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Alterations in dendritic spine numbers are linked to deficits in learning and memory. While we previously revealed that postsynaptic plasticity-related gene 1 (PRG-1) controls lysophosphatidic acid (LPA) signaling at glutamatergic synapses via presynaptic LPA receptors, we now show that PRG-1 also affects spine density and synaptic plasticity in a cell-autonomous fashion via protein phosphatase 2A (PP2A)/β1-integrin activation. PRG-1 deficiency reduces spine numbers and β1-integrin activation, alters long-term potentiation (LTP), and impairs spatial memory. The intracellular PRG-1 C terminus interacts in an LPA-dependent fashion with PP2A, thus modulating its phosphatase activity at the postsynaptic density. This results in recruitment of adhesome components src, paxillin, and talin to lipid rafts and ultimately in activation of β1-integrins. Consistent with these findings, activation of PP2A with FTY720 rescues defects in spine density and LTP of PRG-1-deficient animals. These results disclose a mechanism by which bioactive lipid signaling via PRG-1 could affect synaptic plasticity and memory formation.
Recent studies suggest that synaptic lysophosphatidic acids (LPAs) augment glutamate-dependent cortical excitability and sensory information processing in mice and humans via presynaptic LPAR2 activation. Here, we studied the consequences of LPAR2 deletion or antagonism on various aspects of cognition using a set of behavioral and electrophysiological analyses. Hippocampal neuronal network activity was decreased in middle-aged LPAR2−/− mice, whereas hippocampal long-term potentiation (LTP) was increased suggesting cognitive advantages of LPAR2−/− mice. In line with the lower excitability, RNAseq studies revealed reduced transcription of neuronal activity markers in the dentate gyrus of the hippocampus in naïve LPAR2−/− mice, including ARC, FOS, FOSB, NR4A, NPAS4 and EGR2. LPAR2−/− mice behaved similarly to wild-type controls in maze tests of spatial or social learning and memory but showed faster and accurate responses in a 5-choice serial reaction touchscreen task requiring high attention and fast spatial discrimination. In IntelliCage learning experiments, LPAR2−/− were less active during daytime but normally active at night, and showed higher accuracy and attention to LED cues during active times. Overall, they maintained equal or superior licking success with fewer trials. Pharmacological block of the LPAR2 receptor recapitulated the LPAR2−/− phenotype, which was characterized by economic corner usage, stronger daytime resting behavior and higher proportions of correct trials. We conclude that LPAR2 stabilizes neuronal network excitability upon aging and allows for more efficient use of resting periods, better memory consolidation and better performance in tasks requiring high selective attention. Therapeutic LPAR2 antagonism may alleviate aging-associated cognitive dysfunctions.
Molecular cause and functional impact of altered synaptic lipid signaling due to a prg‐1 gene SNP
(2015)
Loss of plasticity-related gene 1 (PRG-1), which regulates synaptic phospholipid signaling, leads to hyperexcitability via increased glutamate release altering excitation/inhibition (E/I) balance in cortical networks. A recently reported SNP in prg-1 (R345T/mutPRG-1) affects ~5 million European and US citizens in a monoallelic variant. Our studies show that this mutation leads to a loss-of-PRG-1 function at the synapse due to its inability to control lysophosphatidic acid (LPA) levels via a cellular uptake mechanism which appears to depend on proper glycosylation altered by this SNP. PRG-1(+/-) mice, which are animal correlates of human PRG-1(+/mut) carriers, showed an altered cortical network function and stress-related behavioral changes indicating altered resilience against psychiatric disorders. These could be reversed by modulation of phospholipid signaling via pharmacological inhibition of the LPA-synthesizing molecule autotaxin. In line, EEG recordings in a human population-based cohort revealed an E/I balance shift in monoallelic mutPRG-1 carriers and an impaired sensory gating, which is regarded as an endophenotype of stress-related mental disorders. Intervention into bioactive lipid signaling is thus a promising strategy to interfere with glutamate-dependent symptoms in psychiatric diseases.
Lysophosphatidic acid (LPA) is a synaptic phospholipid, which regulates cortical excitation/inhibition (E/I) balance and controls sensory information processing in mice and man. Altered synaptic LPA signaling was shown to be associated with psychiatric disorders. Here, we show that the LPA-synthesizing enzyme autotaxin (ATX) is expressed in the astrocytic compartment of excitatory synapses and modulates glutamatergic transmission. In astrocytes, ATX is sorted toward fine astrocytic processes and transported to excitatory but not inhibitory synapses. This ATX sorting, as well as the enzymatic activity of astrocyte-derived ATX are dynamically regulated by neuronal activity via astrocytic glutamate receptors. Pharmacological and genetic ATX inhibition both rescued schizophrenia-related hyperexcitability syndromes caused by altered bioactive lipid signaling in two genetic mouse models for psychiatric disorders. Interestingly, ATX inhibition did not affect naive animals. However, as our data suggested that pharmacological ATX inhibition is a general method to reverse cortical excitability, we applied ATX inhibition in a ketamine model of schizophrenia and rescued thereby the electrophysiological and behavioral schizophrenia-like phenotype. Our data show that astrocytic ATX is a novel modulator of glutamatergic transmission and that targeting ATX might be a versatile strategy for a novel drug therapy to treat cortical hyperexcitability in psychiatric disorders.
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.