG). We
perform stochastic simulations in the presence of noise in EGF (see
[210]Materials and Methods for details). The mean and the standard
deviation (SD) of species are evaluated at steady states based on
1,000 repeated independent simulations of ODEs in the presence of
noise.
[MATH: mGEF¯ :MATH]
denotes the mean of mGEF; the shading shows the SD. The
dimensionless EGF concentrations in the simulations are obtained
through normalization, that is, dividing the EGF concentration by
217.4 nM (=50 nM/0.23). In all simulations, noise is introduced
only in stimulus (i.e., EGF).
* C, D
A comparative analysis of the Golgi‐localized Arf1 (mG) connectome
with/without coupling to GIV (tGEF) and Gi (tGTPase). Workflow (C)
shows how the list of Golgi‐localized Arf1 and GIV interacting
proteins (Appendix Fig [211]S1 and Dataset [212]EV1) were used as
“seeds” to construct a PPI network from the STRING database to
fetch the linking nodes to connect the seed proteins. The network
was then perturbed by in silico deletion of GIV, followed by a
topological analysis of how such perturbation impacts the shortest
paths associated with Arf1 to all other nodes in the network (see
[213]Materials and Methods). A network representation (D) using the
ClueGo algorithm of the cellular processes associated with the end
proteins that were most frequently encountered in the most impacted
shortest paths associated with Arf1 (listed in Appendix Fig [214]S2
E). The deleted or newly added shortest paths were only considered
using the differential network approach (see [215]Materials and
Methods). The key in the lower left corner displays the color code
of various overarching themes encountered in the network.
Coupled GTPases are predicted to support secretion that is linked to
autocrine signaling and survival
To understand the impact of uncoupling of the GTPase circuit on
Arf1‐dependent secretory functions of the Golgi, we carried out: (i)
PPI network analysis and (ii) the mathematical modeling based on
ordinary differential equations (ODEs).
To restrict the Arf1 interactome to the Golgi, we first extracted a
Golgi‐annotated subcellular localization network of high‐confidence GIV
and Arf1 correlators, based on a proximity‐dependent biotinylation map
of a human cell (Go et al, [216]2021; Appendix Fig [217]S1). Next, the
list of Golgi‐localized proteins was expanded by incorporating the GIV
interactors from BioGRID (Oughtred et al, [218]2021; Appendix
Fig [219]S2A and B). Arf1's connectivity in the coupled network (in
which Arf1·GIV·Gi interactions were intact) was compared against an
uncoupled network created in silico by the removal of GIV from the
network (Appendix Fig [220]S2C). Network analysis (see the workflow in
Fig [221]4C; and as detailed in [222]Materials and Methods) showed that
Arf1's connectivity with many proteins (“nodes”) and pathways was
altered in the uncoupled state (listed in Appendix Fig [223]S2D–G).
These altered pathways share three key themes: (i) “sensing” of diverse
ligands/stimulus, for example, GFs, peptide and steroid hormones, and
cytokines (yellow nodes in Fig [224]4D), (ii) “secreting” proteins to
the extracellular space (red nodes in Fig [225]4D), and (iii)
“survival” signaling via the PI3K‐Akt pathways (teal nodes in
Fig [226]4D). As anticipated in the absence of GIV, Gi, and second
messenger signaling (blue nodes in Fig [227]4D), cellular homeostasis
and cell number (green nodes in Fig [228]4D) were predicted to be
impacted. These findings suggest that removing GIV may impact secretion
that is critical for auto/paracrine sensing/signaling, which maintains
cell number via balanced proliferation and/or death.
We next used computational modeling approaches to interrogate how
coupled (CLC) vs. uncoupled (open loop) GTPase systems at the Golgi
impact cargo secretion and cell number upon sensing GF stimulus
(Fig [229]5A). However, unlike m/tG* activity assays (which happen in a
second to minute), cell secretion may begin within minutes but is
measured in hours, and their impact on cell number requires a longer
time scale (several hours and even days). The secretion function was
predicted to show an ultrasensitive response (n [Hill ] = 1.86) as a
function of the stimulus (i.e., a given dose of EGF) when the two
GTPase switches are coupled; it was predicted to be reduced in the
absence of coupling (Fig [230]5B–D). Though the secretion response is a
constant in the absence of the coupling, it sets the baseline for
EGF‐to‐Arf coupling and thus supplies a platform for the comparison
with the coupled GTPases system. Intriguingly, secretion in the coupled
state shows different responses for most ranges of Gi activity (tG*;
Fig [231]5C), indicating faithful information transduction between Gi
and secretion. Furthermore, the cell number is higher for the coupled
vs. the uncoupled cells (Fig [232]5E and Appendix Fig [233]S3). To
specifically analyze the impact of secrete‐and‐sense autocrine
autonomy, we carried out the simulations under restrictive growth
conditions. These simulations under restrictive growth conditions
revealed that cells with coupled switches display a higher cell number
compared to the cells with uncoupled switches only when the
secrete‐and‐sense loop is highly efficient; this advantage is lost if
the loop is abolished (Fig [234]5F). That coupling of GTPases that is
required for maintaining cell numbers was reproduced using EGF as the
stimulus (Fig [235]5G), providing continuity with prior model‐derived
predictions. We also confirmed that the system and the conclusions are
not only robust to biological noise (Fig [236]5B, C and G) but also
robust to the variations in the kinetic parameter (Appendix
Fig [237]S4).
Figure 5. Coupled GTPases are predicted to support secrete‐and‐sense autonomy
and maintenance of cell number.
Figure 5
[238]Open in a new tab
* A
Schematic of the key features of the auto/paracrine loop that we
hypothesize is regulated by the coupled GTPase circuit (left) and
the corresponding phenomenological models to capture these key
effects (right).
* B, C
Model prediction for secretion as a function of stimulus in cells
with coupled and uncoupled GTPases. Noise is introduced into the
system in a similar way as described in Fig [239]EV4D–G. r
^2 > 0.99 in (B); r ^2 > 0.94 in (C).
* D, E
The secretion (D) or the cell number (E) as a function of stimulus
in coupled and uncoupled switches. The stimulus = 0, 0.00046,
0.0046, 0.046, 0.115, 0.23, and 0.46 correspond to varying doses of
EGF in simulations, ranging from 0, 0.1, 1, 10, 25, 50, and 100 nM,
respectively. The error bar denotes SD based on 1,000 repeated
independent simulations of ODEs when noise is in the stimulus and
connections.
* F
The bar plot depicts cell numbers achieved by cells with either
coupled or uncoupled switches, at different levels of stimulus. For
the first two bars, the height and error bars are the mean and SD
of cell number when stimulus = 0.046 in (E), respectively. For the
last two bars, the height and error bars are the mean and SD of
cell number when stimulus = 0 in (E), respectively.
* G
Relation between cell number and EGF in the presence of noise,
which was introduced in a similar way as described in
Fig [240]EV4D–G. r ^2 > 0.95.
GTPase coupling by GIV is required for time and dose‐dependent secretion of
diverse cargo proteins
We next sought to experimentally validate the predicted impact of
uncoupling on cell secretion by studying the time‐dependent secretion
of a few well‐established transmembrane and soluble cargo proteins. We
began with the transmembrane cargo, vesicular stomatitis virus G
protein (VSVG) using the well‐characterized GFP‐tagged VSVG‐tsO45
mutant (Gallione & Rose, [241]1985). This mutant VSVG is retained in
the ER at 40°C, and accumulates in Golgi stacks at a 20°C temperature
block, from where it escapes to the PM at a permissive 32°C
(Fig [242]EV5A). Considerable VSVG accumulated in the Golgi region in
both control and GIV‐depleted cells under serum‐starved conditions at
20°C. EGF or serum stimulation was permissive to the transport of the
VSV‐G protein to the PM in control cells at 32°C, but such transport
was significantly diminished in GIV‐depleted cells (Fig [243]EV5B and
C). Similar results were observed also in the case of EGF‐stimulated
secretion of three separate soluble cargo proteins, MMP2, MM9
(Fig [244]EV5D–F), and Collagen (Fig [245]EV5G and H); these cargo
proteins were chosen because of GIV's published role in ECM degradation
during cancer metastasis (Rahman‐Zaman et al, [246]2018) and tissue
fibrosis (Lopez‐Sanchez et al, [247]2014). These findings show that the
secretion of diverse proteins in response to GFs is blunted in
GIV‐depleted cells with uncoupled GTPases (Fig [248]6A).
Figure EV5. GIV‐GEM is required for EGF‐triggered secretion of diverse cargo
proteins through the Golgi compartment.
Figure EV5
[249]Open in a new tab
* A
Schematic shows the basis for measuring secretion of transmembrane
cargo protein, ts‐VSVG‐eGFP. This temperature‐sensitive mutant VSVG
is retained in the ER at 40°C, at the Golgi at 20°C, and moves out
of the Golgi to the PM when shifted to 32°C (Gallione &
Rose, [250]1985). When visualized with immunofluorescence under
non‐permeabilized conditions, a VSVG‐ectodomain targeting antibody
selectively detects PM‐localized cargo, whereas a GFP tag allows
the visualization of total VSVG in the cell.
* B, C
Control (sh Control; top) and GIV‐depleted (shGIV; bottom) Cos7
cells were transfected with tsO45‐VSVG‐GFP and cells were shifted
to 40°C for O/N and then incubated at 20°C for 1 h in HEPES
buffered serum‐free media followed by temperature shift at 32°C for
15 min in plain DMEM and or containing 50 nM EGF or 10% serum.
Coverslips were fixed and stained with VSVG‐ectodomain‐specific
monoclonal antibody (red). Representative images are shown in (B).
Scale bar = 10 μm. Green fluorescence indicates total VSVG
expression whereas red fluorescence shows the surface‐localized
pool of VSVG. Bar graphs in (C) display the Red:Green intensity
ratio indicative of the fraction VSVG that is secreted to the cell
surface. Results are expressed as mean ± SEM; n = 3 biological
replicates; P‐values were determined using Mann–Whitney t‐test
compared to t0: *P < 0.05.
* D–H
Control (sh Control) and GIV‐depleted (shGIV) HeLa cells were
analyzed for EGF‐stimulated secretion of three soluble cargo
proteins, MMP2 (D, E), MMP9 (D, F), and Collagen‐Vii RFP (G, H), as
detected from the supernatants at indicated time points after EGF
stimulation. Results are expressed as mean ± SEM; n = 3 replicates;
P‐values were determined using Mann–Whitney t‐test compared to t0:
**P < 0.01; ***P < 0.001. Immunoblots are representative of
findings from at least three independent repeats.
Figure 6. Coupling of GTPases by GIV is required for growth
factor‐independent cell survival that relies upon autocrine secretion.
Figure 6
[251]Open in a new tab
* A
Schematic summarizes the findings showcased in Fig [252]EV5, which
investigate the secretion of diverse cargo proteins
[temperature‐sensitive (ts) VSV‐G, MMP2/9, and ColVII], as
determined by their accumulation in extracellular space over time
after the stimulus (EGF or serum). For each cargo tested, compared
to cells with GIV (shControl), ligand‐stimulated secretion was
impaired in cells without GIV (shGIV).
* B
Immunoblots showing intracellular (left) and secreted (in the
media; right) GFP‐MMP9 at 24 h after stimulation with varying doses
of EGF. Tubulin, used as a loading control, confirms the presence
of a similar number of plated cells in the assay.
* C
Left: Graph displays experimentally determined secretion of
GFP‐MMP9 in response to varying doses of EGF in control (shControl)
and GIV‐depleted (shGIV) HeLa cells (as in B), and quantified by
band densitometry. Results are expressed as mean ± SEM; n = 3
biological replicates. P‐values were determined by a two‐sided
unpaired t‐test. Right: Schematic diagram of dose responses (mG*
and secretion) for the single switch and coupled switches. Coupled
switches stretch the range of proportionate responses. Single mG
switch results in misaligned responses. DoRA, dose–response
alignment.
* D
Left: Schematic summarizing the colorimetric assay used here to
determine the number of metabolically viable cells. Right: The
graph displays formazan absorbance expressed as a measure of cell
viability from the HeLa cells (Y‐axis) cultured at varying conc. of
serum (X‐axis). Results are expressed as mean ± SEM; n = 3
biological replicates. P‐values were determined by a two‐sided
unpaired t‐test.
* E
Bar graphs display the % apoptotic (left) or necrotic (right)
control (parental) and GIV‐depleted (GIV KO) HeLa cells after 24 h
growth in varying concentrations of serum, as assessed by annexin V
staining and flow cytometry. See also Appendix Fig [253]S5 A–C for
dot plots and early and late apoptotic fractions. Results are
expressed as mean ± SEM; n = 3 biological replicates. P‐values were
determined by a two‐sided unpaired t‐test.
* F
Schematic showing the rationale for and mechanism of action of
fungal toxin, BFA, for interrupting the secrete‐and‐sense autocrine
loop in cells.
* G, H
Control (parental) and GIV‐depleted (GIV KO) HeLa cells grown in
different concentrations of serum (FBS%) were treated or not with
varying concentrations of BFA (μM) as indicated. Line graphs in 3D
(G) depict the formazan absorbance expressed as a measure of cell
viability from the HeLa cells in various conditions tested. Bar
graphs (H) depict the cell number in serum‐free growth conditions
that are supported exclusively by the autocrine secrete‐and‐sense
loop (without BFA; BFA = 0.0 μM) or when such loop is interrupted
(BFA = 0.1 μM). Results are expressed as mean ± SEM; n = 3
biological replicates. Statistical significance was determined by
one‐way ANOVA.
* I–K
Control (parental) and GIV‐depleted (GIV KO) MDA MB‐231 cells grown
in different concentrations of serum (FBS%) were treated or not
with varying concentrations of BFA (μM) as in (G, H). Line graphs
in 3D (I) depict the formazan absorbance expressed as a measure of
cell viability from the MDA MB‐231 cells in various conditions
tested. Bar graphs (J) depict the viability of the MDA MB‐231 cells
in serum‐free growth conditions that are supported exclusively by
the autocrine secrete‐and‐sense loop (without BFA; BFA = 0.0 μM) or
when such loop is interrupted (BFA = 0.1 μM). Results are expressed
as mean ± SEM; n = 3 biological replicates. Statistical
significance was determined by one‐way ANOVA. Immunoblots (K) of
equal aliquots of whole cell lysates confirm the depletion of GIV
compared to tubulin (loading control). See also Appendix
Fig [254]S5 D–H for dot plots and early and late apoptotic
fractions. Results are expressed as mean ± SEM; n = 3 biological
replicates.
* L
Summary of conclusions of this work. Top: Coupling of GTPases
within the secretory pathway enables dose–response alignment of
secretion to stimulus, which appears to be essential for
“secrete‐and‐sense” autocrine autonomy in cancer cells. Bottom:
Uncoupling of the GTPases within the secretory pathway disrupts
such autonomy and leads to cell death.
Source data are available online for this figure.
We next asked if the DoRA predicted earlier in the case of Arf1
activity (Fig [255]4B) translates into a similar alignment in the case
of cell secretion. We analyzed the efficiency of secretion of one of
the three cargo proteins, MMP9, from control and GIV‐depleted cells
responding to a range of EGF concentrations for 24 h (Fig [256]6B).
Quantitative immunoblotting confirmed that dose‐dependent secretion was
observed in the case of control cells (coupled GTPases) but not in
GIV‐depleted cells (uncoupled GTPases; Figs [257]6B and C left). We
conclude that the DoRA of Arf1 activity indeed translates into DoRA of
cell secretion in cells with coupled GTPases; by contrast, a misaligned
Arf1 activity (hyperresponsive; Figs [258]4A and [259]6C, right)
translates into misaligned secretion (hyporesponsive; Fig [260]6C,
right) in cells with uncoupled GTPases.
GTPase coupling by GIV is required for cell survival that relies upon
autocrine secretion
We next assessed by MTT assays the total number of metabolically active
cells that develop self‐sufficiency in GF signaling, that is, survive
in GF‐free conditions (0% serum; Fig [261]6D, left). The number of
cells in serum‐free or low‐serum conditions was significantly higher in
the presence of GIV (parental HeLa cells; coupled) than in the absence
of GIV (GIV‐KO cells; uncoupled; Fig [262]6D); this survival gap closed
at higher serum concentrations (see 10% Fetal Bovine Serum (FBS),
Fig [263]6D). Reduced cell number in GIV‐KO cells in the low/no serum
conditions was associated with a concomitant increase in cell death via
apoptosis and necrosis (Fig [264]6E and Appendix Fig [265]S5A–C). We
then sought to validate the results of the simulations in
growth‐restrictive conditions which showed that interrupting the
coupled GTPase circuit at the Golgi will reduce cell numbers
(Fig [266]5F). We analyzed the number of metabolically active cells
with (coupled) or without (uncoupled) GIV across a range of serum
conditions and varying concentrations of the mycotoxin Brefeldin A
(BFA), a well‐known tool to inhibit secretion via its ability to
inhibit Arf1 activation (Prieto‐Dominguez et al, [267]2019;
Fig [268]6F). We made three observations: (i) cells with coupled
circuits have a significant survival advantage in serum‐restricted
conditions (see 0–2.0% FBS; Fig [269]6G); (ii) that advantage depends
on sensing what the cells secrete because blocking secretion with BFA
also eliminates such advantage (Fig [270]6H); and (iii) survival in the
presence of serum (5–10%) is similar for both “coupled” and “uncoupled”
cells, implying non‐secreting cells with uncoupled circuits can survive
if they can “sense” stimuli that they did not generate (e.g., serum
~5–10% range; Fig [271]6G).
To avoid overreliance on a single model system (i.e., HeLa cells), we
generated a second model, GIV‐depleted MDA MB‐231 cells (using
CRISPR/Cas‐mediated genome editing, see [272]Materials and Methods) and
sought to reproduce key findings (Fig [273]6I–K). As in the case of
HeLa cells, the survival advantage of MDA MB‐231 cells with coupled
circuit (with GIV, Parental cells) over those with uncoupled circuit
(GIV KO) was observed exclusively in low/no serum conditions (see
0–2.0% FBS; Fig [274]6I) and blocking secretion with BFA eliminates
such advantage (Fig [275]6J). Reduced cell survival in cells without
GIV (uncoupled state) was associated with higher early and later
apoptosis and necrosis (Appendix Fig [276]S5D–H).
These findings show that the coupled GTPase circuit is required for
cell survival that is supported exclusively by autocrine secretion
(i.e., independent of external GFs), and by that token, essential for a
functional autocrine “secrete‐and‐sense” loop (Fig [277]6L, top).
Interrupting the coupled GTPase circuit at the Golgi appears to disrupt
the “secrete‐and‐sense” loop and abrogate cell survival that is
supported by such secretion (Fig [278]6L, bottom). Because
“secrete‐and‐sense” loop is a key feature of cellular autonomy (Youk &
Lim, [279]2014; Maire & Youk, [280]2015), taken together our findings
show that the coupled GTPase circuit in the cell's secretory pathway
may be critical for autocrine autonomy.
GTPase coupling supports self‐sufficiency in GF signaling
To discern the nature of the pathway/processes whose autocrine autonomy
is supported by the coupled GTPases, we analyzed HeLa and MDA MB‐231
cells with coupled (WT) or uncoupled (GIV KO) circuits by tandem mass
tag (TMT) proteomics. The studies were carried out in
serum‐free/restricted conditions (Fig [281]7A) to maximally enrich the
proteome that supports auto‐/paracrine secretion‐coupled sensing. To
our surprise, the majority (76%; 1,437 proteins, including EGFR; see
the complete list in Dataset [282]EV2) of the differentially
upregulated proteins (DEPs) in the two WT cell lines overlapped
(despite the vast differences between HeLa and MDA MB‐231 cell lines in
origin, genetics, and nearly every other possible way). This suggests
that the presence or absence of GTPase coupling via GIV may impact both
cells similarly. The interactions between the DEPs were fetched from
the STRING database to build a PPI network, in which we found several
major coat proteins (AP1, AP2, COP, and CAV), the monomeric GTPases
(Arfs, Rabs, Rho, CDC42, and Rac1) and trimeric GTPases (GNAI;
Fig [283]7B). A connectivity analysis revealed that EGFR and the Arfs
are some of the most highly connected nodes in the interactome
(Fig [284]7C). A reactome pathway enrichment analysis confirmed that
the most highly connected proteins primarily engage in a variety of GF
signaling pathways (Fig [285]7D).
Figure 7. Differential proteomics of autonomy enabled vs. disabled MDA MB‐231
and HeLa cells.
Figure 7
[286]Open in a new tab
1. Workflow for comparative proteomics on autonomy enabled vs.
disabled cells by tandem mass tag (TMT) multiplex technique
followed by mapping of upregulated proteins in WT cells using the
STRING database (see [287]Materials and Methods).
2. A protein–protein interaction (PPI) network shows the interactions
between upregulated mapped proteins in WT cells. Node and font
sizes correlate positively with the degrees of connectivity.
3. Bar plot shows the degree distribution of highly connected
(degree > 20) nodes in the PPI network in (B).
4. Reactome pathway analysis of the pathways enriched in the most
connected proteins in (C). Red = pathways associated with growth
factor signaling.
5. Workflow for the construction of a multi‐organelle network of
autonomy‐enabled cells using subcellular localization of
upregulated proteins in the WT cells.
6. Visualization of a multi‐organelle network of proteins that partake
in secretion‐coupled autonomy across three compartments, the plasma
membrane, the Golgi, and the ER/ERGIC.
7. Reactome pathway analyses of the pathways enriched within the three
organelles in (F). Red pathways associated with RTK/EGFR signaling
and Green pathways associated with multi‐cellular cell–cell
communication in the plasma membrane.
Because protein functions are determined by subcellular localization,
we sought to map the DEPs that are upregulated in the WT cells based on
their subcellular localization. To this end, we used a Human Cell
Atlas‐supported explorer platform (that uses a large collection of
confocal microscopy images of patterns of subcellular localization of
human proteins) to include the proteins that localize to three
organelles, the Golgi, ER/ERGIC, and the PM (Fig [288]7E; see
[289]Materials and Methods). Visualization of the DEP‐derived PPI
networks as multi‐layered networks that are comprised of intra‐ and
inter‐organelle interactions (Fig [290]7F) revealed greater insights.
As expected, reactome pathway analysis of the ER/ERGIC and the Golgi
interactomes showed an enrichment of protein processing and secretory
processes, respectively, and the PM‐localized interactome showed an
enrichment of GF signaling (Fig [291]7G). The PM‐localized interactome
also showed an enrichment of cell–cell contact and contact‐dependent
signaling pathways (such as Semaphorins and the Eph/ephrin system;
green; Fig [292]7G), which enable cell–cell coordination in
multicellular eukaryotes. These findings indicate that the coupled
GTPase system supports a network of proteins that primarily enable
secretion‐coupled GF sensing and thereby, growth signaling autonomy.
Discussion
The major novelty we report here is the creation of an experimentally
constrained multi‐timescale model for cell survival that relies on
GF‐responsive cell secretion. One major consequence of such a
phenomenon is autonomous growth/survival in the absence of external
GFs. We formally define the molecular basis for such autonomy and
demonstrate the consequences when it is manipulated/perturbed. The
insights and models derived from this study are expected to inform and
impact at least three fields, that is, signal transduction, cell
secretion, and cancer cell biology in the following ways.
In the field of signal transduction, emergent properties of
ectomembrane signaling circuits at the PM have been identified using
mathematical modeling based on ODEs; however, none thus far have
coupled the events at the ectomembrane to the events in the cell's
interior, that is, the endomembrane of organelles. Our study
experimentally validated a Golgi‐localized natural coupling between the
two GTPase switches with exquisite feedback control that enables linear
activation of Arf1 in response to EGF, which in turn enables the Golgi
to mount a response (protein secretion) that is proportionate to the
stimulus (sensed at the PM) and robust to noise. The model reveals two
notable features: First we show that the CLC system generated DoRA,
enabling a linear increase in Arf1/mG* activation and protein
secretion. Such DoRA has been described in several major
receptor‐initiated signaling cascades at the PM (from the pheromone
response system in yeast to the Wnt → βCatenin, TGFβ→SMAD2/3 and
EGFR→MAPK cascades in mammals; Andrews et al, [293]2016), but never in
endomembrane GTPases. Because a linear DoRA maximally preserves any
information during its propagation (Andrews et al, [294]2018), we
conclude that one of the major discernible consequences of the
closed‐loop coupling of two GTPases is its ability to faithfully
transmit information from the PM to the Golgi for the latter to mount a
concordant secretory response. Second, although the first switch, that
is, Arf1/mG* showed a linear response, the subsequent steps (switch #2
and the step of membrane mechanics leading to secretion) become
progressively ultrasensitive. The net result of this is that the
closed‐loop feedback control allows for a tighter alignment of
secretion with respect to EGF by “stretching” out the dose–response
curve across a series of switches to propagate the signal from the
extracellular space to the interior of the cell. Because the stability
behavior of a mathematically simpler version of this closed‐loop system
of coupled GTPases showed that coupling afforded a wide range of steady
states (Stolerman et al, [295]2021), it is tempting to speculate that
the coupled system allows flexibility in responses over a wide range of
stimulus. In fact, follow‐up work has now revealed how ranges of
activity of the mGTPase Arf1, reaction kinetics, the negative feedback
loop (mGAP), and the cascade length affect DoRA (Qiao
et al, [296]2023).
When it comes to the field of protein secretion, the cell's secretory
pathway was originally believed to be a constitutive function that is
regulated by “housekeeping” genes/proteins that maintain the integrity
of the local (membrane or lumenal) environment (Arvan
et al, [297]2002). The earliest evidence that secretion is regulated by
exogenous GFs emerged in 2008 when the phosphoinositide phosphatase
SAC1 was implicated as a “brake” in anterograde Golgi secretion that is
released by GFs (Blagoveshchenskaya et al, [298]2008). Despite these
insights, what remained unknown was how the secretory system (or any
intracellular organelle/system) responds proportionately to external
cues. The functional consequences of an endomembrane coupled GTPase
system we dissected here fill that knowledge gap. We show that coupling
of m/tGTPases with CLC within cells is critical to set up feedback
controls in yet another scale, that is, cell secretion and cell fate
(i.e., survival vs. death).
Finally, when it comes to the field of cancer cell biology, it is
well‐accepted that self‐sufficiency in growth signaling is a hallmark
of all cancer cells (Hanahan & Weinberg, [299]2000); we show here how
cells achieve such self‐sufficiency for the prototypical GF system,
that is, EGF/EGFR. Existing theories linking genetic circuits to
cellular autonomy, although quantifiable and tunable (Youk &
Lim, [300]2014; Maire & Youk, [301]2015; Doğaner et al, [302]2016;
Kamino et al, [303]2017; Tang et al, [304]2021), do not apply to
multicellular eukaryotes. In dissecting the behavior of the coupled
GTPase system, and revealing the consequences of its disruption, both
in silico and in two different cancer cells, we fill that knowledge
gap. Second, intratumoral cellular heterogeneity is known to give rise
to an ecosystem of clonal interactions (Basanta & Anderson, [305]2013;
Tabassum & Polyak, [306]2015) that can drive tumor growth, therapeutic
resistance, and progression (Merlo et al, [307]2006; Basanta &
Anderson, [308]2017; Maley et al, [309]2017; Li &
Thirumalai, [310]2019). Therefore, it is possible that a few autonomous
secretor clones with an intact secrete‐and‐sense loop could be
sufficient to support the survival of neighboring non‐secretor clones.
If so, uncoupling the GTPases and disrupting the secrete‐and‐sense
autonomy could serve as an impactful therapeutic strategy. Finally, the
evolutionary significance of our findings is noteworthy. For example,
the linker between the GTPases, that is, GIV, evolved later in
multicellular organisms such as worms (Nechipurenko et al, [311]2016)
and flies (Puseenam et al, [312]2009; Yamaguchi et al, [313]2010; Ha
et al, [314]2015; Houssin et al, [315]2015). GIV's HOOK module (binds
mGTPase) evolved in worms and flies (Puseenam et al, [316]2009;
Yamaguchi et al, [317]2010; Ha et al, [318]2015; Houssin
et al, [319]2015); its GEM domain (a short motif that binds and
modulates tGTPases) evolved later in fish (DiGiacomo et al, [320]2018)
and remains to date. Thus, the coupled GTPase circuit likely evolved in
higher eukaryotes, and as suggested by our multi‐organelle proteomic
analyses, is geared to support autonomy in multicellular organisms.
This is consistent with the fact that evolution appears to favor
efficient signaling circuits that can accomplish many different tasks
(Milo et al, [321]2002; Shen‐Orr et al, [322]2002). Because GIV is
overexpressed in the most aggressive tumor cells, it is likely that the
GTPase coupled circuit is more frequently assembled in those cells. If
so, the circuit may represent an evolutionary masterpiece of multiscale
feedback control to achieve autonomy, adaptability, and flexibility.
Follow‐up work has now shed light on the importance of this phenomenon
in the orchestration of self‐sustained EGFR/ErbB signaling in tumor
cells (preprint: Sinha et al, [323]2022). Such autonomy in growth
signaling appears to be critical for the maintenance of high metastatic
potential and epithelial–mesenchymal plasticity during the blood‐borne
dissemination of human breast cancer.
Limitations of the study
The multi‐timescale model we built ignores the spatial aspects of the
various feedback control loops. Because the spatial organization of
signaling motifs will influence their temporal behaviors, we anticipate
the need for further refinement of the current model. By depleting GIV,
we disconnect the GTPases and dismantle the entire circuit; selective
disruption of various connections within the Golgi‐localized circuit is
not possible currently due to the lack the experimental tools (e.g.,
specific point mutants of GIV, GEF, or GAPs or perturbagens such as a
small molecule or peptides). Although we studied four different cargo
proteins (VSV‐G, MMP2/9, and Col‐VII) and two types of stimuli (EGF and
serum), a more comprehensive assessment of the cell's secretome is
expected to reveal how the intracellular GTPase circuit controls the
composition of the extracellular space. We chose to use mathematical
modeling to test the experimentally determined key components by
design, but there may be missing components that enable other emergent
properties (such as advantages of AND vs. OR gate mechanisms in the
feedback loops); future work is expected to build upon this framework
to fill these knowledge gaps. Conducting experiments across the full
range of stimuli to assess “proportionality/linearity” of response was
possible in some instances (e.g., cell survival) but not possible in
others (e.g., FRET, Arf1 activity, etc.) due to technical limitations
of the assays and/or detection thresholds. Finally, our mathematical
model ignores the effect of the physical location and heterogeneity of
cells. To explore such homogeneous and heterogeneous cell population
(Gerlee & Anderson, [324]2008; Sottoriva et al, [325]2010; Poleszczuk
et al, [326]2015) future studies will need to include agent‐based
models (Wang et al, [327]2007; Chao Dennis et al, [328]2008; Norton &
Popel, [329]2014), in which each cell is regarded as an individual
agent that “senses” the environment and “decides/acts” in response.
Materials and Methods
Reagents and Tools table
Reagent or Resource Source Identifier
Antibodies
Mouse monoclonal anti‐Gαi‐GTP Graeme Milligan (Lane et al, [330]2008)
26901
Rabbit anti‐Arf1 Paul Randazzo (Marshansky et al, [331]1997) n/a
Rabbit anti‐Mannosidase (Man)‐II Gift from K. Moreman (Velasco
et al, [332]1993) n/a
Anti‐GFP Living Colors, Invitrogen (Thermo Scientific) Catalog #
MA5‐15256
Anti‐RFP Invitrogen (Thermo Scientific) Catalog # MA5‐15257
Anti‐GIV coiled coil antibody Millipore (Sigma) ABT80
Goat anti‐Rabbit IgG, Alexa Fluor 594 conjugated Thermo Fisher
Scientific A11072
Goat anti‐Mouse IgG, Alexa Fluor 488 conjugated Thermo Fisher
Scientific A11017
IRDye 800CW Goat anti‐Mouse IgG Secondary (1:10,000) LI‐COR Biosciences
926‐32210
IRDye 680RD Goat anti‐Rabbit IgG Secondary (1:10,000) LI‐COR
Biosciences 926‐68071
Biological samples
N/a
Chemicals, peptides, and recombinant proteins
DAPI (4′,6‐Diamidino‐2‐Phenylindole, Dilactate) Thermo Fisher
Scientific D3571
MTT Millipore Sigma 475989‐1GM
Puromycin Sigma P9620‐10ML
Brefeldin A Sigma B6542‐5MG
Fetal Bovine Serum PEAK SERUM PS‐FB1
Paraformaldehyde 16% Electron Microscopy Biosciences 15710
Glutathione Sepharose^â 4B Sigma–Aldrich GE17‐0756‐04
Protease inhibitor cocktail Roche 11 873 580 001
Tyr phosphatase inhibitor cocktail Sigma–Aldrich P5726
Ser/Thr phosphatase inhibitor cocktail Sigma–Aldrich P0044
PVDF Transfer Membrane, 0.45 mM Thermo Scientific 88518
Prolong Glass Thermo Fisher Scientific [333]P36980
Paraformaldehyde 16% Electron Microscopy Biosciences 15710
Guava Cell Cycle Reagent Millipore Sigma 4700‐0160
Commercial kits
Dead Cell Apoptosis Kit with Annexin V Alexa Fluor™ 488 & Propidium
Iodide (PI) Thermo Fisher Scientific V13241
Experimental models: Cell lines
HeLa parental ATCC ATCC® CCL‐2
HeLa GIV KO (CRISPR Cas9) Prior work (Abd El‐Hafeez et al, [334]2023)
n/a
MDA‐MB‐231 ATCC ATCC® HTB‐26
MDA‐MB‐231 parental and GIV KO (CRISPR Cas9) lines Prior work (Abd
El‐Hafeez et al, [335]2023) n/a
HeLa shControl Prior work (Lo et al, [336]2015; Lopez‐Sanchez
et al, [337]2015; Rohena et al, [338]2020) n/a
HeLa shGIV Prior work (Rohena et al, [339]2020) n/a
Cos7 shControl Prior work (Ma et al, [340]2015; Rohena
et al, [341]2020) n/a
Cos7 shGIV Prior work (Ma et al, [342]2015; Rohena et al, [343]2020)
n/a
COS7 ATCC ATCC® CRL‐1651™
HEK293T ATCC ATCC® CRL‐1573™
Recombinant DNA
Internally tagged Gαi[1]‐YFP Moritz Bünemann (Bunemann
et al, [344]2003; Gibson & Gilman, [345]2006; Lo et al, [346]2015;
Midde et al, [347]2015) N/A
Girdin CRISPR/Cas9 KO Plasmid (h2) Santa Cruz Biotechnology (SCBT) Inc.
Sc‐402236‐KO‐2
CFP‐Gβ[1] Lo et al ([348]2015) N/A
Temperature sensitive (ts)VSVG‐eGFP Lo et al ([349]2015) N/A
MMP2‐GFP Marc Coppolino (Kean et al, [350]2009) N/A
MMP9‐GFP Marc Coppolino (Kean et al, [351]2009) N/A
Col VII‐RFP Anderzej Fertala (Chung et al, [352]2009) N/A
GST GAT (GGA) Stuart Kornfeld (Dell'Angelica et al, [353]2000) N/A
Other: Software
ImageJ National Institute of Health [354]https://imagej.net/Welcome
IX81 FV1000 inverted confocal laser scanning microscope Olympus n/a
ClueGO Cytoscape Bindea et al ([355]2009)
NetworkX Python [356]https://networkx.org
Gephi Gephi [357]https://gephi.org
Prism GraphPad [358]https://www.graphpad.com/scientific‐software/prism/
LAS‐X Leica
[359]www.leica‐microsystems.com/products/microscope‐software/p/leica‐la
s‐x‐ls
Illustrator Adobe [360]https://www.adobe.com/products/illustrator.html
MATLAB MathWorks [361]https://www.mathworks.com/
ImageStudio Lite LI‐COR
[362]https://www.licor.com/bio/image‐studio‐lite/
[363]Open in a new tab
Methods and Protocols
Modeling approaches
Model assumptions
We restrict our modeling considerations to the secretory pathway on
Golgi and its interactions with cell survival. The secretory pathway on
Golgi consists of mGTPases, tGTPases, their GEFs and GAPs, and the
secretion machinery. In the secretory pathway on Golgi, EGF mediates
the recruitment of GEF for mGTPase (mGEF) and triggers the activation
of corresponding mGTPases. Then active mGTPase can recruit GIV to
vesicles. GIV is GEF for tGTPase (tGEF), and subsequently activates
tGTPase. Upon activation of tGTPase, Gβγ is released and activates the
GAP for mGTPase (mGAP). Besides, mGAP is also regulated by GIV, which
binds to mGAP and works as a co‐factor for GAP activity. mGAP has a
dual role in this circuit: one is to turn “OFF” mGTPase, and the other
is to promote the vesicle formation. The vesicle formation is essential
for secretion, and the secreted GFs leads to cell proliferation. The
increase in cell number in turn enhances the secretion.
To model the above circuit, we assume that
* The total number of each type of GTPases is constant.
* The copy number of GAP for tGTPase (tGAP) is constant since it is
not regulated by other species.
* The species are present in large enough quantities that
deterministic approaches can be used to capture the dynamics of the
system.
* The process of secretion can be modeled using a simplified function
that depends on mGAP.
Therefore, the circuit is modeled by a set of ODEs with six species:
active mGTPase, active tGTPase, mGEF, mGAP, tGEF, and the secreted GFs.
Besides, the cell's survival number is also modeled by an ODE. We note
that our model does not include the spatial or mechanical aspects
associated with these signaling pathways.
Governing equations
Our model consists of two parts: one experimentally constrained module
for coupled switches on the Golgi and the other module to predict the
influence of coupled switches on the secrete‐and‐sense autonomy
(Fig [364]1B). In the module for coupled switches, we modeled all the
species interactions by normalized‐Hill functions (Saucerman &
McCulloch, [365]2004; Cao et al, [366]2020) to capture the overall
input–output relationships. We did not consider all the intermediary
steps in the signaling pathway for the sake of simplicity. When active
tGTPase and tGEF both regulate mGAP, the “AND” logic is applied and
modeled as
[MATH: facttGTPases·facttGEF :MATH]
. Thus, the dynamics of the system can be described by the following
equations:
[MATH: dmGEFdtτmGEF=fact1stimulus+kmGEFY
mGEFmax−mGEF :MATH]
(1)
[MATH: dmGAPdtτmGAP=fact2tGEFfact3tG*+kmGAPY
mGAPmax−mGAP :MATH]
(2)
[MATH: dmG*dtτmG*=fact4mGEF+kmG*1−mG*−fact5mGAPmG* :MATH]
(3)
[MATH: dtGEFdtτtGEF=fact6mG*+ktGEFY
tGEFmax−tGEF :MATH]
(4)
[MATH: dtG*dtτtG*=fact7tGEF+ktG*1−tG*−fact8tGAPtG* :MATH]
(5)
where variables
[MATH: mGEF :MATH]
,
[MATH: mGAP :MATH]
,
[MATH: mG* :MATH]
,
[MATH: tGEF :MATH]
, and
[MATH: tG* :MATH]
denote the fractional activation of mGEF, mGAP, mGTPase, tGEF, and
tGTPase, respectively. Here, the fractional activation is the copy
number divided by the maximal copy number, which changes between 0 and
1. The variable
[MATH: stimulus :MATH]
denotes the input signal EGF; the
[MATH: τ :MATH]
's are time scale;
[MATH: k :MATH]
's are basal production rates, and
[MATH: Yimax,i=mGEF,mGAP,tGEF :MATH]
are maximal fractional activations for species. The function
[MATH: factii=1,2
,⋯,9 :MATH]
is the normalized‐Hill function, which takes the following form:
[MATH: factX=BXn
Kn+Xn,if0≤X<11,<
mspace width="3.5em">ifX≥1 :MATH]
(6)
where
[MATH: B=EC50n−12EC50n−1 :MATH]
and
[MATH: K=B−11/n :MATH]
. Here,
[MATH: EC50 :MATH]
and
[MATH: n :MATH]
are half‐maximal activation and Hill coefficient, respectively. With
these choices of constants
[MATH: B :MATH]
and
[MATH: K :MATH]
, we have
[MATH: fact0=0 :MATH]
,
[MATH: factEC50=0
.5 :MATH]
. It should be noted that constants
[MATH: B :MATH]
and
[MATH: K :MATH]
can be different in different functions
[MATH: facti :MATH]
. In most cases, we used
[MATH: k=0 :MATH]
and
[MATH: Ymax≤1 :MATH]
, so the maximal value of variables
[MATH: 1+kYmax :MATH]
is smaller than 1 to ensure the range of the fractional activation.
But, when we used a non‐zero
[MATH: k :MATH]
, the variable may be larger than 1, and then we regard the variable as
the relative activation, which is normalized by a number smaller than
the maximal copy number. We refer to this model as the coupled system
throughout our study.
To predict the effect of coupled switches on the secrete‐and‐sense
autonomy, we also built a model for secretion (denoted by
[MATH: S :MATH]
) and cell number (denoted by
[MATH: X :MATH]
). Since the activation‐deactivation circle of mGTPase is necessary for
the secretion, we assume the secretion rate is positively correlated to
[MATH: fact9mGAP :MATH]
. In addition, the proliferation of cells is regulated by secreted GFs
to ensure homeostasis (Hart et al, [367]2014; Adler et al, [368]2018).
Then, the dynamics of
[MATH: S :MATH]
and
[MATH: X :MATH]
are governed by:
[MATH: dSdt=βSfact9mGAP+kS−αSSS+K2X−γS :MATH]
(7)
[MATH: dXdt=λSS+K11−XK<
/mfenced>−μX
:MATH]
(8)
where
[MATH: βS :MATH]
is the maximal secretion rate;
[MATH: kS :MATH]
is the basal secretion rate;
[MATH: αS :MATH]
is the maximal endocytosis rate;
[MATH: γ :MATH]
is the degradation rate of secreted GFs;
[MATH: K2 :MATH]
is the binding affinity of secreted GFs. In equation ([369]8),
[MATH: λ :MATH]
and
[MATH: μ :MATH]
are cell proliferation and death rates by the cells, respectively;
[MATH: K1 :MATH]
is the value of
[MATH: S :MATH]
when the Hill function
[MATH:
SS+K1 :MATH]
is 0.5;
[MATH: K :MATH]
is the carrying capacity, that is, once the cell number is
[MATH: K :MATH]
, the cell proliferation rate is zero, preventing the cell number from
exceeding
[MATH: K :MATH]
.
Single switch model
For the circuit that only contains the single switch of mGTPases, its
dynamics is described by equations ([370](1), [371](2), [372](3)),
except that equation ([373]2) is replaced by
[MATH: dmGAPdtτmGAP=kmGAPYmGAP−mGAP :MATH]
(9)
Note that this equation also can be used to describe the dynamics of
mGAP when the regulation from tGEF to mGAP or the regulation from
active tGTPase to mGAP does not exist.
Numerical simulations for the deterministic model
Numerical simulations were implemented in MATLAB. We use the solver
ode15s to simulate the dynamics on the time interval [0, 1,440] min
unless otherwise specified.
Fitting against experimental data
To fit the time course data for control cells and GIV‐depleted cells,
we manually tuned the parameters in our model until the normalized RMSE
between simulated and measured fold changes of active Arf1 was less
than 0.2 and that for active tGTPase less than 0.45. Moreover,
parameters for secretion and cell survival are taken from their
biologically plausible ranges (Adler et al, [374]2018). Our fitting
goal was to capture the experimentally observed trends rather than
obtain kinetic parameters since our model does not include all the
reactions in the pathway(s). Here, the normalized RMSE is the RMSE over
the mean value of all experimental data; the baseline for the
simulation result is the initial fractional activation when simulating
dynamics for control cells, and those for experimental Arf1 and tGTPase
data are initial states in control cells. The obtained parameter values
are listed in Table [375]EV1. In all simulations, the initial condition
is the starved state when
[MATH: stimulus=0
:MATH]
, and then
[MATH: stimulus :MATH]
is set to be 0.23 to simulate the dynamics under the EGF‐stimulated
condition. In all simulations, we use normalized values of EGF
concentrations. The normalization was conducted such that the value of
0.23 EGF used in simulations corresponds to 50 nM in the experiments.
The dimensionless EGF concentrations in the simulations are obtained by
dividing the EGF concentration by 217.4 nM (=50 nM/0.23).
Testing model
We verify that our setting in the model for GIV‐deplete cells indeed
makes the system behave like the uncoupled system. We set the maximal
fractional activation of tGEF as 0.1 (i.e.,
[MATH: YtGEF=0.1 :MATH]
) but keep other parameters unchanged to model the system in
GIV‐depleted cells. The initial state is determined by the steady‐state
values of all species when the stimulus is zero, which are obtained as
follows: we set stimulus zero and chose an arbitrary initial condition
(e.g., all species are 0.5), and then simulated the deterministic
dynamics on the time interval [0, 2,400 h] to ensure that the steady
state is reached.
[MATH: → :MATH]
Then we changed the stimulus to 0.23 to simulate the dynamics of all
species when EGF = 50 nM. We find that GIV‐depleted cells are more
likely behave as the uncoupled system (Appendix Fig [376]S3A). For
these two systems, mGEF and mG* both increase upon the stimulus of EGF,
and mGAP will not increase because of low activation of tGEF in
GIV‐depleted cells or the absence of the positive regulation from tGEF
and tG* in the uncoupled system. Due to the non‐increasing level of
mGAP, these two systems both show non‐decreasing fractional activation
of mG*, low secretion, and low cell number. The only difference between
these two systems is the dynamics of tGTPase switch: tGEF is low in
GIV‐depleted cells and thus cannot activate tG*, while in the uncoupled
system the fractional activations of tGEF and tG* are both high. The
schematics of these three systems are shown in Appendix Fig [377]S3B–D.
Sensitivity analysis
To test the robustness of the model, we performed sensitivity analyses.
The sensitivity measures how the system output is vulnerable to the
parameter change and can be captured by the following quantity:
[MATH: Sensitivity=dlnXdlnα :MATH]
where the
[MATH: X :MATH]
is the system's output and
[MATH: α :MATH]
is the kinetic parameter. In our analyses, we calculated this
sensitivity for each kinetic parameter, that is, the
[MATH: α :MATH]
can be every kinetic parameter. The output
[MATH: X :MATH]
is the normalized RMSE value for simulated mG* or tG* dynamics, or
steady‐state values of the secretion or the cell number. This
derivative is approximated by the ratio of the difference of
[MATH: lnX :MATH]
when
[MATH: α :MATH]
is
[MATH: 1.1×α :MATH]
and
[MATH: 0.9×α :MATH]
to the
[MATH: 0.2×α :MATH]
. We found that, perturbations of the half‐maximal activation
[MATH: EC50 :MATH]
will cause large changes in the normalized RMSEs and the steady‐state
value of the secretion for coupled switches (Appendix Fig [378]S4).
Except
[MATH: EC50 :MATH]
, the mG* and tG* dynamics seem robust to other kinetic parameters,
since the sensitivities for other kinetic parameters are between −0.5
and 0.5. Besides, the steady state of the secretion in coupled switches
is sensitive to the maximal secretion rate
[MATH: αS :MATH]
and the maximal endocytosis rate
[MATH: βS :MATH]
. These not very large sensitivities indicate that the main conclusions
hold under small perturbations.
The stochastic model
To investigate the impact of noise, we consider three different sources
of noise: stimulus, species, and connections. A noisy stimulus is
modeled by the summation of the mean and a noise term
[MATH: ηstit :MATH]
; another type of noise, originated from species, is generated by
adding a noise term
[MATH: ηspet :MATH]
in the equation for each species, and
[MATH: tGAP :MATH]
is also perturbed by a noise term
[MATH: ηspetGAPt :MATH]
; the third type of noise, which comes from connections, is modeled by
adding a noise term
[MATH: ηlinkt :MATH]
to each activation function
[MATH: fact :MATH]
and nonlinear reaction rates in equations for the secretion and the
cell number. Here, these noise terms are independent of each other, and
all modeled by the following Ornstein–Uhlenbeck process:
[MATH: τjnoisedη<
mi>j=−ηjdt+σjd
Wtj :MATH]
(10)
where
[MATH: j=sti,spe,link :MATH]
, and
[MATH: Wtj :MATH]
's are independently and identically distributed standard Wiener
processes. This equation implies that
[MATH: ηjt :MATH]
has zero mean and variance
[MATH:
σj22τjnoise :MATH]
. The equations for active tGEF, the secretory protein, and the cell
number in the presence of noise are taken as an example: when noise
exists only in species, the dynamics of active tGEF, the secretory
protein, and the cell number are described by
[MATH: dtGEFdtτmGEF=fact6mG*+ktGEFYtGEF−tGEF+ηspetGEF :MATH]
(11)
[MATH: dSdt=βSfact9mGAP+kS−αSSS+K2X−γS+ηspeS :MATH]
(12)
[MATH: dXdt=λSS+K11−XK<
/mfenced>−μX+ηspeX :MATH]
(13)
while the corresponding dynamics when noise are present in connections
are governed by
[MATH: dtGEFdtτmGEF=fact6mG*+ηlinktGEF+ktGEFYtGEF−tGEF :MATH]
(14)
[MATH: dSdt=βSfact9mGAP+kS+ηlinkS,1−αSSS+K2+ηlinkS,2X−γS :MATH]
(15)
[MATH: dXdt=λSS+K11−XK<
/mfenced>+ηlinkX−μX
:MATH]
(16)
where
[MATH: ηlink
:MATH]
's with different superscripts are independent noise terms.
Numerical simulations for the stochastic model
Numerical simulations were implemented in MATLAB. We used the Milstein
scheme (Kloeden & Platen, [379]1992) to numerically solve the noise
term
[MATH: ηj :MATH]
(
[MATH: j=sti,spe,link :MATH]
), and used the Euler scheme to solve the dynamics of molecules on the
time interval [0, 1,440 min]. To be specific, the noise term
[MATH: ηj :MATH]
at
[MATH: n+1 :MATH]
time step is determined in the following manner (
[MATH: τjnoise=1 :MATH]
):
[MATH:
ηjn+1=ηjn−ηjndt+σjδWn+12σj2δWn2−dt :MATH]
where
[MATH: dt :MATH]
is the time step and
[MATH: δWn :MATH]
obeys the normal distribution with mean zero and variance
[MATH: dt :MATH]
. Then, the activation of molecules or the cell number is solved by the
Euler scheme. For example, when noise is only in stimulus, the mGEF at
[MATH: n+1 :MATH]
time step, denoted as
[MATH: mGEFn+1 :MATH]
, is obtained by the following equation:
[MATH: [mGEF]n+1
=[mGEF]n+dt1τmGEF((fact1stimulus+ηstin+1<
mrow>+kmGEF)YmGEF−[mGEF]n), :MATH]
the schemes to solve equations ([380]12) and ([381]15) are
[MATH:
Sn+1=Sn+dt(βS<
mfenced>fact9[mGAP]n+kS−αSSnSn+K2X−γSn
+ηspeSn<
/mi>+1), :MATH]
and
[MATH:
Sn+1=Sn+dt((βS<
msubsup>fact9[mGAP]n+kS+ηlinkS,1n+1−αS
SnSn+K2+ηlinkS,2n+1)X−γSn
), :MATH]
respectively.
We compare coupled switches with the single switch of mGTPase for three
different cases of noise: noise in the stimulus, noise in the stimulus
and species simultaneously, and noise in the stimulus and connections
simultaneously. The values of noise amplitudes used for simulations are
listed as follows:
* When noise is only in the stimulus, the parameter
[MATH: σsti
:MATH]
for
[MATH: ηstit :MATH]
is 0.02, and
[MATH: τstinoise :MATH]
is 1.
* When noise is in the stimulus and species simultaneously,
parameters
[MATH: σsti
:MATH]
and
[MATH: τstinoise :MATH]
for
[MATH: ηstit :MATH]
are the same as those when noise is only in the stimulus. In
addition, for the noise term
[MATH: ηspet :MATH]
,
[MATH: τspenoise=1
:MATH]
, and
[MATH: σspe
:MATH]
is 0.02 for all species except the secretion and cell number. Since
the secretion and cell number have small reaction rates,
[MATH: σspeS :MATH]
and
[MATH: σspeX :MATH]
are set to be
[MATH:
2×10−5 :MATH]
and
[MATH:
2×10−6 :MATH]
respectively, and thus the noisy behaviors cannot overwhelm the
deterministic behaviors.
* When noise is in the stimulus and connections simultaneously,
parameters
[MATH: σsti
:MATH]
and
[MATH: τstinoise :MATH]
for
[MATH: ηstit :MATH]
are still the same as those when noise is only in the stimulus.
Moreover,
[MATH: τlinknoise=1
:MATH]
, and
[MATH: σlinknoise=0.02
:MATH]
for all species except the cell number. The
[MATH: σlinkX :MATH]
is 0.002 to ensure the same order of the noise and the production
rate of cell number.
In this study, for a given input signal, we performed 1,000 repeated
simulations on the time interval [0, 1,440 min] (with the steady state
under this signal as the initial state). The time step
[MATH: dt :MATH]
is set to be 0.01.
Computational and bioinformatics approaches
Identification of a Golgi‐localized Arf1 and GIV interactome
We have previously extracted an annotated subcellular localization
network of high‐confidence GIV correlators (Ear et al, [382]2021),
based on Human Cell Map (HCM; Go et al, [383]2021). From the same HCM
data set, a set of high‐confidence Arf1 correlators were also
extracted. Using the combined set of proteins that were correlated with
GIV and Arf1, a full correlation network between every protein was
extracted. Annotated unique GIV interactors from BioGRID (Oughtred
et al, [384]2021) were also incorporated to expand the GIV–Arf1
interaction network. To assign subcellular localization of the GIV
interactors from BioGRID (Oughtred et al, [385]2021), they were first
matched to subcellular localization as annotated by HCM. For those
proteins that were not assigned by HCM, they were then matched to Gene
Ontology (GO) Cellular Component terms, Uniprot (The
UniProt, [386]2019), and Human Protein Atlas (Uhlén et al, [387]2015),
which were all used as a guide to manually assign them based on their
biological function. The complete list of this “Golgi‐localized
Arf1‐GIV interactome” is provided as Dataset [388]EV1.
Protein–protein interaction network construction, in silico perturbation, and
topological analyses
The list of proteins (Dataset [389]EV1) was used as “seed” to generate
the Golgi‐specific Arf1‐GIV network by fetching other connecting
interactions and proteins from STRING database (Franceschini
et al, [390]2013). The shortest path NetworkX algorithm (Sinha
et al, [391]2021) was used to trace the connected proteins and
interactions between every possible pair of proteins from the
above‐mentioned list. The highest possible interaction cutoff score was
used to avoid false positive interactions. To understand the impact of
GIV deletion, a similar network was prepared, except without GIV. The
shortest path alteration fraction (Sinha et al, [392]2021) associated
with Arf1 was calculated using differential shortest path analysis of
the original and GIV‐depleted PPI network. Here only the paths having
shortest path alteration fraction 1 were considered which indicated
only the deleted or newly added shortest paths due to GIV deletion. GO
Biological Process (BP) analysis of the proteins identified using
shortest path alteration fraction analysis was performed using the
Cytoscape tool ClueGO (Bindea et al, [393]2009) and significant GO BP
terms were visualized.
TMT proteomics analysis, network construction, and multi‐layer visualization
Proteins that are upregulated in WT were mapped using the STRING
database ([394]https://string-db.org/). A pathway enrichment analysis
of the most highly connected nodes was performed using the Reactome
database ([395]https://reactome.org/). The compartmentalized
distribution of proteins within the PPI network based on their
organelle‐specific location was mapped using the Cell Atlas Uniform
Manifold Approximation and Projection (UMAP) explorer that was
generated using the large collection of confocal microscopy images
showing the subcellular localization patterns of human proteins,
curated and made available at Human protein atlas
([396]https://www.proteinatlas.org/). Multilayer visualization of an
organelle‐based interaction network was constructed using MultiViz
plugin (preprint: Jayamohan Pillai et al, [397]2022) of Gephi platform.
All the source codes for network analysis are available at
[398]https://github.com/sinha7290/PPIN. MultiViz plugin source code is
available at [399]https://github.com/JSiv/gephi-plugins.
Experimental model and subject details
Cell lines and culture methods
HeLa, Cos7, and MDA‐MB‐231 cells were grown at 37°C in their suitable
media, according to their supplier instructions, supplemented with 10%
FBS, 100 U/ml penicillin, 100 μg/ml streptomycin, 1% l‐glutamine, and
5% CO[2].
GIV CRISPR/Cas9 gene editing and validation
Pooled guide RNA plasmids (commercially obtained from Santa Cruz
Biotechnology; Cat# sc‐402236‐KO‐2) were used to generate both HeLa and
MDA MB‐231 GIV KO lines as described before (Ear et al, [400]2021).
Briefly, these CRISPR/Cas9 KO plasmids consist of GFP and
Girdin‐specific 20 nt guide RNA sequences derived from the GeCKO (v2)
library and target human Girdin exons 6 and 7. Plasmids were
transfected into Hela and MDA‐MB‐231 cells using PEI. Cells were sorted
into individual wells using a cell sorter based on GFP expression. To
identify cell clones harboring mutations in the gene coding sequence,
genomic DNA was extracted using 50 mM NaOH and boiling at 95°C for
60 min. After extraction, pH was neutralized by the addition of 10%
volume 1.0 M Tris‐pH 8.0. The crude genomic extract was then used in
PCR reactions with primers flanking the targeted site. Amplicons were
analyzed for insertions/deletions (indels) using a TBE‐PAGE gel. Indel
sequence was determined by cloning amplicons into a TOPO‐TA cloning
vector (Invitrogen) following manufacturer's protocol.
Reagents and antibodies
All sources for key reagents are listed in the Resource Table above.
Unless otherwise mentioned, all chemicals were purchased from Sigma (St
Louis, MO). A mouse mAb against the active conformation of Gαi was
obtained from Dr. Graeme Milligan (University of Glasgow, UK). Rabbit
anti‐Arf1 IgG was prepared as described (Marshansky et al, [401]1997).
Rabbit polyclonal anti‐α‐mannosidase II (Man II) serum was prepared as
described (Velasco et al, [402]1993). Highly cross‐absorbed Alexa Fluor
594 or 488 F(ab)'[2] fragments of goat anti‐mouse or anti‐rabbit IgG
(H + L) for immunofluorescence were purchased from Invitrogen
(Carlsbad, CA). Goat anti‐rabbit and anti‐mouse Alexa Fluor 680 or
IRDye 800 F(ab)'[2] for immunoblotting, were obtained from LI‐COR
Biosciences.
Cell culture, transfection, ligand stimulation, and lysis
HeLa and MDA MB‐231 (American Type Culture Collection, Manassas, VA)
were maintained in DMEM (Invitrogen) supplemented with 10% FBS
(Hyclone, Logan, UT), 100 U/ml penicillin, 100 μg/ml streptomycin, 1%
l‐glutamine, and 5% CO[2]. Control and GIV shRNA HeLa and Cos7 stable
cell lines were selected with 2 μg/ml of Puromycin (GIBCO) using a
plasmid expressing an shRNA targeting its 3′ UTR (Ghosh
et al, [403]2016a). Depletion of GIV was verified using a GIV‐CT
antibody with an efficiency of ~95% and cells were extensively
validated in prior studies (Lo et al, [404]2015; Ma et al, [405]2015;
Rohena et al, [406]2020). Transfection of cells with fluorescent
plasmids (FRET studies) was carried out using transit‐LT1 (Mirus Bio,
Madison, WI) following the manufacturer's protocol. Cells were checked
for mycoplasma contamination and authenticated by STR profiling
periodically.
For ligand stimulation of cells, serum starvation was carried out
overnight (~16–18 h) by replacing media with 0.2% FBS‐containing media
in the case of HeLa prior to exposing them to the ligands.
Lysates used as a source of proteins in pulldown assays were prepared
by resuspending cells in Tx‐100 lysis buffer [20 mM HEPES, pH 7.2, 5 mM
Mg‐acetate, 125 mM K‐acetate, 0.4% Triton X‐100, 1 mM DTT, supplemented
with sodium orthovanadate (500 mM), phosphatase (Sigma) and protease
(Roche) inhibitor cocktails], after which they were passed through a
28G needle at 4°C, and cleared (10,000 × g for 10 min) before use in
subsequent experiments.
Arf1 activation assays
Purification of GST‐GAT protein and assessment of Arf1 activation was
described previously. In brief, cells were lysed with 1% Triton X‐100,
50 mM Tris, pH 7.5, 100 mM NaCl, 2 mM MgCl[2], 0.1% SDS, 0.5% sodium
deoxycholate, and 10% glycerol with protease inhibitors. Equal amounts
of lysates were incubated with GST‐GGA3 (~40 μg) prebound
glutathione‐Sepharose 4B beads at 4°C for 1 h. Beads were washed, and
the bound proteins were eluted by boiling in Laemmli sample buffer for
5 min, resolved on a 15% SDS–PAGE, and analyzed by immunoblotting.
Quantitative immunoblotting
For immunoblotting, protein samples were boiled in Laemmli sample
buffer, separated by SDS–PAGE and transferred onto 0.45 mM PVDF
membrane (Millipore) prior to blotting. The duration of transfer was
30 min, at 100 V. Post transfer, membranes were blocked using 5%
non‐fat milk or 5% BSA dissolved in PBS. Primary antibodies were
prepared in a blocking buffer containing 0.1% Tween‐20 and incubated
with blots, rocking overnight at 4°C. After incubation, blots were
incubated with secondary antibodies for 1 h at room temperature,
washed, and imaged using a dual‐color Li‐Cor Odyssey imaging system.
Immunofluorescence and confocal microscopy
For immunofluorescence, cells grown on coverslips were fixed in 3%
paraformaldehyde (PFA) and processed as described previously (Ghosh
et al, [407]2010). Antibody dilutions were as follows: Man II, 1:800;
anti‐Gαi·GTP, 1:25; goat anti‐mouse or anti‐rabbit Alexa 488 or Alexa
594, 1:500. DAPI was used at 1:10,000. To estimate the degree of
colocalization (Mander's overlap coefficient; MOC) in
immunofluorescence assays, an ImageJ plugin, JACoP
([408]https://imagej.nih.gov/ij/plugins/track/jacop2.html) was used.
This was preferred over Pearson's because it is a good indicator of the
proportion of the green signal (active G protein) coincident with a
signal in the red channel (Man II, indicative of Golgi membranes) over
its total intensity, which may even apply if the intensities in both
channels are really different from one another. Coverslips were mounted
using Prolong Gold (Invitrogen) and imaged using a Leica SPE CTR4000
confocal microscope.
Image processing
All images were processed on ImageJ software (NIH) and assembled into
figure panels using Photoshop and Illustrator (Adobe Creative Cloud).
All graphs were generated using GraphPad Prism.
FRET studies
Intramolecular FRET was detected by sensitized emission using the
three‐cube method performed as previously reported by Midde
et al ([409]2015). Briefly, previously validated internally tagged
Gαi[1]‐YFP and CFP‐Gβ[1] FRET probe pairs were used (Bunemann
et al, [410]2003; Gibson & Gilman, [411]2006). Cells were transfected
with the probes, serum‐starved overnight, and then stimulated with EGF
(50 nM) exactly as done previously (Midde et al, [412]2015;
Kalogriopoulos et al, [413]2020). All fluorescence microscopy assays
were performed on single cells in a mesoscopic regime to avoid
inhomogeneities from samples as shown previously by Midde et al
(Borejdo et al, [414]2012; Midde et al, [415]2015). Briefly, cells were
sparsely split into sterile 35 mm MatTek glass bottom dishes and
transfected with 1 μg of indicated constructs. To optimize the
signal‐to‐noise ratio in FRET imaging, various expression levels of the
transfected FRET probes were tested. However, to minimize complexities
arising from molecular crowding, FRET probes were overexpressed by
∼1.5‐ to 2‐fold compared with the endogenous proteins. Because the
stoichiometry of FRET probes has a significant impact on FRET
efficiency, cells that expressed equimolar amounts of donor and
acceptor probes (as determined by computing the intensity of the
fluorescence signal by a photon‐counting histogram) were chosen
selectively for FRET analyses. An Olympus IX81 FV1000 inverted confocal
laser scanning microscope was used for live cell FRET imaging
(UCSD‐Neuroscience core facility). The microscope is stabilized on a
vibration‐proof platform, caged in temperature controlled (37°C) and
CO[2] (5%) supplemented chamber. A PlanApo 60× 1.40 N.A. oil immersed
objective designed to minimize chromatic aberration and enhance
resolution for 405–605 nm imaging was used. Olympus Fluoview inbuilt
software was used for data acquisition. A 515 nm Argon‐ion laser was
used to excite EYFP and a 405 nm laser diode was used to excite ECFP as
detailed by Claire Brown's group (Broussard et al, [416]2013). Spectral
bleed‐through coefficients were determined through FRET‐imaging of
donor‐only and acceptor‐only samples (i.e., cells expressing a single
donor or acceptor FP). Enhanced CFP emission was collected from
425–500 nm and EYFP emission was collected through 535–600 nm and
passed through a 50 nm confocal pinhole before being sent to a
photomultiplier tube to reject out‐of‐plane focused light. Every field
of view (FOV) is imaged sequentially through ECFPex/ECFPem,
ECFPex/EYFPem, and EYFPex/EYFPem (3 excitation and emission
combinations) and saved as a donor, FRET, and acceptor image files
through an inbuilt wizard. To obtain the FRET images and efficiency of
energy transfer values a RiFRET plugin in Image J software was used
(Roszik et al, [417]2009). Prior to FRET calculations, all images were
first corrected for uneven illumination, registered, and background
subtracted. For FRET quantification, regions of interest (ROI) were
drawn in the juxtanuclear area presumably in the Golgi region (or at
the cell periphery, presumed to be the plasma membrane regions) to
compute energy transfer. Individual cells with fluorescence intensity
in the mesoscopic regime detected in the donor and acceptor channels
were selected for FRET analysis to avoid inhomogeneities between
samples (Midde et al, [418]2013, [419]2014).
Manual and automatic registration of each individual channel in ImageJ
was critical to correct motion artifacts associated with live cell
imaging. Controls were performed in which images were obtained in
different orders. The order in which images were obtained had no
effect. FRET images were obtained by pixel‐by‐pixel ratiometric
intensity method and the efficiency of transfer was calculated by the
ratio of intensity in the transfer channel to the quenched (corrected)
intensity in the donor channel. The following corrections were applied
to all FOVs imaged: for crosstalk correction, cells transfected with
CFP or YFP alone were imaged under all three previously mentioned
excitation and emission combinations. FRET efficiency was quantified
from 3–4 Regions of Interests (ROI) per cell drawn exclusively along
the P.M. Because expression of FRET probes may have a significant
impact on FRET efficiency, cells that expressed similar amounts of
probes, as determined by computing the fluorescence signal/intensity by
a photon counting histogram were selectively chosen for FRET analyses.
Furthermore, untransfected cells and a field of view without cells were
imaged to correct for background, autofluorescence, and light
scattering. To avoid artifacts of photobleaching, Oxyfluor
([420]www.oxyrase.com) was used to minimize the formation of reactive
oxygen species.
GFP‐tsO45‐VSVG transport assays
To monitor anterograde (ER to Golgi) trafficking control or
GIV‐depleted COS7 cells were transiently transfected with
GFP‐tsO45‐VSV‐G plasmid (Presley et al, [421]1997). Transfected cells
were incubated for 14–16 h at the restrictive temperature (40°C) to
accumulate VSV‐G protein in the ER, shifted to 32°C for 0–60 min to
release VSV‐G protein in the conditions described (i.e., 10% serum,
EGF, or starved condition) and then fixed and processed under
non‐permeabilized conditions (without detergent) for
immunofluorescence. The rate of VSV‐G trafficking from the secretory
compartments to the PM was determined by calculating the ratio of VSV‐G
that was already at the PM (as determined using an anti‐VSV‐G
ectodomain antibody; red pixels) normalized to the total cellular pool
of VSV‐G (GFP; green pixels, using NIH ImageJ software).
Metalloprotease and collagen secretion assays
HeLa cells grown in a 6‐well plate were transfected with 2 μg of
GFP‐MMP2, GFP‐MMP9, or Collagen‐RFP for 5 h. After 5 h, cells were fed
with fresh media without FBS. Media was subsequently changed the next
day (without FBS; exactly 1.5 ml/well) and stimulated with EGF. Media
(100 μl) was collected just before the addition of EGF, as T = 0 h, and
at the indicated time points after EGF stimulation. Each aliquot was
subjected to high‐speed (14,000 × g) spin for 10 min prior to the
addition of 50 μl of Laemmli sample buffer and boiling at 100°C.
MTT assay
Cell proliferation was measured using the MTT reagent and cells
cultured in 96‐well plates. Parental or GIV‐KO HeLaor MDA‐MB‐231 cells
were cultured in different concentrations of FBS (0, 0.25, 2, 5, and
10%). Then the cell lines were incubated with MTT for 4 h at 37°C.
After incubation, culture media was removed and 150 μl of DMSO was
added to solubilize the MTT formazan crystals. Optical density was
determined at 590 nm using a TECAN plate reader. At least three
independent experiments were performed. In an independent experiment,
we tested the effect of using a Brefeldin A (BFA), a well‐known tool to
inhibit secretion, on cell proliferation. The cell lines were cultured
in different concentrations of FBS (0, 0.25, 2, 5, or 10%) and then
treated with different concentrations of BFA (0, 0.01, 0.05, 0.1, 0.5,
1, 10, or 100 μM) and the MTT assays were done as described.
Cell cycle and apoptosis analyses
Cell cycle analysis and apoptotic cell quantification were performed
using the Guava cell cycle reagent (Millipore Sigma) or the annexin
V/propidium iodide (PI) staining kit (Thermo Fisher Scientific),
respectively, according to the manufacturer's instructions. Cells were
quantified on a BD™ (BD Biosciences) LSR II flow cytometer and analyzed
using FlowJo software (FlowJo, Ashland, OR, USA).
Tandem Mass Tag™ (TMT) proteomics
WT and GIV‐KO MDA‐MB231 cells were maintained in 0 and 10% serum
concentration in p10 dishes (Corning) for 16 h prior to harvest, and
cell pellets were subsequently processed for TMT proteomics using LUMOS
Orbitrap‐Fusion analyzer. Samples were processed at the UC San Diego
Biomolecular and Proteomics Mass Spectrometry Core Facility
([422]https://bpmsf.ucsd.edu/). Peptides are identified and mapped
using Peaks X Pro pipeline. The intensity ratio of each identified
protein in WT MDA‐MB231 vs. GIV‐KO MDA‐MB231 cells has been identified
and selected if the significance score > 20. A list of differentially
expressed proteins is provided in Dataset [423]EV2. The mass
spectrometry proteomics data have been deposited to the ProteomeXchange
Consortium via the PRIDE partner repository (Perez‐Riverol
et al, [424]2022) with the dataset identifier PXD037253.
Quantification and statistical analysis
Statistical analyses in modeling approaches
In the deterministic model, we fitted the dose–response curve by
finding the best‐fit function with the form
[MATH:
axnxn+K+d :MATH]
. We solved this optimal problem using “lsqcurvefit” in Matlab, and
[MATH: d :MATH]
can be deleted depending on the effect of the fitting. The only
exception is for the mG vs. mGEF, where we used linear function
[MATH: ax+d :MATH]
. The difference between the fitted curve and the original curve is
measured by R^2, and it is defined as
[MATH:
1−∑iyi−fin∑iyi−y¯n
:MATH]
, where
[MATH: yi :MATH]
is the point in the original curve and
[MATH: fi :MATH]
is the prediction for
[MATH: yi :MATH]
based on the best‐fit curve. In the stochastic model, the standard
deviation is calculated based on the data at 1,440 min, which is
defined as the
[MATH:
∑i=1<
/mn>Nxi−x¯2
N−1,
:MATH]
where
[MATH:
N=1,000
:MATH]
and
[MATH: x¯=∑i=1N<
mi>xiN :MATH]
. The
[MATH: xi :MATH]
is the molecular activation or the cell number at 1,440 min in the
[MATH: i :MATH]
‐th simulation.
Statistical analyses in protein–protein network analyses
An interaction cutoff score has been optimized while fetching the new
proteins and their interactions from the STRING database, such that all
the possible proteins will be included keeping the cutoff very high. In
this instance, an interaction cutoff score of 667 has been used to
include all the proteins from the seed list (Dataset [425]EV1).
Statistical analyses in experimental studies and replication
All experiments were repeated at least three times (biological
replicates, conducted on different days), and results were presented
either as one representative experiment or as average ± SEM.
Statistical significance was assessed with two‐sided unpaired Student
t‐test and Mann–Whitney t‐test. For all tests, a P‐value of 0.05 was
used as the cutoff to determine significance. The actual P‐values are
indicated in each figure. All statistical analysis was performed using
GraphPad Prism 8 or Matlab. Experiments undertaken did not require
blinding; nor did they require sample size calculation or
randomization.
Materials availability
This study did not generate new unique reagent.
Author contributions
Lingxia Qiao: Methodology; investigation; formal analysis;
visualization; writing – original draft; writing – review and editing.
Saptarshi Sinha: Software; methodology; investigation; formal analysis;
validation; visualization; writing – original draft; writing – review
and editing. Amer Ali Abd El‐Hafeez: Validation; investigation; formal
analysis. I‐Chung Lo: Investigation; formal analysis. Krishna K Midde:
Investigation; formal analysis; validation; methodology. Tony Ngo:
Validation; methodology. Nicolas Aznar: Investigation; formal analysis.
Inmaculada Lopez‐Sanchez: Investigation; formal analysis. Vijay Gupta:
Investigation; formal analysis. Marilyn G Farquhar: Resources;
methodology. Padmini Rangamani: Conceptualization; methodology; data
curation; funding acquisition; project administration; supervision;
writing – original draft; writing – review and editing. Pradipta Ghosh:
Conceptualization; methodology; data curation; visualization; funding
acquisition; project administration; supervision; writing – original
draft; writing – review and editing.
Disclosure and competing interests statement
The authors declare that they have no conflict of interest.
Supporting information
Appendix
[426]Click here for additional data file.^ (6.3MB, pdf)
Expanded View Figures PDF
[427]Click here for additional data file.^ (3.4MB, pdf)
Table EV1
[428]Click here for additional data file.^ (35.5KB, xls)
Movie EV1
[429]Click here for additional data file.^ (773.1KB, zip)
Dataset EV1
[430]Click here for additional data file.^ (31.3KB, xlsx)
Dataset EV2
[431]Click here for additional data file.^ (879.3KB, xlsx)
PDF+
[432]Click here for additional data file.^ (23.6MB, pdf)
Source Data for Figure 2
[433]Click here for additional data file.^ (208KB, zip)
Source Data for Figure 3
[434]Click here for additional data file.^ (738.7KB, zip)
Source Data for Figure 6
[435]Click here for additional data file.^ (981.3KB, zip)
Acknowledgements