Biology subjects

Cabral, J.

Publications and source records attributed to Cabral, J..

8 recordsLinked to original sources

bioRxiv2018

Heat Shock in C. elegans Induces Downstream of Gene Transcription and Accumulation of Double-Stranded RNA

We observed that heat shock of Caenorhabditis elegans leads to the formation of nuclear double-stranded RNA (dsRNA) foci, detectable with a dsRNA-specific monoclonal antibody. These foci significantly overlap with nuclear HSF-1 granules. To investigate the molecular mechanism(s) underlying dsRNA foci formation, we used RNA-seq to globally characterize total RNA and immunoprecipitated dsRNA from control and heat shocked worms. We find antisense transcripts are generally increased after heat shock, and a subset of both sense and antisense transcripts enriched in the dsRNA pool by heat shock overlap with dsRNA transcripts enriched by deletion of tdp-1, which encodes the C. elegans ortholog of TDP-43. Interestingly, transcripts involved in translation are over-represented in the dsRNAs induced by either heat shock or deletion of tdp-1. Also enriched in the dsRNA transcripts are sequences downstream of annotated genes (DoGs), which we globally quantified with a new algorithm. To validate these observations, we used fluorescence in situ hybridization (FISH) to confirm both antisense and downstream of gene transcription for eif-3.B, one of the affected loci we identified.

molecular biology↗

bioRxiv2018

The geography of the Anthropocene differs between the land and the sea

O_LIClimate change and other anthropogenic drivers of biodiversity change are unequally distributed across the world. The geographic patterns of different drivers, and the spatial overlap among these drivers, have important implications for the direction and pace of biodiversity change, yet are not well documented. Moreover, it is unknown if the geographic patterns of drivers differ between the terrestrial and marine realm, as expected due to marked differences in how humans interact with the land and ocean. C_LIO_LIWe compiled global gridded datasets on climate change, land-use, resource exploitation, pollution, species invasions, and human population density. We used multivariate statistics to examine the spatial relationships among the datasets and to characterize the typical combinations of drivers experienced by different parts of the world. C_LIO_LIWe found stronger positive correlations among drivers in the terrestrial than in the marine realm, leading to areas of high intensities of multiple drivers on land. Climate change tended to be negatively correlated with other drivers in the terrestrial realm (e.g., in the tundra and boreal forest with high climate change but low human use and pollution) whereas the opposite was true in the marine realm (e.g., in the Indo-Pacific with high climate change and high fishing). C_LIO_LIWe show that different regions of the world can be defined by anthropogenic threat complexes (ATCs), distinguished by different sets of drivers with varying intensities. The ATCs can be used to test hypothesis about the pattern of biodiversity change, especially the joint effects of multiple drivers. More generally, our global analysis highlights the broad conservation priorities needed to mitigate the effects of anthropogenic change on biodiversity responses, with different priorities emerging on land and in the ocean, and in different parts of the world. C_LI

ecology↗

bioRxiv2018

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDAs data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.\n\nImpact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no \"one-size-fits-all\" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.\n\nDATA SUMMARYO_LIData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.\nC_LI

bioinformatics↗

bioRxiv2018

Altered trajectories in the dynamical repertoire of functional network states under psilocybin

Brain activity can be understood as the exploration of a dynamical landscape of activity configurations over both space and time. This dynamical landscape may be defined in terms of spontaneous transitions within a repertoire of discrete metastable states of functional connectivity (FC), which underlie different mental processes. However, it remains unclear how the brains dynamical landscape might be changed in altered states of consciousness, such as the psychedelic state. The present study investigated changes in the brains dynamical repertoire in an fMRI dataset of healthy participants intravenously injected with the psychedelic compound psilocybin, which is found in \"magic mushrooms\". We employed a data-driven approach to study brain dynamics in the psychedelic state, which focuses on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices, and enables the identification of recurrent FC patterns (\"FC-states\"), and their transition profiles over time. We found that a FC state closely corresponding to the fronto-parietal control system was strongly destabilized in the psychedelic state, while transitions toward a globally synchronized FC state were enhanced. These differences between brain state trajectories in normal waking consciousness and the psychedelic state suggest that the latter biases a global mode of functional integration at the expense of locally segregated activity in specific networks. These results provide a mechanistic perspective on subjective quality of the psychedelic experience, and further raise the possibility that mapping the brains dynamical landscape may help guide pharmacological interventions in neuropsychiatric disorders.

neuroscience↗

bioRxiv2017

Disrupted structural connectivity in Pediatric Bipolar Disorder

Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of PBD patients with psychosis and euthymic matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed. Patients IQ and psychotic symptoms significantly correlated with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which correlate with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

neuroscience↗

bioRxiv2017

Plasmid Profiler: Comparative Analysis Of Plasmid Content In WGS Data

SummaryComparative analysis of bacterial plasmids from whole genome sequence (WGS) data generated from short read sequencing is challenging. This is due to the difficulty in identifying contigs harbouring plasmid sequence data, and further difficulty in assembling such contigs into a full plasmid. As such, few software programs and bioinformatics pipelines exist to perform comprehensive comparative analyses of plasmids within and amongst sequenced isolates. To address this gap, we have developed Plasmid Profiler, a pipeline to perform comparative plasmid content analysis without the need for de novo assembly. The pipeline is designed to rapidly identify plasmid sequences by mapping reads to a plasmid reference sequence database. Predicted plasmid sequences are then annotated with their incompatibility group, if known. The pipeline allows users to query plasmids for genes or regions of interest and visualize results as an interactive heat map.\n\nAvailability and ImplementationPlasmid Profiler is freely available software released under the Apache 2.0 open source software license. A stand-alone version of the entire Plasmid Profiler pipeline is available as a Docker container at https://hub.docker.com/r/phacnml/plasmidprofiler_0_1_6/.\n\nThe conda recipe for the Plasmid R package is available at: https://anaconda.org/bioconda/r-plasmidprofiler\n\nThe custom Plasmid Profiler R package is also available as a CRAN package at https://cran.r-project.org/web/packages/Plasmidprofiler/index.html\n\nGalaxy tools associated with the pipeline are available as a Galaxy tool suite at https://toolshed.g2.bx.psu.edu/repository?repository_id=55e082200d16a504\n\nThe source code is available at: https://github.com/phac-nml/plasmidprofiler\n\nThe Galaxy implementation is available at: https://github.com/phac-nml/plasmidprofiler-galaxy\n\nContactEmail: gary.vandomselaar@canada.ca\n\nAddress: National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, Manitoba, Canada\n\nSupplementary informationDocumentation: http://plasmid-profiler.readthedocs.io/en/latest/

bioinformatics↗

bioRxiv2016

SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology

MotivationThe recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research lab and into the front lines of surveillance and outbreak response requires user-friendly, reproducible, and scalable pipelines that have been well validated.\n\nResultsSNVPhyl (Single Nucleotide Variant Phylogenomics) is a bioinformatics pipeline for identifying high-quality SNVs and constructing a whole genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible, and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds, or in the presence of contamination.\n\nAvailabilitySNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

bioinformatics↗

bioRxiv2016

Single or Multi-Frequency Generators in On-going Brain Activity: a Mechanistic Whole-Brain Model following empirical MEG evidences

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.\n\nConsidering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

neuroscience↗