Abnormal regulation of TSG101 in mice with spongiform neurodegeneration

AbstractSpongiform neurodegeneration is characterized by the appearance of vacuoles throughout the central nervous system. It has many potential causes, but the underlying cellular mechanisms are not well understood. Mice lacking the E3 ubiquitin ligase Mahogunin Ring Finger-1 (MGRN1) develop age-dependent spongiform encephalopathy. We identified an interaction between a “PSAP” motif in MGRN1 and the ubiquitin E2 variant (UEV) domain of TSG101, a component of the endosomal sorting complex required for transport I (ESCRT-I), and demonstrate that MGRN1 multimonoubiquitinates TSG101. We examined the in vivo consequences of loss of MGRN1 on TSG101 expression and function in the mouse brain. The pattern of TSG101 ubiquitination differed in the brains of wild-type mice and Mgrn1 null mutant mice: at 1 month of age, null mutant mice had less ubiquitinated TSG101, while in adults, mutant mice had more ubiquitinated, insoluble TSG101 than wild-type mice. There was an associated increase in epidermal growth factor receptor (EGFR) levels in mutant brains. These results suggest that loss of MGRN1 promotes ubiquitination of TSG101 by other E3s and may prevent its disassociation from endosomal membranes or cause it to form insoluble aggregates. Our data implicate loss of normal TSG101 function in endo-lysosomal trafficking in the pathogenesis of spongiform neurodegeneration in Mgrn1 null mutant mice

Optimal charge scheduling of electric vehicles in solar energy integrated power systems considering the uncertainties

Nowadays, vehicle to grid (V2G) capability of the electric vehicle (EV) is used in the smart distribution network (SDN). The main reasons for using the EVs, are improving air quality by reducing greenhouse gas emissions, peak demand shaving and applying ancillary service, and etc. So, in this chapter, a non-linear bi-level model for optimal operation of the SDN is proposed where one or more solar based-electric vehicle parking lots (PLs) with private owners exist. The SDN operator (SDNO) and the PL owners are the decision-makers of the upper-level and lower-level of this model, respectively. The objective functions at two levels are the SDNO’s profit maximization and the PL owners’ cost minimization. For transforming this model into the single-level model that is named mathematical program with equilibrium constraints (MPEC), firstly, Karush–Kuhn–Tucker (KKT) conditions are used. Furthermore, due to the complementary constraints and non-linear term in the upper-level objective function, this model is linearized by the dual theory and Fortuny-Amat and McCarl linearization method. In the following, it is assumed that the SDNO is the owner of the solar-based EV PLs. In this case, the proposed model is a single-level model. The uncertainty of the EVs and the solar system, as well as two programs, are considered for the EVs, i.e., controlled charging (CC) and charging/discharging schedule (CDS). Because of the uncertainties, a risk-based model is defined by introducing a Conditional Value-at-Risk (CVaR) index. Finally, the bi-level model and the single-level model are tested on an IEEE 33-bus distribution system in three modes; i.e., without the EVs and the solar system, with the EVs by controlled charging and with/ without the solar system, and with the EVs by charging/discharging schedule and with/without the solar system. The main results are reported and discussed.fi=vertaisarvioitu|en=peerReviewed

Rosettes & Ribbons: Some Recent Accomplishments of Note at the School

Developing a simple produces for efficient derivation of motor neurons (MNs) is essential for neural tissue engineering studies. Stem cells with high capacity for neural differentiation and scaffolds with the potential to promote motor neurons differentiation are promising candidates for neural tissue engineering. Recently, human olfactory ecto-mesenchymal stem cells (OE-MSCs), which are isolated easily from the olfactory mucosa, are considered a new hope for neuronal replacement due to their neural crest origin. Herein, we synthesized conducting hydrogels using different concentration of chitosan-g-aniline pentamer, gelatin, and agarose. The chemical structures, swelling and deswelling ratio, ionic conductivity and thermal properties of the hydrogel were characterized. Scaffolds with 10 chitosan-g-aniline pentamer/gelatin (S10) were chosen for further investigation and the potential of OE-MSCs as a new source for programming to motor neuron-like cells investigated on tissue culture plate (TCP) and conductive hydrogels. Cell differentiation was evaluated at the level of mRNA and protein synthesis and indicated that conductive hydrogels significantly increased the markers related to motor neurons including Hb-9, Islet-1 and ChAT compared to TCP. Taken together, the results suggest that OE-MSCs would be successfully differentiated into motor neuron-like cells on conductive hydrogels and would have a promising potential for treating motor neuron-related diseases. © 201

Graphene oxide: opportunities and challenges in biomedicine

Desirable carbon allotropes such as graphene oxide (GO) have entered the field with several biomedical applications, owing to their exceptional physicochemical and biological features, including extreme strength, found to be 200 times stronger than steel; remarkable light weight; large surface-to-volume ratio; chemical stability; unparalleled thermal and electrical conductivity; and enhanced cell adhesion, proliferation, and differentiation properties. The presence of functional groups on graphene oxide (GO) enhances further interactions with other molecules. Therefore, recent studies have focused on GO-based materials (GOBMs) rather than graphene. The aim of this research was to highlight the physicochemical and biological properties of GOBMs, especially their significance to biomedical applications. The latest studies of GOBMs in biomedical applications are critically reviewed, and in vitro and preclinical studies are assessed. Furthermore, the challenges likely to be faced and prospective future potential are addressed. GOBMs, a high potential emerging material, will dominate the materials of choice in the repair and development of human organs and medical devices. There is already great interest among academics as well as in pharmaceutical and biomedical industries

Graphene-based materials prove to be a promising candidate for nerve regeneration following peripheral nerve injury

Peripheral nerve injury is a common medical condition that has a great impact on patient quality of life. Currently, surgical management is considered to be a gold standard first-line treatment; however, is often not successful and requires further surgical procedures. Commercially available FDA- and CE-approved decellularized nerve conduits offer considerable benefits to patients suffering from a completely transected nerve but they fail to support neural regeneration in gaps > 30 mm. To address this unmet clinical need, current research is focused on biomaterial-based therapies to regenerate dysfunctional neural tissues, specifically damaged peripheral nerve, and spinal cord. Recently, attention has been paid to the capability of graphene-based materials (GBMs) to develop bifunctional scaffolds for promoting nerve regeneration, often via supporting enhanced neural differentiation. The unique features of GBMs have been applied to fabricate an electroactive conductive surface in order to direct stem cells and improve neural proliferation and differentiation. The use of GBMs for nerve tissue engineering (NTE) is considered an emerging technology bringing hope to peripheral nerve injury repair, with some products already in preclinical stages. This review assesses the last six years of research in the field of GBMs application in NTE, focusing on the fabrication and effects of GBMs for neurogenesis in various scaffold forms, including electrospun fibres, films, hydrogels, foams, 3D printing, and bioprinting

In vitro characterization of human bone marrow mesenchymal stem cell-derived motor neurons induced by epigenetic modifiers

Background: Motor neurons (MNs) are distinct types of cells in the dorso-ventral axis of the spinal cord. These cells are developed in the presence of two main morphogens, including Sonic hedgehog (Shh) and retinoic acid (RA). On the other hand, human bone marrow mesenchymal stem cells (hBM-MSCs) are known as a multipotent type of cells with neural differentiation capacity. In this regard, the aim of this study was to quantitatively evaluate the expression of MN-related genes and the potent epigenetic regulatory genes involved in neurogenesis, including Enhancer of zeste homolog 2 (EZH-2) and P300, during hBM-MSC differentiation into MN-like cells, using RA and Shh. After isolating and inducing the cells with Shh and RA, the results were evaluated using immunocytochemistry and qRT-PCR. Results: Our findings showed that the treated cells could express choline acetyltransferase (ChAT) and insulin gene enhancer binding protein-1 (Islet-1) antigens at the protein level, 2 weeks after induction. Moreover, at the second week after induction, the induced cells expressed MN-related genes (ChAT and ISLET-1) and epigenetic regulatory genes (EZH-2 and P300) at significant levels compared to the control (non-treated BM-MSCs) and to the induced cells at the first week (day 7). In addition, the expression of EZH-2, as a histone-modifying gene, was also significantly upregulated at the first week compared to the control. No significant upregulation was detected in the expression of motor neuron and pancreas homeobox 1 (MNX-1) in the treated groups compared to the control group. Conclusion: We concluded that epigenetic modifiers, P300 and EZH-2, are important mediators for regulating the process of motor neuron differentiation induced by RA and Shh. © 2021, The Author(s)

Measurement of rat brain tumor kinetics using an intravascular MR contrast agent and DCE‐MRI nested model selection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109323/1/jmri24469.pd

An Unsupervised Autoencoder Developed from Dynamic Contrast-Enhanced (DCE)-MRI Datasets for Classification of Acute Tumor Response in an Animal Model

Purpose/Objective(s): Recent studies have shown that vascular parameters of brain tumors derived from DCE-MRI may act as potential biomarkers for radiation-induced acute effects. However, accurate characterization of the spatial regions affected by radiation therapy (RT) remains challenging. Here, we introduce an unsupervised adaptive model for classification and ranking of the RT-affected regions in an animal model of cerebral U-251n tumors. Materials/Methods: Twenty-three immune-compromised-RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, 28 days after implantation, two DCE-MRI studies (Dual Gradient Echo, DGE, FOV: 32 × 32 mm2, TR/(TE1-TE2) = 24 ms/(2 ms-4 ms), flip angle = 18°, 400 acquisitions, 1.55 sec interval with Magnevist contrast agent, CA injection at ∼ 24 sec) were performed 24h apart using a 7T MRI scanner. A single 20 Gy stereotactic radiation exposure was performed before the second MRI, which was acquired 1-6.5 hrs after RT. DCE-MRI analysis was done using a model selection technique to distinguish three different brain regions as follows: Normal vasculature (Model 1: No leakage, only plasma volume, vp, is estimated), leaky tumor tissues with no back-flux to the vasculature (Model 2: vp and forward volumetric transfer constant, Ktrans, are estimated), and leaky tumor tissues with back-flux (Model 3: vp, Ktrans, and interstitial volume fraction, ve, are estimated). Normalized time traces of DCE-MRI information (24 pre, and 24 post-RT for each rat, total of 64108 training datasets) of tumors and their soft surrounding normal tissues were extracted from the 3 different model regions. To eliminate high-dimensional data similarity, an unsupervised autoencoder (AE) was trained to map out the model-derived data into a feature space (latent variables, N=10). For each model, the pre and post RT latent variables were compared (by appropriate tests of significance: ANOVA/Welch, CI=95%) to reveal RT-discriminant features. Pearson correlation coefficients were used to compare the decoded data to rank the effect of RT on different models. Results: The time trace of DCE-MRI information of rat brain in normal (Model 1, non-leaky) and highly permeable (Model 3) regions are less impacted by RT (Higher correlation between pre and post RT: r= 0.8518, p\u3c0.0001 and r= 0.9040, p\u3c0.0001 for Model 1 and Model 3, respectively) compared to the peritumoral regions pertaining to Model 2 (r= 0.8077, p\u3c0.0001). Conclusion: This pilot study suggests that among different brain regions, peritumoral zones (infiltrative tumor borders with enhanced rim) are highly affected by RT. Spatial assessment of RT-affected brain regions can play a key role in optimization of treatment planning in cancer patients, but presents a challenging task in conventional DCE-MRI. This study represents an important step toward classification and ranking the RT-affected brain spatial regions according to their vascular response following hypofractionated RT

Model selection for DCE‐T1 studies in glioblastoma

Dynamic contrast enhanced T 1 ‐weighted MRI using the contrast agent gadopentetate dimeglumine (Gd‐DTPA) was performed on 10 patients with glioblastoma. Nested models with as many as three parameters were used to estimate plasma volume or plasma volume and forward vascular transfer constant ( K trans ) and the reverse vascular transfer constant ( k ep ). These constituted models 1, 2, and 3, respectively. Model 1 predominated in normal nonleaky brain tissue, showing little or no leakage of contrast agent. Model 3 predominated in regions associated with aggressive portions of the tumor, and model 2 bordered model 3 regions, showing leakage at reduced rates. In the patient sample, v p was about four times that of white matter in the enhancing part of the tumor. K trans varied by a factor of 10 between the model 2 (1.9 ↔ 10 −3 min −1 ) and model 3 regions (1.9 ↔ 10 −2 min −1 ). The mean calculated interstitial space (model 3) was 5.5%. In model 3 regions, excellent curve fits were obtained to summarize concentration‐time data (mean R 2 = 0.99). We conclude that the three parameters of the standard model are sufficient to fit dynamic contrast enhanced T 1 data in glioblastoma under the conditions of the experiment. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91323/1/23211_ftp.pd