Skip to main content
NCBI home page
Journal of Industrial Microbiology & Biotechnology logoLink to Journal of Industrial Microbiology & Biotechnology
. 2023 Mar 16;50(1):kuad004. doi: 10.1093/jimb/kuad004

Effect of nitrogen management in cultivation on the stability and microbial community of post-harvest Monoraphidium sp. algae biomass

Bradley D Wahlen 1,✉, Lynn M Wendt 2, Chelsea C St Germain 3, Sarah M Traynor 4, Caitlin Barboza 5, Thomas Dempster 6, Henri Gerken 7, John McGowen 8, Yaqi You 9
PMCID: PMC10548854  PMID: 36928716

Abstract

Long-term storage is necessary to mitigate for seasonal variation in algae productivity, to preserve biomass quality and to guarantee a constant biomass supply to a conversion facility. While ensiling has shown promise as a solution, biomass attributes for successful storage are poorly understood. Storage studies of Monoraphidium sp. biomass indicate a strong correlation between nitrogen management in algae cultivation and stability of post-harvest algae biomass. Algae cultivated with periodic nitrogen addition were stored poorly (>20% loss, dry basis) compared to biomass from nitrogen depleted cultivation (8% loss, dry basis). A follow-up study compared the post-harvest stability of Monoraphidium biomass cultivated in nitrogen-deplete or nitrogen-replete conditions. Replete biomass experienced the largest degradation (24%, dry basis), while deplete biomass experienced the least (10%, dry basis). Dry matter loss experienced among blends of each correlated positively with nitrogen-replete biomass content. The composition of the post-storage algae microbial community was also affected by cultivation conditions, with Clostridia species being more prevalent in stored biomass obtained from nitrogen-replete cultivations. Nitrogen management has long been known to influence algae biomass productivity and biochemical composition; here, we demonstrate that it also strongly influences the stability of post-harvest algae biomass in anaerobic storage.

Keywords: Nitrogen management, Nutrient deplete, Biochemical composition, 16S metagenomic analysis, Wet anaerobic storage

Graphical Abstract

Graphical Abstract.

Graphical Abstract

Open in a new tab

Stability of post-harvest algae biomass is affected by the nitrogen status and associated chemical profile at the time of harvest, affecting how the associated microbial community changes during storage.

Introduction

Seasonal storage is essential to developing commercially viable biofuels and bioproducts from microalgae feedstock (Wendt et al., 2020). Algae biomass production can be as much as 5 times greater in the summer than in the winter (Coleman et al., 2014). This variability in production necessitates effective storage methods that can preserve biomass produced in excess of the average annual production to ensure a constant feedstock supply to a biorefinery during lower-yielding months. Utilizing storage to mitigate for seasonal variability has been found to decrease the cost of producing fuel from microalgae by as much as Inline graphic0.24 per gallon of gasoline equivalent (Davis et al., 2014; Wendt et al., 2019). A common approach to preservation is reducing the water activity of the material through drying to the point where microbial degradation ceases: bacteria, aw < 0.9; yeast, aw < 0.8; mold, aw < 0.7 (Beuchat, 1983; Peleg, 2022; Shinners et al., 2007). However, the high water content of harvested microalgae, containing ∼80% water, makes drying uneconomical and can dramatically increase the carbon intensity of the overall process (Bennion et al., 2015; Wendt et al., 2017). Ensiling is a low-energy input approach to preservation that does not require drying and relies on the metabolism of lactic acid bacteria (LAB) to stabilize the biomass (Rooke & Hatfield, 2003).

Ensiling has been successfully applied to preserve winter feed for livestock from diverse forage crops, such as whole-crop corn (Muck, 2002), corn stover (Wendt et al., 2018), grasses (McGechan, 1990; Parvin et al., 2010), alfalfa (Luo et al., 2021), wheat (Pieper et al., 2011), sweet sorghum (Linden et al., 1987), and others (Muck et al., 2018). Ensiling prevents excessive microbial degradation by leveraging LAB-mediated lactic acid fermentation in an anaerobic environment to reduce the pH to a point where further microbial activity is greatly reduced (∼ <4.5), resulting in stable material (McDonald et al., 1991). In addition to preserving lignocellulosic feedstocks for feed and forage, ensiling has been studied as an effective approach to preserving feedstock for bioenergy production (Wendt & Zhao, 2020).

Ensiling of microalgae and microalgae blended with lignocellulosic biomass has recently been demonstrated to be an effective approach to stably preserving microalgae biomass (Wendt et al., 2017), limiting loss to <10% over 6 months (Wahlen et al., 2020; Wendt et al., 2019). Information on the material attributes of microalgae critical to successful ensiling is not well known with limited examples in the literature. In contrast, factors affecting successful ensiling for lignocellulosic materials are well understood (Rooke & Hatfield, 2003). Rapid pH reduction by lactic acid fermentation is essential for producing well-ensiled herbaceous biomass. The availability of carbohydrates for fermentation, the initial pH of the biomass and its buffering capacity strongly affect how rapidly lactic acid builds up and pH drops (McDonald et al., 1991). It is reasonable to expect that similar attributes impact the stability of ensiled microalgae.

The biochemical composition of microalgae varies by strain and culture conditions. Rapidly growing microalgae strains, such as Scenedesmus spp., have a biochemical composition defined by high protein content and lower levels of lipids and carbohydrates. Carbohydrate and lipid contents increase upon depletion of the nitrogen source, and the rate of microalgae productivity decreases. Little is known about how growth-related fluctuations in carbohydrates, protein, and lipids impact microalgae stability post-harvest. In this study, microalgal biomass from the strain Monoraphidium minutum 26BAM cultivated in nitrogen-replete conditions and nitrogen-deplete conditions was assessed for its potential for successful preservation by ensiling. The impact that cultivation conditions had in storage was assessed by measuring dry matter loss, organic acid production, changes in biochemical composition, and microbial community composition.

Materials and Methods

Algae Biomass

Two separate storage studies were conducted with M. minutum 26BAM biomass. The first study, termed ‘‘initial,’’ utilized 26BAM biomass cultivated continuously in 2000 L raceways containing 400 L of modified BG-11 media outdoors at the Arizona Center for Algae Technology and Innovation in Mesa, AZ in March of 2018. Six raceways operating at a pond depth of 10 cm with modified BG-11 media containing the following components (concentration): NH4HCO3 (5 mM), K2HPO4 (0.31 mM), MgSO4*7H2O (0.3 mM), CaCl2*2H2O (0.24 mM), citric acid (31 μM), ferric ammonium citrate (21 μM), and disodium EDTA (2.7 μM). A set of three raceways was operated in a semi-continuous fashion with periodic (weekly) harvest of a portion (75–84%) of the raceway volume with media replacement (termed ‘‘N-replete’’). The second set of raceways was operated in a similar manner up until a week before harvest (collection of biomass to be sent to INL) with one exception: at night, the entire culture was moved to a holding tank as part of a study on the effect of morning culture temperature on productivity, which is not part of the experimental matrix of this study, but meant to replicate productivity enhancements observed by Crowe et al. (2012). A week before harvest, when the N-replete raceways were reset (84% volume removed and media replaced), the second set of raceways was allowed to continue to grow without removing any culture or adding any nutrients. Algae biomass was dewatered by centrifugation at 1800 × g (Lavin 20–1160 V, AML Industries, Inc., Warren, OH). Biomass was sealed in plastic zip-top bags and sent overnight to Idaho National Laboratory (INL) on ice for storage studies. Solids content of dewatered algae was ∼20%. 26BAM biomass for a second, follow-up storage study, termed ‘‘blend,’’ was cultivated in February of 2019. 26BAM was cultivated as described previously using two flat panel photobioreactors with 4 in. light paths (Wendt et al., 2019). One panel was allowed to go deplete of nitrogen (N-deplete), while the other was maintained replete (N-replete). Flat-panel algae cultures were dewatered as described above and sent on ice in an insulated container to INL for stability studies.

Storage Experiments

Upon receiving the initial biomass storage, studies were initiated for both the nitrogen-replete (biomass with media replacement) and nitrogen-deplete biomass (continued growth, no media addition) as described previously (Wendt et al., 2019). Briefly, each batch of biomass was added to three 125 mL jars without treatment (about 15 g, dry basis, db). Jars were sealed with lids fitted with a through-bulkhead fitting (Swagelok, Solon, OH) with an attached ball valve (Swagelok, Solon, OH). Jars (with and without lids) were weighed before and after adding biomass. Sealed jars were then made anaerobic by alternately exposing the jar to vacuum and nitrogen gas. Tedlar gas collection bags were fitted to the top of the ball valve with C-flex Ultra tubing (Masterflex, P/N EW-06434–16, Cole Parmer, Vernon Hills, IL). The biomass was then incubated at room temperature in the dark for 30 days. Additionally, an aliquot of each batch of biomass for the ‘‘initial’’ experiment was also inoculated with the LAB Lactobacillus buchneri to evaluate its effect on algae preservation in storage. 100 μL of an overnight L. buchneri (NRRL, B-1837, provided by the USDA-ARS Culture Collection (NRRL), Peoria, IL) culture (OD600 = 3.7) was added to 60 g (db) 26BAM culture. The inoculant and algae biomass were sealed in a plastic bag and mixed thoroughly by hand. The inoculated biomass (both N-deplete and N-replete) was added to triplicate storage reactors as described for the untreated biomass and incubated for an identical period of time.

The ‘‘blend’’ study utilized N-replete and N-deplete M. minutum 26BAM biomass without treatment placed in storage reactors as described above. In addition to storing each biomass without treatment, three blends of each biomass were made containing 25, 50, and 75% N-deplete biomass with the remainder comprised of N-replete biomass.

Organic Acid Compositional Analysis

Organic acids resulting from the ensiling process were quantified as previously described (Wendt et al., 2017). Briefly, the quantity of nine organic acids (succinic acid, lactic acid, formic acid, acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid) from each storage replicate was measured by high-performance liquid chromatography (HPLC). The HPLC detector was calibrated with standards at five concentration levels (P/N 95917, Absolute Standards, Inc., Hamden, CT). Duplicate samples from each storage replicate were measured in duplicate by HPLC. Concentration of organic acids is expressed as a dry weight percentage of the algal biomass.

Lactic Acid Titration

The capacity of Monoraphidium biomass grown in different culture conditions to buffer pH change was measured by lactic acid titration. About 1 g of algae biomass (exact weight was recorded) was added to a 100 mL beaker with 50 mL of water. The contents of the beaker were well mixed using a stir bar and stir plate. The initial pH was recorded. The algae sample was then titrated with 0.1 mL 100 mM lactic acid at a time, recording the resultant pH after each acid addition once the pH value stabilized. Titration proceeded until pH stabilized at 3.9. The buffering capacity was expressed as the amount of acid (mL) per g (db) biomass required to reach a pH of 3.9.

Compositional Analysis

Biochemical composition of biomass was determined according to standard laboratory procedures developed by the National Renewable Energy Laboratory (NREL) for microalgal biomass (https://www.nrel.gov/bioenergy/microalgae-analysis.html, accessed December 2022). In preparation for analysis, each initial biomass and biomass after storage were freeze-dried (Labconco, Kansas City, MO) and homogenized with a mortar and pestle. Lipid content is presented as the total fatty acid methyl ester content of algal biomass, which was prepared by in-situ transesterification according to standard procedure (Van Wychen & Laurens, 2015). For each received biomass and storage replicate, triplicate samples were prepared for GC-FID analysis by following the NREL protocol. An equal amount of freeze-dried biomass from each storage replicate was combined for each storage condition to obtain enough material for carbohydrate analysis. Carbohydrate composition was then determined according to the above-mentioned NREL laboratory procedures.

DNA Extraction, Purification, and Evaluation

Algae samples were subjected to DNA extraction using the DNeasy PowerSoil Pro Kit (QIAGEN, Germantown, MD) according to the manufacturer's protocol as before (You et al., 2021). About 200 mg of the raw material was added to a PowerBead Pro tube, and a Mini-Beadbeater-8 (BioSpec Products, Bartlesville, OK) was used to efficiently grind samples with less heat generation than vortexing. Samples were processed for 1 min on the setting ‘‘homogenize.’’ Extracted DNA was eluted in 50 μL and purified using the ethanol precipitation protocol (Green & Sambrook 2016).

The quantity and quality of the resulting DNA extractants were evaluated using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA). DNA extractants were also evaluated using gel electrophoresis. All the DNA samples were stored at −20°C before further processing with freeze-thawing avoided as much as possible.

Library Preparation and Illumina Sequencing

Amplicon libraries were prepared similarly to the Earth Microbiome Project and as before (Caporaso et al., 2012; You et al., 2021). The V4 region of the 16S rRNA gene of archaea and bacteria (∼390 bp) was amplified by PCR using the Invitrogen Platinum Hot Start PCR 2X Master Mix (Thermo Fisher Scientific, Waltham, MA). A typical PCR reaction consisted of 20 ng of template DNA in 1 μL, 0.2 μM of forward-barcoded 515F primer (5′-3′: GTGYCAGCMGCCGCGGTAA), 0.2 μM 806R primer (5′-3′: GGACTACNVGGGTWTCTAAT), and nuclease-free water in a total volume of 25 μL (Apprill et al., 2015; Parada et al., 2016; Walters et al., 2016). For DNA samples with low concentrations, more than 1 μL but <5 μL of the DNA was used as the template in order to avoid matrix effect. Thermal cycler (Applied Biosystems) conditions were: 94°C for 3 min, followed by 35 cycles of 94°C for 45 s, 50°C for 60 s, and 72°C for 90 s, and a final extension at 72°C 10 min.

Triplicate PCR reactions from the same DNA sample were pooled into one library. Libraries were cleaned up using the MagBio HighPrep PCR Clean-up System (Illumina, San Diego, CA) following the Illumina PCR Clean-up 2 Protocol. Library quantity and quality were assessed by qPCR with the NEBNext Library Quant Kit for Illumina (New England BioLabs, Ipswich, MA) on a CFX96 Touch Real-Time PCR System (Bio-Rad, Hercules, CA) and on a 5200 Fragment Analyzer (Agilent Technologies, Santa Clara, CA) utilizing the High Sensitivity NGS Fragment Kit (Agilent Technologies, Santa Clara, CA). Libraries were also evaluated by gel electrophoresis.

Libraries were quantified using the QuantiFluor dsDNA HS System (Promega, Madison, WI) on a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA) and pooled in equimolar concentrations. Sequencing was conducted on a MiSeq platform (Illumina, San Diego, CA) following the manufacturer's guide, using the MiSeq Reagent Kit v2 (2 × 250 bp, Illumina, San Diego, CA). PhiX Control v3 was used as a quality control for sequencing runs, with a spike of 3–5% (increased to 10% if low sample diversity was observed).

Bioinformatic Analysis of Sequences

Paired-end reads were quality controlled using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc), and raw sequences were demultiplexed in Qiime2 and then processed using the Mothur MiSeq SOP (Kozich et al., 2013, accessed October 11, 2021), similar as before (You et al., 2021). Briefly, paired-end reads were merged into contigs, screened for quality, and aligned to the SILVA 16S rRNA database (v 138) (Quast et al., 2012). Chimeras were identified and removed using the VSEARCH algorithm (Rognes et al., 2016), and remaining sequences were then classified using a Bayesian classifier and SILVA 16 s rRNA reference files (v 138). Sequences not classified as prokaryotes were removed (mitochondria, chloroplasts, archaea, eukaryotes, or unknown). Classified sequences were clustered into operational taxonomic units using the OptiClust method with a distance cutoff of 3% (Westcott & Schloss, 2017). The number of sequences in each sample was rarefied to the lowest sequencing depth among all samples to provide for even sampling.

Statistical Analysis

The values for organic acids and dry matter loss were graphed in Sigmaplot 13 (Systat Software, Inc., Palo Alto, CA). Regression analysis of dry matter loss versus deplete biomass content in blends was also conducted in Sigmaplot. R (v.4.1.0) with the tidyverse package was used to create a relative abundance barplot and heatmap. Groupings (by class or genus) with >4% abundance are represented by their group name. All others with abundance <4% are grouped together as ‘‘Other.’’

Results and Discussion

Initial Storage Study

Storage stability was assessed for M. minutum 26BAM cultivated in outdoor raceways at the Arizona Center for Algae Technology and Innovation. The goal of this study was to compare post-harvest stability in ‘‘N-replete’’ and ‘‘N-deplete’’ conditions. In addition, the impact of inoculation with a lactic acid bacterium (LAB), L. buchneri, was performed to encourage proper ensiling. The outcome of the storage studies differed considerably between the N-replete and N-deplete biomass. The highest dry matter loss of 20.6% (db) was observed in the untreated N-replete biomass, followed by LAB-inoculated N-replete biomass (17.3%, db), both registering a final pH of 6.5 or above (Table 1). The N-deplete biomass stored without treatment experienced the least dry matter loss at 8.3% (db) and a final pH of 4.2, indicating that lactic acid fermentation was improved in this condition compared to either case of the N-replete biomass. Interestingly, when N-deplete biomass was inoculated with L. buchneri dry matter loss increased to 15.4% (db) and the final pH was higher at 4.9. L. buchneri has been shown to improve storage stability of herbaceous plant material when added as a silage inoculant (Holzer et al., 2003). Higher dry matter loss compared to the uninoculated algae biomass could be a result of the inoculant not increasing enough in abundance during storage or perhaps is not well suited to enhancing the preservation of Monoraphidium biomass. Additionally, L. buchneri is a heterofermentative LAB whose metabolism produces lactic acid, acetic acid, ethanol, and carbon dioxide.

Table 1.

Effect of treatment on the storage performance of N-replete and N-deplete Monoraphidium minutum 26BAM algae biomass, ‘‘initial’’ study

Monoraphidium biomass Treatment Storage length (days) Dry matter loss (%, dry basis) Final pH
N-replete Untreated 30 20.6 ± 0.0 6.64 ± 0.07
LAB inoculated 30 17.3 ± 0.5 6.50 ± 0.04
N-deplete Untreated 30 8.3 ± 2.1 4.22 ± 0.08
LAB inoculated 30 15.4 ± 0.1 4.91 ± 0.04
Open in a new tab

The complement of organic acids produced during ensiling can be indicative of storage performance (Borreani et al., 2018; Muck et al., 2018). Well-ensiled material has lactic acid as its primary component, while acetic and propionic acids are less prevalent, and butyric acid is absent. Both acetic and propionic acids are less desirable than lactic acid due to their higher pKa (4.76 and 4.88, respectively, compared to 3.86). Butyric acid is formed by Clostridia metabolism and is accompanied by significant loss of dry matter as CO2 (Borreani et al., 2018). The most abundant organic acid in the N-replete biomass after storage was butyric acid followed by acetic acid for both the untreated- and LAB-inoculated conditions (Fig. 1). Lactic acid was not observed in either condition. Conversely, N-deplete biomass had lactic acid as the prominent organic acid in the untreated condition, and butyric acid was not observed. When inoculated with L. buchneri, lactic acid was present at >2% (db) but was less than either acetic or propionic acids. This type of mixed acid fermentation is common to L. buchneri, which employs heterolactic fermentation (Holzer et al., 2003). Heterolactic fermentation of hexose sugars yields lactic acid, ethanol, and CO2, and pentoses are converted to lactic and acetic acid (Borreani et al., 2018; Holzer et al., 2003). The higher dry matter loss in N-deplete biomass inoculated with L. buchneri could in part be explained by this fermentation; however, the extent of loss is greater than anticipated. The organic acid composition characteristic of L. buchneri metabolism indicates that the inoculum grew effectively, but without increased stability. Butyric acid was also not observed in N-deplete biomass inoculated with L. buchneri, also an indication that the LAB inoculum was functional, as L. buchneri has been shown to be effective at inhibiting Clostridia (Carvalho et al., 2012).

Fig. 1.

Fig. 1.

Open in a new tab

Effect of treatment on the content and composition of organic acids after storage of Monoraphidium 26BAM biomass that had been cultivated under nitrogen-replete or nitrogen-deplete conditions. Untreated biomass (22% solids N-replete and 18% N-deplete) was stored anaerobically for 30 days, while LAB-inoculated biomass was inoculated with Lactobacillus buchneri prior to storage. 3.2 N-replete/N-deplete Blended Storage Study.

Several hypotheses attempting to explain observed differences in preservation were considered. N-replete and N-deplete cultures differed in nitrogen availability. Could this difference have altered the microbiome in such a way that N-replete algae biomass was at greater risk of microbial degradation? Could the difference in nitrogen availability have led to higher bacterial numbers in N-replete biomass, leading to higher losses? Or could differences in biomass composition resulting from nitrogen availability impact storage stability? Given the large differences in storage outcomes exhibited by each biomass when stored without treatment, a follow-on experiment was developed to replicate the results and attempt at answering these questions.

The follow-on experiment utilized closed, flat-panel photobioreactors to cultivate Monoraphidium in replete-nitrogen conditions, and in a separate set of flat-panel reactors a Monoraphidium culture was allowed to go nitrogen deplete. Closed photobioreactors were selected in order to minimize environmental contamination and the impact of the initial microbiome on storage stability. Each biomass was stored without additional treatment, as was done in the first experiment and under the same conditions. The storage performance of blended N-replete and N-deplete biomass in ratios of 25:75, 50:50, and 75:25 (N-replete and N-deplete biomass) is shown in Table 2. Stored N-replete biomass had the greatest dry matter loss with 24% (db), comparable to the N-replete biomass from the ‘‘initial’’ study. The pH was similarly circumneutral at 6.92, much higher than the value needed for successful ensiling (4.5). Blends of N-replete and N-deplete biomass experienced less loss with increasing N-deplete biomass content. The pH of the blends experienced a similar pattern of decreasing value with increasing N-deplete biomass content, though none of the blends achieved target pH values for ensiling. The lowest dry matter loss was observed in 100% N-deplete biomass (10%, db) with a pH value of 5.2, still far too high for effective preservation. The inability of the N-deplete biomass to achieve a stable pH in storage could be related to the differences between the N-replete and N-deplete biomass that arise during nitrogen depletion. It is possible that nitrogen depletion was not carried out far enough to achieve the biomass characteristics that would support effective ensiling. The biomass composition will be addressed later in this study.

Table 2.

Effect of N-deplete biomass content on the storage performance of N-replete and N-deplete Monoraphidium minutum 26BAM algae biomass and their blends—‘‘blend’’ study

Monoraphidium biomass N-deplete biomass content Storage length (days) Dry matter loss (%, dry basis) Final pH
N-repletea 0% 30 24.0 ± 0.7 6.92 ± 0.10
Blend 1 25% 30 21.1 ± 1.5 6.41 ± 0.12
Blend 2 50% 30 18.0 ± 1.1 6.00 ± 0.09
Blend 3 75% 30 16.2 ± 1.1 5.72 ± 0.03
N-depleteb 100% 30 10.0 ± 0.5 5.24 ± 0.36
Open in a new tab

aThe pH of the as-received N-replete biomass was 6.93.

bThe pH of the as-received N-deplete biomass was 6.66.

Dry matter loss was negatively correlated with the amount of N-deplete biomass present in each storage condition and exhibited a proportional decrease with increasing N-deplete biomass content. Linear regression analysis identified a negative relationship between dry matter loss and N-deplete biomass content with an R2 value of 0.96 (Fig. 2). This relationship is consistent with both the nitrogen content hypothesis and the biochemical composition hypothesis. If increased amount of nitrogen available in N-replete biomass was detrimental to storage, then addition of N-deplete biomass would dilute its effect. Likewise, if the biochemical composition of N-deplete biomass were favorable to storage, then its addition would improve storage outcomes in a concentration-dependent manner.

Fig. 2.

Fig. 2.

Open in a new tab

Effect of nitrogen addition in Monoraphidium 26BAM cultivation on dry matter loss in storage and on organic acid content and composition. Nitrogen-replete cultivated 26BAM biomass and nitrogen-deplete biomass (% solids, respectively) were each stored anaerobically in separate reactors and after blending at replete: deplete ratios of 25:75, 50:50, and 75:25. Blends are indicated by the amount of deplete biomass present. Dry matter loss occurring in storage is indicated by the red dots and the right vertical axis. The trendline shows the relationship between dry matter loss and deplete biomass content. The R2 value of the trendline is 0.96. Each bar represents storage experiments performed in triplicate.

The organic acid content and composition of stored algae biomass also exhibited a dependence on the nitrogen status during cultivation (Fig. 2). N-replete cultivated biomass had the most organic acid production in storage with >22% (db), and N-deplete biomass had the least production at 9% (db). Organic acid content of blends was intermediate to these values. In addition to observed differences in total organic acid, the composition of the organic acids produced also varied between N-replete and N-deplete biomass. The relative concentration of butyric acid, a byproduct of Clostridia metabolism and often an indicator of silage spoiling, was highest in N-replete biomass and blends (32–36%) and lowest in N-deplete biomass. N-deplete biomass had higher relative concentrations of succinic and lactic acids, 16 and 20% of total acids, respectively. By comparison, lactic and succinic acids together only comprised 3% each of the total organic acid in stored N-replete biomass. Lactic acid content is highly beneficial to achieving stability in ensiled biomass.

Lactic acid production in the ensiling process can be influenced by a number of factors. Water-soluble carbohydrates, or carbohydrates that are readily available for fermentation, and sufficient water are necessary to support lactic acid fermentation of both fresh and senesced material such as grass and wheat straw (McEniry et al., 2010; Thompson et al., 2005) and would likely be important factors in lactic acid fermentation in algae biomass. Algae biomass stored after harvest contains 80% moisture and therefore is not moisture limited. The presence of sufficient carbohydrate to support robust lactic acid fermentation could be impacted by algae cultivation conditions. N-replete conditions are known to favor protein accumulation, while N-deplete conditions promote carbohydrate accumulation as a means of carbon/energy storage. Differences in carbohydrate content of each biomass could shape the microbial community in storage and, consequently, the composition of fermentation products.

Biochemical Composition

The biochemical composition of Monoraphidium biomass varied by nitrogen management in cultivation (N-replete vs. N-deplete) within each storage experiment (‘‘Initial’’ and ‘‘Blend,’’ Table 3). The N-replete biomass obtained for each storage experiment consisted mostly of protein (37% for ‘‘Initial’’ and 47% for ‘‘Blend’’) with considerably less carbohydrate (14.5% ‘‘Initial’’ and 9% ‘‘Blend’’). Carbohydrate content was 2.1 and 2.4 times higher in the N-deplete biomass than the N-replete biomass used in the ‘‘Initial’’ and ‘‘Blend’’ experiments, respectively. The lipid content varies the least across cultivation approaches and experiments. The N-replete biomass in the ‘‘Initial’’ study had the highest lipid content at 14%, which is consistent with rapidly growing cells and likely consists of membrane lipids and not neutral lipids (Dong et al., 2016; Williams & Laurens, 2010). Each remaining biomass used in the study had a lipid content of 11%. Nitrogen depletion can cause algae cells to become enriched for carbohydrates and, if allowed to persist for a sufficient time in cultivation, can lead to neutral lipid accumulation (Dong et al., 2016). The precise impact of nitrogen limitation on biochemical composition can be strain-specific, but there is a general trend of increased carbohydrate and/or lipid content at the expense of protein when nitrogen is limited (Williams & Laurens, 2010).

Table 3.

Biomass composition and carbohydrate composition of each Monoraphidium biomass utilized in this study

Storage experiment Biomass nitrogen status Storage duration Lipid contenta Protein contenta Carbsa Glucan Xylan Galactan Mannan
Initial Storage Study N-replete 0 14.1 ± 0.5 36.7 14.5 7.0 0.1 1.8 5.6
30 days 14.1 ± 1.0 36.5 10.9 4.2 0.2 1.0 5.5
N-deplete 0 11.4 ± 0.6 24.5 30.5 21.2 0.4 2.5 6.5
30 days 11.0 ± 0.6 25.8 22.8 13.6 0.4 2.2 6.6
Blend Storage Study N-replete 0 10.8 ± 0.3 46.6 9.1 2.6 0.1 2.7 3.6
30 days 11.7 ± 1.0 48.2 5.3 1.5 0.2 0.9 2.6
N-deplete 0 11.0 ± 0.5 32.7 22.2 12.3 0.3 2.9 6.8
30 days 10.4 ± 0.2 34.6 15.2 5.9 0.0 2.5 6.9
Open in a new tab

aAll values are presented as a percentage of total biomass on a dry basis. Lipid content is the average of triplicate measurements. All other values are the averages of duplicate measurements.

Glucan is the most abundant carbohydrate in all but one type of biomass used in this study (Table 3). In the N-replete biomass utilized in the ‘‘Blend’’ study, mannan was 38% more abundant than glucan, and galactan was just as prevalent. In all other cases, mannan was the second most abundant carbohydrate. Xylan was observed at trace levels, and arabinan was not detected.

Carbohydrates were the fraction of algae biomass most affected by storage; carbohydrate content was reduced by as much as 42% (Table 3). Degradation of carbohydrates is expected even in well-ensiled biomass, where sugars are consumed by LAB, producing lactic acid that lowers the pH to the point where microbial metabolism is significantly slowed (Rooke & Hatfield, 2003). Protein was not observed to change during storage and even appeared to increase under some conditions. This could be a denominator issue where total protein is being divided by a smaller amount of biomass after degradation in storage. The protein estimation makes the assessment of degradation in storage unclear. Protein is not directly measured but is calculated by multiplying the biomass nitrogen content by a conversion factor (4.78) (Templeton & Laurens, 2015). This is accurate if the total N partitioned to amino acids is the same before and after storage. If amino acids are degraded, but nitrogen remains in solution as a byproduct of metabolism, such as a biological amine, then total nitrogen would not change, and protein would appear to be unchanged. Total amino acid compositional analysis is necessary to definitively determine the impact of storage on protein content. This analysis was not done as part of this experiment.

Significant differences in biochemical composition are evident due to nitrogen management in cultivation. When nitrogen is plentiful, protein content is high, and carbohydrate content is low. When nitrogen becomes deplete in cultivation, protein content is reduced, and carbohydrates accumulate. These differences in composition could impact how the microbial community changes, in response to available substrates, during anaerobic storage of algae biomass.

Metagenomics of the Algae Microbiome

The relative abundance of bacteria in M. minutum 26BAM algae biomass used in these studies was investigated using next-generation sequencing. This facilitated a comparison of the algae biomass microbiome for algae cultivated under different nitrogen regimes (N-replete and N-deplete) and provided an understanding of how anaerobic storage causes the community to shift.

The microbiome of the as-received N-replete and N-deplete biomass used in the ‘‘initial’’ study each consisted of bacteria from the same top seven bacterial classes with Alphaproteobacteria (48 and 35%, respectively) being the most prominent, differing only in the mean relative abundance of each class (Fig. 3). Storage of each biomass, whether untreated or after inoculation with L. buchneri, resulted in the proliferation of bacteria in the class Bacilli, achieving mean relative abundances from 40 to 89%. LAB, which are essential for successful ensiling, are members of Bacilli (Fig. 3) (Pfeiler & Klaenhammer, 2007). In addition to encouraging the growth of beneficial Bacilli, the storage of N-replete biomass also supported the growth of Clostridia, a class of bacteria associated with excessive biomass degradation (Rooke & Hatfield, 2003). In contrast to the N-replete biomass, Clostridia were limited to < 0.1% mean relative abundance in the N-deplete biomass in each storage condition, whether stored untreated or after L. buchneri inoculation. While L. buchneri inoculation was not necessary for the proliferation of Bacilli in either biomass, it did substantially increase the abundance of Bacilli in the N-deplete biomass from 40 to 89% (Fig. 3). Interestingly, the increase in Bacilli facilitated by L. buchneri inoculation did not improve dry matter stability.

Fig. 3.

Fig. 3.

Open in a new tab

Relative abundances of bacterial community dynamics in N-replete and N-deplete algae biomass at the class level. ‘‘R’’ represents N-replete Monoraphidium biomass, and ‘‘D’’ represents N-deplete Monoraphidium biomass. ‘‘RL’’ and ‘‘DL’’ represent either N-replete or N-deplete algae biomass that has been inoculated with Lactobacillus buchneri. ‘‘t0’’ signifies the freshly harvested algae biomass, and ‘‘S’’ designates the microbial community from stored algae biomass. Algae biomass was stored for 30 days. ‘‘B’’ indicates blends of both N-replete and N-deplete biomass: (B1) 25%, (B2) 50%, and (B3) 75% N-deplete biomass. The microbial community composition of the ‘‘Initial’’ experiment is described in the first six bars on the left and that of the ‘‘Blend’’ study in the last seven bars on the right.

A follow-up storage study that looked at the effect of blending N-replete and N-deplete biomass at varying ratios on storage stability (‘‘blend’’) demonstrated similar changes to the microbiome (Fig. 3). The initial microbial communities for each nutrient condition were similar to each other differing only in the relative abundance of each class. Although the algae growth for the ‘‘initial’’ and ‘‘blend’’ storage studies were separated by nearly a year and were cultivated in different environments (open raceway [‘‘initial’’] and closed photobioreactor (‘‘blend’’)] with different potentials for contamination, the community composition was remarkably consistent. The absence of bacteria from the class Fimbriimonadia in algae used for the ‘‘blend’’ study being the only difference. Similar trends in changes to the relative abundance of each bacterial class were observed for the ‘‘blend’’ study. Clostridia were most prevalent in the stored N-replete biomass and blend with higher ratios of N-replete biomass with a high of 31% mean relative abundance (Fig. 3). Bacilli increased in relative abundance with increasing ratios of N-deplete biomass and Clostridia decreased, reaching a low of 4% in stored 100% N-deplete biomass. Comparing the two storage studies, ‘‘initial’’ and ‘‘blend,’’ the mean relative abundance of Clostridia in stored biomass was similar for algae cultivated under nitrogen-replete conditions, but the relative abundance of Bacilli was much greater in biomass from the ‘‘initial’’ study (Fig. 3).

Genera present in the N-replete and N-deplete as-received biomass from either the ‘‘initial’’ or ‘‘blend’’ studies differ only in their relative abundance (Fig. 4a and b). This only considers genera that were present at 4% or greater in any one sample. There could be genera of lower abundance that are present in some of the as-received samples but not others. This indicates that the differences in the microbial community that develop over the course of storage do so due to substrate availability and not the initial composition of the microbial community.

Fig. 4.

Fig. 4.

Open in a new tab

Heat map showing the relative abundance of genera present at >4% in any one sample. (a) Top genera from the ‘‘initial’’ study, including N-replete (R t0) and N-deplete (D t0) initial material, N-replete (R S) and N-deplete (D S) biomass after storage, and post-storage N-replete (RL S), and N-deplete (DL S) biomass inoculated with L. buchneri prior to storage. (b) Top genera from the ‘‘blend’’ study, including N-replete (R t0) and N-deplete (D t0) initial material, N-replete (R S) biomass after storage, N-replete, and N-deplete biomass blends after storage that included 75% (B1 S), 50% (B2 S), and 25% (B3 S) N-replete biomass content with N-deplete biomass comprising the balance, and N-deplete biomass after storage (D S).

Genera such as Enterococcus and Clostridium sensu stricto (groups 1 and 18) in the ‘‘initial’’ study and Lactococcus and Clostridium sensu stricto (group 18) in the ‘‘blend’’ study were only present in the N-replete biomass after storage, where in-storage degradation was considerably greater (Fig. 4). Clostridia are often found in silage with lower dry matter content (higher moisture) and where acidification of silage is delayed; either by pH buffering (Muck, 1988) or by insufficient lactic acid fermentation caused by low concentrations of fermentable sugars (Borreani et al., 2018). Moisture is unlikely to be the cause of differences in relative abundance of Clostridia as the N-deplete biomass in both studies had lower dry matter content than did either N-replete biomass. Lactic acid titrations were conducted with the as-received N-replete and N-deplete biomass from the ‘‘initial’’ study to determine buffering effects. The N-deplete biomass required 3.6 mL of lactic acid solution per gram of biomass (db) to reach the pKa of lactic acid (3.9), while a pH of 3.9 was reached with just 2.5 mL lactic acid solution per gram of biomass for the N-replete biomass. The presence of Clostridia in stored N-replete samples and its absence in N-deplete biomass could be explained by the availability of carbohydrates in each biomass from both studies. Although we cannot rule out the impact of more overall nitrogen present in the N-replete biomass, the total nitrogen in both biomasses is much higher than would be present in a typical feed silage (Pieper et al., 2011; Rooke & Hatfield, 2003). In addition to impacting the microbial community through acidification, LAB have been shown to directly inhibit Clostridia through the production of bacteriocins (Okereke & Montville, 1991). Although bacteriocins, proteinaceous toxins, were not measured in this study; they could limit the growth of Clostridia during storage.

Both Enterococcus and Lactococcus are LAB, known for their lactic acid fermentation that is vital for successful ensiling. It seems out of place for these genera to be present in higher abundance in the poorly ensiled N-replete biomass and absent in the better-preserved N-deplete biomass. Cai et al. report that Enterococcus species isolated from forage crops did not improve silage quality when utilized as silage inoculants (Cai, 1999). The authors attributed the poor performance to the low tolerance of Enterococcus to pH below 4.5. Enterococci thrive initially but begin to die off at pH below 4.5. The authors also noted that Enterococcus was unable to inhibit Clostridia growth and, consequently, their impact on dry matter loss. Other researchers have also noted that enterococci, lactococci, pediococci, and leuconostocs initiate the silage process through lactic acid fermentation and are later supplanted by the more acid-tolerant lactobacilli (Lin et al., 1992; Parvin et al., 2010). This provides an explanation as to why Enterococcus is prevalent in the stored N-replete biomass of the ‘‘initial’’ study but absent in the N-deplete biomass and does not preclude the possibility of Enterococcus contributing in the early phase of storage. The stored N-deplete biomass, where Lactobacillus was present in higher numbers, reached lower pH values that could have caused Enterococcus to decrease in numbers.

Inoculation of forage crops with strains of LAB has been a common practice in the feed and forage industry to ensure enough lactobacilli are present to achieve robust lactic acid fermentation (McDonald et al., 1991). To this end, we inoculated both the N-replete and N-deplete biomass with L. buchneri, a strain that has successfully improved the silage quality of a number of feedstocks (Holzer et al., 2003). Inoculation of algae biomass with L. buchneri did not improve the preservation of N-deplete Monoraphidium biomass compared to storage without inoculation. The >80% relative abundance of Lactobacillus in stored N-deplete (‘‘initial’’ study) biomass that had been inoculated with L. buchneri compared to a relative abundance of 17% for N-deplete biomass that had not been inoculated indicates that the failure of the inoculant to improve storage was not due to a lack of growth but perhaps due to the particular species of Lactobacillus.

Lactobacillus buchneri, a heterofermentative LAB, has a unique metabolism. In addition to producing both lactic acid and acetic acid as is common for heterofermentative LAB, L. buchneri consumes extracellular lactic acid, producing acetic acid and 1,2-propanediol (not measured). The organic acid composition of inoculated N-deplete biomass with its lower lactic acid content and higher acetic and propionic acids compared to the uninoculated biomass is consistent with the metabolism of L. buchneri, demonstrating that inoculants can be used to alter the fermentation and microbial community that occur during storage of algae biomass as has been observed in the ensiling of forage crops. Based on the poor storage performance of L. buchneri-inoculated algae biomass, a different strain of LAB might be more successful in decreasing pH and thus preserving biomass.

Lactobacillus was not found in any of the stored samples in the ‘‘blend’’ study. This could be because it was not found in either of the as-received biomasses or because the necessary conditions that favor its proliferation did not develop during storage in the ‘‘blend’’ study.

Conclusions

This research demonstrates the importance of nitrogen management in cultivation to ensure that algae biomass destined for storage is successfully preserved through ensiling. When grown with sufficient nitrogen (N-replete), the lower carbohydrate content cannot sustain sufficient lactic acid fermentation to stabilize the biomass, permitting butyric acid fermentation and biomass degradation by Clostridia. The higher carbohydrates accumulated by Monoraphidium biomass that was allowed to go deplete of nitrogen in cultivation, in contrast, encouraged the growth of LAB while inhibiting Clostridia. This result, which was repeated in consecutive studies, indicates the importance of nitrogen management to downstream operations in an algae biorefinery.

Acknowledgements

The authors would like to thank Brad Thomas and Kastli Schaller for experimental support and the U.S. Department of Agriculture Research Service Culture Collection for providing the Lactobacillus buchneri strain.

Contributor Information

Bradley D Wahlen, Biological Processing, Idaho National Laboratory, Idaho Falls 83415, USA.

Lynn M Wendt, Biological Processing, Idaho National Laboratory, Idaho Falls 83415, USA.

Chelsea C St. Germain, Biological Processing, Idaho National Laboratory, Idaho Falls 83415, USA.

Sarah M Traynor, Biological Processing, Idaho National Laboratory, Idaho Falls 83415, USA.

Caitlin Barboza, Biological Processing, Idaho National Laboratory, Idaho Falls 83415, USA.

Thomas Dempster, Arizona Center for Algae Technology and Innovation, Arizona State University, Mesa 85212, USA.

Henri Gerken, Arizona Center for Algae Technology and Innovation, Arizona State University, Mesa 85212, USA.

John McGowen, Arizona Center for Algae Technology and Innovation, Arizona State University, Mesa 85212, USA.

Yaqi You, SUNY College of Environmental Science and Forestry, State University of New York, Syracuse 13210, USA.

Author Contributions

Bradley Wahlen: Conceptualization, Investigation, Visualization, Supervision; Lynn Wendt: Conceptualization, Investigation, Supervision, Project Administration, Funding Acquisition; Sarah Traynor: Investigation; Caitlin Barboza: Investigation; Yaqi You: Investigation; Thomas Dempster: Resources; Henri Gerken: Resources; and John McGowen: Resources.

Funding

The research was supported by the U.S. Department of Energy (DOE), the Office of Energy Efficiency and Renewable Energy (EERE), and the Bioenergy Technologies Office (BETO) under Award No. DE-AC07-05ID14517. Yaqi You also received support from the Center for Advanced Energy Studies (CAES), a research, education, and innovation consortium consisting of the Idaho National Laboratory and the public research universities of Idaho. The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the US Government.

Conflict of interest

There is no conflict of interest in conducting or reporting this study.

References

  1. Apprill A., McNally S., Parsons R., Weber L. (2015). Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquatic Microbial Ecology, 75(2), 129–137. https://www.int-res.com/abstracts/ame/v75/n2/p129-137/ [Google Scholar]
  2. Bennion E. P., Ginosar D. M., Moses J., Agblevor F., Quinn J. C. (2015). Lifecycle assessment of microalgae to biofuel: Comparison of thermochemical processing pathways. Applied Energy, 154, 1062–1071. 10.1016/j.apenergy.2014.12.009 [DOI] [Google Scholar]
  3. Beuchat L. R. (1983). Influence of water activity on growth, metabolic activities and survival of yeasts and molds. Journal of Food Protection, 46, 135–141. 10.4315/0362-028x-46.2.135 [DOI] [PubMed] [Google Scholar]
  4. Borreani G., Tabacco E., Schmidt R. J., Holmes B. J., Muck R. E. (2018). Silage review: Factors affecting dry matter and quality losses in silages. Journal of Dairy Science, 101(5), 3952–3979. 10.3168/jds.2017-13837 [DOI] [PubMed] [Google Scholar]
  5. Cai Y. (1999). Identification and characterization of Enterococcus species isolated from forage crops and their influence on silage fermentation. Journal of Dairy Science, 82(11), 2466–2471. 10.3168/jds.S0022-0302(99)75498-6 [DOI] [PubMed] [Google Scholar]
  6. Caporaso J. G., Lauber C. L., Walters W. A., Berg-Lyons D., Huntley J., Fierer N., Owens S. M., Betley J., Fraser L., Bauer M. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal, 6(8), 1621. 10.1038/ismej.2012.8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Carvalho B. F., Ávila C. L. S., Pinto J. C., Pereira M. N., Schwan R. F. (2012). Effects of propionic acid and Lactobacillus buchneri (UFLA SIL 72) addition on fermentative and microbiological characteristics of sugar cane silage treated with and without calcium oxide. Grass and Forage Science, 67(4), 462–471. 10.1111/j.1365-2494.2012.00863.x [DOI] [Google Scholar]
  8. Coleman A. M., Abodeely J. M., Skaggs R. L., Moeglein W. A., Newby D. T., Venteris E. R., Wigmosta M. S. (2014). An integrated assessment of location-dependent scaling for microalgae biofuel production facilities. Algal Research, 5, 79–94. 10.1016/j.algal.2014.05.008 [DOI] [Google Scholar]
  9. Crowe B., Attalah S., Agrawal S., Waller P., Ryan R., Van Wagenen J., Chavis A., Kyndt J., Kacira M., Ogden K. L., Huesemann M. (2012). A comparison of Nannochloropsis salina growth performance in two outdoor pond designs: Conventional raceways versus the ARID pond with superior temperature management. International Journal of Chemical Engineering, 2012, 920608. 10.1155/2012/920608 [DOI] [Google Scholar]
  10. Davis R., Kinchin C., Markham J., Tan E. C. D., Laurens L. M. L., Sexton D., Knorr D., Schoen P., Lukas J. (2014). Process design and economics for the conversion of algal biomass to biofuels: Algal biomass fractionation to lipid- and carbohydrate-derived fuel products. (Technical Report No. NREL/TP-5100-62368). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy14osti/62368.pdf [Google Scholar]
  11. Dong T., Van Wychen S., Nagle N., Pienkos P. T., Laurens L. M. L. (2016). Impact of biochemical composition on susceptibility of algal biomass to acid-catalyzed pretreatment for sugar and lipid recovery. Algal Research, 18, 69–77. 10.1016/j.algal.2016.06.004 [DOI] [Google Scholar]
  12. Green M. R., Sambrook J. (2016). Precipitation of DNA with ethanol. Cold Spring Harbor Protocols, 2016(12), pdb–prot093377. [DOI] [PubMed] [Google Scholar]
  13. Holzer M., Mayrhuber E., Danner H., Braun R. (2003). The role of Lactobacillus buchneri in forage preservation. Trends in Biotechnology, 21(6), 282–287. 10.1016/S0167-7799(03)00106-9 [DOI] [PubMed] [Google Scholar]
  14. Kozich J. J., Westcott S. L., Baxter N. T., Highlander S. K., Schloss P. D. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and Environmental Microbiology, 79(17), 5112–5120. 10.1128/aem.01043-13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Lin C., Bolsen K. K., Brent B. E., Fung D. Y. C. (1992). Epiphytic lactic acid bacteria succession during the pre-ensiling and ensiling periods of alfalfa and maize. Journal of Applied Bacteriology, 73(5), 375–387. 10.1111/j.1365-2672.1992.tb04992.x [DOI] [Google Scholar]
  16. Linden J. C., Henk L. L., Murphy V. G., Smith D. H., Gabrielsen B. C., Tengerdy R. P., Czako L. (1987). Preservation of potential fermentables in sweet sorghum by ensiling. Biotechnology and Bioengineering, 30(7), 860–867. 10.1002/bit.260300708 [DOI] [PubMed] [Google Scholar]
  17. Luo R., Zhang Y., Wang F., Liu K., Huang G., Zheng N., Wang J. (2021). Effects of sugar cane molasses addition on the fermentation quality, microbial community, and tastes of alfalfa silage. Animals, 11(2), 355. https://www.mdpi.com/2076-2615/11/2/355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Okereke A., Montville T. J. (1991). Bacteriocin inhibition of Clostridium botulinum spores by lactic acid bacteria. Journal of Food Protection, 54(5), 349–354. 10.4315/0362-028X-54.5.349 [DOI] [PubMed] [Google Scholar]
  19. McDonald P., Henderson A. R., Heron S. J. E. (1991). The biochemistry of silage (2nd ed.). Calcombe Publications. [Google Scholar]
  20. McEniry J., O'Kiely P., Clipson N. J. W., Forristal P. D., Doyle E. M. (2010). Assessing the impact of various ensilage factors on the fermentation of grass silage using conventional culture and bacterial community analysis techniques. Journal of Applied Microbiology, 108(5), 1584–1593. 10.1111/j.1365-2672.2009.04557.x [DOI] [PubMed] [Google Scholar]
  21. McGechan M. B. (1990). A review of losses arising during conservation of grass forage: Part 2, storage losses. Journal of Agricultural Engineering Research, 45, 1–30. 10.1016/S0021-8634(05)80135-0 [DOI] [Google Scholar]
  22. Muck R. E. (1988). Factors influencing silage quality and their implications for management. Journal of Dairy Science, 71(11), 2992–3002. 10.3168/jds.S0022-0302(88)79897-5 [DOI] [Google Scholar]
  23. Muck R. E. (2002). Effects of corn silage inoculants on aerobic stability St. Joseph, MI. https://elibrary.asabe.org/abstract.asp?aid=9144&t=5 [Google Scholar]
  24. Muck R. E., Nadeau E. M. G., McAllister T. A., Contreras-Govea F. E., Santos M. C., Kung L. (2018). Silage review: Recent advances and future uses of silage additives. Journal of Dairy Science, 101(5), 3980–4000. 10.3168/jds.2017-13839 [DOI] [PubMed] [Google Scholar]
  25. Parada A. E., Needham D. M., Fuhrman J. A. (2016). Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environmental Microbiology, 18(5), 1403–1414. 10.1111/1462-2920.13023 [DOI] [PubMed] [Google Scholar]
  26. Parvin S., Wang C., Li Y., Nishino N. (2010). Effects of inoculation with lactic acid bacteria on the bacterial communities of Italian ryegrass, whole crop maize, guinea grass and rhodes grass silages. Animal Feed Science and Technology, 160(3-4), 160–166. 10.1016/j.anifeedsci.2010.07.010 [DOI] [Google Scholar]
  27. Peleg M. (2022). Models of the water activity effect on microbial growth rate and initiation. Applied Microbiology and Biotechnology, 106(4), 1375–1382. 10.1007/s00253-022-11792-7 [DOI] [PubMed] [Google Scholar]
  28. Pfeiler E. A., Klaenhammer T. R. (2007). The genomics of lactic acid bacteria. Trends in Microbiology, 15(12), 546–553. 10.1016/j.tim.2007.09.010 [DOI] [PubMed] [Google Scholar]
  29. Pieper R., Hackl W., Korn U., Zeyner A., Souffrant W. B., Pieper B. (2011). Effect of ensiling triticale, barley and wheat grains at different moisture content and addition of Lactobacillus plantarum (DSMZ 8866 and 8862) on fermentation characteristics and nutrient digestibility in pigs. Animal Feed Science and Technology, 164(1), 96–105. 10.1016/j.anifeedsci.2010.11.013 [DOI] [Google Scholar]
  30. Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P., Peplies J., Glöckner F. O. (2012). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590–D596. 10.1093/nar/gks1219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Rognes T., Flouri T., Nichols B., Quince C., Mahé F. (2016). VSEARCH: A versatile open source tool for metagenomics. PeerJ, 4, e2584. 10.7717/peerj.2584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rooke J. A., Hatfield R. D. (2003). Biochemistry of ensiling. In Buxton D. R., Muck R. E., Harrison J. H. (Eds.), Silage science and technology (pp. 95–139). Wiley. 10.2134/agronmonogr42.c3 [DOI] [Google Scholar]
  33. Shinners K. J., Binversie B. N., Muck R. E., Weimer P. (2007). Comparison of wet and dry corn stover harvest and storage. Biomass Bioenergy, 31(4), 211–221. 10.1016/j.biombioe.2006.04.007 [DOI] [Google Scholar]
  34. Templeton D. W., Laurens L. M. L. (2015). Nitrogen-to-protein conversion factors revisited for applications of microalgal biomass conversion to food, feed and fuel. Algal Research, 11, 359–367. 10.1016/j.algal.2015.07.013 [DOI] [Google Scholar]
  35. Thompson D. N., Barnes J. M., Houghton T. P. (2005). Effect of additions on ensiling and microbial community of senesced wheat straw. Applied Biochemistry and Biotechnology, 121(1), 21–46. [DOI] [PubMed] [Google Scholar]
  36. Van Wychen S., Laurens L. M. L. (2015). Determination of total lipids as fatty acid methyl esters (FAME) by in situ transesterification. (Technical Report No. NREL/TP-5100-60958). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy16osti/60958.pdf, (accessed December 13, 2022). [Google Scholar]
  37. Wahlen B. D., Wendt L. M., Murphy A., Thompson V. S., Hartley D. S., Dempster T., Gerken H. (2020). Preservation of microalgae, lignocellulosic biomass blends by ensiling to enable consistent year-round feedstock supply for thermochemical conversion to biofuels. Frontiers in Bioengineering and Biotechnology, 8:316. 10.3389/fbioe.2020.00316 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Walters W., Hyde E. R., Berg-Lyons D., Ackermann G., Humphrey G., Parada A., Gilbert J. A., Jansson J. K., Caporaso J. G., Fuhrman J. A., Apprill A., Knight R. (2016). Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems, 1(1), e00009–00015. 10.1128/mSystems.00009-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wendt L. M., Kinchin C., Wahlen B. D., Davis R., Dempster T. A., Gerken H. (2019). Assessing the stability and techno-economic implications for wet storage of harvested microalgae to manage seasonal variability. Biotechnology for Biofuels, 12:80. 10.1186/s13068-019-1420-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Wendt L. M., Murphy J. A., Smith W. A., Robb T., Reed D. W., Ray A. E., Liang L., He Q., Sun N., Hoover A. N., Nguyen Q. A. (2018). Compatibility of high-moisture storage for biochemical conversion of corn stover: Storage performance at laboratory and field scales. Frontiers in Bioengineering and Biotechnology, 6:30. 10.3389/fbioe.2018.00030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wendt L. M., Wahlen B. D., Knoshaug E. P., Nagle N. J., Dong T., Spiller R., Panczak B., Van Wychen S., Dempster T. A., Gerken H., Pienkos P. T. (2020). Anaerobic storage and conversion of microalgal biomass to manage seasonal variation in cultivation. ACS Sustainable Chemistry & Engineering, 8(35), 13310–13317. 10.1021/acssuschemeng.0c03790 [DOI] [Google Scholar]
  42. Wendt L. M., Wahlen B. D., Li C., Kachurin G., Ogden K. L., Murphy J. A. (2017). Evaluation of a high-moisture stabilization strategy for harvested microalgae blended with herbaceous biomass: Part I—Storage performance. Algal Research, 25, 567–575. 10.1016/j.algal.2017.05.016 [DOI] [Google Scholar]
  43. Wendt L. M., Wahlen B. D., Li C., Ross J. A., Sexton D. M., Lukas J. C., Hartley D. S., Murphy J. A. (2017). Evaluation of a high-moisture stabilization strategy for harvested microalgae blended with herbaceous biomass: Part II—Techno-economic assessment. Algal Research, 25, 558–566. 10.1016/j.algal.2017.04.015 [DOI] [Google Scholar]
  44. Wendt L. M., Zhao H. (2020). Review on bioenergy storage systems for preserving and improving feedstock value [Review]. Frontiers in Bioengineering and Biotechnology, 8:370. 10.3389/fbioe.2020.00370 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Westcott S. L., Schloss P. D. (2017). OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere, 2(2). 10.1128/mSphereDirect.00073-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Williams P. J. l. B., Laurens L. M. L. (2010). Microalgae as biodiesel & biomass feedstocks: Review & analysis of the biochemistry, energetics & economics. Energy & Environmental Science, 3(5), 554. 10.1039/b924978h [DOI] [Google Scholar]
  47. You Y., Aho K., Lohse K. A., Schwabedissen S. G., Ledbetter R. N., Magnuson T. S. (2021). Biological soil crust bacterial communities vary along climatic and shrub cover gradients within a sagebrush steppe ecosystem [Original Research]. Frontiers in Microbiology, 12:569791. 10.3389/fmicb.2021.569791 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Industrial Microbiology & Biotechnology are provided here courtesy of Oxford University Press

RESOURCES