A rapid and reliable diagnostic test to distinguish ischemic from hemorrhagic stroke in patients presenting with stroke-like symptoms is essential to optimize management and triage for thrombolytic therapy. The present study measured serum concentrations of ubiquitin C-terminal hydrolase (UCH-L1) and glial fibrillary astrocytic protein (GFAP) in acute stroke patients and healthy controls and investigated their relation to stroke severity and patient characteristics. We also assessed the diagnostic performance of these markers for the differentiation of intracerebral hemorrhage (ICH) from ischemic stroke (IS). Both UCH-L1 and GFAP concentrations were significantly greater in ICH patients than in controls (p < 0.0001). However, exclusively GFAP differed in ICH compared with IS (p < 0.0001). GFAP yielded an AUC of 0.86 for differentiating between ICH and IS within 4.5hrs of symptom onset with a sensitivity of 61% and a specificity of 96% using a cut-off of 0.34ng/ml. Higher GFAP levels were associated with stroke severity and history of prior stroke. Our results demonstrate that blood UCH-L1 and GFAP are increased early after stroke and distinct biomarker-specific release profiles are associated with stroke characteristics and type. We also confirmed the potential of GFAP as a tool for early rule-in of ICH, while UCH-L1 was not clinically useful.

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials forTBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy asTBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.

Global threats to reefs require urgent efforts to resolve coral attributes that affect survival in a changing environment. Genetically different individuals of the same coral species are known to exhibit different responses to the same environmental conditions. New information on coral physiology, particularly as it relates to genotype, could aid in unraveling mechanisms that facilitate coral survival in the face of stressors. Metabolomic profiling detects a large subset of metabolites in an organism, and, when linked to metabolic pathways, can provide a snapshot of an organism’s physiological state. Identifying metabolites associated with desirable, genotype-specific traits could improve coral selection for restoration and other interventions. A key step toward this goal is determining whether intraspecific variation in coral metabolite profiles can be detected for species of interest, however little information exists to illustrate such differences. To address this gap, we applied untargeted 1 H-NMR and LC-MS metabolomic profiling to three genotypes of the threatened coral Acropora cervicornis . Both methods revealed distinct metabolite “fingerprints” for each genotype examined. A number of metabolites driving separation among genotypes were identified or putatively annotated. Pathway analysis suggested differences in protein synthesis among genotypes. For the first time, these data illustrate intraspecific variation in metabolomic profiles for corals in a common garden. Our results contribute to the growing body of work on coral metabolomics and suggest future work could identify specific links between phenotype and metabolite profile in corals.

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