disruptors of homeostasis trigger the release of VLC-PUFAs. 32:6n3 and 34:6n3 are depicted generating hydroperoxy forms, and then ELV-N32 or ELV-N34, respectively. (B) The pool size of free 32:6n3 in retinas of AdipoR1 knockout (KO) mice (red) is decreased as compared with that in wild type (WT) (blue). Insert (1) shows ELV-N32 in KO (red) and WT (blue); insert (2) shows mono-hydroxy 32:6n3, the stable derivative of the hydroperoxy precursor of ELV-N32, in WT (blue) and lack of detectable signal in the KO (red). (C) Similarly, the pool size of free 34:6n3 in retinas of AdipoR1 KO mice (red) is decreased as compared with that in WT (blue). Insert (1) shows ELV-N32 in KO (red) and WT (blue); insert (2) shows mono-hydroxy 34:6n3, the stable derivative of the hydroperoxy precursor of ELV-N34, in WT (blue) and lack of detectable signal in the KO (red). (D) RPE cells sustain PRC functional integrity (left); right, the ablation of AdipoR1 switches off DHA availability, and PRC degeneration ensues. Reproduced from Jun et al. (2017) with permission under the Creative Commons Attribution 4.0 International License (link to the Creative Commons license; http://creativecommons.org/licenses/by/4.0/).

#### Dementia and Alzheimer's Disease

There is tremendous interest in nutritional interventions to prevent cognitive impairment in the elderly, including the potential of fatty acids to prevent or slow cognitive impairment (Bazan et al., 2002; Sinn et al., 2010; Calon, 2011; Jiao et al., 2014; Burckhardt et al., 2016; Rangel-Huerta and Gil, 2018). Change in cell membrane lipid composition has been suggested to be associated with cognitive impairment in Alzheimer's disease (Drolle et al., 2017; Diaz et al., 2018; Penke et al., 2018). Moreover, association between beta-amyloid (Aβ), a main pathological hallmark of Alzheimer's, and arachidonic acid in erythrocyte membrane was found to be specific to Apolipoprotein E ε4 non-carrier patients (Hooper et al., 2017). Treatments with DHA or other PUFAs have not provided significant protection from cognitive decline in the elderly thus far (Carrie et al., 2009; Dacks et al., 2013; Jiao et al., 2014; Phillips et al., 2015;

Nishihira et al., 2016; Zhang et al., 2016), however, assessment of treatments including lipids with longer fatty acids than DHA have started only recently. Ongoing massive clinical trials, like the Cognitive Ageing, Nutrition, and Neurogenesis (CANN) trial, are presently testing potential benefits of combined dietary intervention with long-chain n-3 PUFA and flavonoids on cognition in older adults with mild cognitive impairment or subjective memory impairment (Irvine et al., 2018). The studies summarized in this review pose a number of stimulating questions and open future research directions to explore the potential applicability of VLC-FA to the treatment of specific human brain and retina diseases, like cognitive impairment, epileptic seizures and macular degeneration.

#### IN SUMMARY

Recent studies reveal novel functional roles for ELOVL4 and its VLC-FA products in the CNS, including retina and brain, in health and disease. VLC-PUFA play vital functions in the CNS as precursors of compounds that serve newly recognized roles in homeostatic signaling and regulation of neuronal survival. Most recently it was shown that VLC-SFA have essential functions in synaptic transmission, with disruption of VLC-SFA synthesis leading to seizures and neurodegeneration both in patients lacking ELOVL4 and in mouse models of ELOVL4 mutations. A better understanding of VLC-SFA and VLC-PUFA metabolism and their functions in the CNS holds great promise

#### REFERENCES

for development of new therapeutic avenues for treatment of seizures and neurodegenerative diseases.

#### AUTHOR CONTRIBUTIONS

FD, RA, JF, and DS contributed to the writing and editing of the manuscript.

#### FUNDING

This work was supported by a Presbyterian Health Foundation Team Science grant to FD, RA, and DS and NIH grant P30AG050911-04S1. FD was supported by a bridge grant from the Presbyterian Health Foundation and NIH grant P30AG050911-04S1. JF was supported by the OUHSC Department of Cell Biology and a Presbyterian Health Foundation Seed Grant to DS. RA was supported by the NIH grants R01EY04149, P30EY021725, and R21NS090117.

#### ACKNOWLEDGMENTS

The authors are thankful to Dr. Martin-Paul Agbaga, Dr. Blake Hopiavuori, Dr. Raghavendra Y. Nagaraja, Gyening Yeboah, Richard Brush, Megan Stiles, and Dr. Feng Li for their insights and discussion.

Zoncu, R., Perera, R. M., Sebastian, R., Nakatsu, F., Chen, H., Balla, T., et al. (2007). Loss of endocytic clathrin-coated pits upon acute depletion of phosphatidylinositol 4,5-bisphosphate. Proc. Natl. Acad. Sci. U.S.A. 104, 3793–3798. doi: 10.1073/pnas.0611733104

**Conflict of Interest:** RA has a United States Patent (No. 8,021,874) entitled "Very Long Chain Polyunsaturated Fatty Acids, Methods of Production, and Uses."

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Deák, Anderson, Fessler and Sherry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Processing Bodies Oscillate in Neuro 2A Cells

#### Melisa Malcolm1,2, Lucía Saad1,2, Laura Gabriela Penazzi1,2 and Eduardo Garbarino-Pico1,2 \*

1 Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Córdoba, Argentina, 2 CONICET-UNC, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina

#### Edited by:

Alejandra Alonso, College of Staten Island, United States

#### Reviewed by:

Rachid El Fatimy, Brigham and Women's Hospital, Harvard Medical School, United States Steven Lanier McKnight, UT Southwestern Medical Center, United States

> \*Correspondence: Eduardo Garbarino-Pico

<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

> Received: 31 March 2019 Accepted: 14 October 2019 Published: 29 October 2019

#### Citation:

Malcolm M, Saad L, Penazzi LG and Garbarino-Pico E (2019) Processing Bodies Oscillate in Neuro 2A Cells. Front. Cell. Neurosci. 13:487. doi: 10.3389/fncel.2019.00487 Circadian rhythms are biological variables that oscillate with periods close to 24 h that are generated internally by biological clocks. Depending on the tissue/cell type, about 5–20% of genes are expressed rhythmically. Unexpectedly, the correlation between the oscillations of messengers and the proteins they encode is low. We hypothesize that these discrepancies could be because in certain phases of the circadian cycle some messengers could be translationally silenced and stored. Processing bodies (PBs) are membraneless organelles formed by ribonucleoprotein aggregates located in the cytoplasm. They contain silenced messengers and factors involved in mRNA processing. A previous work showed that the number of cells containing these mRNA granules varies when comparing two time-points in U2OS cell cultures and that these differences disappear when an essential clock gene is silenced. Here we evaluate whether PBs oscillate in Neuro2A cells. We analyzed in cell cultures synchronized with dexamethasone the variations in the number, the signal intensity of the markers used (GE-1/HEDLS and DDX6), and the area of PBs between 8 and 68 h postsynchronization. All three parameters oscillated with periods compatible with a circadian regulated process. The most robust rhythm was the number of PBs. These rhythms could be generated by oscillations in proteins that have been involved in the nucleation of these foci such as LSM1, TTP, and BRF1. The described phenomenon would allow to explain the differences observed in the temporal profiles of some messengers and their proteins and to understand how circadian clocks can control post-transcriptionally cellular functions.

Keywords: processing bodies, circadian rhythms, Neuro 2A, RNA granules, GE1/Hedls, DDX6/P54/RCK, membraneless organelles

### INTRODUCTION

Throughout evolution, living beings have developed mechanisms capable of measuring time and controlling numerous functions in a cyclic manner. Most of these oscillations have a period of 1 day in natural environments and close to 24 h in constant conditions. These mechanisms are called biological clocks and the functions they control circadian rhythms.

They are believed to confer adaptative advantages by predicting the cyclic changes in the environment caused by the rotation of the Earth (e.g., light/dark and temperature cycles) and temporarily organizing physiology (Schibler et al., 2015; Bass and Lazar, 2016; Takahashi, 2017). Examples of circadian rhythms are cycles of sleep/wakefulness, locomotor activity or body temperature. In neurons, rhythms have been described in firing rate, gene expression, the activity of enzymes and channels and even in synaptic plasticity, among others. The molecular clockwork consists of a group of genes that mutually regulate each other through interconnected transcription-translation negative feedback loops (TTFLs) (Takahashi, 2017). These circuits rhythmically regulate the abundance of messengers encoded by ∼5–20% of genes in different tissues/organs and about 50% of mRNAs oscillate in at least some tissue (Zhang et al., 2014). Importantly, post-transcriptional regulation also plays a very important role in the generation of rhythms in protein abundance (Green, 2018). This is reflected in the poor correlation that exists between variations in the abundance of transcripts and their corresponding proteins (Reddy et al., 2006; Mauvoisin et al., 2014; Robles et al., 2014).

In the last two decades, new types of intracellular compartments characterized by not being delimited by membranes have been described in eukaryotic cells, collectively called membraneless organelles or RNA granules. They are condensed liquid-like droplets of ribonuleoprotein complexes (Courchaine et al., 2016; Sfakianos et al., 2016; Shin and Brangwynne, 2017). In cytosol, these subcompartments include processing bodies (PBs), stress granules, germ granules and neuronal granules. PBs are constitutively present, contain translationally silent mRNAs and factors involved in messengers 5 0→3 0 degradation, repression of translation, and RNA interference (Decker and Parker, 2012; Ivanov et al., 2018; Standart and Weil, 2018). Because of this, and other indirect evidence, it has been proposed that these foci would play a role in mRNA degradation (Sheth and Parker, 2003). However, it was also demonstrated that some messengers localizing in PBs can be translated again, consequently they would also serve as storage places (Brengues et al., 2005). Recently, it was possible to purify PBs and determine the identity of many of the proteins and mRNAs that compose them (Hubstenberger et al., 2017). From these studies it was proposed that PBs would store mRNA regulons, that is groups of transcripts coding for proteins with regulatory functions of specific pathways, while messengers with basic functions are virtually excluded (for example, transcripts coding for histone methylases but not for histones were found in PBs) (Hubstenberger et al., 2017; Standart and Weil, 2018). Thus, PBs have a role in the post-transcriptional regulation of gene expression, remarkably they would store silenced the mRNAs of groups of genes that modulate specific functions.

Since it is proposed that PBs store silenced messengers, if these structures were to oscillate, this would help to explain (at least partially) the discrepancies observed when analyzing the mRNA and protein profiles corresponding to a number of genes (Reddy et al., 2006; Mauvoisin et al., 2014; Robles et al., 2014). That is, if the messengers of a particular gene are mostly located in PBs, no matter how abundant, this will not be reflected in their protein levels or translation rate. If it is also considered that these foci contain fundamentally transcripts of regulatory factors (Hubstenberger et al., 2017; Standart and Weil, 2018), changes in their abundance and/or size would contribute to understanding the pathways by which many rhythmic cellular processes are regulated. In fact, Jang et al. (2015) have demonstrated changes in the number of cells containing PBs in cultures of U2OS cells (human osteosarcoma cell line). They decided to study this because previously, by ribosome profiling, they had found that the translation of Lsm1 was circadian modulated into U2OS cells. LSM1 is a marker of PBs (Ingelfinger et al., 2002; Kedersha and Anderson, 2007). They also demonstrated that silencing a fundamental clock gene in the circadian clock molecular mechanism abrogates those differences (Jang et al., 2015). Given that this work analyzes only two time-points postsynchronization with dexamethasone (4 and 16 h), and that one of these is very close to the addition of this synchronizing agent that dramatically affects cellular physiology, we decided to analyze whether the number and size of PBs varies for 68 h in a neuroblastoma cell line. In this Brief Research Report we show that these two parameters oscillate cyclically in cultures of Neuro 2A cells.

### MATERIALS AND METHODS

### Cell Culture

Mouse Albino neuroblastoma (Neuro 2a) cells (ATCC) were cultured in Minimum Essential Medium (MEM, Gibco) supplemented with 10% fetal bovine serum (FBS, Internegocios S.A, Argentina), in a 37◦C incubator with 5% CO2 according ATCC recommendations. These cells have been used before for studying circadian rhythms in cell cultures (Chilov et al., 2001; Margadant et al., 2007; Repouskou et al., 2010; Chang and Guarente, 2013). Cells were grown on coverslips in a 24-well plate until they reached ∼70% of confluence, and then maintained in serum starvation conditions (0.25%, see below) to prevent the progression of the cell cycle. This is important to ensure that changes observed over time are regulated by the circadian clock and not by the cell cycle. Initially we tried to completely eliminate serum from the medium, but cells did not survive the 68 h that the experiment required. Subsequently we tested for 96 h different serum concentrations: 0, 0.1, 0.25, and 0.5%. Cells died at serum concentration lower than 0.25% and proliferate at 0.5% FBS; thus we chose 0.25% FBS, a concentration in which the number of cells did not vary during the 96 h analyzed. To control that the cells were not proliferating, we analyzed cultures grown in the same conditions by flow cytometry with propidium iodide. Indeed, when the cultures contained only 0.25% FBS in the medium, the cells were arrested. For circadian clock synchronization (i.e., to entrain the cell population to the same circadian phase), cells were treated for 1 h with 100 nM dexamethasone (Balsalobre et al., 2000; Nagoshi et al., 2005; Repouskou et al., 2010); then culture medium was replaced with fresh 0.25% FBS-MEM.

#### FIGURE 1 | Continued

fncel-13-00487 October 25, 2019 Time: 17:19 # 4

field was completely covered with cells), average signal intensity and the average area of the PBs. These quantifications are shown in the right panel. The Kruskall–Wallis test followed by Dunn's multiple comparison test was used to determine whether the changes over time were statistically significant. MetaCycle was used to assess whether the time series of data showed rhythmicity. These analyses are presented in Table 1 and Supplementary Tables S5–S10, all three parameters showed cyclic changes with the two markers. With the period, phase and amplitude values obtained by MetaCycle, the data were adjusted to a cosine-fitted curve (CFC). In the case of the PB number, the curve obtained when considering the PBs detected with the two antibodies (colocalization) is also shown.

Cells were fixed every 4 h for 68 h post-synchronization for immunocytochemistry analysis.

#### Immunocytochemistry

Immunocytochemistry (ICC) was achieved according a protocol described by Kedersha and colleagues for analyzing PBs (Kedersha and Anderson, 2007). Briefly, Neuro 2a cells were washed twice with PBS, fixed with paraformaldehyde 4% for 15 min., permeabilized with −20 ◦ C methanol for 10 min., and incubated 1 h in blocking solution (5% Horse serum-PBS). These steps were carried out at room temperature (RT). Then cells were incubated with a mouse monoclonal antibody against p70 S6 kinase α (H-9) (Santa Cruz Biotechnology), diluted 1:1000 in blocking solution, in a humidified chamber overnight at RT. This antibody recognizes p70 S6 kinase α in the nucleus and GE-1/HEDLS/EDC4 in cytoplasm, a known marker for PBs and has been widely used for studying this foci [reviewed in 20]. Then, cells were rinsed three times with PBS, followed by incubation with a polyclonal DyLight 549-AffiniPure donkey anti-mouse IgG secondary antibody (1:2000, Jackson ImmunoResearch Labs), diluted in the same blocking solution, 30 min at RT. Nuclei were stained with Dapi. Cells were rinsed three times with PBS and mounted on slices with mowiol (Sigma-Aldrich). In addition to anti-GE-1/HEDLS, anti-DDX6/P54/RCK (Bethyl Laboratories, 1:500 in blocking solution) was used in double immunolabeling experiments. This is another recognized PB marker (Kedersha and Anderson, 2007).

#### Image Detection and Analysis

Slices were visualized using an BX61 Fluorescence Microscope (Olympus) equipped with a UPLSAPO 60XO oil objective lens (NA 1.35) and U-T31 000v2 specific DAPI/Hoechs, U-TSP101para FITC, and U-T31014 Wide-band excitation set for Elphidium Bromide TRITC, Phenothrin Dil Chroma Filters. The images were acquired with a JAI R CV-M4+CL monochrome camera controlled by Cytovision R (Leica Biosystems). PBs were quantified according the procedure developed by Nissan and Parker (2008) for stress granules with modifications. The entire procedure was conducted using algorithms that guarantee an unbiased treatment of all pictures. Digital images of 1376 × 1038 pixels and 8 bits were processed with the ImageJ program as follow: 1) Process menu > "Filters" > "Gaussian Blur" sigma radius 0.8 pix. 2) Process menu > "Subtract background" (Rolling ball radius: 5 pix). 3) Image > "Adjust" > "Threshold" 40 (red channel) or 35 (green channel). 4) Analyze menu > "Analyze particles" > size 3–300 pix2, circularity 0.7-1. Importantly, the same threshold value was used in the analysis of all the images within each experiment, to ensure an unbiased comparison between time-points. P- bodies are considered circular, ∼300–500 nm in diameter (Cougot et al., 2012), for this reason we exclude particles without a circularity <0.7 (1 correspond to a perfect circle) or out of 3–300 pix2 range. This pixel size range allows quantifying particles with a diameter of 200–2000 nm. The PBs observed in Neuro 2a cells presented an average diameter of 476 nm with this procedure. We were able to decrease the upper limit to a value closer to the largest particle found, even though it would not have affected the results. P-body number was normalized by area covered by cells, for that purpose, masks were created with Fiji ImageJ, and area occupied in each microphotograph was measured.

### Statistical Analysis

Statistical analysis was performed using GraphPad Prism version 5.00 for Windows (GraphPad Software). Values are shown as mean ± SEM, unless otherwise indicated. The Kolmogorov-Smirnov's test was used to check for normality, and Barlett's test to check homogeneity of variances. Because non-normal distribution or homogeneity of variances were found, Kruskal– Wallis's test was used instead of ANOVA, followed by Dunn's multiple comparisons test; p-values ≤0.05 were considered as statistically significant. To evaluate periodicity in time-series data we employed MetaCycle (Wu et al., 2016), which is a R package that runs and integrates three algorithms: ARSER (Yang and Su, 2010), JTK\_CYCLE (Hughes et al., 2010), and Lomb-Scargle (Glynn et al., 2006). The statistics applied with this analysis allow us to determine whether the data are periodic and, if so, their period, amplitude and phase. These parameters were applied to a Cosine function to plot the adjusted curves shown in the graph.

#### RESULTS

As mentioned in the Introduction, Jang and collaborators had shown important evidence that the number of PBs is regulated by the circadian clock (Jang et al., 2015). However, this study was limited to only two time-points, insufficient to describe a rhythm. Importantly, one of the time-points at which the number of PBs was analyzed was too close to synchronization (4 h), when the culture – and therefore the phenomenon studied – are still under the effect of the synchronizing agent (dexamethasone in this case). It is usual to start measuring at least 8 h after synchronization to analyze the rhythms independent of the effect of the agent used. We have also found, by using a database of circadian gene expression

TABLE 1 | PB temporal oscillations in Neuro 2a cells. PB number (normalized per field).

(Continued)

16 10.37 2.15 13.41 1.82 15 48 15.25 1.86 17.88 1.80 20

#### TABLE 1 | Continued

fncel-13-00487 October 25, 2019 Time: 17:19 # 6

Note that the mean was calculated as the mean of the means of each micmphotograph. N indicates the number of microphotographs per time analyzed. Each picture contain an average of 23 cells per field.

[CircaDB, Pizarro et al., 2013], that two proteins involved in the nucleation of PBs, Tristetraprolin (TTP) and BRF-1 (Franks and Lykke-Andersen, 2007), present oscillations in the levels of their messengers in murine liver (**Supplementary Figures S1**, **S2**, from CircaDB). All this together led us to analyze whether PBs show oscillations over time.

In this work we analyze whether PBs present rhythms in cultures of Neuro 2A cells, which have already been used for circadian studies (Chilov et al., 2001; Margadant et al., 2007; Repouskou et al., 2010; Chang and Guarente, 2013). These cells were established from mouse neuroblastoma, so they grow continuously and do not stop dividing under normal culture conditions (10% FBS-MEM). For this reason we first test with which serum concentration the cells stop dividing and thus prevent the progress of the cell cycle from interfering with the determinations of the analyzed variables (see section "Materials and Methods"). When the cultures reached 60–70% confluence, the cell population was synchronized with dexamethasone for 1 h and subsequently maintained at 0.25% FBS-MEM until they were fixed at the indicated time-points. PBs were detected by ICC with an antibody recognizing the GE-1/HEDLS marker. Microphotographs were taken and the PBs were quantified with ImageJ (see section "Materials and Methods"). The number of PBs showed oscillations over time (**Supplementary Figure S3**). The determinations were made between 8 and 68 h postsynchronization, in that lapse two peaks and two valleys can be observed in the three parameters analyzed. In **Supplementary Figure S3A** are shown representative pictures of these timepoints (left panel) and the same photos with the mask that was obtained with the quantification procedure used (right panel). **Supplementary Figure S3B** shows the result of quantifying the number of PBs normalized by the area occupied by cells, the average intensity of the signal obtained after subtracting the background, and the average area of the granules at the different times analyzed. The three variables analyzed showed a similar profile. All three parameters showed significant changes over time (Kruskall–Wallis test, **Supplementary Table S1** and **Supplementary Tables S2–S4** show Dunn's multiple comparison tests for the three parameters). The MetaCycle R package was used to determine whether the time series of data were rhythmic (Wu et al., 2016). This package applies statistical tests that determined that the parameters cycle with periods compatible with circadian rhythms, in addition to estimating amplitude and phase of oscillations (**Supplementary Table S1**). The number of PBs was the rhythm that showed the greatest amplitude. When the experiment was repeated, similar results were obtained. While the period of oscillations in signal intensity and area were comparable in the two experiments (signal intensity 24.17 vs. 22.81; area 23.34 vs. 22.46), the period of the number of PBs showed differences (30.52 vs. 23.15). In any case, taking into account that the variable is not being measured continuously and the sampling frequency (every 4 h), it is to be expected that the period cannot be determined precisely and the differences that are observed in the period estimations.

In order to corroborate with another marker the existence of the oscillations described in PBs, we performed double immunostaining experiments analyzing DDX6 [also known as P54/RCK, another recognized PB marker (Kedersha and Anderson, 2007)] together with GE-1/HEDLS. The cultures were synchronized and the same time-points were taken as in **Supplementary Figure S1**, although in this case two antibodies were used in the ICC. **Figure 1** and **Table 1** show that the number, signal intensity and area of PBs showed relatively similar profiles with both markers. All three variables, with both antibodies, showed statistically significant changes over time in the form of periodic oscillations of at least two cycles (Kruskall–Wallis test and MetaCycle, **Table 1**; **Supplementary Tables S5–S10**,

Dunn's multiple comparison tests. n = 16–24 pictures per timepoint). In each microphotography there were 23.6 ± 9.1 nuclei (average ± SD); that is, between 378 and 566 cells were analyzed at each time-point. Remarkably, all three parameters showed higher levels in the second cycle when PBs were detected with anti-DDX6 (**Figure 1** and **Table 1**). The PB number was the variable that showed the greatest amplitude with the two markers. It was normalized by the surface occupied by the cells since several of them were not completely included in the pictures. Considering this, when the PB number was divided by the number of nuclei, on average this coefficient was between 2.3 and 7.9 per cell when GE-1/HEDLS was used and between 2.7 and 10.8 with DDX6. The period estimated with the two antibodies was very similar, 23.57 h with anti-GE-1/HEDLS and 23.33 h with anti-DDX6. When only granules marked with both markers were considered, as expected the number decreased. Even so the behavior over time remained rhythmic (Fig. 1. p = 2.19E-11 by MetaCycle). When the signal intensity was analyzed, the overall values were different with each antibody, this is to be expected since they are different antibodies and fluorophores, although the time pattern was relatively similar but with greater amplitude for GE-1/HEDLS (**Figure 1** and **Table 1**). In the case of the PB area, as anticipated, the values were reduced since in general the overlap of the signal was not complete, which means that not all the pixels recognized with one antibody as a PB matched exactly with those detected by the other. However, the temporal behavior also showed to be statistically rhythmic (p = 5.3E-4). This experiment was repeated and showed similar results; therefore, these oscillations were demonstrated in 4 independent experiments with anti-GE-1/HEDLS and in 2 with anti-DDX6. In these repetitions minor changes in the phase and amplitude of the rhythms are observed, this is expected since the measurements were not made in a continuous way but with intervals of 4 h and taking different samples in each time-point; nevertheless the temporal profiles are similar in all the cases.

#### CONCLUSION

This work shows for the first time oscillations in the dynamics of PBs. These rhythms were evidenced both in the number, intensity levels of the marker used, and the area of these cytoplasmic ribonucleoprotein granules with two different markers. A previous study had shown differences in the number of PBs when comparing two time-points in U2OS cells, as well as that this difference disappeared by silencing a clock gene essential for the functioning of the circadian clock molecular mechanism (Jang et al., 2015). Taken together, these works indicate that PBs are modulated by circadian clocks. Interestingly, both we (manuscript submitted) and another group (Wang et al., 2019), have found that other type of mRNA granules, the stress granules, also have temporal variations in their number.

In this brief report we limit ourselves to presenting the phenomenon, we have not explored the mechanisms by which the rhythms described in PBs are generated. A plausible hypothesis is that the circadian clock control the levels of factors that can induce the formation of PBs. In fact, changes in the rate of translation of a marker of these foci, LSM1, suggested to Jang and colleagues that PBs could be controlled by the circadian clock (Jang et al., 2015). We searched the CircaDB database (Pizarro et al., 2013) for messengers of two other of these proteins, TTP and BRF1 (Franks and Lykke-Andersen, 2007), that oscillated in their levels, and in fact do so (**Supplementary Figures S1**, **S2**). Other proteins that induce the formation of PBs may also participate in the generation of the observed rhythms. In addition to changes in the concentration of these proteins, post-translational modifications that induce the phase transitions that form PBs have also been described. If any of these modifications are rhythmically controlled, this could also contribute to the phenomenon described.

It is currently postulated that PBs are membrane-free compartments where transcripts are stored (Hubstenberger et al., 2017; Standart and Weil, 2018). They have also been involved in translation silencing and mRNA degradation [discussed in Decker and Parker (2012), Ivanov et al. (2018)]. Since at the beginning of the century technologies were available to analyze the transcriptome and the proteome globally, circadian experiments were performed that demonstrated a significant number of genes (∼50%) showed, contrary to expectations, a poor correlation between the abundance rhythms of their mRNA and protein (Reddy et al., 2006; Mauvoisin et al., 2014; Robles et al., 2014). The presence of many of these messengers in PBs, instead of in polysomes, could explain that even being abundant, their corresponding proteins would not be present in the same magnitude.

Considering the evidence suggesting that PBs store groups of messengers that participate in the regulation of specific pathways [mRNA regulons (Hubstenberger et al., 2017)], the fact that these foci are rhythmically regulated would contribute to understand how clocks can control a number of circadian rhythms at a posttranscriptional level.

#### DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article/**Supplementary Material**.

#### AUTHOR CONTRIBUTIONS

MM, LS, and LP: acquisition, analysis and interpretation of data, making of the figures, revising the work, and design critically. EG-P: conception and design of the work, and manuscript writing. All authors contributed to the manuscript revision, and read and approved the submitted version.

#### FUNDING

The authors would like to acknowledge the support of the Agencia Nacional de Promoción Científica y Tecnológica (PICT 2013-2680), the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), and the Universidad Nacional de Córdoba of Argentina for providing facilities and grants to carry out the research.

#### ACKNOWLEDGMENTS

fncel-13-00487 October 25, 2019 Time: 17:19 # 8

The authors appreciate the technical assistance of Drs. Alejandra Trenchi, Susana Deheza, Gabriela Schachener, and the members

#### REFERENCES

of the "Centro de Microscopia Óptica y Confocal Avanzada de Coìrdoba (CEMINCO)", integrated to the "Sistema Nacional de Microscopia (SNM-MINCyT)" for microscopy assistant.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fncel. 2019.00487/full#supplementary-material

gene expression. Genome Res. 25, 1836–1847. doi: 10.1101/gr.1912 96.115

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Malcolm, Saad, Penazzi and Garbarino-Pico. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Stress, Dietary Patterns and Cardiovascular Disease: A Mini-Review

Luis Pedro Morera1 \*, Georgina Noel Marchiori2,3, Leonardo Adrián Medrano1,4 and María Daniela Defagó2,3

1 Instituto de Organizaciones Saludables, Universidad Siglo 21, Córdoba, Argentina, 2 Instituto de Investigaciones en Ciencias de la Salud (INICSA), CONICET, Universidad Nacional de Córdoba, Córdoba, Argentina, 3 Escuela de Nutrición, Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Argentina, 4 Department of Pyschology, Pontificia Universidad Católica Madre y Maestra, Santiago de los Caballeros, Dominican Republic

According to the World Health Organization, an unhealthy diet and insufficient physical activity are the leading global risks to health. Dietary behavior is a modifiable factor in cardiovascular disease (CVD) prevention. Furthermore, the fact that cardiovascular events and stress-related emotional disorders share a common epidemiology may indicate the existence of pathways linking these two diseases (Chauvet-Gelinier and Bonin, 2017). Psychosocial stress can lead to changes in dietary patterns (DP) and under chronic stress conditions, high caloric and hyperpalatable foods are preferred. The interplay between these two factors impacts on several biological pathways: for example, it can prime the hippocampus to produce a potentiated neuroinflammatory response, generating memory deficits; it can also affect gut microbiota composition, ultimately influencing behavior and brain health and creating a predisposition to the development of diseases such as obesity, CVD, diabetes and metabolic syndrome. Though both cognition and emotion can be heavily affected by caloric intake, diet composition and stress, the molecular pathways involved remain elusive (Spencer et al., 2017). In this review, we describe the interplay between stress and DP at a molecular level, and how these factors relate to brain health and mental fitness. Finally, we show how these findings could give rise to novel therapeutic targets for chronic diseases.

Keywords: stress, cardiovascular disease, dietary patterns, nutrition, microbiome

# INTRODUCTION

Cardiovascular disease (CVD), is the leading cause of mortality and disability worldwide. According to the Global Burden of Disease study, ischemic heart disease and stroke accounted for 25% of total deaths worldwide in 2013 (Rubinstein et al., 2010; GBD 2013 Mortality and Causes of Death Collaborators, 2015).

The current epidemic of CVD is largely explained by several modifiable risk factors associated with lifestyle, feasible to modify. In this sense, an unbalanced diet, excessive alcohol and tobacco consumption, hypercholesterolemia, diabetes mellitus, high blood pressure, visceral obesity, physical inactivity and psychosocial stress increase the risk of future CVD events and are responsible for an estimated 90% of the population-attributable risk fraction of

#### Edited by:

Gabriela Alejandra Salvador, Universidad Nacional del Sur, Argentina

#### Reviewed by:

Marcelo Sergio Vatta, Universidad de Buenos Aires, Argentina Sara Pauwels, KU Leuven, Belgium

> \*Correspondence: Luis Pedro Morera<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neuroendocrine Science, a section of the journal Frontiers in Neuroscience

Received: 15 January 2019 Accepted: 29 October 2019 Published: 12 November 2019

#### Citation:

Morera LP, Marchiori GN, Medrano LA and Defagó MD (2019) Stress, Dietary Patterns and Cardiovascular Disease: A Mini-Review. Front. Neurosci. 13:1226. doi: 10.3389/fnins.2019.01226

**436**

ischemic heart disease and stroke worldwide (Schächinger et al., 2000; O'Donnell et al., 2010; Uthman et al., 2014; Karmali et al., 2017). Even more stress-related psychological disorders such as depression show a higher prevalence in patients with coronary artery disease or heart failure compared with the general population (Lane et al., 2002; Ruo et al., 2003; Rutledge et al., 2006).

Chronic or acute exposure to stress (defined as an ongoing or anticipated threat to homeostasis or well-being) favors to the destabilization of the dynamic balance of the organism, and its response promotes the release of chemical mediators that affect the metabolic and behavioral state in humans. Stress-induced activation of the neuroendocrine hypothalamic-pituitary-adrenal axis (HPA), upon exposure to a stressor cortisol is expected to exert widespread metabolic effects, which is mostly necessary to maintain or restore homeostasis. Stress, perhaps, is the trigger in the cascade of neuroendocrine effects that drive the development of the visceral distribution of adipose tissue, resistance to insulin and the consequent hyperinsulinemia, thus leading to the accumulation of cardiovascular risk factors (Serrano Ríos, 2005). Since glucocorticoids favor increased adiposity, mainly, abdominal fat, they can lead to increased appetite, affecting the quantity and quality of the diet (increased sweet and high fat food intake), and body weight gain. Thus, exposure to stress can modify eating behavior. These stress-induced alterations in food intake and energy balance then interacting with emotional state and health cardiovascular, **Figure 1** (Epel et al., 2001; Groesz et al., 2012). Though a relationship between nutritional changes and emotional state and brain health is already known to exist, the exact nature of this relationship has not been established. Research over the last decade has focused on bidirectional communication between gut and brain, termed the gut-brain axis. Dysbiosis and inflammation of the gut have been linked to several mental illnesses including anxiety and depression, although the molecular pathways involved have not yet been elucidated (Clapp et al., 2017).

The aim of this mini-review is to summarize the findings in the literature on how stress influences the molecular pathways affecting behavior, dietary patterns (DP) and their impact on health, and the synergistic effects of this cycle.

**Figure 1** The potential biological mechanisms that connect the observed relationships between DP, stress and mental health could be modulated by the immune and endocrine systems, brain plasticity and the microbiome-gut-brain axis, being key targets for nutritional therapy in future research.

### METHODS

The authors conducted a literature search of available sources describing issues relating to stress, DP and behavior. Research studies were selected based on research topics found in globally acknowledged databases such as Web of Science, PubMed, Springer, and Scopus, from 2010 up to the present time and classified according to their relevancy. The information provided in the selected studies was carefully evaluated and is described and discussed in the following sections.

### NUTRITION AND CARDIOVASCULAR DISEASE

Diet is one of the most studied factors in the pathogenesis of CVD since it affects diverse cardiometabolic risk factors such

as obesity, lipid blood profile, blood pressure, glucose-insulin homeostasis, endothelial health, adipocyte metabolism, cardiac function, metabolic expenditure, weight regulation, visceral adiposity, and the microbiome (Mozaffarian, 2016).

Dietary patterns refers to the habitual combinations of foods and meals consumed on a daily basis (Hu, 2002). Several DP have been related to beneficial cardiovascular effects. For example, the Mediterranean DP -characterized by moderate energy intake, high consumption of olive oil, fish, legumes, nuts, fruits, and vegetables; fewer red meats and processed (sodiumpreserved) meats; and a regular and moderate consumption of red wine- is associated with low CVD incidence and mortality (Shen et al., 2015; Sanches Machado d'Almeida et al., 2018). This DP and others such as Prudent, DASH or Healthy DP are high in fiber, vitamins, antioxidants, minerals, phenolics, and unsaturated fats, and low in glycemic index, glycemic load, salt, and trans fat (Esmaillzadeh et al., 2007; Mozaffarian et al., 2011). The Western DP on the other hand, characterized by a high intake of red meats, fat dairy products, refined grains and sugars, has been positively correlated with higher concentrations of markers of endothelial dysfunction, the first step in CVD: fasting insulin, C peptide, leptin, C reactive protein, homocysteine, tissue plasminogen activator antigen, interleukin 6 (IL-6), E-selectin, intercellular adhesion molecule 1 (sICAM-1) and vascular cells of molecular adhesion 1 (sVCAM-1) (Defagó et al., 2014; Rodríguez-Monforte et al., 2015; Marchiori et al., 2017).

In the long term, not only the quality but also the quantity of nutrients consumed can influence the neural circuits that regulate motivation, emotion and mood. Evidence can be found in literature about the relationship between saturated, trans fat intake and the risk of mental disorders (Barnard et al., 2014). Chronic exposure to a high-fat diet may affect the underlying neurobiological pathways of emotion and reward via its action on energy metabolism, endocrine function and immunity. Saturated and trans fatty acids favor central fat deposition and have been related with cardiometabolic and neurological diseases (Micha and Mozaffarian, 2008). The omega-3 polyunsaturated fatty acids (PUFAs) participate in modulation inflammation and immunospecific response, cell growth and tissue repair. Omega-3 plays a role in neuronal membrane fluidity and receptor function and lower levels of these PUFAs have been associated with common mental disorders such as depression and generalized anxiety, also with accelerated neurodegeneration (Grant and Guest, 2016).

Acute stress exposure (short term exposure) may shut down appetite by corticotropin-releasing hormone action and epinephrine liberation. However, if the stressor agent persists, the increased cortisol increases appetite and the motivation to eat (Razzoli et al., 2017). Experimental studies have demonstrated how chronic stress exposure increases susceptibility to diet-induced obesity, with induced spontaneous binging and hyperphagia and a preference for highly-palatable food, rich in calories, trans fat, salt and sugar (Packard et al., 2014; Ulrich-Lai et al., 2015). Research on human behavior shows a strong link between exposure to stress and binge eating disorder, often associated with development the overweight and obesity. Stress-induced non-nutritive food selection, is often referred to as eating "comfort food" or highly palatable food (Leigh et al., 2018).

### NUTRITION, MICROBIOME AND CARDIOVASCULAR DISEASE

The human gut microbiome contains 1014 resident microorganisms, among which bacteria are the most well-studied group, predominated by gram positive Firmicutes and gram negative Bacteroidetes (Cresci and Bawden, 2015). The collective genome of the microbiome contains millions of genes compared to the approximately 25,000 genes of the human genome and thus contributes to a wide range of biochemical and metabolic functions, such as nutrient acquisition, the harvesting of energy and large numbers of host metabolic pathways.

Inflammatory related diseases has been related to intestinal microbiome composition (bowel and skin diseases, autoimmune arthritis, type 2 diabetes, and atherosclerosis among others) (Buford, 2017). Specifically, an abnormal change in gut flora has been linked to a range of CVD risk factors, including obesity and diabetes. The first analyses on human intestinal microbiota reported a lower amount of Bacteroidetes than Firmicutes in obese individuals, however, these findings have not been consistently demonstrated in all subsequent metagenomic studies on obesity in humans (Ley, 2010). A lower abundance of butyrate-producing bacteria, in particular Faecalibacterium prausnitzii, a major member of the Firmicutes phylum, in individuals with obesity-related metabolic disturbances and diabetes has been observed. Furthermore, the presence of this bacterium is inversely correlated with the intake of dietary fat. Other studies have identified increased abundance of Bifidobacteria species in healthy individuals compared with individuals with obesity and diabetes (Martín et al., 2017). Dysbiosis has also been linked to CVD and metabolic diseases such as a lower ratio of Bacteroidetes to Firmicutes in obesity and hypertension, or increased Collinsella in atherosclerosis (Brahe et al., 2016).

Several DP have been studied for their ability to modulate the intestinal microbiota. In general terms, a Western DP led to a marked decrease in numbers of total bacteria and beneficial Bifidobacterium and Eubacterium species, whereas Mediterranean and Prudent DP have been linked to increases in Lactobacillus, Bifidobacterium, and Prevotella, and decreases in Clostridium (Koloverou et al., 2016; Singh et al., 2017). However, dietary manipulation to modify gut microbiome is still at the incipient stage It has been detected that dietary fiber including prebiotics has a beneficial effect on gut microbiota and host health, improving insulin sensitivity, low-grade chronic inflammation, and lipid metabolism (Ahmadmehrabi and Tang, 2017). Prebiotics are non-digestible dietary compounds that stimulate growth or activity of autochthonous microorganisms, resulting in health benefits. They are basically oligo- or polysaccharides of fructose or galactose and also lactulose and lactitol; they promote the development of bifidobacteria, which

are capable of degrading various complex carbohydrates. Many Firmicutes and in particular Bacteroides are also able to carry out this function (Suárez, 2013). Prebiotics are naturally present in foods such as wheat, garlic, onion, asparagus, leek, beet, chicory root, and artichoke, among others; they can also be added to products such as milk, yogurt and breakfast cereals. In this regard, adherence to a DP rich in fruits, vegetables and whole grains may modulate the gut microbiome and thus favorably impact on human health.

#### GUT BRAIN AXIS AND STRESS

The mechanisms underlying microbiota-gut-brain axis communication involves neuro-immune-endocrine mediators. This interconnected network includes the central nervous system (CNS), the autonomic nervous system, the enteric nervous system and the HPA (Farzi et al., 2018).

In an organism exposed to stress, the hypothalamus stimulates the pituitary by releasing corticotropin-releasing factor (CRF). In the anterior pituitary, CRF causes the release of the adrenocorticotropic hormone (ACTH), which in turn stimulates the adrenals to cause an increased rate of synthesis and release of cortisol (cortisol in humans and corticosterone in rodents) (Foster et al., 2017). Cortisol serves to maintain homeostasis during normal states of activity (regulating cellular function and metabolism) and during periods of stress (Christiansen et al., 2007).

Chronically elevated cortisol levels can disturb brain function, affecting cognition, emotion and motivation, short and longterm memory; sustained HPA axis activation also impairs the immune system response, as has been demonstrated particularly in early life stress (Shirtcliff et al., 2009). Cortisol exerts it's effects trough the interaction with two corticoid receptors, high affinity mineralocorticoid receptors (MR) and low affinity glucocorticoid receptors (GR). A persistently activated HPA axis will eventually lead to a compensatory downregulation of the expression of GR signaling through epigenetic modifications (Weaver et al., 2004). In addition, glucocorticoids increase intestinal permeability, and negatively affects the gut microbiota composition.

Stress-related changes could be also mediated by neuroendocrine hormones, such as norepinephrine (NE) and dopamine (DA); it has long been known that these catecholamines can increase the growth rate of Gram-negative bacteria (Lyte and Ernst, 1992).

In turn, changes in gut microbiome composition has been already related to cognitive disorders and mental illness. There is strong evidence linking major depression and microbiome, and preclinical evidence related to anxiety disorders (Foster and McVey Neufeld, 2013; Malan-Muller et al., 2017).

#### LINKING MICROBIOME AND NEURONAL SIGNALING: MECHANISTIC EVIDENCE

Further research is required to elucidate the exact mechanisms and mediators involved in brain-microbiota communication. A central issue that remains unresolved is which mediators the microbiota uses to communicate with the brain and how this network ultimately influences behavior and health.

There are three major systems along with their mediators playing a role in inter-talk between the microbiota and the brain: neuronal communication, endocrine signaling mediators and the immune system mediators. Together, these systems create an integrated molecular network that impact both in gut and brain function.

The gut communicates with the brain through a systemic, central route. The vagus nerve sends information to the brain, where it is processed in the nucleus tractus solitarius; this nucleus then projects to the parabrachial nucleus, which in turns projects to the prefrontal cortex (PFC) and amygdala. These latter are key loci that control anxiety and fear responses. It has been reported that both regions display irregularities in germ free animals (GF), including hypermyelination in the PFC.

Previous papers have shown that the N-methyl-D-aspartate (NMDA) receptor subunit (NMDArec2B) has reduced expression in the amygdala of GF animals (Neufeld et al., 2011). Furthermore, it has been demonstrated that feeding prebiotics to these animals elevates their levels of brain-derived neurotrophic factor (BDNF), NMDA receptor subunits, and D-serine in the brain. Moreover, GABA receptor and serotonin 1A receptor are upregulated in the amygdala and hippocampus, respectively, upon supplementing GF animals with a Lactobacillus strain, ultimately regulating behavior in mice (Bravo et al., 2011).

#### TABLE 1 | Molecular signaling pathways linking the microbiome with stress-related health outcomes.

The biogenic amine Serotonin [5-hydroxytryptamine (5-HT)] exerts its function in the brain and in the ENS. Most of 5-HT is synthetized by gut mucosal enterochromaffin cells and ENS neurons. Peripherally, 5-HT is involved in gastrointestinal function, smooth muscle contraction and relaxation, and in pain perception. In the brain it is involved in regulating mood and cognition (Yano et al., 2015). Serotonin synthesis depends on the availability of tryptophan, an essential amino acid that must be supplied in the diet, and microbiota plays a central role in the regulation and synthesis of this amine.

Together these findings reinforce the concept that DP can have either a positive or a negative impact on the CNS by regulating critical neurotransmitters implicated in psychiatric disorders, such as depression (Wallace and Milev, 2017; Zalar et al., 2018).

Finally, the immune system plays an important intermediary role between the brain and the gut. Gut-associated lymphoid tissue is the main component of mucosal-associated lymphoid tissue and corresponds to almost 70% of the entire immune system (Vighi et al., 2008). Dysbiosis of the gut microbiota is linked to abnormal immune responses, which are accompanied by irregular cytokine synthesis and release. It has been reported that TNFα and IFNγ production capacity appears to be influenced by the microbiome, whereas cytokines such as IL-1β, IL-6, and Th17-derived IL-17, and IL-22 display fewer, but more specific, associations with the gut microbiota (Schirmer et al., 2016).

**Table 1** Mechanistic pathways, mediators and main microbiome modifications associated with stress related psychological disorders. Molecular signaling pathways linking the microbiome with stress-related health outcomes.

Several studies related to preclinical models of bacterial infections, probiotics treatment and analysis of germ-free (GF) animals suggest that the microbiota can influence the CNS and consequently behavior. Stress-related disorders such as anxiety and depression are among the main psychiatric conditions associated with microbiome changes (Desbonnet et al., 2010, 2015; Bravo et al., 2011).

#### REFERENCES

#### CONCLUSION

The treatment of multidimensional diseases such as CVD require multidimensional approaches. Nutritional psychiatry is a growing field of research seeking to provide clinically relevant interventions for multifactorial diseases. This emerging line of inquiry has assembled data on biological pathways such as gastrointestinal microbiota and inflammation, on DP and on how these are modulated by the environment. It has become clear that this intertwined network of environmental factors (such as stress and DP) interacting with the above-mentioned pathways combines to modulate behavior.

Some trending foods and (controversial) diets have brought lasting changes to traditional diets, however, in most cases neither these nor dietary supplements have been subjected to adequate clinical trials to test their safety and efficacy. A better understanding of the interplay between diet, stress, neuronal signaling, phenotypes, and the microbiome will provide important insights into the utility of scientific evidence-based nutrition. Future studies are imperative to proactively avoid cardiovascular events in patient population.

#### AUTHOR CONTRIBUTIONS

All authors equally contributed to the drafting, analyses the final version of the manuscript, and read and approved the final version of the manuscript.

#### FUNDING

This work was supported by the Ministry of Science and Technology of Córdoba grant GRFT 2017, Universidad Siglo 21 and SECyT Secretary of Science and Technology of the National University of Córdoba (SECyT).

in mice: implications for brain and behaviour. Brain Behav. Immun. 48, 165–173. doi: 10.1016/j.bbi.2015.04.004

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Morera, Marchiori, Medrano and Defagó. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Function of P2X4 Receptors Is Directly Modulated by a 1:1 Stoichiometric Interaction With 5-HT3A Receptors

Paola Soto, Pablo S. Gaete, Christian Fuentes, Benjamin Lozano, Pamela A. Naulin, Xavier F. Figueroa and Nelson Patricio Barrera\*

Department of Physiology, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile

#### Edited by:

Gabriela Alejandra Salvador, Universidad Nacional del Sur, Argentina

#### Reviewed by:

Ralf Hausmann, RWTH Aachen University, Germany Jeremias Corradi, Instituto de Investigaciones Bioquímicas de Bahía Blanca (INIBIBB), Argentina

> \*Correspondence: Nelson P. Barrera

><EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

> Received: 02 April 2019 Accepted: 07 April 2020 Published: 05 May 2020

#### Citation:

Soto P, Gaete PS, Fuentes C, Lozano B, Naulin PA, Figueroa XF and Barrera NP (2020) Function of P2X4 Receptors Is Directly Modulated by a 1:1 Stoichiometric Interaction With 5-HT3A Receptors. Front. Cell. Neurosci. 14:106. doi: 10.3389/fncel.2020.00106 Interacting receptors at the neuronal plasma membrane represent an additional regulatory mode for intracellular transduction pathways. P2X4 receptor triggers fast neurotransmission responses via a transient increase in intracellular Ca2+ levels. It has been proposed that the P2X4 receptor interacts with the 5-HT3A receptor in hippocampal neurons, but their binding stoichiometry and the role of P2X4 receptor activation by ATP on this crosstalking system remains unknown. Via pull-down assays, total internal reflection fluorescence (TIRF) microscopy measurements of the receptors colocalization and expression at the plasma membrane, and atomic force microscopy (AFM) imaging, we have demonstrated that P2X4/5-HT3A receptor complexes can interact with each other in a 1:1 stoichiometric manner that is preserved after ATP binding. Also, macromolecular docking followed by 100 ns molecular dynamics (MD) simulations suggested that the interaction energy of the P2X4 receptor with 5-HT3A receptor is similar at the holo and the apo state of the P2X4 receptor, and the interacting 5-HT3A receptor decreased the ATP binding energy of P2X4 receptor. Finally, the P2X4 receptor-dependent Ca2+ mobilization is inhibited by the 5-HT3A interacting receptor. Altogether, these findings provide novel molecular insights into the allosteric regulation of P2X4/5-HT3A receptor complex in lipid bilayers of living cells via stoichiometric association, rather than accumulation or unspecific clustering of complexes.

Keywords: receptor-receptor interaction, atomic force microscopy, stoichiometry, P2X4 receptor, 5-HT3A receptor, intracellular Ca2+, ATP

### INTRODUCTION

Receptor-receptor interaction has been an extensive research area with enormous implications in neuronal crosstalking transduction mechanisms, which usually provide new molecular ways to tune and regulate the receptor function (Barrera et al., 2005b; Ferre et al., 2007). Ionotropic receptors located at the central nervous system, such as those belonging to the cys loop family, have been found to form macromolecular complexes composed by two or more different receptors (Ferre et al., 2007). In particular, P2X2 and 5-HT3A receptors are co-expressed at the plasma membrane and physically interact with each other in myenteric neurons and heterologous systems. This interaction depends on the second intracellular loop of the 5-HT3A subunit and an unknown region of the P2X2 receptor that was initially thought to be the C-term tail (Boué-Grabot et al., 2003). As a functional consequence, the inhibition of the additive responses attained with serotonin and ATP was observed. Similar receptor-receptor interactions and inhibitory crosstalking have been observed between P2X and nicotinic acetylcholine (nACh) receptors (Barajas-López et al., 1998) and between P2X and GABA receptors (Jo et al., 2011). Recently, Emerit et al. (2016) showed that the 5-HT3A receptor can colocalize with the P2X4 receptor at the plasma membrane of hippocampal neurons. Nevertheless, it remains unknown the stoichiometry of these interacting receptors, whether the interaction is controlled by the agonist binding and the functional role of this regulatory mechanism.

P2X4 receptor is also regulated by direct interactions with phospholipids. In this context, phosphoinositides PI(4,5)P2 (PIP2) and PI(3,4,5)P3 (PIP3) can modulate P2X4 activity, apparently, by direct interactions with the proximal C-terminal domain of the receptor (C360–V375), which was shown to be required for the development of full receptor function, since depletion of PIP2 and PIP3 dramatically inhibits the P2X4-mediated Ca2+ signal activated by ATP (Bernier et al., 2008). Therefore, as it has been proposed that 5-HT3A receptor may interact with the C-term tail of P2X receptors (e.g., P2X2 receptor), the potential interaction between this receptor and P2X4 receptor may also represent a relevant control mechanism of the phospholipids-mediated P2X4 regulation.

A variety of methods have been continuously developed to tackle the molecular architecture of interacting membrane proteins, from high resolution, such as x-ray crystallography, to complementary biophysical approaches, including cryo-electron microscopy, mass spectrometry and atomic force microscopy (AFM). Based on the surface scanning of the sample, AFM has already been used to characterize the molecular architecture of individual P2X and 5-HT3 receptors (Barrera et al., 2005a,c, 2007; Antonio et al., 2011), among others.

Herein, via pull-down assay of interacting receptors, AFM imaging, macromolecular docking, molecular dynamics (MD) simulations, and total internal reflection fluorescence (TIRF) microscopy analysis, we propose that P2X4 receptor physically interacts in a 1:1 stoichiometric manner with 5-HT3A receptor, which is maintained after ATP binding. By measurements of intracellular Ca2+ levels, we further confirmed that the interacting 5-HT3A receptor inhibits the response to ATP of the P2X4 receptor. Altogether, these findings provide insights into the inhibitory responses triggered via stoichiometric binding of interacting receptors, which consequently support the notion that interacting receptors in specific numbers rather than receptor aggregation are involved in crosstalking neuronal responses.

## MATERIALS AND METHODS

### Expression of P2X4/5-HT3A Receptor Complexes on tsA201 Cells

tsA201 cells (Sigma–Aldrich, St. Louis, MO, USA. Cat. # 96121229-1VL) were grown in DMEM medium (Gibco, Grand Island, NY, USA), supplemented with 10% fetal bovine serum (FBS, Gibco, Grand Island, NY, USA), 100 unit/ml penicillin (Sigma–Aldrich, St. Louis, MO, USA) and 100 µg/ml streptomycin (Sigma–Aldrich, St. Louis, MO, USA). Cells were maintained at 37◦C in a humidified 5% CO2–95% air atmosphere incubator. To induce the expression of P2X4 and 5-HT3A receptors, tsA201 cells with a confluence of 40–60% were transfected with the pDNAs of P2X4 and 5-HT3A receptors using polyethyleneimine (PEI, Sigma–Aldrich, St. Louis, MO, USA) as transfection reagent. Briefly, 10 ml of DMEM without serum were mixed with 275 µl of 1 mg/ml PEI alone (mock transfection) or plus pDNAs for P2X4 and/or 5-HT3A (25 µg each) and left for 15 min at room temperature. Then, cells were incubated with this mixture for 24 h at 37◦C. For transfection, the following constructs were used; rat P2X4 (Rattus norvegicus, GenBank: U47031.1) with a C-terminal hemagglutinin (HA) tag and human 5-HT3A (Homo sapiens, GenBank: AK304630.1) with a C-terminal MYC/His-6 epitope tag, subcloned into the vector pcDNA3.1 (Invitrogen). Immunofluorescence analysis and Ca2+ measurements were performed using cells seeded onto sterile glass coverslips.

### Measurements of Intracellular Ca2+ Levels

Ca2+ measurements were performed using the fluorescent Ca2+ indicator, Fluo 4, as described recently by Lillo et al. (2018). To upload Fluo 4, cells were incubated with 5 µM Fluo 4-AM for 45 min in a MOPS-buffered solution (composition in mM: 118 NaCl; 5.4 KCl; 2.5 CaCl2; 1.2 KH2PO4; 1.2 MgSO4; 11.1 glucose and 5 MOPS) adjusted to pH 7.4. The fluorescent indicator was washed out and the experiments started after 15 min of equilibration. Cells were visualized using an Olympus BX50 WI microscope and the fluorescent signal was recorded using an intensified CCD camera (Retiga Fast 1394, QImaging) and IPLab software. Images were acquired every 2 s at basal conditions and during 2 min of stimulation. Changes in intracellular Ca2+ levels were expressed as the variations of the fluorescence intensity along the time (F/F0), where F is the fluorescence detected during the recording and F0 is the basal fluorescence, or the maximum net fluorescence observed after stimulation (∆F/F0). Three concentrations of ATP (1, 10 and 100 µM) were tested and, to confirm the activation of P2X4 receptors, 30 µM PPADS was preincubated during 15 min before the ATP application. Both reagents were poured over the cultured cells.

#### Confocal and Epifluorescence Microscopy

Cells were fixed with 1% formaldehyde and then washed with PBS solution (composition in mM: 136.9 NaCl; 2.68 KCl; 10.44 NaH2PO4; 1.76 KH2PO4) adjusted to pH 7.4. Nonspecific protein binding sites were blocked with PBS containing 0.1% FBS. Cells were incubated overnight with an anti-MYC monoclonal antibody to detect 5-HT3A receptor (1:250, Thermo Fisher Scientific, Cat. # PA1-981, Rockford, IL, USA) and an anti-HA monoclonal antibody to label P2X4 receptor (1:250 Thermo Fisher Scientific, Cat. # 26183, Rockford, IL, USA). Cells were washed three times for 10 min with PBS and then incubated for 1 h with a secondary antibody conjugated to Alexa Fluorr555 for anti-MYC (1:500, Cat. #A-21424, Molecular Probes, Eugene, OR, USA) or Alexa Fluorr488 for anti-HA (1:500, Cat. # A-11029, Molecular Probes, Eugene, OR, USA). Then, cells were washed and mounted with Fluoromount-G (Electron Microscopy Sciences, Cat. # 17984-25, Hatfield, PA, USA). The fluorescent signal was examined using either an Olympus IX81 confocal inverted microscope coupled with an ORCA R2 Hamamatsu CCD camera or an Olympus BX41 WI microscope coupled with a Jenoptik ProgRes C5 CCD camera. As a negative control, primary antibodies were omitted.

#### TIRF Microscopy

TIRF microscopy imaging was performed using a NIKON Eclipse Ti2-E microscope with the module NIKON H-TIRF with a 100X magnification and up to 100 nm sample depth. Sample preparation was similar to the confocal experiments except for using PBS as mounting protocol and secondary antibodies conjugated to Alexa Fluorr555 and Alexa Fluorr488 to identify anti-HA and anti-MYC primary antibodies, respectively.

### Quantification of P2X4 Receptor Expression and Colocalization of P2X4/5-HT3A Receptor Complexes

Fluorescence intensity of P2X4 receptor expression attained by TIRF and epi microscopies were quantified using the ImageJ software (Schneider et al., 2012). Window and level parameters were optimized to select the Regions of Interest (ROIs) and exclude background noise. Fluorescence intensity was measured as the mean intensity of each ROI in raw images ± SE. For the colocalization analysis of P2X4/5-HT3A receptor complexes, coloc2 plug-in implemented in ImageJ allowed to evaluate the correlation between the pixel's intensity on each channel via Pearson's R coefficient. Note that protein colocalization and expression experiments were performed using the same instrumental acquisition parameters such as light intensity and exposure time for all the images.

## Purification of P2X4/5-HT3A Receptor Complexes

Cells (five flasks of 150 cm2 for each condition) were washed with HBS solution (composition in mM: 50 HEPES; 100 NaCl; 2 EDTA) adjusted to pH 7.6, and then, removed by shaking. Cells were collected in falcon tubes and centrifuged at 6,500 g at 4◦C for 5 min. The pellet was resuspended in a solubilization solution (9 ml, composition in mM: 10 Tris-HCl; 100 NaCl; 5 EDTA, adjusted to pH 7.6), 1% Triton X-100 (Sigma–Aldrich, Cat. # 9002-93-1), a protease inhibitor mixture (complete, EDTA-Free, Roche) and 10 µl PMSF. The sample was incubated on a rotating wheel for 1 h at 4◦C. The supernatant was placed in a Beckman centrifuge tube and subjected to ultracentrifugation at 50,000 g at 4◦C for 1 h. The supernatant, corresponding to the positive control for the plasma membrane protein and named MEMBRANE fraction in Western blot analysis, was mixed with pre-washed anti-HA agarose beads (Thermo Fisher Scientific) and incubated for 3 h at 4◦C. Beads were washed with 10 ml washing buffer (solubilization buffer containing 1% w/v Triton X-100) and centrifuged at 6,500 g three times. One last washing step was performed including a solution of 0.1% CHAPS (3-[(3- Cholamidopropyl)dimethylammonio]-1-propanesulfonate; Cat. # 220201, Calbiochem). Finally, proteins were eluted from beads by incubating them with 200 µl of 0.1% CHAPS plus 6 µl HA peptide (Thermo Fisher Scientific). An identical purification protocol was followed when tsA201 cells were expressing only P2X4 receptors. The ELUTION fraction (200 µl), representing the purified receptor or complex, was stored to perform Western blot analysis and AFM imaging. Five independent purification series were performed for the complex. Note that 100 µM ATP was applied in the eluted sample in solution during 30 min at room temperature, before protein adsorption (50 µl) onto mica.

When P2X4/5-HT3A receptor complexes were pulled down via His6 tag, the purification procedure was similar to that used with the HA tag, except that in this case HisPur Ni-NTA agarose beads were used (Thermo Fisher Scientific) to carry out the extraction of His6 tagged proteins. Later, beads bound to the 5-HT3A-MYC/His6 receptors were deposited in purification columns (Cat. # 450015, Invitrogen), and 200 mM imidazole was used for elution. An identical protocol was followed in tsA201 cells expressing only 5-HT3A receptors. Five independent purification series were performed for each receptor.

### Western Blot Analysis

Purified proteins were separated by 10% SDS-PAGE and transferred onto a nitrocellulose membrane (BioRad, CA, USA). Primary antibodies: anti-MYC (1:500, Thermo Fisher Scientific, Cat. # PA1-981, Rockford, IL, USA), or anti-HA (1:500 Thermo Fisher Scientific, Cat. # 26183, Rockford, IL, USA) were incubated overnight in a PBS solution containing 4% milk at 4 ◦C. HRP-labeled secondary antibodies (1:1,000, Cat. # 621040, Molecular Probes, Eugene, OR, USA) were incubated at room temperature for 1 h in a PBS solution containing 4% milk. The SuperSignalr West Femto chemiluminescent substrate (Thermo Fisher Scientific) was used to detect protein bands. Molecular mass was estimated with prestained markers (Bio-Rad Laboratories, Hercules, CA, USA). The MyECLTM Imager (Thermo Fisher Scientific) was used to reveal the membrane.

### AFM Imaging

Purified protein samples (50 µl from eluted samples) were placed on muscovite micas (Electron Microscopy Sciences, Cat. # 71855- 01, Hatfield, PA, USA) to be adsorbed. After incubation for 15 min at room temperature, the mica was washed with Milli-Q water and dried with nitrogen gas. Images of dry samples were acquired in an AFM (MFP-3D-SA Asylum Research, CA, USA) using the intermittent contact mode. Cantilever was used with a drive frequency of ∼300 kHz and a spring constant of 40 N/m. The force applied to obtain the images was kept as low as possible. The target amplitude was ∼0.5 V and amplitude setpoint ∼0.4 V. Each image (4 µm2 ) was obtained in 15 min and 50 images were analyzed. The molecular volumes of the adsorbed receptor and complex particles were determined from the height and radius of the particle obtained by AFM (Barrera et al., 2008). The molecular volume was calculated from the equation:

$$\text{Vm} = (\pi \text{h/6})(\text{3r}^2 + \text{h}^2) \tag{1}$$

where h is the height of the particle and r is the radius at half the height. The equation considers the protein molecule as a sphere. Histograms of molecular volumes were fit by Gaussian distribution.

To calculate the individual receptor or complex concentration for the analyzed samples, the following equation was used:

$$\text{total micca area } (\mu m^2) \times \text{particle number per}$$

$$\text{M} = \frac{\text{scanning area } (\frac{particle}{\mu m^2}) \times 10^6 (\frac{\mu l}{L})}{\text{dilution volume in micca } (\mu l) \times }\qquad\text{(2)}$$

$$\text{Avogadro Number } (\frac{particle}{mol})$$

where M represents the molarity of the purified particle sample obtained from AFM imaging. For P2X4 receptors, 5-HT3A receptors and P2X4/5-HT3A receptor complexes, molecular volume cut-offs corresponding to their peaks were 403 and 431 (Antonio et al., 2011), 726 and 788 (Barrera et al., 2005a), and 991 and 1209 (**Figure 3**), respectively. Calculated values are derived from five independent purification processes for each sample, where all the samples were adsorbed in the same mica type surface during 15 min.

### Molecular Docking and Molecular Dynamics Simulations of the P2X4/5-HT3A Receptor Complex

The amino acid sequence of rat P2X4 subunit was obtained from UNIPROT (entry P51577) and aligned against the sequence of the zebrafish crystallized structures of P2X4 receptor (Hattori and Gouaux, 2012; PDB codes 4DW0 for the apo structure and 4DW1 for the holo-structure) by using MultAlin (Corpet, 1988). One hundred homology models for the apo and holo-structures were generated by using Modeller 9.18 (Webb and Sali, 2017) and the one having lowest DOPE score for each case was selected and verified by using the web servers MolProbity (Chen et al., 2010) and ProSA (Wiederstein and Sippl, 2007) to evaluate both stereochemical and energetics of each model. From the two final models, N and C-term tails were cut from MET 1 to VAL 28 and from LEU 358 to GLU 388, respectively.

The amino acid sequence of the human 5-HT3A subunit was obtained from UNIPROT (entry P46098) and aligned against the sequence of mouse 5-HT3A receptor structure (Basak et al., 2018; PDB code 6BE1). One hundred homology models for the apo structures were generated by using Modeller 9.18 (Webb and Sali, 2017) and the one having the lowest DOPE were selected and verified by using the web servers MolProbity and ProSA to evaluate both stereochemical and energetics. From the final model, the N-term tail was cut from MET 1 to THR 30 and the intracellular loop consisting from CYS 356 to ALA 411 was cut as well.

Docking of ATP into the P2X4 receptor was performed in AutoDock (Morris et al., 2008). Binding pockets had volumes of 22.5 × 22.5 × 22.5 Å3 and the center of each box was placed as the ligand is seen in the crystal structure. The 100 best conformations were saved and grouped in clusters of root mean square displacement (RMSD) less than 2 Å. Selected dockings for each binding pocket had a negative binding energy and a similar spatial orientation as the crystallized bound ATP.

Due to pentameric and trimeric symmetry of both 5-HT3A and P2X4 receptors, respectively (Hattori and Gouaux, 2012; Basak et al., 2018), there are several possible combinations depending upon their interaction surface. However, we used two experimental evidence to take an appropriate orientation for the receptor complex: (1) choosing the minimal distance between both receptors at the second intracellular loop of the 5-HT3A receptor, which has been postulated as a domain involved in the physical P2X2/5-HT3A receptor interaction (Emerit et al., 2016); and (2) rotating, in the perpendicular plane of the bilayer, all possible conformations of the complex until the largest interaction surface was found since it has been shown that bigger interaction surfaces are more energetically favorable between macromolecular complexes (Casuso et al., 2012). To have a similar comparison between the two dockings, the P2X4 receptor apo structure was aligned to the first resulting docking of the P2X4 holo/5-HT3A receptor complex.

The homology model of the P2X4 receptor in its holo state was combined with three ATP molecules in their binding pocket. Later both apo and holo-structures in the designed position were merged with the structure of the 5-HT3A receptor and were protonated accordingly to the physiological pH. Both complexes were inserted in a pre-equilibrated POPC bilayer (of size 204 × 124 Å2 ) created using the Visual Molecular Dynamics suite (VMD; Humphrey et al., 1996). To do this, both the bilayer and the receptor complex were aligned and all overlapped lipids (nearer than 0.8 Å) and water molecules (nearer than 3 Å) were deleted. The complex of receptor-bilayer was solvated by using the TIP3 water model and NaCl was added to both neutralize and provide a physiological concentration of salt. Simulations were performed using NAMD 2.12 with the temperature at 310 K and pressure at 1 atm with periodic boundary conditions (PBC). The first step was to minimize the energy by simulating 10,000 steps in the NVT ensemble, and then, the NPT ensemble was simulated. One hundred ns MD simulations for both P2X4 apo/5-HT3A and P2X4 holo/5- HT3A receptor complexes were carried out, which correspond to a similar simulation time already applied in our group to study allosteric regulation of P2X4 receptor by ivermectin (Latapiat et al., 2017). To maintain temperature and pressure constant, Langevin dynamics and Nosé–Hoover Langevin piston methods were used for temperature and pressure coupling. To calculate electrostatic interactions, Ewald sums were used with a grid density of 1 Å. Ligand parameterization (ATP) was done using the SwissParam webserver (Zoete et al., 2011) and the CHARMM27 forcefield was used for lipids and protein. Electrostatic and van der Waals interactions between both receptors (or between P2X4 receptor and ATP molecules) within 12 Å cut off, corresponding to the total interaction energy, were measured by using VMD plugin ''NAMDEnergy'' every 40 ps during the simulation.

### Reagents

MOPS, ATP and all chemicals of analytical grade were obtained from Sigma–Aldrich (St. Louis, MO, USA). Fluo 4-AM was purchased in Life Technologies (Eugene, OR, USA) and pyridoxalphosphate-6-azophenol-20 ,40 disulfonic acid (PPADS) in Tocris Bioscience (Ellisville, MO, USA). ATP and PPADS were dissolved in water. Fluo 4-AM was dissolved in DMSO.

### Statistical Analysis

Values are represented as means ± SE. Comparisons between groups were made using unpaired student's t-test or 1-way ANOVA plus Newman–Keuls post hoc test, as appropriate. A value of p < 0.05 was considered significant.

## RESULTS

First, the P2X4 receptor function in transfected tsA201 cells was evaluated by measuring the changes in free intracellular Ca2+ concentration observed in response to ATP application. Stimulation with 100 µM ATP evoked a Ca2+ signal that showed two components: a first fast-transient component that peaked at ∼12 s and decreased thereafter, and a second slow component that starts after ∼60 s (**Figures 1A,B**). In tsA201 cells co-transfected with P2X4 and 5-HT3A receptors, the transient increase in intracellular Ca2+ was blunted (**Figure 1B**) whereas the time course of the second component was similar to that observed in P2X4 transfected cells. Although the Ca2+ response was evaluated at a range of ATP concentrations (1–100 µM), the most striking difference in the initial Ca2+ signaling (first 60 s) between cells transfected with P2X4 receptor alone or co-transfected with P2X4 and 5-HT3A receptors was attained with 100 µM ATP (**Figure 1C**). As expected, the intracellular Ca2+ concentration was not affected by ATP in non-transfected cells (mock, **Figures 1B,C**). Consistent with the activation of P2X4 receptors, the response induced by ATP was almost completely blocked (∼85%) by 15 min pre-incubation with the preferential P2X receptor antagonist PPADS (**Figure 1D**; Lê et al., 1998). Interestingly, the response of co-transfected cells was inhibited by PPADS in the same proportion to that observed in cells transfected with P2X4 alone. As PPADS blocks access to the orthosteric ATP-binding pocket (Huo et al., 2018), these results suggest that the Ca2+ influx via transfected P2X4 receptors is inhibited by the presence of 5-HT3A receptors without interfering with the ATP binding site through direct steric interaction or via a downstream effect.

To test whether or not both P2X4 and 5-HT3A receptors interact with each other, tsA201 cells co-expressing P2X4 and 5-HT3A receptors were identified by confocal, TIRF and epifluorescence analysis using anti-HA and anti-MYC antibodies. As shown in **Figures 2A–C**, confocal imaging revealed that both receptors colocalize in the same cell at the plasma and intracellular membranes. Then, the colocalization of P2X4 and 5-HT3A receptor complexes at the plasma membrane was quantified using TIRF microscopy (**Figures 2D–F**) analysis, which showed a significant colocalization Pearson R index of 0.88 ± 0.09 (**Figure 2G**).

To evaluate a potential direct interaction, P2X4 or 5-HT3A receptors were purified with HA or Ni2+ bound agarose beads from tsA201 cells expressing only one type of receptor. The success and specificity of the purification process was confirmed by Western blot analysis against HA or MYC tag epitopes for P2X4 or 5-HT3A receptors, respectively (**Figures 2J,L**). Electrophoretic migration for each subunit (55 kDa for 5-HT3A and 70 kDa for P2X4) reached a similar molecular weight of that previously published (Barrera et al., 2005a) for 5-HT3A (55kDa) and slightly larger of that reported (Antonio et al., 2011) for P2X4 (65kDa). The difference observed in the electrophoretic migration of P2X4 subunit could correspond to a bigger glycosylation state, which is a posttranslational modification commonly observed for P2X receptors (Barrera et al., 2005c; Ormond et al., 2006; Antonio et al., 2011), since, according to its primary sequence, P2X4 subunit should have a molecular weight of 43.5 kDa. Once both P2X4 and 5-HT3A receptors were co-expressed in tsA201 cells, the purification process was based on pull-down experiments targeting the HA tag epitope in the P2X4 receptor. As a negative control, 5-HT3A receptors expressed alone were not detected in pull-down experiments using the HA-based purification protocol (data not shown). Western blot analysis demonstrated the presence of the P2X4 receptor in the eluted samples, but also a band corresponding to the 5-HT3A subunit was detected by anti-MYC antibody binding, demonstrating that both P2X4 and 5-HT3A receptors interact each other in cells co-expressing both receptors (**Figure 2K**). A similar finding was observed after pulling down the complex by targeting the His6 tag epitope in the 5-HT3A receptor, where a band corresponding to the P2X4 subunit was detected by anti-HA antibody binding (**Figure 2M**). To analyze the stoichiometry of the interaction, complexes formed by interacting P2X4/5-HT3A receptors were recorded by AFM imaging (**Figures 3A–D**). The molecular volumes of the purified samples showed 2 peaks at 489 ± 24 nm3 and 1100 ± 109 nm3 , which are consistent with the detection of P2X4 alone (417 nm3 ; Antonio et al., 2011) and a P2X4/5-HT3A receptor complex with a stoichiometry 1:1; this is 417 nm3 and 757 nm3 (Barrera et al., 2005a; **Figure 3B**). After stimulation of the purified sample with 100 µM ATP in solution before adsorption onto mica for AFM imaging, the proportion of the complex marginally increased from 42% to 51% (area under the double Gaussian curve fit, cut off 800 nm3 ) with no change on the peak values (505 ± 15 nm3 and 1236 ± 62 nm3 ; p > 0.05; **Figure 3D**). All the peaks at histograms were derived from non-linear Gaussian distributions and were elected those that presented the best fits, R2 values corresponding to 0.9307 and 0.9126 for the P2X4/5-HT3A receptor complexes in the absence and presence of ATP, respectively.

To determine the protein concentration of the purified samples obtained from cells transfected with P2X4 or P2X4/5- HT3A receptor complexes, we used the number of protein particles adsorbed into mica, as an indicator of the concentration

batches of cells. Numbers inside bars indicate the number of cells analyzed. Values are the mean ± SE. \*p < 0.05 vs. Mock by 1-way ANOVA plus Newman–Keuls

post hoc test; †p < 0.05 vs. P2X4 by 1-way ANOVA plus Newman–Keuls post hoc test; #p < 0.05 vs. without PPADS by unpaired student's t-test.

determined by the Avogadro Number relationship (equation 2). This analysis showed that P2X4 receptors from single transfected cells and P2X4/5-HT3A receptor complexes from cotransfected cells had a similar concentration of 3.3 ± 0.6 pM and 3.7 ± 1.3 pM (p > 0.05), respectively. However, AFM imaging of purified samples from cotransfected cells also presented an abundant peak at approximately the volume of individual P2X4 receptors, which suggests that pulling down the HA-tagged protein (P2X4 receptor) from the plasma membrane purified the complexes as well as P2X4 receptors alone and, therefore, increased the overall P2X4 expression at the plasma membrane. This correlates well with the TIRF microscopy analysis, where the P2X4 receptor expression in the cotransfected tsA201 cells was approximately two-fold higher than that observed on single transfected cells (**Figure 2H**). Interestingly, according to the analysis of epifluorescence imaging, the total cellular P2X4 receptor expression in tsA201 cells was similar in single and cotransfection experiments (**Figure 2I**), indicating a cellular redistribution of P2X4 receptors depending upon the presence of 5-HT3A receptors.

To calculate interaction energy between P2X4 and 5-HT3A receptors, macromolecular dockings for the interacting receptors were followed by 100 ns MD simulations, considering domains involved in the physical crosstalking between P2X and 5-HT3 receptors (Boué-Grabot et al., 2003; Emerit et al., 2016), and the larger interaction energy between membrane protein complexes observed through larger subunit interfaces (Casuso et al., 2012; **Figures 3E,F**). Total interaction energy (van der Waals and electrostatic energies) of P2X4 receptor at the apo state with 5-HT3A receptor was −301 ± 9 kcal/mol (mean ± SE; **Figure 3E**), which was not statistically different (p > 0.05) from that observed with P2X4 receptor at holo state (−278 ± 12 kcal/mol; **Figure 3F**). Also, the binding interaction energy of 3 ATP molecules to P2X4 receptor holo (−310 ± 12 kcal/mol) was significantly larger than that observed in the presence of interacting 5-HT3A receptor (−168 ± 3 kcal/mol, p < 0.05), suggesting that the inhibitory effect of 5-HT3A receptor could be allosteric, reducing the ATP affinity of the P2X4 receptor.

### DISCUSSION

The P2X4 receptor activation by ATP binding triggered an initial transient increase in intracellular Ca2+ level that was followed by a slow component, where only the transient increase was strongly inhibited by the 5-HT3A receptor, which could correlate well with a reduced Ca2+ influx through P2X receptors. Our data brings a novel outcome for the P2X4/5-HT3A receptor complex interaction. A functional cross-inhibition has been demonstrated between P2X2 and 5-HT3A receptors (Boué-Grabot et al., 2003), but that study was based on simultaneous activation of the receptors by parallel stimulation with ATP and 5-HT. This current report demonstrates that the mere presence of the 5-HT3A interacting receptor in the complex, independent of its agonist, is sufficient to inhibit the P2X4 receptor. A similar inhibitory response has been observed for the mGlu5a

FIGURE 2 | Expression and purification of P2X4/5-HT3A receptor complexes. (A,B) Detection of co-expressed 5-HT3A (A) and P2X4 (B) receptors by confocal immunofluorescence in tsA201 cells via anti-MYC (1:250) and anti-HA (1:250) antibodies, followed by their corresponding secondary antibodies conjugated to Alexa Fluorr555 and Alexa Fluorr488, respectively. The merge of panels (A,B) is shown in (C), where scale bar represents 30 µm. (D,E) Analysis by total internal reflection fluorescence (TIRF) microscopy of co-expressed P2X4 (D) and 5-HT3A (E) receptors individually and merge images (F) in tsA201 cells via anti-HA (1:250) and anti-MYC (1:250) antibodies, followed by their corresponding secondary antibodies conjugated to Alexa Fluorr555 and Alexa Fluorr488, respectively. Scale bar in (F) represents 3 µm. (G) Colocalization analysis of the P2X4/5-HT3A receptor complexes in tsA201 cells (n = 26 from two (Continued)

#### FIGURE 2 | Continued

independent experiments). (H,I) P2X4 receptor expression analysis in tsA201 cells through TIRF (H, n = 26 for P2X4/NRP and P2X4/5-HT3A transfections from two independent experiments) and epifluorescence (I, n = 273 for P2X4 and 254 for P2X4/5-HT3A transfections from two independent experiments) measurements. Non-related plasmid (NRP) corresponds to pDNA IRES-GFP (0.5 µg each well) used in the same amount as the other receptor plasmids. Data are shown as mean ± SE. ∗p < 0.05 vs. P2X4/NRP expression by unpaired t-test. (J,L) Western blot analysis of the purified samples from tsA201 cells expressing only 5-HT3A (J) or P2X4 (L) receptors identified by anti-MYC or anti-HA antibodies, respectively. Specific bands in plasma membrane enriched fraction (MEMBRANE) and purified (ELUTION) samples were observed at 55 kDa (J) for 5-HT3A and 70 kDa (L) for P2X4 subunits. (K,M) Western blot analysis of purified samples from tsA201 cells co-expressing 5-HT3A and P2X4 receptors after pulling down the complexes with either the HA-tag purification (K) or His6-tag purification (M) method. Bands corresponding to 5-HT3A (anti-MYC antibody; K) and P2X4 subunits (anti-HA antibody; M) were detected at similar molecular weights compared to those present after single transfection. Arrowheads indicate molecular mass markers in kDa.

receptor agonist-independent effect on the NMDA receptor (Perroy et al., 2008). Nevertheless, it remains to be determined whether the activation of the 5-HT3A receptor can further inhibit the P2X4 receptor-dependent ATP response or the P2X4 receptor apo could be able to inhibit the activated 5-HT3A receptor, which would enlighten the whole mechanistic scenario of these ionotropic receptors' interaction.

Our data are consistent with the notion that P2X4/5- HT3A receptor interaction results in an allosteric inhibition of ATP-induced P2X4 activation. To further support this proposal, we performed additional experiments to reach the saturation of the concentration-response curve elicited by ATP in cells transfected with P2X4 receptor alone or co-transfected with P2X4 and 5-HT3A receptors. However, stimulation with 1 mM ATP induced an increase in intracellular Ca2+ concentration in non-transfected cells (mock cells), which precluded the direct comparison of the response observed at different ATP concentrations (data not shown), and then, the analysis of the Emax and EC50 observed in response to ATP, as required to confirm the presence of an allosteric interaction of 5-HT3A receptors with P2X4 receptors.

An alternative explanation to the inhibitory action of 5-HT3A receptors on the P2X4 receptor-triggered Ca2+ increase would be a reduction of the expression of P2X4 receptors at the plasma membrane in cotransfected cells. Conversely, we found a similar total expression of the P2X4 receptor between transfected tsA201 cells containing P2X4 and P2X4/5-HT3A subunits. Furthermore, the expression of P2X4 receptors specifically at the plasma membrane, revealed by TIRF microscopy analysis, indicated a two-fold increase of the purinergic receptor in cotransfected cells. Interestingly, a redistribution of P2X4 receptor from intracellular locations to the cell surface without changing total cellular expression has been demonstrated after activation of the C-C chemokine receptor type 2 in microglial cells, which is mediated by delivery of lysosomal P2X4 receptor to the plasma membrane (Toyomitsu et al., 2012). Besides, the P2X4 receptor has been shown to colocalize with the 5-HT3A receptor at the cell surface of

hippocampal neurons when its internalization motif is replaced by a FLAG epitope (Emerit et al., 2016). Our results have shown that this mutation is not needed for a significant plasma membrane expression of both receptors; however, it has yet to be determined whether the physical interaction between P2X4 and 5-HT3A receptors is enough to trigger the protein trafficking or requires intracellular signaling pathway. Remarkably, the expression pattern of purified P2X4/5-HT3A receptor complexes vs. the P2X4 receptor alone, revealed by AFM imaging, resembles relatively well to that observed on the plasma membrane in living cells. Almost half of the particles of the purified samples from tsA201 cells coexpressing P2X4 and 5-HT3A receptors correspond to the assembled complex and present an identical concentration to that from cells expressing only P2X4 receptors. If we consider the other half of particles that match the molecular volume of free P2X4 receptors, then the total purinergic receptor should increase almost twice, reinforcing our notion that the purification process is highly enriched in the plasma membrane fraction.

Our AFM results demonstrate for the first time that the P2X4/5-HT3A receptor complex is formed with a stoichiometry 1:1, which is maintained by the presence of the P2X4 receptor agonist. To propose a molecular way for the allosteric inhibition of 5-HT3A receptor on P2X4 receptor function, we embarked on testing a docking between both receptors, considering the postulated domains for physical crosstalking (Boué-Grabot et al., 2003; Emerit et al., 2016), and interfaces with larger surfaces that maintain a trimeric/pentameric symmetry, which has been already postulated as variables energetically favorable for interacting macromolecules (Casuso et al., 2012). After 100 ns MD simulations, no differences in interaction energy between the P2X4 receptor apo or P2X4 receptor holo complexed with 5-HT3A receptors were found. Also, the presence of the interacting 5-HT3A receptor decreased the ATP binding interaction energy of ATP molecules to the P2X4 receptor, which is consistent with a negative allosteric effect of 5-HT3A receptors that preserves the 1:1 stoichiometric interaction between both receptors. Nevertheless, further bioinformatic analysis should be performed to corroborate these findings, in particular, to explore extensively other receptor-receptor surface dockings via, for example, coarse-grained approaches, which would result in the same or additional conformations of the receptor complex.

Crosstalking mechanisms lead to more complex neuronal signaling by providing additional regulatory roles of agonists and receptors. Taken together, we have shown here that the P2X4 receptor is inhibited by a physical interaction with the

### REFERENCES

5-HT3A receptor in a 1:1 stoichiometry and ATP maintains this complex. As 5-HT3A receptor can interact with other P2X receptors subtypes such as P2X2 and P2X3 (Emerit et al., 2016), it remains to be solved if the same stoichiometry and steric mechanism participate in the functional regulation of these receptors. Furthermore, as P2X4 receptors can be modulated by membrane phosphoinositides (Bernier et al., 2008), it could be further explored whether or not the 5-HT3A receptor and phospholipids can coexist to control the P2X4 receptor function via modifications of the stoichiometric binding.

#### DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article.

### AUTHOR CONTRIBUTIONS

PS performed AFM experiments and data analysis, immunofluorescence and western blot experiments. PG performed Ca2+ measurements and helped writing the article. CF performed homology modeling, molecular docking, and data analysis. BL performed western blot and TIRF experiments, followed by a quantitative analysis of immunofluorescence images. PN performed western blot and TIRF experiments. XF supervised Ca2+ measurements and helped writing the article. NB designed and supervised the investigation, analyzed the data and wrote the article.

### FUNDING

Millennium Scientific Initiative ICM P10-035F, Newton Picarte PCI-DPI Conicyt grant 20140080 and FONDECYT grant 1181326 funded NB. FONDECYT grants 1150530 and 3160348 funded XF and PG, respectively. PS holds a PhD scholarship from Pontificia Universidad Católica de Chile. CF holds an MSc scholarship from CONICYT. This work was supported in part by the Advanced Microscopy Facility UMA-UC.

#### ACKNOWLEDGMENTS

To Prof. Michael Edwardson (University of Cambridge, UK) for providing the P2X4 and 5-HT3A receptor constructs and scientific feedback on the experimental design, analysis, and procedure.

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P2X2 receptors are trimers but that P2X6 receptor subunits do not oligomerize. J. Biol. Chem. 280, 10759–10765. doi: 10.1074/jbc.m412265200

**Conflict of Interest**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Soto, Gaete, Fuentes, Lozano, Naulin, Figueroa and Barrera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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