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Supplementary data 1. Dose values for MRI based RT for the different rats. Dx stands for the dose that x % of the tissue volume received. Supplementary data 2. Dose values for PET/MRI-based RT with PET based sub-volume boosting. Dx stands for the dose that x % of the tissue volume received. Supplementary data 3. representative DVH of 5 Gy boost using a 1 × 1 mm2 collimator. The DVH shows that over 50% of the tissue volume received a dose of 5 Gy. Supplementary data 4. Overview of data obtained. ✓ means the scan was performed, ✕ means the scan was not obtained. Supplementary data 5. Overview tumor volumes (ccm) for all groups. The tumor volumes were based on the volumes of interest manually drawn based on the T1 weighted MRI. For the PET based volumes, a threshold of 60% maximum standardized uptake value was applied within the rat brain. Animals were euthanized when human endpoints were reached (> 20% weight loss, tumor volume > 40% of total brain volume based on MRI or signs of ataxia). (DOCX 249 kb)

Figure S1. In vitro effect of nimorazole monotherapy. Ctrl = control; Nimo = nimorazole; Norm = normoxia; Hypox = hypoxia. Y-axis shows absorbance, analyzed 72 h after treatment. No significant difference was observed between cells treated with PBS (control) or treated with nimorazole, under hypoxic or normoxic conditions (t-test). (DOCX 20 kb)

1. INTRODUCTION Temporal lobe epilepsy (TLE) is the most common form of epilepsy in adults. Research has shown that abnormal functional brain networks could be involved in the development of epilepsy and its comorbidities [1]. Gaining more insight into these networks can be useful for the development of new therapies. Resting-state functional magnetic resonance imaging (rs-fMRI) can visualize changes in functional networks on a whole-brain level [2]. In this study, we aim to map changes in functional networks during epileptogenesis in the intraperitoneal kainic acid (IPKA) rat model for TLE using longitudinal resting-state fMRI and graph theory. 2. MATERIALS AND METHODS Twenty-four adult male Sprague-Dawley rats (276 ± 15 g body weight) were used in this study. Seventeen animals were intraperitoneally injected with kainic acid (KA) according to the protocol of Hellier et al. [3] resulting in status epilepticus (SE). The other 7 animals were injected with saline and used as a control group. Rs-fMRI images were acquired before the KA injections and at 5 time points during the development of epilepsy. At each time point an anatomical image and three resting-state fMRI images were acquired on a 7T system, while the animals were anesthetized with medetomidine. The fMRI images were corrected for slice timing and motion, normalized, smoothed and band-pass filtered using SPM12. The Pearson correlation coefficient was calculated between the fMRI time series of 38 regions of interest (ROIs) and stored in a correlation matrix. After applying different thresholds to remove the weakest connections, several network measures were calculated using a graph theoretical network analysis toolbox (GRETNA), and plotted as a function of time to visualize how the properties of the functional networks change during epileptogenesis. 3. RESULTS AND DISCUSSION In the IPKA rat model the correlation coefficients shift to smaller values during epileptogenesis, indicating that network connections progressively become weaker. Clustering coefficient and local efficiency decrease during epileptogenesis, indicating a decrease in segregation or local interconnectivity in the functional brain network. Characteristic path length increases and global efficiency decreases during epileptogenesis, indicating a decrease in integration or overall communication efficiency. In the next phase of this study, EEG monitoring will be used to characterize the severity of epilepsy in these rats to investigate how changes in functional brain networks during epileptogenesis correlate with epilepsy severity.

Prolyl hydroxylase domain-containing proteins (PHDs) regulate the adaptation of cells to hypoxia. Pan-hydroxylase inhibition is protective in experimental colitis, in which PHD1 plays a prominent role. However, it is currently unknown how PHD1 targeting regulates this protection and which cell type(s) are involved. Here, we demonstrated that Phd1 deletion in endothelial and haematopoietic cells (Phd1(f/f)Tie2:cre) protected mice from dextran sulphate sodium (DSS)-induced colitis, with reduced epithelial erosions, immune cell infiltration, and colonic microvascular dysfunction, whereas the response of Phd2(f/+)Tie2:cre and Phd3(f/f)Tie2:cre mice to DSS was similar to that of their littermate controls. Using bone marrow chimeras and cell-specific cre mice, we demonstrated that ablation of Phd1 in haematopoietic cells but not in endothelial cells was both necessary and sufficient to inhibit experimental colitis. This effect relied, at least in part, on skewing of Phd1-deficient bone marrow-derived macrophages towards an anti-inflammatory M2 phenotype. These cells showed an attenuated nuclear factor-B-dependent response to lipopolysaccharide (LPS), which in turn diminished endothelial chemokine expression. In addition, Phd1 deficiency in dendritic cells significantly reduced interleukin-1 beta production in response to LPS. Taken together, our results further support the development of selective PHD1 inhibitors for ulcerative colitis, and identify haematopoietic cells as their primary target. Copyright (C) 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

1. INTRODUCTION Traumatic brain injury (TBI) is an acquired brain injury that contributes to a substantial number of deaths (mortality rate: 15 per 100 000 in Europe) and a high number of cases of permanent disability (incidence rate: 235 per 100 000 in Europe). Most of the TBI patients have mild TBI (mTBI), a condition that shows no abnormalities on conventional imaging but can result in persisting cognitive defects. Diffusion imaging is an MRI technique sensitive to diffusion of water molecules in the brain and can detect subtle changes in white matter organization. The aim of this study is to investigate whether advanced diffusion MRI scanning can be used to detect microstructural changes in a rat model of mTBI. 2. MATERIALS AND METHODS 2.1 Animal model Nine female Wistar rats weighing 250 ± 19.6 g obtained mTBI utilizing the Marmarou weight drop model [1]. In brief, in anesthetized rats a steel helmet was fixed on the skull 1/3 before and 2/3 behind bregma. The rat was positioned under a 450 g brass weight on a foam bed. The weight was dropped from a height of 1m guided through a plexiglass column. The foam bed together with the rat was rapidly removed away from the column to prevent a second injury. Rats were allowed to recover for one week. 2.2 Imaging and data analysis MRI data was acquired on a 7T MRI scanner (PharmaScan, Bruker, Ettlingen) before and 1 week after injury. T2-weighted images were acquired for anatomical reference. Multishell diffusion data was acquired with multiple directions (b=800, 1500 and 2000; 32, 46 and 64 directions, respectively). Diffusion weighted images were corrected for EPI, motion and eddy current distortions and quantitative maps were calculated for the diffusion tensor and diffusion kurtosis model in ExploreDTI [2]. Furthermore diffusion kurtosis tensor estimation was done using weighted linear least squares method and maps for white matter metrics were calculated using the model of Fieremans et al. [3]. The maps were co-registered in SPM12 with a template based on the local population and a volume-of-interest analysis was performed in the hippocampus, cingulum and corpus callosum using Amide toolbox [4]. Differences between the two time points were calculated for each map using the Wilcoxon signed-rank test in SPSS. P < 0.05 was considered significant. 3. RESULTS AND DISCUSSION The DTI and DKI metrics were not significantly different between the two time points. The axonal water fraction (AWF) was significantly increased in the cingulum, corpus callosum and hippocampus after mTBI and could be explained by axonal swelling. To verify this hypothesis, histological analysis is currently ongoing. Sections will be stained for synapses, astrocytes, neurons and myelin. References Marmarou, A. et al. A new model of diffuse brain injury in rats: Part I. J Neuroscience, 80, 291-300, 1994. Leemans, A. et al. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of Intl Soc Mag Reson Med, p. 3537, Hawaii, USA, 2009 Fieremans, E. et al. White matter characterization with diffusional kurtosis imaging, Neuroimage 58(1): 177-188, 2011. Loening, AM. et al. AMIDE: A Free Software Tool for Multimodality Medical Image Analysis. Molecular Imaging, 2(3):131-137, 2003.