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RBFOX1 is a highly pleiotropic gene that contributes to several psychiatric and neurodevelopmental disorders. Both rare and common variants in RBFOX1 have been associated with several psychiatric conditions, but the mechanisms underlying the pleiotropic effects of RBFOX1 are not yet understood. Here we found that, in zebrafish, rbfox1 is expressed in spinal cord, mid- and hindbrain during developmental stages. In adults, expression is restricted to specific areas of the brain, including telencephalic and diencephalic regions with an important role in receiving and processing sensory information and in directing behaviour. To investigate the effect of rbfox1 deficiency on behaviour, we used rbfox1sa15940, a rbfox1 loss-of-function line. We found that rbfox1sa15940 mutants present hyperactivity, thigmotaxis, decreased freezing behaviour and altered social behaviour. We repeated these behavioural tests in a second rbfox1 loss-of-function line with a different genetic background, rbfox1del19, and found that rbfox1 deficiency affects behaviour similarly in this line, although there were some differences. rbfox1del19 mutants present similar thigmotaxis, but stronger alterations in social behaviour and lower levels of hyperactivity than rbfox1sa15940 fish. Taken together, these results suggest that rbfox1 deficiency leads to multiple behavioural changes in zebrafish that might be modulated by environmental, epigenetic and genetic background effects, and that resemble phenotypic alterations present in Rbfox1-deficient mice and in patients with different psychiatric conditions. Our study thus highlights the evolutionary conservation of rbfox1 function in behaviour and paves the way to further investigate the mechanisms underlying rbfox1 pleiotropy on the onset of neurodevelopmental and psychiatric disorders.
RBFOX1 is a highly pleiotropic gene that contributes to several psychiatric and neurodevelopmental disorders. Both rare and common variants in RBFOX1 have been associated with several psychiatric conditions, but the mechanisms underlying the pleiotropic effects of RBFOX1 are not yet understood. Here we found that, in zebrafish, rbfox1 is expressed in spinal cord, mid- and hindbrain during developmental stages. In adults, expression is restricted to specific areas of the brain, including telencephalic and diencephalic regions with an important role in receiving and processing sensory information and in directing behaviour. To investigate the effect of rbfox1 deficiency on behaviour, we used rbfox1sa15940, a rbfox1 loss-of-function line. We found that rbfox1sa15940 mutants present hyperactivity, thigmotaxis, decreased freezing behaviour and altered social behaviour. We repeated these behavioural tests in a second rbfox1 loss-of-function line with a different genetic background, rbfox1del19, and found that rbfox1 deficiency affects behaviour similarly in this line, although there were some differences. rbfox1del19 mutants present similar thigmotaxis, but stronger alterations in social behaviour and lower levels of hyperactivity than rbfox1sa15940 fish. Taken together, these results suggest that rbfox1 deficiency leads to multiple behavioural changes in zebrafish that might be modulated by environmental, epigenetic and genetic background effects, and that resemble phenotypic alterations present in Rbfox1-deficient mice and in patients with different psychiatric conditions. Our study thus highlights the evolutionary conservation of rbfox1 function in behaviour and paves the way to further investigate the mechanisms underlying rbfox1 pleiotropy on the onset of neurodevelopmental and psychiatric disorders.
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.
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.
Human behaviour is inextricably linked to the interaction of emotion and cognition. For decades, emotion and cognition were perceived as separable processes, yet with mutual interactions. Recently, this differen-tiation has been challenged by more integrative approaches, but without addressing the exact neurophysiological basis of their interaction. Here, we aimed to uncover neurophysiological mechanisms of emotion-cognition interaction. We used an emotional Flanker task paired with EEG/FEM beamforming in a large cohort (N=121) of healthy human participants, obtaining high temporal and fMRI-equivalent spatial resolution. Spatially, emotion and cognition processing overlapped in the right inferior frontal gyrus (rIFG), specifically in pars triangularis. Temporally, emotion and cognition processing overlapped during the transition from emotional to cognitive processing, with a stronger interaction in β-band power leading to worse behavioral performance. Despite functionally segregated subdivisions in rIFG, frequency-specific information flowed extensively within IFG and top-down to visual areas (V2, Precuneus) – explaining the behavioral interference effect. Thus, for the first time we here show the neural mechanisms of emotion-cognition interaction in space, time, frequency and information transfer with high temporal and spatial resolution, revealing a central role for β-band activity in rIFG. Our results support the idea that rIFG plays a broad role in both inhibitory control and emotional interference inhibition as it is a site of convergence in both processes. Furthermore, our results have potential clinical implications for understanding dysfunctional emotion-cognition interaction and emotional interference inhibition in psychiatric disor-ders, e.g. major depression and substance use disorder, in which patients have difficulties in regulating emotions and executing inhibitory control.
Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability
(2023)
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.
The selective autophagy of mitochondria is linked to mitochondrial quality control and is critical to a healthy organism. Ubiquitylation is sometimes needed for marking damaged mitochondria for disposal but also for governing the expression and turnover of critical regulatory proteins. We have conducted a CRISPR/Cas9 screen of human E3 ubiquitin ligases for influence on mitophagy under both basal cell culture conditions and following acute mitochondrial depolarisation. We identify two Cullin RING ligases, VHL and FBXL4 as the most profound negative regulators of basal mitophagy. Here we show that these converge through control of the mitophagy adaptors BNIP3 and BNIP3L/NIX, but that this is achieved through different mechanisms. FBXL4 suppression of BNIP3 and NIX levels is mediated via direct interaction and protein destabilisation rather than suppression of HIF1α-mediated transcription. Depletion of NIX but not BNIP3 is sufficient to restore mitophagy levels. Our study enables a full understanding of the aetiology of early onset mitochondrial encephalomyopathy that is supported by analysis of a disease associated mutation. We further show that the compound MLN4924, which globally interferes with Cullin RING ligase activity, is a strong inducer of mitophagy which can provide a research tool in this context as well as a candidate therapeutic agent for conditions linked to mitochondrial quality control.
To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound -- any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naive techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.
Graph data is an omnipresent way to represent information in machine learning. Especially, in neuroscience research, data from Diffusion-Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) is commonly represented as graphs. Exploiting the graph structure of these modalities using graph-specific machine learning applications is currently hampered by the lack of easy-to-use software. PHOTONAI Graph aims to close the gap between domain experts of machine learning, graph experts and neuroscientists. Leveraging the rapid machine learning model development features of the Python machine learning API PHOTONAI, PHOTONAI Graph enables the design, optimization, and evaluation of reliable graph machine learning models for practitioners. As such, it provides easy access to custom graph machine learning pipelines including, hyperparameter optimization and algorithm evaluation ensuring reproducibility and valid performance estimates. Integrating established algorithms such as graph neural networks, graph embeddings and graph kernels, it allows researchers without significant coding experience to build and optimize complex graph machine learning models within a few lines of code. We showcase the versatility of this toolbox by building pipelines for both resting–state fMRI and DTI data in the hope that it will increase the adoption of graph-specific machine learning algorithms in neuroscience research.
Motivation DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding.
Results We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy.
Availability An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME.