CHARACTERIZING THE MOLECULAR MECHANISMS CONTRIBUTING TO BIOLOGICALLY INDUCED CARBONATE MINERALIZATION AND THROMBOLITE FORMATION
By
ARTEMIS S. LOUYAKIS
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Artemis S. Louyakis
To my mother, for supporting every single goal I’ve ever had, the memory of my father, for keeping me focused, and my partner, for all he’s done
ACKNOWLEDGMENTS
I would like to begin by acknowledging and thanking my mentor, Dr. Jamie
Foster, for all her guidance throughout this Ph.D. I thank my committee members for all of their advice and support - Drs. Eric Triplett, Julie Maupin, Nian Wang, and Eric
McLamore. I’d like to thank the rest of the Department of Microbiology and Cell Science, staff for always keeping my academic life in order, faculty for never turning me away when I came to use equipment or ask for help, especially Drs. K.T. Shanmugan and
Wayne Nicholson, as well as Dr. Andy Schuerger from the Dept. of Plant Pathology for his advice over the years. I’d also like to acknowledge those lab members and extended lab members who made themselves readily available to talk through any problems I came up against and celebrate when all went well, including Drs. Rafael Oliveira,
Jennifer Mobberley, and Giorgio Casaburi, and Lexi Duscher, Rachelle Banjawo,
Maddie Vroom, Hadrien Gourlé, and so many more.
I’d also like to profusely thank my family and friends who have never been anything less than completely supportive of me, specifically my partner Nathan Prince, my mother and siblings Denise Louyakis, Bobbi Louyakis, Nick Newman, Cori Sergi, extended parents and siblings Carol Prince, Barry Prince, Aaron Prince, my nieces and nephew Bailey O’Regan, Bella O’Regan, Layla Newman, Colton Prince, and Summer
Prince, and my dearest friends Tina Pontbriand, Tom Pontbriand, Karen Chan, Dalal
Haouchar, Alexi Casaburi, and Eloise Stikeman.
Finally, I’d like to thank my funding sources for making all of this research possible: the National Science Foundation Graduate Research Fellowship Program and
NASA Exobiology and Evolutionary Biology program.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ...... 4
LIST OF TABLES ...... 8
LIST OF FIGURES ...... 9
LIST OF OBJECTS ...... 11
LIST OF ABBREVIATIONS ...... 12
ABSTRACT ...... 13
CHAPTER
1 LITERATURE REVIEW ...... 15
Introduction to Thrombolites ...... 15 Cyanobacteria Description and Classification ...... 17 Processes of Mineralization in Thrombolites ...... 19 The Alkalinity Engine ...... 21 The Role of EPS in Carbonate Precipitation ...... 22 Study Site: Highborne Cay, The Bahamas ...... 24 Microbial and Functional Gene Diversity In Modern Thrombolite ...... 26 Conclusion ...... 30
2 A STUDY OF THE MICROBIAL SPATIAL HETEROGENEITY OF BAHAMIAN THROMBOLITES USING MOLECULAR, BIOCHEMICAL, AND STABLE ISOTOPE ANALYSES ...... 35
Introduction ...... 35 Methods ...... 39 Sample Collection ...... 39 Microelectrode Measurements ...... 40 Generation and Sequencing of 16S rRNA Gene Libraries ...... 41 Bioinformatic Analysis of 16S rRNA Gene Libraries ...... 42 Reconstruction of Functional Metagenome Using the PICRUSt Algorithm ...... 43 Bulk Stable Isotope Analysis ...... 44 Stable Isotope Analysis Using Secondary Ion Mass Spectrometry (SIMS) ...... 45 Results ...... 47 Microelectrode Profiling of Thrombolite Button Mats ...... 47 Phylogenetic Composition of Bacteria in Thrombolite Communities with Depth ...... 48 Phylogenetic Composition of Archaea in Thrombolite Communities with Depth ...... 51
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Spatial Profiling of Functional Gene Complexity of Thrombolite-Forming Mats Using Predictive Sequencing Analysis ...... 52 Stable Isotope Analyses of Thrombolitic Carbonates ...... 53 Discussion ...... 55 Microbial Diversity within Thrombolite-Forming Mats are Highly Structured ..... 55 Predictive Metagenome Reconstruction Shows Strong Correlation with Taxa and Function ...... 59 Stable Isotope Profiling Suggests Photosynthesis is the Major Inducer of Precipitation in Thrombolite-Forming Mats ...... 60 Conclusion ...... 64
3 CHARACTERIZING THE DOMINANT CYANOBACTERIUM, DICHOTHRIX SPP., AND ITS ASSOCIATED MICROBIAL COMMUNITY USING METAGENOMIC SEQUENCING ...... 75
Introduction ...... 75 Materials and Methods...... 78 Sample Collection and DNA Extraction...... 78 Sequencing and Analysis ...... 79 Results and Discussion...... 80 Optimizing High Quality DNA Extraction From Dichothrix spp. Filaments ...... 80 Assembly and Description of the Dominant Cyanobacteria ...... 81 Community Associated with the Filaments ...... 83 Dichothrix-Associated Cyanobacteria ...... 84 Dichothrix-Associated Bacteria ...... 85 Dichothrix-Associated Archaea ...... 86 Functional Genes Associated with Filament Community ...... 88 Conclusion ...... 91
4 A YEAR IN THE LIFE OF A THROMBOLITE: METATRANSCRIPTOME ANALYSIS OF A BAHAMIAN THROMBOLITE OVER DIEL AND SEASONAL CYCLES ...... 100
Introduction ...... 100 Materials and Methods...... 102 Sample Collection ...... 102 RNA Isolation, Purification, and cDNA Synthesis ...... 103 Generation and Sequencing of RNA Libraries ...... 104 Sequence Quality Control, Assembly, Annotation, and Mapping ...... 104 Results and Discussion...... 105 Taxonomic Dynamics within Thrombolite-Forming Button Mat Communities . 107 Metabolic Activity of the Thrombolite-Forming Community...... 112 Differential Expression Analysis ...... 117 Thrombolite Gene Expression Network ...... 119 Conclusion ...... 120
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APPENDIX
A SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 2 ...... 132
B SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 4 ...... 138
LIST OF REFERENCES ...... 142
BIOGRAPHICAL SKETCH ...... 167
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LIST OF TABLES
Table page
2-1 Summary statistics for thrombolite samples by zone for bacteria and archaea .. 66
3-1 Summary of initial Dichothrix spp. genome assembly methods ...... 99
4-1 Metatranscriptome data summary for four timepoints over three seasons sampled from a Highborne Cay thrombolite...... 122
4-2 Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for October...... 123
4-3 Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for March...... 124
4-4 Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for August...... 125
A-1 Primer list used to generate titanium 454 barcoded libraries for bacteria and archaea ...... 137
B-1 Summary of Trinity assembly statistics ...... 141
B-2 Correlation coefficients for sample pairs; replicates outlined in bold, Pearson correlation on bottom, and Spearman correlation on top...... 141
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LIST OF FIGURES
Figure page
1-1 Overview of Highborne Cay, The Bahamas thrombolites ...... 33
1-2 Diagram of the photosynthesis driven reactions in thrombolite filamentous cyanobacteria and the surrounding EPS matrix...... 34
2-1 The thrombolites of Highborne Cay, The Bahamas...... 67
2-2 Taxonomic distribution of cyanobacteria within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database...... 68
2-3 Taxonomic distribution of Bacteria within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database...... 69
2-4 Comparison of diversity analyses of three spatial zones within the thrombolite-forming mats...... 70
2-5 Taxonomic distribution of Archaea within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database...... 71
2-6 Functional gene comparison of the three thrombolitic mat spatial zones from 16S rRNA metabolic prediction (PICRUSt) ...... 72
2-7 Stable isotope results for calcified filaments located in the upper 3 mm of thrombolite forming button mat...... 73
2-8 Overview of target areas for SIMS analyses within the thrombolite-forming mat...... 74
3-1 Metagenomic binning of the guided, hybrid assembly from the filament- enriched metagenome...... 93
3-2 Krona plot displaying filament-associated taxa with the innermost ring represent phyla and proceeding out to family in the outermost ring ...... 94
3-3 XRD results of thrombolite-forming button mat plotted against aragonite and calcite standards...... 95
3-4 Cyanobacteria tree displaying evolutionary dynamics of Dichothrix- associated members of the phylum based on 16S rRNA gene...... 96
3-5 Krona plot of Archaea associated with the Dichothrix spp. filaments...... 97
3-6 Krona plot depicting metabolic potential of the community associated with Dichothrix spp. filaments...... 98
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4-1 Thrombolite of Highborne Cay, The Bahamas...... 126
4-2 Composition of thrombolite active taxa. Cladograms represent metatranscriptome prokaryotic diversity for each season...... 127
4-3 Relationships between seasons and diel time points...... 128
4-4 Diel and seasonal cycling of major pathway gene expression presenting combined normalized abundances for all the genes in each process...... 129
4-5 Volcano plots visualizing gene expression changes between times and seasons...... 130
4-6 Gene expression network using Bray-Curtis dissimilarity (cutoff: 0.45)...... 131
A-1 Rarefaction plots for number of observed species approaching asymptote at read cutoffs of A) 3691 for Bacteria and B) 3587 for Archaea...... 132
A-2 Relative abundance of bacterial population. Lines depict family-level OTU (97% cutoff) differences between depth zones ...... 133
A-3 Taxonomic abundance diversity of bacteria associated with Zone 1 (0 - 3 mm) of the thrombolite forming mats...... 134
A-4 Taxonomic abundance diversity of bacteria associated with Zone 2 (3 - 5 mm) of the thrombolites as visualized in a hierarchal Krona plot...... 135
A-5 Taxonomic abundance diversity of bacteria associated with Zone 3 (5 - 9 mm) of the thrombolites as visualized in a hierarchal Krona plot...... 136
B-1 Methodology flowchart outlining the experimental parameters and bioinformatics tools used in the analysis of the metatranscriptome data...... 138
B-2 Taxonomic histograms for each sample to family level or higher if unable to classify to family...... 139
B-3 Venn diagrams of up expressed genes for differential expression comparisons ...... 140
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LIST OF OBJECTS
Object page
4-1 Relative abundance of taxa within thrombolites over diel and seasonal cycles (.xlsx file 213 KB)...... 131
4-2 Expressed genes in thrombolites with normalized counts over diel and seasonal cycles (.xlsx file 5 MB)...... 131
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LIST OF ABBREVIATIONS
DIC dissolved inorganic carbon
EPS exopolymeric substance or extracellular polymeric substance
Ga Giga Annum
KEGG Kyoto Encyclopedia of Genes and Genomes
KOs Kyoto Encyclopedia of Genes and Genomes Orthologs
LPS lipopolysaccharide
MEGAN5/6 Meta Genome Analyzer
MetaCV Metagenome Composition Vector
OTUs operational taxonomic units
PCA principal component analysis
PCoA principal coordinate analysis
PICRUSt Phylogenetic Investigation of Communities by Reconstruction of Unobserved States
QIIME Quantitative Insights Into Microbial Ecology
SEM scanning electron microscopy
SIMS secondary ion mass spectrometry, secondary ion microprobe mass spectrometer
SOB sulfide-oxidizing bacteria
SRB sulfate-reducing bacteria
TMM Trimmed mean of M-values
TPM Transcripts per million
VPDB Vienna Pee Dee Belemnite
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
CHARACTERIZING THE MOLECULAR MECHANISMS CONTRIBUTING TO BIOLOGICALLY INDUCED CARBONATE MINERALIZATION AND THROMBOLITE FORMATION
By
Artemis S Louyakis
August 2017
Chair: Jamie S Foster Major: Microbiology and Cell Science
Thrombolites are build-ups of calcium carbonate that exhibit unlaminated internal structures formed through the interactions of microbial communities and their surrounding environment. These long-lived ecosystems have a fossil record that dates back 2.0 Ga and living examples of these systems serve as important analogs to understand the evolution of microbial communities and the biological mechanisms contributing to calcium carbonate precipitation. Despite recent advances, such as next generation sequencing techniques, we are only beginning to understand the microbial and molecular processes associated with their formation. In this research, a three pronged approach was used to address these communities that included: 1) characterization of the vertical spatial diversity; 2) metagenomic analysis of the dominant Cyanobacteria and associated organisms; and 3) metatranscriptomic analysis to unravel the functional complexity over diel and seasonal cycles. The molecular-based approaches were complemented with microelectrode profiling and in situ stable isotope analysis to further examine the dominant taxa and metabolic activity within the thrombolite-forming communities. Analyses revealed three distinctive zones within the
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thrombolite-forming mats that exhibited stratified populations of bacteria and archaea.
Predictive metagenomics also revealed vertical profiles of metabolic capabilities within the community that had not been previously observed. The carbonate precipitates within the thrombolite-forming mats exhibited isotopic geochemical signatures suggesting that the precipitation within the Bahamian thrombolites is photosynthetically induced. The functional genes were then examined to determine active metabolic pathways over diel and seasonal cycling of the thrombolite. Strong diel and seasonal expression patterns were identified and coincide with changes in environmental conditions, e.g. available daylight, temperature, and oxygen concentrations. These trends include diel cycling of photosynthesis genes, nitrogen fixation and denitrification, methanogenesis and dissimilatory sulfate reduction. This study represents the most extensive molecular analysis to date on an actively accreting microbialite and, together, aims to identify the specific metabolic activity leading to lithification and thrombolite formation.
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CHAPTER 1 LITERATURE REVIEW
Introduction to Thrombolites
Microbialites are calcium carbonate, CaCO3, deposits derived from the interactions between microbial communities and their environment (Burne and Moore,
1987; Riding, 2011). These interactions include trapping and binding of sediment grains, as well as the precipitation of CaCO3 driven by the metabolic activity of complex microbial communities known as microbial mats (Burne and Moore, 1987; Riding,
2011). Microbialites represent the oldest communities on Earth dating as far back as 3.7
Ga and signify the only macrostructure produced by living organisms for the first 3 billion years of life dominating 83% of Earth’s history (Burne and Moore, 1987; Awramik
1991; Nutman et al., 2016). Microbialites are further characterized by their internal organization and are dominated by two primary types: stromatolites and thrombolites.
Stromatolites form discrete lamination, while thrombolites are differentiated from other lithifying microbial ecosystems by their clotted internal organization (Aitken 1967;
Burne and Moore, 1987). The fossil record indicates that stromatolites began forming in the early Archean shortly after the formation of the first cells and flourished through the
Proterozoic eon (Burne and Moore, 1987). Communities driven by oxygenic photosynthesizing members are believed to have triggered the Great Oxygenation
Event, which led to the oxygenated atmosphere that allowed higher order organisms to thrive (Buick 1992; Flannery and Walter, 2011). Thrombolites appear after stromatolites in the fossil record with earliest identification about 2.0 Ga, shortly after the Great
Oxygenation Event (Kah and Grotzinger, 1992). However, the late occurrence in the fossil record is not conclusive of their time of origin. Changes in the water chemistry of
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marine environments from the Archean and Paleoproterozoic into the Neoproterozoic resulted in divergent taphonomic conditions (Turner et al., 2000; Arp et al., 2001;
Planavsky and Ginsburg, 2009; Dongjie et al., 2013). Thrombolite fossilization prior to the Proterozoic may be rare or absent due to a lack of marine cementation acting on the clotted mesofabric, which would have filled in the pores and preserved the structures
(Kah and Grotzinger, 1992). Rapid thrombolite and stromatolite decline occurred after higher order organisms became more abundant in the early Phanerozoic with the surge of metazoan grazers (Kah and Grotzinger, 1992; Riding and Liang, 2005a). The inverse relationship between microbialite abundance and number of metazoan genera proceeds through the Phanerozoic (Riding and Liang, 2005a; Riding, 2005). Although thrombolites faced a steep decline in abundance due to the rise in eukaryotes, these ancient ecosystems can still be found across the globe.
Living thrombolites and other microbialites have become crucial analog systems to study microbial interactions and nutrient cycling due to structural similarities between ancient and modern counterparts (Dupraz et al., 2013). Ancient microbialites are thought to have played a key role in global cycling of carbon, oxygen, nitrogen, and sulfur through the evolution of microorganisms with a wide array of metabolic capabilities (Canfield and Raiswell, 1999; Knoll, 2008). One of the single most important events during the evolution of life on Earth has been the development of oxygenic photosynthesis (Buick, 1992). Oxygenic photosynthesis may have initially provided oxygen for the ocean with negligible amounts escaping to the atmosphere (Godfrey and
Falkowski, 2009). As oxygen levels rose in the ancient oceans, oxygen-dependent metabolic processes began to evolve, such as nitrification-denitrification, sulfide
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oxidation, and methanotrophy (Canfield and Raiswell, 1999; Baumgartner et al., 2006;
Godfrey and Falkowski, 2009). Evidence from the fossil record suggests that many of the molecular processes necessary for nutrient cycling in living systems were present and may have even evolved in ancient microbialites (Knoll, 2008; Allwood et al., 2009;
Knoll et al., 2016).
Relatively rare compared to their fossil record, living thrombolites have been found in a wide range of aquatic habitats including the following: fresh water (e.g.,
Green Lake, New York and Pozas Azules II, Mexico; Pavilion Lake and Kelly Lake,
British Columbia); marine (e.g. Exuma Cays, The Bahamas); and hypersaline environments (e.g. Lake Clifton, Western Australia; Los Roques, Venezuela) (Moore and Burne, 1994; Ferris et al., 1997; Desnues et al., 2008; Breitbart et al., 2009; Smith et al., 2010; Petrash et al., 2012; Wilhelm and Hewson, 2012). Within these environments, an array of thrombolite macrostructure types form, including platforms, columns, and domes forming up to several meters in length and height (Riding, 2000).
Cyanobacteria have been shown to play a dominant role in precipitation and thrombolite morphology, including the ecosystem that is the subject of this work, the thrombolitic platforms of Highborne Cay on the western margin of the Exuma Sound, The Bahamas
(Figure 1-1a).
Cyanobacteria Description and Classification
Cyanobacteria are photosynthetic bacteria that have been organized and classified a number of different ways over the past 130 years and some disagreement remains regarding classification (Komarek et al., 2014). Cyanobacteria include three main classes (orders) – Gloeobacteria (Gloeobacterales), Chroobacteria
(Chroococcales, Pleurocapsales, Oscillatoriales), and Hormogoneae (Nostocales,
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Stigonematales). Cavalier-Smith (2002) devised these classes based on previous similar classifications and more modern techniques, while the National Center for
Biotechnology Information (NCBI) lists only orders without division by class.
Cyanobacteria were traditionally classified through a number of morphological features.
Rippka et al. (1979) divides the Cyanobacteria into non-nomenclature Sections I-V instead of orders based on morphological observations. These subsections offer a convenient method to describe the varying morphological characteristics present in
Cyanobacteria. Their corresponding orders are I (Chroococcales), II (Pleurocapsales),
III (Oscillatoriales), IV (Nostocales), and V (Stigonematales) (Komarek et al., 2014).
The morphologically classifications for cyanobacteria take into account many features that may be found across cyanobacteria (Rippka et al., 1979). Thylakoid presence and organization is a major characterization method; Gloeobacteria are the only group that does not contain thylakoids. They are further classified by reproduction method, which can be by binary fission, multiple fission via baeocyte formation, or budding. Cyanobacteria can develop under unicellular or filamentous lifestyles and may or may not form an outer sheath. The filamentous members may form one or more specialized cells, such as heterocysts for nitrogen fixation; akinetes as a survival mechanism, and hyaline cells to avoid dessication. Some cells are also able to form aerotopes to allow for transport through the water column. Using the Rippka classification system, Dichothrix spp. is classified as in the section IV (Nostocales) grouping, as it is filamentous, sheath forming, contains a single basal heterocyst, and apical hyaline cells in the tapered trichome.
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Morphological observations are helpful for describing Cyanobacteria, however they offer limited insight into the evolution and relatedness of organisms. The advent of molecular techniques can offer a more precise approach to classifications that is more reflective of the monophylogeny of taxa and their evolutionary history (Komarek et al.,
2014). Molecular data from high throughput sequencing has also vastly increased the efficiency with which Cyanobacteria can be identified in new environments and allows for recovery of previously unobserved species. This new approach is a significant advance for examinations of complex systems, such as thrombolites, where multiple
Cyanobacteria may contribute to important processes, e.g. mineralization. Based on previous classification methods combined with modern molecular techniques, Komarek, et al (2014) has offered a more complete classification system. Under this proposed classification system, Dichothrix spp. would again fall under Nostocales. They are classified in the family Rivulariaceae and form a monophyletic group with Calothrix and
Rivularia. They are, however, noted as not having enough representative sequencing data beyond the level of genus. For the purposes of this dissertation, the Komarek classification system will be used for the Cyanobacteria identification and descriptions
(Komarek et al., 2014).
Processes of Mineralization in Thrombolites
The global carbon cycle is driven by the interface of biological activity (e.g. microbial interactions in thrombolite-forming mats) with abiotic processes (e.g. ocean sequestration of atmospheric CO2). The highest sequestration of carbon globally occurs in the oceans where CO2 from the atmosphere enters the water and equilibrates with
− 2− bicarbonate, HCO3 , and carbonate, CO3 determining the alkalinity (Siegenthaler and
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Sarmiento, 1993; Post et al., 2004). As atmospheric CO2 increases, for example with the increased burning of fossil fuels, a partial pressure gradient on the oceans causes increases in dissolved CO2 (Post et al., 2004). When ionic concentrations are favorable
2− through evaporation or increases in temperature, CO3 can bind to calcium ions to form
CaCO3 in abiotic calcium carbonate mineralization (Castanier et al., 1999). This process, however, is very inefficient compared to biologically induced mineralization, which occurs when organisms fix dissolved inorganic carbon (DIC) and release byproducts that change the alkalinity of the surrounding environment (Castanier et al.,
1999; Dupraz et al., 2009). There are many contributors to biological mineralization and their effect is seen in varying degrees from passive influencers to full control over precipitation (Dupraz et al., 2009).
Mineralization in lithifying mats can be biologically influenced or biologically induced depending on the alkalinity state and alkalinity is impacted by extrinsic or intrinsic factors (Dupraz et al., 2009). Extrinsic alkalinity changes are a result of abiotic environmental physicochemical changes, namely evaporation and CO2 degassing
(Burne and Moore, 1987; Dupraz et al., 2009; Planavsky et al., 2009), whereas intrinsic alkalinity changes are the result of metabolic activity (Dupraz et al., 2009).
Mineralization can occur as a result of only abiotic mechanisms (e.g. evaporation and
CO2 degassing), biotic activity (i.e. metabolic processes), or a combination of both.
Biologically influenced mineralization describes a system that utilizes extrinsic mechanisms, but type of mineralization is influenced by biological activity. These systems are environmentally dependent (i.e., extrinsic factors) and do not require microbial activity for mineralization, but still utilize organic matter as the products for
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mineralization (Burne and Moore, 1987; Dupraz et al., 2009). Conversely, biologically induced mineralization is a process requiring metabolic interactions with the environment to produce the conditions favorable for precipitation, or intrinsic alkalinity changes (Dupraz et al., 2009). In thrombolite-forming mats, these intrinsic factors induce alkalinity changes within the extracellular polymeric substances (EPS) excreted by microbes and are regulated through the formation of microenvironments. In contrast with these systems, abiotic mechanisms have also been suggested for some fossilized mats, but these may have cryptic microbial influences yet to be identified (Dupraz et al.,
2009). This research examines biologically induced mineralization by focusing on the intrinsic modifications triggered by the metabolic activity of the mat’s microbial consortium (Dupraz et al., 2009).
The Alkalinity Engine
Biologically induced carbonate precipitation is dependent on two key factors, the alkalinity of the local environment and the availability of free calcium (Dupraz et al.,
2009; Gallagher et al., 2012). The alkalinity engine is the accumulation of metabolisms that drive alkalinity changes in an environment thereby promoting either precipitation or dissolution of carbonates (Riding, 2011). Precipitation is promoted by the activity of photoautotrophs (i.e., Cyanobacteria), anoxygenic phototrophs, and sulfate-reducing bacteria (SRB) (Dupraz et al., 2009; Gallagher et al., 2012). These organisms increase alkalinity by releasing negative ions that promote mineralization (Figure 1-2; Dupraz et al., 2009). Examples of this include uptake of carbon dioxide and bicarbonate resulting in release of OH– during photosynthesis and negatively charged functional groups released during degradation of extracellular polymeric substances (EPS) by SRBs
(Figure 1-2; Dupraz et al., 2009). SRBs are also important in that sulfate is an inhibitor
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of aragonite and calcite forms of calcium carbonate, thus its removal from the site of nucleation is integral (Fernandez-Diaz et al., 2010). Alternatively, activity of aerobic heterotrophs, sulfide-oxidizing bacteria (SOBs), and fermenters promote the dissolution of CaCO3 (Dupraz et al., 2009). Aerobic respiration, for example, promotes CaCO3 dissolution by releasing CO2 back into the environment, thereby shifting the carbon equilibrium (Dupraz et al., 2009). It is the combined metabolic activities of these key microbial functional groups that are hypothesized to determine the net precipitation potential of the thrombolites.
The alkalinity engine is also influenced by extrinsic processes in both abiotic and biologically influenced precipitation, but these have only a minor affect on biologically induced precipitation. Briefly, carbonate in seawater is in higher concentration than other chemical species contributing to alkalinity, therefore carbonate concentration determines total alkalinity (Riding, 2011; Gallagher et al., 2012). In an extrinsically driven environment, carbonate can become even more concentrated through evaporation (Dupraz et al., 2009; Riding, 2011). When the concentration of Ca2+ and carbonate ions is ten-fold supersaturated, spontaneous precipitation will occur (Dupraz et al., 2009; Riding, 2011). Degassing of CO2 will further increase alkalinity and drive equilibrium toward CaCO3 precipitation (Dupraz et al., 2009). Overall metabolic activity, environmental processes, or a combination of the two will, together, determine the potential and extent of carbonate precipitation within the thrombolite communities.
The Role of EPS in Carbonate Precipitation
Extracellular polymeric substances (EPS) serve as an important organic matrix to facilitate microbially induced precipitation. Within the EPS matrix, pH, alkalinity, ion concentrations, and microbial activity are all tightly regulated (Dupraz et al., 2009).
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Microbial EPS is predominately composed of polysaccharides with minor components of proteins, nucleic acids, and lipids (Kawaguchi and Decho, 2000; Flemming and
Wingender, 2001; Vu et al., 2009). EPS in the other lithifying systems are predominately made up of sugar monomers and low molecular weight compounds produced by
Cyanobacteria with contributions from other phototrophs and heterotrophs, such as
SRB (Kawaguchi and Decho, 2000; Braissant et al., 2007, Dupraz et al., 2009). EPS physically stabilizes the outer layer of cells protecting the cells from the high-energy wave activity experienced in the intertidal environment where the thrombolites are located (Kawaguchi and Decho, 2000). The matrix also provides the microbial community with protection from harmful substrates, grazing, antimicrobials, competing microbes, UV exposure, desiccation, and/or scavenging of nutrients (Dupraz et al.,
2009). The EPS-protected microbes are then able to form tightly regulated microenvironments in their immediate surroundings through their metabolic activity
(Braissant et al., 2009, Dupraz et al., 2009).
The EPS matrix is extremely effective at binding other molecules from the surrounding environment and this process is integral to CaCO3 precipitation in the thrombolitic mats. One important molecule bound within the matrix is bicarbonate,
− − HCO3 , from the surrounding seawater. As the concentration of trapped HCO3 in the
EPS increases, the Cyanobacteria will import the molecules for photosynthetic carbon fixation, especially as the CO2 in matrix is depleted and the pH increases (Merz, 1992).
− In Cyanobacteria the HCO3 is converted to CO2 carbon-concentrating mechanisms by utilizing the enzyme carbonic anhydrase (Figure 1-2; Merz, 1992; Riding, 2006). As
− previously described, the depletion of CO2 and production of hydroxide ions, OH ,
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during photosynthesis drive the alkalinity engine creating favorable conditions for
− precipitation. As this process elevates the pH, increased HCO3 in the matrix is also
− 2− available to react with OH , to form carbonate, CO3 (Figure 1-2; Riding, 2006).
Simultaneously, EPS is also contributing to mineralization by capturing free Ca2+ cations and providing a site for nucleation when favorable alkalinity conditions are accomplished (Figure 2-2). Bacteria in the EPS matrix produce organic compounds containing major functional groups, predominantly carboxylic acids and sulfate groups.
After deprotonation, these compounds can bind Ca2+ ions and store them for later carbonate nucleation. Heterotrophic activity then degrades EPS releasing bound Ca2+
2− making it available to bind CO3 and precipitate CaCO3, a biologically induced process.
Study Site: Highborne Cay, The Bahamas
The Highborne Cay thrombolites reside in the intertidal region of a 2.5 km fringing reef and cover approximately 0.5 km of coastline (Figure 1-1b; Reid et al.,
1999). Highborne Cay is the only known open marine environment with actively accreting thrombolites and they are easily accessible from the beach as they are located in the intertidal region. Uniquely, Highborne Cay exhibits both stromatolites and thrombolites adjacent to one another, so each can be examined under identical environmental conditions, such as salinity, temperature, and light exposure (Reid et al.,
2000). They were first reported in 1995 and have been dated using Uranium-Thorium measurements to be roughly between 1500 and 1650 years old (Reid et al., 1999;
Andres et al., 2006). Thrombolite-forming microbial mats undergo regular burial events, which are thought to minimize colonization and destruction by grazers (Reid et al.,
1999; Dupraz et al., 2009). As a result, these burial events have resulted in prokaryotic
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dominance of the thrombolite-forming microbial mats, especially by Cyanobacteria.
Activity of Cyanobacteria is thought to be the driving force behind thrombolite formation through photosynthetically induced precipitation and in situ calcification of their sheaths.
Previously, four distinct thrombolite-forming mat morphologies have been identified in
Highborne Cay as black, beige, pink, and button, listed from most shoreward (longest burial period) to most seaward (shortest burial period) (Myshrall et al., 2010; Mobberley et al., 2012). The differing lengths of burial periods result in distinct microbial consortiums residing in each mat type (Mobberley et al., 2012). The black and beige mats are dominated by Pleurocapsales, while the button thrombolites are dominated by a calcifying, filamentous Cyanobacterium, morphologically identified as Dichothrix spp.
(Planavsky et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012). The button mats were also found to be the most metabolically active with higher phototrophic biomass and dissolved oxygen levels than the pink mats or the adjacent stromatolites (Myshrall et al., 2010).
The recent discovery of calcified Dichothrix spp. filaments and the potential for rapid degradation of these microfossils in the thrombolites have an important implication to the fossil record. The unlaminated internal structure of ancient and modern thrombolites is attributed to the presence of calcifying microorganisms, particularly
Cyanobacteria, and in situ calcification is the primary process of sediment formation
(Kennard and James, 1986). Although filaments are absent in some of the older thrombolite fossil record, it is suggested that filamentous Cyanobacteria were also the primary source of carbonate (Planavsky et al., 2009). The absence of fossilized
Cyanobacteria may be a result of diagenesis, rather than nutrient fluxes, such as low
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concentrations of calcium in the ancient oceans as some have suggested, and is not evidence of abiotic mineralization (Planavsky et al., 2009). The evidence of parallel calcification processes between the ancient and living thrombolites indicates that studying the ecotypes and functional genes of the modern systems can provide insight into the mechanisms behind formation of the ancient structures.
Microbial and Functional Gene Diversity In Modern Thrombolite
Metabolic activity from the microbial mat community drives carbonate precipitation in thrombolites (Dupraz et al., 2009). The processes of mineralization are regulated through specific activities within microenvironments in the microbial mat. To define the specific role that microbes have played in the formation and accretion of thrombolites, the community metabolic networks and interconnectivity must be characterized. Before the advent of molecular techniques, microbialites were characterized by examining their mineralogy, mesofabrics, or through microscopy studies of dominant organisms (Riding, 2011). The introduction of molecular sequencing techniques has ushered in a revolutionary mechanism to study complex community dynamics with high resolution and independence from culturing. These techniques began with analyzing microbial diversity through examination of amplicon libraries of the
16S rRNA gene, which is the most conserved rRNA subunit and contains variable regions allowing its use as a valuable comparative tool for archaea and bacteria
(Rajendhran and Gunasekaran, 2011). The functional potential of uncultured organisms was later established through the use of metagenomics via cloning and then through shotgun sequencing allowing in depth analysis of systems that were out of reach not long ago (Handelsman et al., 1998; Eisen, 2007). Molecular analysis of complex systems has advanced further with the direct sequencing of RNA through
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metatranscriptomics to gain insight into the functional activity of complex environmental systems in space, time, and variable conditions.
It is important to know what organisms make up the community, their metabolic capabilities, and which genes are actively expressed. The molecular information can then be combined with the biochemical and geological data to determine the specific mechanisms that result in precipitation and thrombolite formation. Previous molecular studies of thrombolite-forming mats have predominantly examined microbial diversity through analysis of 16S rRNA gene amplification (Myshrall et al., 2010; Mobberley et al., 2012; Wilhelm and Hewson, 2012) or functional gene analysis through metagenomic profiling (Breitbart et al., 2009; Mobberley et al., 2013; White III et al., 2015; Sanghai et al., 2016; Warden et al., 2016). The Highborne Cay thrombolite diversity and metagenome studies have mainly focused on comparisons of the different mat types and, later, a focus on the button mat type. In addition to these studies, a single metatranscriptome from Highborne Cay has been completed on the button mats
(Mobberley et al., 2015). In that study, a midday sample over vertical spatial demarcations resulted in partial delineation of active pathways contributing to calcium carbonate precipitation (Mobberley et al., 2015). These three molecular approaches have begun to reveal the complexities of the microbial community interactions.
Diversity studies examining the 16S rRNA gene of Highborne Cay thrombolites found enrichment of taxa classified under the Proteobacteria and Cyanobacteria phyla
(Myshrall et al., 2010; Mobberley et al., 2012). The Proteobacteria comprised the majority of the OTUs and Alphaproteobacteria dominated aligned reads in both studies.
Proteobacteria dominance has also been observed in the metagenomic analyses of the
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Highborne Cay thrombolites, as well as diversity studies of other thrombolite-forming mats around the world, for example the thrombolites of Lake Clifton in Western
Australia, Rio Mesquites and Pozas Azules II in Mexico, and Pavilion Lake in Canada
(Breitbart et al., 2009; Centeno et al., 2012; Mobberley et al., 2013; Chan et al., 2014;
White III et al., 2015; Warden et al., 2016). Specific Alphaproteobacteria orders that are consistently recovered from thrombolite-forming mats include the photoheterotrophic
Rhodobacterales and Rhodospirillales and the nitrogen-fixing Rhizobiales, all broadly classified as purple nonsulfur bacteria and are common marine organisms that colonize microbial mats (Imhoff et al., 2005; Myshrall et al., 2010; Mobberley et al., 2012; Chan et al., 2014; White III et al., 2015; Warden et al., 2016). Rhodobacterales include species with a wide range of metabolic capabilities and may contribute to the microbial mat community by supplying other organisms with crucial vitamins (Simon et al., 2017).
The marine dwelling clades also play an important role in carbon, nitrogen, and sulfur cycling, for example by exporting molybdoenzymes out of the cell for use by nitrogen fixers, such as the Rhizobiales and some Cyanobacteria (Simon et al., 2017).
Recovery of Cyanobacteria in the microbial mats is wide ranging in both the overall number of identified reads and diversity within the phylum. The most abundant
Cyanobacteria recovered from the thrombolites of Pozas Azules II were from the diazotrophic orders Nostocales and Chroococcales and Pavilion Lake was dominated by the diazotrophic Oscillatoriales and, to a lesser extent, Nostocales (Breitbart et al.,
2009; Chan et al., 2014). The dominant cyanobacterial taxon in the thrombolite-forming button mats of Highborne Cay is a member of the order Nostocales and was determined to be the heterocyst-forming, filamentous Dichothrix spp. from the Rivulariaceae family,
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identified using both morphological and 16S rRNA gene analysis (Figure 1-1c,d;
Planavsky et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012). Previous studies have shown that Dichothrix spp. plays an important role in both EPS production and increasing alkalinity through photosynthesis and is hypothesized to drive calcium carbonate precipitation within the thrombolite-forming mats of Highborne Cay
(Planavsky et al., 2009; Mobberley et al., 2015).
Thrombolites have been molecularly characterized using metagenomics and, for the Highborne Cay thrombolites, metatranscriptomics in order to identify genes linked to the functional guilds promoting and inhibiting precipitation. The metabolic potential of thrombolites in Pavilion Lake, Pozas Azules II, Lake Clifton, and Highborne Cay all showed heterotrophic genes were significantly more enriched than photosynthesis genes (Breitbart et al., 2009; Mobberley et al., 2013; White III et al., 2015; Warden et al., 2016). This result would suggest that the heterotrophic organisms are potentially more active than their photosynthesizing counterparts. However, the metatranscriptome results showed that although photosynthesis genes only comprise 5% of the genes in the Highborne Cay metagenome, they comprise approximately 50% of the active gene expression. Photosynthesis was the dominant pathway, whereas respiration was not as active at midday (Mobberley et al., 2015). This result is an important observation because photosynthesis, specifically from Cyanobacteria, is thought to drive precipitation in the Highborne Cay thrombolites (Dupraz et al., 2009; Planavsky et al.,
2009).
In addition to photosynthesis, nitrogen fixation genes were also highly expressed in the top 3 mm of the thrombolite-forming mat compared to the accompanying
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metagenome and were associated with heterocyst-forming cyanobacteria, such as
Dichothrix spp. Although it may seem unusual to observe nitrogen fixation occurring at the time and location of peak oxygen levels, this result is not surprising. For many diazotrophic bacteria, oxygen would be very destructive to nitrogenase, the enzyme that catalyzes nitrogen fixation (Fay, 1992). Diazotrophic Cyanobacteria, who are also oxygenic photosynthesizers, have evolved a number of mechanisms to protect nitrogenase from the impact of oxygen (Fay, 1992; Paerl, 2017). It’s likely that these important mechanisms evolved in Cyanobacteria in the Precambrian as oxygen was building in the atmosphere (Fay, 1992; Paerl, 2017). Dichothrix spp. is diazotrophic and forms heterocysts, differentiated cells that are specialized for nitrogen fixation. These thick cells protect nitrogenase from the surrounding high oxygen environments and allow for simultaneous expression of the nitrogen fixation pathway and oxygenic photosynthesis (Fay, 1992). Nitrogen metabolism gene abundances in the metagenomes of Pavilion Lake and Pozas Azules II were low and not reported for Lake
Clifton. Nitrogen fixation has been shown to be very low in alkaline environments, so this may be an adaptive trait for the mat communities of Pavilion Lake (Bordeleau and
Prevost, 1994).
Conclusion
Thrombolites represent complex communities of microbes that can aid in understanding processes of global nutrient cycling and microbial evolution. The microbes contributing to the lithification process evolved in the early stages of community organization on Earth and continue to evolve together. Evolved metabolic processes have resulted in highly efficient global cycling of carbon, nitrogen, sulfur, and other important nutrients for life. Through interactions between microbial mat
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communities and their local environment, lithification is promoted and results in thrombolite formation. While initial work has begun to characterize these and other lithifying systems, the mechanisms behind these processes have not been fully elucidated in thrombolite-forming mats.
This dissertation will expand the work on the thrombolites of Highborne Cay by 1) constructing a spatial profile of the microbial diversity and isotope profile, 2) identifying the community activity closely associated with the Cyanobacteria driving precipitation, and 3) characterizing the temporal expression patterns of functional genes. A spatial profile of the mat through a vertical depth gradient will provide a finer resolution of microbial diversity. The diversity information will be used to construct a metabolic prediction to answer the question of where the activity contributing to lithification may be occurring. Stable isotope profiles for carbon, oxygen, and nitrogen will compliment this work by revealing the dominant metabolic activity of precipitated calcium carbonate. The dominant Cyanobacterium, Dichothrix spp., exhibits calcified filaments and is the primary source of mineralization in these thrombolites. Further characterizing the genome of Dichothrix spp. along with the organisms most closely associated with the filaments will provide a clearer picture of the gene potential within the EPS matrix, i.e. the nucleation site. Once the diversity and metabolic potential are described, an in depth functional analysis can be completed. Gene expression will be analyzed over four diel time-points for three Bahamian seasons. This analysis will reveal the nutrient cycling activity contributing to precipitation and, through temporal scaling, illustrate the patterns that ultimately result in mat lithification. Together, this research will represent
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the most extensive molecular analysis to date on a single thrombolite-forming microbial mat.
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A Coral B reef Thrombolites
~1 m
C D
cp 0.5 km
3 mm 10 µm
Figure 1-1. Overview of Highborne Cay, The Bahamas thrombolites A) Satellite map of Highborne Cay, Exuma Sound, The Bahamas showing study site location (red box). B) Thrombolite platform sampled in this study. C) Thrombolite cross section at 40x magnification showing Dichothrix spp. filament bundles. D) Dichothrix spp. filament cell morphology a calcium carbonate precipitation (cp)
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– – 2+ HCO3 HCO3 Ca Anions – released Carbon CO2 ζ-CA OH CO increase fixation 2 OH– RuBisCO alkalinity 2– CO3 CBB Cycle 2– CO3
– R-COOH HCO3 – pH! OH – + Carbonate Calcium R-COO H H O 2 formation binding Ca2+ 2– CO3
(R-COO)2Ca Heterotrophs Ca2+ Mineralization consume LMW under increased OH– compounds alkalinity freeing Ca2+ CaCO3 Ca2+
Ca2+ Ca2+
EPS Cyanobacteria EPS filaments Figure 1-2. Diagram of the photosynthesis driven reactions in thrombolite filamentous cyanobacteria and the surrounding EPS matrix. Carbon fixation by transport – – of HCO3 and CO2 into the cell, conversion of CO2 into HCO3 by carbonic – anhydrase for diffusion into the carboxysome where HCO3 is converted to – 2– CO2 releasing OH ions into the EPS raising the alkalinity favorable to CO3 formation. Ca2+ ions bound by low molecular weight compounds, such as carboxylic acid, are released through heterotrophic degradation of the EPS. 2+ 2– The free Ca ions and the CO3 ions are then able to bind and mineralization of CaCO3 can occur.
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CHAPTER 2 A STUDY OF THE MICROBIAL SPATIAL HETEROGENEITY OF BAHAMIAN THROMBOLITES USING MOLECULAR, BIOCHEMICAL, AND STABLE ISOTOPE ANALYSES
Introduction
With their long evolutionary history, microbialites serve as important model systems to explore and understand the co-evolutionary dynamics among lithifying microbial communities and their local environment. These carbonate structures are formed via the metabolic activity of microbes, which influence and drive biological processes associated with sediment capture and microbiologically induced organomineralization. Microbialites have been found in a wide range of habitats including brackish (e.g., Laval et al., 2000; Breitbart et al., 2009; White et al., 2015;
Chagas et al., 2016), marine (e.g., Dravis, 1983; Reid et al., 2000; Stolz et al., 2009;
Casaburi et al., 2016), and hypersaline (e.g., Logan 1961; Glunk et al., 2011; Wong et al., 2015; Ruvindy et al., 2016; Suosaari et al., 2016; Paul et al., 2016) environments and are classified based on their internal microfabrics (Burne and Moore, 1987; Dupraz et al., 2009). Two of the most well studied types of microbialites are stromatolites, which exhibit laminated internal fabrics (Walter et al., 1994; Reid et al., 2000), and thrombolites with irregular clotted fabrics (Aitken, 1967; Kennard and James, 1986).
Much of our understanding of microbialite formation comes from the study of modern systems (e.g., Reid et al., 2000; Breitbart et al., 2009; Petrash et al., 2012;
Russell et al., 2014; Valdespino-Castillo et al., 2014; Saghaï et al., 2015; Casaburi et al., 2016; White et al., 2015; Ruvindy et al., 2016; Warden et al., 2016; White et al.,
2016; Chagas et al., 2016). Microbialites in The Bahamas have been particularly important in expanding research in this area, as they are the only known modern open
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marine microbialite system and serve as potential analogs to ancient systems (Reid et al., 2000). In Bahamian stromatolites, processes underlying formation include iterative growth by cycling microbial mat communities and seasonal environmental controls; the resulting lamination represents a chronology of past surface communities (Visscher et al., 1998; Reid et al., 2000; Bowlin et al., 2012). In thrombolites, the processes that form the clotted fabrics are not well defined. In some Bahamian thrombolites, the clots appear to be products of calcified cyanobacterial filaments, which through their metabolism cause shifts in the carbonate saturation state and thereby drive precipitation
(Dupraz et al., 2009; Planavsky et al., 2009; Myshrall et al., 2010). Alternatively, it has been suggested that the clotted textures in thrombolites are sometimes linked to disruption or modification of microbial fabrics (Planavsky and Ginsburg, 2009; Bernhard et al., 2013; Edgcomb et al., 2013).
To further explore the formation of clotted fabrics, the thrombolites of Highborne
Cay, The Bahamas, were targeted as they represent one of the few modern locations of actively accreting thrombolitic microbialites in open marine environments (Planavsky et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012; Mobberley et al., 2013;
Mobberley et al., 2015). These marine thrombolites form in the intertidal zone of a 2.5 km fringing reef complex that extends along the eastern margin of Highborne Cay
(Figure 2-1A; Reid et al., 1999). The thrombolites range in size from up to one meter in height to several meters in length (Andres and Reid, 2006; Myshrall et al., 2010) and are covered with several distinct microbial mat types (Mobberley et al., 2012).
The dominant mat type associated with the Bahamian thrombolites, referred to as "button" mat, harbors tufts of vertically orientated calcified cyanobacterial filaments
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(Figure 2-1B; Myshrall et al., 2010; Mobberley et al., 2012). The dominant cyanobacterium identified within these tufts with both morphological and molecular tools is Dichothrix spp. (Planavsky et al., 2009; Mobberley et al., 2012). At the surface, these
Dichothrix-enriched button mats are calcified with aragonite precipitates located in the exopolymeric sheath of the cell. With depth, precipitates undergo dissolution and filaments degrade (Planavsky et al., 2009). In addition to the tufts of calcified filaments, the thrombolite-forming button mats also harbor a genetically diverse and active microbial community that appears to form vertical gradients of metabolic activity
(Myshrall et al., 2010; Mobberley et al., 2013; Mobberley et al., 2015).
Previous work in other microbialite systems, such as stromatolites, has shown that the relationship among active, distinct microbial guilds can alter the local physiochemical environment and generate discrete gradients of both solutes and redox conditions (e.g., Dupraz et al., 2009; Glunk et al., 2011; Wong et al., 2015). Within these microenvironments, the microbial activity can alter both the carbonate saturation index
(i.e., carbonate alkalinity and availability of free calcium) and the cycling of exopolymeric substances (EPS; Braissant et al., 2009), which serve as important nucleation sites for precipitation (Dupraz and Visscher, 2005). Certain metabolisms, such as photosynthesis and some types of sulfate reduction, can lead to an increase in pH and thereby promote precipitation (Visscher et al., 1998; Dupraz et al., 2009; Gallagher et al., 2012). Contrastingly, some metabolisms, such as sulfide oxidation, aerobic respiration, and fermentation, can increase dissolved inorganic carbon (DIC) concentrations but lower the pH and carbonate saturation state of the local environment and promote dissolution (Walter et al., 1994; Visscher et al., 1998; Dupraz et al., 2009).
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Together, it is the parity between categories of metabolisms that determines the extent and net precipitation potential within the lithifying mat community (Visscher and Stolz,
2005).
In addition to the precipitation potential, another component that is critical to the formation of microbialites is the availability of nucleation sites, which can be controlled by the production and degradation of EPS material. The EPS matrix serves several essential roles in the formation of microbialites as it binds cations (e.g., Ca2+) critical for carbonate precipitation, serves as attachment sites for microbes to withstand the high energy wave impacts, and protects microbes from environmental stresses, such as UV exposure and desiccation (Dupraz et al., 2009). Metagenomic analyses of both stromatolites and thrombolites across the globe have shown that Cyanobacteria and
Proteobacteria are the two primary producers of EPS material (Khodadad and Foster,
2012; Mobberley et al., 2013; Mobberley et al., 2015; Casaburi et al., 2016; Ruvindy et al., 2016; Warden et al., 2016). Alteration or restructuring of the EPS through microbial degradation can reduce the cation-binding capability and thereby facilitate the precipitation of calcium carbonate on the EPS matrix (Dupraz et al., 2004; Dupraz and
Visscher 2005; Dupraz et al., 2009).
There have been major advances in understanding the processes controlling stromatolite formation; in contrast, the factors controlling carbonate precipitation in thrombolites are less understood. Several recent studies have begun to use meta-omic approaches to understand thrombolite communities and how they may initiate precipitation. For example, metatranscriptomic sequencing of the Bahamian thrombolite-forming mats at midday revealed distinct profiles of gene expression within
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the thrombolites (Mobberley et al., 2015). This study, however, captured only those metabolically active communities and did not provide a comprehensive assessment of the total microbial population within the observed thrombolites zones. Additionally, previous shotgun metagenomic studies have been used to examine the overall metabolic potential in thrombolite ecosystems, including those at Highborne Cay
(Mobberley et al., 2013) and in hypersaline thrombolites of Lake Clifton (Warden et al.,
2016); however, neither of these metagenomic studies provided spatial information of the thrombolite-forming communities.
In the present study, we build on this previous research by examining the spatial distribution of the bacterial and archaeal diversity associated with the button mats of
Bahamian thrombolites using a targeted phylogenetic marker gene approach coupled with a predictive computational reconstruction of the metagenome to ascertain how thrombolite-forming communities change, both taxonomically and functionally, with depth. These molecular-based approaches are complemented by stable isotope analysis with Secondary Ion Mass Spectrometry (SIMS), a high-resolution technique that has not been previously used in any microbialite study, to provide additional constraints on carbonate precipitation in the Dichothrix calcified filaments. Together, these methodologies elucidate the juxtapositioning of the taxa and metabolic functions associated with the thrombolite-forming mats as well as provide key insight into the metabolic metabolisms that initiate precipitation within these lithifying ecosystems.
Methods
Sample Collection
Thrombolite-forming button mats were collected from the island of Highborne
Cay, The Bahamas, (7649’ W, 2443’N) in February 2010 and October 2013 from an
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intertidal thrombolitic platform from Site 5 (Andres and Reid, 2006). The 2010 mats were partitioned in the field into three distinct vertical sections (0 – 3 mm; 3 – 5 mm; and
5 – 9 mm depth horizons, respectively) with a sterile scalpel to cut the thrombolite- forming mats, and the sections were immediately placed into RNAlater (Life
Technologies, Inc., Grand Island, NY). These samples were transported to Space Life
Sciences Lab, Merritt Island, Florida, where they were stored at -80°C until processing.
The 2013 mats were processed for isotope analyses as described below.
Microelectrode Measurements
Depth profiles of oxygen, sulfide, and pH were determined in triplicate with needle microelectrodes (Visscher et al., 1991, 1998; Pages et al., 2014) either in situ or ex situ under ambient temperature and light intensity. Microelectrodes with a tip diameter between 60 and 150 µm were deployed in 250 µm depth increments with a manual micromanipulator (National Aperture, Salem, NH). Oxygen profiles were measured in submerged mats (in ca. 5-15 cm water) with a polarographic Clark-type needle electrode with an outer diameter of 0.4 mm, and readings were recorded with a picoammeter (PA2000; Unisense, Aarhus, Denmark). Polarographic sulfide electrodes
(Unisense, Denmark) were used in combination with a Unisense PA 2000 picoammeter, and pH and S2- electrodes (Diamond General, Ann Arbor, MI) were connected to a high- impedance millivolt meter (Microscale Measurements, The Netherlands). Both electrode types were encased in needles (outer diameter 0.5 mm). Sulfide electrodes were calibrated before and after each deployment with buffers of three different pH values that span the pH range observed in the thrombolite (i.e., pH 7, 8, and 9). Under an oxygen-free atmosphere, aliquots of a sulfide stock solution were added in increments to the buffer, and electrode signals were recorded. Subsamples of the buffer were taken
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to ascertain the actual concentration of sulfide in the calibration cocktail by using the methylene blue method. The pH electrodes were calibrated at pH 5, 7, and 10. The pH profiles were used to calculate the actual sulfide concentration at each depth.
Generation and Sequencing of 16S rRNA Gene Libraries
DNA was extracted in triplicate from each vertical section with a modified MoBio
PowerSoil DNA isolation kit that included a xanthogenate pre-treatment, as previously described (Green et al., 2008). The DNA was then PCR amplified in triplicate with fusion
454-primers that included a unique eight base pair barcode on the 3’ end (Supplemental
Table S1). The PCR reactions for the bacterial 16S rRNA libraries targeted the V1-2 region and included the following: 1 x Pfu Reaction Buffer (Stratagene, La Jolla, CA),
280 M dNTPs, 2.5 g bovine serum albumin (BSA), 600 nM of each primer, 1 ng of genomic mat DNA, 1.25 U of Pfu DNA Polymerase (Stratagene, La Jolla, CA), and nuclease-free water (Sigma, St. Louis, MO) in a volume of 25 l. The amplification parameters included a 95C denaturation for 5 min, followed by 30 cycles of 95C for 1 min, 64C for 1 min, 75C for 1 min, and a final extension at 75C for 7 min.
The archaeal libraries required a nested PCR approach that included two rounds of amplification and targeted the V3-5 region. The reactions contained the same concentrations as the bacterial library with the exception of 400 nM of 23F and 958R primers (Delong, 1992; Barns et al., 1994) and 10 ng of thrombolitic mat DNA in round one, whereas 400 nM of primers 334F and 915R (Casamayor et al., 2002) with 10 ng of round one amplicon material as a template. The amplification parameters in round one included a denaturation step of 95C for 2 min, followed by 35 cycles of 95C for 30 sec,
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55C for 1 min, 72C for 2 min with an extension of 72C for 10 min. In round two the parameters were similar except that the annealing temperature was changed to 61C.
For each library, the PCR amplicons were purified with the Ultraclean PCR
Clean-Up Kit (MoBio, Carlsbad, CA) and combined into equimolar ratios. Sequencing was performed per manufacturers protocol by a 454 GS-FLX platform with Titanium chemistry (Roche, Branford, CT) at the University of Florida’s Interdisciplinary Center for
Biotechnology Research. The raw sequence data files were deposited into the NCBI sequencing read archive under number SRP068710 (bacteria) and SRP068710
(archaea) under project PRJNA305634.
Bioinformatic Analysis of 16S rRNA Gene Libraries
The recovered bacterial and archaeal 16S rRNA gene sequences were analyzed by Quantitative Insights Into Microbial Ecology (QIIME; version 1.9.1; Caporaso et al.,
2010). Preprocessing was completed to separate the replicate libraries by depth, remove barcode adaptors, and filter for quality by using default parameters including: minimum sequence length of 200 bp; maximum sequence length of 1000 bp; minimum quality score of 25; maximum ambiguous bases of 6; and maximum homopolymer length of 6. Operational taxonomic units (OTUs) were assigned to the filtered reads at
97% identity against the Greengenes database (v13.8; DeSantis et al., 2006) using the
UCLUST method within QIIME. Further filtering was completed including removal of unassigned reads and filtering for most abundant OTUs (> 0.005%). The generated
OTU table was used for taxonomic comparison, filtering the OTUs at 0.005% and producing taxonomic trees with Meta Genome Analyzer (MEGAN5; Huson et al., 2007).
OTU tables were filtered at 0.1%, and hierarchal taxonomic pie charts were created with
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the Krona tool (Ondov et al., 2011). The representative sequences were aligned with
PyNAST (v1.2.2; Caporaso et al., 2010) to the Greengenes Core reference alignment and a phylogenetic tree was built by FastTree (v2.1.3; Price et al., 2010). The phylogenetic tree was used for downstream community analyses. Diversity analyses were performed at a sequence depth of 3587 for archaea and 3691 for bacteria.
Alpha diversity indices were calculated by using observed species and Faith’s
Phylogenetic Diversity (PD) measure (Faith, 1992), and the averaged results were used to generate rarefaction curves. Beta diversity comparisons were visualized by using
Principal Coordinates Analyses (PCoA) and Emperor (Vázquez-Baeza et al., 2013) generated from unweighted UniFrac distance matrices (Lozupone and Knight, 2005).
Statistical significance between the mat depths was calculated by adonis, a nonparametric, permutation-based metric.
Reconstruction of Functional Metagenome Using the PICRUSt Algorithm
Functional gene content from each of the three vertical sections was predicted from the recovered 16S rRNA gene sequences by using the algorithm Phylogenetic
Investigation of Communities by Reconstruction of Unobserved States (PICRUSt v.1 .0;
Langille et al., 2013), as previously described (Casaburi et al., 2016). Results were collapsed at Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologs (KO) Level
3 within the pathway hierarchy of KEGG (Kanehisa and Goto, 2000). For comparison purposes, a shotgun metagenomic dataset of whole Bahamian thrombolite-associated mats previously collected from Highborne Cay (Mobberley et al., 2013) was downloaded from the MG-RAST database with accession number 4513715.3. Raw reads were filtered by SICKLE (v. 1.2; Joshi and Fass, 2011) with default parameters. Filtered reads were re-annotated for functionality at different KEGG levels by the Metagenome
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Composition Vector (MetaCV v. 2.3.0) with default parameters (Liu et al., 2012).
Resulting hits were filtered at a correlation score > 30, collapsed at KO Level 3, and finally compared to the 16S rRNA gene predicted functional profile.
Bulk Stable Isotope Analysis
Samples of thrombolite-forming mats were collected for isotopic analysis during the same collection trip as the molecular samples from Site 5 (Andres and Reid, 2006) of Highborne Cay in October 2013. The mat samples were dried and examined by bulk isotope analysis for both inorganic and organic signatures. Calcified filaments were dissected from the button mats, dried, and ground to a fine powder in triplicate. Aliquots of the carbonate (i.e., aragonite; Planavsky et al., 2009) were measured for inorganic
δ13C and δ18O with a Finnigan-MAT 252 isotope ratio mass spectrometer coupled with a
Kiel III carbonate preparation device.
For isotopic analysis of organic matter, calcified filaments were dissected and treated with an acid solution (6N HCl) at room temperature overnight until all CaCO3 was removed and rinsed with distilled water to remove HCl. Samples were loaded into tin capsules and placed in a 50-position automated Zero Blank sample carousel on a
Carlo Erba NA1500 CNHS elemental analyzer. After flash combustion in a quartz column containing chromium oxide and silvered cobaltous/cobaltic oxide at 1000°C in an oxygen-rich atmosphere, the sample gas was transported in a He carrier stream and passed through a hot reduction column (650°C) consisting of reduced elemental copper to remove oxygen. The effluent stream then passed through a chemical (magnesium perchlorate) trap to remove water followed by a 3 meter GC column at 45°C to separate
N2 from CO2. The sample gas next passed into a ConFlo II preparation system and into the inlet of a Thermo Electron Delta V Advantage isotope ratio mass spectrometer
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running in continuous flow mode where the sample gas was measured relative to laboratory reference N2 and CO2 gases. All carbon and oxygen isotopic results are expressed in standard delta notation relative to Vienna Pee Dee Belemnite (VPDB), whereas nitrogen isotopic results are expressed in standard delta notation relative to air
(AIR). The standard used for bulk C and O measurements was NBS-19, whereas
USGS40 and USGS41 were used for N. Measurements were conducted in triplicate at the Light Stable Isotope Mass Spectrometry Laboratory in the Department of Geological
Sciences at the University of Florida. Instrument precision was better than 0.10‰ for all bulk isotope measurements.
Stable Isotope Analysis Using Secondary Ion Mass Spectrometry (SIMS)
Additional mat samples, collected in Oct 2013, were prepared as thin-sections at the WiscSIMS laboratory, UW-Madison. Samples were cast with EpoxiCure resin in 25 mm epoxy rounds, cut with a Buehler IsoMetTM low speed to expose the most suitable section for analysis, and turned, together with two grains of UWC-3 WiscSIMS calcite standard (δ13C = -0.91 0.04‰; δ18O = -17.87‰ 0.03‰ VPDB (Kozdon et al., 2009), into ~100 µm-thick thin sections. An aragonite standard (UWArg-7, δ13C = 5.99‰; δ18O
= -10.84‰ VPDB; Orland, 2012; Linzmeier et al., 2016) was also run at the beginning of each day of analysis to correct for the differences in instrumental mass fractionation between calcite and aragonite, which was 1.3‰ for δ18O and 1.5‰ for δ13C. The epoxy rounds were ground to expose features of interest for analysis. Petrographic microscopy was conducted with an Olympus BH-2 microscope with plane-polarized and cross- polarized transmitted light at various magnifications to identify potential sites suitable for
SIMS analysis. The samples were then polished and sputter coated with palladium for
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scanning electron microscopy (SEM) at the University of Miami’s Center for Advanced
Microscopy (UMCAM) to identify areas of precipitate for analysis and to screen for potential textural anomalies that might impede in situ δ13C and δ18O measurements.
The SEM analysis was conducted on a FEI XL-30 Field Emission ESEM/SEM instrument with energy dispersive spectrometer (EDS). The SEM analysis was to insure integrity of the sample and to identify specific target sites. After SEM analysis, the palladium coating was removed with 0.25 m polish on a lapidary wheel, dried, and recoated with gold.
The thrombolite mat samples were then analyzed for δ13C and δ18O on a
CAMECA ims-1280 secondary ion microprobe mass spectrometer (SIMS) using a
133Cs+ primary ion beam at the WiscSIMS Laboratory, Department of Geoscience,
University of Wisconsin-Madison. A primary beam of 600 pA, with mean 0.77 ‰ spot-to- spot precision (2SD), was used for δ13C, and 1.7 nA was used for δ18O with a 10 µm spot size (precision ~0.3‰). WiscSIMS carbonate analysis has been described in detail in previous publications (Orland et al., 2009; Valley and Kita, 2009; Kozdon et al., 2011;
Williford et al., 2016).
Analysis of the thrombolitic mat sections (10 – 15 spot analyses per sample) were bracketed by 8 - 10 repeat measurements on the UWC-3 standard grain by using the same parameters as the samples to help determine instrumental mass fractionation corrections for each set of measurements. After completion of each analytical session, the samples were returned to University of Miami for SEM inspection of the pits to evaluate any features that may have impacted accuracy (e.g., cracks or epoxy).
Additionally, for those measurements that penetrated down to epoxy material (depths of
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1-2 µm) and had high secondary ion count rates (i.e., > 100% for 12C of the measured counts per second on the standard grain), the final three to six cycles (of 20) were excluded from computations, and the values for the spots were recalculated as in the work of Vetter et al. (2014). Visualization of the data was conducted in R (v.3.2.2; R
Core Team, 2015) by using the package ggplot2 (Wickham, 2009).
Results
Microelectrode Profiling of Thrombolite Button Mats
The in situ concentrations of oxygen and sulfide were measured with microelectrodes during early afternoon representing peak photosynthesis (i.e., 12:30pm and 2:00pm) and at the end of the night, which marks the end of a prolonged anoxic period (i.e., 4:00am – 6:00am) (Figure 2-1C). The profiles reveal steep vertical gradients that fluctuated throughout the diel cycle. During the day, the oxic zone extended through the first 5 mm of the button mat with the peak of oxygen production (> 600 µM) occurring in the upper 3 mm (Figure 2-1C). At night, however, oxygen levels decreased significantly and were detectable only in the upper 2 mm of the mat suggesting rapid consumption at night and limited diffusion of O2 from the overlying water column.
Contrastingly, sulfide levels were low during the day with levels detectable only below 6 mm. At night, sulfide levels built-up and were detectable at 4 mm with a peak concentration occurring at a depth of 8 - 10 mm within the mat.
In addition to oxygen and sulfide, pH was also monitored throughout the vertical profile of the button mat revealing a wide shift throughout the diel cycle. At peak photosynthesis, the localized pH ranged from 8.4 to 10.4 throughout the depth profile with the highest pH occurring at a depth of 3 mm (Figure 2-1C). At night, however, the pH steadily decreased to as low as 7.1 at depths below 5 mm. Based on these oxygen,
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sulfide, and pH microelectrode profiles three distinct spatial zones emerged. Zone 1 included the upper 3 mm of the button mat and contained a supersaturated oxic zone that was suggestive of high rates of oxygen production and consumption. Zone 2 represented a transitional area 3 – 5 mm beneath the surface where oxygen levels decreased and sulfide levels began to build. Finally Zone 3, which included depths below 5 mm, represented a primarily anoxic region of the thrombolite-forming mat.
Phylogenetic Composition of Bacteria in Thrombolite Communities with Depth
Immediately after the microelectrode profiles were generated, the thrombolite mats were then sectioned based on these three observed zones (Zone 1, 0 – 3 mm;
Zone 2, 3 – 5mm; and Zone 3, 5 – 9 mm), and each of these spatial regions was subsequently examined for taxonomic diversity (Figure 2-1D). Three replicate amplicon libraries were generated for each zone targeting the 16S rRNA gene for both the
Bacteria and Archaea. A summary of the data associated with the amplicon libraries is provided in Table 1-1. The overall bacterial diversity increased with depth (Figure A-1A) with 2044 operational taxonomic units (OTUs) at 97% sequencing similarity in the upper oxic Zone 1 and 2947 and 3525 OTUs recovered from Zone 2 and 3, respectively. The number of recovered OTUs was much higher then previous diversity assessments of the Highborne Cay thrombolites (Myshrall et al., 2010; Mobberley et al., 2012) and likely reflects the increased sequencing coverage as determined by Good’s estimates (Table
2-1).
A total of 16 phyla were recovered from the three spatial zones within the thrombolite-forming mat with the Proteobacteria, Cyanobacteria, Bacteroidetes,
Chloroflexi, and Acidobacteria being highly represented in each zone (Figure A-2).
Distinct taxonomic differences, however, were observed across the three spatial regions
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of the thrombolite mat at the family level (Figure 2-2, 2-3; Figure A-2). In the upper Zone
1, the most abundant family represented within the mat is the cyanobacterial family
Rivulariaceae (Figure 2-2; Figure A-2). This taxon contains the genus Dichothrix, which was previously identified in the thrombolite mats as forming extensive tufts of calcified filaments (Figure 2-1B) and has rarely been found in laminated stromatolites (Foster and Green, 2011). The Rivulariaceae dominated the oxic Zone 1 comprising 21% of annotated reads compared to 15% in the transitional Zone 2 and only 5% of the total recovered reads in Zone 3 (Figure 2-2; Figure A-2). In addition to Rivulariaceae, other prevalent Cyanobacteria in the oxic Zone 1 included Pseudanabaenaceae (11%),
Xenococcaceae (5%), and Synechococcaceae (4%; Figure 2-2; Figure A-2).
Although Cyanobacteria was the dominant phylum recovered from Zone 1, there was also a diverse population of Proteobacteria, specifically, the class
Alphaproteobacteria. Within the Alphaproteobacteria there was enrichment of the photoheterotrophic Rhodobacteraceae (19%) and Rhodospirillaceae (7%) families, and to a lesser extent the Rhizobiales (5%). These taxa were not only abundant in Zone 1 but were highly represented throughout the thrombolite vertical profile (Figure 2-3;
Figure A-2). Other proteobacterial taxa that were abundant in Zone 1 compared to the other two zones included the sulfate-reducing Deltaproteobacteria family
Desulfovibrionaceae (3%) and the Gammaproteobacteria family Thiotrichaceae (0.8%), which harbors several sulfide-oxidizing taxa (Figure 2-3). A detailed krona plot of the upper 3 mm of the thrombolite mat is provided in Figure A-3.
Zone 2 represented a transitional phase in the thrombolite-forming mats with several taxa first appearing in this 3 – 5 mm zone and gradually increasing in relative
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abundance in the anoxic Zone 3 (Figure 2-3; Figure A-2, A-4). For example, in the
Deltaproteobacteria the sulfate-reducing families Desulfobacteraceae and
Syntrophobacteraceae were enriched in Zones 2 and 3 compared to Zone 1.
Additionally, the purple sulfur bacterial Gammaproteobacteria family
Ectothiorhodospiraceae (order Chromatiales) and the sulfide-oxidizing
Piscirickettsiaceae (order Thiotrichales) also exhibited a gradual increase in relative abundance with depth (Figure 2-3). In addition to the more prevalent taxa there were several families that appeared to a lesser extent only at depth and included the photoheterotrophic Gemmatimonadetes, purple non-sulfur bacteria Rhodobiaceae, and nitrite-oxidizing Nitrospiraceae. Detailed taxonomic profiles of Zone 2 and 3 are depicted as krona plots in Appendix A Figures A-4 and A-5.
In addition to analysis of the bacterial composition, a beta diversity analysis was completed to assess whether these observed taxonomic differences were statistically significant. Unweighted UniFrac distance matrices were generated for the Bacteria amplicon libraries and visualized with a jackknifed principal coordinate analysis (PCoA;
Figure 2-4A). The statistical analyses revealed that each of the three spatial zones represented distinctive bacterial communities with low standard deviation amongst the library replicates (Figure 2-4A). The R2 value showed an effect size of 0.402, indicating that approximately 40% of the variation in the bacterial populations could be explained by depth and potentially other environmental factors within the mats (p=0.001;
R2=0.402, adonis; Figure 2-4A). Depth likely accounted for at least 27% (PC1) of the variation among the three zones based on the PCoA plots (Figure 2-4A).
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Phylogenetic Composition of Archaea in Thrombolite Communities with Depth
With regard to the overall archaeal diversity (e.g., Shannon Index), there was little difference across the three zones with the recovered OTUs ranging from 506 to
671 (Table 2-1; Figure A-1B). Of the three recovered phyla, the Thaumarchaeota were dominant in all three zones of the thrombolite forming mats with most of the reads sharing similarity to the ammonia-oxidizing family Cenarchaeaceae (Figure 2-5), specifically the genus Nitrosopumilus. There were, however, some taxonomic differences between the different spatial regions in the thrombolites. For example, phototrophic Halobacteriales showed the highest abundance in the upper oxic Zone 1, as did the ammonia-oxidizer Nitrososphaeraceae (Figure 2-5). Although few methanogenic archaeal taxa were detected in each of the three zones, they had the highest representation in the transitional Zone 2 with most of the reads sharing similarity to the class Thermoplasmata and the family Methanocarcinaceae (Figure 2-5). A beta diversity test was also completed for the archaeal libraries and showed increased statistical variation between replicates (Figure 2-4B). Although the archaeal populations did not have as high of an effect size (R2) as the bacterial population, 30% of variation within the archaea could be explained by environmental factors, such as depth. Based on the beta diversity analysis, just as in the bacterial population, the three zones did appear to have spatially distinct Archaea populations with approximately 20% of the variation between the zones likely being associated with depth (p=0.017; R2=0.307, adonis; Figure 2-4B).
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Spatial Profiling of Functional Gene Complexity of Thrombolite-Forming Mats Using Predictive Sequencing Analysis
In addition to profiling the microbial diversity within the thrombolite button mat, a reconstruction of the functional gene complexity was generated for each zone by using the 16S rRNA gene sequences and the PICRUSt algorithm (Langille et al., 2013). As the number of available reference genomes has steadily increased, PICRUSt has emerged as an effective tool to accurately predict the functional complexity of the metagenomes based on taxonomic information (Langille et al., 2013). The tool has successfully been used to reconstruct the metagenomes of a wide range of ecosystems including nonlithifying microbial mats and stromatolites (Langille et al., 2013; Casaburi et al., 2016). A predicted metagenome was generated for each spatial zone with the
QIIME taxonomic output, which was then statistically compared to a previously published metagenome of the entire button mat (0 – 9 mm; Mobberley et al., 2013) to determine whether differences in the metabolic capabilities could be observed between zones. The previously sequenced thrombolite metagenome was re-annotated by using
MetaCV to update the metagenomic dataset and enabling comparable annotations to the PICRUSt predictive metagenomes.
A total of 272 KEGG functions were identified in the three zones corresponding to 328 level 3 KO entries, which was consistent with the 268 KEGG functions observed in the re-annotated whole-mat metagenome (add s2). Additionally, there was a strong correlation between the PICRUSt predictive metagenomes and the whole mat metagenome (r = 0.93, Pearson), with most of the KOs (n = 222) showing little or no variation between zones (add s2). Of the 59 KOs that did show variation (> 0.1%), several of the differences occurred between the upper oxic Zone 1 and the two deeper
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Zones 2 and 3 (Figure 2-6). In Zone 1, there was an increase in the relative abundance of KO pathways associated with photosynthesis including the antennae proteins, porphyrin, and chlorophyll metabolism, whereas there was a lower abundance of genes associated with carboxylic acid metabolism (e.g., butanoate, benzoate, caprolactam metabolism; Figure 2-6). Deeper within the mat in Zones 2 and 3 there was a higher relative abundance of genes associated with fatty acid metabolism and lipopolysaccharide (LPS) biosynthesis compared to Zone 1. Despite these few select differences, many highly represented pathways in the thrombolite-forming mats, such as
DNA repair proteins, two-component signaling, and bacterial motility, showed no differences among the three spatial zones and likely reflect the central metabolisms associated with the thrombolite microbiome.
Stable Isotope Analyses of Thrombolitic Carbonates
The calcified carbonate filaments associated with the Dichothrix cyanobacteria in the upper Zone 1 were examined by using a combined bulk isotopic analysis and targeted SIMS approach coupled, which enabled an in situ high-spatial resolution analysis (Valley and Kita 2009; Kozdon et al., 2009; Kita et al., 2011) (Figure 2-7). Bulk samples of dissected calcified filaments had δ18O values with a mean of -0.5 0.1‰
VPDB suggesting that the precipitates associated with the filaments were not the result
13 of evaporation, which would cause an enrichment in heavy isotopes. Bulk δ Ccarb values of the dissected filaments had a mean of 5.0 0.03‰, which was similar to the surrounding carbonate sediments within the thrombolite structure (+4.0‰ to +4.9‰; mean = 4.6 0.3‰). The δ13C values for the organic matter associated with the filaments was depleted compared to the sediment with values ranging -9.9‰ and -9.2‰
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(mean = -9.6 0.2‰), suggesting a relatively muted fractionation during organic matter uptake, similar to what has been produced in other modern microbial mats (Canfield
15 and DesMarais, 1993). The δ Norg values associated with the filaments ranged from -
1.1‰ to -0.1‰ (mean = -0.8 0.3‰), suggesting that nitrogen fixation is a predominant means of N assimilation (Sigman et al., 2009) within the thrombolite-forming mats and correlates with the high number of recovered diazotrophic Cyanobacteria and
Alphaproteobacteria from the mats.
To complement the bulk stable isotope analyses, the calcified filaments were also analyzed in situ with SIMS to provide a higher spatial resolution (10 µm spot size) of the δ18O and δ13C compositions of the calcified filaments. Micrographs depicting the
SIMS target sites along the filaments and associated carbonate precipitate are shown in
Fig. 8. The δ18O value of the surrounding carbonate sediments ranged from -2.0‰ to -
0.6‰ (mean = -1.3 0.5‰), whereas the filaments exhibited a more depleted oxygen signature ranging from -7.7‰ to -2.0‰ (mean = -3.2 1.1‰) (Figure 2-7). The δ13C values of the surrounding sediments (i.e., ooids) in the thrombolite button mats had a narrow range of values (+3.6‰ to +4.6‰; mean = 4.1 0.4‰) and matched values from previous studies of sediments that surround the thrombolite structures (e.g. ooids;
Swart and Eberli, 2005), whereas the filaments had a much wider range (+0.1‰ to
+5.5‰; mean = 2.7 1.3‰). All stable isotope measurements are presented in order of analysis in s4 and s5 to be added.
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Discussion
Microbial Diversity within Thrombolite-Forming Mats are Highly Structured
The presence of discrete spatial zones of microbial and biochemical activity have been well documented in stromatolites (e.g., Visscher et al., 1998; Wong et al., 2015); however, the occurrence of similar zonation in mats that form clotted thrombolites has only been recently suggested (Mobberley et al., 2015). In this study, statistical analysis of the bacterial and archaeal communities revealed significantly different profiles of taxa with depth (Figure 2-4) suggesting the microbes are not only active at different depths
(Mobberley et al., 2015) but that there are also distinct populations that are forming discrete microenvironments within the thrombolite-forming mats.
In the upper oxic Zone 1, the dominance of cyanobacterial sequences with similarity to the filamentous Rivulariaceae reinforces the morphological observation that
Dichothrix sp., a member of the Rivulariaceae, serves as a "hot spot" for photosynthetic activity and carbonate deposition within the thick EPS matrix associated with the filaments (Planavsky et al., 2009). Future sequencing of the Dichothrix spp. genome will help to expand the relatively small database of filamentous, heterocystous cyanobacteria as well as delineate the specific pathways associated with EPS production in this keystone organism. In addition to the cyanobacteria, taxonomic analyses also revealed an enrichment of diazotrophic photoorganoheterotrophs primarily associated with the Rhodobacterales, Rhodospirillales, and Rhizobiales increasing with depth (Figure 2-3). These metabolically flexible Alphaproteobacteria are ubiquitous in marine microbial communities including all previously characterized microbialites, coral symbioses, and sediments (e.g., Dang et al., 2013; Houghton et al.,
2014; Wong et al., 2015; Casaburi et al., 2016; Hester et al., 2016; Suosaari et al.,
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2016) and may be contributing to the carbon fixation rates deeper within the thrombolitic mats where there are fewer cyanobacteria due to the reduced light levels and the presence of sulfide. Additionally, the diazotrophic photoheterotrophs may be helping to maintain the bioavailability of nitrogen in the thrombolite-forming communities.
Another key microbial functional group enriched within the thrombolite-forming communities was sulfate-reducing bacteria (SRB), whose activity has been directly correlated to deposition of carbonate in actively accreting stromatolites (Visscher et al.,
2000; Decho et al., 2010). There was a pronounced vertical stratification of SRB in the thrombolite-forming communities. Taxa associated with Desulfovibrionaceae were enriched in the upper oxic Zone 1, whereas the Desulfobacteraceae increased in their relative abundance with depth. This vertical stratification of SRBs has been seen in the non-lithifying hypersaline mats of Guerrero Negro, Mexico (Risatti et al., 1994) and
Solar Lake Egypt (Minz et al., 1999). Several species of sulfate-reducing
Desulfovribionaceae (e.g., Desulfovibrio spp. and Desulfomicrobium spp) have been shown to be prevalent in the oxic zone of microbial mats (Krekeler et al., 1997), and high levels of sulfate reduction activity have been recorded in the upper oxic zone of non-lithifying and stromatolite-forming mats (e.g., Canfield and DesMarais, 1991;
Visscher et al., 1992, 2000). The abundance of SRB in the oxic zone may be, in part, due to the presence of sulfide-oxidizing bacteria (SOB). There was an enrichment of the families Thiotrichaceae and Chromatiaceae in the upper Zone 1, which are known to harbor many sulfide-oxidizing taxa (Pfennig and Trüper, 1992; Lenk et al., 2011). The
2- SOB may be removing the O2 and S generated by the cyanobacteria and SRBs, both of which can be toxic to the SRB at high enough levels (Decho et al., 2010). Together,
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this enrichment of SOB, oxygen-tolerant SRB, and their vertical stratification in the thrombolite-forming may suggest that, much like the stromatolites, these different phylogenetic groups may be playing distinctive community functions in response to variable carbon and electron donor availability at different depths as well as the diel flux of oxygen and sulfide.
The archaeal population also exhibited stratification of certain taxa within the thrombolite-forming mat. There was an enrichment of Halobacteriales in the upper oxic
Zone 1 of the thrombolitic mats. Members of this order are typically chemoorganoheterotrophic and can grow on a wide range sugars, carboxylic acids, alcohols, and amino acids. This aerobic taxon has been observed in both lithifying and nonlithifying microbial mat communities primarily in hypersaline environments (Burns et al., 2004; Arp et al., 2012; Schneider et al., 2013) and may be contributing to the heterotrophic degradation of EPS material associated with the calcified filaments. It should be noted that the salinity of the porewater in the upper part of the microbialites increases significantly (~135 PSU; Visscher unpubl) upon exposure to the atmosphere during low tide, creating temporary hypersaline conditions.
Sequences were also recovered from methanogenic archaea in primarily Zone 2, and these were primarily associated with the Methanocarcinaceae and
Thermoplasmata. These taxonomic results correspond to recovered methyltransferase- encoding genes in the thrombolite metagenome (Mobberley et al., 2013), and there was an enrichment of recovered sequences from Zone 2 (Figure 2-5). Members of the
Methanocarcinaceae can perform methanogenesis using CO2, acetate, and C1 compounds (Feist et al., 2006) and have been shown to elevate pH levels in mat
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communities via CO2 consumption (Kenward et al., 2009). More recently, methanogenic lineages of the Thermoplasmata have been identified in human and rumen gut microbiomes as well as wastewater sludge habitats that also can use methanogenic substrates (Dridi et al., 2012; Poulson et al., 2013; Iino et al., 2013). Although not the dominant archaea within the thrombolite-forming mats, the recovered taxa in this study coupled with functional genes observed in the thrombolite metagenome (Mobberley et al., 2013) suggest that methanogenesis may have a potential, albeit minor, role in promoting an alkaline environment within these thrombolitic mats. Methane production has been observed within the thrombolites and adjacent stromatolites (Visscher unpublished), and further work to more fully characterize methane levels within each zone may help elucidate the role of methanogenesis, if any, in the Bahamian thrombolite formation.
By far the largest component of the archaeal population within the thrombolite forming mat were the Thaumarchaeota specifically Cenarchaeaceae, which harbor many ammonia-oxidizing taxa (Figure 2-5). Although the Cenarchaeaceae were found in all three zones, there was an enrichment in the lower two regions of the mat (Figure
2-5). Thaumarchaeota have been found in a wide range of lithifying and non-lithifying microbial mat habitats (e.g. Ruvindy et al., 2016) and likely play a role in nitrogen cycling within the thrombolite-forming communities. Previous studies in which the metagenomics of lithifying systems have been examined found a paucity of bacterial nitrification genes (Breitbart et al., 2009; Mobberley et al., 2013; Ruvindy et al., 2016), and ammonia-oxidizing archaea, such as those taxa within the Thaumarchaeota, may be facilitating the metabolism of ammonia to nitrite.
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Predictive Metagenome Reconstruction Shows Strong Correlation with Taxa and Function
The PICRUSt predictive metagenome strongly correlated (r = 0.93) with the previously published whole shotgun library, which had targeted the entire thrombolite- forming mat community and provided no spatial information regarding the metagenome
(Mobberley et al., 2013). The PICRUSt reconstruction identified key differences between the different spatial zones thereby providing further evidence that 16S rRNA gene libraries can provide useful insight into the metabolic capabilities of microbial ecosystems. For example, there was extensive overlap in the relative abundance of functional genes between the different depths in several pathways, such as nucleotide and amino acid metabolism, genetic information processing, and environmental information responses with the shotgun sequence library suggesting there are key central metabolisms in the thrombolite-forming mat microbiome at all depths (Figure 2-
6). Additionally, genes associated with several key metabolisms associated with the promotion (e.g., photosynthesis, sulfate reduction) and dissolution (e.g., sulfide oxidation, fermentation, ammonia oxidation) of carbonate precipitation were observed within the thrombolite-forming mats.
Despite the extensive overlap between the core metagenome at each depth, differences were observed between the mat zones. The enrichment of genes associated with photosynthesis pathways in the upper Zone 1 and the increase of genes associated with different carboxylic, fatty acid and LPS metabolisms deeper within the mat reveal distinctive metabolic transitions throughout the mat profile. The increase in LPS production at depth likely reflects the oxygen-limiting environment deeper in the thrombolite-forming mat. Previous studies with model organisms, such as
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Escherichia coli and Pseudomonas aeruginosa, have shown that anaerobic conditions can positively regulate production of LPS (Landini et al., 2002; Sabra et al., 2003).
These spatial differences in metabolic capabilities are also reflected in the biochemical gradients observed within the mats (Figure 2-1). These functional genes could serve as ideal targets to examine the potential regulation of these metabolisms within the thrombolite ecosystems potentially providing insight into the molecular response to changing environmental variables, such as pH, oxygen, and sulfide. Additionally, by tracking these specific molecular pathways, it may be possible to elucidate the specific genes and taxa involved in the diagenetic alteration of organic material in the thrombolites over both spatial and temporal scales, which represents an important area of future microbialite research.
Stable Isotope Profiling Suggests Photosynthesis is the Major Inducer of Precipitation in Thrombolite-Forming Mats
In addition to the microbial and functional gene analyses, the stable isotope profiling provided new insights into the microbial nitrogen cycling and the mechanisms driving carbonate precipitation. Additionally, the SIMS approach enabled for one of the most highly spatially resolved carbonate oxygen and carbon isotopic datasets to date on modern thrombolites. Organic N isotope values approached 0‰, indicating nitrogen fixation was the dominant N source (Hoering and Ford, 1960; Minagawa and Wada,
1986; Sigman et al., 2009), which is consistent with the abundance of heterocystous cyanobacteria, such as Dichothrix sp., and numerous nitrogen fixing anoxygenic phototrophs identified in Zone 1 (Figure 2-8). These results are also consistent with the high number of nitrogen fixation genes (e.g., nifD, nifH, nifK) recovered from the metagenome and metatranscriptome of the thrombolites (Mobberley et al., 2015).
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Additionally, the enrichment of ammonia-oxidizing archaea and high number of recovered transcripts associated with ammonia monooxygenase (amoA; Mobberley et al., 2015) within the mat coupled with the low numbers of bacterial nitrification genes observed in both the predictive and whole shotgun libraries suggest that these archaeal chemolithotrophs may be playing an important role in controlling nitrification and the cycling of fixed nitrogen within the thrombolite-forming mats.
18 Analysis of δ O values by using both bulk and SIMS analyses did not provide evidence of an evaporative signal, the results of which suggest biologically induced precipitation. The high rates of photosynthesis within the thrombolite-forming mats
(Myshrall et al., 2010) coupled with the previously published observations that red algae distributed throughout the tufts of Dichothrix spp. filaments lack precipitates (Planavsky et al., 2009) make it unlikely that non-biological processes, such as CO2 degassing, are driving the precipitation within the thrombolites. The SIMS δ18O values for filaments are highly depleted compared to the values associated with the sediments, and previous studies have shown that increased 18O depletion under elevated pH (Spero and Lea,
1996) is potentially suggestive of rapid rates of carbonate precipitation (McConnaughey,
1989). However, the offset between the bulk and SIMS δ18O values cannot yet be fully explained, as systematically lower SIMS values have been observed up to 2‰ (Orland et al., 2015) and may be the product of water or organics within the sample site. Despite this potential, there is low variability in the 16OH/16O values (add s4), which suggests that the zonation revealed by the SIMS data is accurate. The difference between SIMS and bulk measurements may, in part, reflect the extensive grinding during sample preparation for bulk isotope analysis. Previous studies in corals have shown that the
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friction generated during milling or drilling of the carbonate samples can cause inversion of aragonite to calcite (Waite and Swart, 2015). As a result of extensive processing
(e.g., milling), the δ18O values cause correction errors from 0.2 ‰ per 1% of inversion from aragonite to calcite (Waite and Swart, 2015). Such differences between the two approaches reinforce the value of using a SIMS-based approach to capture the extensive variability that likely exists within the microenvironments of thrombolite forming mats.
The bulk δ13C values of the organic matter associated within the thrombolites were heavy (mean -9.6 0.2‰) relative to RuBisCO-mediated carbon fixation, which exhibits fractionations that typically span between -35 to -23‰ in both plant and microbial ecosystems and can be highly species-dependent (Farquhar et al., 1989;
Falkowski, 1991). The values also appear heavier than other known microbialite systems. For example, unlaminated nodules of Pavilion Lake exhibit a mean bulk organic δ13C value of -26.8‰ (Brady et al., 2010), and microbialites in Cuatros
Ciénegas range from -25‰ and -27‰ (Breitbart et al., 2009). These δ13C –enriched values in the Bahamian thrombolites may reflect diffusion limitations of CO2 into the intertidal microbialites, differences in light intensities (Cooper and DeNiro, 1989), and the relatively high rates of photosynthesis (Myshrall et al., 2010). Values of organic δ13C similar to the Bahamian thrombolites have been observed in microbial mats found in the hypersaline Solar Lake (-5.7 1.4‰) and Gavish Sabkha (-10 2.6‰) and have been attributed to EPS-rich materials on the surface of mats that impede transport of CO2 into the mats (Schidlowski et al., 1984). Previous studies have also shown that external factors, such as increased salinity and temperature, also decrease the solubility of CO2
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(Mucci, 1983). Therefore, the abundance of EPS material within the thrombolite-forming mats coupled with high rates of productivity (Myshrall et al., 2010) may result in a
13 potential shortage of CO2 that may reduce isotopic discrimination of C and is consistent with the idea of HCO3 dissociation driving a pH shift and inducing carbonate precipitation.
The overall carbon isotope profiles of the carbonate suggest that the thrombolites of Highborne Cay are primarily the result of photoautotrophic carbon fixation, which correlates to several lacustrine microbialite systems, such as Lake Clifton, Pavilion
Lake, Great Salt Lake, Green Lake, and Bacalar (for review see Chagas et al., 2016).
The bulk isotope data for carbonates also correlates well with previous analyses on the
Dichothrix calcified filaments (Planavsky et al., 2009), as well as several lacustrine microbialites, such as Pavilion Lake (-1.2 – 2.3 ‰; Brady et al., 2010; Russell et al.,
2014), Kelly Lake (4 – 5‰; Ferris et al., 1997), Lake Van (6‰), Lake Alchichia (6.5‰), and Great Salt Lake (4.2‰) (Chagas et al., 2016). Interestingly, the thrombolite measurements are also higher than the δ13C values of the adjacent stromatolites located only a few meters away in the subtidal zone. The discrepancy may reflect the role of heterotrophic processes in carbonate precipitation in the Bahamian stromatolites
(Andres et al., 2006), and similar results have been observed in the fresh water microbialites of Cuatros Ciénegas (Breitbart et al., 2009) suggesting that heterotrophic process may be also be influencing carbonate precipitation in the Mexican system.
Although SIMS data from Highborne Cay thrombolites show greater variability than bulk isotopes, the means are not statistically different. Some of the extensive
13 variability in the SIMS δ Ccarb values for filaments is tied to variations in the
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microenvironments along the vertically orientated cyanobacteria filaments. The lightest
SIMS δ13C values in filaments may reflect the presence of localized organics (e.g., EPS material) associated with the calcified filaments, given that organic carbon has higher ionization efficiency than carbonate. However, as SIMS threshold cutoffs were applied to eliminate any spots that might include organics, the lower δ 13C values likely accurately capture filament carbonate values. In contrast, the isotopically enriched samples, relative to values predicted from precipitation from local marine DIC, provide evidence for carbonate precipitation in a microenvironment influenced by carbon dioxide uptake, which increases the pH (Visscher et al., 1991, 1998, 2005; Planavsky et al.,
2009). The highest SIMS δ13C values are more isotopically enriched than any previously
13 reported Highborne Cay bulk thrombolite or filament δ C values (Planavsky et al.,
2009). Planavsky et al., (2009) used an offset between Dichothrix filament and detrital sediment δ13C values to argue for photosynthetic carbon dioxide consumption as the initiation factor for carbonate precipitation within the filament sheaths. The observed markedly enriched filament δ13C values strengthen the case for a photosynthetic carbonate precipitation trigger in the Bahamian thrombolites.
Conclusion
The integrated approaches of microbial diversity, metagenome prediction, microelectrode, and stable isotope analysis address several important gaps in our previous understanding of modern thrombolite-forming communities. This study provides a comprehensive spatial portrait of thrombolite-forming communities revealing that, despite having unlaminated, clotted microstructures, these thrombolitic communities form distinct taxonomic and metabolic stratifications. Additionally, the
SIMS results, the first ever generated for a microbialite-forming ecosystem, reveal SIMS
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δ13C values that are more isotopically enriched than any previously reported bulk thrombolite values (Planavsky et al., 2009; Warden et al., 2016), providing direct evidence of a photosynthetic trigger for carbonate precipitation in the thrombolite- forming communities, which differs from stromatolites. Even within the same environment, where thrombolites are juxtaposed to stromatolites under similar environmental conditions (e.g., pH, salinity, temperature, UV flux), these differences between their taxa and metabolic activities appear to generate very distinct carbonate microstructures. Elucidating how these disparate structural fabrics arise will require a more detailed look into the networking and connectivity of the microbial interactions and metabolisms. Regulation of these processes on both diel and seasonal time scales will help assess the patterns associated with microbial activities and their response to their changing environment. Together, these analyses help elucidate the pathways associated with microbialite formation and represent a valuable tool to help reconstruct the microbiological and environmental conditions of the past.
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Table 2-1. Summary statistics for thrombolite samples by zone for bacteria and archaea Bacteria Archaea Zone 1 Zone 2 Zone 3 Zone 1 Zone 2 Zone 3 Depth 0-3 mm 3-5 mm 5-9 mm 0-3 mm 3-5 mm 5-9 mm No. of Reads 25609 21535 31217 14253 22794 21646 Normalized Readsa 3691 3691 3691 3587 3587 3587 Total OTUsb 2044 2947 3525 671 506 654 OTUs >0.005% 729 949 956 178 169 172 Shannon Indexc 6.59 8.67 8.59 4.91 3.58 3.97 ±sd ±0.026 ±0.026 ±0.016 ±0.008 ±0.020 ±0.024 (confidence) (0.029) (0.029) (0.019) (0.009) (0.023) (0.027) % coveraged 94.5 87.8 92.3 97.9 98.2 98.2 ±sd 0.13 5.02 0.71 0.07 0.09 0.31 aRandomized sequence count of each replicate for each zone used to measure diversity. bOTU identification used a 97% similarity threshold. cShannon diversity index calculated over ten iterations for three replicate samples. dGoods coverage estimate.
66 Figure 1
A B
OXYGEN (µM) 0 100 200 300 400 500 600 C D
2 1
H
) T
m 4
P
m
E (
D 2
6 3
8
10 0 50 100 150 200 250 300 SULFIDE (µM)
7 8 9 10 pH
Figure 2-1. The thrombolites of Highborne Cay, The Bahamas. A) Intertidal thrombolite platforms from Site 5. Bar, 1 m. B) Light micrograph of a thrombolite forming button mat revealing extensive vertical assemblages of calcified filaments (arrows). Bar, 500 µm. C) In situ depth profiles of oxygen (square), sulfide (triangle), and pH (circle) collected at peak of photosynthesis (open symbols) or respiration (filled symbols). Shaded areas reflect the targeted areas collected for analysis. Depths below 9 cm were not sampled, as that region shared the same biochemical profile as in Zone 3. D) Cross section of button mat depicting the three spatial regions including an oxic Zone 1 (0 – 3 mm), transitional Zone 2 (3 – 5 mm), and anoxic Zone 3 (5 – 9 mm). Bar, 3 mm.
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Figure 2-2. Taxonomic distribution of cyanobacteria within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database. Overall percentages based on read counts are presented logarithmically depicting the distributions for Zone 1 (blue), Zone 2 (green), and Zone 3 (red). Read abundance data for each taxonomic level are included in parentheses.
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Figure 2-3. Taxonomic distribution of Bacteria within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database. Overall percentages based on read counts are presented logarithmically depicting the distributions for Zone 1 (blue), Zone 2 (green), and Zone 3 (red). Read abundance data for each taxonomic level are included in parentheses.
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Figure 2-4. Comparison of diversity analyses of three spatial zones within the thrombolite-forming mats. Principal coordinate analysis of communities from unweighted UniFrac distance matrix of Zone 1 (0 – 3 mm, blue), Zone 2 (3 – 5 mm, green), and Zone 3 (5 – 9 mm, red) in (A) Bacteria and (B) Archaea populations. Ellipses represent standard deviation over ten rarefaction samplings. Adonis tests suggest that depth is a significant predictor of community composition for both bacterial (R=0.402, p=0.001) and archaeal (R=0.307, p=0.017) communities.
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Figure 2-5. Taxonomic distribution of Archaea within the thrombolite-forming mats derived from MEGAN5 using the Greengenes database. Overall percentages based on read counts are presented logarithmically depicting the distributions for Zone 1 (blue), Zone 2 (green), and Zone 3 (red). Read abundance data for each taxonomic level are included in parentheses.
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Figure 2-6. Functional gene comparison of the three thrombolitic mat spatial zones from 16S rRNA metabolic prediction (PICRUSt) and whole shotgun sequencing. Pearson correlation value (r) is shown for the comparison of metabolic predictions for Zone 1 (blue), Zone 2 (green), and Zone 3 (red) and the whole mat shotgun metagenome.
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Figure 2-7. Stable isotope results for calcified filaments located in the upper 3 mm of thrombolite forming button mat. (A) Oxygen isotope values of organic and inorganic fractions using both bulk and SIMS analysis. Analyses were completed for both background carbonate precipitates (sediment), calcified filaments (filaments) and untreated whole mat samples. (B) Carbon and nitrogen isotope values of both organic and inorganic fractions using both bulk and secondary ion mass spectroscopy (SIMS) analysis. (C) Comparative plot of SIMS values collected for oxygen and carbon isotopes. All results are expressed in delta notation with respect to the carbon/oxygen Vienna Peedee Belemnite (VPDB) or nitrogen air (AIR) standard.
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Figure 2-8. Overview of target areas for SIMS analyses within the thrombolite-forming mat. (A) Petrographic thin section of Dichothrix spp. filaments (f) and associated carbonate precipitate (cp) surrounded by sediments such as ooids (o). (B) Gold-coated reflected light image as viewed by the SIMS instrument. (C) SEM micrograph showing the numerous 6-10 μm pits formed during the SIMS analysis. Boxes depict representative pits that show both high (green) and low (red) quality targets within the sample. (D) Higher resolution SEM micrograph of representative high quality pit (corresponding to green box in C) showing no textural anomalies or cracks. (E) SEM micrograph of low quality pit (corresponding to red box in C) showing crack within the targeted sample site. All low quality target sites were removed from down-stream analyses.
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CHAPTER 3 CHARACTERIZING THE DOMINANT CYANOBACTERIUM, DICHOTHRIX SPP., AND ITS ASSOCIATED MICROBIAL COMMUNITY USING METAGENOMIC SEQUENCING
Introduction
Thrombolite-forming mats harbor a complex microbial consortium that facilitates the conditions for carbonate precipitation through their diverse metabolic activities.
(Myshrall et al., 2010; Mobberley et al., 2012; Wilhelm and Hewson, 2012; Warden et al., 2016). The mineralization process associated with cyanobacterial sheaths, such as the filamentous Dichothrix spp., is primarily driven by photosynthetic activity through the uptake of inorganic carbon (Pentecost and Riding, 1986; Schultze-Lam et al., 1997;
Merz 1992; Riding, 2000; Benzerara et al., 2014). In the thrombolites of Highborne Cay,
The Bahamas, the photosynthetic activity by organisms of the filamentous genus
Dichothrix are the primary drivers of this process (Planavsky et al., 2009; Myshrall et al.,
2010; Mobberley et al., 2012). Thus, deposition of CaCO3 onto the filament sheaths
(i.e., calcified cyanobacterial filaments) in the thrombolite-forming mats may correspond to photosynthetic ‘hot spots’. Additionally, formation and diagenesis of calcified filaments in the thrombolitic mats may have important implications for the detection of biosignatures in the fossil record. Calcified cyanobacterial filaments first appeared in the geologic record ~550 million years ago and were abundant in carbonate reefs and sediments deposited up to 100 million years ago (Riding, 2006). However, since the late
Cretaceous, calcified cyanobacterial filaments were thought to be absent in marine settings (Arp et al., 2001). The presence or absence of calcified cyanobacterial filaments in marine deposits throughout the geological record has been used as evidence for changes in ocean chemistry, in particular changes in calcium ion concentrations (e.g., Arp et al., 2001; Riding, 2006).
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Multiple studies using morphological and molecular approaches have identified the dominant Cyanobacteria that forms calcified filaments in the thrombolites as
Dichothrix spp. (Planavsky et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012;
Mobberley et al., 2015). Morphologically, Dichothrix spp. displays false-branching, sheathed, tapering filaments, basal heterocysts, and apical hyaline cells, but lacks aerotopes and akinetes (Bornet and Flahault, 1888; Komarek et al., 2014). The thick sheath protects the cells from the harsh marine environment and hyaline cells prevent desiccation by storing freshwater for the cells to use within the filaments when needed.
The heterocysts provide a mechanism to fix nitrogen in high oxygen, low nutrient conditions. These adaptations are well suited for life in the thrombolite button mats and have facilitated Dichothrix proliferation. The abundance and observation of this cyanobacterial taxon in the thrombolites suggests that it plays a critical role in the mineralization and accretion process.
To date, no species of Dichothrix, have been cultivated, molecularly characterized, or genetically characterized. Previous cultivation attempts of Dichothrix spp. have been unsuccessful, but are ongoing. With the advent of high throughput sequencing, which provides increases in both sequencing sensitivity and depth, it is now possible to assemble genomes from a metagenome using bioinformatic techniques
(Nielsen et al., 2014). By isolating the filaments through dissection of the thrombolite button, an enriched metagenome was constructed that resulted in a greater concentration of Dichothrix spp. and also included the organisms within the filament
EPS matrix. Assembling the Dichothrix spp. genome would provide a more complete understanding of the mechanisms behind the role this organism plays in thrombolite
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formation and will serve as a reference for future molecular studies. Sequencing of the enriched metagenome will also reveal those taxa and their functional genes that are closely associated with the Dichothrix spp. filaments and will identify functional guilds that may facilitate the carbonate precipitation process.
The process of carbonate precipitation induced through the activity of
Cyanobacteria is predominately an extracellular activity (Benzerara et al., 2014). In most systems and the method occurring in lithifying microbial mats, precipitation is driven by changes in the alkalinity state of the extracellular matrix via removal of inorganic carbonate during photosynthesis and output of hydroxide ions. This process is also compounded by the presence of carbon concentrating mechanisms (CCM), an adaptation to the lower CO2 concentration in the surrounding aquatic environment compared to atmospheric concentrations. CCMs allow for significant increases of the concentration of CO2 in the carboxysome where most of the cell’s RuBisCO is located
– (Kamennaya et al., 2012). Both CO2 and bicarbonate (HCO3 ) transporters are present
− and enable the cells to uptake different forms of carbon, later converting HCO3 into CO2 in the carboxysome, a reaction catalyzed by carbonic anhydrase and consequently resulting in an increased extracellular pH by additional output of hydroxide ions
(Kamennaya et al., 2012). This process has been documented in many cyanobacteria in various systems and prompted the need for a deeper examination for the dominant cyanobacteria in the thrombolite-forming mats.
Although the Cyanobacteria play a driving role in precipitation, community interactions are crucial to the process. One major function is the heterotrophic degradation of EPS surrounding the filaments. Their activity is crucial to free the calcium
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trapped in the EPS to ensure it is available for nucleation to carbonate (Planavsky et al.,
2009). Metabolic activity, e.g. sulfate and nitrate reduction, result in production of bicarbonate and promotes precipitation (Planavsky et al., 2009). These are only some of the roles that the associated community plays in the mineralization process. By further examining the metagenome of the buttons, potential functions can be revealed to have a more complete impression of the microbial mechanisms behind precipitation.
This study takes a metagenomic sequencing approach for full characterization of the microbes associated with the Dichothrix spp. filaments and construction of a partial genome assembly for this organism.
Materials and Methods
Sample Collection and DNA Extraction.
Thrombolite-forming button mat samples were collected in March 2014 from
Highborne Cay, The Bahamas and stored immediately in RNAlater (Life Technologies,
Inc., Grand Island, NY). Upon arrival to the Space Life Sciences Lab, Merritt Island, FL, the samples were moved to -80ºC until extraction. Thrombolites were thawed and
Dichothrix spp. filaments (50 to 125 mg) were carefully dissected from the mats and rinsed with molecular grade water. Extractions were completed using a modified xanthogenate extraction (Green et al., 2008; Foster et al., 2009) with one modification.
After adding the dissected filament to a 1.5 ml tube, the tube was closed and dipped in liquid nitrogen for 60 s, then extraction buffer was added with beads before the initial vortexing step. This step was added due to inefficient penetration of the Dichothrix spp. filaments. Genomic DNA was purified using the Genomic DNA Clean & Concentrator-10
(Zymo Research, Orange, CA, USA). To attain higher molecular weight DNA, additional extractions were completed using a chemical lysis (DTT, 200 mM; T4 lysozyme, 10
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µg/ml; EDTA, 1 mM; Tween-20, 0.2%; 75 µl final volume per 75 mg of filaments; modified from Mehta et al., 2015). After adding lysis solution, tubes of filaments were frozen in liquid nitrogen for 1 m and then incubated in a 65ºC water bath for 5 m. The
PowerBiofilm DNA Isolation Kit (Qiagen, Carlsbad, CA, USA) was used to extract DNA with the following protocol modifications: during the first vortex step in PowerBiofilm protocol was completed on low setting (< 4 on Vortex Genie) and BF3 volume was increased to 200 µl. The manufacturer’s protocol was followed for the remaining steps.
Samples were purified as described above. Genomic DNA was then amplified using the
REPLI-g UltraFast Mini kit (Qiagen, Carlsbad, CA, USA). DNA was examined for quality using an Agilent 2100 Bioanalyzer with a DNA 12000 Chip (Agilent Technologies, Santa
Clara, CA, USA).
Sequencing and Analysis
The xanthogenate-extracted samples were sequenced at University of Florida’s
Interdisciplinary Center for Biotechnology Research using the High Output v3 Kit (paired end, 300 cycles) on a MiSeq sequencing system (Illumina, San Diego, CA, USA). The amplified gDNA was sequenced at the University of Delaware Sequencing and
Genotyping Center using a PacBio RS II Single Molecule, Real-Time (SMRT®) DNA
Sequencing System on six SMRT Cells (Pacific Biosciences, Menlo Park, CA, USA).
MiSeq sequences were initially binned for phylogeny and assembled using IDBA-
UD v1.1.1 (Peng et al., 2010; Peng et al., 2012). The PacBio sequences were assembled using the PacBio SMRTAnalysis tool. Co-assemblies and hybrid assemblies were also completed using IDBA-Hybrid (Peng et al., 2010; Peng et al., 2012) and
SPAdes v3.9.0 (Bankevich et al., 2012). Guided assemblies utilized completed genomes for Calothrix (sp. PCC 7507) refseq I.D. NC_019682.1 and Rivularia (sp. PCC
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7116) refseq I.D. NC_019678.1 (Nordberg et al., 2013). Contigs were binned using
CONCOCT v0.4.1 (Alneberg et al., 2014) and MetaBAT v2.11.1 (Kang et al., 2015) and bin quality was assessed with CheckM v1.0.6 (Parks et al., 2015). Alignments were completed using Bowtie2 v2.2.9 (Langmead and Salzberg, 2012) and SamTools v1.3.1
(Li et al., 2009). Sequences aligning to 16S and 18S rRNA genes were mined and annotated using Metaxa2 (Bengtsson et al., 2015), which uses the SILVA reference database with additionally curated entries (Quast et al., 2012). Sequences were annotated for both taxonomy and function using MetaCV (Liu et al., 2012), as well as with BLASTx (Altschul et al., 1990) using the Swiss-Prot database (UniProt Consortium,
2017). Sequences were visualized using Krona Plots (Ondov et al., 2011). Statistical analysis and additional visualization was completed in R (R Core Team, 2015).
Results and Discussion
Optimizing High Quality DNA Extraction From Dichothrix spp. Filaments
Dichothrix spp. trichomes are enclosed in a tough sheath that offers protection from the harsh marine environment, but inhibits optimal DNA extraction using standard protocols. To penetrate the sheath, protocol optimization included the addition of a chemical lysis step before the mechanical homogenization. Prior to the method described above, multiple chemical lysis treatments were attempted, but were unsuccessful in extracting high molecular weight genomic DNA (>3kb length). Briefly, these include the method described above replacing Tween-20 with SDS 1%,
Proteinase-K extraction, and Proteinase-K + SDS 1%. Each of the chemical cocktails was attempted with no mechanical homogenization, vortexing for 5 min on high, or vortexing for 10 minutes on medium and a range of freeze/thaw steps from 1 to 3 min of freezing and 5 to 10 min in a 65ºC water bath. The chosen method resulted in the least
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DNA shearing confirmed through bioanalyzer and agarose gel electrophoresis examination. Although the chosen method resulted in the highest molecular weight
DNA, a better technique should be devised for penetrating the tough Dichothrix spp. sheath to improve extractions for long read sequencing.
Assembly and Description of the Dominant Cyanobacteria
To date, de novo assemblies have been completed for each set of sequencing data with partial genome completion (Table 3-1). The MiSeq data was assembled using
IDBA-UD (Peng et al., 2012) and the PacBio data was assembled under various parameters using HGAP3 (Chin et al., 2013). A hybrid assembly was completed using
IDBA-hybrid (Peng et al., 2012) and a co-assembly was completed using SPAdes
(Bankevich et al., 2012). Guided assemblies using the complete genomes of Calothrix spp. PCC 7507 and Rivularia spp. PCC 7116, monophyletic sister genera to Dichothrix spp., have also been attempted with limited success. Binning of the guided, hybrid assembly with MetaBAT resulted in eight bins with Dichothrix spp. comprising bin.3
(Figure 3-1). The genome for Dichothrix is 75% assembled to date and additional methods are being explored. Currently, a more comprehensive assembly approach is being attempted with additional sequence data, as well as secondary alignment based techniques with closely related genomes.
The partial assembly for Dichothrix spp. has begun to reveal features that were previously hypothesized through morphological examination, such as heterocyst formation. Basal heterocysts were previously observed in the Highborne Cay Dichothrix spp. indicating that this organism is actively fixing atmospheric nitrogen (Planavsky et al., 2009). Genes linked to heterocyst differentiation were recovered in the Dichothrix genome and include the group 2 sigma factor sigE, but not sigC, which is the primary
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regulator of differentiation genes in Anabaena spp. (Mella-Herrera et al., 2011; Ehira and Miyazaki, 2015). It was found that sigE can compensate for sigC deficiency, so it may be that in Dichothrix spp. sigE is the primary regulator and sigC has been lost
(Ehira and Miyazaki, 2015). Two genes, ntcA and hetR, are required for heterocyst differentiation and multiple copies of each were recovered in Dichothrix spp. Low nitrogen levels result in activation of ntcA and it in turn activates nrrA (OmpR family), which activates hetR, a heterocyst differentiation master gene in Anabaena spp. (Ehira and Miyazaki, 2015). Additionally, multiple nitrogenase activity genes were recovered from the assembled genome with similarity to other heterocyst forming Cyanobacteria that have been described (Thiel and Pratte, 2014; UniProt Consortium, 2017). The Mo- nitrogenase alpha and beta subunits, nifD and nifK respectively, and the FeMo cofactor biosynthesis gene nifB that make up the dinitrogenase, as well as the nitrogenase stabilizing gene nifW were recovered. Other genes recovered that are required for nitrogenase activity include the electron transporter nifH, the sulfur transporter nifS, and nifJ an oxidoreductase that is required under low iron conditions.
The photosynthesis gene structure in Dichothrix spp. includes all the genes found in the genomes of the monophyletic sister genera, Calothrix and Rivularia, with few exceptions (Kanehisa and Goto, 2000; Shih et al., 2013). Present in both sister genera but absent in Dichothrix are psbP and psb27 from photosystem II. psbP is required in higher plants responsible for PSII complex assembly and stability, but it is not well conserved in Cyanobacteria and its absence does not prevent Cyanobacterial growth
(Michoux et al., 2010). When absent, requirements for Cl- and Ca2+ ions increase
(Michoux et al., 2010), thus the marine environment and EPS trapping of ions may have
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contributed to an evolutionary loss or lack of need to gain psbP in the thrombolite
Dichothrix spp. Additionally, psb27, a photosystem II repair protein, is also not found in all Cyanobacteria and may be absent in species with a sufficient manganese cluster in their photosystem II complex (Nowaczyk et al., 2006; Komenda et al., 2012). Only a single photosystem I gene, psaX, was not detected in the Dichothrix spp. assembly, compared to the complete genomes of Rivularia and two of three listed Calothrix genomes. It was not recovered, however, in Calothrix spp. 336/3 isolated from a lake in
Finland. In addition to psaI, psaX function is thought to be in stabilization of subunits and is not necessary for core photosystem I activity (Swingley et al., 2008). The
Dichothrix spp. genome contained all the remaining photosynthesis proteins for the cytochrome b6/f complex, photosynthetic electron transport, and F-type ATPase.
Community Associated with the Filaments
The Dichothrix spp. filaments are host to a diverse community of organisms from numerous taxonomic lineages that include several different Cyanobacteria,
Proteobacteria, Bacteroidetes, as well as 22 other phyla. The community was analyzed by mining reads against small subunit rRNA databases for 16S rRNA (Archaea and
Bacteria) and 18S rRNA (Eukaryota). In addition, taxonomic information was annotated for each functional gene sequence via MetaCV and BLASTx. Both methods were used to garner the greatest amount of information from the dataset. The conserved nature of the rRNA genes is optimal for taxonomic identification, but organisms with multiple copy numbers of the gene can skew abundance estimates and rRNA sequence variation between copies on a single genome can overestimate community diversity (Sun et al.,
2013; Vetrovsky and Baldrian, 2013). Inferring the taxonomy associated with functional genes can provide important information regarding the functional capabilities of various
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members of the community, but abundance is skewed by the wide range of microbial genome sizes and is limited by available genomes in the reference database. This examination will analyze mainly the taxonomic abundance observations from the annotation of functional genes due to low recovery of small subunit rRNA and include rRNA abundance where major divergence was observed.
Dichothrix-Associated Cyanobacteria
There were several additional cyanobacterial taxa associated with the Dichothrix spp. filaments including unicellular (i.e. free-living), colony forming, and additional filamentous groups (Figure 3-2 and Figure 3-4). The filamentous groups that were recovered include members of the Nostocales, Prochlorales, and Oscillatoriales. The diazotrophic Anabaena spp. make up ~10% of the non-Dichothrix spp. portion of the
Cyanobacteria. Only 2.5% of non-Dichothrix spp. is Prochlorococcus spp. in contrast to its high global marine abundance (Partensky et al., 1999). Oscillatoriales abundance was highly divergent between the annotation of all genes (9%) verses the 16S rRNA detected (37%) reflecting the influence of previously mentioned factors, such as genome size and 16S rRNA copy number, on relative richness. Almost all of the
Oscillatoriales recovered from functional gene annotation were identified as the primary producer Trichodesmium sp., an important contributor to the global nitrogen cycle and may have significant implications for regulation of nutrient levels under changing environmental conditions (Capone et al., 1997; Hutchins et al., 2007). This group has an the ability to spatially partition nitrogen fixation within the cell in the absence of heterocysts allowing it to occur under aerobic conditions and restricts the process to daytime (Capone et al., 1997). Combined with the nitrogen fixation abilities of Dichothrix spp. and Anabaena sp., substantial nitrogen cycling could be occurring throughout a 24-
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hour cycle. Non-filamentous Cyanobacteria recovered belonged mostly to the
Chroococcales at 76% (21%) of non-Dichothrix spp. and a smaller number to
Gloeobacter 2.3%. Gloeobacter are an ancient, unique cluster of Cyanobacteria that do not contain thylakoids or phycobilisomes (Komarek, 1992). Interestingly, Gloeobacter have been implicated in the ancient colonization of microbialites through examination of their modern tendency to colonize “wet-rocks” (Courdeau et al., 2012; Mares et al.,
2013). Intracellular calcite precipitation has also been observed in at least one species of Gloeobacter adding further evidence for their potential role in ancient and modern microbialite formation (Courdeau et al., 2012; Mares et al., 2013). Six genera of
Chroococales were recovered in the filament-associated metagenome (percent of order) – Cyanothece spp. (52%), Acaryochloris spp. (14%), Synechococcus spp. (14%),
Microcysistis spp. (6%), Synechocystis spp. (3%), Thermosynechococcus spp. (2%), and 9% could not be assigned. Many members of this order have important evolutionary traits, as well as economically exploitable traits that warrant deeper examination.
Dichothrix-Associated Bacteria
The taxa with the highest relative abundance associated with the filaments are the Proteobacteria (Figure 3-2). Specifically, the Alphaproteobacteria order
Rhodobacterales was found in highest abundance (5% of overall metagenome and 12% of the small subunit rRNA subset) (Figure 3-2). This group contains many members found in pelagic marine environments with a wide array of metabolic capabilities, including uptake of sulfate and oxidation of CO to CO2 (Simon et al., 2017). Production of CO2 within the EPS microenvironment provides additional substrate for photosynthesis activity promoting mineralization. Rhodobacteraceae have also been identified as primary members of biofilm formation and may be contributing to the mat
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by producing additional EPS (Elifantz et al., 2013). The Rhizobiales make up 4% of the filament-associated community and included diazotrophic organisms important to nutrient cycling within the mats. While less abundant, the Deltaproteobacteria orders
Desulfobacterales (0.4% overall) and Desulfovibrionales (0.6% overall) were also identified (Figure 3-2). These sulfate-reducing anaerobes are important nutrient cyclers in the mats (Gibson, 1990; Braissant et al., 2007). Sulfate reduction in the mats is an important promoter of calcium carbonate precipitation (Braissant et al., 2007). Calcium carbonate has multiple polymorphs, such as aragonite or calcite, the former being the predominant form in Highborne Cay thrombolites (Figure 3-3). Sulfate is a known inhibitor to the formation of aragonite crystals, thus its removal from the EPS microenvironment may be necessary before precipitation in thrombolites can occur
(Fernandez-Diaz et al., 2010; Ries, 2010). In addition to these functions, many
Proteobacteria also contribute to mineralization through heterotrophic degradation of the
EPS ultimately freeing calcium and carbonate for nucleation.
Dichothrix-Associated Archaea
Archaea are a diverse group of microorganisms with many unique modes of energy metabolism and differ from Bacteria both genetically (e.g. 16S rRNA composition), structurally (e.g. cell wall structure), and functionally (e.g. unique energy metabolisms) (Whiteman et al., 2006). Archaea make up 3% of the entire metagenome and only 0.1% of the small subunit rRNA (Figure 3-5). The most abundant phylum represented in the community is the Euryarchaeota (73% of Archaea population).
Overall, 38% of the recovered Archaea were assigned to methanogenic taxa, which predominately reduce CO2 to methane (Costa and Leigh, 2014). The orders detected include Methanosarcinales (12%), Methanomicrobiales (9%), Methanobacteriales (7%),
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and Methanococcales (6%), Methanocellales (4%), and Methanopyrales (1%) and represent more than 1% of the entire metagenome. The number of methanogens associated with the filaments may be low (1.1%) due to substrate competition with other carbon-fixing organisms, for example photosynthesizing Cyanobacteria during the day and the sulfate reducing Desulfobacterales that fix carbon via the reductive tricarboxylic acid cycle at night (Saini et al., 2011). Their role, however small, may include contributions to an alkalinity state favorable to precipitation, as well as degradation of low molecular weight compounds (Dupraz et al., 2009).
Additionally, a sizeable portion of the Archaea classify in the Halobacteriaceae family (21%). This taxa is a large and diverse group of aerobic halophilic organisms that were once thought to require a hypersaline environment for survival and are commonly found in lithifying and non-lithifying systems (Burns et al., 2004; Minegishi et al., 2010;
Arp et al., 2012; Schneider et al., 2013). They have more recently been identified in normal marine or lower salinity environments and, in the thrombolites, are likely most active in temporary hypersaline microenvironments in the EPS detected through porewater measurements (~135 PSU; Visscher unpubl; Minegishi et al., 2010). They may be contributing to mineralization in the mat through heterotrophic degradation of
EPS material. Another heterotrophic Euryarchaeota that may be contributing to EPS degradation include the Thermococcaceae (7% of Archaea), identified through functional gene annotation and 16S rRNA genes.
Two genera from the Thaumarchaeota phylum, Cenarchaeum and
Nitrosopumilus were recovered in low concentrations (1% each). Cenarchaeum spp. is best known for its symbiosis with a marine sponge across a diverse range of
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environments (Preston et al., 1996). The chemoautotrophic Nitrosopumilus spp. are abundant in marine, as well as terrestrial environments (Walker et al., 2010; Park et al.,
2012). Both of these organisms are ammonia-oxidizers and contribute to the global nitrogen and carbon cycles (Walker et al., 2010; Park et al., 2012). Ammonia-oxidizing archaea dominate marine sediments over their bacterial counterparts and may offer a greater overall contribution to nitrogen cycling within these environments (Park et al.,
2012). While few have been isolated and genetically analyzed, the contribution of these as well as other archaea in the thrombolite should not be understated, especially when considering their recovery in close proximity to the filaments. More inquiry is needed to fully understand the functional role of these organisms in regard to the processes of mineralization and community dynamics.
Functional Genes Associated with Filament Community
The functional genes in the enriched metagenome were identified using the
Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologs (KO) (Kanehisa and
Goto, 2000; Kanehisa et al., 2011) and UniProt databases (UniProt Consortium, 2017).
Focusing on the metabolic potential within the mats, genes covering a wide array of nutrient cycling, substrates, and energy metabolisms were recovered (Figure 3-6). As discussed in previous sections, photosynthesis, nitrogen cycling, sulfur cycling, and heterotrophic carbon utilization all play important roles in precipitation of calcium carbonate in that mats (Dupraz et al., 2009; Planavsky et al., 2009; Mobberley et al.,
2015). The net activity of the microbial processes will determine whether mineralization will occur and to what extent (Dupraz et al., 2009).
Dichothrix spp. is not the only organism in the mats that is capable of nitrogen fixation. Other Cyanobacteria, such as Anabaena sp., as well as the Proteobacteria
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Rhizobiales and Desulfovibrionales are also diazotrophic. Genes that are associated with nitrogen fixation in Proteobacteria were recovered from the enriched metagenome.
These include the nitrogen regulatory system ntrBC genes that mediate synthesis of the proteins responsible for activating nif expression, NifA and NifL (Thiel and Pratte, 2014).
The genes for nifA, nifL, and ntrBC do not have homologs in Cyanobacteria, so the recovery of these genes indicates the potential for nitrogen fixation from other organisms (Thiel and Pratte, 2014). The nitrogen fixation genes in bacteria other than
Cyanobacteria were most closely associated to the Alphaproteobacteria, Rhizobiales and Rhodospirillales , as well as the Deltaproteobacteria, Desulfovibrionales. In addition to nitrogen fixation in bacteria, nifDHK genes associated with archaea were also recovered from the metagenome. The Methanococcales and Methanosarcinales were both represented in the metagenome with genes from the nifHDK operon. The operon completeness and the abundance of genes within this pathway suggest the potential for extensive nitrogen fixation within and around the filaments.
Sulfur cycling in the mats is an important process that promotes precipitation, i.e. sulfate reduction, or promotes dissolution, i.e. sulfide oxidation, as previously described in chapter one (Dupraz et al., 2009). The key genes associated with sulfate reduction and sulfide oxidation are dsrA and sqr, respectively (Friedrich et al., 2001; Rodriguez-
Mora et al., 2016). The dsrA genes recovered in the filament metagenome are associated with the Deltaproteobacteria orders Desulfovibrionales and
Syntrophobacteriales, the Gammaproteobacteria order Chromatiales, and the
Nitrospirae order Thermodesulfovibrionales. In addition to these bacteria, two sulfate- reducing archaea lineages were also associated with recovered dsrA genes, the
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Thermoproteales genus Caldivirga and the Archaeoglobales genus Archaeoglobus. No sqr genes were detected in the metagenome, nor were any of the genes from the Sox pathway (soxXYZABCD). The lack of sulfide-oxidizing genes should not be taken as evidence for absence of the pathway and is more likely a result of low abundance. Many genes associated with organisms known to oxidize reduced sulfur compounds were recovered and identified the bacterial genera Allochromatium and Chlorobium, as well as the archaeal genus Sulfolobus to name a few (Friedrich et al., 2001).
SRBs in the mat contribute not only by removal of sulfates that inhibit aragonite formation and by producing of bicarbonate that increases alkalinity (Planavsky et al.,
2009), but also by inducing EPS production in other microorganisms. Fatty acid produced in the mats is degraded into acetate for carbohydrate biosynthesis, a substrate utilized by sulfate-reducing bacteria (Berg et al., 2002). The downstream result of this process is metabolism of glyoxylate, which is a known stimulant of EPS production in sulfate-reducers (Pereira et al., 2009). Direct EPS production by SRBs has been well documented, but may be playing a lesser role in the thrombolites based on their low recovery relative to Dichothrix spp. and other Cyanobacteria (Figure 3-1;
Braissant et al., 2007). Examination of the metabolic potential associated with the filaments suggests that SRBs may be playing a secondary role in stimulating EPS production in Dichothrix spp. Overall, sulfur metabolism made up 1% of the metabolic genes in the filament metagenome, so further examination into specific activities is needed, e.g. gene expression of sulfur pathways (Figure 3-6).
Photosynthesis in the metagenome made up only 1% of the genes recovered, slightly lower than previous metagenomic studies of the Highborne Cay thrombolites
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(Figure 3-6; Breitbart et al., 2009; Mobberley et al., 2013). This could indicate that an abundance of the Cyanobacteria and other photosynthesizers commonly found in the mats are not closely associated with the filaments. Although photosynthesis gene abundance is low near the filaments, their expression is likely much higher than other metabolic processes, as has been observed in the metatranscriptome of a Highborne
Cay thrombolite (Mobberley et al., 2015). Genes associated with photosystems I and II for non-cyanobacterial photosynthesis were also recovered in very low numbers. Genes derived from photosystem I (PSI) were limited to two genera of the green sulfur bacteria
Chlorobiales, Chlorobium and Prosthecochloris, and one Acidobacterales genera,
Chloracidobacterium. Photosystem II (PSII) was far more abundant than PSI and included a more diverse group of microorganisms. Detected organisms associated with
PSII genes include members of the purple sulfur bacterial group, Chromatiaceae, as well as genera of the purple nonsulfur group, Rhodospirillum, Rhodobacter, and
Rhodopseudomonas from the orders Rhodospirillales, Rhodobacterales, and
Rhizobiales respectively. Also respectively from those orders, the genera Acidiphilium,
Roseobacter, and Methylobacterium were detected and associated with PSII genes. All these organisms that are actively photosynthesizing are contributing to a favorable alkalinity state promoting precipitation in the mats (Dupraz et al., 2009).
Conclusion
The filament-associated metagenome revealed a wide variety of bacteria and archaea concentrated within the Dichothrix spp. EPS matrix (Figure 3-1). More than 600 organisms were identified, over 500 of which were annotated to genus-level. These results reveal a complex network of microbial contribution that may be implicated in calcium carbonate precipitation and thrombolite formation either directly or in their
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seemingly minor contributions to the overall microbial mat nutrient cycling. More than
3600 unique KEGG orthologs were also recovered from the metagenome and represent the wide array of substrate usage, nutrient cycling, cellular communication abilities, transport mechanisms, and other adaptive traits. This set of important data will help to further the understanding of thrombolite formation and the unique mechanisms that lead to calcium carbonate precipitation in a localized environment. Once completed, the assembled Dichothrix spp. genome will help to contribute to a comprehensive picture of this complex network of microbial activity.
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Figure 3-1. Metagenomic binning of the guided, hybrid assembly from the filament- enriched metagenome using MetaBAT2 and quality checked with CheckM; bin.3 is associated with the filamentous Cyanobacteria, Dichothrix spp.
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N e c o e s s h i t t r s o o lo u c c n h c a a c o c y o c e y C r o a h a e c c e m A u % n i C y 1 4 m a S s m 4 % e % d py 1 o lo % h D b Ch 1 ic es a ro Tr u c o D lf te co es o ra cc % u ba l al 1 lfo c es es v te ia O ib r 0 r sc M r al . te e il yx io e 7 c ycea la oc n s % a noph to ae oc al b Cya rio ce c es 1% o p ta a ia h e le te r Cya y c s 0 o nob ci y e .7 r te acte d om ea 2% % p ria ea pt ac n c e re eri o a St ct il b oba s o 1% yc p te M Com s E o A % am e r a 1 on l i ad p r c a a c i ea a e t e r t t i l n 1 A % c e e o B c urk d a ho l D m ld t eria b ce i a o re e n o 2% o y 31 m h o e c k t b e r o a t u r Bacteria associated a c p B G l t e a P e a t with Dichothrix sp. s r r e s m o i s a e P B t i e t l e m filaments l l s u i o a e a c c l b i l p a i u A a 1 m c % d r B B l o c r ac 7% t i a a illa 0. o e t i cea e t e s F e cea m r e te d B da o r e i s na o i c r mo o m a y s t e ho 1% b m e l ant n o t s 1 X e o a t ia % ea a c e lo ac n c n s d d L d d la le i s i ac na a t P ta o C r to o a e ae r le t b om d ch e ac d l r ro t ia s ill eu % e a i pi c s r 1 al s 8 a S e o % es P . s l a t e l 0 0 e B e t .7 C e % s A c C % lo ea 2 lp id a st ac R ha ro b P rid ell ae h p e o ep iac n e o ro ct v s to ea wa ac d te a a e c e e ri os ob l l 2% oc Sh te p ac B F ga ca ac ir te a 1 ce b ill ria h % F ae ro % a 1 p la e le 1 o nt .7 R s % t vo E 0 hi % y 0 S b % z R C . p o ho 0 9 a 1 b dob h c e ia ac . P % t ter 8 i e a le ales C 1 n r e s % l g i e h a % a c a % n C o c l b a e 7 % o y e d c . c a a 1 % r t t a a 0 S o B o c e l 1 % o t n % p l p f m a e o i 8 h r e 4 l c r e . i e i m i r y t a a a p a e 0 o x e g l c o e r e g s e a c i e a s e o c c in o c e h t i a a e a a d e h d i i c a a a e c a p o r a b i e e l e h e e c e S t t o b c a R a a s c z o e i a r c a z b h i d e e t a o R h a l r c n e y y o a h d t b a m e r o o B d M h p o
y h
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Figure 3-2. Krona plot displaying filament-associated taxa with the innermost ring represent phyla and proceeding out to family in the outermost ring
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Figure 3-3. XRD results of thrombolite-forming button mat plotted against aragonite and calcite standards.
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Chroococcidiopsis Gloeobacter Anabaena Cylindrospermum alatosporum Nostocaceae Cylindrospermum stagnale Nostoc sp. PCC 7120 Nostoc sp. UAM 307 Calothrix sp. Asko 14 Rivularia sp. 1PA19 Nostocales Rivularia sp. 1PA4 Rivulariaceae Rivularia sp. 5PA11 Rivularia sp. 5PA13 Rivularia sp. PCC 7116 Rivularia sp. XP16B Brasilonema Stigonema Tolypothrix Euhalothece sp. ’MPI 95AH10’ Chroococcales Euhalothece sp. ’MPI N303’ Cyanobacterium Microcystis Cyanothece Arthrospira Microcoleaceae Cyanobacteria Microcoleus sp. DAI Planktothrix Oscillatoriales Lyngbya sp. VP417 Oscillatoria Oscillatoriaceae Phormidium sp. Phormidium sp. MBIC10070 Phormidium uncinatum filamentous cyanobacterium GSL035 Pleurocapsales Stanieria Pleurocapsa Spirulina laxissima Acaryochloris Halomicronema sp. Leptolyngbya antarctica Leptolyngbyaceae Leptolyngbya frigida Leptolyngbya sp. CENA13 Synechococcales Leptolyngbya sp. PCC 7375 Leptolyngbya sp. RS03 Prochlorococcus Pseudanabaenaceae Prochlorothrix Pseudanabaena Synechococcaceae Cyanobium sp. PCC 7001 Synechococcus Cyanobacterium 62-2
Figure 3-4. Cyanobacteria tree displaying evolutionary dynamics of Dichothrix- associated members of the phylum based on 16S rRNA gene.
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e b 1% C Potential l sm a oli a c e metab 1% r t s u lacto m M b e Ga bolis % o ta 1 e N me m h M noate olis 2% ta y 3 Buta etab y b d % m a % o ra P ose thw 2 li 1 yr nn pa s te % im ma te sm % m 1% idi and ha li 2 Gl ne se sp bo ) % yc m cto o ta le 3 Fa e eta Fru ph e c 1 1 t ro b se m y % % 2 ty p oli to r c m 3 2 h sm n a % m a o Pe ug A is 1 % ci s l 3 % 1 % o p C 1 T d s o r h e T m 5 % 1 e b o ( % e id b s 3 r i li t e a i P p o p o l l s i t e c m L m e y d e l y e o s m s n n c c i i G i p X o u m b l p o th e n s t / e a l t r e a t e 7 o i r e o u i b t e e d a a t d s o
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Table 3-1. Summary of initial Dichothrix spp. genome assembly methods Seed Max Sequences Assembler Reads Reference Contigs length N50 Miseq IDBA_UD na na 191,321 26,487 807 PacBio HGAP3 6kb na 4,511 39,673 3,476 PacBio HGAP3 4kb na 6,030 27,549 3,557 PacBio HGAP3 minimum na 49 12,147 1,944 MiSeq + PacBio SPAdes na na 1,054,024 41,924 524 MiSeq + PacBio IDBA_Hybrid na Calothrix sp. 1,100,497 26,468 707 MiSeq + PacBio IDBA_Hybrid na Rivularia sp. 1,100,493 26,468 707
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CHAPTER 4 A YEAR IN THE LIFE OF A THROMBOLITE: METATRANSCRIPTOME ANALYSIS OF A BAHAMIAN THROMBOLITE OVER DIEL AND SEASONAL CYCLES
Introduction
Microbially induced mineralization is a critical process that has helped shape
Earth’s geological landscape (Dupraz et al., 2009). This process occurs via the coordinated metabolic activities of lithifying microbial communities that facilitate the precipitation and/or the entrapment of inorganic materials, which together form depositional structures known as microbialites (Burne and Moore, 1987; Reid et al.,
2000; Dupraz and Visscher, 2005; and Dupraz et al., 2009). The ability of these microbialite-forming communities to alter their biological and geological environments has enabled these ecosystems to adapt to a wide range of environmental conditions over the course of Earth’s history (Grotzinger, 1989; Walter, 1994). Microbialites are located throughout the globe in a wide range of habitats (e.g. lacustrine, marine, and hypersaline) and their ecological complexity is just now beginning to be revealed through advancements in high-throughput sequencing of the taxonomic and functional gene diversity (e.g. Breitbart et al., 2009; Petrash et al., 2012; Bernhard et al., 2013;
Khodadad and Foster, 2013; Mobberley et al., 2013; Russell et al., 2014; Valdespino-
Castillo et al., 2014; Saghaï et al., 2015; White et al., 2015; Ruvindy et al., 2016;
Warden et al., 2016; Casaburi et al., 2016; Chagas et al., 2016; Cerqueda-García and
Falcón, 2016; Louyakis et al., 2017).
There are two major categories of microbialites including: stromatolites, with well- laminated internal structures (Walter et al., 1994; Reid et al., 2000); and thrombolites, which have irregular clotted fabrics (Aitken, 1967; Kennard and James, 1986). Although there has been extensive research on the metabolisms and organisms associated with
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formation of stromatolites (e.g. Visscher et al., 1998; Reid et al., 2000; Visscher et al.,
2000; Stolz et al., 2001 Burns et al., 2004; Papineau et al., 2005; Havemann and
Foster, 2008; Allen et al., 2009; Baumgartner et al., 2009; Goh et al., 2009; Foster et al.,
2009; Foster and Green, 2011; Khodadad and Foster, 2012; Casaburi et al., 2016;
Ruvindy et al., 2016; Suosaari et al., 2016), far fewer studies have examined thrombolite-forming communities (Desnues et al., 2008; Planavsky et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012; Edgcomb et al., 2013; Mobberley et al., 2013;
Gleeson et al., 2015; Warden et al., 2016; Louyakis et al., 2017).
One of the more well-studied ecosystems for both stromatolite and thrombolite formation is found on the island of Highborne Cay, located in the Exuma Cays, The
Bahamas, where both microbialite types co-occur in the intertidal and subtidal zones
(Reid et al., 2000; Myshrall et al., 2010). Unlike the stromatolites, which undergo an iterative growth process through microbial mat cycling and environmental controls
(Visscher et al., 1998; Reid et al., 2000; Bowlin et al., 2012), thrombolites form as a result of the activity of bundles calcified cyanobacteria filaments, primarily the taxa
Dichothrix spp. (Planavsky et al., 2009; Louyakis et al., 2017). On the surface of the
Bahamian thrombolite platforms (Figure 4-1a) there are numerous nodular mat formations called ‘button’ mats (Figure 4-1b; Myshrall et al., 2010; Mobberley et al.,
2013) that are comprised of vertically orientated Dichothrix spp. filaments (Figure 4-1c).
The filaments serve as ‘hot-spots’ for carbonate deposition (Figure 4-1d) and previous isotopic analyses has revealed the precipitate is derived from photosynthetic activity
(Planavsky et al., 2009; Louyakis et al., 2017).
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Although the taxonomic complexity and metabolic potential of thrombolite- forming communities is now emerging the transcriptional activities are not well known.
Only a single metatranscriptome analysis of the Bahamian thrombolites has been completed and this study was limited to a single time point (Mobberley et al., 2015). The previous RNA-Seq study did reveal that, at noon, there was pronounced transcriptional activity that formed a distinctive spatial gradient of metabolisms within the thrombolite- forming mats (Mobberley et al., 2015), however, how these activities change over diel cycle throughout the year are not known. In this study we provide the first transcriptional analysis of a lithifying microbial mat system throughout the diel cycle replicated over the course of a year. A comparative metatranscriptomic approach was used to elucidate the activity and functionality of the microbial consortium associated with the thrombolite of
Highborne Cay. Overall, this study provides important snapshots of the thrombolites throughout the year to help more fully characterize the microbiome of these lithifying systems as well as understand how these communities interact and coordinate their activities to drive the formation of these structures in an ever changing environment.
Materials and Methods
Sample Collection
Samples of thrombolite-forming mats were collected in October 2013, March
2014, and August 2014 from the island of Highborne Cay, Exumas, The Bahamas
(7649’ W, 2443’N). For each season samples were collected for two consecutive sunny days at four time points: sunrise (06:00), midday (12:00), sunset (18:00), and midnight (0:00). Three replicate button formations (Figure 4-1B) were collected for each time point using a sterile scalpel and placed immediately in RNAlater (Life
Technologies, Inc., Grand Island, NY) before being transported to Space Life Sciences
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Lab, Merritt Island, FL where they were stored at -80ºC until processing. Metadata for annual temperature and day lengths were attained from archives for Nassau, The
Bahamas from Weather Underground (https://goo.gl/cfHT9t).
RNA Isolation, Purification, and cDNA Synthesis
Upon thawing, samples were gently pressed and centrifuged multiple times for removal of RNAlater. A modified PowerBiofilm RNA Isolation Kit (Qiagen, Carlsbad, CA,
USA) was used to extract total RNA from ~100 mg of sample per reaction. Modifications included adding BFR1 solution and ß-mercaptoethanol (2.8%) directly to the bead tube prior to adding the mat and BFR2 solution. Additionally, to ensure adequate lysing of cyanobacterial filaments without compromising the overall quality of the RNA, the bead tubes underwent homogenization in a BioSpec Mini-Beadbeater-8 in three 1-min increments with 1-min rests on ice in-between. The bead tubes were then heated to
65ºC for 5 min in a water bath and vortexed for 10 min. BFR3 was increased to 200 µl and added to the supernatant. The manufacturer’s protocol was followed for the remaining steps. An additional DNAse treatment the Turbo DNA-free Kit (Ambion, Inc.,
Applied Biosystem Business, CA) was used to remove residual DNA and the samples were purified using RNA Clean & Concentrator-5 (Zymo Research, Orange, CA, USA).
Nucleic acid concentrations were measured with a Qubit 2.0 Fluorometer
(Invitrogen, Life Technologies, Carlsbad, CA, USA). Quality of the RNA was visualized an Agilent 2100 Bioanalyzer with an RNA 6000 Nano Chip (Agilent Technologies, Santa
Clara, CA, USA). To ensure the RNA extracts did not contain any enzymatic inhibitors real-time PCR was used. Universal primers, targeting the 16S rRNA gene (Khodadad et al., 2011), with iTaq Universal SYBR Green One-Step kit (BioRad, Hercules, CA, USA) were used on a CFX96 Touch platform (BioRad, Hercules, CA, USA) as previously
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described (Casaburi et al., 2017). Overall, approximately 6 - 12 extractions for each of the collected thrombolite button formations (n = 2) were pooled to acquire the minimum
1 μg of RNA needed for each library preparation.
Generation and Sequencing of RNA Libraries
Recovered RNA first underwent rRNA depletion with Ribo-Zero Gold rRNA
Removal Kit (Epidemiology, Illumina, San Diego, CA, USA) followed by purification with
RNA Clean & Concentrator-5. Synthesis of cDNA libraries was accomplished using
ScriptSeq v2 RNA-Seq Library Preparation Kit (Illumina, San Diego USA) following manufacturers protocol. Libraries were purified with Axygen AxyPrep Mag kit (Axygen
Biosciences, Union City, CA, USA) and quantified before sequencing. As a result, all time points (n = 4) and seasons (n = 3) had duplicate libraries for a total of 24 libraries that were sequenced at the University of Florida’s Interdisciplinary Center for
Biotechnology Research using NextSeq 500/550 High Output v2 Kit (paired end, 150 cycles, insert size 550 bp) on a NextSeq 500 sequencing system (Illumina, San Diego,
CA, USA). Sequences of thrombolite cDNA were deposited in the NCBI Sequence
Read Archive under BioProject accession number PRJNA305634.
Sequence Quality Control, Assembly, Annotation, and Mapping
Sequences were trimmed using Trimmomatic v0.35 and rRNA was removed using SortMeRNA, as previously described (Kopylova et al., 2012; Bolger et al., 2014).
FastQC was used to analyze read quality (Andrews 2014). Remaining sequences were assembled using Trinity v2.2.0 with the following parameters: fastq assembly (left read file contained forward and unpaired reads), minimum contig length of 75 bp, normalized reads (Grabherr et al., 2011). Detonate v1.11 was used to score assembly tools under altered parameters to aid in selection for downstream analysis (Li et al., 2014).
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Sequences were annotated using the complete Trinotate v3.0 protocol
(https://trinotate.github.io, last accessed June 11, 2017). Alignment was completed using Bowtie2 v2.2.9 (Langmead and Salzberg, 2012) and RSEM v1.2.7 (Li and Dewey,
2011) estimation was used for counts for sample replicates. RSEM estimates were rounded to nearest integer, length corrected (TPM method), TMM adjusted for normalized expression values (EdgeR v3.16.5; McCarthy et al., 2012; Robinson et al.,
2010), and batch corrected with ARSyNseq. NOISeq v2.18.0 (Tarazona et al., 2011;
Tarazona et al., 2015) in Bioconductor v3.4 was used for differential expression analysis with the following noiseqbio parameters: norm = “tmm”, k = 0.5, r = 50, filter = 1, nclust =
15, a0per = 0.9, random.seed = 12200. The networking analysis was completed using the R package Phyloseq v1.19.1 (McMurdie and Holmes, 2013).
Taxonomic analysis was completed on the quality-filtered 16S rRNA gene sequences isolated for each sample using Quantitative Insights Into Microbial Ecology
(QIIME v1.9.1; Caporaso et al., 2010). Operational taxonomic units (OTUs) were assigned to the reads at 97% identity against the Greengenes database v13.8
(DeSantis et al., 2006) using the UCLUST method within QIIME. Representative reads were aligned with PyNAST (v1.2.2; Caporaso et al., 2010) to the Greengenes Core reference alignment and a phylogenetic tree was built with FastTree (v2.1.3; Price et al.,
2010). The resulting biom table data was visualized using GraPhlAn v0.9.7 (Asnicar et al., 2015) after removal of unassigned records.
Results and Discussion
To examine how the thrombolite-forming mats of Highborne Cay coordinate their metabolic activities, metatranscriptomic libraries were generated throughout the diel cycle and replicated over the course of one year. The thrombolite forming button-mats
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from Site 4 (Andres and Reid, 2006) were sampled every six hours, for two consecutive days, in the months of October (2013), March (2014), and August (2014). The diel time points were chosen to reflect the peak of photosynthetic (12:00) and respiration (00:00) activities of the button-mat communities (Myshrall et al., 2010) as well as the light/dark transitions at approximately sunrise (06:00) and sunset (18:00). Although day length did fluctuate between the different months (Figure 4-1e), the objective was to ensure a consistent sampling regime between the treatments. The months were chosen as they reflect the major seasonal transitions in The Bahamas, of fall (October), winter/early spring (March) and summer (August). At the time of collection, the water temperature was 28ºC in October 2013, 24ºC in March 2014, and 30ºC in August 2014. The full dynamic ranges of both water and air temperatures throughout October 2013 to
September 2014 are presented in Figure 4-1e.
In total, 24 libraries were constructed for four time-points over three seasons with two biological replicates for each. More than 1.2 billion reads were recovered and after quality filtering 900 million rRNA and 305 million mRNA reads were retained (Table 4-1).
Sequence data from all seasons and time-points was assembled using Trinity v2.2.0
(Grabherr et al., 2011). A total of 2,349,698 contigs were retrieved and 890,921 contigs had reads mapped to them (Table B-1). Contigs were annotated to 13,983 KEGG
Orthologies (KOs; Table B-1). Mapping of reads to assembled contigs resulted in low overlap between diel time points and seasons, suggesting that the microbial populations within the mat were dynamic throughout the day and year.
Due to limitations in sample collection, only two replicate metatranscriptomes were generated for each time point. To assess reproducibility of thrombolite-forming
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mat sampling, Pearson and Spearman’s rank correlation coefficients were calculated for pairs of replicates. Pearson correlations for replicates ranged from 0.061 to 0.793, mean
0.487 and all p-value <10-11, and Spearman’s rank correlation ranged from 0.296 to
0.506, mean 0.446 and all p-value <10-11 (Table B-2). The standard Pearson correlation for technical replicates of a transcriptome is ≥ 0.9, while no standard has been devised for biological replicates (Conesa et al., 2016). Although efforts were made to collect samples from across the thrombolite platforms in Site 4 (Figure 4-1a) and the correlations were positive between the replicates, the lower values likely reflect spatial heterogeneity within the thrombolite-forming community.
Taxonomic Dynamics within Thrombolite-Forming Button Mat Communities
During library construction the samples underwent rRNA depletion to help improve mRNA recovery. To test whether rRNA depletion would have a biasing effect on the recovered taxonomic data, a parallel study was completed using data from samples collected and handled identically with the exception that one was rRNA depleted and the other was not (NCBI Bioproject ID: PRJNA261361; Mobberley et al.,
2015). A Kruskal-Wallis test showed no significant difference between rRNA depleted and total RNA samples suggesting the depletion step did not significantly alter that relative taxonomic abundances (P > 0.319; data not shown).
The metatranscriptome data was mined for 16S rRNA transcripts to compare relative abundances of bacteria and archaea within the community over diel and seasonal cycles. The use of rRNA transcript levels as proxy for microbial activity does have limitations due to differences in growth rates, life strategies, and environmental conditions (Blazewicz et al., 2013). However, rRNA-based analyses can yield important insight into the community dynamics and provide qualitative information about those
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taxa that have the potential for protein synthesis and how they fluctuate over time.
Together, analyses identified a total 23 bacterial phyla, three archaeal phyla, and 26 candidate phyla that exhibited enriched rRNA transcript expression within the mats over the observed diel and seasonal cycles. A heat map depicting the differential expression of the 16S rRNA genes for each of the time points revealed extensive changes in transcript abundance throughout the day and between seasons with the circle size reflecting the total 16S rRNA transcript number for a given taxa (Figure 4-2). Of the many phyla represented within the thrombolite communities the most diversely represented within the metatranscriptomes were the Proteobacteria, Bacteroidetes and
Cyanobacteria regardless of season (Figure 4-2). A detailed list of those taxa with the highest levels of 16S rRNA transcripts through the diel cycle and seasons are presented in Tables 4-2 through 4-4, Figure B-2, and Object 4-1. Although Proteobacteria
(specifically Gamma-, Alpha-, and Deltaproteobacteria) populations represented a diverse assemblage of taxa, the individual taxa that were the most enriched in the transcriptomes within the thrombolite-forming mats were the Cyanobacteria (Table 4-2 to 4-4).
Regardless of season and time of day, the cyanobacterium with the highest level of transcripts within the community share sequence similarity with the Xenococcaceae, a family of coccoid cyanobacteria within the Order Pleurocapsales. This taxon, along with Pseudoanabaenaceae, which were also highly represented within the metatranscriptome (Table 4-2 to 4-4), have been shown to be abundant in other lithifying mat systems, such as Shark Bay, Western Australia (e.g. Goh et al., 2011;
Suosaari et al., 2016), Lake Alchichica, Mexico (e.g. Kazmierczak et al., 2011) and in
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Lake Van, Turkey (Kazmierczak and Kremer, 2009). Coccoid cyanobacteria have been shown to have important roles in lithifying systems, as carbonate precipitation has been shown to initiate near clusters of coccoid cyanobacteria in some hypersaline mats
(Dupraz et al., 2004) and numerous microfossils, dating back to the Archean, have been interpreted as calcified, colonial coccoid cyanobacteria (e.g. Kazmierczak and
Altermann, 2002; Altermann et al., 2006) suggesting their importance in biologically induced mineralization within the thrombolites.
Unsurprisingly, another highly represented cyanobacterial taxa within the recovered metatranscriptome was the family Rivulariaceae, which harbors the filamentous Dichothrix spp, known to serve as hot spots for carbonate precipitation within the thrombolites (Planavsky et al., 2009; Louyakis et al., 2017). Much like the coccoid cyanobacteria the filamentous strains exhibited very high numbers of transcript levels regardless of season. Although in most cases the cyanobacteria transcript levels peaked at noon, high levels of 16S rRNA transcripts were observed at night (Table 4-2 to 4-4). The accumulation or maintenance of rRNA content during the dark periods has been observed in numerous cultured cyanobacterial studies (Lepp and Schmidt, 1998;
Zinser et al., 2009; Beck et al., 2014) and may provide the cyanobacteria with a competitive advantage to respond quickly during the onset of the light period in the thrombolite communities.
Other highly represented taxa within the thrombolite metatranscriptomes included the alphaproteobacterial families Rhodobacteraceae and Rhizobiales, deltaproteobacterial order Mycoccocales, and the Phycisphaerales (Planctomycetes), which are common in a wide range of marine ecosystems and are thought to play
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important roles in biochemical cycling (Baker et al., 2015; Simon et al., 2017). These taxa exhibited relatively consistent expression of rRNA throughout the diel cycle and over the course of the year (Table 4-2 to 4-4; Object 4-1).
Those enriched taxa that did exhibit seasonal changes in 16S rRNA expression were the Bacteroidetes, Chloroflexi and Firmicutes. In the Bacteroidetes, the families
Saprospiraceae, Cytophagaceae and the cluster BME43 exhibited increased relative abundance in March and October. Heterotrophic Saprospiraceae are known to hydrolyze complex carbon compounds and may contributing to the degradation of exopolymeric substances (Fernandez-Gomez et al., 2013; McIlroy and Nielsen, 2014), which are an essential process to release calcium ions during the precipitation of calcium carbonate (Dupraz et al., 2009) and have been reported in a wide range of lithifying hypersaline mat systems (Mobberley et al., 2012; Schneider et al., 2013). In the warmer month of August, however, there was an overall decrease in the representation of Bacteroidetes, with the exception of the family Flammeovirgaceae, which has been shown to exhibit thermotolerance (Tang et al., 2016) and form associations with cyanobacteria in marine ecosystems, such as corals (Den Uyl et al.,
2016).
There were also seasonal changes in the orders of the Chloroflexi and Firmicutes
(Table 4-2 to 4-4). In the Chloroflexi, the anoxic non-phototrophic Anaerolineae (Class
SBR1031) were enriched in March and October, but lower in August, whereas the photoheterotrophic Chloroflexales (Candidate family Chlorothrixaceae) were enriched in
August. These differences in the relative abundance may simply reflect differences in light abundance throughout the year. In the Firmicutes, the families Bacillaceae and
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Planococcaceae were also enriched at sunset and midnight in August (Table 4-2 to 4-4) and have been isolated from a wide range of extreme environments including salterns, permafrost, hyperalkaline lakes and microbial mats (Reddy et al., 2002; Sorenson et al.,
2005). Members of both families are shown to have a high tolerance to elevated UV and salt conditions (Ordoñez et al., 2009; Paul et al., 2015; See-Too et al., 2017) and the increased expression during August may reflect elevated exposure to UV radiation and desiccation stress in the thrombolites.
Although Bacteria dominated the metatranscriptome at all time points and seasons, Archaea transcripts were recovered with similarity to three phyla, methanogenic members of the Euryarchaeota, ammonia-oxidizing Crenarchaeota, and the newly proposed archaeal phyla Parvarchaeota (Rinke et al., 2013; Object 4-1).
Archaea had very low transcriptional activity in all seasons compared to Bacteria, but were highest in August, comprising just 0.138% of recovered rRNA transcripts. Of the three phyla, the Parvarchaeota were the most active group in all three seasons predominately represented by orders YLA114 and WCHD3-30. YLA114 was previously found enriched in the surface samples of smooth mat-type stromatolites in Shark Bay,
Western Australia and in the same study, WCHD3-30 increased with depth (Wong et al., 2017). The Parvarchaeota were also identified in low temperature (32ºC) Romanian geothermal spring microbialite-forming mats (Coman et al., 2015), marine sediment
(Mahmoudi et al., 2015), and permafrost of Arctic Coastal Plains lake basins (Kao-
Kniffin et al., 2015), but their function within these systems is as yet unknown. While activity of archaea was found to be lower than any of the recovered bacteria, they
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provided detectable transcription levels suggesting the potential for contribution to the community rather than just a passive, opportunistic role.
Metabolic Activity of the Thrombolite-Forming Community
Functional genes grouped by KO classifications were compared using principle component analysis (PCA) based on normalized and batch corrected RSEM estimated counts (Figure 4-3; See Object 4-2 for complete gene table). When all functional genes were considered PCA plots showed little clustering based on time of day (Figure 4-3a) or season (Figure 4-3b) suggesting that the majority of functional processes within the thrombolites are constitutively expressed. This assessment was confirmed when the number of unique gene counts grouped by KOs were compared, as depicted in the
Venn diagrams (Figure 4-3c, d), revealing that majority of unique genes were expressed at all time points (KO = 4898) and during the three seasons (KO = 6013). There were however, unique gene counts associated with each diel time point with the highest number recovered from the midnight samples (KO = 773) at the peak of respiration, followed by noon (KO = 556). With respect to seasons, March (KO = 1063) had the highest number of unique gene counts followed by August (KO = 707), suggesting the communities are differentially responding to changes in their environment over the diel and season cycles.
To begin to elucidate those pathways that might be differentially expressed throughout the diel and seasonal cycles, several key metabolisms were examined that have been typically associated with the promotion or dissolution of carbonate (Visscher and Stolz, 2005; Dupraz et al., 2009). These metabolisms include photosynthesis, nitrogen fixation, denitrification, methanogenesis, and dissimilatory sulfate reduction
(Figure 4-4). The data were normalized for within sample counts (TPM), across sample
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(TMM) and batch corrected, allowing for comparisons between times and seasons. The specific genes used for these metabolic comparisons are listed in Supplemental Table
S4.
For photosynthesis, the transcripts were differentiated based on Photosystem I
(PSI) and II (PSI) and showed pronounced differences (Figure 4-4). Transcripts associated with PSII genes exhibited a pronounced diel cycle with expression increasing rapidly after sunrise and peaking at noon, mirroring many previous studies in both cyanobacterial cultures and non-lithifying microbial mat communities (Figure 4-4;
Steunou et al., 2008; Ludwig and Bryant, 2011). For example, in cyanobacteria the expression of psbA gene, which encodes for the D1 reaction center protein, has long been known to respond to the quality and quantity of light (Golden, 1994). Although PSII expression levels drop precipitously at sunset and midnight, PSII associated transcripts were present in high numbers. At night, psbA transcripts have been shown to be more stable with a half-life of approximately seven hours and play a role in regulating D1 production at the translational level (Mohamed and Jansson, 1989; Mohamed and
Jansson, 1991). Additionally, in diazotrophic coccoid cyanobacteria, isoforms of psbA are transcribed in the dark to form nonfunctional D1 proteins thereby preventing oxygen evolution during the nitrogen fixation period (Wegener et al., 2015). In contrast, transcripts associated with PSI showed no diel cycling and maintained a relatively high, but constant, level of expression even at night. Several studies using cultured cyanobacteria have shown that often under high light intensity almost all of the PSI genes are downregulated (Hihara et al., 2001; Ludwig and Bryant, 2011) to lower the susceptibility of the cells to damage (Hihara et al., 1998). Cyanobacteria often regulate
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their PSII/PSI ratios to rapidly adjust to ever changing light intensities cellular redox states and maintain a balance between CO2 fixation and reductants generated to avoid excess production of reactive oxygen and nitrogen species, which can cause photooxidative damage to the cells (Ludwig and Bryant, 2011). More recently, the advent of high-throughput sequencing and bioinformatics has resulted in the discovery of extensive pigments in cyanobacteria with absorption abilities over a wide range of wavelengths (Ho et al., 2017). Presence of a variety of pigments spanning across wavelengths could also account for expression of photosynthesis genes under very low light conditions.
With regard to nitrogen cycling within the thrombolite-forming mats, metatranscriptome analysis revealed a pronounced diel cycling of nitrogenase transcripts suggesting that nitrogen fixation is the primary N source for the thrombolite communities. In August and October, the expression of the nitrogenase genes (nifD, nifH, nifK) peaked at 18:00, whereas in March nitrogenase transcript levels peaked at midnight (Figure 4-4). These seasonal differences correspond to non-lithifying cyanobacterial mats in Mushroom Springs in Yellowstone and may reflect the increased accumulation of fixed carbon during August and October when the days are longer, thereby providing more available energy for the onset of nitrogenase transcription
(Steunou et al., 2008). The majority of the nitrogenase transcripts were produced by coccoid (Order Chroococcales) and filamentous (Order Nostocales) cyanobacteria throughout the seasons, although there were differences in the peak of transcripts. The nitrogenase transcripts derived from the coccoid were enriched at both 18:00 and 00:00, whereas nif transcripts derived from the filamentous cyanobacteria peaked primarily at
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midnight. Diazotrophic anoxygenic phototrophs showed very low transcript levels within the thrombolite communities at all time points suggesting they are not a dominant source for nitrogen fixation.
The metatranscriptomic results indicated that the primary sink for N in the thrombolite community was assimilatory nitrate reduction, as transcript levels for denitrification (< 700 TPM) and ammonium oxidation (< 300 TMP) were very low at all time points and seasons. It is possible that the sampling regime did not fully capture diel transcriptional activity, as transcripts associated with denitrification pathways (e.g. nirS, nosZ, norC), although relatively stable by mRNA standards, typically have a half-life extending to only 30 minutes (Hartig and Zumft, 1999). There was however, an observable trend of an increase in transcripts associated with denitrification at 00:00 and 06:00, but it was not statistically significant (Figure 4-4). Overall, these results suggest that denitrification and ammonium oxidation do not represent primary sinks for
N within the thrombolite communities.
Although photosynthesis and nitrogen fixation processes dominated the metatranscriptomes, there was evidence of a diverse array of respiration processes, including methanogenesis and dissimilatory sulfate reduction. Although Archaea exhibited very low level of transcription related to methanogenesis, diel and seasonal trends were observed (Figure 4-2, Object 4-1). In August, transcript levels were highest at 00:00, where in October the highest number of recovered methanogenesis transcripts were recovered at 06:00. As there are numerous pathways associated with methanogenesis only transcripts associated with the heterodisulfide reductase complex
(hdrA, B, C) were included in the analysis as it represents the reduction of methyl-S-
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CoM to CH4 that is conserved in all archaeal methanogenesis pathways (Costa and
Leigh, 2014). All of the recovered methanogenic Euryarchaeota within the thrombolite- forming mats were methylotrophic (Orders Methanobacterales, Archaeoglobales,
Methanosarcinales, Methanopyrales) with no hydrogenotrophs present in the community. Although hydrogenotrophic methanogenesis is evolutionarily more ancient
(Sousa et al., 2013), relying on the reduction of CO2 to CH4 with H2 as the primary electron donor, methylotrophic methanogenesis is more varied and may enable these taxa to overcome competition for H2 by other metabolisms (e.g. sulfate reduction). In addition to CO2, methylotrophic archaea can use CO, acetate, and a range methyl compounds as substrates, whereas electrons can come from H2 or through methyl disproportionation, enabling greater metabolic versatility within the thrombolite-forming community.
In the adjacent stromatolites at Highborne Cay there is extensive biogeochemical evidence to suggest sulfate reduction plays an important role in driving the alkalinity towards conditions that promote precipitation (Visscher et al., 1998; Dupraz and
Visscher, 2005; Baumgartner et al., 2006) and numerous taxa typically associated with sulfate reduction have been recovered from the thrombolites (Myshrall et al., 2010;
Mobberley et al., 2012; Louyakis et al., 2017). One of the key steps in this pathway is the direct reduction of sulfite to sulfide by dissimilatory sulfite reductase (dsrAB) (Rees,
1973). However, previous metagenomic analyses recovered very few dissimilatory sulfite reductase (dsrAB) genes from the thrombolite-forming populations (Mobberley et al., 2013; Mobberley et al., 2015) and therefore the transcription of genes typically associated with this pathway was examined (Figure 4-4). Transcripts of dsrAB were
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recovered from Desulfovibrio, the archaea Archaeoglobus, and the phototrophic sulfur bacterium Allochromatium, but exhibited relatively low read counts (< 700 TPM) with transcript levels peaking at midnight in August. Transcripts for dsrAB associated genes, including APS reductase (aprAB) and sulfate adenylyltransferase (sat) were also recovered from a more diverse consortium of taxa, primarily Desulfovibrionales and
Chroococcales, at high abundances. Interestingly, numerous transcripts were also recovered for anaerobic sulfite reductases (asrC) originally described in Salmonella and
Clostridium (Hallenbeck et al., 1989; Czyzewski and Wang, 2012) as well as thiosulfate reductases. These results suggest that much like the stromatolites, dissimilatory sulfate reduction is an important component of the sulfur cycle in the thrombolite-forming community, but is likely a multistep process that may be highly dependent on the nature of electron donors and the environmental conditions. More detailed comparative genomics is required to identify the full range of dissimilatory sulfur metabolisms within these lithifying communities.
Differential Expression Analysis
Extensive numbers of differentially expressed genes were observed for comparisons of midnight vs midday and sunset vs sunrise for all seasons (Figure 4-5).
Genes for photosynthesis (e.g. psbA, psbC, psbD, psbF, and psbE) were highly expressed at noon in all three seasons, while midnight found increased expression of nitrogen fixation genes (e.g. nifB, nifH, and nifW). August had the least number of DE genes for both time comparisons. August midnight and sunset both had comparatively high expression of pufC, photosynthetic reaction center cytochrome c subunit, and other reaction center subunits (i.e. pufL and pufM) that are part of anoxygenic photosynthesis in purple sulfur bacteria. Photosynthesis occurring under very low light conditions has
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been previously documented (Klatt et al., 2016; Ho et al., 2017). Specifically, the inverse activity of anoxygenic and oxygenic photosynthesis over the entire diel cycle has been observed in microbial mats and is likely an ancient adaptation to Precambrian earth conditions that allowed photosynthesis activation with extremely low light and concurrent chemotrophy for additional energy (Klatt et al., 2016).
Fatty aldehyde decarbonylase (ado) was differentially expressed at sunrise in
August and, although not significant, fatty aldehyde-generating acyl-ACP reductase
(aar) was also expressed (Figure 4-5). These two enzymes catalyze the alkane biosynthesis pathway in cyanobacteria (Schirmer et al., 2010). It is not yet understood what the natural function of this pathway may be and although the genes are independently regulated, they occur together across the studied genomes where they are present (Klahn et al., 2014). From an industrial perspective, this pathway has been identified as a potential means to a simpler method of biofuel production without the need for chemical hydrogenation (Schirmer et al., 2010; Klahn et al., 2014). A deeper examination finds that ado was assigned to 28 assembled contigs and the vast majority of those taxonomically assigned to Nostocales. The presence of this pathway highlights another potential function found in Dichothrix spp. of the thrombolite-forming mats and the need for more studies into prospective uses for the organism.
Multiple genes supporting the presence of CO2 concentrating mechanism (CCM) were also identified. The complete family of CCM proteins making up the carboxysome
(ccmKLMNOP) were all identified but not differentially expressed in the presented comparisons. Consistent with the literature, the bicarbonate transport system with a high affinity in fresh water cyanobacteria (cmpABCD) was found with very low
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expression levels (Badger and Price, 2003). In March at sunrise, a SulP-type bicarbonate transporter, bicA, was highly differentially expressed (Figure 4-5) and was also identified with high expression across other time points. This transporter has been identified across a range of marine cyanobacteria and further investigation would benefit the understanding of global sequestration of CO2 (Price et al., 2004). Importantly, bicA has been shown to act independently in uptake of bicarbonate, simplifying the process of carbon concentration within the cyanobacteria carboxylase (Price et al., 2004).
Annotation of bicA was applied to 128 contigs and 107 were classified to the genus
Synechococcus (Object 4-2). Investigation of this bicarbonate transport mechanism has sparked suggestions for modification of agriculturally important C3 plants with the bicA gene to increase photosynthetic efficiency (Price et al., 2011; McGrath and Long, 2014;
Pengelly et al., 2014). Further examination of these contigs may reveal novel homologs of the bicA gene for improved agricultural use.
Thrombolite Gene Expression Network
The overall dynamics of a system can be analyzed through network modeling to identify similar patterns of expression across a large set of genes. Focusing on energy metabolism processes that are linked to mineralization, the thrombolite-forming community expression patterns segregate into two main clusters and a web of less densely clustered expression (Figure 4-6). The main “day” processes occur in one cluster dominated by genes from the photosynthesis pathways, specifically the transcripts that make up the PSII and PSI reaction centers. Other carbon fixation pathways and the nitrogen fixation genes also show similar expression patterns to the
“day” cluster, suggesting that these processes are occurring at the same time.
Additionally, genes for carbonic anhydrase, the enzyme that catalyzes the reaction
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converting bicarbonate to carbon dioxide in the carboxysome, are linked to carbon fixation genes, including photosynthesis, and are found throughout the network. This second cluster is associated with more of the “night” activity in the mat. The second cluster includes carbon fixation occurring in non-photosynthetic organisms, as well as some oxygenic PSI genes that may not be essential for PSI reaction center functioning, but are involved in stability of necessary PSI genes (Duhring et al., 2007). Dissimilatory nitrate reduction and denitrification gene expression are also clustering with the “night” functions away from the oxygenic PS expression, which is not unexpected because nitrate reduction is an anaerobic respiration process (Hallenbeck et al., 1989). Found in the less dense area of the network is expression of sulfate reduction via dsrA and dsrB transcripts. This result is in agreement with the biochemical measurements for sulfide in other microbialites, as well as the pathway analysis for sulfate reduction in this study
(Dupraz et al., 2009; Figure 4-4). Processes found in the less dense web may be less dependent on expression of other genes and, concurrently, less dependent on specific environmental conditions that relate to diel and seasonal cycling.
Conclusion
The thrombolites undergo a high degree of diel variation, as well as seasonal changes. Taxonomically, there appears to be high transition among the less abundant members of the microbial community and stability of the system drivers, i.e.
Cyanobacteria. Functionally, the thrombolites have revealed a higher degree of diversity than has been previously observed. They have a wide range of metabolic capabilities including diverse energy production mechanisms and can utilize a variety of substrates.
Photosynthesis is a dominant metabolic process in the mat and has the strongest influence on the alkalinity state leading to mineralization suggested by the high
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transcript abundance and the weight of photosynthesis gene expression on network clustering. In addition to the genes associated with mineralization, many potentially novel transcripts were identified that could have industrial implications, as well as provide insight into microbialite evolutionary dynamics. This initial examination into thrombolite gene expression patterns is only beginning to scratch the surface and the investigation of this data remains ongoing.
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Table 4-1. Metatranscriptome data summary for four timepoints over three seasons sampled from a Highborne Cay thrombolite. Sample Month Time Rep # of reads #post-QC %rRNA %remaining %alignedb readsa aug06_1 August 6:00 1 38,970,248 27,423,407 73.9 26.1 38.3 aug12_1 August 12:00 1 31,367,092 25,207,290 86.9 13.1 56.1 aug18_1 August 18:00 1 63,575,558 46,390,757 69.7 30.3 41.3 aug24_1 August 0:00 1 46,398,488 31,001,238 71.3 28.7 32.1 aug06_2 August 6:00 2 51,792,988 42,670,313 74.7 25.3 53.8 aug12_2 August 12:00 2 55,154,298 46,063,430 75.8 24.2 59.5 aug18_2 August 18:00 2 55,090,470 45,178,031 81.3 18.7 68.6 aug24_2 August 0:00 2 49,895,344 41,203,480 82.5 17.5 64.0 mar06_1 March 6:00 1 42,286,936 37,108,069 84.0 16.0 61.9 mar12_1 March 12:00 1 43,962,956 36,332,970 53.5 46.5 42.9 mar18_1 March 18:00 1 18,040,772 15,057,539 55.1 44.9 44.6 mar24_1 March 0:00 1 30,358,562 26,183,784 56.7 43.3 47.2 mar06_2 March 6:00 2 50,796,354 42,258,844 54.8 45.2 44.7 mar12_2 March 12:00 2 34,809,204 29,685,603 73.6 26.4 45.6 mar18_2 March 18:00 2 47,205,018 40,876,339 44.1 55.9 47.3 mar24_2 March 0:00 2 78,846,408 68,104,175 55.5 44.5 46.4 oct06_1 October 6:00 1 39,609,094 33,689,382 80.4 19.6 59.4 oct12_1 October 12:00 1 77,945,514 66,083,045 71.6 28.4 60.4 oct18_1 October 18:00 1 71,433,480 59,414,453 70.9 29.1 55.4 oct24_1 October 0:00 1 39,379,596 31,584,081 79.7 20.3 55.2 oct06_2 October 6:00 2 35,044,080 29,260,586 71.0 29.0 46.0 oct12_2 October 12:00 2 75,711,876 65,456,261 56.2 43.8 46.8 oct18_2 October 18:00 2 46,851,856 41,098,015 71.7 28.3 46.3 oct24_2 October 0:00 2 75,785,140 65,230,103 76.2 23.8 46.2 Abbreviations: a, August; m, March; o, October. d, diel replicate; t, timepoint. Sequencing data is listed for each replicate pair. aQuality trimming completed using Trimmomatic within the Trinity pipeline bPercents are non-rRNA portion of raw reads aligned to Trinity assembly using Bowtie2
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Table 4-2. Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for October. October October October October 06:00 12:00 18:00 00:00 Phylum Class Order Family 100,352 129,120 149,798 92,516 Cyanobacteria Oscillatoriophycideae Chroococcales Xenococcaceae 124,770 69,827 87,993 140,317 Cyanobacteria Nostocophycideae Stigonematales Rivulariaceae 141,842 52,996 58,360 145,090 Cyanobacteria Nostocophycideae Nostocales Nostocaceae 94,635 35,655 41,652 98,394 Cyanobacteria Synechococcophycideae 84,135 35,088 41,728 86,040 Cyanobacteria Nostocophycideae Nostocales 73,127 38,795 50,385 79,769 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 35,170 59,275 60,406 33,197 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 44,170 33,857 72,400 34,150 Cyanobacteria Oscillatoriophycideae Chroococcales 43,984 30,519 36,718 49,362 Cyanobacteria 8,070 18,973 39,575 9,781 Proteobacteria Deltaproteobacteria Myxococcales 16,515 27,914 8,729 14,752 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae 11,838 19,604 17,986 11,225 Proteobacteria Alphaproteobacteria 3,066 9,659 28,937 2,561 Cyanobacteria Oscillatoriophycideae Oscillatoriales Phormidiaceae 6,986 12,938 16,185 7,569 Chloroflexi Anaerolineae SBR1031 A4b 6,142 15,348 11,002 9,229 Bacteroidetes [Saprospirae] [Saprospirales] Saprospiraceae 11,817 11,684 7,627 7,739 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 8,815 8,697 9,152 7,546 Planctomycetes Phycisphaerae Phycisphaerales 6,092 8,043 7,688 6,239 Planctomycetes Planctomycetia Pirellulales Pirellulaceae 4,659 8,707 8,012 5,071 Proteobacteria Deltaproteobacteria Myxococcales OM27
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Table 4-3. Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for March. March March March March 06:00 12:00 18:00 00:00 Phylum Class Order Family 161,877 193,626 171,318 172,407 Cyanobacteria Oscillatoriophycideae Chroococcales Xenococcaceae 46,561 48,116 58,289 52,134 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 38,152 56,897 48,108 47,419 Cyanobacteria Oscillatoriophycideae Chroococcales 66,563 55,305 42,924 22,498 Cyanobacteria Nostocophycideae Stigonematales Rivulariaceae 58,452 44,890 33,011 17,324 Cyanobacteria Nostocophycideae Nostocales Nostocaceae 44,032 42,180 28,172 23,396 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 42,697 31,578 23,601 12,204 Cyanobacteria Synechococcophycideae 21,519 34,193 25,054 27,784 Proteobacteria Deltaproteobacteria Myxococcales 40,250 28,379 22,655 12,011 Cyanobacteria Nostocophycideae Nostocales 19,946 23,715 30,490 22,237 Proteobacteria Alphaproteobacteria 12,260 17,150 28,055 20,373 Planctomycetes Planctomycetia Pirellulales Pirellulaceae 25,602 20,515 19,265 9,502 Cyanobacteria 16,617 17,808 15,958 14,918 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 17,221 12,648 15,682 17,582 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae 12,636 11,410 11,411 17,554 Cyanobacteria Oscillatoriophycideae Chroococcales Cyanobacteriaceae 12,677 11,162 9,677 15,241 Chloroflexi Anaerolineae SBR1031 A4b 10,228 10,674 11,782 15,765 Bacteroidetes [Saprospirae] [Saprospirales] Saprospiraceae 12,837 12,355 10,712 11,703 Proteobacteria Deltaproteobacteria Myxococcales OM27 9,663 13,716 13,186 10,569 Planctomycetes Phycisphaerae Phycisphaerales
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Table 4-4. Relative abundance of recovered 16S rRNA transcripts within thrombolites over diel cycle for August. August August August August 06:00 12:00 18:00 00:00 Phylum Class Order Family 215,328 253,340 71,615 63,030 Cyanobacteria Oscillatoriophycideae Chroococcales Xenococcaceae 144,003 201,465 53,328 38,048 Cyanobacteria Oscillatoriophycideae Chroococcales 14,328 2,687 213,623 97,656 Firmicutes Bacilli Bacillales Bacillaceae 11,124 2,960 223,642 88,587 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae 44,024 13,856 40,771 137,687 Cyanobacteria Nostocophycideae Stigonematales Rivulariaceae 56,177 69,714 20,780 46,576 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 62,496 48,752 8,005 18,376 Cyanobacteria Synechococcophycideae Pseudanabaenales Pseudanabaenaceae 17,075 7,593 38,773 66,813 Cyanobacteria Nostocophycideae Nostocales Nostocaceae 23,799 18,666 7,107 31,642 Cyanobacteria 23,864 18,918 19,758 15,307 Planctomycetes Phycisphaerae Phycisphaerales 9,553 5,003 18,148 39,310 Cyanobacteria Nostocophycideae Nostocales 26,915 27,687 3,530 10,241 Proteobacteria Alphaproteobacteria 25,647 19,291 9,740 13,499 Proteobacteria Deltaproteobacteria Myxococcales 20,834 19,602 1,564 15,704 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 7,442 3,577 12,960 25,437 Cyanobacteria Synechococcophycideae 7,166 3,694 25,487 8,512 Chloroflexi Chloroflexi Chloroflexales [Chlorothrixaceae] 15,155 21,369 4,785 3,401 Cyanobacteria Oscillatoriophycideae Chroococcales Cyanobacteriaceae 1,404 355 27,268 14,081 Firmicutes Bacilli Bacillales Planococcaceae 8,098 4,768 13,410 3,963 Bacteroidetes Cytophagia Cytophagales Flammeovirgaceae 9,551 7,905 3,376 9,311 Planctomycetes Planctomycetia Pirellulales Pirellulaceae
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Figure 4-2. Composition of thrombolite active taxa. Cladograms represent metatranscriptome prokaryotic diversity for each season, (a) August, (b) March, and (c) October. Cladogram was constructed using total abundances for the seasons with size of nodes increasing with abundance and colored by phylum. The innermost node (white) represents domain, then phylum, class, order, family, genus, and species on the outermost node. Rings represent relative abundance heatmaps for each time point, innermost is 06:00 (red), then 12:00 (green), 18:00 (orange), and 00:00 (blue) on the outside. GraPhlAn was used for visualization.
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Figure 4-3. Relationships between seasons and diel time points. Principle component analysis of thrombolite metatranscriptomes based on distribution of KEGG Orthology colored by (a) time or (b) season. Venn diagrams displaying uniquely expressed genes grouped by KEGG Orthology for (c) time comparisons across all seasons and (d) comparisons within each season.
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Figure 4-5. Volcano plots visualizing gene expression changes between 00:00 (up on left) and 12:00 (up on right) or 18:00 (up on left) and 06:00 (up on right) for each season, October, March, and August. Points represent unique KEGG Orthologies; log2(FC) < 5 & FDR adjusted p-value < 0.01, green; log2(FC) > 5 & FDR adjusted p-value < 0.01, orange; log2(FC) > 5 & FDR adjusted p-value < 0.001, red; no significance, black.
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Figure 4-6. Gene expression network using Bray-Curtis dissimilarity (cutoff: 0.45). Each node is a KEGG Ortholog for the specified energy metabolism and edges connect similar expression patterns with shorter edges representing a closer connection.
Object 4-1. Relative abundance of taxa within thrombolites over diel and seasonal cycles (.xlsx file 213 KB).
Object 4-2. Expressed genes in thrombolites with normalized counts over diel and seasonal cycles and number of orthologs detected in Trinity assembly (.xlsx file 5 MB).
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APPENDIX A SUPPLEMENTARY TABLES AND FIGURES FOR CHAPTER 2
Figure A-1. Rarefaction plots for number of observed species approaching asymptote at read cutoffs of A) 3691 for Bacteria and B) 3587 for Archaea. Error bars represent standard deviation of three biological replicates for Zone 1 (0 – 3 mm, blue), Zone 2 (3 – 5 mm, green) and Zone 3 (5 – 9 mm, red).
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Figure A-2. Relative abundance of bacterial population. Lines depict family-level OTU (97% cutoff) differences between depth zones grouped by phylum. Taxonomy was assigned using the Greengenes database and filtered by abundance (0.005%).
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Figure A-3. Taxonomic abundance diversity of bacteria associated with Zone 1 (0 - 3 mm) of the thrombolite forming mats as visualized in a hierarchal Krona plot. Each ring within the plot represents a different taxonomic level (i.e., phylum, class, order, family). Taxa comprising less than 0.1% of the community were omitted.
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Figure A-4. Taxonomic abundance diversity of bacteria associated with Zone 2 (3 - 5 mm) of the thrombolites as visualized in a hierarchal Krona plot. Each ring within the plot represents a different taxonomic level (i.e., phylum, class, order, family). Taxa comprising less than 0.1% of the community were omitted.
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Figure A-5. Taxonomic abundance diversity of bacteria associated with Zone 3 (5 - 9 mm) of the thrombolites as visualized in a hierarchal Krona plot. Each ring within the plot represents a different taxonomic level (i.e., phylum, class, order, family). Taxa comprising less than 0.1% of the community were omitted.
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Table A-1. Primer list used to generate titanium 454 barcoded libraries for bacteria and archaea 454 16S rRNA primer Specificity Primer ID Sample Barcodeb Linker 16S Primer Primera referencec bacteria Bac27F-T Bacteria A none TC AGAGTTTGATCCTGGCTCAG Suzuki & Giovannoni, 1996 universal Bac338R-01-T Zone 1 B CCAACCTT CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-02-T Zone 1 B GGAATTGG CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-03-T Zone 1 B AACCAACC CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-04-T Zone 2 B TTAAGGCC CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-05-T Zone 2 B CCGGCCTT CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-06-T Zone 2 B AAGGCCTT CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-07-T Zone 3 B AACGAAGC CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-08-T Zone 3 B TTCGAAGC CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 universal Bac338R-09-T Zone 3 B AATACCGC CA TGCTGCCTCCCGTAGGAGT Suzuki & Giovannoni, 1996 archaea Arc23Fe Archaea none none none ATTCCGGTTGATCCTGC Barns et al., 1994 archaea Arc958Rd,e Archaea none none none YCCGGCGTTGAMTCCATTT Delong, 1992 archaea Arc344F-Td Archaea A none TC ACGGGGYGCAGCAGGCGCGA Casamayor et al., 2002 archaea Arc915R-01-T Zone 1 B CCAACCAA CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-02-T Zone 1 B CGAACCAT CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-03-T Zone 1 B AGACAGTG CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-04-T Zone 2 B AGACACAG CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-05-T Zone 2 B CCAACGTA CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-06-T Zone 2 B CATCTCGT CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-07-T Zone 3 B CATCTCCA CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-08-T Zone 3 B CAGTGTGT CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 archaea Arc915R-09-T Zone 3 B CCGGATTA CA GTGCTCCCCCGCCAATTCCT Casamayor et al., 2002 a. 454 Life Sciences sequence primers A (CTATGCGCCTTGCCAGCCCGCTCAG) and B (CGTATCGCCTCCCTCGCGCCATCAG) with a TC or CA linker, respectively, preceding the 16S primer sequence. b. Barcodes sequences from Hamady et al., 2008. c. References are for 16S rRNA gene primer. d. Primers contain degenerate bases: Y (C,T), M (A,C). e. Archaea specific 16S rRNA gene primers used for initial amplification of a nested PC
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Complete protocol vignetAPPENDIXte located on BGitHub : SUPPLEMENTARYuse TABLESr: alouyaki sAND FIGURES FOR CHAPTER 4 repository: thrombolite_metatranscriptome
Experimental Design: RNA extracted, cleaned, Sampling à concentrated, rRNA depleted, Diel: 6am, 12pm, 6pm, 12am and Illumina libraries prepped Season: Mar, Aug, Oct (see methods for details)
Sequencing: Illumina NextSeq 500 High output v2 kit 2 x 150 cycles; 550 bp insert
Quality Control: FastQC v0.11.4 Trimmomatic v0.36 Taxonomic Diversity: Quality Control on rRNA rRNA removal: depleted vs not depleted Sortmerna v2.1 data
Assembly: Taxonomic Diversity: Trinity v2.2.0 QIIME v1.9.1
Alignment: Annotation: Bowtie2 Trinotate v3.0.1 BLAST v2.4.0 Abundance Estimation: KEGG Orthology RSEM
Quality Control: Filter contigs with <10 aligned reads Correct using contig length (TPM) Batch correction (ARSyNseq) Normalized across samples (TMM)
Pathway Analysis: DE Gene Analysis: Differential Expression: Photosynthesis e.g. Volcano Plots R: NOISeq e.g. Nitrogen Fixation Sulfur Cycle
Figure B-1. Methodology flowchart outlining the experimental parameters and bioinformatics tools used in the analysis of the metatranscriptome data.
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Pseudomonadaceae
1 2 1 2 1 2 1 2
1 2 1 2 1 2 1 2
1 2 1 2 1 2 1 2
______
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_ Enterobacteriaceae
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6 6 2 2 8 8 4 4
6 6 2 2 8 8 4 4
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0 OM27
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u u u u u u u u c c c c c c c c
a a a a a a a m m m m m m m m o o o o o o o o a Myxococcales Rhodospirillaceae Rhodobacteraceae Hyphomicrobiaceae Alphaproteobacteria Pirellulaceae Phycisphaerales Planococcaceae Bacillaceae Cyanobacteria Synechococcophycideae Pseudanabaenaceae Phormidiaceae Chroococcales Xenococcaceae Cyanobacteriaceae Rivulariaceae Nostocales Nostocaceae Gloeobacteraceae Stramenopiles Ulvophyceae Chlorothrixaceae 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 SBR1031 - A4b August March October Flammeovirgaceae Saprospiraceae
Figure B-2. Taxonomic histograms for each sample to family level or higher if unable to classify to family. Legend identifies thirty most abundant taxa over all libraries and lists in order of bar plot from top to bottom (reverse alphabetical).
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Figure B-3. Venn diagrams of up expressed genes for differential expression comparisons filtered using a False Discovery Rate (FDR) threshold of ≤ 0.05 for between 00:00 vs 12:00 and 06:00 vs 18:00.
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Table B-1. Summary of Trinity assembly statistics All Samples No. of Contigs 2,349,698 N50 178 Average Length 186.68 Mapped contigs 890,921 > 500 bases No. of Contigs 13,742 N50 649 Average Length 681.08
Table B-2. Correlation coefficients for sample pairs; replicates outlined in bold, Pearson correlation on bottom (blue scale), and Spearman correlation on top (green scale).
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LIST OF REFERENCES
Aitken, J. D. (1967) Classification and environmental significance of cryptalgal limestones and dolomites, with illustrations from the Cambrian and Ordovician of southwestern Alberta. Journal of Sedimentary Research, 37(4).
Allwood, A. C., Grotzinger, J. P., Knoll, A. H., Burch, I. W., Anderson, M. S., Coleman, M. L., & Kanik, I. (2009) Controls on development and diversity of Early Archean stromatolites. Proceedings of the National Academy of Sciences, 106(24), 9548- 9555.
Alneberg, J., Bjarnason, B.S., De Bruijn, I., Schirmer, M., Quick, J., Ijaz, U.Z., Lahti, L., Loman, N.J., Andersson, A.F. and Quince, C. (2014) Binning metagenomic contigs by coverage and composition. Nature methods, 11(11), 1144-1146.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) Basic local alignment search tool. Journal of molecular biology, 215(3), 403-410.
Altermann, W., Kazmierczak, J., Oren, A., & Wright, D. T. (2006) Cyanobacterial calcification and its rock building potential during 3.5 billion years of Earth history. Geobiology, 4(3), 147-166.
Anbu, P., Kang, C. H., Shin, Y. J., & So, J. S. (2016) Formations of calcium carbonate minerals by bacteria and its multiple applications. Springerplus, 5(1), 250.
Andres, M. S., & Reid, R. P. (2006) Growth morphologies of modern marine stromatolites: a case study from Highborne Cay, Bahamas. Sedimentary Geology, 185(3), 319-328.
Andres, M. S., Sumner, D. Y., Reid, R. P., & Swart, P. K. (2006) Isotopic fingerprints of microbial respiration in aragonite from Bahamian stromatolites. Geology, 34(11), 973-976.
Andrews S. FASTQC. A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 29 September 2014.
Arp, G., Helms, G., Karlinska, K., Schumann, G., Reimer, A., Reitner, J., & Trichet, J. (2012) Photosynthesis versus exopolymer degradation in the formation of microbialites on the atoll of Kiritimati, Republic of Kiribati, Central Pacific. Geomicrobiology Journal, 29(1), 29-65.
Arp, G., Reimer, A., & Reitner, J. (2001) Photosynthesis-induced biofilm calcification and calcium concentrations in Phanerozoic oceans. Science, 292(5522), 1701- 1704.
142
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., ... & Harris, M. A. (2000) Gene Ontology: tool for the unification of biology. Nature genetics, 25(1), 25-29.
Awramik, S. M. (1991) Archaean and Proterozoic stromatolites. In Calcareous algae and stromatolites (pp. 289-304) Springer Berlin Heidelberg.
Awramik, S. M. (1992) The history and significance of stromatolites. In Early Organic Evolution (pp. 435-449). Springer Berlin Heidelberg.
Badger, M. R., & Price, G. D. (2003) CO2 concentrating mechanisms in cyanobacteria: molecular components, their diversity and evolution. Journal of experimental botany, 54(383), 609-622.
Baker, B. J., Lazar, C. S., Teske, A. P., & Dick, G. J. (2015) Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome, 3(1), 14.
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A.A., Dvorkin, M., Kulikov, A.S., Lesin, V.M., Nikolenko, S.I., Pham, S., Prjibelski, A.D. and Pyshkin, A.V. (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of computational biology, 19(5), 455-477.
Barns, S. M., Fundyga, R. E., Jeffries, M. W., & Pace, N. R. (1994) Remarkable archaeal diversity detected in a Yellowstone National Park hot spring environment. Proceedings of the National Academy of sciences, 91(5), 1609- 1613.
Baumgartner, L.K., Reid, R.P., Dupraz, C., Decho, A.W., Buckley, D.H., Spear, J.R., Przekop, K.M. and Visscher, P.T. (2006) Sulfate reducing bacteria in microbial mats: changing paradigms, new discoveries. Sedimentary Geology, 185(3), 131- 145.
Bengtsson Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015) METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403-1414.
Benzerara, K., Skouri-Panet, F., Li, J., Férard, C., Gugger, M., Laurent, T., ... & Margaret-Oliver, I. (2014) Intracellular Ca-carbonate biomineralization is widespread in cyanobacteria. Proceedings of the National Academy of Sciences, 111(30), 10933-10938.
Bernhard, J.M., Edgcomb, V.P., Visscher, P.T., McIntyre-Wressnig, A., Summons, R.E., Bouxsein, M.L., Louis, L. and Jeglinski, M. (2013) Insights into foraminiferal influences on microfabrics of microbialites at Highborne Cay, Bahamas. Proceedings of the National Academy of Sciences, 110(24), 9830- 9834.
143
Blazewicz, S. J., Barnard, R. L., Daly, R. A., & Firestone, M. K. (2013) Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. The ISME journal, 7(11), 2061.
Bolger, A. M., Lohse, M., & Usadel, B. (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114-2120.
Bolhuis, H., Cretoiu, M. S., & Stal, L. J. (2014) Molecular ecology of microbial mats. FEMS microbiology ecology, 90(2), 335-350.
Bordeleau, L. M., & Prévost, D. (1994) Nodulation and nitrogen fixation in extreme environments. Plant and soil, 161(1), 115-125.
Bornet, E., & Flahault, C. (1888) Revision des Nostocacées hétérocystées contenues dans les principaux herbiers de France (Vol. 1).
Bowlin, E. M., Klaus, J. S., Foster, J. S., Andres, M. S., Custals, L., & Reid, R. P. (2012) Environmental controls on microbial community cycling in modern marine stromatolites. Sedimentary Geology, 263, 45-55.
Braissant, O., Decho, A. W., Dupraz, C., Glunk, C., Przekop, K. M., & Visscher, P. T. (2007) Exopolymeric substances of sulfate reducing bacteria: interactions with calcium at alkaline pH and implication for formation of carbonate minerals. Geobiology, 5(4), 401-411.
Braissant, O., Decho, A. W., Przekop, K. M., Gallagher, K. L., Glunk, C., Dupraz, C., & Visscher, P. T. (2008) Characteristics and turnover of exopolymeric substances in a hypersaline microbial mat. FEMS Microbiology Ecology, 67(2), 293-307.
Breitbart, M., Hoare, A., Nitti, A., Siefert, J., Haynes, M., Dinsdale, E., ... & Hollander, D. (2009) Metagenomic and stable isotopic analyses of modern freshwater microbialites in Cuatro Ciénegas, Mexico. Environmental microbiology, 11(1), 16- 34.
Crusoe, M., Edvenson, G., Fish, J., Howe, A., McDonald, E., Nahum, J., Nanlohy, K., Ortiz-Zuazaga, H., Pell, J., Simpson, J. and Scott, C. (2014) The khmer software package: enabling efficient sequence analysis. URL http://dx. doi. org/10.6084/m9. figshare, 979190.
Buick, R. (1992) The antiquity of oxygenic photosynthesis: evidence from stromatolites in sulphate-deficient Archaean lakes. Science, 255(5040), 74.
Burne, R. V. & Moore, L. S. (1987) Microbialites: Organosedimentary deposits of benthic microbial communities. Research Reports, 2, 241-254.
Burns, B. P., Goh, F., Allen, M., & Neilan, B. A. (2004) Microbial diversity of extant stromatolites in the hypersaline marine environment of Shark Bay, Australia. Environmental Microbiology, 6(10), 1096-1101.
144
Calder, J. A., & Parker, P. L. (1973) Geochemical implications of induced changes in C13 fractionation by blue-green algae. Geochimica et Cosmochimica Acta, 37(1), 133-140.
Canfield, D. E., & Des Marais, D. J. (1993) Biogeochemical cycles of carbon, sulfur, and free oxygen in a microbial mat. Geochimica et Cosmochimica Acta, 57(16), 3971- 3984.
Capone, D. G., Zehr, J. P., Paerl, H. W., Bergman, B., & Carpenter, E. J. (1997) Trichodesmium, a globally significant marine cyanobacterium. Science, 276(5316), 1221-1229.
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, J.K., Gordon, J.I. and Huttley, G.A. (2010) QIIME allows analysis of high-throughput community sequencing data. Nature methods, 7(5), 335-336.
Casaburi, G., Duscher, A. A., Reid, R. P., & Foster, J. S. (2016) Characterization of the stromatolite microbiome from Little Darby Island, The Bahamas using predictive and whole shotgun metagenomic analysis. Environmental microbiology, 18(5), 1452-1469.
Casamayor, E.O., Massana, R., Benlloch, S., Øvreås, L., Díez, B., Goddard, V.J., Gasol, J.M., Joint, I., Rodríguez Valera, F. and Pedrós Alió, C. (2002) Changes in archaeal, bacterial and eukaryal assemblages along a salinity gradient by comparison of genetic fingerprinting methods in a multipond solar saltern. Environmental Microbiology, 4(6), 338-348.
Castanier, S., Le Métayer-Levrel, G., & Perthuisot, J. P. (1999) Ca-carbonates precipitation and limestone genesis—the microbiogeologist point of view. Sedimentary Geology, 126(1), 9-23.
Cavalier-Smith, T. (2002) The neomuran origin of archaebacteria, the negibacterial root of the universal tree and bacterial megaclassification. International journal of systematic and evolutionary microbiology, 52(1), 7-76.
Chagas, A. A., Webb, G. E., Burne, R. V., & Southam, G. (2016) Modern lacustrine microbialites: Towards a synthesis of aqueous and carbonate geochemistry and mineralogy. Earth-Science Reviews, 162, 338-363.
Chan, O. W., Bugler-Lacap, D. C., Biddle, J. F., Lim, D. S., McKay, C. P., & Pointing, S. B. (2014) Phylogenetic diversity of a microbialite reef in a cold alkaline freshwater lake. Canadian journal of microbiology, 60(6), 391-398.
Chin, C.S., Alexander, D.H., Marks, P., Klammer, A.A., Drake, J., Heiner, C., Clum, A., Copeland, A., Huddleston, J., Eichler, E.E. and Turner, S.W. (2013) Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nature methods, 10(6), 563-569.
145
Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., & Robles, M. (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 21(18), 3674-3676.
Cooper, L. W., & DeNiro, M. J. (1989) Stable carbon isotope variability in the seagrass Posidonia oceanica: evidence for light intensity effects. Marine Ecology Progress Series, 225-229.
Costa, K. C., & Leigh, J. A. (2014). Metabolic versatility in methanogens. Current opinion in biotechnology, 29, 70-75.
Couradeau, E., Benzerara, K., Gérard, E., Moreira, D., Bernard, S., Brown, G. E., & López-García, P. (2012) An early-branching microbialite cyanobacterium forms intracellular carbonates. Science, 336(6080), 459-462.
Czyzewski, B. K., & Wang, D. N. (2012) Identification and characterization of a bacterial hydrosulphide ion channel. Nature, 483(7390), 494-497.
Dang, H., Yang, J., Li, J., Luan, X., Zhang, Y., Gu, G., Xue, R., Zong, M. and Klotz, M.G. (2013) Environment-dependent distribution of the sediment nifH-harboring microbiota in the northern South China Sea. Applied and environmental microbiology, 79(1), 121-132.
Decho, A. W., & Gutierrez, T. (2017) Microbial Extracellular Polymeric Substances (EPSs) in Ocean Systems. Frontiers in microbiology, 8.
Decho, A. W., Visscher, P. T., & Reid, R. P. (2005) Production and cycling of natural microbial exopolymers (EPS) within a marine stromatolite. Palaeogeography, Palaeoclimatology, Palaeoecology, 219(1), 71-86.
Decho, A. W., Norman, R. S., & Visscher, P. T. (2010) Quorum sensing in natural environments: emerging views from microbial mats. Trends in microbiology, 18(2), 73-80.
DeLong, E. F. (1992) Archaea in coastal marine environments. Proceedings of the National Academy of Sciences, 89(12), 5685-5689.
DeLong, E. F. (2009) The microbial ocean from genomes to biomes. Nature, 459(7244), 200-206.
DeLong, E. F. (2003) Oceans of archaea. ASM News-American Society for Microbiology, 69(10), 503-503.
DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K., Huber, T., Dalevi, D., Hu, P. and Andersen, G.L. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology, 72(7), 5069-5072.
146
Desnues, C., Rodriguez-Brito, B., Rayhawk, S., Kelley, S., Tran, T., Haynes, M., Liu, H., Furlan, M., Wegley, L., Chau, B. and Ruan, Y. (2008) Biodiversity and biogeography of phages in modern stromatolites and thrombolites. Nature, 452(7185), 340-343.
Dongjie, T., Xiaoying, S., Ganqing, J., Yunpeng, P., Wenhao, Z., Yuan, W., & Min, L. (2013) Environment controls on Mesoproterozoic thrombolite morphogenesis: a case study from the North China Platform. Journal of Palaeogeography, 2(3), 275-296.
Dravis, J. J. (1983) Hardened subtidal stromatolites, Bahamas. Science, 219(4583), 385-386.
Dridi, B., Fardeau, M. L., Ollivier, B., Raoult, D., & Drancourt, M. (2012) Methanomassiliicoccus luminyensis gen. nov., spp. nov., a methanogenic archaeon isolated from human faeces. International Journal of Systematic and Evolutionary Microbiology, 62(8), 1902-1907.
Dühring, U., Ossenbühl, F., & Wilde, A. (2007). Late assembly steps and dynamics of the cyanobacterial photosystem I. Journal of Biological Chemistry, 282(15), 10915-10921.
Dupraz, C., Fowler, A., Tobias, C., & Visscher, P. T. (2013) Stromatolitic knobs in Storr's Lake (San Salvador, Bahamas): a model system for formation and alteration of laminae. Geobiology, 11(6), 527-548.
Dupraz, C., Reid, R. P., Braissant, O., Decho, A. W., Norman, R. S., & Visscher, P. T. (2009) Processes of carbonate precipitation in modern microbial mats. Earth- Science Reviews, 96(3), 141-162.
Dupraz, C., & Visscher, P. T. (2005) Microbial lithification in marine stromatolites and hypersaline mats. Trends in microbiology, 13(9), 429-438.
Dupraz, C., Visscher, P. T., Baumgartner, L. K., & Reid, R. P. (2004) Microbe–mineral interactions: early carbonate precipitation in a hypersaline lake (Eleuthera Island, Bahamas). Sedimentology, 51(4), 745-765.
Edgcomb, V.P., Bernhard, J.M., Beaudoin, D., Pruss, S., Welander, P.V., Schubotz, F., Mehay, S., Gillespie, A.L. and Summons, R.E. (2013) Molecular indicators of microbial diversity in oolitic sands of Highborne Cay, Bahamas. Geobiology, 11(3), 234-251.
Ehira, S., & Miyazaki, S. (2015) Regulation of genes involved in heterocyst differentiation in the cyanobacterium Anabaena spp. strain PCC 7120 by a group 2 sigma factor SigC. Life, 5(1), 587-603.
Eisen, J. A. (2007) Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes. PLoS biology, 5(3), e82.
147
Elifantz, H., Horn, G., Ayon, M., Cohen, Y., & Minz, D. (2013) Rhodobacteraceae are the key members of the microbial community of the initial biofilm formed in Eastern Mediterranean coastal seawater. FEMS microbiology ecology, 85(2), 348-357.
Falkowski, P. G. (1991) Species variability in the fractionation of 13C and 12C by marine phytoplankton. Journal of Plankton Research, 13(supp1), 21-28.
Farquhar, G. D., Ehleringer, J. R., & Hubick, K. T. (1989) Carbon isotope discrimination and photosynthesis. Annual review of plant biology, 40(1), 503-537.
Fay, P. (1992) Oxygen relations of nitrogen fixation in cyanobacteria. Microbiological reviews, 56(2), 340-373.
Feist, A. M., Scholten, J. C., Palsson, B. Ø., Brockman, F. J., & Ideker, T. (2006) Modeling methanogenesis with a genome scale metabolic reconstruction of Methanosarcina barkeri. Molecular systems biology, 2(1).
Fernández-Díaz, L., Fernández-González, Á., & Prieto, M. (2010) The role of sulfate groups in controlling CaCO 3 polymorphism. Geochimica et Cosmochimica Acta, 74(21), 6064-6076.
Ferris, F. G., Thompson, J. B., & Beveridge, T. J. (1997) Modern freshwater microbialites from Kelly Lake, British Columbia, Canada. Palaios, 213-219.
Finn, R. D., Clements, J., & Eddy, S. R. (2011) HMMER web server: interactive sequence similarity searching. Nucleic acids research, 39(suppl_2), W29-W37.
Flannery, D. T., & Walter, M. R. (2012) Archean tufted microbial mats and the Great Oxidation Event: new insights into an ancient problem. Australian Journal of Earth Sciences, 59(1), 1-11.
Flemming, H. C., & Wingender, J. (2001) Relevance of microbial extracellular polymeric substances (EPSs)-Part I: Structural and ecological aspects. Water science and technology, 43(6), 1-8.
Foster, J. S., Green, S. J., Ahrendt, S. R., Golubic, S., Reid, R. P., Hetherington, K. L., & Bebout, L. (2009) Molecular and morphological characterization of cyanobacterial diversity in the stromatolites of Highborne Cay, Bahamas. The ISME journal, 3(5), 573-587.
Foster, J. S., & Green, S. J. (2011) Microbial diversity in modern stromatolites. Stromatolites: interaction of microbes with sediments, 383-405.
Friedrich, C. G., Rother, D., Bardischewsky, F., Quentmeier, A., & Fischer, J. (2001) Oxidation of reduced inorganic sulfur compounds by bacteria: emergence of a common mechanism?. Applied and environmental microbiology, 67(7), 2873- 2882.
148
Fuerst, J. A., & Sagulenko, E. (2011) Beyond the bacterium: planctomycetes challenge our concepts of microbial structure and function. Nature Reviews Microbiology, 9(6), 403-413.
Gallagher, K. L., Kading, T. J., Braissant, O., Dupraz, C., & Visscher, P. T. (2012) Inside the alkalinity engine: the role of electron donors in the organomineralization potential of sulfate reducing bacteria. Geobiology, 10(6), 518-530.
Gasc, C., Ribière, C., Parisot, N., Beugnot, R., Defois, C., Petit-Biderre, C., Boucher, D., Peyretaillade, E. and Peyret, P. (2015) Capturing prokaryotic dark matter genomes. Research in microbiology, 166(10), 814-830.
Gibson, G. R. (1990) Physiology and ecology of the sulphate reducing bacteria. Journal of Applied Microbiology, 69(6), 769-797.
Glunk, C., Dupraz, C., Braissant, O., Gallagher, K. L., Verrecchia, E. P., & Visscher, P. T. (2011) Microbially mediated carbonate precipitation in a hypersaline lake, Big Pond (Eleuthera, Bahamas). Sedimentology, 58(3), 720-736.
Golden, S. S. (1994) Light-responsive gene expression and the biochemistry of the photosystem II reaction center. In The molecular biology of cyanobacteria (pp. 693-714). Springer Netherlands.
Grabherr, M.G., Haas, B.J., Yassour, M., Levin, J.Z., Thompson, D.A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q. and Chen, Z. (2011) Full- length transcriptome assembly from RNA-Seq data without a reference genome. Nature biotechnology, 29(7), 644-652.
Green, S. J., Blackford, C., Bucki, P., Jahnke, L. L., & Prufert-Bebout, L. (2008). A salinity and sulfate manipulation of hypersaline microbial mats reveals stasis in the cyanobacterial community structure. The ISME journal, 2(5), 457-470.
Haas, B.J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P.D., Bowden, J., Couger, M.B., Eccles, D., Li, B., Lieber, M. and MacManes, M.D., (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature protocols, 8(8), 1494-1512.
Hallenbeck, P. C., Clark, M. A., & Barrett, E. L. (1989) Characterization of anaerobic sulfite reduction by Salmonella typhimurium and purification of the anaerobically induced sulfite reductase. Journal of bacteriology, 171(6), 3008-3015.
Hanada, S. (2014) The phylum Chloroflexi, the family Chloroflexaceae, and the related phototrophic families Oscillochloridaceae and Roseiflexaceae. In The Prokaryotes (pp. 515-532). Springer Berlin Heidelberg.
Handelsman, J., Rondon, M. R., Brady, S. F., Clardy, J., & Goodman, R. M. (1998) Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chemistry & biology, 5(10), R245-R249.
149
Härtig, E., & Zumft, W. G. (1999). Kinetics of nirS expression (cytochromecd 1 nitrite reductase) in Pseudomonas stutzeri during the transition from aerobic respiration to denitrification: evidence for a denitrification-specific nitrate-and nitrite- responsive regulatory system. Journal of bacteriology, 181(1), 161-166.
Hester, E. R., Barott, K. L., Nulton, J., Vermeij, M. J., & Rohwer, F. L. (2016) Stable and sporadic symbiotic communities of coral and algal holobionts. The ISME journal, 10(5), 1157-1169.
Hihara, Y., Kamei, A., Kanehisa, M., Kaplan, A., & Ikeuchi, M. (2001) DNA microarray analysis of cyanobacterial gene expression during acclimation to high light. The Plant Cell, 13(4), 793-806.
Hihara, Y., Sonoike, K., & Ikeuchi, M. (1998) A Novel Gene, pmgA, Specifically Regulates Photosystem Stoichiometry in the CyanobacteriumSynechocystis Species PCC 6803 in Response to High Light. Plant physiology, 117(4), 1205- 1216.
Ho, M. Y., Soulier, N. T., Canniffe, D. P., Shen, G., & Bryant, D. A. (2017) Light regulation of pigment and photosystem biosynthesis in cyanobacteria. Current Opinion in Plant Biology, 37, 24-33.
Hoering, T. C., & Ford, H. T. (1960) The isotope effect in the fixation of nitrogen by Azotobacter. Journal of the American Chemical Society, 82(2), 376-378.
Houghton, J., Fike, D., Druschel, G., Orphan, V., Hoehler, T. M., & Des Marais, D. J. (2014) Spatial variability in photosynthetic and heterotrophic activity drives localized δ13Corg fluctuations and carbonate precipitation in hypersaline microbial mats. Geobiology, 12(6), 557-574.
Hug, L.A., Thomas, B.C., Sharon, I., Brown, C.T., Sharma, R., Hettich, R.L., Wilkins, M.J., Williams, K.H., Singh, A. and Banfield, J.F. (2016) Critical biogeochemical functions in the subsurface are associated with bacteria from new phyla and little studied lineages. Environmental microbiology, 18(1), 159-173.
Huson, D. H., Auch, A. F., Qi, J., & Schuster, S. C. (2007) MEGAN analysis of metagenomic data. Genome research, 17(3), 377-386.
Hutchins, D.A., Fu, F.X., Zhang, Y., Warner, M.E., Feng, Y., Portune, K., Bernhardt, P.W. and Mulholland, M.R. (2007) CO2 control of Trichodesmium N2 fixation, photosynthesis, growth rates, and elemental ratios: Implications for past, present, and future ocean biogeochemistry. Limnology and Oceanography, 52(4), 1293- 1304.
150
Iino, T., Tamaki, H., Tamazawa, S., Ueno, Y., Ohkuma, M., Suzuki, K.I., Igarashi, Y. and Haruta, S. (2013) Candidatus Methanogranum caenicola: a novel methanogen from the anaerobic digested sludge, and proposal of Methanomassiliicoccaceae fam. nov. and Methanomassiliicoccales ord. nov., for a methanogenic lineage of the class Thermoplasmata. Microbes and environments, 28(2), 244-250.
Isojärvi, J., Shunmugam, S., Sivonen, K., Allahverdiyeva, Y., Aro, E. M., & Battchikova, N. (2015) Draft genome sequence of Calothrix strain 336/3, a novel H2- producing cyanobacterium isolated from a Finnish lake. Genome announcements, 3(1), e01474-14.
Kanehisa, M., & Goto, S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 28(1), 27-30.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2011) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic acids research, 40(D1), D109-D114.
Kah, L. C., & Grotzinger, J. P. (1992) Early Proterozoic (1.9 Ga) thrombolites of the Rocknest Formation, Northwest Territories, Canada. Palaios, 305-315.
Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015) MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3, e1165.
Kawaguchi, T., & Decho, A. W. (2000) Biochemical characterization of cyanobacterial extracellular polymers (EPS) from modern marine stromatolites (Bahamas).
Kaźmierczak, J., Kempe, S., Kremer, B., López-García, P., Moreira, D., & Tavera, R. (2011) Hydrochemistry and microbialites of the alkaline crater lake Alchichica, Mexico. Facies, 57(4), 543-570.
Kazmierczak, J., Altermann, W., Kremer, B., Kempe, S., & Eriksson, P. G. (2009) Mass occurrence of benthic coccoid cyanobacteria and their role in the production of Neoarchean carbonates of South Africa. Precambrian Research, 173(1), 79-92.
Kazmierczak, J., & Altermann, W. (2002) Neoarchean biomineralization by benthic cyanobacteria. Science, 298(5602), 2351-2351.
Kennard, J. M., & James, N. P. (1986) Thrombolites and stromatolites: two distinct types of microbial structures. Palaios, 492-503.
Kenward, P. A., Goldstein, R. H., Gonzalez, L. A., & Roberts, J. A. (2009) Precipitation of low temperature dolomite from an anaerobic microbial consortium: the role of methanogenic Archaea. Geobiology, 7(5), 556-565.
151
Khodadad, C. L., & Foster, J. S. (2012) Metagenomic and metabolic profiling of nonlithifying and lithifying stromatolitic mats of Highborne Cay, The Bahamas. PLoS One, 7(5), e38229.
Kita, N. T., Huberty, J. M., Kozdon, R., Beard, B. L., & Valley, J. W. (2011) High precision SIMS oxygen, sulfur and iron stable isotope analyses of geological materials: accuracy, surface topography and crystal orientation. Surface and Interface Analysis, 43(1 2), 427-431.
Kita, N. T., Ushikubo, T., Fu, B., & Valley, J. W. (2009) High precision SIMS oxygen isotope analysis and the effect of sample topography. Chemical Geology, 264(1), 43-57.
Klähn, S., Baumgartner, D., Pfreundt, U., Voigt, K., Schön, V., Steglich, C., & Hess, W. R. (2014) Alkane biosynthesis genes in cyanobacteria and their transcriptional organization. Frontiers in bioengineering and biotechnology, 2.
Klatt, J. M., De Beer, D., Häusler, S., & Polerecky, L. (2016) Cyanobacteria in sulfidic spring microbial mats can perform oxygenic and anoxygenic photosynthesis simultaneously during an entire diurnal period. Frontiers in microbiology, 7.
Knoll, A. H., Bergmann, K. D., & Strauss, J. V. (2016) Life: the first two billion years. Phil. Trans. R. Soc. B, 371(1707), 20150493.
Knoll, A. H. (2008) Cyanobacteria and earth history. The Cyanobacteria: Molecular Biology, Genomics, and Evolution, 484.
Komarek, J., Kastovsky, J., Mares, J., & Johansen, J. R. (2014) Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia, 86(4), 295-335.
Komenda, J., Sobotka, R., & Nixon, P. J. (2012) Assembling and maintaining the Photosystem II complex in chloroplasts and cyanobacteria. Current opinion in plant biology, 15(3), 245-251.
Koren, S., & Phillippy, A. M. (2015) One chromosome, one contig: complete microbial genomes from long-read sequencing and assembly. Current opinion in microbiology, 23, 110-120.
Kozdon, R., Kelly, D. C., Kita, N. T., Fournelle, J. H., & Valley, J. W. (2011) Planktonic foraminiferal oxygen isotope analysis by ion microprobe technique suggests warm tropical sea surface temperatures during the Early Paleogene. Paleoceanography, 26(3).
Kozdon, R., Ushikubo, T., Kita, N. T., Spicuzza, M., & Valley, J. W. (2009) Intratest oxygen isotope variability in the planktonic foraminifer N. pachyderma: Real vs. apparent vital effects by ion microprobe. Chemical Geology, 258(3), 327-337.
152
Krekeler, D., Teske, A., & Cypionka, H. (1998) Strategies of sulfate-reducing bacteria to escape oxygen stress in a cyanobacterial mat. FEMS Microbiology Ecology, 25(2), 89-96.
Krogh, A., Larsson, B., Von Heijne, G., & Sonnhammer, E. L. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of molecular biology, 305(3), 567-580.
Krumbein, W. E. (1979) Photolithotropic and chemoorganotrophic activity of bacteria and algae as related to beachrock formation and degradation (Gulf of Aqaba, Sinai). Geomicrobiology Journal, 1(2), 139-203.
Lagesen, K., Hallin, P. F., Rødland, E., Stærfeldt, H. H., & Ussery, D. W. (2007) RT: RNammer: consistent annotation of rRNA genes in genomic sequences. Nucleic Acids Res, 35, 3100-3108.
Landini, P., & Zehnder, A. J. (2002) The global regulatory hns gene negatively affects adhesion to solid surfaces by anaerobically grown Escherichia coli by modulating expression of flagellar genes and lipopolysaccharide production. Journal of bacteriology, 184(6), 1522-1529.
Langille, M.G., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Thurber, R.L.V., Knight, R. and Beiko, R.G. (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology, 31(9), 814-821.
Langmead, B., & Salzberg, S. L. (2012) Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357-359.
Laval, B., Cady, S.L., Pollack, J.C., McKay, C.P., Bird, J.S., Grotzinger, J.P., Ford, D.C. and Bohm, H.R. (2000) Modern freshwater microbialite analogues for ancient dendritic reef structures. Nature, 407(6804), 626-629.
Lenk, S., Arnds, J., Zerjatke, K., Musat, N., Amann, R., & Mußmann, M. (2011) Novel groups of Gammaproteobacteria catalyse sulfur oxidation and carbon fixation in a coastal, intertidal sediment. Environmental microbiology, 13(3), 758-774.
Leung, H. C., Yiu, S. M., Parkinson, J., & Chin, F. Y. (2013) IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology. Journal of Computational Biology, 20(7), 540-550.
Li, B., & Dewey, C. N. (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics, 12(1), 323.
Li, B., Fillmore, N., Bai, Y., Collins, M., Thomson, J. A., Stewart, R., & Dewey, C. N. (2014) Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome biology, 15(12), 553.
153
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G. and Durbin, R. (2009) The sequence alignment/map format and SAMtools. Bioinformatics, 25(16), 2078-2079.
Linzmeier, B. J., Kozdon, R., Peters, S. E., & Valley, J. W. (2016) Oxygen isotope variability within Nautilus shell growth bands. PloS one, 11(4), e0153890.
Liu, C.M., Wong, T., Wu, E., Luo, R., Yiu, S.M., Li, Y., Wang, B., Yu, C., Chu, X., Zhao, K. and Li, R. (2012) SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics, 28(6), 878-879.
Liu, J., Wang, H., Yang, H., Zhang, Y., Wang, J., Zhao, F., & Qi, J. (2012) Composition- based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms. Nucleic acids research, 41(1), e3-e3.
Logan, B. W. (1961) Cryptozoon and associate stromatolites from the Recent, Shark bay, Western Australia. The Journal of Geology, 69(5), 517-533.
Louyakis, A.S., Mobberley, J.M., Vitek, B.E., Visscher, P.T., Hagan, P.D., Reid, R.P., Kozdon, R., Orland, I.J., Valley, J.W., Planavsky, N.J. and Casaburi, G. (2017) A Study of the Microbial Spatial Heterogeneity of Bahamian Thrombolites Using Molecular, Biochemical, and Stable Isotope Analyses. Astrobiology, 17(5), 413- 430.
Lozupone, C., & Knight, R. (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Applied and environmental microbiology, 71(12), 8228- 8235.
Ludwig, M., & Bryant, D. A. (2011) Transcription profiling of the model cyanobacterium Synechococcus sp. strain PCC 7002 by Next-Gen (SOLiD™) sequencing of cDNA. Frontiers in microbiology, 2.
Mareš, J., Hrouzek, P., Kaňa, R., Ventura, S., Strunecký, O., & Komárek, J. (2013) The primitive thylakoid-less cyanobacterium Gloeobacter is a common rock-dwelling organism. PloS one, 8(6), e66323.
Masuda, T., & Fujita, Y. (2008) Regulation and evolution of chlorophyll metabolism. Photochemical & Photobiological Sciences, 7(10), 1131-1149.
McBeth, J. M., & Emerson, D. (2016) In situ microbial community succession on mild steel in estuarine and marine environments: exploring the role of iron-oxidizing bacteria. Frontiers in microbiology, 7.
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic acids research, 40(10), 4288-4297.
154
McConnaughey T. (1989) 13C and 18O isotopic disequilibrium in biological carbonates: I. Patterns. Geochimica et Cosmochimica Acta, 53, 151-162.
McGrath, J. M., & Long, S. P. (2014) Can the cyanobacterial carbon-concentrating mechanism increase photosynthesis in crop species? A theoretical analysis. Plant physiology, 164(4), 2247-2261.
McMurdie, P. J., & Holmes, S. (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS one, 8(4), e61217.
McNamara, K. J., & Awramik, S. M. (1992) Stromatolites: a key to understanding the early evolution of life. Science Progress (1933-), 345-364.
Mehta, K. K., Evitt, N. H., & Swartz, J. R. (2015) Chemical lysis of cyanobacteria. Journal of biological engineering, 9(1), 10.
Mella-Herrera, R. A., Neunuebel, M. R., Kumar, K., Saha, S. K., & Golden, J. W. (2011) The sigE gene is required for normal expression of heterocyst-specific genes in Anabaena spp. strain PCC 7120. Journal of bacteriology, 193(8), 1823-1832.
Merz, M. U. (1992) The biology of carbonate precipitation by cyanobacteria. Facies, 26(1), 81-101.
Michoux, F., Takasaka, K., Boehm, M., Nixon, P. J., & Murray, J. W. (2010) Structure of CyanoP at 2.8 Å: implications for the evolution and function of the PsbP subunit of photosystem II. Biochemistry, 49(35), 7411-7413.
Minagawa, M., & Wada, E. (1986) Nitrogen isotope ratios of red tide organisms in the East China Sea: a characterization of biological nitrogen fixation. Marine chemistry, 19(3), 245-259.
Minegishi, H., Kamekura, M., Itoh, T., Echigo, A., Usami, R., & Hashimoto, T. (2010) Further refinement of the phylogeny of the Halobacteriaceae based on the full- length RNA polymerase subunit B′(rpoB′) gene. International journal of systematic and evolutionary microbiology, 60(10), 2398-2408.
Minz, D., Fishbain, S., Green, S. J., Muyzer, G., Cohen, Y., Rittmann, B. E., & Stahl, D. A. (1999) Unexpected population distribution in a microbial mat community: sulfate-reducing bacteria localized to the highly oxic chemocline in contrast to a eukaryotic preference for anoxia. Applied and Environmental Microbiology, 65(10), 4659-4665.
Mobberley, J. M., Khodadad, C. L., & Foster, J. S. (2013) Metabolic potential of lithifying cyanobacteria-dominated thrombolitic mats. Photosynthesis research, 117.
155
Mobberley, J. M., Khodadad, C. L. M., Visscher, P. T., Reid, R. P., Hagan, P., & Foster, J. S. (2015) Inner workings of thrombolites: spatial gradients of metabolic activity as revealed by metatranscriptome profiling. Scientific reports, 5.
Mobberley, J. M., Ortega, M. C., & Foster, J. S. (2012) Comparative microbial diversity analyses of modern marine thrombolitic mats by barcoded pyrosequencing. Environmental microbiology, 14(1), 82-100.
Mohamed, A., & Jansson, C. (1989) Influence of light on accumulation of photosynthesis-specific transcripts in the cyanobacterium Synechocystis 6803. Plant molecular biology, 13(6), 693-700.
Mohamed, A., & Jansson, C. (1991) Photosynthetic electron transport controls degradation but not production of psbA transcripts in the cyanobacterium Synechocystis 6803. Plant molecular biology, 16(5), 891-897.
Moore, L. S., & Burne, R. V. (1994) The modern thrombolites of Lake Clifton, western Australia. In Phanerozoic stromatolites II (pp. 3-29) Springer Netherlands.
Mucci, A. (1983) The solubility of calcite and aragonite in seawater at various salinities, temperatures, and one atmosphere total pressure. American Journal of Science, 283(7), 780-799.
Myshrall, K. L., Mobberley, J. M., Green, S. J., Visscher, P. T., Havemann, S. A., Reid, R. P., & Foster, J. S. (2010) Biogeochemical cycling and microbial diversity in the thrombolitic microbialites of Highborne Cay, Bahamas. Geobiology, 8(4), 337- 354.
Namiki, T., Hachiya, T., Tanaka, H., & Sakakibara, Y. (2012) MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic acids research, 40(20), e155-e155.
Nielsen, H.B., Almeida, M., Juncker, A.S., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D.R., Gautier, L., Pedersen, A.G., Le Chatelier, E. and Pelletier, E. (2014) Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature biotechnology, 32(8), 822-828.
Nordberg, H., Cantor, M., Dusheyko, S., Hua, S., Poliakov, A., Shabalov, I., Smirnova, T., Grigoriev, I.V. and Dubchak, I. (2013) The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic acids research, 42(D1), D26-D31.
Nowaczyk, M. M., Hebeler, R., Schlodder, E., Meyer, H. E., Warscheid, B., & Rögner, M. (2006) Psb27, a cyanobacterial lipoprotein, is involved in the repair cycle of photosystem II. The plant cell, 18(11), 3121-3131.
156
Oliver, R. L., & Walsby, A. E. (1984) Direct evidence for the role of light mediated gas vesicle collapse in the buoyancy regulation of Anabaena flos aquae (cyanobacteria). Limnology and oceanography, 29(4), 879-886.
Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011) Interactive metagenomic visualization in a Web browser. BMC bioinformatics, 12(1), 385.
Ordoñez, O. F., Flores, M. R., Dib, J. R., Paz, A., & Farías, M. E. (2009) Extremophile culture collection from Andean lakes: extreme pristine environments that host a wide diversity of microorganisms with tolerance to UV radiation. Microbial Ecology, 58(3), 461-473.
Orland, I. J. (2012) Seasonality from speleothems: High-resolution ion microprobe studies at Soreq Cave, Israel (Doctoral dissertation, Ph. D. Thesis, University of Wisconsin—Madison).
Orland, I. J., Bar-Matthews, M., Kita, N. T., Ayalon, A., Matthews, A., & Valley, J. W. (2009) Climate deterioration in the Eastern Mediterranean as revealed by ion microprobe analysis of a speleothem that grew from 2.2 to 0.9 ka in Soreq Cave, Israel. Quaternary Research, 71(1), 27-35.
Pages, A., Welsh, D. T., Teasdale, P. R., Grice, K., Vacher, M., Bennett, W. W., & Visscher, P. T. (2014) Diel fluctuations in solute distributions and biogeochemical cycling in a hypersaline microbial mat from Shark Bay, WA. Marine Chemistry, 167, 102-112.
Park, S.J., Kim, J.G., Jung, M.Y., Kim, S.J., Cha, I.T., Ghai, R., Martín-Cuadrado, A.B., Rodríguez-Valera, F. and Rhee, S.K. (2012) Draft genome sequence of an ammonia-oxidizing archaeon,“Candidatus Nitrosopumilus sediminis” AR2, from Svalbard in the Arctic Circle. Journal of bacteriology, 194(24), 6948-6949.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome research, 25(7), 1043-1055.
Partensky, F., Hess, W. R., & Vaulot, D. (1999) Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiology and molecular biology reviews, 63(1), 106-127.
Patterson, W. P., & Walter, L. M. (1994) Depletion of 13C in seawater ΣC02 on modern carbonate platforms: Significance for the carbon isotopic record of carbonates. Geology, 22(10), 885-888.
Paul, D., Kumbhare, S.V., Mhatre, S.S., Chowdhury, S.P., Shetty, S.A., Marathe, N.P., Bhute, S. and Shouche, Y.S. (2015) Exploration of microbial diversity and community structure of lonar lake: the only hypersaline meteorite crater lake within basalt rock. Frontiers in microbiology, 6.
157
Paul, V. G., Wronkiewicz, D. J., Mormile, M. R., & Foster, J. S. (2016) Mineralogy and microbial diversity of the microbialites in the hypersaline Storr's Lake, The Bahamas. Astrobiology, 16(4), 282-300.
Pendleton, M., Sebra, R., Pang, A.W.C., Ummat, A., Franzen, O., Rausch, T., Stütz, A.M., Stedman, W., Anantharaman, T., Hastie, A. and Dai, H. (2015) Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nature methods, 12(8), 780-786.
Peng, Y., Leung, H. C., Yiu, S. M., & Chin, F. Y. (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics, 28(11), 1420-1428.
Peng, Y., Leung, H. C., Yiu, S. M., & Chin, F. Y. (2010) IDBA–a practical iterative de Bruijn graph de novo assembler. In Annual international conference on research in computational molecular biology (pp. 426-440). Springer, Berlin, Heidelberg.
Pengelly, J. J. L., Förster, B., von Caemmerer, S., Badger, M. R., Price, G. D., & Whitney, S. M. (2014) Transplastomic integration of a cyanobacterial bicarbonate transporter into tobacco chloroplasts. Journal of experimental botany, 65(12), 3071-3080.
Pentecost, A. & Riding, R. (1986) Calcification in cyanobacteria. Biomineralization in lower plants and animals, 30, 73-90.
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nature methods, 8(10), 785-786.
Petrash, D. A., Gingras, M. K., Lalonde, S. V., Orange, F., Pecoits, E., & Konhauser, K. O. (2012) Dynamic controls on accretion and lithification of modern gypsum- dominated thrombolites, Los Roques, Venezuela. Sedimentary Geology, 245, 29- 47.
Pfennig, N., & Trüper, H. G. (1992) The family chromatiaceae. In The prokaryotes (pp. 3200-3221). Springer New York.
Pinti, D. L., Hashizume, K., Sugihara, A., Massault, M., & Philippot, P. (2009) Isotopic fractionation of nitrogen and carbon in Paleoarchean cherts from Pilbara craton, Western Australia: Origin of 15 N-depleted nitrogen. Geochimica et Cosmochimica Acta, 73(13), 3819-3848.
Planavsky, N., & Ginsburg, R. N. (2009) Taphonomy of modern marine Bahamian microbialites. Palaios, 24(1), 5-17.
Planavsky, N., Reid, R. P., Lyons, T. W., Myshrall, K. L., & Visscher, P. T. (2009) Formation and diagenesis of modern marine calcified cyanobacteria. Geobiology, 7(5), 566-576.
158
Poretsky, R. S., Hewson, I., Sun, S., Allen, A. E., Zehr, J. P., & Moran, M. A. (2009) Comparative day/night metatranscriptomic analysis of microbial communities in the North Pacific subtropical gyre. Environmental microbiology, 11(6), 1358- 1375.
Post, W.M., Izaurralde, R.C., Jastrow, J.D., McCARL, B.A., Amonette, J.E., Bailey, V.L., Jardine, P.M., West, T.O. and Zhou, J. (2004) Enhancement of carbon sequestration in US soils. AIBS Bulletin, 54(10), pp.895-908.
Poulsen, M., Schwab, C., Jensen, B.B., Engberg, R.M., Spang, A., Canibe, N., Højberg, O., Milinovich, G., Fragner, L., Schleper, C. and Weckwerth, W. (2013) Methylotrophic methanogenic Thermoplasmata implicated in reduced methane emissions from bovine rumen. Nature Communications, 4, 1428.
Powell, S., Szklarczyk, D., Trachana, K., Roth, A., Kuhn, M., Muller, J., Arnold, R., Rattei, T., Letunic, I., Doerks, T. and Jensen, L.J. (2011) eggNOG v3. 0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic acids research, 40(D1), D284-D289.
Preston, C. M., Wu, K. Y., Molinski, T. F., & DeLong, E. F. (1996) A psychrophilic crenarchaeon inhabits a marine sponge: Cenarchaeum symbiosum gen. nov., spp. nov. Proceedings of the National Academy of Sciences, 93(13), 6241-6246.
Price, G. D., Woodger, F. J., Badger, M. R., Howitt, S. M., & Tucker, L. (2004) Identification of a SulP-type bicarbonate transporter in marine cyanobacteria. Proceedings of the National Academy of Sciences, 101(52), 18228-18233.
Price, G. D., Badger, M. R., & von Caemmerer, S. (2011) The prospect of using cyanobacterial bicarbonate transporters to improve leaf photosynthesis in C3 crop plants. Plant Physiology, 155(1), 20-26.
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) FastTree 2–approximately maximum- likelihood trees for large alignments. PloS one, 5(3), e9490.
Punta, M., Coggill, P.C., Eberhardt, R.Y., Mistry, J., Tate, J., Boursnell, C., Pang, N., Forslund, K., Ceric, G., Clements, J. and Heger, A. (2011) The Pfam protein families database. Nucleic acids research, 40(D1), D290-D301.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. and Glöckner, F.O. (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research, 41(D1), D590-D596.
Quail, M.A., Smith, M., Coupland, P., Otto, T.D., Harris, S.R., Connor, T.R., Bertoni, A., Swerdlow, H.P. and Gu, Y. (2012) A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC genomics, 13(1), 341.
159
R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2012 ISBN 3-900051-07-0.
Rajendhran, J., & Gunasekaran, P. (2011) Microbial phylogeny and diversity: small subunit ribosomal RNA sequence analysis and beyond. Microbiological research, 166(2), 99-110.
Rees, C. E. (1973). A steady-state model for sulphur isotope fractionation in bacterial reduction processes. Geochimica et Cosmochimica Acta, 37(5), 1141-1162.
Reichenbach, H. (2015) Myxococcales. Bergey's Manual of Systematics of Archaea and Bacteria.
Reid, R.P., Visscher, P.T., Decho, A.W., Stolz, J.F., Bebout, B.M., Dupraz, C., Macintyre, I.G., Paerl, H.W., Pinckney, J.L., Prufert-Bebout, L. and Steppe, T.F. (2000) The role of microbes in accretion, lamination and early lithification of modern marine stromatolites. Nature, 406(6799), 989-992.
Reid, R. P., Macintyre, I. G. & Steneck, R. S. (1999) A microbialite/algal ridge fringing reef complex, Highborne Cay, Bahamas. Atoll Res Bull, 466, 1-18.
Reid, R. P., Macintyre, I. G., Browne, K. M., Steneck, R. S., & Miller, T. (1995) Modern marine stromatolites in the Exuma Cays, Bahamas: uncommonly common. Facies, 33(1), 1-17.
Riding, R. (2011) Microbialites, stromatolites, and thrombolites. In Encyclopedia of Geobiology (pp. 635-654). Springer Netherlands.
Riding, R. (2006) Cyanobacterial calcification, carbon dioxide concentrating mechanisms, and Proterozoic–Cambrian changes in atmospheric composition. Geobiology, 4(4), 299-316.
Riding, R. (2005) Phanerozoic reefal microbial carbonate abundance: comparisons with metazoan diversity, mass extinction events, and seawater saturation state. Revista Española de Micropaleontología, 37(1), 23-39.
Riding, R., & Liang, L. (2005a) Geobiology of microbial carbonates: metazoan and seawater saturation state influences on secular trends during the Phanerozoic. Palaeogeography, Palaeoclimatology, Palaeoecology, 219(1), 101- 115.
Riding, R., & Liang, L. (2005b) Seawater chemistry control of marine limestone accumulation over the past 550 million years. Revista Española de Micropaleontología, 37(1), 1-11.
Riding, R. (2000) Microbial carbonates: the geological record of calcified bacterial–algal mats and biofilms. Sedimentology, 47(s1), 179-214.
160
Ries, J. B. (2010) Geological and experimental evidence for secular variation in seawater Mg/Ca (calcite-aragonite seas) and its effects on marine biological calcification. Biogeosciences, 7(9), 2795.
Rippka, R., Deruelles, J., Waterbury, J. B., Herdman, M., & Stanier, R. Y. (1979) Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology, 111(1), 1-61.
Risatti, J. B., Capman, W. C., & Stahl, D. A. (1994) Community structure of a microbial mat: the phylogenetic dimension. Proceedings of the National Academy of Sciences, 91(21), 10173-10177.
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.
Rodriguez-Mora, M. J., Edgcomb, V. P., Taylor, C., Scranton, M. I., Taylor, G. T., & Chistoserdov, A. Y. (2016) The diversity of sulfide oxidation and sulfate reduction genes expressed by the bacterial communities of the Cariaco Basin, Venezuela. The open microbiology journal, 10, 140.
Ross, D. E., Marshall, C. W., May, H. D., & Norman, R. S. (2015) Draft genome sequence of Sulfurospirillum spp. strain MES, reconstructed from the metagenome of a microbial electrosynthesis system. Genome announcements, 3(1), e01336-14.
Russell, J. A., Brady, A. L., Cardman, Z., Slater, G. F., Lim, D. S. S., & Biddle, J. F. (2014) Prokaryote populations of extant microbialites along a depth gradient in Pavilion Lake, British Columbia, Canada. Geobiology, 12(3), 250-264.
Ruvindy, R., White III, R. A., Neilan, B. A., & Burns, B. P. (2016) Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high- throughput metagenomics. The ISME journal, 10(1), 183-196.
Sabra, W., Lünsdorf, H., & Zeng, A. P. (2003) Alterations in the formation of lipopolysaccharide and membrane vesicles on the surface of Pseudomonas aeruginosa PAO1 under oxygen stress conditions. Microbiology, 149(10), 2789- 2795.
Saghaï, A., Zivanovic, Y., Zeyen, N., Moreira, D., Benzerara, K., Deschamps, P., Bertolino, P., Ragon, M., Tavera, R., López-Archilla, A.I. and López-García, P. (2015) Metagenome-based diversity analyses suggest a significant contribution of non-cyanobacterial lineages to carbonate precipitation in modern microbialites. Frontiers in microbiology, 6.
161
Saghaï, A., Zivanovic, Y., Moreira, D., Benzerara, K., Bertolino, P., Ragon, M., Tavera, R., López Archilla, A.I. and López García, P. (2016) Comparative metagenomics unveils functions and genome features of microbialite associated communities along a depth gradient. Environmental microbiology, 18(12), 4990-5004.
Saini, R., Kapoor, R., Kumar, R., Siddiqi, T. O., & Kumar, A. (2011) CO 2 utilizing microbes—A comprehensive review. Biotechnology advances, 29(6), 949-960.
Sato, K., & Sakakibara, Y. (2014) MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA research, 22(1), 69-77.
Schidlowski, M., Matzigkeit, U., & Krumbein, W. E. (1984) Superheavy organic carbon from hypersaline microbial mats. Naturwissenschaften, 71(6), 303-308.
Schirrmeister, B. E., Dalquen, D. A., Anisimova, M., & Bagheri, H. C. (2012) Gene copy number variation and its significance in cyanobacterial phylogeny. BMC microbiology, 12(1), 177.
Schneider, D., Arp, G., Reimer, A., Reitner, J., & Daniel, R. (2013) Phylogenetic analysis of a microbialite-forming microbial mat from a hypersaline lake of the Kiritimati Atoll, Central Pacific. PLoS One, 8(6), e66662.
Schultze-Lam, S., Beveridge, T. J., & Des Marais, D. J. (1997) Whiting events: biogenic origin due to the photosynthetic activity of cyanobacterial picoplankton. Limnology and oceanography, 42(1), 133-141.
Schut, G. J., Lipscomb, G. L., Han, Y., Notey, J. S., Kelly, R. M., & Adams, M. M. (2014) The order thermococcales and the family thermococcaceae. In The Prokaryotes (pp. 363-383). Springer Berlin Heidelberg.
See-Too, W.S., Ee, R., Madhaiyan, M., Kwon, S.W., Tan, J.Y., Lim, Y.L., Convey, P., Pearce, D.A., Yin, W.F. and Chan, K.G. (2017) Planococcus versutus spp. nov., isolated from soil. International journal of systematic and evolutionary microbiology, 67(4), 944-950.
Shih, P.M., Wu, D., Latifi, A., Axen, S.D., Fewer, D.P., Talla, E., Calteau, A., Cai, F., De Marsac, N.T., Rippka, R. and Herdman, M. (2013) Improving the coverage of the cyanobacterial phylum using diversity-driven genome sequencing. Proceedings of the National Academy of Sciences, 110(3), 1053-1058.
Siegenthaler, U., & Sarmiento, J. L. (1993) Atmospheric carbon dioxide and the ocean. Nature, 365(6442), 119-125.
Sigman, D. M., Karsh, K. L., & Casciotti, K. L. (2009) Ocean process tracers: nitrogen isotopes in the ocean.
162
Simon, M., Scheuner, C., Meier-Kolthoff, J.P., Brinkhoff, T., Wagner-Döbler, I., Ulbrich, M., Klenk, H.P., Schomburg, D., Petersen, J. and Göker, M. (2017) Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. The ISME journal, 11(6), 1483-1499.
Simpson, J. T., & Pop, M. (2015) The theory and practice of genome sequence assembly. Annual review of genomics and human genetics, 16, 153-172.
Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M., & Kelly, S. (2016) TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Research, 26(8), 1134-1144.
Smith, M. D., Goater, S. E., Reichwaldt, E. S., Knott, B., & Ghadouani, A. (2010) Effects of recent increases in salinity and nutrient concentrations on the microbialite community of Lake Clifton (Western Australia): are the thrombolites at risk?. Hydrobiologia, 649(1), 207-216.
Sørensen, K. B., Canfield, D. E., Teske, A. P., & Oren, A. (2005) Community composition of a hypersaline endoevaporitic microbial mat. Applied and Environmental Microbiology, 71(11), 7352-7365.
Sousa, D.Z., Salvador, A.F., Ramos, J., Guedes, A.P., Barbosa, S., Stams, A.J., Alves, M.M. and Pereira, M.A. (2013) Activity and viability of methanogens in anaerobic digestion of unsaturated and saturated long-chain fatty acids. Applied and environmental microbiology, 79(14), 4239-4245.
Spero, H. J., & Lea, D. W. (1996) Experimental determination of stable isotope variability in Globigerina bulloides: implications for paleoceanographic reconstructions. Marine Micropaleontology, 28(3-4), 231-246.
Steunou, A.S., Jensen, S.I., Brecht, E., Becraft, E.D., Bateson, M.M., Kilian, O., Bhaya, D., Ward, D.M., Peters, J.W., Grossman, A.R. and Kühl, M. (2008) Regulation of nif gene expression and the energetics of N2 fixation over the diel cycle in a hot spring microbial mat. The ISME journal, 2(4), 364.
Stewart, F. J., Ottesen, E. A., & DeLong, E. F. (2010) Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. The ISME journal, 4(7), 896-907.
Stolz, J.F., Reid, R.P., Visscher, P.T., Decho, A.W., Norman, R.S., Aspden, R.J., Bowlin, E.M., Franks, J., Foster, J.S., Paterson, D.M. and Przekop, K.M. (2009) The microbial communities of the modern marine stromatolites at Highborne Cay, Bahamas. Atoll Research Bulletin, 567, 1-29.
Sun, D. L., Jiang, X., Wu, Q. L., & Zhou, N. Y. (2013) Intragenomic heterogeneity of 16S rRNA genes causes overestimation of prokaryotic diversity. Applied and environmental microbiology, 79(19), 5962-5969.
163
Suosaari, E.P., Reid, R.P., Playford, P.E., Foster, J.S., Stolz, J.F., Casaburi, G., Hagan, P.D., Chirayath, V., Macintyre, I.G., Planavsky, N.J. and Eberli, G.P. (2016) New multi-scale perspectives on the stromatolites of Shark Bay, Western Australia. Scientific reports, 6, 20557.
Swart, P. K., & Eberli, G. (2005) The nature of the δ 13 C of periplatform sediments: implications for stratigraphy and the global carbon cycle. Sedimentary Geology, 175(1), 115-129.
Swingley, W.D., Chen, M., Cheung, P.C., Conrad, A.L., Dejesa, L.C., Hao, J., Honchak, B.M., Karbach, L.E., Kurdoglu, A., Lahiri, S. and Mastrian, S.D. (2008) Niche adaptation and genome expansion in the chlorophyll d-producing cyanobacterium Acaryochloris marina. Proceedings of the National Academy of Sciences, 105(6), 2005-2010.
Tang, M., Chen, C., Li, J., Xiang, W., Wu, H., Wu, J., Dai, S., Li, T. and Wang, G. (2016) Fabivirga thermotolerans gen. nov., spp. nov., a novel marine bacterium isolated from culture broth of a marine cyanobacterium. International journal of systematic and evolutionary microbiology, 66(2), 1095-1099.
Ting, C. S., Rocap, G., King, J., & Chisholm, S. W. (2002) Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light- harvesting strategies. Trends in microbiology, 10(3), 134-142.
Treangen, T.J., Koren, S., Sommer, D.D., Liu, B., Astrovskaya, I., Ondov, B., Darling, A.E., Phillippy, A.M. and Pop, M. (2013) MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome biology, 14(1), R2.
Turner, E. C., James, N. P., & Narbonne, G. M. (2000) Taphonomic control on microstructure in Early Neoproterozoic reefal stromatolites and thrombolites. Palaios, 15(2), 87-111.
Tyystjärvi, T., Herranen, M., & Aro, E. M. (2001) Regulation of translation elongation in cyanobacteria: membrane targeting of the ribosome nascent chain complexes controls the synthesis of D1 protein. Molecular microbiology, 40(2), 476-484.
UniProt Consortium. (2017) UniProt: the universal protein knowledgebase. Nucleic acids research, 45(D1), D158-D169.
Valdespino-Castillo, P. M., Alcántara-Hernández, R. J., Alcocer, J., Merino-Ibarra, M., Macek, M., & Falcón, L. I. (2014) Alkaline phosphatases in microbialites and bacterioplankton from Alchichica soda lake, Mexico. FEMS microbiology ecology, 90(2), 504-519.
Valley, J. W., & Kita, N. T. (2009) In situ oxygen isotope geochemistry by ion microprobe. MAC short course: secondary ion mass spectrometry in the earth sciences, 41, 19-63.
164
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A., & Knight, R. (2013) EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience, 2(1), 16.
Větrovský, T., & Baldrian, P. (2013) The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PloS one, 8(2), e57923.
Vetter, L., Kozdon, R., Valley, J. W., Mora, C. I., & Spero, H. J. (2014) SIMS measurements of intrashell δ 13 C in the cultured planktic foraminifer Orbulina universa. Geochimica et Cosmochimica Acta, 139, 527-539.
Visscher, P. T., Beukema, J., & van Gemerden, H. (1991) In situ characterization of sediments: measurements of oxygen and sulfide profiles with a novel combined needle electrode. Limnology and Oceanography, 36(7), 1476-1480.
Visscher, P. T., Prins, R. A., & van Gemerden, H. (1992) Rates of sulfate reduction and thiosulfate consumption in a marine microbial mat. FEMS Microbiology Letters, 86(4), 283-288.
Visscher, P. T., Reid, R. P., & Bebout, B. M. (2000) Microscale observations of sulfate reduction: correlation of microbial activity with lithified micritic laminae in modern marine stromatolites. Geology, 28(10), 919-922.
Visscher, P. T., Reid, R. P., Bebout, B. M., Hoeft, S. E., Macintyre, I. G., & Thompson, J. A. (1998) Formation of lithified micritic laminae in modern marine stromatolites (Bahamas): the role of sulfur cycling. American Mineralogist, 83(11), 1482-1493.
Visscher, P. T., & Stolz, J. F. (2005) Microbial mats as bioreactors: populations, processes, and products. Palaeogeography, Palaeoclimatology, Palaeoecology, 219(1), 87-100.
Vu, B., Chen, M., Crawford, R. J., & Ivanova, E. P. (2009) Bacterial extracellular polysaccharides involved in biofilm formation. Molecules, 14(7), 2535-2554.
Waite, A. J., & Swart, P. K. (2015) The inversion of aragonite to calcite during the sampling of skeletal archives: Implications for proxy interpretation. Rapid Communications in Mass Spectrometry, 29(10), 955-964.
Walker, C.B., De La Torre, J.R., Klotz, M.G., Urakawa, H., Pinel, N., Arp, D.J., Brochier- Armanet, C., Chain, P.S.G., Chan, P.P., Gollabgir, A. and Hemp, J. (2010) Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proceedings of the National Academy of Sciences, 107(19), 8818-8823.
Warden, J. G., Casaburi, G., Omelon, C. R., Bennett, P. C., Breecker, D. O., & Foster, J. S. (2016) Characterization of microbial mat microbiomes in the modern thrombolite ecosystem of Lake Clifton, Western Australia using shotgun metagenomics. Frontiers in microbiology, 7.
165
Wegener, K. M., Nagarajan, A., & Pakrasi, H. B. (2015) An atypical psbA gene encodes a sentinel D1 protein to form a physiologically relevant inactive Photosystem II complex in cyanobacteria. Journal of Biological Chemistry, 290(6), 3764-3774.
White III, R.A., Chan, A.M., Gavelis, G.S., Leander, B.S., Brady, A.L., Slater, G.F., Lim, D.S. and Suttle, C.A. (2015a) Metagenomic analysis suggests modern freshwater microbialites harbor a distinct core microbial community. Frontiers in microbiology, 6.
White III, R. A., Power, I. M., Dipple, G. M., Southam, G., & Suttle, C. A. (2015b) Metagenomic analysis reveals that modern microbialites and polar microbial mats have similar taxonomic and functional potential. Frontiers in microbiology, 6.
Whitman, W. B., Bowen, T. L., & Boone, D. R. (2006) The methanogenic bacteria. In The prokaryotes (pp. 165-207). Springer New York.
Wickham, H. (2009) ggplot2: Elegant Graphics for Data Analysis Springer-Verlag. New York.
Wilhelm, M. B., & Hewson, I. (2012) Characterization of Thrombolitic Bioherm Cyanobacterial Assemblages in a Meromictic Marl Lake (Fayetteville Green Lake, New York). Geomicrobiology Journal, 29(8), 727-732.
Williford, K.H., Ushikubo, T., Lepot, K., Kitajima, K., Hallmann, C., Spicuzza, M.J., Kozdon, R., Eigenbrode, J.L., Summons, R.E. and Valley, J.W. (2016) Carbon and sulfur isotopic signatures of ancient life and environment at the microbial scale: Neoarchean shales and carbonates. Geobiology, 14(2), 105-128.
Wong, H. L., Smith, D. L., Visscher, P. T., & Burns, B. P. (2015) Niche differentiation of bacterial communities at a millimeter scale in Shark Bay microbial mats. Scientific reports, 5.
Ye, Y., & Tang, H. (2015) Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis. Bioinformatics, 32(7), 1001-1008.
Zhu, B., & Cheng, W. (2011) 13C isotope fractionation during rhizosphere respiration of C3 and C4 plants. Plant and Soil, 342(1-2), 277-287.
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BIOGRAPHICAL SKETCH
Artemis S. Louyakis received a Bachelor of Science degree in biological science with a focus on microbiology and a minor in statistics from Murdoch University, School of Veterinary and Life Sciences, in Perth, Western Australia in 2009. Her independent undergraduate research focused on increasing the effective host range of rhizobial bacteria under the guidance of Drs. Wayne Reeve and Lambert Brau. In 2010, Artemis joined the United States Department of Agriculture (USDA) Animal and Plant Health
Inspection Services (APHIS) on Cape Cod, MA as a biological technician. There she worked on molecular profiling and statistical analysis of Gypsy Moths for global regulatory tracking with Drs. John Molongoski, David Lance, and Victor Mastro, as well as other invasive pests including the Sirex woodwasp with Dr. Dave Williams.
In 2012, Artemis’ keen interest in microbial communities led her to accept a
Ph.D. position examining temporal gene expression changes in thrombolite-forming microbial mats under the mentorship of Dr. Jamie Foster in the Department of
Microbiology and Cell Science at the University of Florida. Artemis was awarded an
NSF Graduate Research Fellowship from 2013-2016. As a Ph.D. student, she has presented her research at the American Society for Microbiology (ASM) General
Meeting (2014), the Southeastern Branch ASM Summit (2014), 16th International
Symposium of Microbial Ecology (2016), and the Florida Branch ASM Annual Meeting
(2016). Artemis received her Ph.D. from the University of Florida in summer of 2017.
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