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ABSTRACT Arsenic (As) metabolism genes are generally present in soils, but their diversity, relative abundance, and transcriptional activity in response to different As concentrations remain unclear, limiting our understanding of the microbial activities that control the fate of an important environmental pollutant. To address this issue, we applied metagenomics and metatranscriptomics to paddy soils showing a gradient of As concentrations to investigate As resistance genes ( ars ) including arsR , acr3 , arsB , arsC , arsM , arsI , arsP , and arsH as well as energy-generating As respiratory oxidation ( aioA ) and reduction ( arrA ) genes. Somewhat unexpectedly, the relative DNA abundances and diversities of ars , aioA , and arrA genes were not significantly different between low and high (∼10 versus ∼100 mg kg −1 ) As soils. Compared to available metagenomes from other soils, geographic distance rather than As levels drove the different compositions of microbial communities. Arsenic significantly increased ars gene abundance only when its concentration was higher than 410 mg kg −1 . In contrast, metatranscriptomics revealed that relative to low-As soils, high-As soils showed a significant increase in transcription of ars and aioA genes, which are induced by arsenite, the dominant As species in paddy soils, but not arrA genes, which are induced by arsenate. These patterns appeared to be community wide as opposed to taxon specific. Collectively, our findings advance understanding of how microbes respond to high As levels and the diversity of As metabolism genes in paddy soils and indicated that future studies of As metabolism in soil or other environments should include the function (transcriptome) level. IMPORTANCE Arsenic (As) is a toxic metalloid pervasively present in the environment. Microorganisms have evolved the capacity to metabolize As, and As metabolism genes are ubiquitously present in the environment even in the absence of high concentrations of As. However, these previous studies were carried out at the DNA level; thus, the activity of the As metabolism genes detected remains essentially speculative. Here, we show that the high As levels in paddy soils increased the transcriptional activity rather than the relative DNA abundance and diversity of As metabolism genes. These findings advance our understanding of how microbes respond to and cope with high As levels and have implications for better monitoring and managing an important toxic metalloid in agricultural soils and possibly other ecosystems. 

ABSTRACT Identification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla . Application of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice. IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings.