Abstract Fluid flow and chemokine gradients play a large part in not only regulating homeostatic processes in the brain, but also in pathologic conditions by directing cell migration. Tumor cells in particular are superior at invading into the brain resulting in tumor recurrence. One mechanism that governs cellular invasion is autologous chemotaxis, whereby pericellular chemokine gradients form due to interstitial fluid flow (IFF) leading cells to migrate up the gradient. Glioma cells have been shown to specifically use CXCL12 to increase their invasion under heightened interstitial flow.Computational modeling of this gradient offers better insight into the extent of its development around single cells, yet very few conditions have been modelled. In this paper, a computational model is developed to investigate how a CXCL12 gradient may form around a tumor cell and what conditions are necessary to affect its formation. Through finite element analysis using COMSOL and coupled convection-diffusion/mass transport equations, we show that velocity (IFF magnitude) has the largest parametric effect on gradient formation, multidirectional fluid flow causes gradient formation in the direction of the resultant which is governed by IFF magnitude, common treatments and flow patterns have a spatiotemporal effect on pericellular gradients, exogenous background concentrations can abrogate the autologous effect depending on how close the cell is to the source, that there is a minimum distance away from the tumor border required for a single cell to establish an autologous gradient, and finally that the development of a gradient formation is highly dependent on specific cell morphology.

Abstract Nascent adhesions (NAs) are a general precursor to the formation of focal adhesions (FAs) that provide a fundamental mechanism for cell adhesion that is, in turn, involved in cell proliferation, migration, and mechanotransduction. Nascent adhesions form when cells come into contact with substrates at all rigidities and generally involve the clustering of ligated integrins that may recruit un-ligated integrins. Nascent adhesions tend to take on characteristic sizes in the range of O(100 nm–150 nm) in diameter and tend to contain integrin numbers of O(∼ 20–60). The flexible,adaptable model we present provides and clear explanation of how these conserved cluster features come about. Our model is based on the interaction among ligated and un-ligated integrins that arise due to deformations that are induced in the cell membrane-cell glycocalyx and substrate system due to integrin activation and ligation. This model produces a clearly based interaction potential, and from it an explicit interaction force among integrins, that our stochastic diffusion-interaction simulations then show will produce nascent clusters with experimentally observed characteristics. Our simulations reveal effects of various key parameters related to integrin activation and ligation as well as some unexpected and previously unappreciated effects of parameters including integrin mobility and substrate rigidity. Moreover, the model’s structure is such that refinements are readily incorporated and specific suggestions are made as to what is required for further progress in understanding nascent clustering and the development of mature focal adhesions in a truly predictive manner.

Abstract Fractal dimension, a measure of self-similarity in a structure, is a powerful physical parameter for the characterization of structural property of many partially filled disordered materials. Biological tissues are fractal in nature and reports show a change in self-similarity associated with the progress of cancer, resulting in changes in their fractal dimensions. Here, we report that fractal dimension measurement is a potential technique for the detection of different stages of cancer using transmission optical microscopy. Transmission optical microscopy of a thin tissue sample produces intensity distribution patterns proportional to its refractive index pattern, representing its mass density distribution. We measure fractal dimension detection of different cancer stages and find its universal feature. Many deadly cancers are difficult to detect in their early to different stages due to the hard-to-reach location of the organ and/or lack of symptoms until very late stages. To study these deadly cancers, tissue microarray (TMA) samples containing different stages of cancers are analyzed for pancreatic, breast, colon, and prostate cancers. The fractal dimension method correctly differentiates cancer stages in progressive cancer, raising possibilities for a physics-based accurate diagnosis method for cancer detection.

Abstract Bioluminescence resonance energy transfer (BRET) seems to be a promising biophysical technique to study protein–protein interactions within living cells due to a very specific reaction of bioluminescence that essentially decreases the background of other cellular components and light-induced destruction of biomacromolecules. An important direction of the development of this technique is the study of known strong protein–protein complexes in vivo and the estimation of an average distance between chromophores of the donor and acceptor. Here, we demonstrate an in vivo interaction between barnase fused with luciferase (from Renilla reniformis, RLuc) and barstar fused with EGFP (enhanced green fluorescent protein of Aequorea victoria) monitored by BRET. The distance between the luciferase and EGFP chromophores within the complex has been evaluated as equal to (56 ± 2) Å.

Abstract In this work, we studied the effect of as on the interaction of membrane DPPC with the key antifibrotic drug pirfenidone. Liposomal forms of pirfenidone were obtained using passive loading. The addition of cholesterol reduces the loading efficiency of pirfenidone by 10%. The main binding site of pirfenidone in DPPC liposomes is the carbonyl group: the interaction with PF significantly increases the proportion of low-hydrated carbonyl groups as revealed by ATR-FTIR spectroscopy. The phosphate group acts as an additional binding site; however, due to shielding by the choline group, this interaction is weak. The hydrophobic part of the bilayer is not involved in PF binding at room temperature. Cholesterol changes the way of interaction between carbonyl groups and pirfenidone probably because of the formation of two subpopulations of DPPC and causes a dramatic redistribution of carbonyl groups onto the degrees of hydration. The proportion of moderately hydrated carbonyl groups increases, apparently due to the deepening of pirfenidone into the circumpolar region of the bilayer. For the first time, a change in the microenvironment of pirfenidone upon binding to liposomes was shown: aromatic moiety interacts with the bilayer.

Abstract Alzheimer’s Disease (AD) is a neurodegenerative disorder that is characterized clinically by progressive cognitive decline and pathologically by the β-sheet rich fibril plaque deposition of the amyloid-β (Aβ) peptide in the brain. While plaques are a hallmark of AD, plaque burden is not correlated with cognitive impairment. Instead, Aβ oligomers formed during the aggregation process represent the main agents of neurotoxicity, which occurs 10–20 years before patients begin to show symptoms. These oligomers are dynamic in nature and represented by a heterogeneous distribution of aggregates ranging from low- to high-molecular weight, some of which are toxic while others are not. A major difficulty in determining the pathological mechanism(s) of Aβ, developing reliable diagnostic markers for early-stage detection, as well as effective therapeutics for AD are the differentiation and characterization of oligomers formed throughout disease propagation based on their molecular features, effects on biological function, and relevance to disease propagation and pathology. Thus, it is critical to methodically identify the mechanisms of Aβ aggregation and toxicity, as well as describe the roles of different oligomers and aggregates in disease progression and molecular pathology. Here, we describe a variety of biophysical techniques used to isolate and characterize a range of Aβ oligomer populations, as well as discuss proposed mechanisms of toxicity and therapeutic interventions aimed at specific assemblies formed during the aggregation process. The approaches being used to map the misfolding and aggregation of Aβ are like what was done during the fundamental early studies, mapping protein folding pathways using combinations of biophysical techniques in concert with protein engineering. Such information is critical to the design and molecular engineering of future diagnostics and therapeutics for AD.