Authors: Abel T. Ingle, Nathaniel W. Fortney, Kevin S. Myers, Kevin A. Walters, Matthew J. Scarborough, Timothy J. Donohue, Daniel R. Noguera
Categories: Genome Sequences
Source: Microbiology Resource Announcements
Doi: 10.1128/mra.00292-22
Anaerobic microbiomes can be used to recover the chemical energy in agroindustrial and municipal wastes as useful products. Here, we report a total of 109 draft metagenome-assembled genomes from a bioreactor-fed carbohydrate-rich dairy manure hydrolysate. Studying these genomes will aid us in deciphering the metabolic networks in anaerobic microbiomes.
We are investigating microbial fermentation to valorize agroindustrial residues (1–5). Previously, we reported on the fermentation product profile produced when feeding dairy manure (DM) hydrolysate to an anaerobic bioreactor (3), and here, we report on the microbial community. The bioreactor was inoculated with acid-phase anaerobic digester sludge from the Nine Springs Wastewater Treatment Plant (Madison, WI, USA) (2–5). DNA was extracted from the inoculum and at multiple time points during bioreactor operation using a phenol-chloroform extraction method (4) that excluded a bead-beating step so that the DNA fragment lengths were appropriate for long-read sequencing. The DNA quantity and quality were determined using a Qubit v4.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific), respectively. DNA aliquots of 500 ng (6 samples, all from the bioreactor) and 3,000 ng (4 samples, 2 from the bioreactor and 2 from the inoculum) were submitted to the Joint Genome Institute (JGI; https://jgi.doe.gov/; Berkeley, CA, USA) for paired-end 2 × 150-bp sequencing on the NovaSeq S4 platform (Illumina, Inc., San Diego, CA, USA) and long-read sequencing using the PacBio Sequel II platform (Pacific Biosciences, Inc., Menlo Park, CA, USA), respectively. The Illumina libraries were end repaired, A-tailed, and ligated with Illumina-compatible adapters using the KAPA HyperPrep kit (Roche, USA) as described (6). The PacBio sequencing library preparation included shearing genomic DNA (Blue Pippin size selection; Sage Science, USA) to 6 to 10 kb and performing ligation using the SMRTbell Express template prep kit v2.0 following the manufacturer’s protocol (Pacific Biosciences). All software was used with default parameters unless otherwise noted. The Illumina reads were filtered and error corrected using bbcms from BBMap v38.86 (mincount = 2, highcountfraction = 0.6) (7), assembled using metaSPAdes v3.14.1 (8), and mapped using BBMap v38.86 (ambiguous=random) (7) following the JGI metagenomic workflow (6). The PacBio circular consensus sequencing (CCS) reads were assembled using metaFlye v2.8.1-b1676 (–meta) (9), polished using GCpp v1.0.0-SL-release-8.0.0 (Pacific Biosciences), mapped using minimap2 v2.12-r941 (10), and binned using MetaBAT 2.15 (11). The resulting Illumina metagenomic libraries contained between 78 and 114 million reads with a targeted length of 150 bp, and the PacBio libraries contained between 132,000 and 537,000 CCS reads with a targeted length of 6 to 10 kb. The resulting metagenome-assembled genomes (MAGs) were annotated using the JGI annotation pipeline v5.0.23 (12) and the NCBI Prokaryotic Genome Annotation Pipeline v6.0 (13). The MAGs were then refined by removing contigs deemed to be contaminants using ProDeGe v2.3 (14) and custom scripts that compare tetranucleotide frequency among contigs (run.GC.sh and Calculating_TF_Correlations.R; https://github.com/GLBRC/metagenome_analysis). All refined MAGs were dereplicated into clusters (i.e., replicate genome sets) using dRep v3.2.2 (15). The MAG quality parameters were obtained using CheckM v1.0.11 (16), and taxonomy was assigned using GTDB-Tk v1.5.1 (database release 202) (17). The MAG phylogeny was visualized using RAxML-NG v0.9.0 (Fig. 1) (18) and Interactive Tree of Life v5 (19).
FIG 1 Phylogeny of dRep-identified representative MAGs. Strain code abbreviations are as ACET, Acetobacter; ATO, Atopobiaceae; ACT, Actinomycetaceae; ACUT, Acutalibacteraceae; ANA, Anaerotignaceae; BAC, Bacillus; BACIL, Bacilli; BACTE, Bacteroidales; BIF, Bifidobacteriaceae; BURK, Burkholderiaceae; CLOS, Clostridium; DIAL, Dialister; ENTER, Enterococcaceae; FLAV, Flavobacteriales; LAC, Lactobacillus; LACCAS, Lacticaseibacillus; LCO, Lachnospiraceae; LENLAC, Lentilactobacillus; LEUC, Leuconostoc; LIMLAC, Limosilactobacillus; LIGLAC, Ligilactobacillus; LIQLAC, Liquorilactobacillus; MEG, Megasphaera; PREV, Prevotella; PSEUD, Pseudomonas; SACCH, Saccharofermentans; SCHLAC, Schleiferilactobacillus; SPHING, Sphingobium; SPOR, Sporolactobacillus; XAN, Xanthomonadaceae. The higher taxonomic levels are labeled P, phylum; C, class; and F, family. Phylum Bacter., Bacteroidota; Fir., Firmicutes_C. Class Gamma., Gammaproteobacteria; Alpha., Alphaproteobacteria; Bact., Bacteroidia; Actin., Actinobacteria; Cor., Coriobacteriia; Neg., Negativicutes. Family Xa., Xanthomonadaceae; Ac., Acetobacter; Bifidobac., Bifidobacteriaceae; At., Atopobiaceae; An., Anaerotignaceae; Clo., Clostridiaceae; Acutali., Acutalibacteraceae. The shaded strain code abbreviations indicate representative genomes from the inoculum. The phylogenetic tree was generated in RAxML-NG with 500 bootstraps using the concatenation of 120 bacterial single-copy housekeeping genes generated using GTDB-Tk. Bootstrap values greater than 50 are shown. The scale bar indicates the number of nucleotide substitutions per sequence site.
We provide a resource containing a total of 109 MAGs with greater than 75% completion and less than 3% contamination, grouped into 48 clusters that represent the microbial community diversity (Table 1). The putative classifications of these clusters span the phyla Firmicutes, Actinobacteria, Bacteroidota, and Proteobacteria (Fig. 1).
The raw metagenomic sequence data and the refined MAGs are available at NCBI GenBank under BioProject accession number PRJNA768492. All information on the library construction and sequencing can be found at https://gold.jgi.doe.gov/study?id=Gs0150020 using the JGI GOLD study ID Gs0150020. All custom scripts are available on GitHub (https://github.com/GLBRC/metagenome_analysis).
This material was based upon work supported by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under award number DE-SC0018409; by the National Institute of Food and Agriculture under grant number 2017-67003-26055; and by the Graduate Engineering Research Scholars (GERS) program of the College of Engineering at University of Wisconsin—Madison. The work (proposal 10.46936/10.25585/60001333) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy, operated under contract number DE-AC02-05CH11231.
We thank the Dairy Cattle Center of Madison, WI, for providing the manure and Matt Seib of the Madison Metropolitan Sewerage District for providing the inoculum sludge for the bioreactor experiments.
Daniel R. Noguera, Email: noguera@engr.wisc.edu.
Irene L. G. Newton, Indiana University, Bloomington
The raw metagenomic sequence data and the refined MAGs are available at NCBI GenBank under BioProject accession number PRJNA768492. All information on the library construction and sequencing can be found at https://gold.jgi.doe.gov/study?id=Gs0150020 using the JGI GOLD study ID Gs0150020. All custom scripts are available on GitHub (https://github.com/GLBRC/metagenome_analysis).