Gene Network Approach Research Articles

Brain and blood transcriptome profiles delineate common genetic pathways across suicidal ideation and suicide

Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to the severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with the presence of suicidal ideation were found within 18 co-expressed modules (p < 0.05), as well as in 3 co-expressed modules associated with suicidal ideation severity (p < 0.05, not explained by severity of depression). Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified and investigated using RNA-seq data from postmortem brain that revealed gene expression differences with moderate effect sizes in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity are associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.

S24-02: Gene network approaches predictive of adverse outcomes

S24-02: Gene network approaches predictive of adverse outcomes

Exploring the mechanism of BK polyomavirus-associated nephropathy through consensus gene network approach.

BK polyomavirus-associated nephropathy occurs in kidney transplant recipients under immunosuppressive treatment. BK polyomavirus is implicated in cancer development and invasion, and case reports of renal cell carcinoma and urothelial carcinoma possibly associated with BK polyomavirus has been reported. Further, it has been suggested that the immune responses of KT-related diseases could play a role in the pathogenesis and progression of renal cell carcinoma. Thus, we thought to examine the relationship between BK polyomavirus-associated nephropathy and renal cell carcinoma in terms of gene expression. To identify the common and specific immune responses involved in kidney transplantation-related diseases with a specific focus on BK polyomavirus-associated nephropathy, we performed consensus weighted gene co-expression network analysis on gene profile datasets of renal biopsy samples from different institutions. After the identification of gene modules and validation of the obtained network by immunohistochemistry of the marker across kidney transplantation-related diseases, the relationship between prognosis of renal cell carcinoma and modules was assessed. We included the data from 248 patients and identified the 14 gene clusters across the datasets. We revealed that one cluster related to the translation regulating process and DNA damage response was specifically upregulated in BK polyomavirus-associated nephropathy. There was a significant association between the expression value of hub genes of the identified cluster including those related to cGAS-STING pathway and DNA damage response, and the prognosis of renal cell carcinoma. The study suggested the potential link between kidney transplantation-related diseases, especially specific transcriptomic signature of BK polyomavirus associated nephropathy and renal cell carcinoma.

A holistic gene-network approach linking stressor heterogeneity to resilience and susceptibility.

A holistic gene-network approach linking stressor heterogeneity to resilience and susceptibility.

Drug Repurposing for Atopic Dermatitis by Integration of Gene Networking and Genomic Information.

Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r2 > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.

DNA methylation analysis improves the prognostication of acute myeloid leukemia.

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low‐, intermediate‐, and high‐risk groups. The identified high‐risk group had significantly shorter overall survival compared to the low‐risk group (p‐value ≤10−11). Specifically, our approach identified a particular subgroup of nine high‐risk AML cases that died within 2 years after diagnosis. These high‐risk cases otherwise would be incorrectly classified as intermediate‐risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.

Adaptation and co-adaptation of skin pigmentation and vitamin D genes in native Americans.

We carried out an exhaustive review regarding human skin color variation and how much it may be related to vitamin D metabolism and other photosensitive molecules. We discuss evolutionary contexts that modulate this variability and hypotheses postulated to explain them; for example, a small amount of melanin in the skin facilitates vitamin D production, making it advantageous to have fair skin in an environment with little radiation incidence. In contrast, more melanin protects folate from degradation in an environment with a high incidence of radiation. Some Native American populations have a skin color at odds with what would be expected for the amount of radiation in the environment in which they live, a finding challenging the so-called "vitamin D-folate hypothesis." Since food is also a source of vitamin D, dietary habits should also be considered. Here we argue that a gene network approach provides tools to explain this phenomenon since it indicates potential alleles co-evolving in a compensatory way. We identified alleles of the vitamin D metabolism and pigmentation pathways segregated together, but in different proportions, in agriculturalists and hunter-gatherers. Finally, we highlight how an evolutionary approach can be useful to understand current topics of medical interest.

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease

BackgroundIdentifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration.MethodsA gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.e., established) AD (RAD) genes. Genome-wide association study summary data were integrated with the gene co-expression results and phenotypic information (i.e., memory and aging-related outcomes) from gene knockout studies in Drosophila to generate rankings for other genes that may have a role in AD.ResultsWe found that co-expression of the RAD genes is strongest in the cortical regions where neurodegeneration due to AD is most severe. There was significant evidence for two novel AD-related genes including EPS8 (FDR p = 8.77 × 10−3) and HSPA2 (FDR p = 0.245).ConclusionsOur findings indicate that AD-related risk factors are potentially associated with brain region-specific effects on gene expression that can be detected using a gene network approach.

Development, spread and persistence of antibiotic resistance genes (ARGs) in the soil microbiomes through co-selection.

Bacterial pathogens resistant to multiple antibiotics are emergent threat to the public health which may evolve in the environment due to the co-selection of antibiotic resistance, driven by poly aromatic hydrocarbons (PAHs) and/or heavy metal contaminations. The co-selection of antibiotic resistance (AMR) evolves through the co-resistance or cross-resistance, or co-regulatory mechanisms, present in bacteria. The persistent toxic contaminants impose widespread pressure in both clinical and environmental setting, and may potentially cause the maintenance and spread of antibiotic resistance genes (ARGs). In the past few years, due to exponential increase of AMR, numerous drugs are now no longer effective to treat infectious diseases, especially in cases of bacterial infections. In this mini-review, we have described the role of co-resistance and cross-resistance as main sources for co-selection of ARGs; while other co-regulatory mechanisms are also involved with cross-resistance that regulates multiple ARGs. However, co-factors also support selections, which results in development and evolution of ARGs in absence of antibiotic pressure. Efflux pumps present on the same mobile genetic elements, possibly due to the function of Class 1 integrons (Int1), may increase the presence of ARGs into the environment, which further is promptly changed as per environmental conditions. This review also signifies that mutation plays important role in the expansion of ARGs due to presence of diverse types of anthropogenic pollutants, which results in overexpression of efflux pump with higher bacterial fitness cost; and these situations result in acquisition of resistant genes. The future aspects of co-selection with involvement of systems biology, synthetic biology and gene network approaches have also been discussed.

COVID-19 Hyperinflammation: What about Neutrophils?

COVID-19 is often related to hyperinflammation that drives lung or multiorgan injury. The immunopathological mechanisms that cause excessive inflammation are under investigation and constantly updated. Here, a gene network approach was used on recently published data sets to identify possible COVID-19 inflammatory mechanisms and bioactive genes. First, network analysis of putative SARS-CoV-2 cellular receptors led to the mining of a neutrophil-response signature and relevant inflammatory genes. Second, analysis of RNA-seq data sets of lung cells infected with SARS-CoV-2 revealed that infected cells expressed neutrophil-attracting chemokines. Third, analysis of RNA-seq data sets of bronchoalveolar lavage fluid cells from COVID-19 patients identified upregulation of neutrophil genes and chemokines. Different inflammatory genes mined here, including TNFR, IL-8, CXCR1, CXCR2, ADAM10, GPR84, MME, ANPEP, and LAP3, might be druggable targets in efforts to limit SARS-CoV-2 inflammation in severe clinical cases. The possible role of neutrophils in COVID-19 inflammation needs to be studied further.

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.

Genome-wide DNA methylation profile in the peripheral blood of cocaine and crack dependents.

Objective:Cocaine use disorders (CUDs) represent a major public health problem in many countries. To better understand the interaction between the environmental modulations and phenotype, the aim of the present study was to investigate the DNA methylation pattern of CUD patients, who had concomitant cocaine and crack dependence, and healthy controls.Methods:We studied DNA methylation profiles in the peripheral blood of 23 CUD patients and 24 healthy control subjects using the Illumina Infinium HumanMethylation450 BeadChip arrays.Results:Comparison between CUD patients and controls revealed 186 differentially methylated positions (DMPs; adjusted p-value [adjP] < 10-5) related to 152 genes, with a subset of CpGs confirmed by pyrosequencing. DNA methylation patterns discriminated CUD patients and control groups. A gene network approach showed that the EHMT1, EHMT2, MAPK1, MAPK3, MAP2K1, and HDAC5 genes, which are involved in transcription and chromatin regulation cellular signaling pathways, were also associated with cocaine dependence.Conclusion:The investigation of DNA methylation patterns may contribute to a better understanding of the biological mechanisms involved in CUD.

Gene network approach reveals co-expression patterns in nasal and bronchial epithelium

Nasal gene expression profiling is a new approach to investigate the airway epithelium as a biomarker to study the activity and treatment responses of obstructive pulmonary diseases. We investigated to what extent gene expression profiling of nasal brushings is similar to that of bronchial brushings. We performed genome wide gene expression profiling on matched nasal and bronchial epithelial brushes from 77 respiratory healthy individuals. To investigate differences and similarities among regulatory modules, network analysis was performed on correlated, differentially expressed and smoking-related genes using Gaussian Graphical Models. Between nasal and bronchial brushes, 619 genes were correlated and 1692 genes were differentially expressed (false discovery rate <0.05, |Fold-change|>2). Network analysis of correlated genes showed pro-inflammatory pathways to be similar between the two locations. Focusing on smoking-related genes, cytochrome-P450 pathway related genes were found to be similar, supporting the concept of a detoxifying response to tobacco exposure throughout the airways. In contrast, cilia-related pathways were decreased in nasal compared to bronchial brushes when focusing on differentially expressed genes. Collectively, while there are substantial differences in gene expression between nasal and bronchial brushes, we also found similarities, especially in the response to the external factors such as smoking.

Critical microRNAs and regulatory motifs in cleft palate identified by a conserved miRNA-TF-gene network approach in humans and mice.

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA-TF-gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a-FOXE1-PDGFRA) and eight miRNAs (miR-140, miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-451a and miR-92a) were discovered in both humans and mice. The role of miR-140, which had the strongest association with CP, was investigated in both human and mouse palate cells. The overexpression of miR-140-5p, but not miR-140-3p, significantly inhibited cell proliferation. We further examined whether miR-140 overexpression could suppress the expression of its predicted target genes (BMP2, FGF9, PAX9 and PDGFRA). Our results indicated that miR-140-5p overexpression suppressed the expression of BMP2 and FGF9 in cultured human palate cells and Fgf9 and Pdgfra in cultured mouse palate cells. In summary, our conserved miRNA-TF-gene regulatory network approach is effective in detecting consensus miRNAs, motifs, and regulatory mechanisms in human and mouse CP.

Prediction of survival risks with adjusted gene expression through risk-gene networks.

Network-based analysis of biomedical data has been extensively studied over the last decades. As a successful application, gene networks have been used to illustrate interactions among genes and explain the associated phenotypes. However, the gene network approaches have not been actively applied for survival analysis, which is one of the main interests of biomedical research. In addition, a few previous studies using gene networks for survival analysis construct networks mainly from prior knowledge, such as pathways, regulations and gene sets, while the performance considerably depends on the selection of prior knowledge. In this paper, we propose a data-driven construction method for survival risk-gene networks as well as a survival risk prediction method using the network structure. The proposed method constructs risk-gene networks with survival-associated genes using penalized regression. Then, gene expression indices are hierarchically adjusted through the networks to reduce the variance intrinsic in datasets. By illustrating risk-gene structure, the proposed method is expected to provide an intuition for the relationship between genes and survival risks. The risk-gene network is applied to a low grade glioma dataset, and produces a hypothesis of the relationship between genetic biomarkers of low and high grade glioma. Moreover, with multiple datasets, we demonstrate that the proposed method shows superior prediction performance compared to other conventional methods. The R package of risk-gene networks is freely available in the web at http://cdal.korea.ac.kr/NetDA/. Supplementary data are available at Bioinformatics online.

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

BackgroundThe tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage–tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking.MethodsWe developed a multicellular gene network approach to investigating the prognostic role of macrophage–tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients.ResultsA macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures.ConclusionThe multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Molecular evidence of field cancerization initiated by diabetes in colon cancer patients.

The potential involvement of type 2 diabetes mellitus (T2DM) as a risk factor for colon cancer (CC) has been previously reported. While several clinical studies show a higher incidence of CC and a lower survival rate in diabetics, others report no association. Our own experience indicates that diabetes does not seem to worsen the prognosis once the tumor is present. Despite this controversy, there are no wide‐spectrum molecular studies that delve into the impact of T2DM‐related mechanisms in colon carcinogenesis. Here, we present a transcriptomic and proteomic profiling of paired tumor and normal colon mucosa samples in a cohort of 42 CC patients, 23 of which have T2DM. We used gene set enrichment and network approaches to extract relevant pathways in diabetics, referenced them to current knowledge, and tested them using in vitro techniques. Through our transcriptomics approach, we identified an unexpected overlap of pathways overrepresented in diabetics compared to nondiabetics, in both tumor and normal mucosa, including diabetes‐related metabolic and signaling processes. Proteomic approaches highlighted several cancer‐related signaling routes in diabetics found only in normal mucosa, not in tumors. An integration of the transcriptome and proteome analyses suggested the deregulation of key pathways related to colon carcinogenesis which converged on tumor initiation axis TEAD/YAP‐TAZ as a potential initiator of the process. In vitro studies confirmed upregulation of this pathway in nontumor colon cells under high‐glucose conditions. In conclusion, T2DM associates with deregulation of cancer‐related processes in normal colon mucosa adjacent to tissue which has undergone a malignant transformation. These data support that in diabetic patients, the local microenvironment in normal colon mucosa may be a factor driving field cancerization promoting carcinogenesis. Our results set a new framework to study links between diabetes and colon cancer, including a new role of the TEAD/YAP‐TAZ complex as a potential driver.

Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer.

We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.

Integrative approach to sporadic Alzheimer\u2019s disease:\xa0deficiency of TYROBP\xa0in cerebral A\u03b2 amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing A\u03b2 burden

Integrative gene network approaches enable new avenues of exploration that implicate causal genes in sporadic late-onset Alzheimer’s disease (LOAD) pathogenesis, thereby offering novel insights for drug-discovery programs. We previously constructed a probabilistic causal network model of sporadic LOAD and identified TYROBP/DAP12, encoding a microglial transmembrane signaling polypeptide and direct adapter of TREM2, as the most robust key driver gene in the network. Here, we show that absence of TYROBP/DAP12 in a mouse model of AD-type cerebral Aβ amyloidosis (APPKM670/671NL/PSEN1Δexon9) recapitulates the expected network characteristics by normalizing the transcriptome of APP/PSEN1 mice and repressing the induction of genes involved in the switch from homeostatic microglia to disease-associated microglia (DAM), including Trem2, complement (C1qa, C1qb, C1qc, and Itgax), Clec7a and Cst7. Importantly, we show that constitutive absence of TYROBP/DAP12 in the amyloidosis mouse model prevented appearance of the electrophysiological and learning behavior alterations associated with the phenotype of APPKM670/671NL/PSEN1Δexon9 mice. Our results suggest that TYROBP/DAP12 could represent a novel therapeutic target to slow, arrest, or prevent the development of sporadic LOAD. These data establish that the network pathology observed in postmortem human LOAD brain can be faithfully recapitulated in the brain of a genetically manipulated mouse. These data also validate our multiscale gene networks by demonstrating how the networks intersect with the standard neuropathological features of LOAD.

Transcriptional profiling in rat hair follicles following simulated Blast insult: a new diagnostic tool for traumatic brain injury.

With wide adoption of explosive-dependent weaponry during military activities, Blast-induced neurotrauma (BINT)-induced traumatic brain injury (TBI) has become a significant medical issue. Therefore, a robust and accessible biomarker system is in demand for effective and efficient TBI diagnosis. Such systems will also be beneficial to studies of TBI pathology. Here we propose the mammalian hair follicles as a potential candidate. An Advanced Blast Simulator (ABS) was developed to generate shock waves simulating traumatic conditions on brains of rat model. Microarray analysis was performed in hair follicles to identify the gene expression profiles that are associated with shock waves. Gene set enrichment analysis (GSEA) and sub-network enrichment analysis (SNEA) were used to identify cell processes and molecular signaling cascades affected by simulated bomb blasts. Enrichment analyses indicated that genes with altered expression levels were involved in central nervous system (CNS)/peripheral nervous system (PNS) responses as well as signal transduction including Ca2+, K+-transportation-dependent signaling, Toll-Like Receptor (TLR) signaling and Mitogen Activated Protein Kinase (MAPK) signaling cascades. Many of the pathways identified as affected by shock waves in the hair follicles have been previously reported to be TBI responsive in other organs such as brain and blood. The results suggest that the hair follicle has some common TBI responsive molecular signatures to other tissues. Moreover, various TBI-associated diseases were identified as preferentially affected using a gene network approach, indicating that the hair follicle may be capable of reflecting comprehensive responses to TBI conditions. Accordingly, the present study demonstrates that the hair follicle is a potentially viable system for rapid and non-invasive TBI diagnosis.