vaterite before, and after, adsorbing Cd2+ or Pb2+ were analyzed by XRD.

### The Comparison of Adsorption and Desorption for Heavy Metals by Biogenic CaCO3 and Bacterial Cells

We added 100 mL LB liquid medium to a clean 250 mL conical flask, sterilized it at 115◦C for 20 min, then inoculated 2 mL strain from the bacterial liquid mentioned above in the experimental group, set up 10 parallel trials, and subjected them to shakingculturation at 30◦C and 180 rpm for 7 days. Then, the culture solution was centrifuged at 8000 rpm for 15 min at 4◦C, whereafter, the bacterial cells were collected and dried at 55◦C, and then milled to 200 mesh or finer, by agate mortar in readiness for testing.

The biogenic CaCO3 including CaCO3 and bacterial cells was used to clarify the advantages of biogenic CaCO3 for heavy metals the adsorption. The adsorption and desorption experiments of Cd2+ (74 mg/L), and Pb2+ (94 mg/L) by biogenic CaCO3 and bacterial cells were carried out using the method described in section The Adsorption and Desorption of Cd2+ and Pb2+ by Micro and Nano-sized Biogenic CaCO3.

## The Effect of pH on Adsorption of Biogenic CaCO3 for Heavy Metals

To study the influence of pH on the removal efficiency, 0.05 g biogenic CaCO3 was placed into a 50 mL polyethylene centrifuge tube containing 20 mL solution with different pH values (1, 2, 3, 4, 5, 6, 7, and 8) of 83.13 mg/g Cd2+ (CdCl2), or 99.30 mg/g

Pb2+ (Pb(NO3)2), respectively. The mixture was shaken at 25◦C and 100 rpm for 24 h, and each group was replicated three times. The supernatant was obtained by centrifuging at 8000 rpm for 15 min. The concentrations of Cd2+ or Pb2+ in the supernatant were determined by AAS, and the heavy metal adsorption rates were calculated by use of Equation (2).

### RESULTS AND DISCUSSION

### The Morphological and Elemental Composition Analysis of Biogenic CaCO3 Sediments

Different morphologies of crystals in the sediments were observed by using FESEM. These morphologies included cauliflower-like forms, scaly aggregates, and various irregular aggregates of sediment (part of them shown in **Figures 1A,B**). EDS was used to determine the main component as being CaCO3 (**Figure 1C**). The biogenic CaCO3 exhibited its porous surface, corner-incomplete form, and visible irregular fine lines on its surface, thus it had both a larger internal and external specific surface area and pore volume.

## The Adsorption and Desorption of Biogenic CaCO3 for Cd2+ and Pb2+

Although both Freundlich and Langmuir equations could be used to fit the isothermal adsorption process of Cd2+ and Pb2+ by biogenic CaCO3, the fitting effect of Langmuir adsorption isotherm equation was more favorable, which suggested that the adsorption process was a single-molecule adsorption process (Mikutta et al., 2012; Wang et al., 2015). The maximum adsorption amounts of Cd2+ and Pb2+ by biogenic CaCO3 were 94.340 and 416.667 mg/g, respectively (**Table 1**). CaCO3 is an important mineral that is ubiquitous in soils, shallow grand water aquifers and marine sediments which has good adsorption properties for heavy metals (Davis et al., 1987; Garcia-Sánchez and Alvarez-Ayuso, 2002; Al-Degs et al., 2006; Lee et al., 2007; Yavuz et al., 2007). Yavuz et al. (2007) found that the maximum adsorption capacities of Cd2+ and Pb2+ by natural CaCO3 were

determined as 18.52 and 19.92 mg/g, respectively. This research on the adsorption of heavy metals with biogenic CaCO3 induced by the strain (as a legal strain used in microbial fertilizer) is the first report, and the maximum adsorption capacities of biogenic CaCO3 for heavy metals are apparently higher than that of natural calcite (p < 0.01), which suggests a considerable potential to immobilize or passivate heavy metals in contaminated soil.

**Figure 2A** showed that the adsorption amount (Qe) of Cd2+ or Pb2+ on biogenic CaCO3 increased with increasing Cd2+ or Pb2+ concentration in the equilibrium solution (Ce). When the concentration of Cd2+ or Pb2+ was between 5 and 260 mg/L, the rate of adsorption of heavy metals on biogenic CaCO3

was as high as 87–100%, while the rate of desorption remained steady at 0.1–3% (**Figure 2B**), which suggest that biogenic CaCO3 has a high adsorption capacity for heavy metals and carries little environmental risk. The results provide evidence that bacterial fertilizer and biogenic CaCO3 may play important roles in various environments, and indeed more than previously acknowledged.

### The Adsorption Mechanism

The FTIR results showed that it did not undergo a chemical precipitation reaction to produce new substances after the CaCO3 had adsorbed Cd2+ (74 mg/L) or Pb2+ (94 mg/L)

FIGURE 3 | The adsorptive mechanism analysis of Cd2+ and Pb2+ by biogenic CaCO3. (A) The results of FTIR spectra of biogenic CaCO3 before, and after, adsorbing Cd2+ or Pb2+ (CK: before adsorbing Cd2+ or Pb2+ by biogenic CaCO3). (B) The reticular structure of biogenic CaCO3 by FESEM. (C,E1) The result of biogenic CaCO3 after adsorbing Cd2+ (74 mg/L) by FESEM-EDS: Cd2+ is visible on the surface of the biogenic CaCO3. (D,E2) The result of biogenic CaCO3 after adsorbing Pb2+ (94 mg/L) by FESEM-EDS: Pb2+ is visible on the surface of the biogenic CaCO3. (F) The XRD results of biogenic vaterite before, and after, adsorbing Cd2+ and Pb2+ (CK: before adsorbing Cd2+ and Pb2+ by biogenic CaCO3). The "\*" inside the figure is the site of the EDS spot.

(**Figure 3A**); which indicated that the reaction between the biogenic CaCO3 and Cd2+ or Pb2+ was mainly based on physical adsorption. The FESEM-EDS analysis showed that the adsorbed Cd2+ and Pb2+ were visible on the surface of biogenic CaCO3 (**Figures 3C,E1,D,E2**). Chemical CaCO3 morphology is essentially a diamond-shaped cubic structure with smooth surfaces (Lian et al., 2006; Xiao et al., 2015; Cao et al., 2016), but the biogenic CaCO3 surface is porous, micro and nano-sized, the edge is incomplete, and it can stack to form a fragmented structure or form a reticular aggregate with other different forms of CaCO3 according to FESEM and soft X-ray microscopy analysis (**Figures 1A,B,3B**, **Supplementary Video 1**); thus it has a larger internal and external specific surface area and pore volume, which can provide more adsorption sites and accommodation spaces for heavy metals. The XRD and TEM-SAED results indicated that the biogenic CaCO3 used in this test was mainly amorphous CaCO3 (**Figure 4**), and according to the reports that the amorphous CaCO3 surface area is 20 times that of other crystalline forms of CaCO3 (Yan and Lu, 2012), therefore, it exhibits strong adsorption properties for Cd2+ and Pb2+.

Since there were no diffraction peaks observed from the amorphous biogenic CaCO3 in the XRD result (**Figure 4A**), to clarify the adsorptive mechanism of biogenic CaCO3 for heavy metals, vaterite-type biogenic CaCO3 was used to adsorb Cd2+ (74 mg/L) or Pb2+ (94 mg/L), and the adsorption rates were 98.42 and 100%, respectively, moreover, the desorption rates were all zero. The XRD results revealed that, there was no new phase diffraction peak after the biogenic vaterite had adsorbed the Cd2+ and Pb2+ (**Figure 3F**), which also showed that the reaction between the biogenic CaCO3 and Cd2+ or Pb2+ was mainly based on physical adsorption. It was also indicated that the Cd2+ or Pb2+ was stable in the mineral as a result of binding to the CaCO3 surface adsorption sites, or entry to the CaCO3 crystal pores. Consequently, biogenic CaCO3 offered better adsorption properties for heavy metals. Our findings suggested that the biogenic CaCO3 could be expected to be developed into a new type of heavy metals adsorbent, and might achieve the dual environmental benefits of carbon sequestration and heavy metal immobilization.

### The Comparison of Adsorption and Desorption of Cd2+ and Pb2+ by Biogenic CaCO3 and Bacterial Cells

**Figure 5** illustrates that the adsorption rate of Cd2+ (74 mg/L) and Pb2+ (94 mg/L) by biogenic CaCO3, was significantly higher than that of the bacterial cells, and the desorption rate was significantly smaller than that of the desorption rate of bacterial cells (p < 0.01), it suggested that the adsorption of CaCO3 crystal for heavy metals was dominant and its environmental risk was

very low, but the adsorption rate of bacterial cells for heavy metals was not only low, but also the adsorbed heavy metals would be released to the environment easily, therefore, it posed a higher environmental risk. This also suggested that the biogenic CaCO3 had a larger specific surface area and rich reticular structures which contributed to its high adsorption and low desorption performance. This significant retaining ability of heavy metal ions indicates the remarkable efficiency of biogenic CaCO3 as an adsorbent.

## The Effect of pH on Adsorption of Biogenic CaCO3 for Heavy Metals

The results in **Figure 6A** demonstrate the effects of pH on Cd2+ and Pb2+ adsorption by biogenic CaCO3. The adsorption rate of these heavy metals was quite low at pH ≤ 1, as biogenic CaCO3 could not exist at such a low pH value. At pH values from 1.0 to 4.0, the adsorption percentage increased rapidly with increasing pH; thereafter (pH >4) it did not change to any significant extent with further increases in pH and the adsorption percentage was stable at around 95% (**Figure 6A**). Similar experimental results, such as those from Ma et al. (2012) who used use chemogenic CaCO3 for the adsorption of Cd2+ and Pb2+ and Merrikhpour and Jalali (2012) who used natural CaCO3 for Cd2+, Pb2+, Cu2+, Zn2+ adsorption, etc., can also obtain good adsorption effect when starting from an acidic pH value. Furthermore, we found that the pH value of the adsorption system was increased after adding biogenic CaCO3, and the final pH value after adsorption is around 8.61 (**Figure 6B**), which should be attributable by the biogenic CaCO3 and alkaline metabolites produced by B. subtilis. In addition, the adsorption percentages of Cd2+ and Pb2+ at pH 8 were 16.22 and 41.23% when we did not add biogenic CaCO3, which were significantly lower than biogenic CaCO3 adsorption percentages (p < 0.01). This indicated that the adsorption rate of heavy metals was mainly influenced by the biogenic CaCO3 rather than the formation of heavy metal hydroxides in alkaline conditions. In summary, the high adsorption capability of the biogenic CaCO3 within a wide pH range (3–8) indicated its potential application in the control of the fate of heavy metals in the natural environment.

## CONCLUSIONS

The Langmuir isotherm was preferred to describe the adsorption characteristics of Cd2+ and Pb2+ by biogenic CaCO3 which suggested that the adsorption process was a single molecule layer adsorption process, and the maximum adsorption amounts (Qm) of Cd2+ and Pb2+ were 94.340 and 416.667 mg/g, respectively. Moreover, biogenic CaCO3 could maintain a high adsorption capability for heavy metals within a wide pH range. The biogenic CaCO3 and heavy metal ions formed a physical adsorption process, and the high efficiency and stability of the adsorption of biogenic CaCO3 for Cd2+ and Pb2+ were mainly attributed to its large total specific surface area and their porous structure. These findings revealed a new perspective on the remediation of heavy metal pollution by using biogenic CaCO3.

## AUTHOR CONTRIBUTIONS

BL: designed this study; RL, YG, and LC: performed the laboratory work; RL, YG, LC, and BL: analyzed the data; RL and BL: wrote this manuscript; All authors have read and approved the final manuscript.

## ACKNOWLEDGMENTS

This work was jointly supported by the National Natural Science Foundation of China (Grant No. 41373078) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2018.00041/full#supplementary-material

Supplementary Video 1 | The video of biogenic CaCO3 by soft x-ray microscopy.

### REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Liu, Guan, Chen and Lian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Microbial Diversity and Mineralogical-Mechanical Properties of Calcitic Cave Speleothems in Natural and in Vitro Biomineralization Conditions

#### Navdeep K. Dhami1 \*, Abhijit Mukherjee1 and Elizabeth L. J. Watkin2

1 Biologically Activated Materials Laboratory, Department of Civil Engineering, Curtin University, Perth, WA, Australia, 2 School of Biomedical Sciences, Curtin Health Innovation Research Institute-Biosciences, Curtin University, Perth, WA, Australia

#### Edited by:

Doug LaRowe, University of Southern California, United States

#### Reviewed by:

Amy Michele Grunden, North Carolina State University, United States Juliane Hopf, University of Notre Dame, United States

\*Correspondence:

Navdeep K. Dhami<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

Received: 09 April 2017 Accepted: 09 January 2018 Published: 02 February 2018

#### Citation:

Dhami NK, Mukherjee A and Watkin ELJ (2018) Microbial Diversity and Mineralogical-Mechanical Properties of Calcitic Cave Speleothems in Natural and in Vitro Biomineralization Conditions. Front. Microbiol. 9:40. doi: 10.3389/fmicb.2018.00040 Natural mineral formations are a window into important processes leading to carbon storage and mineralized carbonate structures formed through abiotic and biotic processes. In the current study, we made an attempt to undertake a comprehensive approach to characterize the mineralogical, mechanical, and microbial properties of different kinds of speleothems from karstic caves; with an aim to understand the biogeo-chemical processes in speleothem structures and their impact on nanomechanical properties. We also investigated the biomineralization abilities of speleothem surface associated microbial communities in vitro. Mineralogical profiling using techniques such as X-ray powder Diffraction (XRD) and Tescan Integrated Mineral Analyzer (TIMA) demonstrated that calcite was the dominant mineral in the majority of speleothems with Energy Dispersive X-ray Analysis (EDS) indicating a few variations in the elemental components. Differing proportions of polymorphs of calcium carbonate such as aragonite and vaterite were also recorded. Significant variations in trace metal content were recorded through Inductively Coupled Plasma Mass Spectrometer (ICP-MS). Scanning Electron Microscopy (SEM) analysis revealed differences in morphological features of the crystals which varied from triangular prismatic shapes to etched spiky forms. Microbial imprints and associations were seen in a few sections. Analysis of the associated microbial diversity showed significant differences between various speleothems at Phylum level; although Proteobacteria and Actinobacteria were found to be the predominant groups. Genus level microbial associations showed a relationship with the geochemistry, mineralogical composition, and metal content of the speleothems. The assessment of nanomechanical properties measured by Nanoindentation revealed that the speleothems with a dominance of calcite were stronger than the speleothems with mixed calcium carbonate polymorphs and silica content. The in vitro metabolic activity of the microbial communities associated with the surfaces of the speleothems resulted in calcium carbonate crystal precipitation. Firmicutes and Proteobacteria dominated these populations, in contrast to the

**44**

populations seen in natural systems. The precipitation of calcium carbonate crystals in vitro indicated that microbial metabolic activity may also play an important role in the synthesis and dissociation of biominerals in the natural environment. Our study provides novel evidence of the close relationship between mineralogy, microbial ecology, geochemistry, and nanomechanical properties of natural formations.

Keywords: speleothem, biomineralization, microbial diversity, nanoindentation, calcium carbonate precipitation, mineralogy

### INTRODUCTION

Biomineralization is a naturally occurring process where microbial metabolic activity influences the formation of geological structures (Lowenstam, 1981; Hammes and Verstraete, 2002; Hamilton, 2003). A variety of microbial metabolic pathways including photosynthesis, ammonification, denitrification, ureolysis, and methane oxidation have been shown to influence redox conditions and lead to the precipitation of carbonates in different natural environments (Fortin et al., 1997; Douglas and Beveridge, 1998; Zhu and Dittrich, 2016). Bacterial surfaces including cell walls and extracellular polymeric substances (EPS) have also been reported to act as important sites for carbonate mineral nucleation and growth (Douglas and Beveridge, 1998; Bains et al., 2015). The potential for microbes to synthesize carbonates has recently emerged as a prospective technology, called as Microbially Induced Calcium Carbonate Precipitation (MICCP), for civil and environmental engineering applications (Skinner and Jahren, 2004; Phillips et al., 2013; Zhu and Dittrich, 2016). Current usage of the technology includes remediation of heavy metal polluted sites, sequestration of CO2, and the construction of biocement (Dhami et al., 2012, 2014, 2016, 2017b; Dejong et al., 2013; Zhu and Dittrich, 2016). Several breakthroughs have been made at laboratory and field scale in the creation and utilization of biomineralized carbonates (De Muynck et al., 2010; Phillips et al., 2013; Dhami et al., 2017a; Porter et al., 2017). The application of this technology offers the advantage of high sustainability due to its synthesis at ambient temperatures and has spurred research into exploration of the properties of naturally mineralized structures (Cacchio et al., 2003; Phillips et al., 2013). Although researchers from specializations such as Geology, Microbiology, Environment, and Engineering have contributed to the information on geological formations from separate perspectives; a connection linking mineralogy, microbiology, and mechanical characterization of such biogenically/abiogenically formed natural formations is lacking. A deeper and fundamental understanding of bio-geochemical properties of naturally mineralized structures along with the mechanical properties of these materials is imperative (Northup and Lavoie Diana, 2001; Dhami et al., 2012, 2013b; Zepeda Mendoza et al., 2016).

Geological formations such as caves provide a window into one such environment where mineralized carbonate deposits are formed over geological timescales (Buhmann and Dreybrodt, 1984; Short et al., 2005; Rusznyak et al., 2012). The myriad of rock like deposits in caves are commonly known as speleothems; which vary in range of shapes. These structures are known by different names as stalactites, stalagmites, moonmilk, drip stones, and flowstones depending upon their location (from floors, walls to ceilings) and appearance (soft, spongy, fibrous, or stony) (Borsato et al., 2000; Jones, 2001; Melim et al., 2008). The inorganic and physical chemistry that drive these phenomena is well established (Banks et al., 2010; Pacton et al., 2013), however, a growing body of research indicates that microorganisms can play an important role in carbonate precipitation during speleothem growth (Banks et al., 2010; Rusznyak et al., 2012; Ronholm et al., 2014). Several studies on the analysis of the mineralogy and geochemistry of different cave environments have been conducted (White, 1962; Harmon et al., 1983; Hill and Forti, 1986; Leél-Ossy et al., 2011 ˝ ). The majority of speleothems have been found to be calcareous and composed of calcium carbonate polymorphs such as calcite, vaterite, and aragonite (Ronholm et al., 2014; Demény et al., 2016; Garcia et al., 2016). The growth of speleothems through calcite precipitation has long been viewed as an abiogenic process (Kendall and Broughton, 1978; Broughton, 1983a,b,c). However, mineral deposits that are difficult to explain through geologic or inorganic processes raise the possibility of microbial activity in caves (Jones, 2001). For example, in the case of moonmilk, carbonate precipitation is formed either by direct precipitation of calcite via microbial metabolic activity or by the formation of nucleation surface on which minerals precipitate (Northup et al., 2003; Cuezva et al., 2012). A few studies have also reported the biogenic origins of calcified structures such as "pool fingers" and "U-loops" (Tomczyk-Zak and Zielenkiewicz, 2015 ˙ ). Some studies claim that bacteria produce carbonate minerals as a result of passive growth while others report that chemically reactive cell walls of bacteria act as nucleation sites binding mineral forming elements leading to growth of crystals in oversaturated solutions (Fortin et al., 1997; Barabesi et al., 2007; Dhami et al., 2013a). Researchers have demonstrated that by interacting with minerals, microorganisms play an important role in the formation of caves although little cause and effect has been elucidated (Tomczyk-Zak and Zielenkiewicz, 2015 ˙ ). Cave geochemistry, physico-chemical conditions, and mineralogy has also been reported to have a significant impact on the microbiome and the interactions of these microbes with minerals further play a significant role in the formation and characteristics of biomineralized speleothems (Zepeda Mendoza et al., 2016).

A number of studies have investigated microbial biofilms on cave surfaces (Adetutu et al., 2012; Pacton et al., 2013; Barton et al., 2014; Wu et al., 2015) and others have reported the microbial diversity of caves such as the Lechuguilla Cave in New Mexico (Cunningham et al., 1995; Northup et al., 2003), the

sulfidic Frasassi Cave system in Italy, the Movile Cave in Romania (Macalady et al., 2007; Chen et al., 2009), the nitrate/nitritedominated Nullarbor Cave in Australia (Holmes et al., 2001), karstic caves of Herrenberg Germany (Rusznyak et al., 2012), and Tjuv-Ante's Cave in Sweden (Zepeda Mendoza et al., 2016). The existence of varying calcium carbonate polymorphs in these materials has also been related to biogenic activity. A few authors have attributed the precipitation of different types of carbonate deposits in moonmilk to the activity of indigenous microbial populations. These deposits vary from nano-fibers to micrometer sized needle-fiber crystals in a form of monocrystalline rods and polycrystalline chains (Baskar et al., 2008b; Cuezva et al., 2012; Maciejewska et al., 2017). Microbes have also been reported to mediate a wide range of processes that affect the internal crystal fabric (Jones, 2010) with carbonate precipitation and heterotrophic processes through nitrogen metabolism including ureolysis, ammonification, and nitrate reduction have been reported to play more important roles than photosynthesis (low light conditions) and methanogenesis (Castanier et al., 2000; Banks et al., 2010). Although a few studies have been conducted on the microbial diversity and mineralogy of different cave deposits and speleothems, this area remains relatively unexplored.

Mechanical properties of biomineralized geological formations can provide insights for exploring the potential of microbial carbonate based cements for engineering applications. But in many cases, the non-availability of sufficient quantities of such structures for standard material testing (due to their heritage status) makes it difficult to investigate their mechanical properties. For mechanical testing on (sub) micrometer scale, nanoindentation (also referred to as load and depth-sensing indentation (DSI)) has recently gained attention (Presser et al., 2010). This technique offers the advantage of high accuracy and reliability along with the ability to test small amounts of material; making this method particularly suitable for the speleothems. Fortunately, a few such results are available for rock minerals (Zhu et al., 2007), single calcite crystals (Calvaresi et al., 2013; Dhami et al., 2016), hydroxyapatite crystals (Zamiri and De, 2011), carp otoliths (Ren et al., 2013), and sponge spicules (Müller et al., 2014). This encourages the use of this tool to investigate nano-mechanical properties of different cave speleothems.

Whether the formation of cave speleothems is biogenic or abiogenic or a combination of both is a matter of debate. A few studies have demonstrated that bacteria isolated from cave environments are capable of forming similar crystals from organic calcium salts in vitro using acetate rich B4 medium (Rusznyak et al., 2012; Garcia et al., 2016). Maciejewska et al. (2017) found amino acid/peptide ammonification to be more widespread compared to ureolysis in moonmilk formation. The investigation of microbial communities associated with cave speleothems for their biomineralization potential under supplementation of similar organic calcium salts will shed light on the biogenic routes of mineralization, as well as exploring the change in microbial community dynamics under enrichment conditions in vitro compared to the native profiles.

This paper aims to characterize the mineralogical, elemental and nano-mechanical properties of different cave speleothems as well as the associated microbiomes. We investigated the biomineralization potential of surface associated microbial communities along with microbial community dynamics under in vitro conditions. This is the first study on the collective characterization of mineralogical, microbial, and mechanical properties of cave speleothems to elucidate biogenic mineralization processes.

The Leeuwin-Naturaliste ridge in the Margaret River region of Southwest Western Australia is home to a number of karstic caves stretching for approximately 90 km between Cape Leeuwin and Cape Naturaliste with the Indian Ocean to the west, Geographe Bay to the north and the Southern Ocean to the south (Jasinska, 1997; Eberhard, 2004; Eberhard and Davies, 2011). This provided us with the opportunity to investigate distinctly different cave formations. We examined different cave deposits (stalagmites, stalactites, moonmilk) from three unexplored caves where permission was granted for access. An investigation of mineralized cave speleothems in their natural state as well as under enriched conditions in vitro is presented with the aim to: (a) evaluate mineralogical, chemical, and nanomechanical properties of different cave speleothems, (b) elucidate the microbial diversity associated with different cave speleothems and, (c) investigate the potential of speleothem associated microbial communities in the formation of carbonate minerals under in vitro conditions as well as identification of microbial communities co-responsible for carbonate precipitation in laboratory conditions for analysis of microbial community shifts in comparison to Natural environments.

### MATERIALS AND METHODS

### Description of Caves

Margaret River is located in Western Australia, 277 km (172 mi) SSW of Perth and 48 km (30 mi) SW of Busselton at 33◦ 570 1800S 115◦ 040 3000E. The climate is humid Mediterranean with an average annual rainfall of around 1,130 mm (44 in) (Eberhard, 2004). Complete details about the cave formations and the environment involved have been reported by Eberhard (2004). Permission for the current study was provided by the Augusta Margaret River Tourism Association for sampling calcitic deposits and speleothem sections in a limited number of locations in three caves (Lake Cave, Moondyne Cave, Mammoth Cave) (**Figures 1i,ii**). The speleothems sampled are highlighted in **Figure 2**. In the case of Mammoth Cave, parts of stalactite and stalagmite that had been already shed were provided.

### Sampling of Cave Speleothems

Sampling of calcitic deposits from different formations in each cave (up to 10 g) was conducted using sterile forceps, spatula and chisel. Moonmilk, stalagmite, and stalactite samples were collected from Lake Cave, calcitic deposits were collected from the floor of Moondyne Cave and deposits of stalagmites and stalactites were provided from Mammoth Cave (**Figure 2** and **Table 1**). Sampling locations were selected based on

FIGURE 2 | Cave speleothems collected from (i–iii) Lake Cave (iv) Moondyne Cave (v,vi) Mammoth Cave.

#### TABLE 1 | Cave speleothem type, site and labeling.

fmicb-09-00040 January 31, 2018 Time: 14:54 # 5

accessibility, regulatory restrictions and special features observed. All the specimens were collected aseptically, transported under refrigeration to the laboratory, and stored at −20◦C until use. The samples were separated into three sections in order to correlate mineralogical, mechanical, and microbial characteristics.

### Determination of Carbon, Nitrogen, and Metal Content

One gram of each sample was added to 10 mL deionized water and shaken briefly. Total Al, Mg, Ca, Fe, Mn, and Na analyses were conducted on samples that were dried at 100◦C, extracted with aqua regia (Hoffman, 1991) and analyzed with an inductively coupled plasma mass spectrometer (ICP-MS) (PQ3S; Thermo Electron, United Kingdom) following Rusznyak et al. (2012). For the estimation of Carbon and Nitrogen, the samples were freeze dried, finely ground to 200 mesh size (<75 µm), homogenized and analyzed using a CN analyser (Vario Max; Elementary Analysensysteme GmbH, Germany). Inorganic carbon was determined by measuring the total amount of carbon after removal of organic carbon (OC) and ignition of samples for 4 h at 550◦C (Grüneberg et al., 2010). OC concentrations were then calculated from the difference between total and inorganic carbon concentrations. For each test triplicate samples were analyzed.

### Morphological Characterization of Speleothems by SEM

For scanning electron microscopic analysis (SEM) all samples were fixed in 4% formaldehyde in phosphate-buffered saline (PBS) and kept at 4◦C until examination. The samples were rinsed in 0.2 M PBS (pH 7.4) for 1 h and dehydrated in a series of graded ethyl alcohol then sputter coated with platinum (thickness of approximately 8 nm) using a SCD005 sputter coater (BAL-TEC, Liechtenstein) to avoid surface charging. Finally, the specimens were investigated with Zeiss Neon 40 EsB dual beam FESEM/FIB-SEM for imaging at 10 kV and WD = 10–11 mm.

### Chemical Characterization of Speleothems by XRD, EDS, and TIMA

X ray diffraction (XRD) spectra were obtained by crushing around 1 g sample of each speleothem and subjecting it to X' Pert PRO diffractometer with a Cu anode (40 kV and 30 mA). The scanning was done from 3◦ to 60◦ 2θ with increments of 0.02◦ 2θ and a counting time of 10 s per step. The components of the sample were identified by comparing them with standards established by the International Centre for Diffraction Data. For elemental analysis of the samples, the same SEM equipped with Oxford energy dispersive spectrometer (EDS), coupled with INCA 250 system was used to generate elemental maps in the samples. EDS qualitative analysis and elemental mapping was performed at an accelerating voltage of 20 kV. Data acquisition and analysis was done using AZtec software (Oxford instruments, United Kingdom). Five frames, each with an area varying between 0.5 mm2 to 1 mm2 were mapped. As it can be difficult to identify mineral phases through XRD due to low concentrations or overlapping peaks, poorly crystalline nature of materials with high amorphous content, or high and low temperature variants, the speleothems were also characterized by Tescan Integrated Mineral Analyzer (TIMA). The conditions used for the current analysis were: beam energy 25000 eV, current 6.32 nA, beam intensity 19.56, working distance 15 mm, SEM type TIMA3FE GMU with Pulse Tor 30 detector model at 7.009 µm pixel.

### Mechanical Characterization of Speleothems by Nanoindentation

Cubes of approximately 1 cm were cut from each specimen, embedded in epoxy resin (Buehler eco-thin, Buehler, Lake Bluff, Illinois) and allowed to cure for 24 h (in molds 32 mm × 8 mm). Initial grinding and polishing of samples was performed using silicon carbide paper of reducing gradation 52, 35, 22, and 15 µm to expose the surface. Samples were finally ground and polished using diamond suspensions of reducing gradations as 9, 6, 3, 1, and 0.05 µm on a polishing cloth. Nanoindentation was performed using a G200 nanoindenter (Agilent Technologies) fitted with a Berkovich shaped diamond tip at a Poisson ratio of 0.31. The optical microscope fitted with the nanoindenter was used at 40x magnification to select the points of indentation and a matrix of indentation points on the particles of interest was chosen. At least 50 indentation points were selected for each sample with a minimum spacing between the indents of 10 µm and a maximum depth of indentation set to 1000 nm. The surface approach velocity was set to 20 nm/s and the loading time set to 15 s with a peak hold time of 10 s. On completion, indentation sites were re-inspected with an optical microscope and load-indentation diagrams were plotted. From this data the mechanical properties were estimated (Müller et al., 2014). From the initial slope of the unloading curve, the depth of indentation was estimated. The force corresponding to the displacement was utilized to determine the hardness of the material. The elastic modulus was estimated from the unloading segment of the load–indentation curve. Hardness reflects the resistance of the geological samples to deformation, while the elastic modulus represents the elastic deformation of the material following force application (Müller et al., 2014). A power law function was fitted to the initial unloading portion to determine its slope. The calculations were performed with the software NanoTest Platform Four V.40.08 (Micro Materials Ltd.). We determined Young's modulus, E, as the

maximum slope in the corresponding stress–strain curve. Nonlinear behavior at low strains is the result of varying alignment inaccuracy.

### Microbial Characterization of Cave Speleothems

Bacterial cellular activity or Adenosine triphosphate (ATP) activity of the speleothems was analyzed immediately after returning to the laboratory using the BacTiter-Glo Microbial Cell Viability kit (Promega, United States) as described by Barton et al. (2014).

### DNA Extraction from Speleothem Samples

Genomic DNA (gDNA) was extracted in triplicate from all deposits and speleothems. The speleothem sample (1 g) was suspended in sterile phosphate buffer saline and vortexed at high speed followed by sonication in ultrasonic water bath to detach the surface cells. The cell suspension was harvested by microfiltration (0.2 µm pore size filter; Millipore) and the filter washed in PBS. DNA extraction was conducted using the Power Soil DNA kit (MO BIO Laboratories, Carlsbad, CA, United States) following the manufacturer's instructions. The recovered genomic DNA was pooled and the concentration was quantified using a Nanodrop 8000 Spectrophotometer (Thermo Scientific, Wilmington, DE, United States).

### Amplicon Diversity Sequencing

PCR amplification and sequencing of the V3/V4 region of the 16S rRNA gene was performed by Australian Genome Research Facility (Brisbane, QLD, Australia) using Illumina MiSeq (San Diego, CA, United States) with 2 × 300 base pairs paired-end chemistry. Briefly, extracted gDNA from each of the sample was PCR amplified using primers 341F (50 -CCTAYGGGRBGCASCAG-30 ) and 806R (50 -GGACTAC NNGGGTATCTAAT-30 ). Thermocycling was performed in a Bio-Rad C100 (Bio-Rad Laboratories, Richmond, CA, United States) using AmpliTaq Gold 360 mastermix (Life Technologies, Australia).

### Sequence Analysis

Paired-ends reads were assembled by aligning the forward and reverse reads using PEAR (version 0.9.5) (Zhang et al., 2014). Primers were trimmed using Seqtk (version 1.0)1 and the trimmed sequences were processed using Quantitative Insights into Microbial Ecology (QIIME 1.8) (Caporaso et al., 2010) USEARCH (version 7.1.1090) and UPARSE (Edgar et al., 2011) software. Using USEARCH sequences were quality filtered, full length duplicate sequences were removed and sorted by abundance. Singletons or unique reads in the data set were discarded. Sequences were clustered followed by chimera filtering using "rdp\_gold" database as the reference. To obtain the number of reads in each Operational Taxonomic Unit (OTU), reads were mapped back

1https://github.com/lh3/seqtk

to OTUs with a minimum identity of 97%. Using QIIME, taxonomy was assigned using SILVA database (version silva\_119) (Quast et al., 2013). The obtained sequences were submitted to National Centre for Biotechnology Information (NCBI) (accession number SAMN07489237 to SAMN07489242 and SAMN07498254 to SAMN07498259). The results are provided as percentage of sequencing reads for the identified OTUs in each sample.

### Enrichment of Speleothem Surface Associated Bacterial Communities and Characterization of Precipitated Carbonate Biominerals

In order to investigate the microbial dynamics and biogenic carbonate mineralization potential of the microbial communities associated with speleothem samples under in vitro conditions, one gram of speleothem sample collected from each site was inoculated into flasks containing 100 ml autoclaved B4 medium (4 g yeast extract, 2.5 g calcium acetate, 10 g of glucose) (Banks et al., 2010; Rusznyak et al., 2012). For abiogenic controls, one gram of speleothem samples from each site were subjected to autoclaving in order to remove the associated microbes and then inoculated into sterile B4 media flasks as above. Dry autoclaving may have impacted the mineralogy to but as the aim of this experiment was to differentiate biotic vs. abiotic mineralization, we assumed little effect of the mineral change on crystal precipitations. All the flasks were incubated at 50 rpm in an orbital shaker for 10 days at 30◦C in the dark (the very low rpm were to ensure the least loss of surface deposits from the speleothems which may interfere with the precipitates). These cultures were subcultured and grown for another 10 days to monitor the microbial growth (OD600) as well as precipitate formation as per Rusznyak et al. (2012). The conditions for in vitro carbonate precipitation were significantly different from the actual cave environments with low temperature, high humidity, CO2, and minimal nutrients, but the aim of this experiment was to investigate the biomineralization potential of heterotrophic communities associated with speleothems in a shorter time frame. After 10 days, five ml of culture broth was taken from each set, centrifuged at 13,000 × g (4◦C) for 10 min and the pellet was harvested for DNA extraction using the Bacterial DNA Isolation Kit (Qiagen R ) following the manufacturer's instructions. The microbial diversity of the enriched cultures was analyzed as previously described. For the analysis of the carbonate mineralization potential of the enriched cultures, 10 ml of the culture was filtered using Whatman No. 1 filter paper and the precipitated crystals were harvested as per Zamarreno et al. (2009). The crystals were washed with sterile distilled water, dried at room temperature for 48 h and analyzed for morphological and chemical constituents with SEM and XRD as described previously. All experiments were conducted in triplicate as biological replicates. The data were analyzed by Analysis of Variance (ANOVA) and the means were compared with Tukey's test. All analyses were performed using Graph Pad Prism software R version 6.0.

## RESULTS AND DISCUSSION

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### Carbon Nitrogen and Trace Metal Compositions

Carbon/Nitrogen content and major metal concentrations of different cave deposits were analyzed as per Rusznyak et al. (2012) and shown in **Table 2**. The proportion of Ca was very high in all the samples although Si, Mg, Al, S and Fe were also consistently present in varying amounts. Other metals such as K, Na, and P were recorded in small quantities. The calcium content was lower in the Mammoth Cave speleothems than those of the other two caves. This was also seen for the calcium proportion as carbonate with Lake Cave and Moondyne Cave speleothems having a higher content compared to Mammoth Cave speleothems. Interestingly, the Mammoth Cave samples were the only ones with detectable levels of Silica. Mineral dissolution was observed in the speleothems of Mammoth Cave (shown in the SEM images in next section). The high amount of silica in this case may be an indicative of sandy sediments being washed into the cave or be due to the dissolution of the speleothem or speleogenesis. Previous studies have reported the role of microbial activity in speleogenesis (Banks et al., 2010). Notably the presence of Mg, S, Al and Fe were also higher in Mammoth Cave speleothems suggesting different formation mechanisms and siliceous mineralogy in these samples. A number of cave speleothems have been reported to have similar calcium contents due to the calcitic nature of these formations but variations in the distribution of other metals and elements in the different samples of the study may influence the microbiomes of the speleothems as well as their mechanical properties (Melim et al., 2001; Baskar et al., 2008a; Cacchio et al., 2012; Rusznyak et al., 2012; Banerjee and Joshi, 2014).

### Morphological, Mineralogical and Mechanical Properties

Scanning electron microscopy observations of the speleothems showed a variety of crystalline mineral formations together with a variety of microbial morphotypes and mineralized cells/filaments (**Figure 3**). While there were morphological differences between the different speleothems, most of them displayed associations with hyphae or biofilms. Elemental analysis by EDS, mineralogical analysis by XRD and TIMA demonstrated the presence of multiple mineral polymorphs in the speleothem samples (**Figures 4**–**6**). The microstructures of the speleothem sections for indentation can be seen in **Figure 7**. The elastic modulus and hardness was determined from the load indentation graphs and the values are presented in **Table 3**.

The moonmilk sample was characterized by a mixture of thickly laminated columnar crystals which were widely distributed (**Figures 3i,ii**). The contacts between different layers were associated with rhombohedral crystals and a few cavities were also noticed. The size of the crystals generally varied from 20 to 200 µm but some larger crystals were recorded. A few regions appeared to be covered by a filamentous network of hyphae and cells. The elemental and mineralogical analysis revealed that the sample consisted entirely of calcite and was composed of Ca, C,

FIGURE 3 | Scanning electron micrographs of: (i,ii) Lake Cave moonmilk; (iii,iv) Lake Cave stalagmite; (v,vi) Lake Cave stalactite; (vii,viii) Moondyne Cave stalagmite; (ix,x) Mammoth Cave stalagmite and (xi,xii) Mammoth Cave stalactite (Arrows indicate hyphal networks and microborings) at lower and higher magnifications.

and O (**Figures 4i**, **5i**, **6i**). The organic composition appeared to be high as analyzed from a high Carbon peak in the elemental analysis. XRD and TIMA both demonstrated the presence of pure calcite as the mineral phase. Similar structures were also reported in moonmilk deposits of the Ballynamintra karstic Cave (Rooney et al., 2010). The precipitation of calcite moonmilk is generally attributed to inorganic processes but a few studies have suggested the formation of it through both biogenic and abiogenic processes (Onac and Ghergari, 1993; Hill and Forti, 1997).

The stalagmite sample from Lake Cave had an abundance of triangular crystals interconnected by smaller cementing binders (**Figures 3iii,iv**). All the components seemed radially arranged. Jones (2001) reported similar structures to be formed in cave deposits due to destructive processes of etching and boring. The formation of spiky calcite crystals was reported to be influenced by microbial activity and mucus leading to irregular shaped depressions (Folk et al., 1985). A closer view revealed encrustations of some bacterial cell like fossilized imprints. EDS analysis revealed the presence of Ca, C, and O along with small peaks of Si and Na (**Figure 4ii**). The carbonates were identified as calcite by XRD while TIMA also confirmed the presence of aragonite (**Figures 5ii**, **6ii**).

The stalactite sample of Lake Cave was characterized by alteration of thickly laminated columnar and accretionary crystals in the size range of 500 µm to 1mm which were widely distributed (**Figures 3v,vi**). In some areas the crystals seemed to be compactly cemented. A cover of matrix like film was also recorded in a few sections with some bacterial like imprints. The current sample displayed microbial like associations on the surface, but whether they are playing an associative or dissociative role is not certain. The hypothesis that the growth of stalagmites and stalactites is mediated by microbial activity is postulated in many studies but there are several challenges to identify these processes in natural systems due to fossilization (Barton et al., 2007; Melim et al., 2008). Elemental analysis again revealed the presence of high C, Ca, O, and Mg along with small peaks of Cl and Na (**Figure 4iii**). Unusual mineral morphologies coupled with high Mg content have also been related to EPS influenced mineralization as biofilms help initiate layer formation on the stalactites via organomineralization processes (Banks et al., 2010; Adetutu et al., 2012). The mineralogical analysis demonstrated the presence of calcite and aragonite though XRD along with dolomite identified through TIMA (**Figures 5ii**, **6iii**).

The stalagmite sample from the Moondyne Cave displayed a slightly different morphology with sharp triangular prismatic crystals with acicular needles in a radial arrangement (**Figures 3vii,viii**). The size of the crystals varied from 20 µm to 100 µm with the bigger crystals cemented by smaller crystals. In this case, the rhomboids had regular outlines with possible microbial hyphae over them. Pacton et al. (2013) reported the presence of cement filled microborings in stalactites of the

Botovskaya Caves. Mineralogical analysis of the speleothems in Moondyne Cave revealed that they are composed of calcium carbonates in the form of calcite and vaterite along with a feldspar mineral plagioclase (**Figures 4iv**, **5iv**, **6iv**).

The speleothems collected from Mammoth Cave had different morphological features to those from the other caves due to variety of diagenetic processes, climate and associated environments (Demény et al., 2016). The stalactite sample

displayed sharper trigonal prismatic crystals (**Figures 3ix,x**). The empty microborings were clearly seen on the surface of calcite crystals that were open and flaky. Pacton et al. (2013) in their study found that microborings were generally composed of high Fe-Si phases and Mg-calcite precipitates which are corroded by iron oxides. The microborings were seen associated with EPS in their study. In this case also, partial dissolution of the rhombohedral crystals was observed in some sections. The previous elemental analysis of these samples displayed high content of Si and Fe along with Ca, C, and O (**Figure 4v**) paving way to the hypothesis that iron oxides might be responsible for corroding these sections and hinting the role of EPS. XRD and TIMA revealed the crystal phases as a mixture of calcite and aragonite along with quartz and ankerite (**Figures 5v**, **6v**). Ankerite is closely related to dolomite differing in the presence of iron and manganese instead of magnesium. This mineral has also been found in the Jenolan Caves in New South Wales, Australia (Pogson et al., 2014).

Another speleothem from the Mammoth Cave had rosette like crystals surrounded by flaky aggregates (**Figures 3xi,xii**). The contacts between adjacent crystals appeared to be filled by cement like binders with EPS encrusted mineral precipitates. Some areas revealed a breakdown of the substrate which could be related to the etching/boring activity of microbes (Jones, 2001). Some crusts showed corroded features. Similar features were displayed in stalactites of the Botovskaya Cave (Pacton et al., 2013). EDS analysis in this case also showed the presence of high amounts of Si together with Ca, C, O, Mg, and Fe while the mineralogical analysis demonstrated the presence of calcite, aragonite, dolomite, and quartz (**Figures 4vi**, **5vi**, **6vi**). The presence of intermediate forms and higher amounts of Mg in the Mammoth Cave may be an indicator of active mineralization. The mineralogical and micrographical analysis was closely related to the metal composition.

The variety of textures, morphology, and mineralogy of the different speleothems indicates different processes and controls in the formation of these structures. Different mineralogies

Lake Cave Stalactite; (iv) Moondyne Cave stalagmite; (v) Mammoth Cave stalagmite and (vi) Mammoth Cave stalactite.

and polymorphs such as calcite, vaterite, and aragonite have been analyzed by several studies (Sánchez-Román et al., 2007; Pacton et al., 2013; Demény et al., 2016). Self and Hill (2003) reported that the precipitation of calcite causes enrichment of Mg which favors the formation of aragonite and in a few cases leads to the precipitation of hydro magnesite. In another study, Morse (1983) and Sánchez-Román et al. (2007) explained differences in the mineralogies by kinetic analysis. It was reported that aragonite and calcite nucleate and grow at the same time, but in different settings within the cave. A more continuous water supply in pools and some stalactites promote calcite nucleation, whereas seepage and drip water provided a more favorable environment for aragonite, which forms more delicate speleothems. The variations of mineralogies may also be indicative of different stages of speleothem formation and a varying geochemistry. A recent study of cave speleothems by Demény et al. (2016) reported the transformation of amorphous calcium carbonate into calcite in the presence of cave drip waters where aragonite and vaterite were seen to be intermediate forms, whose formation is dependent upon pH and the concentration of Mg.

Nanoindentation was used to study the microscale mechanical properties of these formations (**Figures 7**, **8** and **Table 3**). This tool also helps in the analysis of the different phases of a

material together with the properties of the interfacial regions. Maintenance of sterile conditions is difficult as the methodology depends upon casting of the materials within resins. Therefore we aimed to determine the overall mechanical properties of the different speleothem samples and investigate the relationship to mineralogy rather than the effect of microbial interactions on the mineral surface. Further tests to determine microbial-mineral interactions and their effect on the nanomechanical properties in the original samples can be conducted using Atomic Force Microscopic techniques.

The mineral constituents of the exposed surface of the speleothem samples after polishing were carefully indented without the interference of the resin matrix (Dhami et al., 2016). The typical microstructures of different speleothem samples recorded through an optical microscope fitted with the Nanoindenter are shown in **Figure 7**. The sites for indentation were selected to cover at least 50% of the image area. Any analysis points on the resin or the interphase between the resin and the sample were discarded from the final analysis. The load indentation curves of the samples are presented in **Figure 8**. Significant variations were recorded between the different speleothems which in some cases were related to their mineralogy. It was observed that the elastic modulus of the speleothems varied within a range of 76–126 GPa while the hardness varied from 2.20 to 20.10 GPa. The Lake Cave Moonmilk had a hardness value of 14–20 GPa and modulus in the range of 120–132 GPa while the stalagmite sample had a hardness value of 10–14.4 GPa with an elastic modulus of 77–84 GPa (**Figures 8i,ii** and **Table 3**). In the case of the Lake Cave stalactite, the hardness and modulus values varied from 5.3–7.5 GPa to 72–83 GPa (**Figure 8iii**). The variation in the mechanical properties was smaller for the stalagmite sample compared to the stalactite sample. It was noticed that the Moonmilk specimen had the highest strength properties which may be in relation to its higher calcite content. In both of the other speleothems, other polymorphs of calcium carbonate were also recorded along with calcite which may play a role in determining the properties of these mineralized formations. However, further investigations of different sections

∗∗Values are mean ± SD (n = 10).

of the same speleothem would be necessary to obtain a more accurate picture. The Moondyne Cave stalagmite had a slightly lower hardness property compared to the Lake Cave samples with a hardness values of 8–10.2 GPa and an elastic modulus value of 75–82 GPa (**Figure 8iv** and **Table 3**). In this case, there was also considerable variation across the cross section of the specimen. The previous mineralogical analysis of this sample had revealed the presence of mixed crystals (small and large) composed of different mineral phases such as calcite, vaterite, and plagioclase and these may be responsible for variations observed. The Mammoth Cave speleothems had the lowest mechanical properties (**Figures 8v,vi**). The stalactite demonstrated hardness in the range of 2.15–4.9 GPa and a modulus value of 64–74 GPa compared to the stalactite sample with hardness and modulus values varying from 2.3-3.9 GPa to 62–66 GPa. Both of these deposits had a much higher content of silicate mineral along with Mg–Fe, and there were also signs of dissolution which may have affected their pore fraction (**Figures 3ix,x**, **4v,vi**). These features may have been responsible for the lower mechanical properties compared to the calcite rich mineralized deposits.

It is believed that different crystal orientations are responsible for differences in mechanical properties (Alonso-Zarza and Wright, 2010). Even within the same sample, there appeared considerable variation in the properties which may be related to the age of the variable accretions inside the geological structures but further studies are required to investigate this hypothesis. There have only been a few reports on the nanomechanical properties of multiphase materials (Constantinides and Ulm, 2007; DeJong and Ulm, 2007; Zhu et al., 2007). Zhu et al. (2007) found that for quartz, the modulus values were 100–110 GPa and the hardness values were 12–14 GPa while orthoclase feldspar recorded modulus values of 80–90 GPa and hardness values of 8–10 GPa. They reported that different crystal orientations of isotropic minerals contribute to the differences in their mechanical properties. The calcium atoms in calcite have a sixfold coordination, nine-fold in aragonite and eightfold coordination in vaterite playing a role in their mechanical properties (Wang and Becker, 2009). It has also been found that the position of CO3 2− ions in different polymorphs such as vaterite are uncertain (disorder displacement); where Ca2+ and CO3 2− are not as closely packed as in calcite and aragonite crystals and therefore their mechanical properties are variable (Ren et al., 2013). Müller et al. (2014) found that the elastic modulus of pure calcite prisms is 72.83 ± 11.68 GPa in comparison to vaterite deposits at 39.13 ± 8.04 GPa but as geological formations are mixtures of different minerals and elements, their final properties vary based upon the crystal orientations and growth stages. This study is the first where an attempt to investigate the mechanical properties of mineralized natural formations was made, however, a comparison of nanoindentation with bulk property testing is essential for a full understanding.

Along with the physical, climatic, environmental, geographical, and chemical factors that play a role in speleothem formation, a few studies have found that microbes can also influence the growth (and dissolution) of these structures by microbially mediated mineralization through trapping and

binding of detrital grains on the substrate, and/or by mediating mineral precipitation (Cañveras and Sanchez-Moral, 2001; Jones, 2001; Baskar et al., 2008a; Banks et al., 2010; Pacton et al., 2013). It is difficult to investigate the role of microbes or demonstrate a cause and effect relationship at this stage, but the study of their associations and diversities may shed more light on biogenic and abiogenic processes in naturally mineralized structures.

### Microbial Characterization of Cave Speleothems

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In order to test for the presence of active microbes associated with the speleothems, a luminescence based ATP test was conducted. Relative luminescent units (RLU) varied from 240 in the Lake Cave stalagmite to 2320 RLUs in the Moondyne Cave stalagmites which signified high microbial activity. Similar microbial activity was seen In the case of speleothems of Venezuelan Cave (Barton et al., 2014). While the detection of microbial activity is not necessarily an indicator of microbial mineralization/dissolution processes in the speleothems, it does confirm their presence in these oligotrophic environments.

The diversity of microbial communities from the speleothems was analyzed. The sequences were grouped into OTUs and classified using the GreenGene database. Information regarding OTUs and sequencing reads is available in the Supplementary Section. Large variation in microbial community structure associated with the different cave speleothems was observed at phylum level (Family and genus level in Supplementary Data) (**Figure 9**).

The microbial community structure of the Lake Cave speleothems revealed huge richness and variability in OTUs. The Moonmilk sample from Lake Cave was associated mainly with Actinobacteria (59%), Proteobacteria (24%) along with Firmicutes (2%). In contrast, the stalagmite sample was dominated by Actinobacteria (46%) and Proteobacteria (34.2%). In the case of the stalactite sample from this cave, again there was a prevalence of Proteobacteria (47%) and Actinobacteria (40%). The Lake Cave stalactite had a relative abundance of both α and β Proteobacteria (Sphingomonadaceae and Oxalobacteraceae) at family level (Supplementary Material) while the Lake Cave stalagmite sample had a higher prevalence of Streptomycetaceae as was also seen by Maciejewska et al. (2017). The stalagmite from Moondyne Cave was dominated by Actinobacteria (43%) and Proteobacteria (39%) along with Chloroflexi (5%). In the case of the Mammoth Cave speleothems, noticeable variations

were observed compared to the other speleothems. At phylum level Actinobacteria (46%) dominated the stalagmite sample followed by Proteobacteria (29%) and Acidobacteria (9.2%). In the stalactite, Proteobacteria were more abundant (39%) followed by Actinobacteria (32.3%) with a higher level of Acidobacteria (13%). The Mammoth Cave speleothems had a much higher proportion of Acidobacteria compared to the other speleothems. At family level, an abundance of Solirubrobacterales and Rhizobiales from the Actinobacteria and α Proteobacteria was observed.

The major phyla in all speleothems were Proteobacteria, Actinobacteria, and Acidobacteria. In general, the dominance of Proteobacteria has been related to their chemoorganotrophic/chemolithotrophic nature. Success of Proteobacteria colonization in several cave environments may be attributed, in part, to their ability to degrade a wide range of organic compounds, their ability to fix atmospheric carbon and transform nitrogen (Tomczyk-Zak and Zielenkiewicz, ˙ 2015). An abundance of Proteobacteria (Alpha, Beta, Delta, and Gammaproteobacteria subclass) has also reported in the karstic caves of Lascaux, Tito Bustillo Caves, Altamira Caves, and Herrenberg Cave Germany (Portillo et al., 2008; Bastian et al., 2009; Rusznyak et al., 2012). The dominance of Sphingomonodales and Rhizobiales in speleothems from Lake and Moondyne Caves support high nitrogen fixation abilities in such oligotrophic environments (Larouche et al., 2012). The presence of a large number of heterotrophic, nitrogen fixing species suggests that microorganisms depend on surface derived carbon for growth, while their ability to fix nitrogen plays a critical role in their survival. Actinobacteria have been reported to play an active role in carbonate biomineralization. Their prevalence was highest in the moonmilk sample from Lake Cave that was composed of pure calcite. Although the formation of moonmilk through biogenic or abiogenic process is controversial, and the dominance of biomineralizing Actinobacteria does not indicate whether these communities subsided on these minerals either during or after their formation, there is a close relationship between the mineralogy and the type of microbial structures. The dominance of Streptomyces in high calcium carbonate rich speleothems was also noted which agrees with previous reports of carbonate mineralization potential of such microbes (Maciejewska et al., 2017). In the case of the Altamira Caves, Streptomyces played a major role in capturing CO2 and precipitating calcium carbonates under low humidity/ CO2 conditions (Cuezva et al., 2012). Firmicutes (Bacillus) have been widely found to be involved in calcite precipitation (Zepeda Mendoza et al., 2016) and were quite noticeable in the Lake and Moondyne Cave calcitic speleothems. At genus level, a large number of previously reported carbonate precipitating bacteria associated with cave speleothems of Lake and Moondyne Cave were observed including species of Sporosarcina, Bacillus, Lysinibacillus, Exiguobacterium, Myxococcus, Paenibacillus, Geobacillus, Syneccococcus, Arthrobacter, Nitrospira, Pseudomonas (Rusznyak et al., 2012; Wu et al., 2015; Garcia et al., 2016). The phylum Acidobacteria, which was found to be more dominating in Mammoth Cave speleothems, has previously been reported to

play an active role in sulfur and iron reduction (Baskar et al., 2008a). The Mammoth Cave speleothems had a lower carbonate content, high Fe and S content as well as surface borings which indicate that these microbes may be playing an active role on the surface of such substrates. Wu et al. (2015) emphasized the impact of cave habitat types such as wall deposits, soils, and aquatic sediments on the bacterial community composition. Previous studies have postulated that the growth of stalactites and stalagmites is mediated by microbes (Cunningham et al., 1995; Jones, 2001; Melim et al., 2001; Baskar et al., 2008b). The diversity studies have confirmed the presence of specific classes of bacteria associated with the speleothems in close connection to their mineralogical composition, however, it does not inform us of the role of microbial formative or dissociative during growth and development. Although mineralogy seems to be the driving force behind the microbial community composition in this study, affecting the properties of these structures, further studies of active cave formations using recent OMICS tools should be conducted to gain further knowledge of the influence of microbes in real environments.

### Enrichment of Mineralizing Communities and Characterization of Biominerals

Microbial carbonate precipitation processes in Cave environments have been reported to be primarily driven by heterotrophic communities which alter the local conditions to promote CaCO3 precipitation (Castanier et al., 2000; Banks et al., 2010; Rusznyak et al., 2012; Maciejewska et al., 2017). In order to gain insights into the actual biogenic process associated with mineral precipitation in caves, mimicking cave conditions in the laboratory will be imperative, including maintenance of similar temperature cycles, humidity, dark conditions, minimal nutrients, and subsurface situations. Establishing field-like conditions in the laboratory has always been challenging, so most of the previous studies investigating the role of biogenic carbonate formation have relied upon the use of acetate rich B4 media at ambient temperatures (Zamarreno et al., 2009; Banks et al., 2010; Shirakawa et al., 2011; Rodriguez-Navarro et al., 2012). Although rich nutrient conditions and ambient temperatures in the laboratory are not a true representation of actual cave processes, such experiments do shed light on the carbonate biomineralization potential of native microbial communities.

In order to further confirm the presence and association of speleothem surface microbes, their growth in B4 media was recorded. A significant increase in optical density after 4 days was recorded in all the biogenic sets (varying from 1.2 ± 0.31 – 2.6 ± 0.53) while little change was observed in the abiogenic set (with sterile speleothem sample input). The community structure and mineralization potential of the enriched cultures were then investigated. The relative abundance at Phylum level of the enriched communities under in vitro conditions can be seen in **Figure 10** and the relative abundance at genus level of the dominant microbial communities in all treatments is presented in Supplementary Table 2.

Variation in the composition of the enrichment microbial community were observed but Firmicutes were found to be

the dominant phylum for Lake Cave and Moondyne Cave cultures while Proteobacteria were dominant in Mammoth Cave enrichments. Firmicutes and Proteobacteria has been previously found to be common genera involved in biomineralization (Rusznyak et al., 2012; Wu et al., 2015; Garcia et al., 2016; Andrei et al., 2017). Although Proteobacteria and Actinobacteria were dominant in the natural cave speleothems, under B4 media enrichments, Firmicutes were found to be the most prevalent along with Proteobacteria.

In the case of the Lake Cave enrichments, Firmicutes completely dominated the culture with a relative abundance of 99% while in the Moondyne Cave speleothem enrichments, it reached around 88% from an initial composition of around 1%. In the case of Mammoth Cave speleothems, dominance of Proteobacteria was recorded. The Firmicutes in case of Mammoth Cave were recorded at 17% in the stalagmite enrichments and around 3% in case of stalactite sample. At genus level, the Lake Cave moonmilk and stalactite enrichments comprised mainly of Bacillus while the stalagmite enrichment had a prevalence of Planococcus (Supplementary Table 2). In the case of the Moondyne speleothem sample, Bacillus and Brevibacillus genus dominated. The majority of communities in Mammoth Cave enrichment were found to be gram negative; belonging to Caulobacter and Burkholderiales genera in case of stalagmite sample and Pseudomonas in stalactite enrichments.

Firmicutes belonging to the Bacilli family have previously been reported to be the most dominant phylum in laboratory studies on bacteria isolated from cave environments (Cacchio et al., 2003; Banerjee and Joshi, 2014; Garcia et al., 2016). Cacchio et al. (2003) found that Bacillus sp. represented about 63% of all isolates from a limestone cave and a loamy soil. Recently Garcia et al. (2016) found that Bacillus sp. dominated the genera isolated from saline soils. Strains of Pseudomonas have also been isolated in a few studies from speleothems in caves (Chekroun et al., 2004; Rusznyak et al., 2012; Garcia et al., 2016). In a recent study of Andrei et al. (2017), both Bacillus and Pseudomonas

genera were found to be the predominant strains enriched from a limestone statue. It was also demonstrated that the predominance of Pseudomonas is linked to its common occurrence in soils. In this study, more Pseudomonas was enriched from the silica rich Mammoth Cave speleothems compared to calcitic speleothems suggesting a connection. The increased biomass (in biogenic sets) and observed diversity indicate the potential of acetate rich B4 media in promoting the growth of culturable bacteria

from cave speleothems under laboratory conditions. However, it also highlights the shift in community structure seen in laboratory conditions which proves that nutrient availability and physical conditions play crucial role in selection of microbial metabolism and community structure. As laboratory enriched community structures are completely different from the natural conditions, preferring a few communities over the others; this study highlights the importance of future studies wherein natural conditions should be mimicked more closely. Variations in microbial community structures associated with different speleothems grown in different media have also been reported by other researchers (Rusznyak et al., 2012; Garcia et al., 2016; Maciejewska et al., 2017). Continuous monitoring of community shifts over regular intervals of time under those conditions will provide a better picture of the real field mechanisms.

Micrographic and mineralogical analysis of the crystals precipitated by microbes associated with the speleothems grown in B4 medium showed successful precipitation in all the biogenic speleothems. SEM analysis of the crystals confirmed the formation of crystals of varying sizes in all the biotic sets. **Figure 11** shows crystals formed by bacterial communities associated with different speleothems under laboratory conditions. In general, a mixture of smooth, rhombohedral, and spherical crystals with sizes varying from 20 to 100 µm was observed in the case of enrichments from Lake Cave (**Figures 11i–iv**), rhombohedral crystals in the case of enrichments from Moondyne Cave (**Figures 11v,vi**) and needle shaped crystals of sizes between 10 and 30 µm were recorded for enrichments from Mammoth Cave speleothems (**Figures 11vii,viii**). Some EPS formation was also noticed in a few sections. In all cases, the carbonate crystals were seen associated with bacterial cells indicating that they acted as nucleation sites, leading to their entrapment and formation of calcium carbonate. XRD analysis of the crystals identified various polymorphs of calcium carbonates in different sets (**Figure 12**). Calcite was seen as the predominant form in the Lake and Moondyne Cave enrichments (with Firmicutes) while vaterite was the major phase in the case of Proteobacteria dominated Mammoth Cave enrichments. No precipitates were seen in the control sets indicating that biotic process plays a crucial role in carbonate formation. The variations in carbonate polymorphs seen in the different communities could be a result of differing bacterial metabolic activities affecting dissolved inorganic carbon (DIC), and saturation index which could influence the type and properties of the calcium carbonate polymorphs (Rodriguez-Navarro et al., 2012; Dhami et al., 2013a; Okyay and Rodrigues, 2015). As microbial metabolic activity plays a role in determining the fate of the carbonate polymorph, this may explain the predominance of calcite formation with Firmicutes and vaterite formation with Proteobacteria as seen in the current study (Jimenez-Lopez et al., 2008; Jroundi et al., 2014; Zhu and Dittrich, 2016). A number of previous studies have reported the formation of biogenic carbonate crystals via bacterial strains isolated from cave environments (Cacchio et al., 2003; Rusznyak et al., 2012; Banerjee and Joshi, 2014). This work further supplements the hypothesis that biomineral production is a general phenomenon by different bacterial isolates and similar microbial activities might play an important role in the formation of speleothems in natural environments but still there is a lack of evidence of cause and effect. The associations between bacterial species, metabolic activities and their effects on mineral polymorphs need further studies. Advanced OMICS and radiolabelling techniques to identify active metabolic processes in speleothem formations from different climatic environments can be carried out in future studies. Our previous studies recorded the nanomechanical properties of different polymorphs of calcium carbonate crystals via ureolytic and carbonic anhydrase route wherein they varied in modulus from 36 ± 10 – 64 ± 2.7 GPa and hardness of 2.32 ± 0.15 – 3.92 ± 0.43 GPa; which are quite low compared to calcite rich cave speleothems of current study (Dhami et al., 2016). Further studies on nanomechanical properties of carbonate minerals synthesized by speleothem surface associated bacterial strains needs to be carried out in the future to investigate how these properties change at different time scales under cave simulated environments to provide deeper understanding of the bio-geo-chemical processes in

nature. Though the generation of calcium carbonate biominerals has been successfully achieved in laboratory conditions but in comparison to their natural counterparts, their mechanical performance is much lower. In order to widen the scope of MICP technology for engineering applications, comprehensive analysis and optimization of physical and chemical conditions to copy mechanical properties of natural biomineralized materials in shorter time spams is required. This challenge demands researchers from diverse areas as microbiology, molecular biology, geology, material/civil engineers and chemists to work together and bridge the gap for production of sustainable bio-cement with properties of naturally biomineralized formations.

### CONCLUSION

In the current study, combined mineralogical, elemental, microbial, and nanomechanical characterization of different cave speleothems such as moonmilk, stalagmite, and stalactite has been conducted for the first time. Mineralogical and elemental analysis of cave speleothems varied from calcite, aragonite, dolomite, ankerite to silicates while micrographic analysis recorded differences in size, type, and shape of structures under different conditions. This analysis indicated the influence of geochemistry on mineralogical properties of different formations. Nanomechanical properties of these mineralized formations indicated that mineralogical signatures had profound effect on the mechanical properties. The moonmilk speleothem sample which was composed purely of calcite had higher strength properties compared to other speleothems indicating a connection between the two. On the other side, microbial diversity study of bacterial communities associated with the different cave speleothems specified that variations in assemblages were related to the mineralogy and geochemistry. Under in vitro conditions, microbial cultures associated with speleothem surfaces were enriched in nutrient rich conditions and all the biotic sets successfully precipitated calcium carbonate minerals. The generation of carbonate biominerals by cave speleothem associated bacterial communities in laboratory conditions support the hypothesis that similar processes may occur in nature. Even in lab conditions, noticeable variations in carbonate polymorphs were recorded highlighting the effect of microbial metabolism and species on carbonate mineralogy. Microbial diversity data under enriched conditions revealed significant variations in the assemblages from real field communities which highlighted the importance of physical and environmental conditions in selection of microbial communities under different conditions. Though the community structures and their metabolisms

### REFERENCES

are selected by nutrient conditions and physico-chemical environments, they in return further affect the mineralogical and nanomechanical fate of the biominerals. Comparison of nanomechanical properties of naturally biomineralized formations with laboratory synthesized carbonate biominerals formed by MICP technology showed significantly higher performance of natural materials. In order to truly mimic the formation of such materials in laboratory conditions for engineering applications and synthesis of sustainable cements, further investigations combining geochemistry, mineralogy and next generation OMICS tools need to be carried out. Whether the performance of laboratory based biominerals formed harnessing different bacterial metabolic routes can be improved through optimization of physical and chemical conditions in shorter time spams; will help in determining the fate of MICP technology.

### AUTHOR CONTRIBUTIONS

ND contributed by providing with concepts and ideas, fieldwork, experiments, and writing. AM contributed with concepts and ideas, fieldwork organization, data interpretation, and writing. EW contributed with microbiological data interpretation and writing.

### FUNDING

The funding for this study was supported by University Western Australia, Australia.

### ACKNOWLEDGMENTS

The authors thank the staff of the Augusta Margaret River Tourism Association (AMRTA) for their technical support in sample collection. Special acknowledgment is owed to Mr. Peter Bell (AMRTA Resource Officer) and Mr. Lindsay Hatcher (Natural Environment and Projects manager) for their assistance in the field and sharing their knowledge of the Margaret River Caves. They acknowledge the use of Curtin University's Microscopy and Microanalysis Facility, whose instrumentation has been partially funded by the University, State and Commonwealth Governments.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2018.00040/full#supplementary-material

Adetutu, E., Thorpe, K., Shahsavari, E., Bourne, S., Cao, X., Fard, R., et al. (2012). Bacterial community survey of sediments at Naracoorte Caves, Australia. Int. J. Speleol. 41, 137–147. doi: 10.5038/1827-806X.41.2.2

communities inhabiting the oldest public stone monument of Cluj-Napoca (Transylvania, Romania). Front. Microbiol. 8:372. doi: 10.3389/fmicb.2017. 00372

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Dhami, Mukherjee and Watkin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Bioenergetic Controls on Microbial Ecophysiology in Marine Sediments

#### James A. Bradley 1 \*, Jan P. Amend1,2 and Douglas E. LaRowe1

1 Department of Earth Sciences, University of Southern California, Los Angeles, CA, United States, 2 Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States

Marine sediments constitute one of the most energy-limited habitats on Earth, in which microorganisms persist over extraordinarily long timescales with very slow metabolisms. This habitat provides an ideal environment in which to study the energetic limits of life. However, the bioenergetic factors that can determine whether microorganisms will grow, lie dormant, or die, as well as the selective environmental pressures that determine energetic trade-offs between growth and maintenance activities, are not well understood. Numerical models will be pivotal in addressing these knowledge gaps. However, models rarely account for the variable physiological states of microorganisms and their demand for energy. Here, we review established modeling constructs for microbial growth rate, yield, maintenance, and physiological state, and then provide a new model that incorporates all of these factors. We discuss this new model in context with its future application to the marine subsurface. Understanding the factors that regulate cell death, physiological state changes, and the provenance of maintenance energy (i.e., endogenous versus exogenous metabolism), is crucial to the design of this model. Further, measurements of growth rate, growth yield, and basal metabolic activity will enable bioenergetic parameters to be better constrained. Last, biomass and biogeochemical rate measurements will enable model simulations to be validated. The insight provided from the development and application of new microbial modeling tools for marine sediments will undoubtedly advance the understanding of the minimum power required to support life, and the ecophysiological strategies that organisms utilize to cope under extreme energy limitation for extended periods of time.

Keywords: bioenergetics, numerical modeling, dormancy, maintenance energy, geobiology, life in extreme environments, low energy, endogenous and exogenous metabolism

### INTRODUCTION

Marine sediments across the globe host a rich microbial biosphere, whose dynamics are important analogs to oligotrophic and extra-terrestrial environments, and whose activity bears a major control on organic carbon (OC) burial and thus global climate. Microbial cells in oligotrophic marine sediments catabolize 104–106 fold more slowly than model organisms in nutrient-rich media (Hoehler and Jørgensen, 2013), yet despite enduring prolonged starvation, they persist over geological timescales (D'Hondt et al., 2015). The vast majority of marine sediments constitute one of the most energy-limited habitats on Earth (Lever et al., 2015) and provide ideal test-cases to study the extreme limits of life over timescales that challenge fundamental notions of what it means to be alive. By studying life at its limit (i.e., low energy, low nutrients), much can be

#### Edited by:

Virginia P. Edgcomb, Woods Hole Oceanographic Institution, United States

#### Reviewed by:

Brent Craig Christner, University of Florida, United States Thomas Maskow, Helmholtz-Zentrum für Umweltforschung (UFZ), Germany

> \*Correspondence: James A. Bradley<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Extreme Microbiology, a section of the journal Frontiers in Microbiology

Received: 26 October 2017 Accepted: 26 January 2018 Published: 13 February 2018

#### Citation:

Bradley JA, Amend JP and LaRowe DE (2018) Bioenergetic Controls on Microbial Ecophysiology in Marine Sediments. Front. Microbiol. 9:180. doi: 10.3389/fmicb.2018.00180 learned about the fundamental ecophysiology of microorganisms. Since most microorganisms in marine sediments appear to be merely surviving rather than growing, factors such as metabolic state and maintenance energy utilization become increasingly important over the long timescales (i.e., thousands to millions of years) associated with this habitat.

 A more comprehensive understanding of the factors that determine physiological state, as well as energy utilized for growth and maintenance, are crucial in addressing these fundamental questions facing deep biosphere research.

Here, we discuss bioenergetics, dormancy and maintenance energy in the subsurface, and consider how quantitative approaches (i.e., modeling) provide opportunities to complement ongoing research. We provide mathematical constructs for simulating microbial growth, yield, maintenance activities, and physiological state changes (i.e., active and dormant), and discuss them in the context of application to the marine subsurface biosphere. We hope to encourage a better integration of theoretical and experimental approaches to subsurface bioenergetics, which we believe is required to advance deep biosphere investigations beyond what can presently be captured by observations alone.

### BIOENERGETICS AS A DRIVER OF MICROBIAL DYNAMICS IN MARINE SEDIMENTS

All organisms require energy to stay alive. That energy is ultimately harvested from the catalysis of redox reactions. Exergonic (energy-yielding) reactions are catalyzed within or nearby living cells at some rate to provide power. This power may ultimately be used to fuel endergonic (energy-requiring) reactions to maintain a cellular steadystate and sometimes (but not always) to grow. The amount of energy available from the catalysis of exergonic redox reactions can be determined by calculating the Gibbs energy of a potential reaction under a given set of geochemical conditions. Gibbs energy calculations demonstrate not only which reactions are thermodynamically favorable and thus constitute conceivable catabolic strategies for microorganisms, but also which environmental variables, including temperature, pressure, pH, salinity, and the concentrations of electron donors and terminal electron acceptors, influence the amount of energy available to microorganisms. In marine sediments, these factors are largely driven by the flux and burial of organic and mineral particles, and living organisms, to the ocean floor. Physical processes such as bioturbation, the diffusion of aqueous species including electron acceptors (e.g., O2, SO2− 4 ) and secondary redox products (e.g., Fe2+, CH4, H2S), the sorption of OC to mineral surfaces, and mineral precipitation, also alter sediment properties. OC is the primary electron donor for microorganisms in marine sediments (Arndt et al., 2013) and O2 and SO2− 4 are the primary electron acceptors for its oxidation (Thullner et al., 2009).

### ENERGY FOR GROWTH AND MAINTENANCE

Growth yields and cellular maintenance requirements are subject to trade-offs based on selective pressures in different environments (Lele and Watve, 2014). Under low-energy conditions, such as in marine sediments, it is thought that microbial activity is limited, more or less, to maintaining cellular integrity through biomolecular repair and replacement (Westerhoff et al., 1983; Tijhuis et al., 1993; del Giorgio and Cole, 1998; Smith and Prairie, 2004; Carlson et al., 2007; Orcutt et al., 2013). Maintenance activities constitute the sum of activities that do not produce growth (e.g., regeneration of enzymes, maintaining membrane integrity, motility, etc.). Accordingly, maintenance activities potentially constitute a much greater fraction of total power utilized by microbial communities in marine sediments compared to other natural settings, or those grown in laboratories.

However, data from (or representative of) marine sediments are lacking, and an accurate determination of the in situ maintenance power utilization of microorganisms in any natural setting is challenging. Empirical approaches are plagued by methodological problems, experimental artifacts, and inconsistencies across studies (Hobson, 1965; Hempfling and Mainzer, 1975; Russell and Baldwin, 1979). Laboratorydetermined values of maintenance powers are also likely a gross over-estimation of power requirement in natural settings due to the favorable (high-energy) conditions under which microorganisms are grown in the laboratory compared to the conditions that microorganisms experience in nature (LaRowe and Amend, 2015a). However, by integrating experimental datasets with numerical modeling, LaRowe and Amend (2015b) derived microbial power use by microorganisms from oligotrophic sediments in the South Pacific Gyre that were several orders of magnitude lower than laboratory-measured maintenance powers. Given the extreme energy-limitation of these sediments, the low rates of OC processing, and the net decline in biomass over the multi-million-year timescales over which cells are buried, it can be assumed that cells present in these sediments are not growing, and thus calculated power utilization represents mostly (if not exclusively) maintenance activities.

Maintenance energy for microorganisms in marine sediments might come from (i) endogenous catabolism, i.e., the utilization of biomass (Herbert, 1958), (ii) exogenous catabolism, i.e., the consumption of additional substrate (Pirt, 1965), or (iii) a combination of the two, which might vary depending on thermodynamic and environmental factors (Wang and Post, 2012).

### ACTIVITY AKIN TO DORMANCY

The idealized conditions under which microorganisms are grown in the laboratory rarely occur in nature. The apparent ubiquity of microorganisms in natural environments that exhibit extraordinarily slow growth, intermittent growth, or even no growth, reflects a general lack of available energy (Morita, 1988). Microorganisms in marine sediments may be considered some of the slowest, most energy-limited living organisms on Earth, generally exhibiting levels of activity that are several orders of magnitude lower than anything measured via cultivation (Jørgensen and Boetius, 2007; D'Hondt et al., 2009, 2015; Røy et al., 2012; Hoehler and Jørgensen, 2013; Jørgensen and Marshall, 2016). The typical vegetative-like state of microorganisms in marine sediments is tantamount to dormancy (Jørgensen and Marshall, 2016), a transient and reversible state of low metabolic activity. Dormancy is thought to enable microorganisms to endure extended periods of unfavorable conditions such as energy-limitation, without the need to divide (Lever et al., 2015). Despite the omnipresence of dormant cells in marine sediments, the exact nature of dormancy and of its bioenergetic controls in relation to the marine sedimentary environment are not well understood. For instance, the rate at which energy is used by dormant cells, or the thermodynamic and environmental parameters that initiate or terminate dormancy, are not known. A quantitative approach toward microbial dormancy incorporating bioenergetics is thus required to truly understand the deep biosphere.

### BIOENERGETICS AND MODELING

Microbial and geochemical models can help determine the fluxes of energy and material between ecosystem components, disentangle processes that are observed experimentally as a net outcome, and predict the sensitivity and response of ecosystems and geochemical environments to perturbations and changing conditions. Further, they are useful to bridge scales, interpolate between observations, and help identify important data and knowledge gaps. Models are also particularly helpful for deep biosphere investigations since the marine subsurface is notoriously difficult to study using traditional sampling strategies, because of its remoteness and relative inaccessibility, the exceedingly slow rates of energy processing, and the vast timescales over which measurements represent.

A review of the diagenetic models commonly used to simulate the degradation of OC, a process that drives biogeochemical reactions in marine sediments, is presented by Arndt et al. (2013). Thermodynamic models have also been used to quantify the power supply to and demand by microorganisms in marine sediments (e.g., LaRowe and Amend, 2015b), and Gibbs energy calculations have been used to infer what types of reactions microorganisms may be catalyzing in the subsurface (e.g., Teske et al., 2014; McKay et al., 2016; Sylvan et al., 2016). However, the majority of models assume that microbial biomass is in a steady state or has negligible influence beyond transient timescales (Thullner et al., 2005). Models need to be complex enough to describe the required properties and processes of the system, but structurally and mechanistically simple enough to be able to constrain and validate parameters and simulations from available data and literature. At present, a suitable microbialbiogeochemical model for the marine subsurface, capturing the ecophysiological factors discussed here, does not exist. In the following sections, we provide formulations for how such factors may be represented. The models presented here are based on differential equations that describe ecological processes in a mathematical sense. For a comprehensive guide to formulating ecological models, we direct the reader to Soetaert and Herman (2009).

### Heterotrophic Growth and Organic Carbon Degradation

The following model (Equations 1 and 2), and some variations of these expressions including logistic growth, a rate limiting term, and mortality, form the basis of many ecosystem models (Soetaert and Herman, 2009; Sierra et al., 2015). Here, heterotrophic growth is dependent on the availability of OC as a substrate, and can be described by:

$$\frac{\delta B}{\delta t} = \left(V\_{\text{max}} \cdot B \cdot \frac{\text{OC}}{K\_{\text{OC}} + \text{OC}}\right) - (\alpha \cdot B) \tag{1}$$

where B denotes the concentration of microbial carbon, t is time, Vmax corresponds to maximum microbial growth rate, OC denotes OC concentration (the non-living organic component), KOC is the half-saturation constant for OC, a kinetic parameter that describes the dependency of microbial growth on OC concentration, and α represents the specific death rate.

The change in OC due to microbial processes can then be represented by:

$$\frac{\delta \text{OC}}{\delta t} = -\left(\frac{1}{Y} \cdot V\_{\text{max}} \cdot B \cdot \frac{\text{OC}}{K\_{\text{OC}} + \text{OC}}\right) + (\alpha \cdot B) \tag{2}$$

where Y represents an observed growth yield, which is the efficiency of converting carbon into microbial products (Sinsabaugh et al., 2013). The contribution of dead biomass to OC is represented by (α·B). Equations (1) and (2) constitute the "Basic" model (**Figure 1A**). This approach lumps maintenance and growth costs into a single parameter (Y), and assumes that all microorganisms are active. Thus, it is not sufficient to describe microbial processes in marine sediments.

### Incorporating Maintenance and Growth Yields

Mechanistically distinguishing between maintenance energy and growth yield is important to accurately quantify bioenergetics in low-energy environments such as marine sediments where non-growing organisms expend a larger proportion of their total power utilization on maintenance.

Two well-known approaches to simulating maintenance energy are provided by Herbert (1958) and Pirt (1965). These approaches differ by the provenance of maintenance energy.

The first of these model types, commonly referred to as the Herbert approach, considers maintenance costs as endogenous

catabolism, i.e., the consumption of biomass (**Figures 1B**, **2A**) (Herbert, 1958; Knapp et al., 1983; Kim and Or, 2016). Thus, the specific maintenance rate is regarded as a negative growth rate:

$$\frac{\delta B}{\delta t} = \left(V\_{\text{max}} \cdot B \cdot \frac{OC}{K\_{OC} + OC}\right) - (a \cdot B) - (a \cdot B) \tag{3}$$

where a indicates maintenance requirement. Correspondingly, the change in OC is:

$$\frac{\delta OC}{\delta t} = -\left(\frac{1}{Y\_G} \cdot V\_{\text{max}} \cdot B \cdot \frac{OC}{K\_{OC} + OC}\right) + (\alpha \cdot B) \tag{4}$$

Here, YG represents the "true growth yield" (Pirt, 1965) reflected by the expenditure of energy solely to generate new biomass (Lipson, 2015). This formulation allows for maintenance activities to continue independently of substrate availability, and thus is potentially useful and appropriate for simulating microbial maintenance under substrate-starved conditions, such as in the vast majority of marine sediments. However, this expression does not allow for microorganisms to cover maintenance requirements from substrate, which may occur when substrate is plentiful (Dawes and Ribbons, 1964), and features a maximum specific growth rate (Vmax) and a "true growth yield" (YG) that are less suitable from an empirical point of view as they cannot be observed or measured directly (Beeftink et al., 1990).

The second model type, commonly referred to as the Pirt approach (Pirt, 1965; Darrah, 1991) considers the additional consumption of substrate for maintenance (**Figures 1C**, **2B**), coupling "Basic" microbial growth (Equation 1) with the consumption of substrate according to:

$$\begin{aligned} \frac{\delta B}{\delta t} &= \left(V\_{\max} \cdot B \cdot \frac{OC}{K\_{OC} + OC}\right) - (\alpha \cdot B) \\ \frac{\delta OC}{\delta t} &= -\left(\frac{1}{Y\_G} \cdot V\_{\max} \cdot B \cdot \frac{OC}{K\_{OC} + OC}\right) - (a \cdot B) + (\alpha \cdot B) \end{aligned} \tag{5}$$

Here, however, the consumption of substrate is numerically possible even in its absence, due to the term that is included for maintenance (a·B). Thus, this expression could be problematic for simulating marine sediments, because continual uptake of OC in low-energy, OC-poor sediments that characterize the deep biosphere may cause a mass imbalance.

In natural settings, the specific maintenance rate, as well the provenance of maintenance energy, can vary under different environmental conditions (Van Bodegom, 2007). A "Variable" model that allows for environmental factors to dictate the supply of maintenance energy from biomass and/or substrate, adapted from Wang and Post (2012), is:

$$\frac{\delta B}{\delta t} = \left(V\_{\text{max}} \cdot B \cdot h\left(OC\right)\right) - \left(m\_q \cdot B \cdot \left(1 - h\left(OC\right)\right)\right) - \left(\alpha \cdot B\right) \tag{6}$$

where mq represents the specific maintenance rate, and h(OC) is a function that varies from 0 to 1, and allows for microorganisms

to cover their maintenance requirements from OC when it is

and varies from 0 to 1. "h(OC)" in the "Variable model" and "f(OC)" in the "Discreet pools model" are functions that vary from 0 to 1.

plentiful [h(OC)→ 1 when OC ≫ KOC], but from biomass when OC becomes scarce (h(0) = 0) (**Figure 2C**).

The change in OC is calculated according to:

$$\frac{\delta OC}{\delta t} = -\left(\frac{1}{Y\_G} \cdot V\_{\max} \cdot B \cdot h \, (OC)\right) - \left(m\_q \cdot B \cdot h \, (OC) \cdot \frac{1}{Y\_G}\right) \tag{7}$$

This model follows the assumptions of (i) net negative growth at limiting concentrations of OC (OC → 0), (ii) no OC consumption when OC is exhausted (OC = 0), and (iii) no biomass degradation (due to endogenous maintenance) and maximum microbial growth when OC is plentiful (OC ≫ KOC).

## Incorporating Active and Dormant Microorganisms

With few exceptions, microbial models do not account for active and dormant biomass, and cells are considered either alive and active, or dead (**Figure 2D**). Of those models that incorporate multiple physiological states, there are two general approaches. First is to directly regard the active fraction of biomass (i.e., the ratio of active biomass to total living biomass) as a state variable (**Figures 1D**, **2E**) (e.g., Panikov, 1995; Blagodatsky and Richter, 1998; Ingwersen et al., 2008):

$$\frac{\delta B}{\delta t} = r \left( V\_{\text{max}} \cdot B \cdot \frac{\text{OC}}{K\_{\text{OC}} + \text{OC}} - \alpha \cdot B \right) \tag{8}$$

where r represents the proportion of actively growing biomass (B) (0≤r≤1), and its value may depend on multiple environmental and biological factors.

The corresponding change in OC is:

$$\frac{\delta OC}{\delta t} = -r\left(\frac{1}{Y} \cdot V\_{\text{max}} \cdot B \cdot \frac{OC}{K\_{OC} + OC}\right) + r\left(\alpha \cdot B\right) \tag{9}$$

Equations (8) and (9) constitute the "Activity-state" model.

Second is to explicitly separate the total live biomass into active (BA) and dormant (BD) pools (**Figures 1E**, **2F**, e.g., Bär et al., 2002; Stolpovsky et al., 2011):

$$\frac{\delta B\_A}{\delta t} = V\_{\text{max}} \cdot B\_A \cdot \frac{OC}{K\_{OC} + OC} - \alpha\_A \cdot B\_A - \xi + \epsilon \tag{10}$$

$$\frac{\delta B\_D}{\delta t} = -\alpha\_D \cdot B\_D - \epsilon + \xi \tag{11}$$

$$\begin{split} \frac{\delta OC}{\delta t} &= -\left(\frac{1}{Y\_G} \cdot V\_{\text{max}} \cdot B\_A \cdot \frac{OC}{K\_{OC} + OC}\right) + (\alpha\_A \cdot B\_A) \\ &+ (\alpha\_D \cdot B\_D) \end{split} \tag{12}$$

where αA and αD denote the specific death rate of active and dormant microorganisms respectively, ǫ denotes the transfer of biomass from dormant to active (BD to BA) (i.e., activation), and ξ denotes the transfer of biomass from active to dormant (BA to BD) (i.e., deactivation). Equations (10–12) constitute the "Discreet pools" model. The transitions between active and dormant microbial pools are typically dependent on environmental or thermodynamic factors. Under favorable conditions there is net activation, and vice-versa.

### Integrating Dormancy and Maintenance

We have developed a new approach that allows for biomass and substrate to supply maintenance energy and that resolves the fraction of active and dormant microorganisms, combining the "Variable" model to represent maintenance (Equations 6 and 7) and the "Discreet pools" model to represent dormancy (Equations 10–12):

$$\frac{\delta B\_{A}}{\delta t} = \left(V\_{\text{max}} \cdot B\_{A} \cdot h\left(OC\right)\right) - \left(m\_{q, \text{BA}} \cdot B\_{A} \cdot \left(1 - h\left(OC\right)\right)\right)$$

$$- \left(\alpha\_{A} \cdot B\_{A}\right) - \xi + \epsilon \tag{13}$$

$$\frac{\delta B\_D}{\delta t} = -\left(m\_{q,BD} \cdot B\_D \cdot \left(1 - h\left(OC\right)\right)\right) - \left(a\mathbf{o} \cdot B\_D\right) - \epsilon + \xi \tag{14}$$

$$\frac{\delta \text{OC}}{\delta t} = -\left(\frac{1}{Y\_G} \cdot V\_{\text{max}} \cdot B\_A \cdot h \text{ (OC)}\right) - \left(m\_{q,BA} \cdot B\_A \cdot h \text{ (OC)} \cdot \frac{1}{Y\_G}\right)$$

$$-\left(m\_{q,BD}\cdot B\_D \cdot h\left(OC\right) \cdot \frac{1}{Y\_G}\right) + \left(\alpha\_A \cdot B\_A\right) + \left(\alpha\_D \cdot B\_D\right) \tag{15}$$

where mqBA and mqBD denote specific maintenance requirements of active and dormant microorganisms respectively (**Figure 1F**). Dormant cells must carry out maintenance (Johnson et al., 2007), and like living cells, are able to utilize biomass and substrate to cover their maintenance demands. Equations (13–15) constitute the "Combined" model. We believe that this model incorporates sufficiently detailed microbial ecophysiology to form the basis of an ecosystem model for the deep biosphere without being overly complex.

### Integration with Experimental Data

We are confident that the "Combined" numerical approach outlined here can be used as a foundation to effectively simulate microbial processes in marine sediments across a range of scales. Plausible values for parameters may be taken from existing datasets and modeling studies (e.g., Stolpovsky et al., 2011; Lomstein et al., 2012; Arndt et al., 2013). We also stress that this approach would be improved by future laboratory and field investigations considering the following measurements:

### Model Developments and Data Integration

The "Combined" model provided in Equations (13–15) can be expanded in the following ways:

## OUTLOOK

The insight provided from the development and application of a new microbial modeling tool for the deep marine subsurface will undoubtedly advance the current understanding of the minimum

energy requirements to support life in marine sediments, and the ecophysiological strategies that organisms utilize to survive low-energy conditions. Such insight might then explain the extraordinary persistence of microbial communities that endure unfavorable conditions over geological timescales.

### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

### REFERENCES

### ACKNOWLEDGMENTS

This research was supported by a Center for Dark Energy Biosphere Investigations (C-DEBI, OCE-0939564) postdoctoral fellowship to JB. JA and DL acknowledge support from C-DEBI and NASA Astrobiology Institute–Life Underground (NAI-LU, NNA13AA92A). This is C-DEBI contribution 415 and NAI-LU contribution 127. Both organizations are based at the University of Southern California. We would like to thank the two reviewers who provided valuable comments on the manuscript.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Bradley, Amend and LaRowe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Low-Light Anoxygenic Photosynthesis and Fe-S-Biogeochemistry in a Microbial Mat

#### Sebastian Haas1,2 \*, Dirk de Beer1 , Judith M. Klatt1,3, Artur Fink1 , Rebecca McCauley Rench4 , Trinity L. Hamilton5 , Volker Meyer1 , Brian Kakuk6 and Jennifer L. Macalady4

1 Max Planck Institute for Marine Microbiology, Bremen, Germany, 2 Department of Oceanography, Dalhousie University, Halifax, NS, Canada, 3 Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, United States, 4 Geosciences Department, Pennsylvania State University, University Park, PA, United States, 5 Department of Plant and Microbial Biology, University of Minnesota, Minneapolis, MN, United States, 6 Bahamas Caves Research Foundation, Marsh Harbour, Bahamas

#### Edited by:

Doug LaRowe, University of Southern California, United States

#### Reviewed by:

John Senko, University of Akron, United States Mustafa Yucel, Middle East Technical University, Turkey

#### \*Correspondence:

Sebastian Haas<EMAIL_ADDRESS><EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

Received: 31 December 2017 Accepted: 13 April 2018 Published: 27 April 2018

#### Citation:

Haas S, de Beer D, Klatt JM, Fink A, Rench RM, Hamilton TL, Meyer V, Kakuk B and Macalady JL (2018) Low-Light Anoxygenic Photosynthesis and Fe-S-Biogeochemistry in a Microbial Mat. Front. Microbiol. 9:858. doi: 10.3389/fmicb.2018.00858 We report extremely low-light-adapted anoxygenic photosynthesis in a thick microbial mat in Magical Blue Hole, Abaco Island, The Bahamas. Sulfur cycling was reduced by iron oxides and organic carbon limitation. The mat grows below the halocline/oxycline at 30 m depth on the walls of the flooded sinkhole. In situ irradiance at the mat surface on a sunny December day was between 0.021 and 0.084 µmol photons m−2 s −1 , and UV light (<400 nm) was the most abundant part of the spectrum followed by green wavelengths (475–530 nm). We measured a light-dependent carbon uptake rate of 14.5 nmol C cm−2 d −1 . A 16S rRNA clone library of the green surface mat layer was dominated (74%) by a cluster (>97% sequence identity) of clones affiliated with Prosthecochloris, a genus within the green sulfur bacteria (GSB), which are obligate anoxygenic phototrophs. Typical photopigments of brown-colored GSB, bacteriochlorophyll e and (β-)isorenieratene, were abundant in mat samples and their absorption properties are well-adapted to harvest light in the available green and possibly even UV-A spectra. Sulfide from the water column (3–6 µmol L−1 ) was the main source of sulfide to the mat as sulfate reduction rates in the mats were very low (undetectable-99.2 nmol cm−3 d −1 ). The anoxic water column was oligotrophic and low in dissolved organic carbon (175–228 µmol L−1 ). High concentrations of pyrite (FeS2; 1–47 µmol cm−3 ) together with low microbial process rates (sulfate reduction, CO2 fixation) indicate that the mats function as net sulfide sinks mainly by abiotic processes. We suggest that abundant Fe(III) (4.3–22.2 µmol cm−3 ) is the major source of oxidizing power in the mat, and that abiotic Fe-S-reactions play the main role in pyrite formation. Limitation of sulfate reduction by low organic carbon availability along with the presence of abundant sulfide-scavenging iron oxides considerably slowed down sulfur cycling in these mats.

Keywords: anoxygenic photosynthesis, green sulfur bacteria, low-light photosynthesis, sulfide scavenging, microbial mat, bacteriochlorophyll e, iron-sulfur-cycling, Proterozoic ocean

**Abbreviations:** AVS, acid volatile sulfide; BChl, bacteriochlorophyll; CRS, chromium reducible sulfur; DIC, dissolved inorganic carbon; DOMS, diver operated microsensor system; GSB, green sulfur bacteria; MBH, magical blue hole; ORP, oxidation-reduction potential; PMT, photomultiplier; RT, retention time.

## INTRODUCTION

fmicb-09-00858 April 25, 2018 Time: 15:28 # 2

A significant part of the Earth's biosphere is exposed to regimes of extreme energy limitation, such as the terrestrial deep biosphere, the deep sea, and deep zones of most marine sediments. Energy limitation may have been even more significant on early Earth in the absence of oxygenic photosynthesis and the associated production of electron acceptors such as molecular oxygen and oxidized forms of nitrogen, iron, and sulfur. Although studies of low-energy communities are technically challenging and therefore relatively rare, they demonstrate how biotic and abiotic processes compete and interact, and may approximate conditions on early Earth and other planetary bodies.

The lower limits of biotic utilization of light are reached by anoxygenic photosynthesis, especially by members of the monophyletic clade of the GSB (Chlorobiaceae), which are obligate anaerobes and obligate anoxygenic phototrophs. They can proliferate at the interface of the sulfidic and photic zone in meromictic or eutrophic lakes (Montesinos et al., 1983; Mori et al., 2013; Crowe et al., 2014) or in microbial mats (Engel et al., 2004; Lau et al., 2009). The extremely low-lightadapted strain Prosthecochloris phaeobacteroides BS1 (formerly Chlorobium phaeobacteroides BS1; Manske et al., 2005; Imhoff and Thiel, 2010; Marschall et al., 2010) forms monospecific assemblages in the Black Sea chemocline and has been shown to fix inorganic carbon by the phototrophic oxidation of sulfide at light intensities as low as 0.015 µmol photons m−2 s −1 , which is five orders of magnitude lower than daylight (Manske et al., 2005). With estimated in situ carbon fixation rates as low as 200 to 1800 ng C m−2 d −1 , however, they have virtually no quantitative effect on the carbon and sulfur cycles in their habitat (Manske et al., 2005). Low-light-adapted GSB are referred to as brown-colored GSB, a non-monophyletic group of low-light specialists (Montesinos et al., 1983) that possess the BChl a and e as well as the carotenes isorenieratene and β-isorenieratene (Glaeser et al., 2002). BChl e and these carotenes are responsible for light-harvesting and energy transfer to BChl a in model GSB strains (Overmann et al., 1992; Hauska et al., 2001). GSB with this combination of pigments have an in vivo absorption maximum at 505 nm (Overmann et al., 1992). Since the physical light attenuation of water is lowest between 400 and 500 nm (Sogandares and Fry, 1997), they are able to thrive at great water depths.

Anoxygenic phototrophs can use a variety of electron donors, including nitrite (Griffin et al., 2007), ferrous iron (Widdel et al., 1993; in GSB: Heising et al., 1999), molecular hydrogen or reduced sulfur compounds (Pfennig, 1975; Overmann and Pfennig, 1989). Hydrogen sulfide is the electron donor most commonly used by GSB. It is converted according to the following stoichiometry:

$$2\text{ HS}^- + \text{CO}\_2 + 2\text{ H}^+ \rightarrow \ 2\text{ S}^0 + \text{CH}\_2\text{O} + \text{H}\_2\text{O} \quad \text{(1)}$$

Hydrogen sulfide is produced biologically by sulfate reduction, a process most prominent in marine sediments, but also frequently observed in biofilms and microbial mats (Canfield and Des Marais, 1991; Kühl and Jørgensen, 1992; Visscher et al., 1992). Wieland et al. (2005) demonstrated how photosynthates in a microbial mat induced significantly increased sulfate reduction rates (SRRs) during the day. CO2 produced by sulfate reduction in turn enhanced photosynthesis. The same study also showed how large amounts of Fe(III) can affect sulfur cycling by precipitating sulfide. In the present study, we describe how a similar effect can slow down sulfur cycling in a mat that is significantly more limited in light and organic carbon.

Green sulfur bacteria may produce sulfate from hydrogen sulfide or more oxidized sulfur compounds (Overmann and Pfennig, 1989), but can also produce elemental sulfur (S0 ), which is deposited extracellularly (Pfennig, 1975). The production of sulfur compounds of intermediate oxidation states rather than sulfate is common also to non-phototrophic sulfide oxidation processes. In fact, the presence of incompletely oxidized sulfur in the form of CRS can be used as an indication for sulfide oxidation (e.g., Thode-Andersen and Jørgensen, 1989; Holmkvist et al., 2011; Lichtschlag et al., 2013), because sulfate reduction typically does not produce sulfur compounds of intermediate oxidation states. In the absence of light, biological sulfide oxidation typically requires either oxygen, nitrate or nitrite as electron acceptor.

Blue holes are sinkholes: vertical, water-filled karst features open to the surface (Mylroie et al., 1995). Inland blue holes of the Bahamas are anchialine caves (Iliffe, 2000), landlocked bodies of water with subterranean connections to the ocean containing meromictic water columns with an upper freshwater lens separated from a lower saltwater column by a stable halocline (Seymour et al., 2007; Gonzalez et al., 2011). Each blue hole displays distinct geochemical traits, making them unique natural laboratories that allow exploring the limits of photosynthesis and the interplay between biotic and abiotic sulfur cycling. Inland blue holes are therefore of particular interest for geomicrobiology, because the unique geochemical features of each blue hole (e.g., depth of halocline, concentration of sulfide, water flow rates, organic matter input) are associated with the formation of specific types of microbial communities (Gonzalez et al., 2011). Few studies have been conducted on the microbiology of inland blue holes to date (Bottrell et al., 1991; Schwabe and Herbert, 2004; Macalady et al., 2010; Gonzalez et al., 2011).

A thick, orange-colored microbial mat with a thin green top layer was discovered in the suboxic, sulfidic water below the halo-chemocline of MBH, The Bahamas (Gonzalez et al., 2011). The green color of surface cells and an abundance of GSB-affiliated 16S rRNA genes in clone libraries suggested the mats were phototrophic (Gonzalez et al., 2011). δ-Proteobacteria were also highly abundant in the clone libraries, suggesting that sulfate reduction might be an important process in the mats (Gonzalez et al., 2011). We aimed to test the hypothesis that the mats are phototrophic and dominated by GSB that are optimally adapted to the quality and low intensity of the ambient light. Secondly, we aimed to test whether the sulfide needed for the GSB activity is supplied by sulfate reduction in the mats. We used a highly sensitive light detection system to quantify ambient irradiance and determine the light quality. The ambient spectra were compared with the absorption spectra of photopigments from the mats. Activities were determined by light dependency of carbon uptake and sulfate reduction rate measurements. To constrain the Fe-S-cycling in the mats, solid phase fractions of the iron and sulfur pools were quantified.

### MATERIALS AND METHODS

### Study Site and Mat

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Magical Blue Hole (also known as Cherokee Road Extension Blue Hole, 26◦ 220 31.9600N, 77◦ 6 0 14.9100W) is an anchialine sinkhole on Abaco Island (The Bahamas). It is approximately 80 m deep and has a surface diameter of approximately 10 m. A stable halo- and chemocline is present at 25 m depth, separating oxic freshwater from anoxic, sulfidic seawater (Gonzalez et al., 2011). Below the surface, the diameter widens and the cave is roughly 40 m wide at 30 m depth. The narrow entry and the shading effect of the overhanging cave walls result in low light intensities below the halo-chemocline. The cave walls below the halo-chemocline are extensively covered by a thick microbial mat. It grows thickest (up to ∼6 cm) on a ledge in the otherwise vertical cave wall situated at approximately 30 m depth. The mat is stratified with a thin green layer of fluffy biological material on top (∼1 mm), a gelatinous orange middle layer (up to ∼3 cm) and a brown bottom-layer (up to ∼3 cm; **Figure 1**).

### Sampling Techniques

Geochemical parameters of the water column were measured in vertical profiles. Water samples were collected in lockable plastic syringes by a diver and sterile-filtered (0.2 µm) immediately after sampling. Samples for dissolved inorganic and organic carbon analysis were stored at 4◦C while samples for ammonium (NH4 +) and nitrate (NO3 −) quantification (n = 5 for 30 m and 40 m; n = 2 for remaining depths) were stored frozen. Total sulfide (H2S + HS− + S 2−) and sulfate (SO4 2−; n = 3 for all depths, respectively) samples were preserved by mixing 1:1 (v/v) with 20% Zn-acetate solution.

Samples for incubation with 13C-labeled substrates and subsamples for photosynthetic pigment analysis (n = 3) were taken by vacuuming the upper green part of the mat into lockable syringes. For clone library construction, a large piece of mat was cut out with a knife and immediately placed into a light-shielded and airtight plastic box by a diver for transport to the laboratory. Clone library samples were separated into the three natural layers based on location and color, preserved with RNAlater and stored at 4◦C until analysis.

Microbial mat samples from around 30 m water depth were taken with push-cores, which were immediately closed by a diver with airtight plugs. Push-cores used to sample for pigment analysis (n = 3; core lengths: 1.2–5.7 cm) and 35S incubation experiments (n = 10; lengths: 3.6–5.4 cm) were wrapped in black tape to shield the samples from light. Cores for solid phase analyses were immediately cut into 0.6 cm slices (AVS/ CRS/ sulfate; lengths: 3.6–5.4 cm) and 0.25 cm slices (dithionite reactive iron/ S0 ; lengths: 3.5–4.5 cm) and fixed in 4 mL 5% Zn-acetate and 3 mL 20% Zn-acetate, respectively. Along with samples for pigment analysis, they were stored frozen. Samples for porosity (n = 4) and density (n = 1) measurements were also taken with push-cores.

### Solid Phase Analyses

Mat porosity was determined as weight reduction after drying at 60◦C in four mat cores (result: 0.92 ± 0.02). Density was determined as wet weight per mat volume. The density average of 0.6 cm/ 1 mL slices (n = 8) from one core sample (1.11 ± 0.12 g cm−3 ) was used for conversions of mat weight into volume.

Acid volatile sulfide (AVS = FeS + Fe3S4 + H2S: Cornwell and Morse, 1987) and CRS (CRS = S0 + FeS2: Cornwell and Morse, 1987) were extracted from six mat core samples in a two-step HCl and Cr(II) distillation according to Fossing and Jørgensen (1989). After distillation of the pellets, AVS and CRS trapped as ZnS were determined according to Cline (1969). Porewater sulfate content was quantified in the supernatant after a centrifugation step, which preceded these distillations. The detection limit was at 12.9 nmol S cm−3 . From five parallel cores, S0 was extracted by methanol and measured as cyclo-S8 by UPLC (Waters, United States) as described previously (Zopfi et al., 2004). Pyrite-S was defined as the difference between CRS and S0 .

A citrate-acetate-dithionite solution was used to extract iron from three mat samples. The procedure extracts most crystalline and amorphous Fe(III) as well as FeS (Kostka and Luther, 1994). Extraction was preceded by a centrifugation step after which the supernatant was discarded. Since Zn-acetate was used to preserve the samples, iron concentrations may be underestimated due to possible formation of non-extractable Fe-acetate. Dithionite extracts were analyzed with the ferrozine method according to Viollier et al. (2000).

### Geochemical Water Analyses

Standard procedures were used to analyze chemical compounds in samples from the water column and in microbial mat extracts. Total sulfide was measured according to Cline (1969). Sulfate ion concentration was analyzed with ion chromatography (761 Compact Ion Chromatographer including 812 valve unit and 838 Advanced Sample Processor, Metrohm, Germany). Phosphate, nitrate and ammonium were quantified with a continuous flow analyzer (San++, Skalar, Netherlands) using standard detection methods (Hansen and Koroleff, 2009). DIC was calculated as the difference between total carbon and non-purgeable organic carbon measured using a Total Carbon Analyzer (TOC-5000A, Shimadzu, Japan) after acidification and N2-purging.

### Photosynthetic Pigment Analysis

Frozen mat cores were horizontally sliced with a resolution of 3 mm. Pigments from both core slices and vacuumed surface layer samples were extracted with acetone as described previously (Al-Najjar et al., 2012). The filtered extract containing the pigments was then injected into a reversed-phase Highperformance liquid chromatograph (HPLC; 2695 Separations Module, Waters, United States). The pigments were separated according to Wright (1991) and absorption spectra were measured using a 996 Photodiode Array Detector. Identification

of pigments was based on RT and absorption spectra. A βcarotene standard (beta-122; 0.813 mg L−1 in 100% acetone, Sigma-Aldrich, United States) and freeze-dried pure culture material of a low-light-adapted Prosthecochloris phaeobacteroides BS1strain isolated from the Black Sea chemocline by Overmann et al. (1992) (DSMZ Braunschweig) were analyzed in parallel as standards.

### Isotope Incubations for Process Rate Determination

Rates of sulfate reduction, photoautotrophic carbon uptake and photoheterotrophic carbon uptake were assessed by incubations with isotopic labels. To assess SRRs inside the mat, the wholecore injection method based on radioactively labeled sulfate was carried out (Jørgensen, 1978). Under N2-atmosphere, 4 µl per cm3 sample of radioactively labeled 35SO4 2− in Na2SO4 carrier solution (∼33 kBq cm−3 ) were vertically injected into push-cores (20 mm diameter) containing up to 20 mL freshly sampled microbial mat. Incubation under anoxic conditions and approximate in situ temperature was stopped after four (n = 6) and six (n = 4) hours by horizontally slicing the cores into 6 mm pieces and immediately mixing them 1:1 (v/v) with 20% Znacetate to stop sulfate reduction and fix hydrogen sulfide as ZnS. After cold Cr(II) distillation (Kallmeyer et al., 2004; as modified by Røy et al., 2014), SRRs were determined (Jørgensen, 1978). A detection limit of 43.73 radioactive decays per minute (95% confidence interval) was calculated based on means and standard deviations of sample blanks prepared (Kallmeyer et al., 2004).