Shifts on gut microbiota composition in an APP/PSS1 transgenic mouse model of Alzheimer s disease during lifespan

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1 Shifts on gut microbiota composition in an APP/PSS1 transgenic mouse model of Alzheimer s disease during lifespan Journal: Applied Microbiology Manuscript ID Draft Journal Name: Letters in Applied Microbiology Manuscript Type: LAM - Original Article Date Submitted by the Author: n/a Complete List of Authors: Bäuerl, Christine; Instituto de Agroquimica y Tecnologia de Alimentos, Biotechnology Collado, Maria Carmen; Spanish National Research Council, Institute of Agrochemistry and Food Science Diaz Cuevas, Ana; Universitat de Valencia Facultat de Medicina i Odontologia, Central Research Unit-INCLIVA Viña, José; Universitat de Valencia Facultat de Medicina i Odontologia, Central Research Unit-INCLIVA Perez-Martinez, Gaspar; Instituto de Agroquimica y Tecnologia de Alimentos, Biotechnology Key Words: Disease processes, Microbial structure, Markers, Intestinal microbiology, Indicators

2 Page 1 of Shifts on gut microbiota composition in an APP/PSS1 transgenic mouse model of Alzheimer s disease during lifespan Christine Bäuerl 1, M. Carmen Collado 1, Ana Diaz Cuevas 2, José Viña 3 and Gaspar Pérez Martínez Department of Biotechnology, Institute of Agrochemistry and Food Technology, Spanish National Research Council, Valencia, Spain. 2 Central Research Unit-INCLIVA, Faculty of Medicine, University of Valencia, Spain 3 Department of Physiology, Faculty of Medicine, University of Valencia, Spain Running head: Gut microbiota in mice Alzheimer s model *Corresponding author: Gaspar Pérez Martínez Institute of Agrochemistry and Food Technology 16 Spanish National Research Council (IATA-CSIC) 17 Department of Biotechnology 18 Av. Agustin Escardino Valencia, Spain 20 Tel.: gaspar.perez@iata.csic.es Significance and Impact of the Study: 1

3 Page 2 of Alzheimer s disease is a neurodegenerative disease and neuroinflammation in the central nervous system appears to have a pivotal role. Using the transgenic TG mouse model, we successfully characterized how AD pathology shifted gut microbiota composition during aging towards an inflammation related bacterial profile related to Proteobacteria and Erysipelotrichaceae and suggest that these changes could contribute to disease progression and severity. Microbiota-targeted interventions could therefore represent a strategy to postpone disease symptoms. 31 Abstract: Alzheimer s disease (AD) is the most common form of dementia and one of the major causes of disability and dependency in older people. Accumulating evidences link gut microbiota with different diseases and its relationship with neurodegenerative diseases is becoming most intriguing. This study was aimed to compare the gut microbiota of transgenic APP/PS1 (TG) mice, a wellestablished mouse model of AD, with their C57BL/6 wild-type (WT) littermates. Faecal samples were collected from 3, 6 and 24 months old mice and analysed by pyrosequencing of the V1 V3 region of the bacterial 16S rrna genes. Bacterial profiles were similar in all young mice (3 months old), and started to diverge so that 6 months old WT and TG mice had different and more diverse microbiota. During ageing, Turicibacteriaceae (typical mice bacterial group) and Rikenellaceae increased in all groups, although total Bacteroidetes remained stable. TG mice were characterized by an increase of Proteobacteria after 6 months, particularly the genus Sutterella (Betaproteobacteria), interestingly also increased in autism disorder. Also the inflammation related family Erysipelotrichaceae was more abundant in TG mice at 24 months compared to wild-type control. In summary, AD pathology in mice shifts the gut microbiota towards profiles that share features with autism and inflammatory disorders. 47 Keywords: Disease processes, Microbial structure, Markers, Intestinal microbiology, Indicators 48 2

4 Page 3 of Introduction Life expectancy has greatly improved in the last century, this raised at the same time important public health and social problems due to the increase of the elderly population. It is estimated that people aged 65 or older will increase from 8 percent of the world s population in 2010 to about 16 percent of the world s population in 2050 (National Institute on Aging et al. 2011). The prolongation of life implies the appearance of age-related diseases, such as cardiovascular diseases, atherosclerosis, cancer, dementia and Alzheimer s disease (AD) among others. Aging affects the brain function and cognitive decline encompasses a large spectrum of clinical manifestations, which may range from mild cognitive impairment (MCI) to 58 more severe degrees of dementia. AD is characterized by the presence of extracellular amyloid plaques and intraneuronal neurofibrillary tangles composed of the 42-amino acid β-amyloid protein (Aβ42) and the microtubule-associated hyperphosphorylated protein tau (Selkoe 2012). There are rare cases of familial AD caused by mutations in the amyloid precursor protein (APP) and presenilin (PSEN1, PSEN2) genes and also more common sporadic heterogeneous AD with unknown causes possibly with both, genetic and environmental risk factors, but always associated to inflammatory processes (Blennow et al. 2016), where proinflammatory cytokines can be detected in the serum of both humans (Mohd Hasni et al. 2016) and mouse models (Martin et al. 2017) The role of the gut microbiota in neurodegenerative diseases is an emerging research field. Since altered gut bacterial profiles were found in several disorders such as obesity, IBD, autism, etc., there is a growing appreciation of the importance of the gut microbiota in health and disease (Clemente et al. 2012). Recently, in Parkinson Disease a different composition of the gut microbiota was described when compared to healthy Controls, characterized by lower abundance of Prevotellaceae, Lactobacillaceae and the butyrate producer Faecalibacterium prausnitzii, whereas Enterobacteriaceae and Bifidobacterium spp. were more abundant 3

5 Page 4 of (Scheperjans et al. 2015; Unger et al. 2016). The gut microbiota composition in healthy adults remains relatively stable, but undergoes gradually changes with advancing age, due to both lifestyle and physical changes. Variations in results from different studies in the aging population may be due to different geographic location, dietary habits, and also differences in sample processing and DNA extraction protocols (Claesson et al. 2012; Choo et al. 2015). However, gut microbiota in older persons appears to be characterized by changes within the phylum Firmicutes, that bring along the decrease of some highly occurring symbiotic bacterial 81 taxa (Ruminococcaceae, Lachnospiraceae, and Bacteroidaceae) and the increase of subdominant species and facultative anaerobes, some of them opportunistic pro-inflammatory species (Biagi et al. 2010; Biagi et al. 2016). At the same time a reduction of beneficial bacteria such as F. prausnitzii and Roseburia is commonly found with advancing age (Wang et al. 2015; Biagi et al. 2016). All these changes may lead to a pro-inflammatory gut environment that may altogether contribute to the chronic-low grade inflammation found in health declining elderly This study followed the changes occurring in the microbiota with ageing but it was also aimed to determine if AD pathology had any influence on the gut microbiota composition along with aging. Therefore, we performed a comparative longitudinal study at 3, 6 and 24 months in both groups, C57/B16 (WT) and its transgenic APP/PSS1 (TG) littermates, a mouse model of familiar AD Results and Discussion The hypervariable V1-V3 region of the 16S rrna genes was sequenced from fecal samples of 3, 6- and 24-month-old (3m, 6m, 24m) WT and TG mice. In order to approach a global depiction of gut bacterial communities, first alpha and beta diversity indicators were calculated. Rarefaction curves calculated for Chao1 index (richness estimator) showed that 24m and 6m TG mice had the greatest bacterial diversity, followed by 24m and 6m WT mice (Figure 1A) and 4

6 Page 5 of younger animals had the lowest diversity, which was confirmed by Shannon diversity index (Figure 1B). Usually reduced microbial diversity has been associated to lower health outcomes in humans (Walters et al. 2014; Giloteaux et al. 2016), also aging human volunteers had a decreased microbial diversity (Zwielehner et al. 2009; Claesson et al. 2012), so that higher diversity has generally been considered a health bound feature. However, as more exhaustive microbiota studies become available, this seems not to be a general paradigm. Recent studies described higher bacterial diversity in major active depressive disorder and autism than in 106 healthy controls (Finegold et al. 2010; Jiang et al. 2015) and in elderly, health seems to be more strongly associated to the specific microbiota composition than to bacterial diversity (Jeffery et al. 2016) Global similarities between bacterial groups found in TG mice and their wild type siblings were analysed by Principal Coodinates Analysis (PCoA) (Figure 2), using unweighted Unifrac (ANOSIM R=0.492; p=0.003). Young WT and TG mice of 3m clustered together, indicating that the genotype at this age does not influence microbiota composition. Adult mice of 6m and 24m WT and TG were clearly separated from the youngest mice, but they also clustered according to their genotype, hence suggesting that gut microbiota composition evolves differently and starts to diverge in TG mice between 3m and 6m. In this case, differences in composition coincide with differences in diversity indices The dominating phyla in all groups were Bacteroidetes (from 60.0% to 87.6 %) followed by Firmicutes (reaching from 5.7 % to 34.0 %) (Figure 3A). Proportions of Firmicutes / Bacteroidetes increased in TG with age (0.10 at 3m, 0.4 at 6m and at 24m), whereas in WT mice decreased strongly from 0.53 at 3m to 0.06 at 6m and again raised to 0.49 at 24m As deduced from diversity and PCoA analysis (Figure 2) little differences can be established among young mice (3m). Closer analyses looking for specific bacterial age associated taxons 5

7 Page 6 of were found in the transition to adulthood, between 3m and 6m, both in WT and TG mice, and it is also the moment we started finding differences between microbiota of WT and TG mice. With respect to general age-dependent changes in the microbiota, the family Turicibacteriaceae, typical habitants of the mouse gut (Nguyen et al. 2015) seem to increase with aging (wt: r=0.7904, p=0.011; TG: r=0.7218, p=0.043) (Figure S1, Table S1 and S2). Although the phylum Bacteroidetes had no clear variations with ageing (Figure 3A), the family Rikenellaceae increased steadily from 3m to 24m in both WT and TG mice, although it was 131 significantly more abundant in WT mice at 24m (12.4 % in WT vs % in TG, p < 0.05) (Figure 4A,4B and S1). As opposed to it, the very abundant family S24.7 showed significant decreases with age in WT and TG mice (Figure 4A, Table S1 and S2) An increase of the phylum Proteobacteria has commonly been reported in the aged gut microbiome (Biagi et al. 2010). In this work, comparison of the aged-matched WT and TG mice revealed that Proteobacteria proportions become significantly higher in TG than WT (p<0.05) after 6m (Figures 3A and 3B). Then, Proteobacteria remained almost stable in both groups until 24m (WT: 1.2 %, TG: 6.7 %) (Figure 3B). Particularly, the class Betaproteobacteria accounted for most of the observed changes at 6m (relative abundances: WT 6m: 0.99 %; TG 6m: 7,46 %; p<0.05). We found that those changes were due to the increase of the genus Sutterella (order Burkholderiales, class Betaproteobacteria) (Figure S1). This bacterial genus is highly represented, together with Enterobacteriaceae, in children with autism disorder (De Angelis et al. 2013). The family Erysipelotrichaceae showed a tendency to increase with age in TG mice, so that 24m TG mice had significantly higher proportions than WT mice (TG: 0.92 % vs. WT: 0.06 %, p<0.05). This bacterial family appears to be very immunogenic and highly coated by IgA in the gut (Palm et al. 2014), it is abundant in inflammatory bowel disease (Schaubeck et al. 2016) and colorectal cancer (Chen et al. 2012). Also Erysilopelotrichaceae correlated positively with TNF-α levels in patients with chronic HIV-infection (Dinh et al. 2015). Thus, a pro-inflammatory gut micro-environment may favour the presence of this bacterial 6

8 Page 7 of family. At OTU level, comparison of the aged-matched WT and TG mice revealed significantly higher (p<0.05) relative abundances of 4 OTUs belonging to Clostridiales in 3m WT mice, 3 OTUs of Suterella in 6m TG, and 24m WT mice were characterized by higher levels of Rikenellaceae (3 OTUs), Bacteroidales (2 OTUs), Ruminococcus (1 OTU), Oscillospira (1 OTU) and Clostridiales (1 OTU) As general conclusion, it can be stated that this study established that gut microbiota in mice started changing after 3m and then remained more or less stable, and at 6m microbiota showed significant differences between TG and WT mice. It coincided with the moment when transgenic APP/PS1 animals start developing β-amyloid deposits in the brain (Garcia-Alloza et 159 al. 2006; Ordonez-Gutierrez et al. 2016; Macklin et al. 2017). Bacterial profiles in TG mice, are characterized by an increase of Proteobacteria mostly belonging to the genus Sutterella at 6m and of Erysilopelotrichaceae at 24m. None of those bacterial groups are pathogenic, although the genus Sutterella has been associated to neonatal piglet diarrhea (Yang et al. 2017), mild in vitro pro-inflammatory effect (Hiippala et al. 2016), metabolic syndrome (Lim et al. 2016), but most remarkably, it has been found consistently abundant in Down Syndrome (Biagi et al. 2014) and in children with autism disorder (Williams et al. 2012; De Angelis et al. 2013). Further, in a recent research with a mouse model of autism, Sutterella correlated with a low performance in social and obsessive compulsive disorder (marble burying) tests, and TNF-α levels (Coretti et al. 2017) In the present study, the phylum Bacteroidetes was dominant in mice of all ages with both genotypes (WT and TG), whereas in a more aggressive AD mouse model (5XFAD) at the age of nine weeks Bacteroidetes decreased (from 61 to 46%) and microbiota was then dominated by Firmicutes (Brandscheid et al. 2017). With this 5XFAD model, the assay finished at the age of 18 weeks. We also found different results than a similar study with APP/PS1 mice (Shen et al. 2017); however, there were substantial differences in the experimental design and 7

9 Page 8 of methodologies used that prevent comparing the results obtained. There are discrepancies in the evolution of microbial diversity associated to age and the presence of Helicobacteriaceae in TG mice, but we could confirm higher abundance of Desulfovibrionaceae in 24m TG mice (0.66% in TG vs. 0.06% in WT mice), although not reaching significance. The intestinal microbiota was shown to be prone to variations in response to genetic and environmental factors present in the different animal care facilities (Rausch et al. 2016), which could explain the differences between the different studies, particularly in less abundant bacterial taxons. 182 The recently emerged concept of the bidirectional communication of the gut microbiota gut brain emphasizes the relevance to study associations between neurodegenerative diseases and the gut microbiota. There exist growing evidence that gut microbiota may affect the central nervous system through communication via the vagus nerve, signalling mediators of the immune system, enteric hormones and gut microbiota-derived products (Sherwin et al. 2016). In this context it has to be mentioned that integrity of the epithelial gut barrier as well of the blood-brain-barrier is reduced during the aging process (Haar et al. 2016; Soenen et al. 2016). Hence, it is proposed that inflammatory mediators or microbial products from the gut may enter into circulation and may reach the brain, thereby influencing the brain function and vice versa and finally entering in a vicious circle In conclusion, we could show that AD pathogenesis in this TG mouse model could shift the gut microbiota towards higher abundances of Proteobacteria, and that these changes started to occur between three and six months of age. In light of these findings, future studies are strongly encouraged to test if microbiota targeted interventions might postpone or alleviate AD pathology Materials and Methods 199 Animals and fecal sample collection 8

10 Page 9 of Female APP/PS1 heterozygous transgenic mice in a C57BL/6 background (B6.Cg- Tg(APPswe,PSEN1dE9)85Dbo/Mmjax) were used in this study as an AD model. APP/PS1 mice express a chimeric mouse/human amyloid precursor protein (Mo/HuAPP695swe) and a mutant human presenilin 1 (PS1-dE9) both directed to CNS neurons. Both mutations are associated with early-onset Alzheimer's disease. WT C57BL/6 mice from the same litter as the heterozygous APP/PS1 mice obtained during the crossing procedures were used as control mice. Animals were bred at the animal care facility at the Central Research Unit INCLIVA, 207 University of Valencia (Spain). For this assay, animals of different ages were selected within the colony, the number of them being limited by the older specimens (24 month old), 3 WT and 2 APP/PS1. Hence, we studied 8 APP/PS1 mice aged 3 months (n=3), 6 months (n=3) and 24 months (n=2) and 9 WT mice aged 3 months (n=3), 6 months (n=3) and 24 months (n=3). All animal work and procedures were approved by the institutional Ethics Committee of the CSIC and University of Valencia and performed following the principles of laboratory animal care mandatory by European Union Law and 2010/63/EU and Spanish Government RD 53/2013 on the protection of animals used for scientific purposes. Mice were maintained under specific pathogen-free conditions under a 12: 12h dark/light cycle at 23 ºC ± 1ºC and 60 % relative humidity. All mice had ad libitum access to water and standard chow. Faecal samples (one or two pellets) of all the animals were collected in eppendorf tubes early in the morning the same day and were quickly frozen until processed Fecal DNA extraction and 16S rrna gene sequencing mg fecal samples were used for DNA extraction using a modified Qiagen stool DNA extraction kit (QIAgen, Hilden, Germany) according to the instructions of the manufacturer. For analysis of the phylogenetic composition of the gut microbiota, the 500 bps fragment encompassing the V1-V3 region of the 16S rrna gene was amplified using barcoded primer set 9

11 Page 10 of based on universal primers 27F and 533R. PCR and quality control of PCR products were carried out as described previously (Cernada et al. 2016). Purified PCR products were pyrosequenced from the forward primer end only using a GS-Junior sequencer with Titanium chemistry (Roche) at the Genomics core facility (SCSIE) of the University of Valencia, Spain. Raw data were quality filtered and low quality sequences or sequences with a length less than 150 nucleotides were discarded, chimeric sequences were removed. The QIIME pipeline (version and green genes data base 13_5) was used for identifying representative 233 sequences for each operational taxonomic unit (OTU) generated from complete linkage clustering with a 97% similarity. Alpha diversity indices were determined from rarefied tables using the Shannon-Wiener index for diversity and the Chao1 index for species richness; Observed Species (number of unique OTUs) and Phylogenetic Distance (PD_whole) were also determined. A beta diversity distance matrix was computed from the previously constructed OTU table using UniFrac analysis. Unweighted (presence/absence matrix) and weighted (presence/absence/abundance matrix) UniFrac distances were used to construct two- and three-dimensional Principal Coordinates Analysis (PCoA) plots. Calypso software version 7.14( was used to remove samples with less than 1000 sequence reads and 0.01 % relative abundance across all samples. In addition, data were transformed using cumulative sum scaling (CSS) with total sum normalization for the statistical analysis between all groups and age-matched groups including anova followed by paired t-tests Acknowledgements: The study was supported by grant financed by the Spanish Ministerio de Economia y Competitividad AGL P Conflict of Interest: 10

12 Page 11 of No conflict of interest declared References: Biagi, E., Candela, M., Centanni, M., Consolandi, C., Rampelli, S., Turroni, S., Severgnini, M., 257 Peano, C., Ghezzo, A., Scurti, M., Salvioli, S., Franceschi, C. and Brigidi, P. (2014) Gut Microbiome in Down Syndrome. PLOS ONE 9, e Biagi, E., Franceschi, C., Rampelli, S., Severgnini, M., Ostan, R., Turroni, S., Consolandi, C., Quercia, S., Scurti, M., Monti, D., Capri, M., Brigidi, P. and Candela, M. (2016) Gut Microbiota and Extreme Longevity. Current Biology 26, Biagi, E., Nylund, L., Candela, M., Ostan, R., Bucci, L., Pini, E., Nikkïla, J., Monti, D., Satokari, R., Franceschi, C., Brigidi, P. and De Vos, W. (2010) Through Ageing, and Beyond: Gut Microbiota and Inflammatory Status in Seniors and Centenarians. PLoS ONE 5, e Blennow, K., de Leon, M.J. and Zetterberg, H. (2016) Alzheimer's disease. The Lancet 368, Brandscheid, C., Schuck, F., Reinhardt, S., Schafer, K.H., Pietrzik, C.U., Grimm, M., Hartmann, T., Schwiertz, A. and Endres, K. (2017) Altered Gut Microbiome Composition and Tryptic Activity of the 5xFAD Alzheimer's Mouse Model. Journal of Alzheimer's disease : JAD 56, Cernada, M., Bäuerl, C., Serna, E., Collado, M.C., Martínez, G.P. and Vento, M. (2016) Sepsis in preterm infants causes alterations in mucosal gene expression and microbiota profiles compared to non-septic twins. Scientific Reports 6, Claesson, M.J., Jeffery, I.B., Conde, S., Power, S.E., O/'Connor, E.M., Cusack, S., Harris, H.M.B., Coakley, M., Lakshminarayanan, B., O/'Sullivan, O., Fitzgerald, G.F., Deane, J., O/'Connor, M., Harnedy, N., O/'Connor, K., O/'Mahony, D., van Sinderen, D., Wallace, M., Brennan, L., Stanton, 11

13 Page 12 of C., Marchesi, J.R., Fitzgerald, A.P., Shanahan, F., Hill, C., Ross, R.P. and O/'Toole, P.W. (2012) Gut microbiota composition correlates with diet and health in the elderly. Nature 488, Clemente, Jose C., Ursell, Luke K., Parfrey, Laura W. and Knight, R. (2012) The Impact of the Gut Microbiota on Human Health: An Integrative View. Cell 148, Coretti, L., Cristiano, C., Florio, E., Scala, G., Lama, A., Keller, S., Cuomo, M., Russo, R., Pero, R., Paciello, O., Mattace Raso, G., Meli, R., Cocozza, S., Calignano, A., Chiariotti, L. and Lembo, F. 283 (2017) Sex-related alterations of gut microbiota composition in the BTBR mouse model of autism spectrum disorder. Scientific Reports 7, Chen, W., Liu, F., Ling, Z., Tong, X. and Xiang, C. (2012) Human Intestinal Lumen and Mucosa- Associated Microbiota in Patients with Colorectal Cancer. PLOS ONE 7, e Choo, J.M., Leong, L.E.X. and Rogers, G.B. (2015) Sample storage conditions significantly influence faecal microbiome profiles. Scientific Reports 5, De Angelis, M., Piccolo, M., Vannini, L., Siragusa, S., De Giacomo, A., Serrazzanetti, D.I., Cristofori, F., Guerzoni, M.E., Gobbetti, M. and Francavilla, R. (2013) Fecal Microbiota and Metabolome of Children with Autism and Pervasive Developmental Disorder Not Otherwise Specified. PLoS One 8, e Dinh, D.M., Volpe, G.E., Duffalo, C., Bhalchandra, S., Tai, A.K., Kane, A.V., Wanke, C.A. and Ward, H.D. (2015) Intestinal Microbiota, Microbial Translocation, and Systemic Inflammation in Chronic HIV Infection. The Journal of Infectious Diseases 211, Finegold, S.M., Dowd, S.E., Gontcharova, V., Liu, C., Henley, K.E., Wolcott, R.D., Youn, E., Summanen, P.H., Granpeesheh, D., Dixon, D., Liu, M., Molitoris, D.R. and Green Iii, J.A. (2010) Pyrosequencing study of fecal microflora of autistic and control children. Anaerobe 16, Garcia-Alloza, M., Robbins, E.M., Zhang-Nunes, S.X., Purcell, S.M., Betensky, R.A., Raju, S., Prada, C., Greenberg, S.M., Bacskai, B.J. and Frosch, M.P. (2006) Characterization of amyloid 12

14 Page 13 of deposition in the APPswe/PS1dE9 mouse model of Alzheimer disease. Neurobiology of Disease 24, Giloteaux, L., Goodrich, J.K., Walters, W.A., Levine, S.M., Ley, R.E. and Hanson, M.R. (2016) Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome 4, 30. Haar, H.J.v.d., Burgmans, S., Jansen, J.F.A., Osch, M.J.P.v., Buchem, M.A.v., Muller, M., Hofman, P.A.M., Verhey, F.R.J. and Backes, W.H. (2016) Blood-Brain Barrier Leakage in Patients with 309 Early Alzheimer Disease. Radiology 281, Hiippala, K., Kainulainen, V., Kalliomäki, M., Arkkila, P. and Satokari, R. (2016) Mucosal Prevalence and Interactions with the Epithelium Indicate Commensalism of Sutterella spp. Frontiers in Microbiology 7, Jeffery, I.B., Lynch, D.B. and O'Toole, P.W. (2016) Composition and temporal stability of the gut microbiota in older persons. ISME J 10, Jiang, H., Ling, Z., Zhang, Y., Mao, H., Ma, Z., Yin, Y., Wang, W., Tang, W., Tan, Z., Shi, J., Li, L. and Ruan, B. (2015) Altered fecal microbiota composition in patients with major depressive disorder. Brain, Behavior, and Immunity 48, Lim, M.Y., You, H.J., Yoon, H.S., Kwon, B., Lee, J.Y., Lee, S., Song, Y.-M., Lee, K., Sung, J. and Ko, G. (2016) The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut. Macklin, L., Griffith, C.M., Cai, Y., Rose, G.M., Yan, X.-X. and Patrylo, P.R. (2017) Glucose tolerance and insulin sensitivity are impaired in APP/PS1 transgenic mice prior to amyloid plaque pathogenesis and cognitive decline. Experimental Gerontology 88, Martin, E., Boucher, C., Fontaine, B. and Delarasse, C. (2017) Distinct inflammatory phenotypes of microglia and monocyte-derived macrophages in Alzheimer's disease models: effects of aging and amyloid pathology. Aging Cell 16,

15 Page 14 of Mohd Hasni, D.S., Lim, S.M., Chin, A.V., Tan, M.P., Poi, P.J.H., Kamaruzzaman, S.B., Majeed, A.B.A. and Ramasamy, K. (2016) Peripheral cytokines, C-X-C motif ligand10 and interleukin-13, are associated with Malaysian Alzheimer's disease. Geriatrics & Gerontology International, n/an/a. National Institute on Aging, National Institutes of Health and U.S. Department of Health and Human Services, W.H.O. (2011) Global Health and Aging. NIH Publication no Nguyen, T.L.A., Vieira-Silva, S., Liston, A. and Raes, J. (2015) How informative is the mouse for 334 human gut microbiota research? Disease Models & Mechanisms 8, Ordonez-Gutierrez, L., Fernandez-Perez, I., Herrera, J.L., Anton, M., Benito-Cuesta, I. and Wandosell, F. (2016) AbetaPP/PS1 Transgenic Mice Show Sex Differences in the Cerebellum Associated with Aging. Journal of Alzheimer's disease : JAD 54, Palm, Noah W., de Zoete, Marcel R., Cullen, Thomas W., Barry, Natasha A., Stefanowski, J., Hao, L., Degnan, Patrick H., Hu, J., Peter, I., Zhang, W., Ruggiero, E., Cho, Judy H., Goodman, Andrew L. and Flavell, Richard A. (2014) Immunoglobulin A Coating Identifies Colitogenic Bacteria in Inflammatory Bowel Disease. Cell 158, Rausch, P., Basic, M., Batra, A., Bischoff, S.C., Blaut, M., Clavel, T., Gläsner, J., Gopalakrishnan, S., Grassl, G.A., Günther, C., Haller, D., Hirose, M., Ibrahim, S., Loh, G., Mattner, J., Nagel, S., Pabst, O., Schmidt, F., Siegmund, B., Strowig, T., Volynets, V., Wirtz, S., Zeissig, S., Zeissig, Y., Bleich, A. and Baines, J.F. (2016) Analysis of factors contributing to variation in the C57BL/6J fecal microbiota across German animal facilities. International Journal of Medical Microbiology 306, Schaubeck, M., Clavel, T., Calasan, J., Lagkouvardos, I., Haange, S.B., Jehmlich, N., Basic, M., Dupont, A., Hornef, M., Bergen, M.v., Bleich, A. and Haller, D. (2016) Dysbiotic gut microbiota causes transmissible Crohn's disease-like ileitis independent of failure in antimicrobial defence. Gut 65,

16 Page 15 of Scheperjans, F., Aho, V., Pereira, P.A.B., Koskinen, K., Paulin, L., Pekkonen, E., Haapaniemi, E., Kaakkola, S., Eerola-Rautio, J., Pohja, M., Kinnunen, E., Murros, K. and Auvinen, P. (2015) Gut microbiota are related to Parkinson's disease and clinical phenotype. Movement Disorders 30, Selkoe, D.J. (2012) Preventing Alzheimer s Disease. Science 337, Shen, L., Liu, L. and Ji, H.F. (2017) Alzheimer's Disease Histological and Behavioral Manifestations in Transgenic Mice Correlate with Specific Gut Microbiome State. J Alzheimers 359 Dis 56, Sherwin, E., Sandhu, K.V., Dinan, T.G. and Cryan, J.F. (2016) May the Force Be With You: The Light and Dark Sides of the Microbiota Gut Brain Axis in Neuropsychiatry. CNS Drugs 30, Soenen, S., Rayner, C.K., Jones, K.L. and Horowitz, M. (2016) The ageing gastrointestinal tract. Current Opinion in Clinical Nutrition & Metabolic Care 19, Unger, M.M., Spiegel, J., Dillmann, K.-U., Grundmann, D., Philippeit, H., Bürmann, J., Faßbender, K., Schwiertz, A. and Schäfer, K.-H. (2016) Short chain fatty acids and gut microbiota differ between patients with Parkinson's disease and age-matched controls. Parkinsonism & Related Disorders 32, Walters, W.A., Xu, Z. and Knight, R. (2014) Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Letters 588, Wang, F., Yu, T., Huang, G., Cai, D., Liang, X., Su, H., Zhu, Z., Li, D., Yang, Y., Shen, P., Mao, R., Yu, L., Zhao, M. and Li, Q. (2015) Gut Microbiota Community and Its Assembly Associated with Age and Diet in Chinese Centenarians. Journal of microbiology and biotechnology 25, Williams, B.L., Hornig, M., Parekh, T. and Lipkin, W.I. (2012) Application of Novel PCR-Based Methods for Detection, Quantitation, and Phylogenetic Characterization of Sutterella Species 15

17 Page 16 of in Intestinal Biopsy Samples from Children with Autism and Gastrointestinal Disturbances. mbio 3, e Yang, Q., Huang, X., Zhao, S., Sun, W., Yan, Z., Wang, P., Li, S., Huang, W., Zhang, S., Liu, L. and Gun, S. (2017) Structure and Function of the Fecal Microbiota in Diarrheic Neonatal Piglets. Frontiers in Microbiology 8. Zwielehner, J., Liszt, K., Handschur, M., Lassl, C., Lapin, A. and Haslberger, A.G. (2009) Combined PCR-DGGE fingerprinting and quantitative-pcr indicates shifts in fecal population 384 sizes and diversity of Bacteroides, bifidobacteria and Clostridium cluster IV in institutionalized 385 elderly. Experimental Gerontology 44, Figure Legends: Figure 1: (A) Rarefaction curve using Chao 1 richness estimator (α-diversity vs. sequencing effort) grouped by age and genotype. (B) Shannon index grouped by age and genotype Figure 2: Principal Coordinate Analysis (PCoA) plot based on the unweighted UniFrac distance matrix. Individual samples of young 3 months-(wt: MID12, MID16, MID17; TG: MID13, MID14, MID15), 6 months-(wt: MID6, MID7, MID10, TG: MID8, MID9, MID11) and 24-months old mice (wt: MID3, MID4, MID5, TG: MID1, MID2) are shown as single points Figure 3: (A) Relative abundances of operational taxonomic units (OTUs) at the phylum level. (B) Mean relative abundances of the phyla Proteobacteria. Statistical differences were 16

18 Page 17 of calculated using ANOVA (p<0.05) after cumulative sum scaling and log2 transformation and pair-wise comparisons are done by t-test and annotated as *: p<0.05; **: p< Figure 4: (A) Relative abundances of operational taxonomic units (OTUs) at the family level. (B) Mean relative abundances of selected families Rikenellaceae and Erysipelotrichaceae. Statistical differences were calculated using ANOVA (p<0.05) after cumulative sum scaling and log2 transformation and pair-wise comparisons are done by t-test and annotated as *: p<0.05; **: p< Supporting Information Table S1 Pearson regression analysis in WT mice using age as variable Table S2 Pearson regression analysis in TG mice using age as variable Figure S1 Mean relative abundances of significantly different operational taxonomic units (OTUs) at the level phylum (A), order (B), genus (C), and family (D). Statistical differences were calculated using ANOVA (p<0.05) after cumulative sum scaling and log2 transformation and pair-wise comparisons are done by t-test and annotated as *: p<0.05; **: p<0.01; ***: p<

19 Page 18 of 26 Figure 1 A B TG24 TG6 WT24 WT6 WT3 TG3

20 Page 19 of 26 Figure 2 ADULT AND OLD MICE Out-group (young healthy mouse) Transgenic Wild-type YOUNG MICE

21 rel. abundance [%] Figure 3 Page 20 of 26 A 100% 90% 80% 70% Unclassified Verrucomicrobia B Proteobacteria ** * * % Tenericutes 50% Actinobacteria % TM % Proteobacteria % Firmicutes 10% Bacteroidetes 2.5 0% WT 3m WT 6m WT 24m TG3m TG 6m TG 24m 0.0 WT 3m WT 6M WT 24m TG 3m TG 6m TG 24m

22 rel. abundance [%] rel. abundance [%] Page 21 of 26 Figure 4 A 100% 75% 50% 25% 0% Family WT 3m WT 6m WT 24m TG 3m TG 6m TG 24m Unclassified S24.7 Rikenellaceae X.Odoribacteraceae. Turicibacteraceae Lachnospiraceae Ruminococcaceae Lactobacillaceae Bacteroidaceae Clostridiaceae Alcaligenaceae F16 Coriobacteriaceae Prevotellaceae X.Mogibacteriaceae. Erysipelotrichaceae Enterobacteriaceae Dehalobacteriaceae Staphylococcaceae Verrucomicrobiaceae B Rikenellaceae * Erysilopelotrichaceae ** ** 20 * * * 1.25 ** * * WT 3m WT 6m WT 24m TG 3m TG 6m TG 24m 0.00 WT 3m WT 6m WT 24m TG 3m TG 6m TG 24m

23 Page 22 of 26 Table S1 Pearson regression analysis in WT mice using age as variable Level Taxa P-value R Mean Abundance Positive [CSS log Samples transformed] Family Turicibacteraceae Unclassified.Bacteroidales Porphyromonadaceae S Desulfovibrionaceae Rikenellaceae Genus Turicibacter Parabacteroides Unclassified.Bacteroidales Unclassified.S Desulfovibrio SpeciesTuricibacter Unclassified.Turicibacter Parabacteroides Unclassified.Parabacteroides Unclassified.S247 Unclassified.S Unclassified.Bacteroidales Unclassified. Bacteroidales Desulfovibrio_C21_c OTU p Bacteroidetes o Bacteroidales_ p Bacteroidetes o Bacteroidales_ p Firmicutes g Turicibacter_ p Bacteroidetes f S247_ p Bacteroidetes f S247_ p Bacteroidetes f S247_ p Firmicutes o Clostridiales_ p Bacteroidetes f S247_ p Firmicutes g Lactobacillus_ p Bacteroidetes f S247_ p Bacteroidetes f Rikenellaceae_ p Bacteroidetes f S247_ p Bacteroidetes f Rikenellaceae_ p Bacteroidetes f Rikenellaceae_

24 Page 23 of 26 Table S2 Pearson regression analysis in TG mice using age as variable Level Taxa P-value R Mean Abundance Positive [CSS log Samples transformed] Phylum Bacteroidetes Family Unclassified.Bacteroidales Unclassified.RF Turicibacteraceae Rikenellaceae Odoribacteraceae S Genus Unclassified.Bacteroidales AF Unclassified.RF Turicibacter Allobaculum Odoribacter Species Unclassified.Bacteroidales Unclassified.Bacte roidales AF12 Unclassified.AF Unclassified.RF39 Unclassified.RF Turicibacter Unclassified.Turicibacter Allobaculum Unclassified.Allobaculum Bacteroides Unclassified.Bacteroides Odoribacter Unclassified.Odoribacter Unclassified.Rikenellaceae Unclassified.Rike nellaceae Unclassified.Christensenellaceae Unclassifie d.christensenellaceae OTU p Bacteroidetes g Bacteroides_ p Firmicutes g Turicibacter_ p Bacteroidetes g Bacteroides_ p Bacteroidetes f S247_ p Bacteroidetes f S247_ p Bacteroidetes f S247_ p Bacteroidetes f S247_ p TM7 f F16_ p TM7 f F16_ p Firmicutes g Lactobacillus_ p Bacteroidetes f S247_

25 Page 24 of 26 Figure S1 Mean relative abundances of significantly different operational taxonomic units (OTUs) at the level phylum (A), order (B), genus (C), and family (D). Statistical differences were calculated using ANOVA (p<0.05) after cumulative sum scaling and log2 transformation and pair-wise comparisons are done by t-test and annotated as *: p<0.05; **: p<0.01; ***: p<0.001 A

26 Page 25 of 26 B C

27 Page 26 of 26 D