RESUMO
Functional outcomes after traumatic brain injury (TBI) vary widely across patients with apparently similar injuries. This variability hinders prognosis, therapy, and clinical innovation. Recently, single nucleotide polymorphism (SNPs) that influence outcome after TBI have been identified. These discoveries create opportunities to personalize therapy and stratify clinical trials. Both of these changes would propel clinical innovation in the field. This review focuses on one of most well-characterized of these SNPs, the Val66Met SNP in the brain-derived neurotrophic factor (BDNF) gene. This SNP influences neurological function in healthy subjects as well as TBI patients and patients with similar acute insults to the central nervous system. A host of other patient-specific factors including ethnicity, age, gender, injury severity, and post-injury time point modulate this influence. These interactions confound efforts to define a simple relationship between this SNP and TBI outcomes. The opportunities and challenges associated with personalizing TBI therapy around this SNP and other similar SNPs are discussed in light of these results.
Assuntos
Lesões Encefálicas Traumáticas/complicações , Fator Neurotrófico Derivado do Encéfalo/genética , Doenças do Sistema Nervoso/etiologia , Doenças do Sistema Nervoso/genética , Polimorfismo de Nucleotídeo Único/genética , Lesões Encefálicas Traumáticas/terapia , Humanos , Medicina de Precisão/métodosRESUMO
Naturally evolved metabolite-responsive biosensors enable applications in metabolic engineering, ranging from screening large genetic libraries to dynamically regulating biosynthetic pathways. However, there are many metabolites for which a natural biosensor does not exist. To address this need, we developed a general method for converting metabolite-binding proteins into metabolite-responsive transcription factors-Biosensor Engineering by Random Domain Insertion (BERDI). This approach takes advantage of an in vitro transposon insertion reaction to generate all possible insertions of a DNA-binding domain into a metabolite-binding protein, followed by fluorescence activated cell sorting to isolate functional biosensors. To develop and evaluate the BERDI method, we generated a library of candidate biosensors in which a zinc finger DNA-binding domain was inserted into maltose binding protein, which served as a model well-studied metabolite-binding protein. Library diversity was characterized by several methods, a selection scheme was deployed, and ultimately several distinct and functional maltose-responsive transcriptional biosensors were identified. We hypothesize that the BERDI method comprises a generalizable strategy that may ultimately be applied to convert a wide range of metabolite-binding proteins into novel biosensors for applications in metabolic engineering and synthetic biology.
