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Bioinformatics, 29 (20), ISSN:1367-4803, ISSN:1460-2059

Active contours constitute a computationally attractive framework for image segmentation. In this paper, we describe a fully parametric design that relies on B-spline bases and is specified by control points on the image. Our technique yields successful segmentation results even for challenging datasets where object contours are not well-defined. We can achieve this because our parametric approach uses few parameters and allows us to constrain the topology of the curve. We provide the details for an efficient implementation and discuss different design alternatives. This work comes with a companion software that gives the end user full control over the snakes. We demonstrate the features of our software which includes an interactive interface where the user can manipulate the position of control points by simple mouse actions. (C) 2013 Elsevier Masson SAS. All rights reserved

Lipids have highly diverse functions that go beyond cellular membrane structure and energy storage. One of the great challenges in lipid research will be to understand how the enormous complexity of lipid homeostasis is maintained. Genetic approaches combined with mass spectrometry-based lipidomics will help to elucidate how cells create and maintain their nonrandom lipid distribution within tissues, cells, organelles and lipid bilayers. Lipid homeostasis is crucial for many cellular processes and we are currently only beginning to understand the specific functions of lipids and the local environment that they create.

Toxoplasma gondii belongs to the coccidian subgroup of the Apicomplexa phylum. The Coccidia are obligate intracellular pathogens that establish infection in their mammalian host via the enteric route. These parasites lack a mitochondrial pyruvate dehydrogenase complex but have preserved the degradation of branched-chain amino acids (BCAA) as a possible pathway to generate acetyl-CoA. Importantly, degradation of leucine, isoleucine and valine could lead to concomitant accumulation of propionyl-CoA, a toxic metabolite that inhibits cell growth. Like fungi and bacteria, the Coccidia possess the complete set of enzymes necessary to metabolize and detoxify propionate by oxidation to pyruvate via the 2-methylcitrate cycle (2-MCC). Phylogenetic analysis provides evidence that the 2-MCC was acquired via horizontal gene transfer. In T. gondii tachyzoites, this pathway is split between the cytosol and the mitochondrion. Although the rate-limiting enzyme 2-methylisocitrate lyase is dispensable for parasite survival, its substrates accumulate in parasites deficient in the enzyme and its absence confers increased sensitivity to propionic acid. BCAA is also dispensable in tachyzoites, leaving unresolved the source of mitochondrial acetyl-CoA.

Although changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. To systematically identify transcription factors (TFs) that can direct chromatin changes during cell fate decisions, we model the relationship between genome-wide dynamics of chromatin marks and the local occurrence of computationally predicted TF binding sites. By applying this computational approach to a time course of Polycomb-mediated H3K27me3 marks during neuronal differentiation of murine stem cells, we identify several motifs that likely regulate the dynamics of this chromatin mark. Among these, the sites bound by REST and by the SNAIL family of TFs are predicted to transiently recruit H3K27me3 in neuronal progenitors. We validate these predictions experimentally and show that absence of REST indeed causes loss of H3K27me3 at target promoters in trans, specifically at the neuronal progenitor state. Moreover, using targeted transgenic insertion, we show that promoter fragments containing REST or SNAIL binding sites are sufficient to recruit H3K27me3 in cis, while deletion of these sites results in loss of H3K27me3. These findings illustrate that the occurrence of TF binding sites can determine chromatin dynamics. Local determination of Polycomb activity by REST and SNAIL motifs exemplifies such TF based regulation of chromatin. Furthermore, our results show that key TFs can be identified ab initio through computational modeling of epigenome data sets using a modeling approach that we make readily accessible.

SummaryGene expression profiling has uncovered the transcription factor Sox4 with upregulated activity during TGF-β-induced epithelial-mesenchymal transition (EMT) in normal and cancerous breast epithelial cells. Sox4 is indispensable for EMT and cell survival in vitro and for primary tumor growth and metastasis in vivo. Among several EMT-relevant genes, Sox4 directly regulates the expression of Ezh2, encoding the Polycomb group histone methyltransferase that trimethylates histone 3 lysine 27 (H3K27me3) for gene repression. Ablation of Ezh2 expression prevents EMT, whereas forced expression of Ezh2 restores EMT in Sox4-deficient cells. Ezh2-mediated H3K27me3 marks associate with key EMT genes, representing an epigenetic EMT signature that predicts patient survival. Our results identify Sox4 as a master regulator of EMT by governing the expression of the epigenetic modifier Ezh2.

In the mammalian neocortex, segregated processing streams are thought to be important for forming sensory representations of the environment(1,2), but how local information in primary sensory cortex is transmitted to other distant cortical areas during behaviour is unclear. Here we show task-dependent activation of distinct, largely non-overlapping long-range projection neurons in the whisker region of primary somatosensory cortex (S1) in awake, behaving mice. Using two-photon calcium imaging, we monitored neuronal activity in anatomically identified S1 neurons projecting to secondary somatosensory (S2) or primary motor (M1) cortex in mice using their whiskers to perform a texture-discrimination task or a task that required them to detect the presence of an object at a certain location. Whisking-related cells were found among S2-projecting (S2P) but not M1-projecting (M1P) neurons. A higher fraction of S2P than M1P neurons showed touch-related responses during texture discrimination, whereas a higher fraction of M1P than S2P neurons showed touch-related responses during the detection task. In both tasks, S2P and M1P neurons could discriminate similarly between trials producing different behavioural decisions. However, in trials producing the same decision, S2P neurons performed better at discriminating texture, whereas M1P neurons were better at discriminating location. Sensory stimulus features alone were not sufficient to elicit these differences, suggesting that selective transmission of S1 information to S2 and M1 is driven by behaviour.

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation.

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image‐based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single‐cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome‐wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell‐based screens at this depth reveals widespread RNAi‐induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell‐to‐cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large‐scale RNAi screens are increasingly performed to reach a systems‐level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single‐cell microenvironment., Molecular Systems Biology, 8 (1), ISSN:1744-4292

Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments. The database is connected to the engineering software Matlab, which allows extracting the desired information by automatically segmenting and quantifying the microscopy images. We implemented three different segmentation methods that recognize individual cells under different conditions, and integrated image analysis protocols that allow measuring and analyzing distinct cellular readouts. To illustrate the power and versatility of YeastQuant, we investigated dynamic signal transduction processes in yeast. First, we quantified the expression of fluorescent reporters induced by osmotic stress to study noise in gene expression. Second, we analyzed the dynamic relocation of endogenous proteins from the cytoplasm to the cell nucleus, which provides a fast measure of pathway activity. These examples demonstrate that YeastQuant provides a versatile and expandable database and an experimental framework that improves image analysis and quantification of diverse microscopy-based readouts. Such dynamic single cell measurements are highly needed to establish mathematical models of signal transduction pathways.

Humans are noted for their capacity to over-ride self-interest in favor of normatively valued goals. We examined the neural circuitry that is causally involved in normative, fairness-related decisions by generating a temporarily diminished capacity for costly normative behavior, a 'deviant' case, through non-invasive brain stimulation (repetitive transcranial magnetic stimulation) and compared normal subjects' functional magnetic resonance imaging signals with those of the deviant subjects. When fairness and economic self-interest were in conflict, normal subjects (who make costly normative decisions at a much higher frequency) displayed significantly higher activity in, and connectivity between, the right dorsolateral prefrontal cortex (DLPFC) and the posterior ventromedial prefrontal cortex (pVMPFC). In contrast, when there was no conflict between fairness and economic self-interest, both types of subjects displayed identical neural patterns and behaved identically. These findings suggest that a parsimonious prefrontal network, the activation of right DLPFC and pVMPFC, and the connectivity between them, facilitates subjects' willingness to incur the cost of normative decisions.

Mitogen-activated protein kinase (MAPK) cascades are conserved signaling modules that control many cellular processes by integrating intra- and extracellular cues. The p38/Hog1 MAPK is transiently activated in response to osmotic stress, leading to rapid translocation into the nucleus and induction of a specific transcriptional program. When investigating the dynamic interplay between Hog1 activation and Hog1-driven gene expression, we found that Hog1 activation increases linearly with stimulus, whereas the transcriptional output is bimodal. Modeling predictions, corroborated by single-cell experiments, established that a slow stochastic transition from a repressed to an activated transcriptional state in conjunction with transient Hog1 activation generates this behavior. Together, these findings provide a molecular mechanism by which a cell can impose a transcriptional threshold in response to a linear signaling behavior.

The simulation of the creation and evolution of biological forms requires the development of computational methods that are capable of resolving their hierarchical, spatial and temporal complexity. Computations based on interacting particles, provide a unique computational tool for discrete and continuous descriptions of morphogenesis of systems ranging from the molecular to the organismal level. The capabilities of particle methods hinge on the simplicity of their formulation which enables the formulation of a unifying computational framework encompassing deterministic and stochastic models. In this paper, we discuss recent advances in particle methods for the simulation of biological systems at the mesoscopic and the macroscale level. We present results from applications of particle methods including reaction–diffusion on deforming surfaces, deterministic and stochastic descriptions of tumor growth and angiogenesis and discuss successes and challenges of this approach.

With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states. The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition. For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome., The metabolic network in the yeast Saccharomyces cerevisiae has been very well characterized in terms of components and topology. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. In this study, we exploited quantitative proteomic assays based on selected reaction monitoring (SRM) mass spectrometry to comprehensively analyze the set of enzymes involved in carbon and amino-acid metabolism in yeast (Figure 1), throughout a set of different metabolic states. To elucidate how this metabolic network of proteins adapts to environmental challenges, we chose five nutritional conditions resulting in maximal difference in magnitude and direction of metabolic fluxes. We could reproducibly detect and quantify across the different conditions, 90% of the targeted metabolic proteome, including complete families of isoenzymes, sharing up to 99.5% sequence identity and multi-subunit enzyme complexes. This yielded an information-rich proteomic data set that represents a nutritionally perturbed biological system with high coverage of its components. Interpreted through flux balance modeling, the data indicate that S. cerevisiae expresses—at least at a basal level—more proteins than are actually necessary for sustaining a given metabolic condition. One potential explanation for the presence of non-necessary proteins is that these enzymes could realize immediate basal metabolic fluxes upon a change to new environmental conditions. Next, we asked whether our data set could contribute to unravel the function of isoenzymes in the metabolic set. Previously proposed roles for isoenzymes include redundancy to buffer against mutations, a means to gene dosage or facilitation of evolutionary innovation and functional diversification. To address the role of isoenzymes in central metabolism, we used hierarchical clustering to analyze the abundance patterns of the metabolic proteins and their relationship to different functional classes and metabolic pathways. Interestingly, while subunits of the same protein complex preferably cluster in proximate branches, members of the same isoenzyme family often clustered in distant branches (Figure 5). The data therefore suggested functional diversification within most isoenzyme families and allowed to propose different functions of divergent isoenzymes. We expect that the comprehensive data set presented in this study will be an ideal blueprint for further developing models of yeast metabolism and for follow-up studies on the function of target metabolic proteins., Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.

DNA technologies such as cloning, DNA microarrays, and next generation sequencing have transformed the life sciences. Protein technologies on the other hand have not seen such explosive progress. This is mainly due to the inherent difficulty of working with proteins because of their manifold physical characteristics as opposed to the well behaved and well understood DNA polymer. Recent technological advancements have increased the throughput of protein biochemistry to levels where it is becoming of interest to systems biology. Here I review methods for high-throughput in situ synthesis and characterization of proteins and their integration with microfluidic devices. In the near future, the use of gene synthesis, microfluidic based protein synthesis and characterization will give rise to a resurgence of protein biochemistry in the current world of high-throughput genomics.

SummaryThe target of rapamycin (TOR) is a highly conserved protein kinase and a central controller of growth. Mammalian TOR complex 2 (mTORC2) regulates AGC kinase family members and is implicated in various disorders, including cancer and diabetes. Here, we investigated the upstream regulation of mTORC2. A genetic screen in yeast and subsequent studies in mammalian cells revealed that ribosomes, but not protein synthesis, are required for mTORC2 signaling. Active mTORC2 was physically associated with the ribosome, and insulin-stimulated PI3K signaling promoted mTORC2-ribosome binding, suggesting that ribosomes activate mTORC2 directly. Findings with melanoma and colon cancer cells suggest that mTORC2-ribosome association is important in oncogenic PI3K signaling. Thus, TORC2-ribosome interaction is a likely conserved mechanism of TORC2 activation that is physiologically relevant in both normal and cancer cells. As ribosome content determines growth capacity of a cell, this mechanism of TORC2 regulation ensures that TORC2 is active only in growing cells.

Specific binding of transcription factors (TFs) determines in a large part the connectivity of gene regulatory networks as well as the quantitative level of gene expression. A multiplicity of both experimental and computational methods is currently used to discover and characterize the underlying TF–DNA interactions. Experimental methods can be further subdivided into in vitro- and in vivo-based approaches, each accenting different aspects of TF-binding events. In this review we summarize the flexibility and performance of a selection of both types of experimental methods. In conclusion, we argue that a serial combination of methods with different throughput and data type constitutes an optimal experimental strategy.

Several stochastic models have tried to capture the architecture of food webs. This approach is interesting, but it is limited by the fact that different assumptions can yield similar results. To overcome this limitation, we develop a purely statistical approach. Body size in terms of an optimal ratio between prey and predator is used as explanatory variable. In 12 observed food webs, this model predicts, on average, 20% of interactions. To analyze the unexplained part, we introduce a latent term: each species is described by two latent traits, foraging and vulnerability, that represent nonmeasured characteristics of species once the optimal body size has been accounted for. The model now correctly predicts an average of 73% of links. The key features of our approach are that latent traits quantify the structure that is left unexplained by the explanatory variable and that this quantification allows a test of whether independent biological information, such as microhabitat use, camouflage, or phylogeny, explains this structure. We illustrate this method with phylogeny and find that it is linked to one or both latent traits in nine of 12 food webs. Our approach opens the door to the formulation of more complex models that can be applied to any kind of biological network.

Contains fulltext : 87926.pdf (Publisher’s version ) (Open Access) Theories of the positive symptoms of schizophrenia hypothesize a role for aberrant reinforcement signaling driven by dysregulated dopamine transmission. Recently, we provided evidence of aberrant reward learning in symptomatic, but not asymptomatic patients with schizophrenia, using a novel paradigm, the Salience Attribution Test (SAT). The SAT is a probabilistic reward learning game that employs cues that vary across task-relevant and task-irrelevant dimensions; it provides behavioral indices of adaptive and aberrant reward learning. As an initial step prior to future clinical studies, here we used functional magnetic resonance imaging to examine the neural basis of adaptive and aberrant reward learning during the SAT in healthy volunteers. As expected, cues associated with high relative to low reward probabilities elicited robust hemodynamic responses in a network of structures previously implicated in motivational salience; the midbrain, in the vicinity of the ventral tegmental area, and regions targeted by its dopaminergic projections, i.e. medial dorsal thalamus, ventral striatum and prefrontal cortex (PFC). Responses in the medial dorsal thalamus and polar PFC were strongly correlated with the degree of adaptive reward learning across participants. Finally, and most importantly, differential dorsolateral PFC and middle temporal gyrus (MTG) responses to cues with identical reward probabilities were very strongly correlated with the degree of aberrant reward learning. Participants who showed greater aberrant learning exhibited greater dorsolateral PFC responses, and reduced MTG responses, to cues erroneously inferred to be less strongly associated with reward. The results are discussed in terms of their implications for different theories of associative learning. 8 p.

Benzodiazepines are widely used in clinics and for recreational purposes, but will lead to addiction in vulnerable individuals. Addictive drugs increase the levels of dopamine and also trigger long-lasting synaptic adaptations in the mesolimbic reward system that ultimately may induce the pathological behaviour. The neural basis for the addictive nature of benzodiazepines, however, remains elusive. Here we show that benzodiazepines increase firing of dopamine neurons of the ventral tegmental area through the positive modulation of GABA(A) (gamma-aminobutyric acid type A) receptors in nearby interneurons. Such disinhibition, which relies on alpha1-containing GABA(A) receptors expressed in these cells, triggers drug-evoked synaptic plasticity in excitatory afferents onto dopamine neurons and underlies drug reinforcement. Taken together, our data provide evidence that benzodiazepines share defining pharmacological features of addictive drugs through cell-type-specific expression of alpha1-containing GABA(A) receptors in the ventral tegmental area. The data also indicate that subunit-selective benzodiazepines sparing alpha1 may be devoid of addiction liability.

Inflammation contributes to both insulin resistance and pancreatic beta cell failure in human type 2 diabetes. Toll-like receptors (TLRs) are highly conserved pattern recognition receptors that coordinate the innate inflammatory response to numerous substances, including NEFAs. Here we investigated a potential contribution of TLR2 to the metabolic dysregulation induced by high-fat diet (HFD) feeding in mice. Male and female littermate Tlr2 +/+ and Tlr2 −/− mice were analysed with respect to glucose tolerance, insulin sensitivity, insulin secretion and energy metabolism on chow and HFD. Adipose, liver, muscle and islet pathology and inflammation were examined using molecular approaches. Macrophages and dendritic immune cells, in addition to pancreatic islets were investigated in vitro with respect to NEFA-induced cytokine production. While not showing any differences in glucose homeostasis on chow diet, both male and female Tlr2 −/− mice were protected from the adverse effects of HFD compared with Tlr2 +/+ littermate controls. Female Tlr2 −/− mice showed pronounced improvements in glucose tolerance, insulin sensitivity, and insulin secretion following 20 weeks of HFD feeding. These effects were associated with an increased capacity of Tlr2 −/− mice to preferentially burn fat, combined with reduced tissue inflammation. Bone-marrow-derived dendritic cells and pancreatic islets from Tlr2 −/− mice did not increase IL-1β expression in response to a NEFA mixture, whereas Tlr2 +/+ control tissues did. These data suggest that TLR2 is a molecular link between increased dietary lipid intake and the regulation of glucose homeostasis, via regulation of energy substrate utilisation and tissue inflammation.

In most plants, a large fraction of photo-assimilated carbon is stored in the chloroplasts during the day as starch and remobilized during the subsequent night to support metabolism. Mutations blocking either starch synthesis or starch breakdown in Arabidopsis thaliana reduce plant growth. Maltose is the major product of starch breakdown exported from the chloroplast at night. The maltose excess 1 mutant (mex1), which lacks the chloroplast envelope maltose transporter, accumulates high levels of maltose and starch in chloroplasts and develops a distinctive but previously unexplained chlorotic phenotype as leaves mature. The introduction of additional mutations that prevent starch synthesis, or that block maltose production from starch, also prevent chlorosis of mex1. In contrast, introduction of mutations in disproportionating enzyme (DPE1) results in the accumulation of maltotriose in addition to maltose, and greatly increases chlorosis. These data suggest a link between maltose accumulation and chloroplast homeostasis. Microscopic analyses show that the mesophyll cells in chlorotic mex1 leaves have fewer than half the number of chloroplasts than wild-type cells. Transmission electron microscopy reveals autophagy-like chloroplast degradation in both mex1 and the dpe1/mex1 double mutant. Microarray analyses reveal substantial reprogramming of metabolic and cellular processes, suggesting that organellar protein turnover is increased in mex1, though leaf senescence and senescence-related chlorophyll catabolism are not induced. We propose that the accumulation of maltose and malto-oligosaccharides causes chloroplast dysfunction, which may by signaled via a form of retrograde signaling and trigger chloroplast degradation.

Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and context-dependent fashion. Although it is generally assumed that anatomical connectivity data is important to guide the construction of neurobiologically realistic models of effective connectivity; the degree to which these models actually profit from anatomical constraints has not yet been formally investigated. Here, we use diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models (DCMs) of fMRI data. We constructed 64 alternative DCMs, which embodied different mappings between the probability of an anatomical connection and the prior variance of the corresponding of effective connectivity, and fitted them to empirical fMRI data from 12 healthy subjects. Using Bayesian model selection, we show that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion. This means that the higher the likelihood that a given connection exists anatomically, the larger one should set the prior variance of the corresponding coupling parameter; hence making it easier for the parameter to deviate from zero and represent a strong effective connection. To our knowledge, this study provides the first formal evidence that probabilistic knowledge of anatomical connectivity can improve models of functional integration.

Live imaging of subcellular structures is indispensible to advance our understanding of cellular processes. The blurred digital images acquired in light microscopy are, however, complex to analyze, and identification and reconstruction of subcellular structures from such images remains a major challenge. We present a novel, model-based image analysis algorithm to reconstruct outlines of subcellular structures using a sub-pixel representation. The algorithm explicitly accounts for the optical properties of the microscope. We validate the reconstruction performance on synthetic data and apply the new method to fluorescence microscopy images of endosomes identified by the GTPase EGFP-Rab5. The benefits of the new algorithm are outlined by comparison to standard techniques. We demonstrate that the new algorithm leads to better discrimination between different endosomal virus entry pathways and to more robust, accurate, and self-consistent quantification of endosome shape features. This allows establishing a set of features that quantify endosome morphology and robustly capture the dynamics of endosome fusion.

Vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs) are key drivers of blood and lymph vessel formation in development, but also in several pathological processes. VEGF-C signaling through VEGFR-3 promotes lymphangiogenesis, which is a clinically relevant target for treating lymphatic insufficiency and for blocking tumor angiogenesis and metastasis. The extracellular domain of VEGFRs consists of seven Ig homology domains; domains 1–3 (D1-3) are responsible for ligand binding, and the membrane-proximal domains 4–7 (D4-7) are involved in structural rearrangements essential for receptor dimerization and activation. Here we analyzed the crystal structures of VEGF-C in complex with VEGFR-3 domains D1-2 and of the VEGFR-3 D4-5 homodimer. The structures revealed a conserved ligand-binding interface in D2 and a unique mechanism for VEGFR dimerization and activation, with homotypic interactions in D5. Mutation of the conserved residues mediating the D5 interaction (Thr446 and Lys516) and the D7 interaction (Arg737) compromised VEGF-C induced VEGFR-3 activation. A thermodynamic analysis of VEGFR-3 deletion mutants showed that D3, D4-5, and D6-7 all contribute to ligand binding. A structural model of the VEGF-C/VEGFR-3 D1-7 complex derived from small-angle X-ray scattering data is consistent with the homotypic interactions in D5 and D7. Taken together, our data show that ligand-dependent homotypic interactions in D5 and D7 are essential for VEGFR activation, opening promising possibilities for the design of VEGFR-specific drugs.

A two step automated high throughput RNAi silencing screen was used to identify host cell factors required during vaccinia virus infection. Validation and analysis of clustered hits revealed previously unknown processes during virus entry including a mechanism for genome uncoating. Viral core proteins were found to be already ubiquitinated during virus assembly. After entering the cytosol of an uninfected cell the viral DNA was released from the core through the activity of the cell's proteasomes. Next a Cullin3 based ubiquitin ligase mediated a further round of ubiquitination and proteasome action. This was needed in order to initiate viral DNA replication. The results accentuate the value of large scale RNAi screens in providing directions for detailed cell biological investigation of complex pathways. The list of cell functions required during poxvirus infection will moreover provide a resource for future virus host cell interaction studies and for the discovery of antivirals. Although smallpox among the most devastating pathogens in human history has been eradicated the spread of other poxvirus species and bioterrorism still poses a danger. To understand the replication cycle of these complex viruses Pelkmans Helenius and colleagues have now analyzed the cell biology of vaccinia virus infection. Using an RNAi based screen the authors identify host cell factors that the virus needs for infection. They find that the ubiquitin proteasome system is needed at two steps: DNA uncoating and DNA replication initiation. © 2012 The Authors.

We use the Dpp morphogen gradient in the Drosophila wing disc as a model to address the fundamental question of how a gradient of a growth factor can produce uniform growth. We first show that proper expression and subcellular localization of components in the Fat tumor-suppressor pathway, which have been argued to depend on Dpp activity differences, are not reliant on the Dpp gradient. We next analyzed cell proliferation in discs with uniformly high Dpp or uniformly low Fat signaling activity and found that these pathways regulate growth in a complementary manner. While the Dpp mediator Brinker inhibits growth in the primordium primarily in the lateral regions, Fat represses growth mostly in the medial region. Together, our results indicate that the activities of both signaling pathways are regulated in a parallel rather than sequential manner and that uniform proliferation is achieved by their complementary action on growth.

Several real-world systems, such as gene expression networks in biological cells, contain coupled chemical reactions with a time delay between reaction initiation and completion. The non-Markovian kinetics of such reaction networks can be exactly simulated using the delay stochastic simulation algorithm (dSSA). The computational cost of dSSA scales with the total number of reactions in the network. We reduce this cost to scale at most with the smaller number of species by using the concept of partial reaction propensities. The resulting delay partial-propensity direct method (dPDM) is an exact dSSA formulation for well-stirred systems of coupled chemical reactions with delays. We detail dPDM and present a theoretical analysis of its computational cost. Furthermore, we demonstrate the implications of the theoretical cost analysis in two prototypical benchmark applications. The dPDM formulation is shown to be particularly efficient for strongly coupled reaction networks, where the number of reactions is much larger than the number of species.

Apical cell surfaces in metazoan epithelia, such as the wing disc of Drosophila, resemble polygons with different numbers of neighboring cells. The distribution of these polygon numbers has been shown to be conserved. Revealing the mechanisms that lead to this topology might yield insights into how the structural integrity of epithelial tissues is maintained. It has previously been proposed that cell division alone, or cell division in combination with cell rearrangements, is sufficient to explain the observed epithelial topology. Here, we extend this work by including an analysis of the clustering and the polygon distribution of mitotic cells. In addition, we study possible effects of cellular growth regulation by mechanical forces, as such regulation has been proposed to be involved in wing disc size regulation. We formulated several theoretical scenarios that differ with respect to whether cell rearrangements are allowed and whether cellular growth rates are dependent on mechanical stress. We then compared these scenarios with experimental data on the polygon distribution of the entire cell population, that of mitotic cells, as well as with data on mitotic clustering. Surprisingly, we observed considerably less clustering in our experiments than has been reported previously. Only scenarios that include mechanical-stress-dependent growth rates are in agreement with the experimental data. Interestingly, simulations of these scenarios showed a large decrease in rearrangements and elimination of cells. Thus, a possible growth regulation by mechanical force could have a function in releasing the mechanical stress that evolves when all cells have similar growth rates.

Autophagy is a lysosomal-mediated degradation process that promotes cell survival during nutrient-limiting conditions. However, excessive autophagy results in cell death. In Drosophila, autophagy is regulated nutritionally, hormonally and developmentally in several tissues, including the fat body, a nutrient-storage organ. Here we use a proteomics approach to identify components of starvation-induced autophagic responses in the Drosophila fat body. Using cICAT labeling and mass spectrometry, differences in protein expression levels of normal compared to starved fat bodies were determined. Candidates were analyzed genetically for their involvement in autophagy in fat bodies deficient for the respective genes. One of these genes, Desat1, encodes a lipid desaturase. Desat1 mutant cells fail to induce autophagy upon starvation. The desat1 protein localizes to autophagic structures after nutrient depletion and is required for fly development. Lipid analyses revealed that Desat1 regulates the composition of lipids in Drosophila. We propose that Desat1 exerts its role in autophagy by controlling lipid biosynthesis and/or signaling necessary for autophagic responses.

Bioinformatics, 29 (7), ISSN:1367-4803, ISSN:1460-2059

Cyclic nucleotide-modulated ion channels are important for signal transduction and pacemaking in eukaryotes. The molecular determinants of ligand gating in these channels are still unknown, mainly because of a lack of direct structural information. Here we report ligand-induced conformational changes in full-length MloK1, a cyclic nucleotide-modulated potassium channel from the bacterium Mesorhizobium loti, analysed by electron crystallography and atomic force microscopy. Upon cAMP binding, the cyclic nucleotide-binding domains move vertically towards the membrane, and directly contact the S1–S4 voltage sensor domains. This is accompanied by a significant shift and tilt of the voltage sensor domain helices. In both states, the inner pore-lining helices are in an ‘open’ conformation. We propose a mechanism in which ligand binding can favour pore opening via a direct interaction between the cyclic nucleotide-binding domains and voltage sensors. This offers a simple mechanistic hypothesis for the coupling between ligand gating and voltage sensing in eukaryotic HCN channels., The molecular determinants underlying ligand gating of cyclic nucleotide-modulated ion channels remain unclear. Kowal et al. determine the conformational changes underlying cAMP binding to the bacterial channel MloK1, and propose a mechanism for coupling of ligand gating and voltage sensing in eukaryotic HCN channels.

Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae , specifically the short-term mechanisms mediating the cells’ recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.

B. cenocepacia is an opportunistic human pathogen that is particularly problematic for patients suffering from cystic fibrosis (CF). In the CF lung bacteria grow to high densities within the viscous mucus that is limited in oxygen. Pseudomonas aeruginosa, the dominant pathogen in CF patients, is known to grow and survive under oxygen-limited to anaerobic conditions by using micro-oxic respiration, denitrification and fermentative pathways. In contrast, inspection of the genome sequences of available B. cenocepacia strains suggested that B. cenocepacia is an obligate aerobic and non-fermenting bacterium. In accordance with the bioinformatics analysis we observed that B. cenocepacia H111 is able to grow with as little as 0.1% O2 but not under strictly anoxic conditions. Phenotypic analyses revealed that H111 produced larger amounts of biofilm, pellicle and proteases under micro-oxic conditions (0.5%–5% O2, i.e. conditions that mimic those encountered in CF lung infection), and was more resistant to several antibiotics. RNA-Seq and shotgun proteomics analyses of cultures of B. cenocepacia H111 grown under micro-oxic and aerobic conditions showed up-regulation of genes involved in the synthesis of the exopolysaccharide (EPS) cepacian as well as several proteases, two isocitrate lyases and other genes potentially important for life in micro-oxia. Data deposition: RNA-Seq raw data files are accessible through the GEO Series accession number GSE48585. MS data have been deposited in the ProteomeXchange database (PXD000270).

In this paper, we report that the PX domain-containing protein SNX16, a member of the sorting nexin family, is associated with late endosome membranes. We find that SNX16 is selectively enriched on tubulo-cisternal elements of this membrane system, whose highly dynamic properties and formation depend on intact microtubules. By contrast, SNX16 was not found on vacuolar elements that typically contain LBPA, and thus presumably correspond to multivesicular endosomes. We conclude that SNX16, together with its partner phosphoinositide, define a highly dynamic subset of late endosomal membranes, supporting the notion that late endosomes are organized in distinct morphological and functional regions. Our data also indicate that SNX16 is involved in tubule formation and cholesterol transport as well as trafficking of the tetraspanin CD81, suggesting that the protein plays a role in the regulation of late endosome membrane dynamics.

Cytoplasmic transport of organelles, nucleic acids and proteins on microtubules is usually bidirectional with dynein and kinesin motors mediating the delivery of cargoes in the cytoplasm. Here we combine live cell microscopy, single virus tracking and trajectory segmentation to systematically identify the parameters of a stochastic computational model of cargo transport by molecular motors on microtubules. The model parameters are identified using an evolutionary optimization algorithm to minimize the Kullback-Leibler divergence between the in silico and the in vivo run length and velocity distributions of the viruses on microtubules. The present stochastic model suggests that bidirectional transport of human adenoviruses can be explained without explicit motor coordination. The model enables the prediction of the number of motors active on the viral cargo during microtubule-dependent motions as well as the number of motor binding sites, with the protein hexon as the binding site for the motors., Author Summary Molecular motors, due to their transportation function, are essential to the cell, but they are often hijacked by viruses to reach their replication site. Imaging of virus trajectories provides information about the patterns of virus transport in the cytoplasm, leading to improved understanding of the underlying mechanisms. In turn improved understanding may suggest actions that can be taken to interfere with the transport of pathogens in the cell. In this work we use in vivo imaging of virus trajectories to develop a computational model of virus transport in the cell. The model parameters are identified by an optimization procedure to minimize the discrepancy between in vivo and in silico trajectories. The model explains the in vivo trajectories as the result of a stochastic interaction between motors. Furthermore it enables predictions on the number of motors and binding sites on pathogens, quantities that are difficult to obtain experimentally. Beyond the understanding of mechanisms involved in pathogen transport, the present paper introduces a systematic parameter identification algorithm for stochastic models using in vivo imaging. The discrete and noisy characteristics of biological systems have led to increased attention in stochastic models and this work provides a methodology for their systematic development.

GABAergic neurons in the neocortex are diverse with regard to morphology, physiology, and axonal targeting pattern, indicating functional specializations within the cortical microcircuitry. Little information is available, however, about functional properties of distinct subtypes of GABAergic neurons in the intact brain. Here, we combined in vivo two-photon calcium imaging in supragranular layers of the mouse neocortex with post hoc immunohistochemistry against the three calcium-binding proteins parvalbumin, calretinin, and calbindin in order to assign subtype marker profiles to neuronal activity. Following coronal sectioning of fixed brains, we matched cells in corresponding volumes of image stacks acquired in vivo and in fixed brain slices. In GAD67-GFP mice, more than 95% of the GABAergic cells could be unambiguously matched, even in large volumes comprising more than a thousand interneurons. Triple immunostaining revealed a depth-dependent distribution of interneuron subtypes with increasing abundance of PV-positive neurons with depth. Most importantly, the triple-labeling approach was compatible with previous in vivo calcium imaging following bulk loading of Oregon Green 488 BAPTA-1, which allowed us to classify spontaneous calcium transients recorded in vivo according to the neurochemically defined GABAergic subtypes. Moreover, we demonstrate that post hoc immunostaining can also be applied to wild-type mice expressing the genetically encoded calcium indicator Yellow Cameleon 3.60 in cortical neurons. Our approach is a general and flexible method to distinguish GABAergic subtypes in cell populations previously imaged in the living animal. It should thus facilitate dissecting the functional roles of these subtypes in neural circuitry. Electronic supplementary material The online version of this article (doi:10.1007/s00424-011-1048-9) contains supplementary material, which is available to authorized users.

Cellular information processing via reversible protein phosphorylation requires tight control of the localization, activity, and substrate specificity of protein kinases, which to a large extent is accomplished by complex formation with other proteins. Despite their critical role in cellular regulation and pathogenesis, protein interaction information is available for only a subset of the 518 human protein kinases. Here we present a global proteomic analysis of complexes of the human CMGC kinase group. In addition to subgroup-specific functional enrichment and modularity, the identified 652 high-confidence kinase-protein interactions provide a specific biochemical context for many poorly studied CMGC kinases. Furthermore, the analysis revealed a kinase-kinase subnetwork and candidate substrates for CMGC kinases. Finally, the presented interaction proteome uncovered a large set of interactions with proteins genetically linked to a range of human diseases, including cancer, suggesting additional routes for analyzing the role of CMGC kinases in controlling human disease pathways., Cell Reports, 3 (4), ISSN:2666-3864, ISSN:2211-1247

SummaryEarly endosomes give rise to multivesicular intermediates during transport toward late endosomes. Much progress has been made in understanding the sorting of receptors into these intermediates, but the mechanisms responsible for their biogenesis remain unclear. Here, we report that F-actin is necessary for transport beyond early endosomes and endosome formation. We found that endosomes captured by actin cables were essentially stationary, but early endosomes also exhibited patches of F-actin and facilitated selective F-actin nucleation and polymerization. Our data show that nucleation of actin patches by early endosomes is strictly dependent on annexin A2, a protein involved in early-to-late endosome transport. It also requires the actin nucleation factor Spire1 and involves Arp2/3, which is needed for filament branching. We conclude that actin patches are nucleated on early endosomes via annexin A2 and Spire1, and that these patches control endosome biogenesis, presumably by driving the membrane remodeling process.

Once viewed simply as a reservoir for carbon storage, lipids are no longer cast as bystanders in the drama of biological systems. The emerging field of lipidomics is driven by technology, most notably mass spectrometry, but also by complementary approaches for the detection and characterization of lipids and their biosynthetic enzymes in living cells. The development of these integrated tools promises to greatly advance our understanding of the diverse biological roles of lipids.

Background Dynamic mathematical models in the form of systems of ordinary differential equations (ODEs) play an important role in systems biology. For any sufficiently complex model, the speed and accuracy of solving the ODEs by numerical integration is critical. This applies especially to systems identification problems where the parameter sensitivities must be integrated alongside the system variables. Although several very good general purpose ODE solvers exist, few of them compute the parameter sensitivities automatically. Results We present a novel integration algorithm that is based on second derivatives and contains other unique features such as improved error estimates. These features allow the integrator to take larger time steps than other methods. In practical applications, i.e. systems biology models of different sizes and behaviors, the method competes well with established integrators in solving the system equations, and it outperforms them significantly when local parameter sensitivities are evaluated. For ease-of-use, the solver is embedded in a framework that automatically generates the integrator input from an SBML description of the system of interest. Conclusions For future applications, comparatively ‘cheap’ parameter sensitivities will enable advances in solving large, otherwise computationally expensive parameter estimation and optimization problems. More generally, we argue that substantially better computational performance can be achieved by exploiting characteristics specific to the problem domain; elements of our methods such as the error estimation could find broader use in other, more general numerical algorithms., BMC Systems Biology, 6, ISSN:1752-0509

Background: In mammals, ChIP-seq studies of RNA polymerase II (PolII) occupancy have been performed to reveal how recruitment, initiation and pausing of PolII may control transcription rates, but the focus is rarely on obtaining finely resolved profiles that can portray the progression of PolII through sequential promoter states. Results: Here, we analyze PolII binding profiles from high-coverage ChIP-seq on promoters of actively transcribed genes in mouse and humans. We show that the enrichment of PolII near transcription start sites exhibits a stereotypical bimodal structure, with one peak near active transcription start sites and a second peak 110 base pairs downstream from the first. Using an empirical model that reliably quantifies the spatial PolII signal, gene by gene, we show that the first PolII peak allows for refined positioning of transcription start sites, which is corroborated by mRNA sequencing. This bimodal signature is found both in mouse and humans. Analysis of the pausing-related factors NELF and DSIF suggests that the downstream peak reflects widespread pausing at the +1 nucleosome barrier. Several features of the bimodal pattern are correlated with sequence features such as CpG content and TATA boxes, as well as the histone mark H3K4me3. Conclusions: We thus show how high coverage DNA sequencing experiments can reveal as-yet unnoticed bimodal spatial features of PolII accumulation that are frequent at individual mammalian genes and reminiscent of transcription initiation and pausing. The initiation-pausing hypothesis is corroborated by evidence from run-on sequencing and immunoprecipitation in other cell types and species.

Background Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on spatial proximity, other interactions typically result in spatial correlations between the interacting structures. Such correlations are the target of microscopy-based co-localization analysis, which can provide hints of potential interactions. Two complementary approaches to co-localization analysis can be distinguished: intensity correlation methods capitalize on pattern discovery, whereas object-based methods emphasize detection power. Results We first reinvestigate the classical co-localization measure in the context of spatial point pattern analysis. This allows us to unravel the set of implicit assumptions inherent to this measure and to identify potential confounding factors commonly ignored. We generalize object-based co-localization analysis to a statistical framework involving spatial point processes. In this framework, interactions are understood as position co-dependencies in the observed localization patterns. The framework is based on a model of effective pairwise interaction potentials and the specification of a null hypothesis for the expected pattern in the absence of interaction. Inferred interaction potentials thus reflect all significant effects that are not explained by the null hypothesis. Our model enables the use of a wealth of well-known statistical methods for analyzing experimental data, as demonstrated on synthetic data and in a case study considering virus entry into live cells. We show that the classical co-localization measure typically under-exploits the information contained in our data. Conclusions We establish a connection between co-localization and spatial interaction of sub-cellular structures by formulating the object-based interaction analysis problem in a spatial statistics framework based on nearest-neighbor distance distributions. We provide generic procedures for inferring interaction strengths and quantifying their relative statistical significance from sets of discrete objects as provided by image analysis methods. Within our framework, an interaction potential can either refer to a phenomenological or a mechanistic model of a physico-chemical interaction process. This increased flexibility in designing and testing different hypothetical interaction models can be used to quantify the parameters of a specific interaction model or may catalyze the discovery of functional relations., BMC Bioinformatics, 11, ISSN:1471-2105