抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Cancer cells possess a broad spectrum of migration and invasion mechanisms. These include both individual and collective cell-migration strategies. Cancer therapeutics that are designed to target adhesion receptors or proteases have not proven to be effective in slowing tumour progression in clinical trials — this might be due to the fact that cancer cells can modify their migration mechanisms in response to different conditions. Learning more about the cellular and molecular basis of these different migration/invasion programmes will help us to understand how cancer cells disseminate and lead to new treatment strategies.

Tumour-cell invasion and migration: diversity and escape mechanisms

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Drug repurposing: progress, challenges and recommendations

Scientific and practical applications of supported lipid-protein bilayers are described. Membranes can be covalently coupled to or separated from solids by ultrathin layers of water or soft polymer cushions. The latter systems maintain the structural and dynamic properties of free bilayers, forming a class of models of biomembranes that allow the application of a manifold of surface-sensitive techniques. They form versatile models of low-dimensionality complex fluids, which can be used to study interfacial forces and wetting phenomena, and enable the design of phantom cells to explore the interplay of lock-and-key forces (such as receptor-ligand binding) and universal forces for cell adhesion. Practical applications are the design of (highly selective) receptor surfaces of biosensors on electrooptical devices or the biofunctionalization of inorganic solids.

https://science.sciencemag.org/content/sci/271/5245/43.full.pdf

Supported Membranes: Scientific and Practical Applications

Cells within tissues are continuously exposed to physical forces including hydrostatic pressure, shear stress, and compression and tension forces. Cells dynamically adapt to force by modifying their behaviour and remodelling their microenvironment. They also sense these forces through mechanoreceptors and respond by exerting reciprocal actomyosin- and cytoskeletal-dependent cell-generated force by a process termed 'mechanoreciprocity'. Loss of mechanoreciprocity has been shown to promote the progression of disease, including cancer. Moreover, the mechanical properties of a tissue contribute to disease progression, compromise treatment and might also alter cancer risk. Thus, the changing force that cells experience needs to be considered when trying to understand the complex nature of tumorigenesis.

/pdf/a-tense-situation-forcing-tumour-progression-4myf9i89al.pdf

A Tense Situation: Forcing Tumour Progression

https://www.cell.com/biophysj/pdf/S0006-3495(73)85983-1.pdf

Strain Energy Function of Red Blood Cell Membranes

Theoretical and experimental studies on viscoelastic properties of erythrocyte membrane

Tumor classification is inexact and largely dependent on the qualitative pathological examination of the images of the tumor tissue slides. In this study, our aim was to develop an automated computational method to classify Hematoxylin and Eosin (H&E) stained tissue sections based on cancer tissue texture features. Image processing of histology slide images was used to detect and identify adipose tissue, extracellular matrix, morphologically distinct cell nuclei types, and the tubular architecture. The texture parameters derived from image analysis were then applied to classify images in a supervised classification scheme using histologic grade of a testing set as guidance. The histologic grade assigned by pathologists to invasive breast carcinoma images strongly correlated with both the presence and extent of cell nuclei with dispersed chromatin and the architecture, specifically the extent of presence of tubular cross sections. The two parameters that differentiated tumor grade found in this study were (1) the number density of cell nuclei with dispersed chromatin and (2) the number density of tubular cross sections identified through image processing as white blobs that were surrounded by a continuous string of cell nuclei. Classification based on subdivisions of a whole slide image containing a high concentration of cancer cell nuclei consistently agreed with the grade classification of the entire slide. The automated image analysis and classification presented in this study demonstrate the feasibility of developing clinically relevant classification of histology images based on micro- texture. This method provides pathologists an invaluable quantitative tool for evaluation of the components of the Nottingham system for breast tumor grading and avoid intra-observer variability thus increasing the consistency of the decision-making process.

/pdf/large-scale-computations-on-histology-images-reveal-grade-5a8e4s9e2b.pdf

Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

Cell lines are used in experimental investigation of cancer but their capacity to represent tumor cells has yet to be quantified. The aim of the study was to identify significant alterations in pathway usage in cell lines in comparison with normal and tumor tissue. This study utilized a pathway-specific enrichment analysis of publicly accessible microarray data and quantified the gene expression differences between cell lines, tumor, and normal tissue cells for six different tissue types. KEGG pathways that are significantly different between cell lines and tumors, cell lines and normal tissues and tumor and normal tissue were identified through enrichment tests on gene lists obtained using Significance Analysis of Microarrays (SAM). Cellular pathways that were significantly upregulated in cell lines compared to tumor cells and normal cells of the same tissue type included ATP synthesis, cell communication, cell cycle, oxidative phosphorylation, purine, pyrimidine and pyruvate metabolism, and proteasome. Results on metabolic pathways suggested an increase in the velocity nucleotide metabolism and RNA production. Pathways that were downregulated in cell lines compared to tumor and normal tissue included cell communication, cell adhesion molecules (CAMs), and ECM-receptor interaction. Only a fraction of the significantly altered genes in tumor-to-normal comparison had similar expressions in cancer cell lines and tumor cells. These genes were tissue-specific and were distributed sparsely among multiple pathways. Significantly altered genes in tumors compared to normal tissue were largely tissue specific. Among these genes downregulation was a major trend. In contrast, cell lines contained large sets of significantly upregulated genes that were common to multiple tissue types. Pathway upregulation in cell lines was most pronounced over metabolic pathways including cell nucleotide metabolism and oxidative phosphorylation. Signaling pathways involved in adhesion and communication of cultured cancer cells were downregulated. The three way pathways comparison presented in this study brings light into the differences in the use of cellular pathways by tumor cells and cancer cell lines.

/pdf/pathway-specific-differences-between-tumor-cell-lines-and-qt7uespwx6.pdf

Pathway-specific differences between tumor cell lines and normal and tumor tissue cells

https://www.cell.com/biophysj/pdf/S0006-3495(92)81660-0.pdf

Aydin Tozeren

Papers

Strain Energy Function of Red Blood Cell Membranes

Theoretical and experimental studies on viscoelastic properties of erythrocyte membrane

Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer

Pathway-specific differences between tumor cell lines and normal and tumor tissue cells

How do selectins mediate leukocyte rolling in venules