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

WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , WHO CLASSIFICATION OF TUMOURS OF HAEMATOPOIETIC AND LYMPHOID TISSUES , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

12 Pathology and Microbiology, and 13 Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma, is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease. We proposed that this variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Using DNA microarrays, we have conducted a systematic characterization of gene expression in B-cell malignancies. Here we show that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour. We identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. The molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer.

/pdf/distinct-types-of-diffuse-large-b-cell-lymphoma-identified-508avrqemv.pdf

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

UMI or not UMI, that is the question for scRNA-seq zero-inflation.

Hodgkin's Disease: Clonal Ig Gene Rearrangements in Hodgkin and Reed‐Sternberg Cells Picked from Histological Sections

Brune V, Tiacci E, Pfeil I, Döring C, Eckerle S, van Noesel CJ et al.. Origin and pathogenesis of nodular lymphocyte-predominant Hodgkin lymphoma as revealed by global gene expression analysis. J Exp Med 205: 2251-2268

CD30 expression in neoplastic T cells of follicular T cell lymphoma is a helpful diagnostic tool in the differential diagnosis of Hodgkin lymphoma

A unique feature of the germinal center B cell-derived Hodgkin and Reed/Sternberg cells of classical Hodgkin lymphoma is their lost B cell phenotype and the aberrant expression of factors of other hematopoietic cell types, including ID2 and NOTCH1. As cellular dedifferentiation and upregulation of ID2 and NOTCH1 are typical consequences of a hypoxic response, we wondered whether hypoxia may impose an HRS cell-like phenotype in B cells. Culturing normal B cells or cell lines of germinal center-type diffuse large B-cell lymphoma under hypoxic conditions caused partial downregulation of several B cell markers, ID2 upregulation, and increased NOTCH1 activity. The hypoxic cells acquired further features of Hodgkin and Reed/Sternberg cells, including increased JUN expression, and enhanced NFκB activity. The Hodgkin and Reed/Sternberg cell-expressed epigenetic regulators KDM4C and PCGF2, as well as the phosphatase DUSP1 were partially induced in hypoxic B cells. Inhibition of DUSP1 was toxic for classical Hodgkin lymphoma cell lines. Thus, hypoxia induces key Hodgkin and Reed/Sternberg cell characteristics in mature B cells. We speculate that hypoxic conditions in the germinal center may impose phenotypic changes in germinal center B cells, promoting their survival and initiating their differentiation towards a Hodgkin and Reed/Sternberg cell-like phenotype. These may then be stabilized by transforming events in the Hodgkin and Reed/Sternberg precursor cells.

Ralf Küppers

Papers

UMI or not UMI, that is the question for scRNA-seq zero-inflation.

Hodgkin's Disease: Clonal Ig Gene Rearrangements in Hodgkin and Reed‐Sternberg Cells Picked from Histological Sections

Brune V, Tiacci E, Pfeil I, Döring C, Eckerle S, van Noesel CJ et al.. Origin and pathogenesis of nodular lymphocyte-predominant Hodgkin lymphoma as revealed by global gene expression analysis. J Exp Med 205: 2251-2268

CD30 expression in neoplastic T cells of follicular T cell lymphoma is a helpful diagnostic tool in the differential diagnosis of Hodgkin lymphoma

Potential role of hypoxia in early stages of Hodgkin lymphoma pathogenesis