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Poster number: 363
Analysis of nitrifying microbial communities by FISH and
16S rRNA amplicon-based sequencing in a wastew...
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2017 - Analysis of nitrifying microbial communities by FISH and 16S rRNA amplicon-based sequencing in a wastewater treatment plant

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Nitrification, the sequential oxidation of ammonia via nitrite to nitrate, is an important process for nitrogen removal from municipal wastewater. This process is catalysed by ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), two different groups of slow-growing microorganisms whose cooperation is needed to achieve complete nitrification. High efficiency and stability of this process is required for wastewater treatment plants (WWTPs) operational optimization due to
nitrification is often subjected to recurring collapse in many WWTPs. Therefore, a better understanding of the microbial ecology of nitrifying bacteria in WWTPs could
potentially improve the nitrification stability. Novel high-throughput molecular methods, as next generation sequencing (NGS), are nowadays providing detailed knowledge on the microorganisms governing wastewater treatment systems. This
methods in conjunction with the environmental ordination of the relationships between biological variables (nitrifying bacterial community) and physicochemical variables (nitrogen compounds and environmental conditions) provide a powerful
tool to elucidate how selection pressures imposed by operational and environmental conditions affect community diversity and dynamics within activated sludge systems.

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2017 - Analysis of nitrifying microbial communities by FISH and 16S rRNA amplicon-based sequencing in a wastewater treatment plant

  1. 1. Poster number: 363 Analysis of nitrifying microbial communities by FISH and 16S rRNA amplicon-based sequencing in a wastewater treatment plant P. Barbarroja1, J.L. Alonso1, R. Pérez-Santonja1, A. Zornoza1, C. Lardín2, L. Pastor3 and E. Morales3. 1Instituto Universitario de Ingeniería del Agua y Medio Ambiente, Universitat Politècnica de València, 46022 Valencia, Spain 2Entidad de Saneamiento y Depuración de Aguas Residuales de la Región de Murcia. Complejo Espinardo CN-301, Calle Santiago Navarro, 4, 30100 Espinardo, Murcia, Spain 3Depuración de Aguas del Mediterráneo. Avda. Benjamin Franklin 21, 46980, Parque Tecnológico - Paterna (Valencia) *Corresponding author: paubaror@iiama.upv.es Introduction Nitrification, the sequential oxidation of ammonia via nitrite to nitrate, is an important process for nitrogen removal from municipal wastewater. This process is catalysed by ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), two different groups of slow-growing microorganisms whose cooperation is needed to achieve complete nitrification. High efficiency and stability of this process is required for wastewater treatment plants (WWTPs) operational optimization due to nitrification is often subjected to recurring collapse in many WWTPs. Therefore, a better understanding of the microbial ecology of nitrifying bacteria in WWTPs could potentially improve the nitrification stability. Novel high-throughput molecular methods, as next generation sequencing (NGS), are nowadays providing detailed knowledge on the microorganisms governing wastewater treatment systems. This methods in conjunction with the environmental ordination of the relationships between biological variables (nitrifying bacterial community) and physicochemical variables (nitrogen compounds and environmental conditions) provide a powerful tool to elucidate how selection pressures imposed by operational and environmental conditions affect community diversity and dynamics within activated sludge systems. Material & Methods Sampling: Samples from activated sludge, influent, and treated effluent, were collected every fifteen days during six months from a bioreactors belonging to a WWTP located in Spain. The plant treats 25 000 m3 day-1 of mainly municipal sewage and adopts an anoxic⁄aerobic (AO) process with a nitrified water recirculation system. DNA extraction and PCR-based Illumina sequencing: Total DNA of 1 ml activated sludge sample was extracted in duplicate. Lysis was performed with the FastPrep® -24 instrument at 6 m/sec for 40 sec (twice) and the DNA was extracted using the FastDNA® SPIN kit for soil (MP Biomedicals) according to the manufacturer’s instructions. OneStep™ PCR Inhibitor Removal Kit (Zymo Research) was used in order to remove sample inhibitors. For Illumina amplicon sequencing of the hypervariable V3–V4 region of bacterial 16S rRNA gene, the primers PRO341F and PRO805R were used (Takahashi et al., 2014). Bioinformatics analysis: Raw Illumina sequences were analysed using Quantitative Insights Into Microbial Ecology (QIIME™ http://qiime.org/) software package version 1.8.0. Forward and reverse reads were joined. Joined reads were ckecked for chimeras using Usearch61 algorithm against 16S SILVA_123 database (Quast et al., 2013). Remaining sequences were clustered at 97% similarity into Operational Taxonomic Units (OTUs) using the denovoOTU clustering script. The most abundant sequence of each OTU was picked as its representative, which was used for taxonomic assignment against 16S SILVA_123 database at 97% identity (cut-off level of 3%) using default parameters. Quantitative Fluorescent in situ hybridization (qFISH): In situ hybridization with fluorescently labelled rRNA- targeted probes was performed, at 46°C for all th[[e probes, as described by Amann et al. (1990). The hybridized samples were analysed by standard epifluorescence microscopy on an Olympus BX50 microscope. Multivariate analysis: Hierarchical cluster analysis was used to evaluate the spatial variability of nitrifying bacterial communities by examining the relative distances among samples in the ordination (abundance square-root transformed data; Bray-Curtis similarity; group-average linking). To assess the contribution of the environmental variables to the variability observed in the nitrifying bacteria community structure, we carried out distance-based linear models (DISTLM), using parsimonious methods (e.g. BIC, AICc). Environmental variables were log-transformed and normalized to eliminate their physical units, prior to multivariate data analyses (euclidean similarity). Distance-based redundancy analysis (dbRDA) was used to visualize the DISTLM. All multivariate analyses were performed with PRIMER v7 (Clarke & Gorley, 2015) with PERMANOVA+ (Anderson et al., 2008). Results & Discussion Several representatives of two different genera of AOB, Nitrosomonas and Nitrosospira, both belonging to a monophyletic group within the beta-proteobacteria, have been found. The NOB community was predominated by genus Nitrospira. Members of the genus Nitrotoga and Brocadia were present at lower relative abundance. Fluctuations of nitrifying bacterial community structures were observed, even thought the stable performance. Cluster analysis revealed tight relation of members of Nitrosomonas and Nitrospira genus (fig. 1). References Anderson, M.J., Gorley R.N., y Clarke, K.R. (2008) PRIMER + for PERMANOVA: Guide to Software and Statistical Methods. PRIMER-E. Ltd, Plymouth. United Kingdom. Belluci M., Curtis T.P. (2011) Ammonia-oxidizing bacteria in wastewater. Methods Enzymol. 496:269-286. Clarke, K.R, y Gorley, R.N. (2015) PRIMER v7: User Manual/Tutorial. PRIMER-E, Plymouth, 296pp. Daims, H., Lücker, S., & Wagner, M. (2016). A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends in microbiology, 24(9), 699-712 Gruber-Dorninger C., Pester M., Kitzinger K., Savio D.F., Loy A., Rattei T., Wagner M., Daims H. (2015) Functionally relevant diversity of closely related Nitrospira in activated sludge ISME J. 9:643-655. d   b  a   b  a   ! Figure   1.   Cluster   analysis   of   the   nitrifying   bacteria.   The   shade   plot   illustrates   the   relative   abundance  of  nitrifying  bacteria  identi:ied  expressed  as  square  root  function.   We investigated models of environmental interpretation of nitrifying variables using of distance-based linear models (DISTLM). The dbRDA plot of the bioreactor revealed a strong association of genus Nitrosomonas, Nitrosospira and Nitrospira with medium values of eflunte nitrite (NO2-N) and effluent soluble total nitrogen (STN) and the species of the genus Nitrotoga correlated with lower values of this variable (figure 2). ! Of the 21 operational and environmental variables tested in this study, chemical oxygen demand (COD) and biological oxygen demand (BOD). emerged in dbRDA as important explanatory variables affecting the dynamics of nitrifying community (fig. 3). Genus Nitrospira, Nitrosospira and Nitrosomonas were strongly and significantly linked high values of biological oxygen demand (BOD) and chemical oxygen demand (COD), however genus Nitrotoga appears related with lower values of this variable. Figure  3.  Distance-­‐based  redundancy  (dbRDA)  bubble   plot   illustrating   the   DISTLM   based         on   the       relationship      between    operational  parameters    and     nitrifying     bacterial   community.   The   “%   of   :itted”   indicates  the  variability  in  the  original  data  explained   by   the   :itted   model   and   “%   of   total   variation”   indicates  the  variation  in  the  :itted  matrix.  The  length   and   direction   of   the   vectors   represent   the   strength   and   direction   of   the   relationship.   BOD,   biological   oxygen   demand   (af:luent);   COD,   chemical   oxygen   demand  (af:luent).   Figure  2.  Distance-­‐based  redundancy  (dbRDA)  bubble   plot  illustrating  the  DISTLM  based  on  the  relationship   between   nitrogen   removal   ef:iciencies   and   the   ef:luent  nitrogen  compounds    and  nitrifying  bacterial   community  .The  “%  of  :itted”  indicates  the  variability   in  the  original  data  explained  by  the  :itted  model  and   “%   of   total   variation”   indicates   the   variation   in   the   :itted  matrix.  The  length  and  direction  of  the  vectors   represent   the   strength   and   direction   of   the   relationship.   NO2-­‐N,   nitrite   nitrogen   (ef:luent);   STN,   soluble  total  nitrogen.    

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