When the most potent combination of antibiotics selects for the greatest bacterial load: the Smile-Frown transition

Final published PDF version of article deposited in accordance with SHERPA RoMEO guidelinesConventional wisdom holds that the best way to treat infection with antibiotics is to ‘hit early and hit hard’. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations

Population genomics reveals that within-fungus polymorphism is common and maintained in populations of the mycorrhizal fungus Rhizophagus irregularis.

Arbuscular mycorrhizal (AM) fungi are symbionts of most plants, increasing plant growth and diversity. The model AM fungus Rhizophagus irregularis (isolate DAOM 197198) exhibits low within-fungus polymorphism. In contrast, another study reported high within-fungus variability. Experiments with other R. irregularis isolates suggest that within-fungus genetic variation can affect the fungal phenotype and plant growth, highlighting the biological importance of such variation. We investigated whether there is evidence of differing levels of within-fungus polymorphism in an R. irregularis population. We genotyped 20 isolates using restriction site-associated DNA sequencing and developed novel approaches for characterizing polymorphism among haploid nuclei. All isolates exhibited higher within-isolate poly-allelic single-nucleotide polymorphism (SNP) densities than DAOM 197198 in repeated and non-repeated sites mapped to the reference genome. Poly-allelic SNPs were independently confirmed. Allele frequencies within isolates deviated from diploids or tetraploids, or that expected for a strict dikaryote. Phylogeny based on poly-allelic sites was robust and mirrored the standard phylogeny. This indicates that within-fungus genetic variation is maintained in AM fungal populations. Our results predict a heterokaryotic state in the population, considerable differences in copy number variation among isolates and divergence among the copies, or aneuploidy in some isolates. The variation may be a combination of all of these hypotheses. Within-isolate genetic variation in R. irregularis leads to large differences in plant growth. Therefore, characterizing genomic variation within AM fungal populations is of major ecological importance

High‐throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations

High-throughput identification technologies provide efficient tools for understanding the ecology and functioning of microorganisms. Yet, these methods have been only rarely used for monitoring and testing ecological hypotheses in plant pathogens and pests in spite of their immense importance in agriculture, forestry and plant community dynamics. The main objectives of this manuscript are the following: (a) to provide a comprehensive overview about the state-of-the-art high-throughput quantification and molecular identification methods used to address population dynamics, community ecology and host associations of microorganisms, with a specific focus on antagonists such as pathogens, viruses and pests; (b) to compile available information and provide recommendations about specific protocols and workable primers for bacteria, fungi, oomycetes and insect pests; and (c) to provide examples of novel methods used in other microbiological disciplines that are of great potential use for testing specific biological hypotheses related to pathology. Finally, we evaluate the overall perspectives of the state-of-the-art and still evolving methods for diagnostics and population- and community-level ecological research of pathogens and pests

deeptools/deepTools: 3.5.4

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deeptools/deepTools: 3.5.4

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deeptools/deepTools: 3.5.4

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Experimental host–pathogen coevolution causes phenotypic changes in both antagonists.

<p><b>A,</b> The five evolution treatments: (i) host control (grey) adapting to general laboratory conditions in the absence of the pathogen, (ii) host one-sided adaptation (blue) where the host adapted to the nonevolving, ancestral pathogen taken from a frozen stock culture at each transfer, (iii) host–pathogen coevolution (red) during which both antagonists were continuously forced to coevolve to each other, (iv) pathogen one-sided adaptation (green) where the pathogen adapted to the nonevolving, ancestral host population taken from a frozen stock culture at each transfer; and (v) pathogen control (grey) adapting to general laboratory conditions in the absence of the host. <b>B–C,</b> Analysis of reciprocal coadaptations in host survival and pathogen killing ability <i>(y</i>-axis) by comparing (along the <i>x</i>-axis) exposures of coevolved hosts with coevolved pathogens from the same replicate population and time point (indicated by Co-H Co-P in the middle of the panels) with either coevolved hosts from the same replicate exposed to ancestral pathogens (Co-H Anc-P, right side) or ancestral hosts exposed to coevolved pathogens from the same replicate (Anc-H Co-P, left side). Results are given for transfers 12 (<b>B</b>) and 20 (<b>C</b>) separately. The lines connect the results for particular replicate populations of the coevolution treatment. <b>D,</b> Survival of evolved host populations from different treatments (colors as in Fig 1A) upon exposure to the ancestral pathogen. <b>E,</b> Pathogen population extinctions under one-sided adaptation (green) and coevolution (red). <b>F–G</b>, Analysis of evolved pathogen populations from different treatments (colors as in Fig 1A) upon exposure to the ancestral host, including pathogen killing ability (measured as host death rate in %) (<b>F</b>) and pathogen infection load (<b>G</b>). Bars denote standard error. The original data is provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s001" target="_blank">S1 Data</a>, and the results of the corresponding statistical analyses are given in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s015" target="_blank">S1 Table</a>, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s016" target="_blank">S2 Table</a>, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s017" target="_blank">S3 Table</a>, <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s018" target="_blank">S4 Table</a>, and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002169#pbio.1002169.s019" target="_blank">S5 Table</a>.</p