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University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 1 The E ffect of G alacto oligosaccharide S upplementation on I ntestinal M icrobiota Madeline Bost C ollege of Agricultur al and L i fe Sciences University of Florida Supplementation with prebiotics, substrates intended to facilitate the growth of beneficial bacteria in the gastrointestinal tract is anticipated to affect health and reduce disease complications. The purpose of this project was to study an interven tion in the diets of University of Florida undergraduate students by providing galacto oligosaccharide (GOS) fiber packets to consume daily. Of the 427 students participating, a subgroup of 66 individuals provided a total of 262 stool samples. DNA was extracte d from fecal samples, and the 16S rRNA gene was amplified non selectively from the bacterial community and analyzed using denaturing gradient gel electrophoresis ( DGGE ). Baseline and treatment samples were compared for each subject. According to DGGE analy sis using both visual comparison and statistical tests GOS intake significantly affect ed the diversity of intestinal microbiota Ongoing DNA sequencing will provide additional data on which species were affected. INTRODUCTION The digestive tract composes a large portion of the immune system. Nearly three quarters of our lymphocytes are housed in the human gastrointestinal (GI) tract (Mueller & Macpherson, 2006) In addition to mucous membranes and stomach acid, the body is protected by a multitude of non pathogenic bacteria living on the epithelial surface lining the GI t ract. These bacteria cover the mucosa and help to prevent pathogenic bacteria from attaching and establishing colonies (Macpherson & Harris, 2004) Additionally, they stimulate an increase in goblet cell proliferation (Mueller & Macpherson, 2006) .They are able to do this by stimulating an immune response without attacking the commensals because they adapt to the internal environment without damaging host tissue (Macpherson et al., 2007) Their activities include fermenting indigestible carbohydrates and producing lactate and short chain fatty acids that are absorbed in the colon (Knol et al., 2005) Most of these bacteria are found in the colon, with the stomach and small intestines having relatively lower quantities (Macpherson & Harris, 2004) ; the majo rity of these bacteria include Bacteroides Clostridium Lactobacillus Eubacterium Faecalibacterium and Bifidobacterium species ( Martin et al., 2010) The community composition for each individual is generally stable over time ( Tzortis & Vulevic, 2009) Intestinal bacteria have been shown to affect obesity, the onset of diabetes, energy metabolism, inflammatory responses, and many other conditions. Microbial communities display a great range of variability, depending on a number of factors including age, health status, diet, and location in the GI tract. Bifidobacteria and lactic acid bacteria are largely recognized as health promoting organisms and widely used as probiotics ( Tzortis & Vulevic, 2009) This is likely due dendritic cells, which help regulate immune response ( Weiss et al., 2010) Administration of probiotic supplementation of certain strains of these bacteria have been shown to beneficially impact the health of infants (Romeo et al., 2010) and breast fe d i nfants tend to have a bifido dominated gut as opposed to formula fed infants, which generally have a much more varied microbial community (Khailova et al., 2010) Significant effort has been put into devising ways to promote the growth specific beneficial bacteria that have been found to improve health and support immune function. One of the ways this is accomplished is through supp lementation, namely prebiotics ( substrates intended for the microbes ) probiotics ( live cultures of the beneficial bacteria ) and synbiotics, combinations of prebiotics and probiotics (Bosscher, 2009). Prebi otics must be non digestible to humans so that they can pass through the digestive system intact and selectively stimulate the growth of the preferred bacteria (Gibson & Roberfroid, 1995; Yang et al., 2010 ). Sources of prebiotics currently investigated include i nulin, fructooligosaccharide, lactulose, and galactooligosaccharide (Satokari et al., 2001) Galacto oligosaccharide ( GOS ) is colorless, water soluble, and has low water activity, which increases shelf life by reduc ing the risk of microbial contamination Additionally, i t is stable with in a range of temperatures which opens wide applications as a supplemental food additive It also has low caloric value of 1 2 kcal /g (Cummings et al., 1997) which makes it an ideal
MADELIN E BOST University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 2 supplement because it will not adversely affect certain groups of individuals (e.g., diabetics ) or raise the energy value of a food. Denaturing gradient gel electrophoresis ( DGGE ) is a technique used to visually determine the population and variety of microbes in a sample and relies on the 16S r RNA gene (Vanhoutte et al., 2004) 16S rDNA sequencing is a particularly useful way to identify bacteria, especially those with unique phen otypes or that cannot be cultured (Woo et al., 2008) In a denaturing polyacrylamide gel, DNA fragments of the same length can be separated based upon base pairs, which affect the mobility of the molecule (Muyzer et al., 1993) As the DNA migrates down the gel, the DNA molecule degenerates from its double stranded conformation to a melted single stranded form that has decreased mobility and its migration stops in specific melting domains based on the number of G C nucleotide bonds The bands indicate group s present in the sample (Wanga et al., 2007) The purpose of this study is to determine the extent to which GOS changes microbial populations in the gut. We hypothesize that beneficia l microbes will be enriched with GOS supplementation specifically, bifidobacteria and lactic acid bacteria and changes in gut microbiota populations will be seen using DGGE analysis and proven with sequencing METHODS Participants Stool samples were obtained from a subgroup of subjects participating in a larger study ex amining the impact of GOS on overall digestive health and immune strength. Undergraduate students were recruited using fliers, messages through listservs, and announcement s in classrooms. 427 students were randomized to three groups: 5 g GOS (provided by P urimmun eTM GTC Nutrition, Golden CO.) treatment, 2.5 g GOS treatment, and 5 g placebo. The procedure dictated a double blind study. Exclusionary criteria included smoking, allergies involving the upper res piratory tract or milk and refusal to discontinue dietary supplements involved with immune strength (including prebiotics, probiotics, fish oil, Echinacea, other fiber supplements, and vitamin E exceeding 100% of the RDA) Participants were required to mix a 5 g treatment packet with a beverage and answer an online questionnaire daily. These questions pertained to stress level, cold symptoms and their severity, and bowel habits. A small subset of study participants volunteered to provide stool samples There were 24, 21, and 22 subjects in groups 0 g GOS 2.5 g GOS and 5 g GOS respectively. Participants dropped off the samples, which were promptly aliquoted within four hours of defecation into tubes for later analysis. The samples were stored at 20C until extraction to minimize DNA degradation. DNA E xtr action DNA was extracted from stool samples using the QIAamp Stool Mini Kit (Qiagen) following the is as follows. A small amount, approximately 0.2 mg stool was homogenized in a 0.05 M phosphate buffer. Zirconia beads were added to assist in the lysing of cells using the Mini B ead B eater (Biospec) Samples were processed using a series of buffers and washes to elute the DNA. Because a phenol chloroform technique is not used, purificati on of the sample is not necessary to remove impurities. Samples were analyzed using a Nanodrop spectrophotometer to assess nucleic acid concentration and purity. PCR A mplification Qiagen reagents 10x reaction buffer, MgCl2 (1 dNTPs, and Taq amplify the V6 V8 region of bacterial RNA. The master mix contained forward and reverse primers. Extracted DNA was diluted 1:10 The PCR series c onsisted of an initial melting temperature of 94 C for 2 min followed by 35 cycles of melting at 94 C for 30 s, annealing at 55 C for 30 s and polymerase copying at 68 C for 30 s. The final annealing step was at 68C for 7 min. R eactions were checked on a 1% agarose gel for the pre sence of a band with length of around 500 base pairs corresponding to the 16S rRNA gene. DGGE A 457 base pair fragment from the V6 to V8 region of the bacterial 16S rDNA was amplified with primers U968 G GCG GGG GCA CGG GGG GAA CGC GAA GAA CCT described by Zoetendal et al. (1998). The GC clamp facilitates separation by DGGE which was performed on an 8% [wt/vol] acrylamide gel with a gradient from 40% at the top to 50% at the bottom at a temperature of 60 C. The 100% denaturing conditions were defined as 7 M urea and 40% formamide. Gels were run for 16 h at 65V and stained with SYBR Gold. Images of the stained gels were scan ned in with Quantity One software (Biorad) and analyzed with Diversity database software (Biorad). DGGE Analysis The DGGE bands were analyzed using Diversity Database ( Biorad ) software T here was a total of 262
THE EFFECT OF GALACT O OLIGOSACCHARIDE SUPP LEMENTATION ON INTES TINAL M ICROBIOTA University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 3 samples Marked bands in gels were visually compared to other gels containing the same subjects to determine if there were obvious differences or similarities across treatment groups with regard to band location and intensity. A phylogenetic tree was derived from all of the gels in the database using the Dice coefficien clustering method. Lane reports for the relative quantity values were exported from each gel and entered into http://www.changbioscience.com/genetics/shannon.html to obtain the Shannon Weimer diversity index, a measure of the occurrence of species in a sample. The Simpson Diversity index (1/D) was also calculated. Th ese values were utilized in an E xcel spreadsheet (Microsoft) using a 2 tailed type 2 est. The groups compared were as follows: first and seco nd baseline sample within groups; baseline and treatment within groups; baseline (first and second donation) samples between groups; treatment (third and fourth) between groups. P values less than 0.05 were considered significant. RESULTS Fecal DNA E xtraction and A mplification The yield of DNA using the Qiagen extraction kit ranged from 75 pair fragment from extracted fecal DNA was initially assessed by visualizatio n in an agarose gel (Figure 1). Figure 1 Agarose gel electrophoresis of PCR products from 16S rDNA from fecal samples. For subject 283, the dilutions of the PCR product are 1:10, 1:20, and 1:40 from left to right. The samples 049, 252, 269, and 290 each have two dilutions in the gel, with 1:10 on the left and 1:20 on the right. Of the three dilutions for sample 283 only the 1:40 dilution is visible in the gel. The negative control shows a faint band but is much lighter than the ladder. Taq Polymerase is the likely cause for this faint band as it is made in E.coli and 16S DNA fragments might be copurified. DGGE For each sample lane, the bands were compared to other lanes of the same treatment group on the basis of location and intensity ( Fig ure 2 ). Rf values between the gels were compared. From this, it was obvious that lanes for a particular subject are more similar to each other than they are to lanes of other subjects, regardless of the group The changes in band intensities between baseline and treatment samples were compared for each subject in the 5 g GOS group When a pattern shared between four or more subjects emerged, it was compared to bands in the 0 and 2.5 g GOS groups No patterns in band intensity after treatment were unique to the 5 g GOS group Phylogenetic clustering was observed for baseline and treatment pairs in 0 g GOS, 2.5 g GOS, and 5 g GOS groups using Diversity Database (Fig ure 3). The ex pected pattern was as follows: treatment sample pairs would have higher similarity than baseline sample pairs, and treatment pairs would be somewhat similar to baseline pairs. The results from this observation are shown in Table 1. For each group, unexpected or seemingly random clusteri ng was observed in about half of the subjects. The expected pattern was low in 0 g GOS and 5 g GOS, but much higher in 2.5 g GOS; over a third of the subjects followed the expected clustering pattern. Cases where the baseline samples were more similar to e ach other than treatment samples were more prevalent than the expected pattern in 0 g GOS and 5 g GOS. Some subjects only donated three samples, so a clustering pattern could not be determined. Ladder control 283 049 252
MADELIN E BOST University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 4 Figure 2. DGGE of PCR products of V6 V8 regions of 16S r DNA from fecal samples. The gel contains three subjects, 038 (5 g GOS), 049 (2.5 g GOS, and 050 (O g GOS), each with two baseline and two treatment samples Figure 3. Excerpt of the phylogenetic cluster of PCR products from 16S rDNA of stool samples. A refers to 0 g GOS, B to 2.5 g GOS, and C to 5 g GOS.
THE EFFECT OF GALACT O OLIGOSACCHARIDE SUPP LEMENTATION ON INTES TINAL M ICROBIOTA University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 5 Table 1 Patterns F ound in the P hylogenetic C lustering Sample Providing Participants 0 g GOS 2.5 g GOS 5 g GOS Number of subjects 24 21 22 Unexpected 15 10 14 Expected 2 8 1 Baseline more similar than treatment 3 2 3 Only three samples provided 4 1 4 Statistical T ests The comparison of the Shannon Weiner 0 g GOS to 5 g GOS was the only statistically significant value (p value = 0.0482); the Simpson value for the same data set was not statistically significant (Table 2 ). From this test, it is evident that the re is not a difference between 5 g GOS and 2.5 g GOS or 2.5 g GOS and 0 g GOS but there is a difference between 0 g GOS and 5 g GOS Table 2 Shannon Weiner and Simpson D iversity I ndexes Parameters Shannon Simpson A1 and A2 0.3529 0.5707 B1 and B2 0.9168 0.8733 C1 and C2 0.9661 0.8062 Ab and At 0.1285 0.0876 Bb and Bt 0.0923 0.0762 Cb and Ct 0.5661 0.2989 Ab and Bb 0.1620 0.2901 Bb and Cb 0.6384 0.8637 Ab and Cb 0.4471 0.2806 At and Bt 0.1978 0.2576 Bt and Ct 0.6059 0.4773 At and Ct 0.0482 0.0638 Note. baseline sample treatment samples respectively. DISCUSSION When considering the diversity index calculations, there was a statistically significant value for the Shannon Weiner calculation for the difference between the 0 g GOS and 5 g GOS post treatment groups (p value = 0.0482) This shows that there is a differ ence between the 5 g treatment and the placebo that may be due to the fiber consumption. These values were not as apparent when considering the 0 g GOS and 2.5 g GOS groups. It is possible that the amount of fiber administered was not high enough to elicit a response. On the other hand, when comparing the DGGE gels visually, no unique patterns emerged for 5 g GOS compared to 0 g GOS This could be due to a number of reasons. Diversity Database is an inexact program, allowing for a large range of subjectivity, specifically with creating the bands. Additionally, there were unexpected r esults in the phylogenetic tree Treatment samples, especially for 5 g GOS were expected to have a greater similarity to each other than the baseline samples, but 5 g GOS having a high similarity to a treatment sample. This indicates that either the particular fiber or the amount consumed did n o t have a strong impact on intestinal microbial populations. Another factor that affected the expected results is that i t is likely that there was a certain level of non compliance. This is indicated by the number of subjects who provided only three samples. Additional sources of non compliance include the manner in which the fiber powder was consumed. According to feedback, the GOS did not completely dissolve in 0.5 L of liquid, and so some participants may have disposed of water bottles or washed cups containing residual fiber and consequently not have consume d the whole dose From these results, the following conclusions may be drawn. The dosage of fiber was not high enough to yield discernible differences between baseline and tr eatment samples between groups 0 g GOS 2.5 g GOS and 5 g GOS by visually comparing DGGE bands ; however the diversity index calculations indicate there is a difference in the diversity of intestinal microbiota DNA sequencing can be done to confirm the species present in the groups. ACKNOWLEDGMENTS I would like to thank Dr. Bobbi Langkamp Henken for exposing me to research studies Dr. Volker Mai for giving me the opportunity to get involved in lab work and Tyler Culpepper for his guidance and patience at the bench.
MADELIN E BOST University of Florida | Journal of Undergraduate Research | Volume 13, Issue 3 | Summer 20 12 6 REFERENCES Bosscher D (2009) Food based strategies to modulate the compos ition of the intestinal microbiota and their associated health effects. Journal of Physiology and P harmacology. 60; 5. Cummings JH, Roberfroid MB, Anderson H, Barth C, Ferro Luzzi A, Ghoos Y, Gibney M, Hermsonsen K, James WPT, Korver O, Lairon D, Pascal G, Voragen AGS (1997) PASSCLAIM Gut health and immunity. European Journal of Clinical Nutrition.51; 417 423. Gibson GR, Roberfroid (1995) Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. The Journal of Nutrition. 125; 1401 1412. Khailova L, Mount Patrick SK, Arganbright KM, Halpern MD, Kinouchi T, Dvorak B ( 2010) Bifidobacterium b ifidum r educes a poptosis in the i ntestinal e pithelium in n ecrotizing e nterocolitis. American Journal of Physiology: GI and liver Physiol ogy. Knol J, Scholtens P, Kafka C, Steenbakkers J, Gro S, Helm K, Klarczyk M, Scho¨pfer H, Bo¨ckler HM, Wells J (2005) Colon m icroflora in i nfants f ed f ormula with g alacto and f ructo o ligosaccharides: m ore l ike b reast f ed Infants. Journal of Pediatric Ga stroenterology and Nutrition. 40; 36 42. Macpherson AJ, Hapfelmeier S, McCoy KD (2007) The armed truce between the intestinal microflora and host mucosal immunity. Seminars in Immunology.19; 57 58. Macpherson AJ, Harris NL (2004) Interactions between co mmensal intestinal bacteria and the immune system. Nature Reviews Immunology.4; 478 485. Martin FPJ, Sprenger N, Montoliu I, Rezzi S, Kochhar S, Nicholson JK (2010) Dietary Modulation of g ut f unctional e cology s tudied by f ecal m etabonomics. Journal of Proteome Research. 9; 5284 5295. Mueller C, Macpherson AJ (2006) Layers of mutualism with commensal bacteria protect us from intestinal inflammation. Gut. 55; 276 284. Muyzer G, De Waal EC, Uitierlinden AG (1993) Profiling of c omplex m icrobial p opulation s by d enaturing g radient g el e lectrophoresis a nalysis of p olymerase c hain r eaction a mplified g enes c oding for 16S rRNA. Applied and Environmental Microbiology. 59; 695 700. Romeo J, Nova E, Wrnberg J, Gmez Martnez S, Daz Ligia LE, Marcos A ( 2010) Immu nomodulatory effect of fibres, probiotics and synbiotics in different life stages. Nutricion Hospitalaria. 25; 341 349. Satokari RM, Vaughan EE, Akkermans ADL, Saarela M, De Vos W ( 2001) Polymerase c hain r eaction and d enaturing g radient g el e lectrophoresis m onitoring of f ecal Bifidobacterium p opulations in a p rebiotic and p robiotic f eeding t rial. Systemic and Applied Microbiology. 24; 227 231. Tzortis G, Vulevic J (2009). Galacto o ligosacc haride p rebiotics. In Prebiotics and Probiotics Science and Technology Springer Science+Business Media, LLC. 207 244 Vanhoutte T, Huys G, De Brandt E, Swings J (2004) Temporal stability analysis of the microbiota in human feces by denaturing gradient gel electrophoresis using universal and group specific 16S rRNA gene primers. FEMS Microbiology Ecology. 48; 437 446. Wanga HF, Zhub WY, Yaob W, Liua J X (2007) DGGE and 16S rDNA sequencing analysis of bacterial communities in colon content and feces of pigs fed whole crop rice. Anaerobe. 13; 127 133. Weiss G, Rasmussen S, Hjerrild Zeuthen L, Nhr Nielsen B, Jarmer H, Jespersen L, Frkir H (2010) Lacto bacillus acidophilus induces virus immune defense genes in murine dendritic cells by a Toll like receptor 2 dependent mechanism. Immunology. 131; 268 281. Weiss G, Rasmussen S, Nielson Fink L, Jarmer H, Nhr Nielsen B, Frkir H (2010) Bifidobacterium bifidum a ctively c hanges the g ene e xpression p rofile i nduced by l actobacillus acidophilus in m urine d endritic c ells. PLoS ONE. 5. Woo PCY, Lau SKP, Teng JLL, Tse H, and Yuen KY ( 2008) Then and now: use of 16S rDNA gene sequencing for bacterial identificat ion and discovery of novel bacteria in clinical microbiology laboratories. Clinical Microbiology and Infection. 14; 908 934. Yang X, Zhao Y, He N, Croft KD (2010) Isolation, c haracterization, and i mmunological e ffects of r Galacto oligosaccharides from a n ew s ource, the h erb l ycopus lucidus t urcz. Journal of Agriculture and Food Chemistry. 58; 8253 8258. Zoetendal EG, Akkermans ADL, De Vos WM ( 1 998) Temperature g radient g el e lectrophoresis a nalysis of 16S rRNA from h uman f ecal s ampl es r eveals s table and h ost s pecific c ommunities of a ctive b acteria. Applie d and Environmental Microbiolo g y. 64; 3854 3 859.
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