Lactate is an energy substrate for rodent cortical neurons and enhances their firing activity

  1. Anastassios Karagiannis
  2. Thierry Gallopin
  3. Alexandre Lacroix
  4. Fabrice Plaisier
  5. Juliette Piquet
  6. Hélène Geoffroy
  7. Régine Hepp
  8. Jérémie Naudé
  9. Benjamin Le Gac
  10. Richard Egger
  11. Bertrand Lambolez
  12. Dongdong Li
  13. Jean Rossier
  14. Jochen F Staiger
  15. Hiromi Imamura
  16. Susumu Seino
  17. Jochen Roeper
  18. Bruno Cauli  Is a corresponding author
  1. Sorbonne Université, CNRS, INSERM, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS-IBPS), France
  2. Brain Plasticity Unit, CNRS UMR 8249, CNRS, ESPCI Paris, France
  3. Institute for Neurophysiology, Goethe University Frankfurt, Germany
  4. Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August- University Göttingen, Germany
  5. Graduate School of Biostudies, Kyoto University, Japan
  6. Division of Molecular and Metabolic Medicine, Kobe University Graduate School of Medicine, Japan
7 figures, 1 table and 15 additional files

Figures

Figure 1 with 1 supplement
Detection of Kcnj11 and Abcc8 KATP channel subunits in cortical neuron subtypes.

(A) Ward’s clustering of 277 cortical neurons (left panel). The x-axis represents the average within-cluster linkage distance, and the y-axis the individuals. (B) Gene detection profile across the different cell clusters. For each cell, colored and white rectangles indicate presence and absence of genes, respectively. (C) Representative voltage responses induced by injection of current pulses (bottom traces) corresponding to −100, −50, and 0 pA, rheobase and intensity inducing a saturating firing frequency (shaded traces) of a regular spiking neuron (black), an intrinsically bursting neuron (gray), a bursting vasoactive intestinal polypeptide (Vip) interneuron (light blue), an adapting Vip interneuron (blue), an adapting Sst interneuron (green), an adapting Npy interneuron (orange), and a Fast Spiking-Parvalbumin interneuron (FS-Pvalb, red). The colored arrows indicate the expression profiles of neurons whose firing pattern is illustrated in (C). (D) Detection of the subunits of the KATP channels in the different clusters. Shaded rectangles represent potential Kcnj11 false positives in which genomic DNA was detected in the harvested material. (E) Single-cell RT-PCR (scRT-PCR) analysis of the regular spiking (RS) neuron depicted in (A–D). (F) Histograms summarizing the detection rate of KATP channel subunits in identified neuronal types. n.s., not statistically significant.

Figure 1—source data 1

Somatic, electrophysiological, and molecular properties of the cortical neurons shown in Figure 1A–D.

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Figure 1—source data 2

Original file of the full raw unedited gel shown in Figure 1E.

https://cdn.elifesciences.org/articles/71424/elife-71424-fig1-data2-v2.zip
Figure 1—source data 3

Uncropped gel shown in Figure 1E with relevant bands labeled.

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Figure 1—source data 4

Statistcal comparisons of the detection of KATP channel subunits in different types of cortical neurons shown in Figure 1F.

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Figure 1—figure supplement 1
Molecular expression of KATP channels.

(A) RT-PCR products generated from 500 pg of total cortical RNAs. M: 100 bp ladder molecular weight marker. (B) Abcc9 splice variants-specific RT-PCR analysis of 1 ng total RNAs from rat heart, neocortex, and forebrain.

Figure 1—figure supplement 1—source data 1

Original file of the full raw unedited gel shown in Figure 1—figure supplement 1A.

https://cdn.elifesciences.org/articles/71424/elife-71424-fig1-figsupp1-data1-v2.zip
Figure 1—figure supplement 1—source data 2

Uncropped gel shown in Figure 1—figure supplement 1A with relevant lanes labeled.

Yellow rectangles denote bands of the expected size.

https://cdn.elifesciences.org/articles/71424/elife-71424-fig1-figsupp1-data2-v2.zip
Figure 1—figure supplement 1—source data 3

Original file of the full raw unedited gel shown in Figure 1—figure supplement 1B.

https://cdn.elifesciences.org/articles/71424/elife-71424-fig1-figsupp1-data3-v2.zip
Figure 1—figure supplement 1—source data 4

Uncropped gel shown in Figure 1—figure supplement 1B with relevant bands labeled.

https://cdn.elifesciences.org/articles/71424/elife-71424-fig1-figsupp1-data4-v2.zip
Figure 2 with 2 supplements
Pharmacological and biophysical characterization of KATP channels in cortical neurons.

(A) Representative voltage responses of a Fast Spiking-Parvalbumin (FS-Pvalb) interneuron induced by injection of current pulses (bottom traces). (B) Protocol of voltage pulses from −70 to −60 mV (left trace). Responses of whole-cell currents in the FS-Pvalb interneurons shown in (A) in control condition (black) and in presence of pinacidil (blue), piazoxide (green) and tolbutamide (red) at the time indicated by a–d in (C). (C) Stationary currents recorded at −60 mV (filled circles) and membrane resistance (open circles) changes induced by KATP channel modulators. The colored bars and shaded zones indicate the duration of application of KATP channel modulators. Upper and lower insets: changes in whole-cell currents and relative changes in membrane resistance induced by KATP channel modulators, respectively. (D) Whole-cell current–voltage relationships measured under diazoxide (green trace) and tolbutamide (red trace). KATP I/V curve (black trace) obtained by subtracting the curve under diazoxide by the curve under tolbutamide. The arrow indicates the reversal potential of KATP currents. Histograms summarizing the KATP current reversal potential (E, F) and relative KATP conductance (G,H) in identified neuronal subtypes (E, G) or between glutamatergic and GABAergic neurons (F, G). Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant. *, ** and *** indicate statistically significant with p< 0.05, 0.01 and 0.001 respectively.

Figure 2—source data 1

Statistical analyses of whole-cell current and membrane resistance changes induced by KATP channel modulators (shown in Figure 2C insets).

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Figure 2—source data 2

Statistical comparisons of KATP current reversal potential and relative KATP conductance between neuronal subtypes and groups (shown in Figure 2E–H) and of whole-cell KATP conductance and current density (shown in Figure 2—figure supplement 2).

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Figure 2—figure supplement 1
Diazoxide-induced current is independent of reactive oxygen species (ROS) production.

(A) Representative stationary currents at −60 mV (filled circles) and membrane resistance (open circles) changes induced by diazoxide and tolbutamide under control condition and in presence of the superoxide dismutase and catalase mimetic, MnTMPyP. The colored bars and shaded zones indicate the duration of application. Histograms summarizing the relative KATP currents (B) and relative whole-cell KATP conductance (C) evoked by two consecutive diazoxide and tolbutamide applications in control condition (Ctrl.) and after the presence of MnTMPyP. Data are normalized by the data measured during first application, expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant.

Figure 2—figure supplement 1—source data 1

Statistical analyses of the effect of MnTMPyP on normalized KATP currents and conducatnce whole-cell KATP conductance (shown in Figure 2—figure supplement 2B,C).

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Figure 2—figure supplement 2
Characterization of KATP channels in different cortical neurons.

Histograms summarizing the whole-cell KATP conductance (A, B) and KATP current density (C, D) and KATP current reversal potential in identified neuronal subtypes (A,C) or between glutamatergic and GABAergic neurons (B,D). Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant.

KCNJ11 is the pore-forming subunit of KATP channels in cortical neurons.

(A) Representative voltage responses of a mouse layer II/III regular spiking (RS) pyramidal cell induced by injection of current pulses (bottom traces). (B) Histograms summarizing the detection rate of Slc17a7, Gad2 and 1, the Atp1a1-3 subunits of the Na/K ATPase and the Kcnj11 and Abcc8 KATP channel subunits in layer II/III regular spiking (RS) pyramidal cells from Kcnj11+/+ mice. (C, D) Whole-cell stationary currents recorded at 50 mV during dialysis with ATP-free pipette solution in cortical neurons of Kcnj11+/+ (C) and Kcnj11−/− (D) mice. Inset: voltage clamp protocol. (E, F) Current–voltage relationships obtained during ATP washout at the time indicated by green and orange circles in (C, D) in cortical neurons of Kcnj11+/+ (E) and Kcnj11−/− (F) mice. (G) Histograms summarizing the whole-cell ATP washout currents in Kcnj11+/+ (black) and Kcnj11−/− (white) cortical neurons. Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. Open symbols in Kcnj11+/+ and Kcnj11−/− bar plots indicate the cells illustrated in (C, D) and (E, F), respectively. (H) Diagram depicting the principle of the ATP washout experiment. *** indicates statistically significant with p< 0.001.

Figure 3—source data 1

Molecular profile of layer II–III pyramidal neurons shown in Figure 3B.

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Figure 3—source data 2

Statistical analysis of whole-cell ATP washout currents in Kcnj11+/+ and Kcnj11−/− cortical neurons (shown in Figure 3G).

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Figure 4 with 1 supplement
Modulation of cortical neuronal excitability and activity by KATP channels.

Representative example of a regular spiking (RS) neurons showing the changes in membrane potential (A), resistance (B, open circles) and spiking activity (C) induced by application of tolbutamide (red) and diazoxide (green). The colored bars and shaded zones indicate the application duration of KATP channel modulators. Relative changes in membrane potential (D), resistance (E), and firing rate (F) induced by tolbutamide and diazoxide in cortical neurons. Histograms summarizing the modulation of membrane potential (G, H(5,32) = 0.15856, p = 0.999, and H, U(8,24) = 96, p = 1.0000) and resistance (I, H(5,32) = 2.7566, p = 0.737, and J, U(8,24) = 73, p = 0.3345) by KATP channels in neuronal subtypes (G, I) and groups (H, J). Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant. * and *** indicate statistically ignificant with p<0.05 and 0.001.

Figure 4—source data 1

Statistical analyses of membrane potential, membrane resistance and firing rate changes induced by KATP channel modulators (shown in Figure 4D, E).

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Figure 4—source data 2

Statistical comparisons between neuronal subtypes and groups of the effect KATP channel modulators on membrane potential, membrane resistance (shown in Figure 4G–J) and firing rate (shown in Figure 4—figure supplement 1C,D) as well as of the proportion of responsive neurons (shown in Figure 4—figure supplement 1A,B).

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Figure 4—figure supplement 1
Modulation of neuronal activity in different cortical neurons by KATP channels.

Histograms summarizing the proportion of responsive neurons (A, Κ2(5) = 7.3125, p = 0.1984, and B, p = 1.0000) and modulation firing rate (C, H(5,32) = 5.0202, p = 0.413, and D, U(8,24) = 87, p = 0.7169) by KATP channels in neuronal subtypes (A, C) and groups (B, D). The numbers in brackets indicate the number of responsive cells and analyzed cells, respectively. Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant.

Lactate enhances cortical neuronal activity via KATP channel modulation.

(A) Representative perforated patch recording of an adapting vasoactive intestinal polypeptide (VIP) neuron showing the modulation of firing frequency induced by changes in the extracellular concentrations of metabolites. The colored bars and shaded zones indicate the concentration in glucose (gray) and lactate (orange). Voltage responses recorded at the time indicated by arrows. The red dashed lines indicate −40 mV. (B) Histograms summarizing the mean firing frequency during changes in extracellular concentration of glucose (black and gray) and lactate (orange). Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. n.s., not statistically significant. *, ** and *** indicate statistically significant with p< 0.05, 0.01 and 0.001, respectively. (C) Dose-dependent enhancement of firing frequency by lactate. Data are normalized by the mean firing frequency in absence of lactate and are expressed as mean ± SEM. Numbers in brackets indicate the number of recorded neurons at different lactate concentrations. (D) Histograms summarizing the normalized frequency under 15 mM lactate (orange) and its modulation by addition of diazoxide (green) or tolbutamide (red). Data are expressed as mean ± SEM, and the individual data points are depicted. n.s., not statistically significant. (E) Histograms summarizing the enhancement of normalized frequency by 15 mM lactate in Kcnj11+/+ (orange) and Kcnj11−/− (pale orange) mouse cortical neurons. The dash line indicates the normalized mean firing frequency in absence of lactate. Data are expressed as mean ± SEM, and the individual data points are depicted. (F) Diagram depicting the enhancement of neuronal activity by lactate via modulation of KATP channels.

Figure 5—source data 1

Statistical analysis of the effect of glucose and lactate on firing rate (shown in Figure 5B).

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Figure 5—source data 2

Statistical analysis of dose-dependent enhancement of firing frequency by lactate (shown in Figure 5C).

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Figure 5—source data 3

Statistical analysis of the effect of diazoxide and tolbutamide on firing rate enhancement by lactate (shown in Figure 5D).

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Figure 5—source data 4

Statistical comparison of lactate enhancement of normalized frequency in Kcnj11+/+ and Kcnj11−/− (shown in Figure 5E).

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Figure 6 with 2 supplements
Lactate enhancement of cortical neuronal activity involves lactate uptake and metabolism.

(A) Histograms summarizing the detection rate of the monocarboxylate transporters Slc16a1, 7, and 3 and Ldha and b lactate dehydrogenase subunits in glutamatergic neurons (black) and GABAergic interneurons (white). The numbers in brackets indicate the number of analyzed cells. (B) Histograms summarizing the enhancement of normalized frequency by 15 mM lactate (orange) and its suppression by the monocarboxylate transporters (MCTs) inhibitor α-cyano-4-hydroxycinnamic acid (4-CIN; purple). Data are expressed as mean ± standard error of the mean (SEM), and the individual data points are depicted. (C) Histograms summarizing the enhancement of normalized frequency by 15 mM lactate (orange) and pyruvate (magenta). Data are expressed as mean ± SEM, and the individual data points are depicted n.s., not statistically significant. (D) Widefield NADH (reduced form of nicotinamide adenine dinucleotide) autofluorescence (upper panel, scale bar: 20 µm) and corresponding field of view observed under IR-DGC (lower panel). The somatic regions of interest are delineated. (E) Mean relative changes in NADH autofluorescence in control condition (gray) and in response to 15 mM lactate (orange) or pyruvate (magenta). The colored bars indicate the duration of applications. Data are expressed as mean ± SEM. Inset: diagram depicting the NADH changes induced by lactate and pyruvate uptake by MCT and their interconversion by lactate dehydrogenase (LDH). (F) Histograms summarizing the mean relative changes in NADH autofluorescence measured during the last 5 min of 15 mM lactate (orange) or pyruvate (magenta) application and corresponding time in control condition (gray). Data are expressed as mean ± SEM, and the individual data points are depicted. (G) Widefield YFP fluorescence of the ATP biosensor AT1.03YEMK (upper left panel, scale bar: 30 µm) and pseudocolor images showing the intracellular ATP (YFP/CFP ratio value coded by pixel hue, see scale bar in upper right panel) and the fluorescence intensity (coded by pixel intensity) at different times under 10 mM extracellular glucose (upper right panel) and after addition of iodoacetic acid (IAA; lower left panel) and potassium cyanide (KCN; lower right panel). (H) Mean relative changes in intracellular ATP (relative YFP/CFP ratio) measured under 10 mM extracellular glucose (gray) and after addition of IAA (yellow) and KCN (blue). Data are expressed as mean ± SEM. The colored bars indicate the time and duration of metabolic inhibitor application. Inset: Histograms summarizing the mean relative changes in intracellular ATP (relative YFP/CFP ratio) ratio under 10 mM extracellular glucose (gray) and after addition of IAA (yellow) and KCN (blue). Data are expressed as mean ± SEM, and the individual data points are depicted. *, ** and *** indicate statistically significant with p< 0.05, 0.05 and 0.001, respectively.

Figure 6—source data 1

Statistcal comparisons of the detection rate of monocarboxylate transporters and lactate dehydrogenase subunits between neuronal groups (shown in Figure 6A) and subtypes (shown in Figure 6—figure supplement 1).

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Figure 6—source data 2

Statistical analysis of the effect of monocarboxylate transporter (MCT) inhibition by α-cyano-4-hydroxycinnamic acid (4-CIN) on lactate-enhanced firing rate (shown in Figure 6B).

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Figure 6—figure supplement 1
Detection rate of monocarboxylate transporters and lactate dehydrogenase subunits in different cortical neuronal types.

Histograms summarizing the detection rate of the monocarboxylate transporterMCTs Slc16a1, 7, and 3 and Ldha and b LDHlactate dehydrogenase subunits in different neuronal subtypes. The numbers in brackets indicate the number of analyzed cells.

Figure 6—figure supplement 2
Neuronal NADH autofluorescence increase by blockade of oxidative phosphorylation.

(A) Mean relative changes in NADH autofluorescence in control condition (gray) and in response to 1 mM potassium cyanide (KCN; blue). The colored bar indicates the duration of KCN applications. Data are expressed as mean ± SEM. (B) Histograms summarizing the mean relative changes in NADH autofluorescence measured during the last 5 min of 1 mM KCN application (blue) and corresponding time in control condition (gray). Data are expressed as mean ± SEM, and the individual data points are depicted. *** indicates statistically significant with p < 0.001.

Figure 6—figure supplement 2—source data 1

Statistcal analysis of effect of potassium cyanide (KCN) on the mean relative changes in NADH autofluorescence (shown in Figure 6—figure supplement 2).

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Diagram summarizing the mechanism of lactate sensing in the cortical network.

Glutamate (Glu) released during synaptic transmission stimulates (1) blood glucose (Glc) uptake in astrocytes, (2) aerobic glycolysis, (3) lactate release, and (4) diffusion through the astrocytic network. Lactate is then (5) taken up by neurons via monobarboxylate transporters (MCT) and (6) oxidized into pyruvate by lactate dehydrogenase (LDH). The ATP produced by pyruvate oxidative metabolism (7) closes KATP channels and increases the spiking activity of both pyramidal cells (black) and inhibitory interneurons (green). The color gradient of the circles represents the extent of glutamate (black) and lactate (orange) diffusion, respectively. Dashed arrows indicate multisteps reactions.

Tables

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Rattus norvegicus, Wistar, male)WistarJanvier LabsjHan:WI
Strain, strain background (Mus musculus, C57BL/6RJ, male and female)Wild type, Kcnj11 +/+Janvier LabsC57BL/6RJ
Strain, strain background (Mus musculus, B6.129P2, male and female)B6.129P2-Kcnj11tm1Sse, Kcnj11−/−PMID:9724715 (Miki et al., 1998)RRID: MGI:5433111
Cell line (Mesocricetus auratus)BHK-21 clone 13 (baby hamster kidneys fibroblasts)ATCCCCL-10, RRID: CVCL_1915
Recombinant DNA reagentpcDNA-ATeam1.03YEMK (plasmid)PMID:19720993( Imamura et al., 2009)
Recombinant DNA reagentpSinRep5 (plasmid)InvitrogenK750-01
Recombinant DNA reagentpDH(26 S) (helper plasmid)InvitrogenK750-01
Sequence-based reagentrat Slc17a7 external sensePMID:16339088 (Gallopin et al., 2006)PCR primersGGCTCCTTTTTCTGGGGGTAC
Sequence-based reagentrat Slc17a7 external antisensePMID:16339088 (Gallopin et al., 2006)PCR primersCCAGCCGACTCCGTTCTAAG
Sequence-based reagentrat Slc17a7 internal sensePMID:16339088 (Gallopin et al., 2006)PCR primersTGGGGGTACATTGTCACTCAGA
Sequence-based reagentrat Slc17a7 internal antisensePMID:16339088 (Gallopin et al., 2006)PCR primersATGGCAAGCAGGGTATGTGAC
Sequence-based reagentrat/mouse Gad2 external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCCAAAAGTTCACGGGCGG
Sequence-based reagentrat/mouse Gad2 external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersTCCTCCAGATTTTGCGGTTG
Sequence-based reagentrat Gad2 internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersTGAGAAGCCAGCAGAGAGCG
Sequence-based reagentrat Gad2 internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersTGGGGTAATGGAAATCAATCACTT
Sequence-based reagentrat Gad1 external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersATGATACTTGGTGTGGCGTAGC
Sequence-based reagentrat Gad1 external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGTTTGCTCCTCCCCGTTCTTAG
Sequence-based reagentrat Gad1 internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCAATAGCCTGGAAGAGAAGAGTCG
Sequence-based reagentrat Gad1 internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGTTTGCTCCTCCCCGTTCTTAG
Sequence-based reagentrat Nos1 external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCCTGGGGCTCAAATGGTATG
Sequence-based reagentrat Nos1 external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersCACAATCCACACCCAGTCGG
Sequence-based reagentrat Nos1 internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCCTCCCCGCTGTGTCCAA
Sequence-based reagentrat Nos1 internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGAGTGGTGGTCAACGATGGTCA
Sequence-based reagentrat Calb1 external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersGAAAGAAGGCTGGATTGGAG
Sequence-based reagentrat Calb1 external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersCCCACACATTTTGATTCCCTG
Sequence-based reagentrat Calb1 internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersATGGGCAGAGAGATGATGGG
Sequence-based reagentrat Calb1 internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersTATCATCCACGGTCTTGTTTGC
Sequence-based reagentrat Pvalb external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersGCCTGAAGAAAAAGAGTGCGG
Sequence-based reagentrat Pvalb external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGTCCCCGTCCTTGTCTCCAG
Sequence-based reagentrat Pvalb internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersGCGGATGATGTGAAGAAGGTG
Sequence-based reagentrat Pvalb internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersCAGCCATCAGCGTCTTTGTT
Sequence-based reagentrat Calb2 external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersTTGATGCTGACGGAAATGGGTA
Sequence-based reagentrat Calb2 external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersCAAGCCTCCATAAACTCAGCG
Sequence-based reagentrat Calb2 internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersGCTGGAGAAGGCAAGGAAAGG
Sequence-based reagentrat Calb2 internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersATTCTCTTCGGTTGGCAGGA
Sequence-based reagentrat Npy external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCGAATGGGGCTGTGTGGA
Sequence-based reagentrat Npy external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersAGTTTCATTTCCCATCACCACAT
Sequence-based reagentrat Npy internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersCCCTCGCTCTATCCCTGCTC
Sequence-based reagentrat Npy internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGTTCTGGGGGCATTTTCTGTG
Sequence-based reagentrat Vip external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersTTATGATGTGTCCAGAAATGCGAG
Sequence-based reagentrat Vip external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersTTTTATTTGGTTTTGCTATGGAAG
Sequence-based reagentrat Vip internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersTGGCAAACGAATCAGCAGTAGC
Sequence-based reagentrat Vip internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGAATCTCCCTCACTGCTCCTCT
Sequence-based reagentrat Sst external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersATGCTGTCCTGCCGTCTCCA
Sequence-based reagentrat Sst external antisensePMID:17068095 (Férézou et al., 2007)PCR primersGCCTCATCTCGTCCTGCTCA
Sequence-based reagentrat Sst internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersGCATCGTCCTGGCTTTGGG
Sequence-based reagentrat Sst internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersAGGCTCCAGGGCATCGTTCT
Sequence-based reagentrat Cck external sensePMID:19295167 (Karagiannis et al., 2009)PCR primersTGTCTGTGCGTGGTGATGGC
Sequence-based reagentrat Cck external antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGCATAGCAACATTAGGTCTGGGAG
Sequence-based reagentrat Cck internal sensePMID:19295167 (Karagiannis et al., 2009)PCR primersATACATCCAGCAGGTCCGCAA
Sequence-based reagentrat Cck internal antisensePMID:19295167 (Karagiannis et al., 2009)PCR primersGGTCGTGTGCGTGGTTGTTT
Sequence-based reagentrat Kcnj8 external senseThis paperPCR primersCTGGCTCACAAGAACATCCG
Sequence-based reagentrat Kcnj8 external antisenseThis paperPCR primersAGCGTCTCTGCCCTTCTGTG
Sequence-based reagentrat Kcnj8 internal sensePMID:26156991 (Varin et al., 2015)PCR primersGCTGGCTGCTCTTCGCTATC
Sequence-based reagentrat Kcnj8 internal antisenseThis paperPCR primersTTCTCCCTCCAAACCCAATG
Sequence-based reagentrat Kcnj11 external senseThis paperPCR primersCCCCACACGCTGCTCATTTT
Sequence-based reagentrat Kcnj11 external antisenseThis paperPCR primersAGGAGCCAGGTCGTAGAGCG
Sequence-based reagentrat Kcnj11 internal senseThis paperPCR primersGCGTCACAAGCATCCACTCC
Sequence-based reagentrat Kcnj11 internal antisenseThis paperPCR primersCCACCCACACCGTTCTCCAT
Sequence-based reagentrat Abcc8 external senseThis paperPCR primersGGTGAAGAAGCCTCCGATGA
Sequence-based reagentrat Abcc8 external antisenseThis paperPCR primersGGTGAAGAAGCCTCCGATGA
Sequence-based reagentrat Abcc8 internal senseThis paperPCR primersGGTTCGGTCCACTGTCAAGG
Sequence-based reagentrat Abcc8 internal antisenseThis paperPCR primersGTCAGCGTCTCCATCCGTGC
Sequence-based reagentrat Abcc9 external senseThis paperPCR primersCGCTGCCTTTTGAGTCCTGT
Sequence-based reagentrat Abcc9 external antisenseThis paperPCR primersGATGGCAAGGAGGAGAGACG
Sequence-based reagentrat Abcc9 internal senseThis paperPCR primersTGGACAACTACGAGCAGGCG
Sequence-based reagentrat Abcc9 internal antisenseThis paperPCR primersCACAACCCACCTGACCCACA
Sequence-based reagentrat Sst intron external sensePMID:17267760 (Hill et al., 2007)PCR primersGGAAATGGCTGGGACTCGTC
Sequence-based reagentrat Sst intron external antisensePMID17267760 (Hill et al., 2007)PCR primersAAACCATGGATGATAGGAAGTCGT
Sequence-based reagentrat Sst intron internal senseThis paperPCR primersGTCCCCTTTGCGAATTCCCT
Sequence-based reagentrat Sst intron internal antisenseThis paperPCR primersTTCGAGCAGCTCCATTTTCC
Sequence-based reagentrat SUR2A/B senseThis paperPCR primersACTTCAGCGTTGGACAGAGACA
Sequence-based reagentrat SUR2A/B antisenseThis paperPCR primersGGTCAGCAGTCAGAATGGTGTG
Sequence-based reagentmouse Slc17a7 external sensePMID:23565079 (Cabezas et al., 2013)PCR primersGGCTCCTTTTTCTGGGGCTAC
Sequence-based reagentmouse Slc17a7 external antisensePMID:23565079 (Cabezas et al., 2013)PCR primersCCAGCCGACTCCGTTCTAAG
Sequence-based reagentmouse Slc17a7 internal sensePMID:23565079 (Cabezas et al., 2013)PCR primersATTCGCAGCCAACAGGGTCT
Sequence-based reagentmouse Slc17a7 internal antisensePMID:23565079 (Cabezas et al., 2013)PCR primersTGGCAAGCAGGGTATGTGAC
Sequence-based reagentmouse Gad2 external sensePMID:22754499 (Perrenoud et al., 2012)PCR primersCCAAAAGTTCACGGGCGG
Sequence-based reagentmouse Gad2 external antisensePMID:22754499 (Perrenoud et al., 2012)PCR primersTCCTCCAGATTTTGCGGTTG
Sequence-based reagentmouse Gad2 internal sensePMID:22754499 (Perrenoud et al., 2012)PCR primersCACCTGCGACCAAAAACCCT
Sequence-based reagentmouse Gad2 internal antisensePMID:22754499 (Perrenoud et al., 2012)PCR primersGATTTTGCGGTTGGTCTGCC
Sequence-based reagentmouse Gad1 external sensePMID:12196560 (Férézou et al., 2002)PCR primersTACGGGGTTCGCACAGGTC
Sequence-based reagentmouse Gad1 external antisensePMID:23565079 (Cabezas et al., 2013)PCR primersCCCAGGCAGCATCCACAT
Sequence-based reagentmouse Gad1 internal sensePMID:23565079 (Cabezas et al., 2013)PCR primersCCCAGAAGTGAAGACAAAAGGC
Sequence-based reagentmouse Gad1 internal antisensePMID:23565079 (Cabezas et al., 2013)PCR primersAATGCTCCGTAAACAGTCGTGC
Sequence-based reagentmouse Atp1a1 external sensePMID:29985318 (Devienne et al., 2018)PCR primersCAGGGCAGTGTTTCAGGCTAAS
Sequence-based reagentmouse Atp1a1 external antisensePMID:29985318(Devienne et al., 2018)PCR primersCCGTGGAGAAGGATGGAGC
Sequence-based reagentmouse Atp1a1 internal sensePMID:29985318 (Devienne et al., 2018)PCR primersTAAGCGGGCAGTAGCGGG
Sequence-based reagentmouse Atp1a1 internal antisensePMID:29985318 (Devienne et al., 2018)PCR primersAGGTGTTTGGGCTCAGATGC
Sequence-based reagentmouse Atp1a2 external sensePMID:29985318 (Devienne et al., 2018)PCR primersAGTGAGGAAGATGAGGGACAGG
Sequence-based reagentmouse Atp1a2 external antisensePMID:29985318 (Devienne et al., 2018)PCR primersACAGAAGCCCAGCACTCGTT
Sequence-based reagentmouse Atp1a2 internal sensePMID:29985318 (Devienne et al., 2018)PCR primersAAATCCCCTTCAACTCCACCA
Sequence-based reagentmouse Atp1a2 internal antisensePMID:29985318 (Devienne et al., 2018)PCR primersGTTCCCCAAGTCCTCCCAGC
Sequence-based reagentmouse Atp1a3 external sensePMID:29985318 (Devienne et al., 2018)PCR primersCGGAAATACAATACTGACTGCGTG
Sequence-based reagentmouse Atp1a3 external antisensePMID:29985318 (Devienne et al., 2018)PCR primersGTCATCCTCCGTCCCTGCC
Sequence-based reagentmouse Atp1a3 internal sensePMID:29985318 (Devienne et al., 2018)PCR primersTGACACACAGTAAAGCCCAGGA
Sequence-based reagentmouse Atp1a3 internal antisensePMID:29985318 (Devienne et al., 2018)PCR primersCCACAGCAGGATAGAGAAGCCA
Sequence-based reagentmouse Kcnj11 external sensePMID:29985318 (Devienne et al., 2018)PCR primersCGGAGAGGGCACCAATGT
Sequence-based reagentmouse Kcnj11 external antisensePMID:29985318 (Devienne et al., 2018)PCR primersCACCCACGCCATTCTCCA
Sequence-based reagentmouse Kcnj11 internal sensePMID:29985318 (Devienne et al., 2018)PCR primersCATCCACTCCTTTTCATCTGCC
Sequence-based reagentmouse Kcnj11 internal antisensePMID:29985318 (Devienne et al., 2018)PCR primersTCGGGGCTGGTGGTCTTG
Sequence-based reagentmouse Abcc8 external sensePMID:29985318 (Devienne et al., 2018)PCR primersCAGTGTGCCCCCCGAGAG
Sequence-based reagentmouse Abcc8 external antisensePMID:29985318 (Devienne et al., 2018)PCR primersGGTCTTCTCCCTCGCTGTCTG
Sequence-based reagentmouse Abcc8 internal sensePMID:29985318 (Devienne et al., 2018)PCR primersATCATCGGAGGCTTCTTCACC
Sequence-based reagentmouse Abcc8 internal antisensePMID:29985318 (Devienne et al., 2018)PCR primersGGTCTTCTCCCTCGCTGTCTG
Sequence-based reagentmouse Sst intron external sensePMID:12930808 (Thoby-Brisson et al., 2003)PCR primersCTGTCCCCCTTACGAATCCC
Sequence-based reagentmouse Sst intron external antisensePMID:12930808 (Thoby-Brisson et al., 2003)PCR primersCCAGCACCAGGGATAGAGCC
Sequence-based reagentmouse Sst intron internal sensePMID:20427660 Cea-del Rio et al., 2010PCR primersCTTACGAATCCCCCAGCCTT
Sequence-based reagentmouse Sst intron internal antisensePMID:20427660 (Cea-del Rio et al., 2010)PCR primersTTGAAAGCCAGGGAGGAACT
Sequence-based reagentrat Slc16a1 external senseThis paperPCR primersGTCAGCCTTCCTCCTTTCCA
Sequence-based reagentrat Slc16a1 external antisenseThis paperPCR primersTCCGCTTTCTGTTCTTTGGC
Sequence-based reagentrat Slc16a1 internal senseThis paperPCR primersTTGTTGCGAATGGAGTGTGC
Sequence-based reagentrat Slc16a1 internal antisenseThis paperPCR primersCACGCCACAAGCCCAGTATG
Sequence-based reagentrat Slc16a7 external senseThis paperPCR primersGCGAAGTCTAAAAGTAAGGTTGGC
Sequence-based reagentrat Slc16a7 external antisenseThis paperPCR primersATTTACCAGCCAGGGGAGGG
Sequence-based reagentrat Slc16a7 internal senseThis paperPCR primersCCGTATGCTAAGGACAAAGGAGT
Sequence-based reagentrat Slc16a7 internal antisenseThis paperPCR primersGGGAAGAACTGGGCAACACT
Sequence-based reagentrat Slc16a3 external senseThis paperPCR primersCATTGGTCTCGTGCTGCTGTS
Sequence-based reagentrat Slc16a3 external antisenseThis paperPCR primersCCCCGTTTTTCTCAGGCTCT
Sequence-based reagentrat Slc16a3 internal senseThis paperPCR primersTGTGGCTGTGCTCATCGGAC
Sequence-based reagentrat Slc16a3 internal antisenseThis paperPCR primersCCTCTTCCTCTTCCCGATGC
Sequence-based reagentrat Ldha external senseThis paperPCR primersGAAGAACAGGTCCCCCAGAA
Sequence-based reagentrat Ldha external antisenseThis paperPCR primersGGGTTTGAGACGATGAGCAGT
Sequence-based reagentrat Ldha internal senseThis paperPCR primersCAGTTGTTGGGGTTGGTGCT
Sequence-based reagentrat Ldha internal antisenseThis paperPCR primersTCTCTCCCTCTTGCTGACGG
Sequence-based reagentrat Ldhb external senseThis paperPCR primersACTGCCGTCCCGAACAACAA
Sequence-based reagentrat Ldhb external antisenseThis paperPCR primersACTCTCCCCCTCCTGCTGG
Sequence-based reagentrat Ldhb internal senseThis paperPCR primersTCTGGGGAAGTCTCTGGCTGA
Sequence-based reagentrat Ldhb internal antisenseThis paperPCR primersTTGGCTGTCACGGAGTAATCTTT
Commercial assay or kitMEGAscript SP6 Transcription KitAmbionAM1330
Chemical compound, drugPinacidil monohydrateSigma-AldrichP154
Chemical compound, drugDiazoxideSigma-AldrichD9035
Chemical compound, drugTolbutamideSigma-AldrichT0891
Chemical compound, drugMn(III)tetrakis(1-methyl-4-pyridyl)porphyrinMillipore475,872
Chemical compound, drugGramicidin from Bacillus aneurinolyticus (Bacillus brevis)Sigma-AldrichG5002
Chemical compound, drugSodium L-lactateSigma-AldrichL7022
Chemical compound, drugα-Cyano-4-hydroxycinnamic AcidSigma-AldrichC2020
Chemical compound, drugSodium pyruvateSigma-AldrichP2256
Chemical compound, drugSodium iodoacetateSigma-AldrichI2512
Chemical compound, drugPotassium cyanideSigma-Aldrich60,178
Chemical compound, drugDithiothreitolVWR443,852 A
Chemical compound, drugPrimer ‘random’4731001
Chemical compound, drugdNTPsGE Healthcare Life Sciences28-4065-52
Chemical compound, drugMineral OilSigma-AldrichM5904
Chemical compound, drugRNasin Ribonuclease InhibitorsPromegaN2511
Chemical compound, drugSuperScript II Reverse TranscriptaseInvitrogen18064014
Chemical compound, drugTaq DNA PolymeraseQiagen201,205
Chemical compound, drugPenicillin-StreptomycinSigma-AldrichP4333-100ML
Software, algorithmPclamp v 10.2Molecular DevicesRRID: SCR_011323
Software, algorithmMatlab v 2018bMathWorksRRID: SCR_001622
Software, algorithmStatistica v 6.1StatsoftRRID: SCR_014213
Software, algorithmGraphPad Prism v 7GraphPadRRID: SCR_002798
Software, algorithmImagingWorkbench v 6.0.25INDEC Systems
Software, algorithmFIJIPMID:22743772 (Schindelin et al., 2012)RRID: SCR_002285MID:29985318
Software, algorithmImage-Pro Analyzer v 7MediaCybernetics
OtherVibratomeLeicaVT1000S RRID: SCR_016495
OtherUpright microscopeOlympusBX51WI
OtherDual port moduleOlympusWI-DPMC
Other×60 ObjectiveOlympusLUMPlan Fl /IR 60 x/0.90 W
Other×40 ObjetiveOlympusLUMPlan Fl /IR 40 x/0.80 W
OtherCCD cameraRoper ScientificCoolSnap HQ2
OtherAxopatch 200BMolecular DevicesRRID: SCR_018866
OtherDigidata 1440AMolecular DevicesRRID: SCR_021038
OtherS900 stimulatorDagan Corporation
OtherpE-2CoolLED
OtherDichroic mirrorSemrockFF395/495/610-Di01-25 × 36
OtherEmission filterSemrockFF01-425/527/685-25
Other780 nm Collimated LEDThorlabsM780L3-C1
OtherDodt Gradient ContrastLuigs and Neumann200-100 200 0155
OtherBeam splitterSemrock725 DCSPXR
OtherAnalogic CCD cameraSonyXC ST-70 CE
OtherMillicellMilliporePICM0RG50
OtherExcitation filterSemrockFF02-438/24-25
OtherDichroic mirrorSemrockFF458-Di02-25 × 36
OtherEmission filterSemrockFF01-483/32-25
OtherEmission filterSemrockFF01-542/27-25
OtherFilter wheelSutter InstrumentsLambda 10B

Additional files

Supplementary file 1

Somatic properties of different neuronal types n, number of cells, < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

n.s., not statistically significant.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp1-v2.doc
Supplementary file 2

Detection rate of molecular markers in different neuronal types.

Detection rates are given in %; n, number of cells; > significantly larger with p ≤ 0.05; >> significantly larger with p ≤ 0.01; >>> significantly larger with p ≤ 0.001. n.s., not statistically significant.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp2-v2.docx
Supplementary file 3

Passive properties of different neuronal types n, number of cells, < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp3-v2.docx
Supplementary file 4

Just above threshold properties of different neuronal types n, number of cells; < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp4-v2.docx
Supplementary file 5

Firing properties of different neuronal types n, number of cells; < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp5-v2.docx
Supplementary file 6

Action potentials properties of different neuronal types n, number of cells; < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp6-v2.docx
Supplementary file 7

AH and AD properties of different neuronal types n, number of cells; < significantly smaller with p ≤ 0.05; << significantly smaller with p ≤ 0.01; <<< significantly smaller with p ≤ 0.001.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp7-v2.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/71424/elife-71424-transrepform1-v2.docx
Source data 1

Statistcal comparisons of somatic properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp8-v2.xls
Source data 2

Statistcal comparisons of detection rate of molecular markers in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp9-v2.xls
Source data 3

Statistcal comparisons of passive properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp10-v2.xls
Source data 4

Statistcal comparisons of just above threshold properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp11-v2.xls
Source data 5

Statistcal comparisons of firing properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp12-v2.xls
Source data 6

Statistcal comparisons of action potentials properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp13-v2.xls
Source data 7

Statistcal comparisons of AH and AD properties in different neuronal types.

https://cdn.elifesciences.org/articles/71424/elife-71424-supp14-v2.xls

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  1. Anastassios Karagiannis
  2. Thierry Gallopin
  3. Alexandre Lacroix
  4. Fabrice Plaisier
  5. Juliette Piquet
  6. Hélène Geoffroy
  7. Régine Hepp
  8. Jérémie Naudé
  9. Benjamin Le Gac
  10. Richard Egger
  11. Bertrand Lambolez
  12. Dongdong Li
  13. Jean Rossier
  14. Jochen F Staiger
  15. Hiromi Imamura
  16. Susumu Seino
  17. Jochen Roeper
  18. Bruno Cauli
(2021)
Lactate is an energy substrate for rodent cortical neurons and enhances their firing activity
eLife 10:e71424.
https://doi.org/10.7554/eLife.71424