Antibiotic-induced microbiota perturbation causes gut endocannabinoidome changes, hippocampal neuroglial reorganization and depression in mice

Brain, Behavior, and Immunity 67 (2018) 230–245

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Antibiotic-induced microbiota perturbation causes gut endocannabinoidome changes, hippocampal neuroglial reorganization and depression in mice F. Guida a,h,⇑, F. Turco b, M. Iannotta a, D. De Gregorio a, I. Palumbo b, G. Sarnelli b, A. Furiano a, F. Napolitano c,d, S. Boccella a, L. Luongo a,h, M. Mazzitelli a, A. Usiello c,e, F. De Filippis f,i, F.A. Iannotti g,h, F. Piscitelli g,h, D. Ercolini f,i, V. de Novellis a,h, V. Di Marzo g,h,⇑, R. Cuomo b,i, S. Maione a,h,⇑ a

Department of Experimental Medicine, Section of Pharmacology L. Donatelli, Università degli Studi della Campania ‘‘Luigi Vanvitelli”, Naples, Italy Department of Clinical Medicine and Surgery, Federico II University of Naples, Naples, Italy c Ceinge Biotecnologie Avanzate, Naples, Italy d Department of Molecular Medicine and Medical Biotechnology, University of Naples ‘‘Federico II”, Naples, Italy e Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università degli Studi della Campania ‘‘Luigi Vanvitelli”, Caserta, Italy f Department of Agricultural Sciences, Division of Microbiology, University of Naples Federico II, Portici, Italy g Institute of Biomolecular Chemistry, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy h Endocannabinoid Research Group, Italy i Task Force on Microbiome Studies, University of Naples Federico II, Italy b

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Article history: Received 22 June 2017 Received in revised form 4 September 2017 Accepted 4 September 2017 Available online 7 September 2017 Keywords: Microbiome Depression Social behavior Hippocampus Endocannabinoidome

a b s t r a c t The microbiota-gut-brain axis (MGBA) regulates the reciprocal interaction between chronic inflammatory bowel and psychiatric disorders. This interaction involves multiple pathways that are highly debated. We examined the behavioural, biochemical and electrophysiological alterations, as well as gut microbiota composition in a model of antibiotic-induced experimental dysbiosis. Inflammation of the small intestine was also assessed. Mice were exposed to a mixture of antimicrobials for 2 weeks. Afterwards, they received Lactobacillus casei DG (LCDG) or a vehicle for up to 7 days via oral gavage. Perturbation of microbiota was accompanied by a general inflammatory state and alteration of some endocannabinoidome members in the gut. Behavioural changes, including increased immobility in the tail suspension test and reduced social recognition were observed, and were associated with altered BDNF/TrkB signalling, TRPV1 phosphorylation and neuronal firing in the hippocampus. Moreover, morphological rearrangements of non-neuronal cells in brain areas controlling emotional behaviour were detected. Subsequent probiotic administration, compared with vehicle, counteracted most of these gut inflammatory, behavioural, biochemical and functional alterations. Interestingly, levels of Lachnospiraceae were found to significantly correlate with the behavioural changes observed in dysbiotic mice. Our findings clarify some of the biomolecular and functional modifications leading to the development of affective disorders associated with gut microbiota alterations. Ó 2017 Elsevier Inc. All rights reserved.

1. Introduction The intestinal microbiota plays an important role in the bidirectional interactions between the central and enteric nervous systems (CNS and ENS, respectively). Thus, the MGBA is involved in ⇑ Corresponding authors at: Department of Experimental Medicine, Section of Pharmacology L. Donatelli, Second University of Naples, 80138 Naples, Italy (F. Guida and S. Maione). Institute of Biomolecular Chemistry, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy (V. Di Marzo). E-mail addresses: [email protected] (F. Guida), [email protected] (V. Di Marzo), [email protected] (S. Maione). http://dx.doi.org/10.1016/j.bbi.2017.09.001 0889-1591/Ó 2017 Elsevier Inc. All rights reserved.

the pathophysiological mechanisms underlying chronic inflammatory bowel disorders leading to psychiatric disorders (Collins and Bercik, 2009; Grenham et al., 2011). Indeed, apart from gastrointestinal functions, the microbiota and its metabolites have been suggested to be involved in the modulation of brain functions, such as emotional behaviours (Foster and McVey Neufeld, 2013) stressrelated responsiveness (Bravo et al., 2012), pain (Bercik et al., 2012), and food intake (Alcock et al., 2014). Consequently, alterations of the ‘‘healthy” microbiota, referred to as dysbiosis, might drive functional and behavioural changes in animals and humans (Bercik et al., 2011; Fond et al., 2015). Therapeutic agents able to

F. Guida et al. / Brain, Behavior, and Immunity 67 (2018) 230–245

recover intestinal bacteria have been proposed as a novel strategy to address psychiatric disorders associated with infection or inflammation, besides their inherent application to inflammatory bowel diseases. Probiotics, selected commensal bacteria ingested through diet or in the form of supplements, can lead to health benefits beyond basic nutrition (Dai et al., 2013). Indeed, preclinical studies have shown that probiotics have the potential to reduce stress and stress-related disorders such as depression or anxiety (Bercik et al., 2010; Slyepchenko et al., 2014). Moreover, beneficial effects of different probiotics have been reported in hospitalised subjects suffering from psychiatric diseases as well as in healthy volunteers (Messaoudi et al., 2011a,b). Interestingly, the recent characterisation of the human microbiome provided new grounds to investigate potential epigenetic mechanisms in neurodevelopmental disorders associated with gastrointestinal pathologies, including autism spectrum disorders (ASDs) or attention-deficit hyperactivity disorder (ADHD) (Fond et al., 2015). Previous reports in children revealed a link between alterations in the gut microbiota and the risk of developing neuropsychiatric disorders in adulthood, pointing to early probiotic treatment as a potentially successful intervention (Frye et al., 2015; Partty et al., 2015; Petra et al., 2015). Exploring the molecular mechanisms at the basis of gut microbiota-brain interactions should lead to improve the therapeutic strategies aimed at managing neuropsychiatric disorders, especially those associated with bowel inflammation. However, the cellular, neurophysiological and biochemical bases through which such improvements can be exerted have been so far poorly investigated. The endocannabinoid system has been shown to be implicated in various pathophysiological states at both peripheral and CNS level, by exerting pleiotropic effects (Rossi et al., 2014; Cristino et al., 2016; Henry et al., 2017; Mela et al., 2016; Russo et al., 2017). In particular, the dysregulation of endocannbinoid tone and the consequent influence on hippocampal neurogenesis have been suggested to play a role in the aetiology of depression (Boorman et al., 2016). Furthermore, substances elevating endocannabinoid levels have been found to ameliorate signs of IBDs and visceral pain sensation in rodents (D’Argenio et al., 2006; Bashashati et al., 2017). Interestingly, the role of endocannabinoids and biochemically elated bioactive long chain fatty acid derivatives, such as the N-acylethanolamines and N-acylserotonins, and their targets (defined all together as the endocannabinoidome (Piscitelli et al., 2011; Witkamp, 2016) in the inflammatory and behavioural consequences of microbiota perturbation have never been investigated, despite the fact that these mediators have been clearly implicated in obesity-mediated dysbiosis (Cani et al., 2016), depression-like signs (Navarria et al., 2014; Rubino et al., 2015) and gut inflammatory conditions (Borrelli and Izzo, 2009; Capasso et al., 2014) in experimental models. In the present study, we used a model of dysbiosis obtained by exposing young healthy mice to a broad-spectrum antimicrobial cocktail (Ampicillin, Streptomycin and Clindamycin, ASC) (Lamouse-Smith et al., 2011), and leading to an imbalanced gut microflora. Moreover, as potent modulator of gut inflammatory/ immune-response (D’Inca et al., 2011; Compare et al., 2017), the probiotic Lactobacillus casei DG (LCDG) was used as pharmacological tool to investigate the effects of microbiota changes on both gut and brain. Mice were submitted to a wide range of behavioural, biochemical and electrophysiological tests in vivo and ex vivo to assess whether such microbiota disruption leads to alterations in gut inflammation, depressive-like symptoms, social behaviour and cognitive deficits, changes in brain neuronal firing and microglial-glial activation, or to alterations of select central and peripheral endocannabinodome members. Our findings suggest that probiotic administration may improve microbiota perturbation-associated affective impairments by acting at both

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central and peripheral levels via several cellular and biochemical mechanisms. 2. Materials and methods 2.1. Animals and treatments Male C57/bl6 mice (6 weeks) obtained from Jackson Laboratory were housed controlled illumination and environmental conditions for 1 week before the commencement of experiments. The experimental procedures were approved by the Animal Ethics Committee of the Università della Campania of Naples. Animal care was in compliance with the IASP and European Community (E.C. L358/1 18/12/86) guidelines on the use and protection of animals in experimental research. All efforts were made to minimize animal suffering and to reduce the number of animals used. Ampicillin, Streptomycin and Clindamycin (ASC) were mixed into sterile drinking water (1 mg/ml, for 2 weeks) as previously described (Lamouse-Smith et al., 2011). Drinking solution containing the antibiotics was available for 14 days ad libitum. Control groups of mice drunk water for 14 days. Afterwards control and Dysb mice they were treated via oral gavage with the probiotic (Lactobacillus casei DG, 109 cells in saline, 100 ml) or saline up to 7 days. Water was replaced every 3 days for the duration of the experiments. An additional group of mice received saline or ASC via intraperitoneal route (absorbable quote of oral daily doses) for 7 days. 2.2. Behavioural testing Behavioural tests (N=10–12) were performed on day 14 and 21 from antibiotic exposure (day 0). At the end of each set of experiments mice were sacrificed for further evaluations. The behavioural tests were scheduled in order to avoid carry-over effects from prior testing experience. 2.2.1. Depressive-like behaviour Tail suspension test (TST). Mice were individually suspended by the tail on a horizontal bar (50 cm from floor) using adhesive tape placed approximately 2 cm from the tip of the tail. The duration of immobility, recorded in seconds, was monitored during the last 4 min of the 6-min test by a time recorder. Immobility time was defined as the absence of escape-oriented behavior. Mice were considered to be immobile when they did not show any body movement, hung passively and completely motionless. Forced swimming test (FST). Mice were placed in a cylinder (30 cm
45 cm) filled with water at a temperature of 27 °C, for a 6-min period. The duration of immobility in seconds was monitored during the last 4 min of the 6-min test. Immobility period was defined as the time spent by the animal floating in the water without struggling and making only movements necessary to keep its head above the water. Immediately afterwards, the trial mice were placed under a heating lamp to dry. 2.2.2. Motor coordination behaviour Possible motor coordination impairment was evaluated by Rotarod test (Ugo Basile). Mice were measured for the time of equilibrium before falling on a rotary cylinder by a magnet that, activated from the fall of the mouse on the plate, allows to record the time of permanence on the cylinder. After a period of adaptation of 30 s, the spin speed gradually increased from 5 to 40 rpm for a maximum time of 5 min. The animals were analyzed by 2 separate tests at 1-h interval in the same day. The time of permanence of the mouse on the cylinder was expressed as latency time (s).

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2.2.3. Muscle strength testing Wire hang. A metallic wire was secured to the top of a transparent, rectangular, open-topped plastic box. The wire was tightly fixed to the top of the tank to avoid vibration, which could interfere with the performance of mouse. The mice were placed on the metallic wire and latency to fall was recorded. This was repeated five times with an interval of 5 min between attempts. The cutoff time was set at 60 s. Mean latency to fall in seconds was determined. 2.2.4. Recognition memory Novel object recognition. Novel object recognition task which consisted in a period of habituation, an acquisition trial, and a test trial was performed in dysbiotic and control mice. During habituation mice were allowed to freely explore for 1 h the apparatus which consists of a rectangular open box (40
30
30 cm width
length
height) made of grey polyvinyl chloride (PVC) illuminated by a dim light. During the acquisition each mouse was allowed to explore two identical objects positioned in the back left and right corners for 5 min. A camera recorded the time spent on exploration of each object. In the 5 min test trial, which was carried out 2 h after the acquisition, one of the two objects was replaced with a new different object. The time spent exploring the object was the time the mouse spent with its nose directed, and within 1 cm, from the object. The recognition index (R.I.) expressed as the percentage of the time the mouse spent exploring the novel object (the time the mouse spent exploring the novel object + the time the mouse spent exploring the familiar object) was recorded by an observer blind to the treatment. 2.2.5. Working memory Y maze. The apparatus consisted of three enclosed black arms 30
5
15 cm (length x width
height) converging on an equilateral triangular black center (5
5
5 cm). At the beginning of each experimental session, each mouse was placed in the center platform and the number of spontaneous alternations (defined as number of successive triplet entry into each of the three arms without any repeated entries) was monitored in a 5 min test session. The percentage of alternation was calculated as the percentage of the ratio of the number of alternations/(total number of arm entries—2). The alternation percentage was calculated as a parameter for the working memory-related behaviour. 2.2.6. Nociceptive pain Hot-plate test. Mice were placed on the hot-plate (Ugo Basile, Varese, Italy) maintained at 55 °C ± 1 °C, and the time between placement of the animal on the hot-plate and licking of the hind paws or jumping was recorded as the index of response latency. The cut-off time of 30 s was set to minimize the tissue damage. 2.2.7. Sociability Three chambers sociability. Mice were placed in a plexiglass three-chambered box custom-built as follows: doorways in the two dividing walls had sliding covers to control access to the outer-side chambers. The test consisted of three consecutive stages of 5 (habituation), 10 (I session) and 10 (II session) mins each. During the first 5 min the mouse was allowed to freely explore the three chambers of the apparatus, detecting at this stage any innate side preference. Afterwards, the mouse was gently encouraged into the central chamber and confined there briefly by closing the side chamber doors. During the following 10-min I session, a custommade stainless-steel barred cup was placed upside down in one of the side chambers. The first intruder mouse (stranger 1), previously habituated, was placed into an upside down identical cup in the other chamber. In the second session, the subject encounters the first intruder, as well as, a second intruder (stranger 2) under

another upside down cup (II session). The time spent in each chamber, the number of transitions between the chambers, the social interaction durations were recorded. The preference for sociability was defined as the time spent in the side chamber with the stranger 1 compared to the chamber with the empty upside down cup or the time spent in the side chamber with the stranger 2 compared to the stranger 1 over the recorded time. Naïve male C57/bl6 mice (6 weeks) were used for social stimuli. 2.3. Western blotting analysis on brain tissue Each animal, previously anesthetized, was decapitated and brains were collected immediately after sacrifice and frozen in liquid nitrogen for subsequent dissection (N = 8–9). Brain regions of interest (cortex and hippocampus) were then dissected and washed twice in cold PBS (without Ca2+ and Mg2+, pH 7.4) and homogenized as previously described (20). Lysates were then centrifuged for 15 min at 13,000
g at 4 °C, and the supernatants transferred into clear tubes and quantified by DC Protein Assay (Bio-Rad, Segrate MI, Italy). Subsequently the samples (80 mg of total protein) were boiled for 5 min in Laemmli SDS loading buffer and loaded on 8–10%SDS-polyacrylamide gel electrophoresis and then transferred to a PVDF membrane. Filters were incubated overnight at 4 °C with the following antibodies: (a) policlonal antiTRPV1 antibody (Cat #: ACC-030 dilution 1:200; Alomone labs, Israel); (b) polyclonal anti-phoshospecific TRPV1 antibody (Cat #: PAB8499; dilution 1:500; Abnova Taipei City, Taiwan); (c) mouse anti-CB1 (Cat #: Y080037, dilution 1:500; Applied Biological Materials Inc., CANADA): d) mouse anti-BDNF (N-20): sc-546, diluted 1:500, Santa Cruz Biotechnology, CA, USA); e) polyclonal anti- TrkB (794): sc-121:1000, Santa Cruz Biotechnology, Dallas, Texas). The monoclonal anti-tubulin clone B-5-1-2 (dilution 1:5000; Sigma– Aldrich) or GAPDH (1:5000, Santa Cruz Biotechnology, Dallas, Texas) antibody was used to check for equal protein loading. Reactive bands were detected bychemiluminescence (ECL or ECL-plus; Perkin-Elmer). Images were analyzed on a Chemi-Doc station with Quantity-one software (Bio-Rad, Segrate MI, Italy (Putra et al., 2012). 2.4. Microbiota analysis 2.4.1. DNA extraction and 16S sequencing Fecal samples (N = 6–8). were fully homogenized in STE buffer (100 mM NaCl, 10 mM Tris-Cl pH 8.0, 1 mM EDTA pH 8.0) and centrifuged (1,000 rpm
1 min) in order to pellet debris. The supernatant was centrifuged again (12,000
g, 2 min) and the pellet was used for DNA extraction, by using the PowerFecal DNA Isolation kit (Mo Bio Laboratories, Inc., Carlsbad, CA). V3-V4 region of the 16S rRNA gene was amplified by using primer S-D-Bact-0341 F50 –CCTACGGGNGGCWGCAG and S-D-Bact-0785R50 -GAC TACHVGGGTATCTAATCC (26) and the following PCR conditions: an initial denaturation at 95 °C for 3 min, followed by 25 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, and a final extension at 72° C for 5 min. Both the primers include overhang adapter sequences for compatibility with Illumina index and sequencing adapters. The PCR mixture contained (in a final volume of 50 ml): 50 ng of template DNA, a final concentration of 0.2 mM of each primer and 0.5 mM of each de-oxynucleoside triphosphate,10 ml of both 5X Q5 Reaction Buffer and High-GC Enhancer and 1 U of Q5 High Fidelity DNA Polymerase. PCR products were purified with the Agencourt AMPure XP beads and quantified using a Plate Reader AF2200. Amplicon multiplexing, pooling and sequencing was carried out following the Illumina 16S Metagenomic Sequencing Library Preparation protocol, on a MiSeq platform and using the MiSeq Reagent kit v3, leading to 2
300 bp, paired-end reads.

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The 16S rRNA gene sequences produced in this study are available at the Sequence Read Archive (SRA) of the National Center for Biotechnology Information, under accession number SRP098544. 2.5. Intestine inflammatory mediator evaluations 2.5.1. Sample collection Mice from each group (N = 6–8) were sacrificed by cervical dislocation. Intestinal samples taken from the duodenum (8 cm distal to the pylorus), jejunum (18 cm distal to the pylorus), ileum (30 cm distal to the pylorus) and colon (distal to the caecum) were washed with Phosphate Saline Buffer (PBS) to remove gut contents. A 0.5–1 cm long sample from each intestinal tissue was cut and immediately stored at 80 °C for subsequent Western Blot and Real-Time PCR analysis. 2.5.2. Protein extraction and western immunoblot analysis To perform western blot analysis, tissues were homogenized in ice-cold hypotonic lysis buffer. Protein concentration was determined by a protein assay kit (Bio-Rad Laboratories, Hercules, CA). For immunoblotting analysis, equivalent amounts of each sample were denatured, separated on a sodium dodecyl sulfatepolyacrylamide gel and transferred to a nitrocellulose membrane (Amersham, Milan, Italy). The membranes were blocked for 2 h at room temperature in 10% milk buffer and were incubated overnight at 4 °C with rabbit anti-IL1b (1:500 vol/vol dilution, Abcam, Cambridge, MA), rabbit anti-TNFa (1:500, Abcam), rabbit antiGFAP (1:1000, Abcam), rabbit anti-S100B (1:1000, Novus Biologicals Europe, Cambridge, UK), or rabbit anti-iNOS (1:1000, Santa Cruz Biotechnology, Santa Cruz, CA) antibodies, using rabbit antia Actin (1:1000, Abcam) as housekeeping protein. Subsequently, membranes were incubated for 2 h at room temperature with the rabbit secondary antibody conjugated to horseradish peroxidase (Abcam). Western blots were analyzed by scanning densitometry (GS-700 imaging densitometer, Bio-Rad) and the results were expressed as optical density [arbitrary units (a.u.; mm2)]. 2.5.3. RNA isolation, Reverse Transcription and Quantitative Real-Time PCR After homogenization, RNA was extracted from intestinal tissues using Trizol reagent (Invitrogen SRL, San Giuliano Milanese, Italy), according to the manufacturer’s instructions. Nanodrop spectrophotometer (Celbio, Milan, Italy) was used to determine the concentration and the quality of RNA (A260/A280 ratio was 1.97 ± 0.06 and A260/A230 ratio resulted to be always above 2.0). Extracted RNA was treated with a DNA-free Kit (Ambion, Austin, TX), according to the manufacturer’s instructions. cDNA synthesis from Dnase-digested RNA was performed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative Real-Time PCR was performed on a StepOne instrument (Thermo Fisher Scientific, Waltham, MA), using a TaqMan Gene Expression Mastermix (Thermo Fisher Scientific). Connexin-43 (Cx-43), Zonula occludens-1 (ZO-1) and GAPDH oligonucleotides were synthesized by Thermo Fisher Scientific. The expression of the housekeeping gene GAPDH was consistent among the various treatments. After each reaction, a melting curve was used to confirm the specificity of PCR products. The DDCT method was used for quantization of mRNA expression in stimulated cells compared to unstimulated ones (Turco et al., 2014), using GAPDH as the housekeeping gene. Each experiment was performed in triplicate. 2.6. Serum collection and Enzyme-linked immunosorbent assay (ELISA) assay Blood from the different group of mice (N = 6–8) was collected by exsanguination. Serum was obtained by centrifugation at 3000
g

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for 10 min and stored at 20 °C for subsequent analysis. ELISA for IL-1b and TNF-a (Biovendor R&D, Brno, Czech Republic) was carried out on serum of mice according to the manufacturer’s protocol. Absorbance was measured on a microtiter plate reader. IL-1b and TNF-a levels were determined using standard curves method. 2.7. Electrophysiology 2.7.1. In-vivo electrophysiological recording preparation Mice were anaesthetized with chloral hydrate (400 mg/kg, i.p.) and placed in a stereotaxic apparatus (David Kopf Instruments, Tujunga, CA, USA). A hole was drilled through the skull. Body temperature of the animals was measured using a rectal thermometer (Yellow Springs Instrument Co., Yellow Springs, OH, USA) and was maintained at 35–36.5 °C using an IR heating lamp (Philips, Infrared Heat). To maintain a full anesthetic state during the experiments, supplemental doses of chloral hydrate (100 mg/kg, i.p.) were periodically administered. Anesthesia was assured by the absence of nociceptive reflex reaction to a tail or a paw pinch and of an eye blink response to pressure. Extracellular single-unit recordings were performed using glass-insulated tungsten filament electrode (3–5 MΏ of impedence) (FHC Frederick Haer & Co., ME) for recordings in prelimbic region (PL) of medial prefrontal cortex (mPFC). Single-barreled glass micropipettes, pulled from 2 mm Stoelting (Wood Dale, IL) capillary glass using a a Narashige (Tokyo, Japan) PE-21 pipette puller and preloaded with fiberglass strands to promote capillary filling with 2% Pontamine Sky Blue dye in 2 M NaCl were instead used to perform recordings in CA3 region of dorsal hippocampus. The micropipette tips used for hippocampal recordings were broken down to diameters of 1–3 lm to reach an impedance of the electrodes ranging from 2 to 6 MX. In all the experiments, the recorded signals were amplified and displayed on a digital storage oscilloscope to ensure that the unit under study was unambiguously discriminated throughout the experiment. Signals were processed by an interface CED 1401 (Cambridge Electronic Design Ltd., UK) and analyzed through Spike2 software (CED, version 4) to create peristimulus rate histograms (PSTHs) online and to store and analyze digital records of single-unit activity off-line. Firing rates were measured as the average rate in spikes/s. Each neuron was recorded for at least 5 min to assess a regular firing rate activity and at least 6 neurons were collected per animal (N = 6–8) Configuration, shape, and height of the recorded action potentials were monitored and continuously recorded. At the end of electrophysiological experiments, the animals were euthanized and the brains were harnessed and stored in paraformaldehyde. Coronal brain slices (20 lm) through regions of interests were prepared to inspect the location and extent of electrode lesions under a light microscope. 2.7.2. Extracellular recording from CA3 region of dorsal hippocampus Single-barreled glass micropipettes was lowered into the CA3 regions of the dorsal hippocampus (AP: 2.5 to 2.7 mm from bregma, L: 2–3 mm from midline and V: 2.5–3 mm below dura) (Fig. 4A). Pyramidal activity of Ca3 neurons was identified by the firing rates (8–15 Hz), (Bambico et al., 2012)) large amplitudes (0.5–1.2 mV), long durations (0.8–1.2 ms) and as single action potentials alternating with complex spike discharges (Kandel and Spencer, 1961). Neuronal burst activity was also evaluated offline using a script designed for Spike 2 (Cambridge Electronic Design, Cambridge, UK). These analyses generated two parameters: number of burst found and mean ratio of the number of spikes within bursts to the sum total of recorded spikes (%), both calculated in an interval of 200 s. The parameters used for analyzing the bursts were based on the criteria previously described (Omura et al., 2015): a train of at least three spikes with an onset defined by a maximum initial interspike interval (ISI) of 6 ms within a regular

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high-frequency firing pattern was categorized as a burst. The longest ISI allowed within a burst was 20 ms. 2.7.3. Extracellular recording from medial prefrontal cortex (mPFC) The electrodes implantation for the extracellular recordings of mPFC pyramidal neurons, following the Basolateral Amigdala (BLA) electrical stimulation, was performed as previously described (Floresco and Tse, 2007)). BLA stimulation (100–200 pulses) evokes 2 distinct types of firing changes: inhibition or excitation in separate populations of mPFC neurons (Ishikawa and Nakamura, 2003). In our study we considered only inhibitory neurons called ‘‘BLA-mPFC(–)”, as they represent the most abundant population detected in mPFC (Gabbott et al., 2006) (Fig. 4K and L). The BLA was stimulated following the protocol proposed (Floresco and Tse, 2007). Moreover, both the extracellular action potentials (EAP) amplitude, indicating synaptic current, and the EAP slope, indicating rate of activation of synaptic receptors, were used for the evaluation of the efficacy of synaptic transmission. The slope was calculated between 20% and 80% of maximal EAPs’ amplitude and was normalized to the average slope recorded during a 15 min baseline interval and measured in millivolt/milliseconds (mV/ms). The amplitude was calculated by measuring the distance between the two horizontal cursors positioned on the baseline and on the negative peak of the spike waveform and it was measured in millivolt (mV). 2.8. Immunohistochemistry Under pentobarbital anesthesia (50 mg/kg, i.p.), animals (N = 5–6) were transcardially perfused with saline solution followed by 4% paraformaldehyde in 0.1 M phosphate buffer. The brains were excised, post fixed for 3 h in the perfusion fixative, cryoprotected for 72 h in 30% sucrose in 0.1 M phosphate buffer and frozen in Optimal cutting temperature embedding compound. Transverse sections (20 lm) were cut using a cryostat and thawmounted onto glass slides. Slides were incubated overnight with primary antibody solutions for the microglial cell marker Iba-1 (rabbit antiionized calcium binding adapter molecule-1; 1:1000; Wako Chemicals, Germany) or for astrocytes marker GFAP (rabbit polyclonal anti-glial fibrillar acidic protein, 1:1000; Dako Cytomation, Denmark). Possible non-specific labeling of mouse secondary antibody was detected by using secondary antibody alone. Following incubation, sections were washed and incubated for 2 h with secondary antibody solution (donkey anti-rabbit Alexa FluorTM 488; 1:1000; Molecular Probes, USA). Slides were washed, coverslipped with Vectashield mounting medium (Vector Laboratories, USA) and visualized under a Leica fluorescence microscope.

(50 mM, pH 7.4) containing deuterated standards (5 pmol of d8AEA, 50 pmol of d5-2AG, d4-PEA, d2-OEA and 500 pmol d8-AA-5HT) and then sonicated (N = 4–6). The homogenates were extracted with chloroform and the organic phase collected and evaporated under nitrogen stream. The extracts were reconstituted in 100 ml of Methanol LCMS (Sigma) for LC-MS and LC-MS/MS analysis. 2.9.2. LC-MS and LC-MS/MS analysis Endocannabinoids and N-acylethanolamines were determined by isotope dilution liquid chromatography–atmospheric pressure chemical ionization–mass spectrometry as described (Bartelt et al., 2011). Tissue N-acylserotonin levels were measured by LC-MS-MS using an LC20AB coupled to a hybrid detector IT-TOF (Shimadzu Corporation, Kyoto, Japan) equipped with an ESI interface. The N-acylserotonins measured included N-arachidonoylserotonin (AA-5-HT) and N-oleoyl-serotonin (OA-5-HT), and the amounts of these species in tissues, quantified using d8AA-5-HT. We acquired full-scan MSn spectra of selected precursor ions by multiple reaction monitoring (MRM), extracted the chromatograms of the high resolution [M+Na]+ values and used the latter chromatograms for calibration and quantification. LC analysis was performed in the isocratic mode using a Kinetex C18 column and methanol/water/acetic acid as the mobile phase, with a flow rate of 0.15 ml/min. Five ml of the extracts were injected in the LC-MS/MS. The quantification was performed by isotope dilution by using m/z values of 485.2971 and 493.2805 corresponding to the molecular ion [M+Na]+ for deuterated and undeuterated AA5-HT, respectively; or m/z value of 463.2873 corresponding to the molecular ion [M+Na]+ of undeuterated OA-5-HT. The most dominant product ion (m/z 185 corresponding to serotonin loss, [M+Na]+) was selected for MRM. 3. Statistical analysis Data were represented as mean ± S.E.M. The behavioural data were analysed using one-way ANOVA, followed by NewmanKeuls multiple comparisons test. The unpaired T-test with Mann Whitney test for intra-groups analysis (N = 10–12). Electrophysiological data were analysed using one-way ANOVA, followed by Holm-Sidack post hoc comparisons or t-test (N = 6–8). Immunohistochemical analysis was performed by using one-way ANOVA followed by Tukey’s post hoc (n = 5–6). Biomolecular data were expressed as mean ± SD and were analysed by one-way ANOVA, Fisher’s or Bonferroni post hoc comparison or (n = 7–9). 3.1. Statistical and bioinformatics data analysis of microbiota

2.8.1. Quantitative image analysis The number of cells positive was determined within a box measuring 2
104 lm2 that was placed in the medial areas of cortex, hippocampus (CA1), thalamus, and hypothalamic paraventricular nucleus (PVN). Eight sections were assessed from one animal. To avoid cell overcounting, only DAPI-counterstained cells were considered as positive profiles. Iba-1 and GFAP-labelled cells were identified as resting (with small soma and long ramified processes) or activated (with hypertrophy together with retraction of processes to a length shorter than the diameter of the somata) (Hains and Waxman, 2006). 2.9. Measurement of N-acylserotonin, N-acylethanolamine and endocannabinoid levels 2.9.1. Lipid extraction Jejunum, ileum and duodenum (80–100 mg) were homogenized with a pestle in 2:1:1 choloroform/methanol/TRIS-HCl

Demultiplexed, forward and reverse reads were joined by using FLASH (Magoc and Salzberg, 2011). Joined reads were quality trimmed (Phred score <20) and short reads (<250 bp) were discarded by using Prinseq (Schmieder and Edwards, 2011). High quality reads were then imported in QIIME (Caporaso et al., 2010). OTUs were picked through de novo approach and uclust method and taxonomic assignment was obtained by using the RDP classifier and the Greengenes (McDonald et al., 2012) database, following a pipeline previously reported (De Filippis et al., 2014). In order to avoid biases due to the different sequencing depth, OTU tables were rarefied to the lowest number of sequences per sample. Statistical analyses and plotting were carried out in R environment (https://www.r-project.org). Alpha-diversity analysis was carried out in QIIME on rarefied OTU tables. Kruskal-Wallis and pairwise Wilcoxon tests were used in order to determine significant differences in alpha diversity parameters or in specific taxa abundance. Permutational Multivariate Analysis of Variance

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(non parametric MANOVA) based on Jaccard and Bray Curtis distance matrices was carried out by using 999 permutations to detect significant differences in the overall bacterial community composition between the groups, by using the adonis function in vegan package. Correction of p-values for multiple testing was performed when necessary. Heatplot, stacked bar-charts and boxplots were plotted in R by using the made4, ggplot2 and ggpubr packages (Fig. 3). 4. Results 4.1. Probiotic effect on the depressive-like behaviour caused by antibiotic-induced microbiota perturbation To test whether the alteration of intestinal flora affected mouse behaviour, we carried out different behavioural tasks. We performed tests on days 14 and 21 and then until recovery. A simplified scheme of treatments in control and Dysb mice is given in Fig. 1A. Dysbiotic (Dysb) mice showed increased immobility time

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(126.2 ± 6 s) compared with control mice (61.5 ± 8.9 s, p < 0.001, F(3, 29) = 26.515), recorded as the absence of escape-oriented behaviour in the tail suspension test (Fig. 1B). Similarly, we found an increased duration of immobility time in Dysb mice (128.4 ± 7.5 s) as compared with control mice (27.2 ± 6.5 s, p < 0.001, F(3, 28) = 17.398) (Fig. 1C), measured as the time spent by the animal floating in the water in the forced swimming test. Repeated probiotic administration in Dysb mice significantly reduced their time of immobility in the tail suspension test (46.5 ± 7.3 s) compared with the group receiving vehicle (119.7 ± 10.3 s, p < 0.001, F(3, 29) = 26.515) (Fig. 1B). Immobility time was also reduced in treated Dysb mice (24.0 ± 5.22 s) compared with the related control (80 ± 13.9 s, p < 0.001, F(3, 28) = 17.398) in the forced swimming test. In the latter test, vehicletreated Dysb mice showed a slight trend to recovery compared to Dysb mice (Fig. 1B). However, this difference was not significant. The wire hang test and rotarod test showed that the muscle strength and motor functioning were not compromised by microbiota perturbation (not shown). Probiotic treatment did not

Fig. 1. Probiotic treatment alleviates behavioural altered responses induced by antibiotic-induced dysbiosis. Effects of chronic administration of vehicle or probiotic on behavioural responses in normal (CTL) or dysbiotic (Dysb) mice. ‘‘A” (left panel) shows the generation of dysbiotic mice. C57BL/6 (6 weeks) mice were treated with Ampicillin, Streptomycin and Clindamycin (ASC) in the drinking water or sterile water only (0–14 days). Afterwards, they were treated with probiotic or saline via gavage (14–21 days). ‘‘A” (right panel) shows the behavioural testing timeline. Dysbiosis was inducted on day 0. Behavioural testing was performed on days 14 and 21 and then until recovery. ‘‘B” and ‘‘C” show the duration of immobility in seconds the tail suspension test and in the forced swimming test, respectively. ‘‘D” and ‘‘E” show the duration of the time spent in seconds exploring the object or mouse I (I session, D) and the duration of the time spent exploring the familiar (mouse I) or intruder mouse (mouse II, II session, E) in the three chambers sociability paradigm. Values are represented as means ± S.E.M. of 10–12 animals per group. ° indicates significant differences vs Ctl, # vs Dysb and * vs Dysb/veh. One-way ANOVA, followed by Newman-Keuls multiple comparisons test. In ‘‘E” ¥ indicates significant differences vs CTL or Dys/Prob animals in presence of mouse 1. The unpaired T-test with Mann Whitney test for intra-groups analysis. P < 0.05 was considered as level of significance. The double symbol indicates P < 0.01 and the triple symbol indicates P < 0.001. Expression of protein levels measured using Western blot analysis in the hippocampus of CTL or Dysb mice treated with vehicle or probiotic. ‘‘F” and ‘‘G” show the protein expression of BDNF and TrkB normalized to GAPDH,. Data are represented as means ± S.E.M. of 8–9 mice per group. ° and * indicate significant differences compared to CTL and Dysb/veh, respectively. P < 0.05 was considered statistically significant. One-way ANOVA, Fisher’s post hoc comparisons. The double symbol indicates P < 0.01.

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Fig. 2. Effects of chronic administration of antibiotics or probiotic on behavioural responses. ‘‘A” and ‘‘B” show the duration of immobility in seconds the tail suspension test and in the forced swimming test, respectively, in antibiotic-treated mice and in the wash-out condition. Values are represented as mean ± S.E.M. of 10–12 animals per group. ° indicates significant differences vs Ctl. One-way ANOVA, followed by Newman-Keuls multiple comparisons test. ‘‘C” shows the duration of the time spent exploring the familiar (mouse I) or intruder (mouse II, II session) mouse in the three-chambers sociability paradigm in antibiotic-treated mice and in the wash-out condition. Values are presented as means ± S.E.M. of 10–12 animals per group. ¥ indicates significant differences vs CTL or Dys/Prob animals in presence of mouse 1. The unpaired T-test with Mann Whitney test for intra-groups analysis. ‘‘D” and ‘‘E” show the duration of immobility in seconds the tail suspension test and in the forced swimming test, respectively. ‘‘F” and ‘‘G” show the duration of the time spent in seconds exploring the object or mouse I (I session) and the duration of the time spent exploring the familiar (mouse I) or intruder (mouse II, II session) mouse in the three-chambers sociability paradigm. ‘‘H” shows the index of recognition in% in the object recognition test; ‘‘I” shows the alternations in% in the Y maze test and ‘‘J” shows the thermal withdrawal latency in seconds in the hot plate test. Values are presented as means ± S.E.M. of 10–12 animals per group.

induce any change per se (Fig. 2C and D). Completely untreated Dysb mice showed a physiological re-establishment of normal behaviour about 2 weeks after the antibiotic suspension (Fig. 2A and B). To rule out a possible systemic effect of antibiotic exposure via oral gavage, we carried out experiments where a group of mice was administered the antibiotic cocktail via intraperitoneal route. The dose was chosen based on the absorbable quota of oral daily doses for 7 days. In this group of mice, no difference was observed in the behavioural responses compared to the controls (CTL = 60 + 4.1 vs Dys = 65 + 3.8 in tail suspension and CTL = 36 + 5.1 vs Dys = 32.5 + 4.2 in the forced swimming test). 4.2. Probiotic effect on social behaviour alterations caused by antibiotic-induced microbiota perturbation Next, we investigated social behaviour and preference for social novelty by using the three-chamber sociability paradigm. No differences in the time spent in each chamber or in the number of transitions between the chambers were observed in Dysb mice compared with controls (not shown). Indeed, both groups of mice showed sociability, spending more time interacting with the other

mouse than with the object during the recorded time (I session, Fig. 1D). However, Dysb mice showed an altered preference for social novelty compared with control mice. The former mice showed no preference between the chamber with the stranger (mouse 2) (46.27 ± 6.7 s) and the chamber with the familiar (mouse 1) (49.6 ± 9 s), whereas the latter spent more time with mouse 2 (41.6 ± 3.6 s) compared to mouse 1 (17.4 ± 1.6 s) (II session, Fig. 1E). The treatment with probiotic in Dysb mice increased the time spent in the chamber with the mouse 2 (33 ± 3.7 s), which proved significantly higher compared with the time spent in the chamber containing the mouse 1 (15.3 ± 0.9 s) p < 0.001) in the second session of the three-chamber sociability paradigm (Fig. 1E). Vehicle-treated Dysb mice showed no difference in the time spent in the two chambers (35 ± 3.4 s vs 27.2 ± 2 s, respectively) (Fig. 1E). Probiotic treatment did not induce any change per se (Fig. 2F–G). Dysb animals subjected to recovery with no treatment showed a physiological re-establishment of normal behaviour about 2 weeks after discontinuation of the antibiotic treatment (Fig. 2C). In the intraperitoneal-injected group of mice, no difference was observed in the behavioural responses compared to the controls (CTL = 42 + 4.1 and 22.3 + 3.7 vs Dys = 38 + 5.3 and 18.5 + 4.8).

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4.3. Effects of antibiotic-induced microbiota perturbation on cognitive and nociceptive behaviours Perturbation of gut microbiota did not affect cognitive performance in the object recognition task since Dysb mice did not show a different recognition index (60.4 ± 9.5%) compared with control mice (54.3 ± 8.5%) (Fig. 2H). Moreover, Dysb mice did not show any difference in percentage of alternation in arm entries (62.2 ± 3.7%) compared with controls (59.0 ± 3.0%) in the y-maze (Fig. 2I). Additionally, thermal threshold in the hot plate test was not different in Dysb mice (8.7 ± 1.2 s) compared with controls (7.9 ± 0.7 s) (Fig. 2J). 4.4. Probiotic treatment normalizes altered hippocampal BDNF expression caused by antibiotic-induced microbiota perturbation To investigate possible changes in brain-derived neurotrophic factor (BDNF) regulation, we analyzed the levels of expression of BDNF and its conjugate tropomyosin receptor kinase B (TrkB) in the hippocampus of Dysb and control mice, with or without probiotic treatment. Our data revealed a significant decrease in BDNF in Dysb mice associated with increased TrkB expression. The treatment with probiotic normalised BDNF protein expression but did not affect TrkB expression compared with vehicle-treated Dysb mice (Fig. 1F and G). 4.5. Antibiotic treatment induces changes in the gut microbiota As expected, antibiotic treatment induced dramatic changes in the overall gut microbiota composition (as assessed by MANOVA, p < 0.05) and Dysb mice clearly clustered apart from all the other groups (Suppl. Fig. S1). Moreover, a decrease in bacterial diversity was observed in Dysb mice compared to controls (p < 0.05, Suppl. Fig. S2). Probiotic treatment failed to restore microbial diversity, but an increase in alpha-diversity parameters was observed 15 days after antibiotic suspension (p < 0.05, Suppl. Fig. S2).

Antimicrobials induced an increase in Proteobacteria and Actinobacteria and a decrease in Bacteroidetes and Firmicutes (p < 0.05; Suppl. Fig. S3). In particular, Lachnospiraceae, Muribaculaceae, and Ruminococcaceae decreased, while Desulfovibrionaceae and Enterobacteriaceae increased in Dysb mice (p < 0.05, Fig. 2). Genus-level analysis revealed a significant reduction of fibre-degrading bacteria in Dysb mice, belonging to Lachnospiraceae (Roseburia, Lachnospira, Lachnobacterium, Lachnoclostridium, Dorea, Acetatifactor) and Ruminococcaceae (Oscillospira, Eubacterium) families (data not shown, p < 0.05). Probiotic-treated mice showed higher levels of Lachnospiraceae and lower levels of Enterococcaceae and Bacillaceae compared to placebo (p < 0.05, Fig. 3). Lachnospiraceae reached levels similar to controls in recovered mice (Rec) (p > 0.05, Fig. 3), although the normal microflora was not completely restored: Proteobacteria and Bacteroidetes levels were still higher and lower than controls, respectively (p < 0.05). Interestingly, Lachnospiraceae abundance was negatively correlated with immobility time (p < 0.05).

4.6. Probiotic effect on gut inflammation caused by antibiotic-induced microbiota perturbation In order to investigate whether microbiota perturbation induced changes in mouse intestinal immune homeostasis, we measured the local expression levels of various proinflammatory mediators in different intestinal segments: duodenum, jejunum, ileum, and colon. We also evaluated the probiotic regarding its effect against inflammatory mediator expression. In the duodenum, IL-1b, TNFa and iNOS expression was significantly higher in Dysb group than in control group (p < 0.05). Probiotic treatment significantly reduced IL-1b, TNFa and iNOS expressions (p < 0.05) (Suppl. Fig. S4 A–C). In the jejunum, TNFa and iNOS expression was significantly increased in Dysb mice compared with control mice (p < 0.05); IL-1b expression also showed an increase, although not statistically

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significant (Suppl. Fig. S4). The probiotic treatment was effective at reversing only the iNOS increase (p < 0.05). In the ileum, IL-1b expression did not show statistically significant changes in Dysb mice with or without vehicle or probiotic treatment (Suppl. Fig. S4). TNFa and iNOS expression was significantly increased in Dysb mice compared with control mice (p < 0.05). Similar to what was observed in the duodenum, probiotic treatment reduced TNFa and iNOS expression (p < 0.05) (Suppl. Fig. S4A–C). In the colon, only TNFa expression was increased in Dysb mice compared with control conditions (p < 0.05). The probiotic treatment significantly reduced this increase (p < 0.05) (Suppl. Fig. S4). We also evaluated whether microbiota perturbation was correlated with enteric glial cells (EGC) activation since these cells directly participate in chronic mucosal inflammation of the gut. As shown in Suppl. Fig. S5D, GFAP expression significantly increased in the jejunum and in the ileum in Dysb mice (p < 0.05); however, this effect was not affected by probiotic treatment. Dysb mice also showed a significant increase of the enteroglial-derived S100B protein expression (p < 0.05) in the ileum and colon regions of the gut (Suppl. Fig. S4E). Probiotic

treatment was effective at decreasing S100B expression only in the ileum (p < 0.05) (Suppl. Fig. S4E).

4.7. Effects of antibiotic-induced microbiota perturbation on intestinal paracellular/intercellular communication and circulating cytokines To elucidate potential effects of microbiota perturbation and/or probiotic treatment on paracellular (tight) and intercellular (GAP) communications in the intestine of mice, we analysed mRNA expression of zonulin (ZO-1) and connexin-43 (Cx-43). In all intestinal segments analysed, ZO-1 expression was not statistically affected by antibiotic or probiotic treatment (Suppl. Fig. S4G). Cx-43 was not affected by dysbiosis. Intriguingly, probiotic treatment increased Cx-43 mRNA expression in the ileum and in the colon portions of the gut (p < 0.05) in Dysb mice (Suppl. Fig. S4H). In addition, to investigate the effect of microbiota perturbation on inflammatory mediators at the systemic level, and hence clarify the potential mechanisms involved in gut–brain axis communication, we also measured the levels of IL-1b and TNFa in the serum. We observed that neither antibiotic nor probiotic

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treatment significantly modified the levels of circulating cytokines compared with control mice (not shown). 4.8. Probiotic effects on the altered hippocampal CA3 pyramidal neuron activity caused by antibiotic-induced microbiota perturbation We carried out in vivo extracellular recordings to monitor possible changes in pyramidal neurons activity of the dorsal hippocampus (CA3 region) in mice exposed to antibiotics and probiotic treatment. Control animals showed a spontaneous ongoing activity of 10.47 ± 0.51 spikes/s (Fig. 4B and G). The burst-firing analysis in the control mice revealed that the number of bursts found in 200 ms and the percentage of spikes per burst were 53.30 ± 7.13 and 75.13 ± 3.04%, respectively (Fig. 4H and I). Remarkably, microbiota perturbation induced a drastic decrease of the firing rate (3.77 ± 0.77 spikes/s, p < 0.001, F(3,16) = 12.04) and significant reductions in the number of bursts and in the percentage of spikes per burst (18.54 ± 4.17, p < 0.01, F(3,16) = 8.37 and 24.09 ± 7.40%, p < 0.001, F(3,16) = 14.55, respectively) (Fig. 4G–I). Vehicle treatment did not change the ongoing firing rate (3.76 ± 0.42 spikes/s), the percentage of spikes per burst (42.48±7.82%), or the number of bursts found in 200 ms (40.45 ± 13.29) (Fig. 4G–I), despite a trend to restore the number of events in 200 ms. Notably, probiotic treatment significantly increased the spontaneous ongoing activity (6.69±13.29 spikes/s, p < 0.05, F(3,16) = 12.04) (Fig. 4D and G) and completely normalized the number of bursts and the percentage of spikes per burst (54.48 ± 4.15 and 63.12 ± 4.47%, respectively) compared with Dysb mice. However, no statistically significant differences in the comparison with the vehicle-treated group were found (Fig. 4G and H). Notably, the decrease of the firing rate in Dysb animals was normalized to similar to controls in recovered mice (Fig. 4E and G). Microbiota perturbation did not induce any significant changes in either amplitude or slope of hippocampal pyramidal neurons as shown in Fig. 4E. 4.9. Effects of antibiotic-induced microbiota perturbation on BLAmPFC (-) neuron activity Since frontal cortex functional reorganisation has been strongly correlated to cognitive impairment in several neurological diseases (Guida et al., 2015), we investigated possible changes in the activity of the medial prefrontal cortex (mPFC) neurons after basolateral amygdala (BLA) stimulation. BLA ? PLC(-) neurons showed a spontaneous firing rate of 2.43 ± 0.17 spikes/s in control mice (Fig. 4K and P). The duration and the onset of the BLA-evoked inhibition were 3.04 ± 0.21 s and 1427 ± 38.10 ms, respectively (Fig. 4Q and R) in control mice. Dysb mice did not show any significant changes in the ongoing activity (Fig. 4M and P), the duration or onset of inhibition (2.21 ± 0.17 spikes/s, 2.64 ± 0.15 s and 1273 ± 58.86 ms, respectively) (Fig. 4Q and R). Moreover, we did not observe any significant difference in the values of EAP amplitude or EAP slope in Dysb mice as compared to controls (Fig. 4N and O). Considering these results, we did not perform mPFC recordings in control or Dysb mice that received probiotic treatment. 4.10. Probiotic effects on microglia and astrocyte activation caused by antibiotic-induced microbiota perturbation Immunohistochemical evaluations were performed to determine the occurrence of non-neuronal cells activation following microbiota perturbation. No difference in the total number of cells was observed across all groups of treatment. However, according to the criteria previously described (Hains and Waxman, 2006), morphological analysis of Iba-1 positive cells revealed that 2 weeks of antibiotics exposure caused an increase in the number of acti-

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vated microglial cells in the CA1 segment of the hippocampus in Dysb mice compared to control mice (Fig. 5A). Moreover, the number of hypertrophic GFAP-labeled astrocytes was enhanced (Fig. 5B). Probiotic treatment significantly attenuated the dysbiosis-induced morphological changes in both microglia and astrocytes compared with vehicle-treated animals (Fig. 5A and B). Animals subjected to the washout (Rec) also showed a normalization of the number of activated microglia (Fig. 5A and C) and astrocytes (Fig. 5B and D). A slight increase in activated microglia or hypertrophic astrocytes number was observed in the medial prefrontal cortex (Fig. 5E–F), in the thalamus and in the PVN in Dysb mice compared to controls. However, such effects were attenuated also in vehicle-treated animals (Supp. Fig. S5). 4.11. Effect of antibiotic-induced microbiota perturbation on intestinal N-acylserotonin, N-acylethanolamine and EC levels Given the well established alterations of serotoninergic signaling in intestinal dysbiosis and its potential consequences on both depression and inflammatory bowel disorders (O’Mahony et al., 2015), the previous finding of N-acylserotonins in the rodent gut (Verhoeckx et al., 2011) and the previously reported role of Narachidonoylserotonin (AA-5-HT) and the molecular targets that it inhibits, i.e. the fatty acid amide hydrolase (FAAH) and the transient receptor potential vanilloid type-1 (TRPV1) channel, in experimental models of anxiety and depression [(Kirkedal et al., 2016; Navarria et al., 2014), and references cited therein], we measured the levels of these new members of the endocannabinoidome, as well as those of the endocannabinoids, AEA and 2-AG, and of two major N-acylethanolamines, which have all been implicated in dysbiosis, depression and/or intestinal inflammation with different roles (Borrelli and Izzo, 2009; Cani et al., 2016; Capasso et al., 2014; Rubino et al., 2015). In Fig. 5 the levels of AA-5-HT and N-oleoylserotonin (OA-5-HT) in mouse jejunum, ileum and duodenum are shown. In this study, for we report for the first time that the intestinal levels of these two lipids decrease (P < 0.05 and P < 0.01, Fig. 6A) significantly in dysbiotic mice and increase after probiotic treatment (P < 0.05 and P < 0.01) in the jejunum. In the ileum and duodenum, only OA-5-HT decreased significantly in dysbiotic mice (P < 0.05), although there was a trend also for AA-5-HT to decrease during this condition, and to increase after probiotic treatment (Fig. 6B and C). Moreover, endocannabinoid and N-acylethanolamine levels are reported (Fig. 6D). The concentrations of the endocannabinoid AEA increased significantly in the jejunum after probiotic treatment (P < 0.05) and decreased in the duodenum in dysbiotic mice (P < 0.05). Oleoylethanolamide (OEA) levels decreased in the duodenum after probiotic treatment (P < 0.05). The concentrations of palmitoylethanolamide (PEA) and the other endocannabinoid, 2AG did not change significantly. 4.12. Probiotic treatment normalizes altered hippocampal TRPV1 phosphorylation caused by antibiotic-induced microbiota perturbation We measured the levels of two major direct or indirect brain molecular targets belonging to the endocannabinoidome, i.e. the cannabinoid CB1 receptor, which is activated directly by the endocannabinoids AEA and 2-AG and indirectly (via inhibition of the AEA-degrading enzyme, FAAH) by N-acylserotonins and some N-acylethanolamines, and the TRPV1 channel, which is activated directly by endocannabinoids and some N-acylethanolamines, and antagonized by some N-acylserotonins. We found that dysbiosis and subsequent treatment with probiotic did not alter either CB1 or TRPV1 protein levels (Fig. 6E and F). However, dysbiosis

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Fig. 5. Probiotic reduces microglia and astrocyte activation induced by antibiotic-induced microbiota perturbation. Iba-1 or GFAP immunoreactivities are shown in hippocampus (A-D) or the cortex (E-H) of normal (CTL) or dysbiotic (Dysb) mice treated with vehicle or probiotic or recovered mice. Quantitative analysis of total or activated cells is shown in the graph. Data are expressed as means ± S.E.M of 5–6 mice per group. ° indicates statistically significant difference versus CTL mice. * indicates statistically significant difference versus Dysb/veh mice. P < 0.05 has been considered as value of significance. The double symbol indicates P < 0.01 and the triple symbol indicates P < 0.001. ANOVA, post hoc Tukey. Scale bars 100 and 25 lm for panoramic and inset image, respectively.

was accompanied by increased phosphorylation (and hence sensitization) of hippocampal TRPV1, and this effect was reversed by probiotic (Fig. 6E and F).

5. Discussion The characterisation of the microbiota-gut-brain axis has crucial implications for the understanding and therapeutic treatment of inflammatory bowel disorders and associated psychiatric disturbances. To date, the mechanisms at the basis of the reciprocal gutbrain interactions and the potential therapeutic use of probiotics for the management of certain neuropsychiatric disorders are still controversial. The present study shows that antibiotic-induced microbiota perturbation leads to a depressive-like behaviour and impaired social activity associated with biochemical and functional changes in the hippocampus. Remarkably, chronic administration of the probiotic LCDG can adjust gut inflammation as well as normalise behavioural responses and the associated biochemical and functional alterations. Prolonged exposure to antimicrobial agents results in the perturbation of gut bacteria composition. Indeed, the treatment schedules and the pharmacokinetic properties of the agents can affect bacterial organisation and, in turn, (positively or negatively) the behaviour (Bercik et al., 2011; Johnston et al., 2014). Here, we used a broad-spectrum activity antibiotics cocktail (Lamouse-Smith

et al., 2011) in order to generate an overall microflora perturbation and identify possible biomolecular mechanisms responsible for behavioural modifications. We found that antibiotic treatment induces depressive behaviours, manifested as enhanced immobility in tests of depression-like behaviour, and impaired social recognition memory, which resemble the lack of motivation and depression often observed in patients with inflammatory bowel disorders. The beneficial effects of consumption of probiotics on behaviour and brain functionality are gaining recognition in the field of inflammatory diseases (D’Mello et al., 2015). In our study, when compared to animals treated with vehicle for the same period of time, repeated probiotic treatment significantly reversed the depressive-like behaviour and the reduced social activity. The antimicrobial exposure for 2-weeks generated a marked dysbiosis, with a loss of bacterial diversity and an increase in Proteobacteria and Actinobacteria, previously associated with depressive disorders (Jiang et al., 2015). Indeed, the translocation of pro-inflammatory mediators, including lipopolysaccharide (LPS) of gram-negative enterobacteria into the systemic circulation (a phenomenon know as ‘‘leaky gut”) may play a role in the pathophysiology of depression (Berk et al., 2013; Foster and McVey Neufeld, 2013; Maes et al., 2008). Interestingly, a partial restoration of the gut microbiota composition was observed after 1 week from antibiotic suspension. More strikingly, probiotic treatment promoted a higher increase of Lachnospiraceae compared to vehicle, reaching an abundance comparable to controls in recovered

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y

2

4

(pmol/g of tissue)

3

2-AG

2

(pmol/mg of tissue)

PEA

0.4

(pmol/mg of tissue)

0.2

OEA

0.0

(pmol/mg of tissue)

D ys

C

TL

b/ Pr ob

0.6

D

D

AEA

ys

ys b D

C

ys b/ Ve h

1.0 0.8 0.6 0.4 0.2 0.0

Jejunum

Ileum Dysb/ Prob

Ctl

Dysb

Dysb/ Veh

47.5 ± 17.90

12.50± 5.10

29.70± 8.10

74.20 ± 9.90

62.40

39.70

±

± 5.10

6.0

0.4± 0.1

0.24± 0.04

0.19± 0.01

0.2± 0.003

0.24± 0.03

0.21± 0.03

0.24± 0.03

0.2± 0.001

0.5± 0.10

0.9± 0.5

0.4± 0.1

0.41± 0.04

0.2± 0.05

0.4± 0.1

0.31± 0.03

0.24± 0.03

0.23± 0.04

0.23± 0.03

0.4± 0.1

0.21± 0.02

0.12

±

0.18± 0.03

0.32± 0.05

0.19±

80.9±

27.40

20.1*

± 7.8

48.80± 6.6

34.4± 5.2

Dysb

Duodenum

Ctl

43.4± 15.1 36.3± 4.6

Dysb/ Veh 68.8± 27.4 33.3± 4.4

Dysb/ Prob

Ctl

97.6± 35.2

23.7

32.7± 2.5

77.4

0± 5.10

±

Dysb 11.1± 2.2 ° 58.2± 11.2

15.2

Dysb/ Veh

Dysb/ Prob

13.30

16.3± 3.1

± 2.40 87.40

±

86± 10.3

14.3

0.04

0.03*

ys b/ Pr ob

3

D

D

4

5

b

5

Dysb/Prob

D ys

OA-5-HT (pmol/mg of tissue)

Dysb/Veh

b/ Ve h

CTL

TL

AA5HT (pmol/mg of tissue)

A

ro b

2,5 2

CTL Dysb Dysb/veh Dysb/prob

2,5

Protein expression levels (ODpTRPV1/ODTRPV1)

Protein expression levels (ODprot/Odα-tubulin)

ys b/ Pr ob D

ys b/ P

F

2

1,5

1,5

1

** +

1

0,5

0,5 0

CB1

TRPV1

0

pTRPV1

D

b/ Ve h

ys b

0

D

b/ Pr ob D ys

ys b/ Ve h D

D

ys b

0

TL

1

C

OA-5-HT (pmol/mg of tissue)

2

b/ Ve h D ys

C

TL

b/ Pr ob

3

C

0

D ys

Ve h ys b/

4

TL

AA5HT (pmol/mg of tissue)

D

D

C

ys b

0

1

D ys

1

2

D

2

3

ys b

OA-5-HT (pmol/mg of tissue)

3

TL

AA5HT (pmol/mg of tissue)

E 4

Fig. 6. Levels of AA-5-HT and OA-5-HT in the jejunum (A), ileum (B) and duodenum (C) following antibiotic-induced microbiota perturbation. Data are expressed as means ± SEM of N = 4–6 mice. * P < 0.05, ** P < 0.01; °P < 0.05, °°P < 0.01 denote statistically significant differences between Ctl vs Dysb and Dysb/Veh vs Dysb/Prob, respectively, as assessed by the Student’s t-test. (D) Endocannabinoid and N-acylethanolamine levels in peripheral tissues. Data are expressed as means ± SEM of separate determinations in N = 9–11 mice. * P < 0.05; °P < 0.05, significant difference between Ctl vs Dysb and Dysb/Veh vs Dysb/Prob, respectively, as assessed by the Student’s t-test. Evaluation of CB1 and TRPV1 protein expression and/or phosphorylation state in the hippocampus of dysbiotic mice. (E) Representative blot showing the expression levels of CB1 and TRPV1 proteins and changes in their phosphorylation at S800 evaluated though western blot analyses of hippocampal tissue isolated from CTL, Dysb, Dysb/veh and Dysb/prob mice. (F left) Bar graph showing the quantification of CB1 and TRPV1 levels normalized to b-tubulin. (F right) Quantification of phospho-TRPV1 (pTRPV1) levels normalized to total TRPV1. The asterisk denotes a p value <0.05 vs CTL. The cross (+) denotes a p value <0.05 vs the other experimental groups; data are means ± SD from at least three independent determinations.

mice. These data support previous findings reporting lowered Lachnospiraceae levels in patients with major depressive disorders. This is intriguing given the fact that several genera of this family are fibre-degrading and short-chain fatty acids (SCFA) producers. Besides having an anti-inflammatory effect, SCFA enhance the intestinal barrier by regulating the assembly of tight junctions; therefore, their levels may be implicated in the leaky gut process. Thus, although specific SCFA were not measured in this study, there may be a positive correlation between Lachnospiraceae abundance and the antidepressant effects mediated by probiotics. Interestingly, in this study the re-establishment of the normal behavioural phenotype appeared in untreated Dysb mice about 2 weeks after the antibiotic suspension. These findings suggest that the probiotic-mediated antidepressant outcomes (in term of behavioural and functional/biochemical changes) might be in part strengthened by the ongoing microbiota retrieval. Thus, the probiotic chronic administration exerts beneficial effects, both at the central/behavioural and intestinal level, possibly by accelerating the physiological recovery, that we observed for some of the measured endpoints following interruption of antibiotic treatment. Microbiota changes were also accompanied by an increase in inflammatory activity in the small intestine. In particular,

increased levels of IL-1b and TNF-a were associated with an enteric reactive gliosis, as indicated by increased levels of GFAP expression. In recent years, EGCs traditionally considered as only supportive cells, are emerging as local GI regulators of gut inflammation, by representing the interface between neurons and non-neuronal cells (Ochoa-Cortes et al., 2016). In particular, we have previously demonstrated that EGCs and S100B proteins are directly involved in gut inflammatory diseases, by regulating i-NOS protein expression and consequent NO release (Cirillo et al., 2011). Here, together with GFAP and S100B, i-NOS protein expression was significantly higher in dysbiotic mice compared to controls. Probiotic significantly reduced gut inflammation. Indeed, the increased levels of cytokines in Dysb mice, as well as the over-expression of i-NOS and S100, were completely normalised by the probiotic treatment. Instead, GFAP protein levels were unchanged, indicating the persistence of EGC activation under probiotic treatment. These data confirm the antiinflammatory property of LCDG (Novotny Nunez et al., 2015) and suggest a possible functional phenotypic shift of enteric GFAPpositive cells towards a protective phenotype. A stringent analysis of surface antigens may identify possible changes in the EGC profile induced by antimicrobials and probiotics.

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Interestingly, a previous study showed that antibiotic-mediated dysbiosis was not associated with gut inflammation or changes in specific enteric neurotransmitters (Bercik et al., 2011). Moreover, a different behavioural phenotype—more exploratory and less apprehensive—was observed in the same animals. Furthermore, unlike what we observed in our model, the short-term antibiotic exposure have been specifically correlated with impaired cognitive performance (Fröhlich et al., 2016). Similar discrepancies might be the consequence of different experimental conditions, including mouse strains and antimicrobials regimen or chemical properties. Indeed, these factors can differently influence microbiota composition and, hence, the intestinal immune response, which in turn may differently affect behaviour. Increased intestinal permeability may trigger systemic inflammation caused by bacterial elements crossing the epithelial barrier, being a well-known marker for intestinal health. Our data suggest that the intestinal barrier was not affected by dysbiosis. In fact, a trend towards increasing ZO-1 mRNA levels was observed in dysbiotic animals, but the statistical analysis revealed no significant differences. Indeed, no changes in the levels of circulating proinflammatory cytokines were detected in dysbiotic animals compared with controls. These data are in line with a previous study showing that bacterial composition, and consequently, intestinal permeability are dependent on the specific target species of the antibiotics; therefore, different classes of antibiotic drugs may have different effects on gut integrity (Tulstrup et al., 2015). However, we cannot exclude that, in our model, despite the observed integrity of the gut barrier, other biomolecules may be released into the systemic circulation, and/or others become less released (like the N-acylserotonins, see below), to exert a role in the pathophysiology of the depression (Fröhlich et al., 2016). Intriguingly, we found that probiotic treatment massively increases Cx-43 mRNA expression in ileum and colon portions of the intestine. As a component of gap junctions, Cx-43 is implicated in communication between intestine cells and its expression on EGCs is tuned by inflammation (Ochoa-Cortes et al., 2016). It has been recently suggested that the loss of Cx-43 from EGCs may increase the permeability of the intestinal vasculature by providing a connection for the gut-brain axis and contributing to brain inflammation in autistic patients (Gupta et al., 2014). Interestingly, the probiotic leads to a significant increase in Cx-43 expression in antibiotic-treated mice, suggesting that the production of this protein might represent a probiotic-mediated curative response to some types of dysbiosis. However, the possible role of Cx-43 in dysbiosis-related psychiatric disorders remains to be investigated. BDNF is a key regulator of synaptic plasticity and neurogenesis. Decreases in BDNF levels within the hippocampus and BDNFdepended alterations in synaptic plasticity have been shown to strongly contribute to the development of depression (Waterhouse and Xu, 2009). Here, Dysb mice beyond a depressive-like behaviour showed reduced levels of hippocampal BDNF associated with an increase in TrkB receptor expression. These results, consistent with literature highlighting BDNF role in altered behaviour in germ free mice (Bercik et al., 2011; Sudo et al., 2004), suggest that the alteration of BDNF/TrkB signalling may play a role in the development of the depressive symptoms induced by dysbiosis. However, the involvement of different players implicated in inflammation-associated depression, including serotonergic and dopaminergic neurotransmission, as well as glucocorticoids (Udina et al., 2016), cannot be excluded. Functional changes in hippocampal CA1-CA3 synapses and morphological modifications in CA3 pyramidal neurons have been reported to be associated with the occurrence of depression (Qiao et al., 2014). The decreased BDNF levels may thus contribute to changes in hippocampal neuronal activity, reducing synaptogenesis and making neurons more vulnerable to other factors, such as

the effect of adrenal glucocorticoids that are produced in response to stress (Angelucci et al., 2005. We found that activity of CA3 pyramidal neurons was deeply depressed in Dysb mice. In particular, the ongoing spontaneous activity, the number of bursts, and the percentage of spikes per burst were significantly reduced. In agreement with the observed behavioural disturbances, in the present study hippocampal BDNF levels and electrophysiological activities were normalised by probiotic treatment. At the same time, microbiota perturbation did not induce electrophysiological modifications in cortical circuits (BLA-mPFC axis), which represent key substrates in cognitive processing. Also these findings are in agreement with the lack of overt cognitive deficits observed here. Recent evidence indicates the involvement of non-neuronal cells in the regulation of synaptic plasticity in cortical circuits involved in emotional processing. In particular, increased levels of circulating pro-inflammatory cytokines and concomitant activation of brain-resident microglia have been shown to play a role in the establishment of depression (Rial et al., 2015). Here, microbiota perturbation caused a significant activation of astrocytes and microglia at the hippocampal level. These changes are considered predictive of a brain malaise that could in turn reflect a malfunction of the neurons. However, further analysis of surface antigens may be necessary to identify the functional phenotype induced by dysbiosis. Consistently with behavioural and functional data, however, microglia/astrocyte activation was reversed by the probiotic treatment. The investigation of the role of microglia/astrocyte activation in Dysb mice included other areas beyond the hippocampus, such as mPFC, PVN, and thalamus in view of the established impact of the microbiota in other brain areas involved in the integration of emotional stimuli, sensorial perception, and metabolism (Sen et al., 2017). In fact, only a slight increase in the number of activated microglia and/or astrocytes was detected. Furthermore, this effect was found physiologically restored 7 days after discontinuation of antibiotics (vehicle-treated animals). These latter data are interesting considering the role of the PVN in neural integration pathways regulating gut functions. Although the underlying mechanisms are not understood yet, our data add to the contention that the gut microbiota influences brain areas controlling emotional behaviours, by affecting, at least at the beginning, structures directly involved in the interface between the CNS and periphery. We also found that dysbiosis was accompanied in the hippocampus by increased phosphorylation, and hence increased sensitization, of TRPV1, whereas no changes in the expression of the cannabinoid CB1 receptor was observed. TRPV1 and CB1 play opposing (exacerbating and inhibiting, respectively) roles in depression in experimental models of this disorder (Huang et al., 2016; Kirkedal et al., 2016; Navarria et al., 2014; Wang et al., 2017), and are targets for both endocannabinoids and, particularly in the case of TRPV1, some other members of the endocannabinoidome, such as N-acylethanolamines, N-acyldopamines and Nacylserotonins (Di Marzo and Wang, 2014). Enhanced TRPV1 expression/activity in the hippocampus has been associated with enhanced stress response and depression-like behaviours and reduced BDNF levels (Navarria et al., 2014; Wang et al., 2017). Therefore, TRPV1 enhanced phosphorylation/sensitization in Dysb mice might contribute to the pro-depression-like effects and reduction of BDNF levels caused by antibiotic treatment. However, in the hippocampus, TRPV1 activity has been associated with enhanced LTD of hippocampal interneurons and (possibly as a consequence) reduced LTP of CA1 pyramidal neurons (Bennion et al., 2011; Brown et al., 2013; Gibson et al., 2008), two effects that should result in enhanced firing activity of the latter neurons, which is in contrast with the observed dysbiosis-induced reduction of CA1 pyramidal neuron firing activity. Therefore, it is possible that the observed enhanced TRPV1 activation in the hippocampus

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occurs in different cells [functional TRPV1 channels have been detected also in the dentate gyrus (Chavez et al., 2010)], or that it also represents an adaptive negative feedback mechanism aimed at counteracting dysbiosis-induced inhibition of pyramidal cell activity. At any rate, the fact that probiotic treatment reversed TRPV1 hyperphosphorylation argues in favour of a role of this channel in the brain response to dysbiosis. Finally, we also observed here that dysbiosis was accompanied by a significant decrease in the intestinal levels of a novel type of lipid mediators, the monounsaturated and polyunsaturated N-acyl-serotonins, that are known to inhibit endocannabinoid inactivation by FAAH and at the same time to antagonise TRPV1 channels (Chavez et al., 2010; Maione et al., 2007; Ortar et al., 2007) and were previously identified in the small intestine of pigs and mice (Verhoeckx et al., 2011). In at least two of the three intestinal tissues analysed, such decrease, when present, was attenuated by the treatment with the probiotic. Disinhibition of FAAH and TRPV1, such as following reduction of N-acylserotonin levels, is expected to worsen both intestinal inflammation (Izzo et al., 2001; McVey and Vigna, 2005) and depressive-like symptoms (Kirkedal et al., 2016; Navarria et al., 2014) in experimental models. Therefore, and also in view of the fact that circulating long chain N-acylserotonins can cross the blood brain and are resistant to hydrolysis to serotonin (de Lago et al., 2005), it is possible that the antibiotic-induced decrease of the levels of these mediators, observed here for the first time, contributes to both intestinal inflammation and depression-like symptoms. Interestingly, only in the jejunum and duodenum we found that dysbiosis and subsequent treatment with probiotic, were accompanied by a reduction and an increase, respectively, of the tissue levels of AEA (the only endocannabinoid that is specifically inactivated by FAAH), but not of 2-AG (which can be inactivated also by other enzymes). This suggests that: 1) indeed, dysbiosis-induced downregulation of N-acylserotonins impacts positively on FAAH activity; 2) this effect is not so evident in the ileum. Interestingly, the levels of N-acylethanolamine substrates of FAAH other than AEA, such as PEA and OEA, were not decreased by microbiota disruption, supporting the notion that the levels of these two compounds and those of AEA can be controlled also by other factors, such as the activity of redundant biosynthetic enzymes or the availability of dietary precursors, and that these lipid mediators play different roles, depending on both the cause of dysbiosis and the type of their fatty acid chain (Geurts et al., 2015). Since the small intestine and the microbiota can be a source, or contribute to the biosynthesis, of serotonin, which is the most likely biosynthetic precursor for N-acylserotonins (Dempsey et al., 2014), further studies are needed to link the observed decrease in the levels of these mediators with specific changes in the composition of one of more microbial species induced by our antibiotic, and counteracted by our LCDG, treatments.

6. Conclusions In conclusion, our findings indicate that changes in gut bacterial composition might cause altered responses in affective behaviours via several concurring cellular and molecular mechanisms. To our knowledge, this is the first study showing hippocampal rearrangements likely responsible for the depressive behaviours induced by mirobiota disruption. Furthermore, our study demonstrates the relationship between particular microbial relative abundances and specific behaviours, and provides unprecedented examples of biomolecules (with particular emphasis on those belonging to the endocannabinoidome) that, through dysbiosis-induced alterations of their levels and/or activity, may explain such behaviours. Finally, we show that chronic administration of LCDG can produce

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