Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.
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ABSTRACT:
SUBMITTER: Liu L
PROVIDER: S-EPMC8575025 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Synaptogenesis is a fundamental step in neuronal development. For spiny glutamatergic synapses in hippocampus and cortex, synaptogenesis involves adhesion of pre and postsynaptic membranes, delivery and anchorage of pre and postsynaptic structures including scaffolds such as PSD-95 and NMDA and AMPA receptors, which are glutamate-gated ion channels, as well as the morphological maturation of spines. Although electrical activity-dependent mechanisms are established regulators of these processes, the mechanisms that function during early development, prior to the onset of electrical activity, are unclear. The Eph receptors and ephrins provide cell contact-dependent pathways that regulate axonal and dendritic development. Members of the ephrin-A family are glycosyl-phosphatidylinositol-anchored to the cell surface and activate EphA receptors, which are receptor tyrosine kinases.Here we show that ephrin-A5 interaction with the EphA5 receptor following neuron-neuron contact during early development of hippocampus induces a complex program of synaptogenic events, including expression of functional synaptic NMDA receptor-PSD-95 complexes plus morphological spine maturation and the emergence of electrical activity. The program depends upon voltage-sensitive calcium channel Ca2+ fluxes that activate PKA, CaMKII and PI3 kinase, leading to CREB phosphorylation and a synaptogenic program of gene expression. AMPA receptor subunits, their scaffolds and electrical activity are not induced. Strikingly, in contrast to wild type, stimulation of hippocampal slices from P6 EphA5 receptor functional knockout mice yielded no NMDA receptor currents.These studies suggest that ephrin-A5 and EphA5 signals play a necessary, activity-independent role in the initiation of the early phases of synaptogenesis. The coordinated expression of the NMDAR and PSD-95 induced by eprhin-A5 interaction with EphA5 receptors may be the developmental switch that induces expression of AMPAR and their interacting proteins and the transition to activity-dependent synaptic regulation.
| S-EPMC2930854 | biostudies-literature
Project description:To study the expression patterns of two Eph family molecules, the receptor EphA5, and the ligand ephrin-A5, during spinal cord development.The receptor expression was analyzed using beta-galactosidase knockin mice, and affinity ligand probe binding. The ligand expression was assessed using two different affinity probes, and knockout mouse tissues as controls.EphA5 was expressed in the ventral spinal cord, while ephrin-A5 was located in the dorsolateral regions of the spinal cord throughout development.These results show that EphA5 and ephrin-A5 are expressed over broad developmental stages and may play important roles in establishing the dorsoventral organization of the spinal cord.
| S-EPMC5550571 | biostudies-other
Project description:BackgroundPeriodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.MethodsTwo periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.ResultsWe produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three "master" immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.ConclusionThis study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.
| S-EPMC7994531 | biostudies-literature
Project description:Regenerative medicine holds great promise for the treatment of degenerative retinal disorders. Krüppel-like factors (KLFs) are transcription factors that have recently emerged as key tools in regenerative medicine because some of them can function as epigenetic reprogrammers in stem cell biology. Here, we show that KLF16, one of the least understood members of this family, is a POU4F2 independent transcription factor in retinal ganglion cells (RGCs) as early as embryonic day 15. When overexpressed, KLF16 inhibits RGC neurite outgrowth and enhances RGC growth cone collapse in response to exogenous ephrinA5 ligands. Ephrin/EPH signaling regulates RGC connectivity. The EphA5 promoter contains multiple GC- and GT-rich KLF-binding sites, which, as shown by ChIP-assays, bind KLF16 in vivo In electrophoretic mobility shift assays, KLF16 binds specifically to a single KLF site near the EphA5 transcription start site that is required for KLF16 transactivation. Interestingly, methylation of only six of 98 CpG dinucleotides within the EphA5 promoter blocks its transactivation by KLF16 but enables transactivation by KLF2 and KLF15. These data demonstrate a role for KLF16 in regulation of RGC neurite outgrowth and as a methylation-sensitive transcriptional regulator of EphA5 expression. Together, these data identify differential low level methylation as a novel mechanism for regulating KLF16-mediated EphA5 expression across the retina. Because of the critical role of ephrin/EPH signaling in patterning RGC connectivity, understanding the role of KLFs in regulating neurite outgrowth and Eph receptor expression will be vital for successful restoration of functional vision through optic nerve regenerative therapies.
| S-EPMC5000058 | biostudies-literature
Project description:The Eph receptor tyrosine kinase/ephrin ligand system regulates a wide spectrum of physiological processes, while its dysregulation has been implicated in cancer progression. The human EphA3 receptor is widely upregulated in the tumor microenvironment and is highly expressed in some types of cancer cells. Furthermore, EphA3 is among the most highly mutated genes in lung cancer and it is also frequently mutated in other cancers. We report the structure of the ligand-binding domain of the EphA3 receptor in complex with its preferred ligand, ephrin-A5. The structure of the complex reveals a pronounced tilt of the ephrin-A5 ligand compared to its orientation when bound to the EphA2 and EphB2 receptors and similar to its orientation when bound to EphA4. This tilt brings an additional area of ephrin-A5 into contact with regions of EphA3 outside the ephrin-binding pocket thereby enlarging the size of the interface, which is consistent with the high binding affinity of ephrin-A5 for EphA3. This large variation in the tilt of ephrin-A5 bound to different Eph receptors has not been previously observed for other ephrins.
| S-EPMC4439037 | biostudies-literature
Project description:BackgroundCurrently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of?~?1000 carefully chosen landmark genes that can capture?~?80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome.ResultsHere, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information.ConclusionsWe believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.
| S-EPMC7821411 | biostudies-literature
Project description:BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a common chronic neurodevelopmental disorder with a high heritability. Much evidence of hemisphere asymmetry has been found for ADHD probands from behavioral level, electrophysiological level and brain morphology. One previous research has reported possible association between BAIAP2, which is asymmetrically expressed in the two cerebral hemispheres, with ADHD in European population. The present study aimed to investigate the association between BAIAP2 and ADHD in Chinese Han subjects. METHODS: A total of 1,397 ADHD trios comprised of one ADHD proband and their parents were included for family-based association tests. Independent 569 ADHD cases and 957 normal controls were included for case-control studies. Diagnosis was performed according to the DSM-IV criteria. Nine single nucleotide polymorphisms (SNPs) of BAIAP2 were chosen and performed genotyping for both family-based and case-control association studies. RESULTS: Transmission disequilibrium tests (TDTs) for family-based association studies showed significant association between the CA haplotype comprised by rs3934492 and rs9901648 with predominantly inattentive type (ADHD-I). For case-control study, chi-square tests provided evidence for the contribution of SNP rs4969239, rs3934492 and rs4969385 to ADHD and its two clinical subtypes, ADHD-I and ADHD-C. However, only the associations for ADHD and ADHD-I retained significant after corrections for multiplicity or logistic regression analyses adjusting the potential confounding effect of gender and age. CONCLUSIONS: These above results indicated the possible involvement of BAIAP2 in the etiology of ADHD, especially ADHD-I.
| S-EPMC3892012 | biostudies-literature
Project description:Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE-/- mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.
| S-EPMC7726562 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data.ResultsIn this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs.ConclusionsThe TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.
| S-EPMC7663897 | biostudies-literature
Project description:The EphA5 receptor tyrosine kinase plays key roles in axon guidance during development. However, the presence of EphA5 protein in the nervous system has not been fully characterized. To examine EphA5 localization better, mutant mice, in which the EphA5 cytoplasmic domain was replaced with beta-galactosidase, were analyzed for both temporal and regional changes in the distribution of EphA5 protein in the developing and adult nervous system. During embryonic development, high levels of EphA5 protein were found in the retina, olfactory bulb, cerebral neocortex, hippocampus, pretectum, tectum, cranial nerve nuclei, and spinal cord. Variations in intensity were observed as development proceeded. Staining of pretectal nuclei, tectal nuclei, and other areas of the mesencephalon became more diffuse after maturity, whereas the cerebral neocortex gained more robust intensity. In the adult, receptor protein continued to be detected in many areas including the olfactory nuclei, neocortex, piriform cortex, induseum griseum, hippocampus, thalamus, amygdala, hypothalamus, and septum. In addition, EphA5 protein was found in the claustrum, stria terminalis, barrel cortex, and striatal patches, and along discrete axon tracts within the corpus callosum of the adult. We conclude that EphA5 function is not limited to the developing mouse brain and may play a role in synaptic plasticity in the adult.
| S-EPMC2724768 | biostudies-literature