Complex Lipids in Microbial Mats and Stromatolites of Hamelin
Pool, Shark Bay, Australia
by
MASsACHUSES INSiTUE
OF TECHNOLOGY
Elise McKenna Myers
OCT 16 0
S.B. Earth, Atmospheric, and Planetary Sciences
LIBRARIES
Massachusetts Institute of Technology
Submitted to the Department of Earth, Atmospheric, and Planetary Sciences
in Partial Fulfillment of the Requirements for the Degree of
Master of Science in Earth, Atmospheric, and Planetary Sciences
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2014
Massachusetts Institute of Technology 2014. All rights reserved
Signature redacted
Signature of Authoi<
Department of Earth, At i 5
heric, and Planetary Sciences
Signature redacted
Certified by
August29,2014
....................
Roger Summons
Professor of Geobiology
Signature redacted
A ccepted by ... ............
Thesis Supervisor
.................................................
Robert D. van der Hilst
Schlumberger Professor of Earth Sciences
Head, Department of Earth, Atmospheric, and Planetary Sciences
Abstract
Stromatolites, columnar rock-like structures, are potentially some of the oldest,
microbially mediated fossils visible in the rock record; if biogenesis is able to be confirmed
for these ancient stromatolites, some being greater than 3 billion years old, these ancient
stromatolites could be used to demonstrate the microbial community assemblages
throughout ancient time. Hamelin Pool, Shark Bay, Australia is an ideal field site for this
task, as stromtolites and modem microbial mats coexist and the microbial mats have been
shown to contribute to the formation of the stromatolites. Comprehensive lipid biomarker
profiles were determined in this study for non-lithified smooth, pustular, and colloform
microbial mats, as well as for smooth and colloform stromatolites. Intact polar lipids,
glycerol dialkyl glycerol tetraethers, and bacteriohopanepolyols were analyzed via liquid
chromatography-mass spectrometry (LC-MS) coupled to a Quadropole Time-of-Flight
(QTOF) mass spectrometer, while the previously studied fatty acids (Allen et al., 2010)
were analyzed using gas chromatography-mass spectrometry
(GC-MS)
to prove
consistent signatures. From the lipid profiles, sulfate-reducing bacteria and anoxygenic
phototrophic bacteria and archaea could be inferred.
The presence of the rare 3-
methylhopanoids (3 Me-BHPs) was discovered in a significant portion of the samples,
which could add to the characterization of this molecule, which has only been concretely
linked to oxygenic conditions for formation. In accordance with Allen et al. in 2010, 2methyhopanoids were detected, as well as limited signals from higher (vascular) plants.
While the lipid profiles for all sediment types were similar, there were some differences
that are likely attributable to morphological differences. However, the overall similarities
suggest microbial communities can be similar between non-lithified microbial mats and
stromatolites.
2
Acknowledgements
I would like to thank the members of the Summons Lab who have been immensely
helpful during this research project. My thanks are particularly compound specific: to
Florence Schubotz for her help on IPLs and GDGTs, to Emily Matys and Julio Sepulveda
for their help on BHPs, and to Roger Summons for his help on FAMEs. I am very grateful
to Roger Summons for this opportunity to work in his lab and also to become a part of the
Geology-Geochemistry-Geobiology groups. I would also like to thank both Lesly AdkinsShellie and Carolyn Colonero who helped to get me whatever I needed, from specific
software to my lab keys. I would like to give a special thank you to Florence Schubotz for
her help in editing even while being in a different time zone and also for her patience in
lab when much of what I said was a question beginning with "hey Flo..." Additionally, I
would like to thank Vicki McKenna for all of her help and support in readying this thesis
for the archives and Jane Connor for her assistance with my writing and presentation.
I would also like to thank all of my friends and family who have been supportive
throughout the past year of this research and the past four years at MIT; there are way too
many people to name, but I am so grateful to have you all in my life. Particularly, I'd like
to thank my grandmothers Edna and Miss Marilyn for your unending love and support
throughout my 4 years here, Reisterstown United Methodist Church for its perfectly
timed care packages, and to my best friends Margo, Helen, and Jamal who have always
been by my side. Very special thanks go to Coach Valerie Handy who convinced me to
come to MIT in the first place, to my mother/best friend whose love and support have
been able to keep me here, and to my grandfather who never knew that our conundrums
over breakfast and other puzzles would lead me to a career in science.
3
Contents
Abstract
Acknowledgements
ii
List of Figures
v
List of Tables
vi
1
Introduction
1
1.1 Microbial Mats and Stromatolites
1
1.2 Membrane Lipid Analysis
2
2
1.2.a Intact Polar Lipids (IPLs)
2
1.2.b Glycerol Dialkyl Glycerol Tetraethers (GDGTs)
1.2.c Bacteriohopanepolyols (BHPs)
3
1.2.d Fatty Acid Methyl Esters (FAMEs)
1.3 Combining Characteristic Lipid Profiles
7
Materials and Methods
9
2.1 Sample Description
9
2.2 Total Lipid Extraction and Preparation of Lipid Fractions
2.2.a Extraction and Basic Preparation of Lipid Fractions
10
2.2.b Derivatization
3
4
7
10
12
2.3 IPLs Preparation and HPLC-MS Analysis
2.4 FAMEs and Hydrocarbon GC-MS Analysis
12
2.5 GDGTs Preparation and HPLC-QTOF-MS Analysis
2.6 BHP HPLC-QTOF-APCI Analysis
14
Results and Discussion
17
3.1 Quantifying Results
17
4
13
15
3.1.a Total Lipid Extract (TLE) Portions
3.1.b Quantification of Data
3.2 Intact Polar Lipids
17
19
3.2.a Results
19
3.2.b Data Validation Efforts
22
3.3 Fatty Acid Methyl Esters
22
3.4 Glycerol Dialkyl Glycerols and Glyco-Glycerol Dialkyl Glycerols
3.4.a Initial Run - Relative Abundance Only
25
3.4.b Secondary Run - Relative Abundance
3.4.c Secondary Run - Quantitative Results
3.4.d Interpreting Both Runs of Relative Results
3.5 Bacteriohopanepolyols
4
17
25
27
30
33
34
3.5.a Quantifying Data and Validating the Instrument
3.5.b Major BHP Signals
34
3.5.c Other BHP Signals
39
36
Conclusions
40
4.1 Overall Significance of These Characteristic Lipid Profiles
4.2 Future Work
40
41
Bibliography
43
Appendices
50
Appendix A - Additional Data
Appendix B - Molecular Structures
50
53
5
List of Figures
1.1
Bacteriohopanepolyol Characteristic Fragmentation.............. ..................
6
1.1
FAMEs Characteristic Fragmentation .....
.....................
7
2.1
Diagram of Extraction and Analysis Procedures.....................
...................
12
3.1
Heat Map of Intact Polar Lipids .................................................
.............
3.2
FAM E Chrom atogram s................................................................
.... ,..............
23
3.3
All GDGTs Relative Abundance (1st Round)............................
...................
26
3.4
All GDGTs Relative Abundance (2nd Round)...........................
.............
3.5
GDGT Relative Abundance of Layered Sections (Both Rounds)............29
3.6
All GDGTs Normalized Abundances........................................
...................
31
3.7
Quantifying BHPs with Extracted Ion Chromatograms......... ................
35
3.8
3.9
Normalized BHP Abundance By Weight..................................
Normalized BHP Abundances ...................................................
37
38
6
19-20
27-28
...................
...................
List of Tables
1.1
1.2
Hydrogen and Ammonium Adducts of Core and Glyco- GDGTs .......... 4
Characteristic Masses (m/z) of Various Bacteriohopanepolyols .............. 6
7
Chapter 1
Introduction
1.1 Microbial Mats and Stromatolites of Hamelin Pool
The Shark Bay World Heritage Site is a 1220 km2 bay located on the westernmost point of
Australia, about 800 kilometers north of Perth, Australia. This "U" shaped bay features
sheltered waters of about 9 meters depth that tend toward hypersalinity partially due to
the high evaporation rates and the lack of substantial contact with fresh water run-off,
rainfall, or lower salinity ocean water. Hamelin Pool, the location of the samples studied
in this report, is one of the most saline parts of Shark Bay.
These areas of hypersalinity are home to characteristic, rock-like structures called
stromatolites which, in conjunction with microbial mats, comprise some of the best
modern analogs to ancient stromatolite microbial communities, some of the earliest forms
of life detectable in the rock record. Hypersalinity prevents the survival of many
predators and competitors, which allows for microbes to create these stromatolite
structures, which had been the only macroscopic evidence of life until about 500 million
years ago. Stromatolites more than 3 billion years old have been found both in Western
Australia and South Africa, which offers a glimpse of these ancient life forms; however,
the information preserved in these fossils is limited (Lowe, 1980; Byerly et al., 1986).
These formations are often defined as microbial organo-sedimentary deposits with planar
to sub-planar laminated internal macro-fabrics of benthic origin (Jahnert & Collins, 2012;
Kalkowsky 1908).
Microbial mats are the other highly studied microbial feature of Hamelin Pool, which, like
stromatolites, have highly distinct morphologies (smooth, colloform, pustular, and tufted)
and varying microbial communities. Grown on moist or submerged surfaces, microbial
mats are held together by microbially excreted slimy substances or by tangled filaments,
8
depending on the mat type. As visible multi-layered sheets of microorganisms, the
community variation in microbial mats is sometimes compartmentalized. The bacteria
and archaea of these mats, while being generally related, occupy different regions of the
multiple centimeters thick mat, which are different chemical environments.
Both stromatolites and microbial mats in Hamelin Pool have been intensely studied in the
hopes of better understanding their different microbial communities and overall
formation. Many arguments have been made linking these microbialites, including one
where the coccoid cyanobacteria Enotphysalis major was described as responsible for both
brown, gelatinous, pustular mats and columnar structures, like stromatolites, by causing
vertical excretion of cells (Golubic 2000). By definition, biotic stromatolites are considered
to have been formed through calcium carbonate precipitation of microbial mats, so the
two general groups can be likened to one another when comparing the microbial
compositions and other potential chemical features, such as membrane lipids.
1.2 Membrane Lipid Analysis
Within stromatolites in Hamelin Pool, degradation products of certain lipids are
preserved, and, because of the growing acceptance of the production of the oldest
stromatolites of Western Australia being biogenic (Allwood et al. 2006), these lipids can
be used as biomarkers. These ancient lipids can then be correlated with lipids forming in
modern microbial systems, such as the various membrane lipids examined in this study.
The structure of the membranes varies among different microbes in order to protect the
internal environment of the microorgansims from external environmental factors, such as
pH or temperature. This need to protect the inner cell has driven high diversity in the
lipid structures of biological membranes, as the membrane structure must be adaptable
and flexible, depending on the external circumstances (Dowhan and Bogdanov 2002).
Because of this ability to adapt, membrane lipids are particularly interesting to study to
see how organisms can adapt to different environments over time. Examining direct
analogues to ancient life forms also provides a look at the potential structure of ancient
membrane lipids, as well as a look at current membrane lipids.
1.2.a Intact Polar Lipids (IPLs)
Lipids and their relative distributions in microbial communities can serve as valuable
characteristic fingerprints of microbiological diversity. Lipids, such as phospholipid fatty
acids, have been used previously to elucidate the composition and quantity of viable
9
biomass in modem microbial ecosystems (White et al., 1997), as well as to indicate the
presence of their respective source organisms. Yet, this interpretation is limited because
these lipids, including membrane phospholipid fatty acids, quickly degrade as a result of
post-mortem processes. In particular, intact polar lipids (IPLs), diacylglycerophospho- or
glycolipids that have a polar head group with various structural moieties, like carboxylic
acid, trimethylamine, or saccharides, quickly lose their polar head group within hours or
days of cell death (White et al. 1979; Moodley et al., 2000), thus making these lipids useful
for the detection of living microbes.
1.2.b Glycerol Dialkyl Glycerol Tetraethers (GDGTs)
Degradation products from some of the lipids that lose their polar head groups,
particularly core Glycerol Dialkyl Glycerol Tetraethers (GDGTs), can be used as more
recalcitrant biomarker lipids, being preserved in immature sediments for <140 Ma
(Schouten et al., 2013). Distributions of these overall more persistent lipids can be used as
proxies for dynamic environmental parameters, such as soil pH (Weijers et al., 2007) or
input of soil organic matter to marine environments (Hopmans et al., 2004). The presence
of some of these lipids is also partially indicative of their microbial origin, such as
isoprenoid GDGT-0, which is the most commonly occurring GDGT in cultivated archaea
(Macalady et al., 2004). Generally speaking, the most abundant archaeol lipids are these
membrane-spanning GDGTs with monoglycosyl (1G), diglycosyl (2G), or triglycosyl (3G),
while trace amounts of tetraglycosyl (4G) are insignificant to the overall composition, so
are often disregarded in analysis, as in this study (see Appendix B-1 and B-2 for
structures). However, the original view that GDGTs were mainly synthesized by archaea
was challenged through environmental samples that show the structural diversity and the
diversity of sources are significant (Schouten et al., 2013). Despite the benefit of the Core
GDGTs being preserved more extensively than its intact form (G-GDGT), some
information is lost by not having the polar head groups, which, with their specific
structural elements, can be correlated to specific organisms.
Distinct GDGTs are identified using compound separation by High Performance Liquid
Chromatography (HPLC) coupled to a quadrupole time of flight mass spectrometer
(Agilent Technologies) that scans for compounds in a particular mass range and then
performs MS/MS scans. Previously determined diagnostic fragments, such as those
described in Sturt et al. 2004, are then used in conjunction with the retention times and
accurate masses of the GDGT molecules. GDGTs can form different adducts during
10
ionization, such as hydrogen or ammonium adducts, influencing the exact mass that need
to be extracted for quantification.
Table 1.1: Hydrogen and Ammonium Adducts
Core-GDGT
H+ion
NH4+ ion
GDGT-0
1302
1319
3227
3492
GDGT-1
1300
1317
3070
3336
GIGT1298
1315
2914
3179
GDGT-3
1296
1313
2757
of Core and Glyco- GDGTs
G-GDGT
H+ion
G-GDGT-0
1481.40
G-GDGT-1
G-GDGT-2
G-GDGT-3
3023
1479
3864
1477
3707
1475
NH4+ion
1643
4549
1641
4392
1621
3897
1637
3551
4079
Crenarchaeol
1292
1309
G-Crenarchaeol
1471
1615
2444
2710
3238
3428
The masses for specific core GDGTs and G-GDGTs are displayed in this table. Data analyzing
software was used to isolate each lipid by this molecular mass and then integratethe extracted ion
chromatogram createdfor the compounds at that given mass.
1.2.c Bacteriohopanepolyols (BHPs)
Similarly recalcitrant to core GDGTs are bacteriohopanepolyols (BHPs), a class of complex
lipids that is one of the primary lipids synthesized by cyanobacteria (Jahnke et al. 2004).
BHPs are recalcitrant due to their carbon skeleton's resistance to abiotic thermal or
pressure degradation (Brocks et al. 2005). As discussed in Ricci et al. 2013, the diagenetic
remains of hopanoids and steroids, hopanes and steranes, are valuable biomarkers, since
they can be interpreted as the remains of the membrane polycyclic triterpenoids of
modem organisms (Rohmer et al., 1984; Ourisson et al. 1987).
BHPs have not been definitively linked to a particular function and not all of them are
specific to certain organisms, yet some interpretations can be made, based on previous
studies. It has been suggested that BHPs may have functions relating to structural
membrane integrity (e.g. Poralla et al. 1984; Horbarch et al., 1991), or even play a role in
preventing cell dessication and overall loss of water (Poralla et al. 2000) or in serving as a
barrier to oxygen for nitrogen-fixing bacteria (Berry et al., 1993), yet all of these theories
have been challenged by subsequent studies (Seipke & Loria, 2009). One still prevailing
theory is that hopanoids may reduce membrane permeability to protons, thereby
protecting the organisms from extreme pH conditions (Welander et al., 2009). As
evidenced by these varying proposed roles, it is important to further study BHPs to gain
11
a better sense of their ecological and physiological significance due to their ubiquitous
presence in soil and sedimentary environments.
Despite the incongruent theories of the functional role of hopanoids, some connections to
bacterial communities can be made by utilizing the highly specific chemical structure of
BHPs (see Appendix B-3 and B-4) that have been well elucidated (Sessions et al., 2013).
For example, BHPs have been correlated to different bacterial communities, like marine
and non-marine cyanobacteria that produce C35 hopanoids methylated at C-2 in the
pentacyclic ring system (Allen et al., 2010; Summons et al., 1999), despite this biosynthetic
capability possibly being more widespread (Welander et al., 2010). Within cyanobacterial
structures, there have been 25 distinct BHP side-chain structures detected, with certain
ones being found exclusively in a particular cyanobacteria, like 35-O-P-3,5-anhydrogalacturonopyranosyl BHP and its 2-methyl homologue in Prochlorothrix hollandica
(Talbot et al. 2008).
Previously, 2-methylhopanoids were considered to be biomarkers of cyanobacteria
(Summons et al., 1999); however, it was demonstrated that cyanobacteria are not the only
group of bacteria that are able to produce significant amounts of 2-MeBHPs (Rashby et al.,
2007). With this discovery, it became imperative that more data be collected about the
modem day function and distribution of 2-MeBHPs in order to better understand what
organisms and what environmental conditions would have resulted in their production
in ancient sediments. Shark Bay stromatolites and microbial mats are particularly useful
in this task, as the ancient sediments with 2Me-BHP signatures were likely similar to
stromatolites: existing in shallow water and supporting growing, abundant bacterial
communities.
In previous studies, it was determined that the majority of 2-Me BHPs produced in
smooth and pustular microbial mats in Hamelin Pool, Shark Bay originate from
cyanobacteria, with the other 2-Me BHP producing microorganism constituting a much
smaller portion of the bacterial communities (Garby et al., 2012). Despite recent research
into 2-Me BHPs in Shark Bay, overall, the vast majority of cyanobacteria screened for
hopanoid production are associated with freshwater environments, which makes studies
to characterize BHPs found in marine environments highly important. Extracting and
analyzing the BHPs found in the microbes that comprise the microbial mats and
stromatolites of Hamelin Pool, as done in this study, is then an important exercise in
exploring the diversity of BHPs in marine cyanobacteria and other marine organisms.
12
Determination of particular BHPs was accomplished through scanning for the exact
masses of specific BHP molecules among other compounds. The most abundant peaks
corresponding to the molecules selected at specific masses were then subjected to
additional fragmentation in order to determine the mass spectra for the compounds.
Examining the resultant mass spectra could confirm a BHP if it demonstrates the
characteristic fragmentations, m/z 191 or m/z 205 for the methylated version, which
correspond to the breaking of the C ring (Figure 1.1).
-E
R2
R1
R2+
A
Via pathway [i]
A B
-R3
B
0,
R3.
Figure 1.1 Bacteriohopanepolyol Characteristic Fragmentation
To the left is a bacteriohopanepolyol(BHP) molecule, showing the characteristicfragmentation
site. The resultantfragment is on the right. For the fragmentation:
If R2 = R3 = H, then the fragment is m/z 191
If R2 or R3 = CH3, then the fragment is m/z 205
Two different pathways of fragmentation are noted because, if broken off via pathway [i], the
positive charge will stay on ring B. Analysis of BHPs relies on a positive ion fragment to
quantify thatfragment.
Table 1.2 Characteristic Masses (m/z) of Various Bacteriohopanepolyols
Adenosylhopane
BHT
BHT-II
2-Me BHT
3-Me BHT
611.47
655.49
655
669.51
669.51
BHPentol
Unsaturated
Aminotetrol Aminopentol 3-Me
Unsat. Amintriol
712
Cyclitol
Aminopentol
Aminotetrol
713
746
722.54
830.54
844.56
1002.62
The characteristicmasses of different BHPs are isolatedfor analysis through a programmed
methodfor a High Performance-LiquidChromatography(HPLC) Instrument. The integrated
extracted ion chromatogramfrom this mass is then used to determine the amount of each specific
hopanoid.
13
1.2.d Fatty Acid Methyl Esters (FAMEs)
To relate the results of this study to previous studies on microbialites in Hamelin Pool (e.g.
Allen et al., 2010), fatty acid methyl esters (FAMEs) were also isolated and analyzed. The
original fatty acids of different microorganisms are able to be treated with methylated HCl
to convert them into Fatty Acid Methyl Esters (FAMEs), compounds that are more stable
and easier to analyze via GC-MS. Analysis of individual FAMEs and their characteristic
distribution within samples can be utilized as biomarkers for different groups of
organisms in environmental samples. These lipids have been extensively studied,
particularly in Hamelin Pool which has dominated FAME profiles of 16:0, 16:1co7 and
18:1o9 (Allen et al. 2010). These signatures correlate well with FAME profiles of cultured
cyanobacteria (Kenyon, 1972; Cohen et al., 1995), which suggests a dominance of
cyanobacteria in the samples, a theory that is supported by microcscopic observation
(Allen et al. 2010). Also noted in previous studies of these samples was the possible
presence of signature lipids 10Me16:0 and i17:w7 that indicate sulfate reducing bacteria
(Orphan et al. 2001; Londry et al. 2004).
FAMEs are best identified by the characteristic 74 Da McLafferty rearrangement ion that
is one of the most readily occurring fragmentations. Other characteristic fragments for
FAMEs were also used, including m/z 55, 87, and 101, among others; another defining
characteristic is an m/z 21 loss from the molecular ion, which corresponds to the loss of a
methoxyl group, thus confirming the compound as a methyl ester.
CH 300C
RO
m/z =74
Figure 1.1 FAMEs Characteristic Fragmentation
The McLafferty rearrangemention readily occurs as a molecularfragment of FAMEs.
1.3 Combining Characteristic Lipid Profiles
Within this study, I extracted, prepared, and analyzed each of the aforementioned lipids
in order to create the most complete picture of microbial communities and their
14
corresponding lipids for the microbial mats and stromatolites of Hamelin Pool, Shark Bay.
Analyzing this suite of lipids will also provide evidence of the distribution of different
types of lipids in this hypersaline environment, which could potentially be used for
identifying the presence of these types of marine microbes in other environments. As
evidenced in the discussion of these lipid types, there is much uncertainty about the
definitive correlations of particular lipid structures to certain types of microbes,
environments, or functions, so correlations of the lipids determined through this study
could be used in enhancing the characterization of lipids, particularly the relatively
unknown BHPs.
Analysis of these lipids is also complementary, in terms of understanding currently living
organisms, as well as those that lived millions of years ago. With the more transient lipids,
IPLs, characterization of the organisms living within the microbial mats and stromatolites
sampled is possible, which strengthens our understanding of the microbial communities
and their overall growth in this location. For this study, a more complete view of the
current biological diversity was pursued by examining the IPLs in conjunction with
GDGTs. Supplementing the analysis of these lipids with analysis of more recalcitrant
lipids then allows for comparison of beyond the current microbial communities.
Recalcitrant lipids from these samples, like the BHPs studied here, allow for comparison
to the recalcitrant lipid profiles of other samples, such as some ancient stromatolites that
have not been determined as biotic or abiotic. This could then result in either evidence to
support or contradict the biogenic origins of some of these ancient stromatolites.
15
Chapter 2
Materials and Methods
2.1 Sample Description
Samples of various microbial mats and stromatolites from Hamelin Pool in Western
Australia were collected during summer field season with a vertical interval of about 5cm
from June 14th, 15t, and 17th of 2011. The samples collected were from distinct
morphological communities: pustular, smooth, and colloform. For three multiples of
different smooth microbial mat samples, two distinct layers were isolated, the lower one
being more silica rich and the upper constituting the microbes directly exposed to
sunlight. These samples were named in the field, but names have been converted for
consistency in publications (Appendix A-1).
On June 14th, 2 smooth mat samples and 2 colloform mat samples were collected for lipids.
On June 15th, a colloform sample was taken at the beach; a smooth sample was taken at
South Carbla Point; a smooth, pustular, and tufted mat were taken from the southern area
of Carbla Point. Additionally, that day the upper layer of a colloform mat was isolated in
three replicates (A, B, and C), lower layer of a colloform mat was isolated in two replicates
(A and B), and colloform composite samples were isolated in three replicates (A, B, and
C). Each of these samples were taken and then separated into the top 5cm and the lower
layer (~5cm). Later that same day, smooth mats covered in 50 cm of water south of the
Carbla Beach fence line were sampled in three replicates (A, B, and C) and later sectioned
into the top 5mm and the bottom 15 mm in the lab. The top layer in this area was
characterized by a pink top and green area below, in addition to some black beneath that,
all adding up to be 5 mm. The bottom layer in this area instead was mostly black
gelatinous material, with some of an older layer clearly visible in the 1 cm thick sample.
16
On June 17th, the pustular mats samples were collected from Carbla Point with the same
vertical interval of 5 cm.
Samples were collected and handled with sterile instruments throughout the time of
study. In the field, samples were removed from the main microbial mat covered regions
and then stored in sterile jar with fired aluminum foil coverings. The samples were then
cooled to -20 C within hours of collection before being shipped frozen to MIT, where they
were then transferred to dark freezer maintained at -20 C.
2.2 Total Lipid Extraction and Preparation of Lipid Fractions
2.2.a Extraction and Basic Preparation of Lipid Fractions
From the jars of lypholized microbial mats, aliquots were removed and ground to a fine
powder using a mortar and pestle that was pre-cleaned first in a muffle oven and then
with organic solvents, hexane, dichloromethane, and methanol (geocleaned). Lipids were
extracted via modified Bligh and Dyer method (Bligh & Dyer, 1959), in which the dried,
crushed biomass (200-500 mg) was placed in a solvent-cleaned 50 mL Teflon centrifuge
tube. To account for sample loss during subsequent sample work-up, 20 Vg from a
solution diluted to 100 ng/VL of C16 PAF, was added as the extraction standard.
For the first step of extraction, every gram of sediment was extracted with 4 mL of Bligh
& Dyer Mixture 1, which is comprised of 0.8 mL 50mM Phosphate buffer (aq), 1 mL
dichloromethane (DCM), and 2 mL methanol (MeOH). The tubes were subsequently
shaken vigorously to fully mix solvent and sample, sonicated for 10 minutes, and then
centrifuged for 10 minutes at 3000 rpm in an Eppendorf 5804 centrifuge. The supernatant
from each step was then decanted into a pre-combusted and geocleaned separatory
funnel. Geocleaned materials were rinsed three times with each of the following solvents:
hexane, dichloromethane, and methanol. The full process of extracting with Mixture 1
was repeated another time and then followed by the same proportions of Bligh & Dyer
Mixture 2, comprised of 0.8 mL 50mM trichloroacetic (TCA) buffer (aq), 1 mL DCM, and
2 mL MeOH. To prepare the aforementioned phosphate buffer, 8.7 grams of K2HPO4
was dissolved in 1 L MilliQ water and add an HCl solution until the final pH is 7.4. For
the TCA buffer, 50 grams of trichloroacetic acid was dissolved in 1 L MilliQ water and
add 20 grams of KOH pellets to result in a pH of 2.
In order to ensure that all non-polar "free" lipids are extracted, the same proportion of 4
mL of solvent to 1 gram of sediment was used for a mixture that was 3:1 by volume
17
DCM:MeOH. DCM and 5 times DCM cleaned water were added in a 1:1 ratio to the
combined supernatant from the former extraction steps in the separator funnel.
The
volume of DCM used should equal the total amount of DCM used throughout the
previous steps. The separatory funnel was vigorously shaken and allowed to sit while
the layers separated.
After the layers had been separated clearly, the organic phase was drawn off the bottom
and collected in an Erlemeyer flask. The remaining aqueous fraction was extracted 3 times
with DCM, with the organic phase being combined after each step.
After this, the
remaining aqueous fraction was discarded and the organic phase was returned to the
separatory funnel to be extracted with DCM cleaned water 3 further times. The remaining
organic fraction was transferred to a pre-combusted 60 mL vial to be blown down under
a stream of N2 in the TurboVap evaporator at 37C. The total lipid extract (TLE) was then
transferred to a 4 mL vial to be weighed for further calculations.
To prepare for five-fraction chromatography, an aliquot of about 1 mg of TLE was
transmethylated with 2.5% methanolic HC in order to yield fatty acid methyl esters
(FAMEs), alcohols, and ether lipids. For this the vial of TLE was heated at 70 IC for 30
minutes. After being gently blown down to dryness with N2, the remaining extract was
taken up and transferred to a column using 3 washes of hexane and 1 wash of DCM. A
10 cm column of silica gel in a Pasteur pipette was used to separate the hydrolyzed lipid
extract using solvents of increasing polarity: saturated and unsaturated hydrocarbons (Fl)
by 3/8 dead volume of hexane; aromatics (F2) by 2 dead volumes of 8:2 Hexane:DCM;
ketones (F3) by 2 dead volumes of DCM; alcohols (F4 - including tetraethers) by 2 dead
volumes of 1:1 DCM:Ethyl acetate (EtOAc); and acids and diols (F5 - polars) by 2 dead
volumes of 7:3 DCM:MeOH. Each fraction was collected in combusted glassware and
transferred using 3 washes of hexane and 1 was of DCM to 2mL vials. The fractions F1
and F3 were transferred directly into 2 mL vials pre-filled with 1 tg of 3-methyl
heneicosane, an anteiso C22 (ai-22) standard for quantifying the saturated and polar
lipids. After transfer, these fractions were evaporated under a stream of N2 again and
then re-dissolved in 200 VL hexane for running on the GC-MS (described in detail later).
The F4 fraction was re-dissolved in hexane and transferred in equal amounts to two 2mL
vials with inserts. Division of this fraction had been to subject the portions to different
derivatization protocols, in order to analyze with different methods. One half of the F4
fraction was stored, while the other half was derivatized. Those vials not being used
-
immediately were evaporated under a stream of N2 and stored in a dark, cold room at
20 C.
18
Dry Bloina
1.
Add
Internal Standards
2. Bligh & Dyer Bxtraction
Total Lipid
Exriact
0.5 Ps anmpi
0.5 p&
-
1. Mild Acid Methanolysis
2. Liquid Chrmatogmphy
LCM-~
A nalysis by AMC
#2
0actIon
Faction #1
RP-A31-M Analyszs
onEJ~wyi
qTENOFwyi
IftCV1&*
to DCM &MeOHJl
Bhd~iahamheoyow
SatMed HydrCmbS
LC-M-M Analysts
on SiO2 fiw 5 haectiona
seliat *byS7
Atamabc Hy*ocubas
FactIon #3
Faction #4
Fgty AdahylaEatr 1
Acahala
raction #5
Da14,
dHP
Figure 2.1 Diagram of Extraction and Analysis Procedures
2.2.b Derivatization
For the polar compound analysis, 100 VL pyridine and 100 IL of N,O-bis(trimethylsilyl)
trifluoro-acetamide (BSTFA) were added to half of F4 and all of F5. The 2mL vials were
then capped and incubated at 70 C for 30 minutes. The lipid fractions were subsequently
blown to dryness under N2 while warm. For analysis via GC-MS (described in detail
later), the F4 and F5 fractions were dissolved in hexane.
2.3IPLs Preparation and HPLC-MS Analysis
The untreated, total lipid extract was analyzed directly by HPLC-MS in accordance with
the methods of W6mer et al., 2013 and Schubotz et al. 2013. By HPLC-MS, lipids were
separated on a Waters Acquity UPLC BEH Amide column (125 mm x 2 mm, 5 Vm) with
a linear solvent gradient through an Agilent 1200 series HPLC systems that is coupled to
an Agilent 6520 Accurate-Mass Quadrupole Time-of-Flight (QTOF) mass spectrometer
equipped with an electrospray ionization interface (ESI). The mass spectrometer was set
to a scan range from m/z 400 to 2000 and performed MS/MS experiments in positive ion
mode. Compounds were identified via exact masses, comparison of retention times with
commercially available standards and published MS/MS fragmentation patters (Sturt et
al., 2004; Schubotz et al., 2013).
19
In order to analyze all IPLs, heat maps are created that plot the relative abundance of a
compounds according to a specific mass that is recorded. Using an electrospray ionization
interface (ESI), molecules are able to be isolated for quantification, assuming the mass falls
within the given mass scanning range of the instrument being used. Plotted on the main
axes are retention time in minutes and mass to charge (m/z), the latter which most often
correlate directly to the mass of the compound, since the charge is typically +1. These
values vary, particularly depending on the side chains of the molecule, which result in
visible shifts of the elution time and mass-to-charge ratio. Marked by the intensity of color
are the abundances of the different lipids. Groups of lipids can be identified based on this
signature and, by tracing the intensities in a given region, the fragmentation of the
particular lipid can be identified.
2.4 FAMEs and Hydrocarbon GC-MS Analysis
An Agilent 7890 gas chromatograph was used to identify the individual fatty acid methyl
esters and alcohols, following the specifics of GC-MS analysis provided in Schubotz et al.
2013. This gas chromatograph, with a programmable temperature vaporizing (PTV)
injector operated in splitless mode and equipped with a Varian CP-Sil-5 fused silica
capillary column (60-m length, 0.32 mm inner diameter, and 0.25-pm film thickness) was
coupled to an Agilent 5975C mass-selective detector.
Fractions F1 and F3 were run with a fatty acids method, while derivatized fractions F4
and F5 were run with a polars method. Data collected on the F1, F4, and F5 were stored
for use in a later paper further detailing the lipid profiles of these samples.
To identify the individual lipids, the overall mass spectra and retention times were
compared with authentic standards and/or samples where these compounds previously
had been characterized. By extracting ion chromatograms of characteristic fragments
within the given lipid, common types of lipid could be identified. Then, by identifying
and comparing the molecular ion of each compound, it could be easily determined
whether or not a compound had an unsaturation (shown by a loss of 2 mass units, 1 for
each Hydrogen).
20
2.5 GDGTs Preparation and HPLC-QTOF-MS Analysis
Core and intact glycerol dialkyl glycerol tetraethers (GDGTs) were analyzed using a
relatively new method (Zhu et al., 2013) that uses a reversed phase liquid
chromatography-electrospray ionization-mass spectrometry (RP-ESI-MS) protocol to
analyze these compounds and others directly from crude total lipid extracts (TLE). This
protocol was run in the positive ion mode on the same instrument, as described above, an
Agilent 1200 series HPLC system coupled with an Agilent 6520 Accurate-Mass
Quadrupole Time-of-Flight (QTOF) mass spectrometer that was equipped with an
electrospray ionization source. Aliquots of TLE were dissolved in a known amount of
methanol in a 2mL insert vial and then run on the HPLC-QTOF-MS.
The scan range of the mass spectrometer was set to m/z 100 to 2000 in positive ion mode
and MS/MS experiments were also performed in a scan range from m/z 100-2000.
Maintaining and monitoring mass accuracy was achieved by a tuning mixture solution
and a lock mass (m/z 922.0098) that was infused throughout the entire course of the run
To be able to quantify the observed GDGTs, 5 ng of a 1ng/VL C46 standard was added as
an injection standard to the TLE aliquot prior to injection. However, due to an overall
lack of reference standards for every class of IPLs, particularly those novel intact branched
GDGTs, the relative concentrations of GDGTs determined with these samples is semiquantitative, similar to the findings of Liu in 2010.
Lipids in these samples were identified via retention time, accurate masses, and diagnostic
fragments (e.g. Liu et al., 2010). To quantify particular GDGTs, methods were run on the
data processing software Agilent Technologies MassHunter Qualitative Analysis that
extracted ion chromatograms of the compounds of interest with molecular weights
corresponding to particular core GDGTs and Glyco-GDGT (G-GDGTs).
Within the expected elution range (retention time of 50-80 minutes) of the compounds of
interest, if a major peak corresponding to the particular GDGT/G-GDGTs was defined, it
was manually integrated to determine the abundance of that particular lipid. When no
peak could be located for a given sample, it was recorded that the abundance of the lipid
in question was 0.
Preparation of the samples varied depending on initially obtained results. Samples of
TLE were dissolved in a known amount of hexane in a 2mL insert vial and then run on
21
the QTOF. If peaks were found to be unclear and the signal to noise ratio was low (3:1 is
the minimum), the samples were re-concentrated (dissolved in less methanol) and run
once more. Re-concentration allowed for clarification if previous non-detect values were
actually low concentrations and also improved the resolution of initial peaks.
2.6 BHP HPLC-QTOF-APCI Analysis
An aliquot of about 0.5pg TLE was derivatized with 25 VL pyridine and 25 VL acetyl
anhydride. Samples were left at room temperature for 24 hours to ensure acetylation of
the BHPs. In order to quantify bacteriohopanepolyols (BHPs) present in these samples,
100 ng of 3a,12a dihydroxy-5p-pregnon-20one, 3,12-diacetate (Pdia) was added to the
derivatized total lipid extract
Later, the amount of a given BHP compound can be
compared in relative abundance to Pdia (via integrated extracted ion chromatograms) in
order to determine their absolute abundance.
Following the procedure used by Welander et al. (2012), a HPLC-MS system was used to
detect the BHPs. The particular LC-MS system contains an Agilent Technologies 1200
Series HPLC that is equipped with an autosampler and a binary pump that links to an
Agilent Technologies QTOF 6520 mass spectrometer via an Agilent Technologies
atmospheric pressure chemical ionization (APCI) interface that was operated in positive
ion mode. BHP compounds were eluted on a Poroshell 120 EC-C18 column (2.1 x 150
mm, 2.7 pm, Agilent Technologies), set at a column temperature of 30 QC, first with
MeOH:water (95:5, v:v) at a flow rate of 0.15 mL min-1 for 2 minutes. Subsequently, a
linear gradient was followed until reaching 20% (v) of isopropyl alcohol (IPA) over 18
minutes at a flow rate of 0.19 mL min-1 and then maintained at 20% (v) for 10 minutes.
The linear gradient was modified to then increase to 30% (v) of IPA at 0.19 mL min-1 for
over 10 minutes and then 30% (v) was maintained for 5 minutes. Then the column was
then eluted using a linear gradient up to 80% IPA (v) over 1 minute at a flow rate of 0.15
mL min-1 and then held for 14 minutes. Finally, the gradient was held for 5 minutes with
MeOH/water (95:5, v:v) at 0.15 mL min-1.
The APCI parameters were set similarly to Welander et al. (2012): gas temperatures 325
LC, drying gas (N2) flow rate of 61 min-1, nebulizer (N2) flow rate 301 min-1, capillary
voltage 1200 V, corona needle 4 pA, and fragmentor 150 V. Data scans were recorded by
scanning from m/z 100 to 1600. To identify BHPs found in these samples, exact masses
were used as well as comparison of the retention time and mass spectra from published
data (Talbot et al., 2003; Talbot et al., 2007).
22
Various characteristics were used to identify the bacteriohopanepolyols, including
fragmentation patterns in the MS-MS, accurate mass measurements of protonated
molecular ions, and a comparison of the resultant relative retention times and mass
spectra with previously reported data. To initially identify the different BHPs, a method
created in Agilent Technologies MassHunter Qualitative Analysis software was run on
the data files that created extracted ion chromatograms (EICs) of the compounds of
interests, sorting them via specific masses. When the characteristic fraction of BHPs, m/z
191 or m/z 205 for the methylated version (Figure 1.1), were found to correlate with the
compound specific mass, an EIC was derived from the peak at that compound specific
mass. This peak was then integrated in order to determine the relative abundance of this
BHP.
23
Chapter 3
Results and Discussion
3.1 Quantifying Results
3.1.a Total Lipid Extract (TLE) Portions
The TLE of the various mat samples ranged from .09% to .24% of the dry weight of the
samples (full data in Appendix A-1), which is a significantly lower range than 0.26% to
0.43% determined by Allen in 2010. Allen extracted a pustular mat to yield .43% TLE,
while the pustular mat in this study yielded only .17% TLE, both by dry weight. For her
smooth mats, she had a yield of 0.26% TLE, while this study averaged .10% of the dry
weight, and she had a yield of 0.38% TLE for a stromatolite, while the stromatolites
(colloform and smooth) included in this study yielded 0.13% of the dry weight as TLE. A
possible explanation for the lower yields of TLE for this study could be seasonality, since
the samples studied here were collected during June, which is winter in Australia, while
Allen's samples were collected in December, the peak of the Australian summer. It would
be very reasonable to have higher yields of organic material from samples collected
during a time of high productivity. The factor is likely not the time between sample
collection and lipid extraction, as some of Allen's samples were stored for 2-3 years before
processing, like the delay in extraction from June 2011 to November 2013 in this study.
Samples were preserved in a similar manner: freeze-dried and at -20 C.
3.1.b Quantification of Data
In order to offer as robust a data set as possible, semi-quantitative analysis of the
abundances of particular lipid classes was conducted from the collected lipid profiles of
24
this study. In order to best determine these values, a combination of a comparison
to an
internal standard and basic mass tracking of the portion of samples being tested was
utilized. Because some samples were run multiple times on different instruments, it was
important to track the dynamic mass of total lipid extract (TLE) represented in each
lipid
profile. The amount of TLE was then directly linked to the overall mass of the microbial
mat or stromatolite to provide a fairly quantitative representation of particular lipid
abundances in different types of mats. A set of Master Tables detailing the quantified
abundances is provided in the Appendices (A-3).
While exact quantification of results is close to impossible, partially due to the lack
of
authentic standards for comparison with some compounds and experimental error, much
can be said about relative abundances for compounds. This type of relative analysis
was
used initially for analysis of GDGTs and BHPs by comparing the integrated areas of
the
Extracted Ion Chromatograms (EICs) of specific molecules of interest and for analyzing
the Total Ion Chromatograms created through the MSD of the different lipid fractions.
Table 3.1 Lipid Composition as Fraction of Total Mass
Sample Name
I/M
IPL /q
1st GDGT /{M} 2nd GDGT / ) BHP / (M)
Smooth Strom 1
1.33E-03
2.22E-05
1.56E-05
4.93E-05 2.46296E-05
Smooth Mat 2 Top
1.03E-03
6.t9E-06
4.92E-06
3.05E-05 1.52629E-05
Smooth Mat 2 Bottom
1. 1RE03
7.84E-06
5.49E-06
3ASE-05 1.73856E-05
Smooth Mat 3 Top
9A5E-04
6306
4AE-06
2.79E-05 1.39677E-05
Smooth Mat 3 Bottom
t.9604
5.97E06
4.18-06
2.65E-05 1.32394E-05
Cob. Strom 1
1.31E-03
S.75E-06
6.13E-06
3.89E-05 1.94052E-05
Cobafosa Mat 2 Top
2.40E-03
1.6a"-5
1.12E-05
7.10E-05 3.5491 IE-05
Cobotm Mat 2 Bottom
1.67E-03
3.70,05
259E-05
1.64E-04 2ORE-05
Cobafom Mat 3 Top
1.27E-03
S.4E06
5.91E-06
3.74E-05 3.43139E-05
Coobam Mat 3 Bottom
2.33E-03
1.55-05
1.09E-05
6.1SF-5 1.6998-O5
Cobafom Mat 4
7.94E-04
1.32E-05
926E06
5.96"-5 2.9321E-05
Pustular Mat 1
1.72E-03
1.43-05
1.OOE05
6-36"5 3.1809E-05
(MJ here is defined as the mass of the initial sample of microbial mat or stromatolite. These masses
rangedfrom 3.6 grams to 15.85 grams, depending on the amount removed for extraction
and
subsequent lipid analysis. The fractional amount, subsequently referred to asf, shows how
much
of the total sample is accountedfor by the lipid quantity reported. Multiplying the quantified
compound specific abundances that are determined in each analysis step by I/f allows
for
quantificationof each lipid in the overall microbial mat or stromatolitesample.
25
3.2 Intact Polar Lipids
3.2.a Results
Relative abundance and diversity of the microbial community of the different mats was
obtained through use of a density map (a.k.a. heat map), which allows for a 3-d view of
the chromatographic separation of different molecules (Figure 3; other heat maps can be
found in the Appendices (A-4)). The signatures represented can be used as a lipid
fingerprint of the respective sample, thereby facilitating a high level comparison between
the different samples.
Different sections of the plot, corresponding to distinct elution times and mass-to-charge
values, were correlated to specific types of lipids based on known values. The distinct
steps shown within the density map correlate to different fatty acid side chains for the
different molecules. On the heat maps featured in this section, the different areas and
their corresponding lipid classes have been annotated to aid in understanding this
analysis.
Smooth Mat 2 Top - Intact Polar Lipid Heat Map
4oco
j1700
DGTS dimer
ji0
-1400
OL dimers
1200
1 -100
-
-
6
DGTA?
--
-TM-OL
4DGTS1
_
w--
4~r--
a
10
12
14
Tuft "ro
26
16
is2128
.
Z
-
SmoothMat 2 Bottom - Intact Polar Lipid Heat Map
1700
loo
1500
1400
The
__
7;100
_
Figure 3.1 Heat Map of Intact Polar Lipids
Teplots above are two examples of density/heat maps createdfrom the samples Smooth Mat 2 Top
(upper plot) and Smooth Mat 2 Bottom (lower plot). Distinct regions of mass-to-charge and
retention time correlate to different types of lipids.
Overall,
the most consistently dominant signatures were from Diacyiglyceryl
hydroxymethyltrimethyl-p-alanine (DGTA), Diacyiglyceryltrimethyihomoserine (DGTS),
Trimethyl ornithine lipids (TM-OL), and Ornithine lipids (OL), with the most consistently
dominant lipid, even between the different layers, was Phosphatidylcholine (PC). PC is a
methylated derivative of Phosphatidylethanolamines (PE), which compose about 25% of
all phospholipids in all living cells. This particular compound can be traced to the
exoplasmic, outer portion of a cell membrane and has a unique soap-like structure that
maintains membrane fluidity while minimizing membrane permeability. In such a
hypersaline environment like Hamelin Pool, this membrane lipid could be extremely
important for the survival of some microorganisms. PC is found in a lower proportion of
bacterial membranes, about 10% of species, so the inclusion of such a strong signal could
be from a consistent bacterial presence. Additional support for this theory is that PC is
not commonly found in cyanobacteria (Barton 2005), so its source could be different
bacteria that thrive in all layers of the samples.
27
The presence of both TM-OL and OL in the samples analyzed suggests a strong presence
of a microbial community that has an anaerobic autotrophic metabolism. TM-OL, often
attributed to planctomycetes, has been found in brackish, marine, and fresh water in
association with anaerobic autotrophic metabolism (Moore et al. 2002). Ornithine lipids
are found in many different source organisms, yet there have been some links made
between their presence and sulfate-reducing bacteria (Makula and Finnerty, 1975;
Schubotz et al., 2009). However, if the presence of TM-OL suggests anaerobic autotrophic
metabolism, the ornithine lipids could be attributed to photosynthetic bacteria, as they
have been previously related (e.g. Zhang 2009).
Both DGTS and DGTA are betaine lipids and suggest a microbial community with a
strong presence of different lower (non-vascular) plants. In particular, many soil, bacteria,
algae, and non-vascular plants synthesize the phosphorus-free DGTS, especially in
response to phosphorus deprivation (e.g. Riekhof, et al. 2014; Geske et al. 2012). Betaine
lipids like these are also found widely within ferns, bryophytes, lichens, and some fungi
and protzoans. For these samples from Hamelin Pool, the DGTS and DGTA lipids could
be a signal of the phototrophic microbes in the microbial mats or they could be from any
soil being brought into the bay, likely being eolian. It is hard to tell any distinction
between the heat maps of upper and lower layers of the smooth mats, although the
signatures appear to be stronger in the bottom samples, which would then negate the
aforementioned reasoning for the origination of DGTS and DGTA. However, without
quantitative data, it is not possible to make any concrete deliberations about the lipid
yields, let alone the corresponding microbial community.
Archaeal intact polar lipid signatures were surprisingly weak for all of the samples that
were analyzed in this way. Archaeal lipids would be represented in the heat maps in the
upper left hand corner, with a low elution time and high mass. The lack of a strong signal
for archaeal lipids is interesting, especially since previous studies have isolated different
archaea, like the Halobacteria from the Euryarchaeota, and confirmed that they most
likely originate from the stromatolites and microbial mats, as opposed to the surrounding
water (Goh et al., 2009).
Differentiating the already weak archaeal intact polar lipid signal allows for some limited
comparison of community distributions for the upper and lower mat. Typically, the lower
layers of microbial mats in Hamelin Pool have been found to have an abundance of subsurface archaea, as well as sulfate-reducing bacteria (Goh et al., 2009). It would then be
sensible to see an increase in the IPL signature of archaea when progressing from the
upper layers of a microbial mat to the lower layers, as occurs in the samples analyzed in
28
this study. However, the intensity of this signal change is rather low, so it would be
difficult to base any claims on the archaeal community present in the samples without
quantifying the data, a process not attempted in this study, since the intact polar
membrane lipid, G-GDGT, was quantified and analyzed, in addition to the core GDGT.
3.2.b Data Validation Efforts
In relation to the dimers, which form during high analyte concentrations, they were
identified and then accounted in the overall lipid distribution for whenever detected. As
noted by Schubotz et al. 2013, it is possible that high molecular weight compounds, such
as intact GDGTs and N-acetyleglucosamine (NAcG)-DAGs, were outside the analytical
window of 500 - 2000 m/z. This could then result in an underrepresentation of these
compounds. Regardless of representation in the samples, accounting for differences in
the response factors for the different lipid classes remains incomplete, due to a lack of
authentic standards.
3.3 Fatty Acid Methyl Esters
Initial identification of individual compounds was conducted via mass chromatograms
that show the FAME characteristic 74 Da McLafferty rearrangement ion. In addition to
the multiple metrics for initial identification based on the extracted ion chromatograms
(EICs), more specific confirmation of the lipids was attained by comparing mass spectra
and retention times with authentic standards, when possible.
Analysis of the FAME total ion chromatograms showed relatively standard distributions.
For the majority of the samples analyzed, the dominant peaks were the ubiquitous, among
bacteria and eukaryotes, C1 6 and Ci8 , with the former being stronger. Overall, branched
FAMEs were identified only in the range of C14 to Cis, while the saturated, straight chain
(normal) FAMEs were found throughout the range observed (C 14 to C26). The abundance
of short-chain odd carbon numbered, branched fatty acids can be attributed to bacteria
(Kaneda, 1991), which offers a general characterization of the microbial mats studied here.
The method used a shorter holding period, so very long carbon chain FAMEs that take
longer to elute, were not observed. The long carbon chains (i.e. >C 20 ) that were included
in the analysis showed dominant even-over-odd signatures.
29
Smooth Mat 3 Top - Fatty Acid Methyl Esters
100
A = br-iS
8 = I-CIS
C = al-C1S
2Me-C18
strd
D= n-C1S
E = 16:1
FGH =br-I7
I=n-C17
n-C16
4'
1&:1
40
n-C18
n-C14
18:2
B
I
A C
E
FGH
I
al-2-.
C22
45.00
50.00
C24
C26
55.00
5D00
Tine (min)
Smooth Mat 2 Top - Fatty Acid Methyl Esters
100
n-C16
2Me-C18
Atnd
4'
V
18:1
16:1
C
15:1
-I
4,
n-C1S
n-C14
CIS
18:2
C17
2M0
30J0
35k
4a
j
C20
C22
C24
4M5.0
ime (min)
Figure 3.2 FAME Chromatograms
These chromatogramsare labelled with the different FAMEs detected, rangingfrom 14 carbon
chains to 24 carbon chains. The injection standard can be clearly seen, which allows for
quantificationof this data.
The fewer long-chain FAMEs that were able to be identified represent those long-chain
fatty acids, C24 to C30, that are not very common in Bacteria, but that likely come from
detrital plant material. Previous studies have determined that these long-chain signatures
originate from the breakdown products of the local vegetation (Rezanka et al., 1989) that
could have been washed in (or blown in for the arid Australian climate). The prominence
of the even-over-odd carbon numbers for these long-chain fatty acids has been often
attributed to an origin from vascular plants (Eglinton & Hamilton, 1967). Both vascular
30
&
plants have this signature throughout their leaf waxes, while sediments with significant
terrigenous plant inputs also have demonstrated the same signature (Eglinton
Hamilton, 1967;
N'ezanka
et al., 1989).
If isotopic analysis were conducted on these
FAMEs, it would be possible to determine if the source of long-chain FAMEs was
consistent or if there were distinct sources; the latter option could then suggest other
sources, like some eukaryotes that have been correlated strongly with long-chain fatty
acids (iezanka et al., 1989; kezanka & Sigler, 2009).
Although the exact FAMEs have not been identified, there are strong n-C16 signatures that
appear to be the 10-Me C16:o fatty acid, which is highly diagnostic of sulfate-reducing
species. The fatty acid was found to be prevalent in multiple environmental samples that
have prominent sulfate-reducing bacteria (e.g. Hinrichs et al., 2000; Labrenz et al. 2000).
The C16 fatty acid was highly distinct between the layers of Smooth Mat 3, with the C16
relative abundance being almost two times greater in the lower layer than in the top, an
observation that is consistent with a lower layer dominated by sulfate-reducing bacteria.
Also, suggesting sulfate reduction are the distinctive C12 to C19 fatty acids that, in
combination with their branched fatty acids from the bacterial phospholipids, have been
shown to correlate to sulfate-reducing bacteria (Taylor & Parkes, 1983). These signatures
are some of the most dominant represented in the FAMEs, which suggests a strong
presence of sulfate-reducing bacteria in the microbial mats and stromatolites in Hamelin
Pool.
Notable signature of different unsaturated FAMEs, particularly C16:1 and C18:1, with the
latter sometimes occurring in multiple forms within one sample, are particularly
interesting. In multiple samples, like Smooth Mat 3 Top, the C16:1peak actually surpassed
the C16:0in terms of relative abundance, while the C18:1 peak(s) regularly surpassed the C18:a
peaks. This high yield of unsaturated Cm and C18 fatty acids has been observed before in
Geobacter metallireducens, a sulfate-reducing species (Lovely et al., 1993), which, based on
this correlation, could be abundant in the microbialites of Hamelin Pool.
Trends in FAMEs for this study closely resemble those of ooids in both Hamelin Pool and
Cat and Andros island in the Bahamas (Summons et al., 2013), which suggests a similar
microbial community. In previous studies, data has suggested the inhabitance of ooids
by specific microbiota, but proof of microbial biofilms being this source is limited
(Summons et al., 2013). However, now adding the FAME signatures from this study to
others of stromatolites and thrombolites undergoing active lithification in association
with a photosynthetic biofilm, there is more evidence suggesting similar microbial origins
for microbial mats and ooids, especially in Hamelin Pool and the Bahamas. For both
31
community assemblages, the molecular evidence for combinations of primary producing
cyanobacteria and sulfate-reducing bacteria indicates sources for both organic matter an
alkalinity, which have been found to drive active carbonate precipitation, especially for
lithifying organosedimentary biofilms (Dupraz & Visscher, 2005; Dupraz et al., 2009), like
in the microbialites studied here.
3.4 Glycerol Dialkyl Glycerols (GDGTs) and Glyco-GDGTs
3.4.a Initial Run - Relative Abundance Only
The initial runs for both GDGTs and G-GDGTs were at a lower concentration of the total
TLE (Table 3.1), which resulted in data that had a signal to noise ratio that was below the
acceptable 3:1 ratio, which allows for better distinction of the data peaks. This initial run,
as previously mentioned, was completed to determine the ideal concentration for future
quantification of core and intact GDGTs. Despite the lower quality of the data, certain
distributions of the different GDGTs and G-GDGTs were robust enough to yield patterns
among the 5/12 different samples that successfully ran and were stored for study on the
Agilent Qualitative Analysis software.
Overall, within the smooth mat samples, the relative abundances were highly dominated
by GDGT-0 (67%), with the subsequent most dominant GDGT being Crenarchaeol (20%),
while the remainder was split among the remaining GDGT molecules identified in this
study (GDGT-1, GDGT-2, GDGT-3). A noticeable shift in the GDGT composition occurred
when 4/5 of the smooth mat samples were divided to show the upper level samples
(denoted as "Top") and lower level samples (denoted as "Bottom"). For Smooth Mat Top
samples, Crenarchaeol GDGTs dominated at 47% relative abundance, followed closely by
GDGT-0 at 45% relative abundance, with the remainder being comprised of GDGT-2.
Conversely, with the Smooth Mat Bottom samples, there was a much lower relative
abundance of Crenarchaeol GDGTs (24% relative abundance), as the majority of the
sample was determined to be GDGT-0 (66% relative abundance). GDGT-2 was also
present in the Smooth Mat Bottom samples at 3% relative abundance, while the remaining
7% was attributed to GDGT-1, a compound not observed in these initial runs on Smooth
Mat Top samples.
32
GDGT Relative Abundance of Layered Sections (1st Round)
Total Smooth
TotalColloform
0%
10%
20%
40%
50%
60%
Relative Abundance (in %)
30%
* GDGT-0
a GDGT-1
GDGT-2 UGDGT-3
70%
80%
90%
100%
a crenachaeol
G-GDGT Relative Abundance (1st Round)
Total Smooth
TotalColloform
0%
20%
40%
60%
Relative Abundance (in %)
- G-GDGT-2
mG-GDGT-3
a G- GDGT-0 MG-GDGT-1
80%
100%
a G-cr anarchaeol
Figure 3.3 GDGT Relative Abundance (1st Round)
The different GDGTs and G-GDGTs were subjected to a ratio comparison to determine any
significant trends in relative abundance.
No other trends could be determined because of the quality of data in the run for
determining the best concentration for clear results and the limited number of samples
that had data files successfully stored on the computer after the runs. The presence of
these trends, when confirmed by the subsequent runs, show the strength of these
33
particularly distinctions.
Based on these results, it was determined that the samples
needed to be much more concentrated to obtain clear, well-defined peaks in the EICs for
all the different GDGT and G-GDGT compounds, which resulted in them being made
more than three times more concentrated.
3.4.b Secondary Run - Relative Abundance
Qualitative data with clear, well-defined peaks for the different GDGT compounds, which
were obtained through the re-concentration of the samples, were found to closely
resemble the overall relative abundances found in the first course of sampling. For the
smooth combined samples, the GDGT data still showed greater than 60% of the relative
abundance of the overall GDGTs were GDGT-0, being 4% lower in relative abundance for
the second round of testing (down to 63% from 67%). Interestingly, the percentage
relative abundance of Crenarchaeol GDGTs is 20% for both the initial run of samples and
the second run. Similar in magnitude were GDGT-1 and GDGT-2 (increasing by 1% in
the second run), while GDGT-3 doubled from 2% to 4% relative abundance.
GDGT Relative Abundances (2nd Round)
Smooth ktrom 1
Smooth Mm 2Top
Smooth Ma 2 Bottom
Smooth Mm STop
Smooth Ma
Bottom
Colobrm Strom I
Cokofrm Mat 2 Top
Colotorm Ma 2 Bottom
Coloorm Mm 3 Top
Coloform Ma 3 Bottom
Co11obmMat4
Putulr Mat 1
0%
10%
20%
30%
*GOGT-0
40%
50%
RelativeAbundance (in %)
mGOGT-1 - GDGT-2 mGDGT-3
34
60%
70%
&Crswchaeo
80%
90%
100%
G-GDGT Relative Abundances (2nd Round)
Smooth Strom1
Smooth Me 2 Top
Smooth Ma
2 Bottom
Smooth Mg Slop
Smooth Ma 5 Bottom
Colofm Stromn 1
Colotarm Ma 2 Top
Cololorm Mg 2 Botom
Coffotrm Ma
Top
Coloftrm Mg 3 Bonom
Co6otmMvM4
Pusuar Ma
I
0%
10%
209
0%
40%
aG-G
GT-0
50%
50
Relative Abundance (in%)
.G40T-1
-G4GT-2
mG-G
%
T-
80%
90%
100%
&G-crgarchasol
Figure 3.4 All GDGTs Relative Abundance (2nd Round)
Similarly to the first round, the GDGTs and G-GDGTs were analyzed in terms of relative
abundance in the samples. With more samples displaying clear results during this round, the data
could be separated into individualsamples to note any further trends.
Despite only having results for 1 colloform mat sample in the first round, the distribution
predicted in the initial runs was very similar to that found by analyzing 5 colloform mat
samples in the second round. The dominant GDGT of the mat was still Crenarchaeol
(though down from 56% to 50% relative abundance), while the next dominant was GDGT0 (24% for both the initial and second rounds). The most significant difference is the
inclusion of GDGT-3 in the overall colloform mat distribution, which could have been
seen only with more samples because of its low abundance. The contribution was likely
overlooked in the initial runs because of data quality, especially since the same colloform
mat sample had a GDGT-3 signature in the second round, where it had none during the
first round.
Unlike the overall relative abundance distributions, the relative abundances determined
in the separate sections of the different mats were significantly different between the first
and secondary round of analysis, which suggests that a better representation of the
detailed breakdown of GDGTs is attained by additional measurements. The most
noticeable difference is between the relative abundances for the lower layer of the smooth
mats. Instead of the highly dominant (66% relative abundance) GDGT-0 and then less
dominant Crenarchaeol GDGT (24%) of the initial round, the second round of testing
revealed GDGT-1 to be the most dominant, at 42%, while GDGT-0 and Crenarchaeol
35
GDGTs were 34% and 19% of the relative abundance respectively. Clearly, more of the
lower GDGT-1 signature for the entire smooth mat are located in the lower sections, while
the Crenarchaeol GDGTs are a bit more prominent in the upper layers of smooth mats,
comprising 35% of the relative abundance at the top and only 19% of the relative
abundance of the top, but 20% of the overall relative abundance.
GDGT Relative Abundance of Layered Sections (1st Round)
Smooth Top
Smooth Bottom
0%
10%
30%
20%
40%
50%
60%
70%
80%
90%
100%
Relative Abundance (in %)
a GDGT-0 mGDGT-1
- GDGT-2
mGDGT-3
a crenarchaeol
GDGT Relative Abundance of Layered Sections (2nd Round)
Totai Smooth Bottom
Total Smooth Top
TotalColoIorm Bottom
TotalColloform Top
0%
10%
20%
30%
40%
50%
60%
70%
80%
Relative Abundance (in %)
* GDGT-0
aGDGT-1
* GDGT-2
* GDGT-3
a Crenarchaeof
Figure 3.5 GDGT Relative Abundance of Layered Sections (Both Rounds)
36
90%
100%
The trend noticed in the initial rounds of testing that Crenarchaeol GDGTs are more
prominent in the upper layers holds true for all samples analyzed during this round. Of
note is the distribution for the colloform mats, which have a fairly stable relative
abundance of GDGT-0 throughout (-2% for the bottom section) and a decreasing relative
abundances of Crenarchaeol GDGTs from top (61%) to bottom (52%) layers. The relative
abundance of Creanarchaol GDGTs remains high throughout the entire mat, despite the
decrease when the depth of sampling increases from 0-5cm to 5-10cm. This suggests a
lesser impact of depth on the presence of Crenarchaeol GDGTs and the microbes that
create them.
For this round, there was also data collected for a pustular sample, which mostly closely
resembled the relative abundances of GDGTs and G-GDGTs of the colloform mats.
Overall, the two mats are both dominated by Crenarchaeol GDGTs and G-GDGTs, with
both GDGT-0 and G-GDGT-0 being the next most dominant GDGT between the range of
22-32%. Based on the relative abundance information, there appear to be more similarities
between the pustular and colloform mats than between either of these and the smooth
mat.
3.4.c Secondary Run - Quantitative Results
While the relative information is useful for detecting potential trends, the quantitative
information is able to be used for proposing theories and drawing connections. In the
second round of testing the higher concentration yielded information about GDGTs and
G-GDGTs for all samples, though only 11/12 could have the results quantified.
Quantitative data was obtained through an integration of the peak shown when an
extracted ion chromatogram (EIC) was taken of a specific mass to isolate the particular
compound. The ratio to determine the amount of each GDGT or G-GDGT was:
5 ng C46 standard
peak areastandard
X ng GDGT
peak area GDGT
The amount of ng of each GDGT was then normalized to the grams of TLE that were
initially extracted to see the fraction of organic matter that can be attributed to each lipid
in the form of a percent. The abundances of GDGTS and G-GDGTs by weight of total TLE
that resulted of these manipulations can be seen in Figure 3.6. The sample Colloform Mat
Top 2 did not have a clear enough peak in the EIC for the C4 standard to allow for a ratio
comparison to determine the amount of each GDGT in the different samples, so it has
been omitted from the quantitative results.
37
Total Core GDGTs
Total G-GDGTs
Total Pustulafl
Total Smooth
Total Smooth
TotalColloform
TotalColloform
-
Total Pustua
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
500
0
4,500
1,000
1,500
2,000
2,500
Total G-GDGTs (in ng G-GDGT / g total TLE)
Total Core GDGTs (in ng GDGT / g total TLE)
Quantified Distributions of Core GDGTs
Quantified Distributions of G-GDGTs
Total Smooth
-
TotalPustul
Total Coflarorm
-
TotalCollofrn
0
500
1,000
1,500
2,000
2,5
3,000
3,5W
Core GDGTs (in ng GDGT / g total TLE)
. GDGT-0 . GDGT-1 - GDGT-2 u GDGT-3 . Crenarchaeol
4,000
4,50
0
500
G GDGT-0
1,000
1,500
2,000
2,500
Total G-GDGTs (inngG-GDGT/g total TLE)
GGDGT-1
G-GDGT-2 mGGGT-3
G-crearchaeol
Figure 3.6 All GDGTs Normalized Abundances
This figure shows the actual normalized abundancesof the different GDGTsfound within the samples and demonstrates the contributionof each
GDGT to the total lipid amount.
....
....
....
.....
...
. ...
............................................................................
......................
I..........
.
O
In an initial comparison of the overall yield of total core GDGTs and total G-GDGTs, based
on the normalized ratio of ng GDGT per g of TLE, a predominance of GDGTs was
demonstrated in the samples. With this, it is clear that the more ancient lipids that have
lost their polar head groups (GDGTs) are more abundant than those representing still
living or recently alive microbes (G-GDGTs). In the pustular mats, there was the most
dramatic difference with the core GDGTs being over 4 times the G-GDGTs, which was
followed closely by the smooth mats, which have core GDGTs being about 3.5 times
greater than the G-GDGTs. Even the lowest difference between the totals was significant,
as it was an almost doubling of the amount of G-GDGT to reach the amount of GDGT.
As previously demonstrated by relative abundance, Crenarchaeol lipid portions for
pustular and colloform mats are similar; however, the quantitative information allows for
further distinctions between the two similar microbial mat forms. For each, the
dominance of Crenarchaeol lipids is represented differently between the pustular and
colloform mats, being 1,729 ng / gTLE and 268 ng/gTLE respectively. Despite the lower
portion of total GDGTs that Crenarchael GDGTs comprise for smooth mats, the amount
of Crenarchaeol GDGT is higher than that of pustular mats, at 300 ng / gTLE. Yet, when
averaged over the total number of smooth samples (5), the amount of Crenarchaeol GDGT
is only 60 ng / gTLE, much lower than the average amount for colloform mats and pustular
mats, 345.8 ng / gTLE and 268 ng / gTLE, respectively. Overall the different types of
GDGTs and G-GDGTs, the colloform mats have higher average amounts of each with
respect to the TLE than pustular mats, which suggests a higher microbial community
density for colloform mats.
Between the smooth and colloform mats, the smooth mats were found to have average
GDGT amounts that were lower than those of colloform mats for all GDGTs except for
GDGT-0. Interestingly, despite other variations, the average amount of GDGT-3 between
the colloform and smooth mat samples was very similar (42 ng / gTLE and 41.8 ng / gTLE,
respectively), while the pustular mat sampled lacked any definite signature of GDGT-3.
In terms of G-GDGTs, however, smooth mats had consistently lower amounts of all GGDGTs than colloform mats. From this data, it can be interpreted that smooth mats are
less dominated by currently (or recently) living microbial biomass, suggesting potentially
unfavorable environmental conditions within the smooth mats for living organisms,
especially in comparison to the colloform mats. Extending this comparison to pustular
mats, it is found that smooth mats have higher average amounts of all G-GDGTs, but only
higher average amounts of GDGT-0, GDGT-2, and GDGT-3. The distribution of lipids
between all three mat types is very different, yet the types of GDGTs and G-GDGTs
represented are relatively consistent, which suggests microbial communities that have
39
similar members, but at different abundances and distributions within the zones of the
smooth and pustular mats.
3.4.d Interpreting Both Runs of Relative Results
The presence of GDGT-0 could signify a small archeael population in an anaeorobic
environment, as the biological sources from which GDGT-0 is often isolated from are
methanotrophs, while the presence of Crenarchaeol GDGT suggests aerobic conditions,
as its common biological source is ammonium oxidizing thaumarchaea (De la Torre et al.,
2008). By expressing a high GDGT-0 percentage and a lower Crenarchaeol percentage,
the smooth mats could be more dominated by anaerobic microbes, with a lower portion
of microbes that would thrive in aerobic conditions. In contrast, the higher abundance of
GDGT-0 in the pustular and colloform mats suggests that the microbes dominating these
mats are aerobic microorganisms.
One possibility for an explanation of the distribution of GDGTs relates to the morphology
of the different microbial mats and stromatolites. With filamentous cyanobacteria being
responsible for the binding and trapping of sand grains (Reid et al. 2000) and
microorganisms boring into the surface, there could be more pore space then left
throughout the mat. The presence of further openings could allow for oxygen to diffuse
to a greater depth within pustular mats. This could be a potential explanation for the
higher dominance of GDGTs in the pustular mats as Crenarchaeol.
A steeper decrease of Crenarchaeal GDGTs was found between the upper and lower
portions of the various smooth mats, a distinction that could be related to not only
&
sampling depth, but also to morphology. Smooth mats and their flat laminae (Jahnert
Collins, 2012) could be responsible for the upper levels of smooth microbial mats having
less oxygen for the microbes to utilize. Progressing deeper within a less porous substance
would logically result in lower amounts of oxygen at a shallower depth, as compared to
more porous substances, like pustular or colloform microbial mats. The significant
decrease of Crenarchaeol GDGTs from the upper samples to lower samples of the smooth
mat (from 35% to 19% relative abundance) supports this theory. However, the
quantitative measurements demonstrate that, despite the decrease in overall portion of
Crenarchaeol GDGTs, the actual amount increases about 50% from the upper samples
(131 ng / gTLE) to the lower samples of the smooth mat (to 196 ng / gTLE), which could
signify another microbial population that dominates the lower mat, while the other
microbes persist at depth.
40
Interestingly enough, the shift from Crenarchaeol GDGTs colloform mats is not to the
anoxic environment signifying GDGT-0, but to GDGT-1. For the smooth mats, the portion
of GDGT-1 increases dramatically from 8.04% to 62.12% when progressing from the upper
layers to the lower layers. The colloform mats show a much less dramatic increase of
6.61% (from 4.33% to 10.94%), demonstrating that this shift is not solely due to the depth
at which a microbial community is located. Current studies on the function of GDGT-1
are predominately focused on its disappearance when temperatures increase (e.g.
Schouten et al. 2002; Pearson et al. 2008), a factor that occurs because of its less
thermotolerant structure with only one pentacyclic ring (as compared to four pentacyclic
and one 6-membered ring in Crenarchaeol GDGTs, which are associated with
Thaumarchaeota).
In contrast to the shift in GDGTs from Crenarchaeol GDGTs to GDGT-1, the G-GDGT
signatures for both smooth mats and colloform mats demonstrate the expected shift to GGDGT-0 in the lower levels. For the upper portions of the smooth mats, Crenarchaeol GGDGT comprises 46.60% by weight, which then drastically decreases to 15.05% by weight.
At the same time, the GDGT-0 increases from 48.78% by weight to 72.17% by weight of
the G-GDGTs in the sample, with G-GDGT-1 and G-GDGT-2 increasing by <5%.
Additionally, it is worth noting that the G-GDGT data show a higher overall portion of
Crenarchaeol G-GDGTs in the upper smooth mats (46.60%) as opposed to the GDGT data
(32.52%), so there is an undeniably greater change in the microbial community
composition suggested by the G-GDGTs.
These differences in the response of the G-GDGTs and GDGTs can be a sign that there are
significant differences between the modern system and the older, ancient microbial
community. Core GDGTs are the molecular fossils of intact G-GDGTs, so represent the
ancient community. According to the results of this study, the ancient community of
smooth mats would experience a less direct shift from an aerobic environment to an
anoxic environment, while the modem microbial communities of smooth mats are
allowing for a clear, fast transition from a more aerobic upper region to a lower region
highly dominated by microbes that thrive in anoxic environments.
41
3.5 Bacteriohopanepolyols
3.5.a Quantifying Data and Validating the Instrument
Semi-quantitative results were attained for BHPs through the use of the internal standard
3a,12a Dihydroxy-5p-pregnon-20one, 3,12-diagetate (Pdia). The exact masses for distinct
BHP compounds and for this standard (which is m/z 359.2579) were selected by the QTOF
for analysis, so an integration of the derived EIC could be taken for both. Because the
ratio of mass to peak area is known for the internal standard Pdia, this fraction can be
related to an unknown mass of a BHP over its peak area and then solved to determine the
amount of mass that is composed of the particular BHP (Figure 3.7).
A high background signal did not allow for some of the hopanoids to be unambiguously
detected using this method, particularly the Bacteriohopanetetrol (BHPentol), which
could only be determined for Smooth Strom 1. For the others samples, the peaks in the
EICs were unable to be distinguished and then integrated to determine a nonzero value
of BHPentol.
08
WPa EI471A.53
MS(a Frg.15I.
11
S-APC EIC71&5300 Scan FaQ.1mW
1404J0.d
I
Smooth Mat 2 Top
x109 *APCI EIK474.9 S30W
14313 sa1OW 144M3..0d
:1
APC EKQ714.530 MS(A Frg-150
140-3003.d
r S mo
A1
APO EIC4714 5300 Scan Frag.150EP 14-04-3004.d
oth Mat 2Bottom
A PC EIC(71453M) Scan Fp1.OGV 1404-3L05.d
1404301d
Smooth Mat 3 Top
4APOI EIC(714.5300 Scan FrIMCM0G
144M30-.O~d
Smooth Mat 3 Bottom
Figure 3.7 Quantifying BHPs with Extracted Ion Chromatograms
The Extracted Ion Chromatograms (EIC) isolatedfrom the different samples by the programmed
method showed the bacteriohopanepolyolAminotriol particularlyclearly (left). These EICs were
then extracted once more at the specific mass of the Aminotriol (m/z 712) to create an EIC that can
be integrated (right) in order to determine the relative quantities of Aminotriol in each sample.
The sensitivity of the QTOF instrument before data collection was tested by creating a
calibration curve derived from data of the initial injections into the qTOF. Initial dilutions
injected were 5 ng, 10 ng, and 15 ng. Each injection was subjected to the standard method
42
protocol described previously and extracted ion chromatograms were created for the
specific fragment corresponding to the standard, Pdia. The integration of this peak was
then plotted against the defined mass of the standard injection, so a calibration could be
determined to see the detection limit and, from that, interpret the current sensitivity of the
QTOF. A thorough understanding of the overall state of the QTOF before use is important
in determining the reliability of data it provides. For this study, the sensitivity of the
QTOF was high enough that the results could be used directly. In the future, a calibration
curve could be utilized to make any quantified results even more precise.
3.5.b Major BHP Signals
The contributions of BHT and aminotriol hopanoids to the overall hopanoid composition
were relatively stable and always significant among the samples analyzed here. As in
other studies, the common BHT intact hopanoid clearly was the most dominant hopanoid,
comprising from 74% to 90% of the total BHPs in the sample (e.g. Taylor 2009). Another
consistent hopanoid found in these samples was aminotriol, which has been previously
correlated to purple non-sulfur bacteria (Neunlist & Rohmer 1985). Based on this
correlation, it would expected that the highest amounts of aminotriol would be found in
the layers that would provide anoxic conditions and light exposure. The samples with
the most aminotriol hopanoids overall were Smooth Strom 1 and Colloform Mat 2 Bottom,
which are not necessarily better for assuring either condition. However, worth noting is
that, while the portion of total BHPs that the aminotriol composes is larger for all the
upper mat sections, except for in Smooth Mat 2, the amount of aminotriol hopanoid is not
larger for any of the upper layers of the smooth mats.
2-Me BHTs (Appendix B-3) were also abundant within the samples studied, which, as
previously mentioned, had been initially considered to be biomarkers of cyanobacteria
until they were found to be produced by other bacteria, as well (Rashby et al., 2007).
However, when the microbial diversity of Hamelin Pool was studied, it was determined
that the majority of these 2-Me BHTs were associated mostly to cyanobacteria for the
smooth and pustular mats (Garby et al., 2013). With the relatively similar and ubiquitous
presence of the 2-Me BHTs in all types of mats in this study, it is probable that they derive
from the same source, thus now adding colloform mats to those that have 2-Me BHT
producing cyanobacteria.
43
Normalized BHP Amounts (ng BHP
0
g.AE)
1
A
n
b
n
ee
1
Snmoth~trm
SmoothMM 2Top
Smooth M altom
SmoxthMM4Tap
SnM~hMat S Botom
EAmowlwme
*BHTf
CoboormStroml
U2-Meffif
Cobo~W.Mat 2 Top
ES-Me-f
UB&fetl
Cok k, , M
2
Colb*mMM Slop
Cyof~m Mat 3
CoIolrm Mat 4
*AmkiwUo
Figure 3.8 Normalized BHP Abundance by Wei gh'-t
After having been normalized to the total grams of TLE, the abundances of the different bacteriohopanepolyols were compared.
PUSUr Mat I
BHP Abundance of Normalized BHP Amounts (ng BHP / g TLE)
Smooth Srom 1
SmoothMat 2Top
Smooth Ma 2 Bottom
Smooth Mat STop
Smooth Mat SBottom
Coloform Strom 1
Cololrm Mat 2 Top
ColoformMa 2 Bottom
ColoformMat
C010form
3 Top
Mat S Bottom
Colofrm Mat 4
Pultulr Mat
1
0%
10%
50%
20%
*AdtenI
pe
U
O
40%
50%
Portion of Total BHPs
E42e
2T
IT MS-Me-SIT
60%
70%
B0%
90%
100%
(%)
EO etWMol
EAmhctrtol
Figure 3.9 Normalized BHP Abundances
BHP abundances are shown here, as represented in percent of the total BHP content.
One of the most interesting results is the presence in trace amounts (1-5%) in 10 of the 12
samples of 3-Me BHT, a compound that has been rarely isolated from various samples.
Methylation at the C3-position of the A-ring (substituting for R3 in Figure 1.1; Appendix
B-4) had been first described in acetic acid bacteria and aerobic methanotrophs. Recently,
genes encoding the potential for C3-methylation were found to be restricted to bacteria
with aerobic metabolism (Welander and Summons, 2012). If following this relatively
recent finding, the 10/12 samples with strong potential 3-Me BHT have more aerobic 3Me BHT producing bacteria than the remaining samples. Interestingly, the only two
samples that did not have the possible 3-Me BHT were the top layers of both smooth mats.
Logically, the exposure to oxygen at that layer should support the growth of bacteria with
aerobic metabolisms, yet the more isolated layers of the same smooth mats had a
noticeable amount of 3-Me BHPs.
As much as possible, the identity of these compounds was confirmed, using differences
in retention time and characteristic fragmentation of the overall molecule, as well as a
similarity in the mass. The 3-Me BHTs and 2-M3 BHTs had the same mass and so were
isolated at 669.51 m/z. Further fragmentation of the molecule resulted in the characteristic
fragments of 191 and 205 m/z throughout 10 of the possible 3-Me BHT peaks. Each of
these potential 3-Me BHT peaks eluted about 1 minute after the confirmed 2-Me BHT
peak, and a difference in elution time would be expected for compounds that are
structurally dissimilar.
45
3.5.c Other BHP Signals
Only one sample, Colloform Mat 3 Top, contained adenosylhopanes, which have been
linked previously to soil bacteria that are then transported to marine environments and,
eventually, marine sediments (Talbot & Farrimond, 2007). Additionally, adenosylhopane
has been identified in purple non-sulfur bacteria (Neunlist & Rohmer, 1985) and
ammonia-oxidisers (Seemann et al., 2009). Since adenosylhopane is a key biosynthetic
intermediate in the pathway to all extended hopanoids (Welander et al., 2012), its presence
in these mats is no surprise and no particular conclusions about its origins can be drawn.
BHPentol has been correlated to cyanobacteria (Bisseret et al. 1985), yet this has not been
proven to be limited only to those bacteria. This minor BHPentol signature cannot then
be interpreted to signify a distinction between the microbial communities of Smooth
Strom 1 and the other samples.
46
Chapter 4
Conclusions
4.1 Overall Significance of These Characteristic Lipid Profiles
Through this study, a detailed profile of various lipids of varying degrees of recalcitrance
was created, which will be beneficial in understanding the overall microbial community
assemblage in the microbial mats and stromatolites of Hamelin Pool. Significant evidence
corroborated among the different lipid analysis techniques supports the expected
presence of microenvironments created in these layered deposits, including two that
support ammonium oxidizing anaerobes and sulfate reducing bacteria. Particularly with
the abundance of GDGT Crenarchaeol, ammonium oxidizing anaerobes are likely
thriving within the layers of the microbial mats, particularly in the colloform lower layers
and the upper layers of the smooth mats. High signatures of Trimethyl ornithine lipids
in the IPL analysis also support the presence of microbes that have anaerobic autotrophic
metabolism. The sulfate reducing bacteria are also prevalent within the microbialites of
Hamelin Pool, as suggested by both IPL data and FAME data.
Another suggested
metabolism by this data is aerobic because of the presence of 3-Me BHTs. However,
without a distinct increase in the amount or concentration of the potential 3-Me BHTs in
the upper layers of the mats, there is not enough evidence in this study to support the
claim that these compounds originate from microbes that need oxygenated environments
(Welander et al., 2010).
Morphology of the microbialites in Hamelin Pool also appears to have a significant impact
on the lipid profiles created, so would, by extension, have an impact on the microbial
community assemblage. The diffusion of oxygen through the surface of a microbial mat
allows for aerobic metabolisms to persist at greater depths in the mats, as is evidenced in
the difference of Crenarchaeol GDGTs between the more porous colloform mats and the
less porous smooth mats. It would be interesting to see the correlation between the upper
and lower layers of pustular mats, as well as overall pustular mats, to confirm any
similarities between colloform and pustular mats.
Any similarities determined could
provide additional evidence of the significance of morphology on microbialite community
compositions.
47
As was the original goal of the study, this full profile of different lipids allows for a more
thorough understanding of current microbial community assemblages, as well as for what
ancient microbial community assemblages might have looked like.
With the more
recalcitrant lipids determined from this study, namely the GDGTs and BHPs, there is now
additional information defining the basic lipid profile for ancient, microbially derived
sedimentary structures. If ancient stromatolites are found to have a similar profile in the
lipids that persist throughout time, they could be made directly analogous to the current
microbialites in Hamelin Pool.
This type of comparison would then provide an
understanding of the most ancient life forms that can be seen in the rock record.
4.2 Future Work
Findings from this study could be made much more robust through additional testing and
analysis, some of which is currently occurring.
FAMEs are being subjected to a
dimethyldisulfide (DMDS) procedure in order to determine the exact location of the
double bonds in the different fatty acids observed, especially those with large signatures,
like C16:1 and C18:1. Such dominance in the overall FAMEs of the sample suggests that there
is some significance to these fatty acids. It is possible that elucidating the structure of
these unsaturated carbon chains could prove to correlate directly with the FAMEs
common in environments dominated by sulfate-reducing bacteria.
Other current FAMEs adjustments include a restructuring of the method executed for GCMS analysis in order to capture signatures of the long-chain carbon molecules surpassing
C26
in size, which would support a better understanding of the potential terrestrial inputs
in this area by demonstrating (or by showing a lack of) long-chain signatures that are
related to the plant waxes of vascular plants.
In addition to the F3 fraction, the F1 fraction is also being more closely analyzed.
Contamination by sulfur, as the product of sulfate reducing bacteria, requires a cleaning
of the sample to oxidize the sulfur to S8 and then result in a clear F3 chromatogram. With
these alkanes, it would be possible to determine whether or not unsaturations within the
FAMEs were representative of the other lipids within the samples and if they could be
linked to different microbes. Also, isotopic analysis for both F3 and F1 would allow for a
robust view of the metabolism of the microbes of these microbialites; unfortunately,
isotopic analysis is not a part of the current continuation of the research, especially
because this analysis was conducted on samples from this location by Allen et al. in 2010.
48
Other possible improvements to the study include further collection of samples, especially
for different layered sections of pustular mats and in different seasons. With a more
detailed data set, conclusions drawn about the microbial community could be much more
reliable because of the additional data. The pustular mats have appeared to be more
similar to the colloform mats, based on the results of this study, yet they were not isolated
into different layers, so it was impossible to compare the lipid profiles at depth. For all of
the mats, it would be interesting to sample even deeper and at different increments to see
any noticeable changes in the microbial community. There could be similar shifts in the
microbial community assemblage that occur at different depths, which could provide
further details about the layered micro-environments of these microbialite structures.
Also, as these samples were collected during the Australian winter, it would be interesting
to see the lipid signatures during the summer to determine if there were any blatant
changes. One would expect there to be an increase in the primary producers, so there
could be an increase in the various lipids relating to cyanobacteria, including 2-Me BHTs
(proven for this location to correlate to cyanobacteria) and some intact polar lipids.
49
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Appendices
Appendix A
A-1
Sample Nomenclature
Published Name
Smooth Strom 1
Smooth Mat 2 Top
Smooth Mat 2 Bottom
Smooth Mat 3 Top
Name at Collection
6/14/11 Smooth Mat Strom 1 Carbla
6/15/11 Smooth B Top
6/15/11 Smooth Bottom B
6/15/11 Smooth C Top
Smooth Mat 3 Bottom
6/15/11 Smooth Bottom C
Colloform Strom 1
Colloform Mat 2 Top
Colloform Mat 2 Bottom
Colloform Mat 3 Top
Colloform Mat 3 Bottom
Colloform Mat 4
Pustular Mat 1
6/14/11
6/15/11
6/15/11
6/15/11
6/15/11
6/15/11
6/17/11
CSB Colloform Strom 2
Colloform Top A
Colloform Bottom A
Colloform Top B
Bottom B Colloform
Colloform Composite
Carbla Pustular 6/17
Collected Samples were initially provided more descriptive names that included details about
location, date collected, and the depth of the sample. Names have been adjusted for nomenclature
consistency in publications.
A-2
Table of Portion TLE per Dry Weight of Samples
% TLE by dry wt.
0.13%
0.10%
0.12%
0.09%
0.09%
0.13%
0.24%
0.17%
0.23%
0.13%
0.08%
0.17%
Sample Name
Smooth Strom 1
Smooth Mat 2 Top
Smooth Mat 2 Bottom
Smooth Mat 3 Top
Smooth Mat 3 Bottom
Colloform Strom 1
Colloform Mat 2 Top
Colloform Mat 2 Bottom
Colloform Mat 3 Top
Colloform Mat 3 Bottom
Colloform Mat 4
Pustular Mat 1
57
91
1-
C=
ai
i -sC=
I
i ei -c .
I
.i~ ..
C2
a.-
ilTIT1HICi
IA
C
-
No_1__
I
C
Lnc r:" --F-
I1.,
C
C4
I
I
1
A-3 Master Table of normalized (ng compound I g TLE) compounds
I
I
I
58
1:L
0n
Q.
oz
o
0
UA
___~
*
I.~
I...
~
'I
A'
LL
4 ~
V
2
f~~Il
''fit 4fl~~JV~1L
~
1 ~~
~
-2
b
4***?
I
A
0
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0
0
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... .. ...... . ..,. .........
1
-
s
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14
-t-
II
bOf
.
1 A !
-
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-ip
-C
-e
-C
O
c
.
"
Appendix B
B-1
Structure of GDGTs (figure from Pitcher et al. 2010)
GTGT-0
GOGT-5
MZ 1304
nfl 1292
GDGT-0
m/z 1302
Crenarcheeol
nfl 1292
GDGT-1
m/z 1300
Cron'
m~z 1292
GDGT.2*H
rnf 1298
GDGT4
m&x 1290
GDGT-3*
m~z 1298
GDGT-7H
nftl 1288
HH
*
H
H
H
GDGT-X
m/z 1290
.
GDGT-4
mIz 1294
H
H
B-2 G-GDGTs have an additional Glyco group in place of one of the terminal OH
bonds
HO
NH4+
HO
0
HNO
0
H
60
B-3
2-Methylbacteriohopanetetrol (2 Me-BHT) acetate molecule, as elucidated
by Sessions et al. in 2012
OAc
OAc
Ac
Ac
B-4
3-methylbacteriohopanetetrol (3-Me BHT) acetate molecule, as elucidated
by Sessions et al. in 2012
OAc
OAc
Ac
61
Ac
B -5
2-methylbacteriohopaneaminotriol (2-Me Aminotriol) acetate molecule, as
elucidated by Sessions et al. in 2012
OH
OH
OH
62
NH2