Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios

SC3: consensus clustering of single-cell RNA-seq data

Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.V.Y.K., T.A., A.Y. and M.H. are supported by Wellcome Trust Grants. K.N.N. is supported by the Wellcome Trust Strategic Award 'Single cell genomics of mouse gastrulation'. M.T.S. acknowledges support from FRS-FNRS; the Belgian Network DYSCO (Dynamical Systems, Control and Optimisation), funded by the Interuniversity Attraction Poles Programme initiated by the Belgian State Science Policy Office; and the ARC (Action de Recherche Concerte) on Mining and Optimization of Big Data Models, funded by the Wallonia-Brussels Federation. M.B. acknowledges support from EPSRC (grant EP/N014529/1). T.C. was funded through a core funded fellowship by the Sanger Institute and a Chancellor′s fellowship from the University of Edinburgh. K.K. and A.R.G. are supported by Bloodwise (grant ref. 13003), the Wellcome Trust (grant ref. 104710/Z/14/Z), the Medical Research Council, the Kay Kendall Leukaemia Fund, the Cambridge NIHR Biomedical Research Center, the Cambridge Experimental Cancer Medicine Centre, the Leukemia and Lymphoma Society of America (grant ref. 07037) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. W.R. was supported by BBSRC (grant ref. BB/K010867/1), the Wellcome Trust (grant ref. 095645/Z/11/Z), EU BLUEPRINT and EpiGeneSys

Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease

Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit

In recent years, there has been a surge of interest in community detection algorithms for complex networks. A variety of computational heuristics, some with a long history, have been proposed for the identification of communities or, alternatively, of good graph partitions. In most cases, the algorithms maximize a particular objective function, thereby finding the `right' split into communities. Although a thorough comparison of algorithms is still lacking, there has been an effort to design benchmarks, i.e., random graph models with known community structure against which algorithms can be evaluated. However, popular community detection methods and benchmarks normally assume an implicit notion of community based on clique-like subgraphs, a form of community structure that is not always characteristic of real networks. Specifically, networks that emerge from geometric constraints can have natural non clique-like substructures with large effective diameters, which can be interpreted as long-range communities. In this work, we show that long-range communities escape detection by popular methods, which are blinded by a restricted `field-of-view' limit, an intrinsic upper scale on the communities they can detect. The field-of-view limit means that long-range communities tend to be overpartitioned. We show how by adopting a dynamical perspective towards community detection (Delvenne et al. (2010) PNAS:107: 12755-12760; Lambiotte et al. (2008) arXiv:0812.1770), in which the evolution of a Markov process on the graph is used as a zooming lens over the structure of the network at all scales, one can detect both clique- or non clique-like communities without imposing an upper scale to the detection. Consequently, the performance of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of equally good results in real networks with local, sparser connectivity.Comment: 20 pages, 6 figure

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

Influence of IFNL3/4 polymorphisms on the incidence of cytomegalovirus infection after solid-organ transplantation

Background. Polymorphisms in the interferon-λ (IFNL) 3/4 region have been associated with reduced hepatitis C virus clearance. We explored the role of such polymorphisms on the incidence of CMV infection in solid-organ transplant (SOT) recipients. Methods. Caucasian patients participating in the Swiss Transplant Cohort Study in 2008-2011 were included. A novel functional TT/-G polymorphism (rs368234815) in the CpG region upstream of IFNL3 was investigated. Results. A total of 840 SOT recipients at risk for CMV were included, among whom 373 (44%) received antiviral prophylaxis. The 12-months cumulative incidence of CMV replication and disease were 0.44 and 0.08, respectively. Patient homozygous for the minor rs368234815 allele (-G/-G) tended to have a higher cumulative incidence of CMV replication (SHR=1.30 [95%CI 0.97-1.74], P=0.07) compared to other patients (TT/TT or TT/-G). The association was significant among patients followed by a preemptive approach (SHR=1.46 [1.01-2.12], P=0.047), especially in patients receiving an organ from a seropositive donor (D+, SHR=1.92 [95%CI 1.30-2.85], P=0.001), but not among those who received antiviral prophylaxis (SHR=1.13 [95%CI 0.70-1.83], P=0.6). These associations remained significant in multivariate competing risk regression models. Conclusions. Polymorphisms in the IFNL3/4 region influence susceptibility to CMV replication in SOT recipients, particularly in patients not receiving antiviral prophylaxi

Influence of surface atomic structure demonstrated on oxygen incorporation mechanism at a model perovskite oxide

Perovskite oxide surfaces catalyze oxygen exchange reactions that are crucial for fuel cells, electrolyzers, and thermochemical fuel synthesis. Here, by bridging the gap between surface analysis with atomic resolution and oxygen exchange kinetics measurements, we demonstrate how the exact surface atomic structure can determine the reactivity for oxygen exchange reactions on a model perovskite oxide. Two precisely controlled surface reconstructions with (4 × 1) and (2 × 5) symmetry on 0.5 wt.% Nb-doped SrTiO3(110) were subjected to isotopically labeled oxygen exchange at 450 °C. The oxygen incorporation rate is three times higher on the (4 × 1) surface phase compared to the (2 × 5). Common models of surface reactivity based on the availability of oxygen vacancies or on the ease of electron transfer cannot account for this difference. We propose a structure-driven oxygen exchange mechanism, relying on the flexibility of the surface coordination polyhedra that transform upon dissociation of oxygen molecules.Austrian Science Fund (SFB “ Functional Oxide Surfaces and Interfaces ” - FOXSI, Project F 45)European Research Council Advanced Grant (“OxideSurfaces” (Project ERC-2011-ADG_20110209))National Science Foundation (U.S.). Division of Materials Research (CAREER Award Grant No. 1055583

Differential effects of cytokines and corticosteroids on Toll-like receptor 2 expression and activity in human airway epithelia

<p>Abstract</p> <p>Background</p> <p>The recognition of microbial molecular patterns via Toll-like receptors (TLRs) is critical for mucosal defenses.</p> <p>Methods</p> <p>Using well-differentiated primary cultures of human airway epithelia, we investigated the effects of exposure of the cells to cytokines (TNF-α and IFN-γ) and dexamethasone (dex) on responsiveness to the TLR2/TLR1 ligand Pam3CSK4. Production of IL-8, CCL20, and airway surface liquid antimicrobial activity were used as endpoints.</p> <p>Results</p> <p>Microarray expression profiling in human airway epithelia revealed that first response cytokines markedly induced TLR2 expression. Real-time PCR confirmed that cytokines (TNF-α and IFN-γ), dexamethasone (dex), or cytokines + dex increased TLR2 mRNA abundance. A synergistic increase was seen with cytokines + dex. To assess TLR2 function, epithelia pre-treated with cytokines ± dex were exposed to the TLR2/TLR1 ligand Pam3CSK4 for 24 hours. While cells pre-treated with cytokines alone exhibited significantly enhanced IL-8 and CCL20 secretion following Pam3CSK4, mean IL-8 and CCL20 release decreased in Pam3CSK4 stimulated cells following cytokines + dex pre-treatment. This marked increase in inflammatory gene expression seen after treatment with cytokines followed by the TLR2 ligand did not correlate well with NF-κB, Stat1, or p38 MAP kinase pathway activation. Cytokines also enhanced TLR2 agonist-induced beta-defensin 2 mRNA expression and increased the antimicrobial activity of airway surface liquid. Dex blocked these effects.</p> <p>Conclusion</p> <p>While dex treatment enhanced TLR2 expression, co-administration of dex with cytokines inhibited airway epithelial cell responsiveness to TLR2/TLR1 ligand over cytokines alone. Enhanced functional TLR2 expression following exposure to TNF-α and IFN-γ may serve as a dynamic means to amplify epithelial innate immune responses during infectious or inflammatory pulmonary diseases.</p