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Record W3159612192 · doi:10.1111/ejn.15268

The challenging diversity of neurons in the ventral tegmental area: A commentary of Miranda‐Barrientos, J. et al., <i>Eur J Neurosci</i> 2021

2021· article· en· W3159612192 on OpenAlexaff
Charles Ducrot, Louis‐Éric Trudeau

Bibliographic record

VenueEuropean Journal of Neuroscience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDiversity (politics)Library scienceMedicineSociologyAnthropology

Abstract

fetched live from OpenAlex

In 1992, a landmark paper by Johnson and North published in the Journal of Physiology and entitled "Two types of neurone in the rat ventral tegmental area (VTA) and their synaptic inputs" (Johnson & North, 1992) provided a first glimpse of the diversity of neurons in this part of the brain. The VTA area was initially better known for the presence of dopamine (DA)-containing neurons projecting to the ventral striatum and for its key role in motivated behaviors and drug addiction. The work by Johnson and North revealed the existence of two distinct categories of neurons that they named "principal cells" and "secondary cells." Principal cells were described as pacemaking neurons with broad action potentials and showing a marked hyperpolarizing response to DA. In contrast, secondary cells were identified as typically quiescent neurons that are hyperpolarized by opioid peptides. The authors concluded that, similarly to the substantia nigra, the VTA thus contained neurons releasing either DA or GABA. Since this paper, work by several teams has considerably expanded our knowledge of neuronal diversity in this brain region. With the discovery of glutamate release and the expression of the type-2 vesicular glutamate transporter (VGLUT2) by subpopulations of DA neurons (Sulzer et al., 1998; Dal Bo et al., 2004), it became obvious that the complexity of the VTA was underestimated. The team of Marisela Morales has since made major contributions to this field by mapping the distribution of neurons with mixed neurotransmitter phenotype in the mesencephalon and by using anatomical tools, optogenetics, and behavioral tasks to examine some of the roles of these neurons (Li et al., 2013; Mongia et al., 2019; Morales & Margolis, 2017; Qi et al., 2016; Root et al., 2014, 2018; Wang et al., 2015; Yamaguchi et al., 2007, 2011, 2013, 2015). In this issue of the European Journal of Neuroscience, Miranda-Barientos and colleagues provide new data that further expand our knowledge of the diversity of neurons in the VTA. In their paper entitled "VTA GABA, glutamate, and glutamate-GABA neurons are heterogeneous in their electrophysiological and pharmacological properties" (Miranda-Barrientos et al., 2021), they took advantage of new intersectional genetic tools to differentially label subsets of neurons using a vglut2-Cre/vgat-Flp mouse (VGAT or VIAAT is the vesicular GABA/glycine transporter). Combining this approach to patch-clamp electrophysiology and the use of a mu-opioid receptor agonist, the authors report that contrarily to what was known in the early 1990s at the time of the paper by Johnson and North, some VTA glutamatergic neurons, like GABA neurons, are hyperpolarized by mu-opioid receptor ligands. Miranda-Barientos and colleagues also reveal additional diversity in the electrophysiological properties of glutamate, GABA, and mixed glutamate/GABA neurons of the VTA. An original contribution of the paper by Miranda-Barientos et al. is the use of intersectional genetic tools. A major goal in neuroscience is to understand how different neuronal cell types contribute to physiological brain functions and to brain diseases. An ever-increasing range of genetic tools is becoming available to help neuroscientists reach these goals. An emerging strategy based on the control of gene expression by the use of recombinase enzymes is the INTRSECT (for "intronic recombinase sites enabling combinatorial targeting") approach that uses Cre-, Dre-, and Flp recombinases acting on gene target sites termed LoxP, ROX, and FRT, respectively (Anastassiadis et al., 2009; Fenno et al., 2014, 2020; Ramírez-Solis et al., 1995; Sadowski, 1995). The Cre-LoxP and Flp-FRT systems are presently the most broadly used. Gene excision, inversion, or translocation can be driven by the orientation of the LoxP or FRT sites in specific cellular sub-populations. Miranda-Barientos et al. (2021) used a vglut2-Cre/vgat-Flp transgenic mouse and strereotaxic injection of Cre- and/or Flp-dependent viral vectors to label VTA neurons with fluorescent proteins. This allowed to identify recorded neurons as putative glutamate, GABA, or mixed glutamate/GABA neurons. In this work, the authors closely examined how opioid receptors modulate the activity of VTA neurons. Opioids compounds bind to Mu (µ), Delta (d) or Kappa (k; MOR, DOR, KOR, respectively) receptors. The opioid system is fundamental for pain modulation but is also involved in a range of other physiological mechanisms. MOR agonists produce positive motivational actions through excitation of VTA DA neurons via direct and indirect mechanisms (Bonci & Williams, 1997; Bozarth & Wise, 1981; Johnson & North, 1992; Margolis et al., 2014; Olmstead & Franklin, 1997). Activation of MORs on local GABA neurons in the VTA reduces the frequency of inhibitory postsynaptic currents in DA neurons, leading to an increase of their excitability (Johnson & North, 1992) and increased DA release in the nucleus accumbens (Di Chiara & Imperato, 1988). The subcellular expression and distribution of MOR is critical for understanding neural circuits and mechanisms involved in the effect of opioids (Le Merrer et al., 2009). In the VTA, it is well established that MORs are localized in the presynaptic and postsynaptic compartment of GABA neurons (Bergevin et al., 2002; Galaj et al., 2020; Margolis et al., 2012). Miranda-Barientos et al. add to this body of knowledge by showing that MORs are expressed by most GABA neurons that are negative for VGluT2 (VGluT2−/VGaT+ neurons) as well as glutamatergic neurons that are negative for VGaT (VGluT2+/VGaT− neurons). Interestingly, neurons co-releasing GABA and glutamate (VGluT2+/VGaT+) do not express MOR. They also show that VGluT2−/VGaT+neurons and VGluT2+/VGaT− neurons are postsynaptically inhibited by DAMGO (a synthetic opioid peptide activating MORs). Further experiments will be required to integrate these new findings in an improved model of how opioids act in the VTA in physiological and pathological contexts. The present work focused on a comparison of glutamate, GABA and mixed glutamate/GABA neurons in the mouse VTA. However, the authors did not compare the electrophysiological properties of these neuronal populations with those of mixed DA/glutamate neurons. They also did not determine whether GABA and/or glutamate neurons from the VTA also contain other neurotransmitters including neuropeptides such as CCK or neurotensin that are heterogeneously found in subsets of DA and non-DA neurons in the ventral midbrain. These are just a few examples of the growing complexity and challenges of such studies, something that is only bound to accelerate with the use of approaches such as single-cell RNASeq. Going forward, a major challenge will be the integration of these findings in a unified vision of the physiological and the pathophysiological functions of these "multilingual" neurons (Trudeau et al., 2014). The peer review history for this article is available at https://publons.com/publon/10.1111/ejn.15268. Data relating to the experiments are available upon request.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0050.013
Scholarly communication0.0070.012
Open science0.0070.004
Research integrity0.0290.056
Insufficient payload (model declined to judge)0.0020.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.324
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2021
Admission routes1
Has abstractyes

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