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Background: Neuronal recovering processes after acute brain injuries such as aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS) and traumatic brain injury (TBI) are complex including brain plasticity and synaptic regeneration mechanisms. Temporal changes in lipidomic profile after different acute brain injuries may reveal new insights about brain plasticity, recovery and biomarker development after acute injury. Hypotheses: Measurable temporal changes in serum lipidomic profile may be similar irrespective of the type of the brain injury and may reveal conserved molecular aberrations after different types of acute brain injuries. These common changes may also be associated to outcome and serve as possible biomarkers. Methods: Prospective cohort (n=74) consisted of IS (n=30), aSAH (n=31) and TBI (n=13) patients. Serum samples were collected in two time points after the insult (early 24-48h and late 120-192h). Lipidomic profiling of 1153 lipids was performed using Lipidyzer Platform. The lipidomics data was analyzed with Metaboanalyst for ANOVA, PCA, heatmap, box plots with following settings: missing values were imputated with KNN using 20% cutoff, log-transformed and auto-scaled. Results: We identified four diacylglycerols (DAG), one cholesterol ester (CE), seven triacylglycerols (TAG) and one sphingomyelin (SM) which showed increased levels in late serum samples across all types of brain injuries (p Conclusion: Changes in lipidomic profiles across time were identified. This suggests common lipidomic characteristics irrespective of the type of brain insult and encourage further studies. Interestingly, lipidomic profiles may be associated to recovery after acute brain injury and further analyses of this data will follow.

Background: Neuronal recovering processes after acute brain injuries such as aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS) and traumatic brain injury (TBI) are complex including brain plasticity and synaptic regeneration mechanisms. Hypotheses: Transient changes in metabolome after different acute brain injuries may reveal affiliated metabolites, metabolic pathways and associations that can also reflect important biological mechanisms. Measurable temporal changes in serum metabolomic profile may be similar irrespective of the type of the brain injury. These metabolomic commonalities may be associated to outcome. Methods: Prospective cohort (n=74) consisted of IS (n=30), aSAH (n=31) and TBI (n=13) patients. Serum samples were collected in two time points after the insult (early 24-48h and late 120-192h). Orbitrap Profiling of 462 metabolites was used to measure metabolites. Outcome was measured 90 days after injury (mRS favorable 0-3, unfavorable 4-6). The metabolomics data was imputed for missing value using KNN with 20% cut off for missing value, log-transformed and autoscaled. For statistics, t-test and ANOVA was performed and FDR-corrected p-value 0.05 was considered to be statistically significant. Results: We identified four similarly temporally increased metabolite levels across all acute brain injury types (p Conclusion: Similar metabolic changes across time were identified with apparent associations to outcome. This suggests important common metabolic characteristics irrespective of the type of brain insult and encourage further studies and validation. Identified metabolites may be further developed as a prognostic biomarker and may indicate new metabolic pathways active irrespective the type of brain injury reflecting biological mechanisms.

Background: Brain plasticity is important processes in recovering after different types of acute brain injuries such as aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS) and traumatic brain injury (TBI). Knowledge gaps still exist which miRNAs contribute to recovery of the acute brain injuries. Hypotheses: Temporally differentially expressed (DE) miRNAs after acute brain injuries may have association to outcome irrespective the type of the acute brain injury. MiRNAs that are DE across different type of brain injuries may reveal important conserved associations that can reflect important biological mechanisms and serve as a biomarker. Methods: Prospective cohort (n=24) consisted of IS (n=8), aSAH (n=8) and TBI (n=8) patients. Two serum samples were collected per patient (early 24-48h and late 120-192h after injury). Outcome was measured 90 days after injury (mRS favorable 0-3 (n=15), unfavorable 4-6 (n=9). MiRNAs were extracted from the samples (total n = 48 samples). Sequencing was performed and DESeq2 was used to identify DE miRNAs. The miRNA putative target genes were predicted with miRWalk. Normalized expression values of identified DE miRNAs were used and linear canonical discriminant analysis (LDA) was performed. Canonical scores were used to build combinatory biomarker with logistic modelling predicting outcome. Results: We identified 22 temporally DE miRNAs (p Conclusions: Results show that combinatory biomarker of identified miRNAs perform well across different type of brain injuries. Validation in larger cohort is justified. Identified common miRNAs also provide new targets for mechanistical validation in disease models of TBI and stroke.

Introduction: Brain plasticity and synaptic regeneration are important processes in recovering after acute brain injuries such as aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS) and traumatic brain injury (TBI). p75 neurotrophic receptor (p75NTR) is a consequential receptor modulating brain plasticity and apoptosis. Hypotheses: Measurable changes in serum levels of p75NTR mechanistically linked proteins are associated to recovery of acute brain injuries. Differentially expressed (DE) miRNAs after acute brain injuries target to p75NTR pathway and have association to outcome. Methods: Concentrations of p75NTR, NGF, sortilin, IL1β, TNFα and cyclophilin were measured from serum using ELISA. Prospective cohort (n=75) consisted of IS (n=30), aSAH (n=31) and TBI (n=14) patients. Serum samples were collected 24-48h after insults. Late samples (120-192h) were also taken from 12 aSAH patients. Outcome was measured 90 days after injury (mRS favorable 0-4, unfavorable 5-6). We generated characteristic curves and area under the curve (AUC) for weighted linear combination of the biomarkers with a linear discriminant analysis. MiRNAs were extracted from the cohort (early and late samples, n=53). Results: In the whole cohort (n=75) combination of cyclophilin, IL1β, sortilin, p75NTR, NGF and TNFα predicted unfavorable mRS (AUC 0.69, p=0.01). In IS group combination of cyclophilin, sortilin, p75NTR, NGF and TNFα predicted unfavorable mRS (AUC 0.84, p=0.02). In aSAH group combination of IL1β, p75NTR and NGF predicted unfavorable mRS (AUC 0.77, p=0.07). In 12 aSAH patient sub-group (late samples included) combination of cyclophilin, IL1β, sortilin, p75NTR and TNFα predicted unfavorable mRS (AUC 1.0, p=0.07). In TBI group combination of cyclophilin, IL1β, p75NTR, NGF and TNFα predicted unfavorable mRS (AUC 0.79, p=0.08). In the aSAH group 2 DE miRNAs were detected, 4 in the IS group and 7 in the TBI group (pp75NTR , NGF , IL1 β , cyclophilin ( PPIA ), and sortilin ( SORT1 ). Conclusions: p75NTR and mechanistically linked proteins predict outcome across different types of brain injuries suggesting a common mechanism irrespective of the type of brain insult. Identified DE miRNAs targeted studied molecules.

Introduction: Therapeutic interest into the neurotrophins resides in their ability to regulate the process of neuronal and synaptic regeneration following acute brain injuries such as intracerebral hemorrhage, subarachnoid hemorrhage and ischemic stroke. P75 neurotrophic receptor (p75NTR) is an important receptor for the role of neurotrophins in modulating brain plasticity and apoptosis. The current understanding of the role of p75NTR in cellular adaptation following pathological insults remains blurred. Methods: We identified p75NTR and related genes through extensive data mining of a PubMed literature search including published works related to p75NTR from the past 20 years. Bioinformatic network and pathway analyses of identified genes (n=235) were performed using ReactomeFIViz in Cytoscape based on the highly reliable Reactome functional interaction network algorithm. This approach merges interactions extracted from human curated pathways with predicted interactions from machine learning. Results: Genome-wide pathway analysis showed total of 16 enriched hierarchical clusters. A total of 278 enriched single pathways were also identified (pp75NTR gene network. This study provides a comprehensive analysis and investigation into the current knowledge of p75NTR signaling networks and pathways. Discussion: This study provides the largest comprehensive gene and functional network library of p75NTR and incorporates current knowledge using a large dataset approach that increases the overall understanding of complex p75NTR networks. These results suggest both new possible target genes for further investigation in p75NTR research, while also validating previously conducted research. These results can be used to generate novel hypotheses to gain a greater understanding of p75NTR in stroke. Future directions: We are currently sequencing miRNA libraries from ischemic and hemorrhagic stroke patients’ plasma with longitudinal plasma samples and clinical data. This data together with presented results will be used to test identified novel targets in p75NTR engineered models in order seek novel therapeutic strategies to increase recovery after stroke.