GeroScience (2018) 40:257–268 https://doi.org/10.1007/s11357-018-0026-y ORIGINAL ARTICLE Composition and richness of the serum microbiome differ by age and link to systemic inflammation Thomas W. Buford & Christy S. Carter & William J. VanDerPol & Dongquan Chen & Elliot J. Lefkowitz & Peter Eipers & Casey D. Morrow & Marcas M. Bamman Received: 8 May 2018 /Accepted: 25 May 2018 /Published online: 5 June 2018 The Author(s) 2018 Abstract Advanced age has been associated with alter- (p = 0.002) differed between young and older adults. ations to the microbiome within the intestinal tract as After correction for false discovery rate (FDR), age well as intestinal permeability (i.e., Bleaky gut^). Prior groups differed (all p values ≤ 0.016) in the relative studies suggest that intestinal permeability may contrib- abundance of the phyla Bacteroidetes, SR1, Spiro- ute to increases in systemic inflammation—an aging chaetes, Bacteria_Other, TM7,and Tenericutes. Signifi- hallmark—possibly via microorganisms entering the cir- cant positive correlations (p values ≤ 0.017 after FDR culation. Yet, no studies exist describing the state of the correction) were observed between IGF1 and circulating microbiome among older persons. To com- Bacteroidetes (ρ = 0.380), Spirochaetes (ρ = 0.528), pare microbiota profiles in serum between healthy young SR1 (ρ = 0.410), and TM7 (ρ = 0.399). Significant in- (20–35 years, n = 24) and older adults (60–75 years, n = verse correlations were observed for IL6 with 24) as well as associations between differential microbial Bacteroidetes (ρ = − 0.398) and TM7 (ρ = − 0.423), as populations and prominent indices of age-related inflam- well as for TNFα with Bacteroidetes (ρ = − 0.344). Sim- mation. Unweighted Unifrac analysis, a measure of β- ilar findings were observed at the class taxon. These data diversity, revealed that microbial communities clustered are the first to demonstrate that the richness and compo- differently between young and older adults. Several sition of the serum microbiome differ between young measures of α-diversity, including chao1 (p =0.001), and older adults and that these factors are linked to observed species (p = 0.001), and phylogenetic diversity indices of age-related inflammation. . . . Keywords Aging Leaky gut Microbiome T. W. Buford (*) C. S. Carter Microbiota Inflammation Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA e-mail: firstname.lastname@example.org : : Introduction W. J. VanDerPol D. Chen E. J. Lefkowitz Biomedical Informatics, Center for Clinical and Translational Sciences, University of Alabama at Birmingham, Birmingham, Chronic low-grade inflammation is one of the most AL, USA consistent biologic features of advanced age, evi- denced by over 10,000 publications in this area E. J. Lefkowitz Department of Microbiology, University of Alabama at (Buford 2017). Yet, despite the common recognition Birmingham, Birmingham, AL, USA of the inflammatory phenomenon, the etiology of age- related inflammation remains poorly understood. Re- : : P. Eipers C. D. Morrow M. M. Bamman cently, a novel hypothesis has emerged from our Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA group and others suggesting that increases in gut 258 GeroScience (2018) 40:257–268 permeability (i.e., Bleaky gut^) and subsequent re- hypothesized that the microbiome found within serum lease of intestinal contents into the circulation may would display age-related differences in measures of be a primary contributor to increases in age-related both alpha- and beta-diversity—key measures to detect inflammation (Buford 2017; Nicoletti 2015). differences in microbiomes between differing popula- Aging is associated with several relevant changes tions (Kumar et al. 2014). Moreover, we also aimed to to overall gut health including increases in intestinal identify specific microbial DNA abundances signifi- permeability (Man et al. 2015;Nicoletti 2015)as well cantly associated with circulating concentrations of as changes to the stability of the gut microbiome IL6 and TNFα as well as insulin-like growth factor 1 (Biagi et al. 2010; Jeffery et al. 2016)—the aggregate (IGF1)—a hormone known to be intricately related to genetic material of microorganisms residing within inflammatory cytokine production (Maggio et al. 2013; the intestinal tract which contribute to regulating host Rajpathak et al. 2008) and recently reported to be stim- health (Human Microbiome Project Consortium ulated by microbiota (Yan et al. 2016). 2012). These changes are relevant in the present context as recent evidence indicates that changes in microbial composition and density can alter immuni- Results ty and inflammation distal to the intestine (Belkaid and Naik 2013). Indeed, early studies in humans Participant characteristics, diet, and inflammatory reported cross-sectional associations between gut parameters Data from a total of 48 participants was microbiome profiles and circulating inflammatory included in the study. Participant descriptive statistics cytokines of older adults (Claesson et al. 2012; are shown in Table 1. The young (n = 24) and older adult Rampelli et al. 2013). However, the mechanisms (n = 24) groups were balanced for sex. Participants in through which gut dysbiosis could contribute to each group were of similar height and body mass, chronic, low-grade inflammation were unclear. Table 1 Participant demographic characteristics and inflammato- Basic and pre-clinical studies have also suggested ry parameters that intestinal permeability, coupled with altered micro- biota profiles (Clark et al. 2015;Reraetal. 2012), may Young adults Older adults drive age-related increases in systemic inflammation. Age, years 27.8 ± 4.0 63.9 ± 3.2** Very recently, Thevaranjan et al. (2017) published a Female, n 14 (58.3%) 14 (58.3%) seminal study in a mouse model definitively demon- Height, cm 170.2 ± 11.0 169.1 ± 10.9 strating that age-related gut microbial dysbiosis drives Body mass, kg 72.7 ± 12.9 74.9 ± 15.0 intestinal permeability, microbial translocation to the Body mass index, kg/m circulation, and ultimately systemic inflammation. Yet, 25.0 ± 3.0 25.9 ± 3.2 despite these important pre-clinical studies, data are Body fat, % 30.1 ± 10.4 35.9 ± 6.4* lacking to link intestinal permeability to inflammation VO max, mL/O /min 37.5 ± 8.4 27.0 ± 4.8** 2 2 in humans. Dietary intake We recently published the first human evidence dem- Total intake, kcal/day 2003 ± 779 1767 ± 625 onstrating that circulating concentrations of zonulin, a Carbohydrate, g/day 253.9 ± 103.6 210.2 ± 75.5* physiologic regulator of intestinal permeability, were Fiber, g/day 20.4 ± 8.3 16.2 ± 8.7 higher—indicating greater permeability—among Fat, g/day 76.5 ± 41.2 67.8 ± 36.4 healthy older adults than younger peers (Qi et al. Protein, g/day 75.4 ± 33.9 73.1 ± 30.1* 2017). Furthermore, zonulin concentrations were posi- Serum inflammatory parameters tively associated with circulating concentrations of in- Interleukin 6, pg/mL 0.38 ± 0.19 0.52 ± 0.20* flammatory cytokines tumor necrosis factor alpha Tumor necrosis factor α, 2.02 ± 0.60 2.20 ± 0.44 (TNFα) and interleukin 6 (IL6) (Qi et al. 2017), two pg/mL Insulin-like growth factor 1, 365.4 ± 129.2 188.4 ± 82.3** of the primary inflammatory cytokines consistently as- μg/L sociated with the aging process. The objective of this study was to expand upon these findings by providing VO2max maximal respiratory capacity (i.e., fitness); *p <0.05, **p <0.0001 the first data comparing microbial DNA profiles within All values (mean ± SD) the circulation of healthy and older adults. We GeroScience (2018) 40:257–268 259 resulting in a similar mean body mass index between Simpson metrics, did not significantly differ between groups. Body fat percentage and fitness were signifi- age groups. cantly different (p < 0.05) between groups. Regarding dietary intake, a trend toward significance (p =0.061) Age-related differences in microbial abundances The was observed for greater daily caloric intake among relative abundance of several bacterial phyla was signifi- younger adults compared to older adults. Young adults cantly different between age groups (Fig. 3). After correc- consumed significantly more carbohydrate (mean dif- tion for false discovery rate (FDR), significant group dif- ference: 43.6 kcal/day, p = 0.008), including significant- ferences were observed for the phyla Bacteroidetes, SR1, ly more fiber (mean difference: 4.2 g/day, p =0.005) Spirochaetes, Bacteria_Other, TM7,and Tenericutes. At than older adults. No differences were observed in daily the class level, significant group differences were observed intake of fat or protein nor in specific sub-types of fats for Bacteroidia, Mollicutes, Bacteria_Other_Other, including cholesterol, saturated fat, or mono/ Cytophagia, Firmicutes_Other,and Leptospirae polyunsaturated fats (data not shown). Serum concen- (Table 2). Additionally, several other families with p values trations of IL6, TNFα, and IGF1 are shown by group in < 0.05 but not significant after FDR correction were iden- Table 1. Significant group differences (p < 0.05) were tified, including Erysipelotrichi, Fusobacteria, observed for IL6 and IGF1, but not TNFα. SR1_unknown,and Acidimicrobiia. Microbial analyses—overall microbiome composition, Associations of identified microbial communities with β-diversity, and α-diversity Figure 1 depicts the overall inflammatory parameters Several phyla were signifi- composition of the serum microbiomes among both cantly associated with serum inflammatory parameters young and older adults at both the phylum (A) and class (Fig. 4), in particular Bacteriodetes which was signifi- (B) levels of taxonomy. Principal coordinate analysis cantly correlated with all three measures. The phylum (PCoA) revealed that age groups differed in the overall TM7 was significantly correlated with both IGF1 and serum microbiota community structure as determined IL6. Additionally, several other phyla displayed p values by Unweighted UniFrac (C). Key measures of α-diver- < 0.05 but were not significant after FDR correction. sity, including richness (chao1 and observed species) These included the following: Bacteria_Other with and phylogenetic diversity, were significantly different IGF1 (ρ =0.329, p =0.025), Tenericutes with IGF1 between young and older adults (Fig. 2). Overall sample (ρ = 0.303, p = 0.041), and Spirochaetes with TNFα diversity, measured according to the Shannon and (ρ = − 0.285, p = 0.050). At the class level, three Fig. 1 Taxonomic distribution of serum microbiome of healthy young and older adults by phylum (a)and class (b). c Comparison of serum microbiome β-diversity (Unweighted UniFrac) between healthy young (blue) and older (red) adults 260 GeroScience (2018) 40:257–268 Fig. 2 Comparison of α-diversity of the serum microbiome be- phylogenetic diversity, and sample diversity (shannon and tween healthy young (blue) and older adults (red). Five indices simpson indices). Box whiskers indicate the range of observed were used to represent the richness (chao1, observed species), values significant correlations were observed including bacteria in both the gut and circulation—was signifi- Bacterioidia with both IGF1 and IL6 as well as cantly lower among older adults. Several of these dif- Cytophagia with IGF1 (Fig. 5). Correlations with other ferentially expressed bacterial DNA were also signifi- families with p values < 0.05 but not significant after cantly correlated with indices of inflammation. DNA FDR correction included Bacteria_Other_Other with from Bacteriodetes in particular displayed strong rela- IGF1 (ρ =0.318, p =0.033), Leptospirae with IGF1 tionships with inflammatory parameters as it was posi- (ρ = 0.321, p = 0.031), and Bacterioidia with TNFα tively associated with IGF1 and negatively associated (ρ = − 0.324, p =0.026). with both IL6 and TNFα. Under healthy conditions, the compartmentalization of bacteria and other microbes to the gastrointestinal tract is maintained by a tight barrier at the intestinal- Discussion vascular interface (Spadoni et al. 2015). Yet, under certain clinical conditions, the integrity of this barrier This is the first study to evaluate the age-related differ- can decrease and result in microbial translocation to the ences in microbial DNA profiles present in serum of systemic circulation. For instance, microbial transloca- healthy humans as well as associations of DNA abun- tion due to a loss of immune control has been reported in dances of specific microbial communities with indices HIV+ patients (Brenchley et al. 2006) as well as in of systemic inflammation. These findings are the first to cirrhotic patients with ascites (Santiago et al. 2016). In indicate that the community structure of the microbiome the case of the HIV+ population, microbial translocation in human serum differs between healthy young and was associated with low-grade systemic inflammation older adults. Compared to younger adults, serum of similar to findings observed in the in recent animal study of aging (Thevaranjan et al. 2017). older adults contained DNA from fewer species representing a lower level of phylogenetic diversity than In the present study, the analysis performed from that of young adults. Numerous bacterial phyla- and whole serum cannot differentiate between microbial class-level differences were observed between age DNA fragments and intact microbes. Even under groups. Notably, the relative abundance of DNA from healthy conditions, human blood contains bacterial the Bacteroidetes phylum—one of the most abundant DNA capable of triggering host innate immune GeroScience (2018) 40:257–268 261 Fig. 3 Microbial DNA populations differentially expressed be- multiple comparisons via false discovery rate. Box whiskers rep- tween young (blue) and older (red) adults at the phylum level. resent the range of observed values Asterisk indicates statistical significance after correcting for responses (Hacker et al. 2002;Muruveetal. 2008; secondary to gut permeability. Future studies are Nikkarietal. 2001). What is notable here, however, is needed to confirm this hypothesis. the differences in the relative abundances between Novel findings of this study include the differences in young and older adults. Several studies have reported β-diversity as well as in the number of species with alterations in circulating bacterial DNA abundances DNA expressed. In particular, DNA from the and corresponding immune/inflammatory profiles in Bacteroidetes phylum differed by age and was signifi- patient populations including those with cirrhosis, cantly correlated with indices of inflammation. Given kidney disease, and cardiovascular disease the lower abundance of Bacteroidetes DNA among (Dinakaran et al. 2014; Frances et al. 2004;Kwan older adults—these data could suggest a causal relation- et al. 2013). In fact, differences in relative bacterial ship between microbial DNA community composition DNA abundances between patients and controls were and lower IGF1/higher inflammatory cytokines ob- served with advanced age. Though speculative, as a proposed as an indicator of cirrhosis progression (Santiago et al. 2016). Though we cannot confirm dominant microbial community, it is possible that re- the cause of these differentially expressed DNA, our ductions in circulating concentrations indicate increases prior findings related to zonulin concentrations in in other potentially more reactive communities. older adults as well as pre-clinical studies in this area Another novel finding of the study is association lead us to hypothesize that these differences may be of serum microbial DNA abundances with IGF1. 262 GeroScience (2018) 40:257–268 Table 2 Serum microbiome composition at the class level (25 most common OTUs) Young adults Older adults p value for group Firmicutes_Clostridia 34.8 ± 14.3 34.5 ± 15.4 0.932 Bacteroidetes_Bacteroidia 18.2 ± 4.7 13.6 ± 5.0 0.003* Firmicutes_Bacilli 12.5 ± 7.0 11.4 ± 4.7 0.831 Proteobacteria_Gammaproteobacteria 9.6 ± 6.3 7.7 ± 3.2 0.580 Actinobacteria_Actinobacteria 4.9 ± 6.4 7.6 ± 7.4 0.093 Firmicutes_Erysipelotrichi 2.3 ± 1.6 8.6 ± 13.4 0.023 Fusobacteria_Fusobacteriia 5.9 ± 4.6 3.4 ± 3.6 0.035 Proteobacteria_Betaproteobacteria 2.7 ± 0.9 3.4 ± 2.3 0.496 Proteobacteria_Alphaproteobacteria 2.3 ± 2.3 2.4 ± 1.8 0.702 Verrucomicrobia_Verrucomicrobiae 1.9 ± 0.8 2.3 ± 1.3 0.217 Proteobacteria_Epsilonproteobacteria 0.8 ± 0.4 0.6 ± 0.4 0.085 Actinobacteria_Coriobacteriia 0.5 ± 0.2 0.6 ± 0.3 0.120 Bacteroidetes_Flavobacteriia 0.5 ± 0.2 0.6 ± 0.7 0.898 Cyanobacteria_Chloroplast 0.5 ± 0.4 0.7 ± 0.5 0.173 Tenericutes_Mollicutes 0.7 ± 0.5 0.5 ± 0.4 0.003* Proteobacteria_Deltaproteobacteria 0.4 ± 0.3 0.4 ± 0.3 0.865 Bacteria_Other_Other 0.3 ± 0.2 0.2 ± 0.2 0.004* Bacteria_SR1_unknown 0.2 ± 0.2 0.1 ± 0.2 0.016 Bacteroidetes_Cytophagia 0.04 ± 0.04 0.25 ± 0.49 0.003* Cyanobacteria_Synechococcophycideae 0.07 ± 0.04 0.16 ± 0.22 0.328 Deferribacteres_Deferribacteres 0.10 ± 0.10 0.10 ± 0.08 0.686 Firmicutes_Other 0.11 ± 0.07 0.06 ± 0.05 0.011* Spirochaetes_Leptospirae 0.10 ± 0.08 0.04 ± 0.06 0.001* Actinobacteria_Acidimicrobiia 0.03 ± 0.03 0.04 ± 0.06 0.045 Cyanobacteria_Oscillatoriophycideae 0.02 ± 0.03 0.10 ± 0.20 0.034 All values (mean ± SD) indicate relative abundance (%) *Statistically significant after correction for false discovery rate Though typically known for its potent anabolic proper- implications for present findings, as microbial LPS ties, IGF1 also has tremendous relevance to the human may stimulate inflammatory cytokine production. immune system. It is well documented that inflamma- Moreover, recent data reported that gut microbiota can tory cytokines attenuate IGF1 production (Maggio et al. stimulate IGF1 (Yan et al. 2016). Despite these links, the 2013; Rajpathak et al. 2008), but IGF1 also plays an present data should not be over-interpreted as they do important role in regulating innate and acquired immu- not provide any indication of directional causality. How- nity—including the production of inflammatory cyto- ever, they do suggest that further follow-up may be kines (Heemskerk et al. 1999). Clinical data have re- warranted given the strength of associations and the cently implicated low IGF1 in flare-ups of inflammatory aforementioned recent literature in this area. bowel disease (Krakowska-Stasiak et al. 2017), while Notably, dietary intake—including intake of dietary basic studies have demonstrated that IGF1 directly in- fiber—and fitness differed between young and older hibits pro-inflammatory cytokines in multiple animal adults. Though these are differences commonly ob- cell types (Ji et al. 2017; Onnureddy et al. 2015), induc- served between young and older adults, these findings ing LPS-induced cytokine expression (Onnureddy et al. are important in the present context as diet and physical 2015). This latter finding may have important activity/exercise are among the primary factors known GeroScience (2018) 40:257–268 263 Fig. 4 Microbial DNA populations at the phylum level signifi- significance after correcting for multiple comparisons via false cantly differing in abundance between young and older adults and discovery rate. Data points are colored separately to indicate correlated with indices of inflammation. Correlation coefficients young (blue) and older (red) adults reflect the Spearman rho comparison. Asterisk indicates statistical to influence gut microbiota communities (Campbell and As with any study, the present investigation is not Wisniewski 2017;Chenetal. 2014;O’Sullivan et al. without limitations. For instance, as noted above, the 2015; Pallister and Spector 2016). To our knowledge, 16S microbiome analysis does not discriminate between no data exist to directly indicate that diet or exercise can microbial DNA fragment and intact microbes. alter systemic bacterial DNA expression. However, both However, as noted, previous studies have shown high-fat meals and highly vigorous exercise are known to that even bacterial DNA fragments are capable of be capable of inducing intestinal permeability, bacterial stimulating immune reactions based on their for- translocation, and even transient endotoxemia (Costa eign structure (Hacker et al. 2002;Muruveetal. et al. 2017; Kelly et al. 2012). It is unclear at present 2008; Nikkari et al. 2001). Again, it is possible how these factors might contribute to age-related differ- that differences in serum microbial DNA expres- ences in serum microbiome profiles, but these factors are sion may be influenced by exercise (as evidenced important to consider for proper interpretation of study by fitness) or diet which are important regulators findings and in moving forward to causal studies. of the intestinal microbiome. However, this is 264 GeroScience (2018) 40:257–268 Fig. 5 Microbial DNA populations at the class level significantly significance after correcting for multiple comparisons via false differing in abundance between young and older adults and cor- discovery rate. Data points are colored separately to indicate related with indices of inflammation. Correlation coefficients re- young (blue) and older (red) adults flect the Spearman rho comparison. Asterisk indicates statistical purely speculative at present. Additionally, only a disease and not obese (body mass index < 30 kg/m ). single time-point was examined; thus, it remains All subjects completed health history questionnaires, unclear if serum microbe composition changes and older adults passed a comprehensive physical exam over time. and a diagnostic exercise stress test with 12-lead ECG to In summary, this study is the first to demonstrate age- confirm health status. All participants were also related differences in the composition of the serum assessed for body composition via dual x-ray absorpti- microbiome and associations between DNA expression ometry and for aerobic fitness (i.e., VO max) via a of microbial communities and circulating indices of maximal exercise challenge with expired gases as fur- inflammation. Future studies are needed to evaluate ther indicators of overall health status. Habitual dietary causal links between these outcomes as well as associ- intake was assessed via 4-day food records analyzed ations between the abundance of microbial communities using Nutrition Data Systems for Research (NDSR) in the serum among those with various chronic diseases. software (Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN). Prior to participation, all participants provided written informed based on docu- Experimental procedures ments approved by Institutional Review Boards of the University of Alabama at Birmingham (UAB) and Bir- Study population A total of 48 healthy, community- mingham Veterans Affairs Medical Center. dwelling adults from the Birmingham, AL metropolitan Blood collection and inflammatory analyses Venous area was included in this study. These participants rep- resented a sub-set from a larger study protocol investi- blood was collected and spun down to obtain serum gating skeletal muscle changes and exercise responsive- using standard clinical practices. Serum IL6 and TNFα ness with aging. Inclusion criteria were based on age were determined using a Meso Scale Discovery (MSD; ranges of 20–35 years for younger adults and 60– Rockville, MD) Quick Plex SQ 120 imager using 75 years for older adults. Subjects were free of chronic electrochemiluminescence technology. Minimum GeroScience (2018) 40:257–268 265 sensitivity for the IL6 assay was 0.07 pg/mL, while Microbiome analyses—sequence data analysis and sensitivity was 0.09 pg/mL for TNFα. Intra-assay coef- composition The sequence data covered the 16S ficients of variation (CV) were 7.84 and 7.67%, and rRNA V4 region with a PCR product length of ~ inter-assay coefficients were 5.78 and 2.5% for IL6 255 bases and 250 base paired-end reads. Since the and TNFα, respectively. IGF1 was assessed via overlap between fragments was approximately 245 immunoradiometric assay (Diagnostic Systems Labora- bases, the information from both ends of the paired tories, Webster, TX). The inter-assay CV, intra-assay reads was merged to generate a single high-quality CV, and assay sensitivity for IGF1 were 9.43, 3.48, read using the module Bfastq_mergepairs^ of and 4.89 ng/mL, respectively. USEARCH (Edgar 2010). Read pairs with an overlap of less than 50 bases or with too many mismatches (> Microbiome analyses—16S PCR amplification The 20) in the overlapping region were discarded. Chi- 16S V4 analysis was done as previously described meric sequences were also filtered using the (Kumar et al. 2014). DNA was extracted from Bidentify_chimeric_seqs.py^ module of USEARCH serum samples with the ZR Fecal DNA Miniprep (Edgar 2010). Overall, read quality was assessed Kit (Zymo Research, Irvine, CA) (Kumar et al. before and after filtering using FASTQC (FASTQC. 2014). PCR was used with unique bar-coded http://Www.bioinformatics.babraham.ac. primerstoamplify the V4regionofthe 16SrRNA uk/projects/fastqc/).The QIIME data analysis gene to create an Bamplicon library^ from individ- package was used for subsequent 16S rRNA data ual samples as described by Kumar et al. (2014). analysis (Caporaso et al. 2010a, b). Sequences were Cycling conditions for the PCR reactions were as grouped into operational taxonomic units (OTUs) follows: initial denature 94 °C for 1 min followed using the clustering program UCLUST at a similarity by 32 cycles of 94 °C for 30 s, 50 °C for 1 min, threshold of 0.97% (Edgar 2010). The Ribosomal 65 °C for 1 min, and a final extension of 65 °C Database Program (RDP) classifier was used to make for 3 min. The entire PCR reaction was electro- taxonomic assignments (to the genus and/or species phoresed on a 1.0% agarose/Tris-borate-EDTA gel. level) for all OTUs at confidence threshold of 80% The PCR product (approximately 250 base pairs) (0.8) (Wang et al. 2007). The RDP classifier was was visualized by UV illumination. The band was trained using the Greengenes (v13_8) 16S rRNA excised and purified from the agarose using database (McDonald et al. 2012). Qiagen QIAquick Gel Extraction Kit according to The resulting OTU table included all OTUs, their tax- the manufacturer’s instructions. onomic identification, and abundance information. OTUs The PCR products were then sequenced using the whose average abundance was less than 0.0005% were Illumina MiSeq platform (Kumar et al. 2014). Paired filtered out. OTUs were then grouped together to summa- end reads of approximately 250 bp from the V4 rize taxon abundance at different hierarchical levels of region of 16S rDNA were analyzed. The samples classification (e.g., phylum, class, etc). Multiple sequence were first quantitated using Pico Green, adjusted to alignment of OTUs was performed with PyNAST a concentration of 4 nM then used for sequencing on (Caporaso et al. 2010a, b). Alpha diversity (diversity with- the Illumina MiSeq (Kumar et al. 2014). Fastq con- in the samples) was calculated using Shannon’sdiversity version of the raw data files was performed following matrix which measures both richness (number of OTUs/ de-multiplexing. Quality control of the fastq files was species present in a sample) and evenness (relative abun- performed which was then subject to quality assess- dance of different OTUs/species and their even distribution ment and filtering using the FASTX toolkit (FASTX). in a sample) (Jost 2007), as implemented in QIIME The remainder of the steps was performed using the (Caporaso et al. 2010a, b). Beta diversity (diversity be- Quantitative Insight into Microbial Ecology (QIIME) tween the samples) was measured using unweighted suite, version 1.8 (Kumar et al. 2014; Lozupone et al. Unifrac analysis (Lozupone and Knight 2005). Principal 2007; Navas-Molina et al. 2013). One sample was coordinate analysis (PCoA) was performed by QIIME to removed from analysis due to failing quality control visualize the dissimilarity matrix between all samples, such procedures. that samples that were more similar were closer in space 266 GeroScience (2018) 40:257–268 Open Access This article is distributed under the terms of the than samples that were more divergent. A 3D PCoA plot Creative Commons Attribution 4.0 International License (http:// was generated using EMPEROR (Vazquez-Baeza et al. creativecommons.org/licenses/by/4.0/), which permits unrestrict- 2013). ed use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if Statistical analysis All data were evaluated for normal- changes were made. ity and homogeneity of variance prior to determination of descriptive statistics and comparative analyses. Group comparisons for demographic, dietary, and in- flammatory data were performed using Student’s t tests References for independent samples. The observed species metric of α-diversity was assessed using Student’s t test. Other Belkaid Y, Naik S (2013) Compartmentalized and systemic control indices of α-diversity were assessed via the Mann- of tissue immunity by commensals. Nat Immunol 14(7):646– Whitney test. 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Correlation coefficients were cal- Sport Sci Rev 45(1):41–47. https://doi.org/10.1249 culated using the Spearman procedure. Correlations /JES.0000000000000096 with p values < 0.05 were flagged, with final determi- Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen nation of significance established after correcting for GL, Knight R (2010a) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics (Oxford, FDR. England) 26(2):266–267. https://doi.org/10.1093 /bioinformatics/btp636 Acknowledgements We are thankful to all study participants for their contributions. This work was supported by grants from the Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman National Institutes of Health, including the National Institute on FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon Aging (R01AG017896 to MMB, R01AG054538 to TWB), the JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, National Center for Medical Rehabilitation Research Lozupone CA, McDonald D, Muegge BD, Pirrung M, (P2CHD086851 to MMB), and the Department of Veterans Af- Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, fairs (Merit Grant to MMB). The following are acknowledged for Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010b) their support of the Microbiome Resource at the University of QIIME allows analysis of high-throughput community se- Alabama at Birmingham: School of Medicine, Comprehensive quencing data. Nat Methods 7(5):335–336. https://doi. 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