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

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

Microglia are the resident macrophages of the central nervous system and are associated with the pathogenesis of many neurodegenerative and brain inflammatory diseases; however, the origin of adult microglia remains controversial. We show that postnatal hematopoietic progenitors do not significantly contribute to microglia homeostasis in the adult brain. In contrast to many macrophage populations, we show that microglia develop in mice that lack colony stimulating factor-1 (CSF-1) but are absent in CSF-1 receptor-deficient mice. In vivo lineage tracing studies established that adult microglia derive from primitive myeloid progenitors that arise before embryonic day 8. These results identify microglia as an ontogenically distinct population in the mononuclear phagocyte system and have implications for the use of embryonically derived microglial progenitors for the treatment of various brain disorders.

/pdf/fate-mapping-analysis-reveals-that-adult-microglia-derive-4q8i4y2udw.pdf

Fate Mapping Analysis Reveals That Adult Microglia Derive from Primitive Macrophages

Monocytes and macrophages are critical effectors and regulators of inflammation and the innate immune response, the immediate arm of the immune system. Dendritic cells initiate and regulate the highly pathogen-specific adaptive immune responses and are central to the development of immunologic memory and tolerance. Recent in vivo experimental approaches in the mouse have unveiled new aspects of the developmental and lineage relationships among these cell populations. Despite this, the origin and differentiation cues for many tissue macrophages, monocytes, and dendritic cell subsets in mice, and the corresponding cell populations in humans, remain to be elucidated.

/pdf/development-of-monocytes-macrophages-and-dendritic-cells-5fkinpnur8.pdf

Development of Monocytes, Macrophages, and Dendritic Cells

Dendritic cells (DCs) form a remarkable cellular network that shapes adaptive immune responses according to peripheral cues. After four decades of research, we now know that DCs arise from a hematopoietic lineage distinct from other leukocytes, establishing the DC system as a unique hematopoietic branch. Recent work has also established that tissue DCs consist of developmentally and functionally distinct subsets that differentially regulate T lymphocyte function. This review discusses major advances in our understanding of the regulation of DC lineage commitment, differentiation, diversification, and function in situ.

The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting.

To understand the system of secreted proteins and receptors involved in cell-cell signaling, we produced a comprehensive set of recombinant secreted proteins and the extracellular domains of transmembrane proteins, which constitute most of the protein components of the extracellular space. Each protein was tested in a suite of assays that measured metabolic, growth, or transcriptional responses in diverse cell types. The pattern of responses across assays was analyzed for the degree of functional selectivity of each protein. One of the highly selective proteins was a previously undescribed ligand, designated interleukin-34 (IL-34), which stimulates monocyte viability but does not affect responses in a wide spectrum of other assays. In a separate functional screen, we used a collection of extracellular domains of transmembrane proteins to discover the receptor for IL-34, which was a known cytokine receptor, colony-stimulating factor 1 (also called macrophage colony-stimulating factor) receptor. This systematic approach is thus useful for discovering new ligands and receptors and assessing the functional selectivity of extracellular regulatory proteins.

https://science.sciencemag.org/content/sci/320/5877/807.full.pdf

Discovery of a Cytokine and Its Receptor by Functional Screening of the Extracellular Proteome

Disclosed is a newly identified secreted molecule, identified herein as “monocyte, granulocyte, and dendritic cell colony stimulating factor” (MGD-CSF), the polypeptide sequence, and polynucleotides encoding the polypeptide sequence. Also provided is a procedure for producing the polypeptide by recombinant techniques employing, for example, vectors and host cells. Additionally, procedures are described to modify the disclosed novel molecules of the invention to prepare fusion molecules. Also disclosed are methods for using the polypeptides and active fragments thereof for treatment of a variety of diseases, including, for example, cancer, autoimmune and inflammatory diseases, infectious diseases, and recurrent pregnancy loss.

Cindy Leo

Papers

Discovery of a Cytokine and Its Receptor by Functional Screening of the Extracellular Proteome

Compositions and methods of use for mgd-csf in disease treatment

87 Regulation of Myeloid Growth and Differentiation by a Novel Cytokine, Interleukin-34 (IL-34), via the CSF-1 Receptor

IL-34, in Synergy with RANK Ligand, Promotes Osteoclast Development through the CSF-1 Receptor

A Novel Cytokine, FPT025, Regulates Myeloid Growth and Differentiation Via the M-CSF Receptor.