APOE genotype influences the gut microbiome structure and function in humans and mice: relevance for Alzheimer's disease pathophysiology

Tam T T Tran, Simone Corsini, Lee Kellingray, Claire Hegarty, Gwénaëlle Le Gall, Arjan Narbad, Michael Müller, Noemi Tejera, Paul W O'Toole, Anne-Marie Minihane, David Vauzour, Tam T T Tran, Simone Corsini, Lee Kellingray, Claire Hegarty, Gwénaëlle Le Gall, Arjan Narbad, Michael Müller, Noemi Tejera, Paul W O'Toole, Anne-Marie Minihane, David Vauzour

Abstract

Apolipoprotein E (APOE) genotype is the strongest prevalent genetic risk factor for Alzheimer's disease (AD). Numerous studies have provided insights into the pathologic mechanisms. However, a comprehensive understanding of the impact of APOE genotype on microflora speciation and metabolism is completely lacking. In this study, we investigated the association between APOE genotype and the gut microbiome composition in human and APOE-targeted replacement (TR) transgenic mice. Fecal microbiota amplicon sequencing from matched individuals with different APOE genotypes revealed no significant differences in overall microbiota diversity in group-aggregated human APOE genotypes. However, several bacterial taxa showed significantly different relative abundance between APOE genotypes. Notably, we detected an association of Prevotellaceae and Ruminococcaceae and several butyrate-producing genera abundances with APOE genotypes. These findings were confirmed by comparing the gut microbiota of APOE-TR mice. Furthermore, metabolomic analysis of murine fecal water detected significant differences in microbe-associated amino acids and short-chain fatty acids between APOE genotypes. Together, these findings indicate that APOE genotype is associated with specific gut microbiome profiles in both humans and APOE-TR mice. This suggests that the gut microbiome is worth further investigation as a potential target to mitigate the deleterious impact of the APOE4 allele on cognitive decline and the prevention of AD.-Tran, T. T. T., Corsini, S., Kellingray, L., Hegarty, C., Le Gall, G., Narbad, A., Müller, M., Tejera, N., O'Toole, P. W., Minihane, A.-M., Vauzour, D. APOE genotype influences the gut microbiome structure and function in humans and mice: relevance for Alzheimer's disease pathophysiology.

Keywords: SCFAs; apolipoprotein E; butyrate; gut microbiota; metabolomics.

Conflict of interest statement

The authors thank the study participants for contributing samples and time to the studies used to generate the data. The authors also acknowledge the many CANN and COB trial colleagues who facilitated this study. This project was supported, in part by a Biotechnology and Biological Sciences Research Council (BBSRC) grant to the laboratory of A.-M.M. and D.V. (BB/M004449/1). Work in P.W.O.’s laboratory was supported, in part, by a Grant from the Science Foundation Ireland (APC/SFI/12/RC/2273) in the form of the Alimentary Pharmabiotic Centre (APC) Microbiome Ireland, the Food Institutional Research Measure (FIRM) Program, and the Immunomet Project of the Government of Ireland’s Department of Agriculture, Food, and the Marine. A.-M.M. and D.V. share senior authorship. The datasets used and/or analyzed during the current study are available at the NCBI Sequence Read Archive (SRA), under BioProject PRJNA533610. The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Box plot of the relative abundance distribution of selected taxa at phylum level (A), order level (B), family level (C), and Clostridium cluster or genus level (D). Significant difference was observed in selected bacterial taxon abundance between human APOE genotypes. Significance values were calculated by the Kruskal-Wallis H test for all genotypes, followed by Dunn’s multiple comparisons and adjusted for false discovery rate using the Benjamini-Hochberg correction. *P < 0.05.
Figure 2
Figure 2
Differences in gut microbiome composition between APOE3-TR and APOE4-TR mice. A) Principal coordinates analysis (PCoA) based on unweighted and weighted UniFrac distances of partial sequences of bacterial 16S rRNA genes showing gut microbiota β diversity grouped by age and APOE genotypes. Samples are projected onto the first (PC1) and second (PC2) principal coordinate axes. β diversity analysis reveals significant gut microbiota differences between APOE3 and APOE4 genotype transgenic mice. Significant differences between groups were calculated by PERMANOVA tests. B) Comparison of relative abundance taxa between APOE3 and APOE4 in young mice samples, old mice samples, and both age groups combined were represented by log2 fold changes. Significant differences in relative abundance of gut microbiota at the family and genus levels between APOE3-TR and APOE4-TR mice were detected in young mice, old mice, and in combined analysis of young and old mice. Statistical significances were determined by the Mann-Whitney U test and were corrected for the multiple comparison using the Benjamini-Hochberg adjustment. E3Y, APOE3 young mice; E4Y, APOE4 young mice; E3O, APOE3 old mice; E4O, APOE4 old mice. *P < 0.05.
Figure 3
Figure 3
Fecal metabolome analysis of APOE3-TR and APOE4-TR mice. A) Heatmap and cluster analysis of 2-way ANOVA of significantly differentially abundant metabolites grouped by age and APOE genotype. Four clusters within significantly differentially abundant metabolites showed distinct APOE genotype and age correlations. Clustering was obtained following similarity analysis using the Ward hierarchical algorithm and Euclidean distance metrics. B) COIA of the association between metabolites and microbiota composition in the gut. The left panel shows the COIA of the microbiota principal component analysis (solid circle) at OTU level and the principal component analysis of metabolomics (empty circle); length of arrow indicates the divergence between 2 data sets. The right panel shows coinertia of metabolome and microbiota data, represented by arrow length between the 2 data points per sample, grouped according to APOE genotype or age. A high overall similarity was found in the structure between the 2 data sets, which was statistically significant, and a higher concordance was found between microbiota composition and metabolites of APOE4 mice compared with APOE3 mice. Length of arrow was estimated using Euclidean distance measurement. E3Y, APOE3 young mice; E4Y, APOE4 young mice; E3O, APOE3 old mice; E4O, APOE4 old mice. *P < 0.05.

References

    1. Tremlett H., Bauer K. C., Appel-Cresswell S., Finlay B. B., Waubant E. (2017) The gut microbiome in human neurological disease: a review. Ann. Neurol. 81, 369–382
    1. Minter M. R., Zhang C., Leone V., Ringus D. L., Zhang X., Oyler-Castrillo P., Musch M. W., Liao F., Ward J. F., Holtzman D. M., Chang E. B., Tanzi R. E., Sisodia S. S. (2016) Antibiotic-induced perturbations in gut microbial diversity influences neuro-inflammation and amyloidosis in a murine model of Alzheimer’s disease. Sci. Rep. 6, 30028
    1. Vogt N. M., Kerby R. L., Dill-McFarland K. A., Harding S. J., Merluzzi A. P., Johnson S. C., Carlsson C. M., Asthana S., Zetterberg H., Blennow K., Bendlin B. B., Rey F. E. (2017) Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 7, 13537
    1. Spor A., Koren O., Ley R. (2011) Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290
    1. Turpin W., Espin-Garcia O., Xu W., Silverberg M. S., Kevans D., Smith M. I., Guttman D. S., Griffiths A., Panaccione R., Otley A., Xu L., Shestopaloff K., Moreno-Hagelsieb G., Paterson A. D., Croitoru K.; GEM Project Research Consortium (2016) Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417
    1. Bonder M. J., Kurilshikov A., Tigchelaar E. F., Mujagic Z., Imhann F., Vila A. V., Deelen P., Vatanen T., Schirmer M., Smeekens S. P., Zhernakova D. V., Jankipersadsing S. A., Jaeger M., Oosting M., Cenit M. C., Masclee A. A., Swertz M. A., Li Y., Kumar V., Joosten L., Harmsen H., Weersma R. K., Franke L., Hofker M. H., Xavier R. J., Jonkers D., Netea M. G., Wijmenga C., Fu J., Zhernakova A. (2016) The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412
    1. Beaumont M., Goodrich J. K., Jackson M. A., Yet I., Davenport E. R., Vieira-Silva S., Debelius J., Pallister T., Mangino M., Raes J., Knight R., Clark A. G., Ley R. E., Spector T. D., Bell J. T. (2016) Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 17, 189
    1. Rothschild D., Weissbrod O., Barkan E., Kurilshikov A., Korem T., Zeevi D., Costea P. I., Godneva A., Kalka I. N., Bar N., Shilo S., Lador D., Vila A. V., Zmora N., Pevsner-Fischer M., Israeli D., Kosower N., Malka G., Wolf B. C., Avnit-Sagi T., Lotan-Pompan M., Weinberger A., Halpern Z., Carmi S., Fu J., Wijmenga C., Zhernakova A., Elinav E., Segal E. (2018) Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215
    1. Pontifex M., Vauzour D., Minihane A. M. (2018) The effect of APOE genotype on Alzheimer’s disease risk is influenced by sex and docosahexaenoic acid status. Neurobiol. Aging 69, 209–220
    1. Liu C. C., Liu C. C., Kanekiyo T., Xu H., Bu G. (2013) Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat. Rev. Neurol. 9, 106–118; erratum: 184
    1. Neu S. C., Pa J., Kukull W., Beekly D., Kuzma A., Gangadharan P., Wang L. S., Romero K., Arneric S. P., Redolfi A., Orlandi D., Frisoni G. B., Au R., Devine S., Auerbach S., Espinosa A., Boada M., Ruiz A., Johnson S. C., Koscik R., Wang J. J., Hsu W. C., Chen Y. L., Toga A. W. (2017) Apolipoprotein E genotype and sex risk factors for Alzheimer disease: a meta-analysis. JAMA Neurol. 74, 1178–1189
    1. Shore V. G., Shore B. (1973) Heterogeneity of human plasma very low density lipoproteins. Separation of species differing in protein components. Biochemistry 12, 502–507
    1. Vauzour D., Minihane A. M. (2012) Neuroinflammation and the APOE genotype: implications for Alzheimer’s disease and modulation by dietary flavonoids and n-3 polyunsaturated fatty acids. Nutr. Aging (Amst.) 1, 41–53
    1. Huang Y., Weisgraber K. H., Mucke L., Mahley R. W. (2004) Apolipoprotein E: diversity of cellular origins, structural and biophysical properties, and effects in Alzheimer’s disease. J. Mol. Neurosci. 23, 189–204
    1. Raffai R. L., Dong L. M., Farese R. V., Jr., Weisgraber K. H. (2001) Introduction of human apolipoprotein E4 “domain interaction” into mouse apolipoprotein E. Proc. Natl. Acad. Sci. USA 98, 11587–11591
    1. Reyland M. E., Williams D. L. (1991) Suppression of cAMP-mediated signal transduction in mouse adrenocortical cells which express apolipoprotein E. J. Biol. Chem. 266, 21099–21104
    1. Ishigami M., Swertfeger D. K., Granholm N. A., Hui D. Y. (1998) Apolipoprotein E inhibits platelet-derived growth factor-induced vascular smooth muscle cell migration and proliferation by suppressing signal transduction and preventing cell entry to G1 phase. J. Biol. Chem. 273, 20156–20161
    1. Pepe M. G., Curtiss L. K. (1986) Apolipoprotein E is a biologically active constituent of the normal immunoregulatory lipoprotein, LDL-In. J. Immunol. 136, 3716–3723
    1. Hui D. Y., Harmony J. A., Innerarity T. L., Mahley R. W. (1980) Immunoregulatory plasma lipoproteins. Role of apoprotein E and apoprotein B. J. Biol. Chem. 255, 11775–11781
    1. Jofre-Monseny L., Minihane A. M., Rimbach G. (2008) Impact of apoE genotype on oxidative stress, inflammation and disease risk. Mol. Nutr. Food Res. 52, 131–145
    1. Rune I., Rolin B., Larsen C., Nielsen D. S., Kanter J. E., Bornfeldt K. E., Lykkesfeldt J., Buschard K., Kirk R. K., Christoffersen B., Fels J. J., Josefsen K., Kihl P., Hansen A. K. (2016) Modulating the Gut microbiota improves glucose tolerance, lipoprotein profile and atherosclerotic plaque development in ApoE-deficient mice. PLoS One 11, e0146439
    1. Friedland R. P. (2015) Mechanisms of molecular mimicry involving the microbiota in neurodegeneration. J. Alzheimers Dis. 45, 349–362
    1. Calabuig-Navarro M. V., Jackson K. G., Walden C. M., Minihane A. M., Lovegrove J. A. (2014) Apolipoprotein E genotype has a modest impact on the postprandial plasma response to meals of varying fat composition in healthy men in a randomized controlled trial. J. Nutr. 144, 1775–1780
    1. Yu Z., Morrison M. (2004) Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36, 808–812
    1. Klindworth A., Pruesse E., Schweer T., Peplies J., Quast C., Horn M., Glöckner F. O. (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1
    1. Zhu Y., Nwabuisi-Heath E., Dumanis S. B., Tai L. M., Yu C., Rebeck G. W., LaDu M. J. (2012) APOE genotype alters glial activation and loss of synaptic markers in mice. Glia 60, 559–569
    1. Maukonen J., Mättö J., Satokari R., Söderlund H., Mattila-Sandholm T., Saarela M. (2006) PCR DGGE and RT-PCR DGGE show diversity and short-term temporal stability in the Clostridium coccoides-Eubacterium rectale group in the human intestinal microbiota. FEMS Microbiol. Ecol. 58, 517–528
    1. Caporaso J. G., Lauber C. L., Walters W. A., Berg-Lyons D., Lozupone C. A., Turnbaugh P. J., Fierer N., Knight R. (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108 (Suppl 1), 4516–4522
    1. Kostidis S., Kokova D., Dementeva N., Saltykova I. V., Kim H. K., Choi Y. H., Mayboroda O. A. (2017) 1H-NMR analysis of feces: new possibilities in the helminthes infections research. BMC Infect. Dis. 17, 275
    1. Lamichhane S., Yde C. C., Schmedes M. S., Jensen H. M., Meier S., Bertram H. C. (2015) Strategy for nuclear-magnetic-resonance-based metabolomics of human feces. Anal. Chem. 87, 5930–5937
    1. Saric J., Wang Y., Li J., Coen M., Utzinger J., Marchesi J. R., Keiser J., Veselkov K., Lindon J. C., Nicholson J. K., Holmes E. (2008) Species variation in the fecal metabolome gives insight into differential gastrointestinal function. J. Proteome Res. 7, 352–360
    1. Wu J., An Y., Yao J., Wang Y., Tang H. (2010) An optimised sample preparation method for NMR-based faecal metabonomic analysis. Analyst (Lond.) 135, 1023–1030
    1. Kellingray L., Gall G. L., Defernez M., Beales I. L. P., Franslem-Elumogo N., Narbad A. (2018) Microbial taxonomic and metabolic alterations during faecal microbiota transplantation to treat Clostridium difficile infection. J. Infect. 77, 107–118
    1. Le Gall G., Guttula K., Kellingray L., Tett A. J., Ten Hoopen R., Kemsley K. E., Savva G. M., Ibrahim A., Narbad A. (2018) Metabolite quantification of faecal extracts from colorectal cancer patients and healthy controls. Oncotarget 9, 33278–33289
    1. Shi Y., Kellingray L., Zhai Q., Gall G. L., Narbad A., Zhao J., Zhang H., Chen W. (2018) Structural and functional alterations in the microbial community and immunological consequences in a mouse model of antibiotic-induced dysbiosis. Front. Microbiol. 9, 1948
    1. Le Gall G., Noor S. O., Ridgway K., Scovell L., Jamieson C., Johnson I. T., Colquhoun I. J., Kemsley E. K., Narbad A. (2011) Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218
    1. Le Gall G. (2015) NMR spectroscopy of biofluids and extracts. Methods Mol. Biol. 1277, 29–36
    1. Caporaso J. G., Kuczynski J., Stombaugh J., Bittinger K., Bushman F. D., Costello E. K., Fierer N., Peña A. G., Goodrich J. K., Gordon J. I., Huttley G. A., Kelley S. T., Knights D., Koenig J. E., Ley R. E., Lozupone C. A., McDonald D., Muegge B. D., Pirrung M., Reeder J., Sevinsky J. R., Turnbaugh P. J., Walters W. A., Widmann J., Yatsunenko T., Zaneveld J., Knight R. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336
    1. Edgar R. C. (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461
    1. Magoč T., Salzberg S. L. (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963
    1. Martin M. (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12
    1. Haas B. J., Gevers D., Earl A. M., Feldgarden M., Ward D. V., Giannoukos G., Ciulla D., Tabbaa D., Highlander S. K., Sodergren E., Methé B., DeSantis T. Z., Petrosino J. F., Knight R., Birren B. W.; Human Microbiome Consortium (2011) Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504
    1. Schloss P. D., Westcott S. L., Ryabin T., Hall J. R., Hartmann M., Hollister E. B., Lesniewski R. A., Oakley B. B., Parks D. H., Robinson C. J., Sahl J. W., Stres B., Thallinger G. G., Van Horn D. J., Weber C. F. (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541
    1. Cole J. R., Wang Q., Cardenas E., Fish J., Chai B., Farris R. J., Kulam-Syed-Mohideen A. S., McGarrell D. M., Marsh T., Garrity G. M., Tiedje J. M. (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145
    1. R Core Team (2016) R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria
    1. Dray S., Dufour A. (2007) The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20
    1. Moonseong H., Ruben Gabriel K. (1998) A permutation test of association between configurations by means of the rv coefficient. Commun. Stat. Simul. Comput. 27, 843–856
    1. Le Chatelier E., Nielsen T., Qin J., Prifti E., Hildebrand F., Falony G., Almeida M., Arumugam M., Batto J. M., Kennedy S., Leonard P., Li J., Burgdorf K., Grarup N., Jørgensen T., Brandslund I., Nielsen H. B., Juncker A. S., Bertalan M., Levenez F., Pons N., Rasmussen S., Sunagawa S., Tap J., Tims S., Zoetendal E. G., Brunak S., Clément K., Doré J., Kleerebezem M., Kristiansen K., Renault P., Sicheritz-Ponten T., de Vos W. M., Zucker J. D., Raes J., Hansen T., Bork P., Wang J., Ehrlich S. D., Pedersen O.; MetaHIT consortium (2013) Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546
    1. Dutta S., Sengupta P. (2016) Men and mice: relating their ages. Life Sci. 152, 244–248
    1. O’Toole P. W., Jeffery I. B. (2015) Gut microbiota and aging. Science 350, 1214–1215
    1. Flemer B., Gaci N., Borrel G., Sanderson I. R., Chaudhary P. P., Tottey W., O’Toole P. W., Brugère J. F. (2017) Fecal microbiota variation across the lifespan of the healthy laboratory rat. Gut Microbes 8, 428–439
    1. Hoffman J. D., Parikh I., Green S. J., Chlipala G., Mohney R. P., Keaton M., Bauer B., Hartz A. M. S., Lin A. L. (2017) Age drives distortion of brain metabolic, vascular and cognitive functions, and the Gut microbiome. Front. Aging Neurosci. 9, 298
    1. Bhattacharjee S., Lukiw W. J. (2013) Alzheimer’s disease and the microbiome. Front. Cell. Neurosci. 7, 153
    1. Ghaisas S., Maher J., Kanthasamy A. (2016) Gut microbiome in health and disease: linking the microbiome-gut-brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. Pharmacol. Ther. 158, 52–62
    1. Sampson T. R., Debelius J. W., Thron T., Janssen S., Shastri G. G., Ilhan Z. E., Challis C., Schretter C. E., Rocha S., Gradinaru V., Chesselet M. F., Keshavarzian A., Shannon K. M., Krajmalnik-Brown R., Wittung-Stafshede P., Knight R., Mazmanian S. K. (2016) Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson’s disease. Cell 167, 1469–1480.e12
    1. Vogt N. M., Romano K. A., Darst B. F., Engelman C. D., Johnson S. C., Carlsson C. M., Asthana S., Blennow K., Zetterberg H., Bendlin B. B., Rey F. E. (2018) The gut microbiota-derived metabolite trimethylamine N-oxide is elevated in Alzheimer’s disease. Alzheimers Res. Ther. 10, 124
    1. Zhang L., Wang Y., Xiayu X., Shi C., Chen W., Song N., Fu X., Zhou R., Xu Y. F., Huang L., Zhu H., Han Y., Qin C. (2017) Altered gut microbiota in a mouse model of Alzheimer’s disease. J. Alzheimers Dis. 60, 1241–1257
    1. Unger M. M., Spiegel J., Dillmann K. U., Grundmann D., Philippeit H., Bürmann J., Faßbender K., Schwiertz A., Schäfer K. H. (2016) Short chain fatty acids and gut microbiota differ between patients with Parkinson’s disease and age-matched controls. Parkinsonism Relat. Disord. 32, 66–72
    1. Scheperjans F., Aho V., Pereira P. A., Koskinen K., Paulin L., Pekkonen E., Haapaniemi E., Kaakkola S., Eerola-Rautio J., Pohja M., Kinnunen E., Murros K., Auvinen P. (2015) Gut microbiota are related to Parkinson’s disease and clinical phenotype. Mov. Disord. 30, 350–358
    1. Forsyth C. B., Shannon K. M., Kordower J. H., Voigt R. M., Shaikh M., Jaglin J. A., Estes J. D., Dodiya H. B., Keshavarzian A. (2011) Increased intestinal permeability correlates with sigmoid mucosa alpha-synuclein staining and endotoxin exposure markers in early Parkinson’s disease. PLoS One 6, e28032
    1. Holmqvist S., Chutna O., Bousset L., Aldrin-Kirk P., Li W., Björklund T., Wang Z. Y., Roybon L., Melki R., Li J. Y. (2014) Direct evidence of Parkinson pathology spread from the gastrointestinal tract to the brain in rats. Acta Neuropathol. 128, 805–820
    1. Stefanis L. (2012) α-Synuclein in Parkinson’s disease. Cold Spring Harb. Perspect. Med. 2, a009399
    1. Jellinger K. A. (2003) Alpha-synuclein pathology in Parkinson’s and Alzheimer’s disease brain: incidence and topographic distribution--a pilot study. Acta Neuropathol. 106, 191–201; erratum: 588
    1. Crews L., Tsigelny I., Hashimoto M., Masliah E. (2009) Role of synucleins in Alzheimer’s disease. Neurotox. Res. 16, 306–317
    1. Bennett M. C. (2005) The role of alpha-synuclein in neurodegenerative diseases. Pharmacol. Ther. 105, 311–331
    1. Larsen N., Vogensen F. K., van den Berg F. W., Nielsen D. S., Andreasen A. S., Pedersen B. K., Al-Soud W. A., Sørensen S. J., Hansen L. H., Jakobsen M. (2010) Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085
    1. Pryde S. E., Duncan S. H., Hold G. L., Stewart C. S., Flint H. J. (2002) The microbiology of butyrate formation in the human colon. FEMS Microbiol. Lett. 217, 133–139
    1. Zhang D., Fu X., Dai X., Chen Y., Dai L. (2016) A new biological process for short-chain fatty acid generation from waste activated sludge improved by Clostridiales enhancement. Environ. Sci. Pollut. Res. Int. 23, 23972–23982
    1. De-Almada B. V., de-Almeida L. D., Camporez D., de-Moraes M. V., Morelato R. L., Perrone A. M., Belcavello L., Louro I. D., de-Paula F. (2012) Protective effect of the APOE-e3 allele in Alzheimer’s disease. Braz. J. Med. Biol. Res. 45, 8–12
    1. Farrer L. A., Cupples L. A., Haines J. L., Hyman B., Kukull W. A., Mayeux R., Myers R. H., Pericak-Vance M. A., Risch N., van Duijn C. M.; APOE and Alzheimer Disease Meta Analysis Consortium (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. JAMA 278, 1349–1356
    1. Gossling J., Moore W. (1975) Gemmiger formicilis, n. gen., n. sp., an anaerobic budding bacterium from intestines. Int. J. Syst. Bacteriol. 25, 202–207
    1. Van den Abbeele P., Belzer C., Goossens M., Kleerebezem M., De Vos W. M., Thas O., De Weirdt R., Kerckhof F. M., Van de Wiele T. (2013) Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J. 7, 949–961
    1. Duncan S. H., Barcenilla A., Stewart C. S., Pryde S. E., Flint H. J. (2002) Acetate utilization and butyryl coenzyme A (CoA):acetate-CoA transferase in butyrate-producing bacteria from the human large intestine. Appl. Environ. Microbiol. 68, 5186–5190
    1. Louis P., Flint H. J. (2009) Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol. Lett. 294, 1–8
    1. Macy J. M., Ljungdahl L. G., Gottschalk G. (1978) Pathway of succinate and propionate formation in Bacteroides fragilis. J. Bacteriol. 134, 84–91
    1. Ho L., Ono K., Tsuji M., Mazzola P., Singh R., Pasinetti G. M. (2018) Protective roles of intestinal microbiota derived short chain fatty acids in Alzheimer’s disease-type beta-amyloid neuropathological mechanisms. Expert Rev. Neurother. 18, 83–90

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera