Systems biology approaches for discovering biomarkers for traumatic brain injury

vCSF Danger-associated Molecular Patterns After Traumatic and Nontraumatic Acute Brain Injury: A Prospective Study

BACKGROUND

Danger-associated molecular patterns (DAMPs) may be implicated in the pathophysiological pathways associated with an unfavorable outcome after acute brain injury (ABI).

METHODS

We collected samples of ventricular cerebrospinal fluid (vCSF) for 5 days in 50 consecutive patients at risk of intracranial hypertension after traumatic and nontraumatic ABI. Differences in vCSF protein expression over time were evaluated using linear models and selected for functional network analysis using the PANTHER and STRING databases. The primary exposure of interest was the type of brain injury (traumatic vs. nontraumatic), and the primary outcome was the vCSF expression of DAMPs. Secondary exposures of interest included the occurrence of intracranial pressure ≥20 or ≥ 30 mm Hg during the 5 days post-ABI, intensive care unit (ICU) mortality, and neurological outcome (assessed using the Glasgow Outcome Score) at 3 months post-ICU discharge. Secondary outcomes included associations of these exposures with the vCSF expression of DAMPs.

RESULTS

A network of 6 DAMPs ( DAMP_trauma ; protein-protein interaction [PPI] P =0.04) was differentially expressed in patients with ABI of traumatic origin compared with those with nontraumatic ABI. ABI patients with intracranial pressure ≥30 mm Hg differentially expressed a set of 38 DAMPS ( DAMP_ICP30 ; PPI P < 0.001). Proteins in DAMP_ICP30 are involved in cellular proteolysis, complement pathway activation, and post-translational modifications. There were no relationships between DAMP expression and ICU mortality or unfavorable versus favorable outcomes.

CONCLUSIONS

Specific patterns of vCSF DAMP expression differentiated between traumatic and nontraumatic types of ABI and were associated with increased episodes of severe intracranial hypertension.

Study on Proteomics-Based Aortic Dissection Molecular Markers Using iTRAQ Combined With Label Free Techniques

Background: Aortic dissection refers to the separation of aortic media and extension along the long axis to form the true and false chambers of the aortic wall. 65–70% of the patients died of cardiac tamponade, arrhythmia, dissection rupture, etc. At present, echocardiography, computed tomography angiography (CTA), etc. are the main diagnosis tools for aortic dissection. To date, there is no rapid serum molecular marker that can be used for differential diagnosis and risk assessment.

Objectives: To screen serum molecular markers systematically amid aortic dissection and acute coronary syndrome and to preliminarily identify the pathogenesis of acute aortic dissection.

Methods: Related disputes cases of all hospitals were statistically analyzed for the AAD medical disputes ratio, early death ratio and misdiagnosis ratio from the database of Guangdong Province Medical Disputes Coordination Committee from 2013 to 2017. Serum and Aortic tissues samples were respectively quantified by iTRAQ and label-free analysis, further validated by ELISA and protein verified by immunofluorescence and Western blot from AAD and control patients enrolled from the Zhujiang Hospital of Southern Medical University and Guangdong Province people's Hospital from 2016 to 2018.

Results: AAD cases ratio accounted for 15.29% in all 150 cardiovascular disputes, 59.26% in all cardiovascular death less than 24 h, and 88.89% in the patients who remained undiagnosed at the time of death, 84 proteins (66 and 18 upregulated and downregulated, respectively) were identified by iTRAQ and 16 proteins (9 and 7 upregulated and downregulated, respectively) by Label-free. Nine proteins (Lumican, FGL1, PI16, MMP9, FBN1, MMP2, VWF, MMRN1, and PF4) related to the pathogenesis of aortic dissection were identified by David /Ease and String techniques as candidate biomarkers for verification test. Four proteins (Lumican, FGL1, PI16, and MMP9) were found to be statistically different after ELISA verification. The expression of FGL1, PI16, and MMP9 proteins was pathologically significantly increased except for Lumican. Histologically, TGF-β1, α-SMA, and Collagen1 were also significantly higher in the aortic group.

Conclusion: Lumican, FGL1, PI16, and MMP9 may be potential biomarkers in AAD patients, and the Lumican-mediated TGF-β1 pathway is likely to be involved in the pathogenesis of aortic dissection.

Brain interstitial glycerol correlates with evolving brain injury in paediatric traumatic brain injury

PURPOSE

A better understanding of the complex pathophysiology of traumatic brain injury (TBI) is needed to improve our current therapies. Cerebral microdialysis (CMD) is an advanced method to monitor the brain, but little is known about its parameters in children. Brain glycerol, one of the CMD variables, is an essential component of the phospholipid bilayer cell membrane and is considered a useful marker of tissue hypoxia in adults. This study examined the time course of glycerol and its associations in paediatric TBI.

METHODS

In this retrospective cohort study, we collected data on children (< 13years) with severe TBI who underwent CMD monitoring. The relationship of glycerol was examined with respect to physiological, radiological variables, and clinical outcome.

RESULTS

Twenty-eight children underwent CMD monitoring and had evaluable data. Lesion progression on head computed tomography (CT) demonstrated a strong relationship with glycerol (median glycerol, maximum and initial-to-maximum) when lesion size increased by > 30% (p=0.01, p=0.04 and p=0.004). Absolute glycerol values had a weak but statistically significant association with intracranial pressure and brain oxygenation. We did not find an association with clinical outcome.

CONCLUSION

This is the first study to provide data on brain interstitial glycerol in children. CMD glycerol, particularly an increase from baseline, is associated with other markers of injury and with a significant increase in lesion size on repeat head CT. As such, it may represent a useful monitorable marker for evolving injury in paediatric TBI.

iTRAQ-Based Quantitative Proteomics Indicated Nrf2/OPTN-Mediated Mitophagy Inhibits NLRP3 Inflammasome Activation after Intracerebral Hemorrhage

Intracerebral hemorrhage- (ICH-) induced secondary brain injury (SBI) is a very complex pathophysiological process. However, the molecular mechanisms and drug targets of SBI are highly intricate and still elusive, yet a clear understanding is crucial for the treatment of SBI. In the current study, we aimed to confirm that nuclear factor-E2-related factor 2 (Nrf2)/Optineurin- (OPTN-) mediated mitophagy alleviated SBI by inhibiting nucleotide-binding oligomerization domain-like receptor pyrin domain-containing 3 (NLRP3) inflammasome activation based on the isobaric tag for relative and absolute quantization (iTRAQ) quantification proteomics. Human ICH brain specimens were collected for iTRAQ-based proteomics analysis. Male Nrf2 wild-type (WT) and knockout (KO) mice were employed to establish ICH murine models. The survival rate, hematoma volume, neurofunctional outcomes, blood-brain barrier (BBB) permeability, brain edema, spatial neuronal death, NLRP3 inflammasome, inflammatory response, mitochondrial function, and mitophagy level were evaluated after ICH. The iTRAQ quantification analysis showed that the differentially expressed proteins (DEPs), Nrf2 and NLRP3, were closely associated with the initiation and development of SBI after ICH. The Nrf2 KO mice had a significantly lower survival rate, bigger hematoma volume, worse neurological deficits, and increased BBB disruption, brain edema, and neuronal death when compared with the Nrf2 WT mice after ICH. Furthermore, Nrf2 KO enhanced NLRP3 inflammasome activation and neuroinflammation as evidenced by the NF-κB activation and various proinflammatory cytokine releases following ICH. Moreover, Nrf2 could interact with and modulate the mitophagy receptor OPTN, further mediating mitophagy to remove dysfunctional mitochondria after ICH. Furthermore, OPTN small interfering RNA (siRNA) increased the NLRP3 inflammasome activation by downregulating mitophagy level and enhancing mitochondrial damage in the Nrf2 WT mice after ICH. Together, our data indicated that Nrf2/OPTN inhibited NLRP3 inflammasome activation, possibly via modulating mitophagy, therefore alleviating SBI after ICH.

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

Metabolomics Analysis of Hippocampus and Cortex in a Rat Model of Traumatic Brain Injury in the Subacute Phase

Traumatic brain injury (TBI) is a complex and serious disease as its multifaceted pathophysiological mechanisms remain vague. The molecular changes of hippocampal and cortical dysfunction in the process of TBI are poorly understood, especially their chronic effects on metabolic profiles. Here we utilize metabolomics-based liquid chromatography coupled with tandem mass spectrometry coupled with bioinformatics method to assess the perturbation of brain metabolism in rat hippocampus and cortex on day 7. The results revealed a signature panel which consisted of 13 identified metabolites to facilitate targeted interventions for subacute TBI discrimination. Purine metabolism change in cortical tissue and taurine and hypotaurine metabolism change in hippocampal tissue were detected. Furthermore, the associations between the metabolite markers and the perturbed pathways were analyzed based on databases: 64 enzyme and one pathway were evolved in TBI. The findings represented significant profiling changes and provided unique metabolite-protein information in a rat model of TBI following the subacute phase. This study may inspire scientists and doctors to further their studies and provide potential therapy targets for clinical interventions.

Biomarkers in traumatic brain injury: new concepts

Traumatic brain injury is a multifaceted condition that encompasses a spectrum of injuries: contusions, axonal injuries in specific brain regions, edema, and hemorrhage. Brain injury determines a broad clinical and disability spectrum due to the implication of various cellular pathways, genetic phenotypes, and environmental factors. It is challenging to predict patient outcomes, to appropriately evaluate the patients, to determine a suitable treatment strategy and rehabilitation program, and to communicate with patient relatives. Biomarkers detected from body fluids are potential evaluation tools for traumatic brain injury patients. These may serve as internal indicators of cerebral damage, delivering valuable information about the dynamic cellular, biochemical, and molecular environments. The diagnostic and prognostic value of biomarkers tested both in animal models of traumatic brain injury is still under question, despite a considerable scientific literature. Recent publications emphasize that a more realistic approach involves combining multiple types of biomarkers with other investigative tools (imaging, outcome scales, and genetic polymorphisms). Additionally, there is increasing interest in the use of biomarkers as tools for treatment monitoring and as surrogate outcome variables to facilitate the design of distinct randomized controlled trials. This review highlights the latest available evidence regarding biomarkers in adults after traumatic brain injury and discusses new approaches in the evaluation of this patient group.

Precision Medicine in Acute Brain Injury: A Narrative Review

Over the past few years, the concept of personalized medicine has percolated into the management of different neurological conditions. Improving outcomes after acute brain injury (ABI) continues to be a major challenge. Unrecognized individual multiomic variations in addition to multiple interacting processes may explain why we fail to observe comprehensive improvements in ABI outcomes even when applied treatments appear to be beneficial logically. The provision of clinical care based on a multiomic approach may revolutionize the management of traumatic brain injury, delayed cerebral ischemia after subarachnoid hemorrhage, acute ischemic stroke, and several other neurological diseases. The challenge is to incorporate all the information obtained from genomic studies, other omic data, and individual variability into a practical tool that can be used to assist clinical decision-making. The effective execution of such strategies, which is still far away, requires the development of protocols on the basis of these complex interactions and strict adherence to management protocols. In this review, we will discuss various omics and physiological targets to guide individualized patient management after ABI.

A Critical Evaluation of Wet Biomarkers for Huntington's Disease: Current Status and Ways Forward

There is an unmet clinical need for objective biomarkers to monitor disease progression and treatment response in Huntington's disease (HD). The aim of this review is, therefore, to provide practical advice for biomarker discovery and to summarise studies on biofluid markers for HD. A PubMed search was performed to review literature with regard to candidate saliva, urine, blood and cerebrospinal fluid biomarkers for HD. Information has been organised into tables to allow a pragmatic approach to the discussion of the evidence and generation of practical recommendations for future studies. Many of the markers published converge on metabolic and inflammatory pathways, although changes in other analytes representing antioxidant and growth factor pathways have also been found. The most promising markers reflect neuronal and glial degeneration, particularly neurofilament light chain. International collaboration to standardise assays and study protocols, as well as to recruit sufficiently large cohorts, will facilitate future biomarker discovery and development.

Artificial Intelligence and the detection of pediatric concussion using epigenomic analysis

Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area ofintensescientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine ('CpG') methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium MethylationEPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FDR p-values were calculated for mTBI detection based on CpG methylation levels. Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent individual accuracy (AUC ≥ 0.90-1.00) while 119 displayed good accuracy (AUC ≥ 0.80-0.89) for the prediction of mTBI. The CpG methylation changes ≥10% were observed in many CpG loci after concussion suggesting biological significance. Pathway analysis identified several biologically important neurological pathways that were perturbed including those associated with: impaired brain function, cognition, memory, neurotransmission, intellectual disability and behavioral change and associated disorders. The combination of epigenomic and clinical predictors were highly accurate for the detection of concusion using AI techniques. Using DL/AI, a combination of epigenomic and clinical markers had sensitivity and specificity ≧95% for prediction of mTBI. In this novel study, we identified significant methylation changes in multiple genes in response to mTBI. Gene pathways that were epigenetically dysregulated included several known to be involved in neurological function, thus giving biological plausibility to our findings.

Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Pain with traumatic brain injury and psychological disorders

Traumatic brain injury (TBI) is the cause for long-term disability in more than 3 million patients in the US alone, with chronic pain being the most frequently reported complain. To date, predisposing mechanisms for chronic pain in TBI patients are largely unknown. Psychological disorders, including post-traumatic stress disorder, depression and anxiety following TBI are commonly reported comorbidities to post-traumatic pain. Long term consequences can be debilitating and affect quality of life even when the injury is mild. In this review, we present the most commonly reported chronic pain conditions across the spectrum of severity of TBI, mainly focusing on mild TBI. We discuss chronic post- traumatic headaches, widespread pain as well as post-traumatic central pain. We discuss pain in the context of injury severity and military versus civilian populations. We are only starting to understand the biological mechanisms behind post-traumatic pain and associated psychological distress following TBI, with genetic, biochemical and imaging studies pointing to the dopaminergic, neurotrophic factors and the role of Apolipoprotein. Physiological and neurological mechanisms are proposed to partially explain this interaction between post-traumatic pain and psychological distress. Nevertheless, the evidence for the role of structural brain damage remains incomplete and to a large extent debatable, as it is still difficult to establish clear causality between brain trauma and chronic pain. Finally, general aspects of management of chronic pain post-TBI are addressed.

Scaled traumatic brain injury results in unique metabolomic signatures between gray matter, white matter, and serum in a piglet model

Traumatic brain injury (TBI) is a leading cause of death and long-term disability in the United States. The heterogeneity of the disease coupled with the lack of comprehensive, standardized scales to adequately characterize multiple types of TBI remain to be major challenges facing effective therapeutic development. A systems level approach to TBI diagnosis through the use of metabolomics could lead to a better understanding of cellular changes post-TBI and potential therapeutic targets. In the current study, we utilize a GC-MS untargeted metabolomics approach to demonstrate altered metabolism in response to TBI in a translational pig model, which possesses many neuroanatomical and pathophysiologic similarities to humans. TBI was produced by controlled cortical impact (CCI) in Landrace piglets with impact velocity and depth of depression set to 2m/s;6mm, 4m/s;6mm, 4m/s;12mm, or 4m/s;15mm resulting in graded neural injury. Serum samples were collected pre-TBI, 24 hours post-TBI, and 7 days post-TBI. Partial least squares discriminant analysis (PLS-DA) revealed that each impact parameter uniquely influenced the metabolomic profile after TBI, and gray and white matter responds differently to TBI on the biochemical level with evidence of white matter displaying greater metabolic change. Furthermore, pathway analysis revealed unique metabolic signatures that were dependent on injury severity and brain tissue type. Metabolomic signatures were also detected in serum samples which potentially captures both time after injury and injury severity. These findings provide a platform for the development of a more accurate TBI classification scale based unique metabolomic signatures.

iTRAQ-Based Quantitative Proteomics Reveals the New Evidence Base for Traumatic Brain Injury Treated with Targeted Temperature Management

This study aimed to investigate the effects of targeted temperature management (TTM) modulation on traumatic brain injury (TBI) and the involved mechanisms using quantitative proteomics technology. SH-SY5Y and HT-22 cells were subjected to moderate stretch injury using the cell injury controller (CIC), followed by incubation at TTM (mild hypothermia, 32°C), or normothermia (37°C). The real-time morphological changes, cell cycle phase distribution, death, and cell viability were evaluated. Moderate TBI was produced by the controlled cortical impactor (CCI), and the effects of TTM on the neurological damage, neurodegeneration, cerebrovascular histopathology, and behavioral outcome were determined in vivo. Results showed that TTM treatment prevented TBI-induced neuronal necrosis in the brain, achieved a substantial reduction in neuronal death both in vitro and in vivo, reduced cortical lesion volume and neuronal loss, attenuated cerebrovascular histopathological damage, brain edema, and improved behavioral outcome. Using an iTRAQ proteomics approach, proteins that were significantly associated with TTM in experimental TBI were identified. Importantly, changes in four candidate molecules (plasminogen [PLG], antithrombin III [AT III], fibrinogen gamma chain [FGG], transthyretin [TTR]) were verified using TBI rat brain tissues and TBI human cerebrospinal fluid (CSF) samples. This study is one of the first to investigate the neuroprotective effects of TTM on the proteome of human and experimental models of TBI, providing an overall landscape of the TBI brain proteome and a scientific foundation for further assessment of candidate molecules associated with TTM for the promotion of reparative strategies post-TBI.

A strategy for evaluating pathway analysis methods

BACKGROUND

Researchers have previously developed a multitude of methods designed to identify biological pathways associated with specific clinical or experimental conditions of interest, with the aim of facilitating biological interpretation of high-throughput data. Before practically applying such pathway analysis (PA) methods, we must first evaluate their performance and reliability, using datasets where the pathways perturbed by the conditions of interest have been well characterized in advance. However, such 'ground truths' (or gold standards) are often unavailable. Furthermore, previous evaluation strategies that have focused on defining 'true answers' are unable to systematically and objectively assess PA methods under a wide range of conditions.

RESULTS

In this work, we propose a novel strategy for evaluating PA methods independently of any gold standard, either established or assumed. The strategy involves the use of two mutually complementary metrics, recall and discrimination. Recall measures the consistency of the perturbed pathways identified by applying a particular analysis method to an original large dataset and those identified by the same method to a sub-dataset of the original dataset. In contrast, discrimination measures specificity-the degree to which the perturbed pathways identified by a particular method to a dataset from one experiment differ from those identifying by the same method to a dataset from a different experiment. We used these metrics and 24 datasets to evaluate six widely used PA methods. The results highlighted the common challenge in reliably identifying significant pathways from small datasets. Importantly, we confirmed the effectiveness of our proposed dual-metric strategy by showing that previous comparative studies corroborate the performance evaluations of the six methods obtained by our strategy.

CONCLUSIONS

Unlike any previously proposed strategy for evaluating the performance of PA methods, our dual-metric strategy does not rely on any ground truth, either established or assumed, of the pathways perturbed by a specific clinical or experimental condition. As such, our strategy allows researchers to systematically and objectively evaluate pathway analysis methods by employing any number of datasets for a variety of conditions.

Current and Emerging Technologies for Probing Molecular Signatures of Traumatic Brain Injury

Traumatic brain injury (TBI) is understood as an interplay between the initial injury, subsequent secondary injuries, and a complex host response all of which are highly heterogeneous. An understanding of the underlying biology suggests a number of windows where mechanistically inspired interventions could be targeted. Unfortunately, biologically plausible therapies have to-date failed to translate into clinical practice. While a number of stereotypical pathways are now understood to be involved, current clinical characterization is too crude for it to be possible to characterize the biological phenotype in a truly mechanistically meaningful way. In this review, we examine current and emerging technologies for fuller biochemical characterization by the simultaneous measurement of multiple, diverse biomarkers. We describe how clinically available techniques such as cerebral microdialysis can be leveraged to give mechanistic insights into TBI pathobiology and how multiplex proteomic and metabolomic techniques can give a more complete description of the underlying biology. We also describe spatially resolved label-free multiplex techniques capable of probing structural differences in chemical signatures. Finally, we touch on the bioinformatics challenges that result from the acquisition of such large amounts of chemical data in the search for a more mechanistically complete description of the TBI phenotype.

Big Data in traumatic brain injury; promise and challenges

Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.

Applying Systems Biology Methodology To Identify Genetic Factors Possibly Associated with Recovery after Traumatic Brain Injury

Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality worldwide. It is linked with a number of medical, neurological, cognitive, and behavioral sequelae. The influence of genetic factors on the biology and related recovery after TBI is poorly understood. Studies that seek to elucidate the impact of genetic influences on neurorecovery after TBI will lead to better individualization of prognosis and inform development of novel treatments, which are considerably lacking. Current genetic studies related to TBI have focused on specific candidate genes. The objectives of this study were to use a system biology-based approach to identify biologic processes over-represented with genetic variants previously implicated in clinical outcomes after TBI and identify unique genes potentially related to recovery after TBI. After performing a systematic review to identify genes in the literature associated with clinical outcomes, we used the genes identified to perform a systems biology-based integrative computational analysis to ascertain the interactions between molecular components and to develop models for regulation and function of genes involved in TBI recovery. The analysis identified over-representation of genetic variants primarily in two biologic processes: response to injury (cell proliferation, cell death, inflammatory response, and cellular metabolism) and neurocognitive and behavioral reserve (brain development, cognition, and behavior). Overall, this study demonstrates the use of a systems biology-based approach to identify unique/novel genes or sets of genes important to the recovery process. Findings from this systems biology-based approach provide additional insight into the potential impact of genetic variants on the underlying complex biological processes important to TBI recovery and may inform the development of empirical genetic-related studies for TBI. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed in TBI.

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis

Background

Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge.

Methods and findings

The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2).

Conclusions

TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients.

Trial Registration

ClinicalTrials.gov Identifier NCT01565551

Post-concussion symptoms and chronic pain after mild traumatic brain injury are modulated by multiple locus effect in the BDNF gene through the expression of antisense: A pilot prospective control study

Background: Mild traumatic brain injury (mTBI) often results in post-concussion symptoms, chronic pain, and sleepiness. Genetic factors are thought to play an important role in poor prognosis.

Aims: The aims of this study are to (1) document the prevalence of pain and post-concussion symptoms in mTBI patients in acute and chronic phases (2) determine whether candidate genes predispose to post-concussive symptoms and pain.

Methods: Posttraumatic symptoms, evaluated using the Rivermead Post-Concussion Symptoms Questionnaire, and pain were assessed in 94 mTBI patients in the acute phase as well as in 22 healthy controls. Assessment was repeated in 36 patients after one year who agreed to participate in the follow-up visit. Gene polymorphisms and expression were assessed in mTBI patients and healthy controls.

Results: In the acute phase, mTBI patients with pain (69%) presented more psychological symptoms and sleepiness and were less able to return to work than those without pain. At one year, 19% of mTBI patients had persistent pain and psychological distress. Two haplotypes (H2 and H3) in the brain-derived neurotrophic factor (BDNF) gene were shown to be respectively deleterious and protective against post-concussion symptoms and pain in both acute and chronic phases. Protective haplotype H3 was associated with a decreased expression of the anti-sense of BDNF (BDNF-AS). Deleterious haplotype H2 predicted the development of chronic pain at one year, whereas H3 was protective.

Conclusions: This pilot study suggests a protective mechanism of a multilocus effect in BDNF, through BDNF-AS, against post-concussion symptoms and pain in the acute phase and possibly chronic pain at one year post-mTBI. The role of antisense RNA should be validated in larger cohorts.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

Protein profiling in serum after traumatic brain injury in rats reveals potential injury markers

INTRODUCTION

The serum proteome following traumatic brain injury (TBI) could provide information for outcome prediction and injury monitoring. The aim with this affinity proteomic study was to identify serum proteins over time and between normoxic and hypoxic conditions in focal TBI.

MATERIAL AND METHODS

Sprague Dawley rats (n=73) received a 3mm deep controlled cortical impact ("severe injury"). Following injury, the rats inhaled either a normoxic (22% O₂) or hypoxic (11% O₂) air mixture for 30min before resuscitation. The rats were sacrificed at day 1, 3, 7, 14 and 28 after trauma. A total of 204 antibodies targeting 143 unique proteins of interest in TBI research, were selected. The sample proteome was analyzed in a suspension bead array set-up. Comparative statistics and factor analysis were used to detect differences as well as variance in the data.

RESULTS

We found that complement factor 9 (C9), complement factor B (CFB) and aldolase c (ALDOC) were detected at higher levels the first days after trauma. In contrast, hypoxia inducing factor (HIF)1α, amyloid precursor protein (APP) and WBSCR17 increased over the subsequent weeks. S100A9 levels were higher in hypoxic-compared to normoxic rats, together with a majority of the analyzed proteins, albeit few reached statistical significance. The principal component analysis revealed a variance in the data, highlighting clusters of proteins.

CONCLUSIONS

Protein profiling of serum following TBI using an antibody based microarray revealed temporal changes of several proteins over an extended period of up to four weeks. Further studies are warranted to confirm our findings.

Chapter 6 state of the science of pediatric traumatic brain injury: biomarkers and gene association studies

OBJECTIVES

Our objective is to review the most widely used biomarkers and gene studies reported in pediatric traumatic brain injury (TBI) literature, to describe their findings, and to discuss the discoveries and gaps that advance the understanding of brain injury and its associated outcomes. Ultimately, we aim to inform the science for future research priorities.

DATA SOURCES

We searched PubMed, MEDLINE, CINAHL, and the Cochrane Database of Systematic Reviews for published English language studies conducted in the last 10 years to identify reviews and completed studies of biomarkers and gene associations in pediatric TBI. Of the 131 biomarker articles, only 16 were specific to pediatric TBI patients, whereas of the gene association studies in children with TBI, only four were included in this review.

CONCLUSION

Biomarker and gene attributes are grossly understudied in pediatric TBI in comparison to adults. Although recent advances recognize the importance of biomarkers in the study of brain injury, the limited number of studies and genomic associations in the injured brain has shown the need for common data elements, larger sample sizes, heterogeneity, and common collection methods that allow for greater understanding of the injured pediatric brain. By building on to the consortium of interprofessional scientists, continued research priorities would lead to improved outcome prediction and treatment strategies for children who experience a TBI.

IMPLICATIONS FOR NURSING RESEARCH

Understanding recent advances in biomarker and genomic studies in pediatric TBI is important because these advances may guide future research, collaborations, and interventions. It is also important to ensure that nursing is a part of this evolving science to promote improved outcomes in children with TBIs.

Differential Neuroproteomic and Systems Biology Analysis of Spinal Cord Injury

Acute spinal cord injury (SCI) is a devastating condition with many consequences and no known effective treatment. Although it is quite easy to diagnose traumatic SCI, the assessment of injury severity and projection of disease progression or recovery are often challenging, as no consensus biomarkers have been clearly identified. Here rats were subjected to experimental moderate or severe thoracic SCI. At 24h and 7d postinjury, spinal cord segment caudal to injury center versus sham samples was harvested and subjected to differential proteomic analysis. Cationic/anionic-exchange chromatography, followed by 1D polyacrylamide gel electrophoresis, was used to reduce protein complexity. A reverse phase liquid chromatography-tandem mass spectrometry proteomic platform was then utilized to identify proteome changes associated with SCI. Twenty-two and 22 proteins were up-regulated at 24 h and 7 day after SCI, respectively; whereas 19 and 16 proteins are down-regulated at 24 h and 7 day after SCI, respectively, when compared with sham control. A subset of 12 proteins were identified as candidate SCI biomarkers - TF (Transferrin), FASN (Fatty acid synthase), NME1 (Nucleoside diphosphate kinase 1), STMN1 (Stathmin 1), EEF2 (Eukaryotic translation elongation factor 2), CTSD (Cathepsin D), ANXA1 (Annexin A1), ANXA2 (Annexin A2), PGM1 (Phosphoglucomutase 1), PEA15 (Phosphoprotein enriched in astrocytes 15), GOT2 (Glutamic-oxaloacetic transaminase 2), and TPI-1 (Triosephosphate isomerase 1), data are available via ProteomeXchange with identifier PXD003473. In addition, Transferrin, Cathepsin D, and TPI-1 and PEA15 were further verified in rat spinal cord tissue and/or CSF samples after SCI and in human CSF samples from moderate/severe SCI patients. Lastly, a systems biology approach was utilized to determine the critical biochemical pathways and interactome in the pathogenesis of SCI. Thus, SCI candidate biomarkers identified can be used to correlate with disease progression or to identify potential SCI therapeutic targets.

Progesterone neuroprotection: The background of clinical trial failure

Since the first pioneering studies in the 1990s, a large number of experimental animal studies have demonstrated the neuroprotective efficacy of progesterone for brain disorders, including traumatic brain injury (TBI). In addition, this steroid has major assets: it easily crosses the blood-brain-barrier, rapidly diffuses throughout the brain and exerts multiple beneficial effects by acting on many molecular and cellular targets. Moreover, progesterone therapies are well tolerated. Notably, increased brain levels of progesterone are part of endogenous neuroprotective responses to injury. The hormone thus emerged as a particularly promising protective candidate for TBI and stroke patients. The positive outcomes of small Phase 2 trials aimed at testing the safety and potential protective efficacy of progesterone in TBI patients then provided support and guidance for two large, multicenter, randomized and placebo-controlled Phase 3 trials, with more than 2000 TBI patients enrolled. The negative outcomes of both trials, named ProTECT III and SyNAPSE, came as a big disappointment. If these trials were successful, progesterone would have become the first efficient neuroprotective drug for brain-injured patients. Thus, progesterone has joined the numerous neuroprotective candidates that have failed in clinical trials. The aim of this review is a reappraisal of the preclinical animal studies, which provided the proof of concept for the clinical trials, and we critically examine the design of the clinical studies. We made efforts to present a balanced view of the strengths and limitations of the translational studies and of some serious issues with the clinical trials. We place particular emphasis on the translational value of animal studies and the relevance of TBI biomarkers. The probability of failure of ProTECT III and SyNAPSE was very high, and we present them within the broader context of other unsuccessful trials.

Application of Systems Biology to Neuroproteomics: The Path to Enhanced Theranostics in Traumatic Brain Injury

The application of systems biology tools in analyzing heterogeneous data from multiple sources has become a necessity, especially in biomarker discovery. Such tools were developed with several approaches to address different types of research questions and hypotheses. In the field of neurotrauma and traumatic brain injury (TBI), three distinct approaches have been used so far as systems biology tools, namely functional group categorization, pathway analysis, and protein-protein interaction (PPI) networks. The databases allow for query of the system to identify candidate targets which can be further studied to elucidate potential downstream biomarkers indicative of disease progression, severity, and improvement. The various systems biology tools, databases, and strategies that can be implemented on available TBI data in neuroproteomic studies are discussed in this chapter.

Role of Systems Biology in Brain Injury Biomarker Discovery: Neuroproteomics Application

Years of research in the field of neurotrauma have led to the concept of applying systems biology as a tool for biomarker discovery in traumatic brain injury (TBI). Biomarkers may lead to understanding mechanisms of injury and recovery in TBI and can be potential targets for wound healing, recovery, and increased survival with enhanced quality of life. The literature available on neurotrauma studies from both animal and clinical studies has provided rich insight on the molecular pathways and complex networks of TBI, elucidating the proteomics of this disease for the discovery of biomarkers. With such a plethora of information available, the data from the studies require databases with tools to analyze and infer new patterns and associations. The role of different systems biology tools and their use in biomarker discovery in TBI are discussed in this chapter.

What's New in Traumatic Brain Injury: Update on Tracking, Monitoring and Treatment

Traumatic brain injury (TBI), defined as an alteration in brain functions caused by an external force, is responsible for high morbidity and mortality around the world. It is important to identify and treat TBI victims as early as possible. Tracking and monitoring TBI with neuroimaging technologies, including functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), and high definition fiber tracking (HDFT) show increasing sensitivity and specificity. Classical electrophysiological monitoring, together with newly established brain-on-chip, cerebral microdialysis techniques, both benefit TBI. First generation molecular biomarkers, based on genomic and proteomic changes following TBI, have proven effective and economical. It is conceivable that TBI-specific biomarkers will be developed with the combination of systems biology and bioinformation strategies. Advances in treatment of TBI include stem cell-based and nanotechnology-based therapy, physical and pharmaceutical interventions and also new use in TBI for approved drugs which all present favorable promise in preventing and reversing TBI.

S100B and Glial Fibrillary Acidic Protein as Indexes to Monitor Damage Severity in an In Vitro Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading and rising cause of death and disability worldwide. There is great interest in S100B and Glial Fibrillary Acid Protein (GFAP) as candidate biomarkers of TBI for diagnosis, triage, prognostication and drug development. However, conflicting results especially on S100B hamper their routine application in clinical practice. To try to address this question, we mimicked TBI damage utilizing a well-validated, simplified in vitro model of graded stretch injury induced in rat organotypic hippocampal slice cultures (OHSC). Different severities of trauma, from mild to severe, have been tested by using an equi-biaxial stretch of the OHSCs at a specified Lagrangian strain of 0 (controls), 5, 10, 20 and 50 %. OHSC were analysed at 3, 6, 18, 24, 48 and 96 h post-injury. Cell death, gene expressions and release into the culture medium of S100B and GFAP were determined at each time point. Gene expression and release of S100B slightly increased only in 20 and 50 % stretched OHSC. GFAP over-expression occurred in 10, 20 and 50 % and was inversely correlated with time post-injury. GFAP release significantly increased with time at any level of injury (p < 0.01 with respect to controls). Consequently, the total amount of GFAP released showed a strong linear relationship with the severity of injury (R(2) = 0.7662; p < 0.001). Under these experimental conditions, S100B seems to be useful in diagnosing only moderate to severe TBI-like injuries. Differently, GFAP demonstrates adequate biomarker requisites since its cellular release is affected by all grades of injury severity.

Multivariate analysis of traumatic brain injury: development of an assessment score

Important challenges for the diagnosis and monitoring of mild traumatic brain injury (mTBI) include the development of plasma biomarkers for assessing neurologic injury, monitoring pathogenesis, and predicting vulnerability for the development of untoward neurologic outcomes. While several biomarker proteins have shown promise in this regard, used individually, these candidates lack adequate sensitivity and/or specificity for making a definitive diagnosis or identifying those at risk of subsequent pathology. The objective for this study was to evaluate a panel of six recognized and novel biomarker candidates for the assessment of TBI in adult patients. The biomarkers studied were selected on the basis of their relative brain-specificities and potentials to reflect distinct features of TBI mechanisms including (1) neuronal damage assessed by neuron-specific enolase (NSE) and brain derived neurotrophic factor (BDNF); (2) oxidative stress assessed by peroxiredoxin 6 (PRDX6); (3) glial damage and gliosis assessed by glial fibrillary acidic protein and S100 calcium binding protein beta (S100b); (4) immune activation assessed by monocyte chemoattractant protein 1/chemokine (C–C motif) ligand 2 (MCP1/CCL2); and (5) disruption of the intercellular adhesion apparatus assessed by intercellular adhesion protein-5 (ICAM-5). The combined fold-changes in plasma levels of PRDX6, S100b, MCP1, NSE, and BDNF resulted in the formulation of a TBI assessment score that identified mTBI with a receiver operating characteristic (ROC) area under the curve of 0.97, when compared to healthy controls. This research demonstrates that a profile of biomarker responses can be used to formulate a diagnostic score that is sensitive for the detection of mTBI. Ideally, this multivariate assessment strategy will be refined with additional biomarkers that can effectively assess the spectrum of TBI and identify those at particular risk for developing neuropathologies as consequence of a mTBI event.

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI.

Systems level analysis and identification of pathways and networks associated with liver fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Characterization of chemically induced liver injuries using gene co-expression modules

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

On the crucial cerebellar wound healing-related pathways and their cross-talks after traumatic brain injury in Danio rerio

Upon injury, the direct damage and the subsequent secondary injury in the brain often result in chronic neurological disorders. Due to multifactorial nature of secondary injury and subsequent complex cellular responses, much of the underlying mechanisms are unclear. This study used an adult zebrafish cerebellum injury model to investigate the phenotypes and the secondary injury responses for recovery mechanisms of injured brain. Using the time course microarray analysis, a candidate protein-protein interaction (PPI) network was refined as cerebellar wound healing PPI network by dynamic modeling and big data mining. Pathway enrichment and ontological analysis were incorporated into the refined network to highlight the main molecular scheme of cerebellar wound healing. Several significant pathways, including chemokine, Phosphatidylinositide 3-kinases, and axon guidance signaling pathway and their cross-talks through PI3K, PAK2, and PLXNA3 were identified to coordinate for neurogenesis and angiogenesis, which are essential for the restoration of the injured brain. Our finding provides an insight into the molecular restoration mechanisms after traumatic brain injury, and open up new opportunity to devise the treatment for traumatic brain injury in human.

Molecular biomarkers of epileptogenesis

Epileptogenesis, a process leading to a reduced threshold for seizures after transient brain insults, as well as the mechanisms underlying the propensity to generate spontaneous epileptic seizures, are highly dynamic processes. Biomarkers--objective measures of biological processes--would be excellent tools for monitoring epileptogenesis and the dynamics of increased seizure propensity, as well as the potential to interfere, for example pharmacologically, with these key pathological aspects of epilepsy. Molecular biomarkers have revolutionized therapies, as well as response prediction and monitoring of therapies in other biomedical fields. However, high-impact molecular biomarkers are still not available in the context of epilepsy. Several factors, such as the large heterogeneity of epileptic syndromes and their underlying pathological patterns, as well as the limited availability of tissue samples, represent a particular challenge to the development of molecular biomarkers in epileptogenesis and epilepsy. However, substantial technical progress has been made recently with respect to biomarker characterization and monitoring by large throughput analysis on the genomic, mRNA, and proteomic levels, starting from minute amounts of brain tissue or body fluids, for example cerebrospinal fluid, blood, serum, or plasma. Given the substantial cellular- and network-level functional pathophysiology involved in epilepsy, it may be beneficial in the future to combine molecular analysis with other methods, such as imaging and electrophysiological biomarkers.