Finding instabilities in the community structure of complex networks

The problem of finding clusters in complex networks has been extensively studied by mathematicians, computer scientists and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters, without overlap. We here introduce a method to identify the nodes lying ``between clusters'' and that allows for a general measure of the stability of the clusters. This is done by adding noise over the weights of the edges of the network. Our method can in principle be applied with any clustering algorithm, provided that it works on weighted networks. We present several applications on real-world networks using the Markov Clustering Algorithm (MCL).Comment: 4 pages, 5 figure

Protein homology reveals new targets for bioactive small molecules.

MOTIVATION: The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. RESULTS: Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. AVAILABILITY AND IMPLEMENTATION: Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

SwissTargetPrediction: a web server for target prediction of bioactive small molecules.

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein-small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch

Shaping the interaction landscape of bioactive molecules.

MOTIVATION: Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. RESULTS: Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Mercury mobility, colloid formation and methylation in a polluted Fluvisol as affected by manure application and flooding–draining cycle

Floodplain soils polluted with high levels of mercury (Hg) are potential point sources to downstream ecosystems. Repeated flooding (e.g., redox cycling) and agricultural activities (e.g., organic matter addition) may influence the fate and speciation of Hg in these soil systems. The formation and aggregation of colloids and particles influence both Hg mobility and its bioavailability to microbes that form methylmercury (MeHg). In this study, we conducted a microcosm flooding–draining experiment on Hg-polluted floodplain soils originating from an agriculturally used area situated in the Rhone Valley (Valais, Switzerland). The experiment comprised two 14 d flooding periods separated by one 14 d draining period. The effect of freshly added natural organic matter on Hg dynamics was assessed by adding liquid cow manure (+MNR) to two soils characterized by different Hg (47.3±0.5 or 2.38±0.01 mg kg−1) and organic carbon (OC: 1.92 wt % or 3.45 wt %) contents. During the experiment, the release, colloid formation of Hg in soil solution and net MeHg production in the soil were monitored. Upon manure addition in the highly polluted soil (lower OC), an accelerated release of Hg to the soil solution could be linked to a fast reductive dissolution of Mn oxides. The manure treatments showed a fast sequestration of Hg and a higher percentage of Hg bound by particulate (0.02–10 µm). Also, analyses of soil solutions by asymmetrical flow field-flow fractionation coupled with inductively coupled plasma mass spectrometry (AF4–ICP–MS) revealed a relative increase in colloidal Hg bound to dissolved organic matter (Hg–DOM) and inorganic colloidal Hg (70 %–100 %) upon manure addition. Our experiment shows a net MeHg production the first flooding and draining period and a subsequent decrease in absolute MeHg concentrations after the second flooding period. Manure addition did not change net MeHg production significantly in the incubated soils. The results of this study suggest that manure addition may promote Hg sequestration by Hg complexation on large organic matter components and the formation and aggregation of inorganic HgS(s) colloids in Hg-polluted Fluvisols with low levels of natural organic matter.</p

Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes

When network and graph theory are used in the study of complex systems, a typically finite set of nodes of the network under consideration is frequently either explicitly or implicitly considered representative of a much larger finite or infinite region or set of objects of interest. The selection procedure, e.g., formation of a subset or some kind of discretization or aggregation, typically results in individual nodes of the studied network representing quite differently sized parts of the domain of interest. This heterogeneity may induce substantial bias and artifacts in derived network statistics. To avoid this bias, we propose an axiomatic scheme based on the idea of node splitting invariance to derive consistently weighted variants of various commonly used statistical network measures. The practical relevance and applicability of our approach is demonstrated for a number of example networks from different fields of research, and is shown to be of fundamental importance in particular in the study of spatially embedded functional networks derived from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure

Dimensional Reduction of Fermions in Brane Worlds of the Gross-Neveu Model

We study the dimensional reduction of fermions, both in the symmetric and in the broken phase of the 3-d Gross-Neveu model at large N. In particular, in the broken phase we construct an exact solution for a stable brane world consisting of a domain wall and an anti-wall. A left-handed 2-d fermion localized on the domain wall and a right-handed fermion localized on the anti-wall communicate with each other through the 3-d bulk. In this way they are bound together to form a Dirac fermion of mass m. As a consequence of asymptotic freedom of the 2-d Gross-Neveu model, the 2-d correlation length \xi = 1/m increases exponentially with the brane separation. Hence, from the low-energy point of view of a 2-d observer, the separation of the branes appears very small and the world becomes indistinguishable from a 2-d space-time. Our toy model provides a mechanism for brane stabilization: branes made of fermions may be stable due to their baryon asymmetry. Ironically, our brane world is stable only if it has an extreme baryon asymmetry with all states in this ``world'' being completely filled.Comment: 26 pages, 7 figure

Revisiting the exercise heart rate-music tempo preference relationship

In the present study, we investigated a hypothesized quartic relationship (meaning three inflection points) between exercise heart rate (HR) and preferred music tempo. Initial theoretical predictions suggested a positive linear relationship (Iwanaga, 1995a, 1995b); however, recent experimental work has shown that as exercise HR increases, step changes and plateaus that punctuate the profile of music tempo preference may occur (Karageorghis, Jones, & Stuart, 2008). Tempi bands consisted of slow (95–100 bpm), medium (115–120 bpm), fast (135–140 bpm), and very fast (155–160 bpm) music. Twenty-eight active undergraduate students cycled at exercise intensities representing 40, 50, 60, 70, 80, and 90% of their maximal HR reserve while their music preference was assessed using a 10-point scale. The Exercise Intensity x Music Tempo interaction was significant, F(6.16, 160.05) = 7.08, p < .001, ηp 2 =.21, as was the test for both cubic and quartic trajectories in the exercise HR–preferred-music-tempo relationship (p < .001). Whereas slow tempo music was not preferred at any exercise intensity, preference for fast tempo increased, relative to medium and very fast tempo music, as exercise intensity increased. The implications for the prescription of music in exercise and physical activity contexts are discussed

CD56 as a marker of an ILC1-like population with NK cell properties that is functionally impaired in AML.

An understanding of natural killer (NK) cell physiology in acute myeloid leukemia (AML) has led to the use of NK cell transfer in patients, demonstrating promising clinical results. However, AML is still characterized by a high relapse rate and poor overall survival. In addition to conventional NKs that can be considered the innate counterparts of CD8 T cells, another family of innate lymphocytes has been recently described with phenotypes and functions mirroring those of helper CD4 T cells. Here, in blood and tissues, we identified a CD56+ innate cell population harboring mixed transcriptional and phenotypic attributes of conventional helper innate lymphoid cells (ILCs) and lytic NK cells. These CD56+ ILC1-like cells possess strong cytotoxic capacities that are impaired in AML patients at diagnosis but are restored upon remission. Their cytotoxicity is KIR independent and relies on the expression of TRAIL, NKp30, NKp80, and NKG2A. However, the presence of leukemic blasts, HLA-E-positive cells, and/or transforming growth factor-β1 (TGF-β1) strongly affect their cytotoxic potential, at least partially by reducing the expression of cytotoxic-related molecules. Notably, CD56+ ILC1-like cells are also present in the NK cell preparations used in NK transfer-based clinical trials. Overall, we identified an NK cell-related CD56+ ILC population involved in tumor immunosurveillance in humans, and we propose that restoring their functions with anti-NKG2A antibodies and/or small molecules inhibiting TGF-β1 might represent a novel strategy for improving current immunotherapies

Topological Strata of Weighted Complex Networks

The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and --more recently-- correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems.Comment: 26 pages, 19 figures, 1 tabl