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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. 

ABSTRACT The use of enterococci as a fecal indicator bacterial group for public health risk assessment has been brought into question by recent studies showing that “naturalized” populations of Enterococcus faecalis exist in the extraenteric environment. The extent to which these naturalized E. faecalis organisms can confound water quality monitoring is unclear. To determine if strains isolated from different habitats display different survival strategies and responses, we compared the decay patterns of three E. faecalis isolates from the natural environment (environmental strains) against three human gut isolates (enteric strains) in laboratory mesocosms that simulate an oligotrophic, aerobic freshwater environment. Our results showed similar overall decay rates between enteric and environmental isolates based on viable plate and quantitative PCR (qPCR) counts. However, the enteric isolates exhibited a spike in copy number ratios of 16S rRNA gene transcripts to 16S rRNA gene DNA copies (rRNA:rDNA ratios) between days 1 and 3 of the mesocosm incubations that was not observed in environmental isolates, which could indicate a different stress response. Nevertheless, there was no strong evidence of differential gene expression between environmental and enteric isolates related to habitat adaptation in the accompanying mesocosm metatranscriptomes. Overall, our results provide novel information on how rRNA levels may vary over different growth conditions (e.g., standard lab versus oligotrophic) for this important indicator bacteria. We also observed some evidence for habitat adaptation in E. faecalis ; however, this adaptation may not be substantial or consistent enough for integration in water quality monitoring. IMPORTANCE Enterococci are commonly used worldwide to monitor environmental fecal contamination and public health risk for waterborne diseases. However, closely related enterococci strains adapted to living in the extraenteric environment may represent a lower public health risk and confound water quality estimates. We developed an rRNA:rDNA viability assay for E. faecalis (a predominant species within this fecal group) and tested it against both enteric and environmental isolates in freshwater mesocosms to assess whether this approach can serve as a more sensitive water quality monitoring tool. We were unable to reliably distinguish the different isolate types using this assay under the conditions tested; thus, environmental strains should continue to be counted during routine water monitoring. However, this assay could be useful for distinguishing more recent (i.e., higher-risk) fecal pollution because rRNA levels significantly decreased after 1 week in all isolates. 

ABSTRACT The phylogenetic and functional diversities of microbial communities in tropical rainforests and how these differ from those of temperate communities remain poorly described but are directly related to the increased fluxes of greenhouse gases such as nitrous oxide (N 2 O) from the tropics. Toward closing these knowledge gaps, we analyzed replicated shotgun metagenomes representing distinct life zones and an elevation gradient from four locations in the Luquillo Experimental Forest (LEF), Puerto Rico. These soils had a distinct microbial community composition and lower species diversity compared to those of temperate grasslands or agricultural soils. In contrast to the overall distinct community composition, the relative abundances and nucleotide sequences of N 2 O reductases ( nosZ ) were highly similar between tropical forest and temperate soils. However, respiratory NO reductase ( norB ) was 2-fold more abundant in the tropical soils, which might be relatable to their greater N 2 O emissions. Nitrogen fixation ( nifH ) also showed higher relative abundance in rainforest than in temperate soils, i.e., 20% versus 0.1 to 0.3% of bacterial genomes in each soil type harbored the gene, respectively. Finally, unlike temperate soils, LEF soils showed little stratification with depth in the first 0 to 30 cm, with ∼45% of community composition differences explained solely by location. Collectively, these results advance our understanding of spatial diversity and metabolic repertoire of tropical rainforest soil communities and should facilitate future ecological studies of these ecosystems. IMPORTANCE Tropical rainforests are the largest terrestrial sinks of atmospheric CO 2 and the largest natural source of N 2 O emissions, two greenhouse gases that are critical for the climate. The microbial communities of rainforest soils that directly or indirectly, through affecting plant growth, contribute to these fluxes remain poorly described by cultured-independent methods. To close this knowledge gap, the present study applied shotgun metagenomics to samples selected from three distinct life zones within the Puerto Rico rainforest. The results advance our understanding of microbial community diversity in rainforest soils and should facilitate future studies of natural or manipulated perturbations of these critical ecosystems. 

ABSTRACT Little is known about the public health risks associated with natural creek sediments that are affected by runoff and fecal pollution from agricultural and livestock practices. For instance, the persistence of foodborne pathogens such as Shiga toxin-producing Escherichia coli (STEC) originating from these practices remains poorly quantified. Towards closing these knowledge gaps, the water-sediment interface of two creeks in the Salinas River Valley of California was sampled over a 9-month period using metagenomics and traditional culture-based tests for STEC. Our results revealed that these sediment communities are extremely diverse and have functional and taxonomic diversity comparable to that observed in soils. With our sequencing effort (∼4 Gbp per library), we were unable to detect any pathogenic E. coli in the metagenomes of 11 samples that had tested positive using culture-based methods, apparently due to relatively low abundance. Furthermore, there were no significant differences in the abundance of human- or cow-specific gut microbiome sequences in the downstream impacted sites compared to that in upstream more pristine (control) sites, indicating natural dilution of anthropogenic inputs. Notably, the high number of metagenomic reads carrying antibiotic resistance genes (ARGs) found in all samples was significantly higher than ARG reads in other available freshwater and soil metagenomes, suggesting that these communities may be natural reservoirs of ARGs. The work presented here should serve as a guide for sampling volumes, amount of sequencing to apply, and what bioinformatics analyses to perform when using metagenomics for public health risk studies of environmental samples such as sediments. IMPORTANCE Current agricultural and livestock practices contribute to fecal contamination in the environment and the spread of food- and waterborne disease and antibiotic resistance genes (ARGs). Traditionally, the level of pollution and risk to public health are assessed by culture-based tests for the intestinal bacterium Escherichia coli . However, the accuracy of these traditional methods (e.g., low accuracy in quantification, and false-positive signal when PCR based) and their suitability for sediments remain unclear. We collected sediments for a time series metagenomics study from one of the most highly productive agricultural regions in the United States in order to assess how agricultural runoff affects the native microbial communities and if the presence of Shiga toxin-producing Escherichia coli (STEC) in sediment samples can be detected directly by sequencing. Our study provided important information on the potential for using metagenomics as a tool for assessment of public health risk in natural environments. 

To what extent multi-omic techniques could reflectin situmicrobial process rates remains unclear, especially for highly diverse habitats like soils. Here, we performed microcosm incubations using sandy soil from an agricultural site in Midwest USA. Microcosms amended with isotopically labeled ammonium and urea to simulate a fertilization event showed nitrification (up to 4.1 ± 0.87 µg N-NO₃⁻g⁻¹dry soil d⁻¹) and accumulation of N₂O after 192 hours of incubation. Nitrification activity (NH₄⁺ → NH₂OH → NO → NO₂^- → NO₃⁻) was accompanied by a 6-fold increase in relative expression of the 16S rRNA gene (RNA/DNA) between 10 and 192 hours of incubation for ammonia-oxidizing bacteriaNitrosomonasandNitrosospira, unlike archaea and comammox bacteria, which showed stable gene expression. A strong relationship between nitrification activity and betaproteobacterial ammonia monooxygenase and nitrite oxidoreductase transcript abundances revealed that mRNA quantitatively reflected measured activity and was generally more sensitive than DNA under these conditions. Although peptides related to housekeeping proteins from nitrite-oxidizing microorganisms were detected, their abundance was not significantly correlated with activity, revealing that meta-proteomics provided only a qualitative assessment of activity. Altogether, these findings underscore the strengths and limitations of multi-omic approaches for assessing diverse microbial communities in soils and provide new insights into nitrification.