In vivo brain GPCR signaling elucidated by phosphoproteomics

Liu, Jeffrey J.; Sharma, Kirti; Zangrandi, Luca; Chen, Chongguang; Humphrey, Sean J.; Chiu, Yueh‐Hsia; Spetea, Mariana; Liu‐Chen, Lee‐Yuan; Schwarzer, Christoph; Mann, Matthias

doi:10.1126/science.aao4927

Cited by 110 publications

(86 citation statements)

References 71 publications

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“…Light from single-cell transcriptomics is now beginning to illuminate dark corners of cellular neuroscience 424 that have long resisted mechanistic and functional analysis (Fan et al, 2018;Fishell and Kepecs, 2019; may be one such corner. While profound impacts of neuropeptide signaling are well-established in a wide 428 range of non-mammalian and sub-cortical neural structures (Borbély et al, 2013;Burbach, 2011;Elphick 429 et al, 2018;Grimmelikhuijzen and Hauser, 2012;Katz and Lillvis, 2014;Kuffler et al, 1979) and there 430 certainly is an excellent literature on cortical neuropeptide signaling (Crawley, 1985;Férézou et al, 2007;431 Gallopin et al, 2006;Gomtsian et al, 2018;Hamilton et al, 2013;Liu et al, 2018;Mena et al, 2013;Mitre 432 et al, 2018;Owen et al, 2013;Rossier and Chapouthier, 1982;Williams and Zieglgänsberger, 1981), 433 published physiological results are surprisingly rare given the breadth of neuroscientific interest in cortex.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Transcriptomic Evidence for Dense Intracortical Neuropeptide Networks

Smith

Sümbül

Graybuck

et al. 2019

Preprint

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ImpactSingle-cell mRNA sequencing data from mouse neocortex expose evidence for peptidergic neuromodulatory networks that locally interconnect every cortical neuron Data Highlights• At least 97% of mouse neocortical neurons express one or more of 18 neuropeptide precursor proteins (NPP) genes at very high levels• At least 98% of cortical neurons express one or more of 29 neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes cognate to the 18 highly expressed NPP genes• Neocortical expression of these 18 NPP and 29 NP-GPCR genes is highly neuron-type-specific and their expression patterns differentiate transcriptomic neuron types with exceptional power• Neuron-type taxonomy and type-specific expression of 37 cognate NPP / NP-GPCR gene pairs generate testable predictions of at least 37 local intracortical neuromodulation networks SummarySeeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective Gprotein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

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Section: Discussionmentioning

confidence: 99%

Single-Cell Transcriptomic Evidence for Dense Intracortical Neuropeptide Networks

Smith

Sümbül

Graybuck

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Such changes have been analyzed by quantitative mass spectrometry-based phosphoproteomics, which can readily analyze thousands of phosphorylation sites Olsen et al, 2006;Sharma et al, 2014). For the more abundant serine and threonine phosphorylation responses, some studies have even done so in tissues (Humphrey et al, 2015;Liu et al, 2018;Lundby et al, 2013). Investigating the signaling network in a tissuespecific context is preferential as it provides insight into the mechanisms encoding specificity (Liu et al, 2018).…”

Section: Main Textmentioning

confidence: 99%

Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites

Lundby

Franciosa

Emdal

et al. 2019

Cell

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HIGHLIGHTS Outline of EGF dependent in-vivo phosphotyrosine signaling in lung tissue  A peptide based screen identifies proteins recruited to regulated phosphotyrosine sites in lung tissue  Several somatic mutations affect proline residues within position +3 of regulated phosphotyrosine sites and introduce molecular switches  A lung cancer oncogenic mutation in EGFR causes aberrant activation of phosphorylation signaling pathways by switching of a recruited protein complex  Cancer mutations in vicinity of phosphotyrosine sites induce molecular switches that alter protein signaling networks  Introduction of cutting-edge LC-MS instrumentation and DIA enables scalable, rapid and highthroughput analysis of phosphotyrosine site interactions

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“…Subsequently, [27] introduced heinz as "the rst approach that really tackles and solves the original problem raised by [48] to optimality." jActiveModules and heinz have become widely-used tools with diverse applications; a few recent examples include mass-spectrometry proteomics [51,58], damaging de novo mutations in schizophrenia and other neurological disorders [36,17], and single-cell RNA-seq [37,85,52].…”

Section: Introductionmentioning

confidence: 99%

NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks

Reyna

Chitra

Elyanow

et al. 2020

Preprint

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A classic problem in computational biology is the identi cation of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are di erentially expressed, highly mutated, or otherwise aberrant compared to other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are o en unknown. For example, some widely-used methods are reported to output very large subnetworks that are di cult to interpret biologically. In this work, we formulate the identi cation of altered subnetworks as the problem of estimating the parameters of a class of probability distributions which we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. We introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identi cation of di erentially expressed genes from both microarray and RNA-seq experiments and the identi cation of cancer driver genes in somatic mutation data. Availability: NetMix is available online at h ps://github.com/raphael-group/netmix.

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In vivo brain GPCR signaling elucidated by phosphoproteomics

Cited by 110 publications

References 71 publications

Single-Cell Transcriptomic Evidence for Dense Intracortical Neuropeptide Networks

Single-Cell Transcriptomic Evidence for Dense Intracortical Neuropeptide Networks

Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites

NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks

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