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Using Network Analysis to Explore the Bacterial Co-Occurrence Patterns

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Using Network Analysis to Explore the Bacterial Co-Occurrence Patterns 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: Bacteria 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 & taxonomy; 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 Proteobacteria (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).
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