Comparable Nutrient Uptake across Diel Cycles by Three Distinct

Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

pubs.acs.org/est

Comparable Nutrient Uptake across Diel Cycles by Three Distinct Phototrophic Communities Anna C. Fedders,† Jennifer L. DeBellis,† Ian M. Bradley,†,§ Maria C. Sevillano-Rivera,‡ Ameet J. Pinto,‡ and Jeremy S. Guest*,† †

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Department of Civil and Environmental Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States ‡ Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States S Supporting Information *

ABSTRACT: The capacity of microalgae to advance the limit of technology of nutrient recovery and accumulate storage carbon make them promising candidates for wastewater treatment. However, the extent to which these capabilities are influenced by microbial community composition remains poorly understood. To address this knowledge gap, 3 mixed phototrophic communities sourced from distinct latitudes within the continental United States (28° N, Tampa, FL; 36° N, Durham, NC; and 40° N, Urbana, IL) were operated in sequencing batch reactors (8 day solids residence time, SRT) subjected to identical diel light cycles with media addition at the start of the nighttime period. Despite persistent differences in community structure as determined via 18S rRNA (V4 and V8−V9 hypervariable regions) and 16S rRNA (V1−V3) gene amplicon sequencing, reactors achieved similar and stable nutrient recovery after 2 months (8 SRTs) of operation. Intrinsic carbohydrate and lipid storage capacity and maximum specific carbon storage rates differed significantly across communities despite consistent levels of observed carbon storage across reactors. This work supports the assertion that distinct algal communities cultivated under a common selective environment can achieve consistent performance while maintaining independent community structures and intrinsic carbon storage capabilities, providing further motivation for the development of engineered phototrophic processes for wastewater management.

1. INTRODUCTION The rapid increase in nitrogen (N) and phosphorus (P) loadings to surface waters over the past several decades1 has been a significant driver of widespread toxic algal and cyanobacterial blooms, eutrophication, and hypoxia,2−4 which are detrimental to aquatic ecosystems, human health, and local economies.5 Improved nutrient management strategies, which can help mitigate these negative impacts,3,6,7 have been an impetus for increasingly strict point-source discharge limits for N and P.8 Technological advancements at water resource recovery facilities (WRRFs, also known as wastewater treatment plants, WWTPs), which, in recent years, have brought the limit of technology (LOT) for nutrient removal to approximately 3 mg-N·L−1 and 0.1 mg-P·L−1,1,9 have also been costly and chemically intensive (relying on exogenous carbon and metal salts).8,10 Thus, an important engineering challenge in modern wastewater management is to advance goals for local and regional water quality and support opportunities for resource recovery while navigating trade-offs among broader initiatives pursuing environmental, economic, and social sustainability.11 Phototrophic bioprocesses are a promising approach to nutrient recovery from aqueous streams12 because microalgae © XXXX American Chemical Society

are capable of taking up N and P to below the limits of detection of standard analytical methods.13−15 In particular, the use of pure algal cultures has been widely investigated for nutrient management16 and for the accumulation of organic carbon (e.g., triacylglycerol, TAG) and other bioproducts.17−20 However, mixed communities may help decrease the susceptibility of biological processes to individual stressors21 and increase productivity and performance.22,23 To date, much of the mixed community work in wastewater has focused on minimally engineered systems such as lagoons,12,24 which exhibit inconsistent nutrient recovery and low biomass productivity.25,26 High-rate algal ponds (HRAPs) provide some operational improvements26 but often still fail to achieve the stability and effluent quality required by WRRFs.12,27,28 More-intensive cultivation systems (e.g., photobioreactors, PBRs) can increase productivity12,29 and nutrient recovery12 through improved light penetration29,30 and process controls (e.g., mixing) but are still subject to uncertainty and variability Received: October 18, 2018 Revised: November 20, 2018 Accepted: November 28, 2018

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DOI: 10.1021/acs.est.8b05874 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology in system performance12 due to our limited understanding of how to design and operate mixed community phototrophic systems.31,32 Consistent with other biological processes at WRRFs, environmental biotechnology can optimize the function of an engineered process by leveraging knowledge of algal metabolism to create a selective environment that favors the proliferation of (i.e., selects for) microorganisms with certain traits.33 This general approach of targeting enriched microbial cultures has drawn inspiration from the work of Lourens Baas Becking (“Everything is everywhere, but the environment selects”).34,35 In the context of algal systems, examples include the work of Mooij and colleagues who proposed and tested system designs aiming to preferentially promote the growth of algal taxa exhibiting desired functional traits, including the manipulation of solids residence time (SRT)36 and the timing and quantity of available N36−38 to enhance carbohydrate content, as well as the presence or absence of silica38 to influence diatom prevalence. Others employed similar principles, incorporating the recycling of settled biomass39 or effluent wasting from the top of the reactor40 to increase the cultures' ability to settle. Moving forward, processes could be designed to optimize wastewater treatment efficacy by achieving adequate diel carbon storage for nighttime nutrient recovery13 and by targeting N and P uptake to undetectable levels.37,41 A critical knowledge gap that remains for algal bioprocesses, however, is how cultivation conditions will influence community composition and process performance when starting with distinct phototrophic communities. Increased understanding of these phenomena is a critical step toward the goal of mixed community algal systems for reliable resource recovery from wastewaters irrespective of geographic location. The objective of this work was to explore how a selective environment incentivizing carbon accumulation would influence structure and function of three phototrophic communities derived from geographically distinct locations. To this end, sequencing batch reactors (SBRs; treatment sequence of fill, react, and decant) were inoculated with naturally occurring microbial communities sourced from surface water and WRRF clarifiers from one of three different geographic locations within the United States (Tampa, FL; Durham, North NC; and Urbana, IL). In doing so, the aim was to incorporate the spatial heterogeneity of inocula, which would be locally available to seed algal wastewater treatment systems in different locations. A total of 3 reactors were operated for >10 SRTs under daytime N-starvation and nighttime Nfeeding to encourage carbon accumulation (consistent with the “survival of the fattest” concept),37 after which batch experiments were conducted to characterize intrinsic kinetic parameters42 and carbon storage capabilities of each community. System function was characterized by nutrient dynamics and the biochemical composition of biomass, while 18S and 16S rRNA gene amplicon sequence data were used for community structure and membership metrics. Results from this study will inform the design of intensive phototrophic wastewater treatment systems targeting consistent and stable performance across diverse geographic locations.

conditions within the reactor while also preventing crosscontamination. Briefly, PBRs had a total volume of 4.7 L and a working volume of 4.0 L. Lighting was provided from one side with a panel of alternating strips of red (630 nm) and blue (460 nm) light emitting diodes (Blaze 12 V LED Tape Light, Elemental LED, Reno, NV) with light intensity controlled by a microcontroller (Red Board, Sparkfun Electronics, Boulder, CO). Mixing was achieved via continuous aeration at approximately 0.1 Lair·Lreactor−1·min−1 with humidified air.41 pH was maintained between 7.00 and 7.75 by adding 100% CO2 to the aeration mix when the pH reached 7.75 (pH 190 Series, Thermo Scientific Eutech, Vernon Hills, IL). Throughout all experiments, attached growth was aseptically resuspended using a magnetic stir bar inside each reactor manipulated with a strong magnet from outside the reactor. 2.2. Inocula Collection, Preparation, and Acclimation. Inocula were sourced from natural surface waters as well as primary and secondary clarifier weirs at WRRFs in 3 geographically distinct locations in the continental United States: Tampa, FL (FL; 28° N, 82°W); Durham, NC (NC; 36° N, 79°W); and Urbana, IL (IL; 40° N, 88°W). Cell counts were performed on each subsample of inoculum, including four surface waters and one secondary clarifier for FL, five surface waters for NC, and three surface waters and one primary clarifier for IL (section S1.1). A mixed inoculum was prepared for each location (FL, NC, and IL) by combining subsamples in equal cell concentrations (Table S1). PBRs contained 4.0 L of modified TAP medium without Tris buffer or acetic acid to minimize dissolved organic carbon (DOC)43 and included the following additions and modifications: NaHCO3 (500 mg· L−1), NH4Cl (98.2 mg-N·L−1), PO43− (31.5 mg-P·L−1) supplied as 2:1 (g·g−1) K2HPO4:KH2PO4, and Na2SiO3· 9H2O (30 mg·L−1). Finally, each mixed inoculum was added to PBRs to a final cell density of 110 cells·mL−1. The low cell density was necessary to accommodate natural stream samples from NC (Table S1) because it was a priority in the experimental design to achieve equal cell densities from all sources of inocula. The microbial communities were acclimated to laboratory conditions over a two-week period (full method details of acclimation are provided in section S1.2), after which long-term operation and sampling began. 2.3. Long-Term Operation. For long-term operation, all PBRs (FL, NC, IL) were operated for 82 days as SBRs (treatment sequence: fill, react, decant) with hydraulic retention times (HRTs) and SRTs of 8 days (this SRT is consistent with past experiments that achieved stable nutrient removal over diel cycles).13 Reactors were subjected to a diel lighting cycle (14:10 light:dark) to mimic natural daylight, with a sinusoidal light intensity having maximum surface irradiance of 190 μE·m−2·s−1. At the start of each night (i.e., immediately after simulated sunset), 0.5 L of well-mixed reactor volume was wasted and was immediately replaced with the addition of 0.5 L of fresh medium having the same composition described in section 2.2, with the exceptions described below. In total, six parallel reactors (two sets of three; FL, NC, and IL) were operated with media designed to be N-limited (58.9 mg-N·L−1 and 29.5 mg-P·L−1; N-to-P mass ratio of 2:1 g-N·g-P−1) or Plimited (186 mg-N·L−1 and 15.5 mg-P·L−1; N-to-P mass ratio of 12:1 g-N·g-P−1) based on typical N-to-P ratios in phototrophs.13,44 The latter set of reactors never reached nutrient limitation and the biomass began to decrease after 28 days of operation (details are provided in section S1.3 and

2. METHODS 2.1. Photobioreactors (PBRs). Flat panel PBRs were constructed as described by Gardner-Dale and colleagues13 (Figure S1), with the goal of maintaining well-defined B


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lipid content was determined via the Folch method as modified by Axelsson and Gentili.49,50 2.7. Aqueous Analyses. Soluble ammonium (NH4+) and orthophosphate (PO43−) concentrations were determined in triplicate using the phenate method (4500-NH3, F)45 and the ascorbic acid method (4500-P, E),45 respectively, each modified for a microplate.13 Method detection limits have been previously determined to be 0.012 mg-N·L−1 and 0.011 mg-P·L−1.13 Nitrate (NO3−) and nitrite (NO2−) concentrations were determined via ion chromatography (ICS 1000, Dionex) with an IonPac AS14A analytical column (Dionex) and eluent containing 8 mM Na2CO3 and 1 mM NaHCO3. Media dissolved organic carbon was measured as non-purgable organic carbon on a Shimadzu TOC-V using a potassium hydrogen phthalate standard and glutamic acid quality control. 2.8. Statistics and Data Analyses. Data from solids and aqueous characterization (sections 2.5−2.7) were compared among reactors using analysis of variance (ANOVA) followed by Tukey’s tests with α = 0.05, unless otherwise specified. A single biomass sample (IL, SRT10) was omitted from these analyses due to freeze-dryer malfunction. Summary statistics for analytical replicates are reported as the average plus or minus the standard deviation. In cases in which only duplicates were available, the difference between the higher individual value and the average was used in place of the standard deviation. To better understand trends in carbon accumulation and mobilization, carbohydrate and lipid content were normalized to protein content.41 Additionally, to connect experimental results from this study with the process modeling literature, biochemical composition and VSS data were also processed following established methods13,41 to distinguish between functional biomass (XCPO), stored carbohydrates (XCH), and stored lipids (XLI). Briefly, the minimum observed mass ratio of carbohydrate·protein−1 and lipid·protein−1 were used to define the composition of functional biomass, and any carbohydrates or lipids in excess of these ratios were defined as storage polymers. Maximum specific storage rates (q̂CH and q̂LI) were calculated as the maximum slope of three consecutive kinetic time points, while maximum specific storage capacities max (f max CH and f LI ) were calculated using the highest storage values observed during the kinetic experiment, both following the approach of the phototrophic process model (PPM).41 2.9. DNA Extraction, PCR, and Amplicon Sequencing. DNA extraction, polymerase chain reaction (PCR), and product purification were conducted as previously described for all eukaryotic evaluation51 Briefly, DNA was extracted using the MP BIO FastDNA SPIN kit for soil (MP Biomedicals, Santa Ana, CA) and stored at −20 °C. DNA extracts were amplified in triplicate via PCR using the KAPA HiFi Hotstart PCR kit (Kapa Biosystems, Wilmington, MA) using primers flanking the V4 and V8−V9 hypervariable regions of the 18S rRNA gene (V4: forward [Reuk454FWD1] = CCAGCASCYGCGGTAATTCC, reverse [V4r] = ACTTTCGTTCTTGAT, V8−V9: forward = [V8f] ATAACAGGTCTGTGATGCCCT, reverse = [1510r] CCTTCYGCAGGTTCACCTAC), reagent concentrations, and thermocycler settings described by Bradley and colleagues.51 Mock community samples corresponding to MC451 and negative controls containing no added DNA were run in triplicate alongside samples. Gel electrophoresis was used to confirm the length of PCR products and excised bands were purified using a QIAquick gel purification kit (Qiagen, Valencia, CA). The

Figure S3). All further discussion in this manuscript focuses on the performance of the three N-limited reactors. 2.4. Sample Collection and Storage. Aqueous samples to determine nutrient concentrations were collected daily for the first 16 days, every 2 days for days 16−32, and approximately every 8 days for the remainder of the experiment (full list of samples given in Table S2). Biomass was analyzed twice per SRT for the first four SRTs and once per SRT for the remainder of the experiment. A total of 10 samples evenly spaced throughout the experiment were used for DNA sequencing and community analysis. During longterm operation, biomass (for solids analysis, biochemical and elemental composition, and DNA extraction) was collected at the end of the light phase (immediately before simulated sunset), and aqueous samples were collected at both the end of the light phase and the end of the dark phase. Samples were collected from a side port in the reactor and were kept wellmixed using a magnetic stir bar and stir plate while being subsampled for subsequent analyses. Samples for solids analysis were analyzed immediately (i.e., no storage). For biochemical composition and aqueous analyses, biomass was pelleted via centrifugation at 10000g for 10 min. After centrifugation, the supernatant was filtered through pre-rinsed 0.22 μm nitrocellulose filters and stored at −20 °C until analysis. Biomass pellets were stored at −20 °C until being lyophilized, ground with a mortar and pestle, and stored under desiccation at room temperature. For DNA analysis, 4 mL samples of biomass suspension were centrifuged at 10000g for 10 min, supernatant was decanted, and pellets were stored at −80 °C until DNA extraction. 2.5. Short-Term Kinetic Assay. After 82 days (>10 SRTs) of operation, a short-term kinetic assay was run to determine the maximum specific carbohydrate and lipid storage rates and maximum storage capabilities of each microbial community.41 Briefly, at the end of the light period, media was spiked into each reactor to achieve a final concentration of 10 mg-N·L−1 (all other media components were scaled to maintain previous operating concentrations from section 2.2); samples were collected 0, 2, 4, 8, 16, 24, 36, 48, 72, 96, 120, 144, 168, and 244 h after the nutrient spike. Light intensity increased at a rate consistent with the sinusoidal curve, and, once it reached the maximum intensity of 190 μE·m−2·s−1 after 7 h, it was maintained at 190 μE·m−2·s−1 for the duration of the kinetic study. 2.6. Biomass Analysis. Total suspended solids (TSS) and volatile suspended solids (VSS) were measured in triplicate for each reactor following standard methods (2540 D and 2540 E, respectively)45 but with GF/F filters (0.7 μm pore size, item no. 09-804-142H, Fisher Scientific)46 and 30 min of desiccation prior to weighing.13 Lyophilized biomass samples were analyzed for carbon, hydrogen, and nitrogen (PerkinElmer 2400 Series II CHNS/O Elemental Analyzer) as well as total phosphorus (PerkinElmer SCIEX ELAN DRC-E inductively coupled mass spectrometer, ICP-MS) by the Microanalysis Laboratory at the University of Illinois at Urbana−Champaign (UIUC) School of Chemical Sciences. Biomass protein content was estimated by multiplying the elemental N percentage by a conversion factor of 6.2 representing the ratio of algal protein to N content.47 Triplicate analysis of biomass carbohydrate content was conducted via two-step acid digestion followed by colorimetric analysis with 3-methyl-2-benzothiazolinone hydrazine (MBTH),48 modified for a microplate reader.13 Total biomass C


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was determined using the summary.single command using the sample with the least number of sequences (3478 for 18S rRNA gene V4 region; 2062 for 18S rRNA gene V8− V9 region; and 122 for 16S rRNA gene V1−V3 region) to set the subsample size. The β diversity, as described by Bray− Curtis distance, was calculated using the summary.shared command, also with the same subsample sizes listed above. The dist.shared command was used to produce Bray−Curtis distance matrices. Nonmetric multidimensional scaling (NMDS) plots were generated using raw data and Bray− Curtis dissimilarity with the vegan package in R.57 Sample richness was computed with the “specnumber” function from the vegan package implemented in R and rarefied with the “rarefy” function based on the minimum read number on a per-sample basis for the V4 and V8−V9 (18S rRNA gene) and V1−V3 (16S rRNA gene) data sets independently. A combined linear model was fitted to relate the variation of richness with environmental variables measured (biomass N:P, biomass carbohydrate:protein, biomass lipid:protein, biomass normalized PO43− uptake, volatile suspended solids, and dissolved organic carbon) at different time points on a per reactor basis with the “lm” function in R for both the V4 and V8−V9 data sets independently. 16S rRNA gene sequence data were omitted from this analysis due to lack of replicate data points. Observations with missing data values (e.g., an environmental variable was not measured at a time point) were also omitted. Furthermore, Pearson correlation coefficients between richness and individual environmental parameters were determined with the “cor” function in R. Scripts used to process the data in Mothur and R are available at: https://doi.org/10.6084/m9.figshare.7205801.v1. 2.11. Accession Numbers. Sequence data used in this analysis may be accessed via the NCBI Sequence Read Archive (SRA; accession nos. SRR5096521 to SRR5096706) and corresponding sample descriptions via BioProject PRJNA394753.

DNA concentration of each sample was measured in triplicate using a Qubit 2.0 (Invitrogen, Carlsbad, CA). Sequencing was performed by the Roy J. Carver Biotechnology Center at UIUC using an Illumina MiSeq with v2 chemistry and 2 × 250 paired-end reads. For bacterial evaluation, DNA was extracted using the same method as for eukaryotic evaluation, and amplification and sequencing were performed by the Roy J. Carver Biotechnology Center. Briefly, amplification of the V1− V3 hypervariable region of the 16S rRNA gene (V1−V3: forward [V1−V3 F28] = GAGTTTGATCNTGGCTCAG, reverse [V1−V3 R519] = GTNTTACNGCGGCKGCTG)52 was carried out with the Biomark HD high-throughput amplification system, and sequencing was performed with an Illumina MiSeq with v3 chemistry and 2 × 300 paired-end reads. 2.10. Sequence-Data Processing and Analysis. Bcl2fastq v1.8.4 Conversion Software (Illumina) was used to demultiplex raw sequence data. Sickle (version 1.33)53 was used to remove all bases with a phred score of less than 25 (V4, V8−V9) or 20 (V1−V3), followed by discarding trimmed reads with less than 70 (V4, V8−V9) or 112 (V1−V3) base pair (bp) overlap between forward and reverse reads. 18S rRNA gene thresholds were determined via mock community analysis (Figure S4) using a similar method to Bradley et al.51 Briefly, a read overlap threshold was chosen for the 18S rRNA gene to maintain the highest number of reads when sequences were clustered at a 97% sequence similarity threshold while accurately characterizing the mock community (within Jaccard dissimilarity of 0.2−0.4 from the theoretical mock community membership). For the V4 region, this read overlap threshold was determined to be 70 bp (Figure S4). Sequencing of the V8−V9 region experienced higher error rates, where all read overlap possibilities at 97% sequence similarity clustering had a Jaccard dissimilarity of 0.6−0.8. Consequently, all figures and data presented here show sequencing results from the V4 region, and sequencing results from the V8−V9 region are shown in the Supporting Information for comparison. Mothur v1.39.0 was used for sequence data processing as described by Kozich et al. (https://www.mothur.org/wiki/ MiSeq_SOP; accessed July 19, 2017) using default settings unless otherwise specified.54 Reads that contained ambiguous bases were removed. Sequences were trimmed using screen.seqs to remove sequences not starting before or ending before positions 4359 and 8460 (for 18S rRNA gene) and 1046 and 13845 (for 16S rRNA gene), respectively, of the seed alignment. Reads were aligned to SILVA v123 using the Needleman−Wunsch algorithm and the alignment trimmed using vertical = T and trump = . options to ensure all reads aligned over similar spans of the hypervariable region. UChime was used to detect and remove chimeras.55 Singletons were removed and the remaining sequences were used in subsequent analyses. Sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity threshold. OTUs which were present in only one of three 18S rRNA gene technical replicates were omitted from further analysis using R.52 Consensus taxonomy was assigned to OTUs by classifying representative sequence from each OTU (centroid ̈ Bayesian Classifier with Silva v123 as the method) using Naive reference database.56 In 16S rRNA gene analysis, the mostabundant OTU corresponded to Mychonastes sp. chloroplast DNA and was removed prior to further analysis. The α diversity, as described by observed OTUs, Chao1 index, inverse Simpson index, and non-parametric Shannon index,

3. RESULTS AND DISCUSSION As is common in bioprocess experimentation, the reactors initially went through a startup phase with variable performance before approaching stable performance. Solely for the purpose of facilitating discussion, the long-term experimental period of the three parallel PBRs inoculated with environmental samples from FL, NC, and IL can be described across three general phases of operation based on performance observations: phase I (rapid change), SRT0 to SRT2 from 0 to 16 days; phase II (moderate change), SRT2 to SRT8 from 16 to 62 days; and phase III (stable performance), SRT8 to SRT10 from 62 to 82 days (a full list of sample names and the day and time of sampling can be found in Table S2). 3.1. Nutrient Recovery. After 2 days of operation and for the remainder of the study, all reactors had undetectable NH4+ by the end of the night period (i.e., before the lights came on; Table S3). NO2− and NO3− were monitored for the first 30 days of the long-term experiment, but all samples were below detection and so monitoring for these anions was discontinued. While effluent PO43− was variable (Figure S5), specific (i.e., VSS-normalized) PO43− uptake rates followed similar decreasing trends with time and stabilized in phases II and III (Figure 1A) but remained distinct across reactors (phase I, FL and NC together, IL distinct, p = 0.00062; phases II and III, all reactors distinct, p = 6.74 × 10−10 and 5.283 × 10−13, respectively). Culture density (as VSS; Figure S7) differed across all three D


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Figure 1. Nutrient performance of parallel PBRs inoculated with environmental samples from Florida (purple circles), North Carolina (green triangles), and Illinois (orange squares). (A) observed specific PO43− uptake rate (g-P·g-VSS−1·day−1) and (B) biomass N:P content (mass·mass−1). A single SRT is equal to 8 days. After 2 days of operation (and for the duration of the study), ammonium concentrations were below detection across all reactors by the end of the night.

Figure 2. Extant (i.e., observed in the reactor) (A) carbohydrate and (C) lipid content of biomass over time during long-term operation of FL (purple circles), IL (orange squares), and NC (green triangles) reactors. Extant storage (i.e., observed in reactor; solid bars) and additional intrinsic storage capacity (i.e., potential storage capacity on top of what was observed in the reactor; hatched bars) of (B) carbohydrates and (D) lipids. Extant carbohydrate and lipid content were largely stable and comparable across the 3 reactors after 10 SRTs of operation, but intrinsic storage capacity (measured after 10 SRTs of operation) varied widely.

reactors during phases I and II (p < 2.2 × 10−16) but began to converge in phase III (p = 0.000165, IL and FL grouped together, NC separate) with final values of 1340 ± 40 mg·L−1 (FL), 1680 ± 20 mg·L−1(NC), and 1470 ± 10 mg·L−1 (IL). Across all reactors and all phases, VSS were 94 ± 1% of TSS. Overall, solids concentrations were consistent with what could be expected in naturally lit PBRs,29 and all three reactors demonstrated consistent and complete nighttime ammonium removal and stable specific phosphorus uptake rates. Initial biomass N·P−1 mass ratios (FL: 6.08 ± 0.00, NC: 5.93 ± 0.02, and IL: 6.64 ± 0.00) were close to the Redfield ratio (16 mol-N·mole-P−1, or 7.2 g-N·g-P−1)58 (Figure 1B) and did not differ across the three reactors during phase I (p = 0.49). Although N:P trends differed across reactors during phases II and III (p = 1.45 × 10−11 and p = 9.88 × 10−7, respectively), all three decreased and stabilized in phase III. The minimum N:P of biomass in each reactor ranged from 2.6 to 3.3 g-N·g-P−1, which is substantially lower than values recently reported for pure cultures of Scenedesmus obliquus (8.5 g·g−1; N-limited) and Chlamydomonas reinhardtii (4.5 g·g−1; Plimited) cultivated at an 8-day SRT with higher media N:P values (7.84:1 in Gardner-Dale et al.13 versus 2:1 g-N·g-P−1 in this study). Lower N:P ratios among phytoplankton have generally been observed in N-limited media,58 but the biomass compositions from this study still approach the lower range of data from the literature (e.g., approximately 2.25 g-N·g-P−1, for Scenedesmus sp.).59 While these values are also influenced by a combination of assimilated and surface-sorbed P,60 the data demonstrate the interspecific and intraspecific (between and within species, respectively) plasticity in biomass N:P, which could be exploited to achieve dual N and P limitation in WRRFs.13 3.2. Carbon Storage. Extant (i.e., observed during operation) carbohydrate and lipid content42 exhibited similar temporal trends across reactors (Figure 2A,C). Carbohydrate content was initially statistically similar in two of three reactors

(phase I; FL, IL together, NC separate, p = 3.06 × 10−3), but all initial values were within 0.20 grams of carbohydrate per gram of protein of each other. The reactors diverged in phase II (FL, IL, and NC separate, p < 2.2 × 10−16), before partially reconverging in phase III (NC and FL together, IL separate, p < 2.2 × 10−16). Carbohydrate content in phase III of operation was roughly double SRT0 values across all three reactors (FL: 0.46 ± 0.02 to 0.98 ± 0.02; IL: 0.37 ± 0.01 to 0.77 ± 0.03; NC: 0.58 ± 0.03 to 1.03 ± 0.02 grams of carbohydrate per gram of protein) but was less than the maximum observed extant value for each reactor. Extant lipid content was initially similar for IL and NC but not FL (phase I, p = 6.06 × 10−8), and diverged in phases II and III (p < 2.2 × 10−16 and p = 2.49 × 10−5, respectively). While final lipid content in FL (SRT10) and IL (SRT9) had a net increase from SRT0 (FL: 0.32 ± 0.02 to 0.43 ± 0.05; IL: 0.23 ± 0.01 to 0.32 ± 0.01 grams of lipid per gram of protein), NC lipid content demonstrated no net change (0.23 ± 0.01 to 0.22 ± 0.01 grams of lipid per gram of protein). Carbohydrate and lipid content stabilized by the end of the experiment, at which time carbohydrate and lipid values across the three reactors were within 32% and 42% of each other, respectively. Thus, despite being inoculated with microbial communities sourced from three different locations, the three reactors exhibited similar extant carbon storage during stable operation (phase III). In contrast to these noted similarities, the final communities differed widely in intrinsic carbon accumulation rates (Figures S8 and S9) and capacities (Figure 2B,D), demonstrating N limitation as a mechanism for harnessing additional carbon E


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Table 1. Comparison of Stoichiometric and Kinetic Parameters from This Study with Literature Values Reported by Guest et al.41 and Mairet et al.61 for the Storage of Carbohydrates (XCH) and Lipids (XLI) by Functional Biomass (XCPO)a this study parameter extant carbohydrate storage ( f extant CH ) extant lipid storage (f extant LI ) maximum intrinsic carbohydrate storage capacity (f max CH ) maximum intrinsic lipid storage capacity (f max LI ) maximum specific rate of carbohydrate accumulation (q̂CH) maximum specific rate of lipid accumulation (q̂LI)

FL

IL

NC

Guest et al . (2013)

Mairet et al . (2011)

units

0.14 ± 0.00 0.20 ± 0.00 0.93 ± 0.07

0.12 ± 0.00 0.14 ± 0.00 0.53 ± 0.10

0.033 ± 0.001 0.025 ± 0.001 1.80 ± 0.15

− − 1.04

− − −

mg-XCH·mg-XCPO−1 mg-XLI·mg-XCPO−1 mg-XCH·mg-XCPO−1

0.55 ± 0.03

0.37 ± 0.03

0.11 ± 0.00

0.77

−

mg-XLI·mg-XCPO−1

0.023 ± 0.009

0.0112 ± 0.001

0.023 ± 0.003

0.032

0.028

mg-XCH·mg-XCPO−1·h−1

0.019 ± 0.004

0.0093 ± 0.0002

0.0026 ± 0.0003

0.0098

0.012

mg-XLI·mg-XCPO−1·h−1

a

Phototrophic process model (PPM) parameter values in this study were comparable with those reported in the literature.

Figure 3. (A) NMDS plot based on Bray−Curtis dissimilarity matrix of all samples and associated technical replicates and (B) Bray−Curtis distance from SRT0 in each reactor based on 18S rRNA gene V4 region sequence data. Panels C and D are analogous to panels A and B, respectively, based on 16S rRNA gene V1−V3 region sequence data. Reactor communities remained distinct from one another throughout the experiment based on both V4 and V1−V3 region analyses. Both eukaryotic and prokaryotic communities within each reactor quickly diverged from their initial structure. Eukaryotic FL and IL communities slowly returned to a state more similar to their starting communities, as did the NC community of prokaryotes. An analogous plot using V8−V9 hypervariable region of the 18S rRNA gene sequencing data may be found in Figure S12.

maximum specific rates of carbohydrate (q̂CH) and lipid (q̂LI) max storage, and carbohydrate (f max CH ) and lipid ( f LI ) storage capacity were calculated as in the PPM (Table 1),41 a bioprocess model developed from the lumped metabolic pathways of Chlamydomonas reinhardtii and calibrated with mixed algal communities derived from a wastewater treatment max plant. Across the three communities, f extant CH and f CH varied by

storage in phototrophs. Maximum intrinsic carbon storage potential exceeded extant values in the three reactors, achieving maximum carbohydrate:protein and maximum lipid:protein ratios that were 1.9−4.7 times and 1.8−3.0 times extant values, respectively (Figure 2B,D). To compare the results of this experiment with values from the literature, extant extant ) and lipid ( f LI ) storage, extant carbohydrate ( f CH F


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Figure 4. Mean relative read abundance of top 10 most abundant eukaryotic (V4) OTUs and top 3 most abundant cyanobacterial (V1−V3) OTUs from each of the 3 reactors (color shading). Bubble area is proportional to mean relative read abundance for 18S or 16S rRNA genes. Hatched shading indicates OTUs that were present but not abundant (i.e., not in the top 10 eukaryotic OTUs or top 3 cyanobacterial OTUs). Bubbles are replaced by “+” for values that are 0.001 for all comparisons, Table S5). FL followed qualitatively similar dynamics (Figure 3B) but with fewer statistically significant differences across phases (Table S6). In all reactors, sharp differences in relative read abundance of numerous dominant OTUs were apparent at the transition points between phases (Figures 4 and S16). While the drivers of these community dynamics are unclear and likely complex, the similar patterns shown in the three reactors suggest that system operation influenced community structure in similar ways. 3.4. Taxonomic Analysis. Despite identification of distinct community structures (Figures 3A and S12A), examination of relative read distribution among OTUs indicates substantial taxonomic overlap among communities (Figures 4 and S15). Over the course of the experiment, FL, NC, and IL had an overall 18S rRNA gene V4 region OTU count of 89 (58 unique), 52 (29 unique), and 85 (53 unique), respectively, which overlapped among reactors as follows: 16 FL-NC-IL, 12 FL-IL only, 4 IL-NC only, and 3 FL-NC only (V8−V9 analysis in section S4.1). A total of 13 of the 16 OTUs shared among all three reactors were in the group composed of the top 10 OTUs with the most abundant reads for at least one reactor (Figure 4, color-shaded rows; V8−V9 analysis in Figure S16). Furthermore, the 10 most abundant OTUs in each reactor represent between 73% and >99% (V4 data) of overall reads for each time point, leaving the remaining minor percentage of reads to be distributed among the remaining majority of OTUs (Figure S16). The dominance and temporal change in relative abundance of a few dominant eukaryotic OTUs in each reactor appears to be responsible for distinct community clusters as determined by the NMDS analysis (Figures 3A and S12A). Based on prokaryotic community analysis (16S rRNA gene V1−V3 region data), a total of 359 OTUs were observed over the course of the experiment, of which FL, NC, and IL had a total of 156 (93 unique), 161 (90 unique), and 146 (94 unique) observed OTUs, respectively. OTUs overlapped between reactors as follows: FL-IL (26), IL-NC (19), FLNC (15), and FL-NC-IL (22). A total of 97 cyanobacterial OTUs were observed, which contributed 18.6%, 58.6%, and 66.3% of 16S rRNA gene sequencing reads in FL, NC, and IL, respectively. Of these, FL, NC, and IL had a total of 48 (30 unique), 41 (24 unique), and 41 (18 unique) cyanobacterial OTUs observed in each (Figure 4), which overlapped between reactors as follows: FL-IL (8), IL-NC (7), FL-NC (2), and FLNC-IL (8). Between 11.5% and 94.7% of total cyanobacterial reads for each sample point were accounted for in the 7 OTUs represented in Figure 4. Consistent with the absence of nitrate in aqueous samples, there were no observed 16S rRNA gene OTUs corresponding to known nitrifiers. While the influence of rare and abundant taxa on community structural dynamics is unclear, system function (nutrient uptake and carbon storage) in the three reactors was likely driven by the relatively small fraction of OTUs with high relative read abundance, many of which were common to all

representing the greatest untapped potential in phototrophic carbon storage. Given the disparity between extant carbohydrate or lipid content and maximum storage capacity (Figure 2), the ability to predict maximum intrinsic carbon storage capacities or maximum storage rates would be valuable to algal and cyanobacterial cultivation operations aiming to increase stored carbon for biomass valorization. To help understand potential relationships among storage parameters, correlation coefficients were determined for pairs of parameters using data from this study’s experimental data set as well as values from the literature (Figure S11). Carbohydrate accumulation characteristics showed limited linear correlation as f max CH demonstrated a slight positive correlation with q̂CH (r2 = 0.53; m [slope] = max 2 71.21) and f extant CH was negatively correlated with f CH (r = 0.84; 2 m = −10.58) and non-correlated with q̂CH (r = 0.16; m = −0.05). Lipid storage characteristics were more strongly and q̂LI were positively correlated with correlated. Both f extant LI 2 f max (r = 1.00 and 0.96 with m = 2.50 and 26.71, respectively), LI and q̂LI was slightly positive and the correlation between f extant LI (r2 = 0.93; m = 0.09). To assess the ability to generalize these observations, the analysis was repeated with the inclusion of values from Guest et al.41 The inclusion of these additional data negatively influenced correlation coefficients (Figure S11), indicating that any effort to develop predictive models of carbon storage based on extant observations or limited calibration of model parameters would require consideration of additional factors not yet addressed in existing process models. 3.3. Community Analysis. 18S and 16S rRNA gene sequencing analyses indicate that eukaryotic communities retained distinct structures for the duration of the experiment (V4, Figure 3A; V8−V9, Figure S12A), while differences between prokaryotic communities were less pronounced (V1− V3, Figure 3C). Specifically, for AMOVA using Bray−Curtis distances comparing each phase (I, II, and III) within and across reactors, all p values for 18S rRNA gene sequencing data were ≤0.004, and only one p value for 16S rRNA gene sequencing data was 15)63 in 16S rRNA gene copy number among bacteria, caution should be exercised in interpreting community composition from read counts. However, because read counts may be interpreted as the best available proxy for cell biomass or biovolume, even among distantly related taxa,62 read-count data in this study may approximate relative biomass contribution of different OTUs to the system (Figures 4 and S15). V4 OTU1 (Acutodesmus) had the highest relative read count in all reactors and accounted for 45.8%, 68.2%, and 25.6% of overall reads across all time points in FL, NC, and IL, respectively (Figure 4). Although the most-dominant V8−V9 OTU was classified as Coelastrella (Figure S16), both genera are members of the Scenedesmaceae. Other OTUs with high relative read abundance represented green algae, diatoms, protozoa, amoeba, fungi, ciliates, and rotifers (Table S7). Coupled with 16S rRNA gene OTUs representing cyanobacteria and broader groups of chemotrophic bacteria, these results indicate the presence of a multitrophic community in reactors achieving stable performance.64 While some of these eukaryotic taxa have larger cell sizes (e.g., rotifers, ciliates, and protozoa),62 which are likely overrepresented due to increased 18S rRNA gene copy number, the 18S rRNA gene OTUs with greatest relative abundance represent smaller celled green algae (e.g., Acutodesmus, Chlorella, Mychonastes, and Monoraphidium; Figure 4). Therefore, despite uncertainty related to copynumber variation, we infer that all three systems were dominated in both biomass content and cell counts (given similar cell sizes) by green algal taxa, which are shared among communities and are probable drivers of system function (i.e., nutrient recovery); rotifers and other predators may have certainly influenced community structure and intrinsic characteristics, but they were not believed to be the main mechanism of nutrient assimilation in the system. Multiple linear regressions relating eukaryotic taxonomic richness (V4 and V8−V9 data sets, independently) to functional metrics did not identify correlations that were directionally consistent across all three reactors either in a combined model or when each functional metric was examined individually (Table S8). These results are consistent with other attempts to relate taxonomic richness to community function.31 However, when individual correlations are compared, VSS is significantly correlated with eukaryotic richness in all reactors (FL: ρ = −0.37, p < 0.05, NC: ρ = 0.64, p < 0.001, and IL: ρ = 0.34, p < 0.1) for V4 data sets, and DOC was significantly correlated to eukaryotic richness in all reactors (FL: ρ = 0.46, p < 0.05, NC: ρ = 0.62, p < 0.001, and IL: ρ = 0.72, p < 0.001) for V8−V9 data sets. Interestingly, VSS and DOC were also identified as collinear covariates in the multiple linear regression model for the IL reactor. Future work may employ alternative modeling techniques coupled with higherresolution time-series data to better understand the relationship between process performance indicators and complex structure−function relationships in mixed phototrophic communities.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b05874. Additional detail on reactor operation and startup, community sequencing, long-term system monitoring data, and community analysis data, including 18S and 16S rRNA gene sequencing analysis results (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Jeremy S. Guest: 0000-0003-2489-2579 Present Address §

Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, 14260, United States Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge funding for the first author from the Illinois Distinguished Fellowship and Carver Fellowship at the University of Illinois at Urbana−Champaign. This work was funded by the National Science Foundation (grant no. CBET1351667) and the Paul L. Busch Award from the Water

4. LEVERAGING LOCAL ALGAE AND CYANOBACTERIA FOR NUTRIENT RECOVERY This research brings much-needed attention to the use of local phototrophic communities for nutrient recovery. Microbial communities remained distinct from each other through time I


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Environment & Reuse Foundation (now called The Water Research Foundation). The authors also thank David GardnerDale for valuable advice and assistance in the lab and Namrata Logishetty for assisting with sample collection and processing.

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Comparable Nutrient Uptake across Diel Cycles by Three Distinct

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