Chromosomal barcoding as a tool for multiplexed phenotypic characterization of laboratory evolved lineages

Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags

Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that require intrastrain resolution. Here we developed a genomic tagging system, termed wild-type isogenic standardized hybrid (WISH)-tags, that can be combined with quantitative polymerase chain reaction and next-generation sequencing for microbial strain enumeration. We experimentally validated the performance of 62 tags and showed that they can be differentiated with high precision. WISH-tags were introduced into model and non-model bacterial members of the mouse and plant microbiota. Intrastrain priority effects were tested using one species of isogenic barcoded bacteria in the murine gut and the Arabidopsis phyllosphere, both with and without microbiota context. We observed colonization resistance against late-arriving strains of Salmonella Typhimurium in the mouse gut, whereas the phyllosphere accommodated Sphingomonas latecomers in a manner proportional to their presence at the late inoculation timepoint. This demonstrates that WISH-tags are a resource for deciphering population dynamics underlying microbiome assembly across biological systems.

High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS)

High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.

Evolution of Resistance to Irinotecan in Cancer Cells Involves Generation of Topoisomerase-Guided Mutations in Non-Coding Genome That Reduce the Chances of DNA Breaks

Resistance to chemotherapy is a leading cause of treatment failure. Drug resistance mechanisms involve mutations in specific proteins or changes in their expression levels. It is commonly understood that resistance mutations happen randomly prior to treatment and are selected during the treatment. However, the selection of drug-resistant mutants in culture could be achieved by multiple drug exposures of cloned genetically identical cells and thus cannot result from the selection of pre-existent mutations. Accordingly, adaptation must involve the generation of mutations de novo upon drug treatment. Here we explored the origin of resistance mutations to a widely used Top1 inhibitor, irinotecan, which triggers DNA breaks, causing cytotoxicity. The resistance mechanism involved the gradual accumulation of recurrent mutations in non-coding regions of DNA at Top1-cleavage sites. Surprisingly, cancer cells had a higher number of such sites than the reference genome, which may define their increased sensitivity to irinotecan. Homologous recombination repairs of DNA double-strand breaks at these sites following initial drug exposures gradually reverted cleavage-sensitive "cancer" sequences back to cleavage-resistant "normal" sequences. These mutations reduced the generation of DNA breaks upon subsequent exposures, thus gradually increasing drug resistance. Together, large target sizes for mutations and their Top1-guided generation lead to their gradual and rapid accumulation, synergistically accelerating the development of resistance.

An improved algorithm for inferring mutational parameters from bar-seq evolution experiments

BACKGROUND

Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the nature of the evolution that is taking place remains a difficult task.

RESULTS

Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters.

CONCLUSION

Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub ( https://github.com/FangfeiLi05/FitMut2 ), in the hope that it can find broader use by the microbial evolution community.

Interpreting and de-noising genetically engineered barcodes in a DNA virus

The concept of a nucleic acid barcode applied to pathogen genomes is easy to grasp and the many possible uses are straightforward. But implementation may not be easy, especially when growing through multiple generations or assaying the pathogen long-term. The potential problems include: the barcode might alter fitness, the barcode may accumulate mutations, and construction of the marked pathogens may result in unintended barcodes that are not as designed. Here, we generate approximately 5,000 randomized barcodes in the genome of the prototypic small DNA virus murine polyomavirus. We describe the challenges faced with interpreting the barcode sequences obtained from the library. Our Illumina NextSeq sequencing recalled much greater variation in barcode sequencing reads than the expected 5,000 barcodes-necessarily stemming from the Illumina library processing and sequencing error. Using data from defined control virus genomes cloned into plasmid backbones we develop a vetted post-sequencing method to cluster the erroneous reads around the true virus genome barcodes. These findings may foreshadow problems with randomized barcodes in other microbial systems and provide a useful approach for future work utilizing nucleic acid barcoded pathogens.

Bacterial age distribution in soil - Generational gaps in adjacent hot and cold spots

Resource patchiness and aqueous phase fragmentation in soil may induce large differences local growth conditions at submillimeter scales. These are translated to vast differences in bacterial age from cells dividing every thirty minutes in close proximity to plant roots to very old cells experiencing negligible growth in adjacent nutrient poor patches. In this study, we link bacterial population demographics with localized soil and hydration conditions to predict emerging generation time distributions and estimate mean bacterial cell ages using mechanistic and heuristic models of bacterial life in soil. Results show heavy-tailed distributions of generation times that resemble a power law for certain conditions, suggesting that we may find bacterial cells of vastly different ages living side by side within small soil volumes. Our results imply that individual bacteria may exist concurrently with all of their ancestors, resulting in an archive of bacterial cells with traits that have been gained (and lost) throughout time-a feature unique to microbial life. This reservoir of bacterial strains and the potential for the reemergence of rare strains with specific functions may be critical for ecosystem stability and function.

Compatibility of Evolutionary Responses to Constituent Antibiotics Drive Resistance Evolution to Drug Pairs

Antibiotic combinations are considered a relevant strategy to tackle the global antibiotic resistance crisis since they are believed to increase treatment efficacy and reduce resistance evolution (WHO treatment guidelines for drug-resistant tuberculosis: 2016 update.). However, studies of the evolution of bacterial resistance to combination therapy have focused on a limited number of drugs and have provided contradictory results (Lipsitch, Levin BR. 1997; Hegreness et al. 2008; Munck et al. 2014). To address this gap in our understanding, we performed a large-scale laboratory evolution experiment, adapting eight replicate lineages of Escherichia coli to a diverse set of 22 different antibiotics and 33 antibiotic pairs. We found that combination therapy significantly limits the evolution of de novode novo resistance in E. coli, yet different drug combinations vary substantially in their propensity to select for resistance. In contrast to current theories, the phenotypic features of drug pairs are weak predictors of resistance evolution. Instead, the resistance evolution is driven by the relationship between the evolutionary trajectories that lead to resistance to a drug combination and those that lead to resistance to the component drugs. Drug combinations requiring a novel genetic response from target bacteria compared with the individual component drugs significantly reduce resistance evolution. These data support combination therapy as a treatment option to decelerate resistance evolution and provide a novel framework for selecting optimized drug combinations based on bacterial evolutionary responses.

Multiplexed Competition in a Synthetic Squid Light Organ Microbiome Using Barcode-Tagged Gene Deletions

Beneficial microbes play essential roles in the health and development of their hosts. However, the complexity of animal microbiomes and general genetic intractability of their symbionts have made it difficult to study the coevolved mechanisms for establishing and maintaining specificity at the microbe-animal host interface.

Beneficial symbioses between microbes and their eukaryotic hosts are ubiquitous and have widespread impacts on host health and development. The binary symbiosis between the bioluminescent bacterium Vibrio fischeri and its squid host Euprymna scolopes serves as a model system to study molecular mechanisms at the microbe-animal interface. To identify colonization factors in this system, our lab previously conducted a global transposon insertion sequencing (INSeq) screen and identified over 300 putative novel squid colonization factors in V. fischeri. To pursue mechanistic studies on these candidate genes, we present an approach to quickly generate barcode-tagged gene deletions and perform high-throughput squid competition experiments with detection of the proportion of each strain in the mixture by barcode sequencing (BarSeq). Our deletion approach improves on previous techniques based on splicing by overlap extension PCR (SOE-PCR) and tfoX-based natural transformation by incorporating a randomized barcode that results in unique DNA sequences within each deletion scar. Amplicon sequencing of the pool of barcoded strains before and after colonization faithfully reports on known colonization factors and provides increased sensitivity over colony counting methods. BarSeq enables rapid and sensitive characterization of the molecular factors involved in establishing the Vibrio-squid symbiosis and provides a valuable tool to interrogate the molecular dialogue at microbe-animal host interfaces.

IMPORTANCE Beneficial microbes play essential roles in the health and development of their hosts. However, the complexity of animal microbiomes and general genetic intractability of their symbionts have made it difficult to study the coevolved mechanisms for establishing and maintaining specificity at the microbe-animal host interface. Model symbioses are therefore invaluable for studying the mechanisms of beneficial microbe-host interactions. Here, we present a combined barcode-tagged deletion and BarSeq approach to interrogate the molecular dialogue that ensures specific and reproducible colonization of the Hawaiian bobtail squid by Vibrio fischeri. The ability to precisely manipulate the bacterial genome, combined with multiplex colonization assays, will accelerate the use of this valuable model system for mechanistic studies of how environmental microbes—both beneficial and pathogenic—colonize specific animal hosts.

Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.