The athletes who run the fastest will have the highest sweat rate

The athletes who run the fastest will have the highest sweat rates. If they do not drink more than others they will finish with the greatest levels of body mass loss and hence the highest levels of dehydration [42]. In some instances fluid may have taken the place of food in terms of energy consumed. In a study of 2,135 endurance athletes TH-302 concentration including marathon runners and triathletes, plasma [Na+] decreased despite of an increased fluid intake, check details however body mass also decreased [39]. Limitations It was not possible in these races to determine urinary excretion of the finishers precisely since the athletes were not able to correctly record

it during the race. Since ultra-endurance performance is associated with skeletal muscle damage [67], we have to investigate also the role of muscle damage in causing a decrease in skeletal muscle mass or https://www.selleckchem.com/products/cb-5083.html fat mass. Conclusions Overall prevalence of EAH was 5.7% and was not higher compared to existing reports for other ultra-endurance athletes competing in other countries. No ultra-MTBer developed EAH in the 24-hour MTB race (R1). One ultra-MTBer

in the 24-hour MTB race (R2), one ultra-runner in the 24-hour running race (R3) and one MTBer in the multi-stage MTB race (R4) developed EAH with mild symptoms. To support the trend of the prevalence of EAH in the Czech Republic and to clarify the cause it is necessary to observe ultra-endurance athletes in a number of different races or a long time and repeatedly. The lower plasma sodium and the subsequent development of EAH may be attributed to overdrinking, a pituitary secretion of the hormone vasopressin, impaired mobilization of osmotically eltoprazine inactive sodium stores, and/or inappropriate inactivation of osmotically active sodium. Future studies need to investigate the change in body composition. A loss in body mass of >3% does not appear to adversely affect performance despite ad libitum fluid consumption being advised. Acknowledgements

The authors gratefully acknowledge the athletes for their splendid cooperation without which this study could not have been done. We thank the organizers and the medical crew of the ,Czech Championship 24-hour MTB race’ in Jihlava (R1), the ‚Bike Race Marathon Rohozec’ in Liberec (R2), the ,Sri Chinmoy Self-transcendence Running Marathon 24-hour race’ in Kladno (R3) and the ‘Trilogy Mountain Bike Stage Race’ in Teplice nad Metují (R4) for their generous support. A special thank goes to the laboratory staff of the University Hospital ,U Svaté Anny’ in Brno, Czech Republic, for their efforts in analyzing hematological and biochemical samples even during the night-times. A special thank goes to Marcus Shortall from the Institute of Technology Tallaght for his help with translation and the extensively correction of the whole text. References 1.

Electronic supplementary material Additional file 1: Results of A

Electronic supplementary material Additional file 1: Results of ATPase search in published genomes of eubacteria from NCBI. Table listing the eubacteria which contain F-type ATPase, V-type ATPase or both F-type and V-type ATPases. (PDF 66 KB) References 1. Demain AL, Newcomb M, Wu JH: Cellulase, clostridia, and ethanol. Microbiol Mol Biol Rev Vactosertib datasheet 2005, 69 (1) : 124–154.PubMedCrossRef 2. Roberts SB, Gowen CM, Brooks JP, Fong SS: Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production. BMC Syst Biol 2010., 4 (31) : 3. Alberts B: The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 1998,

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Heijne G, Daley DO: Protein complexes of the Escherichia coli cell envelope. J Biol Chem 2005, 280 (41) : 34409–34419.PubMedCrossRef 11. Krogh A, Larsson B, von Heijne G, Sonnhammer EL: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001, 305 (3) : 567–580.PubMedCrossRef 12. Sonnhammer EL, von Heijne G, Krogh A: A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol 1998, 6: 175–182.PubMed 13. Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV: The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 2001, 29 (1) : 22–28.PubMedCrossRef 14.

Subjects were instructed not to modify their food intake or eatin

Subjects were instructed not to modify their food intake or eating patterns throughout the study. The days recorded consisted of two days of training followed by a day of rest. Blood lipid Milciclib profile All subjects were reported to a commercial biomedical Laboratory (HBM Inc, Kuwait) after a 12 hour overnight fast. Blood samples were drawn

RGFP966 datasheet from the antecubital vein. Serum total cholesterol and triglycerides were analyzed by enzymatic techniques in a Hitachi 911/904 (Roche Diagnostics, Basel, Switzerland) according to the manufacturer’s protocol. The high density lipoprotein fraction of cholesterol (HDL-C) was measured after precipitation of the very low density lipoprotein (VLDLC) and low density lipoprotein (LDL-C) fractions with phosphotungstic acid. LDL-C was precipitated with Biomerieux reagent. Hemoglobin values were measured using an automatic multi-parameter blood cell counter (Sysmex® KX-21). Maximal Oxygen Consumption (VO2 max) VO 2 Smad inhibitor max was assessed using a modified Bruce protocol. This protocol began after a 2-min warm-up. Treadmill speed, grade, or both

were increased every 2 minutes until cardiopulmonary fatigue was reached and O2 max was obtained. Criteria for attainment of VO 2 max included a < 2 ml/kg increase in oxygen consumption (O2) with an increased work rate, a respiratory exchange ratio (RER) greater than or equal to 1.1, and/or the subject's inability to maintain this work rate. VO 2 for max is expressed in ml/kg/min. Statistical analysis All data were presented as mean, standard deviations (SD) and ± standard errors of the mean (SEM). Differences in mean values of the Kuwaiti fencers in body composition and blood lipids profile were analyzed using the average of the sum of the normal range and by applying a one sample t-test. In addition, the mean dietary intake of different foods and VO2 max values were compared using the one sample t-test. All the variables were compared with the international norm applying a t-test for independent

samples. A probability value of ≤ 0.05 was considered significant. Data was analyzed using the Statistical Package of Social Sciences (SPSS) version 17 (Chicago, IL). Results The results of the present study showed a statistically significant difference in dietary consumption between the athletes daily average nutrient intake and the recommended dietary allowances (RDA) The blood lipids profile, body composition (BMI and %body fat), and VO2 max were within the normal range in comparison with international norms. A complete description of the fencing players physical characteristics (mean and standard deviation), including age, height, weight, body mass index, percent body fat, and maximum oxygen consumption are illustrated in Table 1. Table 1 Baseline characteristics of Kuwaiti fencing players (means ± SD) N Players ID Age (years) Height (cm) Weight (kg) BMI (kg/m2) % Body Fat VO2 max (ml.kg-1.min-1) 1 MK 24.2 181.2 77.2 23.6 13.3 52.6 2 AN 21.

Infect Immun 2009,77(8):3258–3263 PubMedCrossRef 15 Domenech P,

Infect Immun 2009,77(8):3258–3263.P505-15 clinical trial PubMedCrossRef 15. Domenech P, Kolly GS, Leon-Solis L, Fallow A, Reed MB: Massive gene duplication GF120918 price event among clinical isolates of the Mycobacterium tuberculosis W/Beijing family. J Bacteriol 2010,192(18):4562–4570.PubMedCrossRef 16. Reed MB, Gagneux S, Deriemer K, Small PM, Barry CE: The W-Beijing lineage of Mycobacterium tuberculosis overproduces triglycerides and has the DosR dormancy regulon constitutively upregulated. J Bacteriol 2007,189(7):2583–2589.PubMedCrossRef 17. Respicio L, Nair PA, Huang Q, Anil B, Tracz S, Truglio JJ, Kisker C, Raleigh DP, Ojima I, Knudson DL, et al.: Characterizing

septum inhibition in Mycobacterium tuberculosis for novel drug discovery. Tuberculosis (Edinb) 2008,88(5):420–429.CrossRef 18. Huang Q, Kirikae F, Kirikae T, Pepe A, Amin A, Respicio L, Slayden RA, Tonge PJ, Ojima I: Targeting FtsZ for antituberculosis drug discovery: noncytotoxic taxanes as novel antituberculosis agents. J Med Chem 2006,49(2):463–466.PubMedCrossRef 19.

Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE, GDC-0449 purchase et al.: Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998,393(6685):537–544.PubMedCrossRef 20. Rezwan M, Grau T, Tschumi A, Sander P: Lipoprotein synthesis in mycobacteria. Microbiology 2007,153(Pt 3):652–658.PubMedCrossRef 21. Chauhan A, Lofton H, Maloney E, Moore J, Fol M, Madiraju MV, Rajagopalan M: Interference of Mycobacterium tuberculosis cell division by Rv2719c, a cell wall hydrolase. Mol Microbiol 2006,62(1):132–147.PubMedCrossRef 22. Chauhan A, Madiraju MV, Fol M, Lofton H, Maloney E, Reynolds R, Rajagopalan M: Mycobacterium Ibrutinib in vivo tuberculosis cells growing in macrophages are filamentous and deficient in FtsZ rings. J Bacteriol 2006,188(5):1856–1865.PubMedCrossRef 23. Rustad TR, Sherrid AM,

Minch KJ, Sherman DR: Hypoxia: a window into Mycobacterium tuberculosis latency. Cell Microbiol 2009,11(8):1151–1159.PubMedCrossRef 24. Zhang Y, Hatch KA, Wernisch L, Bacon J: A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis. BMC Genomics 2008, 9:87.PubMedCrossRef 25. Park HD, Guinn KM, Harrell MI, Liao R, Voskuil MI, Tompa M, Schoolnik GK, Sherman DR: Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis. Mol Microbiol 2003,48(3):833–843.PubMedCrossRef 26. Bartek IL, Rutherford R, Gruppo V, Morton RA, Morris RP, Klein MR, Visconti KC, Ryan GJ, Schoolnik GK, Lenaerts A, Voskuil MI: The DosR regulon of M. tuberculosis and antibacterial tolerance. Tuberculosis (Edinb) 2009,89(4):310–316.CrossRef 27. Converse PJ, Karakousis PC, Klinkenberg LG, Kesavan AK, Ly LH, Allen SS, Grosset JH, Jain SK, Lamichhane G, Manabe YC, et al.

The decrease in internal

The decrease in internal colonization is not due to differences in the growth rate since the doubling times of H. rubrisubalbicans T3SS mutant strains in NFbHPN medium are identical Sapitinib to the wild type (data not shown). When Pseudomonas syringae pv. tomato T3SS mutant strains were infiltrated in tomato leaves a reduction in the number of recovered bacteria was also observed [35, 36]. These results further support our findings that the genes hrpE

and hrcN are involved in the colonization of V. unguiculata by H. rubrisubalbicans. Mutations in hrpE and hrcN genes reduce the capacity of H. rubrisulbalbicans to colonize rice. H. rubrisubalbicans has been found in roots and leaves of rice [37] but the interaction was not pathogenic. To investigate if H. rubrisubalbicans hrcN and hrpE genes are involved in such non-pathogenic endophytic colonization, rice seedlings were inoculated with H. rubrisubalbicans strains M1, TSE and TSN five days after germination and the number of endophytic bacteria determined 3, 5, 7 and 9 days after inoculation. No disease symptoms were observed in plants inoculated with any of these bacterial strains. Figure 7 shows that three days after inoculation

the number of endophytic wild-type bacteria was 10-fold higher than that of the mutant strains. This difference remained 5 and 7 days after inoculation and increased to 100-fold after nine days. The SC79 results indicate that the genes hrpE and hrcN may also be involved in the endophytic colonization PDK4 of rice by H. rubrisubalbicans. Figure 7 Internal colonization of Oryza ACY-738 price sativa roots by H. rubrisubalbicans . The number of endophytic bacteria colonizing internal rice root tissues was determined 3, 5, 7 and 9 days after inoculation (d.a.i.). The plants were superficially disinfected and the roots were cut, homogenized, diluted and plated. The plates were kept at 30°C for 24 hours and colonies counted. Results are shown as means of Log10 (number of bacteria. g-1 of fresh root) ± standard

deviation (Student t-test; P < 0.05). The experiment contained five different plants for each condition. This experiment was repeated on at least three separate dates. Discussion The type three secretion system of gram-negative plant pathogenic bacteria belonging to the genera Pseudomonas, Ralstonia, Xanthomonas and Erwinia is essential for disease development [35]. Bacteria of the genus Herbaspirillum endophytically colonize plants of the Poaceae family but can also be found in internal tissues of other plants such as Phaseolus vulgaris [38, 39] and soybean (Glycine max) [40], as well as the tropical species banana and pineapple [41]. Most Herbaspirillum species establish neutral or beneficial interaction with plants [42–49]. H.

reinhardtii look like and how is this large number of LHCII’s ass

reinhardtii look like and how is this large number of LHCII’s associated with PSI? And finally, BMS-907351 chemical structure how efficient is the trapping in these large PSI-LHCI-LHCII supercomplexes? Acknowledgments RC is supported by the ERC starting/consolidator grant number 281341 and by the Netherlands Organization for Scientific research (NWO) via a Vici grant. Open AccessThis

article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. References Adolphs J, Muh F, Madjet MA, Busch MS, Renger T (2010) Structure-based calculations of optical spectra of photosystem I suggest an asymmetric light-harvesting process. J Am Chem Soc 132(10):3331–3343. doi:10.​1021/​ja9072222 selleck chemicals llc p38 MAPK activation PubMed Alboresi A, Gerotto C, Cazzaniga S, Bassi R, Morosinotto T (2011) A red-shifted antenna protein associated with photosystem II in Physcomitrella patens. J Biol Chem 286(33):28978–28987. doi:10.​1074/​jbc.​M111.​226126 PubMed Amunts A, Drory O, Nelson N (2007) The structure of a plant photosystem I supercomplex at 3.4 angstrom resolution. Nature

447(7140):58–63PubMed Amunts A, Toporik H, Borovikova A, Nelson N (2010) Structure determination and improved model of plant photosystem I. J Biol Chem 285(5):3478–3486PubMed Ballottari M, Govoni C, Caffarri S, Morosinotto T (2004) Stoichiometry of LHCI antenna SB-3CT polypeptides and characterization of gap and linker pigments in higher plants photosystem I. Eur J Biochem 271(23–24):4659–4665PubMed Ballottari M, Dall’Osto L, Morosinotto T, Bassi R (2007) Contrasting behavior

of higher plant photosystem I and II antenna systems during acclimation. J Biol Chem 282(12):8947–8958PubMed Bassi R, Machold O, Simpson D (1985) Chlorophyll-proteins of two photosystem I preparations from maize. Carlsberg Res Commun 50:145–162 Bassi R, Soen SY, Frank G, Zuber H, Rochaix JD (1992) Characterization of chlorophyll a/b proteins of photosystem I from Chlamydomonas reinhardtii. J Biol Chem 267:25714–25721PubMed Beddard GS, Porter G (1976) Concentration quenching in chlorophyll. Nature 260:366–367 Beddard GS, Carlin SE, Porter G (1976) Concentration quenching of chlorophyll fluorescence in bilayer lipid vesicles and liposomes. Chem Phys Lett 43:27–32 Ben-Shem A, Frolow F, Nelson N (2003) Crystal structure of plant photosystem I. Nature 426(6967):630–635PubMed Boekema EJ, Jensen PE, Schlodder E, van Breemen JF, van Roon H, Scheller HV, Dekker JP (2001) Green plant photosystem I binds light-harvesting complex I on one side of the complex. Biochemistry 40(4):1029–1036PubMed Bossmann B, Knoetzel J, Jansson S (1997) Screening of chlorina mutants of barley (Hordeum vulgare L.) with antibodies against light-harvesting proteins of PS I and PS II: absence of specific antenna proteins.

Medium with 10% FBS was added to the lower chambers as a chemoatt

Medium with 10% FBS was added to the lower chambers as a chemoattractant. After 24 h of incubation, cells that invaded through the membrane

filter were fixed and stained with H&E. The number of invading cells was counted under fluorescence microscope in five random high power fields. Statistical analysis All experiments were repeated see more independently a minimum of three times, and the results were expressed as the mean values ± standard deviation. The differences between groups were analyzed by two-tailed unpaired Student’s t test. A value of p < 0.05 was considered to indicate statistical significance. this website Results MTA1 knockdown leads to the upregulation of miR-125b level in NSCLC cells First we established 95D and SPC-A-1 cell lines with stable knockdown of MTA1 by transfecting the cells with MTA1 shRNA. The knockdown efficiency was confirmed by qRT-PCR and Western blot analysis. Compared to the control cell lines, the expression of MTA1 mRNA and protein was significantly reduced in 95D and SPC-A-1 cells transfected with pLVTHM-MTA1-si plasmid (Figure  1A, B). Figure 1 MTA1 knockdown

leads to the upregulation of miR-125b level in NSCLC cells. A. Quantification of MTA1 mRNA level by quantitative RT-PCR in 95D and SPC-A-1 cells untransfected, transfected with MTA1 shRNA or control shRNA. B. Western blot analysis of MTA1 protein level in 95D and SPC-A-1 Fludarabine nmr cells untransfected, transfected with MTA1 shRNA or control shRNA. B-actin was loading control. C. Quantification of miR-125b level by quantitative RT-PCR in 95D and SPC-A-1 cells transfected with MTA1 shRNA or control shRNA. D. Quantification of miR-125b level by quantitative RT-PCR in 95D and SPC-A-1 cells transfected with MTA1 shRNA or control shRNA, together with miR-125b inhibitor or control. *P < 0.05, **P < 0.01

compared to the controls. Next we detected miR-125b level in the established cell lines. The results showed that miR-125b level was 2.75 and 1.67-fold higher in 95D/MTA1-si and SPC-A-1/MTA1-si cells, compared to the control 95D and SPC-A-1 cells, respectively (Figure  1C). To confirm the negative correlation between MTA1 and miR-125b in NSCLC cells, we transfected miR-125b-inhibitor or nonspecific control miRNA (NC) Liothyronine Sodium into 95D and SPC-A-1 cells. qRT-PCR analysis showed that miR-125b-inhibitor decreased the expression of miR-125b in 95D/CTL-si and SPC-A-1/CTL-si cells only by 30 percent, but it significantly reduced miR-125b expression in 95D/MTA1-si and SPC-A-1/MTA1-si cells (Figure  1D). These data suggest that MTA1 knockdown leads to the upregulation of miR-125b level in NSCLC cells. MTA1 and miR-125b have antagonistic effects on the migration and invasion of NSCLC cells Next we investigated the antagonistic effects of MTA1 and MiR-125b on the migration and invasion of NSCLC cells. Wound healing assay showed that in 95D cells, knockdown of MTA1 led to reduced cell migration.

Despite intensive

investigations on the properties of ZnO

Despite intensive

investigations on the properties of ZnO, little is known about its surface properties. While a few claim that the Fermi level is pinned above the conduction band edge [26], others claim that the Fermi level is pinned below the conduction band edge [27]. Here, we take the Fermi level to be located below the conduction band edge as in the case of n-type ZnO NWs [28]. This is also in accordance with Long et al. [23] who suggested that Zn3N2 with GSK2118436 concentration N substituted by O (ON) is more stable than Zn replaced by O (OZn) or interstitial O (OI). In the case of ON, the Fermi level locates near the bottom of the conduction band, but in the cases of both OZn and OI, the Fermi level is pinned around the top of the valence band [23]. In other words, interstitial oxygen gives p-type Zn3N2, but since it is not energetically favourable, we expect to have the formation of n-type ZnO shell at the surface which surrounds an n-type Zn3N2 core. The energy band diagram

of a 50-nm diameter Zn3N2/ZnO core-shell NW determined from the self-consistent solution of the Poisson-Schrödinger equations (SCPS) in cylindrical coordinates and in the effective mass approximation buy Nirogacestat is shown in Figure  4. In such a calculation, Schrödinger’s equation is initially solved for a trial potential V, and the charge distribution ρ is subsequently determined by multiplying the normalised probability density, ∣ψ k ∣2, by the thermal occupancy of each sub-band with energy E k using Fermi-Dirac statistics and summing over all k. The Poisson equation is then solved for this charge distribution

in order to find Etofibrate a new potential V′, and the process is repeated until convergence is reached. A detailed description of the SCPS solver is given elsewhere [29, 30]. In this calculation, we have taken into account the effective mass m e * = 0.29 mo and static dielectric constant ϵ r = 5.29 of Zn3N2[24, 31], as well as m e * = 0.24 mo and ϵ r = 8.5 for ZnO [32, 33]. In addition, we have taken into account the energy band gap of Zn3N2 to be 1.2 eV [17, 24] and the Fermi level to be pinned at 0.2 eV below the conduction band edge at the ZnO surface [28]. A Vactosertib order flat-band condition is reached at the centre of the Zn3N2/ZnO NW, and a quasi-triangular potential well forms in the immediate vicinity of the surface, holding a total of eight sub-bands that fall below the Fermi level. The one-dimensional electron gas (1DEG) charge distribution is confined to the near-surface region, has a peak density of 5 × 1018 cm−3 (≡5 × 1024 cm−3), as shown in Figure  4, and a 1DEG line density of 5 × 109 m−1. Optical transitions in this case will occur between the valence band and conduction band states residing above the Fermi level similar to the case of InN [1].

Nevertheless, despite all these limitations the phage therapy rem

Nevertheless, despite all these limitations the phage therapy remains an alternative in antibiotic-resistant infections. Although

the majority of studies on phage therapy have been carried out on immunocompetent patients, there are also data indicating www.selleckchem.com/products/pf-06463922.html that phages could be effective and safe in immunocompromised individuals (for review see [16]). Of particular importance are the results achieved in immunocompromised cancer patients, which showed that phages could cure different kinds of bacterial infections without causing any serious side effects [17], as well as preliminary data obtained in a small group of renal transplant recipients (for references see [18]). Interestingly, phages may prolong mouse allograft

survival, which constitutes an important GS-9973 ic50 argument for the safety of phage therapy in transplant recipients [19]. Although GF120918 cyclosporine and steroids may not significantly impair function of cells responsible for innate immunity [20], some myeloablative agents like cyclophosphamide (CP) can transiently deplete the neutrophil pool [21] rendering a patient defenseless against infection. CP is widely used for treatment of autoimmune diseases [22–24] and leukemias [25]. The drug causes a profound, transient leukopenia [26], it also suppresses humoral [27] as well as cellular immune responses [28]. Although the neutropenia is transient and leads later to mobilization of myelopoiesis [29], the impairment of the specific humoral response, crucial for the development of adaptive immunity to pathogens, is long-lasting [27]. Therefore, the aim of this study was to evaluate effectiveness of prophylactic phage administration to CP-immunosuppressed mice on several parameters associated with innate and acquired immune response to many S. aureus such as: number of bacteria in organs of infected mice, serum level of proinflammatory cytokines, blood and bone marrow cell profile and ability to generate specific antibody response to S. aureus. In this work we convincingly demonstrate that

administration of specific phages prior to infection can compensate the deficit of neutrophils in the clearance of S. aureus from the organs of CP-treated and infected mice. Moreover, the phages regulated the levels of proinflammatory cytokines and elicited mobilization of cells from both myelocytic and lymphocytic lineages. Lastly, the application of phages stimulated generation of specific antibodies to S. aureus and to an unrelated antigen sheep red blood cells. Methods Mice, strains and reagents CBA male mice, 10–12 weeks-old, were purchased from Ilkowice/Kraków, Poland. The mice had free access to water and standard rodent laboratory chow. All protocols were approved by the local ethics committee. Staphylococcus aureus L strain was isolated from a 26-year old patient A.L., suffering from pharyngitis.

: Predominant Role of Host Genetics in Controlling the Compositio

: Predominant Role of Host Genetics in Controlling the Composition of Gut Microbiota. PLoS One 2008,3(8):e3064.PubMedCrossRef 8. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI: The Effect of Diet on the Human selleck inhibitor Gut Microbiome: A Metagenomic Analysis in Humanized Gnotobiotic Mice. Sci Transl Med 2009,1(6):6ra14.PubMedCrossRef 9. Turnbaugh PJ, Quince C, Faith JJ, McHardy AC, Yatsunenko T, Niazi F, Affourtit J, Egholm M, Henrissat B, Knight R, Gordon JI: Organismal, genetic, and transcriptional

variation in the deeply sequenced gut microbiomes of identical twins. PNAS 2010,107(16):7503–7508.PubMedCrossRef 10. Gordon JH, Dubos R: The anaerobic bacteria flora of the mouse cecum. J Exp Med 1970, 132:251–260.PubMedCrossRef 11. Harris MA, Reddy CA, Carter GR: Anaerobic bacteria from the large intestine of mice. Appl Environ Microbiol 1976, 31:907–912.PubMed 12. Schloss PD, Handelsman J: Status of the microbial census. Microbiol Mol Biol Rev 2004, 68:686–691.PubMedCrossRef 13. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE, Relman DA: Diversity of the human intestinal microbial flora. Science 2005, 308:1635–1638.PubMedCrossRef 14. Ley RE, Ba ckhed F, Lozupone selleckchem CA, Knightand RD, Gordon JI: Obesity alters gut microbial ecology. Proc Nat Acad Sci USA 2005, 102:11070–11075.PubMedCrossRef 15. Turnbaugh PJ,

Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI: An obesity-associated gut microbiome with selleck increased capacity for energy harvest. Nature 2006, 444:1027–1031.PubMedCrossRef 16. Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P, Flint HJ: Human colonic microbiota associated with diet, obesity and weight loss. Int. J. Obes. (London) 2008, 32:1720–1724.CrossRef 17. Nadal I, Santacruz A, Marcos A, Warnberg J, Garagorri M, Moreno LA, Martin-Matillas M, Campoy C, et al.: Shifts in Clostridia, Bacteroides

and immunoglobulin-coating fecal bacteria associated with weight loss before in obese adolescents. Int J Obes (Lond) 2009, 33:758–767.CrossRef 18. Mariat D, Firmesse O, Levenez F, Guimarăes V, Sokol H, Doré J, Corthier G, Furet JP: The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol 2009, 9:123.PubMedCrossRef 19. Larsen N, Vogensen FK, van den Berg FWJ, Nielsen DS, Andreasen AS, et al.: Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults. PLoS One 2010,5(2):e9085.PubMedCrossRef 20. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO: Development of the Human Infant Intestinal Microbiota. PLoS Biol 2007,5(7):e177.PubMedCrossRef 21. Yajnik CS, Yudkin JS: The Y-Y paradox. Lancet 2004,363(9403):163.PubMedCrossRef 22. Holdeman LV, Elizabeth P, Cato , Moore WEC: Anaerobe Laboratory Manual. 4th edition. Blacksburg, Virginia: Virginia Polytechnic Institute and State University; 1997:1–156. 23. Sambrook , Russell : Molecular Cloning – A Laboratory Manual, volume 1.