The evolutionary logic of sepsis

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

BACKGROUND

Identification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge.

RESULTS

We present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates.

CONCLUSIONS

MGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes. Video Abstract.

Bacterial evolution during human infection: Adapt and live or adapt and die

Microbes are constantly evolving. Laboratory studies of bacterial evolution increase our understanding of evolutionary dynamics, identify adaptive changes, and answer important questions that impact human health. During bacterial infections in humans, however, the evolutionary parameters acting on infecting populations are likely to be much more complex than those that can be tested in the laboratory. Nonetheless, human infections can be thought of as naturally occurring in vivo bacterial evolution experiments, which can teach us about antibiotic resistance, pathogenesis, and transmission. Here, we review recent advances in the study of within-host bacterial evolution during human infection and discuss practical considerations for conducting such studies. We focus on 2 possible outcomes for de novo adaptive mutations, which we have termed "adapt-and-live" and "adapt-and-die." In the adapt-and-live scenario, a mutation is long lived, enabling its transmission on to other individuals, or the establishment of chronic infection. In the adapt-and-die scenario, a mutation is rapidly extinguished, either because it carries a substantial fitness cost, it arises within tissues that block transmission to new hosts, it is outcompeted by more fit clones, or the infection resolves. Adapt-and-die mutations can provide rich information about selection pressures in vivo, yet they can easily elude detection because they are short lived, may be more difficult to sample, or could be maladaptive in the long term. Understanding how bacteria adapt under each of these scenarios can reveal new insights about the basic biology of pathogenic microbes and could aid in the design of new translational approaches to combat bacterial infections.

Monitoring the dead as an ecosystem indicator

Dead animal biomass (carrion) is present in all terrestrial ecosystems, and its consumption, decomposition, and dispersal can have measurable effects on vertebrates, invertebrates, microbes, parasites, plants, and soil. But despite the number of studies examining the influence of carrion on food webs, there has been no attempt to identify how general ecological processes around carrion might be used as an ecosystem indicator. We suggest that knowledge of scavenging and decomposition rates, scavenger diversity, abundance, and behavior around carrion, along with assessments of vegetation, soil, microbe, and parasite presence, can be used individually or in combination to understand food web dynamics. Monitoring carrion could also assist comparisons of ecosystem processes among terrestrial landscapes and biomes. Although there is outstanding research needed to fully integrate carrion ecology and monitoring into ecosystem management, we see great potential in using carrion as an ecosystem indicator of an intact and functional food web.

Distinct Pathogenic Patterns of Burkholderia pseudomallei Isolates Selected from Caenorhabditis elegans and Dictyostelium discoideum Models

Burkholderia pseudomallei is a selective agent that causes septic melioidosis and exhibits a broad range of lethal doses in animals. Host cellular virulence and phagocytic resistance are pathologic keys of B. pseudomallei. We first proposed Caenorhabditis elegans as the host cellular virulence model to mimic bacterial virulence against mammals and second established the resistance of B. pseudomallei to predation by Dictyostelium discoideum as the phagocytosis model. The saprophytic sepsis-causing Burkholderia sp. (B. pseudomallei, Burkholderia thailandensis, Burkholderia cenocepacia, and Burkholderia multivorans) exhibited different virulence patterns in both simple models, but B. pseudomallei was the most toxic. Using both models, attenuated isolates of B. pseudomallei were selected from a transposon-mutant library and a panel of environmental isolates and reconfirmed by in vitro mouse peritoneal exudate cell association and invasion assays. The distinct pathological patterns of melioidosis were inducted by different selected B. pseudomallei isolates. Fatal melioidosis was induced by the isolates with high virulence in both simple models within 4-5 day, whereas the low-virulence isolates resulted in prolonged survival greater than 30 day. Infection with the isolates having high resistance to D. discoideum predation but a low C. elegans killing effect led to 83% of mice with neurologic melioidosis. By contrast, infection with the isolates having low resistance to D. discoideum predation but high C. elegans killing effect led to 20% cases with inflammation in the salivary glands. Our results indicated that individual B. pseudomallei isolates selected from simple biological models contribute differently to disease progression and/or tissue tropism.