Introduction

Stroke leads to loss of brain tissue, causing persistent long-term motor, sensory, and cognitive impairments. Therapeutics for stroke recovery are limited to neurorehabilitative training. Patients, even after rehabilitation, live with severe impairments. Neurorehabilitation exploits behavioral principles of learning and adaption to induce functional gains after stroke1,2. Activities such as task-specific practice3,4, increased dosage5 and duration, and more recently virtual reality-aided feedback and training6,7 have been linked with increased neural plasticity and beneficial outcomes as a result. While much of our conclusions on the relationship between learning and stroke recovery have been drawn from behavioral evidence3,5,7 and functional MRI measures8,9, the exact cellular and molecular mechanisms in play are unknown; specifically—it is unclear if cellular and molecular substrates of learning and memory are recruited for recovery of function after stroke. An understanding of these pathways will allow for targeting the same cellular and molecular mechanisms in learning and memory to enhance recovery of function after stroke.

C–C chemokine receptor-5 (CCR5) and cAMP response element-binding protein (CREB) are critical for the formation of new memories in the normal adult brain10,11,12,13,14. While CREB expression is important for allocating neurons with information pertaining to a memory trace; CCR5 closes this temporal window for linking memories over time11. Allocated neurons that store a memory trace have been classically termed as the engram and have been causally linked with the expression and extinction of a memory15. Recent efforts using transgenic lines that label neurons based on the expression of immediate early genes when paired with behavioral learning paradigms in the adult healthy brain have been able to capture neurons that participate in the engram15,16,17,18,19,20. Transcriptional profiling of participating neurons has uncovered gene signatures that are unique to the engram. Many of the genes are activity-dependent genes that include immediate early genes (IEGs) that are expressed at the onset of an incoming stimulus, succeeded by a wave of late response genes (LRGs) that are normally targets of IEG transcription factors21. The expression of LRGs modulates plasticity at the synapse22,23,24. Existing data on the expression of these activity-dependent genes in the context of memory formation or novel experience allows us to ask if the same gene signatures are expressed under conditions of recovery after stroke.

We have recently shown that downregulation of CCR5 in cortical neurons adjacent to the infarct following a stroke to the motor cortex leads to robust improvements in motor recovery25. Recovery is associated with increased and differential sprouting of axons, preservation of dendritic spines in the secondary motor cortex, and increased signaling through the transcription factor CREB25. Overexpression of CREB alone after a stroke also improves recovery of motor function26. CREB-overexpressing neurons are allocated to a motor network such that chemogenetic inactivation of CREB-overexpressing neurons overturns recovery to a state of motor deficit26.

Here, we transcriptionally profiled neurons at different stages after stroke and under conditions of CCR5 knockdown or CREB overexpression, which enhances motor recovery. We show that under conditions of enhanced motor recovery, cortical neurons express activity-dependent gene sets that are unique to animals that recover and that these gene sets can predict functional outcomes in acute stroke. Low dimensional representations of the data suggest that activity-dependent genes occupy a molecular latent space unique to cortical neurons from animals that have CCR5 knockdown or CREB overexpression after stroke. Moreover, activity-dependent genes form robust gene co-expression networks and transcription factor-target co-expression networks when compared to untreated groups. In response to neuronal knockdown of CCR5 or overexpression of CREB after stroke, we also show that microglia express genes for ligands and receptors that support axonal guidance, dendritic signaling, and synaptogenesis.

Results

Cortical neurons after stroke express activity-dependent gene sets

We collected gene expression data with RNAseq from cohorts of animals with CCR5 knockdown (CCR5kd) or CREB overexpression (CREBoe) in healthy and stroke-induced mice. These two molecular perturbations robustly enhance motor recovery in mouse stroke models25,26 and are associated with improved cognitive function in human stroke25,27. Cortical neurons adjacent to the infarct (Supplementary Fig. S2c), from peri-infarct primary and secondary motor cortices were isolated with fluorescence-activated cell sorting (FACS) (Fig. 1b). Neurons were isolated from multiple cohorts with neuron-specific knockdown of CCR5 or overexpression of CREB in acute (7 days post-stroke) and chronic stroke (30 days post-stroke) along with controls that received the same viral backbone that lacked sequences to target CCR5 or CREB (timelines and groups in Fig. 1a, infarct sizes and location of virus expression relative to infarct in Supplementary Figs. S1 and S2).

Fig. 1: Activity-dependent genes are expressed after stroke.
figure 1

a Experimental timeline. b FACS plots from groups that correspond to treatments and timelines in (a), showing gating and selection of NCAM+ve events that are also positive for fluorescence expression carried by the viral construct (events that fall in the Q2 quadrant). c Gene set enrichment, FDR < 0.1, for various activity-dependent gene sets (y-axis) across conditions compared to Naïve (groups with *) or treatment groups compared with controls that received the same viral backbone. Set size denotes the number of genes in each gene set denoted by the size of the circle: positive enrichment in warmer colors and negative enrichment in cooler colors. d Scatter plot showing behavioral scores for each of the conditions compared to Naïve and their corresponding enrichment scores compared to Naïve for the various activity gene sets in (c). Increasing behavioral scores corresponds to increases in motor deficits. The line drawn represents the line of best fit and gray shaded region is the confidence interval around the slope of the regression line. e Classification of samples from untreated and treated groups before and after stroke using random forest classifiers trained on the various activity-dependent gene sets. Data are median ± SD of prediction error from five iterations. f Heatmap of row normalized gene expression (z-scores) of hierarchically clustered genes from gene sets from top-performing classifiers. Columns are individual samples from 3 groups (Naïve, Ctrl stroke acute, and CCR5kd stroke acute) and gene expression values in rows. g Model performance metrics for each classifier on training (gray) and test data (red). Data are mean values from each iteration with standard error.

Full size image

The timing of neuronal isolations was aligned with when enhanced behavioral recovery was previously reported25,26. Briefly, with CCR5kd in acute stroke, robust enhancements in motor function were observed in week 1. Reducing CCR5 function in chronic stroke led to modest enhancements in function in week 4. In animals with CREBoe, enhancements in function were reported in week 4 with delivery of CREBoe in the first week of stroke. Hence, neurons were FAC-sorted at week 1 for CCR5kd in acute stroke and week 4 for cohorts of animals with the delivery of CCR5kd in chronic stroke or CREBoe in acute stroke (Fig. 1a, S1, S2 for timing of injections, location, and size of infarct, expression of virus relative to the infarct and cortical areas dissected). FAC-sorted neurons were subject to RNA sequencing. Sequencing data were aligned to the mouse reference transcriptome, filtered, and normalized to attain log2 counts per million (log2CPM), and differentially expressed genes were identified (Supplementary Fig. S3, Supplementary Data S1 and SI).

To determine if activity-dependent gene sets are expressed after stroke, we compiled datasets from 19 published studies16,17,18,19,20,22,28,29,30,31,32,33,34,35,36,37,38,39,40 (Supplementary Data S2 and SI) that measured gene expression changes with RNAseq captured from neurons tagged during learning a novel task or during long-term memory storage using activity-dependent transgenic mouse lines; or when exposed to a novel stimulus. These studies characterized genes differentially expressed in neurons of the engram during learning, remote engram during long-term memory storage, and neurons that express IEGs and LRGs when exposed to a novel stimulus. Genes from all studies were categorized into 8 classes: IEGs.up (i.e., IEGs differentially upregulated) and IEGs.down, LRGs.up, LRGs.down, engram.up, engram.down, remote.engram.up; remote.engram.down.

To determine enrichment of activity-dependent gene sets, we compared (FDR < 0.1) enrichment for several hundred genes per identified gene set between samples from animals with CCR5/CREB perturbations and their controls in normal (Supplementary Fig S3j), acute and chronic stroke (Fig. 1c). We found different classes of activity genes expressed in acute and chronic stroke when compared to Naïve (Fig. 1c, Supplementary Fig S3j). Many of these classes are negatively enriched (downregulated) after stroke, with the exception of gene sets for upregulated IEGs (IEGs.up) and downregulated LRGs (LRGs.down). The expression of IEGs is in alignment with previous studies, resulting from the ischemic event41. However, sustained downregulation of LRGs in acute to chronic stroke as seen with positive enrichment for LRGs.down and negative enrichment for LRGs.up in both acute and chronic phases, suggest downregulation or repression in the encoding of transcription factors whose targets are normally LRGs, the expression of which influences synaptic transmission and plasticity22,23,24. Additionally, we found that gene sets for the engram (engram.up) are negatively enriched in acute stroke and not enriched in chronic stroke. Both acute and chronic stroke groups are also negatively enriched for genes downregulated in the engram (engram.down), further supporting the overall downregulated expression of engram gene sets after stroke.

Under conditions of enhanced motor recovery, with CCR5kd in acute stroke (Fig. 1c), we found positive enrichment of gene sets for upregulated IEGs (IEGs.up) showing a further increase in expression of IEGs when compared to its control in acute stroke, but also further downregulation of LRGs as seen with positive enrichment for LRGs.down. Unlike its control, CCR5kd in acute stroke is positively enriched for gene sets upregulated in the engram (engram.up) and remote engram (remote.engram.up). The opposite pattern of expression was seen in chronic stroke, where both CCR5kd and CREBoe when compared to its control, show increased expression of LRGs and reduced expression of IEGs. Positive enrichment for LRGs.up and negative enrichment for LRGs.down (with CCR5kd) show increased LRG expression and positive enrichment for IEGs.down and negative enrichment for IEGs.up show increased expression of IEGs in chronic stroke with treatment.

This trend in the expression of IEGs and LRGs in treated groups with enhanced motor recovery show that IEG expression is restricted to the earlier phases of stroke, associated with an increase in LRG expression in the chronic phases. Similar to CCR5 knockdown in acute stroke, the condition of CCR5kd in chronic stroke is enriched for gene sets upregulated in the engram (engram.up), but also downregulated in the engram (engram.down) suggesting an overall increase in genes differentially expressed in the engram.

Unexpectedly, with CREBoe in stroke we did not find enrichment for the gene set upregulated in the engram (engram.up) but found enrichment for the gene set downregulated in the engram, which is also in contrast with data from CREBoe in the normal brain that shows increased expression of engram genes (Fig. 1c and S3j). The timing of CREB activation plays a critical role in the allocation of active neurons to an engram42. The downregulation of engram genes and the upregulation of LRGs with CREBoe post-stroke at 4 weeks suggest that an earlier onset of expression of engram genes and IEGs may have taken place prior to when motor recovery was observed.

Next, to determine if activity-dependent gene expression is correlated with improved motor function, we used data from previously published studies25,26 that tested CCR5 and CREB signaling in functional motor recovery after stroke. Test scores that represent quantitation of motor deficit compared to naïve, were normalized from the different cohorts of mice across studies. Behavioral scores attained for each of the different conditions were plotted against the normalized enrichment score for the different activity-dependent gene sets for that condition when compared to naïve (Fig. 1d). With a linear regression model to determine the relationship between observed behavioral scores and enrichment of the different activity-dependent genes sets as the predictor variable, we find that 72% of the variance in the dataset was explained by the model (adjusted R2 = 0.717, p = 0.0001). The relationship also exhibited a Pearson’s correlation of −0.66, showing that an increase in motor deficit was associated with lower or negative enrichment of activity-dependent gene expression.

In summary, we show that IEGs are differentially enriched after stroke with downregulation of all other activity-dependent gene sets. However, during conditions of enhanced motor recovery, we show increased enrichment of many activity-dependent gene sets after stroke, further enhanced than its control, including increased enrichment of gene sets for the engram with CCR5kd post-stroke.

Given the enrichment of activity-dependent gene sets after stroke with differential enrichment profiles in conditions of recovery, particularly for sets represented in the engram, we asked if these expression profiles are unique enough to predict recovery. To determine if activity-dependent gene expression can be used to classify samples with enhanced motor recovery from CCR5kd or CREBoe, we trained random forest classifiers on the various activity-dependent gene sets expressed in normal and post-stroke conditions and tested if the classifiers could predict sample type (Fig. 1e–g, Supplementary Fig S3k, Supplementary Data 3). In non-stroke conditions, we found that all classifiers predicted which samples came from CCR5kd or CREBoe or naïve conditions with a prediction error of between 15 ± 12.6% (engram.up; median ± SD) to 10 ± 4.18% (IEGs.up; median ± SD), and aligns with previously determined roles of CCR5 and CREB in memory formation and learning11,12,13,14,15,43. In acute stroke, the highest performing classifiers are those that were trained on remote.engram.up with a prediction error of 8.3 ± 1.8% (median ± SD) and accuracy of 95.5 ± 5.1% (median ± SD); IEGs.up with a prediction error of 8.3 ± 11.4% and accuracy of 90.7 ± 9.6% and engram.up with a prediction error of 16.5 ± 6.6% and accuracy of 86.6 ± 10.2%, further proving that the enrichment of activity-dependent genes represented during memory formation and consolidation are uniquely expressed during motor recovery. Prediction rates of the above classifiers are comparable to classifiers trained on differentially expressed genes from groups with acute stroke (prediction error—12.5 ± 8.5%; Supplementary Fig. S3k) and are better than classifiers trained on inflammatory genes involved in interferon signaling pathways44 (prediction error—27.5 ± 1.6%, Fig. S3k) that are dominantly expressed in acute stroke. Model performances measured by sensitivity (recall) and specificity of the top-performing activity-dependent classifiers are in the range of 0.9–1 (Fig. 1g, Supplementary Data 3) and are superior to most previously reported models45 to predict stroke outcome based on clinical stroke scales and structural imaging data. Furthermore, to determine if individual samples clustered based on the expression of activity-dependent gene sets from the top-performing classifiers as a means to assess sample-to-sample variability in its classification, we performed hierarchical clustering, of samples. We found that all samples that belonged to one condition clustered in the same group based on the expression of gene sets from the top-performing classifiers (Fig. 1f).

On the contrary, all classifiers performed poorly in chronic stroke with prediction error rates between 45.8 ± 12.2% (remote.engram.up) to 64.1 ± 8.3% (IEGs.down) (Fig. 1e) and are worse compared to classifiers trained on differentially expressed genes in chronic stroke groups (13.6 ± 7.8%) (Fig. S3k); suggesting that classifiers trained on activity-dependent genes are poor predictors of recovered motor control in chronic stroke.

Collectively, these data show that activity-dependent gene sets are expressed after a stroke and can a priori identify the stroke condition and treatment category in acute stroke. Cortical neurons with CCR5kd are enriched with genes expressed in the engram, remote engram and immediate early genes and, the expression patterns of these gene sets can predict sample types in acute stroke. CREB-overexpressing neurons in chronic stroke are enriched with late response genes; however none of the classifiers trained on activity-dependent gene sets are able to predict sample-type in chronic stroke.

More at link.