Genome-centric metagenomic insights into the impact of alkaline/acid and thermal sludge pre-treatment on digestion sludge microbiome

Background: Wastewater treatment generates large amounts of waste activated sludge (WAS), which mainly consist of recalcitrant microbial cells and particulate organic matter. WAS pre-treatment is an effective way to destabilize sludge �oc structure and release cellular macromolecules and other organic matter for improvement of digestion e�ciency. Nonetheless, impacts of WAS pre-treatment on the complex digestion sludge microbiome, as well as mechanistic insight into how sludge pre-treatment improve digestion performance, remain to be elucidated. Results: In this study, genome-centric metagenomic approach was employed to investigate the digestion sludge microbiome in four methanogenic sludge digesters with different feeding sludge: APAD, WAS pre-treated with 0.25 mol/L alkaline/acid; HS-APAD, WAS pre-treated with 0.8 mol/L alkaline/acid; Thermal-AD, thermal pre-treated WAS; Control-AD, fresh WAS. We retrieved 254 metagenomic-assembled genomes (MAGs) to identify the key functional populations involved in methanogenic digestion process. These MAGs span 28 phyla with 69 of them as yet-to-be-cultivated lineages, and 30 novel lineages were characterized with metabolic potential associated with hydrolysis and fermentation. Interestingly, functional populations involving carbohydrate digestion were overrepresented in APAD and HS-APAD, while lineages related to protein and lipid fermentation were overrepresented in Thermal-AD, re�ecting different digestion substrates released from alkaline/acid and thermal pre-treatments. Of the three major functional populations, i.e., fermentative bacteria, acetogenic syntrophs and methanogens, signi�cant correlations between genome sizes of the fermentative bacteria and their abundance were observed, particularly in the APAD and HS-APAD with improved digestion performance. Conclusion: These genome-centric metagenomic insights advance our understanding of sludge pre-treatment on digestion sludge microbiomes, shedding light on future optimization of methanogenic sludge digestion and resource recovery.


Introduction
Waste activated sludge (WAS) from wastewater treatment plants contains high levels of organic matter in forms of cells, extracellular polymeric substances (EPS) and macromolecules generated from cell lysis, as well as pathogens and other biohazards [1,2].Anaerobic digestion as a sustainable sludge treatment technology can convert these organic substances into biogas via a multiple-step process consisting of hydrolysis, fermentation, acetogenesis and methanogenesis [3].The organic matter (e.g., microbial cells and EPS), together with metals and other ions, in WAS form stable and complicated sludge ocs.These sludge ocs are recalcitrant to anaerobic digestion and consequently require sludge pre-treatment to destabilize their structure and release organic matter for improving digestion e ciency [4].Many pretreatment techniques (e.g., thermal, ultrasonic, microwave and alkaline pre-treatments) have been developed to effectively release sludge organic matter [5,6,7].For example, thermal hydrolysis (> 100 °C), ultrasonication, microwave and alkaline pre-treatments could enhance sludge fermentation by 4-20%, 1-18%, 1-15% and 3-35%, respectively, compared to their non-pretreated controls [7].In our previous studies, a new alkaline/acid pre-treatment and anaerobic digestion (APAD) process was developed, in which organic carbon removal achieved 52.8 ± 1.7% [2].By contrast, the organic carbon removals were signi cantly lower in other digesters under the same operational conditions but with different in uent sludge, i.e., 42.4 ± 1.6% in Thermal-AD with thermal sludge pre-treatment and 30.9 ± 2.2% in Control-AD with fresh WAS [2].Further increasing alkaline/acid concentrations from 0.25 mol/L (APAD) to 0.8 mol/L (HS-APAD) did not show notable changes in digestion performance and community taxonomic composition in the sludge digesters [8].Consequently, dissolved organic compounds (DOC) derived from sludge pre-treatments, rather than salinity, could be a predominant selective pressure driving the performance and microbiome changes in both APAD and HS-APAD, compared to the Control-AD [8].
Nonetheless, the detailed mechanistic insights into the digestion improvement and impact of WAS pretreatment on digestion sludge microbiome remain unknown.
The conversion of WAS organic matter into biogas highly relies on the complex and tightly coupled synergistic interactions of microbial communities in digestion sludge [9].Previous studies based on 16S rRNA gene amplicon sequencing showed that relative abundance of bacteria and archaea in digestion sludge microbiomes generally accounted for > 95% and < 5%, respectively [10,11], being further con rmed by recent metagenomics analyses [9,12].In view of the bacterial community, Bacteroidetes, Proteobacteria, Spirochaetes and Firmicutes were generally the dominant phyla in sludge digesters, and most of them were fermentative bacteria with high compositional and functional redundancy [9,[13][14][15][16].
By contrast, the slow-growing acetogenic syntrophs and methanogenic archaea in digestion sludge were limited to several lineages [9,11].Accumulating experimental evidences suggested that a variety of factors including feeding substrates and operational parameters (e.g., pH, temperature and ammonia) might change community composition and function of the digestion sludge microbiome [11,12,16,17].
The information generated from the 16S rRNA gene-based analyses was largely limited to community composition and succession [18].Metagenome sequencing could theoretically obtain all microbial genome information within a sample, which might provide direct access to the metabolic potential and networks in the highly complex digestion sludge microbiome.Early metagenomic studies mainly relied on gene-centric analyses, which were biased towards existing databases [9,19,20].Current advances in both high-throughput sequencing technologies and population genome binning algorithms allowed the development of genome-centric approaches for the assessment of complex microbiomes [12,18,21].For example, the genome-centric metagenomics analysis was employed to recover 101 population genomes and revealed their metabolic potential and interactions in a cellulose-degrading digester [16].Very recently, a collection of 1,635 metagenome-assembled genomes were recovered from publicly available datasets derived from different methanogenic digesters, showing high species diversity related to methane generation [12].Also, metagenomics changed the pace of virus discovery by enabling the accurate identi cation of viral genome sequences without requiring isolation of viruses [22,23].In contrast to increasing metagenomic data on anaerobic digestion, no information was available on impacts of WAS pre-treatment on digestion sludge microbiome.It would be rational to assume that the genome-centric and strain-resolved metagenomic approaches could provide a systematic understanding of digestion sludge microbiomes, particularly the yet-to-be elucidated impact of WAS pre-treatment on the digestion sludge microbiome.
In this study, we employed metagenomic approach to explore prokaryotic and DNA viral community composition and function of digestion sludge microbiomes in four sludge digesters (i.e., APAD, HS-APAD, Thermal-AD and Control-AD).Co-assembly of the four metagenomes followed by genomic binning resulted in the recovery of 254 population genomes that constituted the majority of the digestion sludge microbiome.Their metabolic potential and networks were further reconstructed to reveal impacts of WAS pre-treatment on the digestion sludge microbiome.The results provided the rst genome-centric insight into how WAS pre-treatment change community composition and function, as well as metabolic networks of key players and their genomic traits in the digestion sludge microbiome.

Sample collection, DNA extraction and metagenome sequencing
Four mesophilic anaerobic sludge digesters were setup to treat fresh WAS (Control-AD), thermal pretreated sludge (Thermal-AD), and sludge pre-treated with 0.25 mol/L alkaline/acid (APAD) and 0.8 mol/L alkaline/acid (HS-APAD), as described [2,8].Sludge samples for metagenomic sequencing were collected from steady-state digesters (sampling time points were set on day 192 for APAD, Thermal-AD Control-AD, and on day 142 for HS-APAD), and their gDNA was extracted using FastDNA Spin Kit for Soil (MP Biomedicals, Carlsbad, CA, USA) [24].The quality and quantity of the DNA extract were evaluated with gel electrophoresis and Quantus Fluorometer (Promega, Madison, WI, USA).The DNA sequencing libraries and subsequent Illumina HiSeq sequencing services were provided by BGI (Shenzhen, China).

Metagenome assembly and genome binning
All metagenomic sequencing raw data were ltered to remove low quality bases/reads using Sickle [25], with the parameters set to "-q = 20 and − l = 100".Then the four metagenomes were combined and de novo co-assembled using SPAdes (version 3.12.0)[26] with the following parameters, −k 33,55,77 -meta.To generate high-quality metagenome-assembled genomes (MAGs), contigs with length < 1000 bp in the assembly were removed.Three binning methods, i.e., MetaBAT [27], Maxbin [28] and Concoct [29], were employed and compared for the binning of the contigs into population genomes as described [30].To improve binning quality, Re neM [31] was used to lter scaffolds with divergent genomic properties, incongruent taxonomic classi cation and 16S rRNA genes.Manual curation of these population genomes was executed using single copy genes, k-mer frequency distribution, contigs coverage and GC content.To get the optimal genome quality, clean reads for each bin were recruited using BBMap [32] with following parameters: k = 15 minid = 0.9 build = 1.Then, genome bins were reassembled by SPAdes (version 3.12.0)[26] with the following parameters: --careful -k 21,33,55,77.The completeness, contamination and strain heterogeneity of each bin were evaluated using CheckM [33].Finally, 254 bins with > 70% completeness and < 5% contamination were obtained for subsequent analyses.

Genome annotation and metabolic reconstruction
Protein coding sequences (CDS) were determined using prokka [34] with the "--quiet" option for all genome bins.Functional annotations were conducted based on comparisons with the KEGG [35], and eggnog [36] databases using DIAMOND [37] with an E-value threshold of < 1e-5.Carbohydrate-active enzymes were identi ed using the carbohydrate-active enZYmes (CAZy) database [38].A subset of CAZy genes involving in extracellular polysaccharide degradation pathways [39] was selected for further analysis.Peptidases were identi ed using MEROPS [40].To reconstruct the metabolic pathways, genome bins were upload to RAST (Rapid Annotation using Subsystem Technology) [41] for gene prediction and annotation, and their CDS were annotated at the KEGG automatic annotation server (KAAS) [42].The metabolic potential (i.e., hydrolysis, fermentation and syntrophic acetogenesis) of major populations of known lineages were further manually con rmed.
Poorly aligned regions were ltered by TrimAL [46] to remove columns containing > 95% gap positions, and then the 16 ltered ribosomal protein sequences were concatenated for each genome.The phylogenomic tree was constructed using IQ-TREE (version 1.6.10)[47] with the following parameters:mset JTT -mrate I,G,I + G.The resulting newick tree le was uploaded to iTOL v4 [48] for visualization and formatting.To con rm their phylogeny, MAGs were further classi ed with GTDBtk (version 1.0.2,database release 89) [49].

Relative abundance and community composition
Taxonomic classi cation of contigs was performed using Kraken 2 as described [50] with a database comprising 38,156 bacterial, 11,953 viral, and 535 archaeal complete reference genomes.The sequencing reads were then re-aligned back to the contigs using BBMap [32] to calculate their coverages.Taxon read hit counts were obtained by combining the re-aligned reads of all contigs belonging to the taxon, which were further normalized by genome size to calculate taxon abundance.For the relative abundance and diversity analysis, genomes with coverage less than 1 were removed to decrease effect of low abundance misclassi cation.The abundance of MAGs was calculated based on retrieved genome coverage and normalized as reads per kilobase per million (RPKM) as described previously [51,52].

Results
The superkingdom community composition of digestion sludge microbiomes Four metagenomes (144 Gb total raw sequencing reads) from APAD, HS-APAD, Thermal-AD and Control-AD were co-assembled, generating 833,655 contigs with a combined length of 2,395 Mb (N50 = 3,658 bp; Table S1).Community composition investigation based on the relative number of microorganisms showed that Bacteria, Archaea and DNA viruses were the three major taxonomic groups of the digestion sludge microbiome, accounting for an average relative abundance of 92.9%, 2.4% and 4.7%, respectively (Fig. 1A).Notably, the lowest relative abundance of virus in Thermal-AD (i.e., 2.9% in Thermal-AD vs. 5.9% in Control-AD, 4.9% in APAD and 4.9% in HS-APAD) might be due to the e cient removal of bacteriophages in thermal sludge pre-treatment [53].Also, compared to Control-AD, the higher abundance of methanogenic Archaea in APAD and HS-APAD corroborated their enhanced carbon removal and biogas generation [2,8].Principal coordinates analysis (PCoA) showed that thermal and alkaline/acid pretreatment had very different impacts on the digestion sludge microbiome.By contrast, APAD and HS-APAD shared similar microbial community patterns (Fig. 1B), consistent with their 16S rRNA gene-based community clustering (Fig. S1) [2,8].

Metabolic networks in sludge digesters
To construct the metabolic networks, metabolic potential of major microorganisms (or MAGs) involving conversion of sludge organic matter into biogas were identi ed based on the carbon ow from organic macromolecules to methane.The predominant organic macromolecules to be degraded in the sludge digesters were polysaccharides, proteins and lipids.These macromolecules could be converted into methane under mediation of three major groups of functional microorganisms, i.e., hydrolyzing and fermentative bacteria, syntrophic acetogenic bacteria and methanogenic archaea (Table S3).
Pertaining to metabolic networks in the four sludge digesters, presence of multiple populations capable of ful lling the same function (e.g., hydrolysis, fermentation and methanogenesis) suggested high levels of functional redundancy (Fig. 3; Table S4).The high levels of functional redundancy were contributed by both core functional populations (major and essential functional microorganisms) and redundant functional populations (minor and non-essential functional populations), and the two different groups of functional populations could be interconvertible when changing operational conditions of sludge digestion.In line with the microbial community composition, the overall metabolic networks in APAD and HS-APAD were highly similar (p = 0.696; Fig. 3C and D), but different from them in Thermal-AD and Control-AD (Fig. 3A and B).These distinct metabolic networks indicated different digestion substrates released from alkaline/acid and thermal sludge pre-treatments.Notably, the polysaccharide and protein metabolisms were signi cantly enriched in APAD and HS-APAD (Fig. 3C and D), suggesting effective destruction of both EPS and microbial cells to release polysaccharides and proteins in the alkaline/acid pre-treatment.Accordingly, bacterial MAGs of Firmicutes (e.g., bin132 and bin166) and Bacteroidetes (e.g., bin58 and bin252) phyla were selectively enriched as core functional populations in APAD and HS-APAD.In addition, the predominant hydrolyzing and fermentative bacterial populations were different in Thermal-AD and Control-AD, e.g., bin169 of Chloro exi and bin202 of Lentisphaerae in the Thermal-AD, and bin140 of Proteobacteria and bin161 of Actinobacteria in the Control-AD.In syntrophic acetogenic process, propionate and butyrate were major volatile fatty acids (VFAs) as substrates for syntrophic acetogenic bacteria (e.g., bin42 and bin137 of Synergistetes and Firmicutes phyla, respectively, for butyrate oxidation, and bin75 and bin77 of Proteobacteria phylum for propionate oxidation), which were signi cantly enriched as common and core syntrophic acetogens in sludge digesters fed with thermal or alkaline/acid pre-treated WAS (Fig. 3; Table S4).For the methanogenesis, a variety of acetoclastic and hydrogenotropphic methanogens were observed with a high level of functional redundancy in the four digesters.Particularly, Methanosaeta (bin51) capable of producing methane from acetate, formate and H 2 was enriched as core methanogens in APAD and HS-APAD (Fig. 3C and D), being in good agreement with their high methane production [2,8].

Microbial dark matter
The "microbial dark matter" as microorganisms evaded cultivation and yet-to-be-characterized physiologically [54] could play important roles in sludge digesters.From the four digestion sludge microbiomes, 30 MAGs belonging to 12 phyla were identi ed as representative dark matter (Fig. 4 and Table S5), which were phylogenetically distant from their closest known sister lineages and represented novel microorganisms at order and even higher taxonomic levels.Genome-predicted metabolic potential suggested that 24 of 30 MAGs involved in hydrolysis and fermentation of cellular macromolecules in WAS.The abundances of these hydrolyzing and fermentative dark matter were generally < 2.1 RPKM, with an exception of the abundant bin17 and bin106 populations in Thermal-AD.Notably, several populations of candidate phyla with small genomes (< 1 Mb), e.g., Candidatus Stahlbacteria (TM7), Candidatus Shapirobacteria, and Candidatus Dojkabacteria, were identi ed in the sludge microbiomes without functions directly linked to the major methanogenic digestion procedures (Fig. 4).
Of the 30 MAGs, the two most abundant populations (i.e., bin17 and bin106 with 89.2% and 76.6% genome completeness, respectively) belonging to the Planctomycetes phylum were chosen for subsequent metabolic reconstruction (Fig. 5; Table S6).Both genomes encoded enzymes of glycosylic hydrolase (GH) families for hydrolysis of cellulose, hemicellulose and cellobiose, which could convert sludge polysaccharides into simple sugars for their subsequent fermentation and acetogenesis.In contrast to the nearly complete tricarboxylic acid (TCA) cycle in bin106 genome, genes encoding 2oxoglutarate/2-oxoacid ferredoxin oxidoreductase and succinyl-CoA synthetase were absent in bin17 genome.Consequently, the partial TCA cycle might only provide biosynthetic precursors for anabolism of bin17 population.Notably, in addition to the glycolysis pathway, genes in bin106 genome also encoded enzymes for β-oxidation of fatty acids and phenol degradation, as well as a quinone-dependent electron transport chain containing cbb3-type cytochrome-c oxidase with high O 2 -a nity.These potential metabolic traits implied a versatile lifestyle of bin106 population and its aerobic respiration capability under microaerobic conditions.In addition, cell mobility of bin17 and bin106 populations might be different based on their gene contents.For example, genes encoding proteins for basal body, hook and lament assembly, as well as type IV pilus, were detected in bin17 genome, suggesting its high mobility and capability to move toward favorable growth environment.In contrast, only pilus-encoding genes were detected in bin106 genome, implying limited motility of bin106 population.

Genomic traits of digestion sludge microbiomes
Genomic traits (e.g., genome size, coding sequence or CDS, and GC content) of microorganisms could be de ning features of microbial cell growth, and consequently associated with population abundance in a complex microbiome [55,56,57].For instance, populations with small genome size would be expected to grow rapidly and therefore to have high abundance, because small genomes could reduce mutational load and nutrient demands for genome replication [58,59].In the four sludge digesters, average genome size and CDS of functional microorganisms (i.e., fermentative bacteria, acetogenic syntrophs and methanogens) decreased with the successive methanogenic digestion procedures (Fig. 6A; Fig. S3), consistent with the Gibbs free energy changes of each redox reactions.Particularly, the genome size was shown to have a signi cant (p < 0.01) correlation with the abundance of overall digestion sludge microbiome (Fig. 6B).Of the three major functional groups of microorganisms, acetogenic syntrophs and methanogens mediated the rate-limiting steps (i.e., acetogenesis and methanogenesis) and their growth was substrate-replete compared to fermentative bacteria.Accordingly, genome size could best predict the cell growth rate of the substrate-competitive fermentative bacteria (p < 0.01; Fig. 6B; Fig. S4A and B).Interestingly, correlations between genome sizes of fermentative bacteria and their abundance were signi cant (p < 0.01) in APAD and HS-APAD with high carbon removal e ciencies (> 50%), in contrast to their low correlations in Control-AD (p = 0.35) and Thermal-AD (p = 0.78; Fig. 6C).The low correlations in Control-AD and Thermal-AD might be due to their low e ciencies (30-40%) in destruction and removal of sludge ocs and associated microbial cells [2].The low e ciencies further resulted in high retaining of cellular macromolecules (e.g., genomic DNA) of in uent sludge cells (Fig. 6A; Fig. S4C) and consequent complication of the digestion sludge microbiome.

Discussion
This study provided the rst genome-centric metagenomic insight into how WAS pre-treatments changed the digestion sludge microbiome and subsequent digestion performance, as well as into the associated microbial dark matter.The highly complex digestion sludge microbiome guarantied e cient conversion of organic polymers into biogas, of which performance could be changed by a variety of parameters, particularly in uent sludge composition and operational conditions (e.g., pH, temperature and salinity) [60,61].Nonetheless, impacts of these parameters on digestion sludge microbiomes were combined and twisted, and it was consequently challenging to identify the exact impacts of a speci c parameter on the sludge microbiome and to differentiate the core functional populations from functionally redundant and to-be-digested microorganisms derived from in uent WAS.In view of the microbial community composition and function, prokaryotic populations in digestion sludge microbiomes could be classi ed into three major groups: (1) Group-I, core functional populations which were abundant and played essential roles in sludge digestion (e.g., bin166, bin252 and bin58 as the predominant hydrolyzing and fermentative bacteria, bin77 as syntrophic acetogens and bin51 as methanogenic archaeal in APAD and HS-APAD); (2) Group-II, redundant functional populations which had parallel functions with the core functional populations but were minor and non-essential populations; (3) Group-III, non-functional populations which entered digesters as to-be-digested substrates (e.g., aerobic bacteria derived from in uent WAS).Recently, a study investigating the digestion e ciency in full-scale anaerobic sludge digesters estimated that 82% of microbial populations in feeding WAS were the Group-III non-functional populations and could be digested [10].Most of the (Group-III) non-functional populations in feeding sludge were facultative aerobes or anaerobes, functions of which could be barely inferred from their 16S rRNA gene-based information and be easily confused with functional populations (Group-I and Group-II microorganisms) in digestion sludge microbiomes.In this study, composition and function of the three groups of microorganisms, particularly Group-I and Group-II populations, were clearly differentiated by employing genome-centric metagenomics and metabolic network reconstruction.Accordingly, several interesting observations were obtained: (1) WAS pre-treatments changed Group-I assemblies (core functional populations) by releasing different digestion substrates from WAS; (2) Group-II microorganisms with high levels of functional redundancy provided a candidate pool for exible and robust sludge digestion, and the Group-II minor and non-essential functional populations could be converted into Group-I predominant and functionally essential microorganisms when changing operational conditions.These results might guide future optimization of sludge digestion by providing knowledge on speci c impacts of WAS pre-treatment on digestion sludge microbiomes.
The methanogenic sludge digesters with proli c nutrients, optimum temperature and long retention time provided excellent habitats for an extremely wide range of anaerobic microorganisms, supporting diverse and complicated microbiomes for e cient sludge digestion.Of the digestion sludge microbiome, many populations were yet-to-be-cultivated due to their inability to grow in standard culture media [62].Recent advances in single-cell genomic and metagenomic techniques enabled researchers to bypass the complicated microbial cultivation and facilitate the discovery of numerous previously unknown, deep branches of the tree of life without cultivated representatives [63,64].For example, a complete genome sequence of Ca.Cloacamonas Acidaminovorans belonging to Ca. Cloacimonetes (formerly known as WWE1) was retrieved and suggested that Ca. C. Acidaminovorans could derive most of its carbon and energy from the fermentation of amino acids [65].In our study, several novel bacterial lineages were identi ed in the sludge microbiomes, including Group-I core functional populations (e.g., bin17 and bin106), Group-II functionally redundant populations (e.g., bin160 and bin214) and functionally unknown microorganisms (e.g., bin180 and bin224).All Group-I and Group-II functional microorganisms of novel lineages were identi ed to be fermentative bacteria, which suggested the high functional redundancy supported by the thermodynamically favorable fermentation, in contrast to the subsequent rate-limiting steps mediated by both acetogenic syntrophs and methanogens.Interestingly, energy metabolism of the functionally unknown lineages did not directly involved in the sludge digestion procedures.Consequently, these microorganisms might be second-hand metabolizers and their survival might depend on secreta of the Group-I and Group-II populations.Nonetheless, their detailed functional information awaited future indepth analyses.

Conclusions
This study provided unprecedented genome-centric metagenomic insights into how WAS pre-treatments changed the digestion sludge microbiomes, as well as their metabolic networks.Results suggested that (1) WAS pre-treatments governed the core functional population assemblies by changing digestion substrates, while redundant functional populations provided a candidate pool for exible and robust sludge digestion; (2) The genome-predicted metabolic potentials of microbial dark matter in digestion sludge microbiomes suggested their predominant roles in fermentation; (3) The genome sizes of the fermentative bacteria and their abundance were signi cantly correlated; (4) The digestion sludge microbiome could be a unique source for exploring microbial dark matter, as well as bioresource for future biomass industrial implications.Reconstructed metabolic capability of bin17 and bin106 populations.Pathways with genes detected in bin17 and bin106 genomes were depicted in red and blue colors, respectively.Detailed information on genes assigned to speci c metabolic pathways was available in Table S6.

Figure 1 Main
Figure 1