Genome-wide gene expression profiling of stress response in a spinal cord clip compression injury model
© Chamankhah et al.; licensee BioMed Central Ltd. 2013
Received: 27 March 2013
Accepted: 13 August 2013
Published: 28 August 2013
The aneurysm clip impact-compression model of spinal cord injury (SCI) is a standard injury model in animals that closely mimics the primary mechanism of most human injuries: acute impact and persisting compression. Its histo-pathological and behavioural outcomes are extensively similar to human SCI. To understand the distinct molecular events underlying this injury model we analyzed global mRNA abundance changes during the acute, subacute and chronic stages of a moderate to severe injury to the rat spinal cord.
Time-series expression analyses resulted in clustering of the majority of deregulated transcripts into eight statistically significant expression profiles. Systematic application of Gene Ontology (GO) enrichment pathway analysis allowed inference of biological processes participating in SCI pathology. Temporal analysis identified events specific to and common between acute, subacute and chronic time-points. Processes common to all phases of injury include blood coagulation, cellular extravasation, leukocyte cell-cell adhesion, the integrin-mediated signaling pathway, cytokine production and secretion, neutrophil chemotaxis, phagocytosis, response to hypoxia and reactive oxygen species, angiogenesis, apoptosis, inflammatory processes and ossification. Importantly, various elements of adaptive and induced innate immune responses span, not only the acute and subacute phases, but also persist throughout the chronic phase of SCI. Induced innate responses, such as Toll-like receptor signaling, are more active during the acute phase but persist throughout the chronic phase. However, adaptive immune response processes such as B and T cell activation, proliferation, and migration, T cell differentiation, B and T cell receptor-mediated signaling, and B cell- and immunoglobulin-mediated immune response become more significant during the chronic phase.
This analysis showed that, surprisingly, the diverse series of molecular events that occur in the acute and subacute stages persist into the chronic stage of SCI. The strong agreement between our results and previous findings suggest that our analytical approach will be useful in revealing other biological processes and genes contributing to SCI pathology.
KeywordsSpinal cord injury Microarray Pathway analysis GO enrichment
Human spinal cord injury (SCI), often the result of both impact and varying degrees of compression, is initially a primary mechanical tissue and cell injury, but further develops into a cascade of complex secondary damage . Accordingly, the need for biologically relevant animal SCI models has focussed on the development of animal injury models that can reliably mimic human SCI . Various animal SCI models can be classified based on how the primary injury is induced (either physical or chemical), and the duration and extent of the primary injury. Techniques such as weight drop, clip compression, calibrated forceps and chemically-mediated SCI have been introduced and evaluated in laboratory animal models [3–5]. The majority of primary injuries in animal SCI models are physically-induced, by either impact, compression, or a combination of both; the latter most closely mimic SCI in human patients. The nature of the primary injury will dictate the types of secondary events that contribute to common outcomes of all injury models such as acute and chronic spinal cord dysfunction  and loss of regenerative capacity [7, 8]. It may also contribute towards unique features and characteristics of each injury model such as spasticity [9, 10], neuropathic pain [11, 12] and systemic effects . Finally, various methods and devices can be calibrated to injure the spinal cord for various durations of time; hence, the primary injury can be classified as transient or persistent.
Amongst the injury models, the weight drop [14–19] and the aneurysm clip [20–23] are the most standard graded methods of physically-inducing experimental SCI, which have been thoroughly characterized in laboratory animal models. In weight drop models [14–19], the primary injury is a transient impact and compression, hence the name contusive injury, which can be graded as mild, moderate or severe depending on the weight and height of the drop. The clip compression model was introduced as one of the first non-transection models of SCI in rodents . It is an easy and highly reproducible injury model and has the ability to mimic different levels of injury by adjusting the force and duration of clip application. The method of primary injury in the clip model is slightly different from the weight drop model as the compressive force due to the closure of the clip is maintained on the spinal cord for a defined period of time. Consequently, the outcome of a clip injury is usually a more severe form of vascular network disruption, which leads to hemorrhage and shortage of blood supply to the tissue rather than a contusive injury.
Various SCI injury models have been characterized by examining the primary injury (impact, compression, contusion, or laceration of the tissue) and the secondary injuries (blood-spinal cord barrier (BSCB) permeability, ischemia, edema, apoptosis, glutamate excitotoxicity, inflammation, demyelination, axonal degeneration, reactive gliosis, and scar tissue formation) to the spinal cord tissue using low- and high-resolution microscopy and immunohistochemical methods [24, 25]. Additionally, the extent of damage and functional recovery in animals is recorded using kinematic and behavioural studies [26, 27]. Studying the functional state of neurons after injury or during the recovery process is another approach but is only feasible using electrophysiological methods .
Our lab has successfully used the clip compression injury model to injure the rat spinal cord at the thoracic level with consistent and reliable results; both acute and chronic SCI in rats have been characterized using this model [21–23, 29–32], as well as assessment of the efficiency of various intervention strategies such as a combination transplantation of mouse brain-derived neural precursor stem cells, chondroitinase, and growth factors [6, 29, 30, 32–35]. However, molecular events following clip compression injury have not been explored using high throughput strategies. In this study, we used the Affymetrix GeneChip Rat Genome 230 2.0 platform for microarray gene expression analysis of SCI using the clip compression injury model in rats. A unique feature of this study is that a more comprehensive catalogue of the whole genome transcript levels was compared across a wider time frame, i.e. 1, 3, 7, 14 and 56 days post-injury, than has been examined in previous work. In this study, we present the overall picture of biological processes that relate to stress response and are up-regulated and the corresponding molecular events. We show that, by systematically applying the controlled vocabulary of gene functions presented in Gene Ontology (GO) domains, the temporal pattern of biological processes are extracted from microarray gene expression data and that this approach can be applied to discover novel molecular events.
Feature analysis of affymetrix GeneChip Rat genome 230 2.0 array
Analysis and filtering on the resulting file of 31,042 ProbeSets revealed that 10,791 ProbeSet IDs had no annotations, i.e. no Entrez IDs or official gene symbols, which were flagged out. This reduced the number of workable ProbeSet IDs to 20,251. In addition, there were duplicate or multiple ProbeSet IDs which represented a single gene. Conversely, there were ProbeSet IDs with multiple annotations (EntrezID/Gene Symbol) due to sequence identity across more than one gene segment in the genome. This issue could not be easily resolved as the level of uniqueness of the oligonucleotide sequence is not high enough to allow annotation to one gene exclusively. This feature requires manual curation of the data based on Affymetrix instructions to use the latest annotation, which is also the most relevant. Taking the above two features into consideration results in 14,324 gene symbols on the GeneChip RG230 2.0 array. The resulting data file still contains the ProbeSet IDs that have “LOC” or “RGD” identifiers instead of actual gene symbols. These identifiers are applied to genes that are less well characterized and usually belong to similar or orthologous proteins in other species. They may also belong to non-coding regions of the genome. Software platforms developed for GO enrichment and pathway analysis rarely map the LOC and RGD identifiers. In the original Affymetrix GeneChip 230 2.0 array annotation file, the number of LOC and RGD annotated ProbeSets sets are 1163 and 1135, respectively. The same issue of duplicate/multiple entries also applies to these ProbeSets, hence the numbers of “LOC” and “RGD” identifiers in the final output file with 14,324 entries were less: 939 for the LOC and 829 for the RGD identifiers. This means that the total number of annotated ProbeSets in the Affymetrix GeneChip Rat Genome 230 2.0 array annotation file that were mapped to known gene candidates was 12,557, which is equivalent to 62.4% and 71.2% of the total number of genes annotated and listed in the Rat Genome Database (RGD) and European Bioinformatics Institute (EBI) association files, respectively.
Analysis of ProbeSet data
Data normalization and expression/signal value determination resulted in a list of all 31,099 ProbeSets, their fold change values relative to sham (in Log2 scale), and associated ANOVA t test p-values across the time points. Volcano plots of the corresponding fold change values against transformed (−log10) p-values for every time point are displayed in Figure 1C. As shown, all volcano plots display a normal distribution of ProbeSets with fold change values from −8.7 to 11.2 for down- and up-regulated genes, respectively. The shape of the volcano plot changes as time post-injury goes by. Thus, day 3 ProbeSet data plots are not as populated, especially on the down-regulated area and are less similar to other data points. The day 1 plot, on the other hand, looks more similar to the day 7 volcano plot. The more chronic data points of day 14 and day 56 look more similar to each other than to earlier data points.
Examination of the number of ProbeSets with marginal ANOVA t test p-values gave an estimate as to the reliability of data obtained. Thus, we analyzed our data for the number of ProbeSets with ANOVA t test p-values higher than 0.05 at different fold change values (Figure 1D). We found that the majority of changes in gene expression with significance levels of p > 0.05 generally belong to ProbeSets with lower fold change values. For example, the number of ProbeSets with ANOVA t test p > 0.05 did not exceed 6% of the total number of ProbeSets, irrespective of the fold change values. At a more stringent significance level of p ≤ 0.001, however, it would be necessary to filter out the ProbeSets with expression values less than 2 fold changes in order to keep the number of filtered ProbeSets around 10% or less across the time points (data not shown). Thus, filtering the data with higher fold change values automatically targets for transcripts with smaller t test p-values. Based on the results presented in Figure 1D, we performed the functional analysis on the ProbeSet data with fold change values of ≥ 1.5 and p ≤ 0.05.
Analysis of gene set data
Time-point gene set data analysis at different fold change criteria (p ≤ 0.05)
≥ 1.0 fold change
≥ 1.5 fold change
≥ 2.0 fold change
Time-series expression profile clustering by STEM
Time-series gene set data analysis by STEM at different fold change criteria (p ≤ 0.05)
No. missing values allowed
≥ 1.0 fold change
≥ 1.5 fold change
≥ 2.0 fold change
Class II profiles represent fluctuating profiles (44, 41 and 6), with a surprising but more complex pattern of gene expression, most notably during the 24–72 hours post-injury (Figure 3G-H). This results in a bi-phasic expression pattern, which falls into two main clusters. The first cluster comprises profiles 44 and 41 (Figure 3F-G) and is characterized by an initial up-regulation of gene transcript levels early on day 1, followed by a sharp decrease in gene expression on day 3. More than 53% and 83% of the genes in profiles 44 and 41, respectfully, displayed at least a 1.0 (Log2 scale) fold change reduction in transcript levels on day 3 compared to day 1. For profiles 44 and 41, this bi-phasic pattern of gene expression is further followed by escalation of gene expression, which peaks at day 7 and stabilizes on day 14 onward. The second cluster only includes profile 6, which is essentially the mirror of profile 44 and comprise down-regulated genes (Figure 3H). It is characterized by an early and substantial down-regulation on day 1. Next, a period of recovery to normal transcript levels is observed that peaks on day 3 post-injury and then switches direction again and remains low through today 56. Finally, detailed information in Figure 3 indicates that the majority of transcripts belong to profile 44 and 6 with up-regulated transcripts clustering in the former and down-regulated transcripts in the latter.
In summary, the following conclusions can be drawn from the cluster analysis of transcripts both at the ProbeSet and gene level following clip-compression injury of the spinal cord in rats:
Major molecular events after introduction of clip-compression injury occur immediately and up to 72 hours post-injury
For many transcripts a bi-phasic pattern of gene expression is observed, possibly due to switching mechanisms acting between day 1 and day 3 or a shift in the cellular origin of deregulated transcripts or the type of response elicited resulting in chronic deregulations of many genes. Therefore, for many transcripts, the late up or down-regulations seem to be distinct from the early response
The early events seem to stabilize for most transcripts by 1 week post-injury, i.e. no more dramatic global changes in the average gene expression are observed and the level of expressions remains relatively constant.
GO enrichment analysis of deregulated genes
Choice of reference association file
Gene Ontology (GO) enrichment analysis was preferred as the method of choice for functional analysis of the list of deregulated genes as it is based on a controlled vocabulary of terms at all three domains of “Biological Process” (BP), “Molecular Function” (MF) and “Cellular Compartment” (CC). Initially, gene association files from RGD or EBI were analyzed for the number of rat genes that are annotated at each of the three domains (BP, MF and CC) and compared with the list of significantly (ANOVA t test p ≤ 0.05) deregulated genes (Fold Change ≥ 1.0 and 1.5) at each time point. We found that about 70-75% of deregulated transcripts were annotated for all three domains of GO, in reference to the RGD association file whereas the association file from EBI only annotated 55-65% (data not shown). This implies that a minimum of 25-30% of significantly deregulated transcripts are not annotated (in any BP, MF or CC domains) in any gene ontology association files and thus are not considered for analysis regardless of the type of software platform used to perform GO enrichment analysis. Therefore, due to its more extensive annotation coverage, GO enrichment analysis in this study was performed in reference to the RGD association file.
Fold change and p-value criteria affect the number of enriched terms
GO level criteria and term specificity
Gene ontology hierarchy consists of a tree of inter-related terms in a distinct structure called a directed acyclic graph (DAG). In GO tree hierarchy, the terms Biological Process, Molecular Function, and Cellular Component are at level 1. Therefore, more general parent terms are at the top of the hierarchy with lower GO level values and higher GO level values are assigned to more specific child terms. Unless more than one parent is assigned, GO level can be considered as a constant value for each term. As GO level values refer to the position of the enriched terms in the GO hierarchy tree, they can define the specificity or granularity of a given GO term and thus are a valuable parameter for terms prioritization and for inferring biological meaning from GO enrichment analysis . To determine the position of each of the enriched GO terms in the DAG structure of the gene ontology hierarchy, we performed GO Biological Process (BP) enrichment at GO levels between 20 and 3, using STEM. This led to multiple lists of enriched and overlapping GO terms at each level of GO hierarchy. Using this approach, a single GO level was assigned to every GO term. Figure 4B depicts the distribution of all 649 and 329 GO terms obtained at p ≤ 0.001 and p ≤ 0.0001 cut offs, respectively, against their corresponding GO levels. As shown, the enriched terms show a distribution curve that is close to normal against different GO levels though it is slightly skewed at the higher GO level value side. The majority of terms were obtained when GO level parameter was set to 11 and less. On the other hand, examining levels lower than 5 led to GO terms with lower p-values at the cost of more general terms with much broader information about the function of genes in that category (data not shown). It should be mentioned that, although more general terms offer less specific information about the actual biological functions of deregulated transcripts in the list, their significance level, marked by their p-value of enrichment, along with their GO level can help delineate how the specific terms are related to the correct parental signaling pathways or biological processes.
Time-series vs. Time-point analysis
Temporal analysis of gene expression may imply analysis of gene lists in either a time-series and/or a time-point fashion. Although STEM has been designed for time-series expression profiling prior to GO enrichment, it can also be used for time-point GO enrichment analysis. In the time-series approach, clustering by STEM produces significant expression profiles followed by enrichment analysis of the list of genes in each expression profile. The complication with time-series analysis is that not all transcripts have accepted ANOVA t test p-values (e.g. p ≤ 0.05) and thus the insignificant expression values must be removed from the original data prior to STEM analysis. To resolve the issue of many transcripts with missing values across all time-points, STEM offers the option to set the missing value parameter. However, depending on the selected value, this may ultimately reduce the total number of deregulated genes included in the functional analysis. In the time-point approach, however, the input file is the list of genes that belong to a specific time-point, in which case the number of missing values is not an issue. In this study, the time-point GO enrichment analysis was employed to discover common up- and down-regulated biological processes across the time-points as well as possible unique processes to each time-point. The output GO terms were used for inter-relationship analysis and or visualized as a scatter plot or interactive graph using REViGO .
Time-series GO enrichment
Most enriched GO terms visualized in Figure 5 represent deregulated transcripts with the different expression profiles shown in Additional file 1: Figure S1. As expression clustering is performed prior to GO enrichment, the time-series GO enrichment may produce enriched terms that are also significantly represented by the expression profiles shown in Additional file 1: Figure S1. Indeed, we found that the majority of enriched GO terms for up-regulated transcripts represent the expression profiles 44 and 46 and in some cases, profile 48.
Time-point GO enrichment
Common GO terms across all time-points post-injury
Parent term name
Child term name
response to external stimulus
response to extracellular stimulus
response to mechanical stimulus
response to stimulus
response to endogenous stimulus
response to stress
response to wounding
response to lipid
response to lipopolysaccharide
immune effector process
response to oxygen levels
response to hypoxia
blood vessel morphogenesis
response to oxidative stress
response to reactive oxygen species
response to hydrogen peroxide
cytokine production involved in immune response
cytokine biosynthetic process
tumor necrosis factor superfamily cytokine production
response to cytokine stimulus
response to interleukin-1
response to tumor necrosis factor
response to interferon-gamma
innate immune response
response to interferon-gamma
myeloid cell activation involved in immune response
macrophage activation involved in immune response
myeloid leukocyte activation
programmed cell death
apoptotic cell clearance
regulation of immune response
activation of innate immune response
innate immune response-activating signal transduction
immune response-activating cell surface receptor signaling pathway
innate immune response activating cell surface receptor signaling pathway
innate immune response-activating signal transduction
pattern recognition receptor signaling pathway
toll-like receptor signaling pathway
leukocyte cell-cell adhesion
cell surface receptor signaling pathway
integrin-mediated signaling pathway
B cell activation
T cell activation
B cell activation
B cell proliferation
T cell activation
T cell proliferation
T cell activation
T cell differentiation
immune response-activating cell surface receptor signaling pathway
antigen receptor-mediated signaling pathway
antigen receptor-mediated signaling pathway
B cell receptor signaling pathway
adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
B cell mediated immunity
immunoglobulin mediated immune response
single-multicellular organism process
A significant finding is the occurrence of the “apoptotic process” on day 1-day 7 post-injury. This process is accompanied by events whose peak of response also corresponds exclusively to day 7, such as “interleukin-6 production”, “tumor necrosis factor production”, “macrophage activation involved in immune response”, “phagocytosis” and “engulfment and “apoptotic cell clearance” (Figure 7E and 7H).
On both days 1 and 7, a significant up-regulation in induced-innate immunity related GO terms such as “pattern recognition-mediated signaling’ , “Toll-like receptor signaling” and “integrin-mediated signaling pathways” was detected. “Leukocyte cell-cell adhesion” was observed from day 1 to day 7. While genes involved in activation of “innate immune response”, “B and T cell activation”, “cytokine biosynthetic process”, and “phagocytosis” were up-regulated at day 1 and from day 7 onwards to day 56; ‘T cell differentiation” and “B cell mediated immunity” up-regulation is only observed during the chronic phase of injury, i.e. day 14-day 56 (Figure 7O and 7P). Thus, it is not surprising that the “B and T cell proliferation” and the “B cell receptor signalling pathway” peaks of response were on day 7-day 14. Day 14 also marks a peak response to “ossification” (data not shown). Importantly, the peak response to interferon-gamma and the immunoglobulin-mediated immune response is observed on day 56. These two mark the late response biological processes induced after injury to spinal cord.
Our analysis also showed that “programmed cell death” and its related child terms “apoptotic process” and “positive and negative regulation of apoptotic process” are commonly enriched only during day 1- day 7 post-injury. Apoptotic processes significantly increase early after injury on day 1 post-injury and reach a peak between day 3 and day 7 post-injury, after which the contribution of apoptotic processes is diminished but stays significantly enriched (p ≤ 0.001-0.0001). Both positive and negative regulations of apoptosis are significantly enriched, which indicates the fact that the injured cells struggle for survival. However, activation of apoptosis seems to be more predominant than its suppression, as the positive regulation of apoptosis becomes activated earlier than negative regulation and its peak of activity is on day 1 post-injury, although it stays continuously up-regulated up to 1 week post-injury. In contrast, the only significant activity of negative regulation of apoptosis (p ≤ 0.00001) is on day 3 (Figure 7I-J).
We can summarize the biological processes listed in Table 3 into three main categories: stress response including processes such as blood coagulation, complement activation, response to hypoxia and reactive oxygen species, angiogenesis and inflammation. The second category consists of induced innate immune response processes such as activation of macrophages and microglia by Toll-like receptor signaling, cytokine production and secretion, chemokine production and neutrophil chemotaxis, IL6 and tumor-necrosis factor production and their responses. A significant set of induced innate immune-related biological processes involve “Phagocytosis” and “Toll-like receptor signaling pathway”. The third category is mainly the components of adaptive immune response processes such as T cell activation, migration and proliferation, B cell activation and immunoglobulin-mediated immunity. Both humoral and cell-mediated elements of “adaptive immune response” processes seem to actively participate in the pathology of SCI. The pattern of change in mRNA levels for many genes in the above GO biological processes follow the expression profiles observed in profiles 44, 46, 48 and 45, which have been discussed earlier (Figure 3A-H). The continuous up-regulation of the “immunoglobulin mediated immune response” and its parent term “B cell mediated immunity” is striking and may imply that these processes should be categorized as chronic phase responses to SCI as their peak of activity appears after 2–8 weeks, although initiated at early timepoints post-injury.
Our GO analysis also resulted in enrichment of many regulatory processes, the majority of which are positive regulations of the enriched GO biological processes listed in Table 3, as illustrated for apoptotic processes shown in Figures 7I-J.
The injury model
Since its introduction in 1978 as the first SCI model in rats , the clip-compression model has become a standard injury model in animals as it mimics the human primary mechanism of injury to the spinal cord as well as the histo-pathological and behavioural outcomes of human SCI. Our lab has previously characterized this mode of SCI [42–44]. The clip compression of the spinal cord results in central cavitation and axonal loss in the white matter of spinal cord . Rats that receive the clip-compression injury have a very similar pathological progression to humans with SCI including the formation of a cystic cavity surrounded by a glial scar . In addition, animals injured by clip compression will have the same functional recovery profile as is observed in humans [20, 23, 28].
Previous studies have shown that the response of the spinal cord tissue to injury consists of a complex series of cellular responses and events. These cellular events are reflected in a more complex change in temporal and spatial pattern of molecular events at the mRNA level, which, in turn, depends on the type and severity of the primary injury and the following cascade of secondary events . Earlier reports on high throughput gene expression analysis after SCI in animals have been almost exclusively performed in contusion-based models of injury using weight drop method [36–38, 46]. As no such study on the clip-compression injury model has been reported, we aimed to investigate the rat transcriptome dynamics after a moderate to severe injury using hemorrhagic SCI by clip model, similar to most human SCIs. Additionally, the primary injury in the clip model consists of both impact and persistent compression. Therefore, we hypothesized that both similarities and differences between the two models of injury would be evident by examining how the changes in transcriptome occur. Moreover, unlike the majority of earlier studies that chiefly examined the acute and subacute events, we extended the time-frame of our study to 8 weeks post-injury to allow examination of the acute, subacute and chronic phases of the injury. The chosen time-points were based on previous behavioural and immunohistochemical analyses, which showed that following SCI by clip-compression, the first 24 hours post injury would represent a very acute stage and possible involvement of most immediate early stress genes. Days 3 and 7 represent a time during which the peak of delayed apoptotic cell death for the neural cells occurs. Days 10–14 are considered the subacute stage, as the inflammation appears to subside. Finally day 56 is considered the chronic stage as it is the time when the BBB motor recovery test for the spontaneous recovery/improvement in the rat animal model reaches a plateau.
GO enrichment analysis as a tool for biological process inference
Functional analysis of microarray data is a challenging task as the result of initial analysis is only the fold change values representing deregulations in the expression of thousands of transcripts. There are different approaches to analyzing the results of a microarray experiment in order to make efficient biological inferences. Various platforms share a common feature in that they perform an overrepresentation analysis on the list of deregulated genes and statistically analyze if the pool of up- and/or down-regulated transcripts is significantly enriched compared to the list of genes previously annotated to be part of a defined Biological Process, Molecular Function or Cellular Component, as is the case with GO enrichment, or to a certain metabolic or signaling pathway as is observed in pathway analysis platforms. Various pathway analyses are currently in practice for microarray data analysis and there are different approaches to accomplish this. KEGG pathway [47–49], Wikipathways [50–52], and Ingenuity (http://www.ingenuity.com) are amongst the currently available platforms for pathway analysis. A recent analysis showed that among the above three pathway databases, (KEGG, Ingenuity and Wikipathways) there is a low level of consistency, comprehensiveness and compatibility  and the level of consistency varies significantly when different pathways are compared. Due to these limitations, and because GO is considered to represent a relatively current, comprehensive, and, more importantly, a controlled vocabulary for gene function , we analyzed our microarray data using GO enrichment analysis. However, we are also aware of the limitations of GO enrichment analysis . For example, prior to GO enrichment analysis in this study, we determined the number of annotated genes in the list of deregulated transcripts and found that only 55% and 75% of the 14,327 genes on the Rat GeneChip 230 2.0 are annotated in the EBI and RGD association files, respectively (data not shown). The above percentages of annotated genes in Rat genome are similar to the number of annotated genes in all other organisms whose genome has been sequenced and only a subset of known genes are annotated for each of the three domains of GO tree, i.e. BP, MF and CP components .
An advantage in using a controlled vocabulary of gene function such as GO on the SCI microarray data comes from the challenging nature of such analysis due to the inherent complexity of the spinal cord tissue and also the type and level of injury itself. Spinal cord tissue is composed of an array of highly specialized neurons, astrocytes, oligodendrocytes, microglia, and pericytes. Another specialized and complex structure within the cord tissue whose permeability is highly compromised  upon injury is the blood spinal cord barrier (BSCB), which is composed of neurovascular unit (NVU), that maintains the integrity of BSCB and is again comprised of endothelial cells, neurons, astrocytes, and pericytes . Additionally, SCI is generally categorized as a severe injury that leads to loss of normal physiological functions. Thus, the development of a complex series of secondary damage  to the spinal cord after the primary injury is due both to the vast array of cell types affected as well as the injury severity that sets many processes in motion. Such an injury model demands a nonbiased and yet comprehensive coverage of annotations such as GO for clustering of deregulated genes into relevant processes and events. The reliability of this approach is shown by its successful conjecturing of previously known biological processes as well as their dynamic of contribution to the pathology of spinal cord injury as explained below.
Blood coagulation and blood protein signaling
The supply of blood and nutrients is crucial for normal functioning of neural cells. It is well-documented that an early and progressive development of hemorrhage is a common feature of all experimental models of SCI and this includes the clip-compression model [59, 60]. Shearing of the blood vessels and disruption of the vascular architecture within the lesion epicenter by mechanical force leads to hemorrhage, a progressive process which extends to the rostral but more towards the caudal regions of the grey matter [24, 61–63]. As post-traumatic ischemia develops [1, 59], further vasospasm  and loss of autoregulation of blood flow [65, 66] exacerbate the condition. Therefore, the earliest event following compression injury to the spinal cord is a profound damage to the local vasculature (capillaries and venules), hemorrhage (especially in the grey matter) and disruption of cord microcirculation by mechanical, thrombotic or vasospasm mechanisms. Consequently, the normal blood flow to the spinal cord is significantly reduced, which leads to a marked ischemia in the gray and white matter .
The results of our microarray data analysis clearly confirm the outcome of the primary impact and persistent compression injury to the spinal cord, which is disruption of the vasculature and hemorrhage as the major and initial result of the primary injury. Our data indicate that representative genes in the blood coagulation cascade are up-regulated (Figure 7A). For example, the transcript levels of the integral membrane protein tissue factor (coagulation factor III, F3), coagulation factors VIII (F13A1, F8), platelet factor (PF4) and V (F5) are up-regulated, the latter being elevated only on day 1 post-injury (data not shown). Permanent binding of tissue factor F3 to membrane surface is thought to be crucial for the speed of enzymatic reactions in coagulation processes . Additionally, we found that platelet factor (PF4) mRNA levels were increased upon injury. PF4 (CXCL4) is a chemokine released from activated platelets to bind heparin and inhibit its anticoagulant activity. ANO6 is a transmembrane protein that may have a calcium-activated chloride channel activity but it is thought to be essential for calcium-dependent exposure of phosphatidylserine on the surface of activated platelets. Importantly, ANO6 (anoctamin 6 or TMEM16F) transcript level is also elevated early after injury and is continues to be up-regulated up to 8 weeks post-injury. Higher than normal transcript levels of ANO6 during both acute and chronic phases of SCI may explain why the coagulation process is up-regulated even at 8 weeks post-injury. Regulatory proteins such as protein C, a serine protease that is activated in the blood coagulation cascade, along with its receptor (PROCR) are up-regulated as well. Activated protein C has potent anticoagulant activity due to its ability to inactivate factor Va and VIIIa (Yesilirmak et al., 2008) and seems to alleviate the secondary SCI by reducing the ischemia/reperfusion effect by inhibiting neutrophil activation (Hirose et al., 1999) and or leukocyte activation , inducing insulin growth factor-1 and its receptor leading to an increased number of motor neurons .
The GO enrichment analysis identified another 30 coagulation-related genes whose transcripts were up-regulated throughout the course of the study. Amongst these were regulatory proteins with anticoagulant properties such as tissue factor pathway inhibitor 2 (TFPI), which is released by endothelial cells and binds factor VIIa complexes, inhibiting them to generate factor Xa. TFPI function regulates the extrinsic coagulation pathway. Additionally, we found that thrombomodulin (THBD) transcripts were elevated upon SCI up to 2 weeks post-injury. THBD binds thrombin and promotes its interaction with protein C. The resulting complex inactivates factors VIIIa and Va. Elevated levels of these regulatory proteins indicate the importance of endogenous signaling mechanisms to limit excessive spreading of clot formation.
A serious side effect of hemorrhage is the infiltration of blood components such as hemoglobin and fibrinogen to the spinal cord tissue which have been shown to be toxic to CNS tissue [70–73]. Infiltration of hemoglobin creates a hostile environment that is rich in reactive oxygen species and other toxic materials, which induces the cellular response to these toxic mediators of cell death and apoptosis. Hemoglobin, released from red cells after trauma, can promote tissue injury through iron-dependent mechanisms such as inhibiting the Na/K ATPase activity and catalyzing substantial peroxidation of CNS lipids . In our study, the majority of Na/K ATPase enzymes such as ATP1A2, ATP1A3, ATP1B1 and ATP1B2 were down-regulated during the acute as well as the subacute phase of the injury (data not shown). Fibrinogen has been shown to trigger an inhibitory signal transduction pathway in neurons by acting as a ligand for beta-3 integrin, which induces the transactivation of EGF receptor (EGFR) in neurons, thereby inhibiting neurite outgrowth . It also triggers astrocyte scar formation through TGF-beta signaling . The microarray data in our study confirms that genes in the TGF-beta signaling cascade are up-regulated. For example, TGFB1, its receptor and SMAD2 transcripts were up-regulated throughout the 8 weeks post-injury study period (data not shown).
Along with the blood coagulation cascade, a concomitant increase in the complement activation system is observed, whose temporal pattern is not the same as blood coagulation but rather develops in a more delayed fashion. The blood coagulation cascade peak of activity is on day 7 post-injury but stays up-regulated until 8 weeks. The complement activation, however, is turned on with a lag time in the first few days with activity increasing at later time points in the experiment (Figure 7C). Whether the late activation of complement system is due to the effect of reperfusion after ischemia needs further investigation. The complement system can be activated by three different but overlapping classical, lectin and alternative routes .
Representative genes in the complement activation system were deregulated following clip injury to spinal cord. For example, the transcript level of the main activator of the classical pathway of complement activation (C1S) is down-regulated one day after injury. However, it returns to normal values by day 3 and is further up-regulated by day 7 remaining at higher than normal levels even at day 56 post-injury (data not shown). C1S catalyzes the consecutive conversion of C4 to C4a and C4a to active C4b2a (C3 convertase), whose main function is to cleave parental C3 into C3a and C3b. As shown the mRNA levels of C1qa, C1qb, C1qc, Cfd and Cr1l are increased relative to sham un-injured animals. The transcript level of Factor H (CFH), a negative regulator of the alternative pathway for complement activation, is decreased after injury but fluctuates back to higher than normal levels by day 7 post-injury. The elevated level of CFH in our study is in agreement with previous reports that complement inhibitor proteins such as factor H were expressed at elevated levels on neurons and oligodendrocytes after SCI in rats [75, 76].
Using inhibitor approaches, both classical and lectin pathways of complement activation have been shown to participate in SCI pathology [77–79]. C1q Knockout mice showed improved recovery and thus the classical complement activation via C1q is thought to be detrimental to the injured spinal cord . Our data show that the mRNA level of C1 inhibitor (C1-INH, SERPING1), an inhibitor of the lectin pathway, is also increased in a similar profile as observed in CFH mRNA deregulation. C1-INH inhibits complement activation through binding and inactivating MASP1 and MASP2 . Up-regulation of C1-INH has been shown to be protective and independent of C1q and the classical pathway .
Ischemia, response to hypoxia and reactive oxygen species
The decrease in the local blood-flow leads to ischemic-hypoxic damage to the spinal cord tissue. Ischemia generally leads to a decrease in cytoplasmic levels of ATP, cellular swelling through malfunctioning of Na/K ATPases and also the mitochondrial membrane permeability transition . Additionally, hypoxia induces certain transcription factors such as hypoxia inducible factor 1 (Hif-1) heterodimer which is composed of the inducible Hif-1a and the constitutive Hif-1b subunits [83, 84]. The induction of Hif-1a is under the control of NF-kB transcription factor which serves to link hypoxia to innate immune response . This is reflected in an increase in the mRNA level for the genes that function in response to hypoxia. We found that, following clip-compression injury to the spinal cord, the transcript levels of Hif-1a were up-regulated. Negative control of Hif-1 transcriptional activity is under the control of EGLN3, a propyl hydroxylase that, in the presence of oxygen molecule, permits ubiquitination and proteosomal degradation of Hif-1a monomer and Hif1an, which blocks Hif-1 transcriptional activity by preventing Hif-1 association with p300 [83–88]. In this study we found that the transcript level of EGLN3 is decreased upon injury to the spinal cord. EGLN3 acts as the cellular oxygen sensor and is the most important enzyme in promoting Hif-1a degradation. This may explain why its down-regulation causes a positive regulation of the response to hypoxia. EGLN3 has other functions such as NGF-induced proapoptotic effect in neurons, probably through regulating CASP3 activity .
Hif-1a induction and activation under hypoxic condition induces NF-kB and its inhibitor at the same time [83, 86]. In this study, we found that NF-kB related transcripts were all up-regulated. For example, the transcript levels of NFKB2 and of the inhibitors NFKBIA, NFKBIE, NFKBIZ are all up-regulated during the first week after injury (data not shown). Another complication of disruption of blood supply is the phenomenon of ischemia/reperfusion injury causing necrotic injury to oligodendrocytes, neurons, astrocytes, and endothelial cells in the epicenter [90, 91]. This involves many events such as hypoxia, reactive oxygen species (ROS) and lipid peroxidation, cytokines, complement activation, and pro- and anti-apoptotic signaling cascades [91–93]. The ischemia/reperfusion injury is mainly under the regulation of the NF-kB signaling cascade and NF-kB transcription and its signaling cascade are, in turn, responsible for positive regulation of many immune-related responses, anti-apoptotic and equally important but opposing and controversial pro-apoptotic pathways .
Induced innate-immune response and Toll-like receptor signaling: a biphasic process
The inflammatory response to injury is initiated within minutes after SCI . Our enrichment analysis scored inflammation as the most significant process starting immediately after injury and transcription activation of many immune-related genes. Many cytokines and chemokines are produced and secreted by various cells in the spinal cord tissue. It has been shown that IL-1B is produced immediately by astrocytes and neurons [95, 96]. Similar to other studies, our data indicates an up-regulation of IL-1B and TNF-alpha after injury. Most notably, we observed that the inflammatory response, in general, and specifically the cytokines’ expression pattern follow profile 44 (Figure 3F). Profile 44 represents the change in transcript levels of many genes with the first wave of up-regulation on day 1. The early up-regulation then disappears on day 3 and comes back to high levels for many transcripts from day 7 onwards. Such a phenomenon has been reported previously in mice with a contusion injury , although the cessation of primary up-regulation occurred after 24 hours and returned to an increased state on day 14 post-injury. In line with this observation, a biphasic model of cellular inflammatory response has been shown when various immune cells were analyzed using flow cytometry after SCI . Various categories of processes are depicted in Figure 7A-P, which confirm such studies. Accordingly, we can extrapolate our findings and assume the same mechanism of expression or secretion for transcripts with the same profile of expression. This biphasic mode of expression was observed in other enriched GO biological processes such as activation of innate immune response, response to lipopolysaccharide, response to interferon-gamma, tumor necrosis factor superfamily, cytokine production, interleukin-6 production, interleukin-8 production, cytokine secretion, neutrophil chemotaxis, endocytosis and phagocytosis Toll-like receptor signaling pathways, integrin-mediated signaling pathway, T and B cell activation, and immunoglobulin-mediated immune response. The simplest explanation for this observed biphasic response is that the first wave of transcription activation of these genes originates primarily from neurons, astrocytes and microglia cells within the injured area of spinal cord, which subside by day 3. By day 7, post-injury immune cells such as neutrophils, macrophages, T and B lymphocytes have infiltrated the injured cord and amplify the production and secretion of related cytokines and chemokines as the secondary response tends to be at a higher magnitude.
The synthesis of IL-1B in neurons was shown to be dependent on NALP1 inflammasome . In astrocytes, however, overexpression of inflammatory cytokines such as CCL2, CCL3, CXCL1 and CXCL2 is triggered by the IL-1 receptor and not the Toll-like receptor signaling proteins TLR2 and TLR4. Our data indicate that the mRNA levels of IL-1B, IL-1R2 and its accessory protein IL-1RAP, were up-regulated especially on day 3. Central proteins in the Toll-like receptor signaling such as TLR2, TLR4 and MYD88 were all up-regulated, the expression pattern of which follow profile 44. It has been shown that for neutrophils to enter the damaged zone in the spinal cord, the expression of IL-1R and MYD88 are essential . Additionally, the cellular extravasation of neutrophils and other leukocytes into the injured area of spinal cord also requires up-regulation of matrix metalloproteinases (MMPs) [99, 100]. MMPs up-regulations are, in turn, dependent on the interaction of Fas and its ligand and on the peripheral myeloid cells and activation of Syk kinase to trigger recruitment to the injury sites [101, 102]. In our injury model, we observed an increase in the mRNA levels of MMP2, MMP9 and MMP12. We did not observe an increase in transcript levels of Fas or its ligand, but the Sky mRNA was up-regulated on day 1 and afterwards up to 8 weeks post-injury.
Toll-like receptor signaling is initiated after pattern recognition receptors (PRRs) detect pathogen-associated molecular patterns (PAMPs) or danger-associated molecular patterns (DAMPs), which are endogenously generated from tissue and cellular damage. It is now thought that for induction of innate immune response, two signals are required, the first from Toll-like receptors (TLRs) and the second from Nod-like receptors (NLRs). NLRs are responsible for processing of pro-interleukin-1B to IL-1B and pro-IL-18 to IL-18 . Following injury to the spinal cord, processing of pro-IL-1B and pro-IL-18 into the mature form requires NALP1, ASC (PYCARD), CASP11, and finally CASP1 action to cleave the pro- forms [96, 98, 104]. Activation by endogenous signals in response to SCI seems to be the mechanism of activation of inflammation after SCI. We observed the up-regulation of the NOD1 component early after SCI. We also found that, after clip-injury to the spinal cord, PYCARD and CASP1 transcripts are highly up-regulated until 8 weeks post-injury as well as IL-1B and IL-18 transcripts. In addition, the expression of purinergic receptor P2X, ligand-gated ion channel 4 (P2RX4), which has been shown to regulate the inflammasome activation after spinal cord injury  was persistently increased in our injury model.
Adaptive immune response and antibody production
Both IL-1 and IL-18, produced during the first phase of inflammation mediated through the two-signal model of TLRs and NLRs, can induce the cellular and humoral modes of the adaptive immune response. IL-18 affects natural killer (NK) cells, monocytes, dendritic cells, T cells, and B cells, thereby regulating not only the innate, but also the adaptive immune responses . Administration of IL-18 promotes production of interferon-gamma by natural killer (NK) as well as T cells. In our study we observe a late interferon-gamma response, which could be part of the second wave of cytokine production by T cells. T cell migration and activation precede the response to interferon-gamma, but other developing adaptive immune responses such as immunoglobulin-mediated immune response run in parallel to the response to interferon-gamma, which may explain the timing of the two processes (Figure 7).
It has been shown that autoantibodies are generated and detected in patients with chronic SCI [107, 108]. These detected antibodies can recognize a variety of related and unrelated antigens to CNS tissue. Mice defective in production of B cells, and thus antibody production, exhibit reduced pathological symptoms and improved locomotor recovery . The activation of B cells has been shown to be level dependent as T3 injury completely abolished B cell response and T9 injury level induces significant B cell activation and antibody production . It has been postulated that delayed antibody production and accumulation of autoantibodies leads to complement activation through C1q, which triggers the enzymatic cascade of the classic complement activation pathway and recruitment of microglia and macrophages to the site of injury . Our data clearly show that a delayed adaptive immune response is initiated through immunoglobulin-mediated signaling and that this response is consistently and increasingly up-regulated towards the chronic phase in parallel to activation of the complement cascade (Figure 7C and 7N-P). However, the initial events such as T cell migration, T cell and B cell activation and proliferation starts very early after the injury (Figure 7N-P). As shown, the B cell-receptor signaling pathway seems to be a much more significant process than T cell receptor signaling (Figure 7N) which implies that, compared to the cellular T-cell mediated immune response, B cell-mediated immunity and neutralizing antibody production is the dominant immune response during the chronic phase of the injury to the spinal cord (Figure 7).
Microarray expression profiling was used to investigate the temporal changes in the transcriptome of the injured spinal cord in rats. Using GO enrichment analysis we show that it is possible to analyze the fold change in the expression of thousands of genes and obtain the overall picture of the processes involved. Thorough analysis of the expression profiles detected, significant biological processes and events such as response to hypoxia and reactive oxygen species were identified as early events after the injury. We found that both induced innate and adaptive immune responses are strongly and significantly up-regulated, each with relevant sub-categories and deregulated genes. The induced innate immune response may be classified as an acute to subacute type of response, whereas the adaptive immune response and antibody production can be categorized as a late response. The biphasic expression pattern identified in many genes related to immune-response implies that both resident spinal cord cell types as well as infiltrating blood cells may participate in cytokine and chemokine production and general inflammatory response. Our approach in analyzing the fold change in the mRNA levels of many deregulated genes using microarray technology indicates that with careful and systematic analysis of the data, it is possible to reliably delineate the processes involved in injury and recovery and to establish hypotheses for further analysis and intervention strategies.
Animal care and thoracic spinal cord injury
All experimental protocols were approved by the animal care committee of the University Health Network in accordance with the policies established in the guide to the care and use of experimental animals prepared by the Canadian Council of Animal Care. Female Wistar rats (250 g; Charles River Laboratories, 4 sham and 4 injured animals for each time point) were used for this study. Injuries by the aneurysm clip method were performed as previously described [20, 29, 45, 112]. Briefly, under halothane anesthesia (1-2%) and a 1:1 mixture of O2/N2O, the surgical area was shaved and disinfected with 70% ethanol and betadine. A midline incision was made at the thoracic area (T4-T9), and skin and superficial muscles were retracted. Rats underwent a T6-T8 laminectomy and then received a 35 g clip (Walsh) moderate to severe compression injury at T7 for 1 min. The surgical wounds were sutured, and the animals were given Clavamox (Amoxicillin plus Clavulanic acid) for 7d and standard postoperative analgesia treatments and saline (0.9%; 5 ml) to prevent dehydration. Animals were allowed to recover and remained housed under standard condition for the duration of the experiment.
RNA isolation, processing and microarray hybridization
Rats were sacrificed at 1, 3, 7, 14 and 56 days after injury, and a 5 mm sample of the spinal cord containing the epicenter of the injured tissue was extracted for RNA analysis. Total RNA from each individual sample was extracted using TRIzol reagent (Invitrogen, Burlington, ON, Canada). RNeasy mini spin columns (QIAGEN, Mississauga, ON, Canada) were used for purification of total RNA molecules larger than 200 bp, which excludes smaller RNAs such as miRNAs. RNA quality was assessed with a 2100 Bioanalyzer (Agilent). cRNA for microarray hybridization was prepared from 5 ug of starting RNA using the protocol supplied by Affymetrix (Santa Clara, CA). cRNA was hybridized to GeneChip Rat Genome 230 2.0 arrays (24 chips total) at the Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada). Primary data sets were saved in a MIAME-compliant format and uploaded to GEO (series GSE45006).
Microarray data analysis
Data analysis was performed in R with the Affy package (v1.12.2)  in BioConductor . Data were investigated for spatial and distributional homogeneity. Normalization was performed with the sequence-specific GCRMA algorithm (package v2.6.0) in BioConductor . Significance testing of this dataset was performed using linear models and pair-wise comparisons . Each set of animals from a given time point was analyzed and pre-processed separately. The pre-processed data were then significance-tested using a linear modelling implemented in the limma package (v2.9.10) of BioConductor. Each sub-group was fitted to a separate factor in the design matrix, and the pair-wise contrast corresponding to differential expression of injured animals relative to control (sham) animals was extracted using a contrast matrix. Empirical Bayes moderation of the standard error  and false-discovery rate correction for multiple testing  were employed, again as implemented in the limma package. ProbeSets were deemed differentially expressed at p < 0.001 in any given comparison. Significantly different ProbeSets were visualized using the Heatplus package (v1.4.0) of Bioconductor. Euclidean distance was used as the distance metric for unsupervised hierarchical clustering using the DIANA algorithm with the cluster package (v1.11.4) in R (v2.4.1), and scaling was performed across rows. Clustering was used as a tool for replicate visualization and contrast comparison, not for gene selection .
The resulting gene set data with fold change and associated ANOVA t test p-values were analyzed by Short Time-Series Expression Miner (STEM) (discussed below), which allows the temporal expression patterns to be examined and extracted from the pool of up- and down-regulated transcripts across all time-points. Alternatively, individual time-point data were analyzed separately for up- and down-regulated genes, protein classes and signaling pathways. Both approaches were combined with functional analysis of transcripts using gene ontology (GO) enrichment.
Time-series expression profile clustering
We used the non-parametric clustering algorithm of STEM (Short Time-series Expression Miner, version 1.3.7) that is specifically designed to analyze short time-series expression data . STEM implements a novel clustering method that can differentiate between real and random patterns and clusters genes by assigning them to a series of pre-defined patterns, named expression profiles. A profile is considered significant if the number of genes assigned to it exceeds the number of genes that are expected to occur by chance. The statistical significance of the number of genes assigned to each profile versus the expected number was computed and corrected for false discovery rate at p ≤ 0.05.
GO enrichment analysis
STEM is a statistical technique based on unsupervised clustering to find cluster-centroids followed by assignment of genes using distance-classifications, with statistical analysis using enrichment-based techniques. The biological significance of a set of genes can be assessed by GO enrichment analysis. Deregulated transcripts with ANOVA t test p-values ≤0.05 and fold change values > 1.5 were analyzed by the GO enrichment analysis module of STEM. Temporal analysis of the list of deregulated genes was performed using both time-series and time-point approaches. Due to more comprehensive gene coverage of RGD annotation data source file, the enrichment analysis was performed with reference to the RGD association file. For GO analysis of various expression profiles, we applied the annotations of “Biological Process” (BP) domain and the minimum expression fold change (in log2 scale) was set to different values from zero. Other parameters were set to different values as follows: “minimum GO level” to different values from 3 to 20, “minimum number of genes” to 5, and “multiple hypothesis correction method for actual size based enrichment” to Bonferroni. STEM also offers to run the GO enrichment analysis at different GO tree levels, which allows limiting the results to more specific terms in the directed acyclic graph (DAG) structure of the gene ontology hierarchy. In this study, the time-point GO enrichment analysis was also employed to discover common up- and down-regulated biological processes across the time-points as well as possible unique processes to each time-point. The output GO terms were used for inter-relationship analysis and visualization by Venn diagram tool and or visualized as a scatter plot or interactive graph using REViGO .
We would like to acknowledge the funding support for this work from the Gerald and Tootsie Halbert Chair in Neural Repair and Regeneration, and Phillip and Peggy DeZwirek.
- Park E, Velumian AA, Fehlings MG: The role of excitotoxicity in secondary mechanisms of spinal cord injury: a review with an emphasis on the implications for white matter degeneration. J Neurotrauma. 2004, 21 (6): 754-774. 10.1089/0897715041269641.PubMedGoogle Scholar
- Onifer SM, Rabchevsky AG, Scheff SW: Rat models of traumatic spinal cord injury to assess motor recovery. Ilar J. 2007, 48 (4): 385-395. 10.1093/ilar.48.4.385.PubMedGoogle Scholar
- Watson BD, Prado R, Dietrich WD, Ginsberg MD, Green BA: Photochemically induced spinal cord injury in the rat. Brain Res. 1986, 367 (1–2): 296-300.PubMedGoogle Scholar
- Bunge MB, Holets VR, Bates ML, Clarke TS, Watson BD: Characterization of photochemically induced spinal cord injury in the rat by light and electron microscopy. Exp Neurol. 1994, 127 (1): 76-93. 10.1006/exnr.1994.1082.PubMedGoogle Scholar
- Verdu E, Garcia-Alias G, Fores J, Vela JM, Cuadras J, Lopez-Vales R, Navarro X: Morphological characterization of photochemical graded spinal cord injury in the rat. J Neurotrauma. 2003, 20 (5): 483-499. 10.1089/089771503765355559.PubMedGoogle Scholar
- Fehlings MG, Wilson JR: Spine trauma: the challenges in assessing outcomes. J Neurosurg Spine. 2010, 13 (5): 636-637. 10.3171/2010.4.SPINE10243. discussion 637PubMedGoogle Scholar
- Kliot M, Lustgarten JH: Strategies to promote regeneration and recovery in the injured spinal cord. Neurosurg Clin N Am. 1990, 1 (3): 751-759.PubMedGoogle Scholar
- Smith GM, Falone AE, Frank E: Sensory axon regeneration: rebuilding functional connections in the spinal cord. Trends Neurosci. 2012, 35 (3): 156-163. 10.1016/j.tins.2011.10.006.PubMed CentralPubMedGoogle Scholar
- Thompson FJ, Parmer R, Reier PJ, Wang DC, Bose P: Scientific basis of spasticity: insights from a laboratory model. J Child Neurol. 2001, 16 (1): 2-9. 10.1177/088307380101600102.PubMedGoogle Scholar
- Boulenguez P, Liabeuf S, Bos R, Bras H, Jean-Xavier C, Brocard C, Stil A, Darbon P, Cattaert D, Delpire E: Down-regulation of the potassium-chloride cotransporter KCC2 contributes to spasticity after spinal cord injury. Nat Med. 2010, 16 (3): 302-307. 10.1038/nm.2107.PubMedGoogle Scholar
- Hains BC, Waxman SG: Activated microglia contribute to the maintenance of chronic pain after spinal cord injury. J Neurosci. 2006, 26 (16): 4308-4317. 10.1523/JNEUROSCI.0003-06.2006.PubMedGoogle Scholar
- Bedi SS, Lago MT, Masha LI, Crook RJ, Grill RJ, Walters ET: Spinal cord injury triggers an intrinsic growth-promoting state in nociceptors. J Neurotrauma. 2012, 29 (5): 925-935. 10.1089/neu.2011.2007.PubMed CentralPubMedGoogle Scholar
- Popovich P, McTigue D: Damage control in the nervous system: beware the immune system in spinal cord injury. Nat Med. 2009, 15 (7): 736-737. 10.1038/nm0709-736.PubMedGoogle Scholar
- Noyes DH: Electromechanical impactor for producing experimental spinal cord injury in animals. Med Biol Eng Comput. 1987, 25 (3): 335-340. 10.1007/BF02447434.PubMedGoogle Scholar
- Behrmann DL, Bresnahan JC, Beattie MS, Shah BR: Spinal cord injury produced by consistent mechanical displacement of the cord in rats: behavioral and histologic analysis. J Neurotrauma. 1992, 9 (3): 197-217. 10.1089/neu.1992.9.197.PubMedGoogle Scholar
- Bresnahan JC, Behrmann DL, Beattie MS: Anatomical and behavioral outcome after spinal cord contusion injury produced by a displacement controlled impact device. Restor Neurol Neurosci. 1993, 5 (1): 76-PubMedGoogle Scholar
- Gruner JA, Yee AK, Blight AR: Histological and functional evaluation of experimental spinal cord injury: evidence of a stepwise response to graded compression. Brain Res. 1996, 729 (1): 90-101. 10.1016/0006-8993(96)00366-6.PubMedGoogle Scholar
- Scheff SW, Rabchevsky AG, Fugaccia I, Main JA, Lumpp JE: Experimental modeling of spinal cord injury: characterization of a force-defined injury device. J Neurotrauma. 2003, 20 (2): 179-193. 10.1089/08977150360547099.PubMedGoogle Scholar
- Cao Q, Zhang YP, Iannotti C, DeVries WH, Xu XM, Shields CB, Whittemore SR: Functional and electrophysiological changes after graded traumatic spinal cord injury in adult rat. Exp Neurol. 2005, 191 (Suppl 1): S3-S16.PubMedGoogle Scholar
- Rivlin AS, Tator CH: Effect of duration of acute spinal cord compression in a new acute cord injury model in the rat. Surg Neurol. 1978, 10 (1): 38-43.PubMedGoogle Scholar
- Fehlings MG, Nashmi R: A new model of acute compressive spinal cord injury in vitro. J Neurosci Methods. 1997, 71 (2): 215-224. 10.1016/S0165-0270(96)00147-1.PubMedGoogle Scholar
- Nashmi R, Jones OT, Fehlings MG: Abnormal axonal physiology is associated with altered expression and distribution of Kv1.1 and Kv1.2 K + channels after chronic spinal cord injury. Eur J Neurosci. 2000, 12 (2): 491-506. 10.1046/j.1460-9568.2000.00926.x.PubMedGoogle Scholar
- Nashmi R, Fehlings MG: Changes in axonal physiology and morphology after chronic compressive injury of the rat thoracic spinal cord. Neuroscience. 2001, 104 (1): 235-251. 10.1016/S0306-4522(01)00009-4.PubMedGoogle Scholar
- Mautes AE, Weinzierl MR, Donovan F, Noble LJ: Vascular events after spinal cord injury: contribution to secondary pathogenesis. Phys Ther. 2000, 80 (7): 673-687.PubMedGoogle Scholar
- Leal-Filho MB: Spinal cord injury: From inflammation to glial scar. Surg Neurol Int. 2011, 2: 112-10.4103/2152-7806.83732.PubMed CentralPubMedGoogle Scholar
- Basso DM, Beattie MS, Bresnahan JC, Anderson DK, Faden AI, Gruner JA, Holford TR, Hsu CY, Noble LJ, Nockels R: MASCIS evaluation of open field locomotor scores: effects of experience and teamwork on reliability. Multicenter Animal Spinal Cord Injury Study. J Neurotrauma. 1996, 13 (7): 343-359. 10.1089/neu.1996.13.343.PubMedGoogle Scholar
- Broton JG, Nikolic Z, Suys S, Calancie B: Kinematic analysis of limb position during quadrupedal locomotion in rats. J Neurotrauma. 1996, 13 (7): 409-416. 10.1089/neu.1996.13.409.PubMedGoogle Scholar
- Nashmi R, Imamura H, Tator CH, Fehlings MG: Serial recording of somatosensory and myoelectric motor evoked potentials: role in assessing functional recovery after graded spinal cord injury in the rat. J Neurotrauma. 1997, 14 (3): 151-159. 10.1089/neu.1997.14.151.PubMedGoogle Scholar
- Karimi-Abdolrezaee S, Eftekharpour E, Fehlings MG: Temporal and spatial patterns of Kv1.1 and Kv1.2 protein and gene expression in spinal cord white matter after acute and chronic spinal cord injury in rats: implications for axonal pathophysiology after neurotrauma. Eur J Neurosci. 2004, 19 (3): 577-589. 10.1111/j.0953-816X.2004.03164.x.PubMedGoogle Scholar
- Karimi-Abdolrezaee S, Eftekharpour E, Wang J, Morshead CM, Fehlings MG: Delayed transplantation of adult neural precursor cells promotes remyelination and functional neurological recovery after spinal cord injury. J Neurosci. 2006, 26 (13): 3377-3389. 10.1523/JNEUROSCI.4184-05.2006.PubMedGoogle Scholar
- Park E, Liu Y, Fehlings MG: Changes in glial cell white matter AMPA receptor expression after spinal cord injury and relationship to apoptotic cell death. Exp Neurol. 2003, 182 (1): 35-48. 10.1016/S0014-4886(03)00084-0.PubMedGoogle Scholar
- Alluin O, Karimi-Abdolrezaee S, Delivet-Mongrain H, Leblond H, Fehlings MG, Rossignol S: Kinematic study of locomotor recovery after spinal cord clip compression injury in rats. J Neurotrauma. 2011, 28 (9): 1963-1981. 10.1089/neu.2011.1840.PubMedGoogle Scholar
- Karimi-Abdolrezaee S, Eftekharpour E, Wang J, Schut D, Fehlings MG: Synergistic effects of transplanted adult neural stem/progenitor cells, chondroitinase, and growth factors promote functional repair and plasticity of the chronically injured spinal cord. J Neurosci. 2010, 30 (5): 1657-1676. 10.1523/JNEUROSCI.3111-09.2010.PubMedGoogle Scholar
- Eftekharpour E, Karimi-Abdolrezaee S, Wang J, El-Beheiry H, Morshead C, Fehlings MG: Myelination of congenitally dysmyelinated spinal cord axons by adult neural precursor cells results in formation of nodes of Ranvier and improved axonal conduction. J Neurosci. 2007, 27 (13): 3416-3428. 10.1523/JNEUROSCI.0273-07.2007.PubMedGoogle Scholar
- Chua SJ, Bielecki R, Yamanaka N, Fehlings MG, Rogers IM, Casper RF: The effect of umbilical cord blood cells on outcomes after experimental traumatic spinal cord injury. Spine (Phila Pa 1976). 2010, 35 (16): 1520-1526. 10.1097/BRS.0b013e3181c3e963.Google Scholar
- Aimone JB, Leasure JL, Perreau VM, Thallmair M: Spatial and temporal gene expression profiling of the contused rat spinal cord. Exp Neurol. 2004, 189 (2): 204-221. 10.1016/j.expneurol.2004.05.042.PubMedGoogle Scholar
- Di-Giovanni S, Knoblach SM, Brandoli C, Aden SA, Hoffman EP, Faden AI: Gene profiling in spinal cord injury shows role of cell cycle in neuronal death. Ann Neurol. 2003, 53 (4): 454-468. 10.1002/ana.10472.PubMedGoogle Scholar
- De-Biase A, Knoblach SM, Di-Giovanni S, Fan C, Molon A, Hoffman EP, Faden AI: Gene expression profiling of experimental traumatic spinal cord injury as a function of distance from impact site and injury severity. Physiol Genomics. 2005, 22 (3): 368-381. 10.1152/physiolgenomics.00081.2005.PubMedGoogle Scholar
- Rhee SY, Wood V, Dolinski K, Draghici S: Use and misuse of the gene ontology annotations. Nat Rev Genet. 2008, 9 (7): 509-515. 10.1038/nrg2363.PubMedGoogle Scholar
- Supek F, Bosnjak M, Skunca N, Smuc T: REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011, 6 (7): e21800-10.1371/journal.pone.0021800.PubMed CentralPubMedGoogle Scholar
- Barrell D, Dimmer E, Huntley RP, Binns D, O’Donovan C, Apweiler R: The GOA database in 2009--an integrated Gene Ontology Annotation resource. Nucleic Acids Res. 2009, 37 (Database issue): D396-403.PubMed CentralPubMedGoogle Scholar
- Joshi M, Fehlings MG: Development and characterization of a novel, graded model of clip compressive spinal cord injury in the mouse: Part 2. Quantitative neuroanatomical assessment and analysis of the relationships between axonal tracts, residual tissue, and locomotor recovery. J Neurotrauma. 2002, 19 (2): 191-203. 10.1089/08977150252806956.PubMedGoogle Scholar
- Joshi M, Fehlings MG: Development and characterization of a novel, graded model of clip compressive spinal cord injury in the mouse: Part 1. Clip design, behavioral outcomes, and histopathology. J Neurotrauma. 2002, 19 (2): 175-190. 10.1089/08977150252806947.PubMedGoogle Scholar
- Poon PC, Gupta D, Shoichet MS, Tator CH: Clip compression model is useful for thoracic spinal cord injuries: histologic and functional correlates. Spine (Phila Pa 1976). 2007, 32 (25): 2853-2859. 10.1097/BRS.0b013e31815b7e6b.Google Scholar
- Fehlings MG, Tator CH: The relationships among the severity of spinal cord injury, residual neurological function, axon counts, and counts of retrogradely labeled neurons after experimental spinal cord injury. Exp Neurol. 1995, 132 (2): 220-228. 10.1016/0014-4886(95)90027-6.PubMedGoogle Scholar
- Di-Giovanni S, Faden AI, Yakovlev A, Duke-Cohan JS, Finn T, Thouin M, Knoblach S, De-Biase A, Bregman BS, Hoffman EP: Neuronal plasticity after spinal cord injury: identification of a gene cluster driving neurite outgrowth. Faseb J. 2005, 19 (1): 153-154.PubMedGoogle Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27 (1): 29-34. 10.1093/nar/27.1.29.PubMed CentralPubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.PubMed CentralPubMedGoogle Scholar
- Zhang B, Kirov S, Snoddy J: WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005, 33 (Web Server issue): W741-748.PubMed CentralPubMedGoogle Scholar
- Pico AR, Kelder T, van-Iersel MP, Hanspers K, Conklin BR, Evelo C: WikiPathways: pathway editing for the people. PLoS Biol. 2008, 6 (7): e184-10.1371/journal.pbio.0060184.PubMed CentralPubMedGoogle Scholar
- Kelder T, Pico AR, Hanspers K, van-Iersel MP, Evelo C, Conklin BR: Mining biological pathways using WikiPathways web services. PLoS One. 2009, 4 (7): e6447-10.1371/journal.pone.0006447.PubMed CentralPubMedGoogle Scholar
- Kelder T, van-Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, Pico AR: WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012, 40 (Database issue): D1301-1307.PubMed CentralPubMedGoogle Scholar
- Soh D, Dong D, Guo Y, Wong L: Consistency, comprehensiveness, and compatibility of pathway databases. BMC Bioinforma. 2010, 11: 449-10.1186/1471-2105-11-449.Google Scholar
- Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004, 32 (Database issue): D258-261.PubMedGoogle Scholar
- Khatri P, Done B, Rao A, Done A, Draghici S: A semantic analysis of the annotations of the human genome. Bioinformatics. 2005, 21 (16): 3416-3421. 10.1093/bioinformatics/bti538.PubMed CentralPubMedGoogle Scholar
- King OD, Foulger RE, Dwight SS, White JV, Roth FP: Predicting gene function from patterns of annotation. Genome Res. 2003, 13 (5): 896-904. 10.1101/gr.440803.PubMed CentralPubMedGoogle Scholar
- Whetstone WD, Hsu JY, Eisenberg M, Werb Z, Noble-Haeusslein LJ: Blood-spinal cord barrier after spinal cord injury: relation to revascularization and wound healing. J Neurosci Res. 2003, 74 (2): 227-239. 10.1002/jnr.10759.PubMed CentralPubMedGoogle Scholar
- del-Zoppo GJ: Inflammation and the neurovascular unit in the setting of focal cerebral ischemia. Neuroscience. 2009, 158 (3): 972-982. 10.1016/j.neuroscience.2008.08.028.PubMed CentralPubMedGoogle Scholar
- Tator CH, Fehlings MG: Review of the secondary injury theory of acute spinal cord trauma with emphasis on vascular mechanisms. J Neurosurg. 1991, 75 (1): 15-26. 10.3171/jns.1991.75.1.0015.PubMedGoogle Scholar
- Tator CH: Update on the pathophysiology and pathology of acute spinal cord injury. Brain Pathol. 1995, 5 (4): 407-413. 10.1111/j.1750-3639.1995.tb00619.x.PubMedGoogle Scholar
- Tator CH: Review of experimental spinal cord injury with emphasis on the local and systemic circulatory effects. Neurochirurgie. 1991, 37 (5): 291-302.PubMedGoogle Scholar
- Noble LJ, Wrathall JR: Distribution and time course of protein extravasation in the rat spinal cord after contusive injury. Brain Res. 1989, 482 (1): 57-66. 10.1016/0006-8993(89)90542-8.PubMedGoogle Scholar
- Beggs JL, Waggener JD: The acute microvascular responses to spinal cord injury. Adv Neurol. 1979, 22: 179-189.PubMedGoogle Scholar
- Anthes DL, Theriault E, Tator CH: Ultrastructural evidence for arteriolar vasospasm after spinal cord trauma. Neurosurgery. 1996, 39 (4): 804-814. 10.1097/00006123-199610000-00032.PubMedGoogle Scholar
- Smith AJ, McCreery DB, Bloedel JR, Chou SN: Hyperemia, CO2 responsiveness, and autoregulation in the white matter following experimental spinal cord injury. J Neurosurg. 1978, 48 (2): 239-251. 10.3171/jns.1978.48.2.0239.PubMedGoogle Scholar
- Senter HJ, Venes JL: Loss of autoregulation and posttraumatic ischemia following experimental spinal cord trauma. J Neurosurg. 1979, 50 (2): 198-206. 10.3171/jns.1979.50.2.0198.PubMedGoogle Scholar
- Smith SA: The cell-based model of coagulation. J Vet Emerg Crit Care (San Antonio). 2009, 19 (1): 3-10. 10.1111/j.1476-4431.2009.00389.x.Google Scholar
- Mizutani A, Okajima K, Uchiba M, Noguchi T: Activated protein C reduces ischemia/reperfusion-induced renal injury in rats by inhibiting leukocyte activation. Blood. 2000, 95 (12): 3781-3787.PubMedGoogle Scholar
- Yamauchi T, Sakurai M, Abe K, Takano H, Sawa Y: Neuroprotective effects of activated protein C through induction of insulin-like growth factor-1 (IGF-1), IGF-1 receptor, and its downstream signal phosphorylated serine-threonine kinase after spinal cord ischemia in rabbits. Stroke. 2006, 37 (4): 1081-1086. 10.1161/01.STR.0000206280.30972.21.PubMedGoogle Scholar
- Vercellotti GM, Balla G, Balla J, Nath K, Eaton JW, Jacob HS: Heme and the vasculature: an oxidative hazard that induces antioxidant defenses in the endothelium. Artif Cells Blood Substit Immobil Biotechnol. 1994, 22 (2): 207-213. 10.3109/10731199409117415.PubMedGoogle Scholar
- Sadrzadeh SM, Anderson DK, Panter SS, Hallaway PE, Eaton JW: Hemoglobin potentiates central nervous system damage. J Clin Invest. 1987, 79 (2): 662-664. 10.1172/JCI112865.PubMed CentralPubMedGoogle Scholar
- Schachtrup C, Ryu JK, Helmrick MJ, Vagena E, Galanakis DK, Degen JL, Margolis RU, Akassoglou K: Fibrinogen triggers astrocyte scar formation by promoting the availability of active TGF-beta after vascular damage. J Neurosci. 2010, 30 (17): 5843-5854. 10.1523/JNEUROSCI.0137-10.2010.PubMed CentralPubMedGoogle Scholar
- Schachtrup C, Lu P, Jones LL, Lee JK, Lu J, Sachs BD, Zheng B, Akassoglou K: Fibrinogen inhibits neurite outgrowth via beta 3 integrin-mediated phosphorylation of the EGF receptor. Proc Natl Acad Sci U S A. 2007, 104 (28): 11814-11819. 10.1073/pnas.0704045104.PubMed CentralPubMedGoogle Scholar
- Brennan FH, Anderson AJ, Taylor SM, Woodruff TM, Ruitenberg MJ: Complement activation in the injured central nervous system: another dual-edged sword?. J Neuroinflammation. 2012, 9: 137-10.1186/1742-2094-9-137.PubMed CentralPubMedGoogle Scholar
- Anderson AJ, Robert S, Huang W, Young W, Cotman CW: Activation of complement pathways after contusion-induced spinal cord injury. J Neurotrauma. 2004, 21 (12): 1831-1846. 10.1089/neu.2004.21.1831.PubMedGoogle Scholar
- Qiao F, Atkinson C, Song H, Pannu R, Singh I, Tomlinson S: Complement plays an important role in spinal cord injury and represents a therapeutic target for improving recovery following trauma. Am J Pathol. 2006, 169 (3): 1039-1047. 10.2353/ajpath.2006.060248.PubMed CentralPubMedGoogle Scholar
- Reynolds DN, Smith SA, Zhang YP, Mengsheng Q, Lahiri DK, Morassutti DJ, Shields CB, Kotwal GJ: Vaccinia virus complement control protein reduces inflammation and improves spinal cord integrity following spinal cord injury. Ann N Y Acad Sci. 2004, 1035: 165-178. 10.1196/annals.1332.011.PubMedGoogle Scholar
- Li LM, Li JB, Zhu Y, Fan GY: Soluble complement receptor type 1 inhibits complement system activation and improves motor function in acute spinal cord injury. Spinal Cord. 2010, 48 (2): 105-111. 10.1038/sc.2009.104.PubMedGoogle Scholar
- Li L, Li J, Zhu Y, Fan G: Ephedra sinica inhibits complement activation and improves the motor functions after spinal cord injury in rats. Brain Res Bull. 2009, 78 (4–5): 261-266.PubMedGoogle Scholar
- Galvan MD, Luchetti S, Burgos AM, Nguyen HX, Hooshmand MJ, Hamers FP, Anderson AJ: Deficiency in complement C1q improves histological and functional locomotor outcome after spinal cord injury. J Neurosci. 2008, 28 (51): 13876-13888. 10.1523/JNEUROSCI.2823-08.2008.PubMed CentralPubMedGoogle Scholar
- de-Simoni MG, Rossi E, Storini C, Pizzimenti S, Echart C, Bergamaschini L: The powerful neuroprotective action of C1-inhibitor on brain ischemia-reperfusion injury does not require C1q. Am J Pathol. 2004, 164 (5): 1857-1863. 10.1016/S0002-9440(10)63744-3.PubMed CentralPubMedGoogle Scholar
- Lipton P: Ischemic cell death in brain neurons. Physiol Rev. 1999, 79 (4): 1431-1568.PubMedGoogle Scholar
- Walmsley SR, Print C, Farahi N, Peyssonnaux C, Johnson RS, Cramer T, Sobolewski A, Condliffe AM, Cowburn AS, Johnson N: Hypoxia-induced neutrophil survival is mediated by HIF-1alpha-dependent NF-kappaB activity. J Exp Med. 2005, 201 (1): 105-115. 10.1084/jem.20040624.PubMed CentralPubMedGoogle Scholar
- Gorlach A, Bonello S: The cross-talk between NF-kappaB and HIF-1: further evidence for a significant liaison. Biochem J. 2008, 412 (3): e17-19.PubMedGoogle Scholar
- Rius J, Guma M, Schachtrup C, Akassoglou K, Zinkernagel AS, Nizet V, Johnson RS, Haddad GG, Karin M: NF-kappaB links innate immunity to the hypoxic response through transcriptional regulation of HIF-1alpha. Nature. 2008, 453 (7196): 807-811. 10.1038/nature06905.PubMed CentralPubMedGoogle Scholar
- Cummins EP, Berra E, Comerford KM, Ginouves A, Fitzgerald KT, Seeballuck F, Godson C, Nielsen JE, Moynagh P, Pouyssegur J: Prolyl hydroxylase-1 negatively regulates IkappaB kinase-beta, giving insight into hypoxia-induced NFkappaB activity. Proc Natl Acad Sci U S A. 2006, 103 (48): 18154-18159. 10.1073/pnas.0602235103.PubMed CentralPubMedGoogle Scholar
- Belaiba RS, Bonello S, Zahringer C, Schmidt S, Hess J, Kietzmann T, Gorlach A: Hypoxia up-regulates hypoxia-inducible factor-1alpha transcription by involving phosphatidylinositol 3-kinase and nuclear factor kappaB in pulmonary artery smooth muscle cells. Mol Biol Cell. 2007, 18 (12): 4691-4697. 10.1091/mbc.E07-04-0391.PubMed CentralPubMedGoogle Scholar
- Semenza GL: HIF-1, O(2), and the 3 PHDs: how animal cells signal hypoxia to the nucleus. Cell. 2001, 107 (1): 1-3. 10.1016/S0092-8674(01)00518-9.PubMedGoogle Scholar
- Hogel H, Rantanen K, Jokilehto T, Grenman R, Jaakkola PM: Prolyl hydroxylase PHD3 enhances the hypoxic survival and G1 to S transition of carcinoma cells. PLoS One. 2011, 6 (11): e27112-10.1371/journal.pone.0027112.PubMed CentralPubMedGoogle Scholar
- Hall ED, Wolf DL: Post-traumatic spinal cord ischemia: relationship to injury severity and physiological parameters. Cent Nerv Syst Trauma. 1987, 4 (1): 15-25.PubMedGoogle Scholar
- Guth L, Zhang Z, Steward O: The unique histopathological responses of the injured spinal cord. Implications for neuroprotective therapy. Ann N Y Acad Sci. 1999, 890: 366-384. 10.1111/j.1749-6632.1999.tb08017.x.PubMedGoogle Scholar
- Hall ED: Inhibition of lipid peroxidation in central nervous system trauma and ischemia. J Neurol Sci. 1995, 134 (Suppl): 79-83.PubMedGoogle Scholar
- Latanich CA, Toledo-Pereyra LH: Searching for NF-kappaB-based treatments of ischemia reperfusion injury. J Invest Surg. 2009, 22 (4): 301-315. 10.1080/08941930903040155.PubMedGoogle Scholar
- Chen F, Beezhold K, Castranova V: Tumor promoting or tumor suppressing of NF-kappa B, a matter of cell context dependency. Int Rev Immunol. 2008, 27 (4): 183-204. 10.1080/08830180802130327.PubMedGoogle Scholar
- Pineau I, Lacroix S: Proinflammatory cytokine synthesis in the injured mouse spinal cord: multiphasic expression pattern and identification of the cell types involved. J Comp Neurol. 2007, 500 (2): 267-285. 10.1002/cne.21149.PubMedGoogle Scholar
- de-Rivero Vaccari JP, Lotocki G, Marcillo AE, Dietrich WD, Keane RW: A molecular platform in neurons regulates inflammation after spinal cord injury. J Neurosci. 2008, 28 (13): 3404-3414. 10.1523/JNEUROSCI.0157-08.2008.PubMedGoogle Scholar
- Beck KD, Nguyen HX, Galvan MD, Salazar DL, Woodruff TM, Anderson AJ: Quantitative analysis of cellular inflammation after traumatic spinal cord injury: evidence for a multiphasic inflammatory response in the acute to chronic environment. Brain. 2010, 133 (Pt 2): 433-447.PubMed CentralPubMedGoogle Scholar
- Pineau I, Sun L, Bastien D, Lacroix S: Astrocytes initiate inflammation in the injured mouse spinal cord by promoting the entry of neutrophils and inflammatory monocytes in an IL-1 receptor/MyD88-dependent fashion. Brain Behav Immun. 2010, 24 (4): 540-553. 10.1016/j.bbi.2009.11.007.PubMedGoogle Scholar
- Noble LJ, Donovan F, Igarashi T, Goussev S, Werb Z: Matrix metalloproteinases limit functional recovery after spinal cord injury by modulation of early vascular events. J Neurosci. 2002, 22 (17): 7526-7535.PubMed CentralPubMedGoogle Scholar
- Zhang H, Trivedi A, Lee JU, Lohela M, Lee SM, Fandel TM, Werb Z, Noble-Haeusslein LJ: Matrix metalloproteinase-9 and stromal cell-derived factor-1 act synergistically to support migration of blood-borne monocytes into the injured spinal cord. J Neurosci. 2011, 31 (44): 15894-15903. 10.1523/JNEUROSCI.3943-11.2011.PubMed CentralPubMedGoogle Scholar
- Wells JE, Rice TK, Nuttall RK, Edwards DR, Zekki H, Rivest S, Yong VW: An adverse role for matrix metalloproteinase 12 after spinal cord injury in mice. J Neurosci. 2003, 23 (31): 10107-10115.PubMedGoogle Scholar
- Letellier E, Kumar S, Sancho-Martinez I, Krauth S, Funke-Kaiser A, Laudenklos S, Konecki K, Klussmann S, Corsini NS, Kleber S: CD95-ligand on peripheral myeloid cells activates Syk kinase to trigger their recruitment to the inflammatory site. Immunity. 2010, 32 (2): 240-252. 10.1016/j.immuni.2010.01.011.PubMedGoogle Scholar
- Kawai T, Akira S: The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol. 2010, 11 (5): 373-384. 10.1038/ni.1863.PubMedGoogle Scholar
- Kigerl KA, Lai W, Rivest S, Hart RP, Satoskar AR, Popovich PG: Toll-like receptor (TLR)-2 and TLR-4 regulate inflammation, gliosis, and myelin sparing after spinal cord injury. J Neurochem. 2007, 102 (1): 37-50. 10.1111/j.1471-4159.2007.04524.x.PubMedGoogle Scholar
- de-Rivero Vaccari JP, Bastien D, Yurcisin G, Pineau I, Dietrich WD, De-Koninck Y, Keane RW, Lacroix S: P2X4 receptors influence inflammasome activation after spinal cord injury. J Neurosci. 2012, 32 (9): 3058-3066. 10.1523/JNEUROSCI.4930-11.2012.PubMedGoogle Scholar
- Srivastava S, Salim N, Robertson MJ: Interleukin-18: biology and role in the immunotherapy of cancer. Curr Med Chem. 2010, 17 (29): 3353-3357. 10.2174/092986710793176348.PubMedGoogle Scholar
- Hayes KC, Hull TC, Delaney GA, Potter PJ, Sequeira KA, Campbell K, Popovich PG: Elevated serum titers of proinflammatory cytokines and CNS autoantibodies in patients with chronic spinal cord injury. J Neurotrauma. 2002, 19 (6): 753-761. 10.1089/08977150260139129.PubMedGoogle Scholar
- Ankeny DP, Lucin KM, Sanders VM, McGaughy VM, Popovich PG: Spinal cord injury triggers systemic autoimmunity: evidence for chronic B lymphocyte activation and lupus-like autoantibody synthesis. J Neurochem. 2006, 99 (4): 1073-1087. 10.1111/j.1471-4159.2006.04147.x.PubMedGoogle Scholar
- Ankeny DP, Guan Z, Popovich PG: B cells produce pathogenic antibodies and impair recovery after spinal cord injury in mice. J Clin Invest. 2009, 119 (10): 2990-2999. 10.1172/JCI39780.PubMed CentralPubMedGoogle Scholar
- Lucin KM, Sanders VM, Jones TB, Malarkey WB, Popovich PG: Impaired antibody synthesis after spinal cord injury is level dependent and is due to sympathetic nervous system dysregulation. Exp Neurol. 2007, 207 (1): 75-84. 10.1016/j.expneurol.2007.05.019.PubMed CentralPubMedGoogle Scholar
- Ankeny DP, Popovich PG: Mechanisms and implications of adaptive immune responses after traumatic spinal cord injury. Neuroscience. 2009, 158 (3): 1112-1121. 10.1016/j.neuroscience.2008.07.001.PubMed CentralPubMedGoogle Scholar
- Nashmi R, Fehlings MG: Mechanisms of axonal dysfunction after spinal cord injury: with an emphasis on the role of voltage-gated potassium channels. Brain Res Brain Res Rev. 2001, 38 (1–2): 165-191.PubMedGoogle Scholar
- Gautier L, Cope L, Bolstad BM, Irizarry RA: Affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004, 20 (3): 307-315. 10.1093/bioinformatics/btg405.PubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.PubMed CentralPubMedGoogle Scholar
- Zhijin Wu RAI, Gentleman R, Martinez-Murillo F, Spencer F: A Model-Based Background Adjustment for Oligonucleotide Expression Arrays. J Am Stat Assoc. 2004, 99 (December): 909-917.Google Scholar
- Semeralul MO, Boutros PC, Likhodi O, Okey AB, Van-Tol HH, Wong AH: Microarray analysis of the developing cortex. J Neurobiol. 2006, 66 (14): 1646-1658. 10.1002/neu.20302.PubMedGoogle Scholar
- Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: Article3 EpubGoogle Scholar
- Efron B, Tibshirani R: Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002, 23 (1): 70-86. 10.1002/gepi.1124.PubMedGoogle Scholar
- Boutros PC, Okey AB: Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief Bioinform. 2005, 6 (4): 331-343. 10.1093/bib/6.4.331.PubMedGoogle Scholar
- Ernst J, Bar-Joseph Z: STEM: a tool for the analysis of short time series gene expression data. BMC Bioinforma. 2006, 7: 191-10.1186/1471-2105-7-191.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.