Modeling the kinetics of integrin receptor binding to hepatic extracellular matrix proteins

ABSTRACT The composition of the extracellular matrix (ECM) proteins and the expression of their cognate receptors dictate cell behavior and dynamics. In particular, the interactions of ECM

proteins with integrin receptors are key mediators of these cellular processes, playing a crucial role in the progression of several diseases of the liver, including inflammation,

fibrosis/cirrhosis and cancer. This study establishes a modeling approach combining computation and experiments to evaluate the kinetics of integrin receptor binding to hepatic ECM proteins.

ECM ligand concentration was derived from LC-MS/MS quantification of the hepatic ECM from mice exposed to chronic carbon tetrachloride (CCl4); receptor density was derived from published

literature. Mathematical models for ECM-integrin binding kinetics that were developed incorporate receptor divalence and an aggregation scheme to represent clustering. The computer

simulations reproduced positive cooperativity in the receptor aggregation model when the aggregation equilibrium constant (Ka) was positive and greater than Keq for divalent complex

formation. Importantly, the modeling projected an increase in integrin binding for several receptors for which signaling is known to be increased after CCl4 exposure in the liver. The

proposed modeling approach may be of use to elucidate the kinetics of integrin receptor binding to ECM proteins for homeostatic and diseased livers. SIMILAR CONTENT BEING VIEWED BY OTHERS

COMPUTATIONAL SIMULATION OF LIVER FIBROSIS DYNAMICS Article Open access 18 August 2022 MULTICOMPARTMENT MODELING OF PROTEIN SHEDDING KINETICS DURING VASCULARIZED TUMOR GROWTH Article Open

access 07 October 2020 MODELLING HUMAN LIVER FIBROSIS IN THE CONTEXT OF NON-ALCOHOLIC STEATOHEPATITIS USING A MICROPHYSIOLOGICAL SYSTEM Article Open access 15 September 2021 INTRODUCTION The

extracellular matrix (ECM) consists of a broad range of components that interact bi-directionally with neighboring cells to create a dynamic and responsive microenvironment which regulates

cell signaling, recruitment, and tissue function. The ECM not only provides structure and support for the cells in a tissue, but also acts as a reservoir for growth factors and cytokines and

as a signaling mechanism by which cells can intercommunicate with their environment1. Quantitative and qualitative changes to the ECM structure and superstructure can impact overall health

of the organ and the organism. In particular, the hepatic ECM changes predominantly described in published literature occur in the context of hepatic fibrosis, which is characterized by

robust scarring of the liver with collagen fibrils. However, hepatic ECM is significantly more diverse than collagen ECM. Recent studies further indicate that hepatic ECM content changes

dynamically in response to acute stress and injury2,3,4. Moreover, changes to the hepatic ECM may foster an environment that is conducive to cancer and metastasis. Although the concept that

hepatic ECM changes drive hepatic dysfunction under several conditions is well understood, the mechanisms by which these effects are mediated are not. Integrins comprise a family of

heterodimeric transmembrane glycoprotein receptors that facilitate key interactions between cells and the ECM5,6,7. The binding of ECM ligands to integrins mediates critical processes,

including cell adhesion, migration, proliferation, differentiation, inflammation and apoptosis. Indeed, several of the hallmarks of liver diseases and cancer (e.g., altered proliferation,

angiogenesis and apoptosis) are hypothesized to be mediated via changes in ECM:integrin signaling8. Based on this assumption, integrins have become important therapeutic targets for diseases

of dysregulation, including various cancers, fibrosis, and immune dysfunction. However, few integrin-based therapies have been effective to prevent and/or treat these diseases. This

limitation is partly due, to an incomplete understanding of the complexity of the changes to integrin signaling under dysregulated conditions. The kinetics of ECM:integrin interactions are

highly intricate. Integrin receptor complexes are structured as non-covalently linked α and β subunits, the various combinations of which contribute to the diversity of receptor types9 (Fig.

 1). The overall rate of binding is not driven simply by ligand binding to the receptor, but also by clustering at focal adhesion points and an increase in avidity for binding additional

ligand (i.e., positive cooperativity). Masson-Gandais _et al_. described a two-step model wherein the α subunit binds ligand first, influencing ligand recognition and determinant of

association kinetics10. The β subunit binds second, which creates bond stabilization and determines dissociation kinetics. Ligand binding to the extracellular domain activates the receptor

and initiates its conformational changes to a high-affinity state11,12. This two-step process reflects a divalent kinetics model with the α subunit as the high affinity site, and the β

subunit as the low affinity site13. In addition to binding processivity of individual receptors, ligand binding to distinct integrins favors subsequent binding by other receptors (i.e. focal

adhesion clustering). Furthermore, integrin receptors bind promiscuously to various ECM ligands, creating redundancy, competition and diversity in biofunctionality5,9,14. These complex

interdependent factors affect the kinetics of ECM-integrin interactions in the intact organism. Promiscuity among the repertoire of ECM ligands and integrin receptors, particularly those

with RGD-binding motifs, implies a differential pattern of binding relative to the amounts of substrate available15,16. To explore these interactions as a system, several mathematical

descriptions of integrin binding have been reported with outputs related to spatial clustering and signal transduction, liver fibrosis and haptotaxis17,18,19. Although these models

recapitulate certain aspects of ECM-integrin interactions, they typically focus on one ligand (e.g. collagen or fibronectin) as the ECM substrate. In this study, modeling of integrin

receptor binding kinetics is presented that considers divalent receptor characteristics and employs a simple model of integrin clustering. The kinetic indices of each integrin for each of

its ligands were initially determined to establish a single-species integrin profile. Proteomic data were compiled that assess the liver ECM under homeostatic conditions as well as

experimental fibrosis. These proteomic analyses provided information on relative abundance of hepatic ECM components to calibrate substrate concentrations for the kinetic simulations.

Although data from human fibrotic livers has recently been analyzed20, an animal model was chosen here as it provides a more controlled environment for initial model calibration and testing.

Longer term, by testing homeostatic conditions against the experimental treatment models, how the integrin binding phenotype changes in response to injury could be determined and used to

predict the ECM-integrin binding within the context of transitional tissue remodeling. RESULTS As expected, 4 weeks of CCl4 exposure caused robust fibrotic scarring of the liver in our mouse

model. The resultant phenotype of injury and fibrosis has been previously described to include degradation of basement membrane-like ECM and replacement with fibrillar collagens and other

integrin ligands (Fig. 2)21. The canonical change in ECM content during hepatic fibrosis is an increase in collagen 1 deposition. However, as has been previously described22,23, several

other proteins increase in response to CCl4-induced fibrosis. Analysis of the proteomic data (Table 1) revealed ECM protein expression profiles, and a simple conversion for relating

quantitative exponentially modified protein abundance index (emPAI) values to protein mass was employed as a proteomic ruler to estimate protein concentration under homeostatic and

experimental treatment conditions24,25. Weighting the values with the concentration of extraction fractions, we estimated a relative protein concentration for ECM components. The composition

of the liver ECM as quantitated via proteomic analysis has influence on integrin expression of cells that haptotactically migrate towards ECM protein gradients, and provides the pool of

available ligands for subsequent binding. Qualitatively, the majority of proteins identified were found in both the control and treatment groups; with seven proteins uniquely expressed in

the CCl4 group and only one unique to the control group (Fig. 3). Collagens, glycoproteins and proteoglycans identified via proteomic analysis as ECM substrate were quantified and their

relative concentration was determined (Table 1). Beta-1 and Beta-3 integrins selected for the simulations reflect those involved in hepatic events that relate to CCl4 fibrosis. Integrin-ECM

binding microrates have been determined for various cell types and conditions. Proteomic results were previously validated to confirm relative abundance of identified proteins qualitatively

and quantitatively2. In particular, the amounts and distribution of collagens in the treatment group relative to the control were verified. Here, the presence of trace amounts of Col V in

the CCl4 treatment group was validated to explore whether changes on the nanomolar scale would have pathological consequence. (Fig. 4). Next, to provide for the capability of a system-level

analysis, a computational framework was established using proteomic data for binding species to enable evaluation of integrin receptor binding kinetics (see modeling and experimental details

in Methods). The model was developed taking into consideration sequential binding of subunits. _In silico_ simulations of this model were parameterized using rate constants that correlate

with published literature on binding, or otherwise estimated. The rates are listed in Tables 2 and 3. The simulations were initialized using binding constants from published literature;

where values were not available, parameters were estimated accordingly (see Methods). Collagen fragments for collagen I and IV were plotted together (Fig. 5) and assumed to have the same

rates of binding for the purposes of these experiments. For the other fragmented protein, fibrinogen, only the gamma subunit was considered due to the binding motif located within this

fragment26,27. The binding microrates were set to recapitulate positive cooperativity in divalent receptor saturation and in receptor aggregation pairs, as stipulated in Wanant _et al_.28,

wherein the aggregation equilibrium constant, Ka, drove cooperativity in the aggregate model (Table 3). The simulation graphs in Fig. 5 show left-shifted curves with increased ECM ligand

abundance, indicating increased affinity and avidity for ligand. This is reflected in both the curves for fully occupied divalent receptors (_C_ _d_ ) and for fully occupied aggregate

receptor pairs (_A_ _dd_ ). Steady state values (SS) were recorded for each simulation (Table 4). From these simulation data it appears that upregulated ECMPs reached steady state values in

shorter time, and that aggregation of receptors produced positive cooperativity. Considering the single divalent receptor, _C_ _d_ , the ECM:integrin binding pairs that had the highest

steady state values include both collagen 1 and fibrinogen γ chain in association with the αvβ3 integrin receptor. The combinations with the shortest time to SS were collagen 1 binding αvβ3

or α1β1 receptors. For the aggregated receptor pairs, _A_ _dd_ , the pairs with the highest SS values include von Willebrand factor, fibrinogen γ chain, and collagen 1 binding αvβ3, as well

as fibronectin binding α5β1. The ECM:integrin pairings with the shortest time to SS for _A_ _dd_ pairs were collagen 1 binding αvβ3 and α1β1, and fibronectin binding α5β1. In nearly all

cases, the CCl4 model ECM showed faster rise to SS compared to the control ECM; however, fibrinogen γ chain and Col 4α2 behaved in an opposite manner, owing to the fact that CCl4 actually

downregulated these ECMPs in our dataset. Sensitivity analysis was performed (Table 5) by evaluating the equilibrium constants listed in Table 3 for percent change in steady state after a

ten-fold perturbation to the base values (Table 5). For simple integrin complex formation, i.e. when one ligand binds, increasing the Ki causes a significant increase in aggregated

receptors, while a ten-fold decrease causes an approximate 100% decrease in aggregate pairing. Divalent receptors are moderately decreased when Ki increases, showing that decreased affinity

suppresses the capacity for divalent receptor binding. Filling a single divalent receptor is negatively impacted by an increase in Kc, with aggregate receptor pairs decreasing ~83% for all

three receptor:ligand pairings. A decrease in Kc positively increases the steady state for aggregate pairs, because a lower Keq for the second binding event increases affinity for receptors

with one bound ligand. Kp, the constant for filling empty paired receptors, was nominally affected by perturbation, as were perturbations to filling aggregate pairs. Finally, perturbations

to the aggregation constant, Ka, result in a decrease in steady sate for aggregated pairs when Ka is increased ten-fold, and an increase in steady state when Ka is decreased. This reflects

the condition of a decreased equilibrium constant increasing the affinity for ligand binding, which is expected as the aggregation constant drives ligand affinity in this model. DISCUSSION

Integrin binding to ECM is a vital mechanism for cell migration, invasion, proliferation, and signal transduction between cells and their microenvironment. Diseases of chronic inflammation

and injury, including fibroses and cancer, involve persistent dysregulation of ECM-integrin processes and induce remodeling of the ECM. In addition to their intrinsic utility in cellular

processes, association between immune cells and the ECM is regulated via the β1 & β3 integrin receptor subfamilies29. Elucidating these complex cell-ECM-driven pathological conditions

could lead to improved prognostics and clinical outcomes via more precise therapeutic management of the tissue microenvironment. Several mathematical models of integrin binding have been

reported with outputs relating to spatial clustering and signal transduction, liver fibrosis, and cell migration17,19,30,31. These models recapitulated certain aspects of integrin

interactions; however, these previous studies typically modeled only one ligand, mainly fibronectin or collagen, and utilized generic cognate receptor. In this study, the relative abundance

of ECM components that are canonical substrates of integrin receptors was developed for the proposed modeling framework based on experimentally-obtained liver ECM data. With binding

parameters from published literature, an integrin binding pattern was established for each integrin involved in hepatic processes that are involved in fibrosis. The model from Wanant _et

al_. was adapted to implement the basic model for divalent binding28. Specifically, this model aptly describes initial integrin binding leading to a conformational switch of the receptor

complex from low- to high-affinity. A model of receptor aggregation, which can describe integrin clustering upon attachment to ECM via adhesions5, was also implemented. The simulations

include how each integrin binds with cognate ECM ligands and incorporates the varying affinities that drive this interaction. From these calculations, the kinetic indices of each integrin

for each of its binding partners were determined separately. The impact of changes to the ECM (e.g., in response to CCl4-induced fibrosis) on integrin binding was modeled by calibrating the

substrate concentration based on the proteomic analyses. The extracellular matrix proteome was consistent with the known disease phenotype of the mouse model, with upregulation of specific

ECMPs involved in fulminant fibrosis. The computational results show that in simulations using these ECMPs as substrate for key integrin receptors, interactions involving profibrotic

integrins were predominant. The CCl4 mouse model of liver fibrosis was chosen here due to its robustly characterized pathology and ECM/integrin phenotype (Fig. 2). This model is imperfect in

its recapitulation of human liver fibrosis, but it is the current research standard and therefore has well-defined pathology and changes to the ECM32. Using proteomic data from CCl4-exposed

mouse livers, integrin binding can be explored within the context of fulminant fibrosis. Collagen type Iα1, type III and type IV are excessively deposited due to activated hepatic stellate

cells (HSCs) in response to myofibroblastic transformation induced by activated Kupffer cells and damaged hepatocytes33,34. In agreement with these established phenomena, collagens I, III,

and V were upregulated in the CCl4 cohort in the current study (Table 1). Collagen I is aberrantly produced in this mouse model, and collagen V, a potent nucleating effector for the

co-upregulated fibronectin, exhibited a slight increase from trace levels. In contrast, collagen IV and XVIII levels were decreased relative to the control. Interestingly, collagen XVIII was

identified at relatively minimal levels in the controls, and absent in the CCl4 treated animals (Table 1). This is contrary to an expected increase in collagen XVIII following CCl4

treatment22. Nevertheless, interactions simulated with this ECMP are still based on experimental proteomic analysis. Integrin receptors were not able to be resolved with this particular

method of proteomic analysis, so further proteomic analysis of integrin adhesion complexes in culture is a key component of the future directions for this project. Owing to their involvement

in several critical functions that drive homeostasis and dyshomeostasis, integrins have been identified as key druggable targets in several diseases. For example, integrin inhibitors have

been evaluated to suppress liver fibrogenesis, disrupt attachment and invasion of cancer cells, and to mediate immune response35,36,37,38. Regrettably, many of these drugs fail in early

trials and rarely reach clinical use, perhaps due to an incomplete understanding of integrin binding kinetics, which are traditionally based on single-species models and assumptions; indeed,

even antibodies and small peptide mimetics with specificities for multiple integrins have limited clinical application39,40. Though necessary to target multiple integrins to maximize

efficacy _in vivo_, perhaps the missing link is knowing which targeted doses are most effective for each anti-integrin molecule. In attempting to begin to develop a predictive tool for

effective dosing, the primary goal of this work was to create a framework to simulate simple receptor aggregation and reproduce positive cooperativity induced by aggregate pairing. The

simulations were parameterized to analyze for positive cooperativity of binding in the divalent and aggregation cases. The steady state values and time to steady state for each pairing

correlated to upregulation of key ECMPs in CCl4 liver injury (Fig. 5; Table 4). The integrin receptors that predominated simulations of occupancy were consistent with those known to be at

play in the disease model (Fig. 2). This study offers a first step in which the proposed modeling framework has been initially evaluated using data from a model of fulminant fibrosis and by

which other liver pathologies and how the transitional remodeling of the ECM affects ECM-integrin interactions could be explored. We acknowledge that a more comprehensive test of the model

and its assumptions would require further experiments, which will be pursued in follow-up work. By testing homeostatic conditions against experimental treatment models, this platform could

be broadly employed to predict or confirm changes in integrin binding (and by extension, signaling) caused by remodeling of the hepatic ECM in response to insult or injury. Longer term, a

more complex stochastic model for concurrent integrin binding building upon the results of this study could be developed that considers competitive binding of multiple species. This would

lay the foundation for a more detailed and nuanced analysis of ECM:integrin interactions. METHODS All experiments were performed in accordance with the guidelines and regulations of the

University of Louisville Office of Research Integrity and Institutional Review Board and Biosafety Committee. ANIMALS AND TREATMENTS Male C57BL/6J mice (4–6 w) were purchased from Jackson

Laboratory (Bar Harbor, ME). Mice were housed in a pathogen-free barrier facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care, and procedures

were approved by the University of Louisville’s Institutional Animal Care and Use Committee. Food and tap water were provided ad libitum. Mice were administered CCl4 (1 ml/kg i.p.; diluted

1:4 in olive oil; Sigma-Aldrich, St. Louis, MO) 2×/wk for 4 wk. Twenty-four h after the last CCl4 administration, mice were anesthetized by injection of a ketamine HCl/xylazine solution

(100/15 mg/kg i.m.; Sigma-Aldrich, St. Louis, MO). Other animals received the same dose of CCl4, but only once, and were sacrificed 12–72 h after intoxication. Blood was collected from the

vena cava just prior to sacrifice by exsanguination and citrated plasma was stored at −80 °C for further analysis. Portions of liver tissue were frozen immediately in liquid nitrogen, while

others were fixed in 10% neutral buffered formalin or embedded in frozen specimen medium (Tissue-Tek OCT compound, Sakura Finetek, Torrance, CA) for subsequent sectioning and mounting on

microscope slides. 3-STEP ECM EXTRACTION SAMPLE PREPARATION AND WASH Snap-frozen liver tissue (75–100 mg) was immediately added to ice-cold phosphate-buffered saline (pH 7.4) wash buffer

containing commercially available protease and phosphatase inhibitors (Sigma Aldrich) and 25 mM EDTA to inhibit proteinase and metalloproteinase activity, respectively. While immersed in

wash buffer, liver tissue was diced into small fragments and washed five times to remove contaminants. Between washes, samples were pelleted by centrifugation at 10,000 × g for 5 min and

wash buffer was decanted. NACL EXTRACTION Diced samples were incubated in 10 volumes of 0.5 M NaCl buffer, containing 10 mM Tris HCl (pH 7.5), proteinase/phosphatase inhibitors, and 25 mM

EDTA. The samples were gently mixed on a plate shaker (800 rpm) overnight at room temperature. The following day, the remaining tissue pieces were pelleted by centrifugation at 10,000 × g

for 10 min. The supernatant was saved and labeled as the NaCl fraction. SDS EXTRACTION The pellet from the NaCl extraction was subsequently incubated in 10 volumes (based on original weight)

of a 1% SDS solution, containing proteinase/phosphatase inhibitors and 25 mM EDTA. The samples were gently mixed on a plate shaker (800 rpm) overnight at room temperature. The following

day, the remaining tissue pieces were pelleted by centrifugation at 10,000 × g for 10 min. The supernatant was saved and labeled as the SDS extract. GUANIDINE HCL EXTRACTION The pellet from

the SDS extraction was incubated with five volumes (based on original weight) of a denaturing guanidine buffer containing 4 M guanidine HCl (pH 5.8), 50 mM sodium acetate, 25 mM EDTA, and

proteinase/phosphatase inhibitors. The samples were vigorously mixed on a plate shaker at 1200 rpm for 48 h at room temperature; vigorous shaking is necessary at this step to aid in the

mechanical disruption of ECM components. The remaining insoluble components were pelleted by centrifugation at 10,000 × g for 10 minutes. This insoluble pellet was retained and solubilized

as described below. The supernatant was saved and labeled as the GnHCl fraction. DEGLYCOSYLATION AND SOLUBILIZATION The supernatants from each extraction were desalted using Zeba Spin

columns (Pierce) according to manufacturer’s instructions. The desalted extracts were then mixed with five volumes of 100% acetone and stored at −20 °C overnight to precipitate proteins. The

precipitated proteins were pelleted by centrifugation at 16,000× g for 45 min. Acetone was evaporated by vacuum drying in a RotoVap for one hour. Dried protein pellets were resuspended in

500 µL deglycosylation buffer (150 mM NaCl, 50 mM sodium acetate, pH 6.8, 10 mM EDTA, and proteinase/phosphatase inhibitors) that contained chondroitinase ABC (_P_. _vulgaris_; 0.025

U/sample), endo-beta-galactosidase (_B_. _fragilis_; 0.01 U/sample) and heparitinase II (_F_. _heparinum_; 0.025 U/sample). Samples were incubated overnight at 37 °C; those containing the

pellet remaining after the guanidine HCl step received 20 µL DMSO for solubilization. Protein concentrations were estimated by absorbance at 280 nm using bovine serum albumin (BSA) in

deglycosylation buffer for reference standards. LC-MS/MS ANALYSIS OF SAMPLES SAMPLE CLEANUP AND PREPARATION FOR LIQUID CHROMATOGRAPHY Pooled samples in deglycosylation buffer were thawed to

room temperature and clarified by centrifugation at 5,000 × g for 5 min at 4 °C. Samples were reduced by adding 1 M DTT to 50 µL (25 µg) of each sample and then incubating at 60 °C for 30 

min before addition of 8 M urea in 0.1 M Tris-HCl (pH 8.5) was added to each sample. Each reduced and diluted sample was digested with a modified Filter-Aided Sample Preparation (FASP)

method. Recovered material was dried in a SpeedVac and redissolved in 200 µL of 2% v/v acetonitrile (ACN)/0.4% formic acid (FA). The samples were then trap-cleaned with a C18 PROTOTM 300 Å

Ultra MicroSpin Column (The Nest Group). The sample eluates were incubated at −80 °C for 30 min, dried in a SpeedVac, and stored at −80 °C. Before liquid chromatography, dried samples were

warmed to room temperature and dissolved in 2%v/v ACN/0.1% FA to a final concentration of 0.25 µg/µL. A volume of 16 µL (4 µg) of sample was injected into the Orbitrap Elite. LIQUID

CHROMATOGRAPHY Dionex Acclaim PepMap 100, 75 µM × 2 cm nanoViper (C18, 3 µm, 100 Å) trap and Dionex Acclaim PepMap RSLC, 50 µM × 15 cm nanoViper (C18, 2 µm, 100 Å) separating column were

used. An EASY n-LC (Thermo) UHPLC system was used with mobile phase buffer A (2% v/v acetonitrile/0.1% v/v formic acid), and buffer B (80% v/v acetonitrile/0.1% v/v formic acid). Following

injection of the sample onto the trap, separation was accomplished with a 140 min linear gradient from 0% B to 50% B, followed by a 30 min linear gradient from 50% B to 95% B, and lastly a

10 min wash with 95% B. A 40-mm stainless-steel emitter (Thermo P/N ES542) was coupled to the outlet of the separating column. A Nanospray Flex source (Thermo) was used to position the end

of the emitter near the ion transfer capillary of the mass spectrometer. The ion transfer capillary temperature of the mass spectrometer was set at 225 °C, and the spray voltage was set at

1.6 kV. MASS SPECTROSCOPY An Orbitrap Elite – ETD mass spectrometer (Thermo) was used to collect data from the LC eluate. An Nth Order Double Play with ETD Decision Tree method was created

in Xcalibur v2.2. Scan event one of the method obtained an FTMS MS1 scan for the range 300–2000 m/z. Scan event two obtained ITMS MS2 scans on up to ten peaks that had a minimum signal

threshold of 10,000 counts from scan event one. A decision tree was used to determine whether collision induced dissociation (CID) or electron transfer dissociation (ETD) activation was

used. An ETD scan was triggered if any of the following held: an ion had charge state 3 and m/z less than 650, an ion had charge state 4 and m/z less than 900, an ion had charge state 5 and

m/z less than 950, or an ion had charge state greater than 5; a CID scan was triggered in all other cases. The lock mass option was enabled (0% lock mass abundance) using the 371.101236 m/z

polysiloxane peak as an internal calibrant. PROTEOME DATA ANALYSIS Proteome Discoverer v1.4.0.288 was used to analyze the data collected by the mass spectrometer. The database used in Mascot

v2.4 and SequestHT searches was the 6/2/2014 version of the UniprotKB _Mus musculus_ reference proteome canonical and isoform sequences. In order to estimate the false discovery rate, a

Target Decoy PSM Validator node was included in the Proteome Discoverer workflow. The Proteome Discoverer analysis workflow allows for extraction of MS2 scan data from the Xcalibur RAW file,

separate searches of CID and ETD MS2 scans in Mascot and Sequest, and collection of the results into a single file (.msf extension). The resulting.msf files from Proteome Discoverer were

loaded into Scaffold Q + S v4.3.2. Scaffold was used to calculate the false discovery rate using the Peptide and Protein Prophet algorithms. The results were annotated with mouse gene

ontology information from the Gene Ontology Annotations Database. COMPUTATIONAL MODELING First is considered the divalent receptor model that corresponds to ECM ligand binding of the α

subunit occurring prior to the β subunit7,28,41, where _k_ 1 is the first-order association rate constant and _k_ 2 is the dissociation constant for singly occupied receptors (_C_ _m_ ). _C_

_d_ indicates a fully occupied integrin receptor with two bound ECM ligands, and _k_ 3 and _k_ 4 define the rate constants for association and dissociation, respectively, of the doubly

bound integrin receptor. Differential equations for this model are: $$\frac{dI}{dt}={k}_{2}{C}_{m}-{k}_{1}IE,$$ (1) $$\frac{dE}{dt}={k}_{2}{C}_{m}-{k}_{1}IE+{k}_{4}{C}_{d}-{k}_{3}{C}_{m}E,$$

(2) $$\frac{d{C}_{m}}{dt}={k}_{1}IE-{k}_{2}{C}_{m}+{k}_{4}{C}_{d}-{k}_{3}{C}_{m}E,$$ (3) $$\frac{d{C}_{d}}{dt}={k}_{3}{C}_{m}E-{k}_{4}{C}_{d},$$ (4) The scheme for receptor aggregation and

ligand binding is shown in Fig. 6. In the model of receptor aggregation, we utilized the same scheme as Wanant _et al_.28, wherein receptors pair in a manner such that either singly- or

doubly-bound receptors can aggregate only with an unbound receptor with the aggregation equilibrium constant KA (where KA = _k_ 5 _/k_ 6), and disaggregation equilibrium constant KA’ (KA’ = 

_k_ 6 _/k_ 5). Binding constants for an additional ECM ligand binding to the unbound portion of an aggregate pair are the same regardless of whether the bound portion has one or two ligands,

where the equilibrium association constant is KC = _k_ 9 _/k_ 10. The equilibrium constant for adding a second ECM ligand to a singly bound receptor in any pair-configuration is KF = _k_ 7

_/k_ 8. The differential equations describing receptor aggregation are listed below: $$\frac{dI}{dt}={k}_{2}{C}_{m}-{k}_{1}IE+{k}_{6}({A}_{im}+{A}_{id})-{k}_{5}I({C}_{m}+{C}_{d}),$$ (5)

$$\frac{dE}{dt}={k}_{2}{C}_{m}-{k}_{1}IE+{k}_{4}{C}_{d}-{k}_{3}{C}_{m}E+{k}_{10}({A}_{mm}+{A}_{md})-{k}_{9}E({A}_{im}+{A}_{id})+\,{k}_{8}({A}_{id}+{A}_{md}+{A}_{dd})-{k}_{7}E({A}_{im}+{A}_{mm}+{A}_{md}),$$

(6) $$\frac{d{C}_{m}}{dt}={k}_{1}IE-{k}_{2}{C}_{m}+{k}_{4}{C}_{d}-{k}_{3}{C}_{m}E+{k}_{6}{A}_{im}-{k}_{5}I{C}_{m},$$ (7)

$$\frac{d{C}_{d}}{dt}={k}_{3}{C}_{m}E-{k}_{4}{C}_{d}+{k}_{6}{A}_{id}-{k}_{5}I{C}_{d},$$ (8)

$$\frac{d{A}_{im}}{dt}={k}_{5}I{C}_{m}-{k}_{6}{A}_{im}+{k}_{10}{A}_{mm}-{k}_{9}E{A}_{im}+{k}_{8}{A}_{id}-{k}_{7}E{A}_{im},$$ (9)

$$\frac{d{A}_{id}}{dt}={k}_{5}I{C}_{d}-{k}_{6}{A}_{id}+{k}_{10}{A}_{md}-{k}_{9}E{A}_{id}+{k}_{7}E{A}_{im}-{k}_{8}{A}_{id},$$ (10)

$$\frac{d{A}_{mm}}{dt}={k}_{9}E{A}_{im}-{k}_{10}{A}_{mm}+{k}_{8}{A}_{md}-{k}_{7}E{A}_{mm},$$ (11)

$$\frac{d{A}_{md}}{dt}={k}_{9}E{A}_{id}-{k}_{10}{A}_{md}+{k}_{8}E({A}_{dd}-{A}_{md})+{k}_{7}E({A}_{mm}-{A}_{md}),$$ (12) $$\frac{d{A}_{dd}}{dt}={k}_{7}E{A}_{md}-{k}_{8}{A}_{dd},$$ (13) where

_A_ im indicates an aggregate pair comprised of one unbound integrin receptor coupled with a singly-bound receptor; _A_ _id_ is the same combination, except featuring a doubly-bound

receptor. A pair with two singly bound receptors is defined as _A_ _mm_ , with two doubly bound receptors is _A_ _dd_ , and _A_ _md_ indicates a singly bound receptor paired with a doubly

bound one (Fig. 6). The affinity of integrin receptors for ECM proteins fibronectin and laminin are generally in the micromolar range. The Kd measured for ECM:integrin and, in particular,

fibronectin binding, ranges between approximately 10−7–10−6 M42; Takagi _et al_. report nanomolar Kd values for fibronectin binding43. Mallet _et al_. utilized a Kd of 2 × 10−4 M for

tethered RGD peptides in their model of integrin binding17. SIMULATIONS Computer simulations were run using Spyder for Tellurium software version 2.3.5.2; Python version 2.744. Binding

curves were plotted using SigmaPlot 13.0. The model was initialized using ligand concentrations from proteomic analysis (Table 1) and initial integrin concentrations were derived from

published values. Ligand concentration was developed by collapsing the fractionated sample data using MudPIT functionality in Scaffold. Rappsilber _et al_. defined protein abundance index

(PAI) for estimation of absolute protein abundance45, and Ishihama _et al_. report that the emPAI, i.e. exponentially modified PAI, is approximately proportional to protein abundance25.

Using the emPAI quantitative method, proteomic output was normalized by the tissue loading concentration of 0.25 µg/µL; these values for concentration were then divided by the molecular

weight of the protein to convert to molar concentration. Kinetic rates listed in Table 2 were used to calculate microrate parameters relative to the established binding rates from

literature; where exact microrates were unavailable, rates were estimated from various published literature sources. Table 3 relates the equilibrium constants of the system relative to

initial integrin complex formation, such that subsequent binding and clustering steps produce cooperativity when simulated in these proportions. Sensitivity analysis was performed by varying

levels of integrin receptor concentration in 10-fold increments, to explore binding when surface membrane integrin receptor expression is upregulated or downregulated as a consequence of

disease state or in response to microenvironmental fluctuations. DATA AVAILABILITY The datasets generated and/or analysed during the current study are available from the corresponding author

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references ACKNOWLEDGEMENTS We are grateful to William L. Dean (Biochemistry & Molecular Biology, University of Louisville) for useful discussions and advice. AUTHOR INFORMATION Author

notes * Hermann B. Frieboes and Gavin E. Arteel jointly supervised this work. AUTHORS AND AFFILIATIONS * Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY,

40202, USA Shanice V. Hudson, Christine E. Dolin, Lauren G. Poole, Veronica L. Massey, Daniel Wilkey, Juliane I. Beier, Michael L. Merchant, Hermann B. Frieboes & Gavin E. Arteel *

Department of Bioengineering, University of Louisville, Louisville, KY, 40208, USA Shanice V. Hudson & Hermann B. Frieboes * James Graham Brown Cancer Center, University of Louisville,

Louisville, KY, 40202, USA Hermann B. Frieboes * University of Louisville Alcohol Research Center, University of Louisville, Louisville, 40202, KY, USA Gavin E. Arteel Authors * Shanice V.

Hudson View author publications You can also search for this author inPubMed Google Scholar * Christine E. Dolin View author publications You can also search for this author inPubMed Google

Scholar * Lauren G. Poole View author publications You can also search for this author inPubMed Google Scholar * Veronica L. Massey View author publications You can also search for this

author inPubMed Google Scholar * Daniel Wilkey View author publications You can also search for this author inPubMed Google Scholar * Juliane I. Beier View author publications You can also

search for this author inPubMed Google Scholar * Michael L. Merchant View author publications You can also search for this author inPubMed Google Scholar * Hermann B. Frieboes View author

publications You can also search for this author inPubMed Google Scholar * Gavin E. Arteel View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS

Conceived and organized the study (S.V.H., H.B.F., G.E.A.); directed proteomics analysis (M.L.M.); directed animal study for proteomics samples (V.L.M., J.I.B.); prepared proteomic samples

(L.G.P.); performed proteomic analysis (C.E.D., D.W.); implemented modeling and simulations (S.V.H.); analyzed the results (S.V.H., H.B.F.); wrote initial manuscript (S.V.H.); revised and

approved final manuscript (S.V.H., G.E.A., H.B.F.). CORRESPONDING AUTHOR Correspondence to Gavin E. Arteel. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare that they have no

competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Hudson, S.V., Dolin, C.E., Poole, L.G. _et al._ Modeling the Kinetics of Integrin Receptor Binding to Hepatic Extracellular Matrix

Proteins. _Sci Rep_ 7, 12444 (2017). https://doi.org/10.1038/s41598-017-12691-y Download citation * Received: 13 June 2017 * Accepted: 14 September 2017 * Published: 29 September 2017 * DOI:

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