The electrophysiological impact of oligomeric alpha-Synuclein on thick-tufted layer 5 pyramidal neurons in the neocortex of mice

Parkinson’s disease (PD) is one of the most prevalent movement disorders in the world. A clinical hallmark of PD is the appearance of proteinaceous Lewy Bodies throughout the brain that are predominantly formed from aggregation of the presynaptic protein alpha-Synuclein (αSyn). Increasing evidence, however, suggests that the soluble annular αSyn oligomers, formed during early stages of aggregation, are more toxic and pathologically relevant than the larger fibrils which form at later stages of aggregation. The underlying mechanism(s) through which αSyn oligomers exert their toxicity is still largely unknown. This thesis investigates how the toxic nature of αSyn oligomers may affect the electrophysiological properties of neurons. A population of soluble oligomers, termed mOligomers, were isolated from the early stages of in vitro aggregation. In addition, a separate oligomeric species was recovered from the fragmentation of large fibrils; termed fOligomers. Structural characterisation of these two species revealed them to be similar in size and ring-like in shape but showed subtle differences in their secondary structure. Purified, oligomeric αSyn was injected directly into the somata of thick-tufted layer 5 pyramidal neurons in mouse neocortical brain slices during whole-cell patch clamp recording and compared to the effects of equivalent concentrations of αSyn monomer. Using a combined experimental and modelling approach, a wide range of neuronal parameters were extracted and demonstrated oligomer-specific changes in neuronal electrophysiology that were time dependent. Perfusion with αSyn oligomers markedly reduced input resistance, enhanced the current required to trigger an action potential and reduced the firing rate illustrating a reduction in excitability that has the potential to impact both neural circuitry and cognitive output

Intracellular soluble α-synuclein oligomers reduce pyramidal cell excitability

The presynaptic protein α-synuclein (αSyn) aggregates during Parkinson's disease (PD) to form large proteinaceous amyloid plaques, the spread of which throughout the brain clinically defines the severity of the disease. During early stages of aggregation, αSyn forms soluble annular oligomers that show greater toxicity than much larger fibrils. These oligomers produce toxicity via a number of possible mechanisms, including the production of pore-forming complexes that permeabilize membranes. In the present study, two well-defined species of soluble αSyn oligomers were produced by different protocols: by polymerization of monomer and by sonication of fibrils. The two oligomeric species produced were morphologically similar, with both having an annular structure and consisting of approximately the same number of monomer subunits, although they differed in their secondary structure. Oligomeric and monomeric αSyn were injected directly into the soma of pyramidal neurons in mouse neocortical brain slices during whole-cell patch clamp recording. Using a combined experimental and modelling approach, neuronal parameters were extracted to measure, for the first time in the neocortex, specific changes in neuronal electrophysiology. Both species of oligomer had similar effects: (i) a significant reduction in input resistance and the membrane time constant and (ii) an increase in the current required to trigger an action potential with a resultant reduction in the firing rate. Differences in oligomer secondary structure appeared to produce only subtle differences in the activity of the oligomers. Monomeric αSyn had no effect on neuronal parameters, even at high concentrations. The oligomer-induced fall in neuronal excitability has the potential to impact both network activity and cognitive processing

Association of Over-The-Counter Pharmaceutical Sales with Influenza-Like-Illnesses to Patient Volume in an Urgent Care Setting

We studied the association between OTC pharmaceutical sales and volume of patients with influenza-like-illnesses (ILI) at an urgent care center over one year. OTC pharmaceutical sales explain 36% of the variance in the patient volume, and each standard deviation increase is associated with 4.7 more patient visits to the urgent care center (p<0.0001). Cross-correlation function analysis demonstrated that OTC pharmaceutical sales are significantly associated with patient volume during non-flu season (p<0.0001), but only the sales of cough and cold (p<0.0001) and thermometer (p<0.0001) categories were significant during flu season with a lag of two and one days, respectively. Our study is the first study to demonstrate and measure the relationship between OTC pharmaceutical sales and urgent care center patient volume, and presents strong evidence that OTC sales predict urgent care center patient volume year round. © 2013 Liu et al

A mathematical model for human nucleotide excision repair: Damage recognition by random order assembly and kinetic proofreading

A mathematical model of human nucleotide excision repair was constructed and validated. The model incorporates cooperative damage recognition by RPA, XPA, and XPC followed by three kinetic proofreading steps by the TFIIH transcription/repair factor. The model yields results consistent with experimental data regarding excision rates of UV photoproducts by the reconstituted human excision nuclease system as well as the excision of oligonucleotides from undamaged DNA. The model predicts the effect that changes in the initial concentrations of repair factors have on the excision rate of damaged DNA and provides a testable hypothesis on the bio-chemical mechanism of cooperativity in protein assembly, suggesting experiments to determine if cooperativity in protein assembly results from an increased association rate or a decreased dissociation rate. Finally, a comparison between the random order assembly with kinetic proofreading model and a sequential assembly model is made. This investigation reveals the advantages of the random order assembly/kinetic proofreading model

A predictive mathematical model of the DNA damage G2 checkpoint

A predictive mathematical model of the transition from the G2 phase in the cell cycle to mitosis (M) was constructed from the known interactions of the proteins that are thought to play significant roles in the G2 to M transition as well as the DNA damage- induced G2 checkpoint. The model simulates the accumulation of active cyclin B1/Cdk1 (MPF) complexes in the nucleus to activate mitosis, the inhibition of this process by DNA damage, and transport of component proteins between cytoplasm and nucleus. Interactions in the model are based on activities of individual phospho-epitopes and binding sites of proteins involved in G2/M. Because tracking phosphoforms leads to combinatorial explosion, we employ a rule-based approach using the BioNetGen software. The model was used to determine the effects of depletion or over-expression of selected proteins involved in the regulation of the G2 to M transition in the presence and absence of DNA damage. Depletion of Plk1 delayed mitotic entry and recovery from the DNA damage-induced G2 arrest and over-expression of MPF attenuated the DNA damage-induced G2 delay. The model recapitulates the G2 delay observed in the biological response to varying levels of a DNA damage signal. The model produced the novel prediction that depletion of pkMyt1 results in an abnormal biological state in which G2 cells with DNA damage accumulate inactive nuclear MPF. Such a detailed model may prove useful for predicting DNA damage G2 checkpoint function in cancer and, therefore, sensitivity to cancer therapy

A motor association area in the depths of the central sulcus

Cells in the precentral gyrus directly send signals to the periphery to generate movement and are principally organized as a topological map of the body. We find that movement-induced electrophysiological responses from depth electrodes extend this map three-dimensionally throughout the gyrus. Unexpectedly, this organization is interrupted by a previously undescribed motor association area in the depths of the midlateral aspect of the central sulcus. This \u27Rolandic motor association\u27 (RMA) area is active during movements of different body parts from both sides of the body and may be important for coordinating complex behaviors

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different “ideal” and “minimum requirements” brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials

Mcl1 haploinsufficiency protects mice from Myc-induced acute myeloid leukemia

Antiapoptotic BCL2 family members have been implicated in the pathogenesis of acute myelogenous leukemia (AML), but the functional significance and relative importance of individual proteins (e.g., BCL2, BCL-XL, and myeloid cell leukemia 1 [MCL1]) remain poorly understood. Here, we examined the expression of BCL2, BCL-XL, and MCL1 in primary human hematopoietic subsets and leukemic blasts from AML patients and found that MCL1 transcripts were consistently expressed at high levels in all samples tested. Consistent with this, Mcl1 protein was also highly expressed in myeloid leukemic blasts in a mouse Myc-induced model of AML. We used this model to test the hypothesis that Mcl1 facilitates AML development by allowing myeloid progenitor cells to evade Myc-induced cell death. Indeed, activation of Myc for 7 days in vivo substantially increased myeloid lineage cell numbers, whereas hematopoietic stem, progenitor, and B-lineage cells were depleted. Furthermore, Mcl1 haploinsufficiency abrogated AML development. In addition, deletion of a single allele of Mcl1 from fully transformed AML cells substantially prolonged the survival of transplanted mice. Conversely, the rapid lethality of disease was restored by coexpression of Bcl2 and Myc in Mcl1-haploinsufficient cells. Together, these data demonstrate a critical and dose-dependent role for Mcl1 in AML pathogenesis in mice and suggest that MCL1 may be a promising therapeutic target in patients with de novo AML