Inflammation-related gene expression profiles of - 图文

由天下分享时间：2024/12/19 7:49:03 加入收藏我要投稿点赞

NEURAL REGENERATION RESEARCH www.nrronline.orgRESEARCH ARTICLE

Inflammation-related gene expression profiles of salivary extracellular vesicles in patients with head trauma

Yan Cheng1, Mandy Pereira1, *, Neha P. Raukar2, John L. Reagan1, Mathew Quesenberry1, Laura Goldberg1, Theodor Borgovan1, W Curt LaFrance Jr3, Mark Dooner1, Maria Deregibus5, Giovanni Camussi5, Bharat Ramratnam4, Peter Quesenberry1

1 Department of Medicine, Division of Hematology/Oncology, Rhode Island Hospital, Providence, RI, USA 2 Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA

3 Department of Psychiatry and Neurology, Rhode Island Hospital, Providence, RI, USA

4 Department of Medicine, Division of Infectious Diseases, Rhode Island Hospital, Providence, RI, USA 5 Department of Medical Sciences, University of Turin, Torino TO, Italy

Funding: This project was supported by the National Heart, Lungs, and Blood Institute Grant #T32HL116249 (to PQ). Additional support from the National Institute of General Medical Sciences of the NIH through grant (COBRE) #P20GM103468 Flow Cytometry Core (to PQ), National Center for Advancing Translational Sciences of the NIH grant #5UH3TROOO880-05 (to PQ) and institutional support through the Division of Hematology/oncology, Rhode Island Hospital, Providence, RI.

Abstract

At present, there is no reliable biomarker for the diagnosis of traumatic brain injury (TBI). Studies have shown that extracellular vesicles released by damaged cells into biological fluids can be used as potential biomarkers for diagnosis of TBI and evaluation of TBI severity. We hypothesize that the genetic profile of salivary extracellular vesicles in patients with head trauma differs from that in uninjured subjects. Findings from this hypothesis would help investigate the severity of TBI. This study included 19 subjects, consisting of seven healthy controls who denied history of head trauma, six patients diagnosed with concussion injury from an outpatient concussion clinic, and six patients with TBI who received treatment in the emergency department within 24 hours after injury. Real-time PCR analysis of salivary extracellular vesicles in partic-ipants was performed using TaqMan Human Inflammation array. Gene expression analysis revealed nine upregulated genes in emergency department patients (LOX5, ANXA3, CASP1, IL2RG, ITGAM, ITGB2, LTA4H, MAPK14, and TNFRSF1A) and 13 upregulated genes in concussion clinic patients compared with healthy participants (ADRB1, ADRB2, BDKRB1, HRH1, HRH2, LTB4R2, LTB4R, PTAFR, CYSLTR1, CES1, KLK1, MC2R, and PTGER3). Each patient group had a unique profile. Comparison between groups showed that 15 inflammation-related genes had significant expression change. Our results indicate that in-flammation biomarkers can be used for diagnosis of TBI and evaluation of disease severity. This study was approved by the Institutional Review Board on December 18, 2015 (approval No. 0078-12) and on June 9, 2016 (approval No. 4093-16).

Key Words: chronic traumatic encephalopathy; emergency department; extracellular vesicles; inflammation; outpatient concussion clinic; real-time PCR analysis; saliva; traumatic brain injuryChinese Library Classification No. R446; R741; Q4

*Correspondence to: Mandy Pereira, MS, mpereira7@lifespan.org. orcid:

0000-0002-3999-9048 (Mandy Pereira)

doi: 10.4103/1673-5374.266924 Received: March 13, 2024

Peer review started: April 29, 2024 Accepted: August 8, 2024

Published online: October 15, 2024

Introduction

Traumatic brain injury (TBI) broadly describes any change or altered brain function or pathology caused by an exter-nal impact (Cheng et al., 2024; Menon et al., 2010). Brain functions that are evaluated are loss of consciousness, loss of memory, retrograde amnesia, neurologic deficits (weakness, paralysis, sensory loss, aphasia), and altered mental state (confusion, disorientation) (Menon et al., 2010). In 2013, there were approximately 2.5 million emergency department (ED) visits due to head injuries, approximately TBI-related 282,000 hospitalizations and approximately 56,000 TBI-relat-ed deaths (Taylor et al., 2017). Repeated TBI attacks increase the risk of neurodegenerative diseases such as chronic trau-matic encephalopathy (CTE) (McKee et al., 2009; Prins et al., 2013; Mez et al., 2017), Alzheimer’s disease (Prins et al., 2013; Heneka et al., 2015), and Parkinson’s disease (Gyoneva and Ransohoff, 2015). Inflammation is a major feature in

676

neurodegenerative diseases and is considered an underlying cause for chronic neurodegeneration following TBI (Faden et al., 2016; Yang et al., 2024). Inflammatory responses in-volve multiple components, such as release of intracellular components from damaged cells, microglia and astrocyte ac-tivation, cytokine production, and immune cell recruitment to injury (Gyoneva and Ransohoff, 2015). Understanding of inflammatory response can provide insights for drug tar-geting or other potential therapeutic benefits by preventing further damage.

All cells release extracellular vesicles (EVs) (size ranges from 100 to 1000 nm) (Aliotta et al., 2012) and can be found in all biological fluids, such as blood, urine, and saliva. EVs modulate cell communication and interact with target cells by surface receptors, proteins, mRNA/miRNA, and lipids (Svetlov et al., 2009; Michael et al., 2010; Aliotta et al., 2012; Yokobori et al., 2013; Papa et al., 2016). EVs alter the pheno-

analyzed for inflammation-related gene expression in three In the present study, salivary EVs were characterized and participant groups: head trauma emergency department patients, patients diagnosed with concussion from an outpa-tient concussion clinic, and healthy participants. We utilized the Taqman human inflammation array, which contains 92 genes involved in a broad range of inflammatory response. Since healthy central nervous system (CNS) tissue has low (undetectable) levels of inflammatory mediators (Lucas et al., 2006), we hypothesized low expression of inflamma-tion-related genes in the healthy participants. The healthy participants will serve as a normal baseline when compared to the acute and chronic head trauma patients.

based biomarkers that will afford clinicians adjuvant metrics, The goal of this study is the development of surrogate EV-which is correlated to standard neurological testing and im-aging. This may be used to confirm the diagnosis of TBI and perhaps grade its severity. In addition, biomarker expression may serve as ancillary data point to grade therapeutic re-sponse as and prognostic value.

Participants and Methods

Participant selection

The study is a continuation of previous work done by Cheng et al. (2024). Nineteen participants were included due to availability of mRNA to perform arrays and availability of arrays in this study: six patients with acute head trauma from emergency department (EDPT), six patients with concus-sion from an outpatient concussion clinic (CCPT) and seven healthy participants. These 19 participants were randomly chosen, and not matched for age or sex. Patients with acute head trauma had trauma-induced impairment of neurological function. Healthy participants denied history of head trauma. The patient demographic data are summarized in were detailed in Cheng et al. (2024). All participants and/or Table 1 and their family members gave informed consent. Protocol was approved by the Institutional Review Board on December 18, 2015 (approval No. #0078-12) and on June 9, 2016 (#4093-16).Saliva sample collection

Saliva collection was done as previously described by Navazesh et al. (1993). Briefly, participants rinsed with water and then spit saliva into a 50-mL test tube. At least 5 mL sa-liva sample was collected from each participant. For patients with acute head trauma, saliva sample was collected within 24 hours after injury. EVs were evaluated and characterized (data not shown) using NanoSight NS500 instrument, trans-mission electron microscopy (TEM), and western blot analy-sis (results published in Cheng et al., 2024).

Table 1 Participant demographics GroupIDAge at collectionGenderDuration from onsetHealthy controlsC10C1125FC1233FNAC1523FNAC1722MNANAC18UMNAC922FNACCPTCCPT1322NACCPT249F27 dCCPT351M145 dCCPT452F173 dCCPT631F30 dCCPT750F168 dEDPTEDPT228F207 dEDPT336M< 24 hEDPT425M< 24 hEDPT631F< 24 hEDPT926M< 24 hEDPT11

31F27

< 24 h< 24 h

CCPT: Concussion clinic patients (chronic); EDPT: emergency department patients (acute); F: female; M: male; NA: not available; U: unknown.

Salivary EV isolation

Salivary EV was isolated using differential centrifugation (Michael et al., 2010). All salivary samples were kept at –80°C. Saliva samples were diluted with phosphate buffered saline (PBS) (Gibco, Carlsbad, CA, USA). All salivary sam-ples were centrifuged at 1500 × supernatant was centrifuged at 17,000 × g for 10 minutes at 4°C. The The pellets were discarded, and the supernatant was saved. g for 15 minutes. EVs were isolated via ultracentrifugation at 120,000 × 1 hour. Pellets were washed with PBS and centrifuged at g for 120,000 × a final volume of 500-μL PBS.

g for 1 hour. All EV samples were resuspended in Real-time PCR and gene expression analysis

RNA was extracted from EVs using the Trizol reagent (Invit-rogen, Carlsbad, CA, USA) using the protocol recommended by the manufacturer. RNA quantity and purity were deter-mined utilizing the Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA was reverse transcribed to cDNA using the High Capacity cDNA transcription kit (Applied Biosystems, Carlsbad, CA, USA) in a volume of 20 μL using a 9800 Fast Thermal Cycler (Life Technologies, Carlsbad, CA, USA). Reverse transcription PCR consisted of one cycle for 10 minutes at 25°C, one cycle for 120 minutes at 37°C, and one cycle for 5 minutes at 85°C. cDNA was then pre-amplified using a TaqMan PreAamp Master mix (Life Technologies). Pre-amplification reaction was performed according to manufacturer’s guidelines in a final volume of 50 μL prior to real-time PCR. Pre-ampli-fication reaction consisting of an initial 10 minute cycle at 95a final cycle at 60°C followed by 14 cycles of 15 seconds at 95°C for 4 minutes. Pre-amplificated cDNA

°C followed by 677

Statistical analysis

Participant ages were analyzed using one-way analysis of variance with a ence (HSD) test. Participant inflammation-related gene ex-post-hoc Tukey’s honestly significant differ-pression differences were identified by a Wilcoxon sum test (STATA, College Station, TX, USA) on the delta Ct values in EDPT ticipants, and EDPT versus healthy participants, CCPT versus healthy par-was determined to be statistically significant.

versus CCPT. A P-value of less than 0.05 Results

Age comparison among participants

The average age of the CCPT group was significantly higher than that of healthy participants (43.5 years Pvs. 24.5 years, the EDPT group (29.3 years = 0.001), but it was not significantly different from that of

vs. 24.5, P = 0.492) (Table 1).Comparison of gene expression between EDPT and healthy participants

Gene expression profiles were compared between the EDPT group and healthy participants. Among 92 inflammation-re-lated genes on the array, 46 genes were up-regulated between EDPT and healthy participants. Each EDPT with gene show-ing two-fold greater change in expression (greater than two considered biologically relevant) compared to healthy par-ticipants is represented (values between EDPT and healthy participants. Results indi-The Wilcoxon sum test was performed to compare delta Ct Figure 1).

cate that nine genes (ed in EDPT compared with healthy participants (Figure 1) were significantly upregulat-ALOX5 (25.89 ± 7.04), ANXA3 (13.38 ± 13.96), CASP1 P < 0.05): (8.03 ± 3.79), ITGB2 (14.53 ± 6.01), IL2RG (57.5 ± 13.75), ITGAM (22.57 ± 13.67), LTA4H (24.37 ± 30.66), MAPK14 (20.23 ± 9.95), and TNFRSF1A (6.59 ± 1.42).

Comparison of gene expression between CCPT and healthy participants

Inflammation-related gene expression profile in the CCPT group showed that 51 genes were upregulated when com-678

pared with healthy participants. CCPT with genes showing twofold change in expression are shown in Wilcoxon rank sum test performed on the delta Ct values Figure 2. The identified that 13 genes were significantly different (0.05) between CCPT and healthy participants (P < The 13 genes that were significantly upregulated are ADRB1 Figure 2). (130.04 ± 101.86), ADBR2 (25.14 ± 10.25), BDKRB1 (125.85 ± 84.9), HRH1 (253.9 ± 140.72), HRH2 (81.09 ± 67.12), LT-B4R2 (41.80 ± 20.92), LTB4R (50.23±42.85), PTAFR (22.10 ± 13.72), CYSLTR1 (94.64 ± 55.86), CES1 (56.31 ± 40.73), KLK1 (113.11 ± 0.00), MC2R (37.81 ± 0.89), PTGER3 (62.38 ± 0.00).

Comparison of inflammation-related gene expression profile between EDPT and CCPT

Inflammation-related gene expression profile was com-pared between EDPT and CCPT. Results showed that 15 genes were significantly altered in expression in EDPT and CCPT (ITGB2 were significantly higher in EDPT than in CCPT Figure 3; P < 0.05). ALOX5, ANXA3, CASP1, and and ADRB1, ADRB2, BDKRB1, CYSLTR1, HRH1, HRH2, LTB4R2, LTB4R, MC2R, NFKB1, PTAFR were significantly higher in CCPT than in EDPT.

Discussion

TBI begins with an initial injury or impact to the head. Secondary injuries occur when inflammatory cells and cy-tokines are recruited to the area of injury. Inflammatory responses are advantageous when actived in a regulated manner to fight an infection. In TBI, inflammation alters brain homeostasis by changing cellular functions. Brain in-flammation is initiated by microglial activation which leads to the release of cytokines, free radicals, and other macro-molecules (Lucas et al., 2006; Paolicelli et al., 2024). The function of microglia is to rescue neuronal cells from dam-age; however, chronic exposure to inflammation becomes neurotoxic (Paolicelli et al., 2024). Astrocytes help maintain brain homeostasis of the central nervous system. Inflamma-tion activates astrocytes, leading to change in cell phenotype and cell migration to damaged area (Fields and Ghorpade, 2012). TBI induces many cells to react to this damage. EVs released by the activated microglia and astrocytes (as well as other neuronal cells) can be isolated to detect stress level in the brain due to inflammation. EVs are excellent interme-diators that can deliver messages to surrounding cells and tissues and eventually to biological fluids that can be isolated for diagnostic purposes.

ceived treatment within 24 hours after injury, CCPT that In this study, saliva was collected from EDPT who re-have suffered from long term effects from the injury, and healthy individuals with no history of head trauma. Inflam-mation-related genes in isolated EVs were detected using the Taqman human inflammation array. The expression levels of ALOX5, ANXA3, CASP1, IL2RG, ITGAM, ITGB2, LTA4H, MAPK14, and TNFRSF1A were increased in EDPT than in healthy participants. Some of these genes have been reported as altered in TBI. ALOX5 metabolizes arachidonic acid to

(A) Mean delta Ct values of inflammation-related genes (P < 0.05), comparing EDPT (n = 6) with healthy participants (n = 7). (B) Genes with two-fold or higher increase in gene expression. Error bars repre-sent standard deviation. CT: Cycle threshold; EDPT: emergency de-partment patients.

Figure 2 Inflammation-related gene expression information of CCPT

(A) Delta Ct value of inflammation-related genes (P < 0.05), comparing CCPT (n = 6) with healthy participants (n = 7). (B) All genes that had a two-fold or higher increase in expression. Error bars represent standard deviation. CCPT: Concussion clinic patients; CT: cycle threshold.

Mean delta Ct value–2 ADRB1 ADRB2 ALOX5 ANXA3 BDKRB1 CASP1 CYSLTR1 HRH1 HRH2 ITGB2 LTB4R2 LTB4R MC2R NFKB1 PTAFR Figure 3 Mean delta Ct value in CCPT versus EDPT.

The Wilcoxon test sum test was used to compare delta Ct value between EDPT and CCPT. Fifteen inflammation-related genes were significantly (P < 0.05) upregulated. CCPT: Concussion clinic patients; CT: cycle threshold; EDPT: emergency department patients.

679

Cheng Y, Pereira M, Raukar NP, Reagan JL, Quesenberry M, Goldberg L, Borgovan T, LaFrance Jr WC, Dooner M, Deregibus M, Camussi G, Ramratnam B, Quesenberry P (2024) Inflammation-related gene expression profiles of salivary extracellular vesicles in patients with head trauma. Neural Regen Res 15(4):676-681. doi:10.4103/1673-5374.266924leukotrienes. ALOX5 expression increases in the brain after TBI, specifically in glial cells and neutrophils (Zhang et al., 2006; Hijioka et al., 2017). ANXA3 (annexin A3) has been shown to be overexpressed in rodent studies. ANXA3 is upregulated in primary mouse cortical neurons after neuro-nal injury (Chong et al., 2010) and in rat cerebral ischemia (Junker et al., 2007). CASP1 (caspase 1) is involved in apop-tosis. LTA4H (leukotriene A4H) converts LTA4 to LTB4 (Hijioka et al., 2017). LTB4 is a lipid mediator which plays a role in neutrophil infiltration and inflammation in cen-tral nervous system disorders (Hijioka et al., 2017). Studies have shown that LTA4H expression was increased in head trauma patients than in controls (Orr et al., 2015; Hijioka et al., 2017). MAPK pathway was reported to be involved in astrocyte activation due to TBI (Li et al., 2017). Thirteen genes were increased in CCPT than in healthy participants: ADRB1, ADRB2, BDKRB1, HRH1, HRH2, LTB4R2, LTB4R, PTAFR, CYSLTR1, CES1, KLK1, MC2R, and PTGER3. BD-KRB1 (bradykinin) increases blood-brain barrier permeabil-ity and is involved in brain edema relating to ischemic brain injury (Dobrivojevic et al., 2015). Leukotrienes LTB4R2 and LTB4R as discussed previously are believed to be part of inflammatory response. PTAFR (platelet activating factor receptor, aka PAF) is involved in cerebral edema and cere-bral ischemia/reperfusion injury through interactions with PAFR (Yin et al., 2017). PTGER3 (prostaglandin E receptor) expression was significantly increased after unilateral TBI (White et al., 2016). All the genes on the array are involved in more than one inflammatory pathway. Results from this study showed both EDPT and CCPT had many genes sta-tistically significantly upregulated compared with healthy participants, and they did not have any of the 92 genes in common. In this study, we found a unique 15-gene profile between two patient groups. This unique transition of gene transcription could give future insights into the progression of inflammation within the central nervous system.

non-invasive process compared with cerebral spinal fluid or Salivary EVs isolation for diagnosis is a simple and blood collection. Membrane-bound EVs are protected from degradation that serum biomarkers face (Cheng et al., 2024). It is difficult to precisely grade the severity of TBI because it is based on subtle examination and neuroimaging findings (Papa et al., 2015). Salivary EV phenotypes and genetic cargo may allow for early diagnosis. An additional clinical rele-vance of salivary EVs is to monitor inflammatory responses to therapeutic interventions (Cheng et al., 2024).

time, during which multiple genes may change in expression. In this study, CCPT are followed up over a large period of One benefit of EV-based biomarkers is that EVs are free to cross the blood-brain barrier at any time, whereas conven-tional biomarkers can usually only cross a narrow window following TBI and are also limited by variable kinetics and elimination times. Following the initial insult, TBI begins and progresses as an evolving pathology – at each step EVs packed with unique cargo are shed and likely represent this dynamic process (Lucas et al., 2006; Yang et al., 2024). Fol-lowing the initial event, the secondary phase of TBI usually

680

involves a wide spread, systemic immune and neuroinflam-matory response mediated by numerous cytokines – a phe-nomenon that we have focus on and captured in our current work.

origin that have varying expression between the three exper-Our work identifies multiple genes within EVs of neural imental groups. There was considerable difference between multiple gene candidates when comparing the EDPT with healthy controls. This gene palette may represent the acute to sub-acute pathophysiological changes that are triggered following TBI, as many of these genes (and development of inflammation, the development of neural 3) are involved in established pathways involved in the Additional Tables 2 plaques, and amyloid precursor proteins, neural apoptosis, and axon regeneration. Genetic variations that were com-mon between the two TBI groups (EDPT and CCPT) but not seen in the EDPT group identify candidate biomarkers that may be related to long-term recovery of patients who have suffered TBI. As prospective studies detected the expression of these biomarkers over time in patients that recover from their injury versus those who have post-concussion syn-drome, clinicians can identify those patients who need earli-er more aggressive intervention in the early period.

tial of such a study. The current therapeutic landscape for Targeting specific gene candidates is also a future poten-patients with TBI of post-concussion issues is supportive symptom management, including vestibula suppressants for vertigo, or various psychostimulants to address memory and attentions deficits. No therapy aimed at preventing the long-term sequelae of TBI such as the development of post-con-cussion syndrome, and ultimately abating the possibility of dementia. However, the current therapeutic utility of the EV-based genetic biomarkers in this study comes in the form of grading and stratifying TBI severity. Rather than simply relying on variable and often imperfect examination and neuroimaging findings, purported biomarkers may allow clinicians to diagnose patients earlier, and, depending on the flux of genetic expression, TBI stratification may also be ac-complished. Both early detection and accurate stratification can clearly identify patients who need closer monitoring, earlier intervention, and stricter follow-up.

and that only one sample was taken per participant. Two pa-A limitation of this study is the small patient sampling size tient groups: ED head injury (EDPT) and sub-acute/chronic symptomatic concussion (CCPT) groups provided infer-ential data on the longitudinal course of TBI (Cheng et al., 2024). Future studies of patients suffering from TBI will in-clude sample collection over time. This will provide data on intra-subject patterns of post-TBI gene expression (Cheng et al., 2024). Future studies will also include not only more pa-tients but a focus on closer age matched controls. Biomarker expression and TBI pathophysiology may vary slightly be-tween older and younger individuals, such as those seen in our groups.

Author contributions:analysis, and interpretation, drafting and revision of the manuscript, ac-

Study conception and design, data acquisition, Cheng Y, Pereira M, Raukar NP, Reagan JL, Quesenberry M, Goldberg L, Borgovan T, LaFrance Jr WC, Dooner M, Deregibus M, Camussi G, Ramratnam B, Quesenberry P (2024) Inflammation-related gene expression profiles of salivary extracellular vesicles in patients with head trauma. Neural Regen Res 15(4):676-681. doi:10.4103/1673-5374.266924countability for accuracy and integrity of the data: YC. Data acquisition, analysis and interpretation, revision and approval of the manuscript: MPMD, TB, LG, MD, GC. Subject enrollment, data acquisition, revision and , approval of the manuscript: NR, JLR. Data interpretation, revision and approval of the manuscript: WCL, MQ, BR. Study conception and design, data acquisition, analysis, and interpretation, revision of the manuscript, accountability for accuracy and integrity of the data: PQ. All authors ap-proved the final manuscript for publication.

Conflicts of interest: Financial support: All authors have no conflicts of interest to report.Lungs, and Blood Institute Grant #T32HL116249 (to PQ). Additional This project was supported by the National Heart, support from the National Institute of General Medical Sciences of the NIH through grant (COBRE) #P20GM103468 Flow Cytometry Core (to PQ), National Center for Advancing Translational Sciences of the NIH grant #5UH3TROOO880-05 (to PQ) and institutional support through the Division of Hematology/oncology, Rhode Island Hospital Providence RI.

Institutional review board statement: stitutional Review Board on December 18, 2015 (approval No. 0078-12) Protocol was approved by the In-and on June 9, 2016 (approval No. 4093-16).

Declaration of participant consent: obtained all appropriate participant consent forms. In the forms the The authors certify that they have participants have given their consent for their images and other clinical information to be reported in the journal. The participants understand that their names and initials will not be published and due efforts will be made to conceal their identity.

Reporting statement: ing of OBservational studies in Epidemiology (STROBE) statement.

This study followed the STrengthening the Report-Biostatistics statement: viewed by the biostatistician of Rhode Island Hospital in USA.

The statistical methods of this study were re-Copyright license agreement: been signed by all authors before publication.

The Copyright License Agreement has Data sharing statement: available from the corresponding author on reasonable request.Datasets analyzed during the current study are Plagiarism check:Peer review: Checked twice by iThenticate. Open access statement: Externally peer reviewed.

distributed under the terms of the Creative Commons Attribution-Non-This is an open access journal, and articles are Commercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Open peer reviewers: France; Nafisa M Jadavji, Carleton University, Canada.Christophe Pellegrino, Aix-Marseille University, Additional files:

Additional file 1: Additional Table 1: Open peer review report 1.

Additional Table 2: TaqMan human inflammation array gene description.Additional Table 3:

Genes and pathways in emergency department patients.Genes and pathways in concussion clinic patients.Aliotta JM, Lee D, Puente N, Faradyan S, Sears EH, Amaral A, Goldberg L, References

Dooner MS, Pereira M, Quesenberry PJ (2012) Progenitor/stem cell fate determination: interactive dynamics of cell cycle and microvesicles. Stem Cheng Y, Pereira M, Raukar N, Reagan JL, Queseneberry M, Goldberg L, Cells Dev 21:1627-1638.

Borgovan T, LaFrance WC Jr, Dooner M, Deregibus M, Camussi G, Ram-ratnam B, Quesenberry P (2024) Potential biomarkers to detect traumatic brain injury by the profiling of salivary extracellular vesicles. J Cell Physi-Choi DS, Kim DK, Kim YK, Gho YS (2015) Proteomics of extracellular ol 234:14377-14388.

vesicles: Exosomes and ectosomes. Mass Spectrom Rev 34:474-490. Chong KWdoi:10.1002/mas.21420

NS (2010) Annexin A3 is associated with cell death in lactacystin-medi-, Chen MJ, Koay ES, Wong BS, Lee AY, Russo-Marie F, Cheung Dobrivojevi? M, ?piranec K, Sin?i? A (2015) Involvement of bradykinin in ated neuronal injury. Neurosci Lett 485:129-133.

brain edema development after ischemic stroke. Pflugers Arch 467:201-Faden AI, Wu J, Stoica BA, Loane DJ (2016) Progressive inflammation-me-212.

diated neurodegeneration after traumatic brain or spinal cord injury. Br J Fields J, Ghorpade A (2012) C/EBPβ regulates multiple IL-1β-induced hu-Pharmacol 173:681-691.

man astrocyte inflammatory genes. J Neuroinflammation 9:177.

Gyoneva S, Ransohoff RM (2015) Inflammatory reaction after traumatic brain injury: therapeutic potential of targeting cell-cell communication Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein by chemokines. Trends Pharmacol Sci 36:471-480.

DL, Jacobs AH, Wyss-Coray T, Vitorica J, Ransohoff RM, Herrup K, Frautschy SA, Finsen B, Brown GC, Verkhratsky A, Yamanaka K, Koisti-naho J, Latz E, Halle A, Petzold GC, et al. (2015) Neuroinflammation in Hijioka M, Anan J, Ishibashi H, Kurauchi Y, Hisatsune A, Seki T, Koga T, Alzheimer’s disease. Lancet Neurol 14:388-405.

Yokomizo T, Shimizu T, Katsuki H (2017) Inhibition of leukotriene B4 action mitigates intracerebral hemorrhage-associated pathological events Junker H, Suofu Y, Venz S, Sascau M, Herndon JG, Kessler C, Walther in mice. J Pharmacol Exp Ther 360:399-408.

R, Popa-Wagner A (2007) Proteomic identification of an upregulated isoform of annexin A3 in the rat brain following reversible cerebral isch-Li D, Liu N, Zhao HH, Zhang X, Kawano H, Liu L, Zhao L, Li HP (2017) emia. Glia 55:1630-1637.

Interactions between Sirt1 and MAPKs regulate astrocyte activation in-Lucas SM, Rothwell NJ, Gibson RM (2006) The role of inflammation in duced by brain injury in vitro and in vivo. J Neuroinflammation 14:67. McKee AC, Cantu RC, Nowinski CJ, Hedley-Whyte ET, Gavett BE, Budson CNS injury and disease. Br J Pharmacol 147 Suppl 1:S232-40.

AE, Santini VE, Lee HS, Kubilus CA, Stern RA (2009) Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head Menon DK, Schwab K, Wright DWinjury. J Neuropathol Exp Neurol 68:709-735.

Assessment Working Group of the International and Interagency Initia-, Maas AI; Demographics and Clinical tive toward Common Data Elements for Research on Traumatic Brain Injury and Psychological Health. (2010). Position statement: definition of Mez J, Daneshvar DH, Kiernan PT, Abdolmohammadi B, Alvarez VE, traumatic brain injury. Arch Phys Med Rehabil 91:1637-1640.

Huber BR, Alosco ML, Solomon TM, Nowinski CJ, McHale L, Cormier KA, Kubilus CA, Martin BM, Murphy L, Baugh CM, Montenigro PH, Chaisson CE, Tripodis Y, Kowall NWological evaluation of chronic traumatic encephalopathy in players of , Weuve J, et al. (2017) Clinicopath-Michael A, Bajracharya SD, Yuen PS, Zhou H, Star RA, Illei GG, Alevizos I American football. JAMA 318:360-370.

(2010) Exosomes from human saliva as a source of microRNA biomark-Navazesh M (1993) Methods for collecting saliva. Ann N Y Acad Sci 694:72-ers. Oral Dis 16:34-38.

Orr SK, Butler KL, Hayden D, Tompkins RG, Serhan CN, Irimia D (2015) 77.

Gene expression of proresolving lipid mediator pathways is associated Paolicelli RC, Bergamini G, Rajendran L (2024) Cell-to-cell communication with clinical outcomes in trauma patients. Crit Care Med 43:2642-2650. Prins M, Greco T, Alexander D, Giza CC (2013) The pathophysiology of by extracellular vesicles: focus on microglia. Neuroscience 405:148-157. Roozenbeek B, Maas AI, Menon DK (2013) Changing patterns in the epide-traumatic brain injury at a glance. Dis Model Mech 6:1307-1315.

Saatman KE, Duhaime AC, Bullock R, Maas AI, Valadka A, Manley GT; miology of traumatic brain injury. Nat Rev Neurol 9:231-236.

Workshop Scientific Team and Advisory Panel Members (2008) Classi-fication of traumatic brain injury for targeted therapies. J Neurotrauma Taylor CA, Bell JM, Breiding MJ, Xu L (2017) Traumatic Brain Injury-Re-25:719-738.

lated Emergency Department Visits, Hospitalizations, and Deaths - Unit-Tkach M, Théry C (2016). Communication by extracellular vesicles: where ed States, 2007 and 2013. MMWR Surveill Summ 66:1-16.

Werner C, Engelhard K (2007) Pathophysiology of traumatic brain injury. we are and where we need to go. Cell 164:1226-1232.

White TE, Surles-Zeigler MC, Ford GD, Gates AS, Davids B, Distel T, La-Br J Anaesth 99:4-9.

Placa MC, Ford BD (2016) Bilateral gene interaction hierarchy analysis of the cell death gene response emphasizes the significance of cell cycle Yang Y, Boza-Serrano A, Dunning CJR, Clausen BH, Lambertsen KL, Dei-genes following unilateral traumatic brain injury. BMC Genomics 17:130.erborg T (2024) Inflammation leads to distinct populations of extracellu-Yin XJ, Chen ZY, Zhu XN, Hu JJ (2017) Loss of PAFR prevents neuroin-lar vesicles from microglia. J Neuroinflammation 15:168.

flammation and brain dysfunction after traumatic brain injury. Sci Rep Zhang L, Zhang WP7:40614.

Z, Wei EQ (2006) Expression patterns of 5-lipoxygenase in human brain , Hu H, Wang ML, Sheng WW, Yao HT, Ding W, Chen with traumatic injury and astrocytoma. Neuropathology 26:99-106.P-Reviewers: Jadavji NM, Pellegrino C; C-Editors: Zhao M, Li CH; L-Editor:

Song LP; T-Editor: Jia Y

681