Traumatic brain injury (TBI) affects 1.7 million people in the United States each year, causing lifelong functional deficits in cognition and behavior. The complex pathophysiology of neural injury is a primary barrier to developing sensitive and specific diagnostic tools, which consequentially has a detrimental effect on treatment regimens. Biomarkers of other diseases (e.g. cancer) have provided critical insight into disease emergence and progression that lend to developing powerful clinical tools for intervention. Therefore, the biomarker discovery field has recently focused on TBI and made substantial advancements to characterize markers with promise of transforming TBI patient diagnostics and care. This review focuses on these key advances in neural injury biomarkers discovery, including novel approaches spanning from omics-based approaches to imaging and machine learning as well as the evolution of established techniques. Traumatic brain injury (TBI) affects an estimated 1.7 million people in the United States each year and is the leading cause of death in young adults and children in industrialized countries . IndividTBI should be viewed as not a single pathophysiological event, but a cascade that involves two separate injury phases Fig. . The iniSince the complexities of the injury microenvironment are still not fully elucidated, this heterogeneous pathology is a primary barrier to developing sensitive diagnostic tools. The Glasgow Coma Scale (GCS), a commonly used survey in emergency room settings, diagnoses TBI with a battery of observations such as patient’s eye and motor response to stimuli. Despite being a hallmark of TBI diagnosis, the GCS has been found to be a poor predictor of patient outcome and is not appropriate for patients with prior neurological conditions , 21, 22.Researchers are now turning to biomarkers, objective molecular signatures of injury, as a platform for developing more sensitive and specific TBI treatment and diagnosis tools. Identification and quantification of biomarker expression provides the basis for producing these technologies. For example, a biosensor targeting TBI biomarkers can potentially work to both diagnose TBI patients and monitor the severity of their disease progression. Further, these tools may provide insight on treatment efficiency by assessing changes in biomarker expression. Several biomarkers for TBI have been identified, mostly located in serum or cerebral spinal fluid after injury, including indicators of inflammation, necrosis, apoptosis, and astrocytosis , 24. TheDue to the complex heterogeneity of TBI, biomarker discovery in preclinical models must consider the limitations of each model when characterizing candidate biomarkers. Although no one animal model can recapitulate the full complexity of TBI, they have distinct characteristics that can aid researchers in discovery of biomarkers associated with different aspects of TBI pathology. Focal injury models, such as the controlled cortical impact (CCI) model, produce cavitation, contusion, vasogenic and cytotoxic edema , 30. WhiCurrently, there are no approved TBI biomarkers for clinical treatment or diagnostic purposes . BiomarkMicroRNAs (miRNAs) are single-stranded RNAs of 17–25 nucleotides in length and are responsible for regulating gene expression at the post-transcriptional level . These mBiomarker discovery through miRNA expression also has immense clinical utility due to the non-invasive nature of analyzing gene expression through plasma samples and ease of analysis due to advances in microarray and high throughput sequencing technology. Studies utilizing this approach have demonstrated the ability to discriminate TBI patients from non-injured controls. A 2018 study conducted by Qin et al. exhibited this capability by identifying miR-319 and miR-328-5p as miRNAs indicative of severe TBI in comparison to mild or moderate TBI in patients . SimilarScreening for modulated miRNAs in saliva samples is an approach that has demonstrated powerful detection sensitivity while maintaining the non-invasiveness that makes miRNA analysis so beneficial to research in patient populations. In a 2017 case study, Hicks et al. found that 6 specific miRNAs in the saliva of children with TBI were significantly modulated from control samples, with three of those miRNAs associated with neuronal development . FurthermiRNA expression methods have also shown the same promise in identifying markers of severity as studies conducted in animal models. Balankathiresan at al. found that a blast-induced injury model produced five serum miRNAs were significantly altered in injury groups when compared to control animals at three distinct injury timepoints . SimilarNeuroproteomics, the study of protein complements of the genome, seeks to analyze protein expression within the CNS to answer questions about disease states and progression . RecentlThe specificity and sensitivity of neuroproteomics approaches have been successfully demonstrated with animal models of TBI. Boutte et al. used this technique to assess protein expression in cerebral spinal fluid (CSF) and brain tissue within the acute timepoints of a penetrating ballistic-like brain injury (PBBI) rodent model of TBI. In addition to observing significant expression changes of UCH-L1, this method was able to isolate cullin 1, protein phosphotase 2C-alpha, and minichromosome maintenance protein 2 homolog, proteins associated with neurite outgrowth and cell differentiation, as potential candidate biomarkers of injury, demonstrating the power of utilizing bottom-up discovery techniques with advanced proteomic methodology . A similAn alternative to neuroproteomics is metabolomics, the study of global metabolic profiles in specific conditions and diseases using mass spectrometry or nuclear magnetic resonance spectrometry , 73. Thi2α, a marker of oxidative damage, at 6 and 24 h post-injury [Lipidomics, a subset of metabolomics, is emerging as a new approach to biomarker discovery in TBI. The rationale for using lipidomics over neuroproteomics is that lipid expression in blood is reflective of expression in brain tissue and therefore has more clinical utility , 79. Furt-injury . Despitet-injury .6–1011 different ligands) of biological motif-displaying bacteriophages can then be generated to screen against a target antigen or tissue. Collecting only target bound bacteriophage followed by subsequent amplification in bacterial hosts creates a new phage library that is biased toward the target antigen or tissue, thereby completing a single screening cycle, also known as “biopanning”. Biopanning is repeated several times to enrich for biological motifs that have strong affinity for the target antigen or tissue. Upon completion of biopanning rounds, bacteriophage plasmids are sequenced and analyzed for discovering biological motifs that may bind specifically to the target  as a tool for analyzing possible biomarkers of injury in patients. In contrast to traditional MRI, DTI provides the ability to spatially map white matter and analyze its diffusivity via fractional anisotropy. This feature shows promise in being more sensitive to detection of axonal injury as opposed to traditional MR techniques alone , which iClinical applications of DTI are also being heavily researched with high levels of optimism. Rangaprakash et al. applied DTI in an effort to differentiate chronic mild TBI patients from non-injured controls, and found a significant loss of integrity of white matter fibers in hippocampal-striatum pathway in injured patients that was not found in the control population . While t99mTc exametazime in conjunction with SPECT to measure cerebral blood flow (CBF) revealed significantly lower CBF levels in the right temporal lobes of patients with poorer physical health [Single photon emission computed tomography (SPECT) is another imaging modality that has high potential for biomarker discovery applications . Approvel health . Howeverl health .Machine learning involves using advanced algorithms to analyze large sets of data to progressively recognize patterns without being programmed to do so. Machine learning algorithms can be applied to many categories of datasets, from proteomics to imaging data. This approach is well suited for identifying patterns of disease in biomedical data, and as such, has been applied to biomarker research of many diseases including cancers, psychosis, and Huntington’s disease –136. ForMachine learning algorithms are versatile in that they can be applied to non-imaging datasets as well. For example, topological data analysis (TDA), a machine learning tool that clusters patient data based on outcome metrics, was used by Nielson et al. to predict novel biomarkers associated with several variables indicative of unfavorable outcome post-injury . The TDAWhile promising, machine learning algorithms applied to neurotrauma research still have drawbacks. Even though using multivariate analysis is extremely beneficial for analyzing the heterogeneous injury microenvironment, it is critical to consider that larger sample sizes are needed to validate the specificity and sensitivity of the biomarkers selected from these models prior to full utility in clinical applications.Several biomarkers of TBI have been identified but they carry the disadvantage of either not being sensitive or specific to TBI, which diminishes their clinical utility. Biomarkers have the potential for improving diagnostic accuracy, predicting the severity of injury progression, and conveying information to clinicians about injury progression for individual patients. Advancements in biomarker discovery range from improving upon already established techniques to applying novel methods to elucidate mechanisms of the neural injury environment. Many emerging tools and techniques have shown promise in inching the field towards a better comprehension of TBI and have given rise to multiple novel candidate biomarkers to further characterize. While preclinical discovery has not yet lead directly to clinical translation, the technological strides discussed here are immensely promising. Ultimately, future efforts in biomarker discovery should continue to rigorously test potential biomarkers and critically inspect their potential clinical utility.


Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter. Microorganisms in the environment can produce a wide range of secondary metabolites (SM). SMs are natural products with diverse chemical structures. This diversity in chemical structures enables these natural products to carry out a variety of functions. SMs can act as antibiotics, antitumor agents, cholesterol-lowering agents and so on . These nAntimicrobial resistance is an emerging global challenge threatening future public health. Previous reports have predicted a worst-case scenario whereby the global economic impact of antimicrobial resistance will result in more than 10 million annual deaths, which is a loss of 2.0–3.5% of the world gross domestic product that is worth approximately 60–100 trillion USD by 2050 . This grBiosynthetic gene clusters (BGCs) are a physical grouping of all the genes responsible for the assembly of a SM . BGCs coThe developments in sequencing technology and readily available bioinformatic pipelines have enabled large quantities of BGCs to be mined from environmental microorganisms without having to culture them and test their bioactivity . These tAlthough computational tools have been useful for studying secondary metabolism from metagenomic datasets, the analysis of metagenomic data is challenging as the software requires high quality genome-resolved genomes or binned metagenomes . ComputaTo date, studies have screened for SMs in environments hosting large bacterial diversity and presumably large chemical diversity, such as aquatic ecosystems, soils and host microbiomes ,14,15. TPrior to the advent of DNA sequencing and genomics, the search for natural products from microorganisms was conducted primarily using culture-dependent techniques in the laboratory . The disVibrio cholerae cultures. After screening 39,000 crude extracts, 49 were found to block fermentation and three were characterized as novel broad-spectrum antibiotics [Biochemical assays are used primarily to screen for SMs or characterize function. Recently, the development of high throughput biochemical assays has helped to uncover more SMs. A study used thymol blue and bromothymol blue indicators to detect the pH drop in relation to sugar fermentation in ibiotics . Howeveribiotics .Traditional approaches have led to the discovery of many therapeutics that are now used today. However, natural product discovery efforts have since declined largely due to the increasing rediscovery rates of known compounds . In addiDegenerate primers are oligonucleotide sequences, with some positions containing more than one possible nucleotide base. This property can be used to target and amplify areas in the genome that are very similar but have slight variations . This isCustomized primer sets were used to screen for NRPS and type I PKS (PKS-I) systems in Actinomycetes . NRPS anIn a recent study, degenerate primers derived from conserved biosynthetic motifs were used to survey the ketosynthase domains from 185 soil microbiome samples . BGCs enMany bioinformatics tools have now been developed to detect known BGCs in regular genome sequences and genome-resolved metagenomes . There aantiSMASH, NAPDOS and ClustScan are examples of bioinformatics software that provide low novelty but high confidence in its analysis ,26 and tAlthough many bioinformatic tools can identify biosynthetic genes with high accuracy, it implies that only biosynthetic pathways with rules implemented in the software can be detected. Therefore, any pathway that may use unknown or unrelated alternative enzymes will be missed . In addiTo address the limitations of identifying novel BGCs, the ClusterFinder algorithm is a recently developed software providing low confidence but high novelty analysis . PredictStreptomyces viridochromogenes. Activating silent BGCs in native hosts demonstrates great potential for high throughput discovery of natural products [One of the major challenges in natural product discovery is that the vast majority of BGCs are either transcriptionally silent or expressed at very low levels under standard laboratory conditions . To addrproducts .Streptomyces coelicolor [Bacillus subtilis and Escherichia coli, which subsequently led to the discovery of a distinct group of thiotetronic acid natural products by combining this approach with targeted genome mining [The advances in synthetic biology techniques have resulted in silent BGCs being activated in heterologous hosts . Heterolelicolor . Moore ee mining ,33.The use of chemical elicitors provide an alternative route to enhance the expression of silent BGCs . This apThe following section describes several computational perspectives that have demonstrated potential in uncovering new BGCs that encode novel bioactive SMs.S. coelicolor and Streptomyces lividans [The EvoMining approach has been recently proposed as a strategy to identify new BGC classes . EvoMinilividans . These alividans . The EvoLarge-scale comparative genomic alignment has been proposed to identify new types of biosynthetic pathways. This strategy involves detecting syntenic blocks of multiple orthologous genes that are not part of the core genome of a taxon and occur in different genomic contexts in different strains and species . A studyThe following sections, while not exhaustive, are designed to provide some examples of work carried out in BGC ‘mining’ from different environments, specifically providing examples from soil, aquatic and host microbiomes. Noticeably, numerous studies have demonstrated that a combination of existing laboratory approaches along with the use of computational tools have provided new insights in the bioprospecting field. This is particularly crucial for studies looking to identify new natural products from microbial dark matter.Soil environments have been reported to hold a highly diverse range of microorganisms and are known sources for antibiotics, antifungals and other natural products that are involved in bacterial communication and interaction within a given ecosystem ,38,39. HStaphloccous aureus and Mycobacterium tuberculosis [The iChip method developed in 2010 offered a new way to isolate and cultivate unculturable bacteria in situ from many environments . The minrculosis . The BGCA recently published study recovered hundreds of near-complete genomes from the Northern Californian grassland soil and analyzed them using antiSMASH . These gSalinispora tropica and is currently undergoing clinical trials as it has demonstrated high cytotoxicity against breast cancer, human colon carcinoma and non-small lung cancer [Aquatic ecosystems are relatively unexplored but have been reported to harbor a high diversity of microorganisms, which may reflect the biosynthetic potential of these ecosystems ,44. A prg cancer ,47.Early and recent studies have observed specific marine microorganisms, such as Roseobacter and Pseudovibrio having potential for SM production ,49. MembPseudovibrio-related bacteria have previously been isolated from marine sources as free-living and host-associated bacteria ,51. TheyIn a recent study, antiSMASH and NAPDOS were used to screen for SMs in recovered genome bins from Lake Stechlin, north-east Germany . Of the Host microbiome environments house an enormous range of bacteria that have adapted and thrived in the host’s environment. In the past few years, studies have focused on natural product discovery in the human microbiome and have provided new insight into the search for novel SMs in different environments ,58. AnotThe microbiome of three deep sea sponges was screened for secondary metabolite potential by using 454 pyrosequencing and degenerate primers to target sequences associated with ketosynthase domains and adenylation domains . KetosynA recent study utilized a combination of chemistry, metagenomics and metatranscriptomics techniques to examine how microorganisms play a key role in human health. Using 752 metagenomic samples from the National Institute of Health Human Microbiome Project, the study found human-associated bacteria housing 3,118 BGCs that encode small molecules . Many ofSMs have played an important role in pharmaceuticals, research and the industry. There are numerous possibilities for expanding our understanding of chemical diversity and how these metabolites function. Various studies have found an abundance of BGCs encoding known SMs and potentially undiscovered ones, which reinforces the notion that high biodiversity can lead to high chemical diversity. In the future, we should see a continuing development of bioinformatics software to address the current limitations of genome mining, analyzing metagenomic data and compound characterization. We may also see new or improved culturing and screening techniques to better explore the vastness of SMs. Many of the discoveries to date have been attributed to many omics and metaomics approaches. However, the limitations of technology and methods still need to be addressed in order to fully exploit the genome mining process and optimize the workflow from the identification of BGC to the expression of the corresponding natural product.There is growing interest to uncover biosynthetic pathways and new natural products in the environment, particularly environments hosting a rich diversity in microorganisms, such as soils, marine ecosystems and host microbiomes. In addition, other unique microbial ecosystems that occur in often extreme environments, such as microbialites, may also be a new source of novel SM . The ide