NOVEL AND RAPID TECHNOLOGY FOR DISSECTING AND REMOVING MICROBIAL COMMUNITY IN AQUATIC ENVIRONMENT

Document Type : Original Article

Abstract

ABSTRACT:
The present study aims to integrate the benefits of Illumina
microbial sequencing approaches using novel prepared copper oxide
(CuO) nanoparticles for improving water quality monitoring and
management. Our results showed rapid and accurate discovery of novel
different microbial compositions in the collected water samples. The
domestic drains were had pathogenic microbes as viruses, Alpha-, Beta-
Gamma proteobacteria, Bacteroidia, Deinococci, Bacilli also Clostridia
with variety quantitive. The class Pseudomonadales were found to be
higher in quantity in all drains outfall. Viruses, including Enteroviruses
and hepatitis A and E species were found out in high volume of domestic
drains in comparison with the agricultural and industrial drains. On the
other hand, our results showed that decreasing and removing microbial
pathogens using prepared copper oxide nanoparticles (CuO NPs) by
quick precipitation method. The highest antibacterial activity was notified
for CuO NPs with the optimum concentration 102 μg/mL were ranged
between 92 to 96% after contact time 24h at 35ᵒC. The shaking during the
treatment gave a positive effect removing with CuO NPs. Concluding
remarks highlighted the potential of nanoparticles and Illumina mix as
accurate, simple method. The study recommended future efforts to apply
this as a robust, rapid and powerful metagenomics technology to detecting
other disease-causing agents of public health concern to update and
control water pollution.

Highlights

CONCLUSIONS
The synthesized CuO NPs by a rapid precipitation method in
absence and presence of (TOAB). Using a stabilizer to control
nanoparticles size. CuO NPs was tested to examine their antibacterial
activity using (TC), (FC) and (E. faecalis) bacteria in wastewater
samples. several parameters were evaluated to having the optimum
wastewater disinfection factors, as nanoparticles concentration, and
temperature, contact period, pH value, shaking conditions of wastewater.
High antibacterial activity appeared from CuO-TOAB stabilized NPs
more than without TOAB surfactant. the antibacterial activity of both
CuO NPs has slightly effect by the Contact period, where high activity
of CuO happened in wastewater samples when were treated at 25 ºC and
35 ºC, respectively. Noticed that, decreasing the pH values The
antibacterial activity of CuO increasing.When shaking conditions
increase from 70 and 90% the activity of CuO NPs affect. Positive
bacteria E. fecals more effects than gram negative (TC and FC) by
studied conditions. Using a novel technique using for wastewater.
Including Ilumina sequencing technology is a potential and robust way to
investigate microbial structure. In future we could remove and eliminate
the pathogens from the wastewater treatment process. Illumina
sequencing technology detect disease-causing agents in wastewater.
ACKNOWLEDGEMENT
We would like to thank the staff of micro lab and inorganic lab for
Central Laboratory for Environmental Quality Monitoring (CLEQM) for
their cooperation during measurement for the work.

Keywords

Main Subjects


NOVEL AND RAPID TECHNOLOGY FOR
DISSECTING AND REMOVING MICROBIAL
COMMUNITY IN AQUATIC ENVIRONMENT
Mohamed I. Azzam* and Sabah S. Ibrahim
Central Laboratory for Environmental Quality Monitoring,
National Water Research Center, Egypt
*E-mail: dr-mohamed-azzam@hotmail.com
Corresponding Author: Mohamed I, Azzam, Central Lab for Environmental Quality
Monitoring, National Water Research Center, P.O. Box
13621/6, El- Kanater, Egypt. E-mail: dr-mohamedazzam@
hotmail.com
Key Words: Eco-diversity, Elimination, Management, Metagenomics,
Nanoparticles, Water quality.
ABSTRACT:
The present study aims to integrate the benefits of Illumina
microbial sequencing approaches using novel prepared copper oxide
(CuO) nanoparticles for improving water quality monitoring and
management. Our results showed rapid and accurate discovery of novel
different microbial compositions in the collected water samples. The
domestic drains were had pathogenic microbes as viruses, Alpha-, Beta-
Gamma proteobacteria, Bacteroidia, Deinococci, Bacilli also Clostridia
with variety quantitive. The class Pseudomonadales were found to be
higher in quantity in all drains outfall. Viruses, including Enteroviruses
and hepatitis A and E species were found out in high volume of domestic
drains in comparison with the agricultural and industrial drains. On the
other hand, our results showed that decreasing and removing microbial
pathogens using prepared copper oxide nanoparticles (CuO NPs) by
quick precipitation method. The highest antibacterial activity was notified
for CuO NPs with the optimum concentration 102 μg/mL were ranged
between 92 to 96% after contact time 24h at 35ᵒC. The shaking during the
treatment gave a positive effect removing with CuO NPs. Concluding
remarks highlighted the potential of nanoparticles and Illumina mix as
accurate, simple method. The study recommended future efforts to apply
this as a robust, rapid and powerful metagenomics technology to detecting
other disease-causing agents of public health concern to update and
control water pollution.
INTRODUCTION
Increasing demands on clean water for many purposes as drinking,
industrial and irrigation purposes, Clean water sources is shorted to meet
increasing of population, increasing industrial demands and other reasons.
wastewater usage is one of the most source available to meet the clean
Egypt. J. of Appl. Sci., 36 (7-8) 2021 185-204
water demands. pathogens and hazard chemicals which come from
wastewater had an effects on health (Silva et al., 2009).
Copper (II) oxide is semiconducting material have amonoclinic
structure systems, which having useful physical and chemical properties
as superconductivity at relatively stable, high temperature, photovoltaic
properties, and has antimicrobial effect (Rene et al., 2009). CuO
nanoparticles have different technology applications as catalysis (Zhou
et al., 2006), batteries due to high electrochemical capacity (Anandan et
al., 2012), and gas sensors (Borgohain and Mahamuni, 2002). CuO
nanoparticles synthesized by different methods as sonochemical
technique (Anandan et al., 2012), electrochemical method (Borgohain
and Mahamuni, 2002), high temperature combustion (Chang and Zeng,
2004) and novel quick precipitation method (Zhu et al., 2004).
Removing organic pollutants from sewage water such as nitrogen,
sulfur and carbon was considered a hot issue using specific
microorganisms. bacterial community exists inactivated microbial state.
Many methodologies were used for remediation wastewater of plants
such as culture-dependent and culture-independent. Using various
technologies for sequenced and analyzed structure and framework of
microorganisms as 16S rRNA gene, 454 pyrosequencing, metagenomic
sequencing where this tools used for microbial estimation from different
domestic agricultural drains (Ye et al., 2012; Sanchez et al., 2013; Ye
and Zhang, 2013).
Approach clone library sequencing can give non-accurate results
deep-rooted alignment of planning (Aird et al., 2011; Ye et al., 2012).
Some technologies evaluated Illumina sequencing technology where are
an inventive practice to microbial genome (Albertsen et al., 2006;
Bragg and Tyson, 2014). Several studies were evaluated anaerobic
microbial structure using method 454 pyrosequencing (Wong et al.,
2013; Li et al., 2013; Sundberg et al., 2013).
Illumina sequencing technology is a low-cost and effective method
when compared with 454 pyrosequencing investigate microbial
community structure (Mardis, 2008 and Glenn, 2011) where it has been
used for investigation microbial community from water samples
(Mackelprang et al., 2011), also samples from the ocean (Mason et al.,
2014). Anaerobic digested sludge (Ju et al. 2014). A study has tried to
analyze complete data of microbiome from digested sludge samples
(Yang et al., 2014). fecal indicators pollutants such as Escherichia coli
or enterococci is being founded in the effluent part of wastewater
samples, there are other disease-causing agents, shuch as bacteria and
viruses which may be presented in fecal indicators which transmitted
throw different carriers in environment. Ejection of the disease-causing
agents is important for reutilization of water with measurement of
186 Egypt. J. of Appl. Sci., 36 (7-8) 2021
pathogenic microorganisms in the environment (Varela and
Manaia,2013). Pathogenic bacteria “Mycobacteria” has never been
observed and monitored to be measure for human and animal infection in
wastewater where some researchers have quantified the presence of
Mycobacteria in the drainage and surface water samples and they
reported that Pathogenic bacteria "Mycobacteria" were not expected to be
detected by known parameters. (Radomski et al., 2011). Viruses were
reported to be non-predictable in the polluted water by given parameters
(Savichtcheva and Okabe,2006).
Viruses are dangerous for animals and human health more bacteria,
where they diffused easily (Rosa et al.,2010).
This study is important to analyses and dissect the microbiome
structure from various parts. Using samples from El-Rahawy, Tala, Sabal
drains in Egypt. Detecting concentration of viruses and mycobacteria.
Using metagenomic sequencing technology. Pathogenic microbial
species present of drainage water.
MATERIALS AND METHODS
COLLECTION OF SAMPLES
According to Standard Methods for Examination of Water and
Wastewater water samples the water samples analysis was carried out
(APHA, 2012). Samples of water were collected in clean and sterile
polyethylene plastic bottles. Various drains outfalls along the River Nile
at Rosetta branch were chosen for sample collection and represent the
major sector in Egypt, including El-Rahawy drain (R), Sabal drain (S),
El-Tahreer drain (E), Zawiet El-Bahr drain (Z) and Tala drain (T) for 24
h after every 30 min. Drains mixed from sewage, agricultural and
industrial wastes.
Samples were collected under consistent sampling procedures such
that: presence of ample air space in the bottles (at least 2.5 cm) to
facilitate mixing by shaking, keeping sampling bottles closed until it is to
be filled, avoiding external contamination during sample collection,
avoiding internal contamination of stopper or cap and bottle neck and
filling container without rinsing. All samples collected for either chemical
or bacteriological detection and immediately forwarded to the Central
Laboratory for Environmental Quality Monitoring (CLEQM) National
Water Research Center (NWRC), Cairo, Egypt in an iced cooler.
Preparation of Copper oxide nanoparticles (CuO NPs)
Copper oxide nanoparticles with tetraoctylammonium bromide
(TOAB) surfactant were prepared using precipitation method. CuO NPs
stabilized with TOAB surfactant. Dissolving about 15.00 g of
CuSO4.5H2O using 2.34 g of tetraoctylammonium bromide surfactant in
150 mL of ionized water for prepare (CuO-TOAB). Different size of CuO
Egypt. J. of Appl. Sci., 36 (7-8) 2021 187
NPs was prepared at 65, 75 and 85°C using the reflux capacitor. Heating
and stirring for about 15 min at 150 rpm. About 2.0M of sodium
hydroxide were added to 100 ml of the prepared solution. Black
precipitate (ppt) was collected and washing by the ionized water then
allowed to dry.
Physico-chemical analysis
All field parameters were measured in the field and rechecked in
laboratory to ensure data accuracy; Temperature, pH, dissolved oxygen
(DO), electric conductivity (EC) and total dissolved solids (TDS) were
measured in water samples by using the multi-probe system, model
Hydralab– Surveyor, Germany. Once the samples were received in the
lab, they were manually mixed by shaking and examined as follows:
Ammonia (NH3) measured by using Kedah method . Nephelometric
turbidity meter HACH using measured turbidity . (BOD) is known by
Biochemical oxygen demand measuredd by ORION BOD model 890.
(COD) is known Chemical Oxygen Demand tested using potassium
permanganate method. Total hardness, Calcium hardness (Ca. hardness),
and Magnesium hardness (Mg. hardness) measured by Titrimetric
Method. Chloride (Cl-) measured by Argentometric method. Nitrate
(NO3
-), Nitrite
(NO2
-), Phosphate (PO4
3-), and Sulphate (SO4
2-) were measured by
Ion Chromatography. The concentrations of major cations, Calcium
(Ca2+), Sodium (Na+), Magnesium (Mg2+) and Potassium
(K+) . heavy metals as arsenic (As+2), cadmium (Cd+2), chromium
(Cr+3), copper (Cu+2), zink (Zn+2), lead (Pb+2), nickel (Ni+2), aluminum
(Al+3), manganese (Mn+2) and iron (Fe+3) were measured by using ICPOES
Model Varian lab Liberty Series II.
Bacteriological analysis
Collected samples were examined within 6 hours followed the
method from Standard book for Examination of Water and Wastewater
(APHA, 2012). According to standard method Nos. 9222B, 9222 D total
coliforms (TC), fecal coliforms (FC) and fecal streptococci (FS), using
membrane filter technique. All media were obtained form Difco-USA.
Results were recorded as colony forming unit (cfu 100ml-1) by using
equation:
Factors effect on the NPs antibacterial activity Temperature effect
CuO-TOAB was used at 102 and 103 concentrations by μg/mL .
Samples could be control shaking at 150 rpm for 2 h at 15, 25, 35°C.
188 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Contact time effect
Preparing of CuO-TOAB NPs at 75°C. Using CuO-TOAB is better
activity. studying carried out concentrations; 102 and 103μ g/mL of CuOTOAB.
We must control some paramters such as shaking at 150 rpm for
24 h at 25°C.
Testing Flow
Preparing CuO NPs is important for water treatment, testing flow
using to investigate the antibacterial activity of CuO NPs .Increasing
application of preparing CuO NPs for water treatment. Sterile column (L:
44 X D: 12 mm) used to flow 4.00 mL of drainage water. Thickness of
CuO-TOAB layer about 1.0 mm. flow rate of 10 mL/min was constant .
E. faecalis bacteria and TC.
DNA extraction and library preparation
All collected water samples were stirring for 10 min at 6000g to be
concentrated. Filtration using A 0.22 μm filter injection found structure of
microbiome and pathogens. Using 250 mg of pellets for DNA extraction.
DNA could be isolated (MoBio, USA (Kaevska et al., 2011). using gel
electrophoresis was detect DNA quality. Fluorometer (Thermo, USA)
finding out quantity of DNA.. Illumina HiSeq 2000 using conducted at
the Colors Company, Egypt.
DNA exctraction was diluted to 200–300 ng/μL, for using
experimental (Ali et al., 2019a). The procedure of DNA library related to
Campanaro et al., 2016. The short reads were fragmented by a minimum
30 quality score. no ambiguous nucleotides. parameters were get
overlapping about 20 nucleotide length of region was needed. (Ali et al.,
2019b).
Metagenomic determine
A prediction for metagenomic computational of DNA was utilized
from the international of National Center for Biotechnology Information
(NCBI). Prediction server given the metabolic and taxonomic affiliation.
analyzing protein as reported (Meyer et al., 2008). About 10-5 E value for
investigation taxonomic enrollment on MG-RAST (Huson et al., 2011).
Capacity of taxonomic as phyla, and genus, was determined for
explanation. Hierarchical divide to at E value deducation of 10-5 using for
gene comments on profile (Yang et al., 2014). The gentics were divided
successfully into hierarchical metabolic groups. Entering reads were
decoded across the bibliography of the Kyoto Encyclopedia of Genes and
Genomes (KEGG) databases.
Bioinformatic analysis
Sequences of DNA were identified using Basic Local Alignment
Search Tool (blast) on the NCBI database. Using DNAMAN software
Egypt. J. of Appl. Sci., 36 (7-8) 2021 189
performed for many alignments of sequences (Madison, Wisconsin,
USA, version 5.2.9). (Thompson et al., 1994). The nucleotide distances
were measured alignment gaps and using Juckes and Cantor’s method
(Jukes and Cantor, 1969). Development of Molecular Genetic Analysis
(MEGA) software (version 6.0) (Tamura et al., 2013).
Identification for Relationship of Phylogenetic between microbial
community. Evaluaion using unweighting two Method with Arithmetic
Mean (UPGMA) through MEGA 6.0 software. and boot strap analysis
(1000 replicates) was performed to assess the reliability of the constructed
phylogenetic. Nucleotide sequences data determined by the National
Center for Biotechnology Information (NCBI) GenBank database, USA.
Statistical analysis
Data mean values and percentages were calculated using Minitab
16 statistical software program (Minitab, 2010).
RESULTS AND DISCUSSION
Eco-diversity of collected water samples
The process quality of water is an evaluation of physical, chemical
and biological nature of water .Quality of water may effect ont human
health and health of the aquatic system . Our area of study, water quality
drains was carried out. The results obtained from physical, chemical,
microbial community , calculating parameters and statistical analyses.
investigation, both of pH, turbidity, DO, BOD and COD concentrations
showed variable results according to site nature of pollution. in Figure
1(A & B). water samples collected from drains outlets showed high
turbidity, marked depletion in DO values and all COD concentrations
exceeded law 48. The maximum values were at El- Rahawy, Sabal and
Tala drains while. Minimum values were at Zawiet El-bahr and El-
Tahreer drains.
Water quality of drains outlets was improved after treated with by
CuO nanoparticles with different percentages especially in water
turbidity (67%), DO (43.2%), BOD (85%) and COD (85%). Organic
matter concentrations play the important role in water quality and related
strongly with microbial load in aquatic system. CuO nanoparticles used as
antibacterial material in many studies which confirmed the ability of this
particles for bacterial indicators degradations. Suleiman et al., 2013,
prepared and apply CuO nanoparticles in bacterial indicators degradation
(TC, FC and FS) and microbial removing with concentration less than
1000 μg/mL.
190 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Figure 1. Physicochemical properties of collected water samples
A. pH, turbidity, DO, BOD, COD values before treatment with CuO
nanoparticles.
B. Improvement percentage after treatment with CuO nanoparticles.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 191
Different factors effect on NPs antibacterial activity
Assessment for water quality process is an assessed of physical,
chemical and biological nature of water ., This process effecsts human
health and health of aquatic system.
CuO NPs were prepared with and without TOAB surfactant. It
investigated in a real drainage water samples. Many parameters that may
affect the antibacterial activity .We taked the optimum conditions to have
NPs with high activity used in drainage water disinfectant.
The most factors affecting bacterial growth in aquatic environment
is Temperature. Weather of Palestine that temperature almost range
from 10-35°C. Areflection of the slow water response to the weather
temperature. Different incubation effect on temperature; 15, 25 and 35°C,
were studied a factor in antibacterial activity of CuO NPs at
concentration (102 μg/mL).
Studying area antibacterial activity of CuO NPs was studied at
different contact time of 0, 1, 2 and 24 hr at 25°C. Determination
appropriate contact time. Studingfor five samples and our results showed
the best concentration (102 μg/mL) of drainage water samples were 90, 92,
95, 96, 98%.
Testing flow at fixed flow rate of 10 mL/min was cleared
antibacterial CuO NPs. This study needed optimum conditions to
investigation such as pH and temperature. Degradation of bacteria percent
reach to 99%. Indicators of bacteria using in drainage water samples
passing CuO-TOAB. Drainage water samples passing CuO nonstabilized.
Percent degradation for E.faecalis bacterial and TC were 79,
83, 75, 89 and 81%, successively . CuO NPs stabilized with TOAB is
enable effective degradation of bacteria than using CuO NPs without
stabilization.
Biodiversity of Microbial Community
About 35 thousand active sequences abundant quality were
collected for evaluation. Homogeneous sequences with different
quantities were found in about four parts of drain Zawiet El-Bahr.
Concentration comparative active steps was evaluated in studying
(Kaevska et al., 2016 and Azzam et al., 2017). Quantity of microbes is
high.Viruses was get in both of El-Rahawy and Sabal drain outlet. Fewer
in Tala and El-Tahreer drain outlet . Figure 1. The sewage sample was
reported 1730 species where agriculture drain about 1490 species was
discovered in class-level classification. Ye and co-authors reported a
similar finding when studding wastewater samples (Ye and Zhang,
2013). In accordance some studied from researches noticed
diversification were higher in domestic drains when comparison with
agriculture drains (Lee et al., 2015). Data agreement to our investigation
to statistical data. Mixing domestic and agricultural drains samples were
192 Egypt. J. of Appl. Sci., 36 (7-8) 2021
found like samples from agricultural drainsas reported from statically
analysis. Similar variance in the microbial structure founded in El-
Rahawy drain (R), Sabal drain (S), El-Tahreer drain (E) drain, Zawiet El-
Bahr drain (Z) and Tala drain (T).
Bacteria represent the most percent in domestic drains with 97.2%
specially in El-Rahawy, Sabal and Tala drains. Concentration ratio of
archaea, eukaryote, and viruses was measured in the five drains.
publishing studies reported a similar Concentration (Yang et al., 2014).
In El-Rahawy drain the concentration of viruses , accounting
5.82% ,was about three times higher than the other drains. A smaller
bacterial concentration. Analysis details in Figure 2.
Figure (2): Taxonomic profiling at the domain level of the studied Egyptian five
mixed drains. sequences of DNA were assigned to bacteria,
eukaryote, archaea, viruses, other sequences.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 193
Detecting the whole microbial structure and also the functional
profiling of the (DS) digestion sludge, data were showed in Figure 3.
represented short reading where analyzed according to the KEGG
category database.
Figure 3. Krona taxonomy chart for all bacterial and viral strains
discovered in water samples.
In the sewage drains outlets the precent of phyla Proteobacteria,
Bacteroidetes, Firmicutes-associated bacteria were the highest, , as
shown in Table 1.
The founded results were correlated to previous report which
reported by Lee’s group (Lee et al. 2015). In agreement with our results
a high number of Actinobacteria were found as reported by McLellan’s
group (McLellan et al., 2010). Delta-proteobacteria and alpha beta
(Table 1). Significant concentration reporting iii n all outlets domestic
presented Epsilon and Gamma proteobacteria less concentration. Lee and
co-authors proving agreement investigation (Lee et al. 2015).
Previous reports reported that Gamma-proteobacteria, Deinococcus
and Clostridiales were dominant classes (Ye and Zhang 2013).
Parameters difference may cause a variance in microbial composition of
aquatic systems. climate zones, industry , agriculture wastes and human
activity (McLellan et al. 2010).
194 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Table 1: Dominant precent class in major phylum from bacteria and
archaea found in water samples.
Phylum Class
R
Abundance (%)
S
Abundance
(%)
T
Abundance
(%)
Z
Abundance
(%)
G
Abundance
(%)
Proteobacteria Alphaproteobacteria 26 22.1 19.2 17.3 15.4
Betaproteobacteria 26.3 21.9 19.3 17.1 15.4
Gammaproteobacteria 26.5 21.6 19.5 17.3 15.1
Epsilonproteobacteria 25.9 22.2 19.3 17.4 15.2
Others 26.7 18.1 24.9 13.1 17.2
Actinobacteria Actinobacteria 27 23 19 17 14
Others 0.50 0.41 0.30 0.24 0.19
Bacteroidetes Bacteroidia 32 26 17.1 14.4 10.5
Others 1.90 1.86 1.90 1.80 1.65
Deinococcus Deinococci 28 22.8 16 12 8
Others 6.40 5.87 6.02 3.60 4.01
Firmicutes Bacilli 20 20 19 13 15
Clostridia 45 39 32 21 28
Erysipelotrichia 2.01 2.01 1.99 1.67 1.50
In El-Rahawy, Sabal and Tala drain outlets a high concenteration
of viruses with percent 8 % while less concentration was found in El-
Tahreer, Zawiet El-Bahar drains of samples. Some studies detected a small
concentration of viruses in their investigation (Hu, 2012 and Safaa and
Azzam, 2020).
A high concentration of Proteobacterial and virus's classes to
clostridia, actinomycetes, Bacteriodes, Deinococcus and Firmicutes were
reported in the sewage drains when comparing to another drains Figure
3. Several species which belonging to strain Pseudomonades causing
opportunistic disease agents in some humans, plants and animals.
Firmicutes strains are deathly to humans and plants and also
Closteridia species are pathogens which causing some effects to humans,
animals (Godoy et al., 2003). Bifdobacterium considered as Grampositive
and branching. anaerobic bacteria are wide speed in their
inhabitants as gastrointestinal tract and mouth of mammals such as
humans (Schell et al., 2002; Mayo and Sinderen 2010). To understand
the complete microbiota of all parts in the drain outlets the species -level
classification was applied. many species as Staphylococcus, Salmonella,
Shigella, Pseudomonas were found to be in an enhanced concentration in
domestic drains outlets along the Rosetta branch as compared with the
industrial and agricultural drains samples, in Figure 4.
In some publishing literature the same concentration of species was
discussed (Ye and Zhang 2013). aFish technology were conducting by some
researchers to input Actinobacteria and Betaproteobacteria are primary
bacterial in domestic drains and surface water (Muszynski et al., 2015).
About 38% of the strains were existing in the river Nile. Several
Publishing research decide the presence of Betaproteobacteria and
Gammaproteobacteria in samples of activate sludge (Kwon et al., 2010).
Egypt. J. of Appl. Sci., 36 (7-8) 2021 195
Figure 4. Heat map and genetic distance for novel bacterial species
discovered in water samples.
Many of bacterial structure in drains outlets depending locality,
source of pollution, water effluent, and long of the drains effects the
evaluation and removing capacity of microbial pathogens. Enormous
investigation under process dependence cofactor differences of the
bacterial community was behaved (Helbling et al., 2015; Johnson et al.,
2015). bacterial species range existing in mixed drains and agricultural
drains. critical role in the nanoparticle size.Method treatment for water
reporting of many researchers (Hu et al., 2012).
Dominant steps reporting in the case study belonge to the phylum
Proteobacteria and no effect in drains outlets. Case study proving
interesting discovery for a comprehensive pathogenic detection and
removal in various stages along the drains. The domestic drains both of
196 Egypt. J. of Appl. Sci., 36 (7-8) 2021
industrial and agricultural drains. samples had like composition; a less
concentration of Actinobacteria species was reported.
Variety and causing diseases of domestic drain were reported to be
higher in comparison with other types of samples (Figure 5). Result
revealed that the self- purification and precipitation steps is not complete
entirely, microbiota exists in the drains outlets. Transferring in the
surface water. Observing in agreement with beforehand publishing.
Concentration of pathogenic species as Actinobacter and Salmonella
species effects (Bibby et al. 2010; Ye and Zhang 2013). Using Illumina
sequencing technology in casing study proved a potential method to
measured pathogenic microbes.
Figure 5. Abundance of microbial community in drainage water before
treatment by CuO nanoparticles.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 197
CONCLUSIONS
The synthesized CuO NPs by a rapid precipitation method in
absence and presence of (TOAB). Using a stabilizer to control
nanoparticles size. CuO NPs was tested to examine their antibacterial
activity using (TC), (FC) and (E. faecalis) bacteria in wastewater
samples. several parameters were evaluated to having the optimum
wastewater disinfection factors, as nanoparticles concentration, and
temperature, contact period, pH value, shaking conditions of wastewater.
High antibacterial activity appeared from CuO-TOAB stabilized NPs
more than without TOAB surfactant. the antibacterial activity of both
CuO NPs has slightly effect by the Contact period, where high activity
of CuO happened in wastewater samples when were treated at 25 ºC and
35 ºC, respectively. Noticed that, decreasing the pH values The
antibacterial activity of CuO increasing.When shaking conditions
increase from 70 and 90% the activity of CuO NPs affect. Positive
bacteria E. fecals more effects than gram negative (TC and FC) by
studied conditions. Using a novel technique using for wastewater.
Including Ilumina sequencing technology is a potential and robust way to
investigate microbial structure. In future we could remove and eliminate
the pathogens from the wastewater treatment process. Illumina
sequencing technology detect disease-causing agents in wastewater.
ACKNOWLEDGEMENT
We would like to thank the staff of micro lab and inorganic lab for
Central Laboratory for Environmental Quality Monitoring (CLEQM) for
their cooperation during measurement for the work.
REFERENCES
Aird, D. ; M.G. Ross ; W.S. Chen; M. Danielsson ; T. Fennell ; C.
Russ and A. Gnirke (2011). Analyzing and minimizing PCR
amplification bias in Illumina sequencing libraries. Genome
Biology, 12(2): 1-14.
Albertsen, M. ; L.B.S. Hansen ; A.M. Saunders ; Nielsen P.H. and
K.L. Nielsen (2006). A metagenome of a full-scale microbial
community carrying out enhanced biological phosphorus
removal. ISME J., 6(6):1094-1106.
Ali, N. ; H. Gong; A.S. Giwa; Q. Yuan and K. Wang (2019).
Metagenomic analysis and characterization of acidogenic
microbiome and effect of pH on organic acid production.
Archives of Microbiology, 201(9): 1163-1171.
198 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Ali, N. ; H. Gong ; X. Liu ; A.S. Giwa and K. Wang (2020).
Evaluation of bacterial association in methane generation
pathways of an anaerobic digesting sludge via metagenomic
sequencing. Arch Microbiol,202(1):31-41.
APHA,(2012): American Public Health Association (APHA) and
American Water Works Association and Water Environment
Federation,. Standard Methods for the Examination of Water
and Wastewater, 22nd edition. American Public Health
Association, Washington, DC.
Anandan, S. ; G.J. Lee and J.J. Wu (2012). Sonochemical synthesis of
CuO nanostructures with different morphology. Ultrasonics
Sonochemistry, 19(3): 682-686.
Azzam, M.I. ; S.M. Ezzat ; B.A. Othman and K.A. El-Dougdoug (2017).
Antibiotics resistance phenomenon and virulence ability in bacteria
from water environment. Water Science, 31(2): 109-121.
Bibby, K. ; E. Viau and J. Peccia (2010). Pyrosequencing of the 16S
rRNA gene to reveal bacterial pathogen diversity in biosolids.
Water Research, 44(14): 4252-4260.
Bragg, L. and G.W. Tyson (2014). Metagenomics using next-generation
sequencing. In: Paulsen IT, Holmes AJ (eds) Environmental
microbiology: methods and protocols, methods in molecular biology,
vol 1096, 2nd edn. Humana Press, New York, pp 183-201.
Campanaro, S. ; L. Treu ; P.G. Kougias ; D. De Francisci ; G. Valle
and I. Angelidaki (2016). Metagenomic analysis and functional
characterization of the biogas microbiome using high throughput
shotgun sequencing and a novel binning strategy. Biotechnology
for Biofuels, 9(1): 1-17.
Cann, K.F. ; D.R. Thomas ; R.L. Salmon ; A.P. Wyn-Jones and D.
Kay (2013). Extreme water-related weather events and
waterborne disease. Epidemiology & Infection, 141(4): 671-686.
Chang, Y. and H.C. Zeng (2004). Controlled synthesis and selfassembly
of single-crystalline CuO nanorods and nanoribbons.
Crystal Growth & Design, 4(2): 397-402.
Da Silva, L.M. ; D.V. Franco ; I.C. Gonçalves and L.G. Sousa (2009).
Advanced technologies based on ozonation for water treatment.
Water Purification, 1-53.
De Giglio, O. ; G. Barbuti ; P. Trerotoli ; S. Brigida ; A. Calabrese ;
G. Di Vittorio and M.T. Montagna (2016). Microbiological
and hydrogeological assessment of groundwater in southern
Italy. Environmental Monitoring and Assessment, 188(11): 1-9.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 199
Ezzat, S.M. and M.I. Azzam (2020). An approach using a novel phage
mix for detecting Pseudomonas aeruginosa in water. Water and
Environment Journal, 34(2): 189-202.
Fierer, N. and R.B. Jackson (2006). The diversity and biogeography of
soil bacterial communities. Proceedings of the National
Academy of Sciences, 103(3): 626-631.
Gao, P. ; W. Xu ; P. Sontag ; X. Li ; G. Xue ; T. Liu and W. Sun
(2016). Correlating microbial community compositions with
environmental factors in activated sludge from four full-scale
municipal wastewater treatment plants in Shanghai, China.
Applied Microbiology and Biotechnology, 100(10): 4663-4673.
Glenn, T.C. (2011). Field guide to next‐generation DNA sequencers.
Molecular Ecology Resources, 11(5): 759-769.
Godoy, D. ; G. Randle ; A.J. Simpson ; D.M. Aanensen ; T.L. Pitt ;
R. Kinoshita and B.G. Spratt (2003). Multilocus sequence
typing and evolutionary relationships among the causative
agents of melioidosis and glanders, Burkholderia pseudomallei
and Burkholderia mallei. Journal of Clinical Microbiology,
41(5): 2068-2079.
Helbling, D.E. ; D.R. Johnson ; T.K. Lee ; A. Scheidegger and K.
Fenner (2015). A framework for establishing predictive
relationships between specific bacterial 16S rRNA sequence
abundances and biotransformation rates. Water Research, 70:
471-484.
Hu, M. ; X. Wang ; X. Wen and Y. Xia (2012). Microbial community
structures in different wastewater treatment plants as revealed
by 454-pyrosequencing analysis. Bioresource Technology, 117:
72-79.
Hunter, P.R. ; J.M. Colford ; M.W. LeChevallier ; S. Binder and P.S.
Berger (2001). Waterborne diseases. Emerging Infectious
Diseases, 7(3 Suppl), 544.
Huson, D.H. ; S. Mitra ; H.J. Ruscheweyh ; N. Weber and S.C. Schuster
(2011). Integrative analysis of environmental sequences using
MEGAN4. Genome research, 21(9): 1552-1560.
Johnson, D.R. ; T.K. Lee ; J. Park ; K. Fenner and D.E. Helbling
(2015). The functional and taxonomic richness of wastewater
treatment plant microbial communities are associated with each
other and with ambient nitrogen and carbon availability.
Environmental Microbiology, 17(12): 4851-4860.
200 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Ju, F. ; F. Guo ; L. Ye ; Y. Xia and T. Zhang (2014). Metagenomic
analysis on seasonal microbial variations of activated sludge
from a full‐scale wastewater treatment plant over 4 years.
Environmental Microbiology Reports, 6(1): 80-89.
Jukes, T.H. and C.R. Cantor (1969). Evaluation of Protein Molecules.
In: Mammalian Protein Metabolism, Munro, H.N. (Ed.)
Academic Press, New York, pp. 21-132.
Kaevska, M. ; I. Slana ; P. Kralik ; U. Reischl ; J. Orosova ; A.
Holcikova and I. Pavlik (2011). “Mycobacterium avium subsp.
hominissuis” in neck lymph nodes of children and their
environment examined by culture and triplex quantitative realtime
PCR. Journal of Clinical Microbiology, 49(1): 167-172.
Kaevska, M. ; P. Videnska and P. Vasickova (2016). Changes in
microbial composition of wastewater during treatment in a fullscale
plant. Current microbiology, 72(2): 128-132.
Khan, N.H. ; Y. Ishii ; N. Kimata-Kino ; H. Esaki ; T. Nishino ; M.
Nishimura and K. Kogure (2007). Isolation of Pseudomonas
aeruginosa from open ocean and comparison with freshwater,
clinical, and animal isolates. Microbial Ecology, 53(2): 173-186.
Kwon, S.D. ; T.S. Kim ; G.H. Yu ; J.H. Jung and H.D. Park (2010).
Bacterial community composition and diversity of a full-scale
integrated fixed-film activated sludge system as investigated by
pyrosequencing. Journal of Microbiology and Biotechnology,
20(12): 1717-1723.
La Rosa, G. ; M. Pourshaban ; M. Iaconelli and M. Muscillo (2010).
Quantitative real-time PCR of enteric viruses in influent and
effluent samples from wastewater treatment plants in Italy.
Annali Dell'istituto Superiore Di Sanita, 46: 266-273.
Lee, S.H. ; H.J. Kang and H.D.Park (2015). Influence of influent
wastewater communities on temporal variation of activated
sludge communities. Water Research, 73: 132-144.
Li, A. ; Y.N. Chu ; X. Wang ; L. Ren ; J. Yu ; X. Liu and S. Li
(2013). A pyrosequencing-based metagenomic study of
methane-producing microbial community in solid-state biogas
reactor. Biotechnology for Biofuels, 6(1): 1-17.
Mackelprang, R. ; M.P. Waldrop ; K.M. DeAngelis ; M.M. David ;
K.L. Chavarria ; S.J. Blazewicz and J.K. Jansson (2011).
Metagenomic analysis of a permafrost microbial community
reveals a rapid response to thaw. Nature, 480(7377): 368-371.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 201
Mardis, E.R. (2008). The impact of next-generation sequencing
technology on genetics. Trends in genetics, 24(3): 133-141.
Mason, O.U. ; N.M. Scott ; A. Gonzalez ; A. Robbins-Pianka ; J.
Bælum ; J. Kimbrel and J.K. Jansson (2014). Metagenomics
reveals sediment microbial community response to Deepwater
Horizon oil spill. The ISME Journal, 8(7): 1464-1475.
Mayo, B. and D. Van Sinderen (2010). Bifidobacteria: genomics and
molecular aspects. Horizon Scientific Press.
McLellan, S.L. ; S.M. Huse ; S.R. Mueller‐Spitz ; E. N. Andreishcheva
and M.L. Sogin (2010). Diversity and population structure of
sewage‐derived microorganisms in wastewater treatment plant
influent. Environmental Microbiology, 12(2): 378-392.
Meyer, F. ; D. Paarmann ; M. D'Souza ; R. Olson ; E.M. Glass ; M.
Kubal and R.A. Edwards (2008). The metagenomics RAST
server–a public resource for the automatic phylogenetic and functional
analysis of metagenomes. BMC bioinformatics, 9(1): 1-8.
Minitab, (2010). Minitab 16 Statistical Software. Minitab Inc., State
College, Pennsylvania, USA.
Muszyński, A. ; A. Tabernacka and A. Miłobędzka (2015). Long-term
dynamics of the microbial community in a full-scale
wastewater treatment plant. International Biodeterioration &
Biodegradation, 100: 44-51.
Odjadjare, E.E. ; E.O. Igbinosa ; R. Mordi ; B. Igere ; C.L. Igeleke
and A.I. Okoh (2012). Prevalence of multiple antibiotics
resistant (MAR) Pseudomonas species in the final effluents of
three municipal wastewater treatment facilities in South Africa.
International Journal of Environmental Research and Public
Health, 9(6): 2092-210.
Radomski, N. ; L. Betelli ; R. Moilleron ; S. Haenn ; L. Moulin ; E.
Cambau and F.S. Lucas (2011). Mycobacterium behavior in
wastewater treatment plant, a bacterial model distinct from
Escherichia coli and enterococci. Environmental science &
technology, 45(12): 5380-5386.
Rehman, S. ; A. Mumtaz and S.K. Hasanain (2011). Size effects on
the magnetic and optical properties of CuO nanoparticles.
Journal of Nanoparticle Research, 13(6): 2497-2507.
Ren, G. ; D. Hu ; E.W. Cheng ; M.A. Vargas-Reus ; P. Reip and R.P.
Allaker (2009). Characterisation of copper oxide nanoparticles
for antimicrobial applications. International Journal of
Antimicrobial Agents, 33(6): 587-590.
202 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Sánchez, O. ; I. Ferrera ; J.M. González and J. Mas (2013). Assessing
bacterial diversity in a seawater‐processing wastewater
treatment plant by 454‐pyrosequencing of the 16 S rRNA and
amoA genes. Microbial Biotechnology, 6(4): 435-442.
Savichtcheva, O. and S. Okabe (2006). Alternative indicators of fecal
pollution: relations with pathogens and conventional indicators,
current methodologies for direct pathogen monitoring and future
application perspectives. Water Research, 40(13): 2463-2476.
Schell, M.A. ; M. Karmirantzou ; B. Snel ; D. Vilanova ; B. Berger
; G. Pessi and F. Arigoni (2002). The genome sequence of
Bifidobacterium longum reflects its adaptation to the human
gastrointestinal tract. Proceedings of the National Academy of
Sciences, 99(22): 14422-14427.
Stephenson, F.H. (2016). Calculations for molecular biology and
biotechnology. Academic press.
Suleiman, A.K.A. ; L. Manoeli ; J.T. Boldo ; M.G. Pereira and
L.F.W. Roesch (2013). Shifts in soil bacterial community after
eight years of land-use change. Systematic and Applied
Microbiology, 36(2): 137-144.
Sundberg, C. ; W.A. Al-Soud ; M. Larsson ; E. Alm ; S.S. Yekta ;
B.H. Svensson and A. Karlsson (2013). 454 pyrosequencing
analyses of bacterial and archaeal richness in 21 full-scale
biogas digesters. FEMS Microbiology Ecology, 85(3): 612-626.
التکنولوجيا الجديدة والسريعة في ا ا زلة وتشريح المجتمع
الميکروبي في البيئة المائية
محمد اب ا رهيم ع ا زم , صباح سعد اب ا رهيم
المعامل المرکزية لمرصد البيئي. المرکز القومي لبحوث المياه- مصر
الهدف من البحث : يقوم بإ ا زلة البکتيريا مع التقميل من نسبة المواد العالقة والعکارة من الاوساط
المائيو المموثة وايضا الاکسجين الذائب والکميائي باستخدام مادة نانو اکسيد النحاس.
نتائج البحث: يمثل نتائج التجارب المعممية ومناقشتيا وتم فى ىذة الد ا رسة اختيار تأثير العوامل
المختمفة المؤثرة عمى کفاءة إ ا زلة البکتريا من البيئات المائية المموثة وىي تشمل مصارف
) الرىاوي- سبل - تلا وکفر الزيات ( وتمک العوامل تشمل د ا رسة:
-1 تأثير عامل الوقت وفى وجود مادة نانو اکسيد النحاس الممتزة لموصول الى الوقت اللازم
لحدوث اعمي ا ا زلة لمبکتريا وتم د ا رستيا فى البداية استخدام الوقت من 1ساعة الي 46 ساعة
Egypt. J. of Appl. Sci., 36 (7-8) 2021 203
في وجود نانو اکسيد النحاس تکون معدل الا ا زلة لمبکتريا تزداد مع زيادة الوقت فتکون
اعمى قيمة تصل الي 57 فى المائو عند 46 ساعة .
57 درجة سيمزيوس فى إ ا زلة البکتريا باستخدام مادة نانو اکسيد - -4 تأثير درجة الح ا ررة من 11
النحاس وکان احسن درجة ىي 57 لمعدل الا ا زلة.
-5 تأثير ترکيز نانو اکسيد النحاس عمى إ ا زلة انواع البکتريا محل الد ا رسة حيث وجد ان احسن
ترکيز ىو 1 ج ا رم في المتر
-6 تاثير درجة الحموضة عمى معدل الا ا زلة لمبکتريا محل الدرسة کانت اعمى معدل للا ا زلة في
Cuo -TOAB وجود
-5 تأثير معدل الرج عمى معدل الا ا زلة لمبکتريا بزيادة معدل الرج تزداد معدل الا ا زلة مع زيادة
10 ومن انواع البکتريا الموجودة )بکتريا ml/min الرج ويکون معدل النشاط البکتيرى
القولون الکمية:بکتريا المکورة المعوية الب ا رزية: بکتريا القولون الب ا رزية: متقمبات الفا:متقمبات
Novel بيتا سمبية الج ا رم وجاما ايضا( وکان اسم التنقية المستخدمة في تحميل النتائج ىي
و استخدام ايضا تقنية Swage Water Bacterial Disinfection technique
وىو يحدد الام ا رض المسببة في البيئة المائية Illumina Squencing Technology
المموثة وکذاک کمية الحامض النووي الموجود في العينة .
204 Egypt. J. of Appl. Sci., 36 (7-8) 2021

REFERENCES
Aird, D. ; M.G. Ross ; W.S. Chen; M. Danielsson ; T. Fennell ; C.
Russ and A. Gnirke (2011). Analyzing and minimizing PCR
amplification bias in Illumina sequencing libraries. Genome
Biology, 12(2): 1-14.
Albertsen, M. ; L.B.S. Hansen ; A.M. Saunders ; Nielsen P.H. and
K.L. Nielsen (2006). A metagenome of a full-scale microbial
community carrying out enhanced biological phosphorus
removal. ISME J., 6(6):1094-1106.
Ali, N. ; H. Gong; A.S. Giwa; Q. Yuan and K. Wang (2019).
Metagenomic analysis and characterization of acidogenic
microbiome and effect of pH on organic acid production.
Archives of Microbiology, 201(9): 1163-1171.
198 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Ali, N. ; H. Gong ; X. Liu ; A.S. Giwa and K. Wang (2020).
Evaluation of bacterial association in methane generation
pathways of an anaerobic digesting sludge via metagenomic
sequencing. Arch Microbiol,202(1):31-41.
APHA,(2012): American Public Health Association (APHA) and
American Water Works Association and Water Environment
Federation,. Standard Methods for the Examination of Water
and Wastewater, 22nd edition. American Public Health
Association, Washington, DC.
Anandan, S. ; G.J. Lee and J.J. Wu (2012). Sonochemical synthesis of
CuO nanostructures with different morphology. Ultrasonics
Sonochemistry, 19(3): 682-686.
Azzam, M.I. ; S.M. Ezzat ; B.A. Othman and K.A. El-Dougdoug (2017).
Antibiotics resistance phenomenon and virulence ability in bacteria
from water environment. Water Science, 31(2): 109-121.
Bibby, K. ; E. Viau and J. Peccia (2010). Pyrosequencing of the 16S
rRNA gene to reveal bacterial pathogen diversity in biosolids.
Water Research, 44(14): 4252-4260.
Bragg, L. and G.W. Tyson (2014). Metagenomics using next-generation
sequencing. In: Paulsen IT, Holmes AJ (eds) Environmental
microbiology: methods and protocols, methods in molecular biology,
vol 1096, 2nd edn. Humana Press, New York, pp 183-201.
Campanaro, S. ; L. Treu ; P.G. Kougias ; D. De Francisci ; G. Valle
and I. Angelidaki (2016). Metagenomic analysis and functional
characterization of the biogas microbiome using high throughput
shotgun sequencing and a novel binning strategy. Biotechnology
for Biofuels, 9(1): 1-17.
Cann, K.F. ; D.R. Thomas ; R.L. Salmon ; A.P. Wyn-Jones and D.
Kay (2013). Extreme water-related weather events and
waterborne disease. Epidemiology & Infection, 141(4): 671-686.
Chang, Y. and H.C. Zeng (2004). Controlled synthesis and selfassembly
of single-crystalline CuO nanorods and nanoribbons.
Crystal Growth & Design, 4(2): 397-402.
Da Silva, L.M. ; D.V. Franco ; I.C. Gonçalves and L.G. Sousa (2009).
Advanced technologies based on ozonation for water treatment.
Water Purification, 1-53.
De Giglio, O. ; G. Barbuti ; P. Trerotoli ; S. Brigida ; A. Calabrese ;
G. Di Vittorio and M.T. Montagna (2016). Microbiological
and hydrogeological assessment of groundwater in southern
Italy. Environmental Monitoring and Assessment, 188(11): 1-9.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 199
Ezzat, S.M. and M.I. Azzam (2020). An approach using a novel phage
mix for detecting Pseudomonas aeruginosa in water. Water and
Environment Journal, 34(2): 189-202.
Fierer, N. and R.B. Jackson (2006). The diversity and biogeography of
soil bacterial communities. Proceedings of the National
Academy of Sciences, 103(3): 626-631.
Gao, P. ; W. Xu ; P. Sontag ; X. Li ; G. Xue ; T. Liu and W. Sun
(2016). Correlating microbial community compositions with
environmental factors in activated sludge from four full-scale
municipal wastewater treatment plants in Shanghai, China.
Applied Microbiology and Biotechnology, 100(10): 4663-4673.
Glenn, T.C. (2011). Field guide to next‐generation DNA sequencers.
Molecular Ecology Resources, 11(5): 759-769.
Godoy, D. ; G. Randle ; A.J. Simpson ; D.M. Aanensen ; T.L. Pitt ;
R. Kinoshita and B.G. Spratt (2003). Multilocus sequence
typing and evolutionary relationships among the causative
agents of melioidosis and glanders, Burkholderia pseudomallei
and Burkholderia mallei. Journal of Clinical Microbiology,
41(5): 2068-2079.
Helbling, D.E. ; D.R. Johnson ; T.K. Lee ; A. Scheidegger and K.
Fenner (2015). A framework for establishing predictive
relationships between specific bacterial 16S rRNA sequence
abundances and biotransformation rates. Water Research, 70:
471-484.
Hu, M. ; X. Wang ; X. Wen and Y. Xia (2012). Microbial community
structures in different wastewater treatment plants as revealed
by 454-pyrosequencing analysis. Bioresource Technology, 117:
72-79.
Hunter, P.R. ; J.M. Colford ; M.W. LeChevallier ; S. Binder and P.S.
Berger (2001). Waterborne diseases. Emerging Infectious
Diseases, 7(3 Suppl), 544.
Huson, D.H. ; S. Mitra ; H.J. Ruscheweyh ; N. Weber and S.C. Schuster
(2011). Integrative analysis of environmental sequences using
MEGAN4. Genome research, 21(9): 1552-1560.
Johnson, D.R. ; T.K. Lee ; J. Park ; K. Fenner and D.E. Helbling
(2015). The functional and taxonomic richness of wastewater
treatment plant microbial communities are associated with each
other and with ambient nitrogen and carbon availability.
Environmental Microbiology, 17(12): 4851-4860.
200 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Ju, F. ; F. Guo ; L. Ye ; Y. Xia and T. Zhang (2014). Metagenomic
analysis on seasonal microbial variations of activated sludge
from a full‐scale wastewater treatment plant over 4 years.
Environmental Microbiology Reports, 6(1): 80-89.
Jukes, T.H. and C.R. Cantor (1969). Evaluation of Protein Molecules.
In: Mammalian Protein Metabolism, Munro, H.N. (Ed.)
Academic Press, New York, pp. 21-132.
Kaevska, M. ; I. Slana ; P. Kralik ; U. Reischl ; J. Orosova ; A.
Holcikova and I. Pavlik (2011). “Mycobacterium avium subsp.
hominissuis” in neck lymph nodes of children and their
environment examined by culture and triplex quantitative realtime
PCR. Journal of Clinical Microbiology, 49(1): 167-172.
Kaevska, M. ; P. Videnska and P. Vasickova (2016). Changes in
microbial composition of wastewater during treatment in a fullscale
plant. Current microbiology, 72(2): 128-132.
Khan, N.H. ; Y. Ishii ; N. Kimata-Kino ; H. Esaki ; T. Nishino ; M.
Nishimura and K. Kogure (2007). Isolation of Pseudomonas
aeruginosa from open ocean and comparison with freshwater,
clinical, and animal isolates. Microbial Ecology, 53(2): 173-186.
Kwon, S.D. ; T.S. Kim ; G.H. Yu ; J.H. Jung and H.D. Park (2010).
Bacterial community composition and diversity of a full-scale
integrated fixed-film activated sludge system as investigated by
pyrosequencing. Journal of Microbiology and Biotechnology,
20(12): 1717-1723.
La Rosa, G. ; M. Pourshaban ; M. Iaconelli and M. Muscillo (2010).
Quantitative real-time PCR of enteric viruses in influent and
effluent samples from wastewater treatment plants in Italy.
Annali Dell'istituto Superiore Di Sanita, 46: 266-273.
Lee, S.H. ; H.J. Kang and H.D.Park (2015). Influence of influent
wastewater communities on temporal variation of activated
sludge communities. Water Research, 73: 132-144.
Li, A. ; Y.N. Chu ; X. Wang ; L. Ren ; J. Yu ; X. Liu and S. Li
(2013). A pyrosequencing-based metagenomic study of
methane-producing microbial community in solid-state biogas
reactor. Biotechnology for Biofuels, 6(1): 1-17.
Mackelprang, R. ; M.P. Waldrop ; K.M. DeAngelis ; M.M. David ;
K.L. Chavarria ; S.J. Blazewicz and J.K. Jansson (2011).
Metagenomic analysis of a permafrost microbial community
reveals a rapid response to thaw. Nature, 480(7377): 368-371.
Egypt. J. of Appl. Sci., 36 (7-8) 2021 201
Mardis, E.R. (2008). The impact of next-generation sequencing
technology on genetics. Trends in genetics, 24(3): 133-141.
Mason, O.U. ; N.M. Scott ; A. Gonzalez ; A. Robbins-Pianka ; J.
Bælum ; J. Kimbrel and J.K. Jansson (2014). Metagenomics
reveals sediment microbial community response to Deepwater
Horizon oil spill. The ISME Journal, 8(7): 1464-1475.
Mayo, B. and D. Van Sinderen (2010). Bifidobacteria: genomics and
molecular aspects. Horizon Scientific Press.
McLellan, S.L. ; S.M. Huse ; S.R. Mueller‐Spitz ; E. N. Andreishcheva
and M.L. Sogin (2010). Diversity and population structure of
sewage‐derived microorganisms in wastewater treatment plant
influent. Environmental Microbiology, 12(2): 378-392.
Meyer, F. ; D. Paarmann ; M. D'Souza ; R. Olson ; E.M. Glass ; M.
Kubal and R.A. Edwards (2008). The metagenomics RAST
server–a public resource for the automatic phylogenetic and functional
analysis of metagenomes. BMC bioinformatics, 9(1): 1-8.
Minitab, (2010). Minitab 16 Statistical Software. Minitab Inc., State
College, Pennsylvania, USA.
Muszyński, A. ; A. Tabernacka and A. Miłobędzka (2015). Long-term
dynamics of the microbial community in a full-scale
wastewater treatment plant. International Biodeterioration &
Biodegradation, 100: 44-51.
Odjadjare, E.E. ; E.O. Igbinosa ; R. Mordi ; B. Igere ; C.L. Igeleke
and A.I. Okoh (2012). Prevalence of multiple antibiotics
resistant (MAR) Pseudomonas species in the final effluents of
three municipal wastewater treatment facilities in South Africa.
International Journal of Environmental Research and Public
Health, 9(6): 2092-210.
Radomski, N. ; L. Betelli ; R. Moilleron ; S. Haenn ; L. Moulin ; E.
Cambau and F.S. Lucas (2011). Mycobacterium behavior in
wastewater treatment plant, a bacterial model distinct from
Escherichia coli and enterococci. Environmental science &
technology, 45(12): 5380-5386.
Rehman, S. ; A. Mumtaz and S.K. Hasanain (2011). Size effects on
the magnetic and optical properties of CuO nanoparticles.
Journal of Nanoparticle Research, 13(6): 2497-2507.
Ren, G. ; D. Hu ; E.W. Cheng ; M.A. Vargas-Reus ; P. Reip and R.P.
Allaker (2009). Characterisation of copper oxide nanoparticles
for antimicrobial applications. International Journal of
Antimicrobial Agents, 33(6): 587-590.
202 Egypt. J. of Appl. Sci., 36 (7-8) 2021
Sánchez, O. ; I. Ferrera ; J.M. González and J. Mas (2013). Assessing
bacterial diversity in a seawater‐processing wastewater
treatment plant by 454‐pyrosequencing of the 16 S rRNA and
amoA genes. Microbial Biotechnology, 6(4): 435-442.
Savichtcheva, O. and S. Okabe (2006). Alternative indicators of fecal
pollution: relations with pathogens and conventional indicators,
current methodologies for direct pathogen monitoring and future
application perspectives. Water Research, 40(13): 2463-2476.
Schell, M.A. ; M. Karmirantzou ; B. Snel ; D. Vilanova ; B. Berger
; G. Pessi and F. Arigoni (2002). The genome sequence of
Bifidobacterium longum reflects its adaptation to the human
gastrointestinal tract. Proceedings of the National Academy of
Sciences, 99(22): 14422-14427.
Stephenson, F.H. (2016). Calculations for molecular biology and
biotechnology. Academic press.
Suleiman, A.K.A. ; L. Manoeli ; J.T. Boldo ; M.G. Pereira and
L.F.W. Roesch (2013). Shifts in soil bacterial community after
eight years of land-use change. Systematic and Applied
Microbiology, 36(2): 137-144.
Sundberg, C. ; W.A. Al-Soud ; M. Larsson ; E. Alm ; S.S. Yekta ;
B.H. Svensson and A. Karlsson (2013). 454 pyrosequencing
analyses of bacterial and archaeal richness in 21 full-scale
biogas digesters. FEMS Microbiology Ecology, 85(3): 612-626.