Information storage across a microbial community using universal RNA barcoding – Nature Biotechnology

Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).
Google Scholar
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).
Google Scholar
Soucy, S. M., Huang, J. & Gogarten, J. P. Horizontal gene transfer: building the web of life. Nat. Rev. Genet. 16, 472–482 (2015).
Google Scholar
Hussain, F. A. et al. Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. Science 374, 488–492 (2021).
Google Scholar
Wang, T. et al. Horizontal gene transfer enables programmable gene stability in synthetic microbiota. Nat. Chem. Biol. 18, 1245–1252 (2022).
Google Scholar
Kreitz, J. et al. Programmable protein delivery with a bacterial contractile injection system. Nature 616, 357–463 (2023).
Google Scholar
Nikel, P. I., Martínez-García, E. & de Lorenzo, V. Biotechnological domestication of pseudomonads using synthetic biology. Nat. Rev. Microbiol. 12, 368–379 (2014).
Google Scholar
Brophy, J. A. N. et al. Engineered integrative and conjugative elements for efficient and inducible DNA transfer to undomesticated bacteria. Nat. Microbiol. 3, 1043–1053 (2018).
Google Scholar
Blazejewski, T., Ho, H.-I. & Wang, H. H. Synthetic sequence entanglement augments stability and containment of genetic information in cells. Science 365, 595–598 (2019).
Google Scholar
Thomas, C. M. & Nielsen, K. M. Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nat. Rev. Microbiol. 3, 711–721 (2005).
Google Scholar
Heinemann, J. A. & Sprague, G. F. Bacterial conjugative plasmids mobilize DNA transfer between bacteria and yeast. Nature 340, 205–209 (1989).
Google Scholar
Lopatkin, A. J. et al. Antibiotics as a selective driver for conjugation dynamics. Nat. Microbiol. 1, 16044 (2016).
Google Scholar
Lu, T. K. & Collins, J. J. Engineered bacteriophage targeting gene networks as adjuvants for antibiotic therapy. Proc. Natl Acad. Sci. USA 106, 4629–4634 (2009).
Google Scholar
Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).
Google Scholar
Zhou, H., Beltrán, J. F. & Brito, I. L. Functions predict horizontal gene transfer and the emergence of antibiotic resistance. Sci. Adv. 7, eabj5056 (2021).
Google Scholar
Nazarian, P., Tran, F. & Boedicker, J. Q. Modeling multispecies gene flow dynamics reveals the unique roles of different horizontal gene transfer mechanisms. Front. Microbiol. 9, 2978 (2018).
Google Scholar
Cheng, H.-Y., Masiello, C. A., Bennett, G. N. & Silberg, J. J. Volatile gas production by methyl halide transferase: an in situ reporter of microbial gene expression in soil. Environ. Sci. Technol. 50, 8750–8759 (2016).
Google Scholar
Bethke, J. H. et al. Environmental and genetic determinants of plasmid mobility in pathogenic Escherichia coli. Sci. Adv. 6, eaax3173 (2020).
Google Scholar
Davison, J. Genetic exchange between bacteria in the environment. Plasmid 42, 73–91 (1999).
Google Scholar
Hughes, V. M. & Datta, N. Conjugative plasmids in bacteria of the ‘pre-antibiotic’ era. Nature 302, 725–726 (1983).
Google Scholar
Schmidt, M. & de Lorenzo, V. Synthetic constructs in/for the environment: managing the interplay between natural and engineered Biology. FEBS Lett. 586, 2199–2206 (2012).
Google Scholar
Sørensen, S. J., Bailey, M., Hansen, L. H., Kroer, N. & Wuertz, S. Studying plasmid horizontal transfer in situ: a critical review. Nat. Rev. Microbiol. 3, 700–710 (2005).
Google Scholar
Brito, I. L. Examining horizontal gene transfer in microbial communities. Nat. Rev. Microbiol. 19, 442–453 (2021).
Google Scholar
Ronda, C., Chen, S. P., Cabral, V., Yaung, S. J. & Wang, H. H. Metagenomic engineering of the mammalian gut microbiome in situ. Nat. Methods 16, 167–170 (2019).
Google Scholar
Stegemann, S. & Bock, R. Exchange of genetic material between cells in plant tissue grafts. Science 324, 649–651 (2009).
Google Scholar
Hertle, A. P., Haberl, B. & Bock, R. Horizontal genome transfer by cell-to-cell travel of whole organelles. Sci. Adv. 7, eabd8215 (2021).
Google Scholar
Babić, A., Lindner, A. B., Vulić, M., Stewart, E. J. & Radman, M. Direct visualization of horizontal gene transfer. Science 319, 1533–1536 (2008).
Google Scholar
Rubin, B. E. et al. Species- and site-specific genome editing in complex bacterial communities. Nat Microbiol 7, 34–47 (2022).
Google Scholar
Morris, E. R., Grey, H., McKenzie, G., Jones, A. C. & Richardson, J. M. A bend, flip and trap mechanism for transposon integration. eLife 5, e15537 (2016).
Google Scholar
Spencer, S. J. et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J 10, 427–436 (2016).
Google Scholar
Yaffe, E. & Relman, D. A. Tracking microbial evolution in the human gut using Hi-C reveals extensive horizontal gene transfer, persistence and adaptation. Nat. Microbiol. 5, 343–353 (2019).
Google Scholar
Cech, T. R. The chemistry of self-splicing RNA and RNA enzymes. Science 236, 1532–1539 (1987).
Google Scholar
Gambill, L., Staubus, A., Mo, K. W., Ameruoso, A. & Chappell, J. A split ribozyme that links detection of a native RNA to orthogonal protein outputs. Nat. Commun. 14, 543 (2023).
Google Scholar
Waring, R. B., Towner, P., Minter, S. J. & Davies, R. W. Splice-site selection by a self-splicing RNA of Tetrahymena. Nature 321, 133–139 (1986).
Google Scholar
Sullenger, B. A. & Cech, T. R. Ribozyme-mediated repair of defective mRNA by targeted trans-splicing. Nature 371, 619–622 (1994).
Google Scholar
Been, M. D. & Cech, T. R. One binding site determines sequence specificity of Tetrahymena pre-rRNA self-splicing, trans-splicing, and RNA enzyme activity. Cell 47, 207–216 (1986).
Google Scholar
Bremer, H. & Dennis, P. P.Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus 3, 5.2.3 (2008).
Google Scholar
Bernstein, J. A., Khodursky, A. B., Lin, P.-H., Lin-Chao, S. & Cohen, S. N. Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc. Natl Acad. Sci. USA 99, 9697–9702 (2002).
Google Scholar
Bhattarai-Kline, S. et al. Recording gene expression order in DNA by CRISPR addition of retron barcodes. Nature 608, 217–225 (2022).
Google Scholar
Loveless, T. B. et al. Open-ended molecular recording of sequential cellular events into DNA. Nat. Chem. Biol. https://doi.org/10.1038/s41589-024-01764-5 (2024).
Google Scholar
Neil, K., Allard, N., Grenier, F., Burrus, V. & Rodrigue, S. Highly efficient gene transfer in the mouse gut microbiota is enabled by the Incl2 conjugative plasmid TP114. Commun. Biol. 3, 523 (2020).
Google Scholar
Alderliesten, J. B. et al. Effect of donor–recipient relatedness on the plasmid conjugation frequency: a meta-analysis. BMC Microbiol. 20, 135 (2020).
Google Scholar
Li, L. et al. Estimating the transfer range of plasmids encoding antimicrobial resistance in a wastewater treatment plant microbial community. Environ. Sci. Technol. Lett. 5, 260–265 (2018).
Google Scholar
Jahn, M., Vorpahl, C., Hübschmann, T., Harms, H. & Müller, S. Copy number variability of expression plasmids determined by cell sorting and droplet digital PCR. Microb. Cell Fact. 15, 211 (2016).
Google Scholar
Ares-Arroyo, M., Rocha, E. P. C. & Gonzalez-Zorn, B. Evolution of ColE1-like plasmids across γ-Proteobacteria: from bacteriocin production to antimicrobial resistance. PLoS Genet. 17, e1009919 (2021).
Google Scholar
Sheth, R. U. & Wang, H. H. DNA-based memory devices for recording cellular events. Nat. Rev. Genet. 19, 718–732 (2018).
Google Scholar
Munck, C., Sheth, R. U., Freedberg, D. E. & Wang, H. H. Recording mobile DNA in the gut microbiota using an Escherichia coli CRISPR–Cas spacer acquisition platform. Nat. Commun. 11, 95 (2020).
Google Scholar
Schmidt, F. et al. Noninvasive assessment of gut function using transcriptional recording sentinel cells. Science 376, eabm6038 (2022).
Google Scholar
Yang, L. et al. Permanent genetic memory with >1-byte capacity. Nat. Methods 11, 1261–1266 (2014).
Google Scholar
Sheth, R. U., Yim, S. S., Wu, F. L. & Wang, H. H. Multiplex recording of cellular events over time on CRISPR biological tape. Science 358, 1457–1461 (2017).
Google Scholar
Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. Molecular recordings by directed CRISPR spacer acquisition. Science 353, aaf1175 (2016).
Google Scholar
Kempton, H. R., Love, K. S., Guo, L. Y. & Qi, L. S. Scalable biological signal recording in mammalian cells using Cas12a base editors. Nat. Chem. Biol. 18, 742–750 (2022).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
Google Scholar
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Google Scholar
Johns, N. I. et al. Metagenomic mining of regulatory elements enables programmable species-selective gene expression. Nat. Methods 15, 323–329 (2018).
Google Scholar
Ceroni, F., Algar, R., Stan, G.-B. & Ellis, T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12, 415–418 (2015).
Google Scholar
Loveless, T. B. et al. Lineage tracing and analog recording in mammalian cells by single-site DNA writing. Nat. Chem. Biol. 17, 739–747 (2021).
Google Scholar
Jeltsch, A. & Pingoud, A. Horizontal gene transfer contributes to the wide distribution and evolution of type II restriction–modification systems. J. Mol. Evol. 42, 91–96 (1996).
Google Scholar
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
Google Scholar
Engler, C., Kandzia, R. & Marillonnet, S. A one pot, one step, precision cloning method with high throughput capability. PLoS ONE 3, e3647 (2008).
Google Scholar
Weinstock, M. T., Hesek, E. D., Wilson, C. M. & Gibson, D. G. Vibrio natriegens as a fast-growing host for molecular biology. Nat. Methods 13, 849–851 (2016).
Google Scholar
Ferrières, L. et al. Silent mischief: bacteriophage Mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal transposon delivery plasmids mobilized by broad-host-range RP4 conjugative machinery. J. Bacteriol. 192, 6418–6427 (2010).
Google Scholar
Hoeflinger, J. L., Hoeflinger, D. E. & Miller, M. J. A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python. J. Microbiol. Methods 132, 83–85 (2017).
Google Scholar
Untergasser, A. et al. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 35, W71–W74 (2007).
Google Scholar
Untergasser, A., Ruijter, J. M., Benes, V. & van den Hoff, M. J. B. Web-based LinRegPCR: application for the visualization and analysis of (RT)–qPCR amplification and melting data. BMC Bioinformatics 22, 398 (2021).
Google Scholar
Madeira, F. et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res. 50, W276–W279 (2022).
Google Scholar
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
Google Scholar
Chakravorty, S., Helb, D., Burday, M., Connell, N. & Alland, D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69, 330–339 (2007).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Google Scholar
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Janssen, S. et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3, e00021–18 (2018).
Google Scholar
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Google Scholar
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Robeson, M. S. et al. RESCRIPt: reproducible sequence taxonomy reference database management. PLoS Comput. Biol. 17, e1009581 (2021).
Google Scholar
Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).
Google Scholar
Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).
Google Scholar
Klümper, U. et al. Broad host range plasmids can invade an unexpectedly diverse fraction of a soil bacterial community. ISME J. 9, 934–945 (2015).
Google Scholar