Mark S. Alber

Computational Approaches to Studying Thrombus Development

In addition to descriptive biological models, many computational models have been developed for hemostasis/thrombosis that provide quantitative characterization of thrombus development. Simulations using computational models that have been developed for coagulation reactions, platelet activation, and fibrinogen assembly have been shown to be in close agreement with experimental data. Models of processes involved in hemostasis/thrombosis are being integrated to simulate the development of the thrombus simultaneously in time and space. Further development of computational approaches can provide quantitative insights leading to predictions that are not obvious from qualitative biological models.

Mechanism for the catastrophe-promoting activity of the microtubule destabilizer Op18/stathmin

Significance The microtubule (MT) cytoskeleton is a dynamic polymer network that plays a crucial role in cell function and disease. MT assembly and dynamics are precisely controlled; a key regulator is the MT destabilizer known as stathmin. Stathmin’s mechanism of action remains controversial: one well-supported model is that it reduces polymer indirectly by sequestering MT subunits; the alternative is that it acts directly on MTs by an as yet unknown mechanism. We provide a resolution to this debate by presenting experimental evidence that stathmin can act directly on MTs and does so by binding and destabilizing exposed protofilaments. Computer simulations performed in parallel suggest that both the direct and sequestering activities are likely to be significant in a cellular context. Regulation of microtubule dynamic instability is crucial for cellular processes, ranging from mitosis to membrane transport. Stathmin (also known as oncoprotein 18/Op18) is a prominent microtubule destabilizer that acts preferentially on microtubule minus ends. Stathmin has been studied intensively because of its association with multiple types of cancer, but its mechanism of action remains controversial. Two models have been proposed. One model is that stathmin promotes microtubule catastrophe indirectly, and does so by sequestering tubulin; the other holds that stathmin alters microtubule dynamics by directly destabilizing growing microtubules. Stathmin’s sequestration activity is well established, but the mechanism of any direct action is mysterious because stathmin binds to microtubules very weakly. To address these issues, we have studied interactions between stathmin and varied tubulin polymers. We show that stathmin binds tightly to Dolastatin-10 tubulin rings, which mimic curved tubulin protofilaments, and that stathmin depolymerizes stabilized protofilament-rich polymers. These observations lead us to propose that stathmin promotes catastrophe by binding to and acting upon protofilaments exposed at the tips of growing microtubules. Moreover, we suggest that stathmins minus-end preference results from interactions between stathmins N terminus and the surface of α-tubulin that is exposed only at the minus end. Using computational modeling of microtubule dynamics, we show that these mechanisms could account for stathmins observed activities in vitro, but that both the direct and sequestering activities are likely to be relevant in a cellular context. Taken together, our results suggest that stathmin can promote catastrophe by direct action on protofilament structure and interactions.

Modelling platelet–blood flow interaction using the subcellular element Langevin method

In this paper, a new three-dimensional modelling approach is described for studying fluid–viscoelastic cell interaction, the subcellular element Langevin (SCEL) method, with cells modelled by subcellular elements (SCEs) and SCE cells coupled with fluid flow and substrate models by using the Langevin equation. It is demonstrated that: (i) the new method is computationally efficient, scaling as 𝒪(N) for N SCEs; (ii) cell geometry, stiffness and adhesivity can be modelled by directly relating parameters to experimentally measured values; (iii) modelling the fluid–platelet interface as a surface leads to a very good correlation with experimentally observed platelet flow interactions. Using this method, the three-dimensional motion of a viscoelastic platelet in a shear blood flow was simulated and compared with experiments on tracking platelets in a blood chamber. It is shown that the complex platelet-flipping dynamics under linear shear flows can be accurately recovered with the SCEL model when compared with the experiments. All experimental details and electronic supplementary material are archived at http://biomath.math.nd.edu/scelsupplementaryinformation/.

Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa

Significance The opportunistic pathogen Pseudomonas aeruginosa utilizes both its flagellum and type IV pili (TFP) to facilitate motility, attachment, and colonization. Surface motility such as swarming is thought to precede biofilm formation during infection. We combined laboratory and computational methods to probe the physical interactions of TFP during flagellar-mediated swarming and found that TFP of one cell strongly interact with TFP of other cells, which limits swarming expansion rate. Hence, wild-type P. aeruginosa use cell−cell physical interactions via their TFP to control self-organization within motile swarms. This collective mechanism of cell−cell coordination using TFP allows for moderation of swarming direction of individual cells and avoidance of a toxic environment. Pseudomonas aeruginosa is a ubiquitous bacterium that survives in many environments, including as an acute and chronic pathogen in humans. Substantial evidence shows that P. aeruginosa behavior is affected by its motility, and appendages known as flagella and type IV pili (TFP) are known to confer such motility. The role these appendages play when not facilitating motility or attachment, however, is unclear. Here we discern a passive intercellular role of TFP during flagellar-mediated swarming of P. aeruginosa that does not require TFP extension or retraction. We studied swarming at the cellular level using a combination of laboratory experiments and computational simulations to explain the resultant patterns of cells imaged from in vitro swarms. Namely, we used a computational model to simulate swarming and to probe for individual cell behavior that cannot currently be otherwise measured. Our simulations showed that TFP of swarming P. aeruginosa should be distributed all over the cell and that TFP−TFP interactions between cells should be a dominant mechanism that promotes cell−cell interaction, limits lone cell movement, and slows swarm expansion. This predicted physical mechanism involving TFP was confirmed in vitro using pairwise mixtures of strains with and without TFP where cells without TFP separate from cells with TFP. While TFP slow swarm expansion, we show in vitro that TFP help alter collective motion to avoid toxic compounds such as the antibiotic carbenicillin. Thus, TFP physically affect P. aeruginosa swarming by actively promoting cell−cell association and directional collective motion within motile groups to aid their survival.

Strong Binding of Platelet Integrin αIIbβ3 to Fibrin Clots: Potential Target to Destabilize Thrombi

The formation of platelet thrombi is determined by the integrin αIIbβ3-mediated interactions of platelets with fibrinogen and fibrin. Blood clotting in vivo is catalyzed by thrombin, which simultaneously induces fibrinogen binding to αIIbβ3 and converts fibrinogen to fibrin. Thus, after a short time, thrombus formation is governed by αIIbβ3 binding to fibrin fibers. Surprisingly, there is little understanding of αIIbβ3 interaction with fibrin polymers. Here we used an optical trap-based system to measure the binding of single αIIbβ3 molecules to polymeric fibrin and compare it to αIIbβ3 binding to monomeric fibrin and fibrinogen. Like αIIbβ3 binding to fibrinogen and monomeric fibrin, we found that αIIbβ3 binding to polymeric fibrin can be segregated into two binding regimes, one with weaker rupture forces of 30–60u2009pN and a second with stronger rupture forces >60u2009pN that peaked at 70–80u2009pN. However, we found that the mechanical stability of the bimolecular αIIbβ3-ligand complexes had the following order: fibrin polymeru2009>u2009fibrin monomeru2009>u2009fibrinogen. These quantitative differences reflect the distinct specificity and underlying molecular mechanisms of αIIbβ3-mediated reactions, implying that targeting platelet interactions with fibrin could increase the therapeutic indices of antithrombotic agents by focusing on the destabilization of thrombi rather than the prevention of platelet aggregation.