Carbapenem Resistance among Marine Bacteria-An Emerging Threat to the Global Health Sector

Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes

Motivation

Gene function annotation in microbial genomes and metagenomes is a fundamental in silico first step toward understanding metabolic potential and determinants of fitness. The Kyoto Encyclopedia of Genes and Genomes publishes a curated list of profile hidden Markov models to identify orthologous gene families (KOfams) with roles in metabolism. However, the computational tools that rely upon KOfams yield different annotations for the same set of genomes, leading to different downstream biological inferences.

Results

Here, we apply three open-source software tools that can annotate KOfams to genomes of phylogenetically diverse bacterial families from host-associated and free-living biomes. We use multiple computational approaches to benchmark these methods and investigate individual case studies where they differ. Our results show that despite their fundamental similarities, these methods have different annotation rates and quality. In particular, a method that adaptively tunes sequence similarity thresholds substantially improves sensitivity while maintaining high accuracy. We observe particularly large improvements for protein families with few reference sequences, or when annotating genomes from nonmodel organisms (such as gut-dwelling Lachnospiraceae). Our findings show that small improvements in annotation workflows can maximize the utility of existing databases and meaningfully improve in silico characterizations of microbial metabolism.

Availability and implementation

Anvi'o is available at https://anvio.org under the GNU GPL license. Scripts and workflow are available at https://github.com/pbradleylab/2023-anvio-comparison under the MIT license.

Adaptive adjustment of significance thresholds produces large gains in microbial gene annotations and metabolic insights

Gene function annotations enable microbial ecologists to make inferences about metabolic potential from genomes and metagenomes. However, even tools that use the same database and general approach can differ markedly in the annotations they recover. We compare three popular methods for identifying KEGG Orthologs, applying them to genomes drawn from a range of bacterial families that occupy different host-associated and free-living biomes. Our results show that by adaptively tuning sequence similarity thresholds, sensitivity can be substantially improved while maintaining accuracy. We observe the largest improvements when few reference sequences exist for a given protein family, and when annotating genomes from non-model organisms (such as gut-dwelling Lachnospiraceae). Our results suggest that straightforward heuristic adjustments can broadly improve microbial metabolic predictions.

An Extended Reservoir of Class-D Beta-Lactamases in Non-Clinical Bacterial Strains

Bacterial genes coding for antibiotic resistance represent a major issue in the fight against bacterial pathogens. Among those, genes encoding beta-lactamases target penicillin and related compounds such as carbapenems, which are critical for human health. Beta-lactamases are classified into classes A, B, C, and D, based on their amino acid sequence. Class D enzymes are also known as OXA beta-lactamases, due to the ability of the first enzymes described in this class to hydrolyze oxacillin. While hundreds of class D beta-lactamases with different activity profiles have been isolated from clinical strains, their nomenclature remains very uninformative. In this work, we have carried out a comprehensive survey of a reference database of 80,490 genomes and identified 24,916 OXA-domain containing proteins. These were deduplicated and their representative sequences clustered into 45 non-singleton groups derived from a phylogenetic tree of 1,413 OXA-domain sequences, including five clusters that include the C-terminal domain of the BlaR membrane receptors. Interestingly, 801 known class D beta-lactamases fell into only 18 clusters. To probe the unknown diversity of the class, we selected 10 protein sequences in 10 uncharacterized clusters and studied the activity profile of the corresponding enzymes. A beta-lactamase activity could be detected for seven of them. Three enzymes (OXA-1089, OXA-1090 and OXA-1091) were active against oxacillin and two against imipenem. These results indicate that, as already reported, environmental bacteria constitute a large reservoir of resistance genes that can be transferred to clinical strains, whether through plasmid exchange or hitchhiking with the help of transposase genes. IMPORTANCE The transmission of genes coding for resistance factors from environmental to nosocomial strains is a major component in the development of bacterial resistance toward antibiotics. Our survey of class D beta-lactamase genes in genomic databases highlighted the high sequence diversity of the enzymes that are able to recognize and/or hydrolyze beta-lactam antibiotics. Among those, we could also identify new beta-lactamases that are able to hydrolyze carbapenems, one of the last resort antibiotic families used in human antimicrobial chemotherapy. Therefore, it can be expected that the use of this antibiotic family will fuel the emergence of new beta-lactamases into clinically relevant strains.