Integrin-mediated axoglial interactions initiate myelination in the central nervous system

All but the smallest-diameter axons in the central nervous system are myelinated, but the signals that initiate myelination are unknown. Our prior work has shown that integrin signaling forms part of the cell–cell interactions that ensure only those oligodendrocytes contacting axons survive. Here, therefore, we have asked whether integrins regulate the interactions that lead to myelination. Using homologous recombination to insert a single-copy transgene into the hypoxanthine phosphoribosyl transferase (hprt) locus, we find that mice expressing a dominant-negative β1 integrin in myelinating oligodendrocytes require a larger axon diameter to initiate timely myelination. Mice with a conditional deletion of focal adhesion kinase (a signaling molecule activated by integrins) exhibit a similar phenotype. Conversely, transgenic mice expressing dominant-negative β3 integrin in oligodendrocytes display no myelination abnormalities. We conclude that β1 integrin plays a key role in the axoglial interactions that sense axon size and initiate myelination, such that loss of integrin signaling leads to a delay in myelination of small-diameter axons

General practitioners knowledge and management of whiplash associated disorders and post-traumatic stress disorder: Implications for patient care

© 2016 The Author(s). Background: In Australia, general practitioners (GPs) see around two-thirds of people injured in road traffic crashes. Road traffic crash injuries are commonly associated with diverse physical and psychological symptoms that may be difficult to diagnose and manage. Clinical guidelines have been developed to assist in delivering quality, consistent care, however the extent to which GPs knowledge and practice in diagnosing and managing road traffic crash injuries concords with the guidelines is unknown. This study aimed to explore Australian GPs knowledge, attitudes and practices regarding the diagnosis and management of road traffic crash injuries, specifically whiplash associated disorders (WAD) and post-traumatic stress disorder (PTSD). Method: A cross-sectional survey of 423 GPs across Australia conducted between July and December 2014. We developed a questionnaire to assess their knowledge of WAD and PTSD, confidence in diagnosing and managing WAD and PTSD, frequency of referral to health providers, barriers to referral, and attitudes towards further education and training. Factor analysis, Spearman's correlation, and multiple ordered logistic regressions were performed. Results: Overall, GPs have good level knowledge of WAD and PTSD; only 9.6 % (95 % CI: 7.1 %, 12.8 %) and 23.9 % (95 % CI: 20.8 %, 28.2 %) of them were deemed to have lower level knowledge of WAD and PTSD respectively. Key knowledge gaps included imaging indicators for WAD and indicators for psychological referral for PTSD. GPs who were male, with more years of experience, working in the urban area and with higher knowledge level of WAD were more confident in diagnosing and managing WAD. Only GPs PTSD knowledge level predicted confidence in diagnosing and managing PTSD. GPs most commonly referred to physiotherapists and least commonly to vocational rehabilitation providers. Barriers to referral included out-of-pocket costs incurred by patients and long waiting times. Most GPs felt positive towards further education on road traffic crash injury management. Conclusion: This study has enhanced understanding of the knowledge skills and attitudes of GPs towards road traffic crash injury care in Australia, and has identified areas for further education and training. If delivered, this training has the potential to reduce unnecessary imaging for WAD and optimise the early referral of patients at risk of delayed recovery following a road traffic crash

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Cell-free biosensors for biomedical applications

International audienc

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing

Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors

The range of chemicals detectable by cell-free systems is still limited. Here the authors combine metabolic cascades with transcription factor networks to detect small molecules in complex environments