BackgroundWe previously demonstrated that neuregulin-1 (NRG-1) was neuroprotective in rats following ischemic stroke. Neuroprotection by NRG-1 was associated with the suppression of pro-inflammatory gene expression in brain tissues. Over-activation of brain microglia can induce pro-inflammatory gene expression by activation of transcriptional regulators following stroke. Here, we examined how NRG-1 transcriptionally regulates inflammatory gene expression by computational bioinformatics and in vitro using microglial cells.MethodsTo identify transcriptional regulators involved in ischemia-induced inflammatory gene expression, rats were sacrificed 24 h after middle cerebral artery occlusion (MCAO) and NRG-1 treatment. Gene expression profiles of brain tissues following ischemia and NRG-1 treatment were examined by microarray technology. The Conserved Transcription Factor-Binding Site Finder (CONFAC) bioinformatics software package was used to predict transcription factors associated with inflammatory genes induced following stroke and suppressed by NRG-1 treatment. NF-kappa B (NF-kB) was identified as a potential transcriptional regulator of NRG-1-suppressed genes following ischemia. The involvement of specific NF-kB subunits in NRG-1-mediated inflammatory responses was examined using N9 microglial cells pre-treated with NRG-1 (100 ng/ml) followed by lipopolysaccharide (LPS; 10 μg/ml) stimulation. The effects of NRG-1 on cytokine production were investigated using Luminex technology. The levels of the p65, p52, and RelB subunits of NF-kB and IkB-α were determined by western blot analysis and ELISA. Phosphorylation of IkB-α was investigated by ELISA.ResultsCONFAC identified 12 statistically over-represented transcription factor-binding sites (TFBS) in our dataset, including NF-kBP65. Using N9 microglial cells, we observed that NRG-1 significantly inhibited LPS-induced TNFα and IL-6 release. LPS increased the phosphorylation and degradation of IkB-α which was blocked by NRG-1. NRG-1 also prevented the nuclear translocation of the NF-kB p65 subunit following LPS administration. However, NRG-1 increased production of the neuroprotective cytokine granulocyte colony-stimulating factor (G-CSF) and the nuclear translocation of the NF-kB p52 subunit, which is associated with the induction of anti-apoptotic and suppression of pro-inflammatory gene expression.ConclusionsNeuroprotective and anti-inflammatory effects of NRG-1 are associated with the differential regulation of NF-kB signaling pathways in microglia. Taken together, these findings suggest that NRG-1 may be a potential therapeutic treatment for treating stroke and other neuroinflammatory disorders.

BackgroundTraumatic brain injury (TBI) results in irreversible damage at the site of impact and initiates cellular and molecular processes that lead to secondary neural injury in the surrounding tissue. We used microarray analysis to determine which genes, pathways and networks were significantly altered using a rat model of TBI. Adult rats received a unilateral controlled cortical impact (CCI) and were sacrificed 24 h post-injury. The ipsilateral hemi-brain tissue at the site of the injury, the corresponding contralateral hemi-brain tissue, and naïve (control) brain tissue were used for microarray analysis. Ingenuity Pathway Analysis (IPA) software was used to identify molecular pathways and networks that were associated with the altered gene expression in brain tissues following TBI.ResultsInspection of the top fifteen biological functions in IPA associated with TBI in the ipsilateral tissues revealed that all had an inflammatory component. IPA analysis also indicated that inflammatory genes were altered on the contralateral side, but many of the genes were inversely expressed compared to the ipsilateral side. The contralateral gene expression pattern suggests a remote anti-inflammatory molecular response. We created a network of the inversely expressed common (i.e., same gene changed on both sides of the brain) inflammatory response (IR) genes and those IR genes included in pathways and networks identified by IPA that changed on only one side. We ranked the genes by the number of direct connections each had in the network, creating a gene interaction hierarchy (GIH). Two well characterized signaling pathways, toll-like receptor/NF-kappaB signaling and JAK/STAT signaling, were prominent in our GIH.ConclusionsBioinformatic analysis of microarray data following TBI identified key molecular pathways and networks associated with neural injury following TBI. The GIH created here provides a starting point for investigating therapeutic targets in a ranked order that is somewhat different than what has been presented previously. In addition to being a vehicle for identifying potential targets for post-TBI therapeutic strategies, our findings can also provide a context for evaluating the potential of therapeutic agents currently in development.

BackgroundNeuregulin-1 (NRG-1) has been shown to act as a neuroprotectant in animal models of nerve agent intoxication and other acute brain injuries. We recently demonstrated that NRG-1 blocked delayed neuronal death in rats intoxicated with the organophosphate (OP) neurotoxin diisopropylflurophosphate (DFP). It has been proposed that inflammatory mediators are involved in the pathogenesis of OP neurotoxin-mediated brain damage.MethodsWe examined the influence of NRG-1 on inflammatory responses in the rat brain following DFP intoxication. Microglial activation was determined by immunohistchemistry using anti-CD11b and anti-ED1 antibodies. Gene expression profiling was performed with brain tissues using Affymetrix gene arrays and analyzed using the Ingenuity Pathway Analysis software. Cytokine mRNA levels following DFP and NRG-1 treatment was validated by real-time reverse transcription polymerase chain reaction (RT-PCR).ResultsDFP administration resulted in microglial activation in multiple brain regions, and this response was suppressed by treatment with NRG-1. Using microarray gene expression profiling, we observed that DFP increased mRNA levels of approximately 1,300 genes in the hippocampus 24 h after administration. NRG-1 treatment suppressed by 50% or more a small fraction of DFP-induced genes, which were primarily associated with inflammatory responses. Real-time RT-PCR confirmed that the mRNAs for pro-inflammatory cytokines interleukin-1β (IL-1β) and interleukin-6 (IL-6) were significantly increased following DFP exposure and that NRG-1 significantly attenuated this transcriptional response. In contrast, tumor necrosis factor α (TNFα) transcript levels were unchanged in both DFP and DFP + NRG-1 treated brains relative to controls.ConclusionNeuroprotection by NRG-1 against OP neurotoxicity is associated with the suppression of pro-inflammatory responses in brain microglia. These findings provide new insight regarding the molecular mechanisms involved in the neuroprotective role of NRG-1 in acute brain injuries.

The NIH Common Fund Stimulating Peripheral Activity to Relieve Conditions (SPARC) initiative is a large-scale program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. Integral to the SPARC program are the rich anatomical and functional datasets produced by investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural and functional connectivity, mapping of cell types and molecular profiling. These datasets are provided to the research community through an open data platform, the SPARC Portal. To ensure SPARC datasets are Findable, Accessible, Interoperable and Reusable (FAIR), they are all submitted to the SPARC portal following a standard scheme established by the SPARC Curation Team, called the SPARC Data Structure (SDS). Inspired by the Brain Imaging Data Structure (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are coming from all fields of biomedical research. Here we present the rationale and design of the SDS, including a description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. The objective is to provide detailed guidelines for anyone desiring to comply with the SDS. Since the SDS are suitable for any type of biomedical research data, it can be adopted by any group desiring to follow the FAIR data principles for managing their data, even outside of the SPARC consortium. Finally, this manuscript provides a foundational framework that can be used by any organization desiring to either adapt the SDS to suit the specific needs of their data or simply desiring to design their own FAIR data sharing scheme from scratch.

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

Ischemic stroke is a major cause of mortality in the United States. We previously showed that neuregulin-1 (NRG1) was neuroprotective in rat models of ischemic stroke. We used gene expression profiling to understand the early cellular and molecular mechanisms of NRG1’s effects after the induction of ischemia. Ischemic stroke was induced by middle cerebral artery occlusion (MCAO). Rats were allocated to 3 groups: (1) control, (2) MCAO and (3) MCAO + NRG1. Cortical brain tissues were collected three hours following MCAO and NRG1 treatment and subjected to microarray analysis. Data and statistical analyses were performed using R/Bioconductor platform alongside Genesis, Ingenuity Pathway Analysis and Enrichr software packages. There were 2693 genes differentially regulated following ischemia and NRG1 treatment. These genes were organized by expression patterns into clusters using a K-means clustering algorithm. We further analyzed genes in clusters where ischemia altered gene expression, which was reversed by NRG1 (clusters 4 and 10). NRG1, IRS1, OPA3, and POU6F1 were central linking (node) genes in cluster 4. Conserved Transcription Factor Binding Site Finder (CONFAC) identified ETS-1 as a potential transcriptional regulator of NRG1 suppressed genes following ischemia. A transcription factor activity array showed that ETS-1 activity was increased 2-fold, 3 hours following ischemia and this activity was attenuated by NRG1. These findings reveal key early transcriptional mechanisms associated with neuroprotection by NRG1 in the ischemic penumbra.

BackgroundDelayed or secondary cell death that is caused by a cascade of cellular and molecular processes initiated by traumatic brain injury (TBI) may be reduced or prevented if an effective neuroprotective strategy is employed. Microarray and subsequent bioinformatic analyses were used to determine which genes, pathways and networks were significantly altered 24 h after unilateral TBI in the rat. Ipsilateral hemi-brain, the corresponding contralateral hemi-brain, and naïve (control) brain tissue were used for microarray analysis.ResultsIngenuity Pathway Analysis showed cell death and survival (CD) to be a top molecular and cellular function associated with TBI on both sides of the brain. One major finding was that the overall gene expression pattern suggested an increase in CD genes in ipsilateral brain tissue and suppression of CD genes contralateral to the injury which may indicate an endogenous protective mechanism. We created networks of genes of interest (GOI) and ranked the genes by the number of direct connections each had in the GOI networks, creating gene interaction hierarchies (GIHs). Cell cycle was determined from the resultant GIHs to be a significant molecular and cellular function in post-TBI CD gene response.ConclusionsCell cycle and apoptosis signalling genes that were highly ranked in the GIHs and exhibited either the inverse ipsilateral/contralateral expression pattern or contralateral suppression were identified and included STAT3, CCND1, CCND2, and BAX. Additional exploration into the remote suppression of CD genes may provide insight into neuroprotective mechanisms that could be used to develop therapies to prevent cell death following TBI.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2412-0) contains supplementary material, which is available to authorized users.

We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY’s knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.