Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
© Cairelli et al.; licensee BioMed Central. 2015
Received: 4 April 2014
Accepted: 22 April 2015
Published: 18 May 2015
Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury.
Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances.
99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers.
We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.
KeywordsSemantic predications Semantic networks Natural language processing Degree centrality Traumatic brain injury
The diagnosis and treatment of traumatic brain injury (TBI) has received considerable attention. The military community may provide the biggest contribution to this interest because the signature injury of the wars in Iraq and Afghanistan is mild TBI (mTBI) . mTBI is sometimes referred to as concussion, although the latter term is becoming less common in clinical and research contexts. The athletic community is also concerned with this condition, especially football and fighting sports, but also rugby, hockey, and soccer [2-8]. Although less newsworthy, falls cause the majority of head injuries in the US, with nearly 1.7 million TBI cases annually . Worldwide, the annual incidence of mild TBI is estimated to be above 600 per 100,000 and, in addition to falls, motorcycle and bicycle accidents are also major causes . As important as improvements in care are for veterans and athletes, such improvements can have a much broader impact on the health of communities around the world.
Although there is a need to improve the treatment of brain injury, perhaps the most significant hurdle is diagnosing mTBI. Current diagnostic standards are adequate for moderate and severe TBI because their signs and symptoms are more easily identifiable, but about 70-90% of TBI is mild, also known as concussion, and still difficult to recognize . Additionally, the World Health Organization estimates that many mild injuries are not even seen by a health care practitioner because this lack of obvious and urgent symptoms fails to motivate patients to seek care . Unfortunately, this does not mean that there are no long-term sequelae resulting from mTBI. According to current clinical research, mTBI sequelae include cognitive dysfunction, post-traumatic stress disorder, depression, anxiety, and dementia [2,11].
However, there are no currently accepted markers for clinical diagnosis of mTBI. Different organizations have created schematic tools for diagnosis, but these are subjective and the organizations do not completely agree on what constitutes a concussion . For the greatest impact for military applications and throughout the world, as well as to minimize costs, a blood-based test would be ideal. Thus far such a test has not been found. There have been several candidate substances (S100B, neuron-specific enolase, glial acidic fibrillary protein, etc.), but none have succeeded for effective diagnosis of mild injury . Because the search for a single biomarker has not succeeded, a composite panel may be an effective alternative. We present a method to help facilitate the identification of substances that have potential as biomarkers, which can then be validated experimentally.
As demonstrated with systems biology , the molecular interactions that occur after neurological injury are complex. There may be no serum value for any of the individual components of this complicated interplay that are specific to neural injury. However, some specific combination of these values included in a panel has much greater potential for diagnostic accuracy. The first step in investigating which substances belong in such a panel is to identify the potential candidates for inclusion. In this paper, we describe a methodology to provide a list of substances that is intended to establish a base of current knowledge and provide insight into the development of a biomarker panel for mTBI diagnosis. We apply natural language processing to MEDLINE citations to extract semantic predications, which we represent as a network of potentially relevant substances interacting with their physiological environment. These semantic predications are subject-relation-object triples, where the subjects and objects are UMLS concepts and the relation is derived from the UMLS Semantic Network as appropriate for a given concept pair. We then use network analysis techniques to identify a list that is focused on highly significant substances.
Our approach to diagnostic biomarker discovery was inspired by systems medicine, the application of systems biology to medicine. The underlying philosophy looks at biology as ‘information science’ and is concerned with the network of molecular interactions that define biological processes [14,15]. Additionally, disease states are viewed as a perturbation of these molecular networks . In the case of traditional TBI biomarker discovery, the approach has been to seek an individual molecule to represent a disease state, while disregarding any notion of a network let alone its perturbation. Wang et al. describe this approach as pauciparameter, containing an inadequate amount of information and resulting in inadequate characterization . The network must be considered as a whole, because a network perturbation does not necessitate that any of the individual molecules are outside of their normal serum measures, especially at early stages of disease, when prevention is still possible or treatment is optimal. They give prostate specific antigen for prostate cancer screening as an example of a failure of the traditional single marker, pauciparameter approach .
Natural language processing
Basic science and clinical observations supportive of the role of endothelins in the spasm associated with stroke and subarachnoid hemorrhage are presented. (Pubmed ID 15281894)
Endothelin ASSOCIATED_WITH Spasm
Spasm ASSOCIATED_WITH Cerebrovascular accident
The results of this process are stored in a predication database, SemMedDB , which has been used to support a range of biomedical information management research: identifying novel therapeutic approaches , labeling extracted information from clinical text , literature-based discovery [23-26], clinical information retrieval for physicians , retrieving clinical documents , abstraction summarization of biomedical texts , biological entity recognition , identifying disease candidate genes , support for cardiovascular clinical guidelines [32,33], interpreting microarray data , extracting research findings from literature , and supporting formal models of knowledge representation [36,22].
Networks of semantic predications
A simple means of judging the value of a given relationship is the frequency of the relationship, that is, a simple count of how many times it occurs in a given set. When using an automated tool, a single occurrence of a predication is much more susceptible to computational error than a predication with multiple instances. Therefore, a higher frequency may provide more confidence in the validity of the relationship, but at the same time, a high frequency is reflective of an abundance of assertions in the literature which is likely to be indicative of a well-known fact and may be less desirable for novel discovery.
Incorporation of systems medicine, natural language processing, and network theory
This methodology combines ideas and techniques from systems medicine, natural language processing, and network theory. A network of relationships involving substances is created, but the data source is semantic predications from MEDLINE citations rather than genomic or other large-scale experimental data as have often been used for systems medicine. These semantic predications provide a computable form of the knowledge contained in MEDLINE that includes gene, protein, and metabolite relationships analogous to the experimental data traditionally used in systems medicine, as well as additional types of relations at the organism, system, organ, tissue, cell, and molecular level. Statistical approaches are often used to establish correlation and significance of different components in the experimental data of systems medicine, whereas a network of semantic predications provided by SemRep naturally expresses the network of interactions postulated by systems approaches. Network filtering techniques are used to further suggest significance of the individual concepts and their relationships. By coupling components from these three fields, a novel method of biomarker discovery is proposed.
Several manual reviews have been undertaken to survey potential biomarkers for TBI [39,40] and more specifically mTBI [41-43]. These authors search for citations specifically detailing clinical research of mTBI biomarkers and therefore contain only potential biomarkers that have already been investigated. Another limitation of the studies is the small number of citations reviewed (ranging from 26  to 107 ) due to the limitations of human review. Although no automated detection of potential TBI biomarkers exists in the literature, there are automatic systems to help diagnose other disorders, for example diabetes and obesity . Although not related to mTBI, there is research related to the literature-based discovery of other types of interaction networks (though not specifically for biomarkers). One automatically generates an interaction network detailing gene involvement in vaccine-related fever using 170,000 citations from a PubMed search and a vaccine—specific ontology . Another used citations containing the PubMed MeSH term human and containing sentences related to interferon-gamma, from which relationships were extracted and ranked using graph metrics . Jordan et al.  present a keyword search method for identifying putative biomarkers for breast and lung cancer by searching for genes and proteins associated with a biological fluid keyword and either cancer. However, none of this work has made use of semantic predications, as we have, in the formation of an interaction network. There is a large body of work on literature-based discovery approaches many of which use SemRep semantic predications [26,48-54]. These approaches may generate systems for discovery [55-58] or are specific applications to predict various phenomena such as interactions between genes and proteins [46,59], cancer treatments [60,61], adverse drug reactions , drug-drug interactions , drug repurposing [51,62], asthma gene associations , treatments for neovascularization in diabetic retinopathy , relations between psychiatric and somatic diseases , genes related to reactive oxygen species and diabetes , and mechanisms for sleep disturbance  and the obesity paradox .
A PubMed search for all articles containing the MeSH term Trauma, Nervous System was used to generate a list of PubMed identification numbers (PMIDs). This term is a parent to Brain Injuries in the MeSH hierarchy and also includes terms such as Spinal Cord Injuries and Cerebrovascular Trauma. The source publications were limited only in requiring that they included neural injury as a topic, with no limitations on journal, species, location, or type of injury. Although this included non-TBI injury and models, (e.g., stroke, spinal cord injury, hypoglossal-nerve injury, etc.), the goal was to undertake as wide a search as possible in order to retrieve remote and ignored possibilities, with the assumption that a significant level of commonality exists between the various forms of injury included under this broad heading in light of their inclusion of common injury pathways such as inflammation and oxidative damage. 99,437 unique citations were returned by this search.
Semantic predication selection
Semantic predications were extracted from SemMedDB using the PMIDs resulting from the above PubMed search, which yielded 26,441 unique predications. Overall, this set contains 6246 unique concepts, including less informative terms, such as rattus, injury, and patients as well as more specific terms, such as glutamate, brain-derived neurotrophic factor, and methylprednisolone. We then required the predications to contain at least one concept (subject or object) having a UMLS semantic type with potential as a substance biomarker (amino acid sequence; amino acid, peptide, or protein; biologically active substance; body substance; carbohydrate; carbohydrate sequence; chemical; chemical viewed functionally; chemical viewed structurally; eicosanoid; enzyme; gene or gene product; gene or genome; hormone; immunologic factor; inorganic chemical; lipid; neuroreactive substance or biogenic amine; nucleic acid, nucleoside, or nucleotide; nucleotide sequence; organic chemical; organophosphorus compound; receptor; steroid; substance). If only one of the arguments was of this type, the other concept could be of any semantic type. This resulted in the inclusion of some concepts that indicate that the research was performed in animal models such as Rattus and Animals. We did not discard these nodes because they allow the inclusion of potential biomarkers from basic research in the spirit of translational medicine. Although a given semantic type, for instance “Pharmaceutical Substance”, was not included in the list of target semantic types, it could still appear in a resulting predication if the complimentary subject or object met the requirements. As an example, in the predication Dexamethasone INTERACTS_WITH NF-kappa B, the subject, Dexamethasone, is of type Pharmaceutical Substance and the object, NF-kappa B, is of type Amino Acid, Peptide, or Protein. This predication qualifies for inclusion because of the object, not the subject. In the predication Dexamethasone TREATS Rheumatoid Arthritis, the object, Rheumatoid Arthritis, is of type Disease or Syndrome, so the predication would not be selected because neither subject nor object is of an included semantic type. After applying this limitation, 18,085 unique predications remained.
Network of predications
These 18,085 predications extracted from neurological injury MEDLINE citations and containing a potential biomarker as subject or object were then linked together as a network. This network represents all of the known substance activity involved in neurotrauma, as indicated by the semantic predications included in SemMedDB. The nodes of the network represent arguments (subject or object) from the predications, and the edges represent the predicates or relationships between subjects and objects. Each subject-object pair might have multiple predicates. For example, both Melatonin INHIBITS Free Radicals and Melatonin COEXISTS_WITH Free Radicals may have been asserted in the literature. When counting edges in the network, each predicate between the same subject and object in such predications was counted separately. Additionally, each subject-predicate-object triplet could have been asserted once in MEDLINE (and thus in SemMedDB) or as many as dozens of times. When taking into account each predication extracted from multiple citations, the network has 6246 total nodes and 18,085 total edges. When only unique (different) predications are considered (regardless of the number citations they were extracted from), the number of nodes in the network remains 6246, but the number of edges is 14,085. This is still a rather large network; to reduce it to more manageable size, further filtering was carried out.
Network filtering: degree centrality
Network filtering: frequency of occurrence
Substance network visualization
Substance network semantic distribution
The final network was analyzed to outline the distribution of UMLS semantic types and predicates. The semantic types of nodes were sorted and tallied as were the predicate for each token of the edges.
Substance identification precision
Evaluation of biomarker potential
Each of the substances in the final, filtered network was individually reviewed manually as a potential mTBI biomarker. The evaluation was based on 3 questions: is there evidence of a change in the level of this substance during traumatic brain injury, is this change evidenced in blood, and has the substance been previously investigated as a biomarker for traumatic brain injury. We searched PubMed with the query “[substance name] AND traumatic brain injury AND (serum OR blood)” and the resulting articles were explored to provide answers to the evaluation questions.
There are a total of 17 substances out of the 49 concepts in the final network. The first version (Figure 6) shows all concepts (49) and their connections (145), while in the second (Figure 7), a candidate subnetwork is emphasized in black containing 17 substances as labeled nodes and the 48 edges connecting them. The candidate subnetwork also contains 12 unlabeled non-substance nodes. One node shown in the complete network was incorrectly identified as the substance SHAM (salicylhydroxamate) instead of “sham” (meaning a false experimental action) while the 17 other substances were correctly extracted, for a precision of 0.94.
Substance network semantic distribution
Predication frequency in final network
Semantic type frequency in final network
Amino acid, peptide, or protein
Body part, organ, or organ component
Biologically active substance
Injury or poisoning
Neuroreactive substance or biogenic amine
Gene or genome
Patient or disabled group
Sign or symptom
Semantic type frequency of substances in final network
Amino acid, peptide, or protein
Biologically active substance
Neuroreactive substance or biogenic amine
Gene or genome
Evaluation of biomarker potential
Verification of substances in TBI physiology and TBI biomarker research
Changes in trauma?
Changes in blood?
Brain-derived neurotrophic factor
11585248, 22528282, 20679891
Fibroblast growth factor 2
Glial fibrillary acidic protein
16266720, 22528282, 21079180
19257803, 22528282, 21079180
Amyloid beta-protein precursor
Most substances identified in this study as worthy of consideration as mTBI biomarkers fall into four general categories: previously studied biomarkers (amyloid beta-protein precursor, brain-derived neurotrophic factor, fibroblast growth factor 2, glial fibrillary acidic protein, neuron-specific enolase, S100b); neurotransmitters (glutamate, dopamine, norepinephrine); inflammation and cell injury markers (interleukin-6, calpain breakdown products, malondialdehyde, superoxide dismutase); and ubiquitous substances (glucose, lactate, calcium).
Although all of the resulting substances were reviewed in depth during the methodology, the following illustrate the information contained in the resulting mTBI biomarker network and the information retrieved during the validation process. These examples suggest possible implications for clinical practice retrieved directly from the research literature.
Infusion with … 3-(13)C-lactate produced (13)C signals for glutamine … indicating tricarboxylic acid cycle operation followed by conversion of glutamate to glutamine. (PMID 19700417)
These results suggest a new neuroprotective mechanism of 17beta-estradiol by activating glutamate-stimulated lactate production, which is estrogen receptor-dependent. (PMID 11368971)
Glucose and lactate
Following TBI, neuron use initially increases, with subsequent depletion of extracellular glucose, resulting in increased levels of extracellular lactate and pyruvate. (PMID 18826359)
Arterial blood glucose significantly influenced signs of cerebral metabolism reflected by increased cerebral glucose uptake [and] decreased cerebral lactate production… (PMID 19196488)
We conclude that arterial lactate augmentation can increase brain dialysate lactate, and result in more rapid recovery of dialysate glucose after FPI [fluid percussion brain injury]. (PMID 10709871)
Although there have been a limited number of attempts to include multiple biomarkers in panels for TBI [67,76,77], these have not included some of the types of substances returned in our results. To a large degree the absence of consideration for such substances may be explained by their lack of specificity or their ubiquitous nature. The level of specificity as an analyte for these neglected substances is significantly higher for an individual marker to stand on its own, and substances that are frequent if not ubiquitous in normal physiology are not obvious as candidates for TBI identification. Taken on their own, glucose and calcium levels are not useful as measures of brain injury. However, a panel of markers could better represent the complex network of molecular changes that occur during TBI and change the goal from an individual marker/single variable to a panel ameliorates the lack of specificity – as long as the panel as a whole provides adequate sensitivity and specificity.
Limitations of study
These resulting data provide a clinically relevant hypothesis of potential mTBI biomarkers, which requires experimental validation. In our investigation into the validity of the results, it was evident that for some of the substances, especially the previously-studied biomarkers, the background TBI model-based studies have already been completed. For others, this is not the case and basic exploration in models may need to be pursued before moving towards clinical research.
The current result set is limited to the uppermost extreme of node connectedness and therefore potentially overlooks less investigated substances that appear in fewer publications. An elimination of the most frequent predications may enrich the result set for substances less familiar and thereby, potentially, more valuable. The current threshold is principally set to provide a visually comprehensible network in the result, though such a visualization is not required. Reducing the threshold for inclusion would expand the list with significant compounds, including microRNA.
Creating a map of neural injury interactions offers significant potential for basic science research. Additionally, our refinement of the network to identify the most significant interactions according to their degree centrality and frequency facilitates the quick translation of published research data into clinical practice. The resulting compound list is clearly interesting in the context of clinical applicability and merits further study. This technique allows the investigation of potential biomarkers to be focused, potentially reducing the wet-lab effort and reducing the time of assay development.
Now that we have outlined a basic methodology, we would like to compare this method with various other methods combining information extraction and network analysis to understand the advantages and disadvantages to different approaches.
Our current methodology can be expanded as noted above to include different subsets of substances in the final result. Additionally, this methodology is not limited to biomarker discovery but can also be applied to other areas of medical discovery, including novel therapeutic targets, drug repurposing, and others.
We have explored the creation of a molecular interaction network that represents neural injury and is composed of semantic predications automatically extracted from the literature. We achieved our goal of providing substances with potential as biomarkers to support the diagnosis of mTBI. The methodology is based on a network of semantic predications representing the interaction of substances observed subsequent to neural insult. Combining semantic predications of TBI substance interactions into a network in this way correlates well with systems biology (and by extension, systems medicine), which is concerned with the complex network interplay of a biological unit and represents injury and illness as a perturbation to the network.
Predications were extracted by SemRep and the component subject or object concepts were mapped to nodes and their relationships (predicates) mapped to edges, creating a network of relations. This network represents a summary of the physiological and pharmacogenomic space of neurological injury, as presented in the literature included in MEDLINE. To identify clinically significant candidates for mTBI biomarkers, the network was then filtered by degree centrality and frequency, greatly reducing the density of concepts and relationships. The resulting network produced 17 compounds to be considered as mTBI biomarkers, both previously investigated and novel as TBI biomarker candidates. The interaction of several of these is discussed as the basis for a panel of biomarkers to more effectively diagnose mTBI than is currently possible.
Availability of data and software
This research was supported in part by an appointment to the National Library of Medicine Research Participation Program administered by the Oak Ridge Institute for Science and Education through an inter-agency agreement between the US Department of Energy and the National Library of Medicine. This study was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine.
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