Update on GPCR-based targets for the development of novel antidepressants

Mantas, Ioannis; Saarinen, Marcus; Xu, Zhi‐Qing David; Svenningsson, Per

doi:10.1038/s41380-021-01040-1

Cited by 34 publications

(22 citation statements)

References 294 publications

(385 reference statements)

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“…Many topics exhibit high embedding scores for neuronal functions including Neuronal Signal Transduction (GO:0023041), Central Nervous System Projection Neuron Axonogenesis (GO:0021952), and Branchiomotor Neuron Axon Guidance (GO:0021785). Interestingly, Topic 98 in MDD-coding genes only and Topics 22, 51 in MDD-all genes involve Adenylate Cyclase Modulating G-protein Coupled Receptors (GPCRs) Signaling (GO:0007188), which is the target of several recently-developed antidepressant drugs 52 .…”

Section: Scalabilitymentioning

confidence: 99%

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

et al. 2021

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The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 106 cells and confers remarkable cross-tissue and cross-species zero-shot transfer-learning performance. Using gene set enrichment analysis, we find that scETM-learned topics are enriched in biologically meaningful and disease-related pathways. Lastly, scETM enables the incorporation of known gene sets into the gene embeddings, thereby directly learning the associations between pathways and topics via the topic embeddings.

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Section: Scalabilitymentioning

confidence: 99%

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

et al. 2021

View full text Add to dashboard Cite

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“…Many topics exhibit high embedding scores for neuronal functions including Neuronal Signal Transduction (GO:0023041), Central Nervous System Projection Neuron Axonogenesis (GO:0021952), and Branchiomotor Neuron Axon Guidance (GO:0021785). Interestingly, Topic 98 in MDD -coding genes only and Topics 22, 51 in MDD -all genes involve Adenylate Cyclase Modulating G-protein Coupled Receptors (GPCRs) Signaling (GO:0007188), which is the target of several recently-developed antidepressant drugs [53].…”

Section: Pathway-informed Scetm Topicsmentioning

confidence: 99%

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Zhao

Cai

Zhang

et al. 2021

Preprint

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The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, integrative analysis of scRNA-seq data remains a challenge largely due to batch effects. We present single-cell Embedded Topic Model (scETM), an unsupervised deep generative model that recapitulates known cell types by inferring the latent cell topic mixtures via a variational autoencoder. scETM is scalable to over 106 cells and enables effective knowledge transfer across datasets. scETM also offers high inter-pretability and allows the incorporation of prior pathway knowledge into the gene embeddings. The scETM-inferred topics show enrichment in cell-type-specific and disease-related pathways.

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“…Thus, receptor activation is inherently an allosteric process that involves inter-subunit and inter-domain cooperativity. In the class C family, metabotropic glutamate receptors (mGluRs) are responsible for mediating the slow neuromodulatory effects of glutamate to tune synaptic excitability and transmission (Niswender & Conn, 2010; Pin & Bettler, 2016), making them promising therapeutic targets for treating a range of neurological and psychiatric disorders (Conn et al, 2009; Foster & Conn, 2017; Mantas et al, 2021). Based on structural (Doré et al, 2014; Du et al, 2021; Seven et al, 2021; Wu et al, 2014) and mutagenesis (Farinha et al, 2015; Gregory & Conn, 2015; Lundström et al, 2011) studies, the primary mGluR allosteric binding sites were determined to be located within the 7TM domain bundles.…”

Section: Introductionmentioning

confidence: 99%

Conformational fingerprinting of allosteric modulators in metabotropic glutamate receptor 2

Liauw

Foroutan

Schamber

et al. 2022

Preprint

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Activation of G protein-coupled receptors (GPCRs) is an allosteric process. It involves conformational coupling between the orthosteric ligand binding site and the G protein binding site. Factors that bind at sites other than the orthosteric ligand binding site and alter this allosteric activation process are allosteric modulators and are important class of therapeutics. For many receptors, how modulation of signaling is represented at the structural level is unclear. Here, we developed FRET sensors to quantify receptor modulation at each of the three structural domains of metabotropic glutamate receptor 2 (mGluR2). We identified the conformational fingerprint for several allosteric modulators in live cells. This approach enabled us to derive a receptor-centric representation of allosteric modulation and to correlate structural modulation to the standard signaling modulation metrics. Single-molecule FRET analysis revealed that a NAM increases the occupancy of one of the intermediate states while a PAM increases the occupancy of the active state. Moreover, we found that the effect of allosteric modulators on the receptor dynamics is complex and depend on the orthosteric ligand. Collectively, our findings provide a structural mechanism of allosteric modulation in mGluR2 and suggest possible strategies for design of future modulators.

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Update on GPCR-based targets for the development of novel antidepressants

Cited by 34 publications

References 294 publications

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Conformational fingerprinting of allosteric modulators in metabotropic glutamate receptor 2

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