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The Pennsylvania State University
The Graduate School
LIPIDOME COMPARISON BETWEEN PLANKTONIC AND POLYESTER-ADHERED
MICROBES IN MUNICIPAL WASTEWATER OPERATIONS
A Thesis in
Agricultural and Biological Engineering
by
James Scott Pflumm
Ó 2021 James Scott Pflumm
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
August 2021
ii
The thesis of James S. Pflumm was reviewed and approved by the following:
Jeffrey Catchmark Professor of Agricultural and Biological Engineering Thesis Advisor
Heather Preisendanz Associate Professor of Agricultural and Biological Engineering Paul Heinemann Professor Department Head of Agricultural and Biological Engineering
Naomi Altman Professor Emeritus of Statistics
Joshua Kellogg Assistant Professor of Veterinary and Biomedical Sciences
iii
ABSTRACT
Polyethylene terephthalate (polyester) microfibers are one form of microplastic. The rate of
polyester microplastic (MP) pollution entering municipal wastewater currently exceeds the rate of
degradation capable in Municipal Wastewater Treatment Facilities (MWTF). Residential laundry
machine effluent has been estimated to contain 1,900 microfibers after one wash of a single
polyester garment. Despite MWTFs removing up to 99% of microfiber particles, the remaining 1%
of particles is significant. Beyond concerns regarding MP ingestion, MP’s potentially bind heavy
metals and contaminants such as pesticides and pharmaceutical compounds, increasing the amount
of toxic chemicals bioaccumulating in the food chain. Wastewater microbes potentially interact
with MPs. Microbes possess lipid membranes interfacing with and responding to their environment.
Prior work has demonstrated marine microbes can degrade oil pollution. Furthermore, quantifying
the lipid composition of these microbial consortiums (MC) has been shown to provide a
performance index of hydrocarbon degrading activity. This project investigates whether the lipids
of wastewater microbes serve as biochemical indicators of microbial adhesion to a reproducible
polyester-fiber test specimen, hereafter referred to as substrate. Detecting microbial lipid
composition changes in response to this specific polymer substrate potentially benefits future
microbiological and wastewater engineering research efforts to assess microbe-microplastic
interactions under operationally constrained MWTF environmental conditions. I am not aware of
prior published studies that have used lipidomic analysis to quantitatively characterize in-situ
MWTF microbe interactions with plastic specimen substrates. The nature of this research will
characterize the lipid composition of aerobic MWTF microbe-plastic interactions using liquid
chromatography - mass spectrometry (LC-MS). The results of this dataset are not conclusive due
to statistical sampling limitations of this pilot study. The contributions of the analysis documented
herein are the methodological approach and lessons-learned for applying lipidomics to evaluate
wastewater microbial interactions with plastic substrates of interest.
iv
TABLE OF CONTENTS
LIST OF FIGURES ............................................................................................................ vi
LIST OF TABLES .............................................................................................................. viii
ACKNOWLEDGEMENTS................................................................................................. ix
Chapter 1 Introduction ....................................................................................................... 1
Defining the societal context of microplastic pollution ................................................. 1
Chapter 2 Literature review ................................................................................................ 3
What is the societal context of the proposal? ................................................................ 3
Why is microplastic pollution in water a threat to humans and habitat? ........................ 4
Why is microplastic pollution in wastewater a concern? ............................................... 4
Where does microplastics in wastewater come from? ................................................... 5
What wastewater concepts and technologies are relevant to microplastic pollution? ..... 5
What is lipidomics? ..................................................................................................... 12
How do lipids connect to the bacterial anatomy? .......................................................... 13
How has lipidomics been used to study microbial-environment interactions? ............... 23
What is the concept of operation for liquid chromatography mass spectrometry analysis? .............................................................................................................. 24
What are the components of the Orbitrap Tribrid mass spectrometer? .......................... 26
What is the concept of operation for liquid chromatography? ....................................... 27
What is untargeted discovery LC-MS lipidomics? ........................................................ 28
Chapter 3 Goals, Objectives, Hypotheses ........................................................................... 30
Why would a lipidome shift be relevant to MWTF microbe interactions with polyester micro-fibers? ......................................................................................... 30
What biophysical phenomena may be responsible for this lipidome signal shift? .......... 31
v
Goal: ........................................................................................................................... 32
Objective (Specific aim): ............................................................................................. 32
Hypotheses: ................................................................................................................. 32
Chapter 4 Methodology ...................................................................................................... 34
Field sampling ............................................................................................................. 34
Sample preparation ...................................................................................................... 36
Sample analysis ........................................................................................................... 37
Data analysis ............................................................................................................... 39
Chapter 5 Results and Discussion ....................................................................................... 40
Formulating an experimentally-derived index of microbial adhesion to polyester substrates ............................................................................................................. 45
Chapter 6 Conclusion .......................................................................................................... 49
Addressing critiques of this project .............................................................................. 50
Limitations and lessons learned ................................................................................... 51
Bibliography ....................................................................................................................... 54
vi
LIST OF FIGURES
Figure 1: Number of polyester fibers discharged into wastewater from using washing-machines with blankets, fleeces, and shirts (all polyester) (Browne, 2011) .................... 5
Figure 2: Primary plastic waste generation in millions of metric tons and (subset image) chemical structures of polymers including polyethylene terephthalate (PET) (Geyer, 2017) (Krueger, 2015) ................................................................................................. 7
Figure 3: Boxplot summarizing the read abundance of the 50 most abundant microbial species in 20 select Danish wastewater treatment facilities (Nierychlo, 2019) ............... 8
Figure 4: Relative mean abundance of 20 most abundant bacterial families based on 16S sequencing for samples collected on organic and plastic waste in the North Shore Channel, upstream and downstream of Terrence J. O’Brien Water Reclamation Plant, Chicago, IL (McCormick, 2014) ........................................................................ 10
Figure 5: Organisms arranged by genus with respect to plastic substrate degradation. Cyan indicates PET polyester degrading organisms. (Sheth, 2019) ............................... 11
Figure 6: Anatomy and size scale of model microorganism Escherichia coli and Saccharomyces cerevisiae (Milo, 2015) ....................................................................... 14
Figure 7: Order of magnitude quantities for select biomolecules within microbes (Milo, 2015) ........................................................................................................................... 15
Figure 8: Lipid dimensions in cell membrane (Milo, 2015) .................................................. 16
Figure 9: Lipid molecule skeletal model structure (Milo, 2015) ........................................... 17
Figure 10: Gram positive and negative cell wall structures (American Society for Microbiology, 2021) .................................................................................................... 19
Figure 11: Lipid class composition in yeast: CL: cardiolipin; Erg: Ergosterol; IPC: inositolphosphorylceramide; MIPC: mannosyl-inositol phosphorylceramide; M(IP)2C: mannosyl-di-(inositolphosphoryl) ceramide; PA: phosphatidic acid; PC: phosphatidylcholine; PE: phosphatidyl-ethanolamine; PI: phosphatidylinositol; PS: phosphatidylserine; TAG: Triacylglycerols; DAG: diacylglycerol; LPC: Lysophosphatidylcholine (Milo, 2015) ......................................................................... 21
Figure 12: Lipid composition by organelle (Milo, 2015) ...................................................... 22
Figure 13: Lipidomics of yeast grown on different carbon sources (Klose, 2012) ................. 23
Figure 14: Lipid-based Hydrocarbon Degrading Activity Index (HDAI) proposed by Aries et al. (Aries) ....................................................................................................... 24
vii
Figure 15: LC-MS workflow (ThermoFisher, 2021) ............................................................ 25
Figure 16: Orbitrap Tribrid mass spectrometer (ThermoFisher, 2021) .................................. 26
Figure 17: UAJA municipal wastewater treatment facility schematic (Martin, 2011) ........... 36
Figure 18: Composition of unique lipid species across select classes for planktonic and PET bacteria samples. Lines overlaid on data points for visual aid purpose. Lines do not suggest trends across the categorical variable of lipid class. .................................... 41
Figure 19: Diacylglycerol (DG) composition of planktonic control versus PET-adhered microbial sample ......................................................................................................... 43
Figure 20: Phosphotidylcholine (PC) composition of planktonic control versus PET-adhered bacteria sample ............................................................................................... 44
Figure 21: Lipid species composition according to lipid class based on Area Under the Curve [AUC] observations for planktonic vs PET-adhered samples ............................. 45
Figure 22: Lipid-based Hydrocarbon Degrading Activity Index (HDAI) proposed by Aries et al. (Aries) ....................................................................................................... 46
viii
LIST OF TABLES
Table 1: Phylogenetic composition of Archaea, Bacteria and Fungi in the Influent (Blue columns) and Bioreactor (yellow columns) for seven Arctic wastewater facilities in Finland (Gonzalez-Martinez, 2018).............................................................................. 9
Table 2: Lipidomic challenges (ThermoFisher, 2021) .......................................................... 12
Table 3: Lipid classification proposed by Lipid Maps (Fahy, 2009) (ThermoFisher, 2021) ........................................................................................................................... 18
ix
ACKNOWLEDGEMENTS
Thank you to Carol, Jim, Craig, Reece, the Peppy’s, family and friends for their support. To
Dr. Jeffrey Catchmark, Dr. Naomi Altman, Dr. Josh Kellogg and Dr. Heather Preisendanz for
sharing their knowledge and academic support. To Dr. Priyangi Bulathsinhala and Dr. Lin Lin for
their statistics courses. To Art Brant – the Operations Manager at the University Area Joint
Authority for sharing his knowledge and providing facility and data access. To Dr. Andrew
Patterson, Dr. Phil Smith, Dr. Fuhua Hao, Dr. Imhoo Koo for their time and generosity in analyzing
lipid samples , answering questions and providing software training. To Dr. Sara Lincoln and
Odette Mina for the opportunity to contribute to microplastic research and access to ThermoFisher
LipidSearch and Compound Discoverer software. To Dr. Eric Tague for training and support with
ThermoFisher LipidSearch and Compound Discoverer software. To Dr. Andrew Zydney and the
Center for Industrial Biotechnology for financial support. To Caini Chen, Wei-Shu Lin, Hisaaki
Ishihara, Parisa Nazemi for their kindness, knowledge and support as lab members. To Dr. Paul
Heinemann, Wendy Thomas, Stefanie Hugill, Peggy Newell, Amy Maney, Tyler Robinson and the
Department of Agricultural and Biological Engineering for the often behind the scenes work that
allows us to focus on research. To the Jeff Banks, Ed Crow, Dr. Karl Reichard and the Applied
Research Laboratory for their support to return to school. To the Department of Veteran Affairs
and the staff at the Penn State Office of Veterans Programs for their support enabling me to make
use of the GI Bill education benefits. To President Eric Baron and the University community for
working to provide a healthy, safe and welcoming academic community particularly during the
COVID-19 pandemic. To the Center for Open Science for their education and outreach to advance
preregistered experimental designs and transparent scientific practices.
1
Chapter 1
Introduction
Defining the societal context of microplastic pollution
Polyethylene terephthalate (polyester) microfibers are one form of microplastic pollution. The
rate of polyester microplastic pollution entering municipal wastewater treatment facilities (MWTF)
currently exceeds the rate of degradation capable in MWTFs (World Health Organization, 2019)
(Koelmans A. B., 2016) (Koelmans A. M., 2019). As an emerging concern, microplastics (MP) are
known to bind heavy metals, pharmaceutical compounds and pesticides (Talvitie, 2017). These MP
interactions with other contaminants downstream of WWTFs potentially increase the amount of
toxic chemicals bio-accumulating in the food chain. WWTFs can remove up to 99% of MP particles
prior to discharge into surface water bodies by flocculating it into sludge for burial in landfills or
compost products. However the remaining 1% of particles in effluent can be significant. Prior
studies indicated the number of microfibers shed by a single polyester garment after one wash cycle
can be on the order of 1,900 fibers (Browne, 2011).
The research question this proposal aims to answer is the following: Does the microbial lipidome
serve as a biochemical indicator of microbial adhesion to polyester microplastic particles in
municipal wastewater treatment facilities (MWTF)? There are two motivations for this study. First,
Aries et al. demonstrated the lipid composition of a microbial consortium (MC) can serve as a
quantifiable proxy for degradation of oil pollution (Aries, 2001). Second, it takes upwards of
months to years to detect whether microbes metabolize plastic or simply act to break it into smaller
fragments in the natural environment (Jacquin, 2019). This time delay is an obstacle to research
efforts seeking to use microbes to degrade plastics in real-world conditions such as MWTFs. This
project addresses this knowledge gap by proposing microbial lipids as a biochemical indicator of
microbe-microplastic adhesion. To be clear, microbial adhesion does not automatically imply
degradation capability. However knowledge of microbial adhesion to plastic can inform research
optimization decisions regarding microbe selection, their environmental conditions and metabolic
pathways to target for wastewater engineering applications.
The impetus for selecting lipids as the biochemical indicator in this current study stems from
work that used lipid analysis to evaluate the efficacy of microbes to degrade hydrocarbon pollution
such as oil spills (Aries, 2001). Why would lipids be a candidate for a biochemical indicator of
2
microbial adhesion to plastic? Lipids form the cell membrane interface with the microbe’s
environment (Milo, 2015). The fact that lipids make contact with their environment potentially
means these biomolecules change in response to external substrates they interact with. In light of
the aforementioned work which developed a lipid-based hydrocarbon degrading index to address
oil pollution (Aries, 2001), the aim of this present study is to extend this prior work to polyester
microplastic pollution using lipid analysis. Subsequent research would expand this current work
further by conducting genomic, transcriptomic, proteomic, metabolomic analysis. I am not aware
of prior literature documenting microbial multi-omic changes as a function of plastic substrate
interaction in MWTF operations. The current project will document lipid composition differences
between planktonic (also known as a free-catch wastewater sample that is not in contact with a
plastic substrate) and polyester (polyethylene terephthalate - PET), adhered microbes in the aerobic
stage of a single activated sludge municipal wastewater treatment plant. This environmental
significance of the MWTF used in this current study is that it produces wastewater that is partially
beneficially reused and partially discharged to a stream that is designated as a “High Quality Cold
Water Fishery” (Environmental Protection Agency, 2019). The broader long-range objective of this
work is to link the fields of multi-omic and material characterization to address environmental
pollution.
3
Chapter 2
Literature review
This literature review aims to contrast previous work to this current study. I will call attention
to specific results, figures, recommendations presented by authors that have influenced this study.
I will delineate distinctions between this work and prior studies. In some instances, prior studies
dive further into questions beyond those investigated in this study. In other instances, this study
explores questions further than the intended aim of other publications. I will point these out through
the course of this review. The intent for integrating these contrasting statements throughout the
literature review is to explicitly preview key points that influence the extent and boundaries of this
study. In doing so the reader will be more efficiently equipped to anticipate or critique the rationale
for experimental decisions pertaining to goals, objectives, methodology and results described in
Chapters 3,4,5.
What is the societal context of the proposal?
Pervasive microplastic pollution is a growing human, wildlife and habitat health threat.
Much deserved attention is devoted to pervasive plastic pollution in the form of disposable
bottles, bags and straws washing upon ocean shores worldwide (Geyer, 2017). Though the
magnitude of the situation concerning ocean plastics has become well known, the presence of
plastics, and more specifically, microplastics (MPs), in fresh water, potable water, and wastewater
has only more recently been examined. The World Health Organization (WHO) report referenced
50 studies focused on MP in fresh water, drinking water, and wastewater. Nine of these fifty studies
analyzed MPs in drinking water (World Health Organization, 2019). Plastics including
polyethylene (PE), polypropylene (PP), polyester or polyethylene terephthalate (PET), polyvinyl
chloride (PVC) and polystyrene (PS) in the form of fragments, fibers, films, foams, and beads were
the primary types of MPs detected. Particle counts ranged up to 103 particles per liter in fresh water.
For drinking water, mean particle count values spanned orders of magnitude, from 10-3 to 103
particles per liter (World Health Organization, 2019). Polyester microfibers are the form of
microplastic that is the focus of this current study.
4
Why is microplastic pollution in water a threat to humans and habitat?
Known contaminants adsorb to microplastic surfaces.
Investigations to determine whether MPs themselves directly threaten human health are
on-going. MPs are carriers of trace-level pollutants that are known threats to human and ecosystem
health (Talvitie, 2017). MPs can adsorb persistent organic pollutants and heavy metals in aqueous
environments due to their high surface area and hydrophobicity (Koelmans A. B., 2016) (Talvitie,
2017). Polychlorinated biphenyls, polycyclic aromatic hydrocarbons, and organo-chlorine
pesticides were detected on the surface of microplastics in coastal regions, including along the
coasts of the United States, China, UK, Japan, among other countries (Mizukawa, 2013) (Carr,
2016). Metals are also adsorbed onto microplastics, including Fe, Zn, Cu, Pb, Al and TiO2 (Fries,
2013) (Ashton, 2010). MPs also contain additives including flame retardants and plasticizers, dyes
and pigments (Talvitie, 2017). Browne et al. (2011) states that research is needed to determine if
ingested microfibers are taken up by tissues of the gut and release monomers, which may include
adsorbed contaminants. In addition, Browne et al. (2011) states the bioavailability of these
chemicals is likely to be greater for microfiber MPs from polyester and acrylic compared to more
hydrophobic MPs (e.g., polyethylene, polypropylene) (Browne, 2011).
Why is microplastic pollution in wastewater a concern?
Wastewater is reused for agriculture irrigation, thereby placing pollutants in the vicinity of our
food supplies.
The presence of MPs in wastewater is of concern because the water processed by municipal
wastewater treatment facilities (MWTFs) can be used in residential, commercial and agricultural
applications. Reusing treated wastewater has the potential to reduce the amount of freshwater
drawn from surface or groundwater bodies. In 2015 within the US, 147 billion liters of water per
day were withdrawn from ground and surface water sources for use as potable water. Additionally,
more than 40% of the water used for agriculture in the United States is withdrawn from groundwater
(Dieter, 2015). Typically, the amount of water withdrawn is more than what is recharged from
rainfall annually, resulting in a depletion of the groundwater supplies in an unsustainable manner
(Gorelick, 2015). Therefore, it is desirable to replace groundwater withdrawal for irrigation
purposes with treated wastewater. The presence of MPs in the wastewater stream presents concerns.
Irrigation using MP-laden water in an agroecosystems inadvertently introduces MPs to the aquatic
and terrestrial environments.
5
Where does microplastics in wastewater come from?
Synthetic fibers from garments enter wastewater influent through laundry machines.
Panel D of Figure 1 suggests that washing machine effluent can contain +1900 microfibers
(200 fibers/liter) as a result of washing one garment (Browne, 2011) (Hartline, 2016). Despite
MWTFs demonstrating the capability to remove up to 99% of microfibers from effluent, the
remaining 1% of microfibers enter the watershed through discharge. Furthermore, the ~99% of
microfibers flocculated into waste sludge are not permanently removed as a threat to the
environment. These MPs will end up in landfills, incinerated, spread as compost on agricultural
fields or landscape beds.
Figure 1: Number of polyester fibers discharged into wastewater from using washing-
machines with blankets, fleeces, and shirts (all polyester) (Browne, 2011)
What wastewater concepts and technologies are relevant to microplastic pollution?
MWTF have different configurations depending on water quality regulations, service
populations and community funding resources. According to Liu and Lipták, the quality of
wastewater is characterized in terms of physical, chemical and biological properties. Physical
characteristics include color, odor, temperature, turbidity, solid and grease composition. Chemical
characteristics include biochemical oxygen demand (BOC), chemical oxygen demand (COD), total
organic carbon (TOC) and total oxygen demand (TOD). Biological characteristics include plant,
animal material and microbes, including pathogens, viruses and fecal coliforms. Wastewater flow
rate ranges vary differently between small and large communities. Communities with 1,000-
6
100,000 residents can experience flow rate fluctuations from 20-400% with respect to the average
daily flow rate. (Liu, 2000). The Federal Water Pollution Control Act Amendments of 1972 define
US water quality goals (Liu, 2000). Wastewater effluent is typically discharged into surface waters.
With respect to Pennsylvania water quality standards, surface waters are designated with high
quality and exceptional distinctions according to biological and chemical characteristic criteria.
Waterways deemed ‘‘High Quality, Cold Water Fishery, Migratory Fishery Waters’’ have special
protection according to Leeward Construction Co. v. Department of Environmental Protection, 821
A.2d 145 (Pa. Cmwlth. 2003) (Environmental Protection Agency, 2019). The above mentioned
terminology and regulations are relevant to the MWTF sampled in this current study because the
facility’s effluent is discharged approximately 3 kilometers upstream of a High Quality, Cold Water
Fish Hatchery. Furthermore, the extent to which modifications to operational parameters can be
implemented to treat MP pollution in the wastewater is constrained by these regulations.
Wastewater MWTFs are designed to remove chemical and biological waste, not MP. However
Iyare et al’s review notes that advanced treatment technologies may be effective at removing MPs
to varying degrees (Iyare, 2020). Advanced MWTFs typically include three treatment stages
denoted as primary, secondary and tertiary. Primary stage treatment typically includes bulk
screening, grit removal and primary settling. Secondary treatment can consist of anoxic, anaerobic
and aerobic tanks. Secondary clarifiers remove solids from both the top of the water surface as well
as from the bottom of the clarifier tanks. Tertiary filtration removes total suspended solids and
phosphorus. Tertiary treatment can include ultraviolet disinfection and reverse osmosis treatments
(Brant, 2020). The extent of MP particle pollution in wastewater potentially impacts not only
downstream water quality but also UV disinfection effectiveness as well as maintenance and
operation practices of reverse-osmosis equipment. MP can shield pathogens from UV exposure and
microfibers can potentially become lodged in filters. The MWTF sampled in this current study has
primary, secondary and tertiary treatment operations. Further details on this facility will be
described in Chapter 4 and illustrated in Figure 17.
What type of microplastic do we propose to focus on?
Polyester - Polyethylene terephthalate.
Polyethylene terephthalate (PET) accounts for over 50% of global synthetic fiber production.
PET is referred to as polyester for fiber and fabric applications (Intro to PET, n.d.). The inset in
Figure 2 illustrates the ester functional groups in the polyester monomer. For broader context of all
7
global plastic waste generation, Figure 2 shows the waste trajectory for various plastics. In 2015,
an estimated 32 million metric tons of total PET waste was globally generated (Geyer, 2017).
Figure 2: Primary plastic waste generation in millions of metric tons and (subset image)
chemical structures of polymers including polyethylene terephthalate (PET) (Geyer, 2017)
(Krueger, 2015)
What is the prior work characterizing microbes-microplastic interactions in MWTF?
Characterization of microbes on multiple substrates within multiple MWTF stages is unexplored.
Microbiology studies microorganisms or microbes comprised of a single cell, including
viruses (Madigan, 2012). Nierychlo et al. noted there are few published studies characterizing
microbial communities in activated sludge systems (Nierychlo, 2019). Figure 3 summarizes the 50
most abundant microorganisms existing in 20 Danish MWTF.
8
Figure 3: Boxplot summarizing the read abundance of the 50 most abundant microbial
species in 20 select Danish wastewater treatment facilities (Nierychlo, 2019)
Table 1 shows results from Gonzalez et al. metagenomic characterization of archaea, bacteria and
fungi for aerobic treatment stages in 7 Arctic wastewater treatment plants in Finland (Gonzalez-
Martinez, 2018). One longer term goal beyond this current study is to characterize wastewater
archaea, bacteria and fungi adhering to multiple plastic substrates within aerobic and anoxic
treatment tanks of multiple MWTFs.
9
Table 1: Phylogenetic composition of Archaea, Bacteria and Fungi in the Influent (Blue
columns) and Bioreactor (yellow columns) for seven Arctic wastewater facilities in Finland
(Gonzalez-Martinez, 2018)
Figure 4 shows the findings of McCormick et al. documenting bacterial communities on plastic
substrates located up and downstream of a MWRF in Chicago IL (McCormick, 2014). To my
knowledge analysis of archaea, bacteria and fungi ability to flocculate and mineralize microplastic
substrates within the MWRF has not been conducted and remains a knowledge gap.
10
Figure 4: Relative mean abundance of 20 most abundant bacterial families based on 16S
sequencing for samples collected on organic and plastic waste in the North Shore Channel,
upstream and downstream of Terrence J. O’Brien Water Reclamation Plant, Chicago, IL
(McCormick, 2014)
Figure 5 summarizes Sheth et al’s work to map ocean-borne microorganism affinity to plastic
substrates. While this work surveys the marine microbiome, this knowledge is insufficient to inform
MWRF scientists and engineers on how to use individual microbes or microbial consortium (MCs)
to selectively flocculate and mineralize microplastic substrates within the MWRF, so as to prevent
upstream sources of marine pollution.
11
Figure 5: Organisms arranged by genus with respect to plastic substrate degradation. Cyan
indicates PET polyester degrading organisms. (Sheth, 2019)
The studies noted above use genomic techniques to investigate microbial composition of
freshwater, marine and MWTF environments. Genomic analysis is a valuable tool for probing
MWTF microbial ecology. However there are limitations to genomic analysis with respect to
investigating microbe-microplastic interactions. One obstacle of genomic analysis is the still-vast
number of microorganisms that remain uncultured and unidentified (Steen, 2019). This fact
presents challenges to taxonomic classification based on gene sequencing. However even if the
genomes of all microorganisms were catalogued with absolute certainty, identification of microbes
does not directly result in the ability to distinguish microbes that metabolically degrade plastic from
microbes that simply fragment plastic polymers. Nor does genomic analysis have the ability to
differentiate microbes capable of degradation from ones that adhere passively to plastic. Other
techniques are needed to detect microbial function. Despite these limitations, taxonomic
identification informs which microbes physically adhere to substrates.
Techniques with potential to identify plastic degrading microbes include transcriptomic,
proteomic and metabolomic methods. Each of these techniques has merits and limitations. While
it is a longer term goal to systematically integrate of all these techniques with respect to
investigating microbe-microplastic interactions, it is beyond the scope of this thesis to describe
each method in detail. As noted, this study focuses on lipid analysis. The remainder of this chapter
will focus on this field.
12
What is lipidomics?
Before describing lipids at the microscopic or molecular level of detail, the broader macro-
concept of lipidomics will be described. First, what are the definitions for the terms – lipid and
lipidomics? Lipids are biomolecules that are soluble in organic solvents but insoluble in water.
Lipidomics is the classification and quantification of lipids, typically at the cellular or subcellular
level (ThermoFisher, 2021; Reiko Kiyonami, 2016). Lipidomics is one dimension of the broader
omic research field focusing on bioinformatic analysis of a range of biochemicals: proteins
(proteomics), genes (genomics), mRNA (transcriptomics), and metabolites (metabolomics).
What differentiates metabolites from lipids? Metabolites are arbitrarily classified as small
molecules less than approximately 1kDa whereas lipids are can be orders of magnitude larger.
Metabolomics focus on approximately 1000-2000 species whereas lipidomics covers upwards of
50,000 species. Liquid Chromatography – Mass Spectrometry (LC-MS) is able identify 80-90% of
known metabolites . On the other hand LC-MS coverage of lipids however is on the order of 2-5%.
This current limitation of lipid analysis is compounded by the ~12 fold change in concentrations
observed in some cells spanning millimolar to picomolar levels. Detecting concentrations across
this range biases results toward high concentrations (Murphy, 2018). The lipidomic implication is
that despite being present in small concentrations, the MS may not detect cell membrane lipids that
are biologically relevant indicators of plastic interaction.
Table 2 summarizes further challenges of lipid analysis.
Table 2: Lipidomic challenges (ThermoFisher, 2021)
13
How do lipids connect to the bacterial anatomy?
The purpose of this section is to provide a cellular context for lipids as biomolecules in the
broader physical composition of the cell structure. To begin, this section summarizes key anatomy
with respect to size and quantity within a prototypical bacterium, Escherichia coli (E. coli).
Saccharomyces cerevisiae (S. cerevisiae) is a eukaryote and shown here for a second point of
reference. Model organisms are visually instructive to introduce order of magnitude concepts. Later
sections will describe the molecular structure of lipids in further detail.
Bacteria are prokaryotes. They are approximately 1-3 µm in length, 1 µm3 in volume and
contain no organelles as are found in eukaryotes, such as S. cerevisiae as shown in Figure 6 (Milo,
2015). A key point with respect to this microplastic study is to recognize the location of lipids in
bacteria and eukaryotic microbes. The outer cell membrane as well as the membrane of eukaryotic
organelles are comprised of a diverse set of lipids along with proteins and polysaccharides (Milo,
2015) (Sohlenkamp, 2015). These biochemical components are not static throughout the lifespan
of the microbe. Lipid composition changes as a function of growth phase, environmental and
nutrient conditions (Klose, 2012) (Milo, 2015).
14
Figure 6: Anatomy and size scale of model microorganism Escherichia coli and
Saccharomyces cerevisiae (Milo, 2015)
Lipids occupy the cell membrane (Sohlenkamp, 2015). Approximately 107 lipid molecules
comprise a single E. coli cell as shown in Figure 7 (Milo, 2015). Within the context of lipidomic
analysis of bacteria, lipidomics aims to identify and quantify these biomolecules in the cell’s
membrane. In comparison, other -omic methods such as genomics, transcriptomics, proteomics and
metabolomics focus on identifying and quantifying DNA, mRNA, proteins and small molecules
also shown Figure 7. A point of distinction with respect to lipids: Eukaryotes also contain lipids in
other parts of the cell anatomy beyond the cell membrane, namely the organelles.
15
Figure 7: Order of magnitude quantities for select biomolecules within microbes (Milo, 2015)
Lipid membranes form bilayers as shown in Figure 8 (Milo, 2015). The key take-away here is
that the cell membrane is approximately 4 nm thick and comprised of two layers of lipids whereby
the hydrophobic lipid ‘tails’ orient themselves toward each other. The tails are composed of carbon-
carbon chains with an approximate 0.126 nm distance between carbon atoms. As will be shown
later, these tails can be upwards of 20 carbon atoms in length. The ‘knee’ bend depicted in the tail
is a result of carbon-carbon double bonds, or unsaturated bonds (Milo, 2015) (Fahy, 2009). In
contrast carbon-carbon single bonds, or saturated bonds, can pack more closely together and form
straighter chains. The specific molecular composition of these tail segments combined with the
composition of the red ‘head’ component gives rise to unique lipid class molecules.
16
Figure 8: Lipid dimensions in cell membrane (Milo, 2015)
Lipid classification nomenclature has evolved over time and can vary by discipline. Figure 9
shows eight representative classes of lipids (ThermoFisher, 2021). These skeletal molecular
models show the long carbon chains, often referred to as tails, as well as the head groups
covalently bonded to the top of these tails.
17
Table 3 lists the abbreviations for these lipids (Fahy, 2009) (ThermoFisher, 2021). The
pertinent point for this current study with respect to classes of lipids is to acknowledge that
nomenclature and abbreviations are used in subsequent figures below. This study will focus on six
lipid classes: Ceramides (CER), diacylglycerols (DG), fatty acyls (FA), phosphatidylcholines (PC),
phosphatidyl-ethanolamines (PE) and Triacylglycerols (TG). The reason for focusing on these six
is due to the lipid database library used to identify results obtained from this study’s mass
spectrometry analysis.
Figure 9: Lipid molecule skeletal model structure (Milo, 2015)
18
Table 3: Lipid classification proposed by Lipid Maps (Fahy, 2009) (ThermoFisher, 2021)
The head groups vary by lipid class and contain various acyl structures along with more
complicated cyclic carbon and saccharide components. The relevant take-away from the skeletal
structures is that these multi-atom molecules can be decomposed into fragments. Analytical
instruments such as mass spectrometers use high energy collision strategies to fragment these
molecules and identify lipids based on the composite signature of these fragments. Mass
spectrometry (MS) is the method used in this study to identify the lipid composition of the bacteria
samples adhering to the PET substrate. The concept of operation for mass spectrometry will be
described in a later section.
Figure 10 shows the location of lipids in the membranes of Gram positive and negative bacteria
(American Society for Microbiology, 2021) (Sohlenkamp, 2015). The goal of this current study is
to analyze the composite lipid composition of microbial consortium adhered to the PET substrate.
To be clear, the goal is not to isolate lipids from a particular inner or outer membrane or differentiate
between lipids originating from Gram positive and Gram negative bacteria.
19
Figure 10: Gram positive and negative cell wall structures (American Society for
Microbiology, 2021)
Bacterial lipid composition has its own complexity due to cell membrane structural differences
between Gram positive and negative organisms as shown in Figure 10. Gram negative bacteria have
both an inner and outer membrane composed of lipids, along with lipopolysaccharides (LPS)
embedded in the outer membrane. This distinction is relevant to this thesis study because the central
hypothesis is that lipid composition of a microbial consortium changes in response to adhering to
a PET substrate. The presence of Gram negative and positive bacteria together within a microbial
consortium presents a lurking variable. This study does not isolate outer membrane lipids capable
of making contact with PET substrate from inner membranes lipids within an unknown bacterial
consortium. Therefore our analysis assumes detectable lipid signals will be present to permit
association of lipid species composition to the substrate material. This point is important because
if the lipids in the outer membrane do in fact respond to adhesion to a substrate, but the inner
membrane lipids do not, then the inner membrane lipids essentially raise the noise floor for
detecting changes due to substrate interactions.
20
As noted above, the focus of this study is on microbes including both prokaryotes and
eukaryotes. Therefore it is useful to take a moment to point out lipidomic work performed in the
model eukaryotic organism, S. cerevisiae, commonly called yeast. Figure 11 shows the lipid
composition of a homogenous culture of budding yeast (Milo, 2015). Within a given class of lipids
such as phosphatidylcholine (PC) shown in Figure 11, there are unique types of lipids within this
class according to the lipid tail or chain length. Chain lengths are indicated by the nomenclature
10:0-16:1. In this example a lipid has two tails, one tail is 10 carbons long with 0 double bonds
(10:0). The second tail is 16 carbons long with 1 double bond (16:1). The head group and long
chain nomenclature is pertinent for interpreting the results of the lipid study presented in this thesis.
21
Figure 11: Lipid class composition in yeast: CL: cardiolipin; Erg: Ergosterol; IPC:
inositolphosphorylceramide; MIPC: mannosyl-inositol phosphorylceramide; M(IP)2C:
mannosyl-di-(inositolphosphoryl) ceramide; PA: phosphatidic acid; PC: phosphatidylcholine; PE:
phosphatidyl-ethanolamine; PI: phosphatidylinositol; PS: phosphatidylserine; TAG:
Triacylglycerols; DAG: diacylglycerol; LPC: Lysophosphatidylcholine (Milo, 2015)
Figure 12 shows the lipid composition across organelles in a eukaryotic cell (Milo, 2015).
These two figures illustrate the location of lipids in eukaryotic cells as well as the basic
nomenclature and graphical analysis concepts that are incorporated into this present study. To be
clear, this current work will not isolate lipids to specific eukaryotic organelles. Nonetheless, the
key point to recognize here is that lipids are in fact present in organelles. Therefore the lipid signals
detected in this study will be influenced by the presence of eukaryotic cells in the sample. If the
organelles’ lipids respond to the substrate adhesion, then this response is potentially detectable. If
the organelles’ lipids do not respond to the substrate adhesion, then here again, just as in the case
22
of Gram positive and negative bacteria, non-responsive lipids potentially elevate the noise floor,
and thereby complicate detecting a lipid signal response to the plastic substrate.
Figure 12: Lipid composition by organelle (Milo, 2015)
The key take-away’s with respect to the above figures and literature review are: (1) it is possible
to detect multiple classes of microbial lipids, (2) each lipid class has multiple lipid species and, (3)
lipid species use notation to differentiate head groups and chain lengths based on number of carbons
and number of double bonds. This current study will focus on comparing the composite microbial
lipid signal on different substrates. To reiterate, so as to focus the reader’s attention, this study will
not investigate lipid composition as a function of growth phase. It will not distinguish lipids
between prokaryotes and eukaryotes. It will not isolate lipids within eukaryotic organelles.
23
How has lipidomics been used to study microbial-environment interactions?
Microbes’ lipid membranes respond to environmental cues. Environmental conditions can
be difficult to replicate in laboratory conditions. Sohlenkamp et al. suggest that experimental
methods need to enable the investigation of microbial lipid membranes within in-situ natural
environments (Sohlenkamp, 2015). Sohlenkamp et al’s suggestion for in-situ lipid investigation is
directly responsible for my decision to opt to perform in-situ MWTF sample collection in this study
instead of performing an in-vitro experiment.
Klose et al’s lipidome analysis of yeast cells in three growth mediums indicated carbon
source was a significant determinant of overall lipid composition (Klose, 2012). Figure 13
illustrates Klose et al’s findings for the three respective carbon sources, glucose (YPglc), glycerol
(YPgly) and raffinose (YPraf). The concentration of phosphatidylcholine (PC) and
phosphatidylinositol (PI) respond to the growth medium carbon source. Klose et al. also observed
glycerophospholipids became more unsaturated with longer carbon chains in the presence of
raffinose and glycerol.
Figure 13: Lipidomics of yeast grown on different carbon sources (Klose, 2012)
Doumenq et al. documented the fatty acid composition of anaerobic denitrifying marine
microbes. In order of decreasing significance on fatty acid composition, the following factors were
ranked: carbon source>temperature>growth phase≥oxygen (Doumenq, 1999). Aries et al. noted
that the phospholipid fatty acid composition of a 10 microbe consortium of hydrocarbon-degrading
bacteria responded to incubation in the presence of petroleum based oil relative to an ammonium
acetate carbon source. Based on these findings they proposed a quantitative Hydrocarbon
Degrading Activity index (HDAI), shown in Figure 14. The lipid-based index was suggested as a
means to assess the bioremediation potential of microbes for oil contaminated marine sites (Aries,
2001). One limitation of the HDAI proposed by Aries is that despite being based on experimental
measurements of lipid composition, it lacks quantitative bounds of measurement uncertainty.
24
Measurement uncertainty is important in experimental analysis broadly speaking. With respect to
mass spectrometry based quantification of lipid species, measurement uncertainty is important
because the detection efficiency of lipid species is not constant across lipids (Murphy, 2018).
Furthermore sample preparation procedures, liquid chromatography, mass-spectrometry
instrumentation configuration parameters, and calibration standards all contribute to lipid
measurement uncertainty. A secondary goal of this current study is to reformulate Aries’ HDAI
concept into an analogous plastic adhering index (PAI) based on the lipidome response of
wastewater microbes adhering to polyester substrates. This PAI will address measurement
uncertainty.
Figure 14: Lipid-based Hydrocarbon Degrading Activity Index (HDAI) proposed by Aries et
al. (Aries)
What is the concept of operation for liquid chromatography mass spectrometry analysis?
Mass spectrometry (MS) is a primary analytical method used in current lipidomic analysis
(ThermoFisher, 2021) (Bielawski, 2009) (Muro, 2014). Liquid chromatography (LC) is a molecular
separation technique used prior to injecting samples into the MS. The workflow for these two
techniques relevant to this study are outlined in Figure 15.
25
Figure 15: LC-MS workflow (ThermoFisher, 2021)
Mass spectrometry enables identification of molecular lipid species. There are five steps to
mass spectrometry. Within a MS, a molecule is sequentially vaporized, ionized, accelerated,
deflected and detected (ThermoFisher, 2021). Vaporization creates greater surface area for the
ionization electrons to interact with the molecules. Ionization electrons knock electrons free from
the molecule creating ionic molecular fragments. The ionic fragments are accelerated through a
chamber toward a magnetic field. The magnetic field causes the ionic fragments to deflect. The
degree of deflection is determined by two factors: fragment mass and fragment charge. The larger
the mass of the fragment, the less the fragment deflects from its linear trajectory. The greater the
particle charge, the greater the deflection. The fragments collide into the surface of a detection
transducer that records the position of the collision on the detector. The deflection distance is a
function of the ionic fragment’s mass and charge. Mass and charge parameters are combined into
a single ratio called the mass-to-charge ratio (m/z). Deflection distance can be expressed as a
function of this single normalized m/z ratio. Mass spectrometers typically report the m/z ratio
instead of the deflection distance in order to enable comparison of ion fragments across different
instruments. The more ionic fragments of a given m/z ratio that collide with the detector at a given
location, the greater the intensity reading of the output for this m/z value. When intensity is plotted
on the y-axis against m/z ratio on the x-axis ion fragments can be identified by their unique
intensity-m/z ratio signature plots. In this way, a given complex lipid molecule may be fragmented
into upwards of 2 or more ionic fragments. The resulting intensity-m/z ratio plots are used to
differentiate lipid molecules (ThermoFisher, 2021) (Tague, 2021). A microbial cell is composed of
multiple types of lipid molecules at varying quantities (Milo, 2015). Mass spectrometers can be
used to differentiate the lipid composition of a collection of cells (Muro, 2014) (Klose, 2012). The
purposes of this current work is to use mass spectrometry to determine if the lipid composition of
26
microbial consortiums adhering to a polyester substrate are distinguishable from free floating
planktonic microbes.
What are the components of the Orbitrap Tribrid mass spectrometer?
Figure 16: Orbitrap Tribrid mass spectrometer (ThermoFisher, 2021)
Molecules of interest, in this case lipids, are pulled from the LC into the MS by means of a
voltage potential and vacuum suction through the High-Capacity Transfer Tube (HCTT). The
molecules are fragmented and in the process become ionized. The Electrodynamic Ion Funnel is a
series of 23 stacked electromagnetic lenses that focus the ions’ travel path into the MS. Uncharged
or neutral molecular fragments are removed as the ions pass through the curved beam guide.
Removing uncharged fragments reduces signal background noise. The quadrupole mass filter is
used in targeted MS-MS mode to pre-select for specific ions of a specific m/z ratio. This current
study was analyzing all ions. The quadrupole filter was not used in this study because the samples
were analyzed using this exploratory non-targeted workflow. Non-targeted workflow will be
described below. The ions pass into the Ion Routing Multipole (IRM). As the ions enter the IRM
they pass through the Independent Charge Detector (ICD). The ICD counts ions until a user-defined
quantity is accumulated. The Orbitrap has a defined volume. Detection capability requires
regulating the number of ions entering the Orbitrap chamber to avoid what is known as space-
charging due to ion overcrowding. Once a sufficient number of ions is accumulated in the IRM,
this grouping of ions is thermally cooled and shuttled into the C-trap by means of a pulsed voltage
gradient (ThermoFisher, 2021) (Tague, 2021).
27
The C-trap uses a pulsed voltage gradient to channel ions into the (American-style) football
shaped Orbitrap. The Orbitrap is the sub-component in which ions are measured. The ions are
induced into a radial orbiting trajectory around the center spindle. This disk cloud of ions oscillate
longitudinally along the spindle. As the ions approach the narrow ends of the football shaped
housing, they induce a current in a circuit embedded in the housing. Ions of a given m/z ratio induce
a characteristic current. Multiple ‘doughnut disks clouds’ of ions are present in the Orbitrap
chamber at any given time. This creates a complex but periodic current signal. This complex current
signal can be decomposed using Fourier Transform-based signal processing techniques to resolve
the composite signal into a linear sum of individual frequencies and magnitudes. These frequencies
are unique with respect to m/z ratio and serve as signatures of an ion’s molecular identity. The
magnitude of the frequency corresponds to the quantity of the ion. The frequency component is
used to query into a database look-up table for a molecular fragment’s unique m/z identification.
Quantification of the number ions is accomplished by injecting known calibration standards into
the MS. The standard references are used to calibrate the magnitude values measured in the
unknown sample. On completion of analysis, the ions are purged from the Orbitrap by vacuum. At
any given time while a packet of ions are oscillating in the Orbitrap, the ICD and IRM are counting
and accumulating the next packet of ions for analysis. If the user-defined analysis is in full-scan
mode, then packets of positive and negative polarity ions are alternately introduced into the
Orbitrap. A typical scan time is on the order of 500 ms (ThermoFisher, 2021) (Tague, 2021).
What is the concept of operation for liquid chromatography?
Liquid chromatography (LC) is a method to separate a liquid mixture of heterogeneous
molecules by passing them through a filter column. Depending on the interaction properties of the
molecules with the column’s solvent (mobile phase) and filter (stationary phase), the molecules
will take different amounts of time to elute. This time duration is called retention time. When the
filtration is performed under specified pressure, it is possible to establish a constant flow rate of the
mobile phase and consistently replicate retention times for specific molecules across different
instruments. This is referred to as High Pressure Liquid Chromatography (HPLC) also known as
High Performance Liquid Chromatography. Databases for multiple LC columns exist which
enables this retention time to be used as a signature metric for identifying unknown molecules. LC
columns are designed to separate molecules according to various molecular interaction phenomena.
Size, polarity, charge are three examples (Padala, 2018). The type used for this study is Reverse-
28
Phase Chromatography (RPC) (Smith, 2021). In RPC, the mobile phase is polar and the stationary
phase is non-polar. Hydrophobic molecules stick to the stationary phase. Glycerolipids,
phospholipids, sphingolipids and fatty acyls have long carbon chain tails that are hydrophobic.
These biomolecules of interest can be released from the stationary phase by washing the stationary
phase with a non-polar organic solvent. Higher concentrations of non-polar solvents are required
to elute increasingly hydrophobic molecules. Ramping the concentration of the non-polar solvent
enables selective filtration of lipids. In LC-MS analysis, the output, or eluent, from a liquid
chromatography column is injected into the mass spectrometer. LC-MS enables a heterogeneous
lipid mixture to be separated in time based on release conditions from the LC stationary phase.
Spacing the injecting of lipids into the MS helps address the dynamic range limits of the MS and
increases signal to noise ratios. LC improves the detection of low concentration lipids that may
otherwise be mixed with lipids that are present at many fold higher concentrations (Murphy, 2018).
What is untargeted discovery LC-MS lipidomics?
The two broad approaches to lipid analysis are referred to as targeted and untargeted analysis.
Targeted analysis focuses on detecting specific lipids of interest and their substituent fragments.
Specifying LC retention time and MS m/z ratio regions of interest are the two primary tuning
parameters used to conduct targeted analysis. By comparison, untargeted analysis does not pre-
select LC-MS regions of interest. All fragments are recorded and analyzed. Making use of both LC
retention time and MS m/z ratio to identify all fragments is referred to as an untargeted discovery
LC-MS lipidomic workflow, hereafter discovery workflow or (DW) for short. The advantage of
DW is that it thoroughly catalogues the entire lipid composition of the sample. This is useful for
exploratory analysis when particular lipids of interest are not yet known. One disadvantage of DW
is longer sample processing time and larger data files. Both processing time and file size increase
if MS is performed on positive and negative polarity ion fragments for multiple m/z ratios or
retention time bands (ThermoFisher, 2021). This present study used the DW LC-MS workflow.
One limitation of single mode MS with respect to lipids is differentiating lipid isomers. A
glycerolipid with two tails each with 10 carbons (10:0 – 10:0) long would have the same m/z ratio
as a glycerolipid with one 5 carbon tail and one 15 carbon tail (5:0 – 15:0). Single mode MS cannot
differentiate these isomers. However LC can separate these isomers based on retention time. When
an LC-MS workflow is used the isomers can be resolved as a function of retention time and m/z
ratio. This DW LC-MS workflow was used in this study to detect both positive and negative
29
polarity ions in single MS mode. While the spectrometer configuration measured positive and
negative polarity ions, the analysis presented below focuses only on positive polarity ions. The
reason for this is due to a known software bug in the ThermoFisher Lipid Search 4.2.27 software.
At the time of analysis, this bug prevented alignment of lipid species across experimental samples.
The solution for this analysis was to manually import LipidSearch raw output data into
ThermoFisher Compound Discoverer Software 3.2 (Tague, 2021). Manually importing data was
labor intensive. Because the data lacked replication, there was no added knowledge value to
importing both positive and negative polarity fragments into Compound Discoverer. The key point
of this study was to demonstrate the analytical framework as a roadmap for future work. This
LipidSearch bug was corrected by ThermoFisher after this current analysis was completed (Tague,
2021).
30
Chapter 3
Goals, Objectives, Hypotheses
Municipal Wastewater Treatment Facilities (MWTF) are designed to remove contaminants
from wastewater. This infrastructure and the microbes tasked to perform these function were not
designed to selectively degrade or flocculate synthetic micro-fibers. Given the evidence indicating
plastic micro-fibers are now a pervasive pollutant in wastewater, it is useful to evaluate the
capability of MWTF microbial communities to remove this synthetic contaminant. This study
focuses on microbial interactions with a polyester substrate within the aerobic tank at the University
Area Joint Authority located in State College, PA. Specifically the study differentially compares
the lipid composition of microbial consortiums (MC) adhered to a polyester (PET) substrate relative
to the lipid composition of free-floating planktonic microbes not in contact with the PET substrate.
Why would a lipidome shift be relevant to MWTF microbe interactions with polyester
micro-fibers?
Lipidome shifts may aid MWTF operations and microbial consortium design by differentiating
structural vs nutrient interactions of microbes on polyester substrates.
The lipid composition of microbial cell membranes is part of the biological interface separating
microbial metabolic activity from polyester substrates in MWTFs. The lipids of an individual
microbe’s cell membrane contribute to the microbe’s composite lipid signal. The lipid composition
of an entire microbial consortium (MC) contributes to the overall lipid signal of the consortium.
The lipid signal potentially contains information about what is biologically occurring at the cell-
substrate interface. Furthermore, the microbial lipid signal shift may serve as a biochemical
signature of adhesion to a specific substrate, namely polyester. A confounding point to
acknowledge here is that microbes can aggregate in biofilms on surfaces. Biofilms potentially
separate microbes from directly contacting the substrate surface.
The underlying theory motivating this current study’s hypotheses is that microbes
metabolically interacting with polyester substrates will pass PET degrading enzymes and other
molecules across the cell membrane. The cell membrane’s ability to accommodate this
transmembrane mass transfer will manifest itself as a detectable change in the lipid signal of the
MC. The preliminary hypothesis to be examined in this present study is that the there is a detectable
31
lipid signal distinction between planktonic microbes and microbes adhered to polyester. Though
the lipid signal changes detected in this present study may in part be due to enzyme secretion and
nutrient uptake, this initial study is not designed to test the causal relationship between microbial
lipid composition, enzyme secretion and nutrient uptake.
What biophysical phenomena may be responsible for this lipidome signal shift?
This section will outline a biophysical rationale for why enzyme secretion may be a
contributing factor in the lipid signal shift observed in this study. Before proceeding, the reader is
advised here at the outset to keep in mind this project’s scope in terms of both its objectives and
boundaries. This pilot study was originally focused on estimating the minimum surface area of
polyester substrate required to obtain 25 mg of a microbial pellet for LCMS lipidomic analysis.
The current study was not designed to investigate the association between lipid composition and
enzyme secretion.
In terms of physical dimensions, secreted enzymes may be approximated on the order of 5-10
nm length scales. The cell wall thickness is on the order of 4 nm (Milo, 2015). Endo/Exocytosis
may be accommodated by structural compliance and elasticity in the cell wall. Saturated lipids pack
more densely relative to mono-and polyunsaturated lipids (Madigan, 2012). Klose et al. observed
the concentration of unsaturated glycerophospholipids increased with increased hydrocarbon chain
length when incubated in raffinose- and glycerol media compared to glucose media which is a
small-molecule carbon source (Klose, 2012). Lipidome shifts toward unsaturated lipids may
potentially be correlated with cell wall composition changes associated with a cell’s nutrient
interaction with an external substrate. If a sufficiently detectable number of microbes are using a
polyester substrate as a nutrient source, then the hypothesis would predict a lipidome signal increase
in the unsaturated:saturated lipid ratio. Confirming this lipid ratio increase would require Nuclear
Magnetic Resonance (NMR) analysis (Alexandri, 2017). Setting aside the choice of
instrumentation selection, there is potential scientific value in detecting this lipid ratio increase.
Namely the lipid signal can conceivably be used to infer MC interactions with a polyester substrate.
If lipid signal changes can be causally established, replicated over a range of operating conditions,
and the lipid change is unique to specific substrate types, then the lipid change can potentially yield
a characteristic lipidome signature which can be correlated to MC-substrate interaction. This
present study is confined to analyzing a single pilot study sample set using LC-MS analysis. The
goal of this study is to demonstrate a lipidomic differential analysis workflow comparing microbes
32
grown in the presence of a polyester substrate to microbes not grown in the presence of the polyester
substrate.
Goal:
The goal of this study is to identify differences in the lipid signal between aerobic planktonic
microbes and aerobic microbes adhering to a polyester substrate in the UAJA municipal wastewater
treatment facility.
Objective (Specific aim):
Measure and quantify relative differences in the lipid composition between planktonic
microbial consortiums (MC) and MC adhered to a PET substrate. To recap, the term ‘planktonic’
refers here to a free-catch wastewater sample that is not in contact with the plastic substrate.
Hypotheses:
Hypothesis Lipid composition hypothesis
• Null (Ho) The lipid composition of microbes adhered to a polyester
PET substrate is not different than the lipid
composition of planktonic free catch microbes.
• Alternate (H1) The lipid composition of microbes adhered to a polyester
PET substrate is different than the lipid
composition of planktonic free catch microbes.
Variable type Description
• Independent substrate type
• polyester (PET)
• wastewater planktonic free-catch sample
• Dependent lipid species type – reported MS units: Area Under the Curve
[AUC]
• Confounding growth phase | ambient oil/grease
biofilm thickness | dissolved O2 variability
environmental conditions (temperature, light intensity)
presence of PET microfibers in wastewater
33
MWTF glycerol supplement
manual removal of excess solids from substrate during sample
preparation
Unintended contamination from field and laboratory origins
Controls
• Positive None in the present study
• Negative None in the present study
• Internal None in the present study
Analysis
• Instrumentation: Mass spectrometry using Orbitrap Fusion Lumos Tribrid
• Sample size: PET adhered biological sample: 20 mg wet pellet
Planktonic biological sample: 767 mg wet pellet
• Replicates 1 biological replicate | 0 technical replicates
• Randomization No
• Blinding No
34
Chapter 4
Methodology
The specific question this project answers is the following: Is the microbial lipid composition
a biochemical signal that differentiates microbes adhering to polyester substrate versus floating
free? The proposed method to answer this question is lipidomic analysis. The methodology consists
of five steps: field sampling, sample preparation, chromatography, mass spectrometry, sample
analysis and data analysis. The following sections describe these steps.
Prior to describing these steps the rationale for selecting the wastewater treatment facility will
be described. The University Area Joint Authority (UAJA) wastewater treatment facility is located
at 1576 Spring Valley Rd, State College, PA 16801 (Latitude: 40.838850, Longitude: -77.818700).
The UAJA serves the Penn State University and surrounding communities in central Pennsylvania.
The average flow rate is approximately 15-26 million liters per day. The hydraulic retention time
is approximately 8 hours. The solid retention time is approximately 30 days. (Brant, 2020). The
UAJA discharges treated water into Spring Creek approximately 3 km upstream of Benner Spring
State Fish Hatchery located at 1735 Shiloh Road State College, PA 16801 (Latitude: 40.857820,
Longitude: -77.812540). The average trout production of the hatchery is cited as 152,573 kg
(336,366 lbs). (Pennsylvania Fish and Boat Commission, 2021).
The aerobic tank was selected for sampling instead of the anaerobic or anoxic tanks in order
to align this study to prior marine and freshwater studies in which microbe and microplastic sample
collection was conducted at or near the water surface levels in oceans, lakes and rivers.
(McCormick, 2014) (Jacquin, 2019) (World Health Organization, 2019).
Field sampling
The original aim of the pilot study was to estimate the polyester substrate surface area
required to obtain 20-25 mg pellet mass per sample of microbial material to perform lipidomic
analysis. Lipidomic analysis requires 25 mg/sample, with 6 replicates recommended for publication
quality analysis (Smith, 2021). During the time period 2 October – 12 November 2020, 20 m of
high tenacity continuous multi-filament polyester yarn fiber, hereafter referred to as fiber,
(Goodfellow Ltd LS538362 ES / ES 305730/1) was submerged ~0.5-1 meter below the water
surface into aerobic tank number # 1 at the UAJA wastewater treatment facility (Martin, 2011).
35
The sampling location is designated with the purple chevron in Figure 17. The polyester fiber was
composed of 192 filaments. The diameter of each individual filament is 0.023 mm. The overall
approximate width is 2 mm wide and 100 µm thick. This particular polyester fiber was selected
because the supplier provided material specifications and purchasing options online that would
permit internal or independent reproducibility studies. The supplier also provided the polyester
fiber sample at no charge. The 40 day time duration and the October-November time period were
chosen to accommodate scheduling commitments during the semester. Future studies would
determine the optimal time duration and surface area required to produce a 25 mg pellet sample.
Sampling with replicates across different months would inform estimates of sample mass
variability with respect to season.
This polyester fiber material was not sterilized prior to use. The 20 m fiber sample was tied
to non-sterilized stainless steel washers every two meters in order to ensure the sample remained
submerged. The washers were fastened to a non-sterilized stainless steel carriage bolt with a
stainless steel hardware nut so as to compactly coil loop the 20 m fiber sample. A cotton fiber rope
was used to maintain the sample at the approximate 1 m depth below the water level. This study
was conducted at this one depth because the pilot study objective was to estimate the polyester
substrate surface area required to produce a 25 mg pellet. Future studies would collect samples at
multiple depths in order to determine if depth affects microbe and lipid composition. Future studies
might also implement a test fixture which permits the polyester sample to move throughout a larger
portion of the aerobic tank to imitate the unconstrained travel of an actual polyester microplastic
particle of pollutant.
This 20 m sample yielded a 20 mg wet pellet after sample preparation described below. On
12 November 2020, the sample was removed from the aerobic tank. The fiber coil was placed in a
polyethylene infectious waste biohazard bag (similar to Grainger Item # 3UAF2 – exact supplier
catalog number not known) and tied shut with a metal tie strap. The bag containing the sample was
covered with ice until returning to the laboratory for sample preparation. Nitrile chemical resistant
gloves were used to handle the sample insertion and removal from the aerobic tank. On or about
17 November 2021, the planktonic sample was collected from the same aforementioned location
and approximate depth in the aerobic tank of the wastewater treatment facility. To recap, the term
‘planktonic’ refers here to a free-catch wastewater sample that is not in contact with a plastic
substrate. No field blank sample with laboratory-sourced water was collected as a means to account
for potential external contaminants introduced via the sampling equipment and protocol.
36
The sample was collected using the UAJA’s designated plastic scoop cup. Model number and
material properties not known. The approximate 700 mL planktonic sample was placed in a cleaned
and rinsed 40 oz plastic container with screw top lid (Plastic polymer type not known). The plastic
container was placed in a polyethylene infectious waste biohazard bag and tied shut with a metal
tie strap. The bag containing the sample was covered with ice until returning to the laboratory for
sample preparation. Nitrile chemical resistant gloves were used to handle the sample insertion and
removal from the aerobic tank.
Figure 17: UAJA municipal wastewater treatment facility schematic (Martin, 2011)
Sample preparation
Upon retrieval from the UAJA, the samples were brought back to the laboratory. A cotton
laboratory coat and nitrile gloves were worn during all laboratory handling of the sample. A fume
hood was cleaned with 10% bleach solution prior to placing the sample in the hood for preparation.
Excess solid waste was manually removed from the fiber using a non-sterilized razor to repeatedly
scrape the polyester fiber surface at room temperature. The 20 m polyester fiber was cut into ~5
cm segments. These segments were not treated as biological replicates because they originated from
the same 20 m segment of submerged polyester fiber. The segments are not treated as technical
replicates because the entire 20 m length of fiber was required to obtain a single 20 mg wet pellet
for LC-MS analysis. The 5 cm cut-length was selected in order to fit multiple segments into a single
Eppendorf Tube. Adhered biological material bound to fiber segments was removed by placing the
37
5 cm polyester segments in Eppendorf tubes (ET) containing 45 mL Dulbecco’s Phosphate-
Buffered Saline (PBS) (Mediatech Cat No. 21-031-CV) and shaken for 120 min [RPM not recorded]
at room temperature. Sediment collected from the ET was then passed through a 149 µm Spectra
mesh polypropylene filter (Spectra No. 145773) using vacuum filtration and stainless steel utensils.
The vacuum filtration was performed on an unenclosed laboratory bench. Vacuum filtration was
not performed in the fume hood due to the hood’s vacuum ports not being connected to the
building’s vacuum system. Filtrate was centrifuged for 10 min at 24 ºC 8000 rpm (7741 G)
(Beckman Coulter rotor JA-30.50). Approximately 5 ET tubes cracked during centrifuging. A 50
mL serological pipette was used to collect the filtrate and re-centrifuge the sample. The final wet
pellet mass obtained from the 20 m polyester fiber sample was 20 mg. The methodology described
for the polyester substrate sample preparation was also used for preparing the planktonic control
sample. The final pellet mass obtained from the planktonic sample was 767 mg. Three relevant
differences between the preparation of the planktonic and PET adhered pellet samples are: (1) the
planktonic pellet was filtered through a ~40 µm filter instead of a 149 µm filter due to our laboratory
no longer having 149 µm filters in stock. (2) The PET adhered pellet required approximately 6
hours of laboratory preparation time compared to the approximate 2 hours of preparation time for
the planktonic sample. (3) Both pellets were prepared at room temperature. The planktonic pellet
was stored at -20 ºC for 3 days prior to lipid analysis. The PET adhered pellet was stored at 4 ºC
for 7 days. The reason for this was because the -20 ºC freezer was located in another laboratory,
which I did not know at the time that I had permission to use. Interpret the following results of this
pilot study with these factors in mind. Preparing the microbial samples at room temperature for 2-
6 hours potentially impacts the lipid composition. Similarly the difference between the -20 ºC
versus the 4 ºC storage temperature for the PET adhered and planktonic sample may also impact
the lipid composition. Future studies would use one or more microbial model organisms to quantify
whether lipid composition is impacted by filtration protocols conducted across a range of
temperatures and time durations.
Sample analysis
LC-MS analysis was performed using Thermo Scientific Orbitrap Fusion Lumos Tribrid
according to Penn State Metabolomic Facility Specific Protocol # PSU-HILSMCF-UHPLCMS-
005b: UHPLC-MS Protocol for Lipids -Bacteria (Penn State Metabolomics Core Facility Specific
Protocol, 2021). Directly quoting from the core facility protocol: Samples were separated by
38
reverse phase HPLC using a Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA)
with a Waters (Milford, MA) CSH C18 column (150mm x 1mm 1.7 µm particle size) maintained
at 65 ºC and a 15 minute gradient, at a flow rate of 110 µl /min. Solvent A was 40% HPLC grade
water and 60% HPLC grade acetonitrile with 0.1% formic acid and 10 mM ammonium formate,
Solvent B was 90% HPLC grade isopropanol and 10% HPLC grade acetonitrile with 0.1% formic
acid and 10 mM ammonium formate. The initial conditions were 85% A and 15 % B, increasing to
30% B at 2 min, 48% B at 2.5 min., 82% B at 11 min, and 99% B at 11.01 min where it was held
at 99% B until 12.95 min before returning to the initial conditions at 13.00 min. The eluate was
delivered into an Orbitrap Fusion Lumos Tribrid™ mass spectrometer using a H-ESI™ ion source
(Thermo Fisher Scientific). The mass spectrometer was scanned from 200-1000 m/z at a resolution
of 120,000 and operated in polarity switching mode for the first 12 min, and in positive mode only
for the last three minutes. The capillary voltage was set at 4kV in positive ion mode, and 2.5kV in
negative ion mode with an RF lens value of 60%, and an AGC target of 4 x 105 with a maximum
injection time of 50 ms.
ThermoFisher LipidSearch 4.2.27 software was used to create a list of molecular formulas.
ThermoFisher Compound Discoverer 3.2 was used to perform an untargeted lipidomic workflow.
Polarity mode filter scanned both positive and negative spectra. Retention times (RT) alignment
used the adaptive curve model with 2 min maximum shift and 5 ppm mass tolerance. Compound
detection used a minimum peak intensity setting of 1x106 with 30% intensity tolerance and signal
to noise (S/N) threshold of 3. Compound consolidation used a 5 ppm mass tolerance and 0.2 min
RT tolerance. [M+H]+1 ion fragments were selected for analysis. Blank background compounds
were hidden. This means that MS peaks detected in the blank sample were not included in the
alignment of the planktonic and PET adhered lipids. The searched mass list was imported from
LipidSearch 4.2.27. Compound prediction settings specified 5 ppm mass tolerance. Rings-and-
double-bonds -equivalent (RDBE) were set to 0 and 40, respectively. Hydrogen/Carbon ratio
minimum and maximum settings were 0.1 and 4, respectively. Maximum number of candidates
was 10. Pattern matching intensity tolerance was 30%. Intensity threshold was 0.1%. S/N threshold
was 3. Dynamic calibration setting was set to True. Fragmentation data was used for candidate
ranking with 5 ppm mass tolerance and S/N threshold of 3. ChemSpider searched LipidMAPS
database by formulae name with 5 ppm mass tolerance, 100 maximum results stored per compound
and 3 maximum predicted compositions. Minimum spectral fit for a valid candidate was 20.
Maximum allowed difference between spectral fit value of the best and worst candidates was 20.
39
Differential analysis of lipid species was performed by comparing Area Under the Curve (AUC)
magnitudes for the PET adhered sample to the AUC magnitudes for the planktonic sample.
Data analysis
Aligned lipid species AUC values for PET adhered and planktonic samples were downloaded
from ThermoFisher Compound Discoverer 3.2 into a Microsoft Excel (*.xlsx) files. The xlsx files
were imported and analyzed in R version 4.0.3 (2020-10-10) x86_64-apple-darwin17.0 (64-bit).
The following packages and libraries were installed. Dplyr, plyr, plotly, ggplot2, tidyr, splus2R,
ggrepel, treemap, MASS, data.table, regclass. Lipid species were grouped according to lipid class
using R filter-grepl functions on the spreadsheet column text descriptions of lipid identities. The
following lipid classes were selected for study: ceramide (CER), diacylglycerol (DG), fatty acids
(FA), phosphatidylcholines (PC), phosphatidylethanolamine (PE), fatty acids (FA), triglyceride
(TG). Other lipid classes were detected but were not included in the analysis primarily because
they contained very few lipid species. Spreadsheet rows without text descriptions of lipid species
were not used in this analysis. Lipid species with undetectable AUC readings were assigned a scalar
value equal to one, resulting in a log10 reading equal to zero, as shown in the following figures.
40
Chapter 5
Results and Discussion
Total spectra generated was 6,329. Blank 1 and 2 generated 1,568 and 1,563 spectra
respectively. Blanks can generate peaks due to contaminants in laboratory environment, materials
and reagents. PET and planktonic samples generated 1,594, 1,604 spectra, respectively.
LipidMAPS database was used for annotation. In total 3,830 compounds were detected.
Compounds are grouped by molecular weight and retention time across the PET and planktonic
samples, respectively. The minimum and maximum molecular weights detected spanned 264-933
Daltons (Da). Retention times spanned 4.58-15.39 min. The following lipid classes were selected
for study: ceramide (CER), diacylglycerol (DG), fatty acids (FA), phosphatidylcholines (PC),
phosphatidylethanolamine (PE), fatty acids (FA), triglyceride (TG). Other lipid classes were
detected but were not included in the analysis primarily because they contained very few lipid
species.
The first question posed is whether the number of unique microbial lipid species in each lipid
class depends on the sample substrate type, namely planktonic control versus PET substrate. The
answer is no, the data does not suggest a lipid class dependence on substrate type. Figure 18
summarizes the number of unique lipid species detected within each of the six lipid classes. The x-
axis shows the lipid classes. The y-axis shows the counts for unique lipid species within each class.
The red data points show the total number of unique lipid species for each class from the planktonic
control. The cyan data points show the total number of unique lipid species for each class for the
PET-adhered microbial sample. One key take-away of this figure is that the control and PET
samples do in fact contain detectable levels of unique species detected per lipid class. However
these count values cannot be interpreted to make inferences about absolute or relative abundance
of lipid classes or species on a mass or concentration basis between the two samples. The reader is
cautioned to use the lines connecting the data points only as visual aids. The lines are not intended
to convey a trend across the categorical x-axis variable – lipid class. Further caution: This study
lacks sufficient replicates and does not analyze other substrates such as glass and stainless steel.
For these reasons, these results shown here cannot be interpreted to mean that the observed
difference is due exclusively to the microbes adhering to a PET substrate.
41
Figure 18: Composition of unique lipid species across select classes for planktonic and PET
bacteria samples. Lines overlaid on data points for visual aid purpose. Lines do not suggest trends
across the categorical variable of lipid class.
A Chi-square test for independence was performed on the two samples, the planktonic and PET
adhered samples, contained in Figure 18. The purpose was to test the above mentioned hypothesis:
Do the number of lipid species within each of the seven lipid classes shown on the x-axis differ
between the two samples? The Chi-square null hypothesis was that the number of lipid species
within each lipid class was independent of the two samples. The alternate hypothesis is that the
number of lipid species within each lipid class is dependent on the samples. At a 0.05 significance
level, the null hypothesis failed to be rejected. The implication of this conclusion is that microbial
lipid species counts across classes do not depend on the sample. In other words, although there is
a numerical difference in lipid species per class between control and sample, this observed
difference is not greater than would be expected if the planktonic and PET bacterial populations
contained equal number of lipid species in each class. Because no other substrate types were
included in this study, it is not possible to attribute differences observed within or between lipid
classes to the substrate type.
The second question is whether the relative abundance of individual lipid species differs
between the two samples. The answer is yes according to this albeit single-replicate dataset. Prior
to explaining this affirmatory answer to this question, the following interpretation precautions are
recapped up front. Here again, lack of replication, differences in sample preparation and lack of
lipid concentration calibration standards prevents a definitive conclusion. Without calibration
standards, it is not possible to convert the mass spectrometry measurement unit – Area Under the
Curve (AUC), into a concentration value. Lipid detection efficiency is not constant for all lipids.
42
Without the use of calibration standards, a larger AUC for a given lipid does not imply greater
abundance relative to another lipid with a lower AUC. Quantified comparisons of lipids measured
using liquid chromatography-mass spectrometry [LC-MS] require the use of internal standards that
get spiked into samples at the time of injection. Standards allow lipid extraction efficiency and
injection reproducibility to be quantified. These type of standards were not run with this sample
because quantifying the lipid composition was not the intended goal of this pilot study. Without
knowledge of extraction efficiency it is not possible to compare LC-MS Area Under the Curve
[AUC] values between the control (planktonic) and treatment (PET-adhered) samples. Nor is it
valid to quantitatively compare AUC's within the control or treatment because extraction efficiency
varies across individual lipid species. Further, running standards through LC-MS after the samples
are processed does not permit a post-hoc calibration. Extraction efficiency of lipid species is not
only a function of the LC column and MS configuration but also sample preparation prior to loading
(Smith, 2021).
With these caveats in mind, Figure 19 illustrates how the raw data might conceivably suggest
that the relative abundance of certain diaglycerol (DG) lipid species is dependent on whether the
sample was taken from planktonic versus PET-adhered microbes. For orientation - the horizontal
axis indicates the AUC measurement for the planktonic control sample in log10 transformed units.
The vertical axis indicates the AUC measurement for the PET substrate sample also in log10
transformed units. The green 45º line indicates the graphical location of equal AUC readings
between control and PET-adhered sample. Lipid species located on the green line have equal AUC
detection readings between control and sample. Lipid species located above the green line
correspond to lipids with greater AUC readings in the PET-adhered microbe samples relative to the
planktonic control.
43
Figure 19: Diacylglycerol (DG) composition of planktonic control versus PET-adhered
microbial sample
Similarly, Figure 20 illustrates the raw data might likewise suggest that the relative abundance of
certain phosphotidylcholine (PC) lipid species is dependent on whether the sample was taken from
planktonic versus PET-adhered bacteria. For legibility purposes only 30 DG species are annotated
in Figure 19 and 25 PC species in Figure 20.
44
Figure 20: Phosphotidylcholine (PC) composition of planktonic control versus PET-adhered
bacteria sample
Figure 21 illustrates the results for the five other lipid classes with 3 similar categories of lipid
species: (1) Lipid species present in the PET-adhered sample but not the planktonic, (2) Lipid
species present in the planktonic, but not in the PET-adhered sample and, (3) Lipids present in
roughly equal AUC units in both control and PET samples. Reiterating – AUC is not an absolute
measure of abundance and is subject to measurement uncertainty due to detection efficiency,
among other factors. The relevance of measurement uncertainty as it pertains to this study will be
addressed below.
45
Figure 21: Lipid species composition according to lipid class based on Area Under the Curve
[AUC] observations for planktonic vs PET-adhered samples
The key take away is that if these three general categories can be reliably reproduced under
calibrated concentration standards for multiple plastic and control substrates, then it is conceivable
to develop a lipid-based indicator of microbial consortium adhesion to a given plastic substrate.
Formulating an experimentally-derived index of microbial adhesion to polyester substrates
In the previous chapters, the lipid-based Hydrocarbon Degrading Activity Index (HDAI)
developed by Aries et al. was cited as a motivation for pursuing this current study. The HDAI
proposed by Aries is shown in Figure 22.
46
Figure 22: Lipid-based Hydrocarbon Degrading Activity Index (HDAI) proposed by Aries et
al. (Aries)
Aries et al. put forth the HDAI as a metric to infer oil pollution degradation capability of a
microbial consortium based on its lipid composition (Aries, 2001). Similarly, this current study
aims to advance microbial research efforts to metabolically degrade microplastic in wastewater by
using lipids to infer microbial consortiums’ adhesion to polyester substrates. Towards this aim, I
propose a plastic adhering indicator or index.
What biotechnological purpose might a plastic adhering indicator or index (PAI) serve? A PAI
would conceivably be used as a screening metric for prioritizing microbial consortiums for plastic
degradation and flocculation potential. Microbial degradation of plastic is known to take days to
years depending on the bioreactor or natural environmental conditions (Jacquin, 2019). This wait-
and-see time delay logistically impacts experimental iteration by microbiologists and wastewater
engineers seeking to improve degradation and flocculation characteristics of microbial consortiums
or their environmental conditions. A PAI potentially serves as a biochemical-based metric to rank
microbial consortium capabilities relative to the lipid characteristics of known microbial
consortiums.
The following presents a simplified use-case scenario: A microbial consortium is
experimentally observed to degrade or flocculate a polyester substrate. Imagine for a moment, lipid
47
analysis is the only available analytical tool to characterize the biochemical characteristics of the
microbial consortium.
A lipid analysis is performed on the consortiums adhered to the polyester and control substrates.
In this simplified analysis scenario imagine a single lipid species is selected for the PAI metric.
Selection of this single lipid species would be based on analysis of plots similar to Figure 20. In
this scenario, an ideal lipid species for the PAI would be a species that is abundant in the PET
adhered microbial consortia but absent in the planktonic or control consortia. Now fast forward to
a future point in time in which several members of an independent research team each observe
several new microbial consortia adhering to polyester substrates. Given limited funding, personnel
and time constraints, the future research direction must be prioritized to select one single microbial
consortium for further investigation. What indicators should the team rely upon to choose one
consortium from multiple candidates? In this simplified scenario the abundance of the single lipid
species may be used as the scoring index, or PAI. Consortiums with PAI values most similar to, or
greater than, the prior ascertained ‘benchmark-standard’ reference would be given priority.
Undoubtedly in a real-world context, other metrics in addition to lipid composition might be used
to prioritize a research decision. But the underlying rationale for how to prioritize remains
regardless of how many parameters are at the disposal of the researcher.
Returning to this simple single lipid indicator, a PAI (I) can mathematically be constructed
using the following equation.
𝑰 = $𝒅&𝒄𝒑)𝟐 Equation 1
where:
d = the detected concentration of the single lipid molecule from an unknown microbial consortium
on a PET plastic substrate
c = the detected concentration of the single lipid molecule on a control substrate
p = the detected concentration of the single lipid molecule from a known 'benchmark-standard'
microbial consortium on a PET plastic substrate
This formulation of the PAI was developed for this current study. It is intended for lipids with
non-zero concentration benchmark- levels due to the non-zero denominator requirement. Within
this context, larger PAI values suggest lipid profiles associated with adhesion. PAI values greater
than 1 suggest even greater abundance of the lipid species in the unknown microbial consortium
relative to the known ‘benchmark’. Key here is that the PAI is based on experimental data and is
therefore influenced by measurement error. Incorporating measurement uncertainty (UI) into the
48
PAI is based on National Institute of Standards and Technology metrology guidelines (Taylor,
2001). Measurement uncertainty (UI) of the PAI (I) is expressed as follows:
𝑼𝑰𝟐 = $𝝏𝑰𝝏𝒅)𝟐(𝑼𝒅)𝟐 + $
𝝏𝑰𝝏𝒄)𝟐(𝑼𝒄)𝟐 + $
𝝏𝑰𝝏𝒑)𝟐0𝑼𝒑1
𝟐 Equation 2
𝑼𝑰 = 23$𝝏𝑰𝝏𝒅)𝟐(𝑼𝒅)𝟐 + $
𝝏𝑰𝝏𝒄)𝟐(𝑼𝒄)𝟐 + $
𝝏𝑰𝝏𝒑)𝟐0𝑼𝒑1
𝟐4 Equation 3
𝝏𝑰𝝏𝒅= $(𝟐𝒅 − 𝟐𝒄)𝒑&𝟐) Equation 4
𝝏𝑰𝝏𝒄= $(𝟐𝒄 − 𝟐𝒅)𝒑&𝟐) Equation 5
𝝏𝑰𝝏𝒑= $−𝟐𝒑0𝒅𝟐 − 𝟐𝒅𝒄+ 𝒄𝟐1) Equation 6
The uncertainty terms for Ud, Uc, Up are the statistical variances in the lipid species’
concentration calculated by computing variance across experimental replicates.
The PAI using n-lipid species is:
𝑰 = ∑ 3𝒘𝒊 $𝒅𝒊&𝒄𝒊𝒑𝒊)𝟐4𝒏
𝒊:𝟏 Equation 7
The scientific importance of the PAI uncertainty analysis is that it quantitatively accounts
for compounding measurement uncertainty in the PAI interval for any number of n- lipid species
based on an internationally accepted metrology-based standard. While alternative formulations of
the PAI shown in Equation 1 should be evaluated by the research community, the key contribution
of this index inspired by Aries et al’s Hydrocarbon Degrading Activity Index (HDAI) is that from
a metrology perspective, experimentally derived indices are no different than experimental data.
There is uncertainty inherent in both experimental data and indices derived from experimental data.
Therefore it is informative to the end-user if the index’s scalar value is systematically reported
along with its measurement uncertainty.
49
Chapter 6
Conclusion
This pilot study experiment was intended to support a larger research effort to determine if the
microbial lipid composition could serve as a signal for microbial metabolic degradation of polyester
microplastic in municipal wastewater operations. Due to funding limitations the experimental work
to explore this aim has not yet progressed beyond this pilot project that focused on microbial
adhesion. Whether future studies focus on degradation or adhesion, additional funding would
foremost be used to address the replication shortcoming of this current study. The Huck
Metabolomic Core Facility recommends a minimum of 6 biological replicates for publication. This
study used a single 20 m piece of polyester fiber in order to estimate surface area requirements for
follow-on studies that would incorporate replicates. This study therefore used one biological
replicate and one technical replicate. Additional plastic polymers like polyethylene and nylon
would be tested along with negative control substrates such as Teflon, stainless steel and glass.
Stainless steel and glass substrates would serve as internal controls for the laboratory equipment
that will come in contact with the microbes and substrates. Additionally, commercially available
lipid standards would enable quantitative calibration and aid replication by independent research
groups.
The rationale for this experiment was motivated by Aries et al’s research. Aries et al.
demonstrated a microbial consortium exhibited a lipid composition shift when the consortium was
moved from an ammonium acetate medium into a petroleum-oil-based medium. Aries et al.
presented a hydrocarbon degrading activity index (HDAI) based on measuring oil degradation and
quantifying the consortium changes in lipid species concentration levels (Aries, 2001).
Whereas the Aries study measured degradation of the oil, this present study did not measure
degradation of the PET substrate. The lipids detected in this study are only indicative of the
microbes adhering to the substrate. The present study did not have a degradation metric to evaluate
the microbial consortium’s performance and was therefore unable to contemplate a Plastic
Degrading Index. Future studies would consider experiments which combine lipid, metabolic or
gene expression signals with carbon isotope labelled plastic substrates in order to link substrate
degradation to microbial metabolic uptake.
Were this study to measure degradation and lipid parameters, the following hypothesis
would be tested: Is PET substrate degradation associated with an increase in the unsaturated-to-
50
saturated hydrocarbon bond content? The rationale for this hypothesis is as follows: Lipid
membranes are the boundary between the microbe’s interior and its environment. Degradation of
plastic polymer substrates may likely require microbes to secrete enzymes capable of breaking
polymer bonds into smaller molecular substituents. Enzymes can be estimated to have a 5 nm
diameter (Milo, 2015). Prokaryotic microbes can be estimated to be 1 µm in length and 1 µm3 (Milo,
2015). Cell lipid membranes can be estimated to have a 4 nm thickness (Milo, 2015). Exocytosis
of a 5nm diameter enzyme likely requires the cell membrane to be capable of structural compliance
or elasticity. This elasticity can be achieved by incorporating a greater quantity of unsaturated lipids
in the cell wall. The hypothesis would compare the unsaturated:saturated lipid composition ratio of
wastewater microbes adhering to PET relative to control substrates such as glass or free-floating
planktonic microbes under otherwise identical ambient conditions.
Addressing critiques of this project
One criticism of this project is that the hydraulic and solid retention times of this MWTF are
too short to permit non-genetically modified microbes to metabolically biodegrade polyester; 8 hrs
and 30 days respectively. The response to this criticism is the following: (1) Metabolic
biodegradation of polyester by native MWTF microbes is not the only goal. Improving the
flocculation rate of polyester MP is equally important to investigate in order to reduce MP pollution
entering watersheds. MP buoyancy is impacted by microbial adhesion. This study focuses on
microbial adhesion to polyester and can inform solutions that target MP buoyancy. (2) Genetically
modified microbe organisms (GMO) may ultimately be required to accomplish in-situ degradation
with 8 hrs or 30 days. The justification to undertake GMO solutions and assume the potential risks
associated with introducing genetically modified organisms into MWTF would be strengthened by
documenting multiple independent investigations who have attempted but failed to metabolically
degrade polyester in-situ with native MWTF microbes.
Undertaking investigations of native MWTF to metabolize and flocculate polyester, along with
other plastic polymers is incremental and pragmatic. The lessons learned from these attempts will
inform future multi-omic analysis in noisy environmental systems. Promoting incremental and
pragmatic foundational work prior to introducing GMO technologies into MWTF to address MP
pollution may allay public concerns. (3) The retention time constraint of MWTF is a valid concern.
But the question remains- where is a better location to target MP in the waste stream if the goal is
to prevent MP in wastewater from entering watersheds? Should filtration systems upstream of
MWTFs or within households be the primary focus? If a sufficiently effective filtration system
51
were to be invented, how will homeowners be cost-effectively incentivized to retro-fit their
plumbing? How will homeowners be incentivized to perform regular maintenance on it if needed?
Will regulation be required to accomplish these goals? Would regulation and home inspection-
based solutions be more or less costly or likely to succeed relative to targeting microbial metabolic
degradation and flocculation of MP in MWTF? Perhaps synthetic textiles can be manufactured to
improve metabolic degradation and flocculation of MP. Until such changes in manufacturing
procedures, regulations or home-owners practices are realized, society may be faced with a steady
or continually increasing rate of MP pollution entering MWTF. If MP pollution in MWTF effluent
is a concern to animal, human, habitat health, then a multi-approach solution strategy may be
justified. One of these justified approaches for the foreseeable future may be continuing to
investigate native microbe interactions with MP in MWTFs.
A second criticism of this study is that lipid analysis is not a well suited biochemical indicator
of adhesion because of the many confounding factors mentioned in prior chapters. Metabolic or
gene expression analysis may be a more reliable signal. I agree with this criticism. Future work
should investigate these signals as well.
Limitations and lessons learned
The following limitations and lessons learned would be addressed into future studies: (1) Per
the guidance of the Huck Metabolomics Core Facility Director, six biological replicates for each
substrate would be analyzed (Smith, 2021). (2) Ideally three technical replicates would be analyzed
per substrate for quantifying measurement uncertainty variance of the LC-MS instrument. (3) The
following substrates would be analyzed: polyethylene terephthalate (PET), high density
polyethylene (HDPE), low density polyethylene (LDPE), nylon, stainless steel, glass, Teflon, and
a planktonic free catch ‘substrate’. The planktonic sample is a negative control to account for lipid
composition of unadhered bacteria. Teflon is a negative control. No microbes are expected to
adhere to Teflon. Lipids signals detected from the Teflon sample should be analyzed with caution
across the other substrates. LDPE and HDPE substrates are common microplastic polymers. (4)
These substrates should be obtained from sources with documentation to enable independent
studies to acquire and replicate the same quality and purity of materials. (5) Each of these substrates
must possess sufficient surface area to yield a 25 mg wet pellet for LC-MS analysis (Penn State
Metabolomics Core Facility Specific Protocol, 2021) (Smith, 2021). For comparison this current
study used 20 m of PET fiber which yielded only a 20 mg wet pellet. Additional pilot studies for
52
each of these substrates will be required to determine the needed substrate surface area. (6) The
total time between extracting the substrates from the WWTF, through sample preparation, filtering
and cryo-storage needs to be minimized. The lipid composition potentially changes during the
harvest and sample preparation time. This poses a confounding variable. A side-study would
address this confounding variable by systematically quantifying the change in lipid composition as
a function of sample preparation duration. Further, a side-study would also quantify the change in
lipid composition as a function of the temperature at which the sample preparation was performed.
(7) Processing upwards of six substrate types with six replicates each imposes time, material and
logistical challenges. Ideally all substrates and sample replicates are submitted to LC-MS analysis
in the same batch. The feasibility of achieving this requires design of a sampling fixture that quickly
permits samples to be extracted from the WWTF, filtered and cryo-stored at the field site.
Accomplishing this may require coordination with UAJA staff to make use of on-site laboratory
facilities as well as transporting liquid nitrogen from campus to UAJA. Developing the sampling
fixture and the on-site standard operating procedures will require side-studies unto itself. (8)
Internal lipid standards are required during LC-MS analysis in order to quantify and compare the
lipid concentrations across substrates. (9) The hypothesis would be that the mean concentration of
individual lipid species responds to the treatment of selected substrates types. This hypothesis
would be tested using analysis of variance. The null hypothesis is that all substrates have the same
concentration of a specific lipid species. The alternative hypothesis is that at least one substrate
has a different concentration of a specific lipid species. Replicates would enable confidence
intervals to be assigned to the concentration of each respective lipid species. (10) To avoid
contributing to publication bias which has already been noted in (micro)plastic degradation
research (Krueger, 2015), experimental design plans would be submitted to a Pre-Registered
journal publication to obtain phase 1 peer-review feedback and approval. Publishing this study in
a Pre-Registered journal promotes transparent and reproducible scientific practices, improves
public access to project data by recommending adoption of data management plan best practices,
improves public integrity of the scientific process, reduces publication bias and concerns related to
hypothesizing-after-results-are-known (Noseka, 2018).
Indices such as the Aries HDAI or the aforementioned PAI are calculated based on
experimental measurements. An adhesion or degradation index using one or more lipid species
should consider accounting for the uncertainty contribution of each respective species. Lipids with
53
highly efficient extraction factors combined with a low variance in extraction yield should be
weighted higher than lipids with low efficiency extraction and high variability in extraction yield.
Extension of the PAI to include microbial metabolites along with lipids is also conceivable. In
conclusion, multi-omic analysis of municipal wastewater microbes addresses a current societal
concern using current scientific knowledge and methods to evaluate microbial degradation of
microplastic pollution.
54
Bibliography
Alexandri, E., Ahmed, R., Siddiqui, H., Choudhary, M.I., Tsiafoulis, C. G., Gerothanassis, I. P.
"High Resolution NMR Spectroscopy as a Structural and Analytical Tool for Unsaturated
Lipids in Solution." Molecules 22 (2017): 1-71.
American Society for Microbiology. Clinical Microbiology Reviews . 11 05 2021. American
Society for Microbiology. 11 05 2021. <https://cmr.asm.org/content/27/3/631/F6>.
Aries, E., Doumenq, P., Artaud, J., Acquaviva, M. & Bertrand, J. C. "Effects of petroleum
hydrocarbons on the phospholipid fatty acid composition of a consortium composed of
marine hydrocarbon-degrading bacteria." Organic Geochemistry 32 (2001): 891–903.
Ashton, K., Holmes, L. & Turner, A. Association of metals with plastic production pellets in the
marine environment. "Association of metals with plastic production pellets in the marine
environment." Marine Pollution Bulletin 50 (2010): 2050-2055.
Bielawski, Jacek, Pierce, Jason S., Snider, Justin, Rembiesa, Barbara, Szulc, Zdzislaw M.,
Bielawska, Alicja. "Comprehensive Quantitative Analysis of Bioactive Sphingolipids by
High-Performance Liquid Chromatography–Tandem Mass Spectrometry." Armstrong,
Donald. Lipidomics, Methods in Molecular Biology. Humana Press, 2009. 443-467.
Brant, Art. UAJA operations supervisor James S Pflumm. 03 03 2020.
Browne, M., Crump, P., Niven, S., Teuten, E.,Tonkin, A., Galloway, T., Thompson, R.
"Accumulation of Microplastic on Shorelines Woldwide: Sources and Sinks."
Environmental Science & Technology (2011).
55
Carr, S.A., Liu, J., Tesoro, A. "Transport and fate of microplastic particles in wastewater
treatment plants." Water Research (2016): 1-9.
Dieter, C., Maupin, M., Caldwell, R., Harris, M., Ivahnenko, T., Lovelace, J., Barber, N., Linsey,
K. "Estimated Use of Water in the United States in 2015." Circular 1441. 2015.
Doumenq, P. Acquaviva, M. Asia, L. Durbec, J. P. Le Dréau, Y. Mille, G. Bertrand, J. C.
"Changes in fatty acids of Pseudomonas nautica, a marine denitrifying bacterium, in
response to n-eicosane as carbon source and various culture conditions." FEMS
Microbiology Ecology 28 (1999): 151–161.
Environmental Protection Agency. Water Quality Standards Regulations: Pennsylvania. 30 09
2019. Environmental Protection Agency. 22 05 2021.
<https://www.epa.gov/sites/production/files/2014-12/documents/pawqs-chapter93.pdf>.
Fahy, Eoin, Subramaniam, Shankar, Murphy, Robert C., Nishijima, Masahiro, Raetz, Christian R.
H., Shimizu, Takao, Spener, Friedrich, van Meer, Gerrit, Wakelam, Michael J. O.,
Dennis, Edward A. "Update of the LIPID MAPS comprehensive classification system for
lipids." Journal of Lipid Research (2009): S9-S14.
Fries, E., Dekiff, J., Willmeyer, J., Nuelle, M., Ebert, M., Remy, D. "Identification of polymer
types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning
electron microscopy." Environmental Sciences: Processes and Impacts 15 (2013): 1949-
1956.
Geyer, R., Jambeck, J. R. & Law, K. L. "Production, use, and fate of all plastics ever made."
Science Advances 3 (2017).
56
Gonzalez-Martinez, A., Sihvonen, M., Muñoz-Palazon, B., Rodriguez-Sanchez, A., Mikola, A.,
Vahala, R. "Microbial ecology of full-scale wastewater treatment systems in the Polar
Arctic Circle: Archaea, Bacteria and Fungi." Scientific Reports 8 (2018): 1–11.
Gorelick, Steven. "Global change and the groundwater management challenge." Water Resources
Research (2015).
Hartline, N., Bruce, N., Karba, S., Ruff, E., Sonar, S., Holden, P.,. "Microfiber Masses Recovered
from Conventional Machine Washing of New or Aged Garments." Environmental
Science and Technology 50 (2016): 11532–11538.
Intro to PET. n.d. <http://www.petresin.org/news_introtoPET.asp>.
Iyare, P. U., Ouki, S.K., Bond, T. "Microplastics removal in wastewater treatment plants: a
critical review." Environmental Science Water Research & Technology 6 (2020): 2664-
2675.
Jacquin, J. "Microbial ecotoxicology of marine plastic debris: A review on colonization and
biodegradation by the “plastisphere." Frontiers in Microbiology 10 (2019): 1-16.
Klose, C., Surma, M., Gerl, M., Meyenhofer, F., Shevchenko, A., Simons, K. "Flexibility of a
eukaryotic lipidome - insights from yeast lipidomics." PLoS ONE 7 (2012).
Koelmans, A., Bakir, A., Burton, G. A., Janssen, C. R. "Microplastic as a Vector for Chemicals in
the Aquatic Environment: Critical Review and Model-Supported Reinterpretation of
Empirical Studies." Environmental Science and Technology 50 (2016): 3315-3326.
57
Koelmans, A., Mohamed N., Nur H., Hermsen, E., Kooi, M., Mintenig, S.,. "Microplastics in
freshwaters and drinking water: Critical review and assessment of data quality." Water
Research 155 (2019): 410–422.
Krueger, M. C., Harms, H., Schlosser, D. "Prospects for microbiological solutions to
environmental pollution with plastics." Applied Microbiology and Biotechnology 99
(2015): 8857– 8874.
Liu, D.H.F, Liptak, B. Environmental Engineer' Handbook. Boca Raton, FL: CRC Press, 2000.
Madigan, M.T., Martinko, J.M., Stahl, D.A., Clark, D.P. Brock Biology of microorganisms. San
Francisco: Pearson Education, Inc, 2012.
Martin, W., Strohata, M., Book, B., Bruton, T., Hice, B., Smith, D. Nitrate Analyzers,
Supplemental Carbon Addition, and Treatment Plant Modeling Improves Nitrogen
Removal without Capital Expenditures at UAJA. Cincinnati: Hazen and Sawyer , 2011.
McCormick, A., Hoellein, T. J., Mason, S. A., Schluep, J., Kelly, J. J. "Microplastic is an
abundant and distinct microbial habitat in an urban river." Environmental Science and
Technology 48 (2014): 11863–11871.
Milo, R. Phillips, R. Cell Biology by the Numbers. CRC Press, 2015.
Mizukawa, K., Takada, H.,Ito, M.,Geok, Y.,Hosoda, J.,Yamashita, R.,Saha, M.,Suzuki,
S.,Miguez, C., Frias, J.,Antunes, J.,Sobral, P.,Santos, I., Micaelo, C., Ferreira, A.
"Monitoring of a wide range of organic micropollutants on the Portuguese coast using
plastic resin pellets." Marine Pollution Bulletin 70 (2013): 296–302.
58
Muro, E., Atilla-Gokcumen, G. E., Eggert, Ulrike S. "Lipids in cell biology: how can we
understand them better?" Molecular Biology of the Cell 25 (2014): 1819-1823.
Murphy, Robert. "Mass Spectrometric Approaches to Lipidomic Studies." Avanti Polar Lipids,
03 11 2018. Video.
Nierychlo, M., Andersen, K.S., Xu, Y., Green, N., Albertsen, M., Dueholm, M.S., Nielsen, P. H.
"Species-level microbiome composition of activated sludge - introducing MiDAS 3
ecosystem-specific reference database and taxonomy." BioRxiv (2019).
Noseka, B.A., Ebersoleb, C.R., DeHavena, A.C., Mellora, D.T. "The preregistration revolution."
Proceedings of the National Academy of Sciences 115.11 (2018): 2600-2606.
Padala, Reddy, PhD. "High Performance Liquid Chromatography." High Performance Liquid
Chromatography. University Park: Padala, Reddy, PhD, 22 10 2018. Course notes.
Penn State Metabolomics Core Facility Specific Protocol. UHPLC-MS Protocol for Lipids-
Bacteria, FSP Nr.: PSU-HILSMCF-UHPLCMS-005b. Laboratory protocol. University
Park, PA: Penn State Metabolomics Core Facility Specific Protocol, 2021.
Pennsylvania Fish and Boat Commission. Benner Spring State Fish Hatchery. 20 05 2021. 20 05
2021.
<https://www.fishandboat.com/Fish/Stocking/StateFishHatcheries/Pages/BennerSpringSt
ateFishHatchery.aspx>.
Reiko Kiyonami, David Peake, Yingying Huang. "Increased Identification Coverage and
Throughput for Complex Lipidomes." Application note 607. Thermo Fisher Scientific ,
2016.
59
Sheth, M., Kwartler, S., Schmaltz, E., Hoskinson, S., Martz, E. Dunphy-Daly, M., Schultz, T.,
Read, A., Eward, W., Somarelli, J. "Bioengineering a Future Free of Marine Plastic
Waste." Frontiers in Marine Science 6 (2019): 1–10.
Smith, Philip PhD. LC/MS test settings . University Park, 21 04 2021. Email correspondence.
Sohlenkamp, C., Geiger, O. "Bacterial membrane lipids: Diversity in structures and pathways."
FEMS Microbiology Review 40 (2015): 133–159.
Steen, A.D., Crits-Christoph, A., Carini, P. "High proportions of bacteria and archaea across most
biomes remain uncultured." ISME Journal 13 (2019): 3126–3130.
Tague, Eric, PhD. Lipid Search and Compound Discoverer training J.S. Pflumm. 03 2021.
Talvitie, J., Mikola, A., Koistinen, A. & Setälä, O. "Solutions to microplastic pollution –
Removal of microplastics from wastewater effluent with advanced wastewater treatment
technologies." Water Research 123 (2017): 401–407.
Taylor, B., N., Kuyatt, C. E.,. "Guidelines for Evaluating and Expressing the Uncertainty of NIST
Measurement Results." 2001. National Institute of Standards and Technology. 13 05
2021. <http://physics.nist.gov/TN1297>.
ThermoFisher. Lipidomic solutions. 12 05 2021. 12 05 2021.
<https://www.thermofisher.com/us/en/home/industrial/mass-spectrometry/metabolomics-
lipidomics/lipidomics.html#:~:text=Comprehensive%20lipidomics%20solutions,metaboli
sm%20at%20the%20cellular%20level.>.
World Health Organization. Microplastics in drinking-water. Geneva: World Health
Organization, 2019.
