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Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Characterization of a dual BET/HDAC inhibitor for treatment of pancreatic ductal adenocarcinoma
(2020)
Pancreatic ductal adenocarcinoma (PDAC) is resistant to virtually all chemo‐ and targeted therapeutic approaches. Epigenetic regulators represent a novel class of drug targets. Among them, BET and HDAC proteins are central regulators of chromatin structure and transcription, and preclinical evidence suggests effectiveness of combined BET and HDAC inhibition in PDAC. Here, we describe that TW9, a newly generated adduct of the BET inhibitor (+)‐JQ1 and class I HDAC inhibitor CI994, is a potent dual inhibitor simultaneously targeting BET and HDAC proteins. TW9 has a similar affinity to BRD4 bromodomains as (+)‐JQ1 and shares a conserved binding mode, but is significantly more active in inhibiting HDAC1 compared to the parental HDAC inhibitor CI994. TW9 was more potent in inhibiting tumor cell proliferation compared to (+)‐JQ1, CI994 alone or combined treatment of both inhibitors. Sequential administration of gemcitabine and TW9 showed additional synergistic antitumor effects. Microarray analysis revealed that dysregulation of a FOSL1‐directed transcriptional program contributed to the antitumor effects of TW9. Our results demonstrate the potential of a dual chromatin‐targeting strategy in the treatment of PDAC and provide a rationale for further development of multitarget inhibitors.
Autophagy is a membrane-trafficking process that directs degradation of cytoplasmic material in lysosomes. The process promotes cellular fidelity, and while the core machinery of autophagy is known, the mechanisms that promote and sustain autophagy are less well defined. Here we report that the epigenetic reader BRD4 and the methyltransferase G9a repress a TFEB/TFE3/MITF-independent transcriptional program that promotes autophagy and lysosome biogenesis. We show that BRD4 knockdown induces autophagy in vitro and in vivo in response to some, but not all, situations. In the case of starvation, a signaling cascade involving AMPK and histone deacetylase SIRT1 displaces chromatin-bound BRD4, instigating autophagy gene activation and cell survival. Importantly, this program is directed independently and also reciprocally to the growth-promoting properties of BRD4 and is potently repressed by BRD4-NUT, a driver of NUT midline carcinoma. These findings therefore identify a distinct and selective mechanism of autophagy regulation.
The Behavioral Inhibition System (BIS) as defined within the Reinforcement Sensitivity Theory (RST) modulates reactions to stimuli indicating aversive events. Gray’s trait Anxiety determines the extent to which stimuli activate the BIS. While studies have identified the amygdala-septo-hippocampal circuit as the key-neural substrate of this system in recent years and measures of resting-state dynamics such as randomness and local synchronization of spontaneous BOLD fluctuations have recently been linked to personality traits, the relation between resting-state dynamics and the BIS remains unexplored. In the present study, we thus examined the local synchronization of spontaneous fMRI BOLD fluctuations as measured by Regional Homogeneity (ReHo) in the hippocampus and the amygdala in twenty-seven healthy subjects. Correlation analyses showed that Gray’s trait Anxiety was significantly associated with mean ReHo in both the amygdala and the hippocampus. Specifically, Gray’s trait Anxiety explained 23% and 17% of resting-state ReHo variance in the left amygdala and the left hippocampus, respectively. In summary, we found individual differences in Gray’s trait Anxiety to be associated with ReHo in areas previously associated with BIS functioning. Specifically, higher ReHo in resting-state neural dynamics corresponded to lower sensitivity to punishment scores both in the amygdala and the hippocampus. These findings corroborate and extend recent findings relating resting-state dynamics and personality while providing first evidence linking properties of resting-state fluctuations to Gray’s BIS.
Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.
Material and Methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.
Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.
Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.
Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
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.
The extinction of conditioned fear depends on an efficient interplay between the amygdala and the medial prefrontal cortex (mPFC). In rats, high-frequency electrical mPFC stimulation has been shown to improve extinction by means of a reduction of amygdala activity. However, so far it is unclear whether stimulation of homologues regions in humans might have similar beneficial effects. Healthy volunteers received one session of either active or sham repetitive transcranial magnetic stimulation (rTMS) covering the mPFC while undergoing a 2-day fear conditioning and extinction paradigm. Repetitive TMS was applied offline after fear acquisition in which one of two faces (CS+ but not CS−) was associated with an aversive scream (UCS). Immediate extinction learning (day 1) and extinction recall (day 2) were conducted without UCS delivery. Conditioned responses (CR) were assessed in a multimodal approach using fear-potentiated startle (FPS), skin conductance responses (SCR), functional near-infrared spectroscopy (fNIRS), and self-report scales. Consistent with the hypothesis of a modulated processing of conditioned fear after high-frequency rTMS, the active group showed a reduced CS+/CS− discrimination during extinction learning as evident in FPS as well as in SCR and arousal ratings. FPS responses to CS+ further showed a linear decrement throughout both extinction sessions. This study describes the first experimental approach of influencing conditioned fear by using rTMS and can thus be a basis for future studies investigating a complementation of mPFC stimulation to cognitive behavioral therapy (CBT).
A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
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.
