In freshwater lakes, large amounts of methane are formed in anoxic sediments. Methane-oxidizing bacteria effectively convert this potent greenhouse gas into biomass and carbon dioxide. These bacteria are present throughout the water column where methane concentrations can range from nanomolar to millimolar concentrations. In this study, we tested the hypothesis that methanotroph assemblages in a seasonally stratified lake exhibit contrasting methane oxidation kinetics in the methane-rich hypolimnion compared to the epilimnion with low methane concentrations. We further examined the change of methane oxidation kinetics during autumn overturn as more methane becomes available in the epilimnion. Together with the change of methane oxidation kinetics, we investigated changes in the transcription of genes encoding the methane monooxygenase (MMO), which is the enzyme responsible for the first step of methane oxidation. We show that the half-saturation constant (Km) obtained from laboratory experiments with the natural microbial community differed by two orders of magnitude between epi- and hypolimnion during stable stratification. During lake overturn, however, the kinetic constants at the lake surface and in the deep-water converged along with a change of the methanotroph assemblage. Conventional particulate MMO seemed responsible for the methane-oxidation under different methane concentrations. Our results suggest that changing methane availability creates niches for methanotroph assemblages with well-adapted methane-oxidation kinetics. This rapid selection and succession of adapted lacustrine methanotroph assemblages seem to support that the reported high removal efficiency of more than 90% is maintained even under rapidly changing conditions during lake overturn. Consequently, only a small fraction of methane stored in the anoxic hypolimnion is emitted to the atmosphere.

Wastewater treatment plants (WWTPs) are implicated as hotspots for the dissemination of antibacterial resistance into the environment. However, the in situ processes governing removal, persistence, and evolution of resistance genes during wastewater treatment remain poorly understood. Here, we used quantitative metagenomic and metatranscriptomic approaches to achieve a broad-spectrum view of the flow and expression of genes related to antibacterial resistance to over 20 classes of antibiotics, 65 biocides, and 22 metals. All compartments of 12 WWTPs share persistent resistance genes with detectable transcriptional activities that were comparatively higher in the secondary effluent, where mobility genes also show higher relative abundance and expression ratios. The richness and abundance of resistance genes vary greatly across metagenomes from different treatment compartments, and their relative and absolute abundances correlate with bacterial community composition and biomass concentration. No strong drivers of resistome composition could be identified among the chemical stressors analyzed, although the sub-inhibitory concentration (hundreds of ng/L) of macrolide antibiotics in wastewater correlates with macrolide and vancomycin resistance genes. Contig-based analysis shows considerable co-localization between resistance and mobility genes and implies a history of substantial horizontal resistance transfer involving human bacterial pathogens. Based on these findings, we propose future inclusion of mobility incidence (M%) and host pathogenicity of antibiotic resistance genes in their quantitative health risk ranking models with an ultimate goal to assess the biological significance of wastewater resistomes with regard to disease control in humans or domestic livestock.

This is a dataset consisting of donor-specific collections of 78 metagenomes (13 / donor) and 143 metagenome-assembled genomes (MAGs) representing the gut microbiomes of six healthy adult human donors. Raw sequencing data and MAG sequence data will be available in NCBI under BioProject accession PRJNA961974 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA961974). Spreadsheets attached to this dataset include individual accession numbers and sequencing depth for the raw data; and assembly and NCBI accession numbers, binning information, quality metrics, and taxonomic assignments for the MAGs. Resources in this dataset: Resource Title: Metagenome assembled genome (MAG) information File Name: MAGs_information_table.csv Metagenome sample information File Name: Metagenomes_information_table.csv

Organisms living in honey bees and honey bee colonies form large associative holobiont communities that are integral to bee biology. High-throughput sequencing approaches to characterize these holobiont communities from honey bees in various states of health and disease are now commonplace, producing large amounts of nucleotide sequence data that must be accurately and consistently analyzed in order to produce reliable and comparable reports. In addition, new species designations and revisions are actively being made from honey bee holobiont communities, complicating nomenclature in larger databases where taxonomic descriptions associated with archived sequences can quickly become outdated and misleading. To improve the accuracy and consistency of honey bee holobiont research, we have developed HoloBee: a curated database of publicly accessioned nucleotide sequences from the honey bee holobiont community. Except in rare and noted exceptions made by curators, sequences used in HoloBee were obtained from, or in association with, Apis mellifera (Western honey bee) as well as other honey bee species where available (e.g. Apis cerana, Apis dorsata, Apis laboriosa, Apis koschevnikovi, Apis florea, Apis andreniformis and Apis nigrocincta). Sources include: within or on the surface of honey bees (adult, pupae, larvae, egg), corbicular pollen, bee bread, royal jelly, honey, comb, hive surfaces (e.g. bottom board debris, frames, landing platforms), and isolates of microbes, parasites and pathogens from honey bees. HoloBee contains two non-overlapping sets of sequence data, HoloBee-Barcode and HoloBee-Mop, each of which have distinct intended uses. HoloBee-Barcode is a non-redundant database of taxonomically informative barcoding loci for all viruses, bacteria, fungi, protozoans and metazoans associated with honey bees (Apis spp.). It was created from an exhaustive master sequence archive of all valid holobiont sequences. Redundancy was removed from this master archive using a clustering algorithm that grouped sequences with ≥ 99% identity and retained the longest sequence from each cluster as the representative accession for that sequence type (“centroid”). These centroid sequences were concatenated into a fasta formatted file to create the HoloBee-Barcode database. Associated taxonomy for each centroid, including Superkingdom through Species and Strain/Isolate, was individually reviewed and corrected when necessary by a curator. Cross reference tables (separated according to 5 major taxonomic groups) provide a user-friendly outline of information for each centroid accession within HoloBee-Barcode including taxonomy, gene/product name, sequence length, the unaltered NCBI definition line, the number and identity of redundant sequences clustered within each centroid, and any additional information provided by the curator. HoloBee-Barcode centroid counts are: Viruses = 86; Bacteria = 496; Fungi = 41; Protozoa = 4; Metazoa = 60. HoloBee-Barcode is intended to improve and standardize quantitative and qualitative metagenomic descriptions of holobiont communities associated with honey bees by providing a curated set of barcode sequences. The goal of genetic barcoding is to associate a nucleotide sequence sample to a taxonomically valid species. Genomic regions targeted for such barcoding purposes varied by taxonomic group. The small subunit (SSU) ribosomal RNA, or 16S rRNA, is the most commonly used barcode for bacteria and is used in HB-Barcode. These 16S rRNA sequences will support the analysis of data generated with the widely used approach of amplicon-based 16S rRNA deep sequencing to study microbiota communities. Although barcode markers for fungi are less definitive than bacteria, HB-Barcode defaults to the ribosomal RNA internal transcribed spacer region (ITS), which typically includes ITS-1, 5.8S, and ITS-2. For some clades that cannot be resolved by this region, other barcode markers were selected. The majority of barcodes for metazoan taxa are the mitochondrial locus cytochrome c oxidase subunit I (COI). Complete mitochondrial DNA (mtDNA) sequence for Apis cerana (Asian honey bee) and Galleria mellonella (Greater wax moth) are included as barcodes for these species. We note that A. cerana mtDNA is included because it is considered a potentially invasive honey bee species and monitoring for its occurrence is in practice regionally, including in Australia, New Zealand and the USA. Protozoan barcodes include cytochrome b oxidase (Cytb), SSU, or ITS while entire genomes are used for viral barcoding. HoloBee-Mop is a database comprised mostly of chromosomal, mitochondrial and plasmid genome assemblies in order to aggregate as much honey bee holobiont genomic sequence information as possible. For a few organisms without genome assembly data, transcriptome data are included (e.g. Aethina tumida, small hive beetle). Unlike HoloBee-Barcode, redundancy removal was not performed on the HoloBee-Mop database and thus this resource provides an archive of nucleotide sequence assemblies from honey bee holobionts. However, since full viral genomes are used in HoloBee-Barcode, only redundant viral sequences occur in HoloBee-Mop. All accessions within each of these assemblies were concatenated into a single fasta formatted file to create the HoloBee-Mop database. The intended purpose of HoloBee-Mop is to improve honey bee genome and transcriptome assemblies by “mopping-up” as much viral, bacterial, fungal, protozoan and non-honey bee metazoan sequence data as possible. Therefore, sequence data remaining after processing reads through both HoloBee-Barcode and HoloBee-Mop that do not map to the honey bee genome may contain unique data from taxonomic variants or novel species. Details for each sequence assembly within HoloBee-Mop are tabulated in cross reference tables according to each major taxonomic group. HoloBee-Mop assembly counts are: Viruses = 2; Bacteria = 55; Fungi = 5; Protozoa = 1; Metazoa = 6. Follow the HoloBee database on Twitter at: https://twitter.com/HoloBee_db For questions about the HoloBee database, contact: HoloBee database team: holobee.db@gmail.com Jay Evans: Jay.Evans@ars.usda.gov Anna Childers: Anna.Childers@ars.usda.gov