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Article

Capacity of Microbial Strains and Communities to Degrade Sewerage Fats, Oils, and Grease Clog Deposits

by
Allondra M. Woods
1,2,
Catherine J. Pettinger
1,
Catherine Harris
3,
Tanya Soule
4,
Garth Farley
4 and
Erica L.-W. Majumder
1,*
1
Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
2
Biological Engineering Department, Utah State University, Logan, UT 84322, USA
3
Madison Metropolitan Sewerage District, Madison, WI 53713, USA
4
Biodyne USA, Fort Wayne, IN 46808, USA
*
Author to whom correspondence should be addressed.
Appl. Microbiol. 2025, 5(4), 116; https://doi.org/10.3390/applmicrobiol5040116 (registering DOI)
Submission received: 29 August 2025 / Revised: 12 October 2025 / Accepted: 21 October 2025 / Published: 24 October 2025
(This article belongs to the Special Issue Exclusive Papers Collection of Editorial Board Members and Invited Scholars in Applied Microbiology (2025))
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Abstract

Fats, oils, and grease (FOG) deposits are hardened, sticky, insoluble solids that accumulate in sewage systems globally. These deposits contribute to pipe blockages and sanitary sewer overflows, releasing pathogens and pollutants into the environment, posing significant environmental and public health risks. Current removal methods are labor-intensive and costly, emphasizing the need for alternatives. While biological strategies offer a viable alternative, the microbial breakdown of FOG is poorly understood. In this study, we evaluated the potential of individual microbial strains and synthetic microbial communities to biodegrade wastewater-derived FOG deposit samples. These biological agents were applied to a range of FOG samples, and biodegradation was assessed through visual observations such as color change or gas bubbles, particle size, cell counts, pH, weight loss, and changes in fatty acid profile. Results demonstrate that microbial augmentation can enhance FOG degradation, offering an alternative or complementary approach for reducing maintenance burdens and preventing sewer blockages.

1. Introduction

Fats, oils, and grease (FOG) deposits accumulate in sewage systems globally. These hardened, sticky, and insoluble masses can lead to pipe blockages and sanitary sewer overflows (SSOs). These SSOs release untreated wastewater into the environment, creating environmental contamination and public health risks [1,2]. SSOs expose the environment to contaminants, including pathogens, pollutants, and fecal matter, which can pollute drinking water sources, groundwater, air, and land [3]. Raw sewage contains parasitic organisms, intestinal worms, and molds, which can cause diseases including mild gastroenteritis, cholera, and dysentery [3,4]. A U.S. Environmental Protection Agency (EPA) report from 2004 estimated that SSOs cause between 3 and 10 billion gallons of untreated wastewater to be released or discharged each year [1]. A 2018 report for Canadian wastewater systems estimated a total combined sewer overflow volume of 190 million m3 [5]. FOG deposits present a nuisance for wastewater authorities by increasing preventative maintenance needs and wastewater conveyance costs. The EPA estimates that 47% of SSOs are caused by the inappropriate release of grease into the sewers from industrial sources, restaurants, and homes [1,6]. A single Food Service Establishment (FSE) in the United States can produce between 800 and 1700 lbs. of FOG waste a year, which can enter the sewer system if improperly disposed of [6]. Currently, mechanical removal of FOG deposits from sewer systems is necessary, which is time-intensive and expensive [7]. Options for biologically breaking down in sewer wastewater FOG are not well studied but would minimize SSOs and the need for mechanical removal. A few studies have used microbial strains and communities to biodegrade the fatty acids commonly found in wastewater FOG deposits [8,9,10,11], but it is still poorly understood. Even fewer studies have focused on biodegradation using FOG samples from grease interceptors (GIs) and sewer systems [12,13]. There are also several commercial products containing microbial communities and microbial enzymes designed to break down sewer FOG, but their effectiveness is largely unknown [14,15,16].
The microbial degradation of fats and oils typically involves lipolysis mediated by lipase enzymes, which release free fatty acids from the lipids. This is followed by the transport of fatty acids into cells by dedicated transport proteins and fatty acyl-CoA synthases. Fatty acids are then catabolized through β-oxidation, where two carbon atoms are removed at a time as acetyl-CoA [17,18]. Longer chain fatty acids, therefore, take longer to catabolize. Although these enzymatic pathways are well documented and widespread across microbial taxa, their effectiveness in degrading wastewater FOG deposits is poorly understood, as each species can only metabolize certain fats. To address this, we investigated the potential for microorganisms to degrade GI and sewer wastewater FOG. The resident microbial community and fatty acid (FA) composition were characterized for FOG samples from wastewater collection system infrastructure: pumping station (PS), air release valves (ARVs), and FSE GIs. This study builds on prior work by using real wastewater FOG deposits and evaluating both individual strains and microbial communities. Microbial communities are of particular interest due to their potential for synergistic interactions and distributed metabolic capabilities, which may enhance the breakdown of the complex organic materials present in wastewater FOG. We hypothesized that the addition of microbial communities capable of metabolizing organic materials would enhance the degradation of wastewater FOG compared to the controls without added microbes. We also hypothesized that microbial communities would outperform individual strains. We found that the relative ability of microbes or communities to degrade FOG varied by FOG type, microbes, and biodegradation conditions. By exploring biological strategies for FOG degradation, this research contributes to efforts to reduce sewer maintenance costs and environmental risks associated with SSOs.

2. Materials and Methods

2.1. FOG Samples Collection

FOG deposits were collected from three distinct sample types: PS, ARV, and FSE GIs within the Madison Metropolitan Sewerage District service area in Madison, WI. The PS is a low point in the sewers where sewage is pumped toward the wastewater treatment plant. The three samples from this location were taken from a large floating FOG mat that can be up to several feet thick. Air-release valves run along the sewers and are often installed at high points to release air and gases. In the ARVs, FOG deposits float to the top and become stuck above the flowing sewage. Nine air-release valve FOG samples were collected from the floating FOG in five locations. GIs are installed in FSEs to prevent FOG from entering the sewer system. Ideally, FOG gets trapped in the GI, and water can pass through to enter the sewer. When GIs are cleaned out, this waste can be hauled to and disposed of at the wastewater treatment plant. Five samples of this type of aggregated GI cleanout waste from FSEs were collected from loads hauled to the wastewater treatment plant. FOG sample names, collection locations, and characteristics are detailed in Supplementary Table S1 and summarized in Table 1. Once collected, all samples were stored at 4 °C. Analysis of the FOG samples included the identification of FA composition via Fatty Acid Methyl Esters (FAMEs) by Gas Chromatography Flame Ionization Detection (GC-FID) and microbial community identification via Sanger sequencing as described below.

2.2. Fatty Acid Composition Identification

FAs were isolated, converted to FAMEs, and analyzed by GC-FID with modified methods) [19]. Fat isolation was performed by mixing 500 mg of sample with 0.500 mL of MilliQ water and 0.500 mL of 12 M HCl (Sigma-Aldrich 320331-2.5L, St. Louis, MO, USA), heating the mixture at 70 °C for 90 min, cooling to room temperature (22 ± 1 °C), and extracting with 5 mL of n-hexane (95+%, Environmental Grade, Alfa Aesar 042100, Ward Hill, MA, USA). Hexane solvent was removed by vacuum evaporation. 5–38 mg of isolated fat content was mixed with 1 mL of 12% boron trifluoride in methanol (Sigma-Aldrich B1252, St. Louis, MO, USA) and heated to 100 °C for 7 min for transesterification of FAs to FAMEs. The product was cooled to room temperature (22 ± 1 °C) and extracted with 2 mL of water and 1 mL of hexane. The organic layer was collected and FA methyl esters were identified with GC-FID.
GC-FID (Shimadzu Scientific, Kyoto, Japan) was performed with a capillary column DB Fatwax U1 30 m (0.25 mm ID × 0.25 µm, Agilent Cat #G3903-63008, Santa Clara, CA, USA) with a helium carrier gas and 1:5 split ratio. Injector and detector temperature was at 250 °C. Oven temperature was at 100 °C for 1 min after injection, increased 7 °C every minute until 225 °C, and then maintained at 225 °C for 15 min. Sample injection volume was 1 µL. FAs were identified using F.A.M.E. certified reference mix C8–C24 (Sigma-Aldrich CRM18918, St. Louis, MO, USA). Concentrations were determined by peak area and normalized against the isolated fat content weight used prior to transesterification. Results were visualized in R studio (version 4.4.0) [20] with package ggplot2 as part of tidyverse package (version 2.0.0) [21].

2.3. FOG Library Preparation for 16s rRNA Gene Amplicon Sequencing

FOG samples had DNA extractions performed using the standard protocol for the DNeasy PowerSoil Pro kit (Qiagen Cat 47016, Hilden, Germany). Total genomic DNA quality and concentration were verified using an Infinite 200Pro spectrophotometer (Tecan Nano Quant Plate, Männedorf, Switzerland). DNA extract preparation was performed as previously described [22] using PCR amplification and primers targeting the V4 region of the 16S rRNA gene. Library preparation was performed as previously described [22]. Sequences were uploaded to Illumina Sequence Hub and downloaded using BaseSpace Sequence Hub Downloader (Illumina, San Diego, CA, USA).

2.4. FOG Microbiome Community Analyses

Microbial community analysis was performed in Quantitative Insights Into Microbial Ecology (QIIME) 2 (version 2024.5.0) [23]. Demultiplexed data was downloaded from the Illumina Basespace website and uploaded to QIIME2 in Casava 1.8 paired-end demultiplexed format (via QIIME import tools). Demultiplexed reads were denoised with DADA2 (via q2-dada2) and quality-filtered using the chimera consensus pipeline [24]. Samples were filtered to a sampling depth of 16,872, which retained 55.91% (303,696) features in 100% of samples (18 samples). MAFFT22 (version 7.520) and FastTree2 (version 2.1.11) (via q2-phylogeny) [25] were used to align amplicon sequence variants (ASVs) and produce the phylogenetic tree.
Taxonomy was assigned to the ASVs in the feature table using a Naïve Bayes Classifier (via q2-classifier sklearn version 1.4.2) provided by QIIME2 that was trained with SILVA 138 99% operational taxonomic unit reference sequences [26,27]. QIIME2 data was imported to R studio (version 4.4.0) [17] via package qiime2r (version 0.99.6) [23]. Taxonomy relative abundance was visualized with the microViz package (version 0.12.5) [28] at the class level, alpha diversity metrics with the phyloseq package (version 1.50.0) [29], beta diversity (Bray–Curtis dissimilarity with ANOSIM results) with the vegan package (version 2.6-6.1) [30], and visualized calculated MDS1 and MDS2 with ggplot2 as part of the tidyverse package (version 2.0.0) [21].

2.5. Microbial Strains and Communities

2.5.1. Synthetic Microbial Communities

Synthetic microbial communities used to biodegrade FOG deposits were supplied by Biodyne USA, Fort Wayne, IN, USA. These communities were specifically designed to degrade various types of organic matter. A variety of Biodyne communities were tested, labelled BD-A, BD-B, BD-C, BD-D, and BC-1 through BC-11. BD-A was designed to target hydrocarbons, BD-B agricultural residues, BD-C livestock waste, and BD-D crop residue. BC-1 through BC-11 were designed to degrade unspecified organic material. All synthetic communities used are documented in Table 2.

2.5.2. Microbial Strains

Four microbial strains from the Majumder lab strain collection were selected for the biodegradation of FOG deposits. These strains were selected based on their ability to form biofilms and degrade lipids. To assess biofilm formation, a reported crystal violet staining method [31] was adapted for use in PCR strip tubes. Lipid degradation was evaluated using qualitative lipase activity assays [32] on Rhodamine B and Spirit Blue agar (Carolina Biological Supply Item 821428, Burlington, NC, USA). Lipase activity was indicated by the presence of clearing zones around the colonies. Three of the strains, denoted as MS-1, MS-2, and MS-3, were isolated from the Dane County landfill in Madison, WI, and sequenced via 16S rRNA. Landfill isolates were targeted because, like sewerage systems, landfills are also chemically and biologically complex waste environments. The fourth strain, Pseudomonas putida KT2440 (denoted as MS-4), was not subjected to the same preliminary screening assays. Instead, it was selected based on its well-documented capacity for lipid degradation and biofilm formation. All strains were grown in Difco Luria–Bertani Broth media (Becton Dickinson 244620, Sparks, MD, USA) at room temperature (22 ± 1 °C) until they reached an optical density between 0.4 and 0.6 prior to inoculation.

2.5.3. Sequencing-Based Strain Identification

Colony PCR was performed on unknown strains MS-1, MS-2, and MS-3. All strains were grown on Luria Agar at room temperature (22 ± 1 °C) until single colonies formed.
For MS-1 and MS-2, a solid colony was picked with a toothpick and mixed into 100 µL of nuclease-free water. The solution was then boiled for 10 min at 95 °C. Amounts of 25 µL of DreamTaq Green PCR Master Mix (2×) (Thermo Scientific CAT# FERK1082, Vilnius, Lithuania), 4 µL of 10 µM forward primer 27F (Integrated DNA Technologies; 5′-AGAGTTTGATCMTGGCTC-3′, Coralville, IA, USA), 4 µL of 10 µM reverse primer 1492R (Integrated DNA Technologies; 5′-TACGGYTACCTTGTTAYGACTT-3′, Coralville, IA, USA), 15 µL of nuclease-free water, and 2 µL of template DNA were combined. Solution was then added to the thermocycler with the following conditions: 3 min at 95 °C, [30 s at 95 °C, 30 s at 50 °C, 1.5 min at 72 °C] 30×, and 5 min at 72 °C.
For MS-1 and MS-3, a solid colony was picked up with a toothpick and mixed into 50 µL of nuclease-free water. The solution was then boiled for 10 min at 95 °C. Amounts of 25 µL of DreamTaq Green PCR Master Mix (2×), 4 µL of 10 µM forward primer 784F, 4 µL of 10 µM reverse primer 1061R, 17 µL of nuclease-free H2O, and 10 µL of the template DNA were mixed in a solution. Solution was then added to the thermocycler on the following cycle: 5 min at 95 °C, [30 s at 95 °C, 30 s at 50 °C, 1 min at 72 °C] 30×, and 5 min at 72 °C.
A total of 7 µL of 1 KB Plus DNA ladder (GeneRuler CAT# FERSM1334, Vilnius, Lithuania) was injected into a 10% agar agarose gel along with 45 µL of the amplified 16S rRNA DNA from each strain. The electrophoresis apparatus was set to 140 V and run for 45 min. The gel was imaged, and the 16S rRNA bands were cut from the gel and purified using the QIAquick PCR & Gel Cleanup Kit (Qiagen CAT# 28506) following the standard kit procedure. The purified DNA was sent for Sanger sequencing at Functional Biosciences. Resulting sequences were analyzed using the Nucleotide Basic Local Alignment Search Tool (BLAST) (BLASTn version 2.17.0+) tool against the NCBI 16S ribosomal RNA sequences (Bacteria and Archaea) database (updated version date 5 October 2025) to identify microbial strains based on sequence similarity.

2.6. FOG Degradation Tests

2 g of FOG sample was added to a sterile vial with 20 mL of sterile water. Experimental trials had 10 µL of the microbial community or strain added, and control trials had an additional 10 µL of sterile water added. FOG samples were not sterilized prior to inoculation to simulate sewer conditions where endogenous microbial communities are abundant. Vials were stored at room temperature (22 ± 1 °C) and subjected to various conditions, including shaking vs. non-shaking, reapplication vs. no reapplication, and different ratios of inoculum to FOG. Trials were labeled using a structured code indicating FOG source, microbial agent, and experimental conditions (see Supplementary Tables S2 and S3).

2.6.1. Shaking vs. Non-Shaking and Reapplication vs. No Reapplication

Shaking conditions consisted of vials being placed on a shaking rack at ~115 rpm continuously at room temperature (22 ± 1 °C). Reapplication conditions consisted of a small amount of the microbial inoculum being added to the vial every 7 days. To test the effects of shaking and reapplication on FOG degradation, identical vials containing the FOG sample and microbial community were created and placed in varying conditions: one with no shaking and no reapplication, one with shaking and no reapplication, one with no shaking and reapplication, and one with shaking and reapplication. These conditions were selected to account for the movement of sewage in a real sewerage system and to test the impact of a common bioremediation enhancement strategy of adding another dose of microorganisms at a later timepoint.

2.6.2. Varying Ratios

To assess the effects of various ratios of microbial inoculum to FOG and water on degradation, vials were created containing the same FOG, ARV-7, and varying amounts of microbial community BD-D. The ratios tested included 1:500, meaning 1 part microbial community to 500 parts sterile water and FOG deposit, 1:1000, and 1:2000. Each vial contained 2 g of the wastewater FOG and 20 mL of sterile water with varying amounts of the microbial communities.

2.6.3. Dry FOG Degradation Tests

Some vials contained dried FOG. FOG was placed in a drying oven at 38 ± 4 °C until all moisture was evaporated. These vials consisted of 500 mg of the dried FOG deposit, 5 mL of sterile water, and 5 µL of microbial strain or community. For the controls, 5 µL of sterile water was added instead of the microbial strain or community.

2.6.4. Degradation Measurements

Degradation of FOG was monitored via visual observation and measurements such as particle size, cell counts, pH, weight loss, and changes in FA profile. Visual changes to mesocosm properties were recorded at the end of the degradation period. These properties included: changes in coloration, biofilm formation, turbidity, presence of fungal growth, formation of black dots, changes in solution viscosity, production of gas, and the formation of oil droplets. Particle size was identified via the Wentworth Grain Size Scale chart [33] and was measured at two different timepoints and compared. The number of particles per size category was recorded; size categories with more than 20 particles were considered too numerous to count. Particle size measurements were taken only for vials containing the non-dried FOG samples.
Microbial cell counts were measured approximately every two weeks by calculating colony-forming units per milliliter (CFU/mL) from the liquid phase of each vial. To prepare samples, vials were gently inverted to mix. A 20 µL aliquot was transferred into a 96-well plate, and serial dilutions from 10−1 to 10−8 were performed using LB media as the diluent. From each dilution, 10 µL was plated onto Luria agar. Colonies were counted after 48 h of growth at room temperature (22 ± 1 °C). Dilutions yielding between 3 and 30 colonies were used to calculate CFU/mL, and the percent change between the minimum and maximum values was calculated.
The pH of the vials was measured immediately after inoculation, periodically during the degradation period, and at the end of the experiment with pH multi-pad indicator strips (Fisher Scientific, Cat. 13-640-527, Hampton, NH, USA). Weight loss was measured for the vials containing dried FOG samples. Immediately following the degradation test, the FOG was dried to evaporate all moisture and weighed on an analytical balance; the change in weight was then calculated. Changes in FA profile were measured via FAMEs by GC-FID as previously described in the methods (see Section 2.2).

2.6.5. Degradation Performance and Enhancement Rubric

Each vial was scored via an in-house developed degradation performance and enhancement rubric. Rubric categories included particle size changes, CFU/mL percent changes, weight loss, and FA composition changes. Each category was scored between 1 and 5 with a strict set of predefined criteria. The scoring rubrics used can be found in the supplemental materials (Supplementary Tables S4–S8).
Degradation enhancement scores (DESs) were calculated by comparing the experimental group with added microbes to the control group, which had no added microbes. A positive DES indicates more degradation occurring in the experimental trial than in the control trial. A negative DES indicates more degradation occurring in the control trial than in the experimental trial. A score of zero indicates that the experimental and control trial had no difference in the level of degradation. DES was scored as shown in the following equations:
DES (wet FOG) = [(Experimental particle size score − Control particle size score) + (Experimental CFU/mL percent change score − Control CFU/mL percent change score) + Change in fatty acid composition score]/3
DEF (dried FOG) = [(Experimental weight loss score − Control weight loss score) + Change in fatty acid composition score]/2

3. Results

3.1. FOG Sample Analyses

FOG samples were characterized by GC-FID for FA composition and 16S amplicon sequencing for microbial community analysis to better understand FOG composition throughout the sewer system. The FA composition profiles of FOG samples demonstrated that palmitic acid was the most abundant FA, followed by oleic and stearic acids (Figure 1, Supplementary Table S9). In the GI sample, oleic acid was the primary FA component of the FOG. The ARV samples have less variety in the FAs present and higher percentages of palmitic, stearic, and lignoceric acids than the PS samples. In contrast, the PS samples have more variety in the FAs present and a higher percentage of caprylic, decanoic, lauric, myristic, palmitoleic, oleic, and linoleic acids. The GI sample had variety in the number of FAs present. It contained a smaller percentage of palmitic acid compared to the other samples and higher levels of oleic and linoleic acids (Figure 1, Supplementary Table S9).
The microbial community composition (Figure 2, Supplementary Table S10) was analyzed, and when visualized by Class, it showed that PS samples were dominated by the Gammaproteobacteria, which were more abundant than in other sample types (Figure 2A). The GI samples had a low abundance of Gammaproteobacteria and higher abundances of Bacteroidia, Negativacutes, and Bacilli compared to the other samples. The ARV samples had the highest abundance of Clostridia and Alphaproteobacteria. They also contained more Bacteroidia and Gammaproteobacteria than the PS samples, but less than the GI samples. At the genus level (Figure 2B), Pseudomonas, a Gammaproteobacteria known for its role in lipid degradation and biofilm formation [34], was abundant in PS samples and to a lesser extent in ARV samples. Anaerobic fermenters including Prevotellaceae and Veillonellales-Seleomonadales were present in PS and GI samples. These anaerobic fermentors are known for short-chain FA production under anaerobic conditions [35,36]. Additionally, Chromobacteriaceae were abundant in some ARV samples, including facultatively anaerobic microbes with lipase activity [37].
The Bray–Curtis dissimilarity plot of the FOG samples showed that most of the samples within a sample type are grouped closely together and with only partially overlapping ellipses, suggesting that the samples are statistically distinct, especially as neither ellipse incorporates PS samples (Figure 3). However, there is one GI sample, GI-1, that is further from the other GI samples and is closer to the ARV samples. Looking at the microbial community composition data, GI-1 has the most variety compared to the other GI samples, having a smaller abundance of Bacteroidia and Negativicutes with a larger abundance of Bacilli, Gammaproteobacteria, and Alphaproteobacteria (Figure 3). Analysis of similarity (ANOSIM) results of microbial community composition across the three groups, when conducted with 9999 permutations, resulted in an R value of 0.8955 and a significance value of 0.0001. This suggests dissimilarity between groups that is statistically significant. Alpha diversity metrics are shown in Supplementary Table S1. Chao1 and ACE indices for ASV abundance range from 120–594. PS samples were generally lower than ARV samples for Chao1 and ACE indices, and lower than ARV and GI for Shannon Diversity and Simpson Index, indicating lower alpha diversity within PS samples.

3.2. Microbial Strain Sequencing

Strains previously isolated by the Majumder Lab from the Dane County Landfill [38] were used in degradation tests and identified via Sanger sequencing. All strains were identified as having positive lipase production and biofilm formation (Supplementary Table S12). Strains MS-1 and MS-2 were identified as most likely Serratia grimesii, with the highest alignment for each of these strains; however, the alignment was with slightly different regions and had different nucleotide identity values, suggesting the two isolates are different strains. Both strains also had similar match identities to Serratia liquefaciens, Serratia proteamaculans, and Serratia quinivorans. Strain MS-3 was identified as being Alcaligenes faecalis (see Supplementary Table S12 for 16S rRNA gene sequences, and Supplementary Figures S1 and S2 for sequence alignments with identified bacteria).

3.3. Effect of Shaking and Reapplication on Degradation

Three ARV samples were inoculated with synthetic microbial community BD-C under various conditions to test the optimal parameters for FOG degradation. The conditions tested included no shaking or reapplication, shaking, reapplication, or shaking and reapplication. Trials under shaking conditions had the same or smaller DESs (see Supplementary Figure S3 for representative degradation photos). Particle size scores (see Supplementary Table S13 for particle size scores and Supplementary Figure S4 for representative particle size change photos) were seen to be smaller for two of the three FOG samples under shaking conditions than in non-shaking conditions. FA composition score (see Supplementary Table S14 for FA composition results and scores) was smaller or the same for the shaking conditions. CFU scores (see Supplementary Table S15 for CFU results and scores) improved for one of the three FOG samples under shaking conditions.
Reapplication conditions resulted in higher DESs (see Supplementary Table S16 for all DES scores) compared to the trials without reapplication for two of the three FOG samples tested (Figure 4). Reapplication conditions improved particle size scores and CFU scores for two of the three FOG samples tested (Figure 4). Trials under shaking and reapplication conditions did not improve DESs compared to trials with no shaking or reapplication (Figure 4).

3.4. Inoculum Concentration Impact on Degradation

The DES for the 1:1000 ratio was the lowest at −0.77, followed by the 1:500 ratio at −0.33. The 1:2000 ratio has the highest DES at 0.55 (Figure 5).

3.5. Effect of Microbial Strains and Communities on Degradation

MS-2 had a negative average DES, while MS-1 and MS-3 both had average DESs of 0. Strain MS-4 was the only strain with a positive average DES (Figure 6). For the microbial communities, BD-A, BD-B, BC-C, BC-1, BC-2, BC-4, BC-7, BC-8, BC-10, BC-11, and BD-D reapplied with BD-C had positive average DESs. BC-5 and BC-9 had negative average DESs. Microbial communities B-D, BC-3, BC-6, and a mixture of communities BD-C and BD-D had average DESs of 0 (Figure 6).
Overall, the top average DESs were all attributed to microbial communities. BC-2 has the highest average DES, followed by BD-D reapplied with BD-C. The third-highest average DES overall was associated with BC-4, then BD-B, BC-7, and BC-8, which all had the same average DESs, the fourth-highest. BC-9 has the lowest overall average DES, followed by BC-5, which has the second lowest average DES. Though BC-2 has the highest average degradation score across all conditions and FOG samples, other microbial strains and communities had higher average DESs when considering specific FOG types.
When considering only GI samples, BC-8 has the highest average DES, followed by BC-10, then MS-2. BC-11 had the worst average DES for GI samples. On the ARV samples, BC-2 and BC-7 had the highest average DESs, followed by MS-4, then BD-D. BC-9 had the lowest average DES. For PS samples, BC-2 and BC-6 had the highest average DES scores, and BC-5 had the lowest average DES score (Figure 6).

4. Discussion

4.1. FOG Samples

The FOG FA composition from the samples analyzed in this study demonstrated that the primary FAs composing wastewater FOG were long-chain FAs (Figure 1). This includes palmitic acid (16C), palmitoleic acid (16C), stearic acid (18C), oleic acid (18C), linoleic acid (18C), and alpha-linoleic acid (18C). Palmitic acid was present in the highest abundance for ARV and PS samples and was the second highest in abundance for GI samples. The results are consistent with previous studies identifying long-chain fatty acids, particularly palmitic, oleic, and stearic acids, as major components in wastewater FOG and GI samples [9,39,40]. Palmitic acid is derived from a wide variety of foods, including most fats and oils like palm oil, butter substitutes, cheese, baked goods, chips, chocolate, frozen meals, and ice cream [41]. Palmitic acid is also present in personal care products like skincare, makeup, haircare, and cleaning products [42]. Due to its widespread presence, it is unsurprising that it is a primary component of wastewater FOG. The most abundant FA in GI samples and the second most abundant for PS samples was oleic acid. Oleic acid is primarily found in oils, including olive, avocado, and vegetable oils. It is also present in nuts, seeds, meat, dairy products, and some fruits [43,44]. Stearic acid was found to be the second most abundant FA in ARV samples, the fourth most abundant for PS samples, and the third most abundant for GI samples. Stearic acid is primarily found in meat and dairy products like tallow, lard, butter, poultry, and fish [45,46]. Another FA measured in high abundance was myristic acid (14C). Myristic acid was the third most abundant FA in PS and ARV samples but, contrastingly, was only about 3% of the FA content in GI samples. Myristic acid is present in coconut oil, dairy products like butter and milk, and in meats [47]. The higher quantities of myristic acid in PS and ARV samples could arise from the proximity of the sampling sites to manufacturing with high amounts of dairy waste. Overall, there were clear trends across and within sample types in the wastewater FOG FA composition, including the four most abundant FAs present.
The microbial community composition of the wastewater FOG by class, genus (Figure 2), and via the Bray–Curtis dissimilarity plot (Figure 3) demonstrated that the FOG composition within the same sample type remained similar, while it varied across the three sample types. This observation of similarity within sample type is supported by the FA composition. Since both FA and microbial community composition appear dependent on the type of infrastructure the sample was collected from, rather than the location in the sewerage system, it suggests that the physical properties of that infrastructure allow for the accumulation of certain FAs, and those FAs then select for the resident microbial communities. GI samples do have a different source than PS or ARV, but it still holds that infrastructure and FA composition are exerting selective pressure on the microbial community. Additionally, this observation suggests that bioremediation strategies should be designed for the physical constraints of the target piece of infrastructure, the typical FAs accumulating there, and to promote microbial community interactions that serve to enhance FOG degradation.

4.2. Shaking or Reapplication Conditions

We also investigated the effect of other parameters on degradation to inform FOG bioremediation strategy design. Trials under shaking conditions had the same or smaller DESs compared to the non-shaking conditions, contradicting our hypothesis. We initially hypothesized that shaking conditions would enhance the degradation of wastewater FOG because of increased FOG surface area access. However, our results suggest that shaking conditions may have disrupted the biofilms, preventing colonization [48] or distribution of oxygen, which may be detrimental to anaerobic microbes [49]. The lateral flow in a sewerage system may have a different effect than the orbital shaking tested herein.
Reapplication, however, enhanced the degradation of FOG samples in some conditions. In two trials, reapplication increased the DESs, supporting our hypothesis. In one trial, reapplication resulted in a decreased DES score; this may be due to the addition of microbes with a lower affinity for FOG degradation or ones that caused biotic interactions that were unfavorable for the target phenotype of FOG degradation. The observed benefit of reapplication and detriment of shaking to FOG degradation suggests that longer microbial residence time, likely the form of biofilms, and stimulating positive community interactions should be considered in a FOG bioremediation approach.

4.3. Ratio of Added Microbes/FOG

It was hypothesized that higher concentrations of added microbes to wastewater FOG and water would enhance the efficiency of degradation. However, experimental results did not support this. Instead, the lowest concentration of added microbes: FOG, 1:2000, was found to be the most efficient for the enhancement of wastewater FOG degradation. This outcome may be a result of interactions including competition, resource limitation, or microbial crowding. While in some cases competition can stimulate metabolism, higher inoculum levels may have resulted in competition that was ultimately detrimental to growth, resulting in an overall reduction of activity. Additionally, the addition of higher inoculum concentrations may disrupt native microbial community interactions, decreasing degradation, while lower inoculum concentrations may allow added microbes to integrate with the native microbial community, enhancing degradation. Based on these findings, a 1:2000 ratio was selected for all subsequent experiments.

4.4. FOG Degradation Enhancement

4.4.1. Microbial Strain or Community Membership

Microbial strain or community membership was found to affect wastewater FOG degradation. While MS-1, MS-2, and MS-3 did not enhance wastewater FOG degradation, MS-4 was found to enhance degradation. MS-4, Pseudomonas putida, was the most efficient at enhancing degradation of the strains tested (see Supplementary Table S17 for weight loss scores and Supplementary Table S14 for FA degradation and scores). The finding that P. putida (MS-4) enhanced wastewater FOG degradation is consistent with the literature reporting Psuedomonas species and P. putida in particular as strong lipid degraders. P. putida is frequently used for the bioremediation of various organic compounds, including lipids [50,51,52]. While all of the strains tested were known FA degraders and biofilm formers, the results showed that their ability to degrade wastewater FOG differs. It further suggests that while the landfill isolates were adapted to heterogeneous waste environments and tested positive for lipase activity, these isolates were not primed to degrade FOG materials. This implies that bioprospecting for efficient FOG degraders might be more successful from FOG sources.
BD-A, BD-B, BD-C, BC-1, BC-2, BC-4, BC-7, BC-8, BC-10, BC-11, and BD-D reapplied with BD-C were found to enhance FOG degradation. BC-5 and BC-9 were found to decrease FOG degradation. BD-D, BC-3, BC-6, and a 1:1 mixture of BD-C and BD-D had no effect on FOG degradation. Overall, the majority of the microbial communities tested increased the degradation of wastewater FOG, but a few communities were found to decrease degradation. This is likely due to the presence of strong FOG-degrading microbes in some communities that are not in others, as well as synergistic interactions between microbes.

4.4.2. FOG Samples and FOG Sample Types

Results demonstrated that different microbial strains and communities were more efficient at enhancing degradation for certain FOG sample types. For example, when considering only GI samples, BC-8 enhanced degradation the most. When considering only ARV samples, BC-2 and BC-7 enhanced degradation the most. When considering only PS samples, BC-2 and BC-6 enhanced degradation the most. Overall, this suggests that the FOG sample type influences which microorganisms will promote wastewater FOG biodegradation.
Additionally, all GI samples treated with microbes resulted in decreased degradation on average, while all PS samples treated with microbes resulted in enhanced degradation on average. Three of the six ARV samples resulted in enhanced degradation on average, while one ARV sample had a DES of zero, and the last two samples had decreased degradation on average.
It is unsurprising that the FOG sample type affected wastewater FOG degradation, as FOG sample types varied significantly in both FA composition and microbial community. FOG sample types, like GI, that did not have more efficient degradation with the addition of microbes had higher concentrations of long-chain FAs, which are more difficult to degrade than shorter-chain FAs. It is possible that the addition of microbes to the GI FOG samples disrupted and outcompeted microbes naturally present capable of degrading wastewater FOG, resulting in control samples with more efficient wastewater degradation. Furthermore, the addition of microbes introduces oxygen into the vials due to the method of microbe application, and the GI samples had high levels of microbes that are typically anaerobic, such as Negativicutes [53]. The addition of oxygen may have stressed or killed anaerobic microbes, causing shifts in the natural microbial community that hindered the ability to degrade wastewater FOG.

4.4.3. Microbial Communities vs. Microbial Strains

When comparing the ability of microbial strains and communities to enhance FOG degradation, there is no clear indicator that one is more efficient than the other, contradicting our hypothesis. Of the microbial communities tested, most were shown to enhance degradation, while others had no effect or a negative effect on degradation. This was seen in the microbial strains tested as well. More likely, the enhancement of degradation is driven by the particular pairing of the FOG and the microbe or community.

4.5. Other Phenotypic Changes

In addition to measuring DESs, other phenotypic changes were observed that were not accounted for in the FOG degradation DES calculation (see Supplementary Table S18 for all phenotype changes and Supplementary Figure S5 for representative images). Some changes directly indicated microbial growth or activity, including an increase in solution turbidity, biofilm colonization, and the presence of fungal growth. Other changes included the formation of black dots, an increase in solution viscosity, production of gas, increases and decreases in pH (see Supplementary Table S19 for pH changes), and the formation of oil droplets. Interestingly, the formation of black dots only occurred in vials that also had increases in pH over time. These black dots could be evidence of fungal or bacterial spores. While vials were set up in aerobic conditions, the presence of gas that smelled like sulfur may indicate that some vials became anaerobic over time. Production of gas was observed when sealed vials were opened after a week or two. The gas is suspected to be hydrogen sulfide due to its distinct rotten egg-like odor. Oxygen in the vials would need to be consumed before sulfate-reducing bacteria could generate this gas from anaerobic respiration. Another rare, but interesting observation was the formation of oil droplets, likely that leached from the wastewater FOG deposits. Some trials had an increase in viscosity of the liquid; this increase in viscosity was only seen in trials with biofilm formation. This increase in viscosity may be due to the effect of Extracellular Polymeric Substances (EPS) produced by microorganisms in the biofilms. Exopolysaccharides are known to act as texturizers, which can increase viscosity [54,55]. Other components of EPS may also influence the liquid’s viscosity, including proteins, lipids, polysaccharides, and lipopolysaccharides [56]. While we could not score these observations, it further suggests that multiple metabolic processes and possibly abiotic reactions are occurring in the context of FOG degradation.

4.6. Implications for Application

While this study provides insights into the ability of microbial strains and communities to biodegrade wastewater FOG deposits, several factors must be considered when discussing the applicability of microbial degradation of FOG deposits in sewer systems. These factors include flow dynamics, microbial retention time, available nutrient sources, and environmental stressors. In this study, trials were small-scale and tested in lab-created stable conditions where wastewater FOG was the only provided nutrient source. In sewer systems, conditions differ significantly. Sewers experience continuous flow, which greatly impacts microbial retention time and the ability for biofilms to form. In these trials, microbial retention time is much longer than expected in sewer systems. Additionally, in sewer systems, many sources of nutrients are present, and microbes may metabolize nutrients other than the FAs present in FOG deposits. As the majority of the FOG deposits were long-chain FAs, microbes in sewers will preferentially consume small organic carbon source molecules and short and medium-chain FAs before long-chain FAs when available. This further suggests the need for a community that can form a biofilm on the FOG and contains membership capable of long-chain FA transport and β-oxidation. Another consideration is the environmental stressors present in sewer systems that were not present in laboratory conditions, including pH and temperature fluctuations, exposure to surfactants and antimicrobial agents, and changing oxygen availability. The differences between laboratory conditions and sewer systems highlight the need for further research on the ability of microbial strains and communities to degrade wastewater FOG deposits under real-world sewer conditions. Further research is needed to assess the effectiveness and scalability of microbes as a treatment for wastewater FOG deposits.

5. Conclusions

This study found that wastewater FOG is a highly heterogeneous substrate as its composition varies across sample collection locations in its microbial community and fatty acid profiles, but all samples were primarily composed of long-chain fatty acids. The heterogeneous nature of FOG deposits provides many challenges, as seen in tests using GI samples, which demonstrated resistance to microbial degradation. Additionally, physical conditions were found to affect FOG breakdown. Agitation from shaking conditions was found to decrease degradation, suggesting that shear stress affects the formation of useful microbial communities and biofilms. Microbe reapplication was shown to enhance FOG breakdown, highlighting the benefit of longer microbial residence times. Overall, results confirm the potential of biological FOG control and suggest that efficiency is dictated by the specific microbe and FOG sample type pairing. Future work must overcome the challenges posed by the high shear, variable conditions, and low microbial retention times of real sewer systems and GIs to translate controlled laboratory findings into practical applications.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/applmicrobiol5040116/s1, Figure S1A: Alignment of the 16S rRNA gene sequences from MS-1 with the top BLASTn hit, Serratia grimesii, using the NCBI 16S ribosomal RNA sequences (Bacteria and Archaea) database; Figure S1B: Alignment of the 16S rRNA gene sequences from MS-2 with the top BLASTn hit, Serratia grimesii, using the NCBI 16S ribosomal RNA sequences (Bacteria and Archaea) database; Figure S2: Alignment of the 16S rRNA gene sequences from MS-3 with the top BLASTn hit, Alcaligenes faecalis, using the NCBI 16S ribosomal RNA sequences (Bacteria and Archaea) database; Figure S3: Representative images of positive and negative degradation enhancement scores; Figure S4: Representative images of decreases and increases in particle sizes over time; Figure S5: Representative images of observed phenotypes. Table S1: FOG Sample collection location and characteristics; Table S2: Codes for FOG degradation test; Table S3: Trial code examples used in degradation tests; Table S4: Particle size change scoring rubrib; Table S5: Percent change CFU/mL scoring rubric; Table S6: Weight loss scoring rubric for trials with no biofilm present; Table S7: Weight loss scoring rubric when a biofilm is present; Table S8: Change in fatty acid composition scoring rubric; Table S9: Fatty acid compositions of FOG samples by area percent; Table S10: Fog sample microbial community amplicon sequence variants and taxonomy; Table S11: Alpha diversity metrics for microbial communities of FOG samples identified by 16S rRNA amplicon sequencing; Table S12: 16S rRNA sequences and BLAST results of microbial strains; Table S13: Number or particles per size category based on the Wentworth particle size categories; Table S14: Fatty acid composition of control sample treated with water (initial) and no microbes and after degradation period (final) and degradation percentage; Table S15: CFU/mL counts over time; Table S16: Degradation test score results; Table S17: Weight loss of FOG deposit during degradation period; Table S18: Phenotypes observed at the end of degradation period; Table S19: pH observed over tiem and percent change between first minimum and subsequent maximum pH.

Author Contributions

Conceptualization, T.S., G.F. and E.L.-W.M.; methodology, A.M.W. and C.J.P.; validation, A.M.W.; formal analysis, A.M.W. and C.J.P.; investigation, A.M.W.; resources, E.L.-W.M., C.H., T.S. and G.F.; data curation, A.M.W.; writing—original draft preparation, A.M.W.; writing—review and editing, C.J.P. and E.L.-W.M.; visualization, C.J.P. and A.M.W.; supervision, E.L.-W.M.; project administration, E.L.-W.M.; funding acquisition, E.L.-W.M. and C.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a Community-based water research grant provided by Water@UW-Madison and the University of Wisconsin-Madison’s Office of the Provost (Source Identification of Fats, Oils and Grease Clogs in Madison Sewer System FY25). A.M.W’s salary was supported by the UW-PREP program (Postbaccalaureate Research Education Program, National Institute of General Medical Sciences, National Institute of Health, R25GM144251).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data is found in the Supplementary Materials, with the exception of the raw sequencing files that have been uploaded to NCBI and are available under BioProject PRJNA1303665 at the link: https://www.ncbi.nlm.nih.gov/bioproject/?term=1303665 (accessed on 23 January 2023).

Acknowledgments

The authors gratefully acknowledge the use of facilities and instrumentation in the UW-Madison Environmental Engineering Core Facility and the assistance of Jackie Cooper. The core facility is located within the Department of Civil & Environmental Engineering. The authors recognize contributions from the Ricke Lab Sequencing Center within the UW-Madison Department of Animal and Dairy Sciences for 16S amplicon PCR preparation and sequencing. The authors would also like to thank Majumder Lab graduate student Damayanti Rodriguez-Ramos, undergraduate student Hans Nielsen-Fox, and the University of Wisconsin-Madison Microbiology 551 course students from Spring 2024 and their instructor Melissa Christopherson for providing the landfill isolate strains and the results of lipase tests conducted as part of their coursework.

Conflicts of Interest

Authors Soule and Farley were employed by the company Biodyne USA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Biodyne USA did not provide any funding for this study, and employees were not involved in experimental design, data collection, analysis of results, or interpretation.

Abbreviations

The following abbreviations are used in this manuscript:
FOGFats, oils, and grease
SSOSanitary sewer overflow
GIGrease interceptor
FSEFood service establishment
PSPumping station
ARVAir release valve
GC-FIDGas chromatography flame ionization detector
FAFatty acid
QIIME2Quantitative Insights Into Microbial Ecology 2
ASVAmplicon sequence variants
BC OR BDBiodyne community
MSMicrobial strain
BLASTBasic local alignment search tool
DESDegradation enhancement score
ANOMISMAnalysis of similarity
CFUColony-forming unit

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Applmicrobiol 05 00116 g001
Figure 1. Box plots showing the relative abundance (%) of individual fatty acids in FOG samples collected from sewer systems (n = 13), analyzed using GC-FID. Samples are colored based on sampling location, and the box plot summarizes the spread of all samples. Unidentified peaks were added and summarized in the last box of the figure.
Figure 1. Box plots showing the relative abundance (%) of individual fatty acids in FOG samples collected from sewer systems (n = 13), analyzed using GC-FID. Samples are colored based on sampling location, and the box plot summarizes the spread of all samples. Unidentified peaks were added and summarized in the last box of the figure.
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Applmicrobiol 05 00116 g002
Figure 2. Relative abundance of the top 15 bacterial classes (A) and top 15 bacterial genera (B) identified in FOG samples collected from sewer systems based on 16S rRNA gene sequencing and are grouped based on sampling location. Taxa outside the top 15 are grouped as “Other”. Taxa not identified to the genus level are labelled based on the lowest known taxonomic level. Sampled ARV-7-R2 represents a technical replicate of ARV-7. Full ASV results and classifications are available in Supplemental Table S10.
Figure 2. Relative abundance of the top 15 bacterial classes (A) and top 15 bacterial genera (B) identified in FOG samples collected from sewer systems based on 16S rRNA gene sequencing and are grouped based on sampling location. Taxa outside the top 15 are grouped as “Other”. Taxa not identified to the genus level are labelled based on the lowest known taxonomic level. Sampled ARV-7-R2 represents a technical replicate of ARV-7. Full ASV results and classifications are available in Supplemental Table S10.
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Figure 3. Bray–Curtis dissimilarity of microbial communities in FOG samples grouped by FOG type. Ordination plot showing the dissimilarity in microbial community composition among FOG samples, calculated using Bray–Curtis metric based on 16S rRNA gene sequencing data. ARV7-R2 is a technical replicate used as a sequencing control. Ellipses represent the 0.95 confidence level.
Figure 3. Bray–Curtis dissimilarity of microbial communities in FOG samples grouped by FOG type. Ordination plot showing the dissimilarity in microbial community composition among FOG samples, calculated using Bray–Curtis metric based on 16S rRNA gene sequencing data. ARV7-R2 is a technical replicate used as a sequencing control. Ellipses represent the 0.95 confidence level.
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Figure 4. Degradation enhancement scores across different conditions: no shaking nor reapplication (N), reapplication (R), shaking (S), and both shaking and reapplication (S+R). Individual scores used to calculate the degradation enhancement score, including CFU, FA composition, and particle size, are included. Each condition was tested in singlet for three different ARV samples.
Figure 4. Degradation enhancement scores across different conditions: no shaking nor reapplication (N), reapplication (R), shaking (S), and both shaking and reapplication (S+R). Individual scores used to calculate the degradation enhancement score, including CFU, FA composition, and particle size, are included. Each condition was tested in singlet for three different ARV samples.
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Figure 5. Degradation enhancement scores across different ratios of added microbes to FOG. Individual scores used to calculate the degradation enhancement score, including CFU, FA composition, and particle size, are included. Each condition was tested in triplicate, with the range of each metric identified via error bars for replicates.
Figure 5. Degradation enhancement scores across different ratios of added microbes to FOG. Individual scores used to calculate the degradation enhancement score, including CFU, FA composition, and particle size, are included. Each condition was tested in triplicate, with the range of each metric identified via error bars for replicates.
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Figure 6. Degradation enhancement scores sorted by microbial strain or community. Degradation enhancement scores for different FOG types and conditions (wet or dry) are included.
Figure 6. Degradation enhancement scores sorted by microbial strain or community. Degradation enhancement scores for different FOG types and conditions (wet or dry) are included.
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Table 1. FOG Sample Name and Type.
Table 1. FOG Sample Name and Type.
FOG Sample NameFOG Sample Type
PS-1; PS-2; PS-3Pumping Station FOG Samples
ARV-1; ARV-2; ARV-3; ARV-4; ARV-5; ARV-6; ARV-7; ARV-8; ARV-9Air Release Valve FOG Samples
GI-1; GI-2; GI-3; GI-4; GI-5Grease Interceptor FOG Samples
Table 2. Microbial strain and community descriptions.
Table 2. Microbial strain and community descriptions.
Microbial Strain or
Community Name		Description
BD-ABiodyne provided synthetic microbial community, designed to target hydrocarbons
BD-BBiodyne provided synthetic microbial community, designed for general organic breakdown
BD-CBiodyne provided synthetic microbial community, designed to degrade waste and sludge, containing Bacillus amyloliquefaciens (1 × 107 CFU/mL), Bacullus subtilus (1 × 107 CFU/mL), and Pseudomonas balearica (1 × 107 CFU/mL), and other strains
BD-DBiodyne provided synthetic microbial community, designed to digest biological stubble, containing Bacillus licheniformis and subtilis (each at 1 × 107 CFU/mL), Cellulomonas cellasea (1 × 104 CFU/mL), Pseudomonas balearica (1 × 107 CFU/mL), Rhodopseudomonas palustris (1 × 107 CFU/mL), Yarrowia lipolytica (1 × 104 CFU/mL), and other strains
BC-1; BC-2; BC-3; BC-4;
BC-5; BC-6; BC-7; BC-8;
BC-9; BC-10; BC-11		Biodyne provided trial synthetic microbial communities, each stated to contain genomic potential for fatty acid degradation
MS-1Microbial strain isolated from the Dane County Landfill in Madison, WI; best species match is Serratia grimesii
MS-2Microbial strain isolated from the Dane County Landfill in Madison, WI; best species match is Serratia grimesii
MS-3Microbial strain isolated from the Dane County Landfill in Madison, WI; best species match is Alcaligenes faecalis
MS-4Pseudomonas putida strain KT2440
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Woods, A.M.; Pettinger, C.J.; Harris, C.; Soule, T.; Farley, G.; Majumder, E.L.-W. Capacity of Microbial Strains and Communities to Degrade Sewerage Fats, Oils, and Grease Clog Deposits. Appl. Microbiol. 2025, 5, 116. https://doi.org/10.3390/applmicrobiol5040116

AMA Style

Woods AM, Pettinger CJ, Harris C, Soule T, Farley G, Majumder EL-W. Capacity of Microbial Strains and Communities to Degrade Sewerage Fats, Oils, and Grease Clog Deposits. Applied Microbiology. 2025; 5(4):116. https://doi.org/10.3390/applmicrobiol5040116

Chicago/Turabian Style

Woods, Allondra M., Catherine J. Pettinger, Catherine Harris, Tanya Soule, Garth Farley, and Erica L.-W. Majumder. 2025. "Capacity of Microbial Strains and Communities to Degrade Sewerage Fats, Oils, and Grease Clog Deposits" Applied Microbiology 5, no. 4: 116. https://doi.org/10.3390/applmicrobiol5040116

APA Style

Woods, A. M., Pettinger, C. J., Harris, C., Soule, T., Farley, G., & Majumder, E. L.-W. (2025). Capacity of Microbial Strains and Communities to Degrade Sewerage Fats, Oils, and Grease Clog Deposits. Applied Microbiology, 5(4), 116. https://doi.org/10.3390/applmicrobiol5040116

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