Genome-centered metagenomics analysis reveals the symbiotic

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Genome-centered metagenomics analysis reveals the symbiotic organisms possessing ability to cross-feed with anammox bacteria in anammox consortia Yunpeng Zhao, Shufeng Liu, Bo Jiang, Ying Feng, Tingting Zhu, Hu-Chun Tao, Xi Tang, and Sitong Liu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b02599 • Publication Date (Web): 10 Sep 2018 Downloaded from http://pubs.acs.org on September 11, 2018

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Environmental Science & Technology

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Genome-centered metagenomics analysis reveals the

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symbiotic organisms possessing ability to cross-feed with

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anammox bacteria in anammox consortia

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Yunpeng Zhao 1, Shufeng Liu 1, Bo Jiang 1, Ying Feng 1, Tingting Zhu 2, Huchun Tao

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, Xi Tang 1, Sitong Liu 1, 3 *

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Department of Environmental Engineering, Peking University, Beijing 100871, China

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State Environmental Protection Key Laboratory of Drinking Water Source Management and

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Technology, Shenzhen Key Laboratory of Emerging Contaminants Detection & Control in Water

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Environment, Shenzhen Academy of Environmental Sciences, Shenzhen 518001, China

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518055, China

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*Corresponding author: Sitong Liu

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Address: College of Environmental Science and Engineering, Peking University, Yiheyuan Road,

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No.5, Haidian District, Beijing 100871, China.

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E-mail: [email protected]

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Tel/Fax: 0086-10-62754290.

School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen

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1

ACS Paragon Plus Environment


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Abstract

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Although using anammox communities for efficient wastewater treatment has

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attracted much attention, the pure anammox bacteria are difficult to obtain, and the

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potential roles of symbiotic bacteria in anammox performance are still elusive. Here,

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we combined long-term reactor operation, genome-centered metagenomics,

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community functional structure, and metabolic pathway reconstruction to reveal

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multiple potential cross-feedings during anammox reactor start-up according to the 37

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recovered metagenome-assembled genomes (MAGs). We found Armatimonadetes

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and Proteobacteria could contribute the secondary metabolites molybdopterin cofactor

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and folate for anammox bacteria to benefit their activity and growth.

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Chloroflexi-affiliated bacteria encoded the function of biosynthesizing

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exopolysaccharides for anammox consortium aggregation, based on the partial

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nucleotide sugars produced by anammox bacteria. Chlorobi-affiliated bacteria had the

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ability to degrade extracellular proteins produced by anammox bacteria to amino

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acids to affect consortium aggregation. Additionally, the Chloroflexi-affiliated

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bacteria harbored genes for a nitrite loop and could have a dual role in anammox

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performance during reactor start-up. Cross-feeding in anammox community adds a

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different dimension for understanding microbial interactions and emphasizes the

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importance of symbiotic bacteria in the anammox process for wastewater treatment.

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2


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Introduction

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Anaerobic ammonium oxidation (anammox) converts ammonium directly to

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dinitrogen gas, with nitrite as an electron acceptor,1 and has been used for treating

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ammonium-containing wastewater for its energy-efficient advantages.2,3 This process

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has not been widely applied at full scale, and even then, it has been almost exclusively

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for sidestream wastewater.3,4 It is mediated by a group of Planctomycetes bacteria

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called anammox bacteria.1 Five genera of anammox bacteria have been discovered,

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Kuenenia, Brocadia, Anammoxoglobus, Jettenia, and Scalindua,2,5 yet none of these

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anammox bacteria have been purified so far.2,5 The microorganisms belonging to the

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phylum Chloroflexi, Chlorobi, Proteobacteria, Acidobacteria, and Bacteroidetes

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comprise 30–70% of the anammox consortia.2,4,6,7 The members of the anammox

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consortia may share symbiotic relationships, which might be useful for anammox

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metabolism, including growth factor supply or anammox metabolite scavenging.

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Although this phenomenon has been observed for a long time, the potential roles of

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these symbiotic bacteria in the consortia are still elusive.

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Life in the microbial world is structured on the basis of complex interactions

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among microbiomes.8,9 Cross-feeding, the production of metabolites that include

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primary and secondary metabolites and can be used both by the producer and other

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microbes in the community,10 is a type of microbial interaction that is beneficial for

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both the producer and the receiver.11,12 This ubiquitous phenomenon exists in

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microbial communities, especially in those growing under anoxic conditions and

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nutrient and energy constraints.13 It is useful for extending the biochemical and 3



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physiological capabilities of each participant.11 There are two types of cross-feeding

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interactions:14 i) substrate cross-feeding, in which energy-rich organic substances are

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degraded by one and then used by another bacterium; and ii) metabolic cross-feeding,

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where the metabolites utilized by one type of microorganism are produced from

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another type. Recently, metabolic cross-feeding has received increasing attention

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because it involves metabolite exchange in natural and particularly in artificial

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ecosystems, such as carbon source cross-feeding between autotrophic and

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heterotrophic bacteria in the autotrophic denitrifying community in WWTPs.15

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Microbial cross-feedings have been reported in the anammox community,

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especially for carbon and energy sources. Initially, heterotrophs were detected in

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anammox sludge, which could grow on the soluble microbial products of the

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biofilm.16 Subsequent reports showed that the extracellular polysaccharides produced

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by the anammox bacteria could be used as carbon source by certain heterotrophs of

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the Phycisphaerae classes and Anaerolineaceae families.2,7 Recent studies showed

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that less costly extracellular amino acids are used as carbon and energy sources,

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especially by Chlorobi microorganisms.2 Specifically, these species can degrade the

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extracellular proteins and peptides secreted by the anammox bacteria, and the

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resultant amino acids are used either by themselves or by other microorganisms in the

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community that cannot biosynthesize amino acids. Meanwhile, anammox bacteria can

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also supply vitamins to other bacteria in the community.2 Nitrogen exchange between

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anammox and denitrifiers is also considered as a cross-feeding. Nitrate, a product of

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anammox reactions, can be used for the growth of species affiliated to the phyla 4


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Chlorobi, Acidobacteria, and Omnitrophica.3 Simultaneously, the nitrite produced in

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this process can be reused by anammox, which improves effluent quality.3 Although

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metabolic cross-feeding has been identified in the anammox community, whether

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more cross-feedings exist and how the abundance of species containing genes

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potentially involved in cross-feeding remain unclear.

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In this study, a sequencing batch reactor (SBR) was operated for 280 days. Sludge

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samples were taken at different operational phases and analyzed using metagenomic

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sequencing combined with binning. The adopted genome-based microbial ecology

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method was similar to that used in a recent study.3 The Chloroflexi, Armatimonadetes,

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Proteobacteria, Chlorobi bacteria possessing the functions to cross-feed with

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anammox bacteria with respect to secondary metabolite syntrophism,

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exopolysaccharide biosynthesis, or amino acid and nitrite exchange, as well as the

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abundance variations of the genomes containing the genes for the above functions

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during the start-up were investigated. Our results provide deeper insights into

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microbial metabolic cross-feeding in anammox consortia during reactor start-up,

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which will aid in understanding the roles of symbiotic bacteria in such consortia and

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developing operational strategies of anammox processes.

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Material and Methods

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Start-up of the anammox reactor. A lab-scale SBR with a working volume of 3 L

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anammox consortia was inoculated with an initial volatile suspended solids (VSS)

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concentration of 0.1 g/L. It was taken from an SBR which was enriched with

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Candidatus Brocadia sinica (abundance of 50 ± 5% in the whole community) and had 5



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been preserved at 4°C for 90 days before inoculation.17 The SBR was fed with the

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synthetic inorganic wastewater18 (presented in Supporting Information (SI) Text S1)

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and continuously run for 280 days. Each reactor operational cycle included

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continuous feeding (200 min), an anaerobic reaction period (20 h stepwise reduced to

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2 h), settling (35 min), and withdrawal (5 min). The operational strategy was stepwise

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increasing the nitrogen loading rate (NLR), which included increasing the nitrogen

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concentration in the influent from 100 to 550 mg/L and shortening the hydraulic

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retention time (HRT) from 4 days to 18 h. According to the NLR and reactor

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performance, the reactor could be divided into five phases, and the detailed NLR of

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each phase is presented in SI Table S1. The SBR was stirred at 150 rpm with a turbine

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stirrer. Anaerobic conditions inside the reactor were maintained by continuously

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purging N2/CO2 (95/5%) gas. The pH in the influent was kept in the range of 7.5 ± 0.3,

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and the temperature was maintained at 37 ± 1 °C with a thermostatic water jacket.

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Influent and effluent samples were collected every 2 days to measure the

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concentrations of NH4+–N, NO2-–N, and NO3-–N according to standard methods.19

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Five samples were harvested on the initial day (day 0) and at the end of each

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operational phase (day 106, day 166, day 218, and day 280) for DNA extraction, and

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these samples were labeled S1–S5, respectively.

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DNA extraction and meta-genome sequencing. Total genomic DNA was

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extracted using the FastDNA® spin kit for soil (MOBIO Laboratories Inc.) according

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to the manufacturer’s protocol. DNA purity and concentration were determined using

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a NanoDrop 2000 and the TBS-380 PicoGreen assay. DNA quality was checked by 6


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1% agarose gel electrophoresis with ethidium bromide staining. The DNA samples

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were then used for shotgun library construction and Illumina high-throughput

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sequencing on the HiSeq 2500 platform at Majorbio Co. Ltd. (Shanghai, China) to

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generate 150 bp paired-end reads (420 bp mean insert size) with seven runs. The raw

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Illumina reads could be found on the National Center for Biotechnology Information

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(NCBI) website under BioProject PRJNA450161.

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Metagenome assembly, binning, and analysis. The raw metagenomic reads were

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initially trimmed by stripping the adaptor sequences and ambiguous nucleotides using

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SeqPrep version 1.1 based on default parameters,20 and the trimmed sequences were

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quality filtered using Sickle version 1.33 based on a minimum quality score of 20 and

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a minimum sequence length of 50 bp.21 Contigs and scaffolds were then assembled

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individually for each sample using IDBA–UD with default parameters.22 The

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scaffolds generated were binned into the draft genomes based on abundance and the

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tetranucleotide frequency using MetaBAT version 0.32.5 with the sensitive model.23

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CheckM version 1.0.7 was used to assess the completeness and contamination of the

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recovered draft genomes based on the marker gene sets of 111 essential single-copy

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genes.24 Draft genomes have been deposited to GenBank under the accession numbers

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presented in SI Table S2.

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The relative abundance of each MAG was calculated using the method previously

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reported.2 First, the raw metagenome reads from each sample were mapped to all

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contigs assembled to the draft genomes and contigs after assembly using bbmap

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version 37.75 (https://sourceforge.net/projects/bbmap/) with the parameters ‘minid = 7



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0.95’ and ‘ambig = random’. Then, the number of reads that were mapped to each

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MAG was normalized by the genome size of each MAG and the total reads that were

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mapped to all contigs after assembly, which was expressed as reads per kilobase per

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million mapped reads (RPKM). After that, the relative abundance was calculated

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using the ratio between the RPKM value of each genomes and the sum of the RPKM

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values of all genomes in each sample. Additionally, the average nucleotide identity

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and comparison of draft and reference genomes were calculated using OrthoANI

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version 0.93.125 and BRIG version 0.95.26

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The open reading frames (ORFs) of each recovered draft genome were annotated

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using the metagenome implementation of Prodigal version 2.6.3 based on a minimum

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nucleotide length of 60.27 ORFs were then queried against eggNOG database

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(accessed on December 2016) and Kyoto Encyclopedia of Genes and Genomes

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pathway database (KEGG, accessed on August 2017) with the blast e value of 1e-5.

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The subcellular locations of ORFs were predicted using CELLO version 2.5.28

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Construction of phylogenetic tree for the recovered draft genomes. Phylosift

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version 1.0.1 was used to construct the phylogenetic tree.29 First, the taxonomic

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affiliation of each MAG was determined using the Phylosift “all” command. At the

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same time, the marker genes of 58 reference genomes that were related to the

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recovered MAGs and deposited in GenBank were also identified and used to build a

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phylogenetic tree. Then, the concatenated protein alignments of the recovered draft

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genomes and the reference genomes were aligned using MAFFT version 7.31030 and

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reconcatenated. Finally, a maximum likelihood phylogenetic tree was conducted using 8


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RAxML version 8.2.11 with 100 bootstraps and the automatic protein model

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assignment algorithm (PROTGAMMAAUTO).31

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Functional structure analysis of the anammox community. The community

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functional structures of different operational phases were established using the

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functional gene abundance at the level of COG classes with principal component

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analysis (PCA), as previously reported.32 In detail, the functional gene abundance of

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COG functional class was calculated by adding the gene abundance of a certain COG

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class in each MAG, in which the gene abundance was calculated by normalizing the

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number of reads that were mapped to the gene sequence by the number of the 16S

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rRNA gene sequence identified for each metagenomic sequencing data and gene

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sequence length to prevent deviations caused by the slight difference in sequencing

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depth, as reported.33 Then, the functional gene abundance matrix of COG functional

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class was used for PCA in the R environment using the ggbiplot package.

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Results

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Varied nitrogen removal performance during reactor start-up. According to

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the nitrogen loading rate (NLR) and nitrogen removal rate (NRR), the reactor

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operational period could be divided into five operational phases during the reactor

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start-up. In phase I (lag phase, days 1–77), the reactor showed extremely low nitrogen

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removal performance, with a NRR of only 0.005 kg N/(m3 d) (Figure 1c) and nitrogen

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removal efficiency (NRE) of only 10% (Figure 1d). In phase II (early propagation

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phase, days 78–106), the nitrogen removal performance increased sharply, with NRR

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and NRE increasing from 0.005 to 0.076 kg N/(m3 d) and from 9% to 80%, 9



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respectively. The NRR increased further to 0.24 kg N/(m3 d) with a stable NRE of

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80% in phase III (later propagation phase, days 107–167). To avoid nitrite

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accumulation leading to the inhibition of bacterial activity in the reactor, the influent

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nitrogen concentration decreased to 400 mg/L from day 142, and it increased again

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from day 162. However, once NLR increased to 0.5 kg N/(m3 d) on days 173 and 183

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as the HRT shortened and nitrogen concentration increased (Figure 1d), the reactor

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performance deteriorated, and the high NRR and NRE could not be maintained in

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phase IV (inhibition phase). At the same time, the pH difference between the effluent

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and influent also decreased during this period (SI Figure S1). After culturing with

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relatively low influent nitrogen concentrations, the nitrogen removal performance

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increased constantly in phase V (stationary phase, days 221–280), with a NRR of 0.57

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kg N/(m3 d) and a NRE of 80%. The NRR was higher than that obtained in previous

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SBRs34, membrane bioreactors (MBR)7,35 and continuous stirred-tank reactors

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(CSTR).36

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The nitrogen removal ratios were also different between the five phases. Although

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both the nitrite-to-ammonium consumption ratio (△NO2-–N/△NH4+–N) and the

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nitrate-to-ammonium conversion ratio (△NO3-–N/△NH4+–N) varied considerably (SI

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Figure S2 and Table S3) in phase I and the early stage of phase II, their values were

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close to the stoichiometry of the anammox reaction towards the end of phase II, with

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the average △NO2-–N/△NH4+–N and △NO3-–N/△NH4+–N of 1.20 ± 0.10 and 0.48 ±

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0.23. In phase III, the average △NO2-–N/△NH4+–N and △NO3-–N/△NH4+–N were

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1.20 ± 0.08 and 0.28 ± 0.06, respectively, which were in agreement with the 10


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stoichiometry of anammox reaction during the reactor start-up. Afterwards, both

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values decreased while the reactor performance deteriorated in phase IV, with average

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△NO2-–N/△NH4+–N and △NO3-–N/△NH4+–N of 1.18 ± 0.08 and 0.26 ± 0.07,

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respectively. In phase V, the average values of △NO2-–N/△NH4+–N and

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△NO3-–N/△NH4+–N were 1.16 ± 0.08 and 0.23 ± 0.03, respectively, which agreed

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with the previously reported values of 1.1–1.32 of △NO2-–N/△NH4+–N and 0.18–0.26

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of △NO3-–N/△NH4+–N for the anammox reaction.37

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Metagenomic sequencing, binning, and microbial succession. In total,

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382,017,580 reads were generated, and 373,141,944 reads were obtained after read

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quality control for five sludge samples harvested at the start of phase I and the end of

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four phases (SI Table S4). The clean reads were used for assembly, and approximately

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167,980–461,389 contigs with an N50 of 2,533–3,345 bp were generated. Contigs

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were binned to the draft genomes, and 37 high-quality genomes were recovered with

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high completeness and low contamination. Importantly, these 37 draft genomes

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accounted for 55.11% of the original sequencing data from five samples on average

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(SI Table S5), which was higher than that obtained (47.72%) in a previous study about

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anammox granular binning.3 It indicated that these MAGs represented a major

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fraction of the anammox community during reactor start-up.3

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These draft genomes were affiliated to the phyla Planctomycetes, Armatimonadetes,

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Chloroflexi, Proteobacteria, Chlorobi, Acidobacteria, Actinobacteria, Nitrospirae,

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Microgenomates, and some candidate phyla, including Candidate division KSB1 and

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Uhrbacteria (Table 1 and Figure 2). Specifically, AMX1, ATM1, CFX4, and CHB1, 11



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which had high abundance, were similar to Candidatus Brocadia sinica JPN1,

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Armatimonadetes bacterium OLB18, Anaerolineae bacterium UTCFX3, and

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Ignavibacteriales bacterium UTCHB1 (SI Figure S3 and Table S6).2,3,38 Additionally,

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GWF1, KSB1, UCB1, UCB2, and UCB3 had low identities and were genetically

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distant from previously sequenced bacteria (Figure 2a and SI Table S6).

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Although the relative abundances of CFX1, CFX2, and PRO1 were higher than

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that of AMX1 for S1 (Figure 2b and SI Table S5), AMX1 became the predominant

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MAG for S2, with an abundance increasing to 20.95% and then to 56.15% at the end

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of the reactor start-up. Importantly, the second-most abundant MAG was CFX3 for S2,

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whereas it was ATM1 for S3, S4, and S5. MAGs affiliated to Armatimonadetes,

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Chloroflexi, Proteobacteria, and Chlorobi accounted for 54.8 ± 15.9% during S2–S5

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(SI Table S7).

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Variation of community functional structure. The PCA analyses using the

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functional gene abundance of COG class showed the functional profile in the

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anammox consortia (Figure 3), as the MAGs that had similar gene abundances at the

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COG class level would colonize into a cluster. Intriguingly, there were marked

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differences in the colonization-specific clusters between the five samples. In detail,

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there were six groups for S1 within the complex MAG composition in every group

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(Figure 3b). From S2 to S5, the number of clustering groups decreased, with relatively

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smaller parts of MAGs clustering into a group. For example, partial MAGs of the

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phyla Armatimonadetes, Chloroflexi, Proteobacteria, and Chlorobi were always in one

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group that separated from AMX1 (Group II) and groups consisting of most other 12


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MAGs (Figure 3c–f). However, the MAG composition for every group changed

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constantly, while AMX1 (Group II) was separated from other MAGs alone in the last

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four samples. These results suggest that the functional gene abundances of these

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MAGs varied constantly and the symbiotic members had different functional gene

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abundances compared with AMX1. For example, in addition to AMX1, CFX3 (Group

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IV) was also separated from Group I (other MAGs) in S2, whereas ATM1 (Group IV)

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was separated from Group I (other MAGs) in S3 and S4. In S5, groups of ATM1,

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ATM2, PRO1, CFX4, and CHB1 (Group III and Group IV) were also separated from

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Group I (other MAGs) and Group II (AMX1), which also indicated that these MAGs

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had different functional gene abundances of COG class compared to AMX1 and other

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MAGs.

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To identify gene functions that were enriched in the above MAGs, we compared

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the functional difference of each MAG at the level of COG class. Importantly, there

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were distinctive metabolic functions for these MAGs (Data S1 and Figure S4). In

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detail, CFX3 was enriched in COG class G (‘carbohydrate transport and metabolism’),

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whereas CFX4 had more metabolic functions in COG class C (‘energy production and

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conversion’). ATM1, ATM2, and PRO1 were enriched in COG class Q (‘secondary

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metabolites biosynthesis, transport and catabolism’). However, AMX1 contained

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fewer genes associated with such metabolic functions. These results suggest that the

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different metabolic functions and different abundances in the five samples of these

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MAGs made them colonize different clusters in different samples, which implied that

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they might play different roles in different operational phases. 13



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Metabolic pathway restructuration. To further identify the specific functional

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potentials and metabolic pathways of the above MAGs, the ORFs were also blasted

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against the KEGG database. The partial metabolic pathways of these MAGs with

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more than 5% relative abundance in at least one sample are shown in Figure 4. They

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included AMX1 (abundance of 6.72–56.15), ATM1 (abundance of 5.63–10.40%),

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ATM2 (abundance of 5.23–25.01%), PRO1 (abundance of 0.25–6.89%), CFX3

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(abundance of 0.25–10.42%), CFX4 (abundance of 1.58–12.52%), and CHB1

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(abundance of 0.90–6.49%), which accounted for 83.12 ± 14.55% abundance during

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the reactor start-up (Table S7). Interestingly, there were marked differences in

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catabolic potential for different microbial genera: i) The MAGs affiliated to the phyla

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Armatimonadetes and Proteobacteria were enriched in genes encoding components of

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some secondary metabolite biosynthetic pathways, which are important for microbial

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growth and activity. For example, ATM1 and ATM2 harbored genes (mogA and moeA)

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for the biosynthesis of molybdopterin cofactor (MOCO). PRO1 encoded the complete

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pathway for folate production. However, AMX1 lacked these metabolite functions. ii)

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Although CFX3 harbored the complete pathway for biosynthesis of most

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phosphorylated carbohydrates, including UDP-N-acetyl-D-glucosaminuronate

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(UDP-GlcNAcA), GDP-mannose (GDP-Man), and GDP-D-rhamnose (GDP-Rha), it

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lacked functions for other sugar nucleotide biosynthesis, such as for

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UDP-N-acetyl-D-mannosamine (UDP-ManNAc) and CDP-glucose (CDP-Glc),

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whereas AMX1 harbored the most abundant genes for biosynthesis of these molecules.

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At the same time, CFX3 harbored more genes to facilitate macromolecule 14


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polysaccharide biosynthesis, including protein-tyrosine phosphatase,

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polyisoprenyl-phosphate glycosyltransferase, and polymerase, than other MAGs,

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whereas AMX1 lacked them. Importantly, CFX3 also harbored an acidification

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regulation gene for the macro-molecule polysaccharide biosynthesis (ldh), which were

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missing in other bacteria, and contained more polysaccharide transporters in the inner

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and outer membranes. iii) Nitrate reduction was redundant in CFX4, which had two

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copies of nitrate reductase subunits genes (narGHIJ), whereas it harbored only one

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copy of the nitrite reductase (NO-forming) nirS, which could cause nitrite

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accumulation during denitrification. These observations were in agreement with the

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comparative results of COG functional analysis. Furthermore, CHB1 had more

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extracellular peptidases for protein hydrolysis, such as serine-type D-Ala-D-Ala

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carboxypeptidase/endopeptidase, and D-alanyl-D-alanine carboxypeptidase among

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specific peptidases, as well as other unspecific peptidases. At the same time, CHB1

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possessed less complete pathways for amino acid biosynthesis, especially for many

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amino acids with high biosynthetic cost, including tryptophan, phenylalanine, tyrosine,

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methionine, and isoleucine, in line with a previous study.2

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Based on gene abundance calculations in the whole metagenome, significant

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discrepancies for the relative abundance of the above-mentioned genes in different

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operational phases were observed (Figure 4). It was higher for moeA (for MOCO

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production) from ATM1 and ATM2, and folA (for folate biosynthesis) from PRO1 in

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the later propagation and stationary phases. The genes for polysaccharide

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glycosyltransferase, polymerase, and transporters in CFX3 were abundant in the early 15



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propagation phase, which decreased constantly till the end of the start-up. The

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abundance of peptidase-encoding gene from CHB1 was relatively similar for S2, S3,

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and S5, while it was lowest in the inhibition phase (S4). In contrast, narGHIJ of

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CFX4 was highest in the inhibition phase.

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Discussion

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Metabolic cross-feedings in anammox consortia. Although the existence of

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symbiotic bacteria in anammox consortia has been observed and the potential roles of

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these bacteria in amino acid and vitamin exchange in anammox communities have

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been reported recently,2,3 this study attempted to identify additional roles of these

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bacteria in anammox performance with respect to cross-feedings within the anammox

334

consortia in terms of secondary metabolite symbiosis and polysaccharide biosynthesis.

335

Our study not only highlights cross-feedings in the anammox consortia but also

336

implies their potential roles in anammox activity, growth, and aggregation.

337

Here, we presented that multiple cross-feedings could exist in the anammox

338

consortia, and the abundances of recovered genomes containing genes potentially

339

involved in cross-feeding varied during the reactor start-up. Although the abundance

340

of AMX1 increased constantly from S1 to S5, microorganisms belonging to

341

Armatimonadetes, Chloroflexi, Proteobacteria, and Chlorobi showed relatively higher

342

percentages all along, which was in agreement with the results of previous studies.7,39

343

These metagenome-assembled genomes (MAGs) play an important ecological role in

344

the anammox community.2,3,7 Reports indicate functional complementarity of nitrogen

345

and amino acid metabolism between organisms in the microbial consortia, which 16


Page 17 of 43


346

results in metabolite cross-feedings.2,3 We observed that these microorganisms had

347

distinct metabolic potentials with respect to secondary metabolite biosynthesis,

348

carbohydrate metabolism, extracellular protein and peptide degradation, and partial

349

denitrification, which were complementary to the metabolic potential of anammox

350

bacteria. Importantly, most of these functions were vital for anammox metabolism and

351

activity.2,3,7,40,41 Therefore, multiple metabolic cross-feedings among these bacteria

352

could exist in the anammox community (detailed discussion below). Simultaneously,

353

the relative abundances of the core genes encoding the above functions and the

354

microbial functional structures in the different operational phases during reactor

355

start-up were discrepant, which might lead to the varied cross-feeding strength. It has

356

been reported that microbial cross-feeding plays an important role in driving genetic

357

diversity and community stability and even affects the biochemical capacities of

358

ecological niches.11,12 Consequently, these cross-feedings could affect the metabolic

359

activity of anammox bacteria and the reactor performance during reactor start-up.

360

Cross-feedings of the secondary metabolites MOCO and folate. We propose

361

cross-feedings of secondary metabolites in the anammox consortia. Unlike primary

362

metabolites (amino acids, carbohydrates, and vitamins), secondary metabolites are not

363

directly required for microorganism growth.42 However, they are important for many

364

biological processes, particularly those involved in microbial interactions.42,43

365

Cross-feeding of secondary metabolites, including folate and biotin cofactor, has been

366

reported,10,44,45 as synthesizing them is energetically costly and requires complex

367

pathways. We observed that the anammox bacteria lacked the MOCO and folate 17



Page 18 of 43

368

biosynthetic pathways, whereas the abundant Armatimonadetes and Proteobacteria

369

microorganisms in the consortia were enriched with these metabolic functions. At the

370

same time, the deposited GenBank genomes of anammox bacteria are all missing

371

complete pathways for MOCO and folate biosynthesis, including Candidatus Jettenia

372

caeni (BAFH00000000), Candidatus Brocadia sinica (BAFN00000000), and

373

Candidatus Brocadia fulgida (LAQJ00000000). However, MOCO is a cofactor for

374

many oxidoreductases, including the formate dehydrogenases (FDHs),40 which are

375

involved in the Wood–Ljungdahl pathway of anammox CO2 fixation.2 Folate is also a

376

necessary subunit for most compounds for anammox CO2 fixation, including

377

10-formyltetrahydrofolate, 5,10-methenyltetrahydrofolate, and

378

5,10-methylenetetrahydrofolate.41 These secondary metabolites are vital for anammox

379

growth and activity, as they affect carbon fixation or acetyl-coA production.41 The

380

anammox bacterium Candidatus K. stuttgartiensis also lacks some genes required for

381

folate production and has a mutualistic relationship with the symbiotic organisms.41

382

This is the same as the amino acid or nitrogen metabolism complementarity based on

383

microbial genomes.2,3 Therefore, MAGs belonging to the phyla Armatimonadetes and

384

Proteobacteria can produce these metabolites, which could be subsequently

385

transported to the anammox cell instead of direct production by the anammox for

386

energy saving. This process is a cross-feeding, considering the feedback process of

387

amino acid biosynthesis and secretion by anammox bacteria for Armatimonadetes and

388

Proteobacteria (detailed discussion below). As the cross-feedings of secondary

389

metabolites could lead to high bacterial activity,10 this process also could contribute to 18


Page 19 of 43


390 391

the anammox activity and growth. Furthermore, the secondary-metabolite biosynthesis genes had higher abundance in

392

the propagation (S3) and stationary phases (S5), which was in line with the general

393

observation that secondary metabolites are often secreted during the stationary

394

phase.46 Their abundance was also positively correlated with the increasing NRR in

395

these two phases. The growth and activity of folate-deficient bacteria sharply

396

increases after co-culture with folate-producing bacteria.47 Therefore, the

397

cross-feedings of secondary metabolites between anammox and Armatimonadetes or

398

Proteobacteria bacteria could benefit for improving NRR via affecting anammox

399

growth and activity. This also emphasizes the importance of these bacteria for

400

anammox performance and the difficulty of anammox isolation.48 The exchange of

401

secondary metabolites possibly occurs via cell fusion,49 cell-cell connections,50 or

402

membrane vesicles.51 The detailed functional mechanism of secondary metabolite

403

transportation between bacteria is currently being investigated.52

404

Cross-feeding on macromolecular exopolysaccharide biosynthesis. We firstly

405

propose that CFX3 plays important roles in sticky macromolecular exopolysaccharide

406

biosynthesis owing to the cross-feeding of AMX1 and CFX3 in the anammox

407

consortia. Extracellular polysaccharides are usually assembled using hexose and/or its

408

derivative (sugar nucleotide) as repeating units.53,54 We observed that anammox

409

bacteria showed functional complementarity with another high-abundance MAG,

410

CFX3, in sugar nucleotide production, as AMX1 could biosynthesize UDP-ManNAc

411

and CDP-Glc, whereas CFX3 could produce UDP-GlcNAcA, GDP-Man, and 19



Page 20 of 43

412

GDP-Rha. Meanwhile, compared to AMX1 and other microorganisms, CFX3

413

possessed more enzymes for modification, polymerization, and transportation of

414

carbohydrates, and some of them only existed in CFX3, such as protein-tyrosine

415

phosphatases and polyisoprenyl-phosphate glycosyltransferase. These enzymes are

416

used for sticky polysaccharide biosynthesis.55 Importantly, CFX3 encoded an enzyme

417

(ldh) for the biosynthesis of lactate, which is secreted into the extracellular media to

418

maintain acidity, a prerequisite for exopolysaccharide production.55 Hence, partial

419

nucleotide sugars could be biosynthesized by AMX1 and then transported inside

420

CFX3 for exopolysaccharide production. As the exopolysaccharides form a gel-like

421

cross-network, which is important for the formation of microbial aggregates,53–56 the

422

nucleotide sugar cross-feeding and secretion of macromolecule polysaccharides by

423

CFX3 could benefit for anammox bacteria aggregation. It also feeds back to AMX1

424

for the supply of amino acids (detailed discussion below). Thus, most macromolecule

425

exopolysaccharides could be biosynthesized and secreted by the cross-feedings of

426

anammox and Chloroflexi bacteria in the anammox consortia.

427

The anammox aggregation also could contribute to improvements in NRR and

428

NRE.7,57 Here, the relative abundances of core genes for exopolysaccharide

429

production in CFX3were highest in the early propagation phase (S2), which was

430

coincident with the increasing NRE in this phase, as it benefited for the recovery of

431

the anammox aggregation that had been hydrolyzed during storage.58 Accordingly, the

432

biosynthesis of macromolecular exopolysaccharides through cross-feeding between

433

anammox and Chloroflexi bacteria could be useful for the recovery of aggregation 20


Page 21 of 43


434 435

and activity of the anammox consortia. Cross-feeding of amino acids and nitrite. Cross-feeding of amino acids has been

436

reported recently.2 The abundance of genomes containing the genes associated with

437

amino acid cross-feeding varied constantly during the reactor start-up. The Chlorobi

438

microorganisms harbored the maximum number of extracellular peptidases for

439

hydrolyzing proteins and peptides, while they also had the least amino acid

440

biosynthesis-related genes. This indicated that the Chlorobi microorganisms were the

441

dominant degraders of extracellular proteins, which were mainly produced by the

442

anammox bacteria.2 Importantly, this process released the amino acids sequestered in

443

the extracellular matrix for self-use and as carbon and energy sources for the other

444

organisms in the anammox consortia.2 This can also be a feedback mechanism for the

445

contributions of secondary metabolites and exopolysaccharides of Armatimonadetes,

446

Proteobacteria, and Chloroflexi organisms to anammox bacteria. Importantly, the

447

peptidase gene abundance of CHB1 changed constantly throughout S1-S5, and it was

448

lowest in the inhibition phase (S4). The anammox consortia can enhance its

449

aggregation capacity to cope with environmental stress.6 Hence, the low abundance of

450

extracellular peptidases potentially leads to less protein degradation, which improves

451

anammox aggregation by increasing the ratio of extracellular proteins and resisting

452

the high-nitrite phase.7 Therefore, we conclude that amino acid cross-feeding is a

453

self-protective strategy of the anammox consortia for coping with external stress.

454 455

Additionally, the Chloroflexi organisms harbored the genes for the nitrite loop, and they could play dual roles in nitrogen removal. The nitrate produced from the 21



Page 22 of 43

456

anammox reaction can be reduced to nitrite by partial denitrifiers in the anammox

457

consortia. Subsequently, the nitrite can be reused by the anammox bacteria, and the

458

nitrite loop increases the nitrogen removal efficiency.2,3 This process can accelerate

459

the reactor start-up in the early period when the influent nitrite concentration is

460

limited. In contrast, nitrite cross-feeding could intensify the inhibition of anammox in

461

the later period when the influent nitrite concentration was high. Specifically, in the

462

propagation phases (phases II and III), the production and consumption of nitrite was

463

in dynamic balance, and the nitrogen removal rates were relatively higher. In the

464

inhibition phase (Phase IV), the balance was disrupted with the increase in abundance

465

of narGHIJ, from 0.01 in S1 to 0.15 in S4. The increasing amounts of nitrate could be

466

reduced to nitrite, whereas nitrite reductase (nirS) accounted for only 0.07 in S4,

467

which could lead to further inhibition of anammox when the influent nitrite

468

concentration was relatively higher. These data agreed with the low NRR in this phase.

469

Importantly, the average values of △NO3-–N/△NH4+–N and △NO2-–N/△NH4+–N of

470

phase IV were both lower than those of phases II and III, respectively, indicating

471

lower accumulation and removal rates of nitrate and nitrite, respectively.

472

Consequently, nitrite cross-feeding played a double role in nitrogen removal during

473

the reactor start-up.

474

Significance of this study. Microbial activity and aggregation capacity are

475

important for wastewater treatment.6,7,57 Here, we found that MAGs belonging to the

476

phyla Armatimonadetes and Proteobacteria harboring the secondary-metabolite

477

(MOCO and folate) biosynthesis ability to cross-feed with anammox and Chloroflexi 22


Page 23 of 43


478

microorganisms encoding the function for a nitrite loop, which could affect anammox

479

activity. Meanwhile, exchanges of nucleotide sugars between anammox and

480

Chloroflexi bacteria and of amino acids between anammox and Chlorobi bacteria

481

might affect the aggregation capacity of the anammox consortia by producing

482

exopolysaccharides or degrading extracellular proteins. Thus, we emphasize the

483

important roles of MAGs belonging to the phyla Chloroflexi, Armatimonadetes,

484

Proteobacteria, and Chlorobi in the anammox consortia for nitrogen removal. The

485

microbial cross-feeding networks could affect reactor performance by regulating

486

microbial activity and aggregation, which indicates a potential strategy for improving

487

reactor performance by maintaining the balance and diversity of community structure

488

in different operational phases of the anammox reactor.

489

Furthermore, this is the first attempt at studying the microbial succession and

490

start-up process of an anammox reactor using a genome-centered metagenomic

491

analysis. Our proposed ecological interaction offers a deeper understanding of

492

anammox nitrogen removal from the viewpoint of bacterial cross-feeding during the

493

reactor start-up. The adopted genome-based microbial ecology method was similar to

494

that used in a recent study of the PN-anammox reactor.3 It should be noted that our

495

model was based on genomes and their varied abundances and should hence be

496

further validated using metatranscriptomics and metaproteomics2 or isolation of these

497

microorganisms.59 We expect that our results will provide a valuable reference for

498

such studies.

499 23



Page 24 of 43

500

Acknowledgments

501

The financial support from Shenzhen Science and Technology Innovation Committee

502

(No. JSGG20160429162015597) should be highly appreciated. The authors are also

503

grateful to the National Natural Science Foundations of China (No. 51478006) for

504

financial support.

505

Supporting Information

506

Tables showing operational strategies, GenBank accession numbers, nitrogen

507

conversion ratios, sequencing and mapping statistics, average nucleotide identities,

508

partial MAGs abundance, genes definitions, and metabolites abbreviations; Details of

509

mineral medium composition; Figures illustrating the pH difference, nitrogen

510

conversion ratios, comparative genome analysis and gene number comparative

511

analysis of COG class (PDF).

512

File about the details of genes’ functional annotations (XLSX).

513

24


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514

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to methyl jasmonate in Lycoris aurea. Front. Plant Sci. 2017, 7 1971.

664

(53) Flemming, H. C.; Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 2010, 8

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(9), 623–633.

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(54) Jennings, L. K.; Storek, K. M.; Ledvina, H. E.; Coulon, C.; Marmont, L. S.;

667

Sadovskaya, I.; Secor, P. R.; Tseng, B. S.; Scian, M.; Filloux, A.; Wozniak, D. J.; 31



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Howell, P. L.; Parsek, M. R. Pel is a cationic exopolysaccharide that cross-links

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extracellular DNA in the Pseudomonas aeruginosa biofilm matrix. Proc. Natl. Acad.

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Sci. U. S. A. 2015, 112 (36), 11353–11358.

671

(55) Silva, I. N.; Ramires, M. J.; Azevedo, L. A.; Guerreiro, A. R.; Tavares, A. C.;

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Becker, J. D.; Moreira, L. M. The regulator LdhR and D-lactate dehydrogenase LdhA

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of Burkholderia multivorans play a role in carbon overflow and in planktonic cellular

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aggregate formation. Appl. Environ. Microbiol. 2017, 83 (19), 01343-17.

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(56) Li, X.; Luo, J.; Guo, G.; Mackey, H. R.; Hao, T.; Chen, G. Seawater-based

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wastewater accelerates development of aerobic granular sludge: A laboratory

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proof-of-concept. Water Res. 2017, 115, 210–219.

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(57) Tang, X.; Guo, Y.; Wu, S.; Chen, L.; Tao, H.; Liu, S. Metabolomics uncovers the

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regulatory pathway of acyl-homoserine lactones-based quorum sensing in anammox

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consortia. Environ. Sci. Technol. 2018, 52 (4), 2206–2216.

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(58) Adav, S. S.; Lee, D.; Lai, J. Proteolytic activity in stored aerobic granular sludge

682

and structural integrity. Bioresour. technol. 2009, 100 (1), 68–73.

683

(59) Krause, S. M. B.; Johnson, T.; Samadhi Karunaratne, Y.; Fu, Y.; Beck, D. A. C.;

684

Chistoserdova, L.; Lidstrom, M. E. Lanthanide-dependent cross-feeding of

685

methane-derived carbon is linked by microbial community interactions. Proc. Natl.

686

Acad. Sci. U. S. A. 2017, 114 (2), 358–363.

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688

Bin ID AMX1 AMX2 ACD1 ATM1 ATM2 ATN1 ATN2 ATN3 ATN4 CFX1 CFX2 CFX3 CFX4 CFX5 CFX6 CFX7 CFX8


Table 1. 37 genome statistics recovered in this SBR. Taxonomy Bacteria; Planctomycetes; Planctomycetia; Planctomycetales; Planctomycetaceae; Brocadia Bacteria; Planctomycetes; Planctomycetia; Planctomycetales; Planctomycetaceae; Brocadia Bacteria; Acidobacteria Bacteria; Armatimonadetes Bacteria; Armatimonadetes Bacteria; Actinobacteria; Actinobacteria; Corynebacteriales; Nocardiaceae; Rhodococcus Bacteria; Actinobacteria; Actinobacteria Bacteria; Actinobacteria Bacteria; Actinobacteria; Actinobacteria; Micrococcales; Intrasporangiaceae; Ornithinimicrobium Bacteria; Chloroflexi; Anaerolineae Bacteria; Chloroflexi Bacteria; Chloroflexi Bacteria; Chloroflexi; Anaerolineae Bacteria; Chloroflexi; Caldilineae; Caldilineales; Caldilineaceae; Caldilinea Bacteria; Chloroflexi; Anaerolineae Bacteria; Chloroflexi; Caldilineae Bacteria; Chloroflexi; Sphaerobacteridae; Sphaerobacterales;

N50 Length (bp)

Predicted genes

3,796,533 42.25

77,967

3,630

253

3,493,969 45.00

23,006

3,326

0.85 1.82 1.82

132 29 23

2,802,878 54.67 2,809,340 60.98 2,746,897 60.69

36,387 156,133 167,677

2,663 2,587 2,601

95.1

0.38

268

4,790,008 70.96

35,293

4,631

99.2 97.4

2.14 1.28

68 104

4,051,767 67.23 2,867,214 59.93

140,716 42,447

3,731 2,781

85.0

0.54

346

3,062,586 73.54

10,976

3,128

86.2 72.1 96.3 91.8

7.71 2.94 0 4.61

540 908 139 153

3,574,124 3,466,261 3,966,637 2,971,039

60.34 56.47 62.82 61.17

9,222 4,548 51,483 34,524

3,573 4,142 3,454 2,887

96.4

3.03

847

9,366,412 59.40

18,517

8,515

83.0 89.6 87.3

0.18 3.13 0.94

18 1075 535

3,467,924 53.68 5,419,247 64.66 4,430,357 63.96

578,594 6,426 11,330

3,296 5,297 4,345

Completeness (%)

Contamination (%)

# Scaffolds

95.6

0.55

82

98.9

6.04

78.5 95.0 94.2

Genome size (bp)

GC (%)

33



CFX9 CHB1 NOB1 PLA1 PLA2 PLA3 PLA4 PLA5 PRO1 PRO2 PRO3 PRO4 PRO5 PRO6 PRO7 GWF1 KSB1 UCB1 UCB2 UCB3

Sphaerobacterineae Bacteria; Chloroflexi Bacteria; Ignavibacteriae; Ignavibacteria; Ignavibacteriales Bacteria; Nitrospirae; Nitrospirales; Nitrospiraceae; Nitrospira Bacteria; Planctomycetes Bacteria; Planctomycetes Bacteria; Planctomycetes Bacteria; Planctomycetes; Planctomycetia; Planctomycetales; Planctomycetaceae; Blastopirellula Bacteria; Planctomycetes; Planctomycetia; Planctomycetales; Planctomycetaceae; Pirellula Bacteria; Proteobacteria; Alphaproteobacteria; Rhizobiales; Phyllobacteriaceae; Aminobacter Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales; Burkholderiaceae; Lautropia Bacteria; Proteobacteria; Deltaproteobacteria Bacteria; Proteobacteria; Betaproteobacteria; Burkholderiales Bacteria; Proteobacteria; Alphaproteobacteria Bacteria; Proteobacteria; Alphaproteobacteria; Rhizobiales Bacteria; Proteobacteria; Deltaproteobacteria Bacteria; Microgenomates Bacteria; Candidate division KSB1 Bacteria; Candidatus Uhrbacteria Bacteria Bacteria

Page 34 of 43

90.4 94.8 96.8 94.3 97.7 89.8

1.82 0 5.23 3.41 3.41 0

357 30 82 59 65 44

3,726,020 2,332,291 3,961,664 3,885,146 4,711,417 3,355,315

56.77 37.60 60.33 64.60 63.76 63.47

14,876 129,973 86,454 125,775 95,457 100,689

3,597 2,082 3,787 3,207 3,764 2,747

89.4

0.59

417

5,112,838 66.05

17,011

4,309

97.5

0

289

7,048,853 63.88

41,231

5,340

92.3

1.33

46

3,123,305 69.36

101,651

2,980

94.5

1.65

36

3,224,835 69.45

127,685

3,017

90.9 75.9 89.5 77.8 77.4 76.3 92.2 86.2 71.9 76.9

1.94 0 0 0.79 0.84 0 3.3 0 0 0.92

422 209 14 49 643 33 776 4 15 489

5,539,874 2,591,218 3,248,505 2,438,700 2,693,467 825,322 7,248,468 1,064,618 2,159,727 4,407,992

21,504 15,555 654,661 74,166 4,964 38,480 14,792 432,179 196,713 13,236

5,265 2,536 3,044 2,279 2,993 913 6,734 1,023 2,184 3,359

73.14 70.12 60.98 67.02 52.28 44.44 52.87 54.64 66.56 73.19

34


Page 35 of 43


689

Figure captions

690

Figure 1. Profile of nitrogen removal during SBR start-up: (a) Time courses of

691

influent ammonia (NH4+–N) concentration (yellow line), effluent ammonia

692

concentration (green line) and ammonia removal rate (blue line). S1, S2, S3, S4 and

693

S5 represent five samples that were obtained on days 0, 106, 166, 218 and 280,

694

respectively. (b) Time courses of influent nitrite (NO2-–N) concentration (yellow line),

695

effluent nitrite concentration (green line) and nitrite removal rate (blue line). (c) Time

696

courses of influent nitrate (NO3-–N) concentration (yellow line), effluent nitrate

697

concentration (green line) and total nitrogen (TN) removal rate (blue line). (d) Time

698

courses of nitrogen loading rate (NLR) (orange line), nitrogen removal efficiency

699

(NRE) (blue line) and hydraulic retention time (HRT) (black line).

700

Figure 2. The phylogenetic tree and relative abundance of 37 recovered genomes in

701

this SBR. (a) Phylogenetic tree of the recovered 37 draft genomes (red) and 56

702

reference genomes (black) deposited in GenBank. GenBank accession numbers of

703

each genome were provided in parentheses. Branch nodes numbers represent the

704

bootstrap values. The tree was constructed using the software RAxML based on the

705

37 Phylosift marker genes. (b) The relative abundance of 37 microorganisms in five

706

samples. Blue, yellow, grey, coffee and light blue columns represent S1, S2, S3, S4

707

and S5, respectively. Five samples were obtained on day 0, 106, 166, 218 and 280,

708

respectively.

709

Figure 3. (a) The total functional gene abundance on the level of COG classes in 37

710

MAGs for five samples. Blue, yellow, grey, coffee and light blue columns represent 35



Page 36 of 43

711

S1, S2, S3, S4 and S5, respectively. The community functional structure analysis

712

using the PCA analysis of functional gene abundance on the COG classes level based

713

on the sum of gene abundance of a certain COG class in every MAG in in (b) S1, (c)

714

S2, (d) S3, (e) S4, and (f) S5, respectively. The gene abundance was calculated by

715

normalizing the number of reads that were mapped to the gene sequence by the

716

number of the 16S rRNA gene sequence identified for the metagenomic sequencing

717

data and gene sequence length.

718

Figure 4. Potential cross-feedings and variations gene abundances potentially

719

associated to the cross-feeding between Brocadia (AMX1), and Chloroflexi (CFX3

720

and CFX4), Armatimonadetes (ATM1 and ATM2), Proteobacteria (PRO1), Chlorobi

721

(CHB1). The Armatimonadetes and Proteobacteria bacterium encoded the pathways

722

for the production of secondary metabolites for the anammox metabolism. The

723

Chloroflexi bacterium encoded the pathways for the biosynthesis of sticky

724

macromolecular exopolysaccharides (CFX3) and denitrification of nitrate (CFX4).

725

The Chlorobi bacterium had the potential to hydrolyze extracellular proteins and

726

liberate amino acids. The heat map represents the relative abundance of key genes that

727

were complementary between symbiotic and anammox bacteria and were potentially

728

associated with microbial interactions in the studied genomes for five samples. S1-S5

729

represent the samples obtained on days 0, 106, 166, 218 and 280, respectively.

730

Definitions for genes and abbreviations for metabolites are listed in SI Table S8 and

731

S9, respectively.

732 36


Page 37 of 43


733 734

Figure 1. Profile of nitrogen removal during SBR start-up: (a) Time courses of

735

influent ammonia (NH4+–N) concentration (yellow line), effluent ammonia

736

concentration (green line) and ammonia removal rate (blue line). S1, S2, S3, S4 and

737

S5 represent five samples that were obtained on days 0, 106, 166, 218 and 280,

738

respectively. (b) Time courses of influent nitrite (NO2-–N) concentration (yellow line),

739

effluent nitrite concentration (green line) and nitrite removal rate (blue line). (c) Time

740

courses of influent nitrate (NO3-–N) concentration (yellow line), effluent nitrate

741

concentration (green line) and total nitrogen (TN) removal rate (blue line). (d) Time

742

courses of nitrogen loading rate (NLR) (orange line), nitrogen removal efficiency

743

(NRE) (blue line) and hydraulic retention time (HRT) (black line).

744

37



Page 38 of 43

745 746

Figure 2. The phylogenetic tree and relative abundance of 37 recovered genomes in

747

this SBR. (a) Phylogenetic tree of the recovered 37 draft genomes (red) and 56 38


Page 39 of 43


748

reference genomes (black) deposited in GenBank. GenBank accession numbers of

749

each genome were provided in parentheses. Branch nodes numbers represent the

750

bootstrap values. The tree was constructed using the software RAxML based on the

751

37 Phylosift marker genes. (b) The relative abundance of 37 microorganisms in five

752

samples. Blue, yellow, grey, coffee and light blue columns represent S1, S2, S3, S4

753

and S5, respectively. Five samples were obtained on day 0, 106, 166, 218 and 280,

754

respectively.

39



Page 40 of 43

755 756

Figure 3. (a) The total functional gene abundance on the level of COG classes in 37

757

MAGs for five samples. Blue, yellow, grey, coffee and light blue columns represent

758

S1, S2, S3, S4 and S5, respectively. The community functional structure analysis

759

using the PCA analysis of functional gene abundance on the COG classes level based

760

on the sum of gene abundance of a certain COG class in every MAG in in (b) S1, (c)

761

S2, (d) S3, (e) S4, and (f) S5, respectively. The gene abundance was calculated by

762

normalizing the number of reads that were mapped to the gene sequence by the

763

number of the 16S rRNA gene sequence identified for the metagenomic sequencing

764

data and gene sequence length.

765

40


Page 41 of 43


766 767

Figure 4. Potential cross-feedings and variations gene abundances potentially

768

associated to the cross-feeding between Brocadia (AMX1), and Chloroflexi (CFX3

769

and CFX4), Armatimonadetes (ATM1 and ATM2), Proteobacteria (PRO1), Chlorobi

770

(CHB1). The Armatimonadetes and Proteobacteria bacterium encoded the pathways

771

for the production of secondary metabolites for the anammox metabolism. The

772

Chloroflexi bacterium encoded the pathways for the biosynthesis of sticky

773

macromolecular exopolysaccharides (CFX3) and denitrification of nitrate (CFX4).

774

The Chlorobi bacterium had the potential to hydrolyze extracellular proteins and

775

liberate amino acids. The heat map represents the relative abundance of key genes that 41



Page 42 of 43

776

were complementary between symbiotic and anammox bacteria and were potentially

777

associated with microbial interactions in the studied genomes for five samples. S1-S5

778

represent the samples obtained on days 0, 106, 166, 218 and 280, respectively.

779

Definitions for genes and abbreviations for metabolites are listed in SI Table S8 and

780

S9, respectively.

781

42


Page 43 of 43


782

TOC/Abstract graphic

783 784

For Table of Contents Only

43


Genome-centered metagenomics analysis reveals the symbiotic

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