THE DOMINANCE OF THE ARCHAEA IN THE TERRESTRIAL SUBSURFACE A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Michael D. Johnston December, 2013 i THE DOMINANCE OF THE ARCHAEA IN THE TERRESTRIAL SUBSURFACE Michael D. Johnston Thesis Approved: ____________________________________________ Primary Advisor Dr. Hazel A. Barton ____________________________________________ Committee Member Dr. R. Joel Duff ____________________________________________ Committee Member Dr. John Senko ____________________________________________ Department Chair Dr. Monte Turner Accepted: ____________________________________________ Dean of the College Dr. Chand Midha ____________________________________________ Dean of the Graduate School Dr. George R. Newkome ____________________________________________ Date ii ABSTRACT Studies on deep aquatic ecosystems have demonstrated that the distribution of the Bacteria and the Archaea changes dramatically with depth and resource availability. At deeper depths, archaeal populations were shown to dominate and often surpass bacterial populations in abundance. Given this variable trend in deep aquatic systems, we explored whether subterrestrial populations of bacteria and archaea fluctuated similarly. Lechuguilla Cave provided an unprecedented opportunity to examine bacteria and archaea populations to a depth of 400 meters within a well-­‐preserved subsurface ecosystem. The microbial community present at each cave depth was quantified using fluorescent in situ hybridization (FISH) microscopy, absolute qPCR and GDGT lipid analysis, whereas rock/sediment samples were collected for geochemical analysis. The results of our study demonstrated that the Archaea dominated the microbial populations below a depth of 250 m. Our elemental analyses demonstrated that the concentration of organic carbon, nitrogen and iron are important factors regulating the abundance of bacteria and archaea at a particular depth. Geochemical analyses revealed that nitrogen limitation produced exceptionally high organic C:N ratios at the deepest depths, which would suggest that microbial populations should be dramatically declining due to the difficulty in obtaining the nutrients necessary for cellular maintenance and growth. Instead, microbial communities were found to only iii slightly decline in size at deeper depths, suggesting compensating mechanisms (e.g. autotrophy, nitrogen fixation) were responsible in sustaining populations. Microorganisms are known to be critical players in global geochemical cycles through a variety of energy-­‐yielding redox reactions, yet few studies have examined the community structure and physiology of microorganisms within the deep terrestrial subsurface. Our study revealed that archaea dominated microbial populations at deeper depths, presumably through domain-­‐unique adaptations that allow them to survive in these nutrient-­‐stressed conditions. Thus, a better understanding of archaeal-­‐mineral interactions in the subsurface would lead to a greater understanding of the energy dynamics associated with extreme nutrient limitation and the limits of microbial life on Earth. iv ACKNOWLEGEMENTS We would like to thank Beth Cortright, James Hunter, Shawn Thomas, Stan Allison and the Carlsbad Caverns National Park Service for access and in-­‐cave assistance. We would also like to thank Dr. Hongmei Wang (China University of Geosciences), Mark Stauffer (Elemental Analysis Inc.) and the technicians at Advanced Genetics Technologies Center (University of Kentucky) for their assistance in sample analyses. Lastly, we would like to thank the National Science Foundation (Grant # 0643462) for funding our research.
v TABLE OF CONTENTS Page LIST OF TABLES ………………………………………………………………………………………..….…. viii LIST OF FIGURES ………………………………………………………………………………………….…… ix CHAPTER I. INTRODUCTION ……………………………………………………………………………………………... 1 The Dominance of Archaea in Deep Aquatic Environments. ………………………. 1 Adaptations of Archaea to Occupy Nutrient-­‐limited Niches ………….……………. 1 Lechuguilla Cave: An Unprecedented Opportunity to Study Subterranean Microbial Communities ……………………………………………………… 2 Chemolithoautotrophic Bacteria and Archaea ………………………….……………….. 2 II. MATERIALS AND METHODS ………….………………………………………………………………. 4 Sampling of Cave Sites …………………………………………………………………...………... 4 Genomic DNA Extraction from Rock/Sediment Samples …….……………………… 5 Geochemical Analysis ………………………………………….…………………………………… 7 Cell Enumerations by FISH Microscopy ……………………………………………………. 7 Absolute Quantitative PCR Assays ……………………………………………………………. 9 Analysis of GDGT Lipids from Cave Rock/Sediment ………………………………… 11 Principal Component Analysis ……………………………………………………………….. 13 III. RESULTS ……………………………………………………………………………………………………. 14 vi Sample Site Analysis ……………………………………………………………………………… 14 Geochemical Analysis …………………………………………………………………………….. 17 Bacteria and Archaea Cell Counts by FISH Microscopy ……………………………. 18 Bacteria and Archaea Cell Estimations by Absolute qPCR …………...…………… 19 GDGT Lipid Analysis ……………………………………………………………...………………. 20 Principal Component Analysis ……………………………………………...………………... 21 IV. DISCUSSION OF RESULTS ………………………..………………………………………………….. 28 LITERATURE CITED …………….......…………………………..…………………………………….......... 34 APPENDICIES …………………………………………………………………………………………………... 39 APPENDIX A: TABLES …………………………………………………………………………….…… 40 Table 1A: Bacteria-­‐ and Archaea-­‐Specific Probes for FISH Microscopy …….. 41 Table 2A: Hybridization Buffer Recipe for FISH Microscopy ………………….… 42 Table 3A: Wash Buffer Recipe for FISH Microscopy ………………………………… 43 Table 4A: Bacteria-­‐ and Archaea-­‐Specific 16S rRNA Gene Primers for Absolute Quantitative PCR …………………………………………………………...…… 44 Table 5A: Elemental Analysis of Cave Depths (Sites S thru EA) ………………... 45 Table 6A: Elemental Analysis of Cave Depths (Sites BS thru SS) ………………. 46 APPENDIX B: FIGURES ………….……………………………..……………………………………… 47 Figure 1B: Plasmid-­‐Based Standard Curves for Absolute qPCR ……………..…. 47 Figure 2B: GDGT Lipid Analysis (Sites S thru EA) ………………………………….… 48 vii LIST OF TABLES Table Page 3.1 Physical Analysis of Sample Sites …………………………………………………………… 16 3.2 Elemental Analysis of Carbon and Nitrogen ………………………………….………… 17 3.3 Bacterial and Archaeal Cell Estimates by FISH Microscopy …….…………...…… 18 3.4 CT Values and Copy Number Estimations by Absolute PCR ………….…………… 20 3.5 Principal Component Analysis of Cave Geochemistry and Cell Estimates ……………………………………………………………………………………………… 23 3.6 Estimated Proportion of Total Nitrogen Incorporated into Cellular Biomass ……………………………………………………………………………….…… 32 viii LIST OF FIGURES Figure Page 2.1 Lechuguilla Cave Map …………………………………………………………………...…………. 4 2.2 Absolute Quantitative PCR Copy Number Formula…………………………...……… 11 3.1 Lechuguilla Cave Map Portraying Sample Sites …………………………………..…… 14 3.2 Explained Variance Amongst Sample Sites (Including Surface) .................…… 21 3.3 Geochemical Variance Amongst Sample Depths …………………………...…………. 22 3.4 Elemental Concentrations Vs. Bacteria/Archaea Cell Estimates ……………..… 23 3.5 Iron Concentration Vs. Total Cell Counts …………………………..…………………….. 24 3.6 Nitrogen Concentration Vs. Cave Depth …………………………..……………………… 24 3.7 Organic C:N Ratio Vs. Cave Depth …………...……....……………………………………… 25 3.8 Bacteria:Archaea Ratio Vs. Cave Depth …………...…………..………………………….. 25 3.9 Total Cell Counts by FISH Microscopy Vs. Cave Depth ……...................…..……… 26 3.10 ATP Concentrations Vs. Cave Depth ………………...……………………………..…….… 26 3.11 ATP Concentrations Vs. Bacteria Cell Estimates ……………………………..……….. 27 4.1 Comparison of Cell Estimates by FISH Microscopy and Absolute qPCR ………………………………………………………………………………………………..…… 28 4.2 Total Cell Estimates by FISH Microscopy Vs. Cave Depth ……………….………… 29 4.3 Proportion of Microbial Community Represented by Bacteria and Archaea …………………………………………………………………….……….…………… 30 4.4 ATP Concentrations Vs. Bacterial and Archaeal Cell Estimations ……………... 32 ix CHAPTER I INTRODUCTION Quantitative studies on marine and deep aquatic environments have shown that the distribution of bacteria and archaea changes dramatically with depth and resource availability. At greater depths, archaeal populations were shown to dominate and often surpass bacteria in abundance [8, 12, 50 & 51]. Previous studies revealed that archaeal populations dominate at depths where organic carbon and nitrogen sources were scarce and concentrations of phosphate, sulfate and ammonium were high [22 & 30]. Prokaryotic ammonia oxidation had previously been thought only to be performed by bacteria, but recent studies have shown that a vast number of archaea are diazotrophic in nature with an inorganic nitrogen-­‐based primary production strategy [14]. Due to the difficulty in culturing archaea within a laboratory setting, the actual physiological roles of archaea in the environment have remained elusive. Previous studies suggest high chemolithoautotrophic activity by archaea in the environment, and it is hypothesized that archaea have evolved biochemical mechanisms (e.g. low-­‐permeable membranes, well-­‐defined catabolic pathways) that allow them to out-­‐compete bacteria in nutrient-­‐ and energy-­‐limited conditions [41 & 53]. Given the variable shift of bacteria and archaea populations in deep aquatic ecosystems, we wanted to explore whether subterrestrial populations 1 of bacteria and archaea fluctuated similarly under nutrient-­‐ and energy-­‐limited conditions. To test this, we analyzed bacterial and archaeal communities at varying depths within a large, vertical cave system. Lechuguilla Cave provided an exceptional opportunity to examine bacterial and archaeal communities within an oligotrophic, subterranean system, without the need for drilling or other type of ecological disturbance. Lechuguilla Cave, located in Carlsbad Caverns Nation Park, New Mexico, is currently the deepest cave in the continental US (489 m) and the seventh longest cave system in the world (222.6 km in length). Lechuguilla Cave was formed primarily in the Capitan Formation of the Guadalupe Mountains by sulfuric acid speleogenesis, more than 6 million years ago, and is comprised primarily of reef limestones, along with numerous secondary elements (e.g. Ca, S, Fe, Mn, Ti, Si) of mineral origin [15, 21 & 44]. The bedrock of Lechuguilla Cave is known to contain manganese and iron oxide-­‐rich deposits, of which the reduced form is available for lithotropy. Chemolithoautotrophic bacteria and archaea capable of deriving energy from reduced elements (e.g. sulfur, iron, manganese, hydrogen) have been identified within Lechuguilla Cave that are genetically related to species known to subsist in low nutrient, high calcium conditions [38]. Lechuguilla Cave is world-­‐renowned for its pristine condition and limited human disturbance, which allows an unprecedented opportunity to examine microbial communities and microbe-­‐mineral interactions in a natural, subterranean setting. To examine the bacterial and archaeal communities present at various depths within Lechuguilla Cave, rock/sediment samples were collected from various 2 locations. Samples were analyzed geochemically to determine the main constituents within the rock/sediment that could support microbial growth. From these rock/sediment samples, total and domain-­‐specific cell numbers were estimated by fluorescent in situ hybridization (FISH) microscopy, absolute quantitative PCR, and quantitative GDGT core membrane lipid analysis. Such an in-­‐
depth examination of bacterial and archaeal populations within a nutrient-­‐ and energy-­‐limited subterranean environment should lead to a better understanding of community dynamics and nutrient cycling within the subsurface.
3 CHAPTER II Sampling of Cave Sites MATERIALS AND METHODS Samples were collected in a vertical transect through the cave system, starting from the entrance (surface; S) and moving downward at roughly 50 meter intervals until approximately 400 meters below the surface (Figure 2.1). Figure 2.1: Lechuguilla Cave Map Sample collections were conducted in remote locations of the cave from areas with minimal indication of human visitation or contamination. Temperature and humidity measurements were recorded from each site using a H1 Series hygrometer (Nanmac, Framingham, MA), while pH values were noted using a portable SevenGo Pro pH meter (Mettler Toledo, Columbus, OH) with a sediment probe. Adenosine 4 triphosphate (ATP), a major energy currency molecule of cells, was measured at each depth by swabbing a 4-­‐cm2 area using a SystemSURE Plus ATP luminometer along with Ultrasnap ATP Surface Testing swabs (Hygenia, Camarillo, CA), which had been standardized beforehand using Hygenia’s calibration control kit. From each depth, numerous rock/sediment samples were collected using a Dremel tool (Racine, WI) with a flame-­‐sterilized, diamond-­‐coated rotary saw. For geochemical analysis, roughly 10 g of rock/sediment was collected from each site and placed inside a sterile 50 ml conical tube for transport. Rock/sediment samples were also collected for archaea-­‐ and bacteria-­‐specific cell estimations by fluorescent in situ hybridization (FISH) microscopy, absolute quantitative PCR and GDGT core membrane lipid analysis. For cell estimations by FISH microscopy, roughly 10 g of rock/sediment was placed into a 15 ml tube containing 4% paraformaldehyde in phosphate buffered saline (PBS). Once outside of the cave, samples were washed twice over with 1X PBS, placed in a methanol/1X PBS solution (1:1 vol) and stored at -­‐20°C until being processed further. For analysis by absolute qPCR, approximately 20 g of rock/sediment from each depth site was collected and placed in 70% ethanol for transport out of the cave, whereupon samples were stored at -­‐
20°C until environmental DNA extraction and purification was performed. For GDGT lipid analysis, roughly 30 g of pulverized rock/sediment was placed inside a sterile 50 ml conical tube. Genomic DNA Extraction from Cave Rock/Sediment For extraction of environmental DNA for absolute qPCR, approximately 1 g of rock was crushed into a fine powder using a steel pestle-­‐and-­‐mortar and placed into 5 a 2-­‐ml screw-­‐cap eppendorf tube containing 750 μl of 2X buffer AE (200mM Tris [pH 8.0], 200mM NaCl, 300 mM EGTA, 50 mM EDTA, pH 8 with NaOH), 20 μl of RNase A (100 μg/μl) and 45 μl of lysozyme (100 mg/ml). Samples were incubated at 37°C for 30 min, whereupon 90 μl of proteinase K (20 mg/ml) and 15 μl of sodium dodecyl sulfate (SDS, 20% wt/vol) were added and the sample was incubated at 50°C for 30 min. After incubation, 200 μl of sodium dodecyl sulfate (20% SDS wt/vol), 500 μl of chloroform-­‐isoamyl alcohol mixture (24:1 vol/vol) and 0.5 g of acid-­‐washed, zirconium-­‐silica beads (0.1 mm) were added to each sample, with gentle mixing. Samples were then disrupted on a Mini-­‐bead beater (Biospec, Bartlesville, OK) for 2 min, spun down in a microcentrifuge at 10,000 x g for 5 min with the aqueous supernatant pipetted into a new 2-­‐ml screw-­‐cap eppendorf tube. Lysates were extracted twice over with chloroform (1:1 vol) and nucleic acids precipitated using 0.3 M sodium acetate (10% final volume) along with 3 volumes of pure ethanol. Nucleic acids were spun down at 13,000 x g 10 min, air-­‐dried and hydrated in approximately 100 μl of molecular grade, nuclease-­‐free water. Due to the complex geochemistry of Lechuguilla Cave, extracted genomic DNA potentially contained PCR-­‐inhibiting cations (e.g. calcium, iron, potassium), polyphenolic compounds (e.g. fulvic acid, humic acid), alcohols (ethanol, isopropanol, phenol) and/or ionic detergents (e.g. SDS) that would hinder DNA amplification by PCR [13 & 42]. To ensure high quality and yield, the genomic DNA from each sample was purified further using a Aurora Nucleic Acid Extraction System (Boreal Genomics Inc.) machine which uses synchronous coefficient of drag 6 alteration (SCODA) electrophoretic technology to recover and purify DNA from heavily inhibited, low biomass samples [42]. Geochemical Analysis In order to determine the potential influence of geochemistry on microbial community structure, a 10 g rock/sediment sample from each cave site was collected and sent to Elemental Analysis Inc. (Lexington, KY) for geochemical analysis. The amount of organic carbon and nitrogen present in samples was quantified by thermal optical analysis and H, N, C & O combustion using a 2400 Perkin-­‐Elmer CHN Analyzer (PerkinElmer Inc., Santa Clara, CA). Proton induced x-­‐
ray emission (PIXE), which simultaneously detects for 72 inorganic elements (sodium to uranium), was performed to examine the inorganic composition of host rock/sediment. Elements found in high abundance by PIXE analysis were quantified numerous times on sample rock/sediment using energy-­‐dispersive X-­‐ray (EDX) spectroscopy to generate the sample data needed to perform principal component analysis. Cell Enumeration by Fluorescent in situ Hybridization Microscopy To examine the abundance of bacteria and archaea at each depth, several domain-­‐specific quantification methods were employed. Our first method for bacteria-­‐ and archaea-­‐ specific cell estimations was fluorescent in situ hybridization (FISH) microscopy. To improve the enumeration efficiency of FISH microscopy in the presence of mineral contaminants, we modified Kallmeyer’s method of separating cells from a sediment matrix [24]. Briefly, a known weight of sample was crushed into a fine powder, dehydrated, weighed and filled to a volume of 1 ml with 7 1X PBS inside a 2 ml centrifuge tube. To dissolve carbonates, 1 ml of acetate buffer (0.43 M acetic acid and 0.43 M sodium acetate, pH 4.6) was added to each sample and incubated at room temperature for 2 hrs. Samples were then centrifuged at 10,000 x g for 10 min, supernatant discarded and air-­‐dried inside a laminar flow hood for 30 min. Once dry sample weight was recorded, samples were saturated in 1X PBS/ethanol mixture (1:1 vol) and stored for up to a week at 4°C. For total cell counts, samples were centrifuged at 10,000 x g for 10 minutes with the supernatant discarded. Once dry, centrifuge tubes containing samples were filled to a volume of 1 ml with 1X PBS with approximately and roughly 10-­‐100 μl of sample was filtered onto a 0.4 μm Isopore membrane filter, 25 mm (Millipore, Billerica, MA), heat-­‐dried onto a microscope slide at 55°C and stained with 100 μl of SlowFade Gold Lonza, Switzerland) anti-­‐fade reagent containing 1 μg/ml of DAPI [4’,6-­‐diamidino-­‐2’-­‐phenylindole]. Once saturated, slides were sealed using a glass slide cover and stored for up to one week in the dark at -­‐20°C [24]. Cell counts were conducted at a 1000X magnification (frit area = 0.01 mm2) using a BX53 fluorescent microscope (Olympus America Inc., Center Valley, PA). Total cell numbers were determined from the average of one hundred fields-­‐of-­‐view. For domain-­‐specific cell counts, fluorescence in situ hybridization (FISH) was performed using the bacteria-­‐specific primers EUB338, EUB338II & EUB338III, and the archaea-­‐specific primers CREN499 & ARC915 labeled with Cy3 and Cy5 respectively (Appendix A: Table 1). As previously, samples were dried to document weight and resaturated to a volume of 1 ml with 1X PBS. Sample tubes were gently shaken for dispersal, and roughly 1-­‐10 μl of sample was pipetted onto a 8 well of a 12-­‐well, pre-­‐printed slide (Tekdon Inc., Myakka City, FL) and dried inside an oven at 55°C. Samples were gradually dehydrated inside 50 ml conical tubes using an ethanol series (50%, 80% and 98%) for 3 min at each ethanol concentration. Once all ethanol had evaporated, 2 ml of freshly prepared hybridization buffer was prepared for each sample (Appendix A: Table 2). For fluorescent staining, approximately 20 μl of hybridization buffer was placed on top of each sample well along with 0.5 μl of each fluorescently labeled bacteria-­‐specific or archaea-­‐specific probe (50 ng/μl) being used. Next, each slide was placed inside a moisture chamber with roughly 2 ml of hybridization buffer and incubated for 2 hrs at 55°C. Each sample slide was then put inside a 50 ml conical tube containing pre-­‐heated (55°C) hybridization wash buffer (Appendix A: Table 3) and was incubated at 55°C for 20 min. Next, slides were washed with 1X PBS and dried in an oven at 55°C [8 & 49]. Dried slides were saturated with anti-­‐fade reagent, covered with a 22 x 50 mm glass slide cover and sealed. Bacterial and archaeal specific cell enumerations were conducted using a BX53 fluorescent microscope (Olympus America Inc., Center Valley, PA). Samples were examined at a 1000X magnification with final counts being estimated from the average of one hundred fields-­‐of-­‐view. Absolute Quantitative PCR Assays To confirm cell count estimations by FISH microscopy, absolute quantitative PCR (qPCR) analysis was performed using environmental DNA that was previously extracted from each sample along with the bacteria-­‐specific 16S rRNA gene primers EUB338F & EUB518R and the archaea-­‐specific 16S rRNA gene primers ARC85F & ARC313R [18, 31 & 41] (Appendix A: Table 4). Internal plasmid standards were 9 constructed by PCR amplifying partial 16S rRNA gene sequences from the bacterium, Escherichia coli K12 (Carolina #155068), and the archaeon, Halobacterium salinarum (ATCC 33170) using our domain-­‐specific primers. PCR products verified to contain our bacteria-­‐specific 16S rRNA gene sequence (~180 bp) and our archaea-­‐specific 16S rRNA gene sequence (~228 bp) were cloned into pCR 4-­‐TOPO vectors using a TOPO TA Cloning kit (Invitrogen, Grand Island, NY) in accordance with the kit’s protocol [3]. Plasmids containing bacteria-­‐specific and archaea-­‐
specific partial 16S rRNA gene sequences were extracted from the clone colonies using a Zyppy Plasmid Miniprep kit (Zymo Research Corp., Irvine, CA) following the manufacturer’s guidelines. Plasmid inserts were linearized by incubation with the endonuclease EcoRI, isolated on an agarose gel by electrophoresis and recovered using a Zymoclean Gel DNA Recovery Kit (Zymo Research Corp., Irvine, CA). Before creating 10-­‐fold serial dilution series for standard curve calculations, linearized, plasmid-­‐based inserts were quantified using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). Assays for the quantification of bacteria and archaea at each cave depth were performed using Quantifast SYBR Green PCR Kit (Venlo, Netherlands) along with the bacteria-­‐ and archaea-­‐specific 16S rRNA gene primers used to originally construct our internal plasmid standards. For each sample, 25 μl reactions were run in triplicate with 40 ng of previously extracted genomic DNA template, alongside control reactions with no template and no primer. Assays were run on an ABI 7300 (Applied Biosystems Inc., Foster City, CA) under the following conditions: initial 10 denaturation at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 56°C (50°C, archaea primers) for 1 min followed by a final extension at 95°C for 2 min [3]. The initial 16S rRNA gene copy number for each plasmid-­‐based standard dilution series was calculated as a function of DNA concentration, the average molecular weight of a base pair of DNA (660 g mol-­‐1) and the size of our linearized DNA fragment of interest. Gene copy numbers were adjusted to accommodate the potential for bacteria and archaea to carry multiple copies of the 16S rRNA gene with the average values of 4.14 copies per bacterium and 1.70 copies per archaeon used for initial copy number calculations [27] (Figure 2.2). Threshold cycle (CT) values were calculated using the ABI 7300’s Sequence Detection Software, version 1.4 (Applied Biosystems). Standard curves for the bacterial and archaeal qPCR assays were produced via linear regressions by plotting the threshold cycle (Ct) values generated for each standard dilution against the log10 copy number value of each sample’s initial DNA template. The amplification efficiencies of our qPCR assays were calculated manually using the equation: efficiency = 10(-­‐1/slope) -­‐1 [58]. Figure 2.2: Absolute Quantitative PCR Copy Number Formula Analysis of GDGT Lipids from Cave Rock/Sediment Past studies have supported the relative abundance of bacteria and archaea present in an environment by detecting the presence of certain types of membrane-­‐
associated glycerol dialkyl glycerol tetraethers (GDGTs) lipids. Whereas bacterial core membrane lipids typically consist of fatty acids esterified to a glycerol moiety, archaeal core membrane lipids consist of isoprenoidal alcohols ether-­‐linked to 11 glycerol [48 & 53]. Samples for glycerol dialky glycerol tetraether (GDGT) lipids analysis were processed by Dr. Hongmei Wang (China University of Geosciences, Wuhan, China) as previously described by Yang et al. [57]. Briefly, 10 g of sample was freeze-­‐dried and homogenized with a mortar and pestle and lipid fractions were ultrasonically extracted eight times using a 9:1 (vol/vol) mixture of dichloromethane (DCM) and methanol, collected in a round-­‐bottom flask and dried using a rotary evaporator [57]. Extracts were dissolved in DCM and run through a silica gel column to separate apolar and polar fractions by sequentially eluting with DCM and methanol. The polar fractions containing the GDGTs were filtered with 0.45 μm PTFE filters, dried in 3 ml vials with N2 gas, and dissolved with 290 μl of hexane/isopropanol (99:1, vol/vol). A known amount of internal standard (C46 GDGT) was added to quantify the concentration of GDGTs found in each sample [57]. GDGT analysis was performed using liquid chromatography-­‐mass spectrometry (LC/MS). Samples (10-­‐30 μl) were injected and separation was achieved with an Alltect Prevail Cyano Column (150 mm x 2.1 mm, 3 μm). GDGTs were ionized in an atmospheric pressure chemical ionization (APCI) source, with single ion monitoring (SIM) at m/z 1302.3, 1300.3, 1298.3, 1992.3, 1050, 1048, 1046, 1036, 1034, 1032, 1022, and 1018 to enhance sensitivity [57]. Quantification was evaluated by integration of peak area from the extracted ion chromatogram. Final relative masses of GDGTs were obtained using the internal standard with a relative response ratio of 1:1 between chenarchaeol and C46 GDGT being assumed. 12 Principal Component Analysis With the data we obtained using our previously described methods, principal component analysis (PCA) was performed using the R statistical software (http://www.r-­‐project.org), version 2.15.1. Results of principal component analyses were used to assess relationships between geochemistry, depth and bacteria/archaea cell estimations. 13 CHAPTER III RESULTS Sample Site Analysis Sample sites were chosen roughly at depth increments of 50 meters in remote areas showing no sign of human disturbance. A control, surface sample (S) was taken on a limestone shelf containing desert soils 10 m directly above the cave entrance (Figure 3.1). Figure 3.1: Lechuguilla Cave Map Portraying Sample Sites The first in-­‐cave sample at the Liberty Bell speleothem (LB), was at a depth of 36.6 meters below the entrance, where the cave is still influenced by dripping surface water and detritus (hence the speleothems). The area sampled was from a side passage that sees little human visitation; it is also home to cave crickets and beetles, suggesting that the area is still influenced by the surface biosphere. Another ~500 14 meters of passage leads to a significant, vertical drop (Boulder Falls) into a large room, the Colorado Room. Another short, dead-­‐end passage off of the Colorado Room was sampled (CR) at ~115 m below the entrance. The EA sample was chosen at a junction (where the EA survey takes off of the E survey) located ~200 m below the entrance. The physical appearance of the bedrock at sample site EA is different from other sampled sites in that it has a large accumulation of corrosion residue and ferromanganese deposits present. This site was chosen as it has been closed to human visitation for ~20 years, due to experiments occurring in the vicinity. The Big Sky sample (BS) was selected in a remote location near the Big Sky campsite, in an area unlikely impacted by camp activities. The BS sample site was approximately 250 m below the entrance. Sample site LM was chosen near Lake Margaret at a depth near 300 m below the entrance. This was the only sample taken near a lake within the cave; however, it was chosen due to the geochemical similarity of the bedrock to other sampled locations. The FP sample was taken along the FP survey, as the cave headed down to the Sulfur Shores area at a depth ~350 m below the entrance. This sample site was inside a small passageway off of the main trail. Our last sample site (SS) was near the deepest point in this area of the cave, Sulfur Shores. The sample was collected ~400 m below the entrance. In this area the bedrock had a physical appearance very similar to that of sample depth FP, with almost a patina-­‐like coating on the surface of the rock. Below sample site SS, the passageways became extremely narrow and were often coated with secondary deposits of calcite, therefore no samples were collected below this depth, where access to exposed, non-­‐impacted bedrock was difficult. The physical parameters at 15 each sample site demonstrated only subtle changes in temperature, humidity and pH between depths (Table 3.1). Within the cave, the temperature varied no more than roughly 1°C between the sites (19.2°C – 20.3°C), while humidity remained relatively high, ranging from 92.7% to 99.9%. The sediment pH did not vary greatly from the alkali conditions often seen in caves, ranging from a pH of 6.91 (BS) to 8.23 (CR); the BS sample was taken in an area with significant calcite crystals, which may have resulted in a poorer carbonate buffering. In order to provide a surrogate for bacterial activity when sampling at each site, ATP concentrations were measured in triplicate at each depth. ATP levels were found to be four to six orders of magnitude lower within the cave than on the surface, with the deepest depths near the detection limit of our luminometer (106 molecules per gram, ~ 1 femtomole (1 x 10-­‐
15 moles). Table 3.1: Physical Analysis of Sample Sites 16 Geochemical Analysis In order to better characterize each sample site, we conducted comprehensive geochemical analysis. We measured concentrations of organic carbon using thermal optical analysis and combustion analysis. The results demonstrated that total organic carbon (TOC) concentrations were lower at all sites within the cave than on the surface. Nitrogen levels within the cave were too low to detect conventionally using a 2400 Perkin-­‐Elmer CHN Analyzer, therefore new rock/sediment samples were sent to Elemental Analysis Inc. to detect nitrogen using the more sensitive instrumental gas analysis (IGA) spectroscopy technique. With IGA spectroscopy, nitrogen concentrations were found to be extremely low in the cave and approached levels that were four-­‐orders-­‐of-­‐magnitude lower than that measured on the surface (Table 3.2). This resulted in the surface soils having C:N ratios in the standard range (~1227:1) to in-­‐cave samples having extremely high C:N ratios (approaching 14,000:1). Table 3.2: Elemental Analysis of Carbon and Nitrogen Elemental analyses revealed that cave samples were largely composed of inorganic carbon and calcium in the form of calcium carbonate (CaCO3), which is the principle mineral component of the Capitan Limestones that Lechuguilla Cave 17 formed in; however, these analyses also indicated that the mineral matrix of the cave sites also contained moderate levels of iron, magnesium, manganese, sulfur, aluminum, titanium, silicon and strontium. The levels of phosphorus, potassium, nickel, zinc and arsenic were found to be extremely low or undetectable (Appendix A: Table 5 & 6). Bacteria and Archaea Cell Counts by FISH Microscopy Given the similar physical parameters and geochemistry present at our sites, we examined the impact depth had on microbial community structure. We began by enumerating total cell counts by DAPI staining and bacteria-­‐ and archaea-­‐specific cell counts by FISH microscopy. Our results gave total cell estimates ranging from 1.0 x 107 to 7.0 x 109 cells per gram of sediment/rock, and demonstrated that, while bacteria dominated within surface soils (representing 90% of the total population), bacteria represented only a minority of populations deeper in the cave, approaching 30% of the community. Total cell counts using the DAPI staining method suggested that the total cell number did not drop significantly in the deeper parts of the cave, despite a significant drop in measurable ATP. Overall, the total cell counts using DAPI were roughly 4% to 14% higher than the combined cell counts enumerated by bacteria-­‐ and archaea-­‐specific FISH microscopy. This suggests that our 16S rRNA gene primers for identifying bacteria and archaea populations by in situ hybridization may not have been specific for a small portion of the microbial community present (Table 3.3). 18 Table 3.3: Bacterial and Archaeal Cell Estimates by FISH Microscopy Bacteria and Archaea Cell Estimations by Absolute Quantitative PCR The cell counting data suggested that cell numbers within the cave did not drop significantly, even as the cave became deeper. In addition, we saw a dramatic shift in the structure of the community, shifting from a bacteria-­‐dominated population to one dominated by archaea, and such findings would be significant for our understanding of subsurface terrestrial communities. In order to confirm these findings, we employed quantitative PCR amplification. We validated the specificity of our 16S rRNA probes by ensuring the presence of only a single peak in the melting curves recorded by the ABI 7300’s detection software. The slopes for the bacteria and archaea standard curves were -­‐3.593 and -­‐3.564, respectively, with amplification efficiencies of 0.898 and 0.908 (Appendix B: Figure 1). The regression lines of our plasmid-­‐based standard curves generated R2 values of 0.993 for the bacterial standard and 0.977 for the archaeal standard. The absolute copy number of bacteria and archaea present at each depth was estimated by relating each sample’s average CT value to the linear regression equation of its respective 19 plasmid-­‐based standard curve. Final copy numbers were adjusted to account for the possibility of multiple copies of the 16S rRNA gene to be present in a bacterial (4.14 copies) or an archaeal (1.7 copies) genome [28]. Using this approach, our final bacterial and archaeal cell estimations by absolute qPCR ranged from 7.0 x 106 to 3.0 x 109 cells per gram of sediment/rock sampled (Table 3.4), which correlated well with cell estimates calculated by FISH microscopy. Table 3.4: CT Values and Copy Number Estimations by Absolute qPCR GDGT Lipid Analysis With both FISH microscopy and absolute qPCR indicating a higher proportion of archaea at deeper depths, we implemented a chemical marker to further validate our results. To do this, we used GDGT lipid analysis to detect the presence of domain-­‐distinctive core membrane glycerol dialkyl glycerol tetraethers (GDGT) lipids; whereas archaeal GDGT lipids consist of isoprenoidal alcohols ether-­‐
linked to glycerol, bacterial GDGT lipids normally consist of fatty acids esterified to a 20 glycerol moiety. GDGT lipid analysis detected a variety of bacteria-­‐ and archaea-­‐
specific GDGT core membrane lipids in all samples. The generated chromatograms indicated that the overall abundance of archaea-­‐associated GDGT lipids detected, particularly Crenarchaeota-­‐associated GDGT lipids, was present in all samples and increased in concentration with depth (Appendix B: Figure 2). The detection of bacteria-­‐associated GDGT lipids was considerably higher on the surface and decreased in abundance with increased depth. Our results correlated well with GDGT analysis results published beforehand by DeLong in the deep ocean and by Northup et. al in Lechuguilla Cave [12 & 39]. Principal Component Analysis In order to determine qualitative variances in bulk chemistry between all sample depths, we conducted principal component analysis (PCA). The key components of our analysis were total organic carbon (TOC), nitrogen, iron, sulfur, phosphorus, manganese and magnesium. The results demonstrated that the surface sample (S) was geochemically different from our cave sites, as would be expected for a soil sample (Figure 3.2). Excluding the first depth sampled within the cave, Liberty Bell (LB), which is still under the influence of the surface biosphere, all other cave sites clustered together, showing similar geochemistry; the PCA of LB showed significantly higher levels of sulfur and phosphorus than that of lower cave depths. 21 !
LM
!
Org
MCn
BS
0
EA
S
!!
!
!
FP
SS
Mg
!
CR
Fe
pth
De
P
−1
S
PC2 (30.5% explained var.)
N
−2
−3
!
LB
−3
−2
−1
0
PC1 (49.2% explained var.)
1
Figure 3.2: Explained Variance Amongst Sample Sites (Including Surface) To statistically compare the geochemistry of the cave rock amongst the different depths sampled, energy-­‐dispersive X-­‐ray (EDX) spectroscopy was carried out on multiple rock/sediment samples from each cave site to generate geochemical averages. The results were then used to determine the relationships between the inorganic / mineral chemistry of each cave site (Figure 3.3). The results demonstrated that the mineral structure at each site was comparable, with CR, LM, and FP being the most alike, while BS and EA also had similar geochemistry. The phosphorus and sulfur rich sample site LB diverged the most from other cave depths, although there was some overlap with the deepest cave site, SS. The similarity between LB and SS could be related to both sites being located in paths of direct airflow; however a lot more research would need to be done to determine if this is the actual case. The bacteria and archaea cell estimates from each site were compared to elemental averages calculated by EDX spectroscopy (Figure 3.4). 22 1e+09
!
!
! !
!
! !
1e+07
Fe
1e+08
!
!
!
!
1e+07
!
!!
!!
Mn
1e+08
Mg
1e+09
1e+09
1e+08
! !
!
!
! !!
!
domain
1e+09
1e+08
!
!!!!
1e+07
!
!
!
N
Abundance
1e+07
!
Archaea
1e+08
!
1e+07
!
!
!
!
!
!
OrgC
1e+09
P
1e+07
1e+09
1e+08
Bacteria
!
!
!
!
!
!!
!
!
!
!!!!
!
1e+09
1e+07
1e−03
1e+00
1e+03
S
1e+08
!
1e+06
ppm
Figure 3.4: Elemental Concentrations Vs. Bacteria/Archaea Cell Estimates PCA revealed that the community composition of bacteria and archaea at each cave depth was highly correlated to the abundance of iron, nitrogen and organic carbon found at each cave depth (Table 3.5). Table 3.5: Principal Component Analysis of Cave Geochemistry and Cell Estimates 23 The amount of iron present at each depth correlated well with total cell estimates with decreases in iron leading to reductions in overall cell abundance (Figure 3.6). S
0
Total Cell Count
1e+09
CR
−114.3
1e+08
BS
−258.5
LM
−299
LB
−36.6
FP
SS
−347.3
−394.5
1000
10000
Fe
Figure 3.6: Iron Concentration Vs. Total Cell Counts Nitrogen levels were found to be extremely low in the cave, which correlated well with cave depth. Depth also correlated well with C:N ratios, which had previously been reported to a good predictor of the relative abundance of archaea present at a particular depth [7] (Figures 3.6 & 3.7). S
0
10000
N
LB
−36.6
CR
−114.3
BS
−258.5 LM
−299
100
SS
FP
−394.5
−347.3
0
−100
−200
−300
−400
Depth (m)
Figure 3.6: Nitrogen Concentrations Vs. Cave Depth 24 1500
FP
−347.3
1000
SS
−394.5
BS
LM
−258.5 −299
500
0
S
0
LB
−36.6
CR
−114.3
−500
0
−100
−200
−300
−400
Depth (m)
Figure 3.7: Organic C:N Ratio Vs. Cave Depth PCA revealed that the bacteria:archaea ratio estimated at each site was a better predictor of depth than correlation with total cell estimates (Figure 3.8 & 3.9). 10
S
10
CR
−114.3
Bacteria/Archaea)
C:N Ratio
LB
−36.6
BS
−258.5
1
LM
−299
SS
−394.5
−400
FP
−347.3
−300
−200
−100
0
Depth
Figure 3.8: Bacteria:Archaea Ratio Vs. Cave Depth 25 Figure 3.9: Total Cell Counts by FISH Microscopy Vs. Cave Depth PCA also demonstrated a good correlation between depth and ATP concentration with the number of molecules detected per gram being less abundant the deeper in the cave sampled (Figure 3.10). The amount of ATP present at each depth correlated better with bacterial populations with high ATP levels being associated with populations dominated by bacteria (Figure 3.11). 1e+13
CR
−114.3
LB
−36.6
1e+11
BS
−258.5
ATP
LM
−299
1e+09
SS
−394.5
−400
−300
−200
−100
Depth
Figure 3.10: ATP Concentrations Vs. Cave Depth 26 CR
−114.3
LB
−36.6
1e+11
BS
−258.5
ATP
LM
−299
1e+09
SS
−394.5
1e+07
1e+08
Bacteria
Figure 3.11: ATP Concentration Vs. Bacterial Cell Estimates
27 CHAPTER IV DISCUSSION OF RESULTS Data collection from the depths of Lechuguilla Cave provided an unprecedented opportunity to study bacteria and archaea in a well-­‐preserved subterranean ecosystem, un-­‐impacted by the sampling techniques normally required to examine microbial interactions in the subsurface, such as drilling. Very few studies have been conducted on microbial populations in the deep subsurface, and physiological studies have been mainly restricted to soil populations located close to the surface. Lechuguilla Cave permitted us to examine the microbial structure of communities in the subsurface to a depth of 400 m without drilling. Cell estimations by FISH microscopy and absolute qPCR gave similar trends for the proportion of bacteria and archaea present at each cave depth (Figure 4.1) Figure 4.1: Comparison of Cell Estimates by FISH Microscopy and Absolute qPCR 28 Our results illustrated the dominance of archaea over bacteria with increasing depth and nitrogen limitation, while the absolute number of cells only marginally declining with depth (Figure 4.2). This finding correlates well with what has been observed in deep aquatic environments; a higher proportion of archaea is present at deeper depths [8, 12 & 30]. Cell estimations by FISH microscopy demonstrated that bacterial abundance had decreased roughly two orders of magnitude by 400 meters, whereas archaeal populations decreased less than one order of magnitude over the same vertical distance. Cell counts by both methods determine bacteria and archaea populations to become equivalent in abundance roughly around 250 meters in depth which complements results published by Callierie et al. on a deep oligotrophic lake environment [8]. FISH Cell Counts Vs. Cave Depth 0 -­‐50 Depth (m) -­‐100 -­‐150 -­‐200 -­‐250 -­‐300 -­‐350 -­‐400 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Fluorescently-­labled 16S rRNA gene probes count percentage Bacteria Archaea All Probes Bacteria–specific probes: EUBI, EUBII & EUBIII -­ Archaea-­specific probes: ARC 915 & CREN499 Figure 4.2: Total Cell Estimates by FISH Microscopy Vs. Cave Depth 29 Due to the bias that qPCR relies on poorly-­‐supported approximations of bacterial and archaeal 16S rRNA copy numbers, FISH microscopy was deemed a more reliable method for determining cell counts for principal component analysis, and indeed both depth and bacterial:archaeal ratios gave us a better correlation when FISH estimates were used. Results by PCA demonstrated that, with a Pearson’s Correlation Coefficient of 0.6507 (Table 3.5), total cell counts correlated well with depth, but total cell counts wasn’t the strongest predictor of depth. When the ratio of bacteria to archaea was plotted against depth, a stronger correlation with a Pearson’s Correlation Coefficient of 0.9852 was observed, which is significant at the 0.001 level (n=8, df=2). The bacteria to archaea ratio changed from 9:1 at the surface to roughly 1:3 at deeper depths, whereas archaea increased from roughly 10% of the total cell population to approximately 70% of the total population at the deepest depths. The data suggests that depth has a strong influence on the microbial structure of a community with deeper depths having a higher proportion of archaea present (Figure 4.3). 100% Bacteria and Archaea Percentages by FISH Microscopy 80% 60% Archaea 40% Bacteria 20% 0% S (0 m)
CR (-114.3 m) BS (-258.5 m)
FP (-247.3 m)
SS (-394.5 m)
* Sample sites LB, EA and LM were excluded from analysis due to geochemical differences, which were demonstrated by PCA to be significantly different to that of other cave depths. Figure 4.3: Proportion of Microbial Community Represented by Bacteria and Archaea 30 Principal component analysis demonstrated that concentrations of iron, nitrogen and organic carbon were important factors regulating the abundance of bacteria and archaea present at each depth. PCA noted dissimilarity at sample sites LB, EA and LM, which is likely interrelated to the unique geochemistry of these areas (LB is a near-­‐surface site influenced by water and detritus, EA is greatly influenced by its corrosion residue and close proximity to the backreef Yates Formation, while LM is directly above a large lake in the cave). Decreases in organic carbon, iron or nitrogen seemed to influence the overall abundance of bacteria in the subsurface significantly more than the overall abundance of archaea at each cave depth, which is noticeable in figure 3.4. The increasing C:N ratios at deeper depths within the cave suggest that microbial populations should be finding it increasingly difficult to obtain the necessary nutrients for maintenance and growth, but absolute cell numbers only slightly declined, suggesting that compensating mechanisms (such as autotrophy and nitrogen fixation) may be occurring at deeper depths. High C:N ratios was also noted by DeLong in deep marine ecosystems, with archaea (Crenarchaeota) representing approximately half of the population at depths below 2,500 m [12]. The maximum carrying capacity of each depth site was calculated by dividing the nitrogen detected at each site (mg/g) by the average amount of nitrogen needed per bacterial/archaeal cell (5.8 x 10-­‐15 grams) [19]. When compared to our total cell estimates, a very large proportion of the nitrogen was represent as cellular biomass (Table 3.6). Nitrogen fixation is an energetically expensive process, nonetheless, the available energy sources within the rock (e.g. iron, manganese) at our deeper 31 sample sites may allow chemolithoautotrophic microorganisms to propagate and even proliferate in this oligotrophic, subterranean environment. Table 3.6: The Estimated Proportion of Total Nitrogen Incorporated Into Cellular Biomass Principal component analysis of ATP estimates revealed a stronger association between ATP concentrations and bacteria counts, than archaeal or total counts (Figure 4.4). This result suggests that archaeal populations ‘hold on’ to their ATP more effectively than bacteria for energy conservation through unique membrane chemistries that are believed to prevent the leakage of ions and other charged particles (such as ATP) into the environment. Metabolic processes, energy expenditures, and binary fission rates are potential areas of study in the deep ATP (molecules) subsurface microbial populations. 1.00E+12 1.00E+11 . Bacteria Archaea Total Cells 1.00E+10 1.00E+09 1.00E+08 1.00E+06 1.00E+07 1.00E+08 Number of Cells 1.00E+09 Figure 4.4: ATP Concentration Vs. Bacterial and Archaeal Cell Estimates 32 Our study revealed the presence of microbial communities that were naturally and specifically adapted to low nutrient conditions. Previous studies have confirmed the presence of archaea capable of nitrogen fixation exist in chemautotropy-­‐based caves [14]. Archaea have been shown to possess genes for encoding key enzymes for inorganic nitrogen transformation by nitrification, denitrification and ammonium-­‐oxidation [10]. Chemoautotrophs have been shown capable of using reduced sulfur compounds, hydrogen and methane as energy sources to fix inorganic carbon, and the above physiologies suggest syntrophic associations within a metabolically cooperative biofilm [14]. Microorganisms are known to be critical players in the global geochemical cycles, such as carbon, nitrogen, sulfur and phosphorus turnover through a variety of energy-­‐yielding redox reactions. Yet very little is known about the community structure or physiology of microorganisms within the deep, terrestrial subsurface. Our data suggests that, like deep aquatic ecosystems, archaea dominate at deeper depths through domain-­‐unique adaptations to physical stresses such as limited nutrients. The energy dynamics of subterranean ecosystems are not well defined, and better examinations of microbe-­‐mineral interactions and metabolic processes would lead to a greater understanding of nutrient cycling and community assemblages within the subsurface. 33 LITERATURE CITED 1.
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40 APPENDIX A TABLES Table 1A: Bacteria-­‐ and Archaea-­‐Specific Probes for FISH Microscopy
41 Table 2A: Hybridization Buffer Recipe for FISH Microscopy 42 Table 3A: Wash Buffer Recipe for FISH Microscopy 43 Table 4A: Bacteria-­‐ and Archaea-­‐Specific 16S rRNA Gene Primers for Absolute qPCR 44 Table 5A: Elemental Analysis of Cave Depths (Sites S thru EA) 45 Table 6A: Elemental Analysis of Cave Depths (Sites BS thru SS) 46 APPENDIX B FIGURES Bacterial and Archaeal Standard Curves CT Value (Threshold Cycle) 21 19 17 y = -3.564x + 57.394
R² = 0.977
15 E. coli H. sal 13 y = -3.593x + 53.411
R² = 0.992
11 9 7 9 10 11 12 Log10 Copy Number 13 Figure 1B: Plasmid-­‐Based Standard Curves for Absolute Quantitative PCR 47 14 Figure 2B: GDGT Lipid Analysis (Sites S thru EA) 48