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5-iodotubercidin sensitizes cells to RIPK1-dependent necroptosis by interfering with NFκB signaling
(2023)
Receptor-interacting protein kinases (RIPK) −1 and −3 are master regulators of cell fate decisions in response to diverse stimuli and are subjected to multiple checkpoint controls. Earlier studies have established the presence of distinct IKK1/2 and p38/MK2-dependent checkpoints which suppress RIPK1 activation by directly phosphorylating it at different residues. In the present study, we investigated TNF-induced death in MAPK-activated protein kinase 2 (MK2)-deficient cells and show that MK2-deficiency or inactivation predominantly results in necroptotic cell death, even in the absence of caspase inhibition. While MK2-deficient cells can be rescued from necroptosis by RIPK1 inhibitors, RIPK3 inhibition seems to revert the process triggering apoptosis. To understand the mechanism of this necroptosis switch, we screened a 149-compound kinase inhibitor library for compounds which preferentially sensitize MK2-deficient MEFs to TNF-induced cell death. The most potent inhibitor identified was 5-Iodotubericidin, an adenosine analogue acting as adenosine kinase and protein kinase inhibitor. 5-ITu also potentiated LPS-induced necroptosis when combined with MK2 inhibition in RAW264.7 macrophages. Further mechanistic studies revealed that 5-Iodotubericidin induces RIPK1-dependent necroptosis in the absence of MK2 activity by suppressing IKK signaling. The identification of this role for the multitarget kinase inhibitor 5-ITu in TNF-, LPS- and chemotherapeutics-induced necroptosis will have potential implications in RIPK1-targeted therapies.
Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neuron’s size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neuron’s response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neuron’s excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3’ untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
Key Points:
* Deep profiling identifies novel targets of miR-181 associated with global gene regulation.
* miR-181 MREs in repeat elements in the coding sequence act through translational inhibition.
* High-resolution analysis reveals an alternative seed match in functional MREs.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3' untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
Recently, a 15-valent (PCV15) and a 20-valent pneumococcal conjugate vaccine (PCV20) have been licensed by the US Food and Drug Administration and are under evaluation by the European Medicines Agency. PCV15 contains all serotypes of the 13-valent conjugate vaccine (PCV13) plus serotype 22F and 33F and PCV20 includes PCV13 serotypes plus serotypes 8, 10A, 11A, 12F, 15B, 22F, 33F. We investigated pneumococcal serotype distribution, secular trends and proportion of pneumonia caused by serotypes included in PCV13, PCV15, PCV20, and the 23-valent pneumococcal polysaccharide vaccine (PPV23) among adult patients with all-cause community-acquired pneumonia (CAP) between 2013 and 2019. We applied logistic mixed regression modelling to assess annual trends. Urine samples from adult patients with CAP treated in the community or hospital in Germany and included in the CAPNETZ study, a prospective multi-centre cohort study, were analysed by two serotype-specific multiplex urinary antigen detection assays (UAD1/UAD2) at Pfizer’s Vaccines Research and Development Laboratory. UAD1 detects serotypes in PCV13, UAD2 detects additional serotypes in PCV20 plus serotypes 2, 9N, 17F and 20. Out of 1,831 patients screened, urine samples with a valid UAD test result were available for 1,343 patients (73.3%). Among those patients, 829 patients (61.7%) were male, 792 patients (59.0%) were aged ≥60 years, 1038 patients (77.3%) had at least one comorbidity and 1,204 patients (89.7%) were treated in the hospital. The overall proportion of vaccine-type pneumonia among all-cause CAP for PCV13, PCV15, PCV20 and PPV23 was 7.7% (n=103), 9.1% (n=122), 12.3% (n=165) and 13.3% (n=178). Over the entire observation period, we did not observe evidence for significant annual trends in pneumococcal vaccine serotype coverage against pneumonia in adults (PCV13: OR 0.94, 95% CI 0.83-1.05; PCV15: OR 0.93, 95% CI 0.84-1.03; PCV20: OR 0.95, 95% CI 0.86-1.04; PPV23: OR 0.99, 95% CI 0.90-1.08). In conclusion, our data show that i) the infant vaccination program of PCV13, which started in Germany 2010 did not result in a relevant and sustained decrease of PCV13 serotypes in pneumonia in adults and ii) that the gap in the coverage between PCV20 and PPV23 was small and did not increase over the entire observation time.
Recently, a 15-valent (PCV15) and a 20-valent pneumococcal conjugate vaccine (PCV20) have been licensed by the US Food and Drug Administration and are under evaluation by the European Medicines Agency. PCV15 contains all serotypes of the 13-valent conjugate vaccine (PCV13) plus serotype 22F and 33F and PCV20 includes PCV13 serotypes plus serotypes 8, 10A, 11A, 12F, 15B, 22F, 33F. We investigated pneumococcal serotype distribution, secular trends and proportion of pneumonia caused by serotypes included in PCV13, PCV15, PCV20, and the 23-valent pneumococcal polysaccharide vaccine (PPV23) among adult patients with all-cause community-acquired pneumonia (CAP) between 2013 and 2019. We applied logistic mixed regression modelling to assess annual trends. Urine samples from adult patients with CAP treated in the community or hospital in Germany and included in the CAPNETZ study, a prospective multi-centre cohort study, were analysed by two serotype-specific multiplex urinary antigen detection assays (UAD1/UAD2) at Pfizer’s Vaccines Research and Development Laboratory. UAD1 detects serotypes in PCV13, UAD2 detects additional serotypes in PCV20 plus serotypes 2, 9N, 17F and 20. Out of 1,831 patients screened, urine samples with a valid UAD test result were available for 1,343 patients (73.3%). Among those patients, 829 patients (61.7%) were male, 792 patients (59.0%) were aged ≥60 years, 1038 patients (77.3%) had at least one comorbidity and 1,204 patients (89.7%) were treated in the hospital. The overall proportion of vaccine-type pneumonia among all-cause CAP for PCV13, PCV15, PCV20 and PPV23 was 7.7% (n=103), 9.1% (n=122), 12.3% (n=165) and 13.3% (n=178). Over the entire observation period, we did not observe evidence for significant annual trends in pneumococcal vaccine serotype coverage against pneumonia in adults (PCV13: OR 0.94, 95% CI 0.83-1.05; PCV15: OR 0.93, 95% CI 0.84-1.03; PCV20: OR 0.95, 95% CI 0.86-1.04; PPV23: OR 0.99, 95% CI 0.90-1.08). In conclusion, our data show i) no decline of PCV13 serotypes in all-cause CAP between 2013-2019 mainly due to a persistently high proportion of serotype 3 suggesting no meaningful effect of childhood PCV13 vaccination on PCV13 coverage in pneumonia in adults during this time period and ii) that the gap in the coverage between PCV20 and PPV23 was small and did not increase over the entire observation time.
Adaptive threshold estimation procedures sample close to a subject’s perceptual threshold by dynamically adapting the stimulation based on the subject’s performance. Yet, perceptual thresholds not only depend on the observers’ sensory capabilities but also on any bias in terms of their expectations and response preferences, thus distorting the precision of the threshold estimates. Using the framework of signal detection theory (SDT), independent estimates of both, an observer’s sensitivity and internal processing bias can be delineated from threshold estimates. While this approach is commonly available for estimation procedures engaging the method of constant stimuli (MCS), correction procedures for adaptive methods (AM) are only scarcely applied. In this article, we introduce a new AM that takes individual biases into account, and that allows for a bias-corrected assessment of subjects’ sensitivity. This novel AM is validated with simulations and compared to a typical MCS-procedure, for which the implementation of bias correction has been previously demonstrated.
Comparing AM and MCS demonstrates the viability of the presented AM. Besides its feasibility, the results of the simulation reveal both, advantages, and limitations of the proposed AM. The procedure has considerable practical implications, in particular for the design of shaping procedures in sensory training experiments, in which task difficulty has to be constantly adapted to an observer’s performance, to improve training efficiency.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.