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Composting is a process in which organic matter is decomposed by microbial communities resulting into a stable product used as a fertilizer. Because its substrate is mainly composed of recalcitrant lignocellulose biomass, composting material is a source of candidate microorganisms and genes for lignocellulose degradation. In order to better comprehend the microbial composition and some metabolic functions underlying the composting process, in this study, we aim to characterize the microbial profile from the composting facility at the São Paulo Zoo Park focusing on recovering the metagenome-assembled genomes (MAGs) from different composting piles and exploring potential carbohydrate-active enzymes (CAZymes) present on these MAGs. The composting at this facility occurs at high temperatures (50 °C to 75 °C), so these composting piles are a source of thermophilic organisms and enzymes. We obtained samples from seven different composting piles: samples from five inoculum composting piles (ZCI - Zoo Compost Inoculum), and two mature composting piles (ZCM - Zoo Compost Mature). All sequences were obtained by Illumina Next Generation Sequencing, with paired reads of 250 bp. We co-assembled the preprocessed reads using metaSPAdes [Nurk et al. 2017], and used MaxBin2 [Wu, Simmons, Singer 2016], MetaBAT2 [Kang et al. 2019], and Concoct [Alneberg et al. 2014] tools to get bins, a collection of sequences that is a single microbial putative genome. We consolidated the resulting bins using the bin_refinement module from the MetaWRAP pipeline [Uritskiy, DiRuggiero, and Taylor 2018]. We considered a minimum completeness of 50% and a maximum contamination of 10% according to the standards of Minimum Information about a MAG (MIMAG) for a medium-quality MAG [Bowers et al. 2017]. We used the average nucleotide identity (ANI) to compare the obtained MAGs against the ones from the previous study (ZC3 and ZC4) [Braga et al. 2021]. CAZymes were predicted for ZCI and ZCM MAGs using dbCAN2 standalone package [Zhang et al. 2018]. Taxonomic classification was done with GTDB-tk [Chaumeil et al. 2020]. We obtained a total of 166 MAGs (about two times more from ZC3 and ZC4 than in the previous study [Braga et al. 2021]). All 166 MAGs could be classified as bacteria. The phylogenetic breakdown is the following: 69 MAGs from Firmicutes, 24 from Proteobacteria, 20 from Actinobacteriota, 13 from Bacteroidota, 4 from Chloroflexota, 3 Gemmatimonadota, 2 Myxococcota, and 31 MAGs were not classified at the phylum level. All ZCI and ZCM MAGs have CAZymes annotated in the classes glycosyl transferase, carbohydrate esterase, and glycosyl hydrolase. About 30% of all ZCI MAGs and 40% of all ZCM MAGs have CAZymes in the class Auxiliary Activities. This class covers redox enzymes and ligninases. This enlarged MAG dataset will enable us to refine the understanding of the composting process at the microbial and molecular levels, and in particular, the role played by the inoculum material. This work was funded in part by FAPESP and CAPES.

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1	Suzana Guima(suzy.eiko@gmail.com)
2	Layla Martins(laylabiol@gmail.com)
3	Aline Silva(almsilva@iq.usp.br)
4	João Setubal(setubal@iq.usp.br)

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1	Alneberg, J., Bjarnason, B. S., De Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., ... Quince, C. (2014). Binning metagenomic contigs by coverage and composition. Nature methods, 11(11), 1144-1146. Bowers, R. M., Kyrpides, N. C., Stepanauskas, R., Harmon-Smith, M., Doud, D., Reddy, T. B. K., ... & Woyke, T. (2017). Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology, 35(8), 725-731. Braga, L. P. P., Pereira, R. V., Martins, L. F., Moura, L. M. S., Sanchez, F. B., Patané, J. S. L., ... Setubal, J. C. (2021). Genome-resolved metagenome and metatranscriptome analyses of thermophilic composting reveal key bacterial players and their metabolic interactions. BMC genomics, 22(1), 1-19. Chaumeil, P. A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2020). GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics, 36(6), 1925-1927. Kang, D. D., Li, F., Kirton, E., Thomas, A., Egan, R., An, H., & Wang, Z. (2019). MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ, 7, e7359. Nurk, S., Meleshko, D., Korobeynikov, A., Pevzner, P. A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome research, 27(5), p.824-834. Uritskiy, G. V., DiRuggiero, J., & Taylor, J. (2018). MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome, 6(1), 1-13. Wu, Y. W., Simmons, B. A., Singer, S. W. (2016). MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics, 32(4), 605-607. Zhang, H., Yohe, T., Huang, L., Entwistle, S., Wu, P., Yang, Z., ... & Yin, Y. (2018). dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic acids research, 46(W1), W95-W101.

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