Altered microRNA levels in a person’s saliva can help determine if they have experienced a recent concussion

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Doctors may soon be able to more accurately diagnose concussions by measuring the number of certain molecules in a person’s saliva, according to Penn State College of Medicine researchers.

The results of a recent clinical study confirmed that a patient’s spit may be used to aid concussion diagnosis in a non-invasive, non-biased fashion.

Researchers analyzed the saliva of more than 500 study participants for tiny strands of genetic material called micro ribonucleic acid (microRNA).

These molecules play an important role in cellular processes and exist in high amounts in the brain.

The investigators hypothesized that due to the presence of cranial nerves in the mouth, altered microRNA levels could indicate whether a patient is experiencing a concussion.

Concussions occur as a result of physical injury to the head and may result in short-lived symptoms including headaches, dizziness and confusion.

Physicians currently use symptom scales and neurocognitive tests to assess patients and diagnose concussions. Researchers say these methods may not be reliable because they can be subject to patient and physician bias. For example, athletes may underreport a symptom’s severity to return to the field.

“Current methods rely on accurate symptom reporting and honest performance on neurocognitive testing,” said Dr. Steve Hicks, associate professor of pediatrics and principal investigator.

“Analyzing microRNA profiles in saliva following a head trauma is a non-invasive way to test for concussion that can’t be influenced by a patient’s feelings or motives.”

To develop the diagnostic approach, researchers recruited 538 participants across 11 clinical sites. Approximately half the participants had a concussion reported within two weeks of starting the study, while the other half of participants did not, but had conditions that might mimic concussion symptoms including anxiety, depression, attention deficit hyperactivity disorder, exercise-related fatigue or chronic headaches.

The researchers used RNA sequencing to evaluate saliva samples from half of the participants, then used statistical modeling and machine learning to identify noticeable differences between the RNA profiles of participants with concussions and those without.

Once they knew what RNA changes to look for, they tested more than 200 additional participants and were able to successfully identify which patients had concussions.

The accuracy of the saliva approach performed favorably when compared with currently available tests involving balance and reaction time. The results were published in the journal Clinical and Translational Medicine.

“This method has lots of promising applications,” Hicks said. “A rapid, reliable diagnostic means that early, appropriate action can be taken to alleviate the symptoms of patients with concussions.”

Hicks said the work expands upon a smaller pilot study that showed microRNA could be used to predict the duration of concussion symptoms and that further effort is needed to develop the test into a portable technology that can be used field-side by athletic trainers immediately after an injury occurs, by emergency responders at the scene of an accident, or by army medics on a battlefield.

He is working with Quadrant Biosciences, who recently received a grant to do a larger clinical study to validate the method and further develop the technology.


There is growing concern that we may be facing a “concussion epidemic” in sport, military and recreational activities in general with significant associated unrecognized morbidity. Concussion, or mTBI (Harmon et al., 2013) can be defined as a transient impairment of neurological function after mechanical injury and affects up to 3.8 million participants in sports of all kind in the United States alone (Maroon et al., 2014).

However, this injury is also common in the military (DeKosky et al., 2010) and has been described as the signature injury of the recent conflicts in Afghanistan and Iraq. The true incidence of mTBI is difficult to establish and is likely to be far greater than reported figures because type of injury is routinely under-recognized, trivialized and under-reported.

This is partially due to the spontaneous resolution of symptoms in most patients even in the absence of medical care.

However, many studies have demonstrated that individuals who sustain one concussion are potentially more susceptible than others, especially if the new injury occurs before the symptoms from the previous concussion have completely resolved (Tavazzi et al., 2007Vagnozzi et al., 20072008).

Importantly, repeated concussion is associated with depression (Vos et al., 2017) and chronic neurodegenerative conditions in later life, e.g., Parkinson’s disease, motor neuron disease and CTE (Omalu, 2014Gaetz, 2017).

One of the main challenges faced by medical practitioners is the lack of objective parameters to support the diagnosis of concussion and guide return-to play-decisions following the injury (McCrory et al., 2017).

Currently, the vast majority of concussion assessment tools are indirect measures of the response to trauma.

A concussion does not typically cause structural injury to the brain, thus neuroimaging tests such as MRI scan or CT scans are used primarily to rule out a more serious injury, but are not able to exclude the presence of mTBI currently.

Over the last few years, the measurement of biomarkers in biofluids has received growing attention.

Serum/plasma and CSF are most studied biofluids, but it is possible that some central nervous system (CNS)-derived proteins are eventually excreted into body fluids other than CSF and blood.

The presence of the axonal protein Tau in saliva, for example, was demonstrated using mass spectrometry (Shi et al., 2011). In addition, the same research group has also detected Parkinson-related α-synuclein and DJ-1 in this body fluid (Devic et al., 2011).

However, the relationship between salivary concentrations of these proteins and processes within the CNS is far from clear and no conclusive data on disease association have been reported so far.

Saliva is an important physiologic fluid, containing a highly complex mixture of substances, and is rapidly gaining interest for novel approaches to diagnosis, prognosis and management of patients with either oral or systemic diseases. In addition, it is easily collected and stored, and is ideal for POC devices.

In this study, we sought to identify saliva biomarkers from a well-characterized cohort of contact sport-professional and semiprofessional athletes with a view to developing a non-invasive, objective test to support clinical decision-making in sport and military medicine.

Results

Proseek Multiplex Analysis

92 human proteins were analyzed in saliva samples using the Proseek Multiplex Inflammation I panel. All samples met the quality control criteria and 65 of the 92 analyzed proteins were detected in all saliva samples. t-Test was applied for proteins detected and results showed no proteins significantly and differentially expressed between two groups. Volcano plot is represented in Figure 1. A full list of the 65 proteins detected in saliva together with fold changes and standard deviations among groups is also presented in Supplementary Table S2.

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FIGURE 1
Volcano plot of salivary inflammatory proteins. The volcano plot visualizes the p-value (y-axis) and difference in NPX (x-axis) for all 65 analyzed proteins. Proteins on the positive x-axis have higher NPX-values in the non-concussed group and proteins on the negative x-axis have higher NPX-values in the concussed group. The solid line indicates an unadjusted p-value of 0.05 and the dashed line indicates approximated adjusted p-value (FDR) of 0.05.

Nanostring Profiling

Among the 800 microRNAs analyzed by nCounter NanoString in saliva of concussed and non-concussed athletes, 21 miRNAs were selected as differentially expressed across the two populations: hsa-let-7c-5p, hsa-let-7i-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-20a-5p+hsa-miR-20b-5p, hsa-miR-24-3p, hsa-miR-27b-3p, hsa-miR-29a-3p, hsa-miR-29c-3p, hsa-miR-30a-5p, hsa-miR-107, hsa-miR-135b-5p, hsa-miR-142-3p, hsa-miR-148a-3p, hsa-miR-181a-5p, hsa-miR-199b-5p, hsa-miR-221-3p, hsa-miR-324-5p, hsa-miR-424-5p e hsa-miR-652-3p. Results showed a significant upregulation for all the miRNAs mentioned above.

A heat-map is represented in Figure ​2.

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FIGURE 2
Heat-map of DE-miRNAs in saliva of concussed and non-concussed athletes. Heat-map of the miRNAs differentially expressed in saliva of concussed and non-concussed athletes. The values of log2 fold changes for each miRNA are color coded, as shown in the colored bar. Sample clustering obtained through hierarchical clustering approach is shown. NC, non-concussed athletes; C, concussed athletes.

Single TaqMan Assay

In order to validate these findings, we subsequently tested the expression of the 21 selected miRNAs in a separate and independent group, composed of 22 concussed athletes and 10 matched non-concussed athletes. Among these candidate biomarkers for concussion, 5 were significantly upregulated in the validation group. Specifically, miR-27b-3p (p = 0.016), let-7i-5p (p = 0.001), miR-142-3p (p = 0.008), miR-107 (p = 0.028), miR-135b-5p (p = 0.017) confirmed the results obtained by Nanostring analysis (Figure ​3). AUCs for miR-27b-3p (AUC, 0.755; 95% CI, 0.575–0.934), let-7i-5p (AUC, 0.845; 95% CI, 0.681–1), miR-142-3p (AUC, 0.791; 95% CI, 0.634–0.948), miR-107 (AUC, 0.732; 95% CI, 0.565–0.904), miR-135b-5p (AUC, 0.755; 95% CI, 0.573–0.936) are shown in Figure ​4.

We computed four models of multivariate ROC curves, each one built on a combination of 2, 3, 4, or 5 DE miRNAs, respectively, and compared their AUCs (Figure ​5).

The “5 miRNA” model showed the highest AUC: 0.836 (CI: 0.669–0.995). However, the comparison among confusion matrixes of univariate curves and “5 miRNA” model curve did not show an improvement of diagnostic performance for multivariate ROC curve, thus confirming let-7i-5p as the best performing biomarker of mTBI (Table ​2).

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FIGURE 3 – Boxplot of the 5 candidate miRNA biomarkers. Boxplot of relative expression of the five microRNAs which showed a significant upregulation (p < 0.05) in the validation group and assessed by RT-PCR. A non-parametric test (Mann–Whitney U test) was used to compare the level of microRNAs in the two independent groups.
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FIGURE 4
Area under the curve of the five candidate miRNA biomarkers. Receiver-operating characteristic (ROC) curve and corresponding area under the curve (AUC) for biomarkers identified in the validation cohort.
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FIGURE 5
Multivariate ROC Curve analysis. (A) Four models of multivariate ROC curves, each one built on a combination of 2, 3, 4, or 5 DE miRNAs. (B) Five miRNA model ROC curve with confidence intervals highlighted in blue. (C) Sample classification plot and confusion matrix generated from the five miRNA model ROC curve.

Table 2

Comparison of performances of univariate and multivariate ROC curves.

let-7i-5pmiR-142-3pmiR-107miR-27b-3pmiR-135b-5pMultivariate
Sensitivity0.86360.72730.68180.68180.72730.7273
Specificity0.10.20.10.30.20.1
Precision0.67860.66670.6250.68180.66670.64
Negative predictive value0.250.250.1250.30.250.1429
False positive rate0.90.80.90.70.80.9
False discovery rate0.32140.33330.3750.31820.33330.36
False negative rate0.13640.27270.31820.31820.27270.2727
Accuracy0.6250.56250.50.56250.56250.5313

MiRNA Targets and Gene Ontology Analysis

The different distribution of these miRNAs in saliva of concussed athletes is most likely a systemic consequence of the different physiopathology of this typology of trauma. In order to evaluate the biological functions of DE-miRNAs, we computationally searched their validated or predicted targets. Gene ontologies and pathway associations of miRNA targets were analyzed by FatiGo and DAVID.

This analysis showed that they could be involved in important biological processes related to trauma (i.e., response to hypoxia, cell death, neurogenesis, axon repair, myelination) (Figure 6). By screening the Human miRNA tissue atlas, we found that all validated DE-miRNAs, with exclusion of miR-142-3p, were expressed in almost all body tissues, but more abundant in brain (Supplementary Figure S1).FIGURE 6

Pathway enrichment analysis of miRNAs. Over-represented biological functions of let-7i-5p, miR-27b-3p, miR-142-3p, miR-107, miR-135b-5p computed by analyzing their validated molecular targets through FatiGo and David tools. On the left of the histogram are reported the over represented pathways, while, on the right the corresponding p-values of Fisher’s Exact Test. On the x-axis are reported the percentages of gene targets associated to biological functions.

Sperman Correlation Analysis and Paired Comparisons With Neuropsychometric Tests

A summary of the ImPACT and WAIS data obtained from the concussed and non-concussed groups is illustrated in Table ​3. Sperman correlation analysis showed a positive relationship between the ImPACT reaction time percentile and the level of microRNA let-7i-5p (R, 0.49; FRD 0.02) and miR-27b-3p (R, 0.52; FDR 0.02). In addition an inverse correlation was detected between the level of miR-135b-5p and the number of concussions (R, -0.48; FDR 0.05) (Figure ​7).

Table 3

A summary of ImPACT (percentile score with regard to all ImPACT test takers) and WAIS data.

ImPACT domain percentileVerbal memoryVisual memoryMotor speedReaction timeSymptoms scoreCognitive efficiency indexWAIS symbol search score
Mean/Median
Concussed59.8%/67%48%/47%54.6%/55.5%55.6%/72.5%18.8%/4%0.41%/0.4%37.1/36.5
Non-concussed11%/72.5%69.4%/66.5%50.9%/57%41.1%/38%2.63%/1%0.26%/0.3%39.1/40
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FIGURE 7
Sperman correlation of the 5 candidate miRNA biomarkers with neurocognitive assessment tools. Spearman R-values depict correlations between salivary concentrations of the 5 miRNAs of interest and the indexes of ImPACT test and WAIS symbol search and digit span results. Boxes highlight correlations between miRNA levels and clinical indexes which are statistically significant (p < 0.05). Values highlighted with black borders show the statistically significant (p < 0.05) correlations between miRNA levels and clinical indexes.

No statistically significant difference was seen in the ImPACT cognitive efficiency index (summary composite score of all sub domains), or WAIS symbol search score (Mann–Whitney U, p = 0.54 and 0.204, respectively) between the concussed and non-concussed group.

Single TaqMan Assay at Later Time Points

In order to assess the progress or decline of the selected miRNAs at later time points from concussion, we subsequently tested the expression of the 5 selected miRNAs in a separate and independent group, comprising 12 concussed athletes. Expression changes were compared with early concussed athletes (48–72 h) and non-concussed athletes.

Results showed in Supplementary Figure S2 confirmed a p-value < 0.05 between the non-concussed athletes and concussed athletes at early time points. Specifically, miR-27b-3p (p = 0.053), let-7i-5p (p = 0.014), miR-142-3p (p = 0.028), miR-107 (p = 0.015), miR-135b-5p (p = 0.042).

An additional significant result was found in the fold change of let-7i-5p (p = 0.036) between concussed athletes early time point (48–72 h) and concussed athletes later time point (>120 h). No others significant results were found across the groups.

Discussion

Despite the millions of sports-related concussions that occur annually, currently there are no available and sufficiently sensitive molecular-biomarkers to make a clear diagnosis of concussion, to predict recovery and an athlete’s readiness to return to play.

Among the most studied T-tau, NFL in CSF (Hesse et al., 2000Nylen et al., 2006Zetterberg et al., 2006Neselius et al., 2012), S-100β, GFAP and its breakdown products, and UCHL-L1 in plasma/serum, but their utility in the diagnosis of mild to moderate TBI is still unknown (Vos et al., 2010Brophy et al., 2011Papa et al., 2012a,bThelin et al., 2013Di Pietro et al., 2015) and in the majority of the cases their use is limited to alteration of BBB (Zetterberg et al., 2013).

Areas under curve of the most studied protein biomarkers in TBI are presented in Supplementary Table S3 as previously reported by Di Pietro et al. (2018).

In this study, we examined saliva, a novel fluid for discovering new biomarkers within this context. The potential of saliva as a biomarker of mTBI has been increasingly recognized in recent years (Hicks et al., 2018Johnson et al., 2018). A variety of molecular and microbial analytes (Aas et al., 2005Park et al., 2006Bonne and Wong, 2012) have been identified in saliva and considering that salivary glands are highly permeable and enveloped by capillaries, these molecules have the potential to mirror the blood content and diagnose systemic disorders (Burbelo et al., 2012).

Our results showed for the majority of the detected salivary proteins, a moderate but not statistically significant downregulation in concussed athletes. We would interpret this as a programmed shut-down of the synthesis of proteins that are not required following injury but the interplay between different cellular pathways makes the interpretation somewhat speculative. However, this downregulation is in agreement with our previous findings (Di Pietro et al., 2013).

In this previous study, we were able to demonstrate, using an in vitro stretch model of mild TBI, that mTBI triggers a controlled gene program and a hypometabolic state, as an adaptive response finalized to neuroprotection, similar to that found in hibernators and in ischemic preconditioning.

On the contrary, our results show a different and significant expression of microRNAs in the two groups. MiRNAs are a quite recently discovered class of non-coding RNAs, which plays key roles in the regulation of gene expression. They are found in every human tissue and biofluid, are resistant to RNAse degradation and have the ability to cross the BBB (Fire et al., 1998Chan et al., 2005Lim et al., 2005Gauthier and Wollheim, 2006Bi et al., 2009Friedman et al., 2009Li and He, 2012Szafranski et al., 2015).

MiRNAs are attracting increasing interest in clinical research as potential biomarkers for the detection, identification and classification of cancers and other disease states including neurodegenerative diseases and most recently, brain trauma (Redell et al., 2010Vallelunga et al., 2014Ragusa et al., 20162017Di Pietro et al., 2017).

The 5 upregulated microRNAs found in this study, are not brain-specific but they are also expressed in other tissues such as heart, kidney, or testis, as described in two studies aimed to build comprehensive human miRNA tissue atlas catalog and annotating accurate sequence, expression and conservation information for the large number of recently proposed miRNAs (Landgraf et al., 2007Ludwig et al., 2016).

However, 2 of them were described in the context of TBI as potential biomarkers in other biofluids. In particular, miR-27b was described in serum (Bhomia et al., 2016) and CSF (Yang et al., 2016) and miR-142-3p was identified in plasma of patients mildly injured and at greater risk of developing amnesia and therefore, post-concussive syndromes (Mitra et al., 2017).

In addition, other animal studies have described the involvement of these miRs in TBI research.

MiR-27b was found overexpressed in mouse neurons, inhibiting neuronal apoptosis induced by intrauterine hypoxia (Chen et al., 2014). MiR-27a was also found rapidly downregulated in injured cortex and this change coincided with increased expression of the proapoptotic Bcl-2 family members such as Noxa, Puma, and Bax (Sabirzhanov et al., 2014).

MicroRNA let-7i was found in both serum and cerebrospinal fluid immediately after blast wave exposure. In addition, this miRNA plays a role in the regulatory pathways of several inflammatory cytokines and therefore and ideal candidate biomarkers in TBI (Balakathiresan et al., 2012).

Finally, miR-107 regulates granulin/progranulin with implications for traumatic brain injury and neurodegenerative disease (Wang et al., 2010).

According to our results two miRNAs, let-7i-5p and miR-27b-3p, positively correlate with the ImPACT reaction time percentile. This finding suggests that these miRNAs contribute to the process of recovery, enabling natural mechanisms of neuroprotection (Balakathiresan et al., 2012Chen et al., 2014Sabirzhanov et al., 2014) rather than providing a biomarker of biological damage. Interestingly, no information is available about the function of miR-135b-5p, which is negatively correlated with the number of concussions.

Further studies will be required to further elucidate the nature and mechanisms of this miRNA in mTBI. Notably, we found a general upregulation of miRNAs in mTBI patients: we could speculate that the increased release of glutamate observed after concussion can induce an excitatory stimulus on parasympathetic neurons that innervate the submandibular and sublingual salivary glands (Mitoh et al., 2004Guerriero et al., 2015). This should lead to an increased saliva secretion and, theoretically, to a similar effect on miRNA release.

The fact that neither our concussed or non-concussed cohort had statistically different scores in the ImPACT of WAIS assessment may potentially indicate that levels of expressed miRNAs may serve as a more sensitive test to resolve a concussive event. Although possibly useful within the clinical management of concussion, multiple publications have demonstrated that these assessments have significant limitations when used in isolation to diagnose/resolve a concussive event (Schatz et al., 2006Broglio et al., 2007).

Finally, the computational analysis of the present study identified that miRNAs contribute to multiple CNS processes, such as neurogenesis, and axon repair and myelination, providing evidence for further link miRNAs to mechanisms that are widely associated with concussion symptoms and recovery. Incidentally, the comparison among the whole set of inflammatory proteins studied and targets of five DE-miRNAs, showed only about 5% overlap. This may suggest that molecular circuits regulated by DE-miRNAs are quite unrelated to classical inflammation pathways.

Univariate ROC curves showed a good diagnostic accuracy for all the five miRNAs identified in this study, supporting the potential use of these biomarkers in clinical decision-making in sport and military medicine. Specifically, the goal of using miRNA biomarkers would be to detect mTBI with the highest possible accuracy, and a single biomarker often could be not sufficient. For this reason, the use of a combination of biomarkers is preferable, as it should increase diagnostic accuracy. However, by using a multivariate ROC curve model considering all the 5 miRNAs, we did not observe a significant improvement of diagnostic performances with respect to the univariate ROC curves. The comparison of performances of all classification models showed that let-7i-5p is the best classifier.

In conclusion, this study explored a suite of salivary miRNA based biomarkers for diagnosis of concussion. Five biomarkers were identified with potential utility to distinguish concussed athletes from non-concussed athletes after 48–72 h from injury. In addition, preliminary results of later time points (>120 h), showed that these miRs are not able to discriminate concussed from non-concussed athletes.


Cayce Onks, Raymond Kim, Kevin Zhen, Jayson Loeffert, Andrea Loeffert and Robert Olympia of Penn State College of Medicine; Gregory Fedorchak, Samantha DeVita and Aakanksha Rangnekar of Quadrant Biosciences; John Leddy and Mohammad Haider of State University of New York; Zofia Gagnon, Callan McLoughlin, Jason Randall and Miguel Madeira of Marist College; Aaron Yengo-Kahn and Justin Wenzel of Vanderbilt University Medical Center; Matthew Heller and Hallie Zwibel of New York Institute of Technology College of Osteopathic Medicine; Aaron Roberts and Samantha Johnson of Adena Regional Medical Center; Chuck Monteith, Colgate University; Michael Dretsch, Walter Reed Army Institute of Research; Thomas Campbell, Old Dominion University; Rebekah Mannix, Harvard Medical School and Christopher Neville and Frank Middleton of State University of New York Upstate Medical University also contributed to this research.

Funding: This project was supported by a sponsored research agreement between Quadrant Biosciences and the Penn State College of Medicine. National Center for Advancing Translational Sciences through Penn State Clinical and Translational Science Institute (UL1 TR002014) also supported this research. Hicks is a scholar in the institute’s Early Stage Investigator Training Program (KL2 TR002015). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Steve Hicks is a paid consultant for Quadrant Biosciences. Steve Hicks and Frank Middleton are scientific advisory board members for Quadrant Biosciences and are co-inventors of intellectual property related to saliva RNA biomarkers in concussion that are the subject of patent applications owned by the Penn State Research Foundation and the SUNY Upstate Research Foundation and licensed to Quadrant Biosciences. Samantha DeVita, Gregory Fedorchak and Aakanksha Rangnekar are paid employees of Quadrant Biosciences. Christopher Neville is a member of the scientific advisory board and has an equity interest in Quadrant Biosciences Inc. Material has been reviewed by the Walter Reed Army Institute of Research and there is no objection to its presentation and/or publication. The opinions and assertions contained herein are the private views of the author and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for the protection of human subjects as prescribed in AR 70-25.

Source: Penn State

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