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)
Proteobacteria; Escherichia
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)
Proteobacteria; Escherichia
Exonuclease V gamma subunit(COG1330) Isoleucyl-tRNA synthetase(COG0060)
1
1115
16
1120
Proteobacteria; Escherichia
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)
Proteobacteria; Escherichia
Pyruvate carboxylase(COG1038)
Firmicutes; Bacillus
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)
Proteobacteria; Escherichia
15
1246
Nitrate reductase beta subunit(COG1140)
Proteobacteria; Escherichia
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)
Cyanobacteria; Synechocystis
19
1276
ABC-type multidrug transport system, ATPase and permease components(COG1132)
Proteobacteria; Nitrobacter
43
1278
N-methylhydantoinase A/acetone carboxylase, beta subunit(COG0145)
Eukaryota; Dictyostelium
22
1288
HrpA-like helicases(COG1643)
Proteobacteria; Haemophilus
19
1304
(To be continued)
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(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)
Proteobacteria; Escherichia
1
1448
93
1520
1
1641
Glutamate synthase domain 2(COG0069)
Cyanobacteria; Synechocystis
Large extracellular alpha-helical protein(COG2373)
Proteobacteria
ABC-type multidrug transport system, ATPase component(COG1131)
Proteobacteria; Rhizobium
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)
Proteobacteria; Escherichia
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)
Eukaryota; Dictyostelium
85
2150
Adenylate cyclase, family 3 (some proteins contain HAMP domain)(COG2114)
Proteobacteria; Rhizobium
20
2248
Soluble lytic murein transglycosylase and related regulatory proteins (some contain LysM/invasin domains)(COG0741)
Firmicutes; Bacillus
2
2285
Cellobiose phosphorylase(COG3459)
Proteobacteria; Rhizobium
Nucleotidyltransferase/DNA polymerase involved in DNA repair (COG0389)
Actinobacteria
Polyketide synthase modules and related proteins (COG3321)
Firmicutes; Bacillus
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.
5
2832
82
3567
9
5045
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.
No.3
LI et al.: Microorganisms associated with toxic Cyanobacteria
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