Altered gut microbiota in Rett syndrome

Francesco Strati, Duccio Cavalieri, Davide Albanese, Claudio De Felice, Claudio Donati, Joussef Hayek, Olivier Jousson, Silvia Leoncini, Massimo Pindo, Daniela Renzi, Lisa Rizzetto, Irene Stefanini, Antonio Calabrò, Carlotta De Filippo, Francesco Strati, Duccio Cavalieri, Davide Albanese, Claudio De Felice, Claudio Donati, Joussef Hayek, Olivier Jousson, Silvia Leoncini, Massimo Pindo, Daniela Renzi, Lisa Rizzetto, Irene Stefanini, Antonio Calabrò, Carlotta De Filippo

Abstract

Background: The human gut microbiota directly affects human health, and its alteration can lead to gastrointestinal abnormalities and inflammation. Rett syndrome (RTT), a progressive neurological disorder mainly caused by mutations in MeCP2 gene, is commonly associated with gastrointestinal dysfunctions and constipation, suggesting a link between RTT's gastrointestinal abnormalities and the gut microbiota. The aim of this study was to evaluate the bacterial and fungal gut microbiota in a cohort of RTT subjects integrating clinical, metabolomics and metagenomics data to understand if changes in the gut microbiota of RTT subjects could be associated with gastrointestinal abnormalities and inflammatory status.

Results: Our findings revealed the occurrence of an intestinal sub-inflammatory status in RTT subjects as measured by the elevated values of faecal calprotectin and erythrocyte sedimentation rate. We showed that, overall, RTT subjects harbour bacterial and fungal microbiota altered in terms of relative abundances from those of healthy controls, with a reduced microbial richness and dominated by microbial taxa belonging to Bifidobacterium, several Clostridia (among which Anaerostipes, Clostridium XIVa, Clostridium XIVb) as well as Erysipelotrichaceae, Actinomyces, Lactobacillus, Enterococcus, Eggerthella, Escherichia/Shigella and the fungal genus Candida. We further observed that alterations of the gut microbiota do not depend on the constipation status of RTT subjects and that this dysbiotic microbiota produced altered short chain fatty acids profiles.

Conclusions: We demonstrated for the first time that RTT is associated with a dysbiosis of both the bacterial and fungal component of the gut microbiota, suggesting that impairments of MeCP2 functioning favour the establishment of a microbial community adapted to the costive gastrointestinal niche of RTT subjects. The altered production of short chain fatty acids associated with this microbiota might reinforce the constipation status of RTT subjects and contribute to RTT gastrointestinal physiopathology.

Keywords: Constipation; Gut microbiota; Intestinal dysbiosis; Metataxonomics; Mycobiota; Rett syndrome; SCFAs.

Figures

Fig. 1
Fig. 1
Measures of bacterial diversity. aAlpha-diversity calculated on the number of observed OTUs; ***p < 0.0001; **p < 0.001; ns, not significant; Wilcoxon rank-sum test. b, c PCoA plots of bacterial beta-diversity based on b the Weighted UniFrac distance and c the Bray-Curtis dissimilarity analysed according to individuals’ health status. Constipated Rett syndrome subjects (RTT-C), non-constipated Rett syndrome subjects (RTT-NC) and healthy controls (HC) are coloured in red, orange or green, respectively
Fig. 2
Fig. 2
PhyloRelief analysis (RTT vs HC) of bacterial OTUs using the unweighted UniFrac distance. The heat-map shows the relative abundances of the OTUs that are differentially represented in Rett syndrome (RTT) subjects and healthy controls (HC) (PhyloRelief selected clades with FDR-corrected p < 0.01, Kruskal-Wallis test). OTUs are classified according to their genus on the left side of the figure. The OTUs more represented in RTT subjects than HC are highlighted in bold characters. The ultrametric pruned phylogenetic tree of the OTUs is shown on the right side of the figure. RTT subjects and healthy controls are coloured in red and green, respectively. Abundances are expressed in terms of their z-score
Fig. 3
Fig. 3
Cladogram showing the most discriminative bacterial clades identified by LEfSe. Coloured regions/branches indicate differences in the bacterial population structure between Rett syndrome (RTT) subjects and healthy controls (HC). Regions in red indicate clades that were enriched in RTT subjects compared to those in HC, while regions in green indicate clades that were enriched in HC compared to those in RTT subjects
Fig. 4
Fig. 4
Absolute quantification of Bifidobacterium. qPCR analysis of a the genus Bifidobacterium and b the species Bifidobacterium longum ssp. longum in Rett syndrome (RTT) subjects versus healthy controls (HC); **p < 0.01, *p < 0.05, Wilcoxon rank-sum test
Fig. 5
Fig. 5
SCFAs faecal content. Bar-plot representation of the median values of faecal SCFAs in Rett syndrome (RTT) subjects and healthy controls (HC); *p < 0.05, **p < 0.005, ***p < 0.0005, Wilcoxon rank-sum test
Fig. 6
Fig. 6
Measures of fungal beta-diversity. PCoA plots of fungal beta-diversity based on a the Weighted UniFrac distance and b the Bray-Curtis dissimilarity analysed according to individuals’ health status. Constipated Rett syndrome subjects (RTT-C), non-constipated Rett syndrome subjects (RTT-NC) and healthy controls (HC) are coloured in red, orange or green, respectively

References

    1. Chahrour M, Zoghbi HY. The story of Rett syndrome: from clinic to neurobiology. Neuron. 2007;56(3):422–437. doi: 10.1016/j.neuron.2007.10.001.
    1. Liyanage VR, Rastegar M. Rett syndrome and MeCP2. Neruomol Med. 2014;16(2):231–264. doi: 10.1007/s12017-014-8295-9.
    1. Cuddapah VA, Pillai RB, Shekar KV, Lane JB, Motil KJ, Skinner SA, Tarquinio DC, Glaze DG, McGwin G, Kaufmann WE, et al. Methyl-CpG-binding protein 2 (MECP2) mutation type is associated with disease severity in Rett syndrome. J Med Genet. 2014;51(3):152–158. doi: 10.1136/jmedgenet-2013-102113.
    1. Neul JL, Fang P, Barrish J, Lane J, Caeg EB, Smith EO, Zoghbi H, Percy A, Glaze DG. Specific mutations in methyl-CpG-binding protein 2 confer different severity in Rett syndrome. Neurology. 2008;70(16):1313–1321. doi: 10.1212/01.wnl.0000291011.54508.aa.
    1. Gonzales ML, LaSalle JM. The role of MeCP2 in brain development and neurodevelopmental disorders. Curr Psychiatry Rep. 2010;12(2):127–134. doi: 10.1007/s11920-010-0097-7.
    1. Motil KJ, Caeg E, Barrish JO, Geerts S, Lane JB, Percy AK, Annese F, McNair L, Skinner SA, Lee HS, et al. Gastrointestinal and nutritional problems occur frequently throughout life in girls and women with Rett syndrome. J Pediatr Gastroenterol Nutr. 2012;55(3):292–298. doi: 10.1097/MPG.0b013e31824b6159.
    1. Leonard H, Ravikumara M, Baikie G, Naseem N, Ellaway C, Percy A, Abraham S, Geerts S, Lane J, Jones M, et al. Assessment and management of nutrition and growth in Rett syndrome. J Pediatr Gastroenterol Nutr. 2013;57(4):451–460. doi: 10.1097/MPG.0b013e31829e0b65.
    1. Wahba G, Schock SC, Claridge E, Bettolli M, Grynspan D, Humphreys P, Staines WA. MeCP2 in the enteric nervous system. Neurogastroenterol Motil. 2015;27(8):1156–1161. doi: 10.1111/nmo.12605.
    1. Maslowski KM, Mackay CR. Diet, gut microbiota and immune responses. Nat Immunol. 2011;12(1):5–9. doi: 10.1038/ni0111-5.
    1. Kamada N, Seo SU, Chen GY, Nunez G. Role of the gut microbiota in immunity and inflammatory disease. Nat Rev Immunol. 2013;13(5):321–335. doi: 10.1038/nri3430.
    1. Underhill DM, Iliev ID. The mycobiota: interactions between commensal fungi and the host immune system. Nat Rev Immunol. 2014;14(6):405–416. doi: 10.1038/nri3684.
    1. Iliev ID, Funari VA, Taylor KD, Nguyen Q, Reyes CN, Strom SP, Brown J, Becker CA, Fleshner PR, Dubinsky M, et al. Interactions between commensal fungi and the C-type lectin receptor Dectin-1 influence colitis. Science. 2012;336(6086):1314–1317. doi: 10.1126/science.1221789.
    1. Clemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on human health: an integrative view. Cell. 2012;148(6):1258–1270. doi: 10.1016/j.cell.2012.01.035.
    1. Mayer EA, Padua D, Tillisch K. Altered brain-gut axis in autism: comorbidity or causative mechanisms? Bioessays. 2014;36(10):933–939. doi: 10.1002/bies.201400075.
    1. Sampson TR, Mazmanian SK. Control of brain development, function, and behavior by the microbiome. Cell Host Microbe. 2015;17(5):565–576. doi: 10.1016/j.chom.2015.04.011.
    1. Wang Y, Kasper LH. The role of microbiome in central nervous system disorders. Brain Behav Immun. 2014;38:1–12. doi: 10.1016/j.bbi.2013.12.015.
    1. Leoncini S, De Felice C, Signorini C, Zollo G, Cortelazzo A, Durand T, Galano JM, Guerranti R, Rossi M, Ciccoli L, et al. Cytokine dysregulation in MECP2- and CDKL5-related Rett syndrome: relationships with aberrant redox homeostasis, inflammation, and omega-3 PUFAs. Oxidative Med Cell Longev. 2015;2015:421624. doi: 10.1155/2015/421624.
    1. Cortelazzo A, De Felice C, Guerranti R, Signorini C, Leoncini S, Pecorelli A, Zollo G, Landi C, Valacchi G, Ciccoli L, et al. Subclinical inflammatory status in Rett syndrome. Mediat Inflamm. 2014;2014:480980. doi: 10.1155/2014/480980.
    1. Bochen K, Krasowska A, Milaniuk S, Kulczynska M, Prystupa A, Dzida G. Erythrocyte sedimentation rate–an old marker with new applications. Journal of Pre-clinical and Clinical Research. 2011;5(2):50–55.
    1. Joshi S, Lewis SJ, Creanor S, Ayling RM. Age-related faecal calprotectin, lactoferrin and tumour M2-PK concentrations in healthy volunteers. Ann Clin Biochem. 2010;47(Pt 3):259–263. doi: 10.1258/acb.2009.009061.
    1. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5(2):169–172. doi: 10.1038/ismej.2010.133.
    1. Albanese D, De Filippo C, Cavalieri D, Donati C. Explaining diversity in metagenomic datasets by phylogenetic-based feature weighting. PLoS Comput Biol. 2015;11(3):e1004186. doi: 10.1371/journal.pcbi.1004186.
    1. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. doi: 10.1186/gb-2011-12-6-r60.
    1. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–821. doi: 10.1038/nbt.2676.
    1. Mitchell RW, On NH, Del Bigio MR, Miller DW, Hatch GM. Fatty acid transport protein expression in human brain and potential role in fatty acid transport across human brain microvessel endothelial cells. J Neurochem. 2011;117(4):735–746.
    1. Frost G, Sleeth ML, Sahuri-Arisoylu M, Lizarbe B, Cerdan S, Brody L, Anastasovska J, Ghourab S, Hankir M, Zhang S, et al. The short-chain fatty acid acetate reduces appetite via a central homeostatic mechanism. Nat Commun. 2014;5:3611. doi: 10.1038/ncomms4611.
    1. Rizzetto L, De Filippo C, Cavalieri D. Richness and diversity of mammalian fungal communities shape innate and adaptive immunity in health and disease. Eur J Immunol. 2014;44(11):3166–3181. doi: 10.1002/eji.201344403.
    1. Scanlan PD, Marchesi JR. Micro-eukaryotic diversity of the human distal gut microbiota: qualitative assessment using culture-dependent and -independent analysis of faeces. ISME J. 2008;2(12):1183–1193. doi: 10.1038/ismej.2008.76.
    1. Andersen LO, Stensvold CR. Blastocystis in Health and Disease: Are We Moving from a Clinical to a Public Health Perspective? J Clin Microbiol. 2016;54(3):524–528. doi: 10.1128/JCM.02520-15.
    1. Tomova A, Husarova V, Lakatosova S, Bakos J, Vlkova B, Babinska K, Ostatnikova D. Gastrointestinal microbiota in children with autism in Slovakia. Physiol Behav. 2015;138:179–187. doi: 10.1016/j.physbeh.2014.10.033.
    1. Hsiao EY, McBride SW, Hsien S, Sharon G, Hyde ER, McCue T, Codelli JA, Chow J, Reisman SE, Petrosino JF, et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 2013;155(7):1451–1463. doi: 10.1016/j.cell.2013.11.024.
    1. Ventura M, Turroni F, Motherway MO, MacSharry J, van Sinderen D. Host-microbe interactions that facilitate gut colonization by commensal bifidobacteria. Trends Microbiol. 2012;20(10):467–476. doi: 10.1016/j.tim.2012.07.002.
    1. Fanning S, Hall LJ, Cronin M, Zomer A, MacSharry J, Goulding D, Motherway MO, Shanahan F, Nally K, Dougan G, et al. Bifidobacterial surface-exopolysaccharide facilitates commensal-host interaction through immune modulation and pathogen protection. Proc Natl Acad Sci U S A. 2012;109(6):2108–2113. doi: 10.1073/pnas.1115621109.
    1. Pathak P, Trilligan C, Rapose A. Bifidobacterium--friend or foe? A case of urinary tract infection with Bifidobacterium species. BMJ case reports. 2014
    1. Tena D, Losa C, Medina MJ, Saez-Nieto JA. Peritonitis caused by Bifidobacterium longum: case report and literature review. Anaerobe. 2014;27:27–30. doi: 10.1016/j.anaerobe.2014.03.005.
    1. Song Y, Liu C, Finegold SM. Real-time PCR quantitation of clostridia in feces of autistic children. Appl Environ Microbiol. 2004;70(11):6459–6465. doi: 10.1128/AEM.70.11.6459-6465.2004.
    1. Parracho HM, Bingham MO, Gibson GR, McCartney AL. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol. 2005;54(Pt 10):987–991. doi: 10.1099/jmm.0.46101-0.
    1. de Theije CG, Wopereis H, Ramadan M, van Eijndthoven T, Lambert J, Knol J, Garssen J, Kraneveld AD, Oozeer R. Altered gut microbiota and activity in a murine model of autism spectrum disorders. Brain Behav Immun. 2014;37:197–206. doi: 10.1016/j.bbi.2013.12.005.
    1. Kang DW, Park JG, Ilhan ZE, Wallstrom G, Labaer J, Adams JB, Krajmalnik-Brown R. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS One. 2013;8(7):e68322. doi: 10.1371/journal.pone.0068322.
    1. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, et al. Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174–180. doi: 10.1038/nature09944.
    1. Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, Reyes JA, Shah SA, LeLeiko N, Snapper SB, et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13(9):R79. doi: 10.1186/gb-2012-13-9-r79.
    1. Arthur JC, Gharaibeh RZ, Mühlbauer M, Perez-Chanona E, Uronis JM, McCafferty J, Fodor AA, Jobin C. Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer. Nat Commun. 2014;5:4724. doi: 10.1038/ncomms5724.
    1. Oever JT, Netea MG. The bacteriome-mycobiome interaction and antifungal host defense. Eur J Immunol. 2014;44(11):3182–3191. doi: 10.1002/eji.201344405.
    1. Erb Downward JR, Falkowski NR, Mason KL, Muraglia R, Huffnagle GB. Modulation of post-antibiotic bacterial community reassembly and host response by Candida albicans. Sci Rep. 2013;3:2191. doi: 10.1038/srep02191.
    1. Cummings J, Macfarlane G. The control and consequences of bacterial fermentation in the human colon. J Appl Bacteriol. 1991;70(6):443–459. doi: 10.1111/j.1365-2672.1991.tb02739.x.
    1. Duncan SH, Louis P, Flint HJ. Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl Environ Microbiol. 2004;70(10):5810–5817. doi: 10.1128/AEM.70.10.5810-5817.2004.
    1. Barcelo A, Claustre J, Moro F, Chayvialle JA, Cuber JC, Plaisancie P. Mucin secretion is modulated by luminal factors in the isolated vascularly perfused rat colon. Gut. 2000;46(2):218–224. doi: 10.1136/gut.46.2.218.
    1. Squires PE, Rumsey RD, Edwards CA, Read NW. Effect of short-chain fatty acids on contractile activity and fluid flow in rat colon in vitro. Am J Phys. 1992;262(5 Pt 1):G813–817.
    1. Cherbut C, Ferrier L, Roze C, Anini Y, Blottiere H, Lecannu G, Galmiche JP. Short-chain fatty acids modify colonic motility through nerves and polypeptide YY release in the rat. Am J Phys. 1998;275(6 Pt 1):G1415–1422.
    1. Blachier F, Mariotti F, Huneau JF, Tome D. Effects of amino acid-derived luminal metabolites on the colonic epithelium and physiopathological consequences. Amino Acids. 2007;33(4):547–562. doi: 10.1007/s00726-006-0477-9.
    1. Nankova BB, Agarwal R, MacFabe DF, La Gamma EF. Enteric bacterial metabolites propionic and butyric acid modulate gene expression, including CREB-dependent catecholaminergic neurotransmission, in PC12 cells—possible relevance to autism spectrum disorders. PLoS One. 2014;9(8):e103740. doi: 10.1371/journal.pone.0103740.
    1. MacFabe DF. Enteric short-chain fatty acids: microbial messengers of metabolism, mitochondria, and mind: implications in autism spectrum disorders. Microb Ecol Health Dis. 2015;26:28177.
    1. Manokas T, Fromkes JJ, Sundaram U. Effect of chronic inflammation on ileal short-chain fatty acid/bicarbonate exchange. Am J Physiol Gastrointest Liver Physiol. 2000;278(4):G585–590.
    1. Aomatsu T, Yoden A, Matsumoto K, Kimura E, Inoue K, Andoh A, Tamai H. Fecal calprotectin is a useful marker for disease activity in pediatric patients with inflammatory bowel disease. Dig Dis Sci. 2011;56(8):2372–2377. doi: 10.1007/s10620-011-1633-y.
    1. Longstreth GF, Thompson WG, Chey WD, Houghton LA, Mearin F, Spiller RC. Functional bowel disorders. Gastroenterology. 2006;130(5):1480–1491. doi: 10.1053/j.gastro.2005.11.061.
    1. Findley K, Oh J, Yang J, Conlan S, Deming C, Meyer JA, Schoenfeld D, Nomicos E, Park M, Program NIHISCCS, et al. Topographic diversity of fungal and bacterial communities in human skin. Nature. 2013;498(7454):367–370. doi: 10.1038/nature12171.
    1. Rinttila T, Kassinen A, Malinen E, Krogius L, Palva A. Development of an extensive set of 16S rDNA-targeted primers for quantification of pathogenic and indigenous bacteria in faecal samples by real-time PCR. J Appl Microbiol. 2004;97(6):1166–1177. doi: 10.1111/j.1365-2672.2004.02409.x.
    1. Matsuki T, Watanabe K, Fujimoto J, Kado Y, Takada T, Matsumoto K, Tanaka R. Quantitative PCR with 16S rRNA-gene-targeted species-specific primers for analysis of human intestinal bifidobacteria. Appl Environ Microbiol. 2004;70(1):167–173. doi: 10.1128/AEM.70.1.167-173.2004.
    1. Albanese D, Fontana P, De Filippo C, Cavalieri D, Donati C. MICCA: a complete and accurate software for taxonomic profiling of metagenomic data. Sci Rep. 2015;5:9743. doi: 10.1038/srep09743.
    1. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–5267. doi: 10.1128/AEM.00062-07.
    1. Koljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AF, Bahram M, Bates ST, Bruns TD, Bengtsson-Palme J, Callaghan TM, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22(21):5271–5277. doi: 10.1111/mec.12481.
    1. Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26(2):266–267. doi: 10.1093/bioinformatics/btp636.
    1. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–5072. doi: 10.1128/AEM.03006-05.
    1. Notredame C, Higgins DG, Heringa J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol. 2000;302(1):205–217. doi: 10.1006/jmbi.2000.4042.
    1. Hibbett DS. A phylogenetic overview of the Agaricomycotina. Mycologia. 2006;98(6):917–925. doi: 10.3852/mycologia.98.6.917.
    1. Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. doi: 10.1371/journal.pone.0009490.
    1. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. doi: 10.1371/journal.pone.0061217.
    1. Bonferroni CE. Teoria statistica delle classi e calcolo delle probabilità. 8th ed Florence R. Istituto superiore di scienze economiche e commerciali, Libreria internazionale Seeber; 1936.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B (Methodological) 1995;57(1):289–300.
    1. Revelle W. psych: procedures for psychological, psychometric, and personality research. R package version 1.3. 10. Evanston, IL: Northwestern University; 2013.
    1. Team RC. R: A language and environment for statistical computing. 2014. Vienna, Austria: R Foundation for Statistical Computing; 2012.

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