Platelets – the blood clotting cells – have the ability to detect, interpret and respond to mechanical forces, such as those generated by the flow of blood. The magnitude and duration of the forces detected by the platelets influences whether they form a blood clot. Understanding how the platelets respond to mechanical forces is therefore crucial for our knowledge of conditions such as thrombosis, where blood clots form inside vessels and block them. Clots that form within arteries are associated with heart attack and stroke, which account for around one third of all deaths worldwide.

Cells can sense external forces via individual proteins on their surface and transmit the mechanical information across the cell membrane. This triggers signals within the cell that influence how it responds. However, the molecular details of these “mechanosensory” processes remain poorly understood.

To patch up damaged blood vessels, platelets use a protein on their surface named glycoprotein Ibα (GPIbα) to bind to a plasma protein called von Willebrand factor that adheres to the vessel wall. This binding tethers the platelet to the blood vessel and activates it during clot formation. Previous studies suggested that mechanical force affects how this binding triggers the signals that activate platelets.

Ju, Chen et al. used a homebuilt nanotool to pull on platelet GPIbα while it was bound to von Willebrand factor. This revealed that two distinct domains of the GPIbα protein unfold to relay information about the force, such as its magnitude and duration, to the platelet to trigger biochemical signalling inside the cell. The unfolding of each GPIbα domain has a distinct role in determining the quantity and quality of the signals. The unfolding events work synergistically – they occur together to produce an effect that’s greater than the sum of their individual effects. However, pulling on GPIbα via a mutant form of von Willebrand factor eliminated the synergy between the two unfolding events, therefore hindering the effective conversion of mechanical forces into biochemical signals.

Notably, the two GPIbα domains unfolded by force exist in many protein families, including those involved in mediating cell adhesion and detecting signals. The biophysical tools developed by Ju, Chen et al. could be extended to analyze how mechanical cues are presented, received, transmitted and converted into biochemical signals in other cell types and biological systems. Furthermore, the structural insights gained from the platelet GPIbα system may help to design a generic mechanosensory protein machine.

Platelets can serve as a natural model system for studying cell mechanosensing as they rapidly respond to changes in hydrodynamic forces and substrate stiffness due to vascular pathology (Jackson, 2011; Qiu et al., 2015). Previous studies have suggested the role of GPIbα as a mechanoreceptor, for force exerted on it via its ligand VWF induces platelet signaling (Ruggeri, 2015). Conceptually, this coupled mechanical-biochemical process (mechanosensing) can be broken down into four steps: 1) Mechanopresentation: the receptor binding domain A1 is exposed by structural changes in VWF induced by elongational flow and collagen immobilization (Ju et al., 2015a; Springer, 2014); 2) Mechanoreception: GPIbα LRRD receives the force signal via engaging VWF-A1 to tether the platelet against shear stress; 3) Mechanotransmission: force is propagated from the LRRD through the mucin-like macroglycopeptide (MP) stalk (cf. Figure 2A) (Fox et al., 1988) and the MSD across the membrane to adaptor and signaling molecules (e.g. 14-3-3ζ) inside the platelet (cf. Figure 7G); and 4) Mechanotransduction: force induces mechano-chemical changes to convert mechanical cues to biochemical signals. Some of these steps have been characterized separately. For example, GPIbα forms catch-slip bonds with wild-type (WT) A1 in >15 pN, such that the bond lifetime first increases with force, reaches a maximum at ~25 pN, and decreases thereafter; whereas it forms slip-only bonds with type 2B von Willebrand disease (VWD) mutant (e.g. A1R1450E), such that the bond lifetime decreases monotonically with force (Ju et al., 2013; Yago et al., 2008). As another example, force induces unfolding of the LRRD, which prolongs A1–GPIbα bond lifetime (Ju et al., 2015b), and of the MSD, which is hypothesized to play a role in platelet signaling (Zhang et al., 2015). However, how these inter-connected steps are orchestrated to enable the information encoded by force to be translated into biochemical signals is still poorly understood.

We used a biomembrane force probe (BFP) to recapitulate the above process in a single-cell and single-molecular bond level to address the following questions: 1) What molecular events would be induced in GPIbα and how these events are regulated mechanically? 2) Whether, and if so, how changes in presentation of force by VWF-A1 mutation would affect the force reception by GPIbα and its response to force? 3) What features of the force (waveforms) could be sensed by the platelet via GPIbα to initiate intraplatelet calcium fluxes? 4) What proximal events may be responsible for transducing force into a biochemical signal? By manipulating the mechanopresentation and mechanoreception steps then analyzing the resulting mechanotransmission and mechanotransduction steps, we gained insights into the inner workings of this GPIbα-mediated mechanosensory machine.

In the BFP setup, a probe bead was functionalized with VWF-A1 or an anti-GPIb monoclonal antibody (mAb) to serve as a surrogate subendothelial surface (Figure 1A,B). It was attached to the apex of a micropipette-aspirated red blood cell (RBC) to form an ultrasensitive force transducer (Liu et al., 2014). A platelet was aspired by the target pipette to contact the bead in repetitive force-ramp or force-clamp cycles to mimic the sequential formation, force loading, and dissociation of VWF–GPIbα bonds during the translocation of a platelet on the sub-endothelium (Video 1; Figure 1C and Figure 1—figure supplement 1; Materials and methods). Adhesion frequencies from these cycles were kept low (<20%) by adjusting the ligand or antibody density, a condition required for the platelet to be pulled predominantly (>89%) by a single GPIbα bond (Chesla et al., 1998; Zhu et al., 2002). Control experiments using beads lacking ligand showed no binding, and blocking with mAb AK2 (epitope mapped to leucine-rich repeat 1–2 overlapping the A1 binding site, cf. Figure 2A) eliminated GPIbα binding to A1 but not to mAb WM23 (epitope mapped to the MP below LRRD, cf. Figure 2A) (Figure 1D) (Dong et al., 2001). This confirmed binding specificity and that the binding site of A1 is within LRRD but the binding site of WM23 is outside (Zhang et al., 2015).

Figure 1 with 2 supplements see all

Download asset Open asset

Video 1

Download asset

Figure 2 with 1 supplement see all

Download asset Open asset

Identification of LRRD and MSD unfolding

Using an optical trap, Zhang et al. observed force-induced MSD unfolding in purified recombinant full-length GPIb-IX and a GPIbα stalk region construct (Zhang et al., 2015). Using a BFP, we observed LRRD unfolding in glycocalicin (GC) (Ju et al., 2015b), the extracellular segment of GPIbα lacking the MSD (Liang et al., 2013) (Figure 2A–C). Here we pulled GPIbα on platelets via A1 and observed two unfolding signatures, one in the ramping and the other in the clamping phases of the force trace (Figure 1E). Unfolding that occurred in the ramping phase is termed ramped unfolding, which is featured by a sudden force kink at 5–20 pN as observed in previous studies of GPIbα unfolding (Ju et al., 2015b; Zhang et al., 2015). Similar to findings of protein unfolding studies (Kellermayer et al., 1997; Rief et al., 1997; Tskhovrebova et al., 1997; Zhang et al., 2009a, 2015), both the force-extension curves before and after unfolding were well fitted by the worm-like chain (WLC) model (Figure 1—figure supplement 2). Unfolding that occurred in the clamping phase is termed clamped unfolding, which is featured by an abrupt force drop (Figure 1F). Although not observed in the previous studies of GPIbα unfolding (Ju et al., 2015b; Zhang et al., 2015), this feature has been described in protein unfolding studies using force-clamp experiments (Oberhauser et al., 2001; Tskhovrebova et al., 1997).

Unfolding lengths derived from both signatures were measured from the probe bead position vs. time data (Figure 1E insert, 1F and Figure 1—figure supplement 2). The lengths of individual ramped unfolding events distributed tri-modally with three subpopulations (Figure 2D and Figure 2—figure supplement 1; Materials and methods). The first subpopulation coincides with the ramped unfolding length distribution from WM23 vs. platelet experiments (Figure 2E, white bars). WM23 binds the MP region below the LRRD (Figure 2A), hence could unfold MSD only. The average unfolding force vs. length data from the WM23 experiment was well fitted by the WLC model, yielding a contour length of 25.99 ± 0.85 nm (Figure 2H) that matches the previously reported MSD contour length (Zhang et al., 2015). The average unfolding force vs. length data from the A1 experiment overlaid well on the same WLC model fit (Figure 2H). These results identify the first subpopulation in Figure 2D as MSD unfolding.

The second subpopulation in Figure 2D matches the histogram of ramped unfolding lengths of GC pulled via A1 (Figure 2E, blue bars) that ranges from 18–56 nm and peaks at 36 nm (length of leucine-rich repeats 3–6). The average unfolding force vs. length plots derived from the A1 vs. platelet and A1 vs. GC experiments overlaid well on the same WLC model fit (Figure 2I). The best-fit contour length (70.29 ± 3.56 nm) matches the length of LRRD, calculated using a 4-Å contour length per residue (Ju et al., 2015b). These results identify the second subpopulation in Figure 2D as LRRD unfolding.

The third subpopulation in Figure 2D can be identified as concurrent unfolding of both MSD and LRRD that occurred within too short a time elapse to be distinguished by our BFP as two separate events, because its maximum unfolding length (85 nm) matches the sum of the observed maximum MSD and LRRD unfolding lengths.

Similar tri-modally distributed ramped unfolding lengths were obtained by using mAb AN51 (epitope mapped to the N-terminal flanking region above LRRD, cf. Figure 2A) instead of A1 to pull the platelet GPIbα (Figure 2F), and the second subpopulation also matches the ramped unfolding length distribution obtained using AN51 to pull GC (Figure 2G, blue bars). These results are expected because the unfolding lengths are determined by the respective primary structures of the LRRD and MSD, and as such should not depend on the 'grabbing handle' used to pull GPIbα. The consistence of the A1 and AN51 results imparts confidence in our identification of the three subpopulations as unfolding of MSD, LRRD, and both, respectively.

Interestingly, the two force waveforms induced unfolding of different GPIbα domains. Clamped forces unfolded only MSD as the lengths of clamped unfolding distribute as a single peak at 20 nm (Figure 2E,G, red bars), matching the first subpopulation in Figure 2D,F, respectively, regardless of whether platelet GPIbα was engaged by A1 or AN51. Furthermore, unfolding of LRRD in GC was induced only by ramped forces but not clamped forces (Figure 2J,K). By comparison, pulling platelet GPIbα via WM23 with both ramped and clamped forces induced MSD unfolding events with similar occurrence frequencies and unfolding lengths (Figure 2J,K). These results indicate that MSD can be unfolded by increasing forces as well as constant forces. By comparison, LRRD unfolding requires increasing forces. Some force-clamp cycles (Figure 1C; Video 2) generated two consecutive unfolding events, one in the ramping and the other in the clamping phase (Figure 1E). The respective unfolding lengths of the ramped and clamped unfolding events were 34–55 nm and 13–25 nm that totaled 47–80 nm, agreeing with those of the LRRD, MSD, and MSD+LRRD subpopulations in Figure 2D,F. Together, these results provide criteria to determine whether and which GPIbα domain(s) is unfolded (Figure 3—source data 1A).

Video 2

Download asset

Force- and ligand-dependent cooperativity between LRRD and MSD unfolding

To characterize the mechanical response of GPIbα, we measured the frequency, force and length of LRRD and MSD unfolding induced by a range of clamped forces exerted on platelet GPIbα or GC by A1WT or a type 2B VWD mutant A1R1450E. The ramped unfolding frequencies of both domains were extremely low at ≤10 pN but increased with the higher levels of clamped forces (Figure 3A,B). Interestingly, LRRD, but not MSD, unfolded more frequently when platelet GPIbα (Figure 3A) and GC (Figure 3—figure supplement 1) were pulled by A1WT than A1R1450E. The ramped unfolding forces of both domains increased with the clamped force and were indifferent to whether force was applied via WT or R1450E mutant of A1 (Figure 3C,D). In general, a higher force was required to unfold LRRD than MSD. Surprisingly, pulling platelet GPIbα via different ligands generated distinctive MSD clamped unfolding frequency vs. force plots: increasing initially and decreasing after reaching maximal at 25 pN when pulled by A1WT, but decreasing monotonically when pulled by A1R1450E (Figure 3E). These data suggest that the mechanoreceptor GPIbα may be able to interpret mechanical cues and discriminate ligands by responding to different force waveforms applied via different ligands with distinct LRRD and MSD unfolding frequencies. In addition, the distinctive force-dependences of two subpopulations of events that we deemed as respective LRRD and MSD unfolding provide further support for our criteria for their identification and classification.

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

Statistics and cooperativity evaluation of the GPIbα domains unfolding.

(A) Decision rules for and statistical summary of GPIbα domain unfolding in force-clamp experiment mode. Criteria for deciding whether or not (+ or −) and which (LRRD, MSD, or both) GPIbα domain(s) was (were) unfolded are based on BFP profile signatures and the unfolding lengths. YES = observed, NO = not observed. NA = not applicable. (B) Related to Figure 3F–H. Evaluation of LRRD and MSD unfolding cooperativity. All probabilities were calculated from occurrence data in (A). Observed joint probabilities were compared to their predicted counterparts based on the assumption that LRRD and MSD unfolded independently. For example, in 'WT A1 vs. Platelet' under 25 pN: The probability of LRRD unfolding is P(LRRD) = 3.4% + 6.9% + 2.76% = 13.06%. The probability of MSD unfolding is P(MSD) = 7.6% + 13.8% + 6.9% + 2.76% = 31.06%. The probability of MSD ramped unfolding is P(MSD, ramp) = 7.6% + 2.76% = 10.36%. The probability of MSD clamped unfolding is P(MSD, clamp) = 13.8% + 6.9% = 20.7%.

https://doi.org/10.7554/eLife.15447.011

Download elife-15447-fig3-data1-v2.docx

The spatial separation of LRRD and MSD by the >30 nm long MP stalk and the distinctive dependences of their unfolding on the force waveform would seem to favor these two GPIbα domains to unfold independently. This hypothesis predicts that the probability for LRRD and MSD to unfold concurrently should be equal to the product of the respective probabilities for LRRD and MSD to unfold separately. To test this hypothesis, we estimated these probabilities from the observed unfolding occurrence frequencies. At 25 pN, the 34.5% of BFP force-clamp cycles with unfolding events consist of 7.6, 17.2, 6.9, and 2.8% of unfolding of LRRD alone, MSD alone, LRRD and MSD sequentially, and concurrently (Figure 3—source data 1A). Significantly, the frequency of observing both LRRD and MSD unfolding in the same binding cycle, calculated by pooling together both cases of two domains unfolding sequentially and concurrently, P(MSD+LRRD), is much higher than the product of their respective occurrence frequencies, P(MSD)×P(LRRD), which is the joint probability for both to unfold assuming that they were independent (Figure 3—source data 1B).

These data suggest that the two GPIbα domains may unfold cooperatively, i.e., one domain unfolding may increase the likelihood for the other to unfold. To quantify the degree of such cooperativity, we defined a relative probability difference, ∆P/P = [P(MSD+LRRD) - P(MSD)×P(LRRD)]/[P(MSD)×P(LRRD)]. ∆P/P > 0 indicates positive cooperativity between LRRD and MSD unfolding. No cooperativity was observed at 10 pN because this force was insufficient to induce appreciable LRRD unfolding. Pulling with A1WT by a 25 pN clamped force generated high cooperativity, and further increase in force decreased cooperativity (Figure 3F). Remarkably, unfolding cooperativity was completely abolished at all forces when applied via the VWD mutant A1R1450E (Figure 3F).

We used χ² test to determine if the hypothesis that MSD and LRRD unfolded independently should be rejected (Materials and methods). At 25 pN, LRRD unfolding significantly enhanced MSD unfolding (p = 3.09 × 10^–4). The χ² test results are depicted as negative log p-values vs. force plots in Figure 3G,H for A1WT and A1R1450E, respectively. Interestingly, significant (p = 0.05, dashed horizontal lines) unfolding cooperativity was observed only for A1WT at 25 and 40 pN. These data show that the cooperativity between LRRD and MSD unfolding is force- and ligand-dependent.

Model for cooperativity between LRRD and MSD unfolding

To elucidate the mechanism underlying the force- and ligand-dependent unfolding cooperativity, we note that when the MSD unfolding events were separately analyzed according to their occurrence in the ramping or clamping phase, MSD clamped, but not ramped, unfolding was significantly (p= 8.79 × 10^–3 vs. 0.076 at 25 pN) enhanced by LRRD unfolding (Figure 3G), which occurred in the ramping phase only. This dominance of cooperativity by sequential rather than concurrent unfolding suggests a model for LRRD unfolding to impact MSD unfolding, which includes three ideas. The first idea has to do with the MSD time-to-unfold, t_u (cf. Figure 1F). Our force-clamp measurements revealed similar t_u values induced by A1WT or A1R1450E pulling (Figure 4A). The only exception is at 10 pN where a shorter t_u was induced by A1WT than A1R1450E. This can be explained by their differential bond lifetimes (Figure 4B,C). Compared to A1R1450E, the much shorter lifetime of GPIbα bond with A1WT at 10 pN may underestimate t_u because early dissociation of GPIbα would prevent observation of slow MSD unfolding events. This reasoning provides the second idea for our model: MSD clamped unfolding should occur before A1–GPIbα dissociation. The third idea comes from our previous observation (Ju et al., 2015b) that LRRD unfolding significantly prolongs GPIbα bond lifetime with A1WT (Figure 4B) but not A1R1450E (Figure 4C). Combining these three ideas, our model proposes that the A1–GPIbα bond lifetime, regulated by force and prolonged by LRRD unfolding in respective ligand-specific manners, determines the occurrence of MSD clamped unfolding, which, despite its ligand-independent unfolding kinetics, generates a cooperativity pattern that maximizes at the optimal force of 25 pN for A1WT but not for A1R1450E.

Figure 4 with 1 supplement see all

Download asset Open asset

Figure 4—source data 1

MSD unfolding rates (k_u) and the fraction (w₁) and off-rates (k₁, k₂) of GPIbα dissociating from A1WT or A1R1450E under different forces.

w1 represents the fraction of binding events that dissociate with the off-rate k₁. The fraction of events that dissociate with the off-rate of k2 is simply calculated as w2 = 1-w1. NA = not applicable.

https://doi.org/10.7554/eLife.15447.014

Download elife-15447-fig4-data1-v2.docx

To formulate the model mathematically, we multiplied the respective probability densities of the exponentially distributed MSD time-to-unfold (t_u) (Figure 4—figure supplement 1A) and the dual-exponentially distributed lifetime (t_b) of GPIbα bonds with A1WT or A1R1450E (Ju et al., 2013) to construct a joint probability density surface over the t_u-t_b plane (Figure 4D and Figure 4—figure supplement 1B). The predicted MSD clamped unfolding probability is the volume under this surface over the region 0<t_u<t_b<∞ (Materials and methods). When the model was tested against experiment, not only did the calculated force-dependent MSD unfolding frequency match the biphasic pattern for A1WT (Figure 4E) and the monophasic pattern for A1R1450E (Figure 4F), but it also compared well with the observed occurrence frequencies numerically at all forces. Remarkably, the model predicts both the quantitative enhancement of MSD unfolding by LRRD unfolding for A1WT and the lack of enhancement for A1R1450E without a single freely adjustable fitting parameter. The excellent agreement between theory and experiment has provided strong support for our model and explained the data in Figure 4E,F.

Platelet signaling induced by mechanoreception via a single GPIbα

Platelet translocation on VWF signals through GPIbα to induce Ca²⁺ fluxes (Mazzucato et al., 2002; Nesbitt et al., 2002). We optimized the fluorescence BFP (fBFP) method (Chen et al., 2015; Liu et al., 2014) for single-platelet calcium imaging and studied how platelet signaling was triggered by GPIbα mechanoreception via a sequence of intermittent single bonds under a range of clamped forces. The Ca²⁺ signals over the 200-s observation window of repeated platelet contact cycles were classified into three types (Figure 5A,B): i) null-type, featured by a basal trace with a maximum Ca²⁺ intensity increase (normalized by its initial value) ΔI_max<0.05; ii) α-type, featured by an initial latent phase followed by a spike (mostly ΔI_max>0.5) with a quick decay (Video 2); iii) β-type, featured by fluctuating signals around the baseline or gradually increasing signals to an intermediate level (mostly ΔI_max<0.5) followed by a gradual decay to baseline (Video 3). The null type reflects the baseline with background noise, while the α- and β-types match the previous characterization of platelet internal Ca²⁺release triggered by VWF–GPIbα bonds measured in flow chamber experiments (Mazzucato et al., 2002). For each platelet, the calcium trace was overlaid with the sequential binding events, bond lifetimes, and their accumulation over the repeated platelet binding cycles (Figure 5B and Figure 5—figure supplement 1A,B).

Figure 5 with 2 supplements see all

Download asset Open asset

Video 3

Download asset

Pulled by a 25 pN clamp force, A1WT–GPIbα bonds triggered much higher ΔI_max than controls (Figure 5C), showing 28, 42, and 30% of null-, α- and β-types, respectively (Figure 5D). Control experiments that merely held aspirated platelets or contacted them by beads without coating any ligand showed null-type Ca²⁺ only (Figure 5C,D). The α-type Ca²⁺ could also be triggered by pulling GPIbα with AN51 but not with an anti-GPIbβ mAb (Figure 5C,D and Figure 5—figure supplement 1A,B), despite that GPIbβ is tightly connected to GPIbα within one GPIb complex and has been postulated to play a role in signaling through GPIb (Strassel et al., 2006). These data demonstrated the necessity of GPIbα engagement to trigger intraplatelet Ca²⁺ and agree with the previous report that α-type Ca²⁺ peaks occur when platelets are transiently arrested in the whole blood flow (Mazzucato et al., 2002;Nesbitt et al., 2002)

The concurrent measurements of A1–GPIbα binding kinetics and intraplatelet Ca²⁺ allowed us to determine the pre-Ca²⁺ bond lifetimes (Figure 5B), enabling single platelet correlative analysis of binding and signaling. Using the normalized maximum calcium intensity ΔI_max to represent the Ca²⁺ level, we compared its correlations with three statistics of A1WT–GPIbα bond lifetimes occurred prior to calcium onset. We found that ΔI_max correlates best with the pre-Ca²⁺ longest bond lifetime t_max (Figure 5E), similarly well with the pre-Ca²⁺ cumulative lifetime ∑t_i (Figure 5—figure supplement 1C), but poorly with the pre-Ca²⁺ average lifetime <t> (Figure 5—figure supplement 1D). Careful examination of many overlaid calcium and bond lifetime traces, as exemplified in Figure 5B, revealed that the ∑t_i values are generally dominated by the t_max values, which are usually much longer than the rest of the pre-Ca²⁺ bond lifetimes and are immediately followed by the calcium onset before observing additional shorter bond lifetimes. In other words, for each platelet usually ∑t_i could be approximated by t_max but <t> is of a smaller and variable value. This observation explains why calcium correlates equally well with t_max and ∑t_i but not with <t>. Importantly, these results also suggest that a single long-lived GPIbα bond is sufficient to trigger Ca²⁺ in a platelet. This assertion has been further supported by the parallel analysis of the data for A1R1450E. Although for R1450E the t_max value was significantly shorter and the α-type Ca²⁺ population was greatly reduced, the ΔI_max still showed similar correlation with t_max (Figure 5F). Thus the pre-Ca²⁺ longest bond lifetime correlates the Ca²⁺ strength and type.

GPIbα discriminates ligands and shows different force-dependent calcium responses

We next asked whether the mechanoreceptor GPIbα is capable of sensing differences in the force waveform and discriminating the ligand through which force is applied. We first performed force-ramp experiment to generate a wide range of rupture forces using three ramping rates: 100, 1000 and 10,000 pN/s. However, only low levels of β-type Ca²⁺ were resulted (Figure 6A,B), showing no correlation with the largest rupture force prior to calcium onset (Figure 6C,D, right ordinate), regardless of whether platelets were tested by A1WT (Figure 6A,C) or A1R1450E (Figure 6B,D). In sharp contrast, much higher levels of Ca²⁺ of α- and β-types were induced by clamped forces applied to GPIbα via either A1WT (Figure 6E,G) or A1R1450E (Figure 6F,H) despite their much lower levels than the rupture forces seen in the force-ramp experiments (Figure 6C,D). Concurrently, the longest of A1–GPIbα bond lifetimes that occurred prior to Ca²⁺ onset was measured on each platelet and averaged over all platelets in each group. This pre-Ca²⁺ longest bond lifetime, t_max, exhibited catch-slip bond behavior for A1WT and slip-only bond behavior for A1R1450E (Figure 6G,H, right ordinate), just as the corresponding average bond lifetimes previously measured regardless of the intraplatelet calcium (Ju et al., 2013; Yago et al., 2008). Remarkably, the force-dependent pattern of calcium signals matched that of the pre-Ca²⁺ longest bond lifetimes for both A1WT and A1R1450E. The ligand-independent positive correlation of Ca²⁺ signal with t_max is consistent with a previously observed inverse correlation between the cytosolic Ca²⁺ level and the translocation velocity of platelets on immobilized VWF (Nesbitt et al., 2002). This is expected because the platelet translocation velocity is an inverse metric of VWF–GPIbα bond lifetime (Ju et al., 2013; Yago et al., 2008).

Figure 6

Download asset Open asset

Interplay among GPIbα engagement duration, domain unfolding and signal initiation

The findings that durable force is important to both MSD unfolding and Ca²⁺ triggering prompted us to investigate the relationship between GPIbα domain unfolding and platelet signal initiation. We segregated the Ca²⁺ data generated by a 25 pN clamped force on A1WT–GPIbα bonds according to whether or not and, if so, which domain(s) was (were) unfolded prior to calcium onset. Platelets whose tests contained no unfolding event showed short t_max and low calcium of β- and null-types (Figure 7A). Platelets whose tests contained at least one pre-Ca²⁺ MSD unfolding event but no LRRD unfolding showed slightly longer t_max and higher Ca²⁺ of mostly α-type. By comparison, only β-type Ca²⁺ was observed in the rare (2.6%) cases where LRRD unfolded but MSD did not. Since in these cases the t_max values were much longer, this data excludes t_max to be the direct determining parameter for the Ca²⁺ type. Remarkably, the group with pre-Ca²⁺ unfolding of both LRRD and MSD exhibited long t_max and high Ca²⁺ of mostly α-type.

Figure 7 with 2 supplements see all

Download asset Open asset

Consistent results were obtained using WM23 to pull GPIbα to bypass LRRD unfolding (cf. Figure 2A), showing significantly longer t_max and higher calcium for platelets with than without a MSD unfolding event and a clear α- vs. β-type signal distinction between them (Figure 7B). The higher α-type Ca²⁺ triggering efficiency of WM23 than A1 (compare MSD+ columns in Figure 7A and B) may be explained, at least in part, by the slower kinetics of GPIbα dissociation from WM23 than A1, which generated 70% more bond lifetime events by contacting a platelet for 200 s with WM23 than A1 (Figure 7—figure supplement 1A). The bonds were also 74% more durable (Figure 7—figure supplement 1B), resulting in a slightly higher (although not significant) MSD clamped unfolding probability per lifetime event for WM23 (Figure 7—figure supplement 1C). The expected number of MSD clamped unfolding over a 200-s experimental period, calculated as the product of the number of lifetime events and the unfolding probability per lifetime event, was significantly higher for WM23 than A1 (Figure 7—figure supplement 1D). The probability of platelet to have at least one MSD unfolding, calculated as 1– (1– unfolding probability per lifetime event) ^ (# lifetime events), is 40% and 60% for A1 and WM23, respectively (Figure 7—figure supplement 1E), close to the experimental results (51% vs. 69%, Figure 7A,B).

Interestingly, despite their high levels, ramped forces generated very few MSD unfolding events and triggered only null/β- but not α-type Ca²⁺ regardless of whether A1WT or WM23 was used to pull (Figure 7C,D). Together, these data indicate that both force-induced MSD unfolding and bond lifetime are necessary for inducing α-type Ca²⁺ signal.

Using a sensitivity-specificity analysis (Figure 5—figure supplement 2A,B) that slides a putative threshold through the t_max vs. Ca²⁺ type data, we found t_max>2 s to be the best predictor for A1WT to trigger α- rather than β-type Ca²⁺ (Figure 5E, dashed lines), which agrees with the fact that the 2 s threshold is much shorter than the average t_max of α-type Ca²⁺, but exceeds that of β-type Ca²⁺ and MSD time-to-unfold (Figure 5—figure supplement 2C). Thus, a longer-lived pre-Ca²⁺ bond favors MSD unfolding, thereby triggering α-type Ca²⁺; otherwise, it only triggers β-type Ca²⁺. Together, our data suggests separate roles of LRRD and MSD unfolding in GPIbα signaling, with the former intensifying the Ca²⁺ level and the latter determining the Ca²⁺ signal type .

The mechanoreception of GPIbα has been supported by direct observations of transient intracellular Ca²⁺ spike (termed type α/β peak) upon platelet translocation on VWF under flow (Mazzucato et al., 2002; Nesbitt et al., 2002). However, many questions remain. Using fBFP real-time single-bond, single-platelet analysis of force-regulated ligand binding kinetics, receptor unfolding dynamics and intraplatelet calcium mobilization, we have: 1) identified, characterized and mathematically modeled the force- and ligand-dependent cooperativity between LRRD and MSD unfolding (Figure 7G); 2) defined an optimal magnitude and threshold duration of clamped force for platelet signal initiation via a single GPIbα bond (Figure 7—figure supplement 2); 3) uncovered a mechanopresentation defect in a type 2B VWD mutant A1R1450E; 4) delineated the interplay among ligand engagement, GPIbα domain unfolding and signal triggering; and 5) revealed inhibition of GPIbα mechanotransduction by perturbing its cytoplasmic association with 14-3-3ζ.

It is an interesting yet challenging problem to define the minimum mechanical stimulation for inducing signal transduction. We demonstrated that a single A1–GPIbα bond can induce calcium in a platelet without clustering GPIb by multimeric ligands. Specifically, in 83% of the cases where α-type Ca²⁺ was triggered, only a single MSD unfolding event was observed before the Ca²⁺ onset. Of these, 43% had only one pre-Ca²⁺ bond lifetime event. Thus, pulling a single GPIbα by a 25 pN force for >2 s to unfold MSD once is necessary and sufficient to induce α-type Ca²⁺ signals. By comparison, to trigger calcium in a naïve CD8⁺ T lymphocyte requires a sequence of intermittent bonds with a total of >10 s lifetimes under 10 pN accumulated in the first 60 s of contacts between T cell receptors and agonist peptide-major histocompatibility complex molecules (Liu et al., 2014). In both cases a threshold of force duration is required, as ramped forces without a clamp phase are unable to trigger appreciable levels of α-type Ca²⁺ regardless of its magnitude.

The binding defects of VWF-A1 with type 2B VWD mutations have long been recognized (Ruggeri, 2004). A recent study has shown a type 2B mutation, A1V1316M, causes additional signaling defects (Casari et al., 2013). We showed that another type 2B mutation, A1R1450E, also has signaling defect. Interestingly, the defect to induce calcium has the same root as the binding defect, namely, the conversion of the wild-type catch-slip bond to the mutant slip-only bond (Ju et al., 2013; Yago et al., 2008). Consequently, force exerted on GPIbα by A1R1450E is less able to unfold LRRD, lasts shorter at 25 pN to unfold MSD less frequently, does not generate unbinding cooperativity between the LRRD and MSD, and induces lower level and frequency of α-type Ca²⁺ at >10 pN. Thus, the mechanical requirements for signal induction manifest as force-dependencies of VWF–GPIbα bond lifetime, MSD unfolding frequency, unfolding cooperativity, and Ca²⁺ level/type that display similar patterns for the same A1 construct (WT or R1450E) but different patterns between A1WT and A1R1450E. These findings show that the GPIbα mechanoreceptor can discriminate ligands and shed light to the biophysical mechanisms of type 2B VWD.

Our new data on the interplay among VWF binding, GPIbα unfolding, and Ca²⁺ signaling have provided new insights into the inner workings of the A1–GPIb–14-3-3ζ molecular assembly (Figure 7; Video 1). By residing in the juxtamembrane stalk region, the MSD has been shown to be mechanosensitive, and hypothesized to play a role in activating platelets (Zhang et al., 2015). In the present work, we found that MSD unfolding is required to trigger α-type Ca²⁺, showing that this extracellular mechanical event is necessary for transduction of the information embedded in the force waveform into intracellular biochemical signals via the 14-3-3ζ connection (Figure 7G). By overlapping with the ligand-binding site, the LRRD can feel the structural variation in the A1 and respond with an altered unfolding frequency and changed bond lifetime. Importantly, LRRD unfolding prolongs A1–GPIbα bond lifetime to facilitate MSD unfolding, thereby increasing the frequency of α-type Ca²⁺ and its level (Figure 7—figure supplement 2). Thus, our study has elucidated part of a mechanosensor that includes three components: 1) a MSD in the juxtamembrane region whose conformational change results in a binary decision of Ca²⁺ type, 2) a LRRD in the ligand-binding region whose conformational change leads to continuous alterations in ligand-binding duration, signal level and fractions of different signal types, and 3) a MP stalk that transmits force over a distance and provides coupling between the two unfoldable domains. The differential unfolding behaviors of the LRRD and MSD in response to distinct force waveforms provide a simple mechanical mechanism for unfolding cooperativity, by setting the response order such that LRRD unfolds first during force ramp to give more time for MSD to unfold during force clamp. These principles may be helpful for design of a generic mechanosensor, e.g., using synthetic biology approaches.

In addition to an increased MSD unfolding frequency, the cooperativity between LRRD and MSD unfolding may manifest as an increased LRRD unfolding extent. This has been suggested by the observation that the values of the first two peaks (20 and 36 nm) do not add up to that of the third (65-70 nm) in the unfolding length histograms. We note that the observed maximum unfolding length from the GC experiments (56 nm) is smaller than the calculated contour length of LRRD (70 nm). Since LRRD consists of 8 leucine-rich repeats, this suggests that only some but not all of the repeats were unfolded in any given observation. Thus, the LRRD dataset contains mixed populations of partial unfolding events. By comparison, the MSD dataset may be a more uniform population as the observed maximum unfolding length from the WM23 experiments (27 nm) matches the calculated contour length of MSD (26 nm). These considerations suggest possible explanations for the observation that events in which both LRRD and MSD unfold generate more length than the sum of lengths generated from events in which either LRRD or MSD unfolds: LRRD unfolding events with a higher number of unfolded leucine-rich repeats may facilitate MSD unfolding more effectively than those with a lower number of unfolded leucine-rich repeats. Alternatively, MSD unfolding, once happens, may induce more leucine-rich repeats in LRRD to unfold. Note that these two mechanisms are not mutually exclusive.

Studies in mechanosensitive ion channels and enzymes have provided knowledge on how biomolecules respond to force and transduce mechanical stimulations into biochemical signals. For example, ion channels open and close in response to stress within the lipid bilayer or force within a protein link that can do work on the channel and stabilize its state (Sukharev and Sachs, 2012). Mechanosensitive enzymes or substrates, such as vinculin (del Rio et al., 2009; Grashoff et al., 2010) or A2 domain of VWF (Wu et al., 2010; Zhang et al., 2009b), change conformations in response to forces to expose a cryptic site to enable enzymatic reaction. By comparison, for the system studied herein, force signals are received by the receptor via ligand interaction, hence mediated by their binding kinetics. The process of transducing the extracellular mechanical events (i.e., LRRD and/or MSD unfolding) across the cell membrane is likely mechanical rather than chemical (i.e., ion influx).

The study of GPIb mechanosensing may help understand how mechanical force regulates platelet thrombotic functions. For example, in response to shear gradients resulting from flow perturbations, discoid platelets aggregate rapidly in a manner independent of agonist activation pathways (Jain et al., 2016; Nesbitt et al., 2009; Yong et al., 2011). This intriguing phenomenon has significant implication in atherothrombosis and medical device thrombotic fouling. Other than the requirement for VWF–GPIbα binding, the underlying mechanism of such purely force-induced platelet thrombosis remains elusive. Here LRRD unfolding may play a role because it requires an increasing force (resembles shear gradient) and strengthens VWF–GPIbα bonds (Ju et al., 2015b) (Figure 7—figure supplement 2C). In addition, our findings may have broad implications since LRRD is a common structure shared by many adhesion and signaling receptors, e.g., toll-like receptors (Bella et al., 2008).

1. 1. Bell GI

   (1978) Models for the specific adhesion of cells to cells

   Science 200:618–627.

   https://doi.org/10.1126/science.347575

   • Google Scholar
2. 1. Bella J
   2. Hindle KL
   3. McEwan PA
   4. Lovell SC

   (2008) The leucine-rich repeat structure

   Cellular and Molecular Life Sciences 65:2307–2333.

   https://doi.org/10.1007/s00018-008-8019-0

   • Google Scholar
3. 1. Bustamante C
   2. Marko JF
   3. Siggia ED
   4. Smith S

   (1994) Entropic elasticity of lambda-phage DNA

   Science 265:1599–1600.

   https://doi.org/10.1126/science.8079175

   • Google Scholar
4. 1. Calverley DC
   2. Kavanagh TJ
   3. Roth GJ

   (1998)

   Human signaling protein 14-3-3zeta interacts with platelet glycoprotein Ib subunits Ibalpha and Ibbeta

   Blood 91:1295–1303.

   • Google Scholar
5. 1. Casari C
   2. Berrou E
   3. Lebret M
   4. Adam F
   5. Kauskot A
   6. Bobe R
   7. Desconclois C
   8. Fressinaud E
   9. Christophe OD
   10. Lenting PJ
   11. Rosa J-P
   12. Denis CV
   13. Bryckaert M

   (2013) von Willebrand factor mutation promotes thrombocytopathy by inhibiting integrin αIIbβ3

   Journal of Clinical Investigation 123:5071–5081.

   https://doi.org/10.1172/JCI69458

   • Google Scholar
6. 1. Chen W
   2. Lou J
   3. Evans EA
   4. Zhu C

   (2012) Observing force-regulated conformational changes and ligand dissociation from a single integrin on cells

   Journal of Cell Biology 199:497–512.

   https://doi.org/10.1083/jcb.201201091

   • Google Scholar
7. 1. Chen Y
   2. Lee H
   3. Tong H
   4. Schwartz M
   5. Zhu C

   (2016) Force regulated conformational change of integrin αVβ3

   Matrix Biology.

   https://doi.org/10.1016/j.matbio.2016.07.002

   • Google Scholar
8. 1. Chen Y
   2. Liu B
   3. Ju L
   4. Hong J
   5. Ji Q
   6. Chen W
   7. Zhu C

   (2015) Fluorescence biomembrane force probe: concurrent quantitation of receptor-ligand kinetics and binding-induced intracellular signaling on a single cell

   Journal of Visualized Experiments e52975.

   https://doi.org/10.3791/52975

   • Google Scholar
9. 1. Chesla SE
   2. Selvaraj P
   3. Zhu C

   (1998) Measuring two-dimensional receptor-ligand binding kinetics by micropipette

   Biophysical Journal 75:1553–1572.

   https://doi.org/10.1016/S0006-3495(98)74074-3

   • Google Scholar
10. 1. Cruz MA
    2. Diacovo TG
    3. Emsley J
    4. Liddington R
    5. Handin RI

    (2000) Mapping the glycoprotein Ib-binding site in the von willebrand factor A1 domain

    Journal of Biological Chemistry 275:19098–19105.

    https://doi.org/10.1074/jbc.M002292200

    • Google Scholar
11. 1. Dai K
    2. Bodnar R
    3. Berndt MC
    4. Du X

    (2005) A critical role for 14-3-3zeta protein in regulating the VWF binding function of platelet glycoprotein Ib-IX and its therapeutic implications

    Blood 106:1975–1981.

    https://doi.org/10.1182/blood-2005-01-0440

    • Google Scholar
12. 1. del Rio A
    2. Perez-Jimenez R
    3. Liu R
    4. Roca-Cusachs P
    5. Fernandez JM
    6. Sheetz MP

    (2009) Stretching single talin rod molecules activates vinculin binding

    Science 323:638–641.

    https://doi.org/10.1126/science.1162912

    • Google Scholar
13. 1. Dong JF
    2. Berndt MC
    3. Schade A
    4. McIntire LV
    5. Andrews RK
    6. López JA

    (2001) Ristocetin-dependent, but not botrocetin-dependent, binding of von Willebrand factor to the platelet glycoprotein Ib-IX-V complex correlates with shear-dependent interactions

    Blood 97:162–168.

    https://doi.org/10.1182/blood.V97.1.162

    • Google Scholar
14. 1. Fox JE
    2. Aggerbeck LP
    3. Berndt MC

    (1988)

    Structure of the glycoprotein Ib.IX complex from platelet membranes

    Journal of Biological Chemistry 263:4882–4890.

    • Google Scholar
15. 1. Freedman D
    2. Diaconis P

    (1981) On the histogram as a density estimator: L 2 theory

    Probability Theory and Related Fields 57:453–476.

    https://doi.org/10.1007/BF01025868.

    • Google Scholar
16. 1. Grashoff C
    2. Hoffman BD
    3. Brenner MD
    4. Zhou R
    5. Parsons M
    6. Yang MT
    7. McLean MA
    8. Sligar SG
    9. Chen CS
    10. Ha T
    11. Schwartz MA

    (2010) Measuring mechanical tension across vinculin reveals regulation of focal adhesion dynamics

    Nature 466:263–266.

    https://doi.org/10.1038/nature09198

    • Google Scholar
17. 1. Jackson SP

    (2011) Arterial thrombosis—insidious, unpredictable and deadly

    Nature Medicine 17:1423–1436.

    https://doi.org/10.1038/nm.2515

    • Google Scholar
18. 1. Jain A
    2. Graveline A
    3. Waterhouse A
    4. Vernet A
    5. Flaumenhaft R
    6. Ingber DE

    (2016) A shear gradient-activated microfluidic device for automated monitoring of whole blood haemostasis and platelet function

    Nature Communications 7:10176.

    https://doi.org/10.1038/ncomms10176

    • Google Scholar
19. 1. Ju L
    2. Chen Y
    3. Zhou F
    4. Lu H
    5. Cruz MA
    6. Zhu C

    (2015a) Von Willebrand factor-A1 domain binds platelet glycoprotein Ibα in multiple states with distinctive force-dependent dissociation kinetics

    Thrombosis Research 136:606–612.

    https://doi.org/10.1016/j.thromres.2015.06.019

    • Google Scholar
20. 1. Ju L
    2. Dong JF
    3. Cruz MA
    4. Zhu C

    (2013) The N-terminal flanking region of the A1 domain regulates the force-dependent binding of von Willebrand factor to platelet glycoprotein Ibα

    Journal of Biological Chemistry 288:32289–32301.

    https://doi.org/10.1074/jbc.M113.504001

    • Google Scholar
21. 1. Ju L
    2. Lou J
    3. Chen Y
    4. Li Z
    5. Zhu C

    (2015b) Force-induced unfolding of leucine-rich repeats of glycoprotein ibα strengthens ligand interaction

    Biophysical Journal 109:1781–1784.

    https://doi.org/10.1016/j.bpj.2015.08.050

    • Google Scholar
22. 1. Ju L
    2. Qian J
    3. Zhu C

    (2015c) Transport regulation of two-dimensional receptor-ligand association

    Biophysical Journal 108:1773–1784.

    https://doi.org/10.1016/j.bpj.2015.02.023

    • Google Scholar
23. 1. Kellermayer MS
    2. Smith SB
    3. Granzier HL
    4. Bustamante C

    (1997) Folding-unfolding transitions in single titin molecules characterized with laser tweezers

    Science 276:1112–1116.

    https://doi.org/10.1126/science.276.5315.1112

    • Google Scholar
24. 1. Liang X
    2. Russell SR
    3. Estelle S
    4. Jones LH
    5. Cho S
    6. Kahn ML
    7. Berndt MC
    8. Bunting ST
    9. Ware J
    10. Li R

    (2013) Specific inhibition of ectodomain shedding of glycoprotein Ibα by targeting its juxtamembrane shedding cleavage site

    Journal of Thrombosis and Haemostasis 11:2155–2162.

    https://doi.org/10.1111/jth.12425

    • Google Scholar
25. 1. Liu B
    2. Chen W
    3. Evavold BD
    4. Zhu C

    (2014) Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling

    Cell 157:357–368.

    https://doi.org/10.1016/j.cell.2014.02.053

    • Google Scholar
26. 1. Mazzucato M
    2. Pradella P
    3. Cozzi MR
    4. De Marco L
    5. Ruggeri ZM

    (2002) Sequential cytoplasmic calcium signals in a 2-stage platelet activation process induced by the glycoprotein Ibalpha mechanoreceptor

    Blood 100:2793–2800.

    https://doi.org/10.1182/blood-2002-02-0514

    • Google Scholar
27. 1. Morales LD
    2. Martin C
    3. Cruz MA

    (2006) The interaction of von Willebrand factor-A1 domain with collagen: mutation G1324S (type 2M von Willebrand disease) impairs the conformational change in A1 domain induced by collagen

    Journal of Thrombosis and Haemostasis 4:417–425.

    https://doi.org/10.1111/j.1538-7836.2006.01742.x

    • Google Scholar
28. 1. Nesbitt WS
    2. Kulkarni S
    3. Giuliano S
    4. Goncalves I
    5. Dopheide SM
    6. Yap CL
    7. Harper IS
    8. Salem HH
    9. Jackson SP

    (2002) Distinct glycoprotein Ib/V/IX and integrin alpha IIbbeta 3-dependent calcium signals cooperatively regulate platelet adhesion under flow

    Journal of Biological Chemistry 277:2965–2972.

    https://doi.org/10.1074/jbc.M110070200

    • Google Scholar
29. 1. Nesbitt WS
    2. Westein E
    3. Tovar-Lopez FJ
    4. Tolouei E
    5. Mitchell A
    6. Fu J
    7. Carberry J
    8. Fouras A
    9. Jackson SP

    (2009) A shear gradient-dependent platelet aggregation mechanism drives thrombus formation

    Nature Medicine 15:665–673.

    https://doi.org/10.1038/nm.1955

    • Google Scholar
30. 1. Oberhauser AF
    2. Hansma PK
    3. Carrion-Vazquez M
    4. Fernandez JM

    (2001) Stepwise unfolding of titin under force-clamp atomic force microscopy

    PNAS 98:468–472.

    https://doi.org/10.1073/pnas.98.2.468

    • Google Scholar
31. 1. Qiu Y
    2. Ciciliano J
    3. Myers DR
    4. Tran R
    5. Lam WA

    (2015) Platelets and physics: How platelets "feel" and respond to their mechanical microenvironment

    Blood Reviews 29:377–386.

    https://doi.org/10.1016/j.blre.2015.05.002

    • Google Scholar
32. 1. Rief M
    2. Gautel M
    3. Oesterhelt F
    4. Fernandez JM
    5. Gaub HE

    (1997) Reversible unfolding of individual titin immunoglobulin domains by AFM

    Science 276:1109–1112.

    https://doi.org/10.1126/science.276.5315.1109

    • Google Scholar
33. 1. Ruggeri ZM

    (2004) Type IIB von Willebrand disease: a paradox explains how von Willebrand factor works

    Journal of Thrombosis and Haemostasis 2:2–6.

    https://doi.org/10.1111/j.1538-7836.2003.00523.x

    • Google Scholar
34. 1. Ruggeri ZM

    (2015) Platelet GPIb: sensing force and responding

    Blood 125:423–424.

    https://doi.org/10.1182/blood-2014-12-610642

    • Google Scholar
35. 1. Sheather SJ
    2. Jones MC

    (1991) A reliable data-based bandwidth selection method for kernel density estimation

    Journal of the Royal Statistical Society Series B 53:683–690.

    https://doi.org/10.2307/2345597

    • Google Scholar
36. 1. Springer TA

    (2014) von Willebrand factor, Jedi knight of the bloodstream

    Blood 124:1412–1425.

    https://doi.org/10.1182/blood-2014-05-378638

    • Google Scholar
37. 1. Strassel C
    2. David T
    3. Eckly A
    4. Baas MJ
    5. Moog S
    6. Ravanat C
    7. Trzeciak MC
    8. Vinciguerra C
    9. Cazenave JP
    10. Gachet C
    11. Lanza F

    (2006) Synthesis of GPIb beta with novel transmembrane and cytoplasmic sequences in a Bernard-Soulier patient resulting in GPIb-defective signaling in CHO cells

    Journal of Thrombosis and Haemostasis 4:217–228.

    https://doi.org/10.1111/j.1538-7836.2005.01654.x

    • Google Scholar
38. 1. Sukharev S
    2. Sachs F

    (2012) Molecular force transduction by ion channels: diversity and unifying principles

    Journal of Cell Science 125:3075–3083.

    https://doi.org/10.1242/jcs.092353

    • Google Scholar
39. 1. Tskhovrebova L
    2. Trinick J
    3. Sleep JA
    4. Simmons RM

    (1997) Elasticity and unfolding of single molecules of the giant muscle protein titin

    Nature 387:308–312.

    https://doi.org/10.1038/387308a0

    • Google Scholar
40. 1. Wu T
    2. Lin J
    3. Cruz MA
    4. Dong JF
    5. Zhu C

    (2010) Force-induced cleavage of single VWFA1A2A3 tridomains by ADAMTS-13

    Blood 115:370–378.

    https://doi.org/10.1182/blood-2009-03-210369

    • Google Scholar
41. 1. Yago T
    2. Lou J
    3. Wu T
    4. Yang J
    5. Miner JJ
    6. Coburn L
    7. López JA
    8. Cruz MA
    9. Dong JF
    10. McIntire LV
    11. McEver RP
    12. Zhu C

    (2008) Platelet glycoprotein Ibalpha forms catch bonds with human WT vWF but not with type 2B von Willebrand disease vWF

    The Journal of Clinical Investigation 118:3195–3207.

    https://doi.org/10.1172/JCI35754

    • Google Scholar
42. 1. Yin H
    2. Stojanovic-Terpo A
    3. Xu W
    4. Corken A
    5. Zakharov A
    6. Qian F
    7. Pavlovic S
    8. Krbanjevic A
    9. Lyubimov AV
    10. Wang ZJ
    11. Ware J
    12. Du X

    (2013) Role for platelet glycoprotein Ib-IX and effects of its inhibition in endotoxemia-induced thrombosis, thrombocytopenia, and mortality

    Arteriosclerosis, Thrombosis, and Vascular Biology 33:2529–2537.

    https://doi.org/10.1161/ATVBAHA.113.302339

    • Google Scholar
43. 1. Yong AS
    2. Pennings GJ
    3. Chang M
    4. Hamzah A
    5. Chung T
    6. Qi M
    7. Brieger D
    8. Behnia M
    9. Krilis SA
    10. Ng MK
    11. Lowe HC
    12. Kritharides L

    (2011) Intracoronary shear-related up-regulation of platelet P-selectin and platelet-monocyte aggregation despite the use of aspirin and clopidogrel

    Blood 117:11–20.

    https://doi.org/10.1182/blood-2010-04-278812

    • Google Scholar
44. 1. Zhang Q
    2. Zhou Y-F
    3. Zhang C-Z
    4. Zhang X
    5. Lu C
    6. Springer TA

    (2009a) Structural specializations of A2, a force-sensing domain in the ultralarge vascular protein von Willebrand factor

    PNAS 106:9226–9231.

    https://doi.org/10.1073/pnas.0903679106

    • Google Scholar
45. 1. Zhang W
    2. Deng W
    3. Zhou L
    4. Xu Y
    5. Yang W
    6. Liang X
    7. Wang Y
    8. Kulman JD
    9. Zhang XF
    10. Li R

    (2015) Identification of a juxtamembrane mechanosensitive domain in the platelet mechanosensor glycoprotein Ib-IX complex

    Blood 125:562–569.

    https://doi.org/10.1182/blood-2014-07-589507

    • Google Scholar
46. 1. Zhang X
    2. Halvorsen K
    3. Zhang CZ
    4. Wong WP
    5. Springer TA

    (2009b) Mechanoenzymatic cleavage of the ultralarge vascular protein von Willebrand factor

    Science 324:1330–1334.

    https://doi.org/10.1126/science.1170905

    • Google Scholar
47. 1. Zhu C
    2. Long M
    3. Chesla SE
    4. Bongrand P

    (2002) Measuring receptor/ligand interaction at the single-bond level: experimental and interpretative issues

    Annals of Biomedical Engineering 30:305–314.

    https://doi.org/10.1114/1.1467923

    • Google Scholar

Author details

1. Lining Ju

   1. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, United States
   2. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, United States
   3. Heart Research Institute, Camperdown, Australia
   4. Charles Perkins Centre, The University of Sydney, Camperdown, Australia

   Contribution

   LJ, Wrote and revised the paper, Performed research and analyzed data, Analysis and interpretation of data, Designed research, Contributed unpublished essential data or reagents

   Contributed equally with

   Yunfeng Chen

   Competing interests

   The authors declare that no competing interests exist.

2. Yunfeng Chen

   1. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, United States
   2. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, United States

   Contribution

   YC, Wrote and revised the paper, Performed research and analyzed data, Acquisition of data, Analysis and interpretation of data

   Contributed equally with

   Lining Ju

   Competing interests

   The authors declare that no competing interests exist.

3. Lingzhou Xue

   Department of Statistics, The Pennsylvania State University, University Park, United States

   Contribution

   LX, Performed statistical analysis, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Xiaoping Du

   Department of Pharmacology, College of Medicine, University of Illinois at Chicago, Chicago, United States

   Contribution

   XD, Provided key reagents, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

5. Cheng Zhu

   1. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, United States
   2. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, United States
   3. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, United States

   Contribution

   CZ, Wrote and revised the paper, Designed research, Analysis and interpretation of data

   For correspondence

   cheng.zhu@bme.gatech.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1718-565X

• 2,576

  views

• 577

  downloads

• 67

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.