厭氧微生物特性研究

Ir. Dr. Tong Zhang (张 彤）

Environmental Biotechnology Lab Department of Civil Engineering The University of Hong Kong

南京大学环境学院客座教授

生質能源處理技術研討會， 2015年7月17日

1923, HKU Main Bld 4 The first biogas reactor at Shanxi Province of China back to 1970s

http://www.ibtimes.co.uk/xi-jinping-profile-story-who-400967 5 6 Degradation Pathway of Phenol under Methanogenic Condition

Phenol A) Phenol to benzoate Degradation of phenol via Clostridium carboxylation to benzoate by Clostridium (renamed to Benzoate Sedimentibacter hydroxybenzoicus).

Syntrophus B) Benzoate to acetate and H2/CO2 Syntrophus species then further Acetate, H2, CO2 degraded benzoate to acetate and

H2/CO2 for final methanogenesis. Methanogens C) Acetate and H /CO to methane CH4 2 2 The methanogens.

Zhang, T et al. 2005. Microbial characteristics of a methanogenic phenol-degrading sludge. Wat Sci Technol. 52, 73-78 7 Phenol-degrading Anaerobic Granule Sludge Red: cells; Green: Archaea cells (methanogen)

Zhang, T et al. 2005. Microbial characteristics of a methanogenic phenol-degrading sludge. Wat Sci Technol. 52, 73-78

8 Specific methanogenic activities of phenol- degrading consortia

Specific methanogenic activity (SMA) of MT and AT A summary of SMA of different phenol- consortia on Day 193 degrading methanogenic consortia 300.0 Max. MT=282.1 Max. T (oC) phenol con. Reference SMA 250.0 -1

VSS/d (mg.L ) - AT=200.7 26 1260 190 Zhang et al., 2005

200.0 COD/g

- AT (20) 875 201 Ju and Zhang, 2014 4 150.0

CH 37 1260 240 Fang et al., 1996 -

(mg 100.0 37 500 480 Chen et al., 2008 37 634 371 Chen et al., 2009 SMA SMA 50.0 MT (37) 1000 282 Ju and Zhang, 2014

0.0 -1 -1 *SMA: mg. CH4-COD.g.VSS .d 0 200 400 600 800 1000

Initial phenol concentration (mg/L) • Mesophilic (37oC, MT) and ambient (20oC, AT) phenol-degrading methanogenic consortia enriched in anaerobic semi-continuous batch reactors

Ju F, Zhang T*. 2014. Novel microbial populations in ambient and mesophilic biogas-producing and phenol- 9 degrading consortia unraveled by high-throughput sequencing. Microbial Ecology. doi:10.1007/s00248-014-0405-6 Image source: Synbiota/Twitter.

An observation about the history of computing Moore’s law hardware: the price of dense integrated circuit halve every 24 months. Price for one human genome (3 Gb)  Next generation sequencing

10 10 11 Microbial communities

Genomic DNA RNA Protein Metabolites

PCR-HTS HTS cDNA-HTS Mass spectroscopy Analytical methods

16S rRNA gene Metagenome Metatranscriptome Metaproteome Metametabolome

Bioinformatics & statistics analysis

Phylogeny & ; Novel or uncultured microbes; Gene expression & activity Biodiversity & dynamics; Novel biomolecules & enzymes; Protein identification Genetic potentials & genome; Novel makers, primers &probes Global metabolic pathways … … …

12 … Next-generation sequencing of metagenomic DNAs and 16S rRNA gene

DNA extraction HTS

Metagenomic Sludge samples Genomic DNA Illumina Hiseq2000 datasets

16S rRNA gene amplification

HTS

Amplicon PCR amplicons Illumina Miseq datasets

DNA extraction: FastDNA® Spin kit for Soil (MP Biomedicals); Amplification of V3-V4 regions of 16S rRNA gene :

• forward primer 338F (5’-ACTCCTACGGGAGGCAGCAG-3’); • reverse primers: R1 (5’-TACCRGGGTHTCTAATCC-3’, R2 (5’-TACCAGAGTATCTAATTC-3’), R3 (5’- 13 CTACDSRGGTMTCTAATC-3’) and R4 (5’-TACNVGGGTATCTAATCC-3’) (I) Data Pretreatment

Sequences (.fna) Quality Scores (.qual) 16S Analysis Pipeline Flowgram Files (.sff.txt) (II) Construction of OTU Table

De-mutiplexing & Quality Control OTUs Picking

Representative (III) Data Diversity Denoise of Amplicons Sequence Selection Data (e.g., Denoiser) Analysis & Visualization

Representative Construction of OTUs (IV) OTU Occupancy & Sequence Alignment Network Chimera Filtering (e.g. Co-occurrence Analysis ChimeraSlayer) Alpha Diversity & OTU Incidence & Taxonomy Assignment Rarefaction Analysis Abundance Matrixes

Phylogenetic Tree Beta Diversity Analysis Checkerboard Score Construction (C-score) Test

Multivariate Analysis Generalist vs. Specialist; Construction of OTU (e.g., PCoA, NMDS) Persistent vs. Transient Abundance Table

Data Visualization Similarity or Regression (e.g., bar, pie, heatmap) Based Network Analysis

Microbial Interactions & Assembly Patterns14 Metagenomic Genome 1.Uncultured microbes Analysis 2.Genetic potential 3.Clues for enrichment Wheel and/or isolation …

Isolate 1.Physio-biochemical properties 2. Functional validation 3. Materials for genetic engineering … Read Contig 1. Microbial composition 1. Gene catalogue 2. Quantitative analyses 2.Qualitative analyses 3. DNA-level metabolic 3.Primer-probe design potential … … 15 Assembly & Mapping Compositions of Bacteria and Archaea Samples Archaea Bacteria Mesophilic cellulose-converting consortium (~200 days) 9.0% 90.0% Thermophilic cellulose converting consortium (~120 days) 11.0% 83.4% Thermophilic cellulose converting consortium (~545 days) 6.2% 91.2%

Thermophilic beer-lees-converting consortium (45 days) 4.3% 95.5% Thermophilic beer-lees-converting consortium (75days) 7.3% 92.6% Thermophilic beer-lees-converting consortium (110 days) 6.0% 92.5%

Phenol-degrading consortium (ambient) 8.8% 91.2% Phenol-degrading consortium (mesophilic) 12.7% 87.3%

Full-scale ADS (ST) 2.9% 95.0% Full-scale ADS (ST) 4.3% 83.9% Full-scale ADS (ST) 2.0% 97.5% Full-scale ADS (SWH) 2.8% 86.0% Full-scale ADS (SWH) 4.6% 91.0% Full-scale ADS (SWH) 5.2% 94.8% Full-scale ADS (SWH) 4.5% 95.5% 16 OTUs diversity of phenol-degrading methanogenic consortia

500 500 Seed sludge (a) 0.03 Seed sludge 450 450 (b) 0.06 AT AT MT 400 400 0.88 % MT 350 350 0.66% 300 OTUs diversity: 300

250 SEED > AT > MT 250 OTUs OTUs 200 200

150 0.44% 150 0.22% 100 0.22% 100 0.11% 50 50

0 0 0 2000 4000 6000 8000 0 2000 4000 6000 8000 Number of sequences Number of sequences Rarefaction curves of seed, AT and MT samples at cutoff levels of 3% (a) and 6% (b).

A list of OTUs diversity in phenol-degrading consortia using NGS-based and cloing-based methods Temperature Methods Sequences NO. OTUs NO. References 26 0C Cloning 90 13 Zhang et al., 2005 AT (20 0C) NGS 8150 150 Ju and Zhang, 2014 37 0C Cloning 106 20 Chen et al., 2009 37 0C Cloning 107 21 Chen et al, 2008 37 0C Cloning 114 6 Levén and Schnürer, 2010 MT (37 0C) NGS 8150 106 Ju and Zhang, 2014 17 Major shifts in bacterial communities

MT AT SEED AT Adhaeribacter Shifts in (52.9%) Planctomyces 5% 1% 4% Butyrivibrio Parabacteroides Novosphingobium Seed sludge 29% Bradyrhizobium (19.1%) 0% 61% Streptococcus SEED Syntrophomonas 12% Lysobacter 19% Spirochaeta 53% Caldilinea 16% MT Sedimentibacter 1% Turicibacter (43.9%) Thermonema 22% 30% MT Moorella Brachymonas α-subdivision Geobacter β-subdivision 46% Rhodococcus γ-subdivision AT Chlorobaculum Rhodopseudomonas δ-subdivision ε-subdivision 1% Syntrophobacter

ED Thermovirga MT&SE *Nine syntrophic genera are highlighted in rectangles

Levilinea

EED AT&S W22 Candidatus Cloacamonas >99% similarity to Candidatus Cloacamonas acidaminovorans W Syntrophus Pelotomaculum

Desulfovibrio AT&MT Sulfurovum 93.2%-94.4% similarity to Sulfurovum spp. T78 (NBC37-1, lithotrophicum, and aggregans) Acinetobacter Aminobacterium

ED Mycobacterium Synergistes AT&MT&SE Syntrophorhabdus 99.9% similarity to phenol-degrading S. aromaticivorans UI 18 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 Major shifts in archaeal communities

Methanobacterium SEED Methanobacteriaceae (1.6%, 4.3%, 3.8%) (6.9%, 9.0%, 8.0%) AP Methanobacteria Methanobacteriales M. beijingense 5 Hydrogenotrophic (6.9%, 9.0%, 8.0%) (6.9%, 9.0%, 8.0%) (0.0%, 1.0%, 1.2%) methanogens MP (11.2~15.7%) Methanobrevibacter (2.9%, 0.0%, 0.0%) Methanobacteriaceae Methanoculleus (12.3%, 6.4%, 5.0%) (10.8%, 6.4%, 5.0%) M. bourgensis (2.2%, 1.0%, 0.9%) Methanomicrobiales Methanorequlaceae (21.4%, 15.5%, 12.3%) (1.1%, 1.6%, 2.6%) Euryarchaeota Methanolinea (100%, 100%, 100%) (0.0%, 0.7%, 1.6%)

Methanospirillaceae Methanospirillum (1.1%, 1.3%, 0.8%) (1.1%, 1.3%, 0.8%) Methanosaeta (green) M. hungatei (Zhang et al., 2005) (1.1%, 1.3%, 0.8%) Methanosaetaceae (10.1%, 71.4%, 76.5%) 2 Acetoclastic Methanomicrobia Methanosaeta methanogens (92.2%, 91.0%, 92.0%) (10.1%, 71.4%, 76.5%) Methanosarcinales (71.4~76.5%) (70.3%, 71.9%, 77.9%) Methanosarcina Methanosarcinaceae (57.1%, 0.0%, 1.2%) M. acetivorans Methanosarcina (60.6%, 0.0%, 1.2%) (1.1%, 0.0%, 0.0%) (Fang et al., 1996)

M. mazei (5.8%, 0.0%, 0.6%) A major shift of methanogens from Methanosarcina to Methanosaeta 19 Definition of Coverage

C Length of genomic segment (contig): L Number of reads/sequences used to assemble the contig: n Average length of each read : l

Definition: Coverage C = n × l / L

For those genomic segments from the same bacteria species, their coverage should be exactly the same (theoretically) or very close/similar (in reality) to each other.

Conversely, those contigs/genomic segments having similar coverage in a sample are probably (high probability, but not 100% necessarily) from the same species. This could be used as a criteria to pick the contigs of the same species out from the mixture of all the contigs in a sample.

20 Sample 1 Genome size Percentage 1000 cells Size of data Coverage

Bacterium A 2M 20% 200 400 M 200X Sample 1: under Bacterium B 3M 20% 200 600 M 200X mesophilic condition Bacterium C 4M 15% 150 600 M 150X Bacterium D 3M 45% 450 1350 M 450X

100% 2.95 G (total) Sample 2: under thermophilic condition Sample 2 Genome size Percentage 1000 cells Size of data Coverage Bacterium A 2M 10% 100 200 M 100X

Bacterium B 3M 40% 400 1200 M 400X

400 Bacterium C 4M 20% 200 800 M 200X

Bacterium D 3M 30% 300 900 M 300X

100% 3. 1 G (total) 2) - 300

(DNA Using some scripts (a few lines of 200 commands of a computer program, like Python, R, etc.) to pick out these contigs with the same/similar Coverage 100 coverage in the two samples and define them as a bin (draft genome of a bacterium).

100 150 200 300 400 21 Coverage (DNA-1) Bacterial Genome Binning Important criteria for genomic binning:

1.Coverage (depending on percentage of a species in the

2) Separate mixed - Community DNA mixture) genomes into single genomes

(DNA 2.Tetra-nucleotide frequency (depending on the DNA sequences of a species)

Coverage 3.GC content

4.Codon composition

Coverage (DNA-1) 5.Taxonomy

Albertsen et al., 2013. Nature Biotechnology 31, 533–538

22 a 500 b 1.0 Others 450 0.9 Gammaproteobacteria SEED Alphaproteobacteria 400 0.8 Actinobacteria

350 0.7 Synergistetes OTUs - 300 0.6 Firmicutes

0.97 Bacteroidetes 250 0.5 Spirochaetae 200 0.4 AP Chlorobi 150 0.3 Aminicenantes (OP8)

Number Number of Differential coverage binning for reconstruction Chloroflexi 100 0.2 MP 50 of 23 prokaryotic0.1 genomesDeltaproteobacteria 0 0.0 Epsilonproteobacteria 0 2000 4000 6000 8000 SEED AP MP Number of sequences c ParameterS(M) N G Cstatistics(%) C(%) R(%) MT AT G1 1.78 28 37.0 99.1 0.9 G2 Enrichment2.90 133 51 temperature.9 92.5 1.9 (oC) 37 20 G3 1.85 24 46.8 88.8 1.9 G4 Metagenome2.29 33 45. 7size 91 .6(Gbp1.9 ) 11.0 8.6 G5 3.17 77 44.0 87.9 0.9

) G6 Number2.57 44 of 6paired4.6 84.1-end3.7 reads (million) 110 86 C °

0 G7 2.91 28 49.8 95.3 3.7

2 Average length of read (bp) 100 100 (

G8 1.72 35 37.9 88.6 0.0 P G9 1.90 17 56.1 90.7 3.7 1 A

Total assembly size (Mbp) 172 209 n

i G10 2.21 56 57.3 88.6 0.0

e 2 G11N502.4 0( bp84) 39.7 91.6 0.9 6519 6612 g a

r G12 2.43 65 51.8 88.8 1.9 e

v G13Max2.48 contig93 6size0.0 8(6Kbp.0 1).9 42 98 o c

G14 2.97 127 48.2 85.0 0.0 d l G15 4.91 159 68.6 89.7 0.0 o Reads mapped to MT scaffolds 79% 50% f f G16 1.88 103 52.2 84.1 0.9 a c G17 2.16 130 52.5 86.9 0.9 S G18 2Co.13 -assembly38 50.8 9of2.5 shot1.9-gun paired-end reads G19 with2.70 library75 49. 6insert 89.7 lengths0.9 of 180 and 800 bps G20 2.35 132 55.7 73.8 1.9 G211Only2.42 scaffolds106 55.3 ≥9 10002.5 0 .9bp were considered. G222Calculated2.64 240 by66.8 mapping86.0 0.0 reads to scaffold ≥ 1000 bp using G23 2.08 220 52.8 60.0 2.9 CLC workbench 6.0.

Scaffold coverage in MP (37°C) (Reference of the binning approach: Albertsen et al., 2013; NBT) Proteobacteria: G1, G2, G5, G6, G7, G12, G14, G20, G21 and G22 (10 genomes); Chloroflexi: G3, G16, G17 and G18 (4 genomes); Synergistetes: G9 and G13 ; Bacteroidetes: G11 and G19; Actinobacteria: G15; OP8: G4 Euryarchaeota: G8, G10 and G23 (3 genomes); 23 a 500 b 1.0 Others 450 0.9 Gammaproteobacteria SEED Alphaproteobacteria 400 0.8 Actinobacteria

350 0.7 Synergistetes OTUs - 300 0.6 Firmicutes

0.97 Bacteroidetes 250 0.5 Spirochaetae 200 0.4 AP Chlorobi 150 0.3 Aminicenantes (OP8)

Number Number of Chloroflexi 100 0.2 MP Betaproteobacteria 50 Maximum-likelihood0.1 phylogenetic treeDeltaproteobacteria 0 0.0 Epsilonproteobacteria 0 (for 200019 genomes4000 which have 600016S rRNA gene,8000 > 1200 bp. Obtained by de novo assembly, PE tracking and Emirge) SEED AP MP Number of sequences

Relative abundance in metagenome (ppm) SEED Genome size and completeness 220 200 180 160 140 120 100 80 60 40 20 0 AP c M P T78 clade 100 G3 100 Anaerobic digester sludge T78 bacterium clone(CU922173) S(M) N GC(%) C(%) R(%) 84 Bellilinea(HQ183896) 100 100 Longilinea (EU887789) 100 Leptolinea (JN995347) G1 1.78 28 37.0 99.1 0.9 Levilinea (JN457461) Chloroflexi 86 Anaerolinea thermophila UNI-1 (AP012029) 56 Dehalococcoides ethenogenes 195 (CP000027) G2 2.90 133 51.9 92.5 1.9 28 Caldilinea aerophila strain DSM 14535 (NR 074397) 42 Chloroflexus aggregans DSM 9485 (CP001337) 96 Sphaerobacter thermophilus DSM 20745 (CP001824) Gordonia amarae DSM43392T (X80635) G3 1.85 24 46.8 88.8 1.9 Mycobacterium fortuitum DSM 46621 (ALQB01000301.9.1537) 100 Mycobacterium tuberculosis (AJ536031) Actinobacteria 46 100 G4 2.29 33 45.7 91.6 1.9 96 G15 80 Uncultured forest soil Mycobacterium bacterium clone (FJ625357) 100 G14 64 Cryptanaerobacter phenolicus (AY327251) G5 3.17 77 44.0 87.9 0.9 100 Hydrocarbon-contaminated aquifer Cryptanaerobacter sp. clone (JQ086854) Firmicutes

) Pelotomaculum thermopropionicum SI (AP009389.1049070.1050595) 30 58 Aminobacterium colombiense DSM 12261 (CP001997.417874.419393) G6 2.57 44 64.6 84.1 3.7 C Anaerobic digester sludge Synergistaceae bacterium clone (CU920250) 100 ° 100 Aminivibrio pyruvatiphilus (AB623229) Synergistetes 98

0 G13 G7 2.91 28 49.8 95.3 3.7 96 Anaerobic digester sludge Synergistetes bacterium clone (AB780941) 2 42 Candidatus Aminicenans sakinawicola (NZ AWNV00000000)

OP8 ( leachate sediment OP8 bacterium clone (HQ183967) G8 1.72 35 37.9 88.6 0.0 Yellowstone hot spring Aminicenantes bacterium clone OPB95 (AF027060)

P 100 Aminicenantes (OP8) 32 98 G4 98 Anaerobic digester sludge OP8 bacterium clone (CU918664.59.1456) A 100 G9 1.90 17 56.1 90.7 3.7 100 G22 Desulfovibrio 100 Desulfovibrio aminophilus DSM 12254 (AUMA01000001.3418.4949) n Toluene-degrading methanogenic consortium Desulfovibrio bacterium (AF423185) i Desulfovibrio idahonensis (AJ582755) G10 2.21 56 57.3 88.6 0.0 e 96 100 G2 g Syntrophorhabdus aromaticivorans UI (AJUN01000061.89900.91439) 30 G11 2.40 84 39.7 91.6 0.9 54 Phenol-degrading consortium Syntrophorhabdus clone MP1(EF198024) a Syntrophorhabdus 34 DMP-degrading Syntrophorhabdus clone DMPU-68 (EF029845) r 100 Terephthalate-degrading Syntrophorhabdus clone TA10 (AF229783) Deltaproterobacteria G12 2.43 65 51.8 88.8 1.9 e G5 100 Anaerobic digester sludge Syntrophorhabdus clone QEEB3AE12 (CU917624) v 60 62 G20 100 Terephthalate-degrading anaerobic granular sludge Syntrophus clone TA07 (AF229780) G13 2.48 93 60.0 86.0 1.9 o Syntrophaceae 84 94 G12 c 100 Phenol-degrading UASB granular sludge clone (AY261815) Syntrophus aciditrophicus SB (CP000252.738017.739575) G14 2.97 127 48.2 85.0 0.0

d 98 Uncultured sediment Smithella bacterium clone (HE648179) l 98 G21 84 Anaerobic digester sludge Smithella bacterium clone (CU924063) G15 o 4.91 159 68.6 89.7 0.0

f 100 Brachymonas denitrificans (EU434370) f Comamonadaceae Anaerobic digester sludge consortium clone (CU923708) 100 G6 G16 1.88 103 52.2 84.1 0.9 a 100 Brachymonas chironomi (EU346912) Betaproteobacteria

c Advenella kashmirensis WT001 (CP003555.3251467.3253006) 100 G7 G17 2.16 130 52.5 86.9 0.9

S 100 Advenella faeciporci (AB567741) 90 Helicobacter pylori MC903 (U01332) 98 Sulfuricurvum kujiense (AB080645) Sulfurimonas autotrophica DSM 16294 (CP002205) G18 2.13 38 50.8 92.5 1.9 100 92 Anaerobic digester sludge Helicobacteraceae clone BA128(FJ799123) Sulfurovum-like G1 96 Anaerobic digester sludge Helicobacteraceae clone LA103 (FJ799154) 88 94 G1 Epsilonproteobacteria G19 2.70 75 49.6 89.7 0.9 Anaerobic digester sludge Helicobacteraceae clone EtOH-151 (FJ799141) (93.2-94.4%) 82 Nitratifractor salsuginis DSM 16511(CP002452) Sulfate-reducing benzene mineralizing consortium clone SB-17 (AF029044) G20 2.35 132 55.7 73.8 1.9 58 Sulfurovum sp. NBC37-1 (AP009179) 100 100 Sulfurovum lithotrophicum (AB091292) 98 G11 G21 2.42 106 55.3 92.5 0.9 100 SphingobacterialesWCHB1-69anaerobic digester sludge clone R4-52B (GU196214) 94 SphingobacterialesWCHB1-69Anaerobic digester slduge clone 13Cpro-3(AB603834) Anaerobic digester sludge clone QEDV1BD09 (CU919336) Bacteroidetes G22 2.64 240 66.8 86.0 0.0 100 Rikenella microfusus DSM 15922 (ATXW01000002.1335795.1337303) Anaerobic digester sludge Rikenellaceae clone(CU923567) 100 72 G19 G23 2.08 220 52.8 60.0 2.9 100 Anaerobic digester sludge Bacteroidales clone (JF834124) 96 50 G8 Methanobacterium 100 Uncultured Methanobacterium archaeon (FJ167436) Methanobacterium petrolearium (AB542742) Methanobacterium bryantii (AY196657) Estimated by 107 ESCGs and 35 COGs Methanolinea 100 G10 100 Methanolinea tarda NOBI-1 (AGIY02000001.460388.461841) Euryarchaeota Methanosarcina mazei strain zm-15 (KF360023) S(M): genome size; N: contigs number; 100 Toluene-degrading methanogenic Methanosaeta (AF423188) Methanosaeta 82 G23 24 Scaffold cov1e00 rage in MP (37°C) C%: completeness; R%: contamination 0.05 100 Methanosaeta concilii (X51423) Phenol Phenylphosphate 4-Hydroxybenzoate 4-Hydroxybenzoyl-CoA Benzoyl-CoA Cyclohex-1,5-diene-1- 6-Hydroxycyclohex-1-ene-1- carboxyl-CoA HO carbonyl-CoA OH O- (I) P (I) O O SCoA O SCoA O SCoA OH O O O SCoA 2Pi ] ATP AMP + 2Pi CO2 Pi ATP, CoA AMP+ 2Fdr ed 2Fdox 2 ATP, 2[H HO (a) Phenol (Pps) Phenylphosphate (Ppc) 4-Hydroxybenzoat(e4-BCL4)-Hydroxybenzoyl-C(o4A-Hbcr) Benzoyl-CoA (BCR)Cyclohex-1,5-diene-1(-Dch) 6-Hydroxycyclohex-1-ene-1- H2 O carboxyl-CoA carbonyl-CoA HO OH - O OHO O OHScoA + CO2 P O ScoA O ScoA NAD OH O O O ScoA )

ATP AMP + 2Pi Pi ATP, CoA AMP+2Pi 2Fdr ed 2Fdox 2 ATP, 2[H] d

(II) a CO2

HO H (4-OHB decarboxylase) ( (Pps) (Ppc) 2Pi -BCL) (4-Hbcr) (Dch) H P + (4 (BCR) NAD Benzoate AM O ATP H2 O OH OH +O 2 H2O NAD O O- CO2 CL) Butyrate O - O O ) (B Beta oxidization O d a

O H (4-OHB decarboxylase) ( i HO OH (Oah) + 2P NADH Benzoate AMP - P Acetate O O 6-Ketocyclohex-1-ene-1- AT 3-Hydroxypimeloyl-CoA O - 2 H2O carboxyl-CoA O O CL) te O - (B Butyra Beta oxidation O O O O (I) Phosphorylation-carboxylation pathway (by G2 and G5); (II)H carboxylationO O pathwayH (O a(hG14) ) - Acetate O 6-Ketocyclohex-1-ene-1- 3-Hydroxypimeloyl-CoA carboxyl-CoA Pps-Ppc operon encoded by G2, G5 and phenol-degrading Syntrophorhabdus aromaticivorans UI (b) 0 2.5 5 7.5 10 12.5 15 16 (kb) Syntrophorhabdus aromaticivorans G2 (this study) HP1 PpsA B HP2 HP3 LysR-TR HP4 LysR-TR PpcB C C A D X Y 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% h

o Syntrophorhabdus sp. G5 (this study) r t

n HdrB C HP1 PpsA B B HP5 HP2 HP3 LysR-TR PpcB C C A D X Y y 62.3% 72.3% 74.9% 76.4% 69.1% 58.5% 59.1%,55.7% 82.2% 72.4% 69.2% 77.6% 65.5% 65.1% 81.5% S Syntrophorhabdus aromaticivorans strain UI HP1 PpsA B HP2 HP3 LysR-TR HP4 LysR-TR PpcB C C A D X Y 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Thauera aromatica strain DSM6984 Fis-TR ppsA B C PpcB C A D X Y Paak B 59.0% 39.7% 59.1% 40.1%,38.7% 60.1% 39.5% 31.0% 62.8% DDH:R G2 vs. UI: 79.2% (same species); G5 vs. UI: 12.9% (different species)

N Aromatoleum aromaticum strain EbN1 Fis-TR ppsA B C PpcB C A D X Y ANI: G2 vs. UI: 99.8%59.6% (same38.8% species); 58G5.9% vs.40.5% UI:,38.7% 75.6%58.8% 41 .(different0% 30.3% 6species)4.0% Desulfobacterium anilini strain DSM 4660 25

B LysR-TR PpcB ACSL Y B C 4-HbcrA ppsA B TR Paak

R 50.6% 59.3% 44.2% 38.9%

S Desulfotomaculum gibsoniae strain DSM 7213 Sugar diacid utilizing-TR ppsA B PpcB ACSL Y 58.0% 42.9% 53.2% 63.5% Geobacter metallireducens strain GS-15 B

R XylR-TR ppsA B PpcB C Y PAS-TR I 57.8% 40.3% 52.7% 55.3%

Phenylphosphate synthase (Pps) Phenylphosphate carboxylase (Ppc) Transcriptional regulator (TR) CoA ligase Hydrolase Reductase Transferase Hypothetical protein (HP) Other abbreviations: OHB: 4-hydroxybenzoate; 4-BCL: 4-hydroxybenzoate CoA ligase; 4-Hbcr: 4-hydroxybenzoyl-CoA reductase; BCL: benzoate-CoA ligase; BCR: benzoyl-CoA reductase; Dch: cyclohexa-1,5-dienecarbonyl-CoA hydratase; Had: 6-hydroxycylohex-1-en-1-carbonyl-CoA dehydrogenase; Oah: 6-oxo- cyclohex-1-ene-carbonyl-CoA hydrolase; Hdr: heterodisulfide reductase; PaaK: putative phenylacetate-CoA ligase; ACSL: long-chain acyl-CoA synthetase First genomic insights into T78 clade G3: uncultured Chloroflexi T78 clade sp.

Butyrate oxidation (via crotonoyl-CoA)

Relatives: Bellilinea sp. clone De3218 (95.2%); Longilinea sp. clone 48IIISN (95.0%) and Anaerolinea thermophila UNI-1 (87.2%) Potential proteins for electron transfer and energy conservation Electron transfer flavoproteins (2600084386-87); NAD(P)-dependent iron-only hydrogenases (2600085382-85) and NAD(P) transhydrogenases 26 Uncultured ε-Proteobacterium G1: versatile energy

metabolism (S/H2/N)

1.Sulfide oxidation 2.Hydrogen oxidation

+ H2 2H Sulfur 2- g + compounds in SO3 Cyt cox H2S Cyt cox Cyt cox l H H2S Sox system c H2ase 2- Sor 0 Fcc y 2- c 0 Sqr SO4 Cyt cred S Cyt cred (CDXYZAB) - S SO4 Cyt cred 2 H r a MQ MQ MQH2 l lu Cyt b IM l e c MQH2 a r t n MQ MQH2 I + 3.Nitrogen fixation Sulfur and hydrogen oxidation H2ase 2H ATPase Oxaloacetate H2 H2S ADP + ATP Mdh Sqr H Malate 0 Carbonyl group S H2 H2ase MQ MQH2 N Fumarate 2 N2 gas nif Sdh Fdox Fdred - Succinate NH3 NO3

M Nar MQ MQH2 -

O NO NAD N 2 G1 n Glycolysis CO - 2 i o t NO3 i

NDH r t

D-Glucose-6 D-Fructose-6 Glyceraldehyde Phospho- o a g NADH l phosphate phosphate 3-phosphate enolpyruvate Pyruvate Acetyl-CoA Fatty acids MQH2 • 1.78 Mb y e

r Nap n

o MQ

u h • >99%

Flagellum Chemotaxis t p - i l s NO2 assembly proteins z o D-Xylulose 5- D-Erythrose Sedoheptulose Citrate a

h Oxaloacetate t completeness i p o cdNir phosphate 4-phosphate 7-phosphate n

MQ MQH2 e v i t

Cyt a D-Ribulose 5- alpha-D-Ribose Malate Isocitrate NO

Cyt c d bc1 i phosphate 5-phosphate Reductive TCA NO Cyt c x • Not isolated yet. O cycle Pentose Phosphate Cycle CO Nor Cyt O2 2 cbb3 Fumarate 2-oxoglutarate H2O N2O • No close isolated O2 Cyt bd Succinate Succinyl-CoA CO2 N O As5+ 2 H2O HM HM relatives ArsC ArsR ATP ADP ATP ADP Mutidrug HM Nos MQ MQH2 As3+ P- P- ArsP ArsB MRE CDF type type N2

4.Oxygen tolerance As3+ HM HM HM Mutidrug (Co,Zn,Cd) (Cu2, Ca, Zn) (Vancomycin, Acriflavin) ABC Transporters Two-Component Systems (G3, 28 ; NBC37-1, 28) (G3, 22 ; NBC37-1, 28)

Proteins unfound in G1 Proteins unfound in NBC 37-1 Proteins unfound in both

5. Antibiotics, arsenate and Give some hints/directions for isolation. metal resistance 27 Proposed microbial syntrophic and competitive relationships in phenol-degrading consortia

MT & AT: Methanogenic AT (20℃): Sulfate 2005  2015 phenol degradation /sulfite/sulfur/ respiration Phenol Sulfate

G2&G5 G22 G15 G14

Phenylphosphate APS GSB

G2&G5 G22 G15

4-hydroxybenzoate Sulfite

G2& G5 G14 GSB

Benzoyl-CoA G14

G12 G20

Fatty acid & alcohol Sulfur/ G3& G12 G20, G21, G14 polysulfide

GSB Acetate Formate & hydrogen G1 G14 G21 G15 G23 G8&G10 G22 Sulfide/ Methane polysulfide

We know more about the microbial communities. Competition But, how will these knowledges contribute to better performance of the reactors? 28 Consultancy Anaerobic Digestion Projects

• Co-digestion of food waste and combined sludge (Stage I to Stage III)

• Anaerobic digestion of CEPT sludge

29 Co-digestion of food waste with sewage sludge VS reduction ratio in the four reactors

HRT 25 d----R1: FW/FSS=5:5; R2: FW/FSS=2:8; R3: FW/FSS=0:10;

HRT 15 d----R4: FW/FSS=2:8

68%

56%

54% 43%

Days of Digestion

 Consistent with the results obtained in previous batch tests, a higher FW/FSS ratio corresponded with a higher VS reduction ratio.  The two HRTs of 15 days and 25 days did not show obvious impact on the VSR.

30 CH4/VSR and CH4 content HRT 25 d----R1: FW/FSS=5:5; R2: FW/FSS=2:8; R3: FW/FSS=0:10;

HRT 15 d----R4: FW/FSS=2:8

677 598 352

265

Days of Digestion 68% 65% 57%

42%

Days of Digestion  A higher FW/FSS ratio corresponded with lower methane content detected in the biogas generated, and a longer HRT had a larger total volume of biogas generated

31 Anaerobic digestion of CEPT sludge and the microbial ecology • Process optimization for anaerobic digestion of Chemical Enhanced Primary Treatment (CEPT) sludge.

HRT

FeCl 3 OLR dose CEPT Four 0.8-L digesters (M1-M4) sludge

5% Temper Salinity ature Two 8-L digesters (R1 & R2)

CEPT sludge Key operational parameters examined Feed frequency: every 2 days; FeCl3 dose; NaHCO3 supply to control pH 95% above 6.80; Equal SRT and HRT Coagulants and polymer used for CEPT process SCI-STW (600t) Other-STWs (30t)  75% of 2.2 million m3 sewage daily disposed at Stonecutters Island (SCI) STW using CEPT process (2001-now), yielding 600 wet tons (per day) of CEPT sludge. 32 Effect of SRT/HRT and co-varied OLR on methanogenic digestion of CEPT sludge

Accumulative biogas production (L) over 90 days 100 4 Performance HRT (day) 90 3 7 days (average value) 7 9 12 16 80 9 days 2 12 days SMP (m3/kg-VS) 0.31 0.84 0.83 0.97 70 1 16 days 3

OLR (kg/m3/d)OLR SBP (m /kg-VS) 0.45 1.24 1.25 1.46 60 0 12 20 26 34 42 50 58 66 74 82 90 50 Vgas (L/d) 0.56 1.16 0.94 0.73 40 VSR (g/d) 1.29 0.98 0.75 0.51 30 VSR% 53 52 53 48 HRT = 7 days 20 HRT = 9 days CH4 (%) 68.0 68.0 66.3 66.5 HRT = 12 days 10 HRT = 16 days Effluent pH 6.92 6.98 7.08 7.12 0 0 2 4 6 8 1012141618202224262830323436384042444648505254565860626466687072747678808284868890 No. of measure 16 16 16 16 Time (day) o *Other conditions: FeCl3 dose =1.3 mL/L-sludge; T = 35 C; Feed sludge source: SCI STW CEPT slude; Operating time: 90 days.  Shortening of HRT from 16 to 12 or 9 days contributed to significant increases (P-value <0.05) in biogas production by 29-59% (from 0.73 L/d), VSR by 39-92% (from 0.51 g/d), accompanied with slight decreases in SMP by 14% (from 0.97), SBP by 14-15% (from 1.46), and an increase in alkalinity

supply (from 0 to 0.83-1.44 mL/d NaHCO3 solution).  Further decrease of HRT from 9 to 7 days led to sharp drops in SMP (by 63%), SBP (by 64%), and

Vgas (by 48% L/d), and dramatic increase in both alkalinity consumption by 255% and VFAs accumulation by 211% (from 72.8 mg/L).

 Therefore, a HRT of 9-12 days is recommend for design of CEPT sludge digesters in Hong Kong

33 Deterministic factors affect microbial community structure much more than SRT/HRT

(a) NA 7 9 12 16 3D-PCoA (left figures), Procrustes Analysis SRT(d) (of PCs) and BIO-ENV Analysis congruously

Day10 support :

1. Despite of the difference in HRTs (16, 12,

SEED 9 and 7 days), microbial communities in the four digesters were highly synchronised FEED and dynamic over 90 days. Weighted UniFrac 2. Bacteroidales (42.8% in average), Clostridiales (23.0%) and Kosmotoga mrcj (b) (I) (II) (III) NA Stage (8.6%) dominated in all 48 digester samples

Day10 (I) (II) This implicates deterministic factors, such as substrate nature (CEPT) and availability and (III) SEED species interactions (e.g., competition), dominate over operational parameters (SRT and co-varied OLR) in shaping microbial dynamics FEED Weighted UniFrac during startup and initial steady operational periods. Microbial dynamics in four methanogenic digesters with different SRTs. Stage I: Day 10-34; Stage II: Day 42-66; Stage III: Day 74-90. 34 Strong competitive roles of Bacteroidales populations in CEPT sludge digesters

360 Bacteroidetes (27) Firmicutes (26) Proteobacteria (15) Verrucomicrobia (6) (b) s 1 (c) -0.90 Chloroflexi (5) Euryarchaeota (4) Actinobacteria (4) Synergistetes (3) 320 O ther Bacteroidales 317

s Non-Bacteroidales -0.85 N=53 N=26 N=52 n

Tenericutes (3) WWE1 (2) Other phyla (5) Unclassified OTU (1) s

o 280 n i t o -0.80 i a t l 240 a e l (a) r r s19(g)Clostridium e -0.75 r o

200 r c o

f

c -0.70

s30(g)Methanosaeta o 160

e

r 131 v e s28(o)LD1-PB3 i -0.65 t

s40(o)WCHB1-41 b 120 s10(o)Bacteroidales a g m e u -0.60 s35(g)HA73 80 N s15(o)Bacteroidales N s29(g)Syntrophomonas s41(g)Ruminococcus17(s)F. succinogenes s23(g)T78 40 -0.55 s34(f)Thermovirgaceae s1 has 0 -0.50 s18(g)Syntrophomonas Negative Positive Other s1 Non- s27(c)Mollicutes only 3 positive correlations Bacteroidales Bacteroidales s2(s)Kosmotoga-mrcj s1(o)Bacteroidales (d) 10 80 s1 s2 s6 s7 s27 s33 s44 s45 s44(o)Clostridiales s36(o)Clostridiales s40(f)SB-1 s33(f)BA008 s22(s)S. minor (%) 8 s8(g)Streptococcus s3(g)Clostridium 60 6

s26(f)SB-1 s6(g)Syntrophomonas 40 s7(o)Bacteroidales s50(g)Thauera 4 s45(c)Mollicutes s41(o)Bacteroidales s49(f)Marinilabiaceae s42(Unassigned) s43(f)Neisseriaceae 20 2 s25(s)B. adolescentis s4(g)Clostridium

s16(g)Syntrophomonas Relative abundance Relative s1 of 0 0

D SRT=9 days SRT=7 days

E SRT=16 days SRT=12 days

E (Day 10-90) (Day 10-90) S (Day 10-90) (Day 10-90) Left panel: positive correlations Right panels: negative correlations (Spearman’s rho > 0.6, P-value <0.01) (Spearman’s rho < -0.6, P-value <0.01)  Over 60% of negative correlations (i.e., 131 co-exclusion instances) involved the dominant uncultured Bacteroidales species s1 (20%, GreenGenes taxonomy ID: 837605) or other Bacteroidales species-OTUs (40%). 35 Optimization of ARDB Database

ARDB-Antibiotic Resistance Genes Database (Liu and Pop, 2009) http://ardb.cbcb.umd.edu/

Remove non-ARGs sequences Accurate annotation non-ARGs sequences were removed

Remove redundant sequences Shorten computation time 2998 non-redundant sequences

Construct structured database Automatic classification process 618 ARGs subtypes in 25 ARGs types

Yang Y, Li B, Ju F, Zhang T*. 2013. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. Environmental Science and Technology. 47 (18), 10197–10205. 36 Non-redundant Structured ARDB

37 Removal of ARGs during Anaerobic Digestion

38 Elimination of Pathogen during Anaerobic Digestion

39 Environmental Biotechnology Laboratory Department of Civil Engineering The University of Hong Kong Grants: GRF and ITF of Hong Kong Hong Kong DSD

Group members (working on Anaerobic Projects) • Ju Feng • Wang Yubo • Xia Yu • Huang Danping • Wang Yulin 41