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Highlights
• The goal was to assess the intra- and inter-scanner reproducibility of qMRI data.
• Mean scan-rescan variations were not exceeding 2.14%.
• Mean inter-scanner model deviations were not exceeding 5.21%.
• Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low.
Abstract
Background: Quantitative MRI (qMRI) techniques allow assessing cerebral tissue properties. However, previous studies on the accuracy of quantitative T1 and T2 mapping reported a scanner model bias of up to 10% for T1 and up to 23% for T2. Such differences would render multi-centre qMRI studies difficult and raise fundamental questions about the general precision of qMRI. A problem in previous studies was that different methods were used for qMRI parameter mapping or for measuring the transmitted radio frequency field B1 which is critical for qMRI techniques requiring corrections for B1 non-uniformities.
Aims: The goal was to assess the intra- and inter-scanner reproducibility of qMRI data at 3 T, using two different scanner models from the same vendor with exactly the same multiparametric acquisition protocol.
Methods: Proton density (PD), T1, T2* and T2 mapping was performed on healthy subjects and on a phantom, performing each measurement twice for each of two scanner models. Although the scanners had different hardware and software versions, identical imaging sequences were used for PD, T1 and T2* mapping, adapting the codes of an existing protocol on the older system line by line to match the software version of the newer scanner. For T2-mapping, the respective manufacturer’s sequence was used which depended on the software version. However, system-dependent corrections were carried out in this case. Reproducibility was assessed by average values in regions of interest.
Results: Mean scan-rescan variations were not exceeding 2.14%, with average values of 1.23% and 1.56% for the new and old system, respectively. Inter-scanner model deviations were not exceeding 5.21% with average values of about 2.2–3.8% for PD, 2.5–3.0% for T2*, 1.6–3.1% for T1 and 3.3–5.2% for T2.
Conclusions: Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low. The level of systematic differences reported in this work may help to interpret multi-centre data.
Objective: Randomized trials have shown that concomitant methotrexate (MTX) augments the effectiveness of tumour necrosis factor (TNF) inhibitors in rheumatoid arthritis (RA), but its benefit in psoriatic arthritis (PsA) has not been demonstrated. The goal of this study was to examine whether the impact of concomitant MTX on therapeutic outcomes in patients with PsA was similar to its effects in RA.
Methods: We used data from highly comparable and concurrent observational studies of patients with PsA (N = 1424) or RA (N = 3148) who initiated adalimumab therapy during routine clinical care. The 28-joint Disease Activity Score (DAS28) and patient-reported pain scores were evaluated in patients who received 24 months of continuous treatment with adalimumab monotherapy or adalimumab + MTX and in patients who initiated or stopped concomitant MTX during ongoing adalimumab therapy.
Results: Twenty-four months of continuous treatment with adalimumab + MTX was superior to adalimumab monotherapy in RA patients, while no significant difference was observed in patients with PsA. RA patients who added MTX during the study showed significant individual improvements in DAS28 and pain scores at 6 months after the change in therapy, while those who removed MTX had slight increases in disease activity. In contrast, in patients with PsA, neither initiation nor removal of MTX during continuous adalimumab therapy had a significant effect on therapeutic outcomes.
Conclusion: Addition of MTX to adalimumab confers further therapeutic benefit in patients with RA, but not in those with PsA, suggesting differences in MTX effects in these two patient populations.
Clinicaltrials.gov NCT01078090, NCT01077258, NCT01111240
Understanding the complexity of transcriptional regulation is a major goal of computational biology. Because experimental linkage of regulatory sites to genes is challenging, computational methods considering epigenomics data have been proposed to create tissue-specific regulatory maps. However, we showed that these approaches are not well suited to account for the variations of the regulatory landscape between cell-types. To overcome these drawbacks, we developed a new method called STITCHIT, that identifies and links putative regulatory sites to genes. Within STITCHIT, we consider the chromatin accessibility signal of all samples jointly to identify regions exhibiting a signal variation related to the expression of a distinct gene. STITCHIT outperforms previous approaches in various validation experiments and was used with a genome-wide CRISPR-Cas9 screen to prioritize novel doxorubicin-resistance genes and their associated non-coding regulatory regions. We believe that our work paves the way for a more refined understanding of transcriptional regulation at the gene-level.
The small GTPases H, K, and NRAS are molecular switches that are indispensable for proper regulation of cellular proliferation and growth. Mutations in this family of proteins are associated with cancer and result in aberrant activation of signaling processes caused by a deregulated recruitment of downstream effector proteins. In this study, we engineered novel variants of the Ras-binding domain (RBD) of the kinase CRAF. These variants bound with high affinity to the effector binding site of active Ras. Structural characterization showed how the newly identified mutations cooperate to enhance affinity to the effector binding site compared to RBDwt. The engineered RBD variants closely mimic the interaction mode of naturally occurring Ras effectors and as dominant negative affinity reagent block their activation. Experiments with cancer cells showed that expression of these RBD variants inhibits Ras signaling leading to a reduced growth and inductions of apoptosis. Using the optimized RBD variants, we stratified patient-derived colorectal cancer organoids according to Ras dependency, which showed that the presence of Ras mutations was insufficient to predict sensitivity to Ras inhibition.
Attention-Deficit/Hyperactivity Disorder (ADHD) is frequently comorbid with other psychiatric disorders and also with somatic conditions, such as obesity. In addition to the clinical overlap, significant genetic correlations have been found between ADHD and obesity as well as body mass index (BMI). The biological mechanisms driving this association are largely unknown, but some candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the link between ADHD and obesity measures. Using the largest GWAS summary statistics currently available for ADHD (N=53,293), BMI (N=681,275), and obesity (N=98,697), we first tested the association of dopaminergic and circadian rhythm gene sets with each phenotype. This hypothesis-driven approach showed that the dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), while the circadian rhythm gene set was associated with BMI only (P=1.28×10−3). We then took a data-driven approach by conducting genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. This approach further supported the implication of dopaminergic signaling in the link between ADHD and obesity measures, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was significantly enriched in both the ADHD-BMI and ADHD-obesity gene-based meta-analysis results. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering the shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of preventive interventions and/or efficient treatment of these conditions.
Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N=53,293, N=681,275, and N=98,697, respectively). We then conducted genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD-BMI overlapping genes with brain volumes (primary GWAS data N=10,720–10,928; replication data N=9,428). The dopaminergic gene set was associated with both ADHD (P=5.81×10−3) and BMI (P=1.63×10−5), the circadian rhythm was associated with BMI (P=1.28×10−3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD-BMI and ADHD-obesity results. The ADHD-BMI overlapping genes were associated with putamen volume (P=7.7×10−3; replication data P=3.9×10−2) – a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.
Highlights
• This current review covers studies that have identified long non-coding RNAs in aortic aneurysm development and progression.
• We separately discuss transcripts and mechanisms of importance to thoracic as well as abdominal aortic aneurysms.
• Functional data on lncRNAs being identified are highlighted.
• Some have been studied in human as well as experimental models of the disease pathology.
Abstract
Aortic aneurysm (AA) is a complex and dangerous vascular disease, featuring progressive and irreversible vessel dilatation. AA is typically detected either by screening, or identified incidentally through imaging studies. To date, no effective pharmacological therapies have been identified for clinical AA management, and either endovascular repair or open surgery remains the only option capable of preventing aneurysm rupture. In recent years, multiple research groups have endeavored to both identify noncoding RNAs and to clarify their function in vascular diseases, including aneurysmal pathologies. Notably, the molecular roles of noncoding RNAs in AA development appear to vary significantly between thoracic aortic aneurysms (TAAs) and abdominal aortic aneurysms (AAAs). Some microRNAs (miRNA - a non-coding RNA subspecies) appear to contribute to AA pathophysiology, with some showing major potential for use as biomarkers or as therapeutic targets. Studies of long noncoding RNAs (lncRNAs) are more limited, and their specific contributions to disease development and progression largely remain unexplored. This review aims to summarize and discuss the most current data on lncRNAs and their mediation of AA pathophysiology.
The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification
(2019)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
Background. Atherothrombotic disease, including coronary artery disease (CAD) and peripheral artery disease (PAD), can lead to cardiovascular (CV) events, such as myocardial infarction, stroke, limb ischemia, heart failure, and CV death. Aim. Evaluate the humanistic and economic burden of CAD and PAD and identify unmet needs through a comprehensive literature review. Methods. Relevant search terms were applied across online publication databases. Studies published between January 2010 and August 2017 meeting the inclusion/exclusion criteria were selected; guidelines were also included. Two rounds of screening were applied to select studies of relevance. Results. Worldwide data showed approximately 5–8% prevalence of CAD and 10–20% prevalence of PAD, dependent on the study design, average age, gender, and geographical location. Data from the REACH registry indicated that 18–35% of patients with CAD and 46–68% of patients with PAD had disease in one or more vascular beds. Use of medication to control modifiable CV risk factors was variable by country (lower in France than in Canada); statins and aspirin were the most widely used therapies in patients with chronic disease. Survival rates have improved with medical advancements, but there is an additional need to improve the humanistic burden of disease (i.e., associated disability and quality of life). The economic burden of atherothrombotic disease is high and expected to increase with increased survival and the aging population. Conclusion. CAD and PAD represent a substantial humanistic and economic burden worldwide, highlighting a need for new interventions to reduce the incidence of atherothrombotic disease.
The current problem of increasing antibiotic resistance and the resurgence of numerous infections indicate the need for novel vaccination strategies more than ever. In vaccine development, the search for and the selection of adequate vaccine antigens is the first important step. In recent years, bacterial outer membrane proteins have become of major interest, as they are the main proteins interacting with the extracellular environment. Trimeric autotransporter adhesins (TAAs) are important virulence factors in many Gram-negative bacteria, are localised on the bacterial surface, and mediate the first adherence to host cells in the course of infection. One example is the Neisseria adhesin A (NadA), which is currently used as a subunit in a licensed vaccine against Neisseria meningitidis. Other TAAs that seem promising vaccine candidates are the Acinetobacter trimeric autotransporter (Ata), the Haemophilus influenzae adhesin (Hia), and TAAs of the genus Bartonella. Here, we review the suitability of various TAAs as vaccine candidates.