Proteomic Profiling of Extracellular Vesicles Separated from Plasma of Former National Football League Players at Risk for Chronic Traumatic Encephalopathy

The Use of Biofluid Markers to Evaluate the Consequences of Sport-Related Subconcussive Head Impact Exposure: A Scoping Review

BACKGROUND

Amidst growing concern about the safety of sport-related repetitive subconcussive head impacts (RSHI), biofluid markers may provide sensitive, informative, and practical assessment of the effects of RSHI exposure.

OBJECTIVE

This scoping review aimed to systematically examine the extent, nature, and quality of available evidence from studies investigating the effects of RSHI on biofluid markers, to identify gaps and to formulate guidelines to inform future research.

METHODS

PRISMA extension for Scoping Reviews guidelines were adhered to. The protocol was pre-registered through publication. MEDLINE, Scopus, SPORTDiscus, CINAHL, PsycINFO, Cochrane Library, OpenGrey, and two clinical trial registries were searched (until March 30, 2022) using descriptors for subconcussive head impacts, biomarkers, and contact sports. Included studies were assessed for risk of bias and quality.

RESULTS

Seventy-nine research publications were included in the review. Forty-nine studies assessed the acute effects, 23 semi-acute and 26 long-term effects of RSHI exposure. The most studied sports were American football, boxing, and soccer, and the most investigated markers were (in descending order): S100 calcium-binding protein beta (S100B), tau, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), neuron-specific enolase (NSE), brain-derived neurotrophic factor (BDNF), phosphorylated tau (p-tau), ubiquitin C-terminal hydrolase L1 (UCH-L1), and hormones. High or moderate bias was found in most studies, and marker-specific conclusions were subject to heterogeneous and limited evidence. Although the evidence is weak, some biofluid markers-such as NfL-appeared to show promise. More markedly, S100B was found to be problematic when evaluating the effects of RSHI in sport.

CONCLUSION

Considering the limitations of the evidence base revealed by this first review dedicated to systematically scoping the evidence of biofluid marker levels following RSHI exposure, the field is evidently still in its infancy. As a result, any recommendation and application is premature. Although some markers show promise for the assessment of brain health following RSHI exposure, future large standardized and better-controlled studies are needed to determine biofluid markers' utility.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BACKGROUND

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

METHODS

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

Five key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.

CONCLUSION

In summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

ATP1A3 as a target for isolating neuron-specific extracellular vesicles from human brain and biofluids

Neuron-derived extracellular vesicles (NDEVs) are potential biomarkers of neurological diseases although their reliable molecular target is not well established. Here, we demonstrate that ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3) is abundantly expressed in extracellular vesicles (EVs) isolated from induced human neuron, brain, cerebrospinal fluid, and plasma in comparison with the presumed NDEV markers NCAM1 and L1CAM by using super-resolution microscopy and biochemical assessments. Proteomic analysis of immunoprecipitated ATP1A3+ brain-derived EVs shows higher enrichment of synaptic markers and cargo proteins relevant to Alzheimer's disease (AD) compared to NCAM1+ or LICAM+ EVs. Single particle analysis shows the elevated amyloid-β positivity in ATP1A3+ EVs from AD plasma, providing better diagnostic prediction of AD over other plasma biomarkers. Thus, ATP1A3 is a reliable target to isolate NDEV from biofluids for diagnostic research.

Blood-Based Biomarkers in the Diagnosis of Chronic Traumatic Encephalopathy: Research to Date and Future Directions

Chronic Traumatic Encephalopathy (CTE) is a neurodegenerative disease consistently associated with repetitive traumatic brain injuries (TBIs), which makes multiple professions, such as contact sports athletes and the military, especially susceptible to its onset. There are currently no approved biomarkers to diagnose CTE, thus it can only be confirmed through a post-mortem brain autopsy. Several imaging and cerebrospinal fluid biomarkers have shown promise in the diagnosis. However, blood-based biomarkers can be more easily obtained and quantified, increasing their clinical feasibility and potential for prophylactic use. This article aimed to comprehensively review the studies into potential blood-based biomarkers of CTE, discussing common themes and limitations, as well as suggesting future research directions. While the interest in blood-based biomarkers of CTE has recently increased, the research is still in its early stages. The main issue for many proposed biomarkers is their lack of selectivity for CTE. However, several molecules, such as different phosphorylated tau isoforms, were able to discern CTE from different neurodegenerative diseases. Further, the results from studies on exosomal biomarkers suggest that exosomes are a promising source of biomarkers, reflective of the internal environment of the brain. Nonetheless, more longitudinal studies combining imaging, neurobehavioral, and biochemical approaches are warranted to establish robust biomarkers for CTE.

Comprehensive characterization of human brain-derived extracellular vesicles using multiple isolation methods: Implications for diagnostic and therapeutic applications

Extracellular vesicles (EVs) have emerged as critical mediators of intercellular communication and promising biomarkers and therapeutics in the central nervous system (CNS). Human brain-derived EVs (BDEVs) provide a comprehensive snapshot of physiological changes in the brain's environment, however, the isolation of BDEVs and the comparison of different methods for this purpose have not been fully investigated. In this study, we compared the yield, morphology, subtypes and protein cargo composition of EVs isolated from the temporal cortex of aged human brains using three established separation methods: size-exclusion chromatography (SEC), phosphatidylserine affinity capture (MagE) and sucrose gradient ultracentrifugation (SG-UC). Our results showed that SG-UC method provided the highest yield and collected larger EVs compared to SEC and MagE methods as assessed by transmission electron microscopy and nanoparticle tracking analysis (NTA). Quantitative tandem mass-tag (TMT) mass spectrometry analysis of EV samples from three different isolation methods identified a total of 1158 proteins, with SG-UC showing the best enrichment of common EV proteins with less contamination of non-EV proteins. In addition, SG-UC samples were enriched in proteins associated with ATP activity and CNS maintenance, and were abundant in neuronal and oligodendrocytic molecules. In contrast, MagE samples were more enriched in molecules related to lipoproteins, cell-substrate junction and microglia, whereas SEC samples were highly enriched in molecules related to extracellular matrix, Alzheimer's disease and astrocytes. Finally, we validated the proteomic results by performing single-particle analysis using the super-resolution microscopy and flow cytometry. Overall, our findings demonstrate the differences in yield, size, enrichment of EV cargo molecules and single EV assay by different isolation methods, suggesting that the choice of isolation method will have significant impact on the downstream analysis and protein discovery.

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach

Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invasive and enable repeatable measurements based on body fluids. However, EVs from various cells and organs are mixed in the blood, acting as potential obstacles for brain diagnostic systems using BDEVs. Therefore, it is important to screen appropriate brain EV markers to isolate BDEVs in blood. Here, we established a strategy for screening potential BDEV biomarkers. To collect various molecular data from the BDEVs, we propose that the sensitivity and specificity of the diagnostic system could be enhanced using machine learning and AI analysis. This BDEV-based diagnostic strategy could be used to diagnose various brain diseases and will help prevent disease through early diagnosis and early treatment.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Combinational treatments of RNA interference and extracellular vesicles in the spinocerebellar ataxia

Spinocerebellar ataxia (SCA) is an autosomal dominant neurodegenerative disease (ND) with a high mortality rate. Symptomatic treatment is the only clinically adopted treatment. However, it has poor effect and serious complications. Traditional diagnostic methods [such as magnetic resonance imaging (MRI)] have drawbacks. Presently, the superiority of RNA interference (RNAi) and extracellular vesicles (EVs) in improving SCA has attracted extensive attention. Both can serve as the potential biomarkers for the diagnosing and monitoring disease progression. Herein, we analyzed the basis and prospect of therapies for SCA. Meanwhile, we elaborated the development and application of miRNAs, siRNAs, shRNAs, and EVs in the diagnosis and treatment of SCA. We propose the combination of RNAi and EVs to avoid the adverse factors of their respective treatment and maximize the benefits of treatment through the technology of EVs loaded with RNA. Obviously, the combinational therapy of RNAi and EVs may more accurately diagnose and cure SCA.

The evolving role of extracellular vesicles (exosomes) as biomarkers in traumatic brain injury: Clinical perspectives and therapeutic implications

Developing effective disease-modifying therapies for neurodegenerative diseases (NDs) requires reliable diagnostic, disease activity, and progression indicators. While desirable, identifying biomarkers for NDs can be difficult because of the complex cytoarchitecture of the brain and the distinct cell subsets seen in different parts of the central nervous system (CNS). Extracellular vesicles (EVs) are heterogeneous, cell-derived, membrane-bound vesicles involved in the intercellular communication and transport of cell-specific cargos, such as proteins, Ribonucleic acid (RNA), and lipids. The types of EVs include exosomes, microvesicles, and apoptotic bodies based on their size and origin of biogenesis. A growing body of evidence suggests that intercellular communication mediated through EVs is responsible for disseminating important proteins implicated in the progression of traumatic brain injury (TBI) and other NDs. Some studies showed that TBI is a risk factor for different NDs. In terms of therapeutic potential, EVs outperform the alternative synthetic drug delivery methods because they can transverse the blood–brain barrier (BBB) without inducing immunogenicity, impacting neuroinflammation, immunological responses, and prolonged bio-distribution. Furthermore, EV production varies across different cell types and represents intracellular processes. Moreover, proteomic markers, which can represent a variety of pathological processes, such as cellular damage or neuroinflammation, have been frequently studied in neurotrauma research. However, proteomic blood-based biomarkers have short half-lives as they are easily susceptible to degradation. EV-based biomarkers for TBI may represent the complex genetic and neurometabolic abnormalities that occur post-TBI. These biomarkers are not caught by proteomics, less susceptible to degradation and hence more reflective of these modifications (cellular damage and neuroinflammation). In the current narrative and comprehensive review, we sought to discuss the contemporary knowledge and better understanding the EV-based research in TBI, and thus its applications in modern medicine. These applications include the utilization of circulating EVs as biomarkers for diagnosis, developments of EV-based therapies, and managing their associated challenges and opportunities.

Chronic Traumatic Encephalopathy as a Preventable Environmental Disease

In this Perspective we explore the evolution of our understanding of chronic traumatic encephalopathy (CTE) and its relationship with repetitive head injury. As with many neurodegenerative conditions, there is an imperfect correspondence between neuropathology and clinical phenotype, but unlike other neurodegenerative diseases, CTE has a discrete and easily modifiable risk factor: exposure to repetitive head injury. Consequently, evaluation of the evidence regarding exposure to repetitive head injury and CTE risk should be undertaken using public or occupational health frameworks of medical knowledge. The current debate over the existence of CTE as a disease of concern is fuelled in part by immediate medico-legal considerations, and the involvement of high-profile athletes, with inevitable media interest. Moving beyond this debate has significant potential to address and reduce disease impact in the near future, and provide novel insights into mechanisms underlying abnormal protein accumulation in CTE and other neurodegenerative diseases.

Developing Biomarkers of Mild Traumatic Brain Injury: Promise and Progress of CNS-Derived Exosomes

Mild traumatic brain injuries (mTBI) are common injuries across civilian and military populations. Although most individuals recover after mTBI, some individuals continue to show long-term symptoms as well as increased risk for neurodegenerative and neuropsychiatric disorders. Currently, diagnosing TBI severity relies primarily on self-report and subjective symptoms, with limited tools for diagnosis or prognosis. Brain-derived exosomes, a form of extracellular vesicle, may offer a solution for interpreting injury states by aiding in diagnosis as well as outcome prediction with relatively low patient burden. Exosomes, which are released into circulation, contain both protein and RNA cargo that can be isolated and quantified, providing a molecular window into molecular status of the exosome source. Here we examined the current literature studying the utility of exosomes, in particular neuronal- and astrocyte-derived exosomes, to identify protein and miRNA biomarkers of injury severity, trajectory, and functional outcome. Current evidence supports the potential for these emerging new tools to capture an accessible molecular window into the brain as it responds to a traumatic injury, however a number of limitations must be addressed in future studies. Most current studies are relatively small and cross sectional; prospective, longitudinal studies across injury severity, and populations are needed to track exosome cargo changes after injury. Standardized exosome isolation as well as advancement in identifying/isolating exosomes from CNS-specific tissue sources will improve mechanistic understanding of cargo changes as well as reliability of findings. Exosomes are also just beginning to be used in model systems to understand functional effects of TBI-associated cargo such as toxicity. Finally linking exosome cargo changes to objective markers of neuronal pathology and cognitive changes will be critical in validating these tools to provide insights into injury and recovery states after TBI.

Plasma Neurofilament Light Chain Predicts Mortality and Long-Term Neurological Outcomes in Patients with Intracerebral Hemorrhage

Patients with intracerebral hemorrhage (ICH) often suffer from heterogeneous long-term neurological deficits, such as cognitive decline. Our ability to measure secondary brain injury to predict the long-term outcomes of these patients is limited. We investigated whether the blood neurofilament light chain (NfL) can monitor brain injury and predict long-term outcomes in patients with ICH. We enrolled 300 patients with first-episode ICH within 24 h recruited in the Chinese Cerebral Hemorrhage Mechanisms and Intervention study cohort from January 2019 to June 2020. Patients were prospectively followed up for 12 months. Blood samples were collected from 153 healthy participants. Plasma NfL levels determined using a single-molecule array revealed a biphasic increase in plasma NfL in ICH patients compared to healthy controls, with the first peak at around 24 h and a second elevation from day 7 through day 14 post-ICH. Plasma NfL levels were positively correlated with hemorrhage volume, National Institute of Health Stroke Scale, and Glasgow Coma Scale scores of ICH patients. Higher NfL concentration within 72 h after ictus was independently associated with 6- and 12-month worsened functional outcomes (modified Rankin Scale ≥ 3) and higher all-cause mortality. Magnetic resonance imaging and cognitive function evaluation were available for 26 patients at 6 months post-ICH, and NfL levels measured 7 days post-ictus correlated with decreased white matter fiber integrity and poor cognitive function at 6 months after stroke. These findings suggest that blood NfL is a sensitive marker for monitoring axonal injury post-ICH and can predict long-term functional ability and survival.