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

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore

PL02-05 

All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Patterns of Somatic Mutation in Human Cancer Genomes

/pdf/metal-organic-frameworks-and-self-assembled-supramolecular-2xs948xxr9.pdf

Metal–Organic Frameworks and Self-Assembled Supramolecular Coordination Complexes: Comparing and Contrasting the Design, Synthesis, and Functionality of Metal–Organic Materials

The analysis of myo-inositol phosphates (InsPs) and myo-inositol pyrophosphates (PP-InsPs) is a daunting challenge due to the large number of possible isomers, the absence of a chromophore, the high charge density, the low abundance, and the instability of the esters and anhydrides. Given their importance in biology, an analytical approach to follow and understand this complex signaling hub is highly desirable. Here, capillary electrophoresis (CE) coupled to electrospray ionization mass spectrometry (ESI-MS) is implemented to analyze complex mixtures of InsPs and PP-InsPs with high sensitivity. Stable isotope labeled (SIL) internal standards allow for matrix-independent quantitative assignment. The method is validated in wild-type and knockout mammalian cell lines and in model organisms. SIL-CE-ESI-MS enables for the first time the accurate monitoring of InsPs and PP-InsPs arising from compartmentalized cellular synthesis pathways, by feeding cells with either [13C6]-myo-inositol or [13C6]-D-glucose. In doing so, we uncover that there must be unknown inositol synthesis pathways in mammals, highlighting the unique potential of this method to dissect inositol phosphate metabolism and signalling.

/pdf/analysis-of-inositol-phosphate-metabolism-by-capillary-4e6y1a7c3u.pdf

Analysis of Inositol Phosphate Metabolism by Capillary Electrophoresis Electrospray Ionization Mass Spectrometry (CE-ESI-MS)

Supporting Online Material for Rewiring of Genetic Networks in Response to DNA Damage

/pdf/itraconazole-inhibits-endothelial-cell-migration-by-12f2xveg.pdf

Itraconazole inhibits endothelial cell migration by disrupting inositol pyrophosphate-dependent focal adhesion dynamics and cytoskeletal remodeling

The inositol pyrophosphates (PP-InsPs) are a unique group of intracellular messengers that represent some of the most highly phosphorylated molecules in nature. Genetic perturbation of the PP-InsP biosynthetic network indicates a central role for these metabolites in maintaining cellular energy homeostasis and in controlling signal transduction networks. However, despite their discovery over two decades ago, elucidating their physiologically relevant isomers, the biochemical pathways connecting these molecules to their associated phenotypes, and their modes of signal transduction has often been stymied by technical challenges. Many of the advances in understanding these molecules to date have been facilitated by the total synthesis of the various PP-InsP isomers and by the development of new methods that are capable of identifying their downstream signalling partners. Chemical tools have also been developed to distinguish between the proposed PP-InsP signal transduction mechanisms: protein binding, and a covalent modification of proteins termed protein pyrophosphorylation. In this article, we review these recent developments, discuss how they have helped to illuminate PP-InsP structure and function, and highlight opportunities for future discovery.

Chemical Tools for Interrogating Inositol Pyrophosphate Structure and Function

Sialic acids are part of the outermost component of the glycocalyx of all vertebrates; as such, they are fundamental markers in physiological and pathological processes. In this study, we introduce a real-time assay to monitor individual enzymatic steps of sialic acid biosynthesis, either with recombinant enzymes, in particular using UDP-N-acetylglucosamine 2-epimerase (GNE) or N-acetylmannosamine kinase (MNK), or in cytosolic rat liver extract. Using state-of-the-art NMR techniques, we are able to follow the characteristic signal of the N-acetyl methyl group, which displays different chemical shifts for the biosynthesis intermediates UDP-N-acetylglucosamine, N-acetylmannosamine (and its 6-phosphate) and N-acetylneuraminic acid (and its 9-phosphate). Pseudo 2- and 3-D NMR demonstrated that in rat liver cytosolic extract, the phosphorylation reaction of MNK is exclusive for N-acetylmannosamine generated by GNE. Thus, we speculate that phosphorylation of this sugar from other sources (e.g. external application to cells) or N-acetylmannosamine derivatives often applied in metabolic glycoengineering is not conducted by MNK but by a yet unknown sugar kinase. Competition experiments with the most prevalent neutral carbohydrates demonstrated that of these, only N-acetylglucosamine slowed N-acetylmannosamine phosphorylation kinetics, suggesting an N-acetylglucosamine-preferring kinase as the acting enzyme.

/pdf/real-time-monitoring-of-the-sialic-acid-biosynthesis-pathway-10yvv6lf.pdf

Dorothea Fiedler

Papers

Analysis of Inositol Phosphate Metabolism by Capillary Electrophoresis Electrospray Ionization Mass Spectrometry (CE-ESI-MS)

Supporting Online Material for Rewiring of Genetic Networks in Response to DNA Damage

Itraconazole inhibits endothelial cell migration by disrupting inositol pyrophosphate-dependent focal adhesion dynamics and cytoskeletal remodeling

Chemical Tools for Interrogating Inositol Pyrophosphate Structure and Function

Real-time monitoring of the sialic acid biosynthesis pathway by NMR