Predicting Quantitative Genetic Interactions by Means of Sequential Matrix Approximation

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well

Search for Kaluza-Klein Graviton Emission in ppˉp\bar{p} Collisions at s=1.8\sqrt{s}=1.8 TeV using the Missing Energy Signature

We report on a search for direct Kaluza-Klein graviton production in a data sample of 84 ${pb}^{-1}$ of \ppb collisions at $\sqrt{s}$ = 1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and one or two high energy jets. We compare the data with the predictions from a $3+1+n$-dimensional Kaluza-Klein scenario in which gravity becomes strong at the TeV scale. At 95% confidence level (C.L.) for $n$=2, 4, and 6 we exclude an effective Planck scale below 1.0, 0.77, and 0.71 TeV, respectively.Comment: Submitted to PRL, 7 pages 4 figures/Revision includes 5 figure

Measurement of the average time-integrated mixing probability of b-flavored hadrons produced at the Tevatron

We have measured the number of like-sign (LS) and opposite-sign (OS) lepton pairs arising from double semileptonic decays of $b$ and $\bar{b}$-hadrons, pair-produced at the Fermilab Tevatron collider. The data samples were collected with the Collider Detector at Fermilab (CDF) during the 1992-1995 collider run by triggering on the existence of $\mu \mu$ and $e \mu$ candidates in an event. The observed ratio of LS to OS dileptons leads to a measurement of the average time-integrated mixing probability of all produced $b$-flavored hadrons which decay weakly, $\bar{\chi} = 0.152 \pm 0.007$ (stat.) $\pm 0.011$ (syst.), that is significantly larger than the world average $\bar{\chi} = 0.118 \pm 0.005$.Comment: 47 pages, 10 figures, 15 tables Submitted to Phys. Rev.

Identifying Prototypical Components in Behaviour Using Clustering Algorithms

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts

Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms

Nutraceutical agents with anti-inflammatory properties prevent dietary saturated-fat induced disturbances in blood-brain barrier function in wild-type mice

Background: Emerging evidence suggests that disturbances in the blood–brain barrier (BBB) may be pivotal to the pathogenesis and pathology of vascular-based neurodegenerative disorders. Studies suggest that heightened systemic and central inflammations are associated with BBB dysfunction. This study investigated the effect of the anti-inflammatory nutraceuticals garlic extract-aged (GEA), alpha lipoic acid (ALA), niacin, and nicotinamide (NA) in a murine dietary-induced model of BBB dysfunction. Methods: C57BL/6 mice were fed a diet enriched in saturated fatty acids (SFA, 40% fat of total energy) for nine months to induce systemic inflammation and BBB disturbances. Nutraceutical treatment groups included the provision of either GEA, ALA, niacin or NA in the positive control SFA-group and in low-fat fed controls. Brain parenchymal extravasation of plasma derived immunoglobulin G (IgG) and large macromolecules (apolipoprotein (apo) B lipoproteins) measured by quantitative immunofluorescent microscopy, were used as markers of disturbed BBB integrity. Parenchymal glial fibrillar acidic protein (GFAP) and cyclooxygenase-2 (COX-2) were considered in the context of surrogate markers of neurovascular inflammation and oxidative stress. Total anti-oxidant status and glutathione reductase activity were determined in plasma.Results: Brain parenchymal abundance of IgG and apoB lipoproteins was markedly exaggerated in mice maintained on the SFA diet concomitant with significantly increased GFAP and COX-2, and reduced systemic antioxidative status. The nutraceutical GEA, ALA, niacin, and NA completely prevented the SFA-induced disturbances of BBB and normalized the measures of neurovascular inflammation and oxidative stress. Conclusions: The anti-inflammatory nutraceutical agents GEA, ALA, niacin, or NA are potent inhibitors of dietary fat-induced disturbances of BBB induced by systemic inflammations

Enhanced Fear Expression in a Psychopathological Mouse Model of Trait Anxiety: Pharmacological Interventions

The propensity to develop an anxiety disorder is thought to be determined by genetic and environmental factors. Here we investigated the relationship between a genetic predisposition to trait anxiety and experience-based learned fear in a psychopathological mouse model. Male CD-1 mice selectively bred for either high (HAB), or normal (NAB) anxiety-related behaviour on the elevated plus maze were subjected to classical fear conditioning. During conditioning both mouse lines showed increased fear responses as assessed by freezing behaviour. However, 24 h later, HAB mice displayed more pronounced conditioned responses to both a contextual or cued stimulus when compared with NAB mice. Interestingly, 6 h and already 1 h after fear conditioning, freezing levels were high in HAB mice but not in NAB mice. These results suggest that trait anxiety determines stronger fear memory and/or a weaker ability to inhibit fear responses in the HAB line. The enhanced fear response of HAB mice was attenuated by treatment with either the α2,3,5-subunit selective benzodiazepine partial agonist L-838,417, corticosterone or the selective neurokinin-1 receptor antagonist L-822,429. Overall, the HAB mouse line may represent an interesting model (i) for identifying biological factors underlying misguided conditioned fear responses and (ii) for studying novel anxiolytic pharmacotherapies for patients with fear-associated disorders, including post-traumatic stress disorder and phobias

Developing "personality" taxonomies: Metatheoretical and methodological rationales underlying selection approaches, methods of data generation and reduction principles

Taxonomic "personality" models are widely used in research and applied fields. This article applies the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) to scrutinise the three methodological steps that are required for developing comprehensive “personality” taxonomies: 1) the approaches used to select the phenomena and events to be studied, 2) the methods used to generate data about the selected phenomena and events and 3) the reduction principles used to extract the “most important” individual-specific variations for constructing “personality” taxonomies. Analyses of some currently popular taxonomies reveal frequent mismatches between the researchers’ explicit and implicit metatheories about “personality” and the abilities of previous methodologies to capture the particular kinds of phenomena toward which they are targeted. Serious deficiencies that preclude scientific quantifications are identified in standardised questionnaires, psychology’s established standard method of investigation. These mismatches and deficiencies derive from the lack of an explicit formulation and critical reflection on the philosophical and metatheoretical assumptions being made by scientists and from the established practice of radically matching the methodological tools to researchers’ preconceived ideas and to pre-existing statistical theories rather than to the particular phenomena and individuals under study. These findings raise serious doubts about the ability of previous taxonomies to appropriately and comprehensively reflect the phenomena towards which they are targeted and the structures of individual-specificity occurring in them. The article elaborates and illustrates with empirical examples methodological principles that allow researchers to appropriately meet the metatheoretical requirements and that are suitable for comprehensively exploring individuals’ “personality”

Small molecules, big targets: drug discovery faces the protein-protein interaction challenge.

Protein-protein interactions (PPIs) are of pivotal importance in the regulation of biological systems and are consequently implicated in the development of disease states. Recent work has begun to show that, with the right tools, certain classes of PPI can yield to the efforts of medicinal chemists to develop inhibitors, and the first PPI inhibitors have reached clinical development. In this Review, we describe the research leading to these breakthroughs and highlight the existence of groups of structurally related PPIs within the PPI target class. For each of these groups, we use examples of successful discovery efforts to illustrate the research strategies that have proved most useful.JS, DES and ARB thank the Wellcome Trust for funding.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nrd.2016.2