Metagenome of microorganisms associated with the toxic ...

Chinese Journal of Oceanology and Limnology Vol. 29 No. 3, P. 505-513, 2011 DOI: 10.1007/s00343-011-0056-0

Metagenome of microorganisms associated with the toxic Cyanobacteria Microcystis aeruginosa analyzed using the 454 sequencing platform* LI Nan (李楠)1, 2, 3, ZHANG Lei (张蕾)1, 2, 4, LI Fuchao (李富超)2, WANG Yuezhu (王玥珠)5, ZHU Yongqiang (朱永强)5, KANG Hui (康慧)5, WANG Shengyue (王升跃)5, QIN Song (秦松)1, ** 1

Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

2

Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

3

Graduate University of Chinese Academy of Sciences, Beijing 100049, China

4

South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangdong 510301, China

5

Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai 201203, China

Received Mar. 14, 2010; revision accepted Apr. 26, 2010 © Chinese Society for Oceanology and Limnology, Science Press, and Springer-Verlag Berlin Heidelberg 2011

Abstract In this study, the 454 pyrosequencing technology was used to analyze the DNA of the Microcystis aeruginosa symbiosis system from cyanobacterial algal blooms in Taihu Lake, China. We generated 183 228 reads with an average length of 248 bp. Running the 454 assembly algorithm over our sequences yielded 22 239 significant contigs. After excluding the M. aeruginosa sequences, we obtained 1 322 assembled contigs longer than 1 000 bp. Taxonomic analysis indicated that four kingdoms were represented in the community: Archaea (n = 9; 0.01%), Bacteria (n = 98 921; 99.6%), Eukaryota (n = 373; 3.7%), and Viruses (n = 18; 0.02%). The bacterial sequences were predominantly Alphaproteobacteria (n = 41 805; 83.3%), Betaproteobacteria (n = 5 254; 10.5%) and Gammaproteobacteria (n = 1 180; 2.4%). Gene annotations and assignment of COG (clusters of orthologous groups) functional categories indicate that a large number of the predicted genes are involved in metabolic, genetic, and environmental information processes. Our results demonstrate the extraordinary diversity of a microbial community in an ectosymbiotic system and further establish the tremendous utility of pyrosequencing. Keyword: Microcystis aeruginosa; ectosymbiosis; diversity; COGs; algal bloom; metagenome

1 INTRODUCTION Symbioses between bacteria and microalgae are ubiquitous, playing major roles in the evolution and diversity of microalgae. Recently cyanobacteria masses have been recognized as being intimately associated with a complex community of beneficial microbes. This association is essential for cyanobacterial development and is a determinant of bacterial interactions with the environment (Berg et al., 2009; Eiler and Bertilsson, 2004; Pope and Patel, 2008). Several studies have shown that Alphaproteobacteria and Betaproteobacteria tend to dominate in the mucilage of Microcystis during the bloom (Maruyama et al., 2003). There are some metabolism associations between Microcystis and their associated bacteria. Phosphorus exchange

between M. aeruginosa and attached Pseudomonas has been observed in the phycosphere (Jiang et al., 2007). To date, however, little is known about the microbial communities associated with cyanobacteria and the interactions between these organisms. Interaction studies are hampered by difficulties in cultivating symbiontic microbes. Therefore, culture-independent molecular methods that use DNA extracted directly from natural microbial communities have become the methods of choice for investigating complex environment ecosystems. * Supported by the Knowledge Innovation Program of Chinese Academy of Sciences (No. KSCX2-YW-G-073) ** Corresponding author: [email protected] LI Nan and ZHANG Lei contributed equally to this work.

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High-throughput DNA sequencing methodologies use Illumina (Solexa), Roche 454 or other platforms. These technologies produce millions of DNA sequences with tens of hundreds of nucleotides from each environment sample. Although the PCR amplification of 16S rDNA sequences has been of enormous value and still widely used, the absence of functional genomic information in 16S R r NA gene sequences is a drawback. Furthermore, high-throughput sequencing methodologies circumvent cultivation or PCR amplification (Weng et al., 2006). In this study, we have used the Roche 454 platform to study the microbial and functional diversity of the symbiotic community in water bloom of M. aeruginosa in Taihu Lake and the results are presented here.

2 MATERIAL AND METHOD 2.1 Collection of samples Samples of microorganisms associated with the water bloom M. aeruginosa FACHB-912 were harvested from Taihu Lake and filtrate with 0.22 μm membrane, then transported to the laboratory. 2.2 DNA The membrane filters cut into small pieces, ground with liquid nitrogen in a sterilized mortar, and then placed in individual 100 mL conical tubes. Each tube was covered with a lysis buffer (3% CTAB, 100 mmol/L Tris HCl (pH 8.0), 100 mmol/L EDTA, 1.4 mol/L NaCl, 0.2% ME, and 1% PVP) after which a total of 2.7 μL proteinase K (20 mg/mL) was added to the samples. The tubes incubated for 4 h at 55°C. The lysates were removed from the conical tubes, and the nucleic acids were subsequently extracted twice with phenol : chloroform : isoamyl alcohol (25:24:1, v/v/v, Sigma) and once with chloroform : isoamyl alcohol (24:1, v/v, Sigma). The aqueous upper layer removed and transferred to a clean tube. To precipitate the extracted DNA, natrium aceticum (3 mol/L, pH 5.2) and isoamyl alcohol were added in a ratio of 10:1:10 (v/v/v). The sample mixed and left for 2 h at -20°C. The precipitated DNA pelleted by centrifugation at 12 000g for 10 min. The supernatant discarded and the nucleic acid pellet washed twice in 0.5 mL of 70% ethanol. After the second wash, the tube placed in a vacuum desiccator until all remaining traces of ethanol had evaporated and the pellet was completely dry. The DNA pellet resuspended in molecular biology grade distilled water and stored at -20°C.

2.3 Sequencing and analysis The total DNA was purified using QIAfilter midipreps according to the manufacturer’s recommended protocols (Qiagen Pty. Ltd.). Roche 454 sequencing performed at the Chinese National Human Genome Center (Shanghai, China). To measure the taxonomic and functional diversities of the microorganism communities associated with M. aeruginosa, the M. aeruginosa genome sequences (AP009552 and AM778843–AM778958) used as reference sequences in BLASTN searches, with an e-value cut-off of e-30, against all the contigs obtained. M. aeruginosa sequences were removed and the contigs were assembled using the Phred/ Phrap/Consed suite of programs with default parameters (Ewing and Green, 1998; Gordon et al., 1998). Taxonomic information inferred from the best-hit results from BLASTN searches against the NCBI nucleotide database (e-value cut-off e-10). ORFs identified using Glimmer (http://www.ncbi. nlm.nih.gov/genomes/MICROBES/glimmer3.cgi) and COG (clusters of orthologous groups) functions assigned using the BASys Bacterial Annotation System (http://basys.ca/basys/cgi/submit. pl). Sequence similarities between the ORFs and published protein sequences were identified using BLASTX searches against the NCBI non-redundant protein sequences database (e-value cut-off e-10) and the SWISS-PROT protein sequence database (e-value cut-off e-10). Our sequencing data deposited in NCBI’s Sequence Read Archive (accession number SRA010762.3).

3 RESULT 3.1 Microbial assignment

diversity

and

taxonomic

The diversity of M. aeruginosa associated microorganisms surveyed by applying 454 sequencing to the total DNA environment of the samples. The results indicate that four kingdoms represented in the investigated community: Archaea (n = 9; 0.01%), Bacteria (n = 98 921; 99.6%), Eukaryota (n = 373; 3.7%) and Viruses (n = 18; 0.02%). The majority of the bacterial sequences were from Alphaproteobacteria (n = 41 805; 83.3%), Betaproteobacteria (n = 5 254; 10.5%) and Gammaproteobacteria (n = 1 180; 2.4%). The remaining bacterial sequences were Actinobacteridae (n = 918; 1.8%), Deltaproteobacteria (n = 430; 0.9%) and others (n = 581; 1.2%).

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Most of the bacterial sequences assigned at the phylum level with a similarity threshold above 80%. The variety of different genera was greatest within the classes Alphaproteobacteria (>15 genera), Betaproteobacteria (>14 genera), Actinobacteridae (>14 genera) and Gammaproteobacteria (>12 genera). Despite the variety, one predominant genus Streptomyces (n = 418; 45.5%), Sphingopyxis (n = 14 135; 34.8%), Anaeromyxobacter (n = 186; 43.3%) and Pseudomonas (n = 322; 27.3%) was observed for Actinobacteridae, Alphaproteobacteria, Deltaproteobacteria and Gammaproteobacteria respectively. Species were distributed fairly evenly in the Betaproteobacteria and Gammaproteobacteria classes. 3.2 Functional communities

diversity

of

the

microbial

To reveal the gene content, metabolic capability and the roles of specific microorganisms associated with the toxic cyanobacteria, a series of bioinformatic approaches used to analyze the sequence data. All the non-M. aeruginosa sequences were assembled into 22 239 contigs; 20 917 small contigs (0–1 000 bp in length), 1 298 medium contigs (1 001–2 000 bp), and 24 large contigs (>2 000 bp). Although the 1 322 edium m and large contigs constitute only 6% of the contigs, they accounted for 80% of all the assembled sequences. To explore the functional diversity of the microbial community in this study, we analyzed the 1 322 medium and large contigs. Details of the largest contigs, contig numbers, closest relatives and putative functions summarized in Table S2.

4 DISCUSSION In this study, the 454 high-throughput methodology was applied to analyze the diversity of microbial communities associated with M. aeruginosa. Sequence analysis indicated that the symbiontic microbial communities associated with M. aeruginosa belonged to four kingdoms: Archaea, Bacteria, Eukaryota and Viruses. A previous study (Berg et al., 2009) used the cultivation and PCR amplification of 16S rDNA sequences to analyze the microbes associated with cyanobacterial water blooms. The number of bacterial species that they detected was less than the number we found in our study. This may be because of several disadvantages related to the PCR amplification of 16S rDNA sequences. One disadvantage is that “universal” primer design by the highly conserved regions within in some species

507

may result in the primers less efficiently or not at all in other species. For example, the 16S rDNA gene sequence of Nanoarchaeota is so divergent that PCR amplification using universal primers failed to detect this species even in Nanoarchaeota cultures (Huber et al., 2002). In addition, several PCR conditions, such as annealing temperatures and extension times, may allow the formation of chimeras or produce amplification biases that skew the representation of each species in cloned libraries (Kroes et al., 1999; Wang and Wang, 1997; Ishii and Fukui, 2001). For these reasons, we used the 454 high-throughput sequencing method and were successful in identifying high levels of diversity in the M. aeruginosa microbial community. The bacterial species identified from the environment samples of M. aeruginosa FACHB-912 represent a wide range of specific bacteria. A number of the bacteria that we found were able to degrade recalcitrant heavy metal contaminants and organic compounds. For example, the iron (III)-reducing bacteria belonging to the Geobacteraceae family are capable of rapidly catalyzing the reduction and immobilization of uranium (VI) from contaminated subsurface sediments (Petrie et al., 2003). And the microcystin-degrading bacteria, mainly from the Sphingomonadaceae family, degradate the hepatotoxic microcystin produced in the bloom period (Bourne et al., 2001; Harada et al., 2004; Ho et al., 2007; Valeria et al., 2006). We also detected the potentially pathogenic bacteria, Aeromonas, Vibrio, Acinetobacter and Pseudomonas, that may cause adverse health effects in humans and animals (Bourne et al., 2001; Harada et al., 2004; Ho et al., 2007; Ishii et al., 2004; Saito et al., 2003; Valeria et al., 2006). The presence of such pathogens should take into consideration when assessing the risks caused by cyanobacterial blooms. Some microbes that closely related to bacteria are able to convert light energy into chemical energy, also have the ability to regulate the nitrogen fixation pathways. For example, the Alphaproteobacteria R. sphaeroides possesses an extensive range of energy acquiring mechanisms including photosynthesis and lithotrophy, as well as the ability to use aerobic and anaerobic respiration (Mackenzie et al., 2007). Several species, such as actinobacteria use cyanobacteria biomasses for growth (Eiler and Bertilsson, 2004; Webster et al., 2001). The cyanobacteria cyanothece, synechococcus, gloeobacter, and prochlorothrix that can produce cyanobacterial blooms were also found in the M. aeruginosa microbial community.

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Table 1 Selected COG (clusters of orthologous groups) functions identified in the metagenome of the microbial community associated with M. aeruginosa COG category / COG No.

Description

Membrane Transport COG0653

Preprotein translocase subunit SecA (ATPase, RNA helicase)

COG1132

ABC-type multidrug transport system, ATPase and permease components

COG1131

ABC-type multidrug transport system, ATPase component

Signal Transduction COG3696

Putative silver efflux pump

COG5013

Nitrate reductase alpha subunit

COG1140

Nitrate reductase beta subunit

COG0642

Signal transduction system histidine kinase

Replication and Repair COG0209

Ribonucleotide reductase, alpha subunit

COG0208

Ribonucleotide reductase, beta subunit

COG0419

ATPase involved in DNA repair

COG1197

Transcription-repair coupling factor (superfamily II helicase)

COG0507

ATP-dependent exoDNAse (exonuclease V), alpha subunit - helicase superfamilyI member

COG1674

DNA segregation ATPase FtsK/SpoIIIE and related proteins

COG1330

Exonuclease V gamma subunit

COG0389

Nucleotidyltransferase/DNA polymerase involved in DNA repair

Folding, Sorting and Degradation COG1530

Ribonucleases G and E

COG0550

Topoisomerase IA

Transcription COG0664

cAMP-binding proteins - catabolite gene activator and regulatory subunit of cAMP-dependent protein kinases

COG0583

Transcriptional regulator

Translation COG1601

Translation initiation factor 2, beta subunit (eIF-2beta)/eIF-5 N-terminal domain

COG0060

Isoleucyl-tRNA synthetase

COG0495

Leucyl-tRNA synthetase

Carbohydrate Metabolism COG2352

Phosphoenolpyruvate carboxylase

COG1038

Pyruvate carboxylase

COG1014

Pyruvate:ferredoxin oxidoreductase and related 2-oxoacid: ferredoxin oxidoreductases, gamma subunit

COG3250

Beta-galactosidase/beta-glucuronidase

COG0243

Anaerobic dehydrogenases, typically selenocysteine-containing

COG0567

2-oxoglutarate dehydrogenase complex, dehydrogenase (E1) component, and related enzymes

Amino Acid Metabolism COG0146

N-methylhydantoinase B/acetone carboxylase, alpha subunit

COG0060

Isoleucyl-tRNA synthetase

COG0495

Leucyl-tRNA synthetase

COG0145

N-methylhydantoinase A/acetone carboxylase, beta subunit

COG0436

Aspartate/tyrosine/aromatic aminotransferase

COG0458

Carbamoylphosphate synthase large subunit (split gene in MJ)

COG4230

Delta 1-pyrroline-5-carboxylate dehydrogenase

COG0069

Glutamate synthase domain 2

COG0493

NADPH-dependent glutamate synthase beta chain and related oxidoreductases

COG0567

2-oxoglutarate dehydrogenase complex, dehydrogenase (E1) component, and related enzymes

(To be continued)

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COG category / COG No.

509

(Continued)

Description

Lipid Metabolism COG3250

Beta-galactosidase/beta-glucuronidase

Nucleotide Metabolism COG0458

Carbamoylphosphate synthase large subunit (split gene in MJ)

COG0167

Dihydroorotate dehydrogenase

COG0749

DNA polymerase I - 3-5 exonuclease and polymerase domains

COG0209

Ribonucleotide reductase, alpha subunit

COG0208

Ribonucleotide reductase, beta subunit

Energy Metabolism COG0069

Glutamate synthase domain 2

COG5013

Nitrate reductase alpha subunit

COG1140

Nitrate reductase beta subunit

COG0493

NADPH-dependent glutamate synthase beta chain and related oxidoreductases

COG2352

Phosphoenolpyruvate carboxylase

COG0243

Anaerobic dehydrogenases, typically selenocysteine-containing

Enzyme Families Periplasmic protease

COG0515

Serine/threonine protein kinase

In addition to the bacteria that we detected in the environment samples, we also found some species of Archaea and Viruses associated with the cyanobacteria. Methanosaetaceae, an Archaea, was one of the species found in household solid waste (Juliana et al., 2009), suggesting that, when combined with M. aeruginosa blooms, they may be involved in a process of biogas production with the degradation of organic materials and the conservation of energy. Viruses are known to be present in aquatic environments where they regulate the biomass production and species composition of bacteria and phytoplankton, influence biogeochemical cycling, and mediate gene transfers between microorganisms in aquatic ecosystems (Fuhrman, 1999). Thus, our results confirm that the environment samples of M. aeruginosa FACHB-912 obtained from the Taihu Lake contained a great diversity of microorganisms. This information will be helpful in our future investigations into the in situ interactions between Microcystis and their associated bacteria during water bloom. To investigate the biological functions present in the microbial communities, COG functions assigned to the predicted coding sequences (Table 1). Many contigs fell into the genetic and environmental information processing categories, reflecting the expected abundance of these functions in nature (Fig.1). Other COG categories represented enzymes

including kinases (Serine/threonine protein kinase, COG0515) and enzymes that function in membrane transport (ABC-type multidrug transport system, COG1131), and in signal transduction (signal transduction system histidine kinase, COG0642) (Table 1). 700 600 500

Number of contigs

COG0793

400 300 200 100 0

M

T

S

L

J

K

Environmental Genetic Information Information Processing Processing

E

F

G

C

A

I

S

Metabolism Function unknown

Fig.1 Categorization of the microbial community metagenome contigs according to COG functional categories M. Membrane Transport; T. Signal Transduction; S. Folding, Sorting and Degradation; L. Replication and Repair; J. Translation; K. Transcription; E. Amino Acid Metabolism; F. Nucleotide Metabolism; G. Carbohydrate Metabolism; C. Energy Metabolism; A. Enzyme Families; I. Lipid Metabolism; S. Function unknown

510

Table S1 The proportions of taxonomic groups represented by microorganisms associated with water bloom caynobacteria M. aeruginosa FACHB-912 Bacterial strains distribution on class level

Number

Percent

Deltaproteobacteria

430

0.90%

Actinobacteridae

918

1.80%

Gammaproteobacteria

1180

2.40%

Betaproteobacteria

5254

10.50%

Alphaproteobacte

41805

83.30%

581

1.20%

other bacterium

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Actinobacteridae Nocardia

10

1.10%

Salinispora

11

1.20%

Table S1 Bacterial strains distribution on class level

(Continued) Number

Percent

Verminephrobacter

264

5.00%

Cupriavidus

270

5.10%

Methylibium

358

6.80%

Delftia

378

7.20%

Diaphorobacter

386

7.30%

Burkholderia

393

7.50%

Leptothrix

492

9.40%

Polaromonas

573

10.90%

Bordetella

659

12.50%

Acidovorax

822

15.60%

other Betaproteobacteria

222

4.20%

13

3.00%

Leifsonia

12

1.30%

Thermobifida

13

1.40%

Beutenbergia

15

1.60%

Kineococcus

16

1.70%

Desulfatibacillum

17

4.00%

38

8.80%

Deltaproteobacteria Pelobacter

Saccharopolyspora

19

2.10%

Desulfovibrio

Rhodococcus

22

2.40%

Geobacter

41

9.50%

54

12.60%

Arthrobacter

25

2.70%

Myxococcus

Frankia

39

4.20%

Sorangium

Clavibacter

64

7.00%

Anaeromyxobacter

Mycobacterium

75

8.20%

other Deltaproteobacteria

Nocardioides

86

9.40%

Gammaproteobacteria

Streptomyces

418

45.50%

30

32.70%

Mesorhizobium

645

1.50%

Agrobacterium

719

Rhodopseudomonas

65

15.10%

186

43.30%

16

3.70%

Aeromonas

32

2.70%

Enterobacter

32

2.70%

Methylococcus

32

2.70%

Escherichia

38

3.20%

1.70%

Cronobacter

40

3.40%

774

1.90%

Xylella

42

3.60%

Bradyrhizobium

899

2.20%

Azotobacter

46

3.90%

Dinoroseobacter

982

2.30%

Klebsiella

59

5%

Sphingomonas

1003

2.40%

Ruegeria

1181

2.80%

Xanthomonas

109

9.20%

Rhizobium

1307

3.10%

Rhodospirillum

1422

3.40%

Stenotrophomonas

205

17.40%

Paracoccus

1542

3.70%

Pseudomonas

322

27.30%

Methylobacterium

1610

3.90%

Other Gammaproteobacteria

152

12.90%

Novosphingobium

2489

6.00%

Erythrobacter

2703

6.50%

Rhodobacter

7097

17.00%

Sphingopyxis

14135

34.80%

3297

7.90%

other Actinobacteridae Alphaproteobacteria

Other Alphaproteobacteria Betaproteobacteria Rhodoferax

74

1.40%

Azoarcus

90

17.10%

Thauera

127

2.40%

Ralstonia

146

2.80%

(To be continued)

Enterobacteriaceae.

Previous studies have suggested that the water bloom cyanobacteria M. aeruginosa can affect surrounding species, including zooplankton (Aleya et al., 2006), phytoplankton (Sedmak and Elersek, 2005) and microbes (Eiler and Bertilsson, 2004; Pope and Patel, 2008; Yoshida et al., 2006). Environmental elements (Baptista and Vasconcelos, 2006) and other species (Yoshida et al., 2006; Zhang et al., 2009) have also been shown to interact with the cyanobacteria. Currently, it is widely recognized

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Table S2 Details of the largest contigs, contig numbers, closest relatives and putative functions of the assembled contig sequences from the metagenome of microorganisms associated with the cyanobacteria M. aeruginosa Putative function: BLAST(COG accession number if available)

Closest relative (phylum; genera)

Number of contigs

Largest contig

ATPase involved in DNA repair(COG0419)

Firmicutes; Staphylococcus

3

1013

Beta-galactosidase/beta-glucuronidase(COG3250)

Actinobacteria; Arthrobacter

10

1015

Anaerobic dehydrogenases, typically selenocysteine-containing(COG0243)

Proteobacteria; Escherichia

12

1016

DNA polymerase I - 3-5 exonuclease and polymerase domains(COG0749)

Proteobacteria; Rhizobium

56

1016

Phosphoenolpyruvate carboxylase(COG2352)

Cyanobacteria; Anabaena

6

1023

Dihydroorotate dehydrogenase(COG0167)

Proteobacteria; Rhodospirillum

19

1025

Translation initiation factor 2, beta subunit (eIF-2beta)/eIF-5 N-terminal domain(COG1601)

Bacteroidetes; Bacteroides

70

1040

Preprotein translocase subunit SecA (ATPase, RNA helicase)(COG0653)

Chlorobi; Chloroherpeton

51

1043

Ribonucleotide reductase, beta subunit(COG0208)

Chlamydiae

14

1047

Ribonucleases G and E(COG1530)


18

1061

Putative silver efflux pump(COG3696)

Proteobacteria; Alcaligenes

5

1063

Leucyl-tRNA synthetase(COG0495)

Actinobacteria; Frankia

84

1064

Cation/ efflux pump(COG0841)

Proteobacteria; Yersinia

68

1065

Periplasmic protease(COG0793)

Actinobacteria; Streptomyces

29

1068

Membrane-fusion protein(COG0845)

Proteobacteria; Actinobacillus

34

1076

FOG: PAS/PAC domain(COG2202)

Firmicutes; Bacillus

34

1105

RTX toxins and related Ca -binding proteins(COG2931)

Proteobacteria; Neisseria

Small-conductance mechanosensitive channel(COG3264)


Exonuclease V gamma subunit(COG1330) Isoleucyl-tRNA synthetase(COG0060)

1

1115

16

1120


2

1122

Bacteroidetes; Bacteroides

80

1162

Carbamoylphosphate synthase large subunit (split gene in MJ)(COG0458)

Proteobacteria; Agrobacterium

64

1167

Cation transport ATPase(COG2217)

Cyanobacteria; Synechocystis

79

1172

11

1173

1

1174

30

1185

2+

Serine/threonine protein kinase(COG0515)

Actinobacteria; Streptomyces

Pyruvate:ferredoxin oxidoreductase and related 2-oxoacid:ferredoxin oxidoreductases, gamma subunit(COG1014)


Pyruvate carboxylase(COG1038)


Uncharacterized conserved protein(COG1354)

Actinobacteria-Mycobacterium

6

1205

Topoisomerase IA(COG0550)

Proteobacteria; Zymomonas

46

1217

Transcription-repair coupling factor (superfamily II helicase)(COG1197)

Actinobacteria-Mycobacterium

48

1234

Nitrate reductase alpha subunit(COG5013)


15

1246

Nitrate reductase beta subunit(COG1140)


38

1247

2-oxoglutarate dehydrogenase complex, dehydrogenase (E1) component, and related enzymes(COG0567)

Actinobacteria; Corynebacterium

66

1257

Aspartate/tyrosine/aromatic aminotransferase(COG0436)

Eukaryota; Dictyostelium

141

1265

Ribonucleotide reductase, alpha subunit(COG0209)

Proteobacteria; Agrobacterium

30

1272

Cobalamin biosynthesis protein CobN and related Mg-chelatases(COG1429)

Proteobacteria; Pseudomonas

60

1275

FOG: EAL domain(COG2200)


19

1276

ABC-type multidrug transport system, ATPase and permease components(COG1132)

Proteobacteria; Nitrobacter

43

1278

N-methylhydantoinase A/acetone carboxylase, beta subunit(COG0145)


22

1288

HrpA-like helicases(COG1643)

Proteobacteria; Haemophilus

19

1304

(To be continued)

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CHIN. J. OCEANOL. LIMNOL., 29(3), 2011 Table S2

(Continued)

Putative function: BLAST(COG accession number if available)

Closest relative (phylum; genera)

N-methylhydantoinase B/acetone carboxylase, alpha subunit(COG0146)

Saccharomyces

Delta 1-pyrroline-5-carboxylate dehydrogenase(COG4230)

Number of contigs

Largest contig

5

1317

Proteobacteria; Salmonella

31

1320

DNA segregation ATPase FtsK/SpoIIIE and related proteins(COG1674)

Proteobacteria; Salmonella

29

1351

GMP synthase, PP-ATPase domain/subunit(COG0519)

Proteobacteria; Bradyrhizobium

56

1375

Antirestriction protein(COG4227)


1

1448

93

1520

1

1641

Glutamate synthase domain 2(COG0069)


Large extracellular alpha-helical protein(COG2373)

Proteobacteria

ABC-type multidrug transport system, ATPase component(COG1131)


19

1642

cAMP-binding proteins - catabolite gene activator and regulatory subunit of cAMP-dependent protein kinases(COG0664)

Eukaryota; Saccharomyces

4

1679

Uncharacterized protein containing caspase domain(COG4249)

Cyanobacteria; Anabaena

2

1683

ATP-dependent exoDNAse (exonuclease V), alpha subunit - helicase superfamily I member(COG0507)


1

1756

Allophanate hydrolase subunit 2(COG1984)

Eukaryota; Saccharomyces

23

1835

Transcriptional regulator(COG0583)

Saccharomyces

83

1953

NADPH-dependent glutamate synthase beta chain and related oxidoreductases(COG0493)

Saccharomyces

19

2111

Signal transduction histidine kinase(COG0642)


85

2150

Adenylate cyclase, family 3 (some proteins contain HAMP domain)(COG2114)


20

2248

Soluble lytic murein transglycosylase and related regulatory proteins (some contain LysM/invasin domains)(COG0741)


2

2285

Cellobiose phosphorylase(COG3459)


Nucleotidyltransferase/DNA polymerase involved in DNA repair (COG0389)

Actinobacteria

Polyketide synthase modules and related proteins (COG3321)


Non-ribosomal peptide synthetase modules and related proteins (COG1020)

Firmicutes; Brevibacillus

that the dynamics of cyanobacterial harmful algal blooms (CHABs) is influenced by the amount, proportion, and chemical composition of the nitrogen and phosphorus that ends up in the water (Paerl, 2008). Enzymes related to energy metabolism like the nitrate reductase alpha/beta subunits and phosphoenolpyruvate carboxylase found in the Proteobacteria (Table 1 and Table S2), may be particularly important for the bloom of non-nitrogen fixing M. aeruginosa. Thus, our study indicates that there must be complex molecular regulatory mechanisms in natural systems that trigger these blooms (and toxin production). Our analysis of the high-throughput sequences using the COG functional categories indicates that there may be extra- and intra-cellular mechanisms that determine bloom dynamics and termination that have not yet been completed elucidated.

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29

7756

5 ACKNOWLEDGMENT We would like to thank Professor SONG Lirong (Institute of Hydrobiology, Chinese Academy of Sciences) for providing the Taihu Lake microalga strain Microcystis aeruginosa FACHB-912 samples. We also thank Professor Russell HILL for his kind suggestions. References Aleya L, Michard M, Khattabi H, Devaux J. 2006. Coupling of the biochemical composition and calorific ocntent of zooplankters with the Microcystis aeruginosa proliferation in a highly eutrophic reservoir. Environ. Technol., 27(11): 1 181-1 190. Baptista M S, Vasconcelos M T. 2006. Cyanobacteria metal interactions: requirements, toxicity, and ecological implications. Crit. Rev. Microbiol., 32(3): 127-137.

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