ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks.

Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/.

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

BACKGROUND: Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq). RESULTS: Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT. CONCLUSIONS: TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

Factors influencing usage of antimicrobial drugs among pastoralists in Kenya.

Agricultural use of antimicrobials in food animal production may contribute to the global emergence of antimicrobial resistance (AMR). However, considerable gaps exist in research on the use of antimicrobial drugs (AMDs) in food animals in small-scale production systems in low- and middle-income countries, despite the minimal regulation of antimicrobials in such regions. The aim of this study was to identify factors that may influence AMD use in livestock among pastoral communities in Kenya. We collected data related to household and herd demographics, herd health, and herd management from 55 households in the Maasai Mara ecosystem, Kenya, between 2018 and 2019. We used multi-model logistic regression inference (supervised machine learning) to ascertain trends in AMD use within these households. AMD use in cattle was significantly associated with AMD use in sheep and goats (p = 0.05), implying that decisions regarding AMD use in cattle or sheep and goats were interdependent. AMD use in sheep and goats was negatively associated with vaccination against the foot and mouth disease (FMD) virus in cattle (OR = 0.06, 95% CI 0.01-0.67, p = 0.02). Less AMD use was observed for vaccine-preventable diseases like contagious ecthyma when households had access to state veterinarians (OR = 0.06, p = 0.05, 95% CI 0.004-0.96). Overall, decisions to use AMDs were associated with vaccine usage, occurrence of respiratory diseases, and access to animal health advice. This hypothesis-generating study suggests that applying community-centric methods may be necessary to understand the use of AMDs in pastoral communities.

Syotti: scalable bait design for DNA enrichment.

MOTIVATION: Bait enrichment is a protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ('baits') are designed, manufactured and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. Metsky et al. demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. RESULTS: We formalize the problem of designing baits by defining the Minimum Bait Cover problem, show that the problem is NP-hard even under very restrictive assumptions, and design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 min to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% of the data in 72 h. AVAILABILITY AND IMPLEMENTATION: https://github.com/jnalanko/syotti. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.

BACKGROUND: Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. RESULTS: We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. CONCLUSIONS: AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.

Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.

Beyond Antimicrobial Use: A Framework for Prioritizing Antimicrobial Resistance Interventions.

Antimicrobial resistance (AMR) is a threat to animal and human health. Antimicrobial use has been identified as a major driver of AMR, and reductions in use are a focal point of interventions to reduce resistance. Accordingly, stakeholders in human health and livestock production have implemented antimicrobial stewardship programs aimed at reducing use. Thus far, these efforts have yielded variable impacts on AMR. Furthermore, scientific advances are prompting an expansion and more nuanced appreciation of the many nonantibiotic factors that drive AMR, as well as how these factors vary across systems, geographies, and contexts. Given these trends, we propose a framework to prioritize AMR interventions. We use this framework to evaluate the impact of interventions that focus on antimicrobial use. We conclude by suggesting that priorities be expanded to include greater consideration of host-microbial interactions that dictate AMR, as well as anthropogenic and environmental systems that promote dissemination of AMR.

Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful?

Antimicrobial resistance (AMR) poses a global human and animal health threat, and predicting AMR persistence and transmission remains an intractable challenge. Shotgun metagenomic sequencing can help overcome this by enabling characterization of AMR genes within all bacterial taxa, most of which are uncultivatable in laboratory settings. Shotgun sequencing, therefore, provides a more comprehensive glance at AMR "potential" within samples, i.e., the "resistome." However, the risk inherent within a given resistome is predicated on the genomic context of various AMR genes, including their presence within mobile genetic elements (MGEs). Therefore, resistome risk stratification can be advanced if AMR profiles are considered in light of the flanking mobilizable genomic milieu (e.g., plasmids, integrative conjugative elements (ICEs), phages, and other MGEs). Because such mediators of horizontal gene transfer (HGT) are involved in uptake by pathogens, investigators are increasingly interested in characterizing that resistome fraction in genomic proximity to HGT mediators, i.e., the "mobilome"; we term this "colocalization." We explored the utility of common colocalization approaches using alignment- and assembly-based techniques, on clinical (human) and agricultural (cattle) fecal metagenomes, obtained from antimicrobial use trials. Ordination revealed that tulathromycin-treated cattle experienced a shift in ICE and plasmid composition versus untreated animals, though the resistome was unaffected during the monitoring period. Contrarily, the human resistome and mobilome composition both shifted shortly after antimicrobial administration, though this rebounded to pre-treatment status. Bayesian networks revealed statistical AMR-MGE co-occurrence in 19 and 2% of edges from the cattle and human networks, respectively, suggesting a putatively greater mobility potential of AMR in cattle feces. Conversely, using Mobility Index (MI) and overlap analysis, abundance of de novo-assembled contigs supporting resistomes flanked by MGE increased shortly post-exposure within human metagenomes, though > 40 days after peak dose such contigs were rare (∼2%). MI was not substantially altered by antimicrobial exposure across all cattle metagenomes, ranging 0.5-4.0%. We highlight that current alignment- and assembly-based methods estimating resistome mobility yield contradictory and incomplete results, likely constrained by approach-specific data inputs, and bioinformatic limitations. We discuss recent laboratory and computational advancements that may enhance resistome risk analysis in clinical, regulatory, and commercial applications.

Haptoglobin enhances cardiac transplant rejection.

RATIONALE: Early graft inflammation enhances both acute and chronic rejection of heart transplants, but it is unclear how this inflammation is initiated. OBJECTIVE: To identify specific inflammatory modulators and determine their underlying molecular mechanisms after cardiac transplantation. METHODS AND RESULTS: We used a murine heterotopic cardiac transplant model to identify inflammatory modulators of early graft inflammation. Unbiased mass spectrometric analysis of cardiac tissue before and ≤72 hours after transplantation revealed that 22 proteins including haptoglobin, a known antioxidant, are significantly upregulated in our grafts. Through the use of haptoglobin-deficient mice, we show that 80% of haptoglobin-deficient recipients treated with perioperative administration of the costimulatory blocking agent CTLA4 immunoglobulin exhibited >100-day survival of full major histocompatibility complex mismatched allografts, whereas all similarly treated wild-type recipients rejected their transplants by 21 days after transplantation. We found that haptoglobin modifies the intra-allograft inflammatory milieu by enhancing levels of the inflammatory cytokine interleukin-6 and the chemokine MIP-2 (macrophage inflammatory protein 2) but impair levels of the immunosuppressive cytokine interleukin-10. Haptoglobin also enhances dendritic cell graft recruitment and augments antidonor T-cell responses. Moreover, we confirmed that the protein is present in human cardiac allograft specimens undergoing acute graft rejection. CONCLUSIONS: Our findings provide new insights into the mechanisms of inflammation after cardiac transplantation and suggest that, in contrast to its prior reported antioxidant function in vascular inflammation, haptoglobin is an enhancer of inflammation after cardiac transplantation. Haptoglobin may also be a key component in other sterile inflammatory conditions.

Proximity to natural gas wells and reported health status: results of a household survey in Washington County, Pennsylvania.

BACKGROUND: Little is known about the environmental and public health impact of unconventional natural gas extraction activities, including hydraulic fracturing, that occur near residential areas. OBJECTIVES: Our aim was to assess the relationship between household proximity to natural gas wells and reported health symptoms. METHODS: We conducted a hypothesis-generating health symptom survey of 492 persons in 180 randomly selected households with ground-fed wells in an area of active natural gas drilling. Gas well proximity for each household was compared with the prevalence and frequency of reported dermal, respiratory, gastrointestinal, cardiovascular, and neurological symptoms. RESULTS: The number of reported health symptoms per person was higher among residents living < 1 km (mean ± SD, 3.27 ± 3.72) compared with > 2 km from the nearest gas well (mean ± SD, 1.60 ± 2.14; p = 0.0002). In a model that adjusted for age, sex, household education, smoking, awareness of environmental risk, work type, and animals in house, reported skin conditions were more common in households < 1 km compared with > 2 km from the nearest gas well (odds ratio = 4.1; 95% CI: 1.4, 12.3; p = 0.01). Upper respiratory symptoms were also more frequently reported in persons living in households < 1 km from gas wells (39%) compared with households 1-2 km or > 2 km from the nearest well (31 and 18%, respectively) (p = 0.004). No equivalent correlation was found between well proximity and other reported groups of respiratory, neurological, cardiovascular, or gastrointestinal conditions. CONCLUSION: Although these results should be viewed as hypothesis generating, and the population studied was limited to households with a ground-fed water supply, proximity of natural gas wells may be associated with the prevalence of health symptoms including dermal and respiratory conditions in residents living near natural gas extraction activities. Further study of these associations, including the role of specific air and water exposures, is warranted.

Influence of plant-earthworm interactions on SOM chemistry and p,p'-DDE bioaccumulation.

Laboratory experiments assessed how bioaccumulation of weathered p,p'-DDE from soil and humic acid (HA) chemistry are affected by interactions between the plants Cucurbita pepo ssp. pepo and ssp. ovifera and the earthworms Eisenia fetida, Lumbricus terrestris, and Apporectodea caliginosa. Total organochlorine phytoextraction by ssp. pepo increased at least 25% in the presence of any of the earthworm species (relative to plants grown in isolation). Uptake of the compound by ssp. ovifera was unaffected by earthworms. Plants influenced earthworm bioaccumulation as well. When combined with pepo, p,p'-DDE levels in E. fetida decreased by 50%, whereas, in the presence of ovifera, bioconcentration by L. terrestris increased by more than 2-fold. Spectral analysis indicated a decrease in hydrophobicity of HA in each of the soils in which both pepo and earthworms were present. However, HA chemistry from ovifera treatments was largely unaffected by earthworms. Risk assessments of contaminated soils should account for species interactions, and SOM chemistry may be a useful indictor of pollutant bioaccumulation.

Surfactant-facilitated remediation of metal-contaminated soils: efficacy and toxicological consequences to earthworms.

The effectiveness of surfactant formulations to remove aged metals from a field soil and their influence on soil toxicity was investigated. Batch studies were conducted to evaluate the efficacy of cationic (1-dodecylpyridinium chloride; DPC), nonionic (oleyl dimethyl benzyl ammonium chloride; trade name Ammonyx KP), and anionic (rhamnolipid biosurfactant blend; trade name JBR-425) surfactants for extracting Zn, Cu, Pb, and Cd from a soil subjected to more than 80 years of metal deposition. All three surfactants enhanced removal of the target metals. The anionic biosurfactant JBR-425 was most effective, reducing Zn, Cu, Pb, and Cd in the soil by 39, 56, 68, and 43%, respectively, compared with less than 6% removal by water alone. Progressive acidification of the surfactants with citric acid buffer or addition of ethylenediaminetetra-acetic acid (EDTA) further improved extraction efficiency, with more than 95% extraction of all four metals by surfactants acidified to pH 3.6 and generally greater than 90% removal of all metals with addition of 0.1 M EDTA. In two species of earthworm, Eisenia fetida and Lumbricus terrestris, metal bioaccumulation was reduced by approximately 30 to 80%, total biomass was enhanced by approximately threefold to sixfold, and survival was increased to greater than 75% in surfactant-remediated soil compared with untreated soil. The data indicate that surfactant washing may be a feasible approach to treat surface soils contaminated with a variety of metals, even if those metals have been present for nearly a century, and that the toxicity and potential for metal accumulation in biota from the treated soils may be significantly reduced.

Technical note: evaluation of extraction methodologies for the determination of an organochlorine pesticide residue in vegetation.

Numerous extraction methodologies are used to quantify pesticide levels in vegetation. Sample availability, resource use, efficiency, time consumption, space allocation, and cost vary considerably among the commonly employed techniques. A study was conducted to compare the efficiency of microwave assisted extraction (MAE), blender homogenized extraction (BE), Soxhlet extraction (SE), the QuEChERS ("Quick, Easy, Cheap, Effective, Rugged, and Safe") method, and a simple oven assisted extraction (OAE), to recover p,p'-DDE from the tissues of Cucurbita pepo. A hot-solvent soak of stem or root tissues in a 2-propanol/hexane mixture, OAE yields recoveries that are statistically equivalent to the other procedures. The method recovered 1800 +/- 190 ng g(-1) and 8100 +/- 900 ng g(-1) (BCF = 87 +/- 9.7) p,p'-DDE from stem and root tissue, respectively. Recoveries for the other methods ranged from 1400-2200 ng g(-1) for the stems and 3600-7200 ng g(-1) for the roots. Statistical analyses for stem and root extraction indicate that there is no significant difference among the variances of each method. Given its simplicity, precision, and efficiency, OAE appears to be suitable for the extraction of an organic pollutant such as p,p'-DDE from plant tissues and for use in phytotechnology development and risk assessment.

Soil sterilization affects aging-related sequestration and bioavailability of p,p'-DDE and anthracene to earthworms.

Laboratory experiments investigated the effects of soil sterilization and compound aging on the bioaccumulation of spiked p,p'-DDE and anthracene by Eisenia fetida and Lumbricus terrestris. Declines in bioavailability occurred as pollutant residence time in both sterile and non-sterile soils increased from 3 to 203 d. Accumulation was generally higher in sterile soils during initial periods of aging (from 3-103 d). By 203 d, however, bioavailability of the compounds was unaffected by sterilization. Gamma irradiation and autoclaving may have altered bioavailability by inducing changes in the chemistry of soil organic matter (SOM). The results support a dual-mode partitioning sorption model in which the SOM components associated with short-term sorption (the 'soft' or 'rubbery' phases) are more affected than are the components associated with long-term sorption (the 'glassy' or microcrystalline phases). Risk assessments based on data from experiments in which sterile soil was used could overestimate exposure and bioaccumulation of pollutants.

Sterilization affects soil organic matter chemistry and bioaccumulation of spiked p,p'-DDE and anthracene by earthworms.

Laboratory experiments were conducted to assess the effects of soil sterilization on the bioavailability of spiked p,p'-DDE and anthracene to the earthworms Eisenia fetida and Lumbricus terrestris. Physical and chemical changes to soil organic matter (SOM) induced by sterilization were also studied. Uptake of both compounds added after soil was autoclaved or gamma irradiated increased for E. fetida. Sterilization had no effect on bioaccumulation of p,p'-DDE by L. terrestris, and anthracene uptake increased only in gamma-irradiated soils. Analyses by FT-IR and DSC indicate sterilization alters SOM chemistry and may reduce pollutant sorption. Chemical changes to SOM were tentatively linked to changes in bioaccumulation, although the effects were compound and species specific. Artifacts produced by sterilization could lead to inaccurate risk assessments of contaminated sites if assumptions derived from studies carried out in sterilized soil are used. Ultimately, knowledge of SOM chemistry could aid predictions of bioaccumulation of organic pollutants.

Effects of incubation time and organism density on the bioaccumulation of soil-borne p,p'-DDE by the earthworm, Eisenia fetida.

Laboratory-scale experiments were conducted to assess the influence of incubation time and organism density on bioaccumulation of weathered p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) from soil by the earthworm Eisenia fetida. Bioaccumulation was measured after 14, 28, 42, and 56 days of exposure. Tissue concentration increased with incubation time and steady state was not reached until at least 42 days. Organism density had no effect on the bioaccumulation of weathered p,p'-DDE. Ratios of 10, 20, 40, and 80 earthworms/350 g of soil led to the same tissue concentrations in test organisms. Risk assessments of contaminated soil should account for these experimental variables.