Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications

Whole genome analysis and antimicrobial resistance of Neisseria gonorrhoeae isolates from Ghana

Introduction

Gonorrhoea is a major public health concern. With the global emergence and spread of resistance to last-line antibiotic treatment options, gonorrhoea threatens to be untreatable in the future. Therefore, this study performed whole genome characterization of Neisseria gonorrhoeae collected in Ghana to identify lineages of circulating strains as well as their phenotypic and genotypic antimicrobial resistance (AMR) profiles.

Methods

Whole genome sequencing (WGS) was performed on 56 isolates using both the Oxford Nanopore MinION and Illumina MiSeq sequencing platforms. The Comprehensive Antimicrobial Resistance Database (CARD) and PUBMLST.org/neisseria databases were used to catalogue chromosomal and plasmid genes implicated in AMR. The core genome multi-locus sequence typing (cgMLST) approach was used for comparative genomics analysis.

Results and Discussion

In vitro resistance measured by the E-test method revealed 100%, 91.0% and 85.7% resistance to tetracycline, penicillin and ciprofloxacin, respectively. A total of 22 sequence types (STs) were identified by multilocus sequence typing (MLST), with ST-14422 (n = 10), ST-1927 (n = 8) and ST-11210 (n = 7) being the most prevalent. Six novel STs were also identified (ST-15634, 15636-15639 and 15641). All isolates harboured chromosomal AMR determinants that confer resistance to beta-lactam antimicrobials and tetracycline. A single cefixime-resistant strain, that belongs to N. gonorrhoeae multiantigen sequence type (NG-MAST) ST1407, a type associated with widespread cephalosporin resistance was identified. Neisseria gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR), identified 29 unique sequence types, with ST-464 (n = 8) and the novel ST-3366 (n = 8) being the most prevalent. Notably, 20 of the 29 STs were novel, indicative of the unique nature of molecular AMR determinants in the Ghanaian strains. Plasmids were highly prevalent: pTetM and pblaTEM were found in 96% and 92% of isolates, respectively. The TEM-135 allele, which is an amino acid change away from producing a stable extended-spectrum β-lactamase that could result in complete cephalosporin resistance, was identified in 28.5% of the isolates. Using WGS, we characterized N. gonorrhoeae strains from Ghana, giving a snapshot of the current state of gonococcal AMR in the country and highlighting the need for constant genomic surveillance.

Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)

The fluctuation assay remains an important tool for analyzing the levels of mutagenesis in microbial populations. The mutant counts originating from some average number of mutations are usually assumed to obey the Luria-Delbrück distribution. While several tools for estimating mutation rates are available, they sometimes lack accuracy or versatility under non-standard conditions. In this work, extensions to the Luria-Delbrück protocol to account for phenotypic lag and cellular death with either perfect or partial plating were developed. Hence, the novel MLE MUtation Rate calculator, or mlemur, is the first tool that provides a user-friendly graphical interface allowing the researchers to model their data with consideration for partial plating, differential growth of mutants and non-mutants, phenotypic lag, cellular death, variability of the final number of cells, post-exponential-phase mutations, and the size of the inoculum. Additionally, mlemur allows the users to incorporate most of these special conditions at the same time to obtain highly accurate estimates of mutation rates and P values, confidence intervals for an arbitrary function of data (such as fold), and perform power analysis and sample size determination for the likelihood ratio test. The accuracy of point and interval estimates produced by mlemur against historical and simulated fluctuation experiments are assessed. Both mlemur and the analyses in this work might be of great help when evaluating fluctuation experiments and increase the awareness of the limitations of the widely-used Lea-Coulson formulation of the Luria-Delbrück distribution in the more realistic biological contexts.

Solving the puzzle of Fe homeostasis by integrating molecular, mathematical, and societal models

To ensure optimal utilization and bioavailability, iron uptake, transport, subcellular localization, and assimilation are tightly regulated in plants. Herein, we examine recent advances in our understanding of cellular responses to Fe deficiency. We then use intracellular mechanisms of Fe homeostasis to discuss how formalizing cell biology knowledge via a mathematical model can advance discovery even when quantitative data is limited. Using simulation-based inference to identify plausible systems mechanisms that conform to known emergent phenotypes can yield novel, testable hypotheses to guide targeted experiments. However, this approach relies on the accurate encoding of domain-expert knowledge in exploratory mathematical models. We argue that this would be facilitated by fostering more "systems thinking" life scientists and that diversifying your research team may be a practical path to achieve that goal.

Precise measurement of the fitness effects of spontaneous mutations by droplet digital PCR in Burkholderia cenocepacia

Understanding how mutations affect survivability is a key component to knowing how organisms and complex traits evolve. However, most mutations have a minor effect on fitness and these effects are difficult to resolve using traditional molecular techniques. Therefore, there is a dire need for more accurate and precise fitness measurements methods. Here, we measured the fitness effects in Burkholderia cenocepacia HI2424 mutation accumulation (MA) lines using droplet-digital polymerase chain reaction (ddPCR). Overall, the fitness measurements from ddPCR-MA are correlated positively with fitness measurements derived from traditional phenotypic marker assays (r = 0.297, P = 0.05), but showed some differences. First, ddPCR had significantly lower measurement variance in fitness (F = 3.78, P < 2.6 × 10-13) in control experiments. Second, the mean fitness from ddPCR-MA measurements were significantly lower than phenotypic marker assays (-0.0041 vs -0.0071, P = 0.006). Consistent with phenotypic marker assays, ddPCR-MA measurements observed multiple (27/43) lineages that significantly deviated from mean fitness, suggesting that a majority of the mutations are neutral or slightly deleterious and intermixed with a few mutations that have extremely large effects. Of these mutations, we found a significant excess of mutations within DNA excinuclease and Lys R transcriptional regulators that have extreme deleterious and beneficial effects, indicating that modifications to transcription and replication may have a strong effect on organismal fitness. This study demonstrates the power of ddPCR as a ubiquitous method for high-throughput fitness measurements in both DNA- and RNA-based organisms regardless of cell type or physiology.