Predicting protein functions with message passing algorithms

Motivation: In the last few years a growing interest in biology has been shifting towards the problem of optimal information extraction from the huge amount of data generated via large scale and high-throughput techniques. One of the most relevant issues has recently become that of correctly and reliably predicting the functions of observed but still functionally undetermined proteins starting from information coming from the network of co-observed proteins of known functions. Method: The method proposed in this article is based on a message passing algorithm known as Belief Propagation, which takes as input the network of proteins physical interactions and a catalog of known proteins functions, and returns the probabilities for each unclassified protein of having one chosen function. The implementation of the algorithm allows for fast on-line analysis, and can be easily generalized to more complex graph topologies taking into account hyper-graphs, {\em i.e.} complexes of more than two interacting proteins.Comment: 12 pages, 9 eps figures, 1 additional html tabl

Effect of voluntary running on adult hippocampal neurogenesis in cholinergic lesioned mice

<p>Abstract</p> <p>Background</p> <p>Cholinergic neuronal dysfunction of the basal forebrain is observed in patients with Alzheimer's disease and dementia, and has been linked to decreased neurogenesis in the hippocampus, a region involved in learning and memory. Running is a robust inducer of adult hippocampal neurogenesis. This study aims to address the effect of running on hippocampal neurogenesis in lesioned mice, where septohippocampal cholinergic neurones have been selectively eliminated in the medial septum and diagonal band of Broca of the basal forebrain by infusion of mu-p75-saporin immunotoxin.</p> <p>Results</p> <p>Running increased the number of newborn cells in the dentate gyrus of the hippocampus in cholinergic denervated mice compared to non-lesioned mice 24 hours after injection of bromodeoxyuridine (BrdU). Although similar levels of surviving cells were present in cholinergic depleted animals and their respective controls four weeks after injection of BrdU, the majority of progenitors that proliferate in response to the initial period of running were not able to survive beyond one month without cholinergic input. Despite this, the running-induced increase in the number of surviving neurones was not affected by cholinergic depletion.</p> <p>Conclusion</p> <p>The lesion paradigm used here models aspects of the cholinergic deficits associated with Alzheimer's Disease and aging. We showed that running still increased the number of newborn cells in the adult hippocampal dentate gyrus in this model of neurodegenerative disease.</p

Identification of compounds with anti-human cytomegalovirus activity that inhibit production of IE2 proteins

Using a high throughput screening methodology we surveyed a collection of largely uncharacterized validated or suspected kinase inhibitors for anti-human cytomegalovirus (HCMV) activity. From this screen we identified three structurally related 5-aminopyrazine compounds (XMD7-1, -2 and -27) that inhibited HCMV replication in virus yield reduction assays at low micromolar concentrations. Kinase selectivity assays indicated that each compound was a kinase inhibitor capable of inhibiting a range of cellular protein kinases. Western blotting and RNA sequencing demonstrated that treatment of infected cells with XMD7 compounds resulted in a defect in the production of the major HCMV transcriptional transactivator IE2 proteins (IE2-86, IE2-60 and IE2-40) and an overall reduction in transcription from the viral genome. However, production of certain viral proteins was not compromised by treatment with XMD7 compounds. Thus, these novel anti-HCMV compounds likely inhibited transcription from the viral genome and suppressed production of a subset of viral proteins by inhibiting IE2 protein production

Some protein interaction data do not exhibit power law statistics

It has been claimed that protein-protein interaction (PPI) networks are scale-free based on the observation that the node degree sequence follows a power law. Here we argue that these claims are likely to be based on erroneous statistical analysis. Typically, the supporting data are presented using frequency-degree plots. We show that such plots can be misleading, and should correctly be replaced by rank-degree plots. We provide two PPI network examples in which the frequency-degree plots appear linear on a log-log scale, but the rank-degree plots demonstrate that the node degree sequence is far from a power law. We conclude that at least these PPI networks are not scale-free.Comment: 4 pages, 2 figure

Graph theoretic analysis of protein interaction networks of eukaryotes

Thanks to recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are universal across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and the yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interlogs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interlogs addition, for which we present a possible scenario through an in silico modeling.Comment: 7 pages, 6 figures, 2 table

Impact of observational incompleteness on the structural properties of protein interaction networks

The observed structure of protein interaction networks is corrupted by many false positive/negative links. This observational incompleteness is abstracted as random link removal and a specific, experimentally motivated (spoke) link rearrangement. Their impact on the structural properties of gene-duplication-and-mutation network models is studied. For the degree distribution a curve collapse is found, showing no sensitive dependence on the link removal/rearrangement strengths and disallowing a quantitative extraction of model parameters. The spoke link rearrangement process moves other structural observables, like degree correlations, cluster coefficient and motif frequencies, closer to their counterparts extracted from the yeast data. This underlines the importance to take a precise modeling of the observational incompleteness into account when network structure models are to be quantitatively compared to data.Comment: 17 pages, 7 figures, accepted by Physica

Yeast Protein Interactome Topology Provides Framework for Coordinated-Functionality

The architecture of the network of protein-protein physical interactions in Saccharomyces cerevisiae is exposed through the combination of two complementary theoretical network measures, betweenness centrality and `Q-modularity'. The yeast interactome is characterized by well-defined topological modules connected via a small number of inter-module protein interactions. Should such topological inter-module connections turn out to constitute a form of functional coordination between the modules, we speculate that this coordination is occurring typically in a pair-wise fashion, rather than by way of high-degree hub proteins responsible for coordinating multiple modules. The unique non-hub-centric hierarchical organization of the interactome is not reproduced by gene duplication-and-divergence stochastic growth models that disregard global selective pressures.Comment: Final, revised version. 13 pages. Please see Nucleic Acids open access article for higher resolution figure

APJ1 and GRE3 Homologs Work in Concert to Allow Growth in Xylose in a Natural Saccharomyces sensu stricto Hybrid Yeast

Creating Saccharomyces yeasts capable of efficient fermentation of pentoses such as xylose remains a key challenge in the production of ethanol from lignocellulosic biomass. Metabolic engineering of industrial Saccharomyces cerevisiae strains has yielded xylose-fermenting strains, but these strains have not yet achieved industrial viability due largely to xylose fermentation being prohibitively slower than that of glucose. Recently, it has been shown that naturally occurring xylose-utilizing Saccharomyces species exist. Uncovering the genetic architecture of such strains will shed further light on xylose metabolism, suggesting additional engineering approaches or possibly even enabling the development of xylose-fermenting yeasts that are not genetically modified. We previously identified a hybrid yeast strain, the genome of which is largely Saccharomyces uvarum, which has the ability to grow on xylose as the sole carbon source. To circumvent the sterility of this hybrid strain, we developed a novel method to genetically characterize its xylose-utilization phenotype, using a tetraploid intermediate, followed by bulk segregant analysis in conjunction with high-throughput sequencing. We found that this strain’s growth in xylose is governed by at least two genetic loci, within which we identified the responsible genes: one locus contains a known xylose-pathway gene, a novel homolog of the aldo-keto reductase gene GRE3, while a second locus contains a homolog of APJ1, which encodes a putative chaperone not previously connected to xylose metabolism. Our work demonstrates that the power of sequencing combined with bulk segregant analysis can also be applied to a nongenetically tractable hybrid strain that contains a complex, polygenic trait, and identifies new avenues for metabolic engineering as well as for construction of nongenetically modified xylose-fermenting strains

Tips for Teachers of Evidence-based Medicine: Making Sense of Decision Analysis Using a Decision Tree

Decision analysis is a tool that clinicians can use to choose an option that maximizes the overall net benefit to a patient. It is an explicit, quantitative, and systematic approach to decision making under conditions of uncertainty. In this article, we present two teaching tips aimed at helping clinical learners understand the use and relevance of decision analysis. The first tip demonstrates the structure of a decision tree. With this tree, a clinician may identify the optimal choice among complicated options by calculating probabilities of events and incorporating patient valuations of possible outcomes. The second tip demonstrates how to address uncertainty regarding the estimates used in a decision tree. We field tested the tips twice with interns and senior residents. Teacher preparatory time was approximately 90 minutes. The field test utilized a board and a calculator. Two handouts were prepared. Learners identified the importance of incorporating values into the decision-making process as well as the role of uncertainty. The educational objectives appeared to be reached. These teaching tips introduce clinical learners to decision analysis in a fashion aimed to illustrate principles of clinical reasoning and how patient values can be actively incorporated into complex decision making