Inducing different severities of traumatic brain injury in Drosophila using a piezoelectric actuator.
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ABSTRACT:
SUBMITTER: Saikumar J
PROVIDER: S-EPMC8063611 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. In addition, there has been a growing appreciation that even repetitive, milder forms of TBI (mTBI) can have long-term deleterious consequences to neural tissues. Hampering our understanding of genetic and environmental factors that influence the cellular and molecular responses to injury has been the limited availability of effective genetic model systems that could be used to identify the key genes and pathways that modulate both the acute and long-term responses to TBI. Here we report the development of a severe and mild-repetitive TBI model using Drosophila. Using this system, key features that are typically found in mammalian TBI models were also identified in flies, including the activation of inflammatory and autophagy responses, increased Tau phosphorylation and neuronal defects that impair sleep-related behaviors. This novel injury paradigm demonstrates the utility of Drosophila as an effective tool to validate genetic and environmental factors that influence the whole animal response to trauma and to identify prospective therapies needed for the treatment of TBI.
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Project description:Traumatic brain injury (TBI) is a substantial health issue worldwide, yet the mechanisms responsible for its complex spectrum of pathologies remains largely unknown. To investigate the mechanisms underlying TBI pathologies, we developed a model of TBI in Drosophila melanogaster. The model allows us to take advantage of the wealth of experimental tools available in flies. Closed head TBI was inflicted with a mechanical device that subjects flies to rapid acceleration and deceleration. Similar to humans with TBI, flies with TBI exhibited temporary incapacitation, ataxia, activation of the innate immune response, neurodegeneration, and death. Our data indicate that TBI results in death shortly after a primary injury only if the injury exceeds a certain threshold and that age and genetic background, but not sex, substantially affect this threshold. Furthermore, this threshold also appears to be dependent on the same cellular and molecular mechanisms that control normal longevity. This study demonstrates the potential of flies for providing key insights into human TBI that may ultimately provide unique opportunities for therapeutic intervention.
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Project description:Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.
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Project description:Traumatic brain injury (TBI) can cause persistent pathological alteration of neurons. This may lead to cognitive dysfunctions, depression, and even increased susceptibility to life threatening diseases, such as epilepsy. To investigate the underlying genetic and molecular basis of TBI, Wasserman and colleagues developed an inexpensive and reproducible model for simulating TBI in Drosophila melanogaster (Fruit Fly). Using a modified version of this model, we subjected w1118 fruit flies to mild closed head trauma. To determine the transcriptomic changes which contribute to survival post TBI, we collected fly heads from the survivors at 2 time points; 4 hours and 24 hours post-trauma. We observed that the induction of transcriptomic changes occur 24 hours post-trauma in fly heads. Classification of these AS changes showed selective retention of long introns (>81bps), with a mean size of ~3000bps. Gene ontology analysis of the genes containing retained introns showed significant enrichment for critical regulators of glycolysis, citric acid cycle as well as vesicle mediated neurotransmitter release. Some of these genes also showed a significant reduction in transcript abundance. The retained introns were enriched for CA-rich motifs known to bind to Smooth (SM), an hnRNPL class of splicing factor.
2017-09-22 | GSE104100 | GEO
Project description:We demonstrate a low-power (<0.1 mW), low-voltage (<10 V(p-p)) on-chip piezoelectrically actuated micro-sorter that can deflect single particles and cells at high-speed. With rhodamine in the stream, switching of flow between channels can be visualized at high actuation frequency (micro1.7 kHz). The magnitude of the cell deflection can be precisely controlled by the magnitude and waveform of input voltage. Both simulation and experimental results indicate that the drag force imposed on the suspended particle/cell by the instantaneous fluid displacement can alter the trajectory of the particle/cell of any size, shape, and density of interest in a controlled manner. The open-loop E. Coli cell deflection experiment demonstrates that the sorting mechanism can produce a throughput of at least 330 cells/s, with a promise of a significantly higher throughput for an optimized design. To achieve close-loop sorting operation, fluorescence detection, real-time signal processing, and field-programmable-gate-array (FPGA) implementation of the control algorithms were developed to perform automated sorting of fluorescent beads. The preliminary results show error-free sorting at a sorting efficiency of micro 70%. Since the piezoelectric actuator has an intrinsic response time of 0.1-1 ms and the sorting can be performed under high flowrate (particle speed of micro 1-10 cm/s), the system can achieve a throughput of >1,000 particles/s with high purity.
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Project description:ObjectiveThe aim of the study was to predict suicidal ideation 1 yr after moderate to severe traumatic brain injury.DesignThis study used a cross-sectional design with data collected through the prospective, longitudinal Traumatic Brain Injury Model Systems network at hospitalization and 1 yr after injury. Participants who completed the Patient Health Questionnaire-9 suicide item at year 1 follow-up ( N = 4328) were included.ResultsA gradient boosting machine algorithm demonstrated the best performance in predicting suicidal ideation 1 yr after traumatic brain injury. Predictors were Patient Health Questionnaire-9 items (except suicidality), Generalized Anxiety Disorder-7 items, and a measure of heavy drinking. Results of the 10-fold cross-validation gradient boosting machine analysis indicated excellent classification performance with an area under the curve of 0.882. Sensitivity was 0.85 and specificity was 0.77. Accuracy was 0.78 (95% confidence interval, 0.77-0.79). Feature importance analyses revealed that depressed mood and guilt were the most important predictors of suicidal ideation, followed by anhedonia, concentration difficulties, and psychomotor disturbance.ConclusionsOverall, depression symptoms were most predictive of suicidal ideation. Despite the limited clinical impact of the present findings, machine learning has potential to improve prediction of suicidal behavior, leveraging electronic health record data, to identify individuals at greatest risk, thereby facilitating intervention and optimization of long-term outcomes after traumatic brain injury.
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