Recently, there has been a rapid increase in studies on the relationship between brain diseases and gut microbiota, and clinical evidence on gut microbial changes in Parkinson's disease (PD) has accumulated. 6-Hydroxydopamine (6-OHDA) is a widely used neurotoxin that leads to PD pathogenesis, but whether 6-OHDA affects gut microbial environment has not been investigated. Here we performed the 16S rRNA gene sequencing to analyze the gut microbial community of mice. We found that there were no significant changes in species richness and its diversity in the 6-OHDA-lesioned mice. The relative abundance of Lactobacillus gasseri and L. reuteri probiotic species in feces of 6-OHDA-lesioned mice was significantly decreased compared with those of sham-operated mice, while the commensal bacterium Bacteroides acidifaciens in 6-OHDA-treated mice was remarkably higher than sham-operated mice. These results provide a baseline for understanding the microbial communities of 6-OHDA-induced PD model to investigate the role of gut microbiota in the pathogenesis of PD.
Citation: Choi JG, Huh E, Kim N, Kim D-H, Oh MS (2019) High-throughput 16S rRNA gene sequencing reveals that 6-hydroxydopamine affects gut microbial environment. PLoS ONE 14(8): e0217194. https://doi.org/10.1371/journal.pone.0217194
Editor: David I. Finkelstein, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, AUSTRALIA
Received: May 2, 2019; Accepted: July 24, 2019; Published: August 12, 2019
Copyright: © 2019 Choi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2015R1A2A2A01004341), to MSO; Medical Research Center Program through the NRF funded by the Ministry of Science and ICT (NRF-2017R1A5A2014768), to MSO. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Parkinson’s disease (PD) is a multicentric neurodegenerative disease clinically defined by motor deficits and progressive degeneration of dopaminergic neurons in brain . Non-motor manifestations, which precede the motor disabilities in PD patients, play a key role in the disease progression and evidence for their significance has gradually accumulated [2–4]. Among the non-motor symptoms of PD, gastrointestinal (GI) dysfunction, including drooling, impaired gastric emptying, and constipation are frequently reported [5, 6].
Accumulating evidence suggests that the brain is directly involved in gut dysbiosis, an alteration in gut microbiota composition leading to its imbalance, and GI dysfunction following exposure to central stress-like depression [7–9]. Gut dysbiosis may cause gut permeability, affecting the GI epithelial barriers and immune system [10, 11]. The immune responses triggered by gut microbiota changes could enhance the inflammatory reactions that induce misfolded α-synuclein, which is a pathological hallmark of PD [12, 13].
Scheperjans and his colleagues explored the relationship between gut microbiota changes and clinical phenotypes of PD with fecal microbiome analysis, which showed a reduction in beneficial Prevotellaceae and an elevation of pathogenic Enterobacteriaceae in PD patients with severe gait disturbance . In addition, Keshavarzian et al  reported that anti-inflammatory bacterial genera such as Blautia, Coprococcus, and Roseburia were less abundant in feces of PD patients whereas Ralstonia known as a pro-inflammatory bacterial genus was more abundant in the mucosa of PD patients. These studies suggest that changes in gut microbiota composition are closely associated with PD pathogenesis, however, whether gut microbial environment could be affected by intracerebral injection of chemical neurotoxins that cause the pathogenesis of PD has not been studied yet.
This study aimed to investigate whether unilateral brain lesions induced by intracerebral injection of 6-hydroxydopamine (6-OHDA) neurotoxin, which not only causes the death of nigrostriatal dopaminergic neurons in brain but also GI dysfunctions such as gastroparesis [16, 17], affects gut microbial community. To do this, we administrated 6-OHDA directly to the striatum of mouse brain and performed high-throughput sequencing of 16S rRNA genes from fecal samples. Then, we analyzed the alterations of species richness, bacterial diversity, relative abundance at several taxonomic levels, and predicted the functional composition of microbial communities.
Materials and methods
Animals and surgery
Male ICR mice (8 weeks-old) were purchased from Daehan Biolink (Eumseong, Korea). The animals were housed into total 9 cages (3 cages (n = 2/cage) and 1 cage (n = 3/cage) per sham-operated group; 5 cages (n = 2/cage) per 6-OHDA-lesioned group) at an ambient temperature of 23 ± 1°C and relative humidity 60 ± 10% under a 12 h light/dark cycle and were allowed free access to water and food. This study was carried out in accordance with the Principles of Laboratory Animal Care (NIH publication number 80–23, revised 1996). The protocol was approved by the Animal Care and Use Guidelines of Kyung Hee University, Seoul, Korea (Permit number: KHUASP(SE)-16-127). Mice were monitored for total schedule once daily. Mice were euthanized in case of 35% weight loss (humane endpoints) according to the approved protocol, but there were no euthanized mice in this study. The unilateral injection of 6-OHDA was performed as modified methods . Briefly, mice were anesthetized with tribromoethanol (312.5 mg/kg, i.p.) and placed in a stereotaxic apparatus (myNeuroLab, St. Louis, MO, USA). They received a unilateral injection of 2 μl 6-OHDA (16 μg/2 μl; 6-OHDA-lesioned group (n = 10)) or equal volume of vehicle (saline with 0.1% ascorbic acid; sham-operated group (n = 9)) into the right striatum according to the mouse brain atlas (coordinates with respect to bregma: AP +0.5 mm, ML +2.0 mm, DV: -3.0 mm) . After the surgery, mice were maintained body temperature by using heating pads or blankets.
Fecal sample collection, DNA extraction, and sequencing
Fecal samples were collected at 14 days after the surgery and before the injection of apomorphine. All samples were placed immediately into sterile plastic tubes and stored at -80°C until analysis. DNA was extracted from the samples using the FastDNA SPIN Kit for Soil (MP Biomedicals Inc., Solon, USA) according to the manufacturer’s instructions.
PCR amplification was performed using primers targeting from V3 to V4 regions of the 16S rRNA gene with extracted DNA. For bacterial amplification, primers of 341F (5’-TCGTCGGCAGCGTC-AGATGTGTATAAGAGACAG-CCTACGGGNGGCWGCAG-3’; underlining sequence indicates the target region primer) and 805R (5’-GTCTCGTGGGCTCGG-AGATGTGTATAAGAGACAG-GACTACHVGGGTATCTAATCC-3’). The amplifications were carried out under the following conditions: initial denaturation at 95°C for 3 min, followed by 25 cycles of denaturation at 95°C for 30 sec, primer annealing at 55°C for 30 sec, and extension at 72°C for 30 sec, with a final elongation at 72°C for 5 min. Then, secondary amplification for attaching the Illumina NexTera barcode was performed with i5 forward primer (5’-AATGATACGGCGACCACCGAGATCTACAC-XXXXXXXX-TCGTCGGCAGCGTC-3’; X indicates the barcode region) and i7 reverse primer (5’-CAAGCAGAAGACGGCATACGAGAT-XXXXXXXX-AGTCTCGTGGGCTCGG-3’). The condition of secondary amplification is equal to the former one except the amplification cycle set to 8. The PCR product was confirmed by using 2% agarose gel electrophoresis and visualized under a Gel Doc system (BioRad, Hercules, CA, USA). The amplified products were purified with the QIAquick PCR purification kit (Qiagen, Valencia, CA, USA). Equal concentrations of purified products were pooled together and short fragments (non-target products) were removed with an Ampure beads kit (Agencourt Bioscience, MA, USA). The quality and product size were assessed on a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA) using a DNA 7500 chip. Mixed amplicons were pooled and the sequencing was carried out at ChunLab, Inc. (Seoul, Korea), with an Illumina MiSeq Sequencing system (Illumina, USA) according to the manufacturer’s instructions.
Taxonomic assignment of sequence reads
Processing raw reads started with quality checking (QC) and filtering of low quality (<Q25) reads by trimmomatic 0.32 . After the QC pass, paired-end sequence data were merged together using PandaSeq . Primers were then trimmed with ChunLab’s in-house program at a similarity cut off of 0.8. Sequences were denoised using Mothur’s pre-clustering program, which merge sequences and extracts unique sequences allowing up to 2 differences between sequences . The EzTaxon database (http://www.eztaxon-e.org/) was used for Taxonomic Assignment using BLAST 2.2.22 and pairwise alignment was used to calculate similarity [23, 24]. Microbiome taxonomic profiling was analyzed by BIOiPLUG program (ChunLab Inc., Seoul, Korea). The uchime and non-chimeric 16S rRNA database from EzTaxon were used to detect chimera on reads that contained less than 97% best hit similarity rate . Sequence data is then clustered using -Hit and UCLUST, and alpha diversity analysis was carried out [26, 27].
All statistical parameters were calculated using GraphPad Prism 5.0 software (GraphPad Software Inc., San Diego, USA). Values were expressed as the median and quartiles of the data. The results were analyzed with Student’s t-test between two groups. Differences with a p value less than 0.05 were deemed to be statistically significant.
Linear discriminant analysis (LDA) effect size (LEfSe) based on Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict how taxonomic differences between fecal microbiota of two groups impact their microbial metabolic potential in the main functional classes (KEGG categories) . LEfSe uses the Kruskal–Wallis rank-sum test to identify features with significantly different abundances between assigned taxa compared to the groups, and LDA to estimate the size effect of each feature, based on a p < 0.05 and LDA score > 2.0. The PICRUSt and LEfSe were analyzed by the EzBioCloud database (ChunLab Inc., Seoul, Korea) .
The alteration of bacterial species richness in 6-OHDA-lesioned mice
All analyzed sequences contained at least 2 of the V3 and V4 16S rRNA gene regions . Each read was taxonomically assigned according to the EzTaxon database. When each phylotype at species level was defined using a baseline of 97% nucleotide sequence similarity from 883 to 1,510 phylotypes (average 1,097) were found in the analyzed samples. To investigate the alterations of species richness in feces of 6-OHDA-lesioned mice, we estimated the abundance-based coverage estimators (ACE), Chao1, and the number of operational taxonomic unit (OTU) at species level. The values observed in the sham-operated and 6-OHDA-lesioned mice were comparable (Fig 1).
The alteration of bacterial diversity in 6-OHDA-lesioned mice
To investigate the alterations of bacterial diversity in mice feces followed by 6-OHDA lesions, we estimated several diversity indexes such as Shannon’s diversity index, Simpson’s diversity index, and phylogenic diversity at species level. We found that there was no difference between the groups (Fig 2). These data show that bacterial species diversity was unaffected by 6-OHDA lesion.
These data are analyzed by (A) Shannon index, (B) Simpson index, and (C) phylogenic diversity index. Values are expressed as box and whisker which presents the median and quartiles of the data. n = 9/sham-operated group; n = 10/6-OHDA-lesioned group.
The alteration of relative abundance in 6-OHDA-lesioned mice
To investigate whether 6-OHDA treatment could affect gut microbial environment, the proportion of each taxon at the family, genus, and species levels was compared between sham-operated and 6-OHDA-lesioned groups. The ratio was limited to each taxa containing 1% or more. At the family level, the relative abundance of Lactobacillaceae showed a significant decrease in the 6-OHDA-lesioned group (4.07 ± 0.82%) compared with the sham-operated group (13.54 ± 4.65%, p = 0.0498), while that of Bacteroidaceae was significantly increased in the 6-OHDA-lesioned group (19.32 ± 5.49%) compared with the sham-operated group (6.33 ± 1.94%, p = 0.0476) (Fig 3). The genera Lactobacillus was significantly decreased in 6-OHDA-lesioned mice (3.99 ± 0.78%) compared with those of the sham-operated group (13.28 ± 4.39%, p = 0.0424), while the Bacteroides genus was significantly increased in 6-OHDA-lesioned mice (18.94 ± 4.91%) compared with those of the sham-operated group (6.96 ± 2.18%, p = 0.0466) (Fig 4). In Fig 5, two Lactobacillus species like L. gasseri and L. reuteri were significantly reduced in the 6-OHDA-lesioned group (1.30 ± 0.44% and 0.84 ± 0.18%, respectively) compared to the sham-operated group (6.14 ± 2.22%, p = 0.0378 and 3.82 ± 1.37%, p = 0.0355, respectively), while the relative abundance of commensal bacteria B. acidifaciens was markedly increased in the 6-OHDA-lesioned group (10.85 ± 4.12%) compared with those of the sham-operated group (1.59 ± 0.40%, p = 0.0494). These results suggest that the remarkable changes of bacterial composition may occur in the mouse intestine after 6-OHDA administration, showing the significant decrease of beneficial bacteria such as Lactobacillus probiotic species.
Notable bacterial family over 1% in at least is marked. Values are expressed as box and whisker which presents the median and quartiles of the data. #p<0.05 compared with the sham-operated group. n = 9/sham-operated group; n = 10/6-OHDA-lesioned group.
Notable bacterial genus over 1% in at least is marked. Values are expressed as box and whisker which presents the median and quartiles of the data. #p<0.05 compared with the sham-operated group. n = 9/sham-operated group; n = 10/6-OHDA-lesioned group.
Notable bacterial species over 1% in at least is marked. Values are expressed as box and whisker which presents the median and quartiles of the data. #p<0.05 compared with the sham-operated group. n = 9/sham-operated group; n = 10/6-OHDA-lesioned group.
Predicted functional composition using LEfSe analysis based on the PICRUSt dataset
Within the main KEGG categories, LEfSe analysis indicated that homologous recombination; galactose metabolism; other glycan degradation; starch and sucrose metabolism; amino sugar and nucleotide sugar metabolism; biosynthesis of antibiotics; biosynthesis of amino acids; biosynthesis of secondary metabolites; and metabolic pathways were enriched in 6-OHDA-lesioned group, whereas ribosome; carbon metabolism; quorum sensing; tryptophan metabolism; alanine, aspartate and glutamate metabolism; hepatitis C; ABC transporters metabolism pathway genes were enriched in sham-operated group (Fig 6).
LEfSE analysis performed on metabolic functions based on PICRUSt results shows several significant differences of KEGG categories between sham and 6-OHDA group. The threshold for the logarithmic LDA score was 2.0 and p < 0.05 for the factorial Kruskal-Wallis test among classes.
The composition of gut microbiota can have a remarkable impact on disease status as well as normal physiology in brain [31, 32]. Emerging evidence supports that gut dysbiosis by altered gut microbiota has the potential to be closely linked with neurodegenerative diseases, including PD [33–36]. In this study, we found that 6-OHDA, a well-known neurotoxin that leads to PD pathogenesis, creates gut microbial environment that affects specific microbes in mice using the high-throughput 16S rRNA gene sequencing method.
First, we confirmed the well-established PD mouse model induced by intracerebral injection of 6-OHDA as shown in severe motor deficits and dopaminergic neuronal damage (S1 Fig). In this model, we found little difference between the sham-operated and 6-OHDA-lesioned groups on species richness, bacterial diversity (Figs 1 and 2). We also found no significance between two groups on a principal coordinates analysis evaluated by Permutational multivariate analysis of variance (p = 0.153, S2 Fig). It has been reported that microbial species richness and its diversity indicate whether gut microbiota impacts biological entities, but the ecological meaning of this index simply shows the number of bacterial species, and is not an indicator of gut dysbiosis [37–39].
Next, we observed a significant differentiation in several microbes at the phylogenic family, genus, and species levels between the sham-operated and 6-OHDA-lesioned groups (Figs 3–5). We showed a relative lower abundance of Lactobacillaceae and Lactobacillus at the family and genus level, respectively, thereby the relative abundance of L. gasseri and L. reuteri was significantly reduced in the 6-OHDA-lesioned group compared to the sham-operated group. Several studies showed that exposure to excessive stress induced a shift in microbial composition that was the reduced proportion of anti-inflammatory bacteria, including Lactobacillus [40, 41]. These bacteria regulated emotional behavior by inducing transcription of γ-aminobutyric acid receptors and suppressed the disease progression and excessive T cells-mediated immune responses in experimental autoimmune encephalomyelitis-induced mouse model of multiple sclerosis [42–44]; especially L. reuteri supplementation restored gut motility in patients with chronic constipation, which is a major non-motor symptoms in PD . We found a clear difference in microbial community patterns between the sham-operated and 6-OHDA-lesioned groups. Several reports showed that Lactobacillus was more abundant in advanced PD patients than controls on the contrary to our results [14, 46–48]. According to the report of Gerhardt and Mohajeri , Lactobacillus was increased in three of total 10 PD studies and this increase may be caused by constipation frequency in PD patients because Lactobacillus has been known to be increased in constipation-type irritable bowel syndrome (IBS) and decreased in diarrhea-type IBS . In addition, an experimental result indicated that monitoring on rhesus monkeys induced by systemic 6-OHDA injection showed soft stool or diarrhea in 2 of 5 animals . This implies that Lactobacillus abundance may be different by various factors. We also found the remarkable increase of Bacteroides abundance after the 6-OHDA lesion. Keshavarzian and his colleagues reported that the counts of Bacteroides were significantly increased in fecal samples of PD patients . This remarkable increase is closely related to the immunoglobulin A (IgA), which has been increased in mice exposure to repeated 6-OHDA or inoculation of germ-free mice with B. acidifaciens [52, 53]. It is reported that IgA plays a critical role in maintenance of gut microbiota composition and is presumed to prevent intestinal damages from external pathogen [54, 55]. Lactobacillus and B. acidifaciens are widely known as sensitive bacteria against alterations of gut environment induced by external insults. It has been reported that the abundance of B. acidifaciens which modulated host immunity was remarkably increased in large intestine of trinitrobenzenesulfonic acid-induced colitis and Salmonella-infected gastroenteritis, respectively [56, 57]. Thus, Lactobacillus and B. acidifaciens are expected to be more sensitive to external toxins like 6-OHDA than other bacteria.
In a recent microbiome analysis of the cecal sample in mice induced by intraperitoneal injection of 6-OHDA, peripheral 6-OHDA induced an approximately 20-fold decrease in the Prevotellaceae compared with control group . This implies that peripheral 6-OHDA, which does not cross the blood-brain barrier , can directly affect enteric microbiota. Our results showed the different patterns of gut microbial environment unlike those of peripheral 6-OHDA injection. It is expected that intracerebral injection of 6-OHDA influenced gut microbial community through indirect regulation. Recent studies indicate that activation of the hypothalamic-pituitary-adrenal (HPA) axis following exposure to chronic or acute stress affects gut microbiota composition directly via stimulation of the vagus nerve [40, 60, 61]. Moreover, the release of neurotransmitters in the brain through cholinergic sympathetic activation influences changes in gut microbiota composition and GI functions . It is possible that brain damage induced by 6-OHDA, which can regulate the HPA axis and eliminate cholinergic sympathetic innervation [63, 64], may interfere with the signaling from brain to gut along the HPA axis, thereby affects gut microbial environment.
LEfSe analysis identified several categories of functional metabolism that were enriched in sham and 6-OHDA mice. Mice with 6-OHDA lesion showed the effect size for microbial metabolic pathways responsible for carbohydrate metabolism (eg. galactose, starch, sucrose and glycan degradation), homologous recombination, biosynthesis of antibiotics, and biosynthesis of secondary metabolites. Especially, alterations in secondary metabolites have been observed in the gut microbiota metagenomics data of PD patients, suggesting that metabolic dysfunction of secondary metabolites may be an important feature of PD . However, this result is only originated from predicted metagenomic functions and metabolomic approaches are preferred to identify factual changes in metabolic function of microbiota of 6-OHDA-lesioned mice.
There are several limitations to this study. First, analysis of the mouse gut microbiota was performed fourteen days after 6-OHDA injection. The time-course study after intracerebral injection of 6-OHDA on gut microbial changes would provide more detailed information. Second, how 6-OHDA could affect gut microbial environment in mice was unexplored, then further study should focus on the mechanism of 6-OHDA-induced alteration of several microbes. We first identified the relative abundances at taxonomic levels and predicted functional metabolic pathways between sham-operated and 6-OHDA-lesioned groups by the analysis of bacterial 16S rRNA gene sequencing. This study demonstrated that the levels of some microorganisms in the feces, such as Lactobacillus and Bacteroides, are associated with the exposure to 6-OHDA. These results provides a baseline for understanding the microbial communities of 6-OHDA-induced PD model to investigate the role of gut microbiota in the pathogenesis of PD.
S1 Fig. Intrastriatal injection of 6-OHDA induces motor deficits and dopaminergic neuron death.
(A-C) open field test, rotarod test, and apomorphine-induced rotation test were performed to explore whether 6-OHDA induces motor deficits in mice, respectively. (D-F) immunohistochemical staining of TH was analyzed in SNpc and ST, respectively. Values were expressed as mean ± SEM (6-OHDA-lesioned group; n = 10, sham-operated group; n = 9). Scale bar = 100 μm. #p<0.05 and ###p<0.001 (vs. sham-operated group).
S2 Fig. The relationship between the bacterial profiles of the samples, represented by a principal coordinates analysis plot from a weighted UniFrac tool.
OTUs were determined based on 97% similarity of reads. Green and blue dots denote the sham and 6-OHDA group, respectively.
- 1. Jankovic J. Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008;79(4):368–76. Epub 2008/03/18. pmid:18344392.
- 2. Chaudhuri KR, Healy DG, Schapira AH, National Institute for Clinical E. Non-motor symptoms of Parkinson's disease: diagnosis and management. Lancet Neurol. 2006;5(3):235–45. pmid:16488379.
- 3. Antonini A, Barone P, Marconi R, Morgante L, Zappulla S, Pontieri FE, et al. The progression of non-motor symptoms in Parkinson's disease and their contribution to motor disability and quality of life. J Neurol. 2012;259(12):2621–31. pmid:22711157.
- 4. Chaudhuri KR, Schapira AH. Non-motor symptoms of Parkinson's disease: dopaminergic pathophysiology and treatment. Lancet Neurol. 2009;8(5):464–74. pmid:19375664.
- 5. Georgescu D, Ancusa OE, Georgescu LA, Ionita I, Reisz D. Nonmotor gastrointestinal disorders in older patients with Parkinson's disease: is there hope? Clin Interv Aging. 2016;11:1601–8. pmid:27956826; PubMed Central PMCID: PMC5113937.
- 6. Mukherjee A, Biswas A, Das SK. Gut dysfunction in Parkinson's disease. World J Gastroenterol. 2016;22(25):5742–52. pmid:27433087; PubMed Central PMCID: PMC4932209.
- 7. Soderholm JD, Yates DA, Gareau MG, Yang PC, MacQueen G, Perdue MH. Neonatal maternal separation predisposes adult rats to colonic barrier dysfunction in response to mild stress. Am J Physiol Gastrointest Liver Physiol. 2002;283(6):G1257–63. pmid:12388189.
- 8. Varghese AK, Verdu EF, Bercik P, Khan WI, Blennerhassett PA, Szechtman H, et al. Antidepressants attenuate increased susceptibility to colitis in a murine model of depression. Gastroenterology. 2006;130(6):1743–53. pmid:16697738.
- 9. Park AJ, Collins J, Blennerhassett PA, Ghia JE, Verdu EF, Bercik P, et al. Altered colonic function and microbiota profile in a mouse model of chronic depression. Neurogastroenterol Motil. 2013;25(9):733–e575. pmid:23773726; PubMed Central PMCID: PMC3912902.
- 10. Carabotti M, Scirocco A, Maselli MA, Severi C. The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol. 2015;28(2):203–9. pmid:25830558; PubMed Central PMCID: PMC4367209.
- 11. Zhu X, Han Y, Du J, Liu R, Jin K, Yi W. Microbiota-gut-brain axis and the central nervous system. Oncotarget. 2017;8(32):53829–38. pmid:28881854; PubMed Central PMCID: PMC5581153.
- 12. Devos D, Lebouvier T, Lardeux B, Biraud M, Rouaud T, Pouclet H, et al. Colonic inflammation in Parkinson's disease. Neurobiol Dis. 2013;50:42–8. pmid:23017648.
- 13. Olanow CW, Wakeman DR, Kordower JH. Peripheral alpha-synuclein and Parkinson's disease. Mov Disord. 2014;29(8):963–6. pmid:25043799.
- 14. Scheperjans F, Aho V, Pereira PA, Koskinen K, Paulin L, Pekkonen E, et al. Gut microbiota are related to Parkinson's disease and clinical phenotype. Mov Disord. 2015;30(3):350–8. pmid:25476529.
- 15. Keshavarzian A, Green SJ, Engen PA, Voigt RM, Naqib A, Forsyth CB, et al. Colonic bacterial composition in Parkinson's disease. Mov Disord. 2015;30(10):1351–60. pmid:26179554.
- 16. Simola N, Morelli M, Carta AR. The 6-hydroxydopamine model of Parkinson's disease. Neurotox Res. 2007;11(3–4):151–67. pmid:17449457.
- 17. Zheng LF, Song J, Fan RF, Chen CL, Ren QZ, Zhang XL, et al. The role of the vagal pathway and gastric dopamine in the gastroparesis of rats after a 6-hydroxydopamine microinjection in the substantia nigra. Acta Physiol (Oxf). 2014;211(2):434–46. pmid:24410908.
- 18. Thiele SL, Warre R, Nash JE. Development of a unilaterally-lesioned 6-OHDA mouse model of Parkinson's disease. J Vis Exp. 2012;(60). pmid:22370630; PubMed Central PMCID: PMC3376941.
- 19. Paxinos G, Franklin K. Paxinos and Franklin's the Mouse Brain in Stereotaxic Coordinates 4th Edition. 4th ed: Academic Press; 2012 25th October 2012.
- 20. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. pmid:24695404; PubMed Central PMCID: PMC4103590.
- 21. Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics. 2012;13:31. pmid:22333067; PubMed Central PMCID: PMC3471323.
- 22. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537–41. pmid:19801464; PubMed Central PMCID: PMC2786419.
- 23. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10. pmid:2231712.
- 24. Myers EW, Miller W. Optimal alignments in linear space. Comput Appl Biosci. 1988;4(1):11–7. pmid:3382986.
- 25. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200. pmid:21700674; PubMed Central PMCID: PMC3150044.
- 26. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2. pmid:23060610; PubMed Central PMCID: PMC3516142.
- 27. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. pmid:20709691.
- 28. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. pmid:21702898; PubMed Central PMCID: PMC3218848.
- 29. Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67(5):1613–7. pmid:28005526; PubMed Central PMCID: PMC5563544.
- 30. Chakravorty S, Helb D, Burday M, Connell N, Alland D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods. 2007;69(2):330–9. pmid:17391789; PubMed Central PMCID: PMC2562909.
- 31. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci. 2012;13(10):701–12. pmid:22968153.
- 32. Diaz Heijtz R, Wang S, Anuar F, Qian Y, Bjorkholm B, Samuelsson A, et al. Normal gut microbiota modulates brain development and behavior. Proc Natl Acad Sci U S A. 2011;108(7):3047–52. pmid:21282636; PubMed Central PMCID: PMC3041077.
- 33. Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson's Disease. Cell. 2016;167(6):1469–80 e12. pmid:27912057.
- 34. Unger MM, Spiegel J, Dillmann KU, Grundmann D, Philippeit H, Burmann J, et al. Short chain fatty acids and gut microbiota differ between patients with Parkinson's disease and age-matched controls. Parkinsonism Relat Disord. 2016;32:66–72. pmid:27591074.
- 35. Pistollato F, Sumalla Cano S, Elio I, Masias Vergara M, Giampieri F, Battino M. Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease. Nutr Rev. 2016;74(10):624–34. pmid:27634977.
- 36. Bonfili L, Cecarini V, Berardi S, Scarpona S, Suchodolski JS, Nasuti C, et al. Microbiota modulation counteracts Alzheimer's disease progression influencing neuronal proteolysis and gut hormones plasma levels. Sci Rep. 2017;7(1):2426. pmid:28546539; PubMed Central PMCID: PMC5445077.
- 37. Delang CO, Li WM. Species Richness and Diversity. Ecological Succession on Fallowed Shifting Cultivation Fields: A Review of the Literature. Dordrecht: Springer Netherlands; 2013. p. 39–66.
- 38. Miyake S, Kim S, Suda W, Oshima K, Nakamura M, Matsuoka T, et al. Dysbiosis in the Gut Microbiota of Patients with Multiple Sclerosis, with a Striking Depletion of Species Belonging to Clostridia XIVa and IV Clusters. PLoS One. 2015;10(9):e0137429. pmid:26367776; PubMed Central PMCID: PMC4569432.
- 39. Ribiere C, Peyret P, Parisot N, Darcha C, Dechelotte PJ, Barnich N, et al. Oral exposure to environmental pollutant benzo[a]pyrene impacts the intestinal epithelium and induces gut microbial shifts in murine model. Sci Rep. 2016;6:31027. pmid:27503127; PubMed Central PMCID: PMC4977522.
- 40. Bailey MT, Dowd SE, Galley JD, Hufnagle AR, Allen RG, Lyte M. Exposure to a social stressor alters the structure of the intestinal microbiota: implications for stressor-induced immunomodulation. Brain Behav Immun. 2011;25(3):397–407. pmid:21040780; PubMed Central PMCID: PMC3039072.
- 41. Galley JD, Nelson MC, Yu Z, Dowd SE, Walter J, Kumar PS, et al. Exposure to a social stressor disrupts the community structure of the colonic mucosa-associated microbiota. BMC Microbiol. 2014;14:189. pmid:25028050; PubMed Central PMCID: PMC4105248.
- 42. Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, et al. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad Sci U S A. 2011;108(38):16050–5. pmid:21876150; PubMed Central PMCID: PMC3179073.
- 43. Kwon HK, Kim GC, Kim Y, Hwang W, Jash A, Sahoo A, et al. Amelioration of experimental autoimmune encephalomyelitis by probiotic mixture is mediated by a shift in T helper cell immune response. Clin Immunol. 2013;146(3):217–27. pmid:23416238.
- 44. Lavasani S, Dzhambazov B, Nouri M, Fak F, Buske S, Molin G, et al. A novel probiotic mixture exerts a therapeutic effect on experimental autoimmune encephalomyelitis mediated by IL-10 producing regulatory T cells. PLoS One. 2010;5(2):e9009. pmid:20126401; PubMed Central PMCID: PMC2814855.
- 45. Ojetti V, Ianiro G, Tortora A, D'Angelo G, Di Rienzo TA, Bibbo S, et al. The effect of Lactobacillus reuteri supplementation in adults with chronic functional constipation: a randomized, double-blind, placebo-controlled trial. J Gastrointestin Liver Dis. 2014;23(4):387–91. pmid:25531996.
- 46. Hasegawa S, Goto S, Tsuji H, Okuno T, Asahara T, Nomoto K, et al. Intestinal Dysbiosis and Lowered Serum Lipopolysaccharide-Binding Protein in Parkinson's Disease. PLoS One. 2015;10(11):e0142164. pmid:26539989; PubMed Central PMCID: PMC4634857.
- 47. Petrov VA, Saltykova IV, Zhukova IA, Alifirova VM, Zhukova NG, Dorofeeva YB, et al. Analysis of Gut Microbiota in Patients with Parkinson's Disease. Bull Exp Biol Med. 2017;162(6):734–7. pmid:28429209.
- 48. Hopfner F, Kunstner A, Muller SH, Kunzel S, Zeuner KE, Margraf NG, et al. Gut microbiota in Parkinson disease in a northern German cohort. Brain Res. 2017;1667:41–5. pmid:28506555.
- 49. Gerhardt S, Mohajeri MH. Changes of Colonic Bacterial Composition in Parkinson's Disease and Other Neurodegenerative Diseases. Nutrients. 2018;10(6). pmid:29857583; PubMed Central PMCID: PMC6024871.
- 50. Malinen E, Rinttila T, Kajander K, Matto J, Kassinen A, Krogius L, et al. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am J Gastroenterol. 2005;100(2):373–82. pmid:15667495.
- 51. Shultz JM, Resnikoff H, Bondarenko V, Joers V, Mejia A, Simmons H, et al. Neurotoxin-Induced Catecholaminergic Loss in the Colonic Myenteric Plexus of Rhesus Monkeys. J Alzheimers Dis Parkinsonism. 2016;6(6). pmid:28090391; PubMed Central PMCID: PMC5225669.
- 52. Gonzalez-Ariki S, Husband AJ. Ontogeny of IgA(+) cells in lamina propria: effects of sympathectomy. Dev Comp Immunol. 2000;24(1):61–9. pmid:10689098.
- 53. Yanagibashi T, Hosono A, Oyama A, Tsuda M, Suzuki A, Hachimura S, et al. IgA production in the large intestine is modulated by a different mechanism than in the small intestine: Bacteroides acidifaciens promotes IgA production in the large intestine by inducing germinal center formation and increasing the number of IgA+ B cells. Immunobiology. 2013;218(4):645–51. pmid:22940255.
- 54. Kato LM, Kawamoto S, Maruya M, Fagarasan S. Gut TFH and IgA: key players for regulation of bacterial communities and immune homeostasis. Immunol Cell Biol. 2014;92(1):49–56. pmid:24100385.
- 55. Donaldson GP, Ladinsky MS, Yu KB, Sanders JG, Yoo BB, Chou WC, et al. Gut microbiota utilize immunoglobulin A for mucosal colonization. Science. 2018;360(6390):795–800. pmid:29724905; PubMed Central PMCID: PMC5973787.
- 56. Alrafas HR, Busbee PB, Nagarkatti M, Nagarkatti PS. Resveratrol modulates the gut microbiota to prevent murine colitis development through induction of Tregs and suppression of Th17 cells. J Leukoc Biol. 2019. pmid:30897248.
- 57. Lo BC, Shin SB, Canals Hernaez D, Refaeli I, Yu HB, Goebeler V, et al. IL-22 Preserves Gut Epithelial Integrity and Promotes Disease Remission during Chronic Salmonella Infection. J Immunol. 2019;202(3):956–65. pmid:30617224.
- 58. Houlden A, Goldrick M, Brough D, Vizi ES, Lenart N, Martinecz B, et al. Brain injury induces specific changes in the caecal microbiota of mice via altered autonomic activity and mucoprotein production. Brain Behav Immun. 2016;57:10–20. pmid:27060191; PubMed Central PMCID: PMC5021180.
- 59. Williams JM, Peterson RG, Shea PA, Schmedtje JF, Bauer DC, Felten DL. Sympathetic innervation of murine thymus and spleen: evidence for a functional link between the nervous and immune systems. Brain Res Bull. 1981;6(1):83–94. pmid:7193506.
- 60. Dinan TG, Cryan JF. Regulation of the stress response by the gut microbiota: implications for psychoneuroendocrinology. Psychoneuroendocrinology. 2012;37(9):1369–78. pmid:22483040.
- 61. Bailey MT, Lubach GR, Coe CL. Prenatal stress alters bacterial colonization of the gut in infant monkeys. J Pediatr Gastroenterol Nutr. 2004;38(4):414–21. pmid:15085020.
- 62. Petra AI, Panagiotidou S, Hatziagelaki E, Stewart JM, Conti P, Theoharides TC. Gut-Microbiota-Brain Axis and Its Effect on Neuropsychiatric Disorders With Suspected Immune Dysregulation. Clin Ther. 2015;37(5):984–95. pmid:26046241; PubMed Central PMCID: PMC4458706.
- 63. Yodlowski ML, Fredieu JR, Landis SC. Neonatal 6-hydroxydopamine treatment eliminates cholinergic sympathetic innervation and induces sensory sprouting in rat sweat glands. J Neurosci. 1984;4(6):1535–48. pmid:6427423.
- 64. Bundzikova-Osacka J, Ghosal S, Packard BA, Ulrich-Lai YM, Herman JP. Role of nucleus of the solitary tract noradrenergic neurons in post-stress cardiovascular and hormonal control in male rats. Stress. 2015;18(2):221–32. pmid:25765732; PubMed Central PMCID: PMC4503520.
- 65. Wang S, Li N, Zou H, Wu M. Gut microbiome-based secondary metabolite biosynthetic gene clusters detection in Parkinson's disease. Neurosci Lett. 2019;696:93–8. pmid:30572101.