Increasing evidence indicate that astrocytes are able to sense and interact with neurons in a network-specific manner (Ben Haim and Rowitch, 2017). It has also become evident that astrocytes are involved in respiratory behavior. The preBötzinger Complex (preBötC), a brainstem respiratory center (Grass et al., 2004), contain electrically active astrocytes, that exhibit rhythmic calcium (Ca²⁺) oscillations associated with the respiratory-related neuronal rhythm (Schnell et al., 2011; Okada et al., 2012). Further, brainstem astrocytes respond to changes in blood gas levels (Angelova et al., 2015), especially in the chemosensitive region the parafacial respiratory group/retrotrapezoid nucleus (pFRG/RTN) (Huckstepp et al., 2010). During the hypercapnic response, carbon dioxide (CO₂) causes ATP release from astrocytes (Fukuda et al., 1978; Erlichman et al., 2010; Gourine et al., 2010; Huckstepp et al., 2010; Turovsky et al., 2015; Turovsky et al., 2016), increasing inspiratory frequency (Lorier et al., 2007; Gourine et al., 2010). We recently provided evidence that CO₂ also elicits a gap junction-dependent release of PGE2 (Forsberg et al., 2016), increasing the signaling frequency of the pFRG/RTN (Forsberg et al., 2016). Astrocytes in the pFRG/RTN express the primary enzyme involved in PGE2 synthesis (i.e., mPGEs-1), and might be responsible for the PGE2 release during hypercapnia (Forsberg et al., 2016). We also display the functional connectivity of the respiratory networks (Forsberg et al., 2016). Taken together, these findings inspired us to further investigate the respiratory rhythm-generating networks, especially the contribution of astrocytes and if they could interact with neurons and modulate respiratory network activity, possibly through the release of PGE2.

In the present study, we utilized organotypic brainstem slice cultures (Forsberg et al., 2016; Phillips et al., 2016) of B6.Cg-Tg(hGFAP-tTA:: tetO-MrgA1)1^Kdmc/Mmmh mice (GFAP^MrgA1+) in which green fluorescent protein (GFP) and the MrgA1 receptor (MrgA1R, encoded by Mrgpra1) were expressed under control of the promoter for glial fibrillary acidic protein (GFAP), a generally used marker for astrocytes (Brenner et al., 1994). The MrgA1R is an endogenous G_q-coupled receptor that is normally not expressed in the brain. Rather, MrgA1R is expressed exclusively in the dorsal root ganglion nociceptive sensory terminals of the spinal cord (Fiacco et al., 2007) and is activated by RF peptides (Dong et al., 2001). Consequently, we could selectively activate brainstem astrocytes using synthetic Phe-Leu-Arg-Phe-amide (FLRF) (Young et al., 2010; Cao et al., 2013).

Expression of GFP in the pFRG/RTN and preBötC of the GFAP^MrgA1+ mice was evident (Figure 1), and GFP was co-localized with GFAP immunolabeling (Figure 1b and h, n = 11 slices; see Supplementary file 1 for the number of times each experiment was conducted). Although subgroups of GFAP-expressing cells have been previously defined using fluorescence intensity (Grass et al., 2004), we were unable to detect measurable differences in the GFP fluorescence among our samples. The astrocyte and oligodendrocyte marker S100β was expressed by 12 ± 3% of the GFP-positive cells in the respiratory networks (n = 409 S100β and 3527 GFP positive cells, 13 slices). Conversely, 92 ± 4% of the S100β-positive cells also expressed GFP (Figure 1c and i, n = 13 slices). Notably, GFAP antibody penetration in our immunohistochemistry experiments only reached 24 ± 8% of the brainstem slice thickness (Figure 1g). This could explain the low ratio of S100β-GFP double-labeled cells. When the data was re-analyzed with only the portion of the slice where S100β-positive cells were found included, 44 ± 11% of the GFP-positive cells also expressed S100β. However, genetically encoded labeling of S100β should be used to investigate this in more detail in future studies. The GFP-positive cells followed the neuronal spread, but expressed neither the neuronal markers NK1R or MAP2 (Figure 1d,e and j,k, n = 1254 cells, 21 slices), nor the microglial marker Iba1 (Figure 1f and l, n = 248 cells, 11 slices). No GFP expression was detected in littermate controls (WT; Figure 1m, n = 12 slices). Although we cannot fully exclude that a minority of GFAP expressing cells could be undifferentiated precursor cells, our findings strongly suggest that the GFAP-driven expression of GFP (and thus MrgA1R) is astrocyte specific, as expected (Brenner et al., 1994; Fiacco et al., 2007; Cao et al., 2013).

Figure 1

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IHC Quantification.

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The Ca²⁺ time-lapse imaging of brainstem slices derived from both GFAP^MrgA1+ and WT control mice displayed similar Ca²⁺ signaling activity (Figure 2a). This was observed for both frequency (4.9 ± 0.8 cycles/min vs 4.9 ± 0.8 cycles/min, N.S., in the pFRG/RTN and 5.4 ± 1.1 cycles/min vs 5.3 ± 1.0 cycles/min, N.S., in the preBötC, Figure 2b and d), coefficient of variation (33 ± 10 vs 32 ± 11, N.S., in the pFRG/RTN and 35 ± 9 vs 35 ± 13, N.S., in the preBötC, Figure 2b and d) and network structure. Thus, the induced expression of GFP and MrgA1R did not affect respiratory network activity or function. Together with the immunohistochemical expression analyses, this demonstrates that the MrgA1 mouse line has astrocyte-specific expression of the inserted genes. It is also clear that they have no physiological effects in the untreated animal, making this mouse line useful for investigation of the astrocytes role in respiratory networks.

Figure 2

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Control frequencies.

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Figure 2—source data 2

Network quantification.

https://doi.org/10.7554/eLife.29566.006

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Figure 2—source data 3

Number of cells.

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When examining the Ca²⁺-signaling activity of cells in the pFRG/RTN and in the preBötC, we identified two subgroups among the GFP-expressing astrocytes. The first contained cells with a rhythmic Ca²⁺-signaling pattern, and the second cells that appeared inactive, that is, the Fura-2 fluorescence intensity in these cells was stable over time. A similar subgrouping of astrocytes, with approximately 10% exhibiting calcium transients preceding inspiratory-related neuronal signals, has been suggested previously on the basis of both electrophysiological and Ca²⁺ imaging methods (Grass et al., 2004; Schnell et al., 2011; Oku et al., 2016). The present study also found that the majority of the astrocytes appeared inactive (Figure 2c and e; 82 ± 9% or 208 ± 74 cells per network in the pFRG/RTN, n = 19 slices; 87 ± 7% or 216 ± 82 cells per network in the preBötC, n = 22 slices). In the preBötC, the proportion of active astrocytes was similar to that detected by Schnell and colleagues (Schnell et al., 2011), but lower than that reported by both Grass and colleagues (Grass et al., 2004) and Oku and colleagues (Oku et al., 2016). However, we consider the respiratory networks individually, whereas previous studies evaluated the proportion of the total number of recorded cells. To our knowledge, the present study is the first to describe active and inactive astrocytes within the pFRG/RTN. We did not see any difference in the ratio of astrocytes and neurons between the two regions (70 ± 13% astrocytes in the pFRG/RTN and 61 ± 11% astrocytes in the pFRG/RTN, N.S.). This ratio is slightly higher than what is observed in acutely frozen and sectionend brainstems, where 55 ± 6% of the pFRG/RTN cells (n = 3) and 53 ± 4% of the preBötC cells (n = 6) were astrocytes. The larger proportion of astrocytes compared to neurons could in part be due to reactive gliosis induced by the organotypic brainstem slice culture preparation (Stoppini et al., 1991; Herlenius et al., 2012; Forsberg et al., 2016). However, in the living brainstem slice cultures, 40 ± 12% of the active cells in pFRG/RTN were astrocytes (42 ± 27 astrocytes out of 106 ± 62 active cells per network), whereas only 20 ± 9% of active cells in the preBötC were astrocytes (29 ± 17 out of 146 ± 39 active cells per network; Figure 2c and e, p<0.05 when comparing the pFRG/RTN and the preBötC, n = 41; see Supplementary file 2 for a list of all statistical tests used for all comparisons in the present study).

On the basis of a correlated cell activity analysis (Smedler et al., 2014; Forsberg et al., 2016), we determined that the active astrocytes within the pFRG/RTN and the preBötC formed specific networks. These networks differed from those of non-astrocytes, that is, neurons (Figure 2f and g; see Supplementary file 1 for the number of times each experiment was conducted), but displayed similar network structures (Figure 2h and j). Defining a cluster as Hartelt and colleagues (Hartelt et al., 2008), we found an average of 5.8 ± 1.1 cells per cluster, where each neuron connected to on average 4.9 ± 0.9 other neurons. The astrocytic network showed similar connections between each other (6.2 ± 1.3 cells per cluster and 3.8 ± 1.1 connections to other astrocytes). For the pFRG/RTN, there were 4.9 ± 0.8 neurons and 5.6 ± 1.8 astrocytes per cell-specific cluster. The pFRG/RTN neurons were on average connected to 3.7 ± 0.8 other neurons, the astrocytes to 3.7 ± 0.9 other astrocytes. Thus, our analysis of functional connections rendered similar results as the morphological analysis by Hartelt and colleagues regarding the neuronal network. The structure of the functional astrocytic network presented here, is novel and underlines their importance. There was no difference in network structure between the preBötC and pFRG/RTN, as previously described (Forsberg et al., 2016). The astrocytic and neuronal networks connected with each other at multiple nodes, and together they formed the respiratory networks of the pFRG/RTN and of the preBötC. Some of the active astrocytes specifically displayed correlated Ca²⁺ activity with NK1R-expressing (i.e. respiratory) neurons, where approximately every second astrocytic peak was precisely timed with a neuronal peak (Figure 2a). This suggests that the astrocytic activity and respiratory output are synchronized in our system, similarly to what have been described in previous studies (Schnell et al., 2011; Okada et al., 2012). However, we did not measure the respiratory output, and are therefore unable to conclude whether astrocytic activity is phase-locked with respiratory cycles or not.

The combined, larger network had larger clusters (7.1 ± 1.3 cells per cluster in preBötC and 6.8 ± 1.4 cells per cluster in the pFRG/RTN). In the pFRG/RTN, astrocytes constituted 37 ± 13% of the total network (number of correlating cell pairs), whereas astrocytes only constituted 16% ± 5% of the preBötC network (p<0.05 when comparing the pFRG/RTN and the preBötC; Figure 2f,g,i and k). We also observed a significantly larger number of connections between astrocytes and neurons in the pFRG/RTN than in the preBötC (33 ± 12% vs. 24% ± 11% of all correlating cell pairs, p<0.05; Figure 2f,g,i and k). Thus, astrocytes are both independently active and part of the respiratory networks, but appear to have different actions in the two central pattern generators. Previous studies demonstrate a large heterogeneity of both astrocyte function and astrocyte-neuronal interactions (Ben Haim and Rowitch, 2017). However, the cross-correlation analysis in our study does not reveal any details on the cellular mechanisms, so this has to be investigated in future studies.

Under control conditions, astrocytes displayed a low oscillating frequency (1.1 ± 0.5 cycles/min in the preBötC (n = 22 slices) vs. 1.1 ± 0.4 cycles/min in the pFRG/RTN (n = 19 slices); difference not statistically significant), similar to that described previously for active astrocytes (Grass et al., 2004; Schnell et al., 2011; Okada et al., 2012; Oku et al., 2016). The frequency displayed by the astrocytes in each region was lower than that shown by the neurons in the respective regions (preBötC: 5.4 ± 2.4 cycles/min, n = 22 slices, p<0.05 compared with astrocytes; pFRG/RTN: 6.4 ± 3.5 cycles/min, n = 19 slices, p<0.05 compared with astrocytes, Figure 2a).

Next, we activated the GFAP^MrgA1+ astrocytes by applying the MrgA1R ligand FLRF, which increased the Ca²⁺-signaling frequency of astrocytes in both the pFRG/RTN and the preBötC (Figure 3a and b). In the pFRG/RTN, astrocyte activation also induced an increase in the signaling frequency of the non-astrocytes (Figure 3a and b). This is in contrast to the preBötC, where non-astrocytes retained their Ca²⁺-signaling frequency independent of astrocyte stimulation (Figure 3a and b). Inactive astrocytes exhibited a single Ca²⁺ peak shortly after the application of FLRF, but returned to the inactive state within 10 s (Figure 3a). The stimulation of astrocytes did not affect the network structures of the astrocytic, neuronal, or complete respiratory networks (Figure 3c). Astrocytes in slices derived from WT mice did not react to the application of FLRF (Figure 3b).

Figure 3

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Frequency data.

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Figure 3—source data 2

Network quantification.

https://doi.org/10.7554/eLife.29566.010

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While these findings suggest that astrocytes do not directly modify neuronal activity in the preBötC, astrocytes may nonetheless be important for the maintenance of the respiratory rhythm generation in this complex. For instance, the astrocyte inhibitor methionine sulfoximine depresses breathing in vivo (Young et al., 2005), and glial inhibitors (fluorocitrate, fluoroacetate, and methionine sulfoximine) reduce respiratory-related activity in the preBötC in vitro (Erlichman et al., 1998; Huxtable et al., 2010). We have not examined such inhibition of astrocytes in the present study. Previous studies have shown an effect on respiration after astrocyte modulation (Angelova et al., 2015; Fukushi et al., 2016; Gourine and Funk, 2017; Rajani et al., 2017). Hypoxia and mechanical stimulation can induce a Ca²⁺ influx in preBötC astrocytes, causing a release of ATP (Angelova et al., 2015; Rajani et al., 2017), which maintains the inspiratory-related rhythm generation in the preBötC during post-hypoxic depression (Gourine et al., 2005). Although the ATP release is caused by an increase in [Ca²⁺]_i, the involved ion channels remain to be determined (Rajani et al., 2017). These could be different from the ones involved in the MrgA1-induced astrocyte stimulation. Further, our results indicate a low proportion (~20%) of astrocytes participating in the preBötC network, and stimulation of these might not be sufficient to affect the neuronal rhythm. The effect of MrgA1-induced astrocyte stimulation also remains to be investigated under hypoxic conditions.

Our data suggest that astrocytes in the pFRG/RTN modulate ongoing respiratory network activity, which is in contrast to their function in the preBötC. Astrocytes are known to modulate synaptic transmission between neurons, but the mechanism remains a mystery (Ben Haim and Rowitch, 2017). They may also use connexins to propagate Ca²⁺ waves (Parpura et al., 2012), but we recently showed that gap junction inhibition does not affect the network synchronization of the pFRG/RTN (Forsberg et al., 2016). Therefore gap junctions are not likely to constitute the main bridge between the cells during normoxic and normocapnic conditions in the pFRG/RTN. However, gap junctions can be utilized by astrocytes to release the gliotransmitter ATP, and purinergic signaling plays an important role for astrocyte-neuron interactions during hypercapnia (Erlichman et al., 2010; Gourine et al., 2010; Huckstepp et al., 2010; Gourine and Kasparov, 2011; Angelova et al., 2015). In addition, we recently suggested that astrocytes also release PGE2 in response to a hypercapnic challenge (Forsberg et al., 2016). This would in part explain the interaction between chemosensitivity and the inflammatory system observed in several studies (Hofstetter et al., 2007; Siljehav et al., 2012; Siljehav et al., 2014; Forsberg et al., 2016). Moreover, in human neonates, rapid elevation of brainstem PGE2 during infectious events is associated with, and may explain, the initial presenting symptoms of infection, which are apnea, bradycardia, and desaturation (Siljehav et al., 2015).

Because a hypercapnic challenge triggers a gap junction mediated release of PGE2 (Forsberg et al., 2016), we hypothesized that astrocyte stimulation would trigger a similar release. Indeed, we found that after FLRF application, the PGE2 levels in the artificial cerebrospinal fluid (aCSF) doubled in the pFRG/RTN (Figure 4a, n = 8 slices, p<0.05). The increase was transient, similar to the PGE2 release that occurs during hypercapnia (Forsberg et al., 2016). By contrast, FLRF application did neither affect the PGE2 levels in the GFAPMrgA1+ preBötC slices (n = 3 slices), nor in the pFRG/RTN and preBötC regions in the WT slices (n = 2 slices for each region). These results indicate that the pFRG/RTN contains chemosensitive astrocytes that release PGE2 upon hypercapnic challenge. The physiological effect of the released PGE2 is likely multifactorial. We previously demonstrated that PGE2 is involved in the stimulation of the pFRG/RTN (Forsberg et al., 2016), similar to the effects of ATP (Gourine et al., 2010). Although ATP counteracts the vasodilatory effect of CO₂/H⁺ during hypercapnia in the ventral brainstem (Hawkins et al., 2017), understanding the role of PGE2 in the microcircuits controlling brainstem blood flow will require further investigations.

Figure 4

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Elisa.

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Figure 4—source data 2

Frequency data.

https://doi.org/10.7554/eLife.29566.013

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We also determined that pre-activation of astrocytes blunts the hypercapnic response in the pFRG/RTN (Figure 4b, n = 8 slices). This result suggests that existing PGE2, and likely other gliotransmitters, is released after activation, depleting the stores. Extracellular pH is retained during hypercapnia in our system (Forsberg et al., 2016), but CO₂ can pass over the cell membrane and decrease the intracellular pH. This decreased pH drives bicarbonate and sodium ions into the cell, triggering a Ca²⁺ influx (Turovsky et al., 2016). In parallel, CO₂ can directly modify connexin 26 hemichannels to induce an open state (Huckstepp et al., 2010; Meigh et al., 2013). Thus, it is possible that the mechanism behind the gliotransmitter release during hypercapnic challenge is different from that of the FLRF-induced activation and Ca²⁺ influx. The design of the present study could not discern between PGE2 released from active and inactive astrocytes or determine whether PGE2 directly affected neurons or acted via intermediate astrocytes. In the pFRG/RTN, prostaglandin EP3 receptors have been found on both neurons and astrocytes (Forsberg et al., 2016). Thus, the different pathways as well as the kinetics associated with the effects of PGE2 will require further investigation. Hypercapnia did not affect the preBötC activity (Figure 4c, n = 6 slices), consistent with the results of our previous study (Forsberg et al., 2016). In summary, these results indicated that the pFRG/RTN contains astrocytes that are able to react to CO₂ and release PGE2 (and possibly other gliotransmitters) to modify the behavior of the neuronal population. This type of astrocytic responsiveness and modifying effect was not detected in the preBötC.

Taken together, our results led us to conclude that a subgroup of astrocytes with oscillatory Ca²⁺ activity interacted with and was an essential part of the respiratory neural networks. To our knowledge, this is the first study that describes how the astrocytes are organized in a specific network separate from, but interacting with, the neuronal network. Our results, together with the previous study (Forsberg et al., 2016), strongly suggest that the release of gliotransmitters is the main signaling system. This is in accordance with the findings of other investigators (Gourine et al., 2010; Huckstepp et al., 2010; Turovsky et al., 2016). Specifically, we observed that almost half of the cells in the pFRG/RTN network were astrocytes and that these astrocytes could entrain the neuronal rhythm, suggesting that gliotransmitters from pFRG/RTN astrocytes can modify respiratory activity. Moreover, a transient release of PGE2 induced by a Ca²⁺ influx in the astrocytes of the pFRG/RTN substantially reduced the subsequent hypercapnic response, confirming that PGE2 is one of the central gliotransmitters in chemosensitivity and indicating that it is of astrocytic origin. Thus, subgroups of astrocytes participate in the respiratory rhythm of both the preBötC and the pFRG/RTN, and modify respiratory network behavior in the pFRG/RTN. Therefore, we suggest that astrocytes constitute an important link between the respiratory and inflammatory systems, and stand out as a potential target for the treatment of central respiratory dysfunction.

1. 1. Angelova PR
   2. Kasymov V
   3. Christie I
   4. Sheikhbahaei S
   5. Turovsky E
   6. Marina N
   7. Korsak A
   8. Zwicker J
   9. Teschemacher AG
   10. Ackland GL
   11. Funk GD
   12. Kasparov S
   13. Abramov AY
   14. Gourine AV

   (2015) Functional oxygen sensitivity of astrocytes

   Journal of Neuroscience 35:10460–10473.

   https://doi.org/10.1523/JNEUROSCI.0045-15.2015

   • PubMed
   • Google Scholar
2. 1. Ben Haim L
   2. Rowitch DH

   (2017) Functional diversity of astrocytes in neural circuit regulation

   Nature Reviews Neuroscience 18:31–41.

   https://doi.org/10.1038/nrn.2016.159

   • PubMed
   • Google Scholar
3. 1. Brenner M
   2. Kisseberth WC
   3. Su Y
   4. Besnard F
   5. Messing A

   (1994)

   GFAP promoter directs astrocyte-specific expression in transgenic mice

   Journal of Neuroscience 14:1030–1037.

   • PubMed
   • Google Scholar
4. 1. Cao X
   2. Li LP
   3. Wang Q
   4. Wu Q
   5. Hu HH
   6. Zhang M
   7. Fang YY
   8. Zhang J
   9. Li SJ
   10. Xiong WC
   11. Yan HC
   12. Gao YB
   13. Liu JH
   14. Li XW
   15. Sun LR
   16. Zeng YN
   17. Zhu XH
   18. Gao TM

   (2013) Astrocyte-derived ATP modulates depressive-like behaviors

   Nature Medicine 19:773–777.

   https://doi.org/10.1038/nm.3162

   • PubMed
   • Google Scholar
5. 1. Dong X
   2. Han S
   3. Zylka MJ
   4. Simon MI
   5. Anderson DJ

   (2001) A diverse family of GPCRs expressed in specific subsets of nociceptive sensory neurons

   Cell 106:619–632.

   https://doi.org/10.1016/S0092-8674(01)00483-4

   • PubMed
   • Google Scholar
6. 1. Erlichman JS
   2. Li A
   3. Nattie EE

   (1998)

   Ventilatory effects of glial dysfunction in a rat brain stem chemoreceptor region

   Journal of Applied Physiology 85:1599–1604.

   • PubMed
   • Google Scholar
7. 1. Erlichman JS
   2. Leiter JC
   3. Gourine AV

   (2010) ATP, glia and central respiratory control

   Respiratory Physiology & Neurobiology 173:305–311.

   https://doi.org/10.1016/j.resp.2010.06.009

   • PubMed
   • Google Scholar
8. 1. Fiacco TA
   2. Agulhon C
   3. Taves SR
   4. Petravicz J
   5. Casper KB
   6. Dong X
   7. Chen J
   8. McCarthy KD

   (2007) Selective stimulation of astrocyte calcium in situ does not affect neuronal excitatory synaptic activity

   Neuron 54:611–626.

   https://doi.org/10.1016/j.neuron.2007.04.032

   • PubMed
   • Google Scholar
9. 1. Forsberg D
   2. Horn Z
   3. Tserga E
   4. Smedler E
   5. Silberberg G
   6. Shvarev Y
   7. Kaila K
   8. Uhlén P
   9. Herlenius E

   (2016) CO2-evoked release of PGE2 modulates sighs and inspiration as demonstrated in brainstem organotypic culture

   eLife 5:e14170.

   https://doi.org/10.7554/eLife.14170

   • PubMed
   • Google Scholar
10. 1. Forsberg D
    2. Thonabulsombat C
    3. Jäderstad J
    4. Jäderstad LM
    5. Olivius P
    6. Herlenius E

    (2017) functional stem cell integration into neural networks assessed by organotypic slice cultures

    Current protocols in stem cell biology 42:2D 13 11–21312.

    https://doi.org/10.1002/cpsc.34

    • PubMed
    • Google Scholar
11. 1. Fukuda Y
    2. Honda Y
    3. Schläfke ME
    4. Loeschcke HH

    (1978) Effect of H+ on the membrane potential of silent cells in the ventral and dorsal surface layers of the rat medulla in vitro

    Pflugers Archiv European Journal of Physiology 376:229–235.

    https://doi.org/10.1007/BF00584955

    • PubMed
    • Google Scholar
12. 1. Fukushi I
    2. Takeda K
    3. Yokota S
    4. Hasebe Y
    5. Sato Y
    6. Pokorski M
    7. Horiuchi J
    8. Okada Y

    (2016) Effects of arundic acid, an astrocytic modulator, on the cerebral and respiratory functions in severe hypoxia

    Respiratory Physiology & Neurobiology 226:24–29.

    https://doi.org/10.1016/j.resp.2015.11.011

    • PubMed
    • Google Scholar
13. 1. Gourine AV
    2. Llaudet E
    3. Dale N
    4. Spyer KM

    (2005) Release of ATP in the ventral medulla during hypoxia in rats: role in hypoxic ventilatory response

    Journal of Neuroscience 25:1211–1218.

    https://doi.org/10.1523/JNEUROSCI.3763-04.2005

    • PubMed
    • Google Scholar
14. 1. Gourine AV
    2. Kasymov V
    3. Marina N
    4. Tang F
    5. Figueiredo MF
    6. Lane S
    7. Teschemacher AG
    8. Spyer KM
    9. Deisseroth K
    10. Kasparov S

    (2010) Astrocytes control breathing through pH-dependent release of ATP

    Science 329:571–575.

    https://doi.org/10.1126/science.1190721

    • PubMed
    • Google Scholar
15. 1. Gourine AV
    2. Kasparov S

    (2011) Astrocytes as brain interoceptors

    Experimental Physiology 96:411–416.

    https://doi.org/10.1113/expphysiol.2010.053165

    • PubMed
    • Google Scholar
16. 1. Gourine AV
    2. Funk GD

    (2017) On the existence of a central respiratory oxygen sensor

    Journal of Applied Physiology jap.00194.2017.

    https://doi.org/10.1152/japplphysiol.00194.2017

    • Google Scholar
17. 1. Grass D
    2. Pawlowski PG
    3. Hirrlinger J
    4. Papadopoulos N
    5. Richter DW
    6. Kirchhoff F
    7. Hülsmann S

    (2004) Diversity of functional astroglial properties in the respiratory network

    Journal of Neuroscience 24:1358–1365.

    https://doi.org/10.1523/JNEUROSCI.4022-03.2004

    • PubMed
    • Google Scholar
18. 1. Hartelt N
    2. Skorova E
    3. Manzke T
    4. Suhr M
    5. Mironova L
    6. Kügler S
    7. Mironov SL

    (2008) Imaging of respiratory network topology in living brainstem slices

    Molecular and Cellular Neuroscience 37:425–431.

    https://doi.org/10.1016/j.mcn.2007.10.011

    • PubMed
    • Google Scholar
19. 1. Hawkins VE
    2. Takakura AC
    3. Trinh A
    4. Malheiros-Lima MR
    5. Cleary CM
    6. Wenker IC
    7. Dubreuil T
    8. Rodriguez EM
    9. Nelson MT
    10. Moreira TS
    11. Mulkey DK

    (2017) Purinergic regulation of vascular tone in the retrotrapezoid nucleus is specialized to support the drive to breathe

    eLife 6:e25232.

    https://doi.org/10.7554/eLife.25232

    • PubMed
    • Google Scholar
20. 1. Herlenius E
    2. Thonabulsombat C
    3. Forsberg D
    4. Jäderstad J
    5. Jäderstad LM
    6. Björk L
    7. Olivius P

    (2012) Functional stem cell integration assessed by organotypic slice cultures

    Current Protocols in Stem Cell Biology Chapter 2:13.

    https://doi.org/10.1002/9780470151808.sc02d13s23

    • PubMed
    • Google Scholar
21. 1. Hofstetter AO
    2. Saha S
    3. Siljehav V
    4. Jakobsson PJ
    5. Herlenius E

    (2007) The induced prostaglandin E2 pathway is a key regulator of the respiratory response to infection and hypoxia in neonates

    PNAS 104:9894–9899.

    https://doi.org/10.1073/pnas.0611468104

    • PubMed
    • Google Scholar
22. 1. Huckstepp RT
    2. id Bihi R
    3. Eason R
    4. Spyer KM
    5. Dicke N
    6. Willecke K
    7. Marina N
    8. Gourine AV
    9. Dale N

    (2010) Connexin hemichannel-mediated CO2-dependent release of ATP in the medulla oblongata contributes to central respiratory chemosensitivity

    The Journal of Physiology 588:3901–3920.

    https://doi.org/10.1113/jphysiol.2010.192088

    • PubMed
    • Google Scholar
23. 1. Huxtable AG
    2. Zwicker JD
    3. Alvares TS
    4. Ruangkittisakul A
    5. Fang X
    6. Hahn LB
    7. Posse de Chaves E
    8. Baker GB
    9. Ballanyi K
    10. Funk GD

    (2010) Glia contribute to the purinergic modulation of inspiratory rhythm-generating networks

    Journal of Neuroscience 30:3947–3958.

    https://doi.org/10.1523/JNEUROSCI.6027-09.2010

    • PubMed
    • Google Scholar
24. 1. Lin W
    2. Kemper A
    3. McCarthy KD
    4. Pytel P
    5. Wang JP
    6. Campbell IL
    7. Utset MF
    8. Popko B

    (2004) Interferon-gamma induced medulloblastoma in the developing cerebellum

    Journal of Neuroscience 24:10074–10083.

    https://doi.org/10.1523/JNEUROSCI.2604-04.2004

    • PubMed
    • Google Scholar
25. 1. Lorier AR
    2. Huxtable AG
    3. Robinson DM
    4. Lipski J
    5. Housley GD
    6. Funk GD

    (2007) P2Y1 receptor modulation of the pre-Bötzinger complex inspiratory rhythm generating network in vitro

    Journal of Neuroscience 27:993–1005.

    https://doi.org/10.1523/JNEUROSCI.3948-06.2007

    • PubMed
    • Google Scholar
26. 1. Meigh L
    2. Greenhalgh SA
    3. Rodgers TL
    4. Cann MJ
    5. Roper DI
    6. Dale N

    (2013) CO₂directly modulates connexin 26 by formation of carbamate bridges between subunits

    eLife 2:e01213.

    https://doi.org/10.7554/eLife.01213

    • PubMed
    • Google Scholar
27. 1. Okada Y
    2. Sasaki T
    3. Oku Y
    4. Takahashi N
    5. Seki M
    6. Ujita S
    7. Tanaka KF
    8. Matsuki N
    9. Ikegaya Y

    (2012) Preinspiratory calcium rise in putative pre-Botzinger complex astrocytes

    The Journal of Physiology 590:4933–4944.

    https://doi.org/10.1113/jphysiol.2012.231464

    • PubMed
    • Google Scholar
28. 1. Oku Y
    2. Fresemann J
    3. Miwakeichi F
    4. Hülsmann S

    (2016) Respiratory calcium fluctuations in low-frequency oscillating astrocytes in the pre-Bötzinger complex

    Respiratory Physiology & Neurobiology 226:11–17.

    https://doi.org/10.1016/j.resp.2015.02.002

    • PubMed
    • Google Scholar
29. 1. Parpura V
    2. Heneka MT
    3. Montana V
    4. Oliet SH
    5. Schousboe A
    6. Haydon PG
    7. Stout RF
    8. Spray DC
    9. Reichenbach A
    10. Pannicke T
    11. Pekny M
    12. Pekna M
    13. Zorec R
    14. Verkhratsky A

    (2012) Glial cells in (patho)physiology

    Journal of Neurochemistry 121:4–27.

    https://doi.org/10.1111/j.1471-4159.2012.07664.x

    • PubMed
    • Google Scholar
30. 1. Pascual O
    2. Casper KB
    3. Kubera C
    4. Zhang J
    5. Revilla-Sanchez R
    6. Sul JY
    7. Takano H
    8. Moss SJ
    9. McCarthy K
    10. Haydon PG

    (2005) Astrocytic purinergic signaling coordinates synaptic networks

    Science 310:113–116.

    https://doi.org/10.1126/science.1116916

    • PubMed
    • Google Scholar
31. 1. Phillips WS
    2. Herly M
    3. Del Negro CA
    4. Rekling JC

    (2016) Organotypic slice cultures containing the preBötzinger complex generate respiratory-like rhythms

    Journal of Neurophysiology 115:1063–1070.

    https://doi.org/10.1152/jn.00904.2015

    • PubMed
    • Google Scholar
32. 1. Rajani V
    2. Zhang Y
    3. Jalubula V
    4. Rancic V
    5. SheikhBahaei S
    6. Zwicker JD
    7. Pagliardini S
    8. Dickson CT
    9. Ballanyi K
    10. Kasparov S
    11. Gourine AV
    12. Funk GD

    (2017) Release of ATP by pre-Bötzinger complex astrocytes contributes to the hypoxic ventilatory response via a Ca(2+) -dependent P2Y1 receptor mechanism

    The Journal of Physiology.

    https://doi.org/10.1113/JP274727

    • PubMed
    • Google Scholar
33. 1. Ruangkittisakul A
    2. Schwarzacher SW
    3. Secchia L
    4. Poon BY
    5. Ma Y
    6. Funk GD
    7. Ballanyi K

    (2006) High sensitivity to neuromodulator-activated signaling pathways at physiological [K+] of confocally imaged respiratory center neurons in on-line-calibrated newborn rat brainstem slices

    Journal of Neuroscience 26:11870–11880.

    https://doi.org/10.1523/JNEUROSCI.3357-06.2006

    • PubMed
    • Google Scholar
34. 1. Ruangkittisakul A
    2. Panaitescu B
    3. Ballanyi K

    (2011) K(+) and Ca²(+) dependence of inspiratory-related rhythm in novel "calibrated" mouse brainstem slices

    Respiratory Physiology & Neurobiology 175:37–48.

    https://doi.org/10.1016/j.resp.2010.09.004

    • PubMed
    • Google Scholar
35. 1. Ruangkittisakul A
    2. Kottick A
    3. Picardo MC
    4. Ballanyi K
    5. Del Negro CA

    (2014) Identification of the pre-Bötzinger complex inspiratory center in calibrated "sandwich" slices from newborn mice with fluorescent Dbx1 interneurons

    Physiological Reports 2:e12111.

    https://doi.org/10.14814/phy2.12111

    • PubMed
    • Google Scholar
36. 1. Schnell C
    2. Fresemann J
    3. Hülsmann S

    (2011) Determinants of functional coupling between astrocytes and respiratory neurons in the pre-Bötzinger complex

    PLoS One 6:e26309.

    https://doi.org/10.1371/journal.pone.0026309

    • PubMed
    • Google Scholar
37. 1. Siljehav V
    2. Olsson Hofstetter A
    3. Jakobsson PJ
    4. Herlenius E

    (2012) mPGES-1 and prostaglandin E2: vital role in inflammation, hypoxic response, and survival

    Pediatric Research 72:460–467.

    https://doi.org/10.1038/pr.2012.119

    • PubMed
    • Google Scholar
38. 1. Siljehav V
    2. Shvarev Y
    3. Herlenius E

    (2014) Il-1β and prostaglandin E2 attenuate the hypercapnic as well as the hypoxic respiratory response via prostaglandin E receptor type 3 in neonatal mice

    Journal of Applied Physiology 117:1027–1036.

    https://doi.org/10.1152/japplphysiol.00542.2014

    • PubMed
    • Google Scholar
39. 1. Siljehav V
    2. Hofstetter AM
    3. Leifsdottir K
    4. Herlenius E

    (2015) Prostaglandin E2 mediates cardiorespiratory disturbances during infection in neonates

    The Journal of Pediatrics 167:1207–1213.

    https://doi.org/10.1016/j.jpeds.2015.08.053

    • PubMed
    • Google Scholar
40. 1. Smedler E
    2. Malmersjö S
    3. Uhlén P

    (2014) Network analysis of time-lapse microscopy recordings

    Frontiers in Neural Circuits 8:111.

    https://doi.org/10.3389/fncir.2014.00111

    • PubMed
    • Google Scholar
41. 1. Stoppini L
    2. Buchs PA
    3. Muller D

    (1991) A simple method for organotypic cultures of nervous tissue

    Journal of Neuroscience Methods 37:173–182.

    https://doi.org/10.1016/0165-0270(91)90128-M

    • PubMed
    • Google Scholar
42. 1. Turovsky E
    2. Karagiannis A
    3. Abdala AP
    4. Gourine AV

    (2015) Impaired CO2 sensitivity of astrocytes in a mouse model of Rett syndrome

    The Journal of Physiology 593:3159–3168.

    https://doi.org/10.1113/JP270369

    • PubMed
    • Google Scholar
43. 1. Turovsky E
    2. Theparambil SM
    3. Kasymov V
    4. Deitmer JW
    5. Del Arroyo AG
    6. Ackland GL
    7. Corneveaux JJ
    8. Allen AN
    9. Huentelman MJ
    10. Kasparov S
    11. Marina N
    12. Gourine AV

    (2016) Mechanisms of CO2/H+ sensitivity of astrocytes

    Journal of Neuroscience 36:10750–10758.

    https://doi.org/10.1523/JNEUROSCI.1281-16.2016

    • PubMed
    • Google Scholar
44. 1. Young JK
    2. Dreshaj IA
    3. Wilson CG
    4. Martin RJ
    5. Zaidi SI
    6. Haxhiu MA

    (2005) An astrocyte toxin influences the pattern of breathing and the ventilatory response to hypercapnia in neonatal rats

    Respiratory Physiology & Neurobiology 147:19–30.

    https://doi.org/10.1016/j.resp.2005.01.009

    • PubMed
    • Google Scholar
45. 1. Young SZ
    2. Platel JC
    3. Nielsen JV
    4. Jensen NA
    5. Bordey A

    (2010) GABA(A) increases calcium in subventricular zone astrocyte-like cells through l- and t-type voltage-gated calcium channels

    Frontiers in cellular neuroscience 4:8.

    https://doi.org/10.3389/fncel.2010.00008

    • PubMed
    • Google Scholar

Author details

1. David Forsberg

   Department of Women’s and Children’s Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4719-2201

2. Thomas Ringstedt

   Department of Women’s and Children’s Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

   Contribution

   Conceptualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0294-9351

3. Eric Herlenius

   Department of Women’s and Children’s Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

   For correspondence

   eric.herlenius@ki.se

   Competing interests

   employed at the Karolinska Institutet and the Karolinska University Hospital and is a coinventor of a patent application regarding biomarkers and their relation to breathing disorders, WO2009063226.

   "This ORCID iD identifies the author of this article:" 0000-0002-6859-0620

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