Blood-Brain Barrier Dysfunction and Exposure to Head Impacts in University Football Players.

OBJECTIVE: To investigate the link between dysfunction of the blood-brain barrier (BBB) and exposure to head impacts in concussed football athletes. DESIGN: This was a prospective, observational pilot study. SETTING: Canadian university football. PARTICIPANTS: The study population consisted of 60 university football players, aged 18 to 25. Athletes who sustained a clinically diagnosed concussion over the course of a single football season were invited to undergo an assessment of BBB leakage. INDEPENDENT VARIABLES: Head impacts detected using impact-sensing helmets were the measured variables. MAIN OUTCOME MEASURES: Clinical diagnosis of concussion and BBB leakage assessed using dynamic contrast-enhanced MRI (DCE-MRI) within 1 week of concussion were the outcome measures. RESULTS: Eight athletes were diagnosed with a concussion throughout the season. These athletes sustained a significantly higher number of head impacts than nonconcussed athletes. Athletes playing in the defensive back position were significantly more likely to sustain a concussion than remain concussion free. Five of the concussed athletes underwent an assessment of BBB leakage. Logistic regression analysis indicated that region-specific BBB leakage in these 5 athletes was best predicted by impacts sustained in all games and practices leading up to the concussion-as opposed to the last preconcussion impact or the impacts sustained during the game when concussion occurred. CONCLUSIONS: These preliminary findings raise the potential for the hypothesis that repeated exposure to head impacts may contribute to the development of BBB pathology. Further research is needed to validate this hypothesis and to test whether BBB pathology plays a role in the sequela of repeated head trauma.

Concussion susceptibility is mediated by spreading depolarization-induced neurovascular dysfunction.

The mechanisms underlying the complications of mild traumatic brain injury, including post-concussion syndrome, post-impact catastrophic death, and delayed neurodegeneration remain poorly understood. This limited pathophysiological understanding has hindered the development of diagnostic and prognostic biomarkers and has prevented the advancement of treatments for the sequelae of mild traumatic brain injury. We aimed to characterize the early electrophysiological and neurovascular alterations following repetitive mild traumatic brain injury and sought to identify new targets for the diagnosis and treatment of individuals at risk of severe post-impact complications. We combined behavioural, electrophysiological, molecular, and neuroimaging techniques in a rodent model of repetitive mild traumatic brain injury. In humans, we used dynamic contrast-enhanced MRI to quantify blood-brain barrier dysfunction after exposure to sport-related concussive mild traumatic brain injury. Rats could clearly be classified based on their susceptibility to neurological complications, including life-threatening outcomes, following repetitive injury. Susceptible animals showed greater neurological complications and had higher levels of blood-brain barrier dysfunction, transforming growth factor ß (TGFß) signalling, and neuroinflammation compared to resilient animals. Cortical spreading depolarizations were the most common electrophysiological events immediately following mild traumatic brain injury and were associated with longer recovery from impact. Triggering cortical spreading depolarizations in mild traumatic brain injured rats (but not in controls) induced blood-brain barrier dysfunction. Treatment with a selective TGFß receptor inhibitor prevented blood-brain barrier opening and reduced injury complications. Consistent with the rodent model, blood-brain barrier dysfunction was found in a subset of human athletes following concussive mild traumatic brain injury. We provide evidence that cortical spreading depolarization, blood-brain barrier dysfunction, and pro-inflammatory TGFß signalling are associated with severe, potentially life-threatening outcomes following repetitive mild traumatic brain injury. Diagnostic-coupled targeting of TGFß signalling may be a novel strategy in treating mild traumatic brain injury.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.

BACKGROUND: The gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks. METHODS: A total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels. RESULTS: Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus. CONCLUSIONS: Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.

Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease.

BACKGROUND: Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. RESULTS: We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response. CONCLUSIONS: We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.