BackgroundBrain volume loss is an important surrogate marker for assessing disability in MS; however, contribution of gray and white matter to the whole brain volume loss needs further examination in the context of specific MS treatment.ObjectivesTo examine whole and segmented gray, white, thalamic, and corpus callosum volume loss in stable patients receiving natalizumab for 2–5 years.MethodsThis was a retrospective study of 20 patients undergoing treatment with natalizumab for 24–68 months. Whole brain volume loss was determined with SIENA. Gray and white matter segmentation was done using FAST. Thalamic and corpus callosum volumes were determined using Freesurfer. T1 relaxation values of chronic hypointense lesions (black holes) were determined using a quantitative, in-house developed method to assess lesion evolution.ResultsOver a mean of 36.6 months, median percent brain volume change (PBVC) was -2.0% (IQR 0.99–2.99). There was decline in gray (p = 0.001) but not white matter (p = 0.6), and thalamic (p = 0.01) but not corpus callosum volume (p = 0.09). Gray matter loss correlated with PBVC (Spearman’s r = 0.64, p = 0.003) but not white matter (Spearman’s r = 0.42, p = 0.07). Age significantly influenced whole brain volume loss (p = 0.010, multivariate regression), but disease duration and baseline T2 lesion volume did not. There was no change in T1 relaxation values of lesions or T2 lesion volume over time. All patients remained clinically stable.ConclusionsThese results demonstrate that brain volume loss in MS is primarily driven by gray matter changes and may be independent of clinically effective treatment.
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, which makes p75NTR’s related signaling networks an interesting and challenging initial point of investigation. 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. Genome-wide pathway analysis showed total of 16 enriched hierarchical clusters. A total of 278 enriched single pathways were also identified (p < 0.05, false discovery rate corrected). Gene network analyses showed multiple known and new targets in the p75NTR gene network. This study provides a comprehensive analysis and investigation into the current knowledge of p75NTR signaling networks and pathways. These results also identify several genes and their respective protein products as involved in the p75NTR network, which have not previously been clearly studied in this pathway. These results can be used to generate novel hypotheses to gain a greater understanding of p75NTR in acute brain injuries, neurodegenerative diseases and general response to cellular damage.
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<0.05) that were in common across all acute brain injury types when compared between favorable and unfavorable groups. From this pool miRWalk target analysis identified 4 miRNAs (hsa-miR-146b-3p, hsa-miR-485-3p, hsa-miR-5010-5p, hsa-miR-485-5p) that targeted to known plasticity mechanism. LDA of these four miRNAs resulted an equation with canonical scores: 0.636 x [hsa-miR-146b-3p] + 0.576 x [hsa-miR-485-3p] + 0.652 x [hsa-miR-5010-5p] + 0.372 x [hsa-miR-485-5p]. The receiver operating characteristic curve generated showed AUC = 94.1%, 95% CI = (0.849, 1.00), p = 0.016 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.
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