Dataset Information

No Cost of Complexity in Bacteriophages Adapting to a Complex Environment.

ABSTRACT: A long-standing prediction in evolutionary biology is that organisms experience a so-called "cost of complexity" manifested as a decreasing rate of adaptation in populations as organisms or selective environments become increasingly complex. This theory assumes the ubiquity of antagonistic pleiotropy, or trade-offs in fitness, for mutations affecting multiple traits or phenotypes. A particular manifestation of antagonism thought to be at play in adaptive dynamics involves the relationship between viral growth rate and capsid stability, an interaction that may impede the adaptation of viral pathogens to novel hosts and environments. Here, we present a comparison of the genetics of adaptation for populations of bacteriophages undergoing complete adaptive walks under both simple and complex selective conditions, with complexity being determined by the number of traits under directional selection. We found no evidence for a long-term cost of complexity in viruses experiencing complex selection, with on average at least as great a rate of adaptation under more complex conditions, and rampant evidence for synergistic, rather than antagonistic, pleiotropy. The lack of evident trade-offs between multiple phenotypes implies that emerging pathogens may be able to improve their growth in many different hosts or environments simultaneously, and to do so at a faster rate than previously anticipated.

SUBMITTER: Sackman AM

PROVIDER: S-EPMC6499518 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

No Cost of Complexity in Bacteriophages Adapting to a Complex Environment.

Sackman Andrew M AM Rokyta Darin R DR

Genetics 20190226 1

A long-standing prediction in evolutionary biology is that organisms experience a so-called "cost of complexity" manifested as a decreasing rate of adaptation in populations as organisms or selective environments become increasingly complex. This theory assumes the ubiquity of antagonistic pleiotropy, or trade-offs in fitness, for mutations affecting multiple traits or phenotypes. A particular manifestation of antagonism thought to be at play in adaptive dynamics involves the relationship betwee ...[more]

PMID: 30808620

Similar Datasets

Project description:The bacteriophage is an effective adjunct to existing antibiotic therapy; however, in the course of bacteriophage therapy, host bacteria will develop resistance to bacteriophages, thus affecting the efficacy. Therefore, it is important to describe how bacteria evade bacteriophage attack and the consequences of the biological changes that accompany the development of bacteriophage resistance before the bacteriophage is applied. The specific bacteriophage vB3530 of Pseudomonas aeruginosa (P. aeruginosa) has stable biological characteristics, short incubation period, strong in vitro cleavage ability, and absence of virulence or resistance genes. Ten bacteriophage-resistant strains (TL3780-R) were induced using the secondary infection approach, and the plaque assay showed that vB3530 was less sensitive to TL3780-R. Identification of bacteriophage adsorption receptors showed that the bacterial surface polysaccharide was probably the adsorption receptor of vB3530. In contrast to the TL3780 parental strain, TL3780-R is characterized by the absence of long lipopolysaccharide chains, which may be caused by base insertion of wzy or deletion of galU. It is also intriguing to observe that, in comparison to the parent strain, the bacteriophage-resistant strains TL3780-R mostly exhibited a large cost of fitness (growth rate, biofilm formation, motility, and ability to produce enhanced pyocyanin). In addition, TL3780-R9 showed increased susceptibility to aminoglycosides and chlorhexidine, which may be connected to the loss and down-regulation of mexX expression. Consequently, these findings fully depicted the resistance mechanism of P. aeruginosa to vB3530 and the fitness cost of bacteriophage resistance, laying a foundation for further application of bacteriophage therapy.IMPORTANCEThe bacteriophage is an effective adjunct to existing antibiotic therapy; However, bacteria also develop defensive mechanisms against bacteriophage attack. Thus, there is an urgent need to deeply understand the resistance mechanism of bacteria to bacteriophages and the fitness cost of bacteriophage resistance so as to lay the foundation for subsequent application of the phage. In this study, a specific bacteriophage vB3530 of P. aeruginosa had stable biological characteristics, short incubation period, strong in vitro cleavage ability, and absence of virulence or resistance genes. In addition, we found that P. aeruginosa may lead to phage resistance due to the deletion of galU and the base insertion of wzy, involved in the synthesis of lipopolysaccharides. Simultaneously, we showed the association with the biological state of the bacteria after bacteria acquire bacteriophage resistance, which is extremely relevant to guide the future application of therapeutic bacteriophages.

Project description:The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems. We compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment. We demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200-$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.

Dataset Information

No Cost of Complexity in Bacteriophages Adapting to a Complex Environment.

Publications

No Cost of Complexity in Bacteriophages Adapting to a Complex Environment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets