MBL Microbial Diverstity Course 2012

A mat gone mad – Metagenomics of a microbial mat

Thiele, S. Dept. of Molecular Ecology, Max-Planck-Institute for Marine Microbiology, Bremen, Germany Contact: [email protected]

Abstract

Microbial mats are widely occurring ecosystems of distinct bacterial populations forming different layers in a stacked manner. Within and among these layers, a tight coupling of different metabolic pathways, namely photosynthesis, sulfur metabolism, methane metabolism and nitrogen metabolism have been found. These pathways are tightly coupled over the diurnal cycle, leading to changes in chemical gradients during a days turn. By using chemical analyzes, like microsensor measurements and anion ion-exchange chromatography combined with molecular tools like 454 pyrosequencing and metagenomics, the metabolic pathways within an intertidal microbial mat from the Great Sippewisset Salt Marsh was investigated. Oxygenic photosynthesis was confirmed within the cyanobacterial layer of the mat, while hints for anoxygenic photosynthesis were found throughout the mat. Sulfate reduction as well as sulfite oxidation was predicted throughout the mat, while methane oxidation was only indicated for the uppermost layer (???). In contrast, indications for methanogenesis were not found. Nitrogen fixation, ammonium assimilation, denitrification and DNRA were indicated throughout the microbial mat, while amoA, the marker gene for nitrification was only found in the two uppermost layers. In addition to the prediction of pathways, several marker genes could be assigned to taxonomic information and then compared to data retrieved from 454 pyrosequencing, linking functional features with identity. Thus some light was shed on the microbial communities of microbial mats from the Great Sippewisset Salt Marsh in context of their function in the ecosystem.These findings showed that metagenomics in combination with chemical measurements can be used to answer some of the major questions in microbial ecology.

Introduction

Laminated microbial ecosystems, widely referred to as microbial mats, are found in various environments, like hot springs (e. g. Ward et al., 1997) or hypersaline ponds (e. g. Sørensen et al., 2005), but also in intertidal sediments, like those found in Great Sippewisset Salt Marsh (Nicholson et al., 1987). Due to different chemical environments, distinct layers are formed by microbial groups with different ecological functions. These layers are tightly linked in their metabolic processes (Fig 1). Due to these processes, different ecological niches are created and colonized by different organisms. Several chemical cycles have been described for microbial mats. Photosynthesis is the main driver of primary production in the upper layers of the mat. Here oxygen is produced by mainly Cyanobacteria, creating an oxic environment. In deeper anoxic layers anoxygenic photosynthesis, by e.g. Green Sulfur Bacteria occurs (Nicholson et al., 1987). Tightly coupled with the oxygen production is the reduction of hydrogen sulfide to sulfate. In the oxygen depleted zone of the mat hydrogen sulfite is produced by sulfate reducing bacteria and gases out into higher layers. Reaching the oxic zone, H2S is oxidized again. This chemical gradient of oxygen and hydrogen sulfite is influenced by the diurnal cycle. During the night, when photosynthesis is intermitted, oxygen is depleted and hydrogen sulfite concentrations increases. During the day the process is reversed by aerobic sulfite oxidation and H2S concentrations decreases (Jørgensen et al., 1979). In addition to the sulfur cycle, also the nitrogen cycle of microbial mats is dependent on the separation of oxic and anoxic layer. In the upper layer nitrogen is fixed, most likely by Cyanobacteria, and the resulting ammonia is either assimilated or nitrified to nitrate. In the anoxic layer, the anaerobe part of the nitrogen cycle takes part. In contrast to anaerobic fixation of N2 (Bonin and Michotey, 2006), Denitrification and Anammox lead to the formation of N2, while dissimilatory nitrate reduction to ammonium (DNRA) and ammonification can produce ammonium (Bonin and Michotey, 2006). Besides sulfur and nitrogen metabolism, the finding of methane production and oxidation in microbial mats added another metabolic pathway (Buckley et al., 2008). Potential methane production rates were found highest at the chemocline between 5 and 10 mm depth in mats from Great Sippewisset Salt Marsh near Cape Cod, Massachusetts, USA. Nevertheless methane production was also found in anoxic and the oxic cyanobaterial layer (Buckley et al., 2008). In contrast to high production, low amounts of methane gas were found at the mat surface, indicating aerobic methane oxidation in the top layer and anaerobic methane oxidation in the deeper layers (Buckley et al., 2008).

Fig. 1: Photosynthesis, sulfur and nitrogen cycling in a microbial mat during the diurnal cycle. L. Villanueva, 2011, unpublished.

Besides investigation of the methane cycling, the microbial mats of Great Sippewisset Salt Marsh have been subject to research for a long time. It usually consists of a Cyanobacteria dominated green/ brown layer, a pink layer dominated by purple sulfur bacteria, a black layer inhabiting green sulfur bacteria and a gray layer with methylotrophic methanogenes (Nicholson et al., 1987; Imhoff and Pfennig, 2001; Zaar et al., 2003; Buckley et al., 2008). However, most studies were focused on phototrophic organisms and few molecular tools were applied to the system. Here I describe the investigation of photosynthesis and sulfur, nitrogen and methane metabolisms within a microbial mat sampled in 2010. I used metagenomics to approach the metabolic potential of the 5 different layers of the microbial mat. This method, based on culture independent sequencing of environmental DNA, is used to investigate three major questions of microbial ecology. Who is there? What are they doing? How is it compared to other environments? In other words, the taxonomic content of a sample can be analyzed, the functional content of the sample can be explored and results can be compared to other environments (Reviewed e.g. in Wooley et al., 2010). Therefore metagenomics is a powerful tool to test hypothesis based on chemical measurements and explore the metabolic potentials of microbial communities. In this study hypothesis about metabolic pathways were developed based on microsensor measurements and nutrient analyzes. These hypotheses were then tested using the metagenomics approach combined with 454 tag pyrosequencing to determine the taxonomic identity of the key organisms in the different metabolic pathways throughout the 5 layers of the microbial mat.

Material & Methods

Sampling

A microbial mat was collected on the 3rd of July 2012 during day time at low tide in Great Sippewisset Salt Marsh (41°58.599 N/ 70°64.083 W). A 30 mm thick microbial mat was taken from an air exposed area and brought back to the laboratory. The mat displayed 5 distinct layers. A brown layer covering the top of the mat, a green layer from 0-4 mm, a purple layer from 4-6 mm, black layer from 6-12 mm and a thick sand layer on the bottom. The mat was covered with Sea Water Base (SWB) with a pH of ~4.6 for approx. 2 hours after sampling and stored until and during microsensor measurements. An additional mat of ~25 mm thickness was sampled from the same spot on the 17th of July during mid day and stored in ambient water taken from a stream next to the mat. This consisted from 5 layers, showing an aging process of the mats. A very small brown layer was found on the top, followed by a green layer (0-2 mm), a pink layer (2-4 mm), an orange layer (4-6 mm), a black layer (6-9 mm) and a sandy layer (9-25 mm).

Microsensor measurements

In order to characterize the microbial mats and build hypothesis about the metabolic pathways taking place, I used oxygen, pH and hydrogen sulfite sensors from Unisense (Arrhus, Denmark) to measure depth profiles of both mats during light and dark phases. Light phases were simulated with a 75 W bulb emitting 1300 mE m-2 s-1, while dark phases were measured in darkness. Between the light and dark phase, the mat was allowed to acclimatize for min. 30 minutes in the dark. Depth profiles of the first sampling were obtained in steps of 60 µm starting at the water surface and ending at 2.9 cm depth of the mat. For the second mat similar profiles were done to a depth of 2.5 cm. All sensors were sensitive in a range of 50 µm.

Nutrient measurements

After both microsensor applications pore water was extracted from all 4 or 5 layers of the mat. I used the microsensor stand to navigate syringes with 26.1 G needles to the specific layers in order to extract 1-1.5 ml of pore water. The water was rinsed according to the protocol for Anion Ion-Exchange Chromatography (IC) and 1:10 dilutions (in water) were analyzed using an ICS2100 (Thermo Fischer, Sunnyvale, USA). Ammonium concentrations of the old mat were measured using a colorimetric assay after Solorzano (Solorzano et al., 1969).

Sampling for 454 tag pyrosequencing

After microsensor measurements the mat from the 3rd of July was skimmed and sub samples of all layers were used for DNA extraction. I used the MoBio PowerSoil DNA Isolation Kit (MoBio, Carlsbad, CA, USA) for extraction. Amplification of the V6 region of the 16S rRNA was done with universal primers tagged with marker sequences. I used sequences C1-C4. The PCR fot the 16S rRNA V9 region was done using 15 µl of Phusion Master Mix, 2.4 µl DMSO, 0.6 µl Reverse and 0.3 µl Forward Primer (provided by course staff) and 2 µl of template DNA. The PCR was run using a touch down program. Denaturing was done for 5 s at 98°C and elongation for 7 s at 72°C. Annealing was done for 10 s with a touch down step in 10 cycles starting at 68°C and decreasing for 1°C every cycle. The following 12 cycles were done at 58°C. The final 10 cycles excluded the annealing step and elongation step was prolonged to 21 s. The samples were sequenced at Pennsylvania State University. After retrieval the data I used Qiime (Caporaso et al., 2010) for quality control and tag removal. Due to the high quality of sequences only sequences between 400 and 450 bp were used. Assignment was done against the V9 region of the SILVA ref 108 database (Pruesse et al., 2007). The data were analyzed on the family level.

Metagenomeic analyses

Metagenomeic analyses were done on a metagenome sampled in the same area in Great Sippewisset Salt Marsh by Chuck Pepe-Ranney and Daniel Buckley on the 23rd of June 2010 and sequenced during the course 2010. This mat showed 5 distinct layers, namely a brown top layer, a green layer, a pink layer and two sandy layer called Sand1 and Sand2. The DNA was extracted and amplified during the course in 2010 and data were provided by Daniel Buckley and Chuck Pepe-Ranney. The quality of the sequences was controlled by Lebusha Kelly using Qiime (Caporaso et al., 2010). For analyses I used MG-RAST (Meyer et al., 2008) utilizing the SEED database (Overbeek et al., 2005) for annotation, the silva SSU database (Pruesse et al., 2007) for taxonomic comparison, and KEGG pathways (Kanehisa et al., 2012) for the comparison of pathways. Evaluation of methane, nitrogen, and sulfur metabolisms were done using KEGG maps, while photosynthesis was evaluated by direct search of all genes of the pathway in the MG RAST metagenomes. All annotations were done using the non-redundant multi source protein annotation database with an e value cut off of 1E-5, 60% sequence identity and 15 bp alignment lengths. Pivotal genes of all pathways which were not found in the MG RAST annotation were blasted against Pfam databases (Punta et al., 2011) using blastx (http://blast.ncbi.nlm.nih.gov/). The results from the blastx analyses were sorted according to the following criteria. First all sequences with e-values higher than 1x10-3 were excluded. The resulting sequences were checked for sequences length using a threshold of 50 base pairs. Exceptions were made for small genes, namely psbT, psaF, psaI, psaJ, psaM, psaX, and psa28, were 25 base pairs were chosen and psbX were 40 base pairs were chosen. The final quality check was done using a threshold of 60% for sequence identity. Exceptions were made for psbT, psbX, and adhN, were 50% identity was used as threshold. The resulting sequences per layer were counted. Databases for genes not found in Pfam (nirK, nosZ, nrfA, dsrAB) were retrieved from Fungenes (Schulz et al., 2009) and analysed in the same way. Marker gene analyses were done using the same blastx protocol as described above. All sequences were normalized against recA counts per layer. I used nifH for nitrogen fixation, amoA for nitrification, nirS and nirK for denitrification, nrfA for dissimilatory nitrate reduction to ammonia (DNRA), glnA for ammonia assimilation, dsrAB for sulfur oxidation/ sulfate reduction, mcrA for methanogenesis, pmoA for methane oxidation, psbA for oxygenic photosynthesis, and pufM for anoxygenic photosynthesis. Taxonomic correlation of the marker genes (beside nirK and nosZ) was done using blastx against the BLAST non-redundant database (http://blast.ncbi.nlm.nih.gov/) and blast2lca (https://github.com/emepyc/Blast2lca) against the NCBI taxonomy. A threshold of 2 hits minimum was used to the most abundant taxa. These taxa were validated with the 454 database.

Results

Mat characterization and chemical parameters

Two mats were sampled during the course, the first and younger one showing 4 distinct layers, the second and older mat showing an additional orange layer between the pink and the black layer. Due to the storage in SWB the young mat hat an ambient pH ~4.6, while the old mat was stored in ambient water and showed pH values between7-8. Both mats were characterized using microsensors measurements and nutrient analyses. Microsensor profiles of oxygen taken in both mats showed oxygen saturation in the green layer and a steep decrease towards anoxic conditions in the pink layer (Fig. 2) during light, while dark measurements showed a depletion of the mat in oxygen after few micrometers. Measurements of the pH in the older mat followed the trend of oxygen and ranged between 7 and 8.

Fig. 2: Microsensor profiles of oxygen and hydrogen sulfite concentrations in two different mats A) young mat B) old mat. The different layers of the mats are indicated by gray lines. Note different scales for both concentrations and depth.

In addition to microsensor measurements I measured anion concentrations using IC and Ammonium using a colorimetric assay. Nitrite concentrations above the base line level were only found in the green and orange layer of the old mat, resulting in highly fluctuating nitrite concentrations during the day and values as low as -169.5 µM at night (Fig. 3). Nitrate was only measurable with 23.9 µM in the green layer during daylight, resulting in a decrease to 0 µM to deeper depth in the old mat. During night values decreased from 27.1 µM in the green layer to 16.9 µM and 16.5 µM in the orange and black layer, while the sandy layer was again below the baseline of the IC (Fig. 3). In the young mat nitrite could not be determined for the sandy layer. A steep decrease from 193.9 µM to 4.0 µM was found from the green layer to the pink layer during day time, while the concentration was constant at 3.2 ± 0.5 µM (Fig 3). Nitrate decreased from green to pink layer from 16.7 µM to 12.3 µM and further to 10.7 µM in the black layer, to incrase to highest values in the sand (21.7 µM) during day light. Night values decreased with depth from 25.8 µM in the green layer to 14.7 ± 1.5 µM all other layers (Fig. 3). Sulfate concentrations as low as 0.02 ± 0.03 mM and 0.04 ± 0.04 were measured in the old mat during daytime and night respectively. In the young mat the sulfate concentration increased from 4.0 to 10.0 mM from the green to the pink layer and decreased to 0.03 ± 0.005 mM afterwards during the day (Fig. 3). During night the concentration increased from 7.25 ± 0.26 mM in the top two layers to 16.0 and 11.9 mM in the lower layers (Fig. 3).

Fig. 3: Concentrations of nitrite, nitrate, sulfate and ammonium in both the old (left column) and the young (right column) mat. Metagenome data quality

After the quality control steps, the metagenomes uploaded to MG RAST had an average size of 41,364,256 ± 16,670,086 bp resulting from 127,890 ± 46386 reads per metagenome. The average read size was 323 ± 108 bp. From these reads an average of 38.9 ± 1.5% was annotated as protein.

Taxonomic classification

A first taxonomic classification was done using the taxonomic annotation of MG RAST with the implemented Silva database. The diversity was highest in the pink layer and lowest in the Sand1 layer ranging from 73 to 28 OTUs, while 46 ± 2 OTUs were found I all other layers (Fig. 4). Within the Eukaryota the diversity was rather low with Danio rerio, the zebra fish, dominating the sequences (Sup. 1). An even lower diversity was found for Archaea with only 3 OTUs in the Sand2 layer and no OTUs in the upper layer (Fig. 1A-E). Among the Bacteria the diversity was again highest in the pink layer and lowest in the Sand1 layer. The brown layer was dominated by mainly sulfate reducing bacteria and Pseudomonas filiscindens (Sup. 1). In the green layer was dominated from Pseudomonas aeruginosa, Nitrosococcus halophilus, Calothrix desertica and some members of the Bacteroidetes (Sup. 1). In the pink layer the diversity was higher, dominated by Thiococcus pfenningii and Prolixibacter bellariivorans, among other Gammaproteobacteria, Firmicutes and Cyanobacteria (Sup. 1). The lowest diversity was found in the Sand1 layer dominated by Microcoleus chtonoplastes and Rhodopirellula sp. (Sup.1). The Sand2 layer was dominated by Marichromatium sp. and Planctomyces limnophilus besides several Campylobacter and Desulfobacterales (Sup. 1). I did not calculate diversity indices, since it is not the scope of this study to analyze the diversity of the Sippewisset mats in details.

Fig. 4: Overview of general OUT abundances in the 5 layers of the microbial mat.

454 Tag pyrosequencing

In order to investigate the diversity of the young mat, I used 454 Tag pyrosequencing. For the investigation of the sequences I chose a cut off of 2% of the abundance for binning. All taxa below are compiled ass “Other Bacteria” In the green layer a clear dominance of Microcoleus chtonoplastes was found. In minor abundances the MI602j-37 clade, Flavobacteriaceae, Saprospireae, Rhodobacteraceae and Lyngbya were present, while no archaeal sequences were found (Fig. 5). A different community, dominated by Chlorobiaceae and uncultured Bacteroidetes was found in the pink layer. Here also the Wchb1-69 and Phos-he36 clade, Rhodobacteraceae, Microcoleus chtonoplastes pcc 7420, Sulfobacteriaceae and Sulfobulbaceae were present. Archaeal sequences were found in abundances as low as 0.2% (Fig. 5). The black layer was dominated by Desulfobacteriaceae, followed by Spirochaetaceae, Rhodobacteraceae, Anaerolinaceae, Flavobacteriaceae and Uncultured Acidobacteria. Archaeal sequences were found in 2.5% abundance (Fig. 5). A dominance of Desulfobacteriaceae was also found in the sandy layer accompanied by Spirochaetaceae, uncultured Bacteroidetes and the archaeal Deep sea hydrothermal vent g6-clade. Archaea were found as high as 4.8% in this layer (Fig. 5).

Fig. 5: Taxonomic overview over the 4 layers of the young mat. For simplicity all sequences below a cut off of 2% were classified as “Other Bacteria”.

Metabolic pathways

Within the metagenome I investigated several metabolic pathways throughout the different layers of the microbial mat, namely photosynthesis, nitrogen, sulfur and methane metabolism. An overview was gained by normalizing the abundance of marker genes against recA. The oxygenic photosynthesis marker gene psbA, and pufM, the marker gene for anoxygenic photosynthesis, were found in higher abundances in the green layer. In this layer the abundance of dsrAB, marker gene for sulfate reduction, increased (Fig. 6A). Within the nitrogen metabolism, no drastic changes over the depth of the mat were found. Only nifH, marker gene for nitrogen fixation, was higher in the green layer and nirS, marker gene for denitrification, decrease slightly with depth (Fig. 6B). Only low numbers of genes for methane metabolism were found. While mcrA, marker gene for methanogenesis, was not present in any of the layers, the marker gene for methane oxidation, pmoA, was as low as 2% and 4% of the recA genes in the pink and Sand1 layer.

Fig. 6: Marker genes for different pathways normalized against recA.

The gene libraries of the marker genes were then blasted against the metagenomes and resulting sequences were assigned to a taxonomic path. Mostly the taxonomic classification was rather broad such as uncultured Proteobacteria. Nevertheless for all marker genes besides pmoA several genera or even species were found. A validation with the 454 dataset resulted in various species containing marker genes (Tab. 1). In order to examine the pathways in more detail, I used KEGG annotations of MG RAST. Gaps in pathways were tried to fill by blasting the metagenomes against Pfam or Fungenes annotations of the genes, if available. Even though psbA was found in both the brown and pink layer, only the green layer showed satisfying coverage of the genes necessary for photosystem I and II (Fig. 7A; Sup. 2). In both sand layers very low coverage of the photosystem genes were found. Despite the finding of pufM, anoxygenic photosynthesis was not further investigated. Good coverage of pathways for assimilatory, and dissimilatory sulfate reduction, as well as sulfur oxidation were found throughout the whole mat, while genes necessary for the reduction of sulfite to elemental sulfur were not found (Fig. 7B; Sup. 2). Within the nitrogen metabolism, pathways for nitrogenfixation and assimlitaion were found in all layers of the mat. Pathways for denitrifation and DNRA also showed good coverage throughout the metagenomes. Nitrification instead was only found in the brown and green layer of the mat (Fig. 7C; Sup. 2). Most genes for aerobic methane oxidation, using the pathway with S-Hdroxy-methylglutathione and S- Formylglutathione, were found in all layers even though some genes were missing. In the green, pink and sand layer the gene for methenyltetrahydromethanopterin cyclohydrolase was found, a hint for anaerobic methane oxidation. Nevertheless the pathway was rather incomplete (Fig. 7D; Sup. 2). Tab. 1: Taxonomically assigned marker genes of different pathways. Assignment was controlled against the 454 data set.

glnA pufM nrfA nirS amoA nifH pmoA dsrAB Chloroflexus Synechococcus Lyngbya Microcoleus Green Phyllobacteriaceae aggregans sp. sp. chthonoplastes Synechococcus Microcoleus Rhodobacteraceae sp. chthonoplastes Lyngbya sp. Microcoleus chthonoplastes Hoeflea Ignavibacterium Microcoleus Thiorhodococcus Pink Flammeovirgaceae phototrophica album chthonoplastes drewsii Desulfococcus Rhodobacteraceae oleovorans Rhodospirillaceae Desulfobacteraceae Chromatiaceae Spirochaetaceae

Sand1 Porphyromonadaceae Planctomycetaceae Rhodobacteraceae Desulfobacteraceae A

B

Fig. 7: Metabolic pathways from different layers, showing KEGG (blue) and Pfam (light blue) annotated genes and abundances of marker genes. C

D

Fig. 7: Metabolic pathways from different layers, showing KEGG (blue) and Pfam (light blue) annotated genes and abundances of marker genes. Discussion

Microbial mats are ecological systems with tightly linked chemical cycles. Metabolism of nitrogen, sulfur and methane as well as photosynthesis occur dependent on each other to form this unique ecosystem. From measurements of intermediates of these cycles, hypothesis can be developed about the metabolic functions within the given layer of the mat. Still the predictions should be treated carefully. First of all a mat from 2010 was compared to 2 mats from 2012. Due to shifts in either environmental parameter or the microbial community within the mats, results of the comparison can be biased. Furthermore, the mats from 2012 contained different layers than the mat from 2010. In addition, the two mats from 2012 were at a different succession state and were treated with different pH during the measurements. All of this factors have to be taken care of, when comparing results between the mats. Furthermore, biases can be introduced into the taxonomic dataset by the choice of the reference sets. The NCBI database is e.g. biased towards pathogenic bacteria which are important in medical studies, thus many taxonomic hits in the metagenomes are pathogens. Even though we used the SILVA ref dataset, the blast algorithm allowed only for 6 taxonomic levels, altering the results of the taxonomic classification and making a comparison between these data and those from the metagenome difficult. The main primary production by photosynthesis can be assumed in the green layer by increased oxygen concentrations in the green layer during the day, combined with decreased sulfite concentrations and increased sulfate concentrations. This hypothesis was supported by the finding of Cyanobacteria, like Synechococcus and Lyngbya species in the taxonomic data of the metagenome and of Microcholeus chtonoplastis as the dominant organism in the 454 data. Most of the genes coding for the two photosystems were present in this layer, as well as psbA, marker gene for oxygenic photosynthesis. In addition, psbA was found in the brown and the pink layer, but the genes for photosynthesis were not found in convincing abundance. Nevertheless, the low coverage of the metagenomes could bias this assumption. Even higher abundances were found for the pufM gene, indicating for anoxygenic photosynthesis. This is not surprising, especially for the pink layer, since green and purple sulfur bacteria are known to use anoxygenic photosynthesis (Bryant and Frigaard, 2006). These bacteria were previously found in the pink layer and Chlorobiaceae were also detected in the 454 database. In addition, Rhodobacteraceae were found in the 454 data in the pink and black layer, and were found to have the pufM gene (s. also report of Thea Whitman, 2012). However, Hoeflea phototrophica was the only organism assigned to the pufM genes in the pink layer in my data set. Nevertheless, several species with pufM genes were found in the green layer, which supports the highest abundances of this gene in this layer. Both sandy layers did not show any hint of photosynthetic activity. The diurnal behavior of the sulfite and sulfur concentrations could be shown by microsensor and IC measurements. Thus, the occurrence sulfite oxidation was assumed in the upper three layers, while sulfate reduction was assumed to occur in the lower layers. In the metagenome all genes necessary for a convincing prediction of both pathways, as well as the abundance of the marker genes dsrAB (sulfate reduction) were found throughout the whole mat. However, a taxonomic assignment for dsrAB was only found in the pink layer. Still organisms capable for sulfate reduction were found in the sand, black and the pink layer in the 454 data and throughout the mat. Even though the black layer is not present in the metagenome data, still sulfate reducing and sulfite oxidizing bacteria can be found throughout the whole microbial mat according to taxonomic annotations of the metagenome. In the light of the diurnal cycle conditions favoring one or the other group of organism can be explained. Thus during the day, when oxygen levels are high, sulfite oxidation occurs in the top three layer and maybe even deeper due to flushes of oxygenated water into the deeper layers. On the other hand are sulfate reducers favored by the anoxic conditions in the deep layers and the depletion of oxygen and the enrichment of hydrogen sulfite in the upper layers during night.

Methane production and consumption in in Great Sippewisset microbial mats was proposed by Buckley and co-workers (Buckley et al., 2008). Surprisingly no hint for methanogenesis was found in the metagenome data. This indicates that methane might be produced by other pathways than methanogenesis. Methyl phosphonate metabolism from Archaea, resulting in methane production, was indicated by the finding of the marker gene phnJ in the mats (s. report of Elizabeth Stuter, 2012). Still very few indicators for Archaea were found in this study. Methanol consumption on the contrary could be predicted throughout the whole mat, even though the marker gene, pmoA, was only found in the pink and Sand1 layer. However, no taxonomic assignment was able for this gene. Given that methane is produced, but is not found to be emitted from the mat in high amounts (Buckley et al., 2008), methane consumption in every layer can be expected. This was not supported by any taxonomic data and thus predictions about methane metabolisms have to remain unclear. The marker gene for nitrogen fixation, nifH, was found throughout the entire mat, being highest in the green layer. Here it could be assigned to Microcholeus chtonoplastis. In the green layer, nitrogen fixation of nitrogen gas from the air is done by either Cyanobacteria or more likely purple sulfur bacteria (s. report of Heidi Smith, 2012), while in deeper layers nitrogen resulting from denitrification can be assumed. The ammonium resulting from the nitrogen fixation is either assimilated or nitrified. Strong hints were found that assimilation occurs in all layers, such as glnA gene abundance. Since ammonium is a major source of nitrogen in the environment, high uptake can be expected in all layers. Nitrification can occur in two steps, either from ammonium to nitrite or from nitrite to nitrate. High values of nitrite in the brown and pink layer support the hypothesis of nitrification to nitrite, even though this can also be due to errors in the measurement of the nitrite concentration. Still constant values for nitrate support the hypothesis. In the upper layers amoA was found, thus completing the genes for the nitrification pathway, and assigned to Lyngbya spec. in the green layer. Findings of Lyngbya in the 454 dataset in this layer support the hypothesis of strong nitrification in the green layer. In the anoxic layers of the mat, denitrifiaction and DNRA were assumed to be more abundant. Both pathways, including the marker genes nirS and nrfA, were found throughout the whole mat. Similar to sulfate reduction, these pathways can occur during oxygen depletion in the night and thus are likely to be present in the whole microbial mat. A stronger DNRA activity in the two sandy layers is supported by high ammonium concentration, even though ammonification, mineralization or even nitrogen fixation of nitrogen gas fron denitrification could produce ammonium in these layers. This is supported by the assignment of Synechococcus and Microcholeus chtonoplastis to nirS in the green layer and Microcholeus chtonoplastis and Desulvococcus oleovorans in the pink layer, but no assignment in the sandy layer. However, only Ignavibacterium album was assigned to nrfA and was found in the pink layer. Therefore the source of ammonium in deeper layers has to remain unclear. Anammox processes could reduce ammonium concentrations in deep layers, but were not investigated in this study. Concluding, within the given biases I was able to predict metabolic pathways from chemical measurements and verify the hypothesis using genetic information. By comparison of 16S rRNA data from the metagenome and the 454 Tag sequencing approach, I was able to show the most abundant groups of organisms in the mats. In addition, most of the hypothesized pathways were found in the metagenome, leaving some gaps, likely due to the bad coverage of the metagenome. I was able to assign taxonomic classification to the marker genes and by that link the taxonomic information to the metabolic information. Even though the focus of the study was to explain the processes within the mat rather than compare the different layers, I was able to compare the different metagenomes from the different layers, but more statistical work is need. Thus within the microbial mats from Great Sippewisset Salt Marsh, I was able to confirm the known chemical cycles on a genetic level. Unexplained remains the lack of hints for methane production, where further research is needed. In general, metagenomics proved to be a powerful tool for the investigation of microbial environment according to the metabolic capabilities and for predicting metabolic pathways to build up hypothesis for further studies. Acknowledgements

I thank Steve and Dan for the great opportunity to be part of the course, their organization, their help and their patience. All the TAs, namely Verena, Suzanna, Mallory, Ashley, Sara, Adam and Shuiquan. Special thanks go to Chuck, without whom all this wouldn’t have been possible. Last but not least I thank all my course mates for the great summer in Woods Hole. This study was funded by NASA and MPI Bremen.

References

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Suplement

Sup. 1: Taxonomic overview on species level over different layers of the mat. The color code shows the order level.

Supl. 2: Overview over all investigated taxonomic pathways in all layers of the mat. Dark blue squares show KEGG annotations, while light blue squares show Pfam or Fungenes annotations. The different pathways are color coded.

Brown

Sup. 1A: Taxonomic overview of the 16S rRNA data retrieved from the brown layer of the metagenome Green

Sup. 1B: Taxonomic overview of the 16S rRNA data retrieved from the green layer of the metagenome Pink

Sup. 1C: Taxonomic overview of the 16S rRNA data retrieved from the pink layer of the metagenome Sand1

Sup. 1D: Taxonomic overview of the 16S rRNA data retrieved from the Sand1 layer of the metagenome Sand2

Sup. 1E: Taxonomic overview of the 16S rRNA data retrieved from the Sand2 layer of the metagenome Brown

PsbA = 6 KEGG Pfam/Fungenes Green

PsbA = 33 KEGG Pfam/Fungenes Pink

PsbA = 5 KEGG Pfam/Fungenes Sand1

KEGG Pfam/Fungenes Sand2

KEGG Pfam/Fungenes Brown

(no disproportionation)

As. SO4 red. Dis. SO4 red. Sulfur red. Sulfur oxidation Green

(no disproportionation)

As. SO4 red. Dis. SO4 red. Sulfur red. Sulfur oxidation Pink

(no disproportionation)

As. SO4 red. Dis. SO4 red. Sulfur red. Sulfur oxidation Sand1

(no disproportionation)

As. SO4 red. Dis. SO4 red. Sulfur red. Sulfur oxidation Sand2

(no disproportionation)

As. SO4 red. Dis. SO4 red. Sulfur red. Sulfur oxidation Brown

No mcrA

KEGG Pfam/ Fungenes Aerobic Anaerobic Green

No mcrA

KEGG Pfam Aerobic Anaerobic Pink

No mcrA

KEGG Pfam PmoA=2 Aerobic Anaerobic Sand1

No mcrA

KEGG Pfam PmoA=2 Aerobic Anaerobic Sand2

No mcrA

KEGG Pfam Aerobic Anaerobic Brown

amoA = 2

glnA = 72

nrfA = 1 nifH = 20 KEGG nirS = 50 Pfam/ Fungenes N2 fixation Nitrification Denitrification DNRA Assimilation Green

amoA = 1

glnA =97

nrfA = 1 nifH = 48 KEGG nirS = 50 Pfam/ Fungenes N2 fixation Nitrification Denitrification DNRA Assimilation Pink

amoA = 0

glnA = 128

nrfA = 4 nifH = 44 KEGG nirS = 80 Pfam/ Fungenes N2 fixation Nitrification Denitrification DNRA Assimilation Sand1

amoA = 0

glnA = 77

nrfA = 1 nifH = 20 KEGG nirS = 40 Pfam/ Fungenes N2 fixation Nitrification Denitrification DNRA Assimilation Sand2

amoA = 0

glnA = 34

nrfA = 1 nifH = 4 KEGG NirS = 13 Pfam/ Fungenes N2 fixation Nitrification Denitrification DNRA Assimilation