Anaerobic gut bacterial metabolites alter morphology and survival of neurons and glia in a hippocampal culture model of Parkinson’s Disease

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Hoffmann, Jennifer C. Martin, Ana Evora, François Brillet, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8852210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Parkinson’s disease (PD) is associated with alpha-synuclein (α-syn) accumulation and spread. Recent evidence suggests a connection between PD and gastrointestinal changes early in the disease process due to host-bacterial interactions and association between the microbiome and PD pathology. Methods : Supernatants (SNs) from 16 bacterial strains, characterised via HPLC and GC, were tested on rat hippocampal cultures. PD pathology was modelled via viral gene delivery targeting neurons with human mutated A53T α-synuclein (hA53T-αsyn). Cell viability was assessed +/- SNs and hA53T-αsyn. Immunohistochemistry combined with semi-automated image analyses was developed to determine neuronal and glial density and morphology. Results : A microbiome panel of 16 different species yielded a range of SN-specific fermentation products (e.g. acetate, formate and lactate). HA53T-αsyn transfection created an in vitro synucleinopathy model with ~-50% viability after 6 days. Most bacterial SNs ameliorated gross viability loss caused by hA53T-αsyn. Segmentation of cell types identified microglia as most impacted by hA53T-αsyn, with an increased size indicative of activation. Some SN treatments boosted neurone and microglia numbers per se , and reduced hA53T-αsyn toxicity , with partial neuro-protection detected for B. subtilis and B. hansenii , while A. mucinipila, E. rectale and R. intestinalis offered some protection for glia. Conclusions : Specific SNs improved viability per se and affected hA53T-αsyn toxicity. Actions were highly cell type specific with some improving neuronal or glia morphology changes. The observed CNS-modulatory effects indicate that therapeutic routes targeting the microbiome may ameliorate PD pathology. Parkinson’s disease synuclein primary hippocampal culture gut bacteria microbiome CellProfiler Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting the ageing population with prevalence forecasted to surpass 12 million by 2040 (Dorsey et al., 2018; Rani & Mondal, 2021). Official clinical manifestations of PD comprise hand, leg or jaw tremors, muscle stiffness, slowed movement, and coordination and balance impairments. Clinically, established motor-symptoms are used for diagnosis, but by this stage 50–70% of substantia nigra dopaminergic neurons are already lost (Blesa et al., 2012). Histo-pathologically, the disease is associated with aggregation and spreading of the protein alpha-synuclein (α-syn), a protein involved in synaptic vesicular release. In synucleinopathies, it is prone to misfolding and aggregation resulting in monomers, oligomers and eventual development of Lewy bodies (Braak et al., 1996; Cheng et al., 2011; Marczynski et al., 2019). Toxicity associated with α-syn causes neuronal cell death, which releases their contents to the environment (Sidhu et al., 2004). Although PD is considered a neurodegenerative brain disease, findings highlight spread of α-syn to or from peripheral systems (Braak et al., 2003; Chiu et al., 2021). Animal models of PD using pre-formed fibrils of α-syn have demonstrated a prion-like spread from intestinal or vagal structures to the brain (Yang et al., 2023). In patients, gut effects can be seen up to 20 years before the clinical diagnosis of PD, including expression of misfolded α-syn within various intestinal tissues such as the myenteric plexus (Hilton et al., 2014; Stokholm et al., 2016). Gastrointestinal symptoms can include altered gut permeability, slower transit time, and reduced gastrointestinal short-chain fatty acid (SCFA) levels (Romano et al., 2021). Patients also present with an altered gut microbiome composition (Stokholm et al., 2016). Undigested fibres are the major substrate for microorganisms residing within the colon. Bacterial metabolites such acetate, propionate, and butyrate comprise most SCFAs produced by bacterial fermentation (Frolova et al., 2022). In anaerobic fermentation electron transfer uses metabolic intermediates and produces various metabolites simultaneously (Mukhopadhya & Louis, 2025), e.g. lactate production from pyruvate regenerates NADH from NAD + for further glycolysis monosaccharide fermentation. Substrate identity, presence of cross-feeding bacteria and whether the bacterium is a generalist or specialised fermenter affect metabolite outputs. For instance, Bacteroidota species produce propionate using the succinate pathway (Bhatia & Yang, 2017; Hays et al., 2024; Mukhopadhya & Louis, 2025); while butyrate production occurs through two routes: Faecalibacterium and Roseburia species use the acetyl-CoA pathway, consuming acetate while other bacteria including Butyrivibrio and Subdoligranulum species use the butyrate kinase pathway from butyryl-CoA (Hays et al., 2024; Lange et al., 2023) (Fig. 1). SCFAs elicit both local and systemic effects in the host and can enter the brain; therefore, changes in microbiome composition are likely to implicate host health in PD (Jameson et al., 2020; Nankova et al., 2014). Experimentally, antibiotics that destroy bacteria indiscriminately altered gut microbiome composition temporarily (Cryan et al., 2019; Sampson et al., 2016), and faecal microbiome transplants (FMT) from PD patients to mice induced Parkinsonian symptoms, while healthy control FMT to PD mice reduced symptoms (Sampson et al., 2016). Gut microbiota influenced the activation of brain microglia and astrocytes, and short-chain fatty acid (SCFA) concentrations were rescued in PD mice receiving FMT from healthy individuals. Levels of dopamine and serotonin were also restored in these mice (Sun et al., 2018). These findings have paved the way for research into the role of the gut-microbiome-brain axis in PD, including possible routes of pathology spread and treatment targets. Human population studies have indicated that some bacterial species are specific to PD patients, absent from healthy controls, and these may be possible disease promoters and/ or indicative of a disrupted microbiome (Nuzum et al., 2020; Wallen et al., 2022). In vitro research has demonstrated that α-syn delivery (wildtype or mutated) can impair neuronal outgrowth and branching (H. J. Lee et al., 2008). This aligns with other neurodegenerative diseases such as Alzheimer’s disease, where overexpression of Tau causes neuronal trunk and branch count changes (Su et al., 2008). In contrast, others were unable to identify an effect of α-syn on the morphology of neurons using hA53T viral transfection compared to GFP controls (Vieira et al., 2020). Other non-neuronal central nervous system (CNS) cells essential for defence mechanisms, homeostasis, and inflammation such as astrocytes and microglia have also been shown to be (indirectly) morphologically affected by α-syn. Microglia as immune scavengers of the brain present opportunities to remove and degrade α-syn, but prolonged activation of microglia in PD exacerbates α-syn related inflammation (H. J. Lee et al., 2008; Su et al., 2008). Similarly, astrocytes treated with α-syn become reactive (increased GFAP levels) and when co-cultured with neurons, are found to inhibit neuronal outgrowth (Vieira et al., 2020). Together, toxicity, inflammation and morphological changes accompany degenerative pathways in PD (Sidhu et al., 2004; Sohrabi et al., 2023). In this study bacterial supernatants (SNs) were tested as possible modulators in rat hippocampal cultures, in the presence or absence of human mutated A53T α-syn, delivered via a viral vector. This models the early, prodromal stage of PD in vitro , where neurodegeneration is detectable and first cell death emerges (Postuma & Berg, 2019; Siderowf & Lang, 2012). The selection of anaerobic bacteria was based on published links reported with the PD microbiome, and the understanding of relevant pathways in gut and brain health (Table 1). Primary neuronal cells were investigated for morphological and viability changes in the presence of α-syn and/ or bacterial metabolites. Evidence was obtained for species-specific impact on cell viability and morphology in this in vitro model of PD. Table 1 Bacterial species and their origin strains relevant to PD. ‘+’ and ‘-’ denote gram-positive and gram-negative respectively, ↓ ; decrease and ↑ ; increase in PD patients. Bacterial Strains Gram PD ↓/↑ Strain Code Commentary Reference Akkermansia muciniphila - ↑ DSM 22959 Mucin degrader, causes α-syn aggregation. (Amorim Neto et al., 2022) Alistipes shahii - ↑ D5-AX-PE M15 May be involved in mild cognitive impairment. (Bedarf et al., 2017; Ren et al., 2020) Anaerostipes hadrus + ↓ DSM 3319 Butyrate producer and present in healthy individuals. (Liu et al., 2024; Wallen et al., 2022) Bacillus subtilis + Unknown B524ATN916 C. elegans fed on B. subtilis have reduced α-syn levels. (Goya et al., 2020) Bacteroides coprocola - ↓ DSM 17136 Found to positively correlate with a healthy gut. (Gacesa et al., 2022; Petrov et al., 2017) Bacteroides dorei - ↓ DSM 17855 Abundant part of the microbiome; mainly produces acetate and propionate. (Bedarf et al., 2017; Petrov et al., 2017; Zafar & Saier, 2021) Blautia coccoides + ↓ DSM 935 Produce the highest levels of H 2 , which may be neuroprotective in PD. (Hasegawa et al., 2015; Van Kessel & El Aidy, 2019) Blautia hansenii + ↓ DSM 20583 Acetate, lactate and succinate producer. (J. Chen et al., 2016; Wallen et al., 2022) Collinsella aerofaciens + ↑ DSM 3979 Increases gut permeability in specific disease conditions. (Wallen et al., 2022) Dorea longicatena + ↓ DSM 13814 Acetate and formate producer. (Petrov et al., 2017; Taras et al., 2002) Escherichia coli - ↓ HB101 Curli can increase α-synuclein aggregation. (Wallen et al., 2022) Eubacterium rectale (also known as Agathobacter rectale ) + ↓ AI-86 (DSM 17629) Universal healthy microbiome resident. (Gacesa et al., 2022; Wallen et al., 2022) Fusicatenibacter saccharivorans + ↓ DSM 26062 Reduced populations in PD. (Wallen et al., 2022) Parabacteroides distasonis - ↑ DSM 20701 Increases with age but also specifically in PD. (Sharon et al., 2016; Wallen et al., 2022) Roseburia intestinalis + ↓ LI-82 Associated with anti-inflammatory properties. (Sun & Shen, 2018; Wallen et al., 2022) Segatella copri (Formerly Prevotella ) - ↓ DSM 18205 Associated with lower risk of irritable bowel disease and positively associated with general health. (Bedarf et al., 2017; Petrov et al., 2017; Wallen et al., 2022) 2. Materials and Methods 2.1 Bacterial strains, and growth conditions A. muciniphila was cultured in Brain Heart Infusion broth (BHI from Oxoid), made using 37.7g to 1L dH 2 O with 0.1g resazurin per 100mL. All other strains were cultured in yeast, casitone, fatty acids (YCFA) broth (Duncan et al., 2002 ) supplemented with 0.2% glucose (Sigma G5767), 0.2% cellobiose (Sigma C7252), 0.2% soluble starch (Sigma S2004), forming YCFAGSC. The media was prepared anaerobically; the pH adjusted to pH 7.5–7.6 and 7.5mL aliquots dispensed into Hungate tubes while simultaneously flushing with CO 2 . Tubes were sealed with butyl rubber septa (Bellco Glass) and autoclaved. Bacteria were inoculated using anaerobic methods (Bryant, 1972 ) and incubated anaerobically at 37°C without agitation. Bacteria were grown until their respective stationary phase (assessed by performing growth curve analysis) before harvesting the supernatant through centrifugation, 5,000×g for 5 min at room temperature. Collected supernatants (SNs) were filtered through 0.22 µm filters and frozen until further use. Full-length 16S rRNA gene PCR amplification was performed using cell pellets from harvested supernatants following standard methods. PCR products were cleaned using the Wizard PCR product purification kit (Promega, Southampton, United Kingdom) and bidirectional partial 16S rRNA gene sequences obtained using primers 519F, RP2 and FD1 from Eurofins MWG (Table 2 ). Inaccurate Bacillus species identification occurred using rRNA gene amplification after PCR product cleaning. An alternative approach using the primers pyrA and aroE applying a PCR amplification protocol (G. Lee et al., 2022 ) was performed on pellets of the same bacterial samples to successfully identify Bacillus subtilis . Table 2 Primers. Primer details used in 16S rRNA gene PCR amplification and sequencing the panel of bacteria. A different target gene sequence alongside PCR amplification was required to distinguish Bacillus subtilis from other species of Bacillus (G. Lee et al., 2022 ). Primers Sequence Target Reference 519F-F ACGGCTACCTTGTTACGACTT 16S rRNA gene Durack & Lynch, 2019 RP2-R CAGCMGCCGCGGTAATWC 16S rRNA gene Van Immerseel et al., 2010 FD1-F AGAGTTTGATCCTGGCTCAG 16S rRNA gene pyrA-F GTC TTC CGT TCA GGA AAG GC Bacillus subtilis only G. Lee et al., 2022 pyrA-R GAT CTC CCG TTT GGA TCG GCTC Bacillus subtilis only aroE-F GGG GAA GGC TTC GTG AAG TC Bacillus subtilis only aroE-R CCC ACA GAC GTT GTA TGG ATG Bacillus subtilis only 2.2 Gas chromatography of bacterial fermentation products Gas chromatography was carried out at the Analytical Department, Rowett Institute, University of Aberdeen. Supernatants collected of a single stationary phase cultured bacterium were run in duplicates, 2× 1mL derivatised to t -butyldimethyl-silyl derivatives and the concentrations of SCFAs measured. Acid production was determined by capillary GC following standard methodology (Richardson et al., 1989 ). GC measured Acetate, formate, propionate, butyrate, iso-butyrate, valerate, iso-valerate, lactate and succinate level. For data analysis, replicates were averaged and bacterial medium control values subtracted to identify specific bacterial metabolites. Output was presented with standard deviation error bars; however no statistical tests were performed due to the low experimental repeat. GC is a well-established technique for measuring SCFAs and other bacterial metabolite fermentation products. It requires derivatisation to increase the phenolic analyte volatility (Nolvachai & Marriott, 2013 ). This process itself induces a technical difficulty as metabolites may not respond quantitatively to extraction or derivatisation, introducing sources of error (Kanani & Klapa, 2007 ). 2.3 High-performance liquid chromatography (HPLC) testing of bacterial supernatant fermentation products HPLC was applied to measure acetate, propionate, butyrate, valerate, lactate, succinate, and the additional SN components, glucose, glycolic acid, pyruvate and fumarate. Sampling of the SNs occurred at SBiomedic, Belgium. Bacterial SN samples and corresponding bacterial growth medium samples were thawed overnight at 4°C, each split in 3× 400µL aliquots, transferred into HPLC glass vials (Waters, Cat. 186000282C) and directly processed. Briefly, samples were analysed using a Prominence-I LC-2030 plus HPLC (Shimadzu) equipped with an Aminex HPX-87H ion exclusion column (BioRad, Cat. 1250140) coupled with RID-20A detector (Shimadzu). Glucose (Cat. 170080025 Thermo Scientific) and the SCFAs (sodium acetate Cat. 241245, sodium propionate Cat. P1880, sodium butyrate Cat. 303410, valeric acid Cat. W310107, succinate acid Cat. 398055, all Sigma, and lactic acid Cat. 79-33-4, Guinama S.L) were quantified against a six non-zero level calibration curve using standard solutions > 3.125, 6.5, 12.5, 25, 50, 100mmol.l − 1 . Data analysis was performed as in section 2.2 . All replicates were averaged and presented data subtracted from medium controls to identify production or use of metabolites and energy sources. Standard deviation error bars are presented but without statistical tests. 2.4 A53T viral construct AAV1/2-CMV/CBA-human-A53T-alpha-synuclein-WPRE-BGH-polyA (the α-syn delivery virus, subsequently referred to as hA53T-αsyn) and AAV1/2-CMV/CBA-Null/empty-WPRE-BGH-polyA (control construct, subsequently referred to as Null-Empty) vectors were obtained from AMSBIO (Oxford, UK), aliquoted and stored at -80°C until use at the Institute of Medical Sciences, University of Aberdeen. The titre concentration of both constructs upon purchase was 5.1×10 12 vg/mL. 2.5 Primary rat hippocampal cultures Primary rat hippocampal culture preparation was adapted from (Drysdale et al., 2006 ) and performed in accordance with Animals (Scientific Procedures) Act 1986 (ASPA). Male and female neonatal Sprague-Dawley rat pups between the age of postnatal (P) 0 to P2 were culled via cervical dislocation. In brief, 24-well plates containing glass coverslips (VWR) or 96-well plates were coated with poly-L-lysine, washed and allowed to dry. Rat hippocampi were dissected and placed into ice cold HEPES Buffer Solution (HBS; 130mM sodium chloride, 5.4mM potassium chloride, 18mM calcium chloride, 1mM magnesium chloride, 10mM HEPES, pH 7.4) with 25mM D-glucose. Tissue was chopped and transferred to 1mg/mL protease type XIV (Sigma) in HBS with D-glucose for 30 min at 37°C for enzymatic digestion. After washing, tissue was dissociated through trituration. HBS solution was replaced with 0.22µm double filtered Neurobasal (Fisher Scientific) supplemented with 2% B27, 10% heat inactivated foetal bovine serum (FBS), 2mM GlutaMAX (Gibco), and 1ng/mL fibroblast growth factor-basic (bFGF) (all Fisher Scientific), and 1% penicillin-streptomycin (Sigma-Aldrich). Cells were seeded at 75,000 or 25,00 cells per well in 50µL for 24-well plates containing coverslips and 96-well plates, respectively. Cultures were kept at 37°C, 5% CO 2 in an incubator for 1.5 hours to allow cell adhesion. Thereafter, wells were topped up with 250µL FBS free Neurobasal mix with supplements as above in 24-well plates, or 100µL in 96-well plates. Cells were left for 3 days in vitro to grow and settle without disturbances. At 3 days in vitro , media change was performed again with Neurobasal and supplements without FBS during which treatment conditions were added as required. Cultures received an equated final concentration of 5.1×10 10 vg/mL as a one-off exposure to induce PD pathology and/ or received 1% of total medium as supernatant. Cells received a media top-up 3 days thereafter. Final experimental endpoints were taken 9 days in vitro (DIV) 6 days after treatment addition. All primary culturing and subsequent experimental tests and endpoints were carried out at the Institute of Medical Sciences at Aberdeen University. 2.6 Viability assay Cellular viability was assessed using Cell Counting Kit-8 (CCK-8) according to manufacturer’s guidelines (Sigma, UK). Primary cultures were treated with 5.1×10 10 vg/mL virus vectors alone, 1% bacterial supernatant alone or 1% growth medium, or the combination of supernatant and hA53T-αsyn for 6 days (9 DIV) prior to running of the assay. Medium was removed and replaced with 90µL fresh Neurobasal and supplements without FBS (section 2.5 ) and 10µL of CCK-8 solution and maintained for 4 hours at 37°C, 5% CO 2 in an incubator. Absorbance was measured on the FLUOstar Omega plate reader (BMG Labtech) at 450nm with a reference wavelength measured at 600 nm. Percentage viability was calculated by subtracting absorbance values at the reference wavelength from those measured at 450nm, averaged per condition, subtracting blanks and normalising against media controls or the Null-Empty vector as indicated. 2.7 Immunofluorescence After 9 DIV, media were removed from each well containing coverslip grown cells followed by a HBS wash, then fixed for 10 min at -20°C with ice-cold methanol. Permeabilization and blocking was performed together for 20–30 min at room temperature on a rocker using HBS, 0.1% Triton X-100, 1% milk powder, 1% goat serum and 2% BSA. Samples were washed prior to primary antibody addition 3× with HBS with 0.1% tween-20 (HBS-Tween). The primary antibodies GFAP (1:2000, Invitrogen), IBA1 (1:1000, Wako), MAP2 (1:2000, Invitrogen), 4B12 (synuclein 1:1000, Invitrogen) and pSer129 (P-synuclein, 1:5000, Abcam) were made up in HBS, plus 1% goat serum, 2% bovine serum albumin (BSA), and 0.1% Tween-20 and left on samples overnight at 4°C on a shaker. For cell type specificity, conjugated GFAP-Cy3 (1:500, Invitrogen) was also used. Following overnight incubation, primary antibody mix was washed off with 3× HBS-Tween washes. Secondary antibodies anti-rabbit Alexa Fluor 488 (1:500, Invitrogen), anti-chicken Alexa Fluor 594 (1:500, Molecular Probes, USA), and anti-mouse Alexa Fluor 647 (1:500, Invitrogen) were added to the same HBS mix as primary antibodies for 1 hour on a shaker light-protected with foil. Cells were washed 3× with HBS-Tween and incubated 3min with HBS containing 1:1000 DAPI (Sigma), washed again, dried and mounted onto Epredia superfrost adhesion slides (Fisher Scientific) with ProlongTM Diamond Antifade mountant (Invitrogen, Thermo Fisher). 2.8 Image Acquisition and Assessment of Cellular Morphology For quantitative analysis of primary cultures with microtubule associated protein II (MAP2: neurons, in red), ionized calcium-binding adapter molecule 1 (IBA1: microglia, in green), glial fibrillary acidic protein (GFAP: astrocytes, in magenta), and nuclei (DAPI, in blue), fluorescence microscopy was performed using the EVOS M5000 (Invitrogen) with a 20× objective. Three images were taken per coverslip, with imaging criteria consisting of: avoiding extremely dense clusters of neurons that cannot be reliably analysed; avoiding bubbles and artifacts present in the DAPI and RFP channels including debris or discoloured background affecting cell visualisation. Image analysis was performed using CellProfiler’s (v4.2.6) automatic image segmentation. Three pipelines were developed and validated inhouse for segmenting and measuring morphology of neurons, microglia and astrocyte cells. Total image cell counts were calculated by the number of segmented cells (red, green or magenta) with an associated DAPI nucleus. The identified objects of interest (cells) were used in subsequent morphology analysis. The number of independent replications was n = 7–10. Qualitative exemplar images were obtained at 40× using the Zeis LSM880 Airyscan confocal microscope at consistent exposure times. A pinhole size of 1 airy unit was used for all images. 2.9 Statistical Analysis Data from image analyses pipelines were exported to MS Excel for quantification. Experimental repeats and pipeline data were pooled; technical replicates (image replicates, 3×) were averaged to receive a mean value per experimental replicate per parameter. Percentage area stained was calculated based on image MAP2, IBA1, or GFAP pixel intensity of the define object (cell). Parameters measured per object per image were automatically averaged. Parameter outputs for all objects were divided by object count to create average parameter outputs per cell . Cell counts are the total number of cells that possess a nucleus and were associated with a specific cell type stain. Morphological shape descriptors trunk and branch were based on counts per cell, while area and perimeter were measured in pixels. Solidity and eccentricity yielded units between 0 and 1, based on a circular shape or the ratio between the length and width of the object. Data from both CCK-8 readouts and CellProfiler analyses are expressed as percentage (%) of controls. Outliers were identified using ROUT method (GraphPad Prism, 10.5.0), and normal distribution of data confirmed before statistical analysis. Significances for CCK-8 data and media comparison for cell counts were determined using two-way ANOVAs and Bonferroni’s post hoc test. For morphology parameters, group comparisons for a given supernatant employed a one-way ANOVA with Sidak’s post hoc test. If the overall ANOVA was significant but not the post hoc test, planned group comparisons via unpaired t-tests were conducted. Spearman correlation heatmaps were prepared using GraphPad Prism. R studio (2024.09.1) and Inkscape (version 1.3.2) were used to visualise the data together. For visualisation purposes only, immunohistochemistry images were enhanced by adjusting the contrast. 3. Results 3.1 Bacteria produced distinctive fermentation products Fermentation products of stationary phase cultures were measured using gas chromatography (GC) and high-performance liquid chromatography (HPLC). Comparing data from derivatised GC detection with HPLC analysis of neat bacterial supernatants enabled the reproducibility of fermentation analyses for acetate, butyrate, propionate, valerate, lactate and succinate to be assessed and allow more extensive supernatant profiling of glucose, pyruvate, fumarate and glycolic acid (Fig. 2). Analysis of bacterial supernatants using GC and HPLC identified similar trends in fermentation production but varied in the exact mM range of samples (Fig. 2D). Concentrations of propionate, butyrate and valerate were consistent between bacteria across both analytical methods. Acetate concentrations measured higher by HPLC than GC for A. hadrus , B. coprocola , F. duncaniae , and F. saccharivorans , (Fig. 2A). Acetate can be an end-product of bacterial fermentation, but only select bacteria are able to convert it to butyrate (Fig. 1) (Louis & Flint, 2017). A. hadrus produced both 4.5-6.3mM acetate and 7.3-9.1mM butyrate via the butyryl-CoA acetate-CoA transferase pathway (Figs. 1 & 2). In contrast, E. rectale and R. intestinalis only formed butyrate (Fig. 2) assumed through the butyryl-CoA acetate-CoA transferase pathway (Fig. 1). Formate was detected only by GC, and in 7 of 16 bacteria, likely generated by the Wood-Ljungdahl pathway. Highest concentrations were present in R. intestinalis (12.38mM), F. saccharivorans (10.58mM), and D. longicatena (10.10mM) supernatants, with low concentrations detected for A. muciniphila and P. distasonis (0.72mM and 0.59mM, respectively) and none for the Blauti a species. Succinate, which was not a constituent of the growth medium, was detected in 10 (GC) and 15 (HPLC) bacteria using the two methods. The difference was mainly due to the detection of low concentrations (below 1mM) in 8 isolates using HPLC but not GC (for instance A. muciniphila supernatant contained 0.5mM succinate based on HPLC data). Concentrations of lactate were not consistent between the two measurement techniques, with HPLC detecting less (Fig. 2B). GC often gives higher lactate readings than HPLC due to derivatization enhancing signal, greater sensitivity of GC detectors, less susceptibility to co-elution, or matrix interferences affecting HPLC more. Formate, iso-butyrate and iso-valerate were detected at low levels by GC and not measured by HPLC (Fig. 2A & 2B). Both glucose and pyruvate present in the growth media as measured by HPLC (YCFA 8.6mM glucose and 9.8mM pyruvate; BHI; 12.4mM and 17.9mM; Fig. 3C) were found to be exhausted by most bacteria after growth reached stationary phase (Fig. 2C & 3B). Less than 2mM of pyruvate were detected in A. shahii , B. hansenii and E. coli after growth medium subtraction suggesting that monosaccharides were not fully utilised for downstream fermentation, however pyruvate itself is not an end product (Fig. 3B). HPLC was the only method to measure fumarate and glycolic acid levels (Fig. 2C & 3B), which could therefore not be compared. 3.2 Bacterial supernatants improved viability of primary cultures exposed to hA53T-αsyn The neuronal expression of hA53T-αsyn was confirmed through staining of total and phosphorylated α-syn and cell specific staining in cultures 6 days after virus addition (Fig. 3). Alpha-synuclein was detected predominantly in neurons, and rarely in astrocytes. Phosphorylation (pSer synuclein antibody) only occurred in select neurons while the majority presented with total α-syn (4B12 stain) in the cell body and towards the axon (Fig. 3B – C). Addition of hA53T-αsyn for 6 days in vitro significantly reduced viability of cell cultures (range: 5.5% − 44.5% viability, average: -80% reduction) vs. the Neurobasal and YCFA controls without hA53T-αsyn ( p < 0.0001, Fig. 4A). A 2-way ANOVA over all control conditions (with media composition and α-synuclein as factors) confirmed the viability loss caused by α-synuclein; (F (1, 35) = 106.6, p < 0.0001), with no effect of media or interaction (F < 1; P = 0.9). Co-application of SNs with hA53T-αsyn also revealed a significant detrimental effect of a-synuclein (F (1, 99) = 358.6, P < 0.0001), but not SN treatment (F (10, 99) = 0.4835, p = 0.8972) (Fig. 4B). Normalising treatment by bacterial SNs with hA53T-αsyn against growth medium YCFA with hA53T-αsyn (= 100%) indicated that most bacterial supernatants provided significant protection to hA53T-αsyn triggered toxicity, i.e. an increase in viability (Fig. 4C). The biggest improvements in viability were obtained for B. coccoides , C. aerofaciens , and P. distasonis in the presence of hA53T-αsyn with viability scores of 220%, 235%, and 207%, respectively. There was a lot of variability in the consequences of exposure to hA53T-αsyn, with the most consistent results observed for A. shahii (192%; Fig. 4C). 3.3 hA53T-αsyn induces cell loss, most strongly affecting microglia Mitochondria-based viability assays cannot differentiate between cell types in this mixed cell culture system. Cell type-specific effects were therefore determined next using three separate CellProfiler pipelines, based on staining with MAP2, IBA1 and GFAP for neurons, microglia, and astrocytes, respectively. Exemplar immunofluorescent images are presented in Fig. 5A. Initial visual validation confirmed that segmentation allowed successful and reproducible identification of cell-type specific counts and morphological characteristics. After confirming normal distribution of all data sets and the absence of mathematical outliers; 2-way ANOVAs were run over all conditions (with media composition and α-synuclein as factors) for cell counts of each cell type (neurons, microglia, astrocytes; Fig. 5B, E & H, all treatment replications are based on n = 8–10 independent experiments). In neurons , we confirmed that α-synuclein but not media was an overall significant factor (α-synuclein: F (1, 46) = 12.32, p = 0.001; media: F (2, 46) = 0.7608, p = 0.4731), and that there was no interaction F (2, 46) = 0.4724; p = 0.6265). The null-empty vector did not affect neuronal count (n = 7; p > 0.05 vs control). Consequently, media controls (and synuclein only data) were pooled (comprising blank control, BHI and YCFA controls), yielding an overall control count (mean) of 83.5 vs. a α-synuclein mean of 59.5 neurons (paired comparison: t = 3.56, df = 50; p = 0.0008). Individual scatter plots, pooled data as well as estimation plots (Fig. 5B) illustrate the distribution of the data and the difference between groups (mean and range). An increase in neuronal counts alongside a reduced perimeter (Fig. 6) due to SN only treatment was detected for A. shahii, B. corprocola and B. hansenii (vs. YCFA control; Table 3, Fig. 5&6, and Supplements). For the latter, the combined treatment (hA53T-αsyn plus B. hansenii SN) had the most pronounced protective effect on neuronal morphology, reducing neuronal MAP2 levels as well as area and perimeter (see also below). Although B. subtilis did not differ from YCFA, it improved neuronal counts and adjusted the size (perimeter) in primary cultures treated with hA53T-αsyn by 53.3% ( p 0.05; Fig. 5D), yet numbers were significantly reduced in the combined presence with hA53T-αsyn ( p < 0.05). For most SNs, co-treatment with hA53T-αsyn led to reduced neuronal counts, comparable to hA53T-αsyn only, thus mirroring αsyn associated reduction seen in SN-free conditions (Table 3). Glia cell count was also significantly reduced by α-synuclein (microglia: F (1, 46) = 35.12, p < 0.0001 and astrocytes: F (1, 41) = 10.94, p = 0.002). Both cell types also confirmed an absence of media impact and lack of an interaction (microglia media factor: F (2, 46) = 1.861, p = 0.1671, interaction: F (2, 46) = 2.374, p = 0.1044; astrocytes media factor: F (2, 41) = 0.3931, p = 0.6775; interaction: F (2, 41) = 0.04296, p = 0.9580). The presence of hA53T-αsyn most dramatically reduced microglia count compared to controls (67.3% p < 0.01, Fig. 5E) and specifically in (the more strongly powered) YCFA media (88% p < 0.0001, Fig. 5E and 61.6%, p < 0.01 Fig. 5H). Pooled data yielded a control mean count of 105.1 and an α-syn mean of 38.9 microglia ( t = 5.93, df = 50; p < 0.0001, Fig. 5E). Astrocyte pooled data identified a control count of 104.3 and an α-synuclein mean of 51.1 cells ( t = 3.55, df = 45; p = 0.0009, Fig. 5H). For individual SNs, most yielded higher microglia numbers while astrocyte counts did not change (Fig. 5, Suppl. Figures 2 & 3, Table 3). Only E. rectale increased microglia numbers but also their eccentricity. Co-treatment of bacterial SNs and hA53T-αsyn transfection caused microglia ( p’s < 0.05) and astrocyte loss ( p’s < 0.5) vs SN only, accompanied by distinct and cell-type specific morphological changes (details below; all results summarised in Table 3). These effects confirmed that overall glia and neurones were strongly affected by SN and hA53T-αsyn treatments, and the interactions between the treatments yielded a diverse, cell type and bacterial strain dependent profile. 3.4 Alpha-synuclein dramatically alters glial morphology Features of glia activation comprise morphological changes such as changes in size, branching and arborisation. Parameters indicative of cell size (area and perimeter), and arborisation (trunk and branch counts), as well as eccentricity and solidity were measured (Table 3 and Supplements). In neurones (see also above), 3 SNs boosted neurone numbers alongside smaller perimeters, and cell perimeters were exclusively affected by 2/9 SNs ( E. coli and A. muciniphila) , while other neuronal parameters remained largely unchanged. Occasional changes in neuronal trunk and branch counts (affected by E. rectale and B. coprocola ) delivered identical outcomes, therefore, exemplar branch count is shown in Fig. 6 (see also Supplementary Fig. 7–9) and Table 3. To determine the associations between treatments on morphological parameters, correlation analysis was performed. High correlations were identified between neuronal branch/trunk count vs. perimeter, and % area stained for MAP2 vs. perimeter. Correlation analyses are graphically separated for controls only, controls with hA53T-αsyn, bacterial supernatants only, and bacterial supernatants with hA53T-αsyn (Fig. 6G-J). For surviving neuronal cells, lower cell counts correlated strongly with larger perimeters in the presence of hA53T-αsyn, but bacterial supernatant co-treatment abolished (normalised) this negative correlation between perimeter and cell count (Fig. 6H & J). Surviving microglia (cell loss due to hA53T-αsyn: 38%) presented with a larger size (area and perimeter) and elongation (eccentricity, ratio of the axes); p < 0.05, Fig. 7A & B and Supplementary Fig. 10–11). The remaining cells also displayed stronger IBA1 staining ( p < 0.05), further suggesting microglia cell activation (Supplementary Fig. 5 and Table 3). Overall, most (5/9) bacterial supernatants improved microglia survival (count) after 6 days (Table 3). Some SNs alone (3/9) changed morphology (enhanced eccentricity), yet activation brought about by hA53T-αsyn was similar with or without SNs. Only E. rectale SN somewhat improved the perimeter of enlarged microglia in the presence of hA53T-αsyn (Fig. 6B; and see Supplementary Fig. 9 and Table 3). Further investigations of linear trends over all treatments as well as planned paired comparisons found A. shahii and B. coprocola and E. rectale to significantly increase in length (eccentricity: p’s < 0.05) in SN with and without α-synuclein (Fig. 7; Supplementary Fig. 12 and Table 3). In general, microglia tend to become more irregular as they increased in perimeter and eccentricity. However, parameters for roundness (circularity & solidity) failed to identify a direct treatment effect (Fig. 7E & F, see Supplementary Fig. 13). However, parametric correlations verified that hA53T-αsyn treatment resulted in fewer cells with increased IBA1 intensity (negative correlation), larger areas and more irregular cell surfaces alongside, while solidity negatively correlated with perimeter and area (Fig. 7H). All parameters other than eccentricity were normalised by co-incubation with SNs (Fig. 7J). 3.6 Enlargement of surviving astrocytes due to α-syn is ameliorated by R. Intestinalis and A. muciniphila Astrocyte size (area and perimeter trend) was increased by hA53T-αsyn exposure compared to YCFA media alone. Only the bacterial SN from B. subtilis reduced the perimeter per se ( p < 0.05, Supplementary Fig. 15), while A. shahii significantly increased the astrocyte size further vs. hA53T-αsyn alone (Fig. 8A & B). Only one SN significantly reduced the perimeter in the presence of hA53T-αsyn ( R. intestinalis; p = 0.0220, Fig. 8C), while A. muciniphila suggested a beneficial effects on cell count (p = 0.07), with significant reductions in GFAP and area. Similar to microglia, correlation analysis for controls with hA53T-αsyn indicated that when cells are lost due to hA53T-αsyn exposure, the remaining cells enlarge and express more GFAP (i.e. negative correlation; p < 0.001, Fig. 8E), and area and perimeters correlated for all conditions. The negative correlation between GFAP, perimeter and cell count was again abolished by SN treatment. For astrocytes no other correlations between cell count and morphological parameters following SN treatment alone were detected. 3.7 Overall summary of cellular effects caused by hA53T-αsyn and / or bacterial SN treatment Segmentation and analysis of neuron and glia morphologies were performed following exposure to SN collected from 9 bacterial species selected based on their population change in PD, gram stain, and metabolite profile. Table 3 summarises all parameters measured across the three cell types in primary hippocampal (mixed) cultures and the effects of the bacterial SNs against control medium alone and comparisons with hA53T-αsyn conditions. We have outlined significant effects and correlations alongside interpretations regarding assumed beneficial/detrimental changes based on previously published work (e.g. Fernández-Arjona et al., 2019; Jovanovic et al., 2022; Koch et al., 2015; Liddelow et al., 2017). For glia, changes in size have been considered in the context of changes in cell numbers, e.g. less but larger glia are considered a (negative) sign of activation. For hA53T-αsyn with or without SNs, an increase in GFAP or Iba1 staining (especially alongside lower numbers) and increases in size are therefore assumed to be detrimental. In post-mitotic non-proliferating neurones, a reduction in e.g. size, trunk count or MAP2 staining may be indicative of degenerative processes, i.e. detrimental. However, this cannot be unequivocally classified as impaired neuronal health. Reductions in such parameters caused by some SNs may indicate that specific neuronal types had higher survival rates. Since hA53T-αsyn did not reliably enhance MAP2 levels per se , respective reductions caused by SNs could therefore not be unequivocally categorised as beneficial. Generally, survival rates and morphological changes due to treatment were most profound in microglia, indicative of irregularities and hence cellular activation. For glia, perimeter was identified as the most robust parameter, showing clear negative correlations when cell counts were decreased or increased, while neuronal numbers provided the clearest readout for this cell type. 4. Discussion Gut changes can become apparent up to 20 years before clinical diagnosis of PD, i.e. during or even prior to prodromal PD. Research on the gut microbiome (bacterial genera and species), as well as mouse model research based on faecal transplantation, support an active participation of the gastrointestinal environment in PD (Lin et al., 2019 ; Sampson et al., 2016 ; Stokholm et al., 2016 ). The gut-brain axis and more specifically, the gut-microbiome-brain axis, may therefore offer a window of opportunity for intervention and prevention. In recent years, PD has been investigated predominantly on a systems level, with models developed on propagating synuclein pathology between organs (brain, gut, vagus nerve). Here, we determined the impact of commensal bacteria previously implicated in PD in hippocampal cell cultures by measuring their bacterial fermentation and metabolite products, and changes in viability and morphology of hippocampal cell cultures. Overall, most SNs improved gross viability, as well as 3/9 neuronal counts and 5/9 microglia counts, while none were reliably changing astrocyte density. The SN from B. subtilis (positively reversing neuronal loss), and E. rectale (reversing some synuclein toxicity parameters of microglia) were arguably the most promising when co-applied with hA53T-αsyn. The most beneficial action on astrocytes affected by hA53T-αsyn was observed for A. muciniphila and R. intestinalis . 4.1 Bacterial metabolite characterisation and sensitivity PD is accompanied by a reduction in faecal SCFAs (Hill-Burns et al., 2017 ; Unger et al., 2016 ). Therefore, our work aimed to identify the relevant fermentation products produced by bacteria in a nutrient rich medium (YCFA). Half of the bacteria produced succinate and propionate, with propionate previously suggested to be protective in a PD mouse model (Nishiwaki et al., 2024 ). The three bacteria (A. hadrus, B. hansenii, and D. longicatena ) that produced fumarate did not produce any succinate as end products. GC and HPLC broadly identified similar quantities of acetate, propionate, butyrate, valerate and succinate, confirming the robustness of both procedures, with minor sensitivity differences. Our work has provided transparent results for both methods improving on previous reports (e.g. Ahmed et al., 2019 ). We were however not able to determine which technical procedure can ultimately considered to be most robust. In the wider field, various groups have characterised bacterial metabolite profiles from cultures, or animal models with varied approaches and choice of analytical method (Ahmed et al., 2019 ; Ghaisas et al., 2019 ; Lopez-Siles et al., 2012 ; Sampson et al., 2016 ). A comparative approach alongside full validation has not been provided yet. Therefore, our work goes some way to identify potential sources for inconsistencies between analytical methods. 4.2 Bacterial supernatants can improve viability and neuronal survival Only B. subtilis improved neuronal (but not glia) density when combined with hA53T-αsyn exposure. Research using C. elegans has suggested various S ubtilis strains to be protective against α-syn, potentially due to a slowing and reversing of aggregation (Goya et al., 2020 ). It is possible that species- and cell type- specific components of the Subtilis species metabolome, for example specific enzymes present in the supernatant, produced an improvement of neurons beyond that of SCFAs. B. subtilis and B. hansenii were the only two SNs that reduced the hA53T-αsyn-related enlarged neuronal size. Interestingly, the SNs from B. hansenii , A. muciniphila, A. shahii and E. coli reduced the neuronal perimeter vs control medium, indicating that bacteria might produce metabolites that are detrimental to neuronal outreach or growth. Although a healthy gut microbiome may be protective for PD pathology and therefore some bacterial metabolites may be beneficial, discrepancies exist in the literature. SCFAs are known to reduce inflammation of the CNS, but findings also suggest that high SCFA levels can cause neuronal mitochondrial and lipid alterations (Fillier et al., 2022 ; Sampson et al., 2016 ; Wenzel et al., 2020 ). Importantly, our work did not indicate negative effects on cell viability following treatment with bacterial SNs. We did not identify general effects of SNs or α-syn exposure on neuronal trunk or branch count (except E. rectale and B. coprocola ); this differed from previous studies (Jovanovic et al., 2022 ). In organotypic rat hippocampal slices, 1–5mM synthetic formate was reported to induce neuronal death, which was prevented with 1µM folic acid supplementation (Kapur et al., 2007 ). Neurobasal medium contains 9µM folic acid which may have mitigated the effect of the mM amounts of formate present in C. aerofaciens, E. coli and R. intestinalis , respectively, as these conditions presented similar cell counts to media controls. Future work will characterise actions of bacterial compound SNs, media composition and specific SCFAs on cell viability. 4.2 Glial cells react strongly to both α-syn and bacterial metabolite exposure Microglia, smooth in a resting state, become irregular when activated or apoptotic, the latter outlined by blebbing spheres along the cell border (Ren et al., 2020 ; Schiess et al., 2010 ). While some SNs generally improved microglia counts (5 out of 9 bacteria), we identified α-syn to reduce viability and glia counts, and shift glia towards a non-resting state. This was exacerbated in combination with SNs from e.g. A. shahii , B. coprocola and E. rectale SN. Treatment of mixed hippocampal cell cultures with YCFA did not cause changes vs. standard medium in the parameters measured. Scrutiny of the data indicated that exposure to SNs together with hA53T-αsyn potentiated microglial reactivity and activation. We therefore hypothesize that microglia may become overwhelmed by combined α-syn and bacterial metabolite challenges. This indicates that bacterial metabolites may exacerbate stress or inflammation rather than offering protection in an already cellularly stressed environment (Fernández-Arjona et al., 2019 ). hA53T-αsyn-surviving astrocytes enlarged in area and perimeter, but only R. intestinalis and A. muciniphila were able to ameliorate this (vs hA53T-αsyn). Glial shape changes indicate that α-syn caused cellular activation and stress, with no singular SN able to reverse altered glial morphology completely. As varying concentrations of individual fermentation products were produced by each bacterium, effects on cell viability, survival or shape could be the result from either individual or combined level of specific SCFAs, or the presence of other metabolites (Wenzel et al., 2020 ). 4.3 Limitations It is necessary to consider that primary neuronal cultures, even when counted and homogenously resuspended, lead to somewhat variable initial cell ratios and surviving populations, not necessarily comparable to in situ conditions. Additionally, use of neonatal tissue lacks the aspect of ageing-related changes in cell organisation and physiology (Slanzi et al., 2020 ). The use of highly supplemented media does also not accurately represent a natural environment for either bacteria or primary culture preparations. Further factors may also be released from fermentation processes or additional metabolic factors, following interactions between bacteria and the host organism (Chen et al., 2019 ; Medlock et al., 2018 ). While there is evidence of SCFAs (acetate, propionate and butyrate) reaching the brain through the blood-brain barrier, there are still uncertainties regarding other metabolites (Mirzaei et al., 2021 ; Mukhopadhya & Louis, 2025 ). 5. Conclusions The current study approached gut-brain axis research by utilising defined bacterial supernatant interventions in an in vitro synuclein model of PD, addressing a knowledge gap in our understanding of their potential effects on the CNS. Virally delivered α-synuclein offered an in vitro model for controlled testing of treatments with short experimental duration, to determine actions via a validated cell-type-specific workflow. To our knowledge, this is the first analysis of synuclein toxicity in which the morphology of three co-cultured cell types was measured and quantified, and where mutated hA53T-αsyn and/ or bacterial supernatants are tested in a comprehensive manner. In general, most bacterial SNs increased total cell counts, indicative of a protective potential, alongside pronounced changes in glial morphology. Our results offer an initial understanding of α-syn and bacterial metabolite interactions on nervous tissue viability and morphology, and can therefore advance our understanding of PD relevant pathologies. Dietary adjustments that can alter the microbiome might prevent, or at least modify, the impact of α-syn associated pathologies and thus PD development. Abbreviations α-syn alpha-synuclein hA53T human A53T HPLC high-performance liquid chromatography GC gas chromatography central nervous system CNS PD,Parkinson’s Disease SCFA short-chain fatty acid YCFA yeast,casitone,fatty acid medium NAD(H) Nicotinamide adenine dinucleotide (hydrogen). Declarations Availability of Data and Materials Data and CellProfiler pipelines used to generate the data are not publicly available as these are currently used for ongoing research and further publications, but are available on request. Author Contributions BP conceived, planned and designed the project and revised the manuscript; BP, KS and PH designed the experiments. PH with help from JM performed anaerobic bacterial culturing and analysed gas chromatography experiments. AE and FB performed HPLC analysis of the supernatants and performed data calculations. All other data was collected and presented by PH. PH wrote the initial manuscript with input from all authors, and prepared figures and tables. All authors have read and approved the final version of the manuscript. Ethics Approval and Consent to Participate Not applicable. Acknowledgment We thank the Rowett Institute analytical chemistry department for the gas chromatography testing of the bacterial supernatants. Funding This research was part-funded by 4D Pharma PLC and by the University of Aberdeen Development Trust SCIO. Conflict of Interest The authors declare no conflict of interest. Declaration of AI and AI-assisted Technologies in the Writing Process The authors declare that no AI and AI-associated technologies were used in the process of writing. References Ahmed S, Busetti A, Fotiadou P, Vincy Jose N, Reid S, Georgieva M, Brown S, Dunbar H, Beurket-Ascencio G, Delday MI, Ettorre A, Mulder IE (2019) In vitro Characterization of Gut Microbiota-Derived Bacterial Strains With Neuroprotective Properties. 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Bellwether Publishing, Ltd, pp 1–20. 1 https://doi.org/10.1080/19490976.2020.1848158 Tables Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table3BP.docx SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8852210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590452692,"identity":"7257bfb0-d0bb-4d45-8578-78a8e82ce3a1","order_by":0,"name":"Philip A. Hoffmann","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"A.","lastName":"Hoffmann","suffix":""},{"id":590452693,"identity":"98a7b1bf-855f-4cba-98f2-e9214f498097","order_by":1,"name":"Jennifer C. Martin","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"C.","lastName":"Martin","suffix":""},{"id":590452694,"identity":"fd47b777-b16f-4bb6-9e29-e0698abaeb89","order_by":2,"name":"Ana Evora","email":"","orcid":"","institution":"S-Biomedic NV","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Evora","suffix":""},{"id":590452695,"identity":"1dedac88-f012-46fe-83dc-6166c8934e94","order_by":3,"name":"François Brillet","email":"","orcid":"","institution":"S-Biomedic NV","correspondingAuthor":false,"prefix":"","firstName":"François","middleName":"","lastName":"Brillet","suffix":""},{"id":590452696,"identity":"5481462d-ed21-4e69-97d6-d771b7acf741","order_by":4,"name":"Karen P. Scott","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"P.","lastName":"Scott","suffix":""},{"id":590452697,"identity":"66c77558-3362-486b-a180-59030a6010f1","order_by":5,"name":"Bettina Platt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACAwYGxgNAWo5BAsQGAR7CWhgOMCQYGJOuJbGBaC3mDLwHDvz88Se9f3bztgcMNXYMBmcO4Ndi2cCXcLAnwSB3xp1j5QYMx5IZDM42EHDYAR4gAmppuJFjJsHAdoDB4DwhvwC1HPyTYJAuD9byj0gth4G2JBiAtDC2HSDsMMtmvoTDMmnGhhvvHCuTSOxL5pEk5H1z9t6DD9/YyMnL3W7eJvHhm50c35kEAi5jRo6GBMKxAgLEqBkFo2AUjIKRDQCyFENKAHuOxwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Aberdeen","correspondingAuthor":true,"prefix":"","firstName":"Bettina","middleName":"","lastName":"Platt","suffix":""}],"badges":[],"createdAt":"2026-02-11 13:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8852210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8852210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505875,"identity":"8a4e28e9-8ac5-4381-a4fc-5c3c6d390e56","added_by":"auto","created_at":"2026-02-26 13:33:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216347,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed relevant metabolic pathways and fermentation products from gut bacteria.\u003c/strong\u003e Bacteria species investigated here utilise the following pathways: Succinate pathway (green), classical pathway (blue: acetate) and Butyryl-CoA Acetate-CoA transferase pathway (teal). Wood-Ljungdahl Pathway (purple), Acrylate Pathway (pink), Oxidative pathway (orange) and classical pathway (red: butyrate production) are also present. Citric acid cycle (grey) is oxidative and within the colon only few microorganisms can perform substrate oxidation by aerobic respiration. This cycle outlines production of glycolate. The schematic is based on information provided in: Bhatia \u0026amp; Yang, 2017; Hays et al., 2024; Lachaux et al., 2019; Lange et al., 2023; and Veras et al., 2020. Not all intermediate steps are expanded upon.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/50fa5eefa0065545f02b3f87.jpg"},{"id":103216077,"identity":"c7fd44bb-9adc-4ca0-ab76-90936332ab70","added_by":"auto","created_at":"2026-02-23 09:29:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFermentation products of anaerobic bacteria.\u003c/strong\u003e Metabolite results per bacterial SN corrected for growth medium and measured via (\u003cstrong\u003eA\u003c/strong\u003e) gas chromatography (GC) and (\u003cstrong\u003eB\u003c/strong\u003e) HPLC (in mM). Negative values indicate utilisation of growth medium components. (\u003cstrong\u003eC\u003c/strong\u003e) Representative chromatographs of GC (left panel) measured as peak area (pA) over time (min), and HPLC (right panel) measured as intensity of refraction index signals (mV) over time (min) for supernatant metabolite identification. One technical repeat of \u003cem\u003eFusicatenibacter saccharivorans \u003c/em\u003eis shown for each detection method. (\u003cstrong\u003eD\u003c/strong\u003e) Comparison of paired GC and HPLC results for products identified in both experimental approaches (from (\u003cstrong\u003eA \u003c/strong\u003e\u0026amp;\u003cstrong\u003e B\u003c/strong\u003e), in mM). GC measured samples in 2 technical repeats, whereas HPLC was based on 3 technical repeats, both from one biological sample. \u003cem\u003eB. coccoides \u003c/em\u003eand \u003cem\u003eB. dorei \u003c/em\u003eHPLC data (identified by the presence of an asterisks) were analysed in duplicates. Technical repeat variability is presented as error bars across analytical output (no statistical tests performed).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/71ac2179c38bf119b671d63c.jpg"},{"id":103216083,"identity":"1ce14ba2-cb63-4beb-a36b-e6f9aadfad32","added_by":"auto","created_at":"2026-02-23 09:29:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExemplar immunofluorescence images of primary cultures exposed to α-syn for 6 days.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) MAP2, IBA1 and 4B12 staining of Neurobasal control and hA53T-αsyn. (\u003cstrong\u003eB\u003c/strong\u003e) MAP2, 4B12 and pSer staining, (\u003cstrong\u003eC\u003c/strong\u003e) GFAP conjugated (conj.), 4B12 and pSer staining of Neurobasal control and hA53T-αsyn each. All images were taken with a 40× objective on a ZEISS LSM880 confocal microscope. Brightness and contrast were adjusted for visualisation only. Scale bar: 20µm.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/d591e4850f07a1f39f27e51e.jpg"},{"id":103216079,"identity":"7ce92f48-fe8f-4e1b-9473-055759e2d9b7","added_by":"auto","created_at":"2026-02-23 09:29:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":279175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrimary cell culture viability after hA53T-αsyn and/ or bacterial supernatant addition.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Expression of hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003eand/ or YCFA only treatment after 6 days normalised against controls (n = 7-10 per condition, each with 3 technical replicates). Exposure to hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003ereduced viability significantly. (\u003cstrong\u003eB\u003c/strong\u003e): Bacterial supernatants with/without hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003e(+/-), normalised to supernatant only, all yielded a significant reduction in viability. (\u003cstrong\u003eC\u003c/strong\u003e): Bacterial supernatants plus hA53T-αsyn, normalised against hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003eonly. Statistical differences (two-way ANOVAs with Bonferroni’s post hoc tests) are displayed and noted as: \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003ep \u0026lt;\u003c/em\u003e0.01 (**), \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.001 (***), and \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.0001 (****). Red bar: hA53T-αsyn, blue bars: bacterial supernatant, purple bars: supernatant with hA53T-αsyn, black denotes control condition (\u003cstrong\u003eA\u003c/strong\u003e \u0026amp; \u003cstrong\u003eC\u003c/strong\u003e only).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/95f5a58f40b5cda435380cb8.jpg"},{"id":103504912,"identity":"b5bc2acb-9ea2-43e1-ae9e-9e3ced9e011c","added_by":"auto","created_at":"2026-02-26 13:22:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":600036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell population changes in hippocampal cultures treated with A53T-αsyn and / or bacterial supernatants.\u003c/strong\u003e Exemplar visualisation of culture composition (\u003cstrong\u003eA\u003c/strong\u003e) for Neurobasal medium (control), YCFA (supernatant media control) and two exemplar supernatants with or without hA53T-αsyn, stained for neurons (MAP2, red), microglia (IBA1, green), and astrocytes (GFAP, magenta). Scale bar (A): 20µm. (\u003cstrong\u003eB\u003c/strong\u003e – \u003cstrong\u003eJ\u003c/strong\u003e): Cell count of the three different cell types per condition. Counts for neuron (\u003cstrong\u003eB\u003c/strong\u003e), microglia (\u003cstrong\u003eE\u003c/strong\u003e), and astrocyte (\u003cstrong\u003eH\u003c/strong\u003e) populations normalised against control. Results from two-way ANOVA and Bonferroni’s post hoc tests; and planned unpaired t-test as well as an estimation plot for pooled groups (with or without α-synuclein). Two examples of bacterial supernatant effects for neurons\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eC\u003c/strong\u003e) \u0026amp; (\u003cstrong\u003eD\u003c/strong\u003e), microglia (\u003cstrong\u003eF\u003c/strong\u003e) \u0026amp; (\u003cstrong\u003eG\u003c/strong\u003e), and astrocytes (\u003cstrong\u003eI\u003c/strong\u003e) \u0026amp; (\u003cstrong\u003eJ\u003c/strong\u003e) are shown. All data are summarised in Table 3. Statistical significance is reported as follows: \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01 (**), \u003cem\u003ep \u0026lt;\u003c/em\u003e0.001 (***), and \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.0001 (****). All groups had independent replicates (\u003cem\u003en) \u003c/em\u003eof 7-10.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/461c0b8d5061c69152a9632e.jpg"},{"id":103216081,"identity":"02f0d883-9918-4435-aff9-70c04e1b1a63","added_by":"auto","created_at":"2026-02-23 09:29:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":301498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeuronal morphology changes and the parameter correlations.\u003c/strong\u003e Examples of perimeter changes by hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003eand/ or supernatant (\u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eB\u003c/strong\u003e \u0026amp; \u003cstrong\u003eC\u003c/strong\u003e). Effects of either hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003eand/ or supernatant on neuronal branch count (\u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eE\u003c/strong\u003e \u0026amp; \u003cstrong\u003eF\u003c/strong\u003e). All \u003cem\u003en = \u003c/em\u003e5-8. Results from one-way ANOVA and Sidak’s post-hoc test; statistical significance as follows: \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01 (**). Spearman correlation analysis of all neuronal parameters measured for controls only (\u003cstrong\u003eG\u003c/strong\u003e), controls and hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003etreatment (\u003cstrong\u003eH\u003c/strong\u003e), bacterial supernatants only (\u003cstrong\u003eI\u003c/strong\u003e), and supernatants with hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eJ\u003c/strong\u003e). Blue indicates a positive correlation; red indicates a negative correlation. Horizontal stripes within correlation box (\u003cstrong\u003eH\u003c/strong\u003e) indicate the row parameter to increase as the column parameter decreases.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/35bf611d17fed3688109d7be.jpg"},{"id":103216084,"identity":"cd0d59aa-d104-41d1-b853-9ea50e6d447f","added_by":"auto","created_at":"2026-02-23 09:29:58","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":314277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglia morphology changes and parameter correlations. \u003c/strong\u003eMicroglia examples of perimeter (A \u0026amp; B); eccentricity (range 0-1; C \u0026amp; D); and solidity (E \u0026amp; F). Results from one-way ANOVAs and Sidak’s post-hoc test (\u003cem\u003en \u003c/em\u003e\u0026gt; 5; statistical significance as follows: \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.001 (***), and \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.0001 (****), for unpaired t tests indicated by #. Spearman correlations of neuronal parameters measured for controls only (\u003cstrong\u003eG\u003c/strong\u003e), controls and hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003etreatment (\u003cstrong\u003eH\u003c/strong\u003e), bacterial supernatants only (\u003cstrong\u003eI\u003c/strong\u003e), and supernatants with hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eJ\u003c/strong\u003e). Blue indicates positive correlations, red negative correlations. Horizontal stripes within correlation boxes (\u003cstrong\u003eH\u003c/strong\u003e) indicate the row parameter increases as the column parameter decreases. Vertical stripes within correlation boxes (\u003cstrong\u003eH\u003c/strong\u003e– \u003cstrong\u003eJ\u003c/strong\u003e) indicate that the row parameter decreases as the column parameter increases.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/25bf20e24c19ac84f11adc2b.jpg"},{"id":103505368,"identity":"30610b38-6985-4173-9a67-9011df52568c","added_by":"auto","created_at":"2026-02-26 13:30:28","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":202455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAstrocyte\u003c/strong\u003e \u003cstrong\u003emorphology changes and correlations between parameters.\u003c/strong\u003e Examples of perimeter changes by hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003eand / or supernatants are shown in (\u003cstrong\u003eA\u003c/strong\u003e), (\u003cstrong\u003eB\u003c/strong\u003e), \u0026amp; (\u003cstrong\u003eC\u003c/strong\u003e).\u003cstrong\u003e \u003c/strong\u003eResults\u003cstrong\u003e \u003c/strong\u003efrom one-way ANOVAs and Sidak’s post-hoc test (\u003cem\u003en \u003c/em\u003e\u0026lt; 5); statistical significance: \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.05 (*), \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.01 (**). Significant Spearman correlations of neuronal parameters measured for controls only (\u003cstrong\u003eD\u003c/strong\u003e), controls and hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003etreatment (\u003cstrong\u003eE\u003c/strong\u003e), bacterial supernatants only (\u003cstrong\u003eF\u003c/strong\u003e), and supernatants with hA53T-αsyn\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eG\u003c/strong\u003e). Blue indicates positive correlations, red negative correlations. Horizontal stripes within correlation boxes indicate the row parameter to increase as the column parameter decreases.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/36cc71d0c8f5b0d81ed6ec81.jpg"},{"id":108805349,"identity":"ca4d0e86-3576-4c6a-b25d-b9137ab707cd","added_by":"auto","created_at":"2026-05-08 15:25:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3006631,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/74c93c2c-dcfc-42d4-a481-5a3d505ae5a0.pdf"},{"id":103504958,"identity":"b939a7e4-41a7-4e23-bc41-14f44cf66991","added_by":"auto","created_at":"2026-02-26 13:22:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30736,"visible":true,"origin":"","legend":"","description":"","filename":"Table3BP.docx","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/e52fceaf5e87d506ffbcbbc3.docx"},{"id":103504967,"identity":"3f147271-23bb-4970-aa07-46c2e9c43df0","added_by":"auto","created_at":"2026-02-26 13:22:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5454335,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8852210/v1/f3839fb1411545b3396908ec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anaerobic gut bacterial metabolites alter morphology and survival of neurons and glia in a hippocampal culture model of Parkinson’s Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is the second most common neurodegenerative disease affecting the ageing population with prevalence forecasted to surpass 12\u0026nbsp;million by 2040 (Dorsey et al., 2018; Rani \u0026amp; Mondal, 2021). Official clinical manifestations of PD comprise hand, leg or jaw tremors, muscle stiffness, slowed movement, and coordination and balance impairments. Clinically, established motor-symptoms are used for diagnosis, but by this stage 50\u0026ndash;70% of substantia nigra dopaminergic neurons are already lost (Blesa et al., 2012). Histo-pathologically, the disease is associated with aggregation and spreading of the protein alpha-synuclein (\u0026alpha;-syn), a protein involved in synaptic vesicular release. In synucleinopathies, it is prone to misfolding and aggregation resulting in monomers, oligomers and eventual development of Lewy bodies (Braak et al., 1996; Cheng et al., 2011; Marczynski et al., 2019). Toxicity associated with \u0026alpha;-syn causes neuronal cell death, which releases their contents to the environment (Sidhu et al., 2004).\u003c/p\u003e\n\u003cp\u003eAlthough PD is considered a neurodegenerative brain disease, findings highlight spread of \u0026alpha;-syn to or from peripheral systems (Braak et al., 2003; Chiu et al., 2021). Animal models of PD using pre-formed fibrils of \u0026alpha;-syn have demonstrated a prion-like spread from intestinal or vagal structures to the brain (Yang et al., 2023). In patients, gut effects can be seen up to 20 years before the clinical diagnosis of PD, including expression of misfolded \u0026alpha;-syn within various intestinal tissues such as the myenteric plexus (Hilton et al., 2014; Stokholm et al., 2016). Gastrointestinal symptoms can include altered gut permeability, slower transit time, and reduced gastrointestinal short-chain fatty acid (SCFA) levels (Romano et al., 2021). Patients also present with an altered gut microbiome composition (Stokholm et al., 2016).\u003c/p\u003e\n\u003cp\u003eUndigested fibres are the major substrate for microorganisms residing within the colon. Bacterial metabolites such acetate, propionate, and butyrate comprise most SCFAs produced by bacterial fermentation (Frolova et al., 2022). In anaerobic fermentation electron transfer uses metabolic intermediates and produces various metabolites simultaneously (Mukhopadhya \u0026amp; Louis, 2025), e.g. lactate production from pyruvate regenerates NADH from NAD\u003csup\u003e+\u003c/sup\u003e for further glycolysis monosaccharide fermentation. Substrate identity, presence of cross-feeding bacteria and whether the bacterium is a generalist or specialised fermenter affect metabolite outputs. For instance, \u003cem\u003eBacteroidota\u003c/em\u003e species produce propionate using the succinate pathway (Bhatia \u0026amp; Yang, 2017; Hays et al., 2024; Mukhopadhya \u0026amp; Louis, 2025); while butyrate production occurs through two routes: \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e species use the acetyl-CoA pathway, consuming acetate while other bacteria including \u003cem\u003eButyrivibrio\u003c/em\u003e and \u003cem\u003eSubdoligranulum\u003c/em\u003e species use the butyrate kinase pathway from butyryl-CoA (Hays et al., 2024; Lange et al., 2023) (Fig.\u0026nbsp;1). SCFAs elicit both local and systemic effects in the host and can enter the brain; therefore, changes in microbiome composition are likely to implicate host health in PD (Jameson et al., 2020; Nankova et al., 2014).\u003c/p\u003e\n\u003cp\u003eExperimentally, antibiotics that destroy bacteria indiscriminately altered gut microbiome composition temporarily (Cryan et al., 2019; Sampson et al., 2016), and faecal microbiome transplants (FMT) from PD patients to mice induced Parkinsonian symptoms, while healthy control FMT to PD mice reduced symptoms (Sampson et al., 2016). Gut microbiota influenced the activation of brain microglia and astrocytes, and short-chain fatty acid (SCFA) concentrations were rescued in PD mice receiving FMT from healthy individuals. Levels of dopamine and serotonin were also restored in these mice (Sun et al., 2018). These findings have paved the way for research into the role of the gut-microbiome-brain axis in PD, including possible routes of pathology spread and treatment targets. Human population studies have indicated that some bacterial species are specific to PD patients, absent from healthy controls, and these may be possible disease promoters and/ or indicative of a disrupted microbiome (Nuzum et al., 2020; Wallen et al., 2022).\u003c/p\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cp\u003eIn vitro research has demonstrated that \u0026alpha;-syn delivery (wildtype or mutated) can impair neuronal outgrowth and branching (H. J. Lee et al., 2008). This aligns with other neurodegenerative diseases such as Alzheimer\u0026rsquo;s disease, where overexpression of Tau causes neuronal trunk and branch count changes (Su et al., 2008). In contrast, others were unable to identify an effect of \u0026alpha;-syn on the morphology of neurons using hA53T viral transfection compared to GFP controls (Vieira et al., 2020).\u003c/p\u003e\n\u003cp\u003eOther non-neuronal central nervous system (CNS) cells essential for defence mechanisms, homeostasis, and inflammation such as astrocytes and microglia have also been shown to be (indirectly) morphologically affected by \u0026alpha;-syn. Microglia as immune scavengers of the brain present opportunities to remove and degrade \u0026alpha;-syn, but prolonged activation of microglia in PD exacerbates \u0026alpha;-syn related inflammation (H. J. Lee et al., 2008; Su et al., 2008). Similarly, astrocytes treated with \u0026alpha;-syn become reactive (increased GFAP levels) and when co-cultured with neurons, are found to inhibit neuronal outgrowth (Vieira et al., 2020). Together, toxicity, inflammation and morphological changes accompany degenerative pathways in PD (Sidhu et al., 2004; Sohrabi et al., 2023).\u003c/p\u003e\n\u003cp\u003eIn this study bacterial supernatants (SNs) were tested as possible modulators in rat hippocampal cultures, in the presence or absence of human mutated A53T \u0026alpha;-syn, delivered via a viral vector. This models the early, prodromal stage of PD \u003cem\u003ein vitro\u003c/em\u003e, where neurodegeneration is detectable and first cell death emerges (Postuma \u0026amp; Berg, 2019; Siderowf \u0026amp; Lang, 2012). The selection of anaerobic bacteria was based on published links reported with the PD microbiome, and the understanding of relevant pathways in gut and brain health (Table\u0026nbsp;1). Primary neuronal cells were investigated for morphological and viability changes in the presence of \u0026alpha;-syn and/ or bacterial metabolites. Evidence was obtained for species-specific impact on cell viability and morphology in this \u003cem\u003ein vitro\u003c/em\u003e model of PD.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBacterial species and their origin strains relevant to PD. \u0026lsquo;+\u0026rsquo; and \u0026lsquo;-\u0026rsquo; denote gram-positive and gram-negative respectively, \u003cstrong\u003e\u0026darr;\u003c/strong\u003e; decrease and \u003cstrong\u003e\u0026uarr;\u003c/strong\u003e; increase in PD patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBacterial Strains\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGram\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD \u0026darr;/\u0026uarr;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStrain Code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCommentary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAkkermansia muciniphila\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 22959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMucin degrader, causes \u0026alpha;-syn aggregation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Amorim Neto et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAlistipes shahii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD5-AX-PE M15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay be involved in mild cognitive impairment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Bedarf et al., 2017; Ren et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAnaerostipes hadrus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 3319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eButyrate producer and present in healthy individuals.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Liu et al., 2024; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB524ATN916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eC. elegans\u003c/em\u003e fed on \u003cem\u003eB. subtilis\u003c/em\u003e have reduced\u003c/p\u003e\n \u003cp\u003e\u0026alpha;-syn levels.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Goya et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides coprocola\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 17136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFound to positively correlate with a healthy gut.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Gacesa et al., 2022; Petrov et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides dorei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 17855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundant part of the microbiome; mainly produces acetate and propionate.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Bedarf et al., 2017; Petrov et al., 2017; Zafar \u0026amp; Saier, 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBlautia coccoides\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProduce the highest levels of H\u003csub\u003e2\u003c/sub\u003e, which may be neuroprotective in PD.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Hasegawa et al., 2015; Van Kessel \u0026amp; El Aidy, 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBlautia hansenii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 20583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetate, lactate and succinate producer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(J. Chen et al., 2016; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCollinsella aerofaciens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 3979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreases gut permeability in specific disease conditions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDorea longicatena\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 13814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetate and formate producer.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Petrov et al., 2017; Taras et al., 2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHB101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurli can increase \u0026alpha;-synuclein aggregation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEubacterium rectale\u003c/em\u003e (also known as \u003cem\u003eAgathobacter rectale\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAI-86 (DSM 17629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversal healthy microbiome resident.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Gacesa et al., 2022; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFusicatenibacter saccharivorans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 26062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduced populations in PD.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eParabacteroides distasonis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 20701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreases with age but also specifically in PD.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Sharon et al., 2016; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRoseburia intestinalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLI-82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociated with anti-inflammatory properties.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Sun \u0026amp; Shen, 2018; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSegatella copri\u003c/em\u003e (Formerly \u003cem\u003ePrevotella\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSM 18205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociated with lower risk of irritable bowel disease and positively associated with general health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Bedarf et al., 2017; Petrov et al., 2017; Wallen et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cem\u003e2.1 Bacterial strains, and growth conditions\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eA. muciniphila\u003c/em\u003e was cultured in Brain Heart Infusion broth (BHI from Oxoid), made using 37.7g to 1L dH\u003csub\u003e2\u003c/sub\u003eO with 0.1g resazurin per 100mL. All other strains were cultured in yeast, casitone, fatty acids (YCFA) broth (Duncan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) supplemented with 0.2% glucose (Sigma G5767), 0.2% cellobiose (Sigma C7252), 0.2% soluble starch (Sigma S2004), forming YCFAGSC. The media was prepared anaerobically; the pH adjusted to pH 7.5\u0026ndash;7.6 and 7.5mL aliquots dispensed into Hungate tubes while simultaneously flushing with CO\u003csub\u003e2\u003c/sub\u003e. Tubes were sealed with butyl rubber septa (Bellco Glass) and autoclaved. Bacteria were inoculated using anaerobic methods (Bryant, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) and incubated anaerobically at 37\u0026deg;C without agitation. Bacteria were grown until their respective stationary phase (assessed by performing growth curve analysis) before harvesting the supernatant through centrifugation, 5,000\u0026times;g for 5 min at room temperature. Collected supernatants (SNs) were filtered through 0.22 \u0026micro;m filters and frozen until further use.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eFull-length 16S rRNA gene PCR amplification was performed using cell pellets from harvested supernatants following standard methods. PCR products were cleaned using the Wizard PCR product purification kit (Promega, Southampton, United Kingdom) and bidirectional partial 16S rRNA gene sequences obtained using primers 519F, RP2 and FD1 from Eurofins MWG (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInaccurate \u003cem\u003eBacillus\u003c/em\u003e species identification occurred using rRNA gene amplification after PCR product cleaning. An alternative approach using the primers pyrA and aroE applying a PCR amplification protocol (G. Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was performed on pellets of the same bacterial samples to successfully identify \u003cem\u003eBacillus subtilis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePrimers.\u003c/b\u003e Primer details used in 16S rRNA gene PCR amplification and sequencing the panel of bacteria. A different target gene sequence alongside PCR amplification was required to distinguish \u003cem\u003eBacillus subtilis\u003c/em\u003e from other species of \u003cem\u003eBacillus\u003c/em\u003e (G. Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e519F-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACGGCTACCTTGTTACGACTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16S rRNA gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDurack \u0026amp; Lynch, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGCMGCCGCGGTAATWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16S rRNA gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVan Immerseel et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD1-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAGTTTGATCCTGGCTCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16S rRNA gene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epyrA-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTC TTC CGT TCA GGA AAG GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eG. Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epyrA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAT CTC CCG TTT GGA TCG GCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e only\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earoE-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGG GAA GGC TTC GTG AAG TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e only\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earoE-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCC ACA GAC GTT GTA TGG ATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e only\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Gas chromatography of bacterial fermentation products\u003c/h2\u003e \u003cp\u003eGas chromatography was carried out at the Analytical Department, Rowett Institute, University of Aberdeen. Supernatants collected of a single stationary phase cultured bacterium were run in duplicates, 2\u0026times; 1mL derivatised to \u003cem\u003et\u003c/em\u003e-butyldimethyl-silyl derivatives and the concentrations of SCFAs measured. Acid production was determined by capillary GC following standard methodology (Richardson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). GC measured Acetate, formate, propionate, butyrate, iso-butyrate, valerate, iso-valerate, lactate and succinate level. For data analysis, replicates were averaged and bacterial medium control values subtracted to identify specific bacterial metabolites. Output was presented with standard deviation error bars; however no statistical tests were performed due to the low experimental repeat.\u003c/p\u003e \u003cp\u003eGC is a well-established technique for measuring SCFAs and other bacterial metabolite fermentation products. It requires derivatisation to increase the phenolic analyte volatility (Nolvachai \u0026amp; Marriott, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This process itself induces a technical difficulty as metabolites may not respond quantitatively to extraction or derivatisation, introducing sources of error (Kanani \u0026amp; Klapa, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 High-performance liquid chromatography (HPLC) testing of bacterial supernatant fermentation products\u003c/h2\u003e \u003cp\u003eHPLC was applied to measure acetate, propionate, butyrate, valerate, lactate, succinate, and the additional SN components, glucose, glycolic acid, pyruvate and fumarate. Sampling of the SNs occurred at SBiomedic, Belgium. Bacterial SN samples and corresponding bacterial growth medium samples were thawed overnight at 4\u0026deg;C, each split in 3\u0026times; 400\u0026micro;L aliquots, transferred into HPLC glass vials (Waters, Cat. 186000282C) and directly processed. Briefly, samples were analysed using a Prominence-I LC-2030 plus HPLC (Shimadzu) equipped with an Aminex HPX-87H ion exclusion column (BioRad, Cat. 1250140) coupled with RID-20A detector (Shimadzu). Glucose (Cat. 170080025 Thermo Scientific) and the SCFAs (sodium acetate Cat. 241245, sodium propionate Cat. P1880, sodium butyrate Cat. 303410, valeric acid Cat. W310107, succinate acid Cat. 398055, all Sigma, and lactic acid Cat. 79-33-4, Guinama S.L) were quantified against a six non-zero level calibration curve using standard solutions\u0026thinsp;\u0026gt;\u0026thinsp;3.125, 6.5, 12.5, 25, 50, 100mmol.l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Data analysis was performed as in section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e. All replicates were averaged and presented data subtracted from medium controls to identify production or use of metabolites and energy sources. Standard deviation error bars are presented but without statistical tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 A53T viral construct\u003c/h2\u003e \u003cp\u003eAAV1/2-CMV/CBA-human-A53T-alpha-synuclein-WPRE-BGH-polyA (the α-syn delivery virus, subsequently referred to as hA53T-αsyn) and AAV1/2-CMV/CBA-Null/empty-WPRE-BGH-polyA (control construct, subsequently referred to as Null-Empty) vectors were obtained from AMSBIO (Oxford, UK), aliquoted and stored at -80\u0026deg;C until use at the Institute of Medical Sciences, University of Aberdeen. The titre concentration of both constructs upon purchase was 5.1\u0026times;10\u003csup\u003e12\u003c/sup\u003evg/mL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Primary rat hippocampal cultures\u003c/h2\u003e \u003cp\u003ePrimary rat hippocampal culture preparation was adapted from (Drysdale et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and performed in accordance with Animals (Scientific Procedures) Act 1986 (ASPA). Male and female neonatal Sprague-Dawley rat pups between the age of postnatal (P) 0 to P2 were culled via cervical dislocation.\u003c/p\u003e \u003cp\u003eIn brief, 24-well plates containing glass coverslips (VWR) or 96-well plates were coated with poly-L-lysine, washed and allowed to dry. Rat hippocampi were dissected and placed into ice cold HEPES Buffer Solution (HBS; 130mM sodium chloride, 5.4mM potassium chloride, 18mM calcium chloride, 1mM magnesium chloride, 10mM HEPES, pH 7.4) with 25mM D-glucose. Tissue was chopped and transferred to 1mg/mL protease type XIV (Sigma) in HBS with D-glucose for 30 min at 37\u0026deg;C for enzymatic digestion. After washing, tissue was dissociated through trituration. HBS solution was replaced with 0.22\u0026micro;m double filtered Neurobasal (Fisher Scientific) supplemented with 2% B27, 10% heat inactivated foetal bovine serum (FBS), 2mM GlutaMAX (Gibco), and 1ng/mL fibroblast growth factor-basic (bFGF) (all Fisher Scientific), and 1% penicillin-streptomycin (Sigma-Aldrich). Cells were seeded at 75,000 or 25,00 cells per well in 50\u0026micro;L for 24-well plates containing coverslips and 96-well plates, respectively.\u003c/p\u003e \u003cp\u003eCultures were kept at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e in an incubator for 1.5 hours to allow cell adhesion. Thereafter, wells were topped up with 250\u0026micro;L FBS free Neurobasal mix with supplements as above in 24-well plates, or 100\u0026micro;L in 96-well plates. Cells were left for 3 days \u003cem\u003ein vitro\u003c/em\u003e to grow and settle without disturbances. At 3 days \u003cem\u003ein vitro\u003c/em\u003e, media change was performed again with Neurobasal and supplements without FBS during which treatment conditions were added as required. Cultures received an equated final concentration of 5.1\u0026times;10\u003csup\u003e10\u003c/sup\u003evg/mL as a one-off exposure to induce PD pathology and/ or received 1% of total medium as supernatant. Cells received a media top-up 3 days thereafter. Final experimental endpoints were taken 9 days \u003cem\u003ein vitro\u003c/em\u003e (DIV) 6 days after treatment addition. All primary culturing and subsequent experimental tests and endpoints were carried out at the Institute of Medical Sciences at Aberdeen University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Viability assay\u003c/h2\u003e \u003cp\u003eCellular viability was assessed using Cell Counting Kit-8 (CCK-8) according to manufacturer\u0026rsquo;s guidelines (Sigma, UK). Primary cultures were treated with 5.1\u0026times;10\u003csup\u003e10\u003c/sup\u003evg/mL virus vectors alone, 1% bacterial supernatant alone or 1% growth medium, or the combination of supernatant and hA53T-αsyn for 6 days (9 DIV) prior to running of the assay. Medium was removed and replaced with 90\u0026micro;L fresh Neurobasal and supplements without FBS (section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e) and 10\u0026micro;L of CCK-8 solution and maintained for 4 hours at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e in an incubator. Absorbance was measured on the FLUOstar Omega plate reader (BMG Labtech) at 450nm with a reference wavelength measured at 600 nm. Percentage viability was calculated by subtracting absorbance values at the reference wavelength from those measured at 450nm, averaged per condition, subtracting blanks and normalising against media controls or the Null-Empty vector as indicated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immunofluorescence\u003c/h2\u003e \u003cp\u003eAfter 9 DIV, media were removed from each well containing coverslip grown cells followed by a HBS wash, then fixed for 10 min at -20\u0026deg;C with ice-cold methanol. Permeabilization and blocking was performed together for 20\u0026ndash;30 min at room temperature on a rocker using HBS, 0.1% Triton X-100, 1% milk powder, 1% goat serum and 2% BSA. Samples were washed prior to primary antibody addition 3\u0026times; with HBS with 0.1% tween-20 (HBS-Tween). The primary antibodies GFAP (1:2000, Invitrogen), IBA1 (1:1000, Wako), MAP2 (1:2000, Invitrogen), 4B12 (synuclein 1:1000, Invitrogen) and pSer129 (P-synuclein, 1:5000, Abcam) were made up in HBS, plus 1% goat serum, 2% bovine serum albumin (BSA), and 0.1% Tween-20 and left on samples overnight at 4\u0026deg;C on a shaker. For cell type specificity, conjugated GFAP-Cy3 (1:500, Invitrogen) was also used.\u003c/p\u003e \u003cp\u003eFollowing overnight incubation, primary antibody mix was washed off with 3\u0026times; HBS-Tween washes. Secondary antibodies anti-rabbit Alexa Fluor 488 (1:500, Invitrogen), anti-chicken Alexa Fluor 594 (1:500, Molecular Probes, USA), and anti-mouse Alexa Fluor 647 (1:500, Invitrogen) were added to the same HBS mix as primary antibodies for 1 hour on a shaker light-protected with foil. Cells were washed 3\u0026times; with HBS-Tween and incubated 3min with HBS containing 1:1000 DAPI (Sigma), washed again, dried and mounted onto Epredia superfrost adhesion slides (Fisher Scientific) with ProlongTM Diamond Antifade mountant (Invitrogen, Thermo Fisher).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Image Acquisition and Assessment of Cellular Morphology\u003c/h2\u003e \u003cp\u003eFor quantitative analysis of primary cultures with microtubule associated protein II (MAP2: neurons, in red), ionized calcium-binding adapter molecule 1 (IBA1: microglia, in green), glial fibrillary acidic protein (GFAP: astrocytes, in magenta), and nuclei (DAPI, in blue), fluorescence microscopy was performed using the EVOS M5000 (Invitrogen) with a 20\u0026times; objective. Three images were taken per coverslip, with imaging criteria consisting of: avoiding extremely dense clusters of neurons that cannot be reliably analysed; avoiding bubbles and artifacts present in the DAPI and RFP channels including debris or discoloured background affecting cell visualisation. Image analysis was performed using CellProfiler\u0026rsquo;s (v4.2.6) automatic image segmentation. Three pipelines were developed and validated inhouse for segmenting and measuring morphology of neurons, microglia and astrocyte cells. Total image cell counts were calculated by the number of segmented cells (red, green or magenta) with an associated DAPI nucleus. The identified objects of interest (cells) were used in subsequent morphology analysis. The number of independent replications was n\u0026thinsp;=\u0026thinsp;7\u0026ndash;10. Qualitative exemplar images were obtained at 40\u0026times; using the Zeis LSM880 Airyscan confocal microscope at consistent exposure times. A pinhole size of 1 airy unit was used for all images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData from image analyses pipelines were exported to MS Excel for quantification. Experimental repeats and pipeline data were pooled; technical replicates (image replicates, 3\u0026times;) were averaged to receive a mean value per experimental replicate per parameter. Percentage area stained was calculated based on image MAP2, IBA1, or GFAP pixel intensity of the define object (cell). Parameters measured per object per image were automatically averaged. Parameter outputs for all objects were divided by object count to create average parameter outputs \u003cem\u003eper cell\u003c/em\u003e. Cell counts are the total number of cells that possess a nucleus and were associated with a specific cell type stain. Morphological shape descriptors trunk and branch were based on counts per cell, while area and perimeter were measured in pixels. Solidity and eccentricity yielded units between 0 and 1, based on a circular shape or the ratio between the length and width of the object.\u003c/p\u003e \u003cp\u003eData from both CCK-8 readouts and CellProfiler analyses are expressed as percentage (%) of controls. Outliers were identified using ROUT method (GraphPad Prism, 10.5.0), and normal distribution of data confirmed before statistical analysis. Significances for CCK-8 data and media comparison for cell counts were determined using two-way ANOVAs and Bonferroni\u0026rsquo;s post hoc test. For morphology parameters, group comparisons for a given supernatant employed a one-way ANOVA with Sidak\u0026rsquo;s post hoc test. If the overall ANOVA was significant but not the post hoc test, planned group comparisons via unpaired t-tests were conducted. Spearman correlation heatmaps were prepared using GraphPad Prism. R studio (2024.09.1) and Inkscape (version 1.3.2) were used to visualise the data together. For visualisation purposes only, immunohistochemistry images were enhanced by adjusting the contrast.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 Bacteria produced distinctive fermentation products\u003c/h2\u003e\n \u003cp\u003eFermentation products of stationary phase cultures were measured using gas chromatography (GC) and high-performance liquid chromatography (HPLC). Comparing data from derivatised GC detection with HPLC analysis of neat bacterial supernatants enabled the reproducibility of fermentation analyses for acetate, butyrate, propionate, valerate, lactate and succinate to be assessed and allow more extensive supernatant profiling of glucose, pyruvate, fumarate and glycolic acid (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eAnalysis of bacterial supernatants using GC and HPLC identified similar trends in fermentation production but varied in the exact mM range of samples (Fig.\u0026nbsp;2D). Concentrations of propionate, butyrate and valerate were consistent between bacteria across both analytical methods. Acetate concentrations measured higher by HPLC than GC for \u003cem\u003eA. hadrus\u003c/em\u003e, \u003cem\u003eB. coprocola\u003c/em\u003e, \u003cem\u003eF. duncaniae\u003c/em\u003e, and \u003cem\u003eF. saccharivorans\u003c/em\u003e, (Fig.\u0026nbsp;2A). Acetate can be an end-product of bacterial fermentation, but only select bacteria are able to convert it to butyrate (Fig.\u0026nbsp;1) (Louis \u0026amp; Flint, 2017). \u003cem\u003eA. hadrus\u003c/em\u003e produced both 4.5-6.3mM acetate and 7.3-9.1mM butyrate via the butyryl-CoA acetate-CoA transferase pathway (Figs.\u0026nbsp;1 \u0026amp; 2). In contrast, \u003cem\u003eE. rectale\u003c/em\u003e and \u003cem\u003eR. intestinalis\u003c/em\u003e only formed butyrate (Fig.\u0026nbsp;2) assumed through the butyryl-CoA acetate-CoA transferase pathway (Fig.\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003eFormate was detected only by GC, and in 7 of 16 bacteria, likely generated by the Wood-Ljungdahl pathway. Highest concentrations were present in \u003cem\u003eR. intestinalis\u003c/em\u003e (12.38mM), \u003cem\u003eF. saccharivorans\u003c/em\u003e (10.58mM), and \u003cem\u003eD. longicatena\u003c/em\u003e (10.10mM) supernatants, with low concentrations detected for \u003cem\u003eA. muciniphila\u003c/em\u003e and \u003cem\u003eP. distasonis\u003c/em\u003e (0.72mM and 0.59mM, respectively) and none for the \u003cem\u003eBlauti\u003c/em\u003ea species.\u003c/p\u003e\n \u003cp\u003eSuccinate, which was not a constituent of the growth medium, was detected in 10 (GC) and 15 (HPLC) bacteria using the two methods. The difference was mainly due to the detection of low concentrations (below 1mM) in 8 isolates using HPLC but not GC (for instance \u003cem\u003eA. muciniphila\u003c/em\u003e supernatant contained 0.5mM succinate based on HPLC data).\u003c/p\u003e\n \u003cp\u003eConcentrations of lactate were not consistent between the two measurement techniques, with HPLC detecting less (Fig.\u0026nbsp;2B). GC often gives higher lactate readings than HPLC due to derivatization enhancing signal, greater sensitivity of GC detectors, less susceptibility to co-elution, or matrix interferences affecting HPLC more. Formate, iso-butyrate and iso-valerate were detected at low levels by GC and not measured by HPLC (Fig.\u0026nbsp;2A \u0026amp; 2B).\u003c/p\u003e\n \u003cp\u003eBoth glucose and pyruvate present in the growth media as measured by HPLC (YCFA 8.6mM glucose and 9.8mM pyruvate; BHI; 12.4mM and 17.9mM; Fig.\u0026nbsp;3C) were found to be exhausted by most bacteria after growth reached stationary phase (Fig.\u0026nbsp;2C \u0026amp; 3B). Less than 2mM of pyruvate were detected in \u003cem\u003eA. shahii\u003c/em\u003e, \u003cem\u003eB. hansenii\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e after growth medium subtraction suggesting that monosaccharides were not fully utilised for downstream fermentation, however pyruvate itself is not an end product (Fig.\u0026nbsp;3B). HPLC was the only method to measure fumarate and glycolic acid levels (Fig.\u0026nbsp;2C \u0026amp; 3B), which could therefore not be compared.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 Bacterial supernatants improved viability of primary cultures exposed to hA53T-\u0026alpha;syn\u003c/h2\u003e\n \u003cp\u003eThe neuronal expression of hA53T-\u0026alpha;syn was confirmed through staining of total and phosphorylated \u0026alpha;-syn and cell specific staining in cultures 6 days after virus addition (Fig.\u0026nbsp;3). Alpha-synuclein was detected predominantly in neurons, and rarely in astrocytes. Phosphorylation (pSer synuclein antibody) only occurred in select neurons while the majority presented with total \u0026alpha;-syn (4B12 stain) in the cell body and towards the axon (Fig.\u0026nbsp;3B \u0026ndash; C).\u003c/p\u003e\n \u003cp\u003eAddition of hA53T-\u0026alpha;syn for 6 days \u003cem\u003ein vitro\u003c/em\u003e significantly reduced viability of cell cultures (range: 5.5% \u0026minus;\u0026thinsp;44.5% viability, average: -80% reduction) vs. the Neurobasal and YCFA controls without hA53T-\u0026alpha;syn (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001, Fig.\u0026nbsp;4A). A 2-way ANOVA over all control conditions (with media composition and \u0026alpha;-synuclein as factors) confirmed the viability loss caused by \u0026alpha;-synuclein; (F (1, 35)\u0026thinsp;=\u0026thinsp;106.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with no effect of media or interaction (F\u0026thinsp;\u0026lt;\u0026thinsp;1; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9). Co-application of SNs with hA53T-\u0026alpha;syn also revealed a significant detrimental effect of a-synuclein (F (1, 99)\u0026thinsp;=\u0026thinsp;358.6, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), but not SN treatment (F (10, 99)\u0026thinsp;=\u0026thinsp;0.4835, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8972) (Fig.\u0026nbsp;4B). Normalising treatment by bacterial SNs with hA53T-\u0026alpha;syn against growth medium YCFA with hA53T-\u0026alpha;syn (=\u0026thinsp;100%) indicated that most bacterial supernatants provided significant protection to hA53T-\u0026alpha;syn triggered toxicity, i.e. an increase in viability (Fig.\u0026nbsp;4C). The biggest improvements in viability were obtained for \u003cem\u003eB. coccoides\u003c/em\u003e, \u003cem\u003eC. aerofaciens\u003c/em\u003e, and \u003cem\u003eP. distasonis\u003c/em\u003e in the presence of hA53T-\u0026alpha;syn with viability scores of 220%, 235%, and 207%, respectively. There was a lot of variability in the consequences of exposure to hA53T-\u0026alpha;syn, with the most consistent results observed for \u003cem\u003eA. shahii\u003c/em\u003e (192%; Fig.\u0026nbsp;4C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 hA53T-\u0026alpha;syn induces cell loss, most strongly affecting microglia\u003c/h2\u003e\n \u003cp\u003eMitochondria-based viability assays cannot differentiate between cell types in this mixed cell culture system. Cell type-specific effects were therefore determined next using three separate CellProfiler pipelines, based on staining with MAP2, IBA1 and GFAP for neurons, microglia, and astrocytes, respectively. Exemplar immunofluorescent images are presented in Fig.\u0026nbsp;5A. Initial visual validation confirmed that segmentation allowed successful and reproducible identification of cell-type specific counts and morphological characteristics.\u003c/p\u003e\n \u003cp\u003eAfter confirming normal distribution of all data sets and the absence of mathematical outliers; 2-way ANOVAs were run over all conditions (with media composition and \u0026alpha;-synuclein as factors) for cell counts of each cell type (neurons, microglia, astrocytes; Fig.\u0026nbsp;5B, E \u0026amp; H, all treatment replications are based on n\u0026thinsp;=\u0026thinsp;8\u0026ndash;10 independent experiments). In \u003cstrong\u003eneurons\u003c/strong\u003e, we confirmed that \u0026alpha;-synuclein but not media was an overall significant factor (\u0026alpha;-synuclein: F (1, 46)\u0026thinsp;=\u0026thinsp;12.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; media: F (2, 46)\u0026thinsp;=\u0026thinsp;0.7608, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4731), and that there was no interaction F (2, 46)\u0026thinsp;=\u0026thinsp;0.4724; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6265). The null-empty vector did not affect neuronal count (n\u0026thinsp;=\u0026thinsp;7; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 vs control).\u003c/p\u003e\n \u003cp\u003eConsequently, media controls (and synuclein only data) were pooled (comprising blank control, BHI and YCFA controls), yielding an overall control count (mean) of 83.5 vs. a \u0026alpha;-synuclein mean of 59.5 neurons (paired comparison: \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.56, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008). Individual scatter plots, pooled data as well as estimation plots (Fig.\u0026nbsp;5B) illustrate the distribution of the data and the difference between groups (mean and range).\u003c/p\u003e\n \u003cp\u003eAn increase in \u003cstrong\u003eneuronal counts\u003c/strong\u003e alongside a \u003cstrong\u003ereduced perimeter\u003c/strong\u003e (Fig.\u0026nbsp;6) due to SN only treatment was detected for \u003cem\u003eA. shahii, B. corprocola\u003c/em\u003e and \u003cem\u003eB. hansenii\u003c/em\u003e (vs. YCFA control; Table\u0026nbsp;3, Fig.\u0026nbsp;5\u0026amp;6, and Supplements). For the latter, the combined treatment (hA53T-\u0026alpha;syn plus \u003cem\u003eB. hansenii\u003c/em\u003e SN) had the most pronounced protective effect on neuronal morphology, reducing neuronal MAP2 levels as well as area and perimeter (see also below).\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n \u003cp\u003eAlthough \u003cem\u003eB. subtilis\u003c/em\u003e did not differ from YCFA, it improved neuronal counts and adjusted the size (perimeter) in primary cultures treated with hA53T-\u0026alpha;syn by 53.3% (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) compared to YCFA\u0026thinsp;+\u0026thinsp;hA53T-\u0026alpha;syn conditions (Fig.\u0026nbsp;5C). In contrast, \u003cem\u003eC. aerofaciens, E. coli, E.rectale\u003c/em\u003e and \u003cem\u003eR. intestinalis\u003c/em\u003e did not significantly affect neuronal count vs. YCFA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;5D), yet numbers were significantly reduced in the combined presence with hA53T-\u0026alpha;syn (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). For most SNs, co-treatment with hA53T-\u0026alpha;syn led to reduced neuronal counts, comparable to hA53T-\u0026alpha;syn only, thus mirroring \u0026alpha;syn associated reduction seen in SN-free conditions (Table\u0026nbsp;3).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGlia cell count\u003c/strong\u003e was also significantly reduced by \u0026alpha;-synuclein (microglia: F (1, 46)\u0026thinsp;=\u0026thinsp;35.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and astrocytes: F (1, 41)\u0026thinsp;=\u0026thinsp;10.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Both cell types also confirmed an absence of media impact and lack of an interaction (microglia media factor: F (2, 46)\u0026thinsp;=\u0026thinsp;1.861, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1671, interaction: F (2, 46)\u0026thinsp;=\u0026thinsp;2.374, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1044; astrocytes media factor: F (2, 41)\u0026thinsp;=\u0026thinsp;0.3931, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6775; interaction: F (2, 41)\u0026thinsp;=\u0026thinsp;0.04296, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9580). The presence of hA53T-\u0026alpha;syn most dramatically reduced \u003cstrong\u003emicroglia count\u003c/strong\u003e compared to controls (67.3% \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;5E) and specifically in (the more strongly powered) YCFA media (88% p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;5E and 61.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 Fig.\u0026nbsp;5H). Pooled data yielded a control mean count of 105.1 and an \u0026alpha;-syn mean of 38.9 microglia (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.93, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;5E). \u003cstrong\u003eAstrocyte\u003c/strong\u003e pooled data identified a control count of 104.3 and an \u0026alpha;-synuclein mean of 51.1 cells (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.55, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0009, Fig.\u0026nbsp;5H).\u003c/p\u003e\n \u003cp\u003eFor individual SNs, most yielded higher microglia numbers while astrocyte counts did not change (Fig.\u0026nbsp;5, Suppl. Figures\u0026nbsp;2 \u0026amp; 3, Table\u0026nbsp;3). Only \u003cem\u003eE. rectale\u003c/em\u003e increased microglia numbers but also their eccentricity. Co-treatment of bacterial SNs and hA53T-\u0026alpha;syn transfection caused microglia (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and astrocyte loss (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5) vs SN only, accompanied by distinct and cell-type specific morphological changes (details below; all results summarised in Table\u0026nbsp;3).\u003c/p\u003e\n \u003cp\u003eThese effects confirmed that overall glia and neurones were strongly affected by SN and hA53T-\u0026alpha;syn treatments, and the interactions between the treatments yielded a diverse, cell type and bacterial strain dependent profile.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4 Alpha-synuclein dramatically alters glial morphology\u003c/h2\u003e\n \u003cp\u003eFeatures of glia activation comprise morphological changes such as changes in size, branching and arborisation. Parameters indicative of cell size (area and perimeter), and arborisation (trunk and branch counts), as well as eccentricity and solidity were measured (Table\u0026nbsp;3 and Supplements). In neurones (see also above), 3 SNs boosted neurone numbers alongside smaller perimeters, and cell perimeters were exclusively affected by 2/9 SNs (\u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eA. muciniphila)\u003c/em\u003e, while other neuronal parameters remained largely unchanged. Occasional changes in neuronal trunk and branch counts (affected by \u003cem\u003eE. rectale\u003c/em\u003e and \u003cem\u003eB. coprocola\u003c/em\u003e) delivered identical outcomes, therefore, exemplar branch count is shown in Fig.\u0026nbsp;6 (see also Supplementary Fig.\u0026nbsp;7\u0026ndash;9) and Table\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003eTo determine the associations between treatments on morphological parameters, correlation analysis was performed. High correlations were identified between neuronal branch/trunk count vs. perimeter, and % area stained for MAP2 vs. perimeter. Correlation analyses are graphically separated for controls only, controls with hA53T-\u0026alpha;syn, bacterial supernatants only, and bacterial supernatants with hA53T-\u0026alpha;syn (Fig.\u0026nbsp;6G-J). For surviving neuronal cells, lower cell counts correlated strongly with larger perimeters in the presence of hA53T-\u0026alpha;syn, but bacterial supernatant co-treatment abolished (normalised) this negative correlation between perimeter and cell count (Fig.\u0026nbsp;6H \u0026amp; J).\u003c/p\u003e\n \u003cp\u003eSurviving microglia (cell loss due to hA53T-\u0026alpha;syn: 38%) presented with a larger size (area and perimeter) and elongation (eccentricity, ratio of the axes); \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05, Fig.\u0026nbsp;7A \u0026amp; B and Supplementary Fig.\u0026nbsp;10\u0026ndash;11). The remaining cells also displayed stronger IBA1 staining (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), further suggesting microglia cell activation (Supplementary Fig.\u0026nbsp;5 and Table\u0026nbsp;3).\u003c/p\u003e\n \u003cp\u003eOverall, most (5/9) bacterial supernatants improved microglia survival (count) after 6 days (Table\u0026nbsp;3). Some SNs alone (3/9) changed morphology (enhanced eccentricity), yet activation brought about by hA53T-\u0026alpha;syn was similar with or without SNs.\u003c/p\u003e\n \u003cp\u003eOnly \u003cem\u003eE. rectale\u003c/em\u003e SN somewhat improved the perimeter of enlarged microglia in the presence of hA53T-\u0026alpha;syn (Fig.\u0026nbsp;6B; and see Supplementary Fig.\u0026nbsp;9 and Table\u0026nbsp;3). Further investigations of linear trends over all treatments as well as planned paired comparisons found \u003cem\u003eA. shahii\u003c/em\u003e and \u003cem\u003eB. coprocola\u003c/em\u003e and \u003cem\u003eE. rectale\u003c/em\u003e to significantly increase in length (eccentricity: \u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in SN with and without \u0026alpha;-synuclein (Fig.\u0026nbsp;7; Supplementary Fig.\u0026nbsp;12 and Table\u0026nbsp;3).\u003c/p\u003e\n \u003cp\u003eIn general, microglia tend to become more irregular as they increased in perimeter and eccentricity. However, parameters for roundness (circularity \u0026amp; solidity) failed to identify a direct treatment effect (Fig.\u0026nbsp;7E \u0026amp; F, see Supplementary Fig.\u0026nbsp;13). However, parametric correlations verified that hA53T-\u0026alpha;syn treatment resulted in fewer cells with increased IBA1 intensity (negative correlation), larger areas and more irregular cell surfaces alongside, while solidity negatively correlated with perimeter and area (Fig.\u0026nbsp;7H). All parameters other than eccentricity were normalised by co-incubation with SNs (Fig.\u0026nbsp;7J).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.6 Enlargement of surviving astrocytes due to \u0026alpha;-syn is ameliorated by R. Intestinalis and A. muciniphila\u003c/h2\u003e\n \u003cp\u003eAstrocyte size (area and perimeter trend) was increased by hA53T-\u0026alpha;syn exposure compared to YCFA media alone. Only the bacterial SN from \u003cem\u003eB. subtilis\u003c/em\u003e reduced the perimeter \u003cem\u003eper se\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig.\u0026nbsp;15), while \u003cem\u003eA. shahii\u003c/em\u003e significantly increased the astrocyte size further vs. hA53T-\u0026alpha;syn alone (Fig.\u0026nbsp;8A \u0026amp; B). Only one SN significantly reduced the perimeter in the presence of hA53T-\u0026alpha;syn (\u003cem\u003eR. intestinalis; p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0220, Fig.\u0026nbsp;8C), while \u003cem\u003eA. muciniphila\u003c/em\u003e suggested a beneficial effects on cell count (p\u0026thinsp;=\u0026thinsp;0.07), with significant reductions in GFAP and area.\u003c/p\u003e\n \u003cp\u003eSimilar to microglia, correlation analysis for controls with hA53T-\u0026alpha;syn indicated that when cells are lost due to hA53T-\u0026alpha;syn exposure, the remaining cells enlarge and express more GFAP (i.e. negative correlation; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001, Fig.\u0026nbsp;8E), and area and perimeters correlated for all conditions. The negative correlation between GFAP, perimeter and cell count was again abolished by SN treatment. For astrocytes no other correlations between cell count and morphological parameters following SN treatment alone were detected.\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.7 Overall summary of cellular effects caused by hA53T-\u0026alpha;syn and / or bacterial SN treatment\u003c/h2\u003e\n \u003cp\u003eSegmentation and analysis of neuron and glia morphologies were performed following exposure to SN collected from 9 bacterial species selected based on their population change in PD, gram stain, and metabolite profile. Table\u0026nbsp;3 summarises all parameters measured across the three cell types in primary hippocampal (mixed) cultures and the effects of the bacterial SNs against control medium alone and comparisons with hA53T-\u0026alpha;syn conditions.\u003c/p\u003e\n \u003cp\u003eWe have outlined significant effects and correlations alongside interpretations regarding assumed beneficial/detrimental changes based on previously published work (e.g. Fern\u0026aacute;ndez-Arjona et al., 2019; Jovanovic et al., 2022; Koch et al., 2015; Liddelow et al., 2017). For glia, changes in size have been considered in the context of changes in cell numbers, e.g. less but larger glia are considered a (negative) sign of activation. For hA53T-\u0026alpha;syn with or without SNs, an increase in GFAP or Iba1 staining (especially alongside lower numbers) and increases in size are therefore assumed to be detrimental. In post-mitotic non-proliferating neurones, a reduction in e.g. size, trunk count or MAP2 staining may be indicative of degenerative processes, i.e. detrimental. However, this cannot be unequivocally classified as impaired neuronal health. Reductions in such parameters caused by some SNs may indicate that specific neuronal types had higher survival rates. Since hA53T-\u0026alpha;syn did not reliably enhance MAP2 levels \u003cem\u003eper se\u003c/em\u003e, respective reductions caused by SNs could therefore not be unequivocally categorised as beneficial.\u003c/p\u003e\n \u003cp\u003eGenerally, survival rates and morphological changes due to treatment were most profound in microglia, indicative of irregularities and hence cellular activation. For glia, perimeter was identified as the most robust parameter, showing clear negative correlations when cell counts were decreased or increased, while neuronal numbers provided the clearest readout for this cell type.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGut changes can become apparent up to 20 years before clinical diagnosis of PD, i.e. during or even prior to prodromal PD. Research on the gut microbiome (bacterial genera and species), as well as mouse model research based on faecal transplantation, support an active participation of the gastrointestinal environment in PD (Lin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sampson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stokholm et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The gut-brain axis and more specifically, the gut-microbiome-brain axis, may therefore offer a window of opportunity for intervention and prevention.\u003c/p\u003e \u003cp\u003eIn recent years, PD has been investigated predominantly on a systems level, with models developed on propagating synuclein pathology between organs (brain, gut, vagus nerve). Here, we determined the impact of commensal bacteria previously implicated in PD in hippocampal cell cultures by measuring their bacterial fermentation and metabolite products, and changes in viability and morphology of hippocampal cell cultures. Overall, most SNs improved gross viability, as well as 3/9 neuronal counts and 5/9 microglia counts, while none were reliably changing astrocyte density.\u003c/p\u003e \u003cp\u003eThe SN from \u003cem\u003eB. subtilis\u003c/em\u003e (positively reversing neuronal loss), and \u003cem\u003eE. rectale\u003c/em\u003e (reversing some synuclein toxicity parameters of microglia) were arguably the most promising when co-applied with hA53T-αsyn. The most beneficial action on astrocytes affected by hA53T-αsyn was observed for \u003cem\u003eA. muciniphila\u003c/em\u003e and \u003cem\u003eR. intestinalis\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Bacterial metabolite characterisation and sensitivity\u003c/h2\u003e \u003cp\u003ePD is accompanied by a reduction in faecal SCFAs (Hill-Burns et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Unger et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, our work aimed to identify the relevant fermentation products produced by bacteria in a nutrient rich medium (YCFA). Half of the bacteria produced succinate and propionate, with propionate previously suggested to be protective in a PD mouse model (Nishiwaki et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The three bacteria (A. \u003cem\u003ehadrus, B. hansenii, and D. longicatena\u003c/em\u003e) that produced fumarate did not produce any succinate as end products.\u003c/p\u003e \u003cp\u003eGC and HPLC broadly identified similar quantities of acetate, propionate, butyrate, valerate and succinate, confirming the robustness of both procedures, with minor sensitivity differences. Our work has provided transparent results for both methods improving on previous reports (e.g. Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We were however not able to determine which technical procedure can ultimately considered to be most robust. In the wider field, various groups have characterised bacterial metabolite profiles from cultures, or animal models with varied approaches and choice of analytical method (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghaisas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lopez-Siles et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sampson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A comparative approach alongside full validation has not been provided yet. Therefore, our work goes some way to identify potential sources for inconsistencies between analytical methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Bacterial supernatants can improve viability and neuronal survival\u003c/h2\u003e \u003cp\u003eOnly \u003cem\u003eB. subtilis\u003c/em\u003e improved neuronal (but not glia) density when combined with hA53T-αsyn exposure. Research using \u003cem\u003eC. elegans\u003c/em\u003e has suggested various S\u003cem\u003eubtilis\u003c/em\u003e strains to be protective against α-syn, potentially due to a slowing and reversing of aggregation (Goya et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is possible that species- and cell type- specific components of the \u003cem\u003eSubtilis\u003c/em\u003e species metabolome, for example specific enzymes present in the supernatant, produced an improvement of neurons beyond that of SCFAs. \u003cem\u003eB. subtilis\u003c/em\u003e and \u003cem\u003eB. hansenii\u003c/em\u003e were the only two SNs that reduced the hA53T-αsyn-related enlarged neuronal size. Interestingly, the SNs from \u003cem\u003eB. hansenii\u003c/em\u003e, \u003cem\u003eA. muciniphila, A. shahii\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e reduced the neuronal perimeter vs control medium, indicating that bacteria might produce metabolites that are detrimental to neuronal outreach or growth.\u003c/p\u003e \u003cp\u003eAlthough a healthy gut microbiome may be protective for PD pathology and therefore some bacterial metabolites may be beneficial, discrepancies exist in the literature. SCFAs are known to reduce inflammation of the CNS, but findings also suggest that high SCFA levels can cause neuronal mitochondrial and lipid alterations (Fillier et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sampson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wenzel et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Importantly, our work did not indicate negative effects on cell viability following treatment with bacterial SNs. We did not identify general effects of SNs or α-syn exposure on neuronal trunk or branch count (except \u003cem\u003eE. rectale\u003c/em\u003e and \u003cem\u003eB. coprocola\u003c/em\u003e); this differed from previous studies (Jovanovic et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In organotypic rat hippocampal slices, 1\u0026ndash;5mM synthetic formate was reported to induce neuronal death, which was prevented with 1\u0026micro;M folic acid supplementation (Kapur et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Neurobasal medium contains 9\u0026micro;M folic acid which may have mitigated the effect of the mM amounts of formate present in \u003cem\u003eC. aerofaciens, E. coli\u003c/em\u003e and \u003cem\u003eR. intestinalis\u003c/em\u003e, respectively, as these conditions presented similar cell counts to media controls. Future work will characterise actions of bacterial compound SNs, media composition and specific SCFAs on cell viability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Glial cells react strongly to both α-syn and bacterial metabolite exposure\u003c/h2\u003e \u003cp\u003eMicroglia, smooth in a resting state, become irregular when activated or apoptotic, the latter outlined by blebbing spheres along the cell border (Ren et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schiess et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While some SNs generally improved microglia counts (5 out of 9 bacteria), we identified α-syn to reduce viability and glia counts, and shift glia towards a non-resting state. This was exacerbated in combination with SNs from e.g. \u003cem\u003eA. shahii\u003c/em\u003e, \u003cem\u003eB. coprocola\u003c/em\u003e and \u003cem\u003eE. rectale\u003c/em\u003e SN. Treatment of mixed hippocampal cell cultures with YCFA did not cause changes vs. standard medium in the parameters measured. Scrutiny of the data indicated that exposure to SNs together with hA53T-αsyn potentiated microglial reactivity and activation. We therefore hypothesize that microglia may become overwhelmed by combined α-syn and bacterial metabolite challenges. This indicates that bacterial metabolites may exacerbate stress or inflammation rather than offering protection in an already cellularly stressed environment (Fern\u0026aacute;ndez-Arjona et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ehA53T-αsyn-surviving astrocytes enlarged in area and perimeter, but only \u003cem\u003eR. intestinalis\u003c/em\u003e and \u003cem\u003eA. muciniphila\u003c/em\u003e were able to ameliorate this (vs hA53T-αsyn). Glial shape changes indicate that α-syn caused cellular activation and stress, with no singular SN able to reverse altered glial morphology completely. As varying concentrations of individual fermentation products were produced by each bacterium, effects on cell viability, survival or shape could be the result from either individual or combined level of specific SCFAs, or the presence of other metabolites (Wenzel et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eIt is necessary to consider that primary neuronal cultures, even when counted and homogenously resuspended, lead to somewhat variable initial cell ratios and surviving populations, not necessarily comparable to \u003cem\u003ein situ\u003c/em\u003e conditions. Additionally, use of neonatal tissue lacks the aspect of ageing-related changes in cell organisation and physiology (Slanzi et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of highly supplemented media does also not accurately represent a natural environment for either bacteria or primary culture preparations. Further factors may also be released from fermentation processes or additional metabolic factors, following interactions between bacteria and the host organism (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Medlock et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While there is evidence of SCFAs (acetate, propionate and butyrate) reaching the brain through the blood-brain barrier, there are still uncertainties regarding other metabolites (Mirzaei et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mukhopadhya \u0026amp; Louis, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe current study approached gut-brain axis research by utilising defined bacterial supernatant interventions in an \u003cem\u003ein vitro\u003c/em\u003e synuclein model of PD, addressing a knowledge gap in our understanding of their potential effects on the CNS. Virally delivered α-synuclein offered an \u003cem\u003ein vitro\u003c/em\u003e model for controlled testing of treatments with short experimental duration, to determine actions via a validated cell-type-specific workflow.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first analysis of synuclein toxicity in which the morphology of three co-cultured cell types was measured and quantified, and where mutated hA53T-αsyn and/ or bacterial supernatants are tested in a comprehensive manner. In general, most bacterial SNs increased total cell counts, indicative of a protective potential, alongside pronounced changes in glial morphology. Our results offer an initial understanding of α-syn and bacterial metabolite interactions on nervous tissue viability and morphology, and can therefore advance our understanding of PD relevant pathologies. Dietary adjustments that can alter the microbiome might prevent, or at least modify, the impact of α-syn associated pathologies and thus PD development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eα-syn\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealpha-synuclein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehA53T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman A53T\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-performance liquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egas chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecentral nervous system CNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePD,Parkinson\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshort-chain fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eyeast,casitone,fatty acid medium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAD(H)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNicotinamide adenine dinucleotide (hydrogen).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eData and CellProfiler pipelines used to generate the data are not publicly available as these are currently used for ongoing research and further publications, but are available on request.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eBP conceived, planned and designed the project and revised the manuscript; BP, KS and PH designed the experiments. PH with help from JM performed anaerobic bacterial culturing and analysed gas chromatography experiments. AE and FB performed HPLC analysis of the supernatants and performed data calculations. All other data was collected and presented by PH. PH wrote the initial manuscript with input from all authors, and prepared figures and tables. All authors have read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eWe thank the Rowett Institute analytical chemistry department for the gas chromatography testing of the bacterial supernatants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was part-funded by 4D Pharma PLC and by the University of Aberdeen Development Trust SCIO.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eDeclaration of AI and AI-assisted Technologies in the Writing Process\u003c/p\u003e\n\u003cp\u003eThe authors declare that no AI and AI-associated technologies were used in the process of writing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed S, Busetti A, Fotiadou P, Vincy Jose N, Reid S, Georgieva M, Brown S, Dunbar H, Beurket-Ascencio G, Delday MI, Ettorre A, Mulder IE (2019) In vitro Characterization of Gut Microbiota-Derived Bacterial Strains With Neuroprotective Properties. 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Bellwether Publishing, Ltd, pp 1\u0026ndash;20. 1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19490976.2020.1848158\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2020.1848158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, synuclein, primary hippocampal culture, gut bacteria, microbiome, CellProfiler","lastPublishedDoi":"10.21203/rs.3.rs-8852210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8852210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Parkinson’s disease (PD) is associated with alpha-synuclein (α-syn) accumulation and spread. Recent evidence suggests a connection between PD and gastrointestinal changes early in the disease process due to host-bacterial interactions and association between the microbiome and PD pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Supernatants (SNs) from 16 bacterial strains, characterised via HPLC and GC, were tested on rat hippocampal cultures. PD pathology was modelled via viral gene delivery targeting neurons with human mutated A53T α-synuclein (hA53T-αsyn). Cell viability was assessed +/- SNs and hA53T-αsyn. Immunohistochemistry combined with semi-automated image analyses was developed to determine neuronal and glial density and morphology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A microbiome panel of 16 different species yielded a range of SN-specific fermentation products (e.g. acetate, formate and lactate). HA53T-αsyn transfection created an \u003cem\u003ein vitro\u003c/em\u003e synucleinopathy model with ~-50% viability after 6 days. Most bacterial SNs ameliorated gross viability loss caused by hA53T-αsyn.\u003c/p\u003e\n\u003cp\u003eSegmentation of cell types identified microglia as most impacted by hA53T-αsyn, with an increased size indicative of activation. Some SN treatments boosted neurone and microglia numbers \u003cem\u003eper se\u003c/em\u003e, and reduced hA53T-αsyn toxicity , with partial neuro-protection detected for \u003cem\u003eB. subtilis\u003c/em\u003e and \u003cem\u003eB. hansenii\u003c/em\u003e, while \u003cem\u003eA. mucinipila, E. rectale \u003c/em\u003eand \u003cem\u003eR. intestinalis \u003c/em\u003eoffered some protection for glia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Specific SNs improved viability per se and affected hA53T-αsyn toxicity. Actions were highly cell type specific with some improving neuronal or glia morphology changes. The observed CNS-modulatory effects indicate that therapeutic routes targeting the microbiome may ameliorate PD pathology.\u003c/p\u003e","manuscriptTitle":"Anaerobic gut bacterial metabolites alter morphology and survival of neurons and glia in a hippocampal culture model of Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 09:29:47","doi":"10.21203/rs.3.rs-8852210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"429ab0a6-7cce-43bb-9792-07b2a552e59a","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T21:09:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 09:29:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8852210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8852210","identity":"rs-8852210","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}