Gerald Birk

Seeding induced by α‐synuclein oligomers provides evidence for spreading of α‐synuclein pathology

Lewy bodies, α‐synuclein (α‐syn) immunopositive intracellular deposits, are the pathological hallmark of Parkinson’s disease (PD). Interestingly, Lewybody‐like structures have been identified in fetal tissue grafts about one decade after transplantation into the striatum of PD patients. One possible explanation for the accelerated deposition of α‐syn in the graft is that the aggregation of α‐syn from the host tissue to the graft is spread by a prion disease‐like mechanism. We discuss here an in vitro model which might recapitulate some aspects of disease propagation in PD. We found here that in vitro‐generated α‐syn oligomers induce transmembrane seeding of α‐syn aggregation in a dose‐ and time‐dependent manner. This effect was observed in primary neuronal cultures as well as in neuronal cell lines. The seeding oligomers were characterized by a distinctive lithium dodecyl sulfate‐stable oligomer pattern and could be generated in a dynamic process out of pore‐forming oligomers. We propose that α‐syn oligomers form as a dynamic mixture of oligomer types with different properties and that α‐syn oligomers can be converted into different types depending on the brain milieu conditions. Our data indicate that extracellular α‐syn oligomers can induce intracellular α‐syn aggregation, therefore we hypothesize that a similar mechanism might lead to α‐syn pathology propagation.

The Selective mGlu5 Receptor Antagonist MTEP, Similar to NMDA Receptor Antagonists, Induces Social Isolation in Rats

It has repeatedly been shown that uncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists can mimic certain aspects of positive and negative symptoms of schizophrenia in human volunteers and laboratory animals. The purpose of the present study was to expand these findings and to determine whether the selective metabotropic glutamate receptor subtype 5 (mGluR5) antagonist, MTEP (3-[(2-methyl-1,3-thiazol-4-yl)ethynyl]pyridine), could induce similar effects in Wistar rats. First, MTEP (1.0–10.0 mg/kg; intraperitoneally) after acute and subchronic (daily for 5 days) administration as well as the uncompetitive antagonists of the NMDA receptor of either high affinity, phencyclidine (0.5–4.0 mg/kg; subcutaneously (s.c.)) and (+)-MK-801 (0.03–0.25 mg/kg; s.c.), or low–moderate affinity, ketamine (2.0–16.0 mg/kg; s.c.) and memantine (0.15–20.0 mg/kg; s.c.), following daily administration for 3 days were tested in the social interaction test to determine their ability to reproduce the negative and positive symptoms measured by social isolation and stereotyped behavior, respectively. Second, the compounds were tested in the motility test following acute administration to determine their ability to induce locomotor hyperactivity reflecting the positive symptoms. In line with previous findings, all examined NMDA receptor antagonists produced social interaction deficits, locomotor hyperactivity, and stereotypy except memantine. Notably, this study found that MTEP following both acute and subchronic administration dose-dependently induced social isolation, but did not cause either locomotor hyperactivity or stereotypy. These data demonstrate that social behavior deficits in rats can be caused by both the blockade of the NMDA receptor and the inhibition of mGluR5, whereas mGluR5 antagonists may not independently be able to mimic the positive symptoms.

Automated classification of the behavior of rats in the forced swimming test with support vector machines

The forced swimming test of rats or mice is a frequently used behavioral test to evaluate compounds for antidepressant activity in vivo. The aim of this study was to replace the human observer, needed to score and analyze the behavior of animals, by a fully automated method. For this purpose, in a first step from a video recording of each rat, an activity profile was calculated, from which subsequently a set of meaningful properties was extracted. This set was finally used to train a Support Vector Machine (SVM). Furthermore, specialized kernel functions, namely the so-called time resolved p-spectrum and modified optimal assignment kernels, were developed to calculate the similarity of activity profiles. Our method allows for a very reliable discrimination of animals treated with antidepressants of different classes (tricyclics imipramine and desipramine as well as selective serotonin reuptake inhibitor, SSRI, fluoxetine) versus a vehicle-treated group. Moreover, our technique is able to classify between tricyclic antidepressants and SSRIs. The results of this work enabled the development of an automated and highly accurate behavior classification system.

Three-dimensional accurate detection of lung emphysema in rats using ultra-short and zero echo time MRI.

Emphysema is a life‐threatening pathology that causes irreversible destruction of alveolar walls. In vivo imaging techniques play a fundamental role in the early non‐invasive pre‐clinical and clinical detection and longitudinal follow‐up of this pathology. In the present study, we aimed to evaluate the feasibility of using high resolution radial three‐dimensional (3D) zero echo time (ZTE) and 3D ultra‐short echo time (UTE) MRI to accurately detect lung pathomorphological changes in a rodent model of emphysema.Porcine pancreas elastase (PPE) was intratracheally administered to the rats to produce the emphysematous changes. 3D ZTE MRI, low and high definition 3D UTE MRI and micro‐computed tomography images were acquired 4 weeks after the PPE challenge. Signal‐to‐noise ratios (SNRs) were measured in PPE‐treated and control rats. T2* values were computed from low definition 3D UTE MRI. Histomorphometric measurements were made after euthanizing the animals. Both ZTE and UTE MR images showed a significant decrease in the SNR measured in PPE‐treated lungs compared with controls, due to the pathomorphological changes taking place in the challenged lungs. A significant decrease in T2* values in PPE‐challenged animals compared with controls was measured using UTE MRI. Histomorphometric measurements showed a significant increase in the mean linear intercept in PPE‐treated lungs. UTE yielded significantly higher SNR compared with ZTE (14% and 30% higher in PPE‐treated and non‐PPE‐treated lungs, respectively).This study showed that optimized 3D radial UTE and ZTE MRI can provide lung images of excellent quality, with high isotropic spatial resolution (400 µm) and SNR in parenchymal tissue (>25) and negligible motion artifacts in freely breathing animals. These techniques were shown to be useful non‐invasive instruments to accurately and reliably detect the pathomorphological alterations taking place in emphysematous lungs, without incurring the risks of cumulative radiation exposure typical of micro‐computed tomography. Copyright

Gene delivery to adipose tissue using transcriptionally targeted rAAV8 vectors.

In recent years, the increasing prevalence of obesity and obesity-related co-morbidities fostered intensive research in the field of adipose tissue biology. To further unravel molecular mechanisms of adipose tissue function, genetic tools enabling functional studies in vitro and in vivo are essential. While the use of transgenic animals is well established, attempts using viral and non-viral vectors to genetically modify adipocytes in vivo are rare. Therefore, we here characterized recombinant Adeno-associated virus (rAAV) vectors regarding their potency as gene transfer vehicles for adipose tissue. Our results demonstrate that a single dose of systemically applied rAAV8-CMV-eGFP can give rise to remarkable transgene expression in murine adipose tissues. Upon transcriptional targeting of the rAAV8 vector to adipocytes using a 2.2 kb fragment of the murine adiponectin (mAP2.2) promoter, eGFP expression was significantly decreased in off-target tissues while efficient transduction was maintained in subcutaneous and visceral fat depots. Moreover, rAAV8-mAP2.2-mediated expression of perilipin A – a lipid-droplet-associated protein – resulted in significant changes in metabolic parameters only three weeks post vector administration. Taken together, our findings indicate that rAAV vector technology is applicable as a flexible tool to genetically modify adipocytes for functional proof-of-concept studies and the assessment of putative therapeutic targets in vivo.

Functional Proton MRI in Emphysematous Rats.

ObjectiveTo demonstrate the feasibility of proton magnetic resonance imaging (MRI) ventilation–related maps in rodents for the evaluation of lung function in the presence of pancreatic porcine elastase (PPE)-induced emphysema. Materials and MethodsTwelve rats were equally divided into 3 groups: group 1 (no administration of PPE); group 2 (PPE selectively only in the left lung); and group 3 (PPE administered in both lungs). Magnetic resonance imaging (MRI) and computed tomographic (CT) data were acquired at baseline, at 2 weeks and 4 weeks after administration, after which the animals were euthanized. The MRI protocol comprised a golden angle 2-dimensional ultrashort echo time MRI sequence [echo time, 0.343 millisecond (ms); repetition time, 120 ms; 12 slides with thickness, 1 mm; acquisition time, 30 minutes], from which inspiration and expiration images were reconstructed after the extraction of a self-gating signal. Inspiration images were registered to images at expiration, and expansion maps were created by calculating the specific difference in signal intensity. The lungs were segmented, and the mean specific expansion (MSE) calculated as an established surrogate for fractional ventilation. Computed tomographic data provided lung density (peak of the Hounsfield unit histogram, HU_P), whereas histology provided the mean linear intercept for each lung. ResultsTwo weeks after administration, the control group had a mean MSE in both lungs corresponding to 96% of the baseline. Group 2 had 85% of the baseline, and group 3 had 57%. Considering the PPE-treated lungs alone, a significant reduction in MSE of 27% at 2 weeks and 40% at 4 weeks was found with respect to nontreated lungs. Significant correlations between HU_P and MSE were found at all time points (baseline: r = 0.606, P = 0.0017; 2 weeks: r = 0.837, P ⩽ 0.0001; 4 weeks: r = 0.765, P < 0.0001; all time points: r = 0.739, P < 0.0001). Mean linear intercept values significantly correlated both with MRI MSE (r = −0.770, P < 0.0001) and with CT HU_P (r = −0.882, P < 0.0001). DiscussionThe calculated ventilation-related maps showed a reduction of function in the PPE-treated lungs, both compared to the nontreated lungs and to the baseline values. Moreover, a good agreement between MRI-measured MSE, CT, and histology data quantitatively supports the presence of ventilation deficit in emphysematous lungs.In this work, we have demonstrated the feasibility of ventilation-related maps from non–contrast-enhanced 1H lung MRI, which were capable of tracking changes in lung function over time in emphysematous rats.

Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system

Preclinical studies of novel compounds rely on quantitative readouts from animal models. Frequently employed readouts from histopathological tissue scoring are time consuming, require highly specialized staff and are subject to inherent variability. Recent advances in deep convolutional neural networks (CNN) now allow automating such scoring tasks. Here, we demonstrate this for the case of the Ashcroft fibrosis score and a newly developed inflammation score to characterize fibrotic and inflammatory lung diseases. Sections of lung tissue from mice exhibiting a wide range of fibrotic and inflammatory states were stained with Masson trichrome. Whole slide scans using a 20x objective were acquired and cut into smaller tiles of 512x512 pixels. The tiles were subsequently classified by specialized CNNs, either an “Ashcroft fibrosis CNN” or an “inflammation CNN”. For the Ashcroft fibrosis score the CNN was fine-tuned by using 14000 labelled tiles. For the inflammation score the CNN was trained with 3500 labelled tiles. After training, the Ashcroft fibrosis CNN achieved an accuracy of 79.5% and the inflammation CNN an accuracy of 80.0%. An error analysis revealed that misclassifications are almost exclusively with neighboring scores, which reflects the inherent ambiguity of parts of the data. The variability between two experts was found to be larger than the variability between the CNN classifications and the ground truth. The CNN generated Ashcroft score was in very good agreement with the score of a pathologist (r2 = 0.92). Our results demonstrate that costly and time consuming scoring tasks can be automated and standardized with deep learning. New scores such as the inflammation score can be easily developed with the approach presented here.