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.

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.
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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.