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.

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

The B-cell receptor (BCR) signaling pathway is a crucial pathway of B cells, both for their survival and for antigen-mediated activation, proliferation and differentiation. Its activation is also critical for the genesis of many lymphoma types. BCR-mediated lymphoma proliferation may be caused by activating BCR-pathway mutations and/or by active or tonic stimulation of the BCR. BCRs of lymphomas have frequently been described as polyreactive. In this review, the role of specific target antigens of the BCRs of lymphomas is highlighted. These antigens have been found to be restricted to specific lymphoma entities. The antigens can be of infectious origin, such as H. pylori in gastric MALT lymphoma or RpoC of M. catarrhalis in nodular lymphocyte predominant Hodgkin lymphoma, or they are autoantigens. Examples of such autoantigens are the BCR itself in chronic lymphocytic leukemia, LRPAP1 in mantle cell lymphoma, hyper-N-glycosylated SAMD14/neurabin-I in primary central nervous system lymphoma, hypo-phosphorylated ARS2 in diffuse large B-cell lymphoma, and hyper-phosphorylated SLP2, sumoylated HSP90 or saposin C in plasma cell dyscrasia. Notably, atypical posttranslational modifications are often responsible for the immunogenicity of many autoantigens. Possible therapeutic approaches evolving from these specific antigens are discussed.

/pdf/role-of-specific-b-cell-receptor-antigens-in-lymphomagenesis-4dz66vfazt.pdf

Role of Specific B-Cell Receptor Antigens in Lymphomagenesis.

Memory B cells are generated during the course of T cell-dependent immune responses in histological structures called germinal centres. These cells have undergone somatic hypermutation of their rearranged Ig V region genes and many of them have also performed Ig class switch recombination. About 10 years ago, the expression of CD27 was proposed as a surface marker for human memory B cells.1,2 However, there is now evidence that additional subsets of memory B cells exist.3 One of these subsets is defined by the expression of FCRL4, an inhibitory receptor that was previously known as IRTA1 or FcRH4.4,5 Two studies published in a recent issue of the Journal of Experimental Medicine shed more light on these peculiar B cells. The study by Ehrhardt et al.6 provides a detailed account of the particular gene expression pattern of these cells compared to FCRL4 memory B cells, thereby further defining FCRL4+ cells as a distinct B-cell population. On the other hand, Moir et al.7 report the unexpected finding that FCRL4+ B cells are present at increased frequencies in the peripheral blood of human immunodeficiency virus (HIV) viraemic patients and have an ‘exhausted’ phenotype. In healthy individuals, FCRL4+ B cells are largely restricted to mucosal tissues and mesenteric lymph nodes, and are very rare in peripheral blood, lymph nodes and the spleen.4,5Most FCRL4+ B cells do not express CD27, and B90% of them have undergone class switching predominantly to IgG, whereas a smaller proportion express IgA.4,5 FCRL4+ B cells express high levels of CD20 and intermediate levels of CD21, but lack the expression of markers that identify germinal centre B cells and plasma cells.4,5 Similar to classical memory B cells, most of the FCRL4+ B cells also express high levels of the CD80 and CD86 costimulatory molecules, have a propensity to quickly differentiate into antibody-secreting cells upon in vitro stimulation with mimics of T-cell help and carry somatically mutated Ig V genes (Figure 1).4,5 These features collectively strongly suggest that FCRL4+ B cells represent memory cells. The study by Ehrhardt et al.6 now reveals multiple differences in terms of gene expression between FCRL4+ and FCRL4 memory B cells. These differences involve transcription factors, cell cycle regulators, signalling molecules and surface proteins. For example, FCRL4+ B cells display increased levels of the adhesion molecule and complement receptor CD11c, and about 10-fold higher levels of the Runx2 transcription factor but about 10-fold lower levels of Runx1 than FCRL4 memory B cells. As little is known about the genes regulated by Runx1 and Runx2 in mature B cells, it will be important to determine the role of these factors in the generation and function of the distinct memory B cells. Overall, this study defines multiple differences in gene expression between FCRL4+ and FCRL4 memory B cells and supports the concept that these B-cell subsets have different functions in immune responses. The FCRL4+ B cells detected by Moir et al.7 in the peripheral blood of HIV+ viraemic individuals share many phenotypic features with the tissue-based FCRL4+ memory B cells. Thus, this might indicate that in the disease situation of viraemic HIV-positive patients, FCRL4+ B cells exit mucosal tissues and circulate through the blood. However, the situation is likely not that simple, as there are also important differences between the FCRL4+ B cells in healthy individuals and in these viraemic HIV+ individuals. These differences include a low or largely absent expression of CD80 and CD86 on FCRL4+ B cells in HIV+ individuals, whereas these molecules are expressed at high levels in FCRL4+ B cells from healthy individuals (Figure 1). Moreover, many tissue-based memory B cells in healthy persons are Ki-67+ and in the G1 phase of the cell cycle, whereas FCRL4+ B cells in the setting of HIV viraemia appear largely Ki-67-negative, and hence are quiescient non-cycling cells. Importantly, there are also functional differences. FCRL4+ B cells isolated from tonsils of HIVnegative persons are induced to proliferate and differentiate into plasmablasts upon stimulation by CD40 ligand and interleukins, but FCRL4-expressing B cells from HIV+ persons do not proliferate strongly upon such stimulation and are also impaired in their ability to differentiate into antibodysecreting cells in response to polyclonal stimuli (Figure 1). Although FCRL4+ memory B cells in healthy humans show the same mutation load in their Ig V region genes as classic memory B cells, Ig V genes of FCRL4+ B cells from HIV viraemic patients are also less mutated. These characteristics of FCRL4+ B cells indicate that the cells may be exhausted, as proposed by Moir et al.7 ‘Exhaustion’ is a well-known phenomenon from T-cell immunology and describes a differentiation stage with expression of multiple inhibitory receptors, reduced expression of activating molecules and impaired effector functions. Importantly, as anti-HIV specificities were enriched among the FCRL4+ B cells, the impaired function of these cells may contribute to the poor humoral response against HIV.7 Although the two studies gave important and exciting novel insights into FCRL4+ B cells in humans, many questions remain to be resolved. What is the relationship between FCRL4+ memory B cells and the mostly FCRL4-negative CD27+ memory B cells? Can they develop from the same germinal centre B-cell clones? Is one population the Dr Ralf Küppers is at the Institute for Cell Biology (Tumor Research), Medical School, University of DuisburgEssen, Essen, Germany. E-mail: ralf.kueppers@uk-essen.de Immunology and Cell Biology (2008) 86, 635–636 & 2008 Australasian Society for Immunology Inc. All rights reserved 0818-9641/08 $32.00

Human memory B cells: memory B cells of a special kind.

Chromosomal translocations involving an immunoglobulin (IG) locus and a proto-oncogene play a major role in diffuse large B-cell lymphoma (DLBCL) pathogenesis. Recurrent IG translocation partners in DLBCL are the BCL6, BCL2, and MYC genes, but other rare translocation partners are also known. We studied 20 DLBCL with fluorescence in situ hybridization-based evidence for IG heavy chain (IGH) locus-associated translocations not involving BCL6, BCL2, MALT1, or MYC by long distance inverse PCR to identify the translocation partners. Moreover, we studied eight DLBCL with MYC translocations not involving IG or known non-IG loci as translocation partner to search for novel MYC translocations. We identified three novel IGH-associated translocations. Chromosomal breakpoints involved the IMMP2L gene in 7q31, the BCAS2 gene in 1p13, and the PVRL2 gene in 19q13. The latter gene, which is recurrently translocated in T-cell lymphomas, is significantly higher expressed in the biopsy with the translocation compared to cases without this genetic aberration, indicating a pathogenetic role of PVRL2 also in DLBCL. In one case with a MYC break we obtained a novel MYC-SOCS1 translocation representing an unusual translocation of a proto-oncogene with a tumor suppressor gene. Indeed, we demonstrate that the oncogene was deregulated and the tumor suppressor gene inactivated. As both genes undergo aberrant somatic hypermutation in the region of the chromosomal breakpoints, this translocation likely happened as a byproduct of the hypermutation process. Overall, our study suggests that chromosomal translocations in DLBCL are more heterogeneous than previously known. © 2016 Wiley Periodicals, Inc.

Novel IGH and MYC Translocation Partners in Diffuse Large B-Cell Lymphomas.

Forkhead box P1 (FOXP1) protein is a transcription factor involved in cell signaling and regulation of gene expression. The overexpression of FOXP1 in a subgroup of systemic diffuse large B-cell lymphomas has been associated with an exceptionally poor clinical outcome. Data on FOXP1 expression in primary central nervous system lymphomas (PCNSL), that is, diffuse large B-cell lymphomas confined to the central nervous system, are not yet available. We analyzed 43 PCNSL from immunocompetent patients. Immunohistochemistry showed expression of FOXP1 protein in 21 (88%) of 24 cases. All 19 PCNSL analyzed by quantitative gene expression analysis showed overexpression of truncated FOXP1 Isoforms 3 and 9 and downregulation of normal-size FOXP1 compared with nonmalignant germinal center B cells, the normal counterpart of PCNSL tumor cells. Thus, truncated FOXP1 isoforms are preferentially overexpressed in PCNSL as they are in diffuse large B-cell lymphomas. Although the mechanisms are presently unclear, this overexpression may contribute to a poor prognosis in PCNSL.

Ralf Küppers

Papers

Role of Specific B-Cell Receptor Antigens in Lymphomagenesis.

Human memory B cells: memory B cells of a special kind.

Novel IGH and MYC Translocation Partners in Diffuse Large B-Cell Lymphomas.

Preferential expression of truncated isoforms of FOXP1 in primary central nervous system lymphoma.

L&H cells in lymphocyte-predominant Hodgkin's disease.