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

The process of programmed cell death, or apoptosis, is generally characterized by distinct morphological characteristics and energy-dependent biochemical mechanisms. Apoptosis is considered a vital component of various processes including normal cell turnover, proper development and functioning of the immune system, hormone-dependent atrophy, embryonic development and chemical-induced cell death. Inappropriate apoptosis (either too little or too much) is a factor in many human conditions including neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer. The ability to modulate the life or death of a cell is recognized for its immense therapeutic potential. Therefore, research continues to focus on the elucidation and analysis of the cell cycle machinery and signaling pathways that control cell cycle arrest and apoptosis. To that end, the field of apoptosis research has been moving forward at an alarmingly rapid rate. Although many of the key apoptotic proteins have been identified, the molecular mechanisms of action or inaction of these proteins remain to be elucidated. The goal of this review is to provide a general overview of current knowledge on the process of apoptosis including morphology, biochemistry, the role of apoptosis in health and disease, detection methods, as well as a discussion of potential alternative forms of apoptosis.

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Apoptosis: A Review of Programmed Cell Death

At high concentrations, free radicals and radical-derived, nonradical reactive species are hazardous for living organisms and damage all major cellular constituents. At moderate concentrations, how...

/pdf/free-radicals-in-the-physiological-control-of-cell-function-g3w3iyr0ph.pdf

Free Radicals in the Physiological Control of Cell Function

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Comprehensive Integration of Single-Cell Data.

Phospho Flow Cytometry: Single‐Cell Signaling Networks in Next‐Generation Drug Discovery and Patient Stratification

Verfahren und Zusammensetzungen zur Risikostratifizierung

Methods of developing and using models of cellular networks by applying a probabilistic graphical model are provided.

Method for modeling cell signaling systems by means of bayesian networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.

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Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Resistance to glucocorticoids (GC) is associated with an increased risk of relapse in B-cell progenitor acute lymphoblastic leukemia (BCP-ALL). Performing transcriptomic and single-cell proteomic studies in healthy B-cell progenitors, we herein identify coordination between the glucocorticoid receptor pathway with B-cell developmental pathways. Healthy pro-B cells most highly express the glucocorticoid receptor, and this developmental expression is conserved in primary BCP-ALL cells from patients at diagnosis and relapse. In-vitro and in vivo glucocorticoid treatment of primary BCP-ALL cells demonstrate that the interplay between B-cell development and the glucocorticoid pathways is crucial for GC resistance in leukemic cells. Gene set enrichment analysis in BCP-ALL cell lines surviving GC treatment show enrichment of B cell receptor signaling pathways. In addition, primary BCP-ALL cells surviving GC treatment in vitro and in vivo demonstrate a late pre-B cell phenotype with activation of PI3K/mTOR and CREB signaling. Dasatinib, a multi-kinase inhibitor, most effectively targets this active signaling in GC-resistant cells, and when combined with glucocorticoids, results in increased cell death in vitro and decreased leukemic burden and prolonged survival in an in vivo xenograft model. Targeting the active signaling through the addition of dasatinib may represent a therapeutic approach to overcome GC resistance in BCP-ALL. 

/pdf/dasatinib-overcomes-glucocorticoid-resistance-in-b-cell-2s38qwnr.pdf

Garry P. Nolan

Papers

Phospho Flow Cytometry: Single‐Cell Signaling Networks in Next‐Generation Drug Discovery and Patient Stratification

Verfahren und Zusammensetzungen zur Risikostratifizierung

Method for modeling cell signaling systems by means of bayesian networks

Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Dasatinib overcomes glucocorticoid resistance in B-cell acute lymphoblastic leukemia