Neutrophil degranulation is increased at seven days after human intracerebral hemorrhage, but not at 72 h, and correlates with decreased miR-3613 and miR-3690

Abstract

Background

The molecular mechanisms of inflammatory pathophysiology after intracerebral hemorrhage (ICH) are not well established. We report mRNA-seq and miRNA-seq in the peripheral blood of ICH patients with three serial samples during the first week after the stroke.

Methods

Twenty-seven ICH patients were enrolled via 24/7 screening and peripheral blood sampled at < 24 h (baseline), 72 h (+/-12 h), and 7 days (+/- 2 days) from last known normal. mRNA-seq and miRNA-seq were assessed for differential expression (DE) between the time point comparisons. Pathways identified via enrichment analysis (STRING, Reactome, Ingenuity Pathway Analysis) were tested via paired t-test and principal component analysis (PCA). Correlations between miRNA/mRNA pairs were computed.

Results

For DE mRNA at 72 h vs. baseline, the main enriched pathways pertained to neuronal function and synaptic transmission; prominent neuron-related genes from these pathways are also implicated in platelet activation. For 7 days vs. baseline, neutrophil degranulation and ribosomal biogenesis were the most enriched pathways; PCA also suggested neutrophil degranulation was the pathway most significantly different at 7 days vs. baseline compared with 72 h vs. baseline (p = 0.02). miR-3613 and miR-3690 had decreased expression at 7 days and were the miRNA most correlated with DE genes from the neutrophil degranulation pathway.

Conclusion

Neutrophil degranulation was the prominent enriched pathway at 7 days after ICH and correlated with decreased miR-3613 and miR-3690. Further research is warranted of neutrophils in post-ICH inflammatory pathophysiology and functional validation studies of miR-3613 and miR-3690 as potential regulators of neutrophil degranulation after ICH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-025-04408-w.

Background

Intracerebral hemorrhage (ICH) occurs when there is spontaneous, non-traumatic bleeding into the brain; it is the second most common type of stroke and accounts for 50% of stroke-related mortality [1]. Findings support the existence of inflammatory pathophysiology following ICH, including processes in the brain and in the circulating blood which contribute to damage and potentially repair [2–5]. While ICH has been found to induce inflammation which directly correlates with ICH severity [6, 7], the molecular mechanisms of these dynamic processes post-ICH remain poorly characterized. RNA-sequencing (RNA-seq) is a robust technique and the current standard approach to quantify RNA molecules in samples.

microRNA (miRNA) are small (18–25 nucleotides in length), evolutionarily conserved, non-coding RNA molecules which are powerful regulators of gene expression. A single miRNA can affect hundreds of genes. Conventionally, miRNA have an inverse relationship with gene expression; for example, increased expression of an miRNA results in decreased expression of its target genes [8–10]. miRNA play critical roles in regulating the development and function of leukocytes [11], present in circulating blood, and leukocyte-derived miRNA have been investigated in many diseases such as atherosclerosis [12], diffuse large B cell lymphoma [13], cryptococcus infection [14], gastric and breast cancer [15], and spondyloarthritis [16]. There are exciting potential applications for miRNA in diagnostics, prognostics, and particularly as miRNA-based therapeutics [17].

We now report findings from an investigation of mRNA-seq and miRNA-seq in three serial peripheral blood samples from ICH patients at baseline (< 24 h from last known normal), 72 h (+/- 12 h) and 7 days (+/- 2 days). The overall goal of the work is to gain new insights into the molecular mediators of inflammatory pathophysiology in the first week after ICH. The study provides a unique opportunity to characterize the dynamic temporal changes in these biomarkers in the acute period following ICH and to identify associations between the miRNA and correlated genes in the same samples.

Methods

ICH Subjects

Patients with ICH were enrolled at the University of Cincinnati Medical Center, Cincinnati, Ohio, United States, via 24/7 screening. Inclusion criteria included ≥ 18 years of age, history and radiographic findings consistent with primary spontaneous ICH in the lobar or basal ganglia/deep white matter brain structures, and less than 24 h from last known normal. Exclusion criteria included non-English speaking, cerebellar or brainstem ICH, ruptured aneurysm, arteriovenous malformation or other vascular anomaly, traumatic etiology of brain hemorrhage, hemorrhagic conversion of an ischemic stroke, prior history of ICH, active cancer or brain metastases, current pregnancy, and incarceration, in police custody, or institutionalized.

Blood sample collection

Peripheral blood was collected from the subject via venipuncture into Zymo DNA/RNA Shield blood collection tubes (Irvine, California), according to the manufacturer instructions and frozen at −80 degrees Celsius until the time of RNA-seq. Blood was collected from each subject at the following times from last known normal: <24 h, 72 h (+/- 12 h), and 7 days (+/- 2 days). The total RNA was extracted using Zymo Quick-RNA Whole Blood kit with DNase I digestion. Using RNA 6000 Pico Kit (Agilent, Santa Clara, CA), Bioanalyzer QC showed good RNA quality (RIN score approximately 7 to 8). The extracted RNA was then used for both miRNA-seq and globin mRNA/rRNA depletion RNA-seq.

mRNA sequencing

Directional globin mRNA/rRNA depletion RNA-seq was performed by the Genomics, Epigenomics and Sequencing Core at the University of Cincinnati [18, 19]. To summarize, using NEBNext Globin & rRNA Depletion Kit (NEB), 50 to 100 ng whole blood RNA was globin mRNA/rRNA depleted according to the protocol. Next, NEBNext Ultra II Directional RNA Library Prep kit (NEB) was used for library preparation under PCR cycle number of 14. After library Bioanalyzer QC (Agilent, Santa Clara, CA) and Qubit quantification (ThermoFisher, Waltham, MA), the normalized libraries were sequenced using NextSeq 2000 Sequencer (Illumina, San Diego, CA) under the setting of PE 2 × 61 bp to generate an average of approximately 50 M reads clusters.

miRNA sequencing

miRNA-seq was performed by the Genomics, Epigenomics and Sequencing Core at the University of Cincinnati [20, 21]. To prepare the library, NEBNext Small RNA Sample Library Preparation kit (NEB, Ipswich, MA) was used with modified approach for precise miRNA library size selection; this makes it possible for the kit to process low input and low-quality RNA with better library recovery and miRNA reads alignment. Specifically, using 20 ng total RNA as input, after 15 cycles of PCR, the libraries with unique indices were first equal-10 µl pooled, column cleaned up and mixed with custom-designed DNA ladder that contains 135 and 146 bp purified PCR amplicons. This size range corresponds to an miRNA library with 16–27 nucleotide insert which covers all miRNA. After high resolution agarose gel electrophoresis and gel excision, the library pool ranging from 135 to 146 base pairs, including the DNA marker, were purified and quantified by NEBNext Library Quant kit (NEB) using QuantStudio 5 Real-Time PCR System (Thermofisher, Waltham, MA). The first round of sequencing was performed on NextSeq 2000 sequencer (Illumina, San Diego, CA) to generate a few million reads to quantify the relative concentration of each library. The volume of each library was then adjusted to generate about 5 M read clusters from each sample under the same PE 2 × 61 bp setting for the final data analysis.

Filtering, alignment and normalization of mRNA and miRNA

The quality of sequence data was assessed using FastQC version 0.11.8 [22]. Adaptors were removed using TrimGalore version 0.6.10 [23] and sequences were retained if they had a Phred score > 20; miRNA sequences were removed if less than 18 nucleotides in length. The mRNA sequences were aligned to genome reference GRCh38 using STAR version 2.7.11a [24]. The miRNA sequences were aligned to the reference library miRbase v22 implemented in miRge3.0 [25]. For both mRNA and miRNA an average read count > 5 was required and then normalization was completed using the median of ratios implemented in the program DESeq2 [26]. The normalized read counts were log₂-transformed for downstream analyses.

Statistical analysis

Demographic and clinical history for the enrolled ICH patients were summarized as mean ± standard deviation or percentages. Differential expression (DE) of mRNA and miRNA between baseline and each of the follow-up timepoints were tested using a paired t-test (i.e., baseline vs. 72 h, baseline vs. 7 days, 72 h vs. 7 days). Spearman’s correlation coefficients were computed between DE mRNA and DE miRNA for each timepoint comparison, with a focus on those with r < −0.4, since increased miRNA generally downregulates target gene expression [27, 28]. For miRNA highlighted by the bioinformatic analyses (see below), we examined whether the change in the gene expression between the two timepoints differed by clinical and demographic characteristics using the Wilcoxon test for categorical traits (e.g., sex, ICH location) and Spearman’s correlation (e.g., age, hemorrhage volume). Since the goal of the research was to identify biological pathways enriched for differential expression at the respective timepoints, the raw p-value thresholds were selected to enable a sufficient number of genes and miRNA for informatic bioinformatic analyses: 72 h vs. baseline mRNA P < 0.01, miRNA P < 0.05; 7 days vs. baseline mRNA P < 0.01, miRNA P < 0.05; 7 days vs. 72 h mRNA P < 0.05, miRNA P < 0.05). As a secondary goal, a false discovery rate adjusted p-value was computed to identify individual mRNA and miRNA exhibiting differential expression in the pairwise comparison of timepoints.

A principal component analysis (PCA) was computed on the differences in the expression for the timeframe identifying specified pathway identified in the bioinformatic analyses. Using the first timeframe, the loadings defining the first principal component (PC) were used to project the first PC onto the differences in expression in the second timeframe; the first PC captures the dominant shared variation across the differences in gene expression between the two timepoints. For example, if pathway A was identified by the bioinformatic analysis of the differential expression between 72 h and baseline, the PCA was computed on the set of genes in pathway A, using the differences of 72 h vs. baseline, and the resulting first PC was then projected onto the differences for 7 days vs. baseline. For each pathway, a paired t-test was computed to test whether the first principal component was statistically different between the two timepoints. The first PC captures the dominant variation and is our a priori focus. However, the process was repeated for the second, third, and fourth PC.

Bioinformatic pathway analysis

The DE mRNA and miRNA at the three timepoints were tested for pathway enrichment via Ingenuity Pathway Analysis (IPA, QIAGEN Inc., https://digitalinsights.qiagen.com/IPA), Cytoscape [29], STRING [30], and Reactome [31]. For each pathway identified in the enriched analysis of the DE mRNA, the genes comprising that pathway were identified via IPA. The miRNA/mRNA pairs with statistically significant correlation coefficients were then analyzed by TargetScan (version 8.0) [32] and MirTarBase (version 9.0) [33] to explore whether the pairs have been reported as miRNA/mRNA targets in at least one of these bioinformatic tools.

Results

ICH subjects

A total of 27 ICH patients were enrolled (Table 1), with three serial blood samples each, resulting in 81 samples collected. Four samples were excluded due to inadequate RNA quality at the time of miRNA-seq and/or mRNA-seq (1 sample excluded at 72 h, 3 samples excluded at 7 days; all white males with diabetes). These exclusions were solely based on RNA quality and not clinical characteristics and reflect a common patient profile (Table 1). Further, for an individual subject, no more than one of the three serial samples were excluded, again supporting that the exclusions were not related to patient characteristics. The most notable effect of these exclusions was reducing the sample size for the comparisons including the 7-day samples.

Table 1.

Demographics, clinical variables, and past medical history for enrolled subjects with intracerebral hemorrhage (ICH)

	Cohort (n = 27)
Age (years)	66 ± 13
Sex
Female (%)	30%
Male (%)	70%
Race/ethnicity
White	78%
Black	18%
Asian	4%
ICH location
Deep	63%
Lobar	37%
ICH Volume, mL median [IQR]	11.5 [6.5, 24.5]
Surgery for ICH	4%
External Ventricular Drain	25%
Infectiona	37%
History of
ICH	0%
Stroke	15%
Myocardial Infarction	26%
Diabetes	52%
Atrial Fibrillation	26%
Hypertension	78%
Hyperlipidemia	48%
Cancer	4%
Current smoking	19%
Past smoking	26%

^aInfection defined as treatment with antibiotics or antifungals during the study period

72 h vs. baseline

For 72 h (n = 26) vs. baseline (n = 26), there were 1863 differentially expressed (DE) mRNA (p < 0.01) and 52 DE miRNA (P < 0.05). Supplemental Tables S1a and S2a present all mRNA and miRNA identified, respectively, for 72 h vs. baseline, including values for statistical significance and expression differences. The DE genes were enriched in pathways related to neuronal function such as neurexins and neuroligins, protein kinase A signaling (a regulator of synaptic plasticity) [34], synaptogenesis signaling pathway, and axonal guidance signaling (Table 2). Genes which were prominently featured in the network analysis, and more substantially upregulated at 72 h compared with baseline, included NRGN (neurogranin), KCNH3 (potassium voltage-gated channel subfamily H, member 3), ITGA2B (integrin subunit alpha 2b), and ITGB3 (integrin beta 3). The correlation coefficients identified 1159 miRNA/mRNA pairs with r < −0.4, and 6 of the miRNA-mRNA pairs were identified in TargetScan or miRTarBase (see Supplemental Table S3 for more information about the mRNA/miRNA pairs, including statistical values and whether identified in TargetScan or miRTarBase). miR-224-5p was downregulated at 72 h vs. baseline and had two connections with mRNA (ABCC1 and BCL2). One mRNA connection was noted for each of the other miRNA: miR-21-5p (upregulated at 72 h, connected with TIMP3), miR-1908-5p (downregulated, NRGN), miR-186-5p (upregulated, FOXK2), and miR-744-5p (downregulated, LAIR1).

Table 2.

Canonical pathways enriched for genes differentially expressed for 72 h vs. baseline and 7 days vs. baseline

72 h vs. baseline
IPA Canonical Pathways	-log(p-value)	Ratio	Entities
Neurexins and neuroligins	10.2	0.333	19/57
Protein Kinase A Signaling	8.59	0.13	53/408
Synaptogenesis Signaling Pathway	8.11	0.139	44/317
Serotonin Receptor Signaling	7.61	0.119	56/472
Semaphorin Neuronal Repulsive Signaling Pathway	7.07	0.176	26/148

7 days vs. baseline
IPA Canonical Pathways	-log(p-value)	Ratio	Entities
Neutrophil degranulation	9.75	0.109	52/477
Major pathway of rRNA processing in the nucleolus and cytosol	7.91	0.145	27/186
PI3K/AKT Signaling	7.73	0.139	28/202
Estrogen Receptor Signaling	7.62	0.104	43/412
Chronic Myeloid Leukemia Signaling	7.19	0.117	33/281
Chromatin organization	7.13	0.122	30/246

1863 genes with p<0.01 were analyzed in Ingenuity Pathway Analysis (IPA)

1232 genes with p<0.01 were analyzed in Ingenuity Pathway Analysis (IPA)

7 Days vs. baseline

For 7 days (n = 24) vs. baseline (n = 24), there were 1232 DE mRNA (P < 0.01) and 71 DE miRNA (p < 0.05). Supplemental Tables S1b and S2b present all mRNA and miRNA identified, respectively, for 7 days vs. baseline, including values for statistical significance and expression differences. These DE expressed genes were enriched in pathways related to neutrophil degranulation, ribosomal biogenesis and processing, and P13K/AKT signaling which has a role in ribosomal biogenesis and other cellular processes; [35] (Table 2) the neutrophil degranulation pathway was the most statistically significant for 7 days vs. baseline. The DE genes from the neutrophil degranulation pathway were represented in a network analysis (Fig. 1). The principal component analysis (PCA) of the top three pathways for 72 h vs. baseline and 7 days vs. baseline suggested the change in neutrophil degranulation pathway at 7 days was significantly greater than at 72 h (p = 0.02, Table 3, Supplemental Table S4); principal component (PC1) explained approximately 33% of the variation in the differences in gene expression between baseline and 7 days. This result suggests that as time from ICH increases, from 72 h to 7 days, the neutrophil degranulation pathway plays an increasing role in inflammatory pathophysiology. mRNA from the neutrophil degranulation pathway were correlated with miRNA as represented in Fig. 2 and Supplemental Figure S1. Two miRNA had the greatest number of correlations with mRNA from the neutrophil degranulation pathway: miR-3613-5p and miR-3690-5p. Each of these miRNA were correlated with 24 mRNA.

Fig. 1.

A network of differentially expressed genes from the neutrophil degranulation pathway, 7 days vs. baseline. As shown above, for a given gene, increasing intensity of green represents increased mean difference between 7 days and baseline, and greater size of the node represents increased statistical significance. The network integrates findings from multiple bioinformatics tools including Cytoscape, STRING, MCODE and Reactome

Table 3.

Comparison of dominant principal component of change in expressed mRNA with paired projections between stated time points for enriched pathways^a

	Paired T-test P-value
72 h vs. baseline (projected to 7 days vs. baseline)
Neurexins and neuroligins	0.1898
Protein Kinase A Signaling	0.5721
Synaptogenesis Signaling Pathway	0.2384
7 days vs. baseline (projected to 72 h vs. baseline)
Neutrophil degranulation	0.0235
Major pathway of rRNA processing in the nucleolus and cytosol	0.3506
PI3K/AKT Signaling	0.2677

Neutrophil degranulation was the only enriched pathways with P < 0.05

^aAdditional principal components and proportion of variation explained reported in Supplemental Table S4

Fig. 2.

Clustered heatmap for Spearman correlation coefficients (Spearman correlation) between miRNA at 7 days and mRNA at 7 days from the neutrophil degranulation pathway. miR-3613-5p and miR-3690-5p are noted at the bottom of the figure and each correlate with 24 miRNA; this is the most correlations between an miRNA and mRNA for the entire reported study. The darkest blue corresponds to the strongest negative correlation between miRNA and mRNA after filtering Spearman correlation coefficients (r) < 0 and p-values < 0.05. The focus on negative correlations is based on the biologic relationship between miRNA and mRNA expression. The clustering algorithm utilized Euclidean distance

In addition, for the 7 days vs. baseline comparison, genes related to ribosomal biogenesis were repeatedly noted in the networks and were strongly upregulated at 7 days; this included RPS27A (ribosomal protein 27 A), RPSA (ribosomal protein SA), RPLP1 (ribosomal protein lateral stalk subunit P1), RPS19 (ribosomal protein S19), and RPL13a (ribosomal protein L13a). The correlation coefficients identified 3832 miRNA/mRNA pairs with rho <−0.4, 31 of which were also identified in either TargetScan or MirTarBase (see Supplemental Table S3). miR-150-5p (upregulated at 7 days vs. baseline) had a connection with 10 mRNA (mRNA of MRVI1, GSK3B, AGTBP1, BASP1, APC, VMP1, SPARC, GAB1, ENTPD1, and CPD). Two miRNA were paired with four mRNA including miR-1301-3p (downregulated at 7 days, connected with genes PKM, MINK1, HMGA1, RPS2) and miR-744-5p (downregulated, NCL, RPL3, TLE3, and IRAK1). Two miRNA were paired with three mRNA including miR-328-3p (downregulated, RPL5, RPS2, and YWHAQ) and miR-423-5p (downregulated, AK2, TPCN1, and PKM). One miRNA was paired with two mRNA: miR-1908-5p (downregulated, TOR4A and C12orf49). Finally, five miRNA were paired with one mRNA: miR-122-5p (upregulated, RBM47), miR-505-5p (downregulated, PLD3), miR-223-5p (downregulated, RTL8A), miR1343-3p (downregulated, GSR), and miR-3613-5p (downregulated, HNRNPA1).

7 Days vs. 72 h

For 7 days (n = 23) vs. 72 h (n = 23), there were 1147 DE mRNA (p < 0.05) and 8 DE miRNA (p < 0.05). Supplemental Tables S1c and S2c present all mRNA and miRNA identified, respectively, for 7 days vs. 72 h, including values for statistical significance and expression differences. The correlation coefficients identified 424 miRNA/mRNA pairs with r <−0.4, three of which were noted in either TargetScan or miRTarBase (see Supplemental Table S3). miR-122-5p (upregulated at 7 days, connected with the mRNA CHST12 and NLGN3), and miR-4677-5p (upregulated, ZNF516).

Secondary PCA and bioinformatics

The secondary principal components (PC2, PC3 and PC4) explain a decreasing proportion of the variation in the differences in gene expression for identified pathways (Supplemental Table S4). The PC2 explained a modest 18% of the variation in the difference in gene expression for the Protein Kinase A Signaling pathway which showed significant differences between 72 h vs. baseline (p = 0.01). The remaining PC3 or PC4 explained a trivial amount of variation and do not likely represent a global pattern for the respective pathways. Supplemental Figures S2 and S3 provide a complete graphical summary of the pathways for the 72 h vs. baseline mRNA DE and 7 days vs. baseline mRNA DE, respectively.

miRNA/Variable Associations

Finally, we tested whether miRNA miR-3613-5p or miR-3690-5p were associated with key demographic, comorbidities or clinical characteristics of the ICH (Supplemental Tables S5a and S5b). In this modest sample size, there was no evidence of an association between any of these patient characteristics and either miRNA (P ≥ 0.10). However, these results should be viewed with caution as the statistical power is limited.

Discussion

In this integrated analysis of mRNA and miRNA in serial peripheral blood samples from ICH patients, we found neutrophil degranulation was a significantly upregulated pathway at 7 days vs. baseline. miR-3613-5p and miR-3690-5p were downregulated at 7 days vs. baseline and were the two miRNA most correlated with the neutrophil degranulation pathway. To the best of our knowledge, this is the first report of these two miRNA in ICH. A single miRNA can regulate expression of a large number of target genes [17]. miR-3613 is known to influence factors important for cell proliferation; [36] this includes targeting polyamines which have been associated with an increased risk of adverse outcomes in stroke patients [37] and neuronal injury in stroke [38]. miR-3690 is also a known regulator of cellular proliferation [39, 40] miR-3613 and miR-3690 have been found to share similar sequence and function with other miRNA, such as let-7b, which are key for the development and function of neutrophils [41]. The miRNA let-7b induces anti-inflammatory effects in neutrophils; [42] therefore, decreased let-7b expression would be expected to increase inflammatory pathophysiology in neutrophils. Similarly, in the current study, miR-3613 and miR-3690 had decreased expression at 7 days vs. baseline while the neutrophil degranulation pathway was increased at 7 days.

While we found neutrophil degranulation was a significantly upregulated pathway at 7 days vs. baseline, it was not at 72 h vs. baseline. The principal component analysis (PCA) provided additional support for these findings, as the results suggested neutrophil degranulation was significantly greater at 7 days vs. baseline compared with 72 h vs. baseline. While the timeline of inflammatory changes after ICH varies between patients and may depend on their biological variables and/or the cause of their disease, generally, it is thought inflammatory cell infiltration and cytokine production peak during the first 72 h post-ICH [43]. In this context, our finding of more substantial neutrophil activation after 72 h is hypothesis generating regarding both the potential mediators of this more delayed inflammatory pathophysiology in ICH, as well as opportunities for inhibiting harmful inflammation and/or promoting reparative mechanisms. In light of this work, a more fine-grained exploration of the natural history of neutrophil activation in ICH would be informative.

Regarding prior reports of neutrophils in post-ICH pathophysiology, Carmona-Mora et al. found peripheral neutrophil count increased within a few hours after ICH and remained elevated throughout the first week; the same authors reported, in human ICH studied with RNA-seq, monocyte-related genes were generally downregulated while neutrophil gene expression was upregulated [44]. Neutrophils are known to migrate from peripheral blood to the location of the cerebral hemorrhage where the cells secrete factors such as cytokines, reactive oxygen species, proteases, and neutrophil extracellular traps (NETs). Neutrophils have been implicated in both beneficial and harmful effects after ICH with the associated time course being insufficiently understood [45, 46]. In vitro studies have indicated heme, which is produced by erythrocyte hemolysis and would be present in cerebral hemorrhages, triggers neutrophil activation and promotes release of NETs [47]. In a post-mortem study of ICH patients, NETs were found in the hematoma and the surrounding brain tissue, suggesting NETs not only interact with early hemostasis in the hematoma core, but also the surrounding neuroinflammatory response [48]. When 78 ICH subjects were compared with 35 healthy controls, biomarkers of neutrophil presence and NET activation were increased in plasma from ICH subjects, and such markers were also upregulated in intracerebral hematoma samples collected during surgical evacuation [49].

In the reported study, the first 72 h were dominated by enriched pathways related to neuronal function and synaptic transmission. However, several genes from these pathways are also important for platelet activation and function. Goubau et al. described similarities in granule trafficking mechanisms between neurons and platelets [50]. NRGN (neurogranin), a gene prominent in our network analysis and with increased expression at 72 h compared with baseline, is found in granule-like structures in pyramidal cells of the brain and has reported involvement with synaptic plasticity, synaptic regeneration, and long-term potentiation [51]. NGRN is also highly expressed in platelets [52]. The upregulation of the integrins ITGB3 (integrin beta 3) and ITGA2B (integrin subunit alpha 2b) at 72 h is consistent with reports of integrins being cell adhesion receptors which play essential roles in the control of neuronal connectivity including synapse formation, maintenance, and plasticity [53]. These same integrins are also essential for platelet function [54]. KCNH3 (potassium voltage-gated channel subfamily H, member 3), upregulated at 72 h, is a member of a family of potassium voltage-gated channels and a potent regulator of neuron excitability [55]. There is uncertainty to what extent the mRNA findings at 72 h in our study, which analyzed circulating whole blood, were due to changes in platelets versus brain-specific processes like the release of neuronal microparticles. The literature reports brain-derived microparticles, which are known to contain mRNA from the parent cell, are released into the circulation after damage to the brain like ischemic stroke [56, 57] and traumatic brain injury [58]. Release of such microparticles could account for the observed changes in mRNA expression, related to neuronal function, in our study. However, platelets also have a dynamic transcriptome in the setting of inflammation [59–61] and could contribute to the transcriptomic signal we identified. Additional investigation is needed to characterize the potential contribution of neuronal microparticles or cells released into the circulation after ICH compared with changes derived from platelets.

In our study, genes related to ribosomal biogenesis were upregulated at 7 days vs. baseline and highly represented in the network analyses of differentially expressed genes for this timepoint comparison. Considering reports that reparative processes may become more prominent by 7 days after ICH [62], this would be consistent with our finding of ribosomal biogenesis, a process associated with repair, being increased at 7 days. Ribosomes have a primary function of using mRNA as a template to synthesize proteins with amino acids as the building blocks [63]. While ribosomal biogenesis is a critical component of the response of neurons to stimuli, many aspects of this response are poorly understood for neurons, specifically, such as the spatiotemporal kinetics and the relationship of these changes to synaptic signaling [64].

Strengths of the study include the utilization of clinical samples from ICH patients, acute enrollment with collection of the first sample within 24 h of last known normal, three serial samples for each subject, RNA-seq technology which provides a non-biased and highly accurate transcriptomic assessment, data from both mRNA-seq and miRNA-seq, utilization of principal component analysis in addition to bioinformatic methodology, and calculation of correlation coefficients to determine miRNA/mRNA pairs prior to bioinformatic tools to predict miRNA targets of the mRNA. Limitations include the moderate sample size of 27 subjects, the testing of peripheral whole blood as opposed to also including plasma biomarkers and/or cell-specific enrichment, and a lack of clinical/functional outcomes.

Conclusion

We successfully performed a study of mRNA-seq, miRNA-seq and bioinformatic analysis in ICH patents with three serial peripheral blood samples drawn in the first week after the stroke. There was shift in differentially expressed mRNA from processes related to synaptic transmission, neuronal function, and inflammation in the first 72 h followed by a dominance of neutrophil degranulation and ribosomal biogenesis by 7 days. miR-3613-5p and miR-3690-5p were downregulated at 7 days vs. baseline and were the miRNA most correlated with the neutrophil degranulation-related differentially expressed genes. Further research would be beneficial regarding neutrophils in post-ICH inflammatory pathophysiology and functional validation studies of miR-3613 and miR-3690 as potential regulators of neutrophil degranulation after ICH.

Abbreviations

ICH

Intracerebral hemorrhage

RNA

Ribonucleic acid

mRNA-seq

mRNA-sequencing

miRNA-seq

microRNA-sequencing

DE

Differential expression

QC

Quality control

bp

Base pairs

ng

Nanograms

PCR

Polymerase chain reaction

PC

Principal component

Authors’ contributions

KBW led the study design, was PI for the grant funding the work, contributed to the main manuscript text, and facilitated communication with the coauthors.MCM, DL, HCA., AZ, and CDL performed the bioinformatics/biostatistical analysis, contributed to composing the manuscript text, and either directly produced or provided the data for all tables and figures.XZ directed the mRNA-seq and miRNA-seq laboratory analysis and contributed to the methodology of the manuscript text.RC and FD provided consultation and manuscript text for neutrophil-related pathophysiology and associated molecular biology.All authors contributed to the manuscript through discussion of the study concept, related background, and findings with KBW, and, in many cases, other coauthors; all authors were given an opportunity to provide edits and comments of the full manuscript prior to approving the text.

Funding

The research was funded by a Transformational Project Award from the American Heart Association, Grant #20TPA35490044.

Data availability

The datasets generated and/or analysed during the current study, including mRNA-seq and miRNA-seq data, are available in the Gene Expression Omnibus (GEO) repository, access number GSE296792. The data will be publicly available upon acceptance of the manuscript for publication.

Declarations

Ethics approval and consent to participate

The research reported in the manuscript was approved by the Institutional Review Board at the University of Cincinnati, Cincinnati, Ohio, United States of America, and was conducted in accordance with the Declaration of Helsinki. Consent to participate was provided by all subjects or a legally authorized representative.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.