610 Medizin und Gesundheit
Refine
Year of publication
Document Type
- Preprint (265) (remove)
Language
- English (265)
Has Fulltext
- yes (265) (remove)
Is part of the Bibliography
- no (265)
Keywords
- EEG (2)
- MRI (2)
- aging (2)
- alpha power (2)
- hippocampus (2)
- natural scenes (2)
- neuronal populations (2)
- primary visual cortex (2)
- stimulus encoding (2)
- visual attention (2)
Institute
- Medizin (155)
- Ernst Strüngmann Institut (43)
- Frankfurt Institute for Advanced Studies (FIAS) (40)
- MPI für Hirnforschung (30)
- Biochemie, Chemie und Pharmazie (14)
- Biowissenschaften (13)
- Buchmann Institut für Molekulare Lebenswissenschaften (BMLS) (13)
- Physik (10)
- MPI für Biophysik (9)
- Psychologie (8)
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.
Coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and can affect multiple organs, among which is the circulatory system. Inflammation and mortality risk markers were previously detected in COVID-19 plasma and red blood cells (RBCs) metabolic and proteomic profiles. Additionally, biophysical properties, such as deformability, were found to be changed during the infection. Based on such data, we aim to better characterize RBC functions in COVID-19. We evaluate the flow properties of RBCs in severe COVID-19 patients admitted to the intensive care unit by using in vitro microfluidic techniques and automated methods, including artificial neural networks, for an unbiased RBC analysis. We find strong flow and RBC shape impairment in COVID-19 samples and demonstrate that such changes are reversible upon suspension of COVID-19 RBCs in healthy plasma. Vice versa, healthy RBCs immediately resemble COVID-19 RBCs when suspended in COVID-19 plasma. Proteomics and metabolomics analyses allow us to detect the effect of plasma exchanges on both plasma and RBCs and demonstrate a new role of RBCs in maintaining plasma equilibria at the expense of their flow properties. Our findings provide a framework for further investigations of clinical relevance for therapies against COVID-19 and possibly other infectious diseases.
Coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and can affect multiple organs, among which is the circulatory system. Inflammation and mortality risk markers were previously detected in COVID-19 plasma and red blood cells (RBCs) metabolic and proteomic profiles. Additionally, biophysical properties, such as deformability, were found to be changed during the infection. Based on such data, we aim to better characterize RBC functions in COVID-19. We evaluate the flow properties of RBCs in severe COVID-19 patients admitted to the intensive care unit by using in vitro microfluidic techniques and automated methods, including artificial neural networks, for an unbiased RBC analysis. We find strong flow and RBC shape impairment in COVID-19 samples and demonstrate that such changes are reversible upon suspension of COVID-19 RBCs in healthy plasma. Vice versa, healthy RBCs immediately resemble COVID-19 RBCs when suspended in COVID-19 plasma. Proteomics and metabolomics analyses allow us to detect the effect of plasma exchanges on both plasma and RBCs and demonstrate a new role of RBCs in maintaining plasma equilibria at the expense of their flow properties. Our findings provide a framework for further investigations of clinical relevance for therapies against COVID-19 and possibly other infectious diseases.
Upon infection, human immunodeficiency virus (HIV-1) releases its cone-shaped capsid into the cytoplasm of infected T-cells and macrophages. As its largest known cargo, the capsid enters the nuclear pore complex (NPC), driven by interactions with numerous FG-repeat nucleoporins (FG-Nups). Whether NPCs structurally adapt to capsid passage and whether capsids are modified during passage remains unknown, however. Here, we combined super-resolution and correlative microscopy with cryo electron tomography and molecular simulations to study nuclear entry of HIV-1 capsids in primary human macrophages. We found that cytosolically bound cyclophilin A is stripped off capsids entering the NPC, and the capsid hexagonal lattice remains largely intact inside and beyond the central channel. Strikingly, the NPC scaffold rings frequently crack during capsid passage, consistent with computer simulations indicating the need for NPC widening. The unique cone shape of the HIV-1 capsid facilitates its entry into NPCs and helps to crack their rings.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
The spike protein of SARS-CoV-2 is a highly flexible membrane receptor that triggers the translocation of the virus into cells by attaching to the human receptors. Like other type I membrane receptors, this protein has several extracellular domains connected by flexible hinges. The presence of these hinges results in high flexibility, which consequently results in challenges in defining the conformation of the protein. Here, We developed a new method to define the conformational space based on a few variables inspired by the robotic field’s methods to determine a robotic arm’s forward kinematics. Using newly performed atomistic molecular dynamics (MD) simulations and publicly available data, we found that the Denavit-Hartenberg (DH) parameters can reliably show the changes in the local conformation. Furthermore, the rotational and translational components of the homogenous transformation matrix constructed based on the DH parameters can identify the changes in the global conformation of the spike and also differentiate between the conformation with a similar position of the spike head, which other types of parameters, such as spherical coordinates, fail to distinguish between such conformations. Finally, the new method will be beneficial for looking at the conformational heterogeneity in all other type I membrane receptors.
Background: Trauma-related guilt and shame are crucial for the development and maintenance of PTSD (posttraumatic stress disorder). We developed an intervention combining cognitive techniques with loving-kindness meditations (C-METTA) that specifically target these emotions. C-METTA is an intervention of six weekly individual treatment sessions followed by a four-week practice phase.
Objective: This study examined C-METTA in a proof-of-concept study within a randomized wait-list controlled trial.
Method: We randomly assigned 32 trauma-exposed patients with a DSM-5 diagnosis to C-METTA or a wait-list condition (WL). Primary outcomes were clinician-rated PTSD symptoms (CAPS-5) and trauma-related guilt and shame. Secondary outcomes included psychopathology, self-criticism, well-being, and self-compassion. Outcomes were assessed before the intervention phase and after the practice phase.
Results: Mixed-design analyses showed greater reductions in C-METTA versus WL in clinician-rated PTSD symptoms (d = −1.09), guilt (d = −2.85), shame (d = −2.14), psychopathology and self-criticism.
Conclusion: Our findings support positive outcomes of C-METTA and might contribute to improved care for patients with stress-related disorders. The study was registered in the German Clinical Trials Register (DRKS00023470).
HIGHLIGHTS
C-METTA is an intervention that addresses trauma-related guilt and shame and combines cognitive interventions with loving-kindness meditations.
A proof-of-concept study was conducted examining C-METTA in a wait-list randomized controlled trial
C-METTA led to reductions in trauma-related guilt and shame and PTSD symptoms.
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.