Geoffrey M. Wahl

Bio: Geoffrey M. Wahl is an academic researcher from Salk Institute for Biological Studies. The author has contributed to research in topics: Gene & DNA damage. The author has an hindex of 86, co-authored 213 publications receiving 35144 citations. Previous affiliations of Geoffrey M. Wahl include University of Texas at Austin & Howard Hughes Medical Institute.

Efficient transfer of large DNA fragments from agarose gels to diazobenzyloxymethyl-paper and rapid hybridization by using dextran sulfate.

Abstract: We describe a technique for transferring electrophoretically separated bands of double-stranded DNA from agarose gels to diazobenzyloxymethyl-paper. Controlled cleavage of the DNA in situ by sequential treatment with dilute acid, which causes partial depurination, and dilute alkali, which causes cleavage and separation of the strands, allows the DNA to leave the gel rapidly and completely, with an efficiency independent of its size. Covalent attachment of DNA to paper prevents losses during subsequent hybridization and washing steps and allows a single paper to be reused many times. Ten percent dextran sulfate, originally found to accelerate DNA hybridization in solution by about 10-fold [J.G. Wetmur (1975) Biopolymers 14, 2517-2524], accelerates the rate of hybridization of randomly cleaved double-stranded DNA probes to immobilized nucleic acids by as much as 100-fold, without increasing the background significantly.

Cancer stem cells--perspectives on current status and future directions: AACR Workshop on cancer stem cells.

Abstract: A workshop was convened by the AACR to discuss the rapidly emerging cancer stem cell model for tumor development and progression. The meeting participants were charged with evaluating data suggesting that cancers develop from a small subset of cells with self-renewal properties analogous to organ

Regulating the p53 pathway: in vitro hypotheses, in vivo veritas

Abstract: Mutations in TP53, the gene that encodes the tumour suppressor p53, are found in 50% of human cancers, and increased levels of its negative regulators MDM2 and MDM4 (also known as MDMX) downregulate p53 function in many of the rest. Understanding p53 regulation remains a crucial goal to design broadly applicable anticancer strategies based on this pathway. This Review of in vitro studies, human tumour data and recent mouse models shows that p53 post-translational modifications have modulatory roles, and MDM2 and MDM4 have more profound roles for regulating p53. Importantly, MDM4 emerges as an independent target for drug development, as its inactivation is crucial for full p53 activation.

DNA damage triggers a prolonged p53-dependent G1 arrest and long-term induction of Cip1 in normal human fibroblasts.

Abstract: The tumor suppressor p53 is a cell cycle checkpoint protein that contributes to the preservation of genetic stability by mediating either a G1 arrest or apoptosis in response to DNA damage. Recent reports suggest that p53 causes growth arrest through transcriptional activation of the cyclin-dependent kinase (Cdk)-inhibitor Cip1. Here, we characterize the p53-dependent G1 arrest in several normal human diploid fibroblast (NDF) strains and p53-deficient cell lines treated with 0.1-6 Gy gamma radiation. DNA damage and cell cycle progression analyses showed that NDF entered a prolonged arrest state resembling senescence, even at low doses of radiation. This contrasts with the view that p53 ensures genetic stability by inducing a transient arrest to enable repair of DNA damage, as reported for some myeloid leukemia lines. Gamma radiation administered in early to mid-, but not late, G1 induced the arrest, suggesting that the p53 checkpoint is only active in G1 until cells commit to enter S phase at the G1 restriction point. A log-linear plot of the fraction of irradiated G0 cells able to enter S phase as a function of dose is consistent with single-hit kinetics. Cytogenetic analyses combined with radiation dosage data indicate that only one or a small number of unrepaired DNA breaks may be sufficient to cause arrest. The arrest also correlated with long-term elevations of p53 protein, Cip1 mRNA, and Cip1 protein. We propose that p53 helps maintain genetic stability in NDF by mediating a permanent cell cycle arrest through long-term induction of Cip1 when low amounts of unrepaired DNA damage are present in G1 before the restriction point.

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

Abstract: 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。

Machine learning

Abstract: 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.).