Biology subjects

Specht, K.

Publications and source records attributed to Specht, K..

10 recordsLinked to original sources

bioRxiv2024

Cultured Bacteria Isolated from Primary Sclerosing Cholangitis Patient Bile Induce Inflammation and Cell Death

BackgroundPrimary sclerosing cholangitis (PSC) is a chronic liver disease characterized by inflammation and progressive fibrosis of the biliary tree. The pathogenesis of PSC remains poorly understood, and there are no effective therapeutic options. Previous studies have observed associations between changes in the colonic and biliary microbiome and PSC. We aimed to determine whether bacterial isolates cultured from PSC patient bile induced disease-associated phenotypes in cells. MethodsBile was collected from PSC patients (n=10) by endoscopic retrograde cholangiography and from non-PSC controls (n=3) undergoing cholecystectomies. Biliary bacteria were cultured anaerobically, and 50 colonies per sample were identified by 16S rRNA sequencing. The effects of supernatants from seven PSC-associated bacterial strains on cellular phenotypes were characterized using human colonic (Caco-2), hepatic (HepG2), and biliary (EGI-1) cells. ResultsNo bacteria were isolated from non-PSC controls, while bacteria were cultured from most PSC patients. The PSC bile microbiomes exhibited reduced diversity compared to the gut or oral cavity, with one or two bacterial strains predominating. Overall, PSC-associated bacteria produced factors that were cytotoxic to hepatic and biliary cells. Enterococcus faecalis, and to a lesser extent Veillonella parvula, induced epithelial permeability, while Escherichia coli, Fusobacterium necrophorum, and Klebsiella pneumoniae induced inflammatory cytokines in biliary cells. ConclusionsOur data suggest that bacteria cultured from PSC bile induce cellular changes that may contribute to PSC disease pathogenesis. Enterococcus may promote intestinal permeability, facilitating bacterial migration to the biliary tree. Once there, Escherichia, Fusobacterium and Klebsiella, may cause inflammation and damage in biliary and liver cells.

microbiology↗

bioRxiv2024

The within-subject stability of cortical thickness, surface area, and brain volumes across one year

T1-weighted (T1w) imaging is widely used to examine brain structure based on image-derived phenotypes (IDPs) such as cortical thickness, surface area, and brain volumes. The reliability of these IDPs has been extensively explored, mainly focusing on the inter-subject variations, whereas the stability of the within-subject variations has often been overlooked. Additionally, how environmental factors such as time of day and daylight hours impact the structural brain is poorly understood. Therefore, we aimed to address the stability of T1w-derived phenotypes and explore the effecting factors including processing stream, brain region size, time of day, daylight hours, and head movement. Three subjects in their late 20s, early 30s, and early 40s were scanned repeatedly on the same scanner over one year, from which a densely sampled dataset was acquired with 38, 40, and 25 sessions for subjects 1, 2, and 3, respectively. The temporal stability was evaluated using within-subject percentage change and coefficients of variation (CV). The effects of time of day and daylight hours were assessed by fitting general linear models, aggregating the effect size with meta-analysis, and equivalent analysis. The longitudinal processing stream generates more stable results than the cross-sectional processing stream, hence the following results are all derived from the longitudinal processing stream. First and foremost, most IDPs demonstrated percentage changes within 5% and CVs within 2% for almost all brain regions, indicating high stability. Interestingly, we found that the brain region size negatively correlates with the CVs. Specifically, several small brain regions, including the temporal pole, frontal pole, pericalcarine, entorhinal cortex, and accumbens area, showed low stability. In addition, cortical thickness change was strongly and positively correlated with that of volume change while being negatively correlated with change in surface area, illustrating their distinct roles in brain anatomy. Moreover, the time of day could be ignored when evaluating the total surface area and total cortical brain volume but not the average cortical thickness and total subcortical brain volume. Furthermore, daylight hours could be left out when evaluating IDPs since there was no appreciable effect of daylight hours on the IDPs stability. Lastly, apparent head motion causes cortical thickness and volume underestimated and surface area overestimated.

neuroscience↗

bioRxiv2023

The intra-individual reliability of 1H-MRS measurement in the anterior cingulate cortex across one year

Magnetic resonance spectroscopy (MRS) is the primary method that can measure the levels of metabolites in the brain in vivo. To achieve its potential in clinical usage, the reliability of the measurement requires further articulation. Although there are many studies that investigate the reliability of gamma-aminobutyric acid (GABA), comparatively few studies have investigated the reliability of other brain metabolites, such as glutamate (Glu), N-acetyl-aspartate (NAA), creatine (Cr), phosphocreatine (PCr), or myo-inositol (mI), which all play a significant role in brain development and functions. In addition, previous studies which predominately used only two measurements (two datapoints) failed to provide the details of the time effect (e.g., time-of-day) on MRS measurement within subjects. Therefore, in this study, MRS data located in the anterior cingulate cortex (ACC) were repeatedly recorded across one year leading to at least 25 sessions for each subject with the aim of exploring the variability of other metabolites by using the index coefficient of variability (CV); the smaller the CV, the more reliable the measurements. We found that the metabolites of NAA, tNAA, and tCr showed the smallest CVs (between 1.61 and 4.90 %), and the metabolites of Glu, Glx, mI, and tCho showed modest CVs (between 4.26 and 7.89 %). Furthermore, we found that the concentration reference of the ratio to water with tissue correction results in smaller CVs compared to the ratio to tCr. In addition, we did not find any time-of-day effect on the MRS measurements. Collectively, the results of this study indicate that the MRS measurement is reasonably reliable in quantifying the levels of metabolites. Key pointsThe MRS measurement is reliable within subject. The ratio to water provides more reliable results compared to the ratio to tCr. The ratio to water is recommended as the internal concentration reference.

neuroscience↗

bioRxiv2023

The Bergen Breakfast Scanning Club dataset: a deep brain imaging dataset

Populational brain imaging methods based on group averages provide valuable insights into the general functions of the brain. However, they often overlook the inherent inter- and intra-subject variability, limiting our understanding of individual differences. To address this limitation, researchers have turned to big datasets and deep brain imaging datasets. Big datasets enable the exploration of inter-subject variations, while deep brain imaging datasets, involving repeated scanning of multiple subjects over time, offer detailed insights into intra-subject variability. Despite the availability of numerous big datasets, the number of deep brain imaging datasets remains limited. In this article, we present a deep brain imaging dataset derived from the Bergen Breakfast Scanning Club (BBSC) project. The dataset comprises data collected from three subjects who underwent repeated scanning over the course of approximately one year. Specifically, three types of data chunks were collected: behavioral data, functional brain data, and structural brain data. Functional brain images, encompassing magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI), along with their anatomical reference T1-weighted brain images, were collected twice a week during the data collection period. In total, 38, 40, and 25 sessions of functional data were acquired for subjects 1, 2, and 3, respectively. On the other hand, structural brain images, including T2-weighted brain images, diffusion-weighted images (DWI), and fluid-attenuated inversion recovery (FLAIR) images, were obtained once a month. A total of 10, 9, and 6 sessions were collected for subjects 1, 2, and 3, respectively. The primary objective of this article is to provide a comprehensive description of the data acquisition protocol employed in the BBSC project, as well as detailed insights into the preprocessing steps applied to the acquired data.

neuroscience↗

bioRxiv2023

Brain age predictions in longitudinal data reveal the importance of scan quality and field strength

IntroductionBrain age, the estimation of a persons age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for for the same individual at various time points and validate our findings with age-matched healthy controls. MethodsWe used densly sampled T1-weighted MRI data from four individuals (from two datasets) to observe how brain age corresponds to age and is influenced by acquision and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pre-trained deep learning model. ResultsWe find small within-subject correlations between age and brain age. We also find evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data, and inconclusive effects of scan quality. ConclusionThe absence of maturation effects for the age range in the presented sample, brain age model-bias (including training age distribution and field strength) and model error are potential reasons for small relationships between age and brain age in longitudinal data. Future brain age models should account for differences in field strength and intra-individual differences.

neuroscience↗

bioRxiv2021

Why Blood Pressure and Body Mass Should be Controlled for in Resting-State Functional Magnetic Resonance Imaging Studies.

Replicability has become an increasing focus within the scientific communities with the ongoing "replication crisis". One area that appears to struggle with unreliable results is resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, the current study aimed to improve the knowledge of endogenous factors that contribute to inter-individual variability. Arterial blood pressure, body mass, hematocrit, and glycated hemoglobin were investigated as potential sources of between-subject variability in rs-fMRI, in healthy individuals. Whether changes in resting state-networks (rs-networks) could be attributed to variability in the BOLD-signal, changes in neuronal activity, or both, was of special interest. Within-subject parameters were estimated utilizing Dynamic Causal Modelling (DCM) as it allows to make inferences on the estimated hemodynamic (BOLD-signal dynamics) and neuronal parameters (effective connectivity) separately. The results of the analyses imply that blood pressure and body mass can cause between-subject and between-group variability in the BOLD-signal and that all the included factors can affect the underlying connectivity. Given the results of the current and previous studies, rs-fMRI results appear to be susceptible to a range of factors, which is likely to contribute to the low degree of replicability of these studies. Interestingly, the highest degree of variability seems to appear within the much-studied Default Mode Network and its connections to other networks.

neuroscience↗

bioRxiv2020

Time-of-day influence on resting-state fMRI: an overlooked factor contributing to the replication crisis?

The current project explored the hypothesis that time-of-day dependent metabolic variations may contribute to reduced reliability in resting-state fMRI studies. We have investigated time-of-day effects in the spontaneous fluctuations (>0.1Hz) of the blood oxygenation level dependent (BOLD) signal. Using data from the human connectome project (HCP) release S1200, cross-spectral density dynamic causal modelling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. Hierarchical group-parametric empirical Bayes (PEB) found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect. We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies.

neuroscience↗

bioRxiv2019

Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

In a stable environment the brain can minimize processing required for sensory input by forming a predictive model of the surrounding world and suppressing neural response to predicted stimuli. Unpredicted stimuli lead to a prediction error signal propagation through the perceptual network, and resulting adjustment to the predictive model. The inter-regional plasticity which enables the model-building and model-adjustment is hypothesized to be mediated via glutamatergic receptors. While pharmacological challenge studies with glutamate receptor ligands have demonstrated impact on prediction-error indices, it is not clear how inter-individual differences in the glutamate system affect the prediction-error processing in non-medicated state. In the present study we examined 20 healthy young subjects with resting-state proton MRS spectroscopy to characterize glutamate+glutamine (rs-Glx) levels in their Heschls gyrus (HG), and related this to HG functional connectivity during a roving auditory oddball protocol. No rs-Glx effects were found within the frontotemporal prediction-error network. Larger rs-Glx signal was related to stronger connectivity between HG and bilateral inferior parietal lobule during unpredictable auditory stimulation. We also found effects of rs-Glx on the coherence of default mode network (DMN) and frontoparietal network (FPN) during unpredictable auditory stimulation. Our results demonstrate the importance of Glx in modulating long-range connections and wider networks in the brain during perceptual inference.

neuroscience↗

bioRxiv2019

Exosomes transmit retroelement RNAs to drive inflammation and immunosuppression in Ewing Sarcoma

Ewing sarcoma (EwS) is an aggressive childhood malignancy with a high propensity for metastasis. By analyzing cohorts of patients and age-matched healthy donors, we establish that EwS metastatic progression is accompanied by elevated plasma levels of multiple proinflammatory cytokines, interferons and extracellular vesicles (EVs). The latter were enriched with transcripts derived from LINE, SINE and ERV retroelements and from locus-specific pericentromeric regions, including HSAT2. We show that some of these RNAs, including HSAT2 and HERV-K, are selectively transmitted in EwS EVs and taken up by stromal fibroblasts and peripheral blood CD33+ myeloid cells and CD8+ T-cells, inducing immune exhaustion, immunosuppressive phenotypes and proinflammatory responses. Moreover, EwS EV-derived repeat RNAs were propagated and serially transmitted in recipient cell EVs, reminiscent of viral infection. As such, this study uncovers a novel mechanism driving cancer-associated inflammation, immunosuppression and metastatic progression.

cancer biology↗

bioRxiv2019

Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

neuroscience↗