An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

AbstractLarge amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface

Acanthamoeba and Dictyostelium as Cellular Models for Legionella Infection

Environmental bacteria of the genus Legionella naturally parasitize free-living amoebae. Upon inhalation of bacteria-laden aerosols, the opportunistic pathogens grow intracellularly in alveolar macrophages and can cause a life-threatening pneumonia termed Legionnaires' disease. Intracellular replication in amoebae and macrophages takes place in a unique membrane-bound compartment, the Legionella-containing vacuole (LCV). LCV formation requires the bacterial Icm/Dot type IV secretion system, which translocates literally hundreds of "effector" proteins into host cells, where they modulate crucial cellular processes for the pathogen's benefit. The mechanism of LCV formation appears to be evolutionarily conserved, and therefore, amoebae are not only ecologically significant niches for Legionella spp., but also useful cellular models for eukaryotic phagocytes. In particular, Acanthamoeba castellanii and Dictyostelium discoideum emerged over the last years as versatile and powerful models. Using genetic, biochemical and cell biological approaches, molecular interactions between amoebae and Legionella pneumophila have recently been investigated in detail with a focus on the role of phosphoinositide lipids, small and large GTPases, autophagy components and the retromer complex, as well as on bacterial effectors targeting these host factors

Type IV secretion in Gram-negative and Gram-positive bacteria

Type IV secretion systems (T4SSs) are versatile multiprotein nanomachines spanning the entire cell envelope in Gram‐negative and Gram‐positive bacteria. They play important roles through the contact‐dependent secretion of effector molecules into eukaryotic hosts and conjugative transfer of mobile DNA elements as well as contact‐independent exchange of DNA with the extracellular milieu. In the last few years, many details on the molecular mechanisms of T4SSs have been elucidated. Exciting structures of T4SS complexes from Escherichia coli plasmids R388 and pKM101, Helicobacter pylori and Legionella pneumophila have been solved. The structure of the F‐pilus was also reported and surprisingly revealed a filament composed of pilin subunits in 1:1 stoichiometry with phospholipid molecules. Many new T4SSs have been identified and characterized, underscoring the structural and functional diversity of this secretion superfamily. Complex regulatory circuits also have been shown to control T4SS machine production in response to host cell physiological status or a quorum of bacterial recipient cells in the vicinity. Here, we summarize recent advances in our knowledge of ‘paradigmatic’ and emerging systems, and further explore how new basic insights are aiding in the design of strategies aimed at suppressing T4SS functions in bacterial infections and spread of antimicrobial resistances

Icm/Dot-dependent inhibition of phagocyte migration by Legionella is antagonized by a translocated Ran GTPase activator

The environmental bacterium Legionella pneumophila causes a severe pneumonia termed Legionnaires' disease. L. pneumophila employs a conserved mechanism to replicate within a specific vacuole in macrophages or protozoa such as the social soil amoeba Dictyostelium discoideum. Pathogen-host interactions depend on the Icm/Dot type IV secretion system (T4SS), which translocates approximately 300 different effector proteins into host cells. Here we analyse the effects of L. pneumophila on migration and chemotaxis of amoebae, macrophages or polymorphonuclear neutrophils (PMN). Using under-agarose assays, L. pneumophila inhibited in a dose- and T4SS-dependent manner the migration of D. discoideum towards folate as well as starvation-induced aggregation of the social amoebae. Similarly, L. pneumophila impaired migration of murine RAW 264.7 macrophages towards the cytokines CCL5 and TNFα, or of primary human PMN towards the peptide fMLP respectively. L. pneumophila lacking the T4SS-translocated activator of the small eukaryotic GTPase Ran, Lpg1976/LegG1, hyper-inhibited the migration of D. discoideum, macrophages or PMN. The phenotype was reverted by plasmid-encoded LegG1 to an extent observed for mutant bacteria lacking a functional Icm/Dot T4SS.Similarly, LegG1 promoted random migration of L. pneumophila-infected macrophages and A549 epithelial cells in a Ran-dependent manner, or upon 'microbial microinjection' into HeLa cells by a Yersinia strain lacking endogenous effectors. Single-cell tracking and real-time analysis of L. pneumophila-infected phagocytes revealed that the velocity and directionality of the cells were decreased, and cell motility as well as microtubule dynamics was impaired. Taken together, these findings indicate that the L. pneumophila Ran activator LegG1 and consequent microtubule polymerization are implicated in Icm/Dot-dependent inhibition of phagocyte migration

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

International audienceRecent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto-or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connec-tome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered. (C) 2016 Elsevier Inc. All rights reserved

Murine tissue factor disulfide mutation causes a bleeding phenotype with sex specific organ pathology and lethality

Tissue factor is highly expressed in sub-endothelial tissue. The extracellular allosteric disulfide bond Cys186-Cys209 of human tissue factor shows high evolutionary conservation and in vitro evidence suggests that it significantly contributes to tissue factor procoagulant activity. To investigate the role of this allosteric disulfide bond in vivo, we generated a C213G mutant tissue factor mouse by replacing Cys213 of the corresponding disulfide Cys190-Cys213 in murine tissue factor. A bleeding phenotype was prominent in homozygous C213G tissue factor mice. Pre-natal lethality of 1/3rd of homozygous offspring was observed between E9.5 and E14.5 associated with placental hemorrhages. After birth, homozygous mice suffered from bleedings in different organs and reduced survival. Homozygous C213G tissue factor male mice showed higher incidence of lung bleedings and lower survival rates than females. In both sexes, C213G mutation evoked a reduced protein expression (about 10-fold) and severely reduced pro-coagulant activity (about 1000-fold). Protein glycosylation was impaired and cell membrane exposure decreased in macrophages in vivo. Single housing of homozygous C213G tissue factor males reduced the occurrence of severe bleeding and significantly improved survival, suggesting that inter-male aggressiveness might significantly account for the sex differences. These experiments show that the tissue factor allosteric disulfide bond is of crucial importance for normal in vivo expression, post-translational processing and activity of murine tissue factor. Although C213G tissue factor mice do not display the severe embryonic lethality of tissue factor knock-out mice, their postnatal bleeding phenotype emphasizes the importance of fully functional tissue factor for hemostasis