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Bioresources and Bioprocessing

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Research
Open Access

Fungal pretreatment of raw digested piggery wastewater enhancing the survival of algae as biofuel feedstock

Bioresources and Bioprocessing20174:6

https://doi.org/10.1186/s40643-016-0136-2

©  The Author(s) 2017

Received: 28 September 2016

Accepted: 29 December 2016

Published: 12 January 2017

Abstract

Background

Understanding about the impact of white rot fungi on indigenous bacterial communities, NH4 + and turbidity in digested piggery wastewater, will allow the optimization of wastewater treatment methods and its use as a feasible medium for algal growth. Here, the white rot fungi were inoculated into undiluted and unsterilized digested piggery wastewater under different temperatures and pH regimes in order to lower the pretreatment cost. Diversity and abundance of the bacterial communities in the pretreated wastewater were assessed by PCR-denaturing gradient gel electrophoresis coupled with 16S rDNA sequencing.

Results

The research showed a significant reduction on the microbial diversity with the presence of white rot fungi which occur at pH 6. The distribution and presence of bacteria taxa were strongly correlated with NH4 + concentration, pH, and the presence of white rot fungi. Variance partition analysis also showed that the effect on the chlorophyll content of algae in fungi-filtered wastewater was as the following hierarchy: bacterial diversity > NH4 + > turbidity. Therefore, the algae in treated wastewater with less abundance of bacteria proliferated more successfully, indicating that bacterial community not only played an important role in algal growth but also imposed a strong top-down control on the algal population. The algae grown in wastewater treated with fungi reached the highest specific growth rate (0.033 day−1), whereas the controls displayed the negative specific growth rate. The fatty acid composition varied markedly in C16:0 and C18:0 between these treatments, with a higher content of C16:0.

Conclusions

This study firstly showed that Chlorella can grow as cost-effective biofuel feedstocks in undiluted and unsterilized digested wastewater with high ammonium concentration and dark brown color because the bacterial abundance of digested piggery wastewater could be reduced greatly by the white rot fungi.

Keywords

FungiBacterial communitiesNH4 + TurbidityAlgaeDigested piggery wastewater

Background

One approach for biofuel production is based on the utilization of algal biomass harvested from the application of an algae-based method to treat agricultural wastewater or manure effluent (Craggs et al. 2013). Thus, an efficient and economically viable alternative pretreatment should be employed to treat digested effluent before being used for large-scale algal cultivation (Chen et al. 2011). The toxicity of many pollutants lessens the remediation efficiency of most algae but white rot fungi can withstand most of these toxic levels. Current physical and/or chemical methodologies are effective for decolorization of wastewater, but these methods are unsuitable for large-scale use, owing to their high cost, low efficiency, and poor adaptability to a wide range of pollutants (Waghmode et al. 2011). Moreover, high energy requirements and the secondary pollution problems in the form of sludge inhibit their wide application (Waghmode et al. 2011). Compared to chemical and/or physical methods, biological processes have received much more attention due to their cost-effectiveness, lower sludge residue, and eco-friendliness (Waghmode et al. 2012). The effectiveness of microbial decolorization depends on the adaptability and activity of the chosen microorganisms and the characteristics of the nutrient itself. Therefore, many factors such as strain selection, microbial ecology, and any environmental constraints must be taken into account to improve the biodegradation of digested piggery wastewater (Tyagi et al. 2011). Treatments with white rot fungi offer the possibility to expand the substrate range of existing treatments via biodegradation that cannot be removed by chemical reagents. Furthermore, several studies have also demonstrated that the use of white rot fungi is safer (e.g., non-hazardous) and eco-friendly compared to the conventional use of chemicals (Fulekar et al. 2013). Fungi perform more efficiently in the decolorization of wastewater compared to bacteria which require preconditioning to the particular pollutant (Garg and Tripathi 2011; Pakshirajan and Radhika 2013). Phanerochaete chrysosporium (PC) is the most investigated species and has been shown to be very promising for treating effluents from pulp and paper, coal conversion, textile, and olive oil industries (Taccari et al. 2009). It has now become a model species in studying pollutant bioremediation, especially in the decolorization of different dyes (Moredo et al. 2003). The pH has a significant impact on the decolorization potential of white rot fungi (Kiran et al. 2012; Pratt et al. 2012). However, due to the significant modifications on the microbial community with the presence of white rot fungi, it is difficult to distinguish the effects attributable to bacterial abundance or those inherent to the sample on algal growth in digested swine wastewater.

Another important composition of the medium is the bacterial communities which may act as algal diseases to some extent, especially when considering alternative water sources like wastewater for algal cultivation. Indigenous bacterial communities naturally mediate many ecological processes and play fundamental roles in the degradation of pollutants present in a local environment, i.e., swine wastewater (Zhang et al. 2013). Knowledge on how bacterial communities respond to different environmental conditions can provide deeper understanding of the parameters useful to control waste degradation and removal using microorganisms. Substantial research indicates that different physico-chemical factors affect bacterial communities in different extents. According to previous researches, the white rot fungi have shown selective effects on indigenous bacterial communities by competing for degradable substrates or changing the sample properties (e.g., pH, temperature, and carbon sources). The usage of bacteria in wastewater for algal cultivation has not been closely considered because, on one hand, bacteria usually occur in low density due to the high dilution and, on the other hand, they are unlikely to have no significant effects on algal growth due to sterilization in lab scale (Bartley et al. 2014). So far, there are no studies that have closely looked into the effects of bacterial community on algae growth in digested and non-sterilized fungal-pretreated swine wastewater. Compared with the cultivation-based techniques, PCR-DGGE and 16S rDNA sequencing offer comprehensive and high-resolution analytical advantageous means to investigate the bacterial communities (Dong and Reddy 2010).

This study then aimed to determine the optimum conditions (e.g., NH4 +, turbidity, and bacterial abundance) for wastewater to be useful as an algal growth medium pre-treated with white rot fungi. Many researches have also demonstrated that Chlorella species are an ideal candidate for biofuel production owing to its high adaptability to wastewater and ability to inhibit bacterial growth by chemical defenses. Chlorella vulgaris is considered as one of the promising strains for lipid production due to its high growth rate and relatively high lipid content (Shu et al. 2012). Thus, in this study, the ability of Chlorella species to survive in the optimized wastewater with fungal removal was also tested.

Methods

Preparation of fungal inoculant and medium

The PC strain ATCC 24725 was provided by the China Center for Type Culture Collection (CCTCC; Wuhan, China). Test strain of PC was transferred to sterile PDA slants and stored at 4 °C before use. To produce enough fungus, the inoculant was grown in 200-mL sterile broth in 500-mL Erlenmeyer flasks with a 0.5 cm2 agar plug and then incubated at 30 °C in a rotary shaker at 135 rpm for 8 days. After which, 5 mL of the cultures containing fungal pellets of different sizes was filtered through a 0.45-µm nylon filter. The filtrates containing the fungi pellets were homogenized using sterile glass beads (8 mm) before use in effluent treatment. The moisture weight of inoculant mycelium pellets was 0.5 g mL−1. Before being applied for the subsequent studies, the wastewater was stored at low temperature (4 °C) to prevent natural degradation. Characteristics of this wastewater are shown in Table 1.
Table 1

Characterization of initial effluents from piggery anaerobic digestion

Parameter

Pre-treated value (mean ± SD)

After treatment valuea

pH

8.6 ± 0.2

7.2 ± 0.5

Color (Abs. 400 nm)

1.1 ± 0.08

0.6 ± 0.07

TSS (mg L−1)

1133.3 ± 68.1

254.2 ± 31.7

COD (mg L−1)

625.8 ± 16.9

262.5 ± 12.6

TN (mg L−1)

330.6 ± 14.3

255.9 ± 10.9

TP (mg L−1)

50.3 ± 6.7

29.6 ± 8.5

NH4 + (mg L−1)

287.1 ± 25.2

226.6 ± 19.4

aThe average data of 6 pretreated wastewater by fungi

Experimental set-up and sample collection

Preliminary experiments showed that pH, temperature, and the amount of inoculum are the most significant variables affecting efficiency of effluent pre-treatment (Ye et al. 2014). The relative influence of the variables in the experiment is shown in Table 2. A total of 8 experimental conditions were tested. A final volume of 250 mL piggery wastewater was used and inoculated with different amounts of fungi in 500-mL flasks. Cultures were grown for 9 days under 24-h light (100 μmol photon m−2 s−1) provided by cool, white fluorescent tubes, and two different temperatures (i.e., 20 and 25 °C). Cultures were maintained under static conditions without aeration. The pH condition of the media was modified or maintained by adding either HCl or NaOH. All experiments were carried out in triplicate. Finally, the wastewater was used to grow algal cells after filtering by using a screen filter (0.66 mm) to remove fungi followed with fungus treatments at different temperatures (i.e., 20 and 25 °C) according to the experimental design (Table 2).
Table 2

Environmental parameters used in digested piggery wastewater and Shannon-Wiener

Code

Fungi

pH

Temperature

Shannon-Wiener

PC1

Phanerochaete chrysosporium

4

20

2.8161

PC2

Phanerochaete chrysosporium

4

25

2.8335

PC3

Phanerochaete chrysosporium

6

20

2.2597

PC4

Phanerochaete chrysosporium

6

25

2.5019

PC5

Phanerochaete chrysosporium

8

20

2.6528

PC6

Phanerochaete chrysosporium

8

25

2.4781

CK1

No fungal inoculum

8.6

20

2.8870

CK2

No fungal inoculum

8.6

25

2.6138

The C. vulgaris (FACHB 25) used in this study was purchased from the Freshwater Algal Culture Collection of the Institute of Hydrobiology, the Chinese Academy of Sciences (Wuhan, China). The algal inoculum was grown in 500-mL flasks containing 200 mL BG11 medium. The cultures were incubated in chambers illuminated at the light density of 100 µmol m−2 s−1 using cool white fluorescent lamps under a 12-h/12-h light–dark cycle at temperature regimes described in the aforementioned sections. The pH of the medium was adjusted to 7.2.

Characterizing the samples

Physico-chemical characteristics (NH4 +, total nitrogen (TN), phosphorous (TP), COD, and turbidity) of the samples throughout the study were determined following protocols described in the Standard Methods for the Examination of Water and Wastewater (APHA 2005). The kinetics coefficients for TN and TP have been calculated in this study according to the previous studies (Liu 2012; Wang et al. 2014). Algal biomass was measured by means of dry weight and chlorophyll content. Samples (10 mL) of cultures were centrifuged at 6000g at 4 °C for 5 min. Pellets were washed twice with distilled water and centrifuged again to remove impurities. Pellets were freeze dried for two days to determine dry weight. Chlorophyll content was also generally used as algal biomass and differentiates contribution from heterotrophs such as bacteria and fungi. The chlorophyll content was determined using modified spectrophotometric methods (Wellbum 1994). In brief, 2 mL of sample was collected every day from the treatments, and the pigment was extracted by acetone and quantified using a spectrophotometer (Thermo UV–Vis). The specific growth rate was determined as described in the previous study based on the change of chlorophyll content (Liu 2015). Lipid and protein contents of algae were determined as described in the previous study (Liu 2015; Volker et al. 1979). The carbohydrate was analyzed according to the method (DuBois et al. 1956). The fatty acid compositions were analyzed as described in the previous study (Liu 2015).

The kinetics coefficient for total nitrogen (TN) and phosphorous (TP)

The relationship between the specific growth rate of C. vulgaris in shake flasks and nutrient concentration was investigated by estimating the parameters of the Monod equation (Doran 1995), a homologue of the Michaelis–Menten expression (Eq. 1):
$$\mu = \frac{{\mu_{\rm{max} } S}}{{K_{S} + S}},$$
(1)
where S is the concentration of the growth-limiting substrate; \(\mu_{\rm{max} }\) is maximum specific growth rate (day−1); and \(K_{S}\) is a substrate constant with the same dimensions as substrate concentration that is the substrate concentration at which the growth rate is half its maximum value.
The equation was transformed as follows into a suitable form for a straight line plot
$$\frac{1}{\mu } = \frac{{K_{S} }}{{\mu_{\rm{max} } }} \times \frac{1}{S} + \frac{1}{{\mu_{\rm{max} } }}.$$
(2)

According to this regression Eq. (2), the maximum specific growth rate (µ max) for the nitrate as growth-limiting substrate and substrate constant (K S ) were calculated (Liu 2012).

Genomic DNA extraction and PCR-DGGE

Fifty milliliters of wastewater was concentrated to 2-mL samples for genomic extraction by centrifugation at 8000 rpm. The genomic DNA of the microbial community from 2-mL samples was extracted using DNA Kits (Qiagen, Germany). Extracted DNA was quantified by a 200 microplate reader (Tecan, Switzerland). The V3 region of bacterial 16S rDNA was amplified using the universal primers 338F and 534R, with a GC clamp of 39 bases added to the 5-terminus. PCR amplification was performed in 50 µL reaction mixtures according to the methods described in previous study (Zhao et al. 2012). Denaturant Gradient Gel Electrophoresis (DGGE) was carried out to further analyze diversity of the amplified fragments of the 16S rDNA bacterial gene, and PCR products were placed under denaturing conditions as described in previous study (Zhao et al. 2012). The gels were run with 40 µL and were silver stained after (DuBois et al. 1956; Volker et al. 1979). Targeted bands of the 16S rDNA corresponding to possible different species were further isolated and purified using purification kits (Watson Biotechnologies, China) and cloned into E. coli DH5α. Three clones were randomly selected from each band, followed by PCR amplification of the cloned inserts (Zhao et al. 2012). Sequencing was performed as described in previous research (Xu et al. 2011).

Data analysis

Generated 16S rDNA gene sequences were compared against GenBank database to obtain closely related sequences by BLASTn search. All hits with >97% similarity were downloaded and aligned with the unknown sequences for phylogenetic analysis (Matsunaga et al. 2005). Phylogenetic trees were constructed in MEGA 4.0. DGGE bands were analyzed to study the relatedness of the microbial communities with similarity coefficients. Two bands were considered to be related if they migrated the same distance on the DGGE gel (Patil et al. 2010; Zhang et al. 2008). Relative band intensities or peaks on DGGE community profiles were also determined. Each band was considered to be an operational taxonomic unit or species, and the band densities as proxy for abundance, which were then used to calculate the Shannon–Weaver diversity (H′) and equitability indices (J) (Shannon 2001). The noise levels and minimum peak thresholds of the software were set to optimum values in order to reduce background noise which only allowed the detection of genuine peaks (Patil et al. 2010).

Canonical correspondence analysis (CCA) was used to determine multivariate relationships between DGGE banding profiles and environmental factors. The analysis was performed using CANOCO 4.5, and the significance was measured by Monte Carlo test using 1000 permutations. Analysis of variance (ANOVA) was done in SPSS 19.0. The main and interaction effects of turbidity and NH4 + concentration were determined. Duncan tests were applied to assess statistical differences between treatments at 95% confidence level (p < 0.05).

Results

Changes in wastewater treated with fungi

Changes in the properties of the wastewater treated with fungi are shown in Fig. 1. Results suggested that pollutant removal (NH4 + and turbidity) was tightly dependent on the selection of environmental conditions. In this study, the removal of NH4 + seemed to vary with pH levels. Treatments with pH 4 resulted in the decrease of a more acidic condition of pH 3.56, while the treatment with pH 6 led to an increase of a more alkaline condition of around pH 8.5. The treatments with pH 8 caused a slight increase at the end of experiment to around pH 9. The percent removal of NH4 + increased with the rising of pH levels but the maximum removal effect (i.e., 43% by PC) occurred at conditions with an initial pH of 6. The maximum color reduction occurred in PC3 and PC4 with an initial pH of 6, while the minimum turbidity reduction (10%) occurred in PC2 with an initial pH of 4 at 25 °C, which was even less than the control (20%). As for turbidity reduction of total treatments, there were significant differences between fungus treatment and controls.
Figure 1
Fig. 1

Nutrient removal efficiency (NH4 +, TP, COD, TSS, and turbidity) of piggery wastewater by fungi at the end of experiment. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls. The initial physico-chemical characteristics are shown in Table 1

Bacterial community diversity based on PCR-DGGE

PCR-DGGE was used to investigate the impact of the fungi on the structure of the indigenous bacterial community. Bands of the DGGE profile corresponding to different PCR-amplified 16S rDNA fragments were obtained from different species or strains (Table 3; Additional file 1: Figure S1). Each sample had distinct DGGE pattern, and bands from each lane showed big difference with each other. Bands were visible until approximately 50% denaturant. On one hand, the treatments with an initial pH of 6 inoculated with PC showed less bacterial diversity compared to the other treatments. On the other hand, the treatments with an initial pH of 4 with the presence of white rot fungi showed the most bacterial species diversity. It suggested that the bacterial communities were affected by pH condition. It was further supported by the data of lowest Shannon-Wiener index H′ value of 2.2597 for PC3, whereas the treatment with an initial pH 4 had the highest Shannon-Wiener index H′ value (2.8161 for PC1), which was close to the value of CK (2.8870).
Table 3

16S rDNA gene sequence obtained from the pretreated wastewater by white rot fungi in this study

Clone No.

Best match database (Accession)

Similarity (%)

Environmental functions

References

S1

Bacteroides sp. (AB596884.1)

99

Break down macromolecules in anaerobic digesters

Auerbach et al. (2007)

S2

Cloacibacterium normanense (LN613116.1)

100

Anaerobic, rod-shaped bacterium to recycle fibers

Ntougias et al. (2015)

S3

Petrimonas sulfuriphila (NR_042987.1)

100

Exist in removal process of ammonium in the wastewater

Nakasaki et al. (2009)

S4

Flavobacterium cucumis (KF261012.1)

100

Degrade potentially toxic compounds

Spain et al. (2007)

S5

Achromobacter sp. (LK936601.1)

100

The ammonia and nitrite oxidizing bacteria

Honda and Osawa (2002)

S6

Xenophilus sp. (KC010298.1)

98

Nitrification treatment of methane fermentation digestion

Qiao et al. (2010)

S7

Dechloromonas sp.(EF632559.1)

97

Degrade phenol and benzene

Spain et al. (2007)

S8

Stenotrophomonas sp. (EF221774.1)

100

Efficiently degrade NO3–N in semi-anaerobic condition

Yu et al. (2009)

S9

Uncultured Bacteroidetes bacterium (KF630625.1)

99

Remove ammonium in the wastewater

Zhou et al. (2010)

S10

Alcaligenes sp. (FR745404.1)

99

Relatively high ammonium removal potential

Spain et al. (2007)

S11

Bacillus sp. (KC430992.1)

100

Remove ammonium in the wastewater

Zheng et al. (2011)

S12

Castellaniella defragrans (FJ982930.1)

100

Degrade phenol and benzene

Spain et al. (2007)

S13

Denitrobacter sp. (EF471227.1)

99

Degradation of wood and cycling of nitrogen and sulfur

Lu et al. (2003)

S14

Magnetospirillum sp. (KM289194.1)

100

Contribute to the global iron cycle

Matsunaga et al. (2005)

Environmental functions were derived from literature

Bacterial 16S rDNA sequences re-amplified from the dominant DGGE bands were further used for phylogenetic analysis (Table 3; Additional file 1: Figure S2). The phylogenetic tree showed that there were mainly four groups of bacterial species dominant in the wastewater. Most corresponded to bands S1, S3, S4, S6, S9, and S11, which were closely related to Bacteroides sp., Petrimonas sulfuriphila, Flavobacterium cucumis, Xenophilus sp., uncultured Bacteroidetes, Bacillus sp., with at least 99% similarity to the reference sequences. Bands S10 and S13 appeared only in treatments with pH 4, whose closest match (99%) was Alcaligenes sp. and Denitrobacter sp. (99%), respectively. The presence of these species corresponds well with the relatively high ammonium removal potential found in this treatment. However, since only abundant microbial populations can be detected by PCR-DGGE of 16S rDNA, it can be assumed that the Achromobacter sp. constitutes the majority of the ammonia and nitrite oxidizing bacteria in the studied treatments, implying the presence of these oxidizing species to the removal of pollutants (Table 3). Interestingly, only band S9 seemed to be different in DGGE profiles of microbial communities under the same initial pH level with fungal treatment. It was mostly similar to an uncultured Bacteroidetes with 99% similarity. In addition, bands S5 (ID, 99%) and S6 (ID, 99%) were the most distributed and appearing in all treatments. These species were characterized as ubiquitous predatory bacteria and thus were not expected to be specific to any treatment. This bacterium was found in all treatments, suggesting it possibly plays an important role in the removal of ammonium in the wastewater.

Algal cells grown in different wastewater pretreated by fungi

The raw digested piggery wastewater after treatment by fungi was used to cultivate C. vulgaris (Figs. 2, 3). As expected, in the first few days, cells (indexed by chlorophyll content and dry weight) in pretreated wastewater showed a little growth compared to those in raw untreated wastewater. On the first three days, all treatments reached the stationary phase, but the CK in the raw untreated wastewater showed declined trend. In contrast, cells grown in PC3 of the treated wastewater reached a relative high biomass. A significant difference was observed among all treatments at the end of the study. All of algal cells in the treated wastewater showed a slight increase in chlorophyll concentration until day 7. PC1 and PC2 showed a slight decline in biomass (18%), other treatments in pretreated wastewater continued to grow and reached the maximum biomass potential at day 9, whereas CKs had the most dramatically declined in biomass (nearly 70%). Therefore, the PC3 reached the highest specific growth rate (0.033 day−1), whereas the CKs with the negative specific growth rate (−0.14 day−1). The nutrient removal efficiency was higher by algae in PC4 and PC3 than the other treatments (Fig. 4). The maximum specific growth rate (µ max) for the nitrate as growth-limiting substrate (TN) was 0.390 day−1 and the substrate constant (K S ) was calculated as 14.23 mg L−1, while the value was 0.309 day−1 and 4.15 mg L−1, respectively, for TP. CCA ordination of chlorophyll and other environmental parameters also revealed that NH4 +, turbidity, and bacterial community were strongly correlated with the first CCA axis (r = −0.081, −0.050 and −0.107, respectively), while temperature was the most correlated parameter with the second axis (Fig. 5). These two axes alone already explained 99.99% of total variance. Furthermore, CCA showed that all treatments were centered in map, indicating that there was other parameter which can distinguish these treatments (such as carbon sources).
Figure 2
Fig. 2

Chlorophyll concentration of algal cells grown in different pretreated wastewater by Phanerochaete chrysosporium. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Figure 3
Fig. 3

Dry weight of algal cells grown in different pretreated wastewater by Phanerochaete chrysosporium. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Figure 4
Fig. 4

The removal of nutrients (NH4 +, TP and COD) by algae after 9 days. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Figure 5
Fig. 5

Algae-environment biplot from canonical correspondence analysis (CCA) summarizing differences in chlorophyll, protein, carbohydrate, and lipid of different treatments (PC1–PC6, CK1, CK2) and environmental variables (i.e., pH, temperature, NH4 +, turbidity, and bacterial diversity). PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Effects of environmental parameters on the composition of the bacterial community

Canonical analysis revealed that the first two factors (principal components) explained 82.55% (56.69% for F1 and 25.87% for F2) of the variability in species data (Fig. 6). The CCA ordination of the species and environmental variables demonstrated that NH4 +, fungi, and the pH of the media were correlated with the first CCA axis (r = −0.328, 0.183 and 0.339, respectively). Whereas, pH was the most correlated parameter with the second axis (r = −0.255) and temperature was weakly correlated with the second axis (r = 0.133). The distribution of species was mainly related to NH4 +, pH, turbidity, and the presence of fungi but most of the variation (39%) in the first axis was contributed by NH4 + in this experiment. However, the total explanatory effect of the environmental parameters was not significant as confirmed by the Monte Carlo permutation test (p > 0.05).
Figure 6
Fig. 6

Species-environment biplot from canonical correspondence analysis (CCA) summarizing differences in bacterium species (S1–S14) of different treatments (PC1–PC6, CK1 CK2) along with environmental variables (i.e., pH, temperature, NH4 +, turbidity, and fungal pretreatment. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Fatty acid composition of algal lipid

The lipids extracted after 9 days of cultivation from C. vulgaris was converted to fatty acid methyl ester (FAME), and their compositions are summarized in Fig. 7. We found that the major constituents composed of palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), and linolenic acid (C18:3) regardless the growth conditions, such that the sum of these four fatty acid accounted for approximately 90% of the total fatty acids in the cells. However, the amount of saturated (C16:0 and C18:0), mono-unsaturated (C16:1 and C18:1), and poly-unsaturated (C18:2 and C18:3) fatty acids in the microalgae grown with different wastewater accounted for 8.32–47.45%, 3.25–28.12%, and 1.58–11.28% of the total fatty acid, respectively. The fatty acid composition varied markedly in C16:0 and C18:0 between these treatments, with a higher content of C16:0. The highest concentration of saturated fatty acid (47.45% of C16:0) was observed in wastewater treated by fungi at pH 6 and 25 °C. The lipid productivities of the algae were calculated in this study (PC1: 5.29, PC2: 21.57, PC3: 36.92, PC4: 24.32, PC5: 23.37, PC6: 20.80, CK1: 24.19, CK2: 28.88 mg L−1 day−1, respectively).
Figure 7
Fig. 7

The fatty acid composition of Chlorella vulgaris after 9-day cultivation. PC1 (pH 4, 20 °C), PC2 (pH 4, 25 °C), PC3 (pH 6, 20 °C), PC4 (pH 6, 25 °C), PC5 (pH 8, 20 °C), PC6 (pH 4, 25 °C). CK1 (20 °C), and CK2 (25 °C) are controls

Discussion

The optimization on more economic and eco-friendly ways for treating and recycling swine wastewater has attracted growing interest resulting from the potential utilization of raw wastewater for algal biofuel production (Table 4). The energy balance and economic viability of biodiesel production from algae improved with the application of wastewater, which results in 71% cost reduction of producing per ton of algal biomass (Olguín 2012). Our previous studies showed that high ammonium concentration and bacterial communities may inhibit algal growth. The pretreatment of the effluent becomes a challenge because of the requirement of non-toxic and eco-friendly methods compared to chemical reagents. Specially, the bacterial community is a critical issue needed to be addressed for pretreatment methods especially in digested effluents since the abundance of bacteria can control their host populations (Gachon et al. 2010). P. chrysosporium has already been used in treating digested effluents and capable of affecting the physical and biochemical properties of the wastewater (Liu et al. 2015). Results indicated a significant decrease on the microbial diversity with the presence of white rot fungi at pH 6. Therefore, to determine the factors significantly affecting the indigenous bacterial species is an important step to have a good understanding on changes in physico-chemical conditions during wastewater treatment. Direct multivariate statistics (e.g., CCA) provided powerful means to determine different parameters that affect the indigenous bacterial communities within different treatments. The results also indicated that NH4 +, pH, and the presence of fungi as well as the interactions led to the most influence on the bacterial community when the fungi were introduced. It was inferred that the mechanism underlying this phenomenon may be because the fungus could influence the bacterial community structure indirectly through changing pH, reducing ammonium concentration, and competing for substrate and space. Moreover, changes in C/N ratio of wastewater also changed other physico-chemical parameters (e.g., pH, dissolved organic carbon, and total suspended solid), which in turn also affected the bacterial community, for instance, P. chrysosporium. Phylogenetic analyses revealed that the majority of the taxonomic groups were found to play a role in the nitrogen cycle. Most of the detected sequences were found to be related to Bacteroides genus, which had already been reported to be abundant in wastewater treatment plants before (Auerbach et al. 2007). The secondary taxonomic group is highly diverse and is well-known to comprise communities in anaerobic digesters where they break down macromolecules of feeding fermentation systems (Nakasaki et al. 2009).
Table 4

Comparison of nutrient removal and biomass and lipid production in microalgae grown in various wastewater conditions

Microalgal species

Wastewater type

Nutrient removal

Biomass production

Lipid content (%)

References

Chlorella vulgaris

Tertiary municipal wastewater

100%

0.29 g L−1

30

Ji et al. (2013b)

Chlorella vulgaris

Piggery wastewater effluent

68% N, 42% P,

1.1 g L−1

–

Ji et al. (2012)

Chlorella vulgaris

Treated domestic sewage

73.77% N, 100% P

–

7.6

Sydney et al. (2011)

Chlorella sorokiniana

Palm oil mill effluent

–

8.0 mg L−1 day−1

28.27

Putri et al. (2011)

Scenedesmus obliquus

Piggery wastewater effluent

26 mg L−1N, 1.9 mg L−1 P

0.18 g L−1

27

Ji et al. (2013a)

Scenedesmus obliquus

Tertiary municipal wastewater

100%

0.31 g L−1

27

Ji et al. (2013b)

Scenedesmus obliquus

Municipal wastewater

97% N, 98% P

0.41 wt L−1

22

Abou-Shanab et al. (2013)

Scenedesmus sp.

Electric factory wastewater

46% N, 100% P

4.5 g L−1

–

Su et al. (2011)

Scenedesmus rubescens

Synthetic wastewater

95.55% P

–

14.91

Aravantinou et al. (2013)

Nannochloropsis salina

Anaerobic digestion effluent

89% N, 82.8% P

155.3 mg L−1 day−1

24.9

Cai et al. (2013)

Nannochloropsis Salina

Diluted digester effluent

97% TN

204.12 mg L−1 day−1

32

Sheets (2013)

Nannochloropsis sp.

50% municipal wastewater

–

2.23 g L−1

59.9

Jiang et al. (2011)

Micratinium reisseri

Mine wastewater

97%

0.8 g L−1

17

Ji et al. (2014)

Micractinium reisseri

Municipal wastewater

–

0.26 wt. L−1

19

Abou-Shanab et al. (2013)

Botryococcus braunii

Treated domestic sewage

60.02% N, 51.15% P

1.88 g L−1

36.14

Sydney et al. (2011)

Ourococcus multisporus

Tertiary municipal wastewater

100%

0.31 g L−1

31

Ji et al. (2013b)

Neochloris vigensis

Synthetic wastewater

94.92% P

–

19.29

Aravantinou et al. (2013)

Chlorococcum spec.

Synthetic wastewater

94.34% P

–

6.93

Aravantinou et al. (2013)

A good review on the research before 2011 about the algal biofuel production using wastewater resources has been made by Pittman et al. (2011)

In addition, results also showed that bacterial abundance was the most limiting parameter for algal growth in untreated wastewater as they directly influenced photosynthetic activities, followed by NH4 + levels and turbidity. Therefore, the algae in treated wastewater with less abundance of bacteria proliferated more successfully, indicating that bacterial community played an important role in algal growth, imposing a strong top-down control on the algal population. Moreover, a similar growth rate of algae from all fungi-treated wastewater indicated that both of algae and bacteria were limited by the availability of carbon (i.e., CO2) such as the carbon source in wastewater (e.g., humic acid), which has been consumed up or degraded by fungi. The raw digested piggery wastewater with high concentration of ammonium and dark brown color, which has been used to grow algae with a high dilution (up to 20 times of dilution rate) was greatly different from the general municipal wastewater (Pittman et al. 2011). The high dilution rate would consume huge amount of freshwater which increase the cultivation cost. Therefore, this study firstly showed that Chlorella can grow as biofuel feedstocks in undiluted and unsterilized digested wastewater originally with high ammonium concentration and dark brown color because the bacterial abundance of digested piggery wastewater could be reduced greatly by the white rot fungi. The result also showed that the efficiency of nutrient removal (e.g., NH4 +, TP and COD) was relatively poor for all the experimental variations but the TP uptake was strongly correlated with the microalgal growth. Because the NH4 + stripping increased the TN uptake, the differentiation of TN removal between biological uptake and abiotic precipitation could be carried out according to the theoretical formula of microalgae as shown in previous study (Ji et al. 2012; Wang et al. 2014). Therefore, the result demonstrated again that the bacterial abundance was the most limiting parameter for algal growth in untreated wastewater. However, the fatty acid compositions were affected mostly by pH and temperature. The results showed that comparatively higher saturated fatty acid compositions (C16:0 and C18:0) were found at pH 6 and 25 °C, while the polyunsaturated fatty acids had a higher amount at 20 °C than that at 25 °C. The differences in saturated fatty acid contents and C-chain length would cause noticeable change in the biodiesel properties. Relatively higher content of polyunsaturated fatty acid in algal oil causes deterioration in the quality of biodiesel upon storage (Zhang et al. 2008).

Conclusion

This study demonstrated that the introduction of fungal species induced changes in the indigenous microbial community of the swine wastewater through affecting its pH and nutrient concentrations. On one hand, canonical correspondence analysis showed that fungi inoculation provided direct evidence that the contribution of the variation in the bacterial community, whereas the wastewater property changes induced by different inoculated conditions also contributed to algal growth to some extent. Variance partitioning, on the other hand, revealed that the bacterial community played an important role in algal growth, which was supported by the observation that the algal cells could survive in undiluted digested piggery wastewater pretreated with fungi in comparison to cells in untreated wastewater which had a decline of 70% in biomass. The algae grown in wastewater treated with fungi reached the highest specific growth rate (0.033 day−1), whereas the CK showed a negative specific growth rate. The fatty acid composition varied markedly in C16:0 and C18:0 between these treatments, with a higher content of C16:0. Therefore, this was the first study showing that the bacterial diversity could be reduced greatly by the white rot fungi and then the algae can grow in undiluted and unsterilized digested wastewater.

Declarations

Authors’ contributions

JYL conceived the experimental design, ran the experiment, and wrote the manuscript; WQ analyzed the data and revised the manuscript, and YPW polished the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank Song YM and Yang H for their lab assistance. JYL is supported by the International collaboration in genome-wide metabolic network reconstruction of oleaginous algae (20151BDH80007) and National High-tech R&D Program of China (2012AA021205). The authors are most grateful for the constructive comments of the two reviewers. Work in the State Key Lab of Food Science and Technology is supported by the Food Department.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The dataset(s) supporting the conclusions of this article is (are) included within the article and its Additional file 1. Reprints and permissions information is available at http://bioresourcesbioprocessing.springeropen.com/.

Funding

This work was financially supported by International collaboration in genome-wide metabolic network reconstruction of oleaginous algae (20151BDH80007) and National High-tech R&D Program of China (2012AA021205).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
The Engineering Research Center for Biomass Conversion, Ministry of Education, Nanchang University, Nanchang, China
(2)
State Key Laboratory of Rice Biology, Institute of Biotechnology, Zhejiang University, Hangzhou, China

References

  1. Abou-Shanab RAI, Kim S-H, Ji M-K, Lee S-H, Roh H-S, Jeon B-H (2013) Municipal wastewater utilization for biomass and biodiesel production by Scenedesmus obliquus HM103382 and Micractinium reisseri JN169781. J Renew Sustain Energy 5(5):052006. doi:10.1063/1.4821504 View ArticleGoogle Scholar
  2. APHA (2005) Standard methods for the examination of water and wastewater, 21st edn. American Public Health Association/American Water Works Association/Water Environment Federation, WashingtonGoogle Scholar
  3. Aravantinou AF, Theodorakopoulos MA, Manariotis ID (2013) Selection of microalgae for wastewater treatment and potential lipids production. Bioresour Technol 147:130–134. doi:10.1016/j.biortech.2013.08.024 View ArticleGoogle Scholar
  4. Auerbach EA, Seyfried EE, McMahon KD (2007) Tetracycline resistance genes in activated sludge wastewater treatment plants. Water Res 41(5):1143–1151. doi:10.1016/j.watres.2006.11.045 View ArticleGoogle Scholar
  5. Bartley M, Boeing W, Dungan B, Holguin FO, Schaub T (2014) pH effects on growth and lipid accumulation of the biofuel microalgae Nannochloropsis salina and invading organisms. J Appl Phycol 26(3):1431–1437. doi:10.1007/s10811-013-0177-2 View ArticleGoogle Scholar
  6. Cai T, Park SY, Racharaks R, Li Y (2013) Cultivation of Nannochloropsis salina using anaerobic digestion effluent as a nutrient source for biofuel production. Appl Energy 108:486–492. doi:10.1016/j.apenergy.2013.03.056 View ArticleGoogle Scholar
  7. Chen C-Y, Yeh K-L, Aisyah R, Lee D-J, Chang J-S (2011) Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review. Bioresour Technol 102(1):71–81. doi:10.1016/j.biortech.2010.06.159 View ArticleGoogle Scholar
  8. Craggs R, Lundquist T, Benemann J (2013) Wastewater treatment and algal biofuel production. In: Borowitzka MA, Moheimani NR (eds) Algae for biofuels and energy: developments in applied phycology, vol 5. Springer, Amsterdam, pp 153–163View ArticleGoogle Scholar
  9. Dong X, Reddy GB (2010) Soil bacterial communities in constructed wetlands treated with swine wastewater using PCR-DGGE technique. Bioresour Technol 101(4):1175–1182. doi:10.1016/j.biortech.2009.09.071 View ArticleGoogle Scholar
  10. Doran PM (ed) (1995) Reactions and reactors. Elsevier, New YorkGoogle Scholar
  11. DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith F (1956) Colorimetric method for determination of sugars and related substances. Anal Chem 28(3):350–356. doi:10.1021/ac60111a017 View ArticleGoogle Scholar
  12. Fulekar MH, Pathak B, Fulekar J, Godambe T (2013) Bioremediation of organic pollutants using Phanerochaete chrysosporium. In: Goltapeh EM, Danesh YR, Varma A (eds) Fungi as bioremediators: soil biology, vol 32. Springer, Berlin, pp 135–157View ArticleGoogle Scholar
  13. Gachon C, Sime-Ngando T, Strittmatter M, Chambouvet A, Kim G (2010) Algal diseases: spotlight on a black box. Trends Plant Sci 15:633–640View ArticleGoogle Scholar
  14. Garg S, Tripathi M (2011) Strategies for decolorization and detoxification of pulp and paper mill effluent. In: Whitacre DM (ed) Reviews of environmental contamination and toxicology, vol 212. Springer, New York, pp 113–136Google Scholar
  15. Honda Y, Osawa Z (2002) Microbial denitrification of wastewater using biodegradable polycaprolactone. Polym Degrad Stab 76(2):321–327. doi:10.1016/S0141-3910(02)00028-9 View ArticleGoogle Scholar
  16. Ji M-K, Kim H-C, Sapireddy VR, Yun H-S, Abou-Shanab RAI, Choi J, Lee W, Timmes TC, Inamuddin Jeon B-H (2012) Simultaneous nutrient removal and lipid production from pretreated piggery wastewater by Chlorella vulgaris YSW-04. Appl Microbiol Biotechnol 97(6):2701–2710. doi:10.1007/s00253-012-4097-x View ArticleGoogle Scholar
  17. Ji M-K, Abou-Shanab RAI, Hwang J-H, Timmes TC (2013a) Removal of nitrogen and phosphorus from piggery wastewater effluent using the green microalga Scenedesmus obliquus. J Environ Eng 139(9):1198–1205. doi:10.1061/(ASCE)EE.1943-7870.0000726 View ArticleGoogle Scholar
  18. Ji M-K, Abou-Shanab RAI, Kim S-H, Salama E-S, Lee S-H, Kabra AN, Lee Y-S, Hong S, Jeon B-H (2013b) Cultivation of microalgae species in tertiary municipal wastewater supplemented with CO2 for nutrient removal and biomass production. Ecol Eng 58:142–148. doi:10.1016/j.ecoleng.2013.06.020 View ArticleGoogle Scholar
  19. Ji M-K, Kabra AN, Salama E-S, Roh H-S, Kim JR, Lee DS, Jeon B-H (2014) Effect of mine wastewater on nutrient removal and lipid production by a green microalga Micratinium reisseri from concentrated municipal wastewater. Bioresour Technol 157:84–90. doi:10.1016/j.biortech.2014.01.087 View ArticleGoogle Scholar
  20. Jiang L, Luo S, Fan X, Yang Z, Guo R (2011) Biomass and lipid production of marine microalgae using municipal wastewater and high concentration of CO2. Appl Energy 88(10):3336–3341. doi:10.1016/j.apenergy.2011.03.043 View ArticleGoogle Scholar
  21. Kiran SA, Ali S, Asgher M, Anwar F (2012) Comparative study on decolorization of reactive dye 222 by white rot fungi Pleurotus ostreatus IBL-02 and Phanerochaete chrysosporium IBL-03. Afr J Microbiol Res 6(15):3639Google Scholar
  22. Liu J (2012) Investigation of optimal growth environments for large-scale algal biodiesel production. PhD thesis, University of SheffieldGoogle Scholar
  23. Liu J (2015) Interspecific biodiversity enhances biomass and lipid productivity of microalgae as biofuel feedstock. J Appl Phycol. doi:10.1007/s10811-015-0535-3 Google Scholar
  24. Liu J, Song Y, Ruan R, Liu Y (2015) Removal of humic acid from composted hog waste by the white-rot fungus, Phanerochaete chrysosporium. Water Sci Technol 72(1):92–98View ArticleGoogle Scholar
  25. Lu J, Sanchez S, Hofacre C, Maurer JJ, Harmon BG, Lee MD (2003) Evaluation of broiler litter with reference to the microbial composition as assessed by using 16S rRNA and functional gene markers. Appl Environ Microbiol 69(2):901–908. doi:10.1128/aem.69.2.901-908.2003 View ArticleGoogle Scholar
  26. Matsunaga T, Okamura Y, Fukuda Y, Wahyudi AT, Murase Y, Takeyama H (2005) Complete genome sequence of the facultative anaerobic magnetotactic bacterium Magnetospirillum sp. strain AMB-1. DNA Res 12(3):157–166. doi:10.1093/dnares/dsi002 View ArticleGoogle Scholar
  27. Moredo N, Lorenzo M, Domínguez A, Moldes D, Cameselle C, Sanroman A (2003) Enhanced ligninolytic enzyme production and degrading capability of Phanerochaete chrysosporium and Trametes versicolor. World J Microbiol Biotechnol 19(7):665–669. doi:10.1023/A:1025198917474 View ArticleGoogle Scholar
  28. Nakasaki K, Tran LTH, Idemoto Y, Abe M, Rollon AP (2009) Comparison of organic matter degradation and microbial community during thermophilic composting of two different types of anaerobic sludge. Bioresour Technol 100(2):676–682. doi:10.1016/j.biortech.2008.07.046 View ArticleGoogle Scholar
  29. Ntougias S, Melidis P, Navrozidou E, Tzegkas F (2015) Diversity and efficiency of anthracene-degrading bacteria isolated from a denitrifying activated sludge system treating municipal wastewater. Int Biodeterior Biodegrad 97:151–158. doi:10.1016/j.ibiod.2014.11.009 View ArticleGoogle Scholar
  30. Olguín EJ (2012) Dual purpose microalgae–bacteria-based systems that treat wastewater and produce biodiesel and chemical products within a Biorefinery. Biotechnol Adv 30(5):1031–1046. doi:10.1016/j.biotechadv.2012.05.001 View ArticleGoogle Scholar
  31. Pakshirajan K, Radhika P (2013) Enzymatic decolourization of textile dyeing wastewater by the white rot fungus Phanerochaete Chrysosporium. Text Light Ind Sci Technol (TLIST) 2(1):42–48Google Scholar
  32. Patil S, Kumar M, Ball A (2010) Microbial community dynamics in anaerobic bioreactors and algal tanks treating piggery wastewater. Appl Microbiol Biotechnol 87(1):353–363. doi:10.1007/s00253-010-2539-x View ArticleGoogle Scholar
  33. Pittman JK, Dean AP, Osundeko O (2011) The potential of sustainable algal biofuel production using wastewater resources. Bioresour Technol 102(1):17–25. doi:10.1016/j.biortech.2010.06.035 View ArticleGoogle Scholar
  34. Pratt C, Parsons SA, Soares A, Martin BD (2012) Biologically and chemically mediated adsorption and precipitation of phosphorus from wastewater. Curr Opin Biotechnol 23(6):890–896. doi:10.1016/j.copbio.2012.07.003 View ArticleGoogle Scholar
  35. Putri EV, Din MFM, Ahmed Z, Jamaluddin H, Chelliapan S (2011) Investigation of microalgae for high lipid content using palm oil mill effluent (Pome) as carbon source. IPCBEE 12:85–89Google Scholar
  36. Qiao S, Kanda R, Nishiyama T, Fujii T, Bhatti Z, Furukawa K (2010) Partial nitrification treatment for high ammonium wastewater from magnesium ammonium phosphate process of methane fermentation digester liquor. J Biosci Bioeng 109(2):124–129. doi:10.1016/j.jbiosc.2009.07.014 View ArticleGoogle Scholar
  37. Shannon CE (2001) A mathematical theory of communication. SIGMOBILE Mob Comput Commun Rev 5(1):3–55. doi:10.1145/584091.584093 View ArticleGoogle Scholar
  38. Sheets JP (2013) Cultivation of Nannochloropsis Salina in diluted anaerobic digester effluent under simulated seasonal climatic conditions and in open raceway ponds. The Ohio State UniversityGoogle Scholar
  39. Shu C-H, Tsai C-C, Liao W-H, Chen K-Y, Huang H-C (2012) Effects of light quality on the accumulation of oil in a mixed culture of Chlorella sp. and Saccharomyces cerevisiae. J Chem Technol Biotechnol 87(5):601–607. doi:10.1002/jctb.2750 View ArticleGoogle Scholar
  40. Spain AM, Peacock AD, Istok JD, Elshahed MS, Najar FZ, Roe BA, White DC, Krumholz LR (2007) Identification and Isolation of a Castellaniella species important during biostimulation of an acidic nitrate- and uranium-contaminated aquifer. Appl Environ Microbiol 73(15):4892–4904. doi:10.1128/aem.00331-07 View ArticleGoogle Scholar
  41. Su Z-F, Li X, Hu H-Y, Wu Y-H, Noguchi T (2011) Culture of Scenedesmus sp. LX1 in the modified effluent of a wastewater treatment plant of an electric factory by photo-membrane bioreactor. Bioresour Technol 102(17):7627–7632. doi:10.1016/j.biortech.2011.05.009 View ArticleGoogle Scholar
  42. Sydney EB, da Silva TE, Tokarski A, Novak AC, de Carvalho JC, Woiciecohwski AL, Larroche C, Soccol CR (2011) Screening of microalgae with potential for biodiesel production and nutrient removal from treated domestic sewage. Appl Energy 88(10):3291–3294. doi:10.1016/j.apenergy.2010.11.024 View ArticleGoogle Scholar
  43. Taccari M, Stringini M, Comitini F, Ciani M (2009) Effect of Phanerochaete chrysosporium inoculation during maturation of co-composted agricultural wastes mixed with olive mill wastewater. Waste Manag 29(5):1615–1621. doi:10.1016/j.wasman.2008.12.014 View ArticleGoogle Scholar
  44. Tyagi M, da Fonseca MMR, de Carvalho CCCR (2011) Bioaugmentation and biostimulation strategies to improve the effectiveness of bioremediation processes. Biodegradation 22(2):231–241View ArticleGoogle Scholar
  45. Volker N, Klaus P, Hans-Georg Z, Senta M (1979) A simple, versatile, sensitive and volume-independent method for quantitative protein determination which is independent of other external influences. Hoppe-Seyleŕs Zeitschrift für physiologische Chemie 360:1657View ArticleGoogle Scholar
  46. Waghmode TR, Kurade MB, Govindwar SP (2011) Time dependent degradation of mixture of structurally different azo and non azo dyes by using Galactomyces geotrichum MTCC 1360. Int Biodeterior Biodegrad 65(3):479–486. doi:10.1016/j.ibiod.2011.01.010 View ArticleGoogle Scholar
  47. Waghmode T, Kurade M, Kabra A, Govindwar S (2012) Degradation of Remazol Red dye by Galactomyces geotrichum MTCC 1360 leading to increased iron uptake in Sorghum vulgare and Phaseolus mungo from soil. Biotechnol Bioprocess Eng 17(1):117–126. doi:10.1007/s12257-011-0307-0 View ArticleGoogle Scholar
  48. Wang M, Kuo-Dahab WC, Dolan S, Park C (2014) Kinetics of nutrient removal and expression of extracellular polymeric substances of the microalgae, Chlorella sp. and Micractinium sp., in wastewater treatment. Bioresour Technol 154:131–137. doi:10.1016/j.biortech.2013.12.047 View ArticleGoogle Scholar
  49. Wellbum A (1994) The spectral determination of chlorophyll a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J Plant Physiol 144:307–313View ArticleGoogle Scholar
  50. Xu J, Zhao J, Wang J, Zhao Y, Zhang L, Chu M, Li N (2011) Molecular-based environmental risk assessment of three varieties of genetically engineered cows. Transgenic Res 20(5):1043–1054. doi:10.1007/s11248-010-9477-3 View ArticleGoogle Scholar
  51. Ye G, Zhu Y, Liu J, Chen X, Huang K (2014) Preparation of glycerol-enriched yeast culture and its effect on blood metabolites and ruminal fermentation in goats. PLoS ONE 9(4):e94410. doi:10.1371/journal.pone.0094410 View ArticleGoogle Scholar
  52. Yu L, Liu Y, Wang G (2009) Identification of novel denitrifying bacteria Stenotrophomonas sp. ZZ15 and Oceanimonas sp. YC13 and application for removal of nitrate from industrial wastewater. Biodegradation 20(3):391–400. doi:10.1007/s10532-008-9230-2 View ArticleGoogle Scholar
  53. Zhang E, Wang B, Wang Q, Zhang S, Zhao B (2008) Ammonia–nitrogen and orthophosphate removal by immobilized Scenedesmus sp. isolated from municipal wastewater for potential use in tertiary treatment. Bioresour Technol 99(9):3787–3793. doi:10.1016/j.biortech.2007.07.011 View ArticleGoogle Scholar
  54. Zhang J, Zeng G, Chen Y, Yu M, Huang H, Fan C, Zhu Y, Li H, Liu Z, Chen M, Jiang M (2013) Impact of Phanerochaete chrysosporium inoculation on indigenous bacterial communities during agricultural waste composting. Appl Microbiol Biotechnol 97(7):3159–3169. doi:10.1007/s00253-012-4124-y View ArticleGoogle Scholar
  55. Zhao J, Xu J, Wang J, Zhao Y, Zhang L, He J, Chu M, Li N (2012) Impacts of human lysozyme transgene on the microflora of pig feces and the surrounding soil. J Biotechnol 161(4):437–444. doi:10.1016/j.jbiotec.2012.05.018 View ArticleGoogle Scholar
  56. Zheng X, Chen Y, Wu R (2011) Long-term effects of titanium dioxide nanoparticles on nitrogen and phosphorus removal from wastewater and bacterial community shift in activated sludge. Environ Sci Technol 45(17):7284–7290. doi:10.1021/es2008598 View ArticleGoogle Scholar
  57. Zhou S, Zhang X, Feng L (2010) Effect of different types of electron acceptors on the anoxic phosphorus uptake activity of denitrifying phosphorus removing bacteria. Bioresour Technol 101(6):1603–1610. doi:10.1016/j.biortech.2009.09.032 View ArticleGoogle Scholar

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