A wide range of bacterial and viral porcine pathogens are routinely isolated from the tonsils [1]. In this study, we identified large numbers of sequences whose closest affiliate in the database were Haemophilus parasuis and Pasteurella multocida (on average 12.8% and 9.3%, respectively, of reads identified at the 97% cutoff), as well as small numbers of sequences closest Daporinad solubility dmso to Streptococcus suis (on average 0.4% of reads identified) from almost all

samples. However, we did not find sequences affiliated with Actinobacillus pleuropneumoniae or A. suis, which have been reported to be found in most swine herds in Ontario, Canada [7]. Small numbers of sequences closest to Mycoplasma were found in a few pigs, but these were not identified beyond the Classifier function

of the RDP. Herd 1 has been regularly tested and found to be free of A. pleuropneumoniae, A. suis, and Mycoplasma, which is substantiated by these results. We were surprised not to find sequences consistent with the presence of pathogenic Actinobacillus species in Herd 2, which has had a history of chronic but undefined selleckchem respiratory problems. It is possible that these chronic problems are related to the higher numbers of Pasteurella sequences found in Herd 2, or to the presence of another known respiratory pathogen, Arcanobacterium, found in Herd 2 but not Herd 1. In addition to porcine pathogens, many bacterial agents of foodborne infections of humans have been isolated from pig tonsils, including members of the Enterobacteriaceae such as Salmonella species, Escherichia

Amisulpride coli, and Yersinia enterocolitica as well as Campylobacter species and Listeria monocytogenes [9–13]. We found low numbers of Campylobacter (0.17% of total reads) and Escherichia (0.59% of total reads) in most of the pig tonsils in this study. In addition, we found other Enterobacteriaceae (1.9% of the total) that are rarely associated with human foodborne illness, including Citrobacter, Enterobacter, Morganella, Proteus, and Providencia, in one or more pigs. We did not find Salmonella, Yersinia, or Listeria in these tonsil samples from healthy pigs. The only other mammalian system where the tonsillar microbiota has been reported is in humans. Culture-based studies of human tonsils have identified Streptococcus pyogenes; S. pneumoniae; Group C, F, and G β-hemolytic streptococci; several α-hemolytic and non-hemolytic streptococci; Staphylococcus aureus; Haemophilus influenzae; H. parainfluenzae; and Moraxella catarrhalis in aerobic cultures [25–31]. Many species of the Bacteroides-Prevotella-Porphyromonas group, Fusobacterium, Lactobacillus, Peptostreptococcus, and Veillonella have also been isolated using anaerobic cultures.

However, the

Trp-2 AuNVs remained in solution when ethanol (0.2% v/v Tween 20) was added to the tubes due to the decrease in polarity of the solvent and the addition of surfactants (Additional file 1: Figure S8). Thus, AuNV particle behavior in solution is dependent on the peptide properties. Having high peptide density on AuNVs is important for vaccine function because the peptide-coated nanocarriers collect in the endosomes and can mimic the size of pathogens, stimulating DC maturation. The induction of DCs to mature and to present tumor antigens is crucial for engineering a successful vaccine. This stimulation by nanomaterials has been shown by Moon et al. to cause DCs to induce large amounts of cross-presentation for stronger and sustained anti-tumor immune responses [27]. Cross-presentation is very important for CTL stimulation because it is required to allow peptides to enter the MHC class I (cytosolic) pathway from the MHC find more class II (endosomal) pathway. By using MHC class I peptides, DC-to-splenocyte ELISPOTs can be used to evaluate the extent of cross-presentation. Additionally, the assay itself is of interest because it can screen LEE011 in vivo large numbers of nanovaccines in vitro, simulating the process of antigen presentation and preventing extensive use of animals. Once the AuNVs enter the endosomes, it is critical that the peptides can come off the particles

and enter the MHC class I pathway. Therefore, the conjugation optimization of conjugation duration and schemes is a key for an effective AuNV. From the

optimization results, we concluded that the 1-h conjugation time was most effective. We hypothesize that the peptides link linearly during the 1-h conjugation but will begin to cross-link transversely via peptide side groups by 2 h. The non-linear cross-linking could disrupt the peptide sequence or presentability, thus lowering the efficacy and size of those AuNVs. As for the method optimization, the buffers used for the conjugation process cause a significant impact on the AuNV efficacy. MES buffer has a pKa of 6.15, which is within the range for EDC coupling to selleck chemicals llc carboxyl groups generating O-acylisourea [28, 29]. Sulfo-NHS was then added to replace the O-acylisourea to form semi-stable amine-reactive NHS esters. Amine binding to the NHS esters reacts better at neutral to higher pH [29]. Thus, switching to PBS at pH 7.4 prevents excessive self cross-linkage. Furthermore, the one-step (MES) method allows better carboxyl activation and a higher chance of extra linkages or cross-linkage, but it can also cause excessive cross-linkages from the side changes of the peptides, which can lower the efficacy of the vaccine peptides. Conversely, the two-step method (MES-PBS) allows less side chain linkage but lowers overall peptide linkage. From the results, the one-step method AuNVs were significantly better at stimulating CTLs than the two-step method.

J Biol Chem 1993,268(10):7503–7508.PubMed 48. Wilderman PJ, Vasil AI, Johnson Z, Wilson MJ, Cunliffe HE, Lamont Maraviroc IL, Vasil ML: Characterization of an endoprotease (PrpL) encoded by a PvdS-regulated gene in Pseudomonas aeruginosa . Infect Immun 2001,69(9):5385–5394.PubMedCrossRef 49. Nouwens AS, Beatson SA, Whitchurch CB, Walsh BJ, Schweizer HP, Mattick JS, Cordwell SJ: Proteome analysis of extracellular proteins regulated by the las and

rhl quorum sensing systems in Pseudomonas aeruginosa PAO1. Microbiology 2003,149(Pt 5):1311–1322.PubMedCrossRef 50. Noreau J, Drapeau GR: Isolation and properties of the protease from the wild-type and mutant strains of Pseudomonas fragi . J Bacteriol 1979,140(3):911–916.PubMed 51. Thompson SS, Naidu YM, Pestka JJ: Ultrastructural localization of an extracellular protease in Pseudomonas fragi by using the peroxidase-antiperoxidase reaction. Appl Environ Microbiol 1985,50(4):1038–1042.PubMed 52. Ashida H, Maki R, Ozawa H, Tani Y, Kiyohara M, Fujita M, Imamura A, Ishida H, Kiso M, Yamamoto K: Characterization of two different endo-alpha- N -acetylgalactosaminidases from probiotic and pathogenic enterobacteria, Bifidobacterium

longum and Clostridium perfringens . Glycobiology 2008,18(9):727–734.PubMedCrossRef check details 53. Simpson PJ, Jamieson SJ, Abou-Hachem M, Karlsson EN, Gilbert HJ, Holst O, Williamson MP: The solution structure of the CBM4–2 carbohydrate binding module from a thermostable Rhodothermus marinus xylanase. Biochemistry 2002,41(18):5712–5719.PubMedCrossRef 54. Pesci EC, Pearson JP, Seed PC,

Iglewski BH: Regulation of las and rhl quorum sensing in Pseudomonas aeruginosa . J Bacteriol 1997,179(10):3127–3132.PubMed 55. Colmer-Hamood JA, Aramaki H, Gaines JM, Hamood AN: Transcriptional analysis of the Pseudomonas aeruginosa toxA regulatory gene ptxR . Can J Microbiol 2006,52(4):343–356.PubMedCrossRef 56. Sambrook JF, Russell DW: Molecular Cloning: A Laboratory Manual. 3rd edition. Cold Spring Harbor, NY: CSHL Press; 2001. 57. Smith AW, Iglewski BH: Transformation of Pseudomonas aeruginosa by electroporation. Nucleic Acids Res 1989,17(24):10509.PubMedCrossRef 58. Sobel ML, McKay GA, Poole K: Contribution of the MexXY multidrug transporter to aminoglycoside resistance in Pseudomonas aeruginosa clinical isolates. Antimicrob Agents Chemother 2003,47(10):3202–3207.PubMedCrossRef before 59. Cheng KJ, Ingram JM, Costerton JW: Interactions of alkaline phosphatase and the cell wall of Pseudomonas aeruginosa . J Bacteriol 1971,107(1):325–336.PubMed 60. Sokol PA, Ohman DE, Iglewski BH: A more sensitive plate assay for detection of protease production by Pseudomanas aeruginosa . J Clin Microbiol 1979,9(4):538–540.PubMed 61. Rumbaugh KP, Griswold JA, Iglewski BH, Hamood AN: Contribution of quorum sensing to the virulence of Pseudomonas aeruginosa in burn wound infections. Infect Immun 1999,67(11):5854–5862.

This can be absorbable such as vicryl or biologic mesh, non-absorbable such as polypropylene (PPE) or expanded polytetrafluoroethylene (ePTFE), or Selleck Antiinfection Compound Library a Wittman patch. The material is initially applied loosely to allow for bowel expansion and prevent ACS. Serial examinations of the wound at the bedside or in the operating room must be done and the mesh is pleated or refastened to gradually pull the fascial edges together [47–49]. The primary benefit of these systems is their ability

to maintain and recover fascial domain. Drawbacks include damage to the fascia, inability to prevent adhesions and difficulty with fluid management. EC fistula rates vary with type of graft material; as high as 7-26% with non-absorbable mesh [42, 50–52], followed by 4.6-18% with absorbable mesh [49, 53, 54], and the Wittman patch which has the lowest reported rates of 0–4.2% [55–58]. Risk of ECF is reduced if omentum is interposed between the mesh and bowel [52]. Primary closure has been reported as late as >50 days after the initial damage control

operation [49]. ACS rates associated with interposition grafts are seldom sited in the literature; most that did reported no incidences [48, 53, 54]. Resuscitation The second stage MLN8237 mouse of DCL is resuscitation focused on correction of physiologic derangements, acidosis, oxygen debt, coagulopathy and hypothermia [1]. Hemodynamic derangements due to hypovolemic shock should be reversed as quickly as possible with volume resuscitation. However, over use of crystalloids can result in third spacing worsening bowel edema, anastomotic leaks, ACS and multi-organ failure [59, 60]. Accordingly, the use of massive transfusion protocols (MTP) has been recommended for DCL patients [60–62]. MTP’s advocate using blood transfusion earlier in resuscitation, using blood and blood products instead of crystalloid or colloid, and the infusion of red cells, plasma,

and platelets in a 1:1:1 ratio. There is evidence to suggest that MTP’s and use of 1:1:1 transfusion ratios results in lower overall fluid requirements, blood utilization, and possibly improved mortality in patients with massive blood loss, severe injury and severe physiological derangements, such as are encountered in DCL patients [63, 64]. In addition, before fluid resuscitation should be guided by hemodynamic parameters such as stroke volume variance or pulse pressure differentials and central venous or left atrial pressures. Improved fluid management may decrease the incidence of ACS and promote early fascial closure [28, 65, 66]. There is also some evidence that the use of hypertonic fluids in the postoperative period may decrease time to primary closure and improve the primary closure rate [67]. Patients should be monitored for development of ACS and if exhibiting symptoms, the TAC should be removed and replaced with a looser device immediately [2].

e. ampicillin, gentamicin, sulfa/trimethoprim, rifampicin, tetracycline, amoxy/clavulan, cephalotin, clindamycin, enrofloxacin, fusidic acid and oxacillin. No change in MIC values was observed when the wild type S. aureus and L. monocytogenes and the corresponding response regulator

mutants were compared (data not shown). Thus, as opposed to the CovRS TCS, HssR/RR23 from S. aureus and L. monocytogenes do not seem to sense other types of stress. The results for RR23 correspond with previous experiments, showing no stress phenotype for an rr23 mutant [22]. Discussion In the present study, we investigated how the antimicrobial peptide, plectasin, affects two human pathogens. Our results indicate that plectasin and another defensin, eurocin, do not BGJ398 supplier perturb the S. aureus and L. monocytogenes membrane, but differentially affect the bacterial survival. These results are in agreement with recent findings, which show that plectasin does not compromise membrane integrity [6, 12]. However, the non-defensins, novicidin and protamine did lead to increased leakage, implying that the antimicrobial activity of these peptides involves disruptions of the bacterial membranes (Figure 1). To identify genes involved in resistance to plectasin, we screened transposon Small molecule library price mutant libraries of L. monocytogenes and S. aureus. We were unable to identify any L. monocytogenes

mutants more resistant to the peptide compared to wild type. The L. monocytogenes wild-type is more tolerant to plectasin (MIC >64 μg/ml) compared to the S. aureus wild type (MIC = 8-16 μg/ml), which might explain the difficulties in obtaining L. monocytogenes mutants with decreased sensitivity [[6, 7], Methocarbamol this work]. Four isolated S. aureus mutants, more resistant to plectasin, had the transposon element inserted in the response regulator hssR that is part of a TCS, HssRS,

involved in sensing heme concentrations [14]. A primary mechanism by which bacterial cells respond to changes in the environment is through the action of TCSs. TCSs typically consist of a membrane-bound histidine kinase that responds to environmental signals by undergoing autophosphorylation followed by transfer of the phosphoryl group to the regulator [23]. During contact with a host, S. aureus acquire heme as iron source, but surplus heme can be toxic. The HssRS system is important for sensing the level of heme, and for activating the ABC transporter system HrtAB, which protects the bacteria against heme-mediated damage [16, 17]. Changes in iron availability are an environmental signal indicative of mammalian host-pathogen interaction and the HssRS TCS seems to be important for S. aureus to sense and respond to heme as a component of vertebrate blood [24, 14]. Our results reveal that a mutation in hssR increases the resistance of S. aureus to two defensin-like HDPs, suggesting that the mutation of hssR leads to enhanced bacterial resistance to immune clearance.

To investigate the effects of colicin M on the whole genome expression profile, an overnight culture of the

E. coli strain MG1655 (F-lambda-ilvG-rfb-50 rph-1) was grown as described above. One part was treated with colicin M (30 ng/ml), while the untreated part served as the control. For gene expression analysis by microarray and qPCR, total RNA was isolated from 2-ml samples removed from each flask following 30 min and 60 min incubations at 37°C. The experiments were repeated at least two times. For quantification of colanic acid, the growth conditions and the application of subinhibitory concentrations of colicin M were as described above. learn more Colanic acid was purified from 50 ml cultures treated with colicin M for 60 min, 90 min and 120 min at 37°C, with aeration. The experiment was repeated at least two times. RNA isolation Total RNA was extracted using the RNAProtect Bacteria Reagent (Qiagen) and RNeasy Mini kits (Qiagen), according to the manufacturer instructions. To remove residual DNA, on-column DNase digestion was performed during the RNA purification using the RNase-Free DNase Set (Qiagen). A Nanodrop ND 1000 spectrophotometer (Thermo Scientific) was used to confirm

total RNA concentrations, while an Agilent 2100 Bioanalyser (Agilent Technologies, CA, NVP-BGJ398 ic50 USA) was used to evaluate the RNA quality. The isolated RNA was stored at −80°C until use. Microarray procedures Gene expression analysis was performed using Affymetrix GeneChip® E. coli Genome 2.0 arrays. Target preparation, hybridization, washing, staining and scanning were performed as recommended by the Affymetrix GeneChip® Expression Analysis Technical Manual. The experiment was repeated at least two times. The acquisition of array images and the data quality assessment were performed using an Affymetrix

GeneChip Command Console. The GeneChip data was processed using several different R/Bioconductor packages. The Affymetrix raw data were normalized using the RMA algorithm from the XPS package. The data have been deposited in the NCBI Gene Expression Omnibus database (GEO, http://​www.​ncbi.​nlm.​nih.​gov/​geo) under GEO series accession number GSE37026. Annotation of the genes and the data representation was managed using the G protein-coupled receptor kinase ANNAFFY and AFFYCORETOOLS packages. The normalized data, converted to log2 values, were first limited to the ENTREZ-annotated probes from strain K12 (10208 probes). The remaining data were tested for differential expression, which was performed using the LIMMA package for the 30-min treated versus the 30-min untreated control and for the 60-min treated versus the 60-min untreated control bacterial culture. Differential expression was assessed using the 2-way factorial ANOVA model constructed using LIMMA package. Differential expression was assessed using the false discovery rate multiple test correction [82] and controlling type I error at α = 0.05.

This study provides important insights into our understanding of the feedback response of soil microbial communities to elevated CO2 and global change. Methods Site, sampling and environmental variable analysis This study was conducted within the BioCON experiment site [6] located at the Cedar Creek Ecosystem Science Reserve, MN, USA. The main BioCON field experiment has 296 plots (2 by 2 m) in six 20-meter-diameter rings, three for an aCO2 concentration of 368 μmol/mol and three for an Ibrutinib elevated CO2 concentration of 560 μmol/mol using a FACE system as described by Reich et al. [6]. In this

study, soil samples without plant root from 24 plots (12 biological replicates from ambient CO2 and 12 biological replicates from elevated NVP-AUY922 CO2. All with 16 native plant species including four C4 grasses,

four C3 grasses, four N-fixing legumes and four non-N-fixing herbaceous species, and no additional N supply) were collected in July 2007. The aboveground and belowground biomass, plant C and N concentrations, soil parameters, and in situ net N mineralization and net nitrification were measured as previously described [6, 32]. More detailed information about sampling is provided in Additional file 13. GeoChip analysis DNA extraction, amplification and labeling, as well as the purification of labeled DNA, were carried out according the methods described by Xu et al. [23]. GeoChip 3.0 [26] was used to analyze the functional structure of the soil microbial communities. Details for GeoChip hybridization, image processing and data pre-processing

are described in Additional file 13. Statistical analysis Pre-processed GeoChip data were further analyzed with different statistical methods: (i) detrended correspondence analysis (DCA) [48], combined with analysis of similarities (ANOSIM), non-parametric multivariate analysis of variance (Adonis) and Multi-Response ifoxetine Permutation Procedure (MRPP), for determining the overall functional changes in the microbial communities; (ii) microbial diversity index, Significant Pearson’s linear correlation (r) analysis, analyses of variance (ANOVA) and response ratio (RR) [3]; (iii) redundancy analysis (RDA) for revealing the individual or set of environmental variables that significantly explained the variation in functional microbial communities; (iv) variation partitioning for RDA were used to select the minimum number of environmental variables explaining the largest amount of variation in the model [20, 49]. More details about the data analysis are described in Additional file 13.

The number of bacteria at time 0 was identical for the LVS strain and the ΔpdpC derivatives in all experiments performed, so the distinct phenotypes https://www.selleckchem.com/products/Methazolastone.html of the mutant could not be explained by differences in its uptake by phagocytes. While LVS and the complemented strain replicated approximately three log10 CFU within the first 24 h, the ΔpdpC mutant showed no growth (Figure 8). In additional experiments, there were no significant increases in bacterial numbers during the first 6 h, or at 48 or 72 h (data not shown). The results unambiguously demonstrated that the ΔpdpC mutant had a markedly impaired ability to replicate intracellularly. Replication

was also assessed in BMDM and PEC and the results were similar to those obtained with J774 cells; the mutant showed no replication whereas the complemented strain replicated as well as LVS (data not shown). To further verify the inability of the mutant to grow intracellularly, bacterial RNA was isolated from infected cells and the expression of the F. tularensis 16S rRNA gene was measured. We observed a 1.4 log10 decrease of 16S rRNA in ΔpdpC-infected cells see more during the first 24 h, while LVS infected cells showed

an increase of 2.8 log10. The data demonstrate that, regardless of method and macrophage type utilized, the ΔpdpC mutant showed no significant intracellular replication and the deficient phenotype could be restored by complementation of the mutation in cis. Figure 8 Intracellular growth of F. tularensis in J774 cells. LVS,

the ΔpdpC mutant, the complemented ΔpdpC mutant, or the ΔiglC mutant was used to infect J774 cells for 2 h, after which the monolayers were washed and medium added (corresponds to 0 h). Cells were then incubated for 24 or 48 h before being lysed, serially diluted and plated to estimate Thymidine kinase the number of CFU. The graph presents a representative experiment out of eight performed. Each bar represents the mean value and the error bars represent the standard deviation. The asterisk indicates that the log10 data differs significantly from LVS (***: P < 0.001). Two-sided Student’s t-test was used to compare means. The ΔpdpC mutant shows attenuation in vivo The lack of intracellular replication observed for the ΔpdpC mutant suggested that it is likely attenuated in vivo. To test this, mice were infected by the intradermal route with LVS, the ΔpdpC mutant, or the complemented mutant. The model has been widely used [25, 28–32] and identifies even marginal levels of attenuation since the LD50 for LVS is estimated to be approximately 2 × 107 CFU [33]. With an infection dose of 4 × 107 CFU, LVS caused 80% mortality (mean time to death 4.3 ± 0.5 days) and all mice infected with the complemented strain died within 4 days (mean time to death 3.6 ± 0.5 days).

Antibody selections were performed against L. acidophilus using two methods. In the first, the bacteria were coated on Immunotubes (Nunc),

while, in the second, selection was carried out by centrifugation. For each selection we used a previously described naïve scFv library displayed on M13 filamentous phage [36]. Two to three rounds of selection, with increasing stringency, were performed prior to re-cloning enriched scFvs into pEP-GFP11 CHIR-99021 order [37] for screening. This vector generates scFv proteins in fusion with two different detection tags: SV5, recognized by a monoclonal antibody [38] and S11, a split green fluorescent protein (GFP) tag that fluoresces when complemented with GFP1-10 [39]. The simultaneous use of both tags enhances signal-to-noise ratio when testing putative clones for binding activity against L. acidophilus in flow cytometry. ScFv culture supernatant was incubated with L. acidophilus followed by staining and the L. acidophilus bacteria analyzed using an LSRII flow cytometer (Becton Dickinson). Sequencing revealed one unique scFv (α-La1) from the immunotube selection, and three unique scFvs (α-La2, α-La3, and α-La4) from the selection by centrifugation (Additional file 1). The α-La1 selleck inhibitor scFv was found to be highly specific for L.

acidophilus, binding to all tested L. acidophilus strains (ATCC strains 4356 and 832), but not to a panel of other gut bacteria, including Bifidobacterium sp., Peptoniphilus sp., E. coli, and six different species of Lactobacillus (Figure 1 and Table 1). Our analysis this website included Lactobacillus helveticus, the closest species to L. acidophilus, the 16S rRNA sequence of which shares >98% identity [40]. The other three α-La scFvs showed similar degrees of specificity. We proceeded with the α-La1 scFv for the remainder of the study due to greater expression and apparent

affinity relative to the other α-La scFvs (Additional file 2). The specificity of the α-La1 scFv was also further validated using the AMNIS Image-Stream Mark II flow cytometer (Amnis Corporation), which captures microscope images in a flow cytometric configuration (Figure 1B). Figure 1 A phage display derived single chain fragment (scFv) was selected that binds Lactobacillus acidophilus (L.a.) specifically. Various bacterial species (see Table 1 for abbreviations) were mixed with the α-La scFv-SV5-GFP-s11 fusion protein and stained with α-SV5-IgG-PE and/or GFP1-10. Binding specificity was confirmed using both standard (A) and imaging (B) flow cytometry (BF = Bright Field, GFP = Green Fluorescent Protein, PE = Phycoerytherin).

The sample S3 showed high diversity of novel isolates with presence of 4 novel isolates closely related to Parabacteroides distasonis, Megasphaera elsdenii, Clostridium subterminale, Bacteroides fragilis respectively. This suggests that there is difference in culturable anaerobic bacteria diversity with age within individuals

in a family. Table 2 Identification of obligate anaerobic isolates by 16 S rRNA gene sequence analysis Sample Isolate Closest BLAST hit Percent similarity Gene bank accession numbers S2 SLPYG 1 Bifidobacteria adolescentis 97% JN389522 (8 months) SLPYG 2 Parabacteroides BGB324 mw distasonis 99% JN038555   SLPYG 3 Parabacteroides distasonis 99% JN038556   SLBE 4 Parabacteroides distasonis 99% JN038557   SLBE 5 Parabacteroides distasonis 99% JN038558 S1 VLPYG 2 Clostridium subterminale 99% JN093125 (26 years) VLPYG 3 Bacteroides vulgates 99% JN084207   VLPYG 4 Parabacteroides distasonis 99% JN038554   VLPYG 5 Clostridium difficile 96% JN093126 learn more   VLPYG 6 Clostridium mangenotii 98% JN093127   VLBE 7 Bacteroides fragilis 99% JN084198   VLBE 8 Bacteroides thetaiotaomicron 99% JN084201   VLBE 9 Bacteroides thetaiotaomicron 99% JN084202 S3 BLBE 1 Parabacteroides distasonis 97% JN038559 (56 years) BLBE 2 Bacteroides ovatus 98% JN084211   BLPYG 5 Bacteroides uniformis 99% JN084205   BLBE 6

Bacteroides xylanisolvens 99% JN084212   BLPYG 7 Megasphaera elsdenii 97% HM990964   BLPYG 8 Clostridium subterminale 96% JN093128   BLPYG 9 Bacteroides fragilis 97% JN084199   BLBE 11 Parabacteroides distasonis 99% JN038560   BLBE 12 Parabacteroides distasonis 99% JN038561 Biochemical characteristics of the isolates Fenbendazole were analyzed using BIOLOGTM. The isolates

were grouped in 5 different phenotypes based on obtained characteristics. The identifications and accession numbers of the 16SrRNA gene sequence of the isolates are represented in Table  2. DGGE analysis The DGGE analysis revealed the difference in gut flora composition of individuals of different age belonging to the same family as shown in Figure  1. The band intensity and number of bands observed in DGGE profile of samples suggests that different bacterial species are dominating the gut flora of individuals of varying age. Figure 1 DGGE analysis of the stool DNA, denaturation gradient 40%-60%. Family S: S1 (26 years), S2 (8 months), S3 (56 years) and Family T: T1 (14 years), T2 (42 years), T3 (62 years). Legend : Lane 1- S2, lane 2- S1, lane 3- S3, lane 4- T1, lane 5- T2, lane 6- T3. Clone library analysis Total 960 clone sequences from the 6 clone libraries were obtained and analyzed. The sequences are submitted to NCBI with accession numbers from JQ264784 to JQ265743.