Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm

The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. However there are different interpretations in the literature and there are different implementations of the Ward agglomerative algorithm in commonly used software systems, including differing expressions of the agglomerative criterion. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward's hierarchical clustering method.Comment: 20 pages, 21 citations, 4 figure

NMR Derived Model of GTPase Effector Domain (GED) Self Association: Relevance to Dynamin Assembly

Self-association of dynamin to form spiral structures around lipidic vesicles during endocytosis is largely mediated by its ‘coiled coil’ GTPase Effector Domain (GED), which, in vitro, self-associates into huge helical assemblies. Residue-level structural characterizations of these assemblies and understanding the process of association have remained a challenge. It is also impossible to get folded monomers in the solution phase. In this context, we have developed here a strategy to probe the self-association of GED by first dissociating the assembly using Dimethyl Sulfoxide (DMSO) and then systematically monitoring the refolding into helix and concomitant re-association using NMR spectroscopy, as DMSO concentration is progressively reduced. The short segment, Arg109 - Met116, acts as the nucleation site for helix formation and self-association. Hydrophobic and complementary charge interactions on the surfaces drive self-association, as the helices elongate in both the directions resulting in an antiparallel stack. A small N-terminal segment remains floppy in the assembly. Following these and other published results on inter-domain interactions, we have proposed a plausible mode of dynamin self assembly

HIV Services Utilization in Los Angeles County, California

Recipients of HIV/AIDS prevention services in Los Angeles County California were surveyed in 2004 by 220 HIV prevention service provider staff from 51 agencies funded by the Office of AIDS Programs and Policy. This resulted in 2,102 usable surveys for cluster analysis purposes. This Countywide Risk Assessment Survey assessed demographics, sexual history, substance use, perceptions regarding HIV/AIDS, and use of 18 different services at both the agency administering the survey and at other agencies. The 36 types of service use data were subjected to a cluster analysis that found five clusters. These service pattern clusters differed from each other on proportion HIV positive, HIV testing history, history of abuse, education, type of residence, type of funding, intervention type, and ethnicity. The analysis also suggests that domestic violence services availability and utilization should be examined more thoroughly in the future for HIV infected/affected populations

Solution structure of a repeated unit of the ABA-1 nematode polyprotein allergen of ascaris reveals a novel fold and two discrete lipid-binding sites

Parasitic nematode worms cause serious health problems in humans and other animals. They can induce allergic-type immune responses, which can be harmful but may at the same time protect against the infections. Allergens are proteins that trigger allergic reactions and these parasites produce a type that is confined to nematodes, the nematode polyprotein allergens (NPAs). These are synthesized as large precursor proteins comprising repeating units of similar amino acid sequence that are subsequently cleaved into multiple copies of the allergen protein. NPAs bind small lipids such as fatty acids and retinol (Vitamin A) and probably transport these sensitive and insoluble compounds between the tissues of the worms. Nematodes cannot synthesize these lipids, so NPAs may also be crucial for extracting nutrients from their hosts. They may also be involved in altering immune responses by controlling the lipids by which the immune and inflammatory cells communicate. We describe the molecular structure of one unit of an NPA, the well-known ABA-1 allergen of Ascaris and find its structure to be of a type not previously found for lipid-binding proteins, and we describe the unusual sites where lipids bind within this structur

Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p

Pan-Pathway Based Interaction Profiling of FDA-Approved Nucleoside and Nucleobase Analogs with Enzymes of the Human Nucleotide Metabolism

To identify interactions a nucleoside analog library (NAL) consisting of 45 FDA-approved nucleoside analogs was screened against 23 enzymes of the human nucleotide metabolism using a thermal shift assay. The method was validated with deoxycytidine kinase; eight interactions known from the literature were detected and five additional interactions were revealed after the addition of ATP, the second substrate. The NAL screening gave relatively few significant hits, supporting a low rate of “off target effects.” However, unexpected ligands were identified for two catabolic enzymes guanine deaminase (GDA) and uridine phosphorylase 1 (UPP1). An acyclic guanosine prodrug analog, valaciclovir, was shown to stabilize GDA to the same degree as the natural substrate, guanine, with a ΔTagg around 7°C. Aciclovir, penciclovir, ganciclovir, thioguanine and mercaptopurine were also identified as ligands for GDA. The crystal structure of GDA with valaciclovir bound in the active site was determined, revealing the binding of the long unbranched chain of valaciclovir in the active site of the enzyme. Several ligands were identified for UPP1: vidarabine, an antiviral nucleoside analog, as well as trifluridine, idoxuridine, floxuridine, zidovudine, telbivudine, fluorouracil and thioguanine caused concentration-dependent stabilization of UPP1. A kinetic study of UPP1 with vidarabine revealed that vidarabine was a mixed-type competitive inhibitor with the natural substrate uridine. The unexpected ligands identified for UPP1 and GDA imply further metabolic consequences for these nucleoside analogs, which could also serve as a starting point for future drug design

Caffeine Consumption Prevents Diabetes-Induced Memory Impairment and Synaptotoxicity in the Hippocampus of NONcZNO10/LTJ Mice

Diabetic conditions are associated with modified brain function, namely with cognitive deficits, through largely undetermined processes. More than understanding the underlying mechanism, it is important to devise novel strategies to alleviate diabetes-induced cognitive deficits. Caffeine (a mixed antagonist of adenosine A1 and A2A receptors) emerges as a promising candidate since caffeine consumption reduces the risk of diabetes and effectively prevents memory deficits caused by different noxious stimuli. Thus, we took advantage of a novel animal model of type 2 diabetes to investigate the behavioural, neurochemical and morphological modifications present in the hippocampus and tested if caffeine consumption might prevent these changes. We used a model closely mimicking the human type 2 diabetes condition, NONcNZO10/LtJ mice, which become diabetic at 7–11 months when kept under an 11% fat diet. Caffeine (1 g/l) was applied in the drinking water from 7 months onwards. Diabetic mice displayed a decreased spontaneous alternation in the Y-maze accompanied by a decreased density of nerve terminal markers (synaptophysin, SNAP25), mainly glutamatergic (vesicular glutamate transporters), and increased astrogliosis (GFAP immunoreactivity) compared to their wild type littermates kept under the same diet. Furthermore, diabetic mice displayed up-regulated A2A receptors and down-regulated A1 receptors in the hippocampus. Caffeine consumption restored memory performance and abrogated the diabetes-induced loss of nerve terminals and astrogliosis. These results provide the first evidence that type 2 diabetic mice display a loss of nerve terminal markers and astrogliosis, which is associated with memory impairment; furthermore, caffeine consumption prevents synaptic dysfunction and astrogliosis as well as memory impairment in type 2 diabetes

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects