Abundant Variations

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  3. Astrophysics > Astrophysics of Galaxies
  4. Shoulder Stand Variations for the Abundant Yogi | DOYOUYOGA

Methylomonas is a genus of type I methanotrophic bacteria, which obtain their carbon and energy from the oxidation of methane. Methylomonas sequences have been detected previously in drinking water [32] , and a recent study of water meter biofilms also detected sequences corresponding to the family Methylococcaceae , the bacterial family that includes the genus Methylomonas , [7]. Methylotrophic bacteria, specifically Methylophilus , were also detected in biofilms within drinking water meters [7].


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The factors favoring high abundance of methanotrophic and methylotrophic taxa within drinking water distribution systems are unclear. We would not expect high concentrations of methane or methanol in drinking water, however these compounds could be produced in anoxic sites within DWDS via anaerobic processes such as methanogenesis or fermentation. Another process that might have supported the growth of methylotrophic bacteria is utilization of haloacetic acid, which is a common by-product of chlorination of drinking water.

A recent study isolated a Methylobacterium strain from a DWDS biofilm that was capable of growth with haloacetic acid as the sole carbon source [25]. The results of our study, which showed a significant correlation between the relative abundance of Methylomonas sequences and HAA concentration in the source water, lends further support to this hypothesis.


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  6. Our results demonstrated that there was significant variation in the taxonomic composition of the biofilm bacterial communities within the pipe sections across our sampling dates. Acinetobacter is a genus of Gram-negative, heterotrophic bacteria [33] that is commonly found in soils and groundwater [34] — [36]. Acinetobacter is also one of the most common groups of bacteria isolated from drinking water [1] , and a number of Acinetobacter species have been shown to produce biofilms [37] — [39].

    Therefore the presence of Acinetobacter in the pipe biofilms was not surprising. However, the dramatic variation in Acinetobacter abundance that we observed between August and the other sampling dates was remarkable. There were several unique features of the August sampling date that may have contributed to its distinct biofilm composition. First, the source of the drinking water within the distribution system changed prior to August to predominantly groundwater for a period of three weeks, with this period of groundwater dominance occurring approximately two months prior to the August sampling.

    Since Acinetobacter are regularly detected in soil and groundwater, this switch to a groundwater dominated system prior to August might have provided an additional inoculum of Acinetobacter that were able to become established within the pipe biofilms. Another related feature of the August samples was that the drinking water at that sampling time had a much higher nitrate concentration at least four times higher than all of the other sampling dates.

    The nMDS analysis indicated that nitrate concentration was one of the main drivers of the composition of the bacterial communities within the August biofilms. The August biofilms also showed bacterial counts that were more than two times higher than any of the other sampling dates, suggesting more biofilm mass which could have generated more anoxic microsites. Although bacteria from the genus Acinetobacter are generally aerobes, there are some species within the genus that can utilize nitrate as an electron acceptor when oxygen is not present [40].

    Therefore, the higher drinking water nitrate concentration and possibly more anoxic microsites caused by higher bacterial biofilm growth on the August sampling date may have provided Acinetobacter with a competitive advantage over Methylomonas. Mycobacteria are frequently detected in DWDS and are considered a significant public health issue [42]. The genus Mycobacterium consists of approximately species, including a large number of species that are either non-pathogenic or pathogenic under certain situations [26].

    For example, nontuberculosis Mycobacterium are a major cause of opportunistic infections in immunocompromised hosts [42]. MAC is the group of non-tuberculosis Mycobacterium most commonly associated with human disease, causing primarily pulmonary infections in individuals who are immunocompromised [45]. In this study, we were unable to discriminate the Mycobacterium sequences down to the species level, so it is unclear whether the sequences we detected represented potentially pathogenic species.

    Several characteristics of Mycobacteria enhance their survival in DWDS, including their ability to grow under oligotrophic conditions [46] , form biofilms and resist chlorine disinfection [26]. Several recent studies have detected species related to Mycobacterium in chlorinated drinking water [47] — [49]. Previous work at the DWDS considered here indicated that the frequency of detection of Mycobacteria increased when the disinfectant was switched from chlorine to chloramine in [43] , suggesting that Mycobacteria might be less sensitive to chloramine than chlorine.

    However, a recent study using a model DWDS observed that the relative sensitivity of Mycobacterium avium biofilms to chlorine and monochloramine depended on the pipe material [50]. Specifically, M. Here we found that Mycobacterium sequences were most abundant in biofilms from the July sampling date, which also had the highest level of free ammonia and the lowest levels of total chlorine and monochloramine. These constituents are related, as monochloramine is reductively dehalogenated to ammonia, and monochloramine is the major component of total chlorine in this system.

    The results of our nMDS analysis indicate that the monochloramine concentration strongly influenced bacterial community composition for the July sampling date. Therefore, the fact that Mycobacterium was predominant only on the sampling date with the lowest level of monochloramine suggests that within the ductile iron pipes in this DWDS monochloramine significantly reduced the abundance of Mycobacterium within the biofilms. The Xanthomonadaceae are obligate aerobic chemoorganotrophs, and this family includes some well-known plant pathogens [33].

    Organisms from the family Xanthomonadaceae family were not detected in a previous pyrosequencing survey of drinking water biofilms [7] , but these organisms have been isolated from drinking water and from drinking water pipe biofilms using culture based techniques [52]. In this study, we were unable to discriminate the Xanthomonadaceae sequences down to the genus or species level, and the reason for their high abundance in the March samples is unclear. The biofilm communities from July and March showed much higher variation in composition among replicates than did biofilm communities from December and August The December and August samples were each dominated by a single bacterial genus Methylomonas in December and Acinetobacter in August with very little variation between replicates.

    These data suggest that the environmental conditions in December and August each favored one specific bacterial genus that dominated all of the biofilms on that sampling date. In contrast, biofilms from July and March had several dominant bacterial genera that showed high variations between the replicates, suggesting that conditions during those months produced greater variability by enabling several genera to compete for dominance within the biofilms.

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    Nitrification is a significant concern in drinking water distribution systems that use chloramine as the secondary residual, as nitrification can lead to a decrease in the chloramine residual, an increase in the growth of heterotrophic bacteria and an increase in concentrations of nitrate and nitrite, which pose risks to human health [53].

    Multiple studies have identified nitrifying bacteria in DWDS [53] — [55]. No sequences corresponding to any known ammonia oxidizing bacterial genera were detected in the biofilms analyzed in this study. However, a few sequences from a known nitrite oxidizing genus, Nitrospira , were detected in the July samples, but not in samples from any of the other sampling dates. July also showed the highest concentration of free ammonia at least two times higher than all other sampling dates and unpublished data from PCU confirm that there was a peak in nitrification activity in June and July of The nMDS analysis indicated that ammonia concentration was a significant driver of the composition of the biofilm bacterial communities from July , and previous studies have indicated that the presence of free ammonia is the principal cause of nitrification in DWDS [56].

    Therefore, these data suggest that the high free ammonia concentration combined with the high temperature in July enabled nitrification to occur within the pipes; however, the lack of detection of ammonia oxidizing bacteria suggests that ammonia oxidation within the biofilms may have been driven by ammonia oxidizing archaea AOA. Previous studies have detected AOA in drinking water distribution systems [54] , but archaea would not have been detected by the bacterial primers used in the current study. In summary, the results of our study demonstrate that the biofilms within the DWDS pipes were dominated by a few bacterial taxa, specifically Methylomonas , Acientobacter and Mycobacterium , and that the dominant taxa within the biofilms varied dramatically between sampling times.

    It is likely that these differences in dominant taxa were driven by differences in environmental conditions, and our analysis suggests that nitrate, ammonium, total chlorine, and monochloramine concentrations were key drivers of biofilm bacterial community composition. Another possibility is that these differences in dominant taxa could have been the result of the founder effect, which stipulates that the founding member of a biofilm will have an advantage over subsequent colonizers and will remain dominant. The founder effect has been suggested as a possible driver of biofilm community composition in a variety of habitats [57] — [60] and could have been a contributing factor to the differences in dominant community members in our biofilms.

    Further experimental work, which is ongoing in our lab, will be needed to explore the relative contributions of environmental factors and founder effects on the composition of biofilms within drinking water distribution systems. The authors thank Sharon Waller for coordinating the pipe sampling effort and Kesha Baxi for assistance with data analysis at the start of the project.

    Submission history

    The authors thank Tim LaPara for his helpful comments on an earlier version of this manuscript. Performed the experiments: NM AC. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Pipes that transport drinking water through municipal drinking water distribution systems DWDS are challenging habitats for microorganisms. Introduction The pipes that are used to transport drinking water through municipal drinking water distribution systems DWDS are challenging habitats for microorganisms.

    Sample Processing In the laboratory one cap was removed from the pipe section and the water was carefully poured out. Download: PPT. Statistical Analyses Plate count data and diversity scores were analyzed by one-way analysis of variance ANOVA based on sampling date and pairwise comparisons were made by Tukey's post hoc test. Figure 1.

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    Relative percentages of source waters within PCU drinking water distribution system. Water Chemistry Water temperature varied seasonally in the water main from which the biofilm samples were obtained, being higher in summer months July and August than winter months February, March and December Table 1. Figure 2. Table 2. Numbers of heterotrophic bacteria in pipe biofilms based on plate count assay. Bacterial Community Analysis Tag pyrosequencing of 16S rRNA genes was used to profile the bacteria within the biofilms lining the drinking water pipes.

    Figure 3. Rarefaction curves for biofilm bacterial communities based on tag pyrosequencing of 16S rRNA genes. Table 3. Comparison of the number of observed and estimated bacterial OTUs in pipe biofilm communities based on 16S tag pyrosequencing data. Figure 4. Non-metric multidimensional scaling ordination of biofilm bacterial communities based on tag pyrosequencing of 16S rRNA genes.

    Table 4. Figure 5. Diversity of bacterial biofilm communities and relationship between bacterial abundance and diversity. Table 5. Relative abundance of most numerically dominant bacterial genera 1. Discussion Pipe samples were collected from the same region of a drinking water distribution system in Pinellas County, FL on five dates over an month period between February and August Acknowledgments The authors thank Sharon Waller for coordinating the pipe sampling effort and Kesha Baxi for assistance with data analysis at the start of the project.

    References 1. Annual Reviews in Microbiology 81— View Article Google Scholar 2. Marshall KC Adhesion and growth of bacteria at surfaces in oligotrophic habitats.

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    More Information. Package Forwarding Service Save on shipping by combining your shipments. Ship products from shops that don't provide international shipping. Stetson Stetson In Fig. To calibrate the magnitudes in the U Johnson, we have matched our photometry with the catalogue of photometric secondary standards by Stetson and derived calibration equation by using least-squares fitting of straight lines of stellar magnitudes and colours.

    Very accurate photometry is crucial to identify different sequences along the CMD for the analysis of multiple stellar populations. Therefore, we have used proper motions to separate most of the field stars from cluster members. Results of our proper motions analysis are illustrated in Fig. The red circles have been drawn by eye and are used to separate probable cluster members black points from the most evident field stars grey crosses.

    Right panels show the VPDs of stellar displacement in the five intervals of V magnitude indicated by the horizontal lines in the middle-panel CMD. The red circles separate probable cluster members and field stars, which have been represented with black dots and grey crosses, respectively, in all the panels of this figure. In the next section, we describe the investigation of the chemical composition of these two sequences using FLAMES data.

    D A PI: A. Data reduction involving bias-subtraction, flat-field correction, wavelength-calibration, sky-subtraction, has been done by using the dedicated pipelines. For the template, we used a synthetic spectrum obtained through the March version of moog Sneden Observed RVs were then corrected to the heliocentric system. Only cluster members, selected on the basis of proper motions and RVs have been included in the following analysis.

    The status of probable cluster members is listed in the last column. On the other hand, for UVES data we derive atmospheric parameters by using a standard fully spectroscopic approach, independent of the photometry. Details on the estimate of stellar parameters for both sets of spectra are presented below. We emphasize that a variation of 0. Similarly, a difference in log g by 0.

    We assume masses taken from isochrones of 0. Regarding the most reliable T eff scale, both spectroscopic and photometric scales are likely affected by systematics. Then, we fixed the other parameters and varied the temperature until the slope of the line that best fits the relation between abundances and EP became equal to the respective mean error. This difference in temperature can be considered a rough estimate of the error in temperature itself. We obtained a mean error of 0. This error agrees with that estimated from the comparison with photometric values 0. The exceptions from the EW analysis are discussed below.

    Line list for the program stars. But, even with such a subtraction procedure, we caution that residual telluric feature contamination might be of concern for the analysis of the Magnesium and aluminium abundances were possible only for the UVES data. Spectral synthesis of the analysed Al transitions allow us to account for possible blending caused by CN molecules, that are substantial in the case of the star U, which is the coolest in our UVES sample.

    Both hyperfine and isotopic splitting were included in the Cu analysis, with well-studied spectral line component structure from the Kurucz compendium. This line has no significant hyperfine or isotopic substructures, and was treated as a single absorber. Some examples of our spectral synthesis are plotted in Fig. In each panel, the observed spectrum has been represented in black. The cyan spectra have been computed with no contribution from La and Nd, and Cu for stars U and U, respectively, and no contribution from O and La for stars G and G, respectively.

    Line-to-line scatter is listed when more than one line has been analysed. The last column lists the status of s -rich or s -poor assigned to each star. The internal uncertainty associated with the photometric surface gravities are formally small, so we conservatively adopt an error of 0. In addition to the contribution introduced by internal errors in atmospheric parameters, we estimated the contribution due to the limits of our spectra, e. The variations in the abundances obtained by varying the EWs have been then divided by the square root of the number of available spectral lines minus one.

    Since the EWs measurement errors are random, the error associated with those elements with a larger number of lines is lower. For the other elements, we have a lower number of lines, so the error contribution introduced by EWs uncertainties is higher. To evaluate the error affecting this measurement, we re-derive EWs from single not combined exposures for three stars observed with GIRAFFE, and derived the error associated with the mean EW obtained for each star.

    The impact of this uncertainty to the Ba abundance has been derived in the same manner as for the UVES data, e. We then analysed the chemical abundances of all these synthetic spectra in the same manner as the observed spectra. Similarly to the discussion for EWs, these errors are random, and the corresponding uncertainty in chemical abundances is lower for those elements with a large number of lines e. These total uncertainties have been obtained following the formalism given in Johnson , and also account for correlations in the atmospheric parameters determination.

    We remark here that we are interested in star-to-star abundance variations. For this reason, we are only marginally interested in external sources of error which are systematic and much more difficult to evaluate. Later in the paper, we will discuss mostly internal uncertainties affecting our abundances, while systematic effects will be discussed only when relevant, e. The systematically lower Fe abundances inferred from UVES can be easily explained by systematic differences in atmospheric parameters, which have been derived in a different manner for the two sets of data.

    We remark here that we are mostly interested in the internal variations in chemical abundances present in the cluster. We are aware of systematics in the abundances derived from GIRAFFE and UVES, which are due in part to the systematics in atmospheric parameters, but also to the different transitions used for the two data sets, as UVES spectra span a significantly wider range in wavelength. We anticipate here that in these plots, our abundance results are best represented by dividing the two samples of stars into different groups, having different abundance patterns in n -capture elements and overall metallicity.

    The vertical tails extending from the boxes indicate the total range of abundances determined for each element, excluding outliers. Outliers those 1. Summary of the abundance results obtained from UVES spectra.

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    Although both Ba and La are expected to be produced mostly by s -process in the Solar system, their production can be influenced by the r -process at low metallicity e. However, we will refer to them as s -process elements because results from UVES suggest that the enrichment in this object has been due primarily to material that has undergone s -process nucleosynthesis see the discussion below in this section. The abundance patterns of Ba and La are clearer when we consider how they vary with the overall metallicity. For each stellar group we show the average values, with associated dispersions grey bars and errors blue and red bars.

    The total number of analysed stars for each group is also listed. Having identified the main stellar groups by means of the large sample available from GIRAFFE data, we were able to better chemically characterize them by using the higher-resolution and larger spectral range of the UVES sample. The UVES sample was carefully chosen to ensure that stars on both RGBs were selected to allow us to conduct a more detailed chemical characterization. From the UVES results the separation between the s -poor stars and the s -rich stars is clearer, making the identification of the two s -groups straightforward.

    The UVES stars appear to cluster around two different values in all these plots: at a lower and at a higher level of Fe and n -capture element contents.

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    Summary of the abundance results for n -capture process elements obtained from the UVES sample. The horizontal and vertical ranges are identical in all panels. Symbols are as in Fig. Mean UVES abundances for the total number of analysed stars, the s -rich and the s -poor group.

    The kernel-density distributions corresponding to the observed data strongly differ from the distribution for a mono-metallic GCs expected from our observational errors. The red continuous lines are the normalized kernel density distributions of the observed metallicities, while the red dotted lines are the normalized kernel-density distributions corresponding to measurement errors only.

    To derive each kernel density distribution, we used a Gaussian Kernel and a dispersion equal to the measurement error. Inspection of other contrasting pairs of stars and other spectral lines yields the same conclusion. In each panel, we represent pairs of stars with similar atmospheric parameters, so that the difference in the represented lines Ba, Nd, Ce are due almost entirely to a different chemical content in these elements.

    The blue spectrum represents a star belonging to the s -poor group, the red spectrum to a star belonging to the s -rich one. For most elements including O, Na, and Al , internal dispersions remain high even when the sample is separated into s -poor and s -rich groups. As shown in Fig. Symbols and colours are as in Fig. Despite clear variations in Al, which correlates with Na, also implying a O—Al anticorrelation, there is no strong evidence for a Mg—Al anticorrelation, at least from our relatively small sample of UVES targets.

    We note, however, that the star with the highest Al also has the lowest Mg and low O, and possibly a larger sample of stars with Al and Mg abundances can reveal the presence of clear Mg—Al anticorrelation.