Proper regulation of the midbrain dopamine system is essential for numerous brain functions and behavior (Bissonette and Roesch, 2016). Disruption in the balance of midbrain dopamine neuron activity has been linked to several neurological and psychiatric conditions, including autism (Pavăl, 2017), schizophrenia (Hietala and Syvälahti, 1996), and substance use disorders (Ostroumov and Dani, 2018). Within the VTA, the activity of dopamine neurons is regulated in part by inhibitory (GABAergic) and excitatory (glutamatergic) synaptic input. The molecular mechanisms that maintain the balance of inhibitory and excitatory connectivity in the adult midbrain, however, remain poorly resolved.

Genome-wide association studies and analysis of de novo mutations have strongly implicated genes regulating neuronal axon guidance in neurodevelopmental disorders (Gilman et al., 2012; Gulsuner et al., 2013). Although the impact of mutations in these genes early in development is likely critical for their role in neurodevelopmental disorders, many of the genes maintain high levels of expression in the adult brain, and their functions in this context are less understood. We previously demonstrated that the axonal guidance receptor Robo2 is necessary for the maintenance of inhibitory synaptic connectivity in the adult VTA (Gore et al., 2017), suggesting that axonal guidance proteins have a critical function in maintaining synaptic connectivity in the adult midbrain.

Netrin-1 is predominately recognized for its role in neurodevelopmental processes (Gore et al., 2017; Manitt et al., 2010; Winberg et al., 1998; Glasgow et al., 2018; Yetnikoff et al., 2010). During development, the gene encoding netrin-1 (Ntn1) is highly expressed throughout the central nervous system (CNS). Following this critical period global expression decreases (Manitt et al., 2010), but expression within the limbic system, particularly in the ventral midbrain, persists. Consistent with the continued function of Ntn1 following early development, genetic inactivation of either Ntn1 (Winberg et al., 1998) or its receptor Dcc Glasgow et al., 2018 from forebrain glutamatergic neurons in late postnatal development results in significantly impaired spatial memory in adult mice that corresponds to a loss of hippocampal plasticity. Within the VTA, Dcc expression levels in adult mice are significantly upregulated following amphetamine exposure (Yetnikoff et al., 2010), and Dcc haploinsufficient mice display blunted locomotor response to amphetamine (Flores et al., 2005), consistent with increased excitatory synaptic strength in the VTA following amphetamine treatment (Saal et al., 2003). These results suggest a potential role for Ntn1 signaling through Dcc in regulating excitatory tone in the adult dopamine system.

To determine whether Ntn1 regulates excitatory synaptic connectivity in the VTA of adult mice, we used viral-mediated, Cre-inducible CRISPR/Cas9 (Hunker et al., 2020) to selectively mutate Ntn1 in midbrain dopamine and GABA neurons. We find that Ntn1 loss of function significantly reduces postsynaptic glutamate receptor-mediated currents in a cell-autonomous manner similar to what has been reported previously in the adult hippocampus (Glasgow et al., 2018). We further show that Ntn1 loss of function in VTA GABA neurons has a more profound effect on behavior than the loss of function in VTA dopamine neurons. Intriguingly, the simultaneous loss of function of Ntn1 in both cell types of the VTA largely rescues the behavioral phenotypes observed following mutagenesis in VTA GABA neurons alone. These data support a model in which the balance of excitatory synaptic connectivity between dopamine and GABA neurons within the VTA is maintained by the persistent expression of the developmental gene Ntn1. This continued function of Ntn1 in adulthood sustains the excitatory/inhibitory equilibrium onto dopamine neurons that is critical to the function of the mesolimbic dopamine system.

Here, we show that Ntn1 is present in both dopamine and GABA-producing neurons of the adult VTA, and loss of Ntn1 function via genetic inactivation in either cell type results in a significant disruption of excitatory synaptic connectivity. The exact mechanisms by which Ntn1 regulates glutamatergic connectivity remain to be resolved. Likely mechanisms include Ntn1 regulation of the actin cytoskeleton and receptor transport vesicles through its activation of the cognate receptor DCC (Yetnikoff et al., 2010; Rajasekharan and Kennedy, 2009). The latter is consistent with our observed decrease in the amplitude of mEPSCs and reduced AMPA-evoked currents and with previous reports of Ntn1 regulating the delivery of GluA1-containing AMPA receptors to the postsynaptic density (Jasmin et al., 2021). Our finding that mEPSC frequency, but not paired-pulse ratio, was affected by Ntn1 loss further suggests netrin’s role in modulating excitatory synaptic connectivity is likely confined to postsynaptic mechanisms.

Loss of Ntn1 in dopamine neurons had little effect on behavior; however, we did observe an increase in anxiety-like behavior as measured by the open field assay consistent with the proposed role of dopamine in the modulation of anxiety-related behavior (Zarrindast and Khakpai, 2015). The general lack of effect of reduced glutamatergic synaptic connectivity on appetitive behavior, locomotion, and sensory-motor gating is consistent with previous observations that reduced glutamatergic signaling in dopamine neurons largely does not affect these behaviors (Zweifel et al., 2009; Hutchison et al., 2018). However, this does not discount the importance of glutamatergic inputs to the VTA (see additional discussion below). In contrast, loss of Ntn1 in VTA GABA neurons had a significant effect on multiple behaviors including locomotion, motivation, and acoustic pre-pulse inhibition, all of which are consistent with a hyperdopaminergic phenotype and with previous reports that disrupting GABA neuron function in the VTA induces similar phenotypes (Gore et al., 2017; Soden et al., 2020).

Given the robust nature of the behavioral effects observed following Ntn1 mutagenesis in VTA GABA neurons, we were initially surprised that simultaneous loss of Ntn1 in both GABA and dopamine neurons largely rescued the observed hyperdopaminergic phenotype. These results suggest that a balance of glutamatergic signaling in these two cell types is essential for the normal functioning of the mesolimbic dopamine system (Figure 6A). This finding is similar to what has been reported previously in the striatum.(Beutler et al., 2011) , demonstrated that loss of NMDA receptor signaling in dopamine D1 receptor-expressing neurons prevented the development of amphetamine sensitization; however, inactivation of NMDA receptors in D1R and D2R-expressing medium spiny neurons reversed this phenotype (Beutler et al., 2011). We have previously shown that blocking all synaptic transmission from GABA neurons in the VTA (Gore et al., 2017), or blocking selectively GABA release from VTA GABA neurons (Hutchison et al., 2018) results in hyperactivity and increased operant responding, though to a much greater degree than the effects observed here. We and others have also shown that loss of glutamate signaling in dopamine neurons has only a modest behavioral effect (Zweifel et al., 2009; Hutchison et al., 2018) so it is not surprising that reducing mEPSCs onto VTA dopamine neurons has little effect. The partial rescue of the GABA-Ntn1 knockout experiment suggests that reducing glutamatergic input onto dopamine neurons can abrogate effects associated with reduced inhibitory tone; thus, restoring the excitatory and inhibitory balance. Future research directly measuring the amount of inhibitory and excitatory current from the same cell (for example, by recording mIPSC at the reversal potential for EPSCs and mEPSCs at the reversal potential for mIPSCs) would be helpful in supporting this hypothesis, but was not performed in the current paper.

There are numerous disinhibitory projections to the VTA (Soden et al., 2020), and it is hypothesized that activation of GABAergic inputs onto VTA GABA neurons during behavior suppresses the activity of these neurons releasing the inhibitory brake onto dopamine neurons. Simultaneously, excitatory inputs onto VTA dopamine neurons, such as those from the pedunculopontine tegmental nucleus (Soden et al., 2020; Lodge and Grace, 2006), or dorsal raphe nucleus (Qi et al., 2014) would drive the glutamate-dependent burst activation of dopamine neurons (Zweifel et al., 2009; Lodge and Grace, 2006). Loss of excitatory drive onto dopamine neurons can likely be compensated for through the many disinhibitory circuits of the VTA. Indeed, inhibitory synaptic connectivity is not altered by the loss of Ntn1, and suppression of inhibition onto dopamine neurons is a potent means to drive the burst activation of these cells (Paladini and Tepper, 1999). The loss of glutamatergic inputs onto VTA GABA neurons is less likely to be bypassed at the circuit level, and we do not observe compensatory changes in excitatory or inhibitory synapses onto dopamine neurons following Ntn1 mutagenesis in VTA GABA neurons or compensatory changes in inhibitory synaptic connectivity onto VTA GABA neurons. The effects of Ntn1 loss of function in VTA GABA neurons can be abrogated if the excitatory drive onto dopamine neurons is also reduced by Ntn1 mutagenesis. Excitatory inputs onto GABA neurons within the VTA proper, as well as the caudal tail of the VTA, or RMTg, arise from multiple locations including the lateral habenula and ventral pallidum (Omelchenko et al., 2009; Brinschwitz et al., 2010; Tooley et al., 2018). Activation of these circuits is aversive, likely through the activation of GABAergic inputs onto dopamine neurons (Tooley et al., 2018; Wulff et al., 2019; Faget et al., 2018; Stamatakis and Stuber, 2012). Thus, reduced glutamatergic inputs onto VTA GABA neurons would suppress the inhibition of dopamine neurons allowing excitatory drive onto these cells to go unchecked, resulting in the phenotypes observed here. When excitatory inputs onto dopamine neurons are also reduced the combined driving forces of disinhibition and excitation of the dopamine neurons are re-equilibrated, restoring balance to the system.

While our findings shed light on the role of Ntn1 in adult VTA neurons, the question remains as to which netrin-1 receptors may be involved. Indeed, though Dcc is considered to be its canonical receptor, Ntn1 is a known ligand for several additional receptors, including DSCAM, Neogenin, and Unc5 homologs A-D (Rajasekharan and Kennedy, 2009; Lai Wing Sun et al., 2011). Previous work has identified the presence of both Dcc and Unc5c receptors in the adult VTA (often in the same cells) (Manitt et al., 2010). It is also interesting to note that during the development of the spinal cord, Ntn1 expression in the floor plate attracts commissural axons to the midline, but following the arrival of these axons at the floor plate, Unc5 expression increases to suppress the attractive actions of DCC signaling (Lai Wing Sun et al., 2011). Whether a similar relationship exists for the formation of nascent synapses and the maintenance of excitatory synapses occurs in the VTA will be important to resolve. Of further note, in addition to the role of Ntn1/Dcc/Unc5 signaling in the regulation of commissural axons crossing the midline, Slit/Robo signaling repels axons away from the floor plate (Kidd et al., 1998; Kidd et al., 1999) setting up a push-pull relationship between these pathways. We previously demonstrated that Robo2 maintains inhibitory synaptic connectivity in the adult VTA (Gore et al., 2017), suggesting the existence of another ‘push/pull’ relationship between these two pathways in which netrin/Dcc/Unc5 regulates excitation and Slit/Robo signaling regulates inhibition.

We find that Ntn1 is expressed in both Vgat- and Th-expressing neurons which is consistent with single nuclear RNA sequencing data recently obtained in rats from Phillips et al., 2022, though the levels of nuclear Ntn1 mRNA levels in this study were relatively low. This may reflect differences between rat and mouse, or the increased sensitivity of in situ hybridization in detecting cytosolic mRNA compared to nuclear mRNA levels observed with snRNA seq. Interestingly, (Phillips et al., 2022) also observed the expression of Ntn1 in glial cells. Thus, the identity and potential role of the roughly 10% of Ntn1 expressing neurons that do not co-label with markers of dopamine or GABA neurons in the VTA, potentially glutamatergic neurons or glial cells, warrants further investigation.

In our proposed model, reduced excitatory input onto GABA neurons results in a reduced inhibition of dopamine neurons causing the observed phenotypes, consistent with previous observations that reducing VTA GABA neuron function causes hyperactivity (Gore et al., 2017; Soden et al., 2020). However, it is also likely that GABA neurons projecting outside the VTA (for example, to the NAc Brown et al., 2012) are also affected and could contribute to the observed behavioral phenotypes.

Mutations in NTN1 (netrin-1) and DCC in humans have been associated with several dopamine-associated psychiatric conditions, including neurodevelopmental disorders such as schizophrenia (Wang et al., 2018; Tang et al., 2019; Grant et al., 2012) and major depressive disorder (Tang et al., 2019; Zeng et al., 2017; Vosberg et al., 2020; Torres-Berrío et al., 2020), as well as multiple neurodegenerative disorders (Lesnick et al., 2007; Lesnick et al., 2008; Lin et al., 2009). Our findings that Ntn1 plays a key role in maintaining excitatory connectivity in the adult midbrain and controlling the inhibitory/excitatory balance in this region highlights the importance of understanding these critical developmental signaling pathways in the adult nervous system that are likely important for therapeutic considerations in targeting these pathways.

All data generated or analysed during this study are included in the manuscript and supporting file.

1. 1. Beutler LR
   2. Wanat MJ
   3. Quintana A
   4. Sanz E
   5. Bamford NS
   6. Zweifel LS
   7. Palmiter RD

   (2011) Balanced NMDA receptor activity in dopamine D1 receptor (D1R)- and D2R-expressing medium spiny neurons is required for amphetamine sensitization

   PNAS 108:4206–4211.

   https://doi.org/10.1073/pnas.1101424108

   • PubMed
   • Google Scholar
2. 1. Bissonette GB
   2. Roesch MR

   (2016) Development and function of the midbrain dopamine system: What we know and what we need to

   Genes, Brain, and Behavior 15:62–73.

   https://doi.org/10.1111/gbb.12257

   • PubMed
   • Google Scholar
3. 1. Brinschwitz K
   2. Dittgen A
   3. Madai VI
   4. Lommel R
   5. Geisler S
   6. Veh RW

   (2010) Glutamatergic axons from the lateral habenula mainly terminate on gabaergic neurons of the ventral midbrain

   Neuroscience 168:463–476.

   https://doi.org/10.1016/j.neuroscience.2010.03.050

   • PubMed
   • Google Scholar
4. 1. Brown MTC
   2. Tan KR
   3. O’Connor EC
   4. Nikonenko I
   5. Muller D
   6. Lüscher C

   (2012) Ventral tegmental area GABA projections pause accumbal cholinergic interneurons to enhance associative learning

   Nature 492:452–456.

   https://doi.org/10.1038/nature11657

   • PubMed
   • Google Scholar
5. 1. Faget L
   2. Zell V
   3. Souter E
   4. McPherson A
   5. Ressler R
   6. Gutierrez-Reed N
   7. Yoo JH
   8. Dulcis D
   9. Hnasko TS

   (2018) Opponent control of behavioral reinforcement by inhibitory and excitatory projections from the ventral pallidum

   Nature Communications 9:849.

   https://doi.org/10.1038/s41467-018-03125-y

   • PubMed
   • Google Scholar
6. 1. Flores C
   2. Manitt C
   3. Rodaros D
   4. Thompson KM
   5. Rajabi H
   6. Luk KC
   7. Tritsch NX
   8. Sadikot AF
   9. Stewart J
   10. Kennedy TE

   (2005) Netrin receptor deficient mice exhibit functional reorganization of dopaminergic systems and do not sensitize to amphetamine

   Molecular Psychiatry 10:606–612.

   https://doi.org/10.1038/sj.mp.4001607

   • PubMed
   • Google Scholar
7. 1. Gilman SR
   2. Chang J
   3. Xu B
   4. Bawa TS
   5. Gogos JA
   6. Karayiorgou M
   7. Vitkup D

   (2012) Diverse types of genetic variation converge on functional gene networks involved in schizophrenia

   Nature Neuroscience 15:1723–1728.

   https://doi.org/10.1038/nn.3261

   • PubMed
   • Google Scholar
8. 1. Glasgow SD
   2. Labrecque S
   3. Beamish IV
   4. Aufmkolk S
   5. Gibon J
   6. Han D
   7. Harris SN
   8. Dufresne P
   9. Wiseman PW
   10. McKinney RA
   11. Séguéla P
   12. De Koninck P
   13. Ruthazer ES
   14. Kennedy TE

   (2018) Activity-dependent netrin-1 secretion drives synaptic insertion of glua1-containing AMPA receptors in the hippocampus

   Cell Reports 25:168–182.

   https://doi.org/10.1016/j.celrep.2018.09.028

   • PubMed
   • Google Scholar
9. 1. Gore BB
   2. Miller SM
   3. Jo YS
   4. Baird MA
   5. Hoon M
   6. Sanford CA
   7. Hunker A
   8. Lu W
   9. Wong RO
   10. Zweifel LS

   (2017) Roundabout receptor 2 maintains inhibitory control of the adult midbrain

   eLife 6:e23858.

   https://doi.org/10.7554/eLife.23858

   • PubMed
   • Google Scholar
10. 1. Grant A
    2. Fathalli F
    3. Rouleau G
    4. Joober R
    5. Flores C

    (2012) Association between schizophrenia and genetic variation in DCC: A case-control study

    Schizophrenia Research 137:26–31.

    https://doi.org/10.1016/j.schres.2012.02.023

    • PubMed
    • Google Scholar
11. 1. Gulsuner S
    2. Walsh T
    3. Watts AC
    4. Lee MK
    5. Thornton AM
    6. Casadei S
    7. Rippey C
    8. Shahin H
    9. Nimgaonkar VL
    10. Go RCP
    11. Savage RM
    12. Swerdlow NR
    13. Gur RE
    14. Braff DL
    15. King MC
    16. McClellan JM
    17. Consortium on the Genetics of Schizophrenia
    18. PAARTNERS Study Group

    (2013) Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network

    Cell 154:518–529.

    https://doi.org/10.1016/j.cell.2013.06.049

    • PubMed
    • Google Scholar
12. 1. Hietala J
    2. Syvälahti E

    (1996) Dopamine in schizophrenia

    Annals of Medicine 28:557–561.

    https://doi.org/10.3109/07853899608999120

    • PubMed
    • Google Scholar
13. 1. Hunker AC
    2. Soden ME
    3. Krayushkina D
    4. Heymann G
    5. Awatramani R
    6. Zweifel LS

    (2020) Conditional single vector CRISPR/sacas9 viruses for efficient mutagenesis in the adult mouse nervous system

    Cell Reports 30:4303–4316.

    https://doi.org/10.1016/j.celrep.2020.02.092

    • PubMed
    • Google Scholar
14. 1. Hutchison MA
    2. Gu X
    3. Adrover MF
    4. Lee MR
    5. Hnasko TS
    6. Alvarez VA
    7. Lu W

    (2018) Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior

    Molecular Psychiatry 23:1213–1225.

    https://doi.org/10.1038/mp.2017.7

    • PubMed
    • Google Scholar
15. 1. Jasmin M
    2. Ahn EH
    3. Voutilainen MH
    4. Fombonne J
    5. Guix C
    6. Viljakainen T
    7. Kang SS
    8. Yu L-Y
    9. Saarma M
    10. Mehlen P
    11. Ye K

    (2021) Netrin-1 and its receptor DCC modulate survival and death of dopamine neurons and parkinson’s disease features

    The EMBO Journal 40:e105537.

    https://doi.org/10.15252/embj.2020105537

    • PubMed
    • Google Scholar
16. 1. Kidd T
    2. Brose K
    3. Mitchell KJ
    4. Fetter RD
    5. Tessier-Lavigne M
    6. Goodman CS
    7. Tear G

    (1998) Roundabout controls axon crossing of the CNS midline and defines a novel subfamily of evolutionarily conserved guidance receptors

    Cell 92:205–215.

    https://doi.org/10.1016/s0092-8674(00)80915-0

    • PubMed
    • Google Scholar
17. 1. Kidd T
    2. Bland KS
    3. Goodman CS

    (1999) Slit is the midline repellent for the robo receptor in Drosophila

    Cell 96:785–794.

    https://doi.org/10.1016/s0092-8674(00)80589-9

    • PubMed
    • Google Scholar
18. 1. Lai Wing Sun K
    2. Correia JP
    3. Kennedy TE

    (2011) Netrins: Versatile extracellular cues with diverse functions

    Development 138:2153–2169.

    https://doi.org/10.1242/dev.044529

    • PubMed
    • Google Scholar
19. 1. Lein ES
    2. Hawrylycz MJ
    3. Ao N
    4. Ayres M
    5. Bensinger A
    6. Bernard A
    7. Boe AF
    8. Boguski MS
    9. Brockway KS
    10. Byrnes EJ
    11. Chen L
    12. Chen L
    13. Chen T-M
    14. Chin MC
    15. Chong J
    16. Crook BE
    17. Czaplinska A
    18. Dang CN
    19. Datta S
    20. Dee NR
    21. Desaki AL
    22. Desta T
    23. Diep E
    24. Dolbeare TA
    25. Donelan MJ
    26. Dong H-W
    27. Dougherty JG
    28. Duncan BJ
    29. Ebbert AJ
    30. Eichele G
    31. Estin LK
    32. Faber C
    33. Facer BA
    34. Fields R
    35. Fischer SR
    36. Fliss TP
    37. Frensley C
    38. Gates SN
    39. Glattfelder KJ
    40. Halverson KR
    41. Hart MR
    42. Hohmann JG
    43. Howell MP
    44. Jeung DP
    45. Johnson RA
    46. Karr PT
    47. Kawal R
    48. Kidney JM
    49. Knapik RH
    50. Kuan CL
    51. Lake JH
    52. Laramee AR
    53. Larsen KD
    54. Lau C
    55. Lemon TA
    56. Liang AJ
    57. Liu Y
    58. Luong LT
    59. Michaels J
    60. Morgan JJ
    61. Morgan RJ
    62. Mortrud MT
    63. Mosqueda NF
    64. Ng LL
    65. Ng R
    66. Orta GJ
    67. Overly CC
    68. Pak TH
    69. Parry SE
    70. Pathak SD
    71. Pearson OC
    72. Puchalski RB
    73. Riley ZL
    74. Rockett HR
    75. Rowland SA
    76. Royall JJ
    77. Ruiz MJ
    78. Sarno NR
    79. Schaffnit K
    80. Shapovalova NV
    81. Sivisay T
    82. Slaughterbeck CR
    83. Smith SC
    84. Smith KA
    85. Smith BI
    86. Sodt AJ
    87. Stewart NN
    88. Stumpf K-R
    89. Sunkin SM
    90. Sutram M
    91. Tam A
    92. Teemer CD
    93. Thaller C
    94. Thompson CL
    95. Varnam LR
    96. Visel A
    97. Whitlock RM
    98. Wohnoutka PE
    99. Wolkey CK
    100. Wong VY
    101. Wood M
    102. Yaylaoglu MB
    103. Young RC
    104. Youngstrom BL
    105. Yuan XF
    106. Zhang B
    107. Zwingman TA
    108. Jones AR

    (2007) Genome-wide atlas of gene expression in the adult mouse brain

    Nature 445:168–176.

    https://doi.org/10.1038/nature05453

    • PubMed
    • Google Scholar
20. 1. Lesnick TG
    2. Papapetropoulos S
    3. Mash DC
    4. Ffrench-Mullen J
    5. Shehadeh L
    6. de Andrade M
    7. Henley JR
    8. Rocca WA
    9. Ahlskog JE
    10. Maraganore DM

    (2007) A genomic pathway approach to A complex disease: Axon guidance and parkinson disease

    PLOS Genetics 3:e98.

    https://doi.org/10.1371/journal.pgen.0030098

    • PubMed
    • Google Scholar
21. 1. Lesnick TG
    2. Sorenson EJ
    3. Ahlskog JE
    4. Henley JR
    5. Shehadeh L
    6. Papapetropoulos S
    7. Maraganore DM

    (2008) Beyond parkinson disease: Amyotrophic lateral sclerosis and the axon guidance pathway

    PLOS ONE 3:e1449.

    https://doi.org/10.1371/journal.pone.0001449

    • PubMed
    • Google Scholar
22. 1. Lin L
    2. Lesnick TG
    3. Maraganore DM
    4. Isacson O

    (2009) Axon guidance and synaptic maintenance: Preclinical markers for neurodegenerative disease and therapeutics

    Trends in Neurosciences 32:142–149.

    https://doi.org/10.1016/j.tins.2008.11.006

    • PubMed
    • Google Scholar
23. 1. Lodge DJ
    2. Grace AA

    (2006) The hippocampus modulates dopamine neuron responsivity by regulating the intensity of phasic neuron activation

    Neuropsychopharmacology 31:1356–1361.

    https://doi.org/10.1038/sj.npp.1300963

    • PubMed
    • Google Scholar
24. 1. Manitt C
    2. Labelle-Dumais C
    3. Eng C
    4. Grant A
    5. Mimee A
    6. Stroh T
    7. Flores C

    (2010) Peri-pubertal emergence of UNC-5 homologue expression by dopamine neurons in rodents

    PLOS ONE 5:e11463.

    https://doi.org/10.1371/journal.pone.0011463

    • PubMed
    • Google Scholar
25. 1. Morales M
    2. Margolis EB

    (2017) Ventral tegmental area: Cellular heterogeneity, connectivity and behaviour

    Nature Reviews. Neuroscience 18:73–85.

    https://doi.org/10.1038/nrn.2016.165

    • PubMed
    • Google Scholar
26. 1. Omelchenko N
    2. Bell R
    3. Sesack SR

    (2009) Lateral habenula projections to dopamine and GABA neurons in the rat ventral tegmental area

    The European Journal of Neuroscience 30:1239–1250.

    https://doi.org/10.1111/j.1460-9568.2009.06924.x

    • PubMed
    • Google Scholar
27. 1. Ostroumov A
    2. Dani JA

    (2018) Inhibitory plasticity of mesocorticolimbic circuits in addiction and mental illness

    Trends in Neurosciences 41:898–910.

    https://doi.org/10.1016/j.tins.2018.07.014

    • PubMed
    • Google Scholar
28. 1. Paladini CA
    2. Tepper JM

    (1999) Gabaa and GABAB antagonists differentially affect the firing pattern of substantia nigra dopaminergic neurons in vivo

    Synapse 32:165–176.

    https://doi.org/10.1002/(SICI)1098-2396(19990601)32:3<165::AID-SYN3>3.0.CO;2-N

    • Google Scholar
29. 1. Pavăl D

    (2017) A dopamine hypothesis of autism spectrum disorder

    Developmental Neuroscience 39:355–360.

    https://doi.org/10.1159/000478725

    • PubMed
    • Google Scholar
30. 1. Phillips RA
    2. Tuscher JJ
    3. Black SL
    4. Andraka E
    5. Fitzgerald ND
    6. Ianov L
    7. Day JJ

    (2022) An atlas of transcriptionally defined cell populations in the rat ventral tegmental area

    Cell Reports 39:110616.

    https://doi.org/10.1016/j.celrep.2022.110616

    • PubMed
    • Google Scholar
31. 1. Qi J
    2. Zhang S
    3. Wang H-L
    4. Wang H
    5. de Jesus Aceves Buendia J
    6. Hoffman AF
    7. Lupica CR
    8. Seal RP
    9. Morales M

    (2014) A glutamatergic reward input from the dorsal raphe to ventral tegmental area dopamine neurons

    Nature Communications 5:5390.

    https://doi.org/10.1038/ncomms6390

    • PubMed
    • Google Scholar
32. 1. Rajasekharan S
    2. Kennedy TE

    (2009) The netrin protein family

    Genome Biology 10:239.

    https://doi.org/10.1186/gb-2009-10-9-239

    • PubMed
    • Google Scholar
33. 1. Saal D
    2. Dong Y
    3. Bonci A
    4. Malenka RC

    (2003) Drugs of abuse and stress trigger a common synaptic adaptation in dopamine neurons

    Neuron 37:577–582.

    https://doi.org/10.1016/s0896-6273(03)00021-7

    • PubMed
    • Google Scholar
34. 1. Seibenhener ML
    2. Wooten MC

    (2015) Use of the open field maze to measure locomotor and anxiety-like behavior in mice

    Journal of Visualized Experiments 01:e52434.

    https://doi.org/10.3791/52434

    • PubMed
    • Google Scholar
35. 1. Simon P
    2. Dupuis R
    3. Costentin J

    (1994) Thigmotaxis as an index of anxiety in mice influence of dopaminergic transmissions

    Behavioural Brain Research 61:59–64.

    https://doi.org/10.1016/0166-4328(94)90008-6

    • PubMed
    • Google Scholar
36. 1. Soden ME
    2. Chung AS
    3. Cuevas B
    4. Resnick JM
    5. Awatramani R
    6. Zweifel LS

    (2020) Anatomic resolution of neurotransmitter-specific projections to the VTA reveals diversity of gabaergic inputs

    Nature Neuroscience 23:968–980.

    https://doi.org/10.1038/s41593-020-0657-z

    • PubMed
    • Google Scholar
37. 1. Stamatakis AM
    2. Stuber GD

    (2012) Activation of lateral habenula inputs to the ventral midbrain promotes behavioral avoidance

    Nature Neuroscience 15:1105–1107.

    https://doi.org/10.1038/nn.3145

    • PubMed
    • Google Scholar
38. 1. Tan KR
    2. Yvon C
    3. Turiault M
    4. Mirzabekov JJ
    5. Doehner J
    6. Labouèbe G
    7. Deisseroth K
    8. Tye KM
    9. Lüscher C

    (2012) GABA neurons of the VTA drive conditioned place aversion

    Neuron 73:1173–1183.

    https://doi.org/10.1016/j.neuron.2012.02.015

    • PubMed
    • Google Scholar
39. 1. Tang J
    2. Chen X
    3. Cai B
    4. Chen G

    (2019) A logical relationship for schizophrenia, bipolar, and major depressive disorder part 4: Evidence from chromosome 4 high-density association screen

    The Journal of Comparative Neurology 527:392–405.

    https://doi.org/10.1002/cne.24543

    • PubMed
    • Google Scholar
40. 1. Tooley J
    2. Marconi L
    3. Alipio JB
    4. Matikainen-Ankney B
    5. Georgiou P
    6. Kravitz AV
    7. Creed MC

    (2018) Glutamatergic ventral pallidal neurons modulate activity of the habenula-tegmental circuitry and constrain reward seeking

    Biological Psychiatry 83:1012–1023.

    https://doi.org/10.1016/j.biopsych.2018.01.003

    • PubMed
    • Google Scholar
41. 1. Torres-Berrío A
    2. Hernandez G
    3. Nestler EJ
    4. Flores C

    (2020) The netrin-1/DCC guidance cue pathway as a molecular target in depression: translational evidence

    Biological Psychiatry 88:611–624.

    https://doi.org/10.1016/j.biopsych.2020.04.025

    • PubMed
    • Google Scholar
42. 1. Vosberg DE
    2. Leyton M
    3. Flores C

    (2020) The netrin-1/DCC guidance system: Dopamine pathway maturation and psychiatric disorders emerging in adolescence

    Molecular Psychiatry 25:297–307.

    https://doi.org/10.1038/s41380-019-0561-7

    • PubMed
    • Google Scholar
43. 1. Wang Z
    2. Li P
    3. Wu T
    4. Zhu S
    5. Deng L
    6. Cui G

    (2018) Axon guidance pathway genes are associated with schizophrenia risk

    Experimental and Therapeutic Medicine 16:4519–4526.

    https://doi.org/10.3892/etm.2018.6781

    • PubMed
    • Google Scholar
44. 1. Winberg ML
    2. Mitchell KJ
    3. Goodman CS

    (1998) Genetic analysis of the mechanisms controlling target selection: Complementary and combinatorial functions of netrins, semaphorins, and igcams

    Cell 93:581–591.

    https://doi.org/10.1016/s0092-8674(00)81187-3

    • PubMed
    • Google Scholar
45. 1. Wulff AB
    2. Tooley J
    3. Marconi LJ
    4. Creed MC

    (2019) Ventral pallidal modulation of aversion processing

    Brain Research 1713:62–69.

    https://doi.org/10.1016/j.brainres.2018.10.010

    • PubMed
    • Google Scholar
46. 1. Yetnikoff L
    2. Eng C
    3. Benning S
    4. Flores C

    (2010) Netrin-1 receptor in the ventral tegmental area is required for sensitization to amphetamine

    The European Journal of Neuroscience 31:1292–1302.

    https://doi.org/10.1111/j.1460-9568.2010.07163.x

    • PubMed
    • Google Scholar
47. 1. Zarrindast MR
    2. Khakpai F

    (2015)

    The modulatory role of dopamine in anxiety-like behavior

    Arch Iran Med 18:591–603.

    • Google Scholar
48. 1. Zeng Y
    2. Navarro P
    3. Fernandez-Pujals AM
    4. Hall LS
    5. Clarke T-K
    6. Thomson PA
    7. Smith BH
    8. Hocking LJ
    9. Padmanabhan S
    10. Hayward C
    11. MacIntyre DJ
    12. Wray NR
    13. Deary IJ
    14. Porteous DJ
    15. Haley CS
    16. McIntosh AM
    17. Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

    (2017) A combined pathway and regional heritability analysis indicates NETRIN1 pathway is associated with major depressive disorder

    Biological Psychiatry 81:336–346.

    https://doi.org/10.1016/j.biopsych.2016.04.017

    • PubMed
    • Google Scholar
49. 1. Zweifel LS
    2. Parker JG
    3. Lobb CJ
    4. Rainwater A
    5. Wall VZ
    6. Fadok JP
    7. Darvas M
    8. Kim MJ
    9. Mizumori SJY
    10. Paladini CA
    11. Phillips PEM
    12. Palmiter RD

    (2009) Disruption of NMDAR-dependent burst firing by dopamine neurons provides selective assessment of phasic dopamine-dependent behavior

    PNAS 106:7281–7288.

    https://doi.org/10.1073/pnas.0813415106

    • PubMed
    • Google Scholar

Author details

1. Marcella M Cline

   1. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States
   2. Molecular and Cellular Biology Program, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9590-3725

2. Barbara Juarez

   1. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States
   2. Department of Pharmacology, University of Washington, Seattle, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

3. Avery Hunker

   Department of Pharmacology, University of Washington, Seattle, United States

   Present address

   Allen Brain Institute, Seattle, United States

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

4. Ernesto G Regiarto

   Department of Pharmacology, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Bryan Hariadi

   Department of Pharmacology, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Marta E Soden

   Department of Pharmacology, University of Washington, Seattle, United States

   Contribution

   Data curation, Formal analysis, Methodology

   Competing interests

   No competing interests declared

7. Larry S Zweifel

   1. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States
   2. Department of Pharmacology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   larryz@u.washington.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3465-5331

• 1,053

  views

• 116

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.