Cross-order detection of bacteriophage transduction in microbial communities using RNA barcoding

Domain: biology
Published: 2026-03-23
Editorial outcome: minor_revision

Abstract
Bacteriophages (phages) facilitate gene transfer and microbial evolution in all ecosystems and have applications as tools for engineering microbiomes and as antimicrobials. Historic efforts to map phage hosts, such as plaque assays, are limited to cultured bacteria, are low throughput, and are hard to apply in microbial communities and environmentally-relevant contexts. To overcome these limitations, we integrate a synthetic ribozyme that stores information about participation in horizontal gene transfer in 16S ribosomal RNA (rRNA) into the phage-plasmid P1, and perform targeted 16S rRNA sequencing following transduction to identify phage-host interactions. Experiments in synthetic and wastewater communities reveal Aeromonadales as a previously unreported P1 host order and show P1 transduction into pathogens. In wastewater, host range varies across phagemids having different origins of replication and phage-derived particles having different tail fibers. This work shows how autonomous barcoding can be used in phages to identify the molecular controls on their host range in microbial communities.

Introduction
Bacteriophages (phages) play a crucial role in the environment. They drive ecological interactions in microbial communities by killing or augmenting the metabolic capabilities of their hosts.1,2 At an estimated abundance of 1031 particles on Earth, viruses are the most abundant lifeform and are an order of magnitude more abundant than bacteria.3 Conservative diversity estimates suggest 107-109 distinct viral species, and since many viruses infect more than one host, this suggests an even greater number of unique virus-host interactions.4 Phages act as agents of horizontal gene transfer (HGT), functioning as both disseminators of antibiotic resistance genes and as vectors for in situ gene delivery.5–9 Phages that kill pathogens also represent a promising alternative to antibiotics, as antibiotic-resistant infections are projected to kill up to 10 million people per year by 2050.10 To realize their full potential as therapeutics, there is pressing need to understand their ecology by connecting phage to their different microbial hosts.

Approaches for mapping phage-host interactions are limited in their ability to study transduction in microbial communities. For example, plaque assays can only be applied to plaque-forming phage that infect culturable microbes.11 Viral tagging and electrophoretic methods overcome this challenge by isolating phage bound to bacteria and their cell wall components, but they cannot differentiate between phage absorbed to bacterial surfaces and phage that transduce cells.12,13 Methods that use fluorescence in situ hybridization to observe transduction are hampered by the low efficiency of fluorescent probe delivery to cells.14 Techniques that link phage genetic material to cellular DNA, like epicPCR and high-throughput chromosome conformation capture (Hi-C), require arduous sample processing.15–17 Environmental transformation sequencing (ET-seq) simplifies this processing by autonomously barcoding host genomes.18 However, ET-seq only yields signals in cells that transcribe and translate the synthetic components that modify chromosomes, and requires metagenomic sequencing, limiting throughput and affordability. Thus, there is a need for simpler experimental approaches that overcome these limitations.

Recently, a synthetic biology tool for studying HGT was reported that allows for sensitive detection of mobile genetic element host range.19 This approach, called RNA-addressable modification (RAM), uses an engineered ribozyme to write information about participation in conjugation in the 16S rRNA of recipient cells in a community. The ribozyme is transcribed using a broad host range promoter and consists of a guide sequence targeting 16S rRNA, a catalytic core, and a synthetic barcode. The guide sequence directs the ribozyme to a conserved region of 16S rRNA, where it catalyzes a trans-splicing reaction that adds the barcode adjacent to variable regions in the 16S rRNA that can be used for taxonomic identification (Fig. 1a). When used to monitor plasmid host range via conjugation in wastewater, RAM revealed plasmid uptake by ~50% of the amplicon sequence variants (ASVs).19 When encoded into multiple plasmids, RAM revealed how changes in the replication origins alters conjugative host range. These observations suggest that RAM could be used with other types of mobile DNA, like phage, to enable sensitive detection of host range.

To connect phages with hosts in a microbial community, we encode RAM into P1 phage and assess transduction by applying amplicon sequencing to barcoded rRNA. P1 was targeted because it is a lysogenic phage that exists as an extrachromosomal element during the lysogenic phase of its life cycle; these phage-plasmids cannot be connected to hosts via metagenomic sequencing.20,21 P1 host range is important to understand because it infects many human-associated enteric bacteria and similar phages are thought to contribute to the spread of antibiotic-resistant genes via HGT.22–26 By coding RAM in phage P1 and phagemids having different origins of replication, we demonstrate RAM as a viable technology for reporting on phage-host relationships in diverse contexts. We also discover P1 hosts not previously reported using synthetic and wastewater communities. Finally, we investigate how two different tail fibers natively encoded in the P1 genome impact its host range. This study represents a scalable blueprint for uncovering the determinants of phage host range, and engineering phages to enable high-efficiency DNA delivery or species-specific killing for microbiome engineering.27–29

Results
RAM can link phage and host genetic information

To investigate if RAM can be used in phages to record information on phage-host connections, we coded RAM in phage P1 using two strategies (Fig. 1b). First, we inserted RAM into the genome of P1 (Supplementary Fig. 1a) using the CRISPR-associated transposon system (CAST).30,31 We also incorporated RAM into a pair of P1 phagemids, synthetic plasmids that contain the P1 phage packaging site but lack other P1 genes (Supplementary Fig. 1b),25,32 including phagemids with pBBRI1 and P1 origins of replication (oriR); the latter was designated “mini P1” to differentiate from native P1. The pBBR1 origin was included because it is broad host range and expected to extend the diversity of hosts that stably maintain phagemid DNA following transduction.

To evaluate whether RAM-containing phage and phagemids are competent for rRNA barcoding, Escherichia coli MG1655 was transformed with both phagemids and transduced with phage and then exposed to antibiotics to select for homogenous populations of cells. Barcoded 16S rRNA was observed with all constructs (Fig. 1c). Using RT-qPCR, we found that the pBBR1 phagemid presented a slightly higher barcoding signal than cells harboring a strictly plasmid version of pBBR1. The signal from the pBBR1 phagemid was 40- and 112-fold higher than from P1 and the mini P1 phagemid, respectively. These findings show that RAM can report on phage transduction and phagemid transformation in E. coli.

To assess whether RAM can report on phage transduction in other species, we generated phage and phage-derived particles containing the constructs and exposed them to enteric bacteria that are susceptible to P1 transduction, including E. coli, Citrobacter freundii, Klebsiella pneumoniae, and Shigella flexneri.25 Following selective plating for transduced cells, P1 phage with genomically-integrated RAM produced a higher CFU count than both phagemids across all species (Fig. 1d). We evaluated E. coli transduced by both phagemid lysates to investigate whether some phage-derived particles still contained wildtype P1 genome (Supplementary Fig. 2). A large number of E. coli exposed to either phagemid lysate contained wildtype P1, 7.3E8 CFUs/mL lysate for pBBR1 and 8.7E6 CFUs/mL lysate for mini P1 as measured by selective plating on E. coli (relative to 1.1E8 CFUs/mL lysate and 1.2E7 CFUs/mL lysate containing the respective phagemids). These values were lower than transduction of P1 phage lysate alone (1.2E10 CFUs/mL lysate), suggesting differences in the absolute amount or relative transduction efficiency of phage-derived particles in phagemid lysates. These findings show that P1 phage and phagemids can infect a variety of microbes, but variation in RAM signal was observed across constructs due to differences arising during phage-derived particle generation and/or transduction.

RT-qPCR was next used to evaluate the barcoding signal arising from transduction of phage and phage-derived particles into these different enteric bacteria. In these experiments we analyzed barcoding following a two-hour incubation with undiluted lysates, since these experimental conditions yielded a strong signal with E. coli MG1655 (Supplementary Fig. 3, 4). With enteric bacteria, the strongest barcoding signal was observed with P1 phage (Fig. 1e). However, the relative rank order of the barcoding signal for each phagemid varied across species, similar to that observed with selective plating (Fig. 1d). Concentrations of barcoded 16S rRNA and total barcode, that is barcode in unreacted ribozyme and barcoded 16S rRNA, were positively correlated (R2 = 0.8), and no correlation was observed between barcoded 16S rRNA and total 16S rRNA concentrations (R2 = 0.01; Supplementary Fig. 5). With K. pneumoniae, a RAM signal could be observed with both phagemids. This contrasts with selective plating measurements, which were not possible for K. pneumoniae, as indicated by “X” in Fig. 1d, since this strain is already resistant to the selection marker used in the phagemids. These results show that RAM can be used to report on phage and phage-derived particle transduction in diverse enteric bacteria.

RAM captures new transduction hosts in a synthetic community

To investigate if RAM can identify phage hosts in a community context, we transduced an eight-member synthetic microbial community with the engineered phage and phagemids (Fig. 2a). In addition to the four microorganisms tested as isolates, we included Enterobacter cloacae and Shigella sonnei, neither of which have reported susceptibility to P1 transduction. We also included Salmonella enterica and Pseudomonas aeruginosa, as P1 phage and P1 phagemid transduction, respectively, has been observed with related strains.25,33 The community captures most gram-negative ESKAPET pathogens, a group of microorganisms responsible for the bulk of multi-drug resistant infections, and are thus targets for phage interventions.34,35 To discern between ESKAPE pathogens transduced by P1, RAM provides ~400 bp of 16S rRNA sequence that spans the V6-V8 variable regions (Fig. 2b).

RT-qPCR analysis was used to monitor RAM barcoding in the synthetic community (Fig. 2c). The strongest barcoding signal was observed with P1 phage. The signal from P1 was 2-fold higher than from the mini P1 phagemid, and the signal from the mini P1 phagemid was 9-fold higher than the pBBR1 phagemid. Amplicon sequencing of the barcoded rRNA revealed reproducible results across each particle type and distinct host ranges of the constructs (Fig. 2d). E. coli, S. flexneri, and S. sonnei were the most abundant hosts across all constructs, although the RAM signals could not differentiate between the host species because all the Shigella rRNA and a subset of the E. coli rRNA are identical. Similarly, the E. cloacae rRNA were all identical to a subset of K. pneumoniae rRNA. The pBBR1 phagemid host range was distinct from the mini P1 and P1 communities, while the mini P1 phagemid and P1 had similar host ranges. With the pBBR1 phagemid, barcoding of S. enterica rRNA was observed, which represents a transduction host for a P1 phagemid not previously reported. These results show that RAM allows for facile comparisons of phage and phagemid host range in a community, and they extend the stable transduction range of P1 phage-derived particles carrying pBBR1 phagemids to S. enterica.

Previously unreported order of P1 hosts was revealed in a wastewater community

To understand P1 host range in an environmental microbial community, the rRNA-barcoding phage and phage-derived particles carrying RAM constructs were added to a microbial community derived from wastewater influent sampled from a wastewater treatment plant in Houston, Texas (Fig. 3a). Untreated wastewater was used because it contains an abundance of enteric bacteria that P1 is known to transduce. Each of the phage constructs yielded a RAM signal by RT-qPCR (Fig. 3b). P1 phage again yielded the strongest signal, 7-fold higher than the mini P1 phagemid, while the mini P1 phagemid signal was 2-fold higher than the pBBR1 phagemid. Across all phage and phage-derived particles, 127 unique amplicon sequencing variants (ASVs) were barcoded, 99 of which also appeared in the native community (Fig. 3c). Additionally, 95 ASVs were observed in the community that lacked a barcoding signal. These observations show that the three phage and phage-derived particles collectively transduce approximately half of the detected ASVs in the native community. They also show that RAM provides a signal for transduction into rare ASVs that were not observed in the native community 16S rRNA pool. This phenomenon is thought to arise because the ASVs rare in the natural community occupy a smaller fraction of total reads in the native community than they do in the barcoded community, limiting their amplification to a detectable level in the former but not the latter.19

The native community was primarily composed of Aeromonas, a common genus in wastewater, with lesser amounts of Escherichia-Shigella, Enterobacteriaceae, Proteus, and Comamonadaceae (Fig. 3d, Supplementary Fig. 6). With all the phage constructs, the three most abundant barcoded ASVs were Escherichia-Shigella, Enterobacteriaceae, and Aeromonas. Each construct barcoded similar numbers of ASVs: 81, 90, and 106 for pBBR1, mini P1, and P1, respectively (Fig. 3e). In total, 57 ASVs were transduced by all three phage and phage-derived particles, 78 were transduced by mini P1 and P1, 66 were transduced by pBBR1 and P1, and 65 were transduced by pBNBR1 and mini P1. A weighted Unifrac analysis on all samples found that replicates from each construct clustered into unique groups (Fig. 3f, Supplementary Fig. 7). The pBBR1 phagemid clustered more closely to P1 than mini P1, and the mini P1 host community was distinct from the P1 host community. These findings show that the construct has a significant impact on the transduction host range observed in microbial communities, even though the particle packaging each construct was the same.

While Escherichia-Shigella and other Enterobacteriaceae have been reported as P1 transduction hosts,25 to the best of our knowledge, the barcoding signal in Aeromonas represents the first evidence for this genera as P1 hosts. To confirm this finding, ten Aeromonas species isolated from wastewater were screened for P1 transduction. Many isolates were antibiotic resistant, 20% for trimethoprim and 70% for carbenicillin (the antibiotics used to select for cells with P1 containing RAM and phagemids containing RAM, respectively), limiting the use of selections to monitor transduction. Among the eight trimethoprim-susceptible isolates, one was susceptible to P1 transduction as measured by selective plating (Supplementary Fig. 8). Whole genome sequencing of the infected host revealed the presence of the P1 and host genomes; the latter was Aeromonas hydrophila TN-97-08. Exposure of this isolate to RAM-containing phage and phage-derived particles generated barcoded 16S rRNA for every construct (Fig. 3g). These findings show how RAM can be used to discover host taxa for phages in microbial communities.

Natural P1 tail variation alters wastewater community host range

To study host range determinants of P1, we used RAM to compare the host ranges of phage-derived particles having different tail fibers that are found within an invertible cassette in the P1 genome (Fig. 4a). Among the genes in this cassette, S and S’ represent the tail fibers, U and U’ support tail fiber maturation, R attaches tail fibers to the phage base plate, and cin regulates cassette inversion.36 The S and S’ tail fibers share a conserved (Sc) region at the N-terminus but vary at the C-terminus (Sv or Sv’). The Sv/Sv’ elements control host specificity and exhibit a 46% pairwise identity (Supplementary Fig. 9). The S tail fiber binds to sugars in the outer core of the LPS, while S’ binds to O-antigens.22,37,38 To produce phage-derived particles having either S or S’ tail fibers, we removed the coding sequences for Sc, Sv, U, Sv’, U’, and cin from the P1 genome and created a plasmid that express either Sc, Sv, and U or Sc, Sv’, and U’ (Fig. 4b).22 E. coli harboring the modified P1 genome, the tail-fiber plasmid, and the pBBR1 phagemid harboring RAM were used to produce phage-derived particles.

To investigate if S and S’ phage-derived particles present unique host ranges, particles were first added to E. coli MG1655 and S. flexneri 2a 24570. Transduction monitored using selection (Fig. 4c) and barcoding (Supplementary Fig. 10) revealed S particles transduced both microbes, similar to particles produced using an unmodified P1 genome (designated wildtype), while S’ transduced S. flexneri 2a 24570 alone, as previously observed.22 These results also show the lack of false positive RAM barcoding, as no barcoded 16S rRNA was detected for S’ particles via RT-qPCR. Next, we exposed a wastewater microbial community to these particles. With particles containing the S’ tail fiber, significantly more (3-fold) barcoded rRNA was observed compared to those having the S tail fiber (Fig. 4d); wildtype particles yielded an even higher signal than S’ (2-fold). Escherichia-Shigella and Enterobacteriaceae were the most prevalent ASVs barcoded upon incubation with all three phage-derived particle types (Fig. 4e). Amongst the lower abundance ASVs, variable composition in transduced hosts was observed with the different tail fibers. A comparison of the host range of the three phage-derived particles revealed 59 ASVs were barcoded by all three particles (Fig. 4f), 40 ASVs were barcoded by two particles, 7 to 17 ASVs were barcoded by a single particle. These results illustrate how RAM can be used to assess the host range of phage-derived particles that vary in tail fiber composition in a community context.

To understand how phage host range varies with each tail fiber, we compared the taxonomic groups transduced by S, S’, and wildtype phage particles. A PERMANOVA on a weighted Unifrac analysis showed significant differences in the community structures of the barcoded ASVs (Fig. 4g, Supplementary Fig. 11). To identify taxonomic groups that were significantly enriched or depleted between the tail fibers’ host communities, an ANCOM-BC analysis was performed at the genus level (Fig. 4h). The S’ tail fiber was enriched in Enterobacter and Klebsiella relative to the S tail fiber, while the wildtype tail fibers were enriched relative to the S tail fiber with Enterobacter and Klebsiella. The wildtype tail fibers were additionally enriched in Morganella and Aeromonas relative to the S tail fiber, and Acinetobacter and Aeromonas relative to the S’ tail fiber. Together, these results indicate that the production of phage-derived particles using the wildtype P1 lysogen originating in the S configuration leads to a mixture of tail fibers in both configurations. The findings illustrate how RAM can be applied to study the impact of tail fiber composition on viral ecology.

Methods
Strains, Plasmids, and Cloning

A list of strains can be found in Supplementary Table 1. All strains were cultured in Lysogeny Broth (LB) consisting of 10 g/L tryptone, 5 g/L yeast extract, and 10 g/L NaCl with an additional 15 g/L of agar for plates. Phage lysis media (PLM) consisted of LB with an additional 100 mM MgCl and 5 mM CaCl . M9 minimal medium consisted of 12.8 g/L NaHPO *7H O, 3 g/L KH PO , 0.5 g/L NaCl, 0.4% glucose, 2 mM MgSO , 0.1 mM CaCl , and 0.2% casamino acids. Antibiotics were added at concentrations of 100 μg/mL carbenicillin, 20 μg/mL chloramphenicol, 50 μg/mL kanamycin, or 10 μg/mL trimethoprim in liquid culture and on plates. A concentration of 40 μg/mL trimethoprim was used for A. hydrophila. Inducers were added at concentrations of 13 mM arabinose for inducing lysis or 100 ng/mL anhydrotetracycline for inducing λ-Red genes in BioDesignER.

A list of plasmids, phagemids, and phages can be found in Supplementary Table 2. Plasmids and phagemids were constructed via Golden Gate assembly or Gibson assembly.90,91

Modifications to the P1 genome were implemented through the CAST system or the BioDesignER E. coli strain, a λ-Red based system.30,92 CAST was used to add RAM and BioDesignER was used to knockout the native tail fiber coding region. A CAST plasmid was constructed containing the full complement of CAST components, including gRNA, Cas/Tn7 proteins, and genetic payload. A gRNA was chosen that would insert the genetic payload away from essential genes. The final location of the genetic payload was upstream of the mod gene, a component of the modification system, and downstream of the lxc gene, a modulator of the master C1 repressor, both non-essential genes.61 A depiction of the genetic payload, which includes a single operon to express RAM and TmpR, can be found in Supplementary Figure 1a. The CAST plasmid was transformed into a host that contained the P1 genome as well as a plasmid to induce P1 lysis. A colony from the transformation was used to generate a lysate, protocol detailed below, with a mixture of phage particles containing modified and unmodified P1 genomes. The lysate was then subjected to the transduction to plating protocol using trimethoprim to select for colonies infected with a successfully modified version of P1. The standard protocol for λ-Red based modifications in BioDesignER were followed.92 Homology arms corresponding to the tail fiber coding regions were taken from prior literature.22 Oligos that included tails for the homology arms and primer regions corresponding to a TmpR gene were used to generate dsDNA for transformation via PCR. The version of P1 with KanR in the genome called CM01 was a gift from a collaborator.

The packaging site used to generate P1 phagemids was a gift from the same collaborator and is slightly different in overall architecture, but functionally the same as previously published packaging sites used for P1 phagemids.32 Exact packaging site architecture can be found in Supplementary Figure 1b.

Lysate Generation

E. coli MG1655 was used as the host strain to generate all phage and phage-derived particles. The lysates containing the two different tail fibers were generated from the BioDesignER strain, a derivative of E. coli MG1655.92 For RAM encoded in the P1 genome, the host contained RAM encoded P1 and an inducible coi plasmid. For RAM encoded on a P1 phagemid, the host contained P1 with a kanamycin resistance marker, the phagemid, and an inducible coi plasmid. For tail fiber related experiments, the host contained P1 with the tail fiber coding region knocked out, the phagemid, and an inducible coi plasmid that also contained coding regions for either of the two tail fibers. The host was cultured overnight directly from a glycerol stock or from a colony with appropriate antibiotics. Saturated cultures were then subcultured 100x into 2 mL of LB media containing appropriate antibiotics. After 2 hours, subcultures were pelleted by centrifugation at 4100xg for 10 minutes. Pellets were resuspended without antibiotics in 2 mL of PLM. Arabinose was added at a final concentration of 13 μM to induce the lytic cycle. Cultures were given 2 hours to fully lyse and then mixed with chloroform at a final concentration of 2.5%. Lysates were then vortexed for 30 seconds and centrifuged for 2 minutes at 12,200xg. Supernatant was removed and stored at 4 °C. All growth and lysis steps occurred in an incubator kept at 37 °C and 250 RPM.

We included a PEG concentration step for the lysates containing the S and S’ tail fibers as the modifications appeared to lower the number of functional phage-derived particles. Cultures were scaled up to 50 mL following our previous lysate preparation protocol. After the 2 hour lysis step, 2.5% chloroform and 0.3 M NaCl (including NaCl in PLM) were added directly to 50 mL flasks containing lysate and allowed to shake at 37 °C and 250 rpm for 15 minutes. Lysates were then centrifuged at 4100g for 30 minutes, and the supernatant carefully collected. PEG 6000 was then added to the lysates to a final concentration of 4g/100mL and the lysates allowed to sit overnight at 4 °C. The lysates were then spun down at 4100g for 45 minutes at 4 °C. The supernatant was poured off and 500 μL of SM buffer added to the pellet. The pellet was resuspended by gentle, up and down pipetting and then allowed to sit for 2 hours. The resuspension was then filtered through a 0.22 μM filter and stored at 4 °C until use.

Transduction for Plating

Host organisms were cultured overnight to stationary phase directly from a glycerol stock. Hosts were then resuspended to OD 0.5 in PLM and placed on ice. Meanwhile, a dilution series of the phage/phagemid lysates was prepared with PLM in a 96 well plate. Next, 50 μL of host suspension was mixed with 50 μL of phage/phagemid dilution in a different 96 well plate. The plate was then incubated at 37 °C and 250 RPM for 1 hour. Finally, 5 μL of host and phage/phagemid mixture was plated on antibiotic plates to select for appropriate subpopulations of phage/phagemid. Colonies were then counted at the lowest dilution with visibly distinct colonies.

Transduction for RT-qPCR

Similar to transduction efficiency, host organisms were cultured overnight to stationary phase directly from a glycerol stock. Hosts were then resuspended to OD 0.5 in PLM for all wastewater and Aeromonas experiments and M9 supplemented with 1% casamino acids and 0.4% glucose for the initial monoculture and synthetic community experiments. To create the synthetic community, equal volumes of host suspension were combined together. Phage lysates were spiked at a ratio of 1:10 of host suspension. The mixture was then incubated at 37 °C and 250 RPM for 2 hours before harvesting for RNA extraction.

RNA or DNA Extraction

RNA was extracted on a Promega Maxwell RSC 48 with a modified protocol for the Maxwell RSC Purefood GMO and Authentication Kit. Modifications are centered on the lysis step. Biomass equivalent to 1 mL of 0.4 OD culture was pelleted and resuspended in 1 mL of CTAB buffer. Samples were then incubated for 5 minutes at 90 °C. Next, samples were removed and given roughly 5 minutes to cool at room temperature before adding 40 μL of Proteinase K. Finally, samples were incubated for 10 minutes at 70 °C before being added to Maxwell cartridges and continuing with the standard kit pipeline. Samples were eluted into 50 μL of elution buffer and immediately processed to remove DNA.

DNA from A. hydrophila containing P1 was extracted on a Promega Maxwell RSC 48 with the standard DNA extraction protocol from the RSC Purefood GMO and Authentication Kit. Biomass equivalent to 1 mL of 0.4 OD culture was used in the extraction.

DNA Removal

DNA was removed using the Invitrogen TURBO DNA-free Kit with a modified protocol. All reactions consisted of 30 μL sample extract, 13 μL H O, 5 μL 10X TURBO DNase buffer, and 2 μL TURBO DNase. Reactions were incubated at 37 °C for 1 hour after which 5 μL of DNase Inactivation Reagent was added. Reactions were then incubated for an additional 10 minutes at 37 °C with brief inversions/flicking of tubes every 2.5 minutes to resuspend settled DNase Inactivation Reagent. Samples were stored at -80 °C until further processing for RT-qPCR or RT-PCR.

Reverse transcription quantitative PCR (RT-qPCR)

Primers and probes for RT-qPCR can be found in Supplementary Table S3. Reactions were prepared with PCR Biosystem’s qPCRBIO Probe 1-Step Go reaction mix and run on a Quantstudio-3 qPCR thermocycler. A single 10 μL reaction consisted of 0.4 μL of H O, 5 μL of probe master mix, 0.1 μL of reverse transcriptase, 0.5 μl of a previously prepared primer/probe stock solution, and 4 μL of RNA extract. The final concentration of oligos in the reaction was 0.4 μM for primers and 0.2 μM for probes. RNA extracts were diluted 10x or 1000x prior to addition to the reaction to minimize the effect of inhibitors. RT-qPCR data from the thermocycler was first analyzed in Thermofisher’s Design and Analysis Software 2.7.0. Standard curves were generated from a dilution series of barcoded RAM amplicons from E. coli MG1655 that had been converted to DNA through RT-PCR. Concentration of the undiluted DNA standard was determined via qubit. New standard curves were generated for each preparation of primer/probe stock solution. The thresholds determined in the standard curve for a primer/probe stock preparation were manually set to the same value in all RT-qPCR runs using that stock preparation. All RT-qPCR data in a specific figure panel was generated on the same RT-qPCR run.

Reverse transcription PCR (RT-PCR)

Primers for RT-PCR can be found in Supplementary Table 3. Reactions were prepared with New England Biolab’s LunaScript® Multiplex One-Step RT-PCR Kit on either a BioRad C1000 Touch or T100 thermocycler. A single 25 μL reaction consisted of 8.75 μL H O, 1.25 μL DMSO, 5 μL of reaction buffer, 1 μL of enzyme mix, 5 μL of a previously prepared primer stock solution, and 4 μL of RNA extract. The final concentration of primers in the reaction was 0.5 μM. RNA extracts were diluted 10x prior to addition to the reaction to minimize the effect of inhibitors. Reactions were scaled up to 50 μL for RAM amplicons to increase the amount of DNA generated. Reactions were subjected to gel electrophoresis and appropriately sized bands extracted with New England Biolab’s Monarch® Spin DNA Gel Extraction Kit. Extracted DNA was then sent to Genewiz for sequencing through their Amplicon-EZ pipeline.

Amplicon sequencing analysis

A depiction of the workflow for amplicon sequencing analysis can be found in Supplementary Figure 12. Sequencing analysis was conducted in Qiime 2 2024.10.93 Data for barcoded 16S and whole community 16S were initially imported into Qiime separately due to their unique trimming requirements. The Cutadapt plugin was used to trim the forward primer (5’-GCAACGCGAAGAACCTTACC) for both data sets and the unique reverse primer for each data set, (5’-AAGTCATGCCGTTTTCATGTGATC) for barcoded 16S and (5’-TGACGGGCGGTGWGTRCA) for the whole community 16S.94 A residual barcode sequence was trimmed from the barcoded 16S data set (5’-CGGATAACGGGAAAAGCATTGAACACCAT). To make the sequence comparisons between the two datasets equivalent, an additional sequence was trimmed from the whole community 16S data set (5’-AGGCCCGGGAACGT). Primers and the extra sequence from the whole community 16S data set were required to have at least 5 nt aligning to the end of the read and 10 nt for the residual barcode sequence. Primers and the residual barcode sequence were also required to have less than 10% sequence mismatch and 14% sequence mismatch for the extra sequence from the whole community 16S data set. Reads that did not contain these sequences were discarded. The two data sets were then exported from Qiime, combined, and imported back into Qiime together. The DADA2 plugin was used to denoise, merge paired-end sequences, dereplicate, and then filter chimeras.95 ASVs generated from this process that did not occur more than three times across two samples were removed, along with any ASVs below 250 bp. Taxonomy was assigned via the feature-classifier sklearn-classify command using the Silva 138 99% OTU database.96 Taxonomy for the synthetic community was assigned with the feature-classifier blast command using a custom BLAST database constructed from the genomes of the included organisms.

Statistics & Reproducibility

Statistical analysis of sequencing data was conducted in Qiime 2 2024.10 using default settings unless otherwise specified.93 Diversity metrics were calculated with the diversity core-metrics-phylogenetic command. We chose to report the weighted Unifrac analysis over the unweighted Unifrac analysis because it includes information on relative infection efficiency, an important element of host range. The resulting PCoA of the weighted Unifrac analysis was exported from Qiime 2 and visualized in R.97 The ANCOM-BC analysis was conducted with the composition ancombc command.98 The resulting Qiime 2 files were exported and visualized in R. ANCOM-BC results were only reported in the main text if they were statistically significant (q-value < 0.05).

No statistical method was used to predetermine sample size. One replicate of the microbial community experiments detailed in Figure 4 was mishandled during sample processing. This replicate, as well as one random replicate from each other experimental condition (to make replicates equal across experimental conditions), was removed from any further reporting and analysis. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a well-written and concise manuscript describing an exciting novel technique to identify the host range for genetically\nmodified phage using culture-independent, high-throughput amplicon sequencing techniques. It represents a novel\nadvancement with important applications to the fields of viral ecology and phage therapy. The technique builds on previous\nresearch using RAM elements to identify the host range for conjugative plasmids, largely focused on applications associated\nwith HGT. By adding RAM elements into phage genomes or packaging RAM-containing plasmids into phage particles, it\ncreates a novel tool to probe phage host range in a high throughput and culture-independent way.\nThe authors demonstrate this tool can be used to reveal novel hosts for the P1 phage in wastewater communities. They not\nonly identified Aeromonas as a novel P1 host with this tool, but follow up by confirming that P1 can deliver the RAM system\nand modify Aeromonas hydrophila rRNA in the lab. They also demonstrate that changing tail fibers changes the host range\nin a significant way. Both of these are noteworthy results.\nWhile RAM has been demonstrated to reveal the conjugative host range for plasmids, the application to phage host range is\ncompletely novel in this paper. It complements other host-range approaches, as outlined in the paper, adding several\nfeatures that other methods are lacking. This approach is targeted to a specific gene making it cost-effective, while still being\nopen-ended, not requiring a priori knowledge of the host range. It also requires phage RNA expression, a step beyond some\ntechniques that only probe phage proximity or attachment to the host. One drawback is that it requires the use of a\ngenetically modified phage, limiting this approach to the small fraction of phage that are amenable to genetic manipulation.\nAnother drawback is that it doesn't reveal complete lytic cycle, a caveat that the authors acknowledge. Still, there are many\nuseful applications of this technology, including as a tool that could rapidly advance phage therapy or improve\nunderstanding of how phage tail fibers or other components impact host-range in a high-throughput way.\nThe conclusions drawn by the authors are backed by the evidence in their figures and tables. The paper would be stronger if\nthe authors included hosts that are unable to be infected by P1 in their synthetic community and demonstrate a comparison\nbetween the observed and expected host range. Although the authors show that not all organisms in their wastewater\ncommunity are targeted by this method, demonstrating some amount of specificity, it would be good to know what the\nlikelihood of seeing or missing some phage host targets would be. Could the phagemids be transferred to other host via\nanother mechanism not associated with the phage capsid itself (e.g. transformation or conjugation)? Demonstrating non-\ninfection of bacteria that are not P1 hosts in their synthetic community would ensure other mechanisms of HGT do not\ninfluence their results.\nThe methods are sound, reproducible, and meet the standard in the field.\nSpecific comments:\nFig. 1e- What are the XXs? Not tested? Consider mentioning why this is not tested in the legend not just the text.\nFig. 1f- How many 16S rRNA copies of each strain are present without barcoding? Useful to understanding the efficiency of\nbarcode-labeling.\nLine 204: P1 infection was already demonstrated for S enterica (i.e. line 182-185), so it’s not clear why this sentence\nsuggests these results extends the host range for P1 to S enterica. Do the authors mean something else (e.g. pBBR1 host\nrange)? If so, consider rephrasing.\nLine 235-239: While these unique ASVs may be only detectable because of they represent a small portion of the community,\nthere is also the possibility they are just sequencing error. Are these similar to other sequences in the community (more\nlikely sequencing error) or substantially different from any observed in the community but similar to other known species\n(such that they are likely just underrepresented)?\nLine 257-262: While the authors state that this technique doesn’t necessarily identify organisms that can undergo lytic\ninfections in the barcoded hosts, now that they have the an Aeromonas barcoded strain in culture, they should state whether\nP1 can completely infect in this strain (e.g. form plaques).\nLine 312-314: Is a weighted Unifrac the right approach to apply for this analysis? It seems like the weighted approach will be\nsubject to variations in rRNA between species more than host range, which is not the goal of the analysis. Using an\nunweighted analysis seems like a better approach to get the information you are interested in (different in host range).\nLine 315: At what level was the ANCOM-BC approach applied? At ASVs level?\nLine 376-377: Not sure how this approach would be applicable to culture collections compared to the cheaper methods of\nstandard plaque assays. Are the authors proposing to construct synthetic communities from the culture collection and then\napply this technique? It should be clarified.", "reviewer_2": "In this manuscript, LaTurner et al., adapted a recently reported microbiome editing tool, RNA-addressable modification\n(RAM), into temperate plasmid-phage P1 and phagemids thereof. This enables new lines of investigation into phage-host\nrange biology through RT-qPCR of host-rRNA barcoding (introduced by the edited RAM-Phage-derived particles). They\nshowcase the technology’s functionality across a range of microbial complexity: isolate culture, synthetic microbial\ncommunities, and complex, wastewater microbial communities. Consistent with being P1-derived, these rRNA edits only\ndelivered/functioned in gammaproteobacteria. However, aided by the sensitivity of RAM barcoding, the authors succeeded\nin identifying new potential transduction hosts for phage P1, Salmonella enterica and Aeromonas species. Finally, they\nprovide a nice demonstration of how to assay the impact of different tail fibers on P1\ntransduction host range.\nOverall, this manuscript presents a creative use of phage-derived particles for studying the microbiome and phage gene\ncontributions to host range. Both topics are of strong interest for microbiome science and phage biology communities. I have\na few major comments (below) that I believe can be easily addressed through text and figure edits. When addressed, I\nbelieve this manuscript will be in good condition to publish in Nature Communications.\nMajor comments:\nThe discussion lacks inclusion of recent, pertinent phage-derived particles microbiome engineering literature. For instance,\nengineered phages (and phage-derived particles) have delivered Cas9-targeting capability, base-editors, Cas transposases\nin both monoculture and complex microbiome environments. Many of these endeavors are well-known from the last few\nyears and contextualizes the timeliness of this manuscript. I was looking forward to seeing a discussion section comparing\nand highlighting opportunities for these technologies as delivered by phages, but was saddened to not see these discussion\npoints.\nA key limitation of the technology as presented in this manuscript is the sole use of P1-derived\nphage scaffolds. P1 itself is a temperate plasmid-phage and may not be as aggressive in killing the target bacterium after\ntransduction. It also benefits from being one of the longest studied phages with 75 years of research behind it. This robust\nhistory of knowledge enables complex circuitry behind phagemids. Are temperate or nonlytic “lifestyles” required to use RAM in a\nphage? Are there limitations of phage engineering that prevent choice of other scaffolds? What are the impacts on host-range that these\nphages can target? I don’t think the authors necessarily need to show that they can do this with experiments, but discussing\nthese points will facilitate adoption of the technology instead of limiting potential users to preexisting phagemids.\nRelatedly, there is need to be careful with the ambiguity of terms relating to phage biology when discussing transduction\nagents such as a packaged phagemid as “phage particles”. “Phage-derived particle” is a field-standard term to refer to\nphagemid-derived phages that may not have a complete infectious cycle, but “phage particles” typically refers to phage\nvirions that package replicative phage genomes unless also describing the modification of interest to clarify. The shortened\nuse of “phage particles” without immediate clarification reads ambiguous to a phage biologist in many places and deviates\nfrom phage engineering field standards. I would recommend using the formalisms adopted by Brodel and Charpenay et al.,\n2024 (https://doi.org/10.1038/s41586-024-07681-w). While this is, at its core, a semantic critique, addressing this should be\nsimple (text edits would be sufficient) and will greatly increase the clarity of the manuscript. This will increase recognition\nwithin the phage biology community. There are many places in the manuscript this slightly impacts including figures.\nAll of the citations in the Methods section appear to be incorrectly assigned to seemingly random papers. Maybe this is a\ncitation-editor mix up? Please check through the citations carefully. There are likely more places that got swapped.\nMinor comments\n1. The genetic diagram in Figure 1b is quite confusing while describing some pretty advanced concepts central to the\nmanuscript. In particular, the piece that is more confusing than it needs to be is that it is unclear (without pausing for 10\nminutes) which elements are circular in the cell (i.e. phage episome, phagemid, plasmid), genomic or packaged by the\nphage. These elements don’t appear to be organized by time either (until Fig1c). Providing more cues linking the elements\nin Fig 1b to Fig 1c would greatly enhance readability.\n2. “mini P1” should be “mini-P1” to be consistent with its original source material.\n3. Early in the manuscript, the authors assess whether or not differences in transduction efficiency from Fig 1e comes from\nheterogeneous particles. However, the authors propose an answer to this question with another measurement of phage\ntransduction in Fig S2. While this is a valid measurement of transduction efficiency, it is not a physical measurement of\nphage particles as described in the manuscript. There are a lot of ways to address this ranging from direct TEM\nmeasurements, indirect measurements such as dynamic light scattering or statements of packaging efficiency such as\nqPCR. As it stands however, there is only evidence of transduction heterogeneity agnostic to particle identity. The claim of\nheterogeneous particles does not appear appropriately substantiated in the current form of the manuscript.\n4. Line 186: “P1 phagemid infection has been observed with this strain “ phagemids do not possess infection properties, just\ntransduction properties\n5. Line 225: “environmental community” reads ambiguous. I would recommend “environmental microbial community”.\n6. The color palette used in Fig 3d (and Fig S5), is repeating, making interpretation ambiguous. Please use a more\nappropriate color palette for this figure.\n7. For Fig 3d (and Fig S5), It may be advisable to (1) lump low abundance assignments to an “other” category and/or (2)\npick a single level of genus, family, order in its own right. Because of the nature of rRNA readout having overlap between close\nclades, it might be worth considering an unassigned category (ex. “unassigned Enerobacteriaceae”)\n8. Lines 256-257: “Many isolates were antibiotic resistant, 20% for trimethoprim and 70% for carbenicillin, limiting the use of\nselections to monitor transduction.” My understanding is that these microbes being antibiotic resistant isn’t a real technical\nlimitation here as kanamycin was typically used to measure transduction efficiency via selection. I agree that there is value\nto RAM-encoding phage-derived particles, specifically on this point, but is more appropriate to highlight in the discussion.\n9. Line 258: “one was susceptible to P1 infection”. Data is not shown for this claim in Fig. S6. Data is only shown for\ntransduction. Please correct phrasing.\n10. Line 262- 263: (similar to above) this is a statement about transduction. Please correct to address this.\n11. Considering the observation in Fig 4e, that S’ tail fibers infect a wide range of species relative to S tail fibers, I would limit\nthe usage of the word “only” when describing host range imparted by S’ recognition of hosts.\n12. Fig 4h: Please report what the log fold change is relative to.\n13. I would suggest not phrasing organisms as “unculturable”, rather “uncultivated” or “difficult to cultivate”.\n14. Several claims in the discussion are either too speculative or missing citations\nLines 370-371: “The ability to monitor barcoding in diverse communities is expected to be scalable, enabling high-\nthroughput studies in diverse microbiomes in parallel.”\nLines 371-372: “RAM can advance the discovery of unculturable hosts in other communities”. This seems a bit speculative\ngiven the limitations of the current technologies to not only cultivated hosts but also ones with well-defined phagemids.\nLines 377-380: “Also, knock-in studies that add genes of interest or knock-down studies that interrupt specific genes could\nbe paired with RAM measurements to identify phage genes essential for infections…”\nLines 385-386: “These approaches could be expanded to other elements of phage biology, such as anti-defense systems”\n15. All citations in the methods section appear to be misaligned\n[Several places] “BioDesignER strain”. Is this from a company and missing a company acknowledgement or a paper and\nmissing a citation? Looking closer, there appears to be a misaligned citation (update: see major comments).\n[Several places] Please add a citation for CAST.\nCitations for source phagemids seem to be missing in the Methods section."}, "author_response": "Response to Reviewer Comments\nManuscript Reference Number: NCOMMS-25-67744-T\nTitle: Cross-order detection of bacteriophage transduction in microbial communities using\nRNA barcoding\nZachary W. LaTurner, Matt J. Dysart, Samuel K. Schwartz, Elizabeth Zeng, James Chappell,\nJonathan J. Silberg, Lauren B. Stadler\nWe thank the reviewers for their constructive comments and suggestions. We provide a point-by-\npoint response to their comments below, corresponding revisions in the manuscript are\nhighlighted in blue. Bolded blue text indicates specific changes.\nREVIEWER #1:\nThis is a well-written and concise manuscript describing an exciting novel technique to identify\nthe host range for genetically modified phage using culture-independent, high-throughput\namplicon sequencing techniques. It represents a novel advancement with important applications\nto the fields of viral ecology and phage therapy. The technique builds on previous research using\nRAM elements to identify the host range for conjugative plasmids, largely focused on\napplications associated with HGT. By adding RAM elements into phage genomes or packaging\nRAM-containing plasmids into phage particles, it creates a novel tool to probe phage host range\nin a high throughput and culture-independent way.\nThe authors demonstrate this tool can be used to reveal novel hosts for the P1 phage in\nwastewater communities. They not only identified Aeromonas as a novel P1 host with this tool,\nbut follow up by confirming that P1 can deliver the RAM system and modify Aeromonas\nhydrophila rRNA in the lab. They also demonstrate that changing tail fibers changes the host\nrange in a significant way. Both of these are noteworthy results.\nWhile RAM has been demonstrated to reveal the conjugative host range for plasmids, the\napplication to phage host range is completely novel in this paper. It complements other host-\nrange approaches, as outlined in the paper, adding several features that other methods are\nlacking. This approach is targeted to a specific gene making it cost-effective, while still being\nopen-ended, not requiring a priori knowledge of the host range. It also requires phage RNA\nexpression, a step beyond some techniques that only probe phage proximity or attachment to the\nhost. One drawback is that it requires the use of a genetically modified phage, limiting this\napproach to the small fraction of phage that are amenable to genetic manipulation. Another\ndrawback is that it doesn't reveal complete lytic cycle, a caveat that the authors acknowledge.\nStill, there are many useful applications of this technology, including as a tool that could rapidly\nadvance phage therapy or improve understanding of how phage tail fibers or other components\nimpact host-range in a high-throughput way.\nThe conclusions drawn by the authors are backed by the evidence in their figures and tables. The\npaper would be stronger if the authors included hosts that are unable to be infected by P1 in\ntheir synthetic community and demonstrate a comparison between the observed and expected\nhost range. Although the authors show that not all organisms in their wastewater community are\ntargeted by this method, demonstrating some amount of specificity, it would be good to know\nwhat the likelihood of seeing or missing some phage host targets would be.\nWe thank the reviewer for their comments. We appreciate the reviewer’s suggestion to\ninclude hosts that are unable to be infected by P1 to demonstrate the expected and\nobserved host range. While not included in the synthetic community, we did perform\nsuch experiments, shown in Figures 4C and S9. In these experiments, we tested P1’s two\ntail fibers on two host species with previously characterized transduction profiles: (i) the\nS tail fiber, which can infect E. coli and S. flexneri, and (ii) the S’ tail fiber, which can\ninfect S. flexneri but cannot infect E. coli. Importantly, when E. coli was exposed to P1\nparticles with the S’ tail fiber, we observed no detectable spliced product via RT-qPCR, a\nmethod substantially more sensitive than amplicon sequencing. Thus, these experiments\naddress the reviewer’s concern by confirming that the RAM output is consistent with the\nexpected host range specificity of P1’s tail fibers. To emphasize this important point, we\nadded a sentence highlighting this result to the main text.\n(Line 311-313) These results also show the lack of false positive RAM barcoding, as\nno barcoded 16S rRNA was detected for S’ particles via RT-qPCR.\nCould the phagemids be transferred to other host via another mechanism not associated with the\nphage capsid itself (e.g. transformation or conjugation)? Demonstrating non-infection of\nbacteria that are not P1 hosts in their synthetic community would ensure other mechanisms of\nHGT do not influence their results.\nAdditional modes of horizontal gene transfer, such as conjugation or transformation,\ncould mobilize constructs beyond transduction hosts, but we believe that the prevalence\nof this phenomenon is likely negligible. In the case of conjugation, none of our constructs\ncontain known motifs required for conjugal transfer, such as oriTs or relaxases. In the\ncase of transformation, several prior studies showed that it is difficult to detect natural\ntransformation in a microbial community even when spiking in high concentrations of\nnaked plasmid and then selecting for them with antibiotics.1–4 (see end of response\ndocument for citations.) Since the majority of construct DNA in our samples is housed\nwithin the phage particle, it is even less likely for our constructs to undergo natural\ntransformation. Additionally, in our unpublished work, we have been unable to detect\nRAM signal when spiking a wastewater microbial community with a high concentration\nof RAM-containing plasmids (~20 ng/µL).\nThe methods are sound, reproducible, and meet the standard in the field.\nSpecific comments:\nFig. 1e- What are the XXs? Not tested? Consider mentioning why this is not tested in the legend\nnot just the text.\nIn Fig. 1e the X’s refer to instances where our K. pneumoniae strain was already resistant\nto the carbenicillin used for selective plating of phagemids. We sought to use this\nnotation to demonstrate the limits of selective plating methods, as identifying appropriate\nselectable markers for every species in a high-throughput or community experiment is\nlikely not feasible. RAM overcomes the need for a selectable marker to read out\ntransduction host range. We added additional text in the legend clarifying the Xs.\n(Line 152-154) This contrasts with selective plating measurements, which were not\npossible for K. pneumoniae, as indicated by “X” in Fig. 1e, since this strain is already\nresistant to the selection marker used in the phagemids.\n(Line 180-182) The symbol “X” indicates instances where the host strain was already\nresistant to the selectable marker in the construct.\nFig. 1f- How many 16S rRNA copies of each strain are present without barcoding? Useful to\nunderstanding the efficiency of barcode-labeling.\nWe quantified the concentration of 16S rRNA in each strain, as well as the concentration\nof total barcode (which is the sum of barcode from barcoded 16S rRNA and barcode still\nin the unreacted RAM ribozyme), which are both useful for understanding the efficiency\nof barcoding (data shown below). The total amount of barcoded 16S rRNA displayed in\nFig. 1f is a function of multiple species-specific parameters, including: (i) variation in the\ninfection efficiencies of phage particles with each species, and (ii) the variation in the\nactivity of the promoter used to drive RAM ribozyme production. With this in mind, we\nfind the ratio of barcoded 16S rRNA to total barcode (a sum of barcode from barcoded\n16S rRNA and barcode still in the unreacted RAM ribozyme) is an informative metric for\nsplicing efficiency as it controls for the aforementioned factors. In our prior work (see\nFig. S9 of Kalvapalle, et al. 2025),5 we present the splicing efficiency as measured by\nbarcoded 16S rRNA versus total barcode across 5 different species of bacteria, which\nshows that there is some variation across species and an association between total\nbarcode and barcoded 16S rRNA. In our data below, we observed a similar correlation\n(R2 = 0.80) between concentrations of barcoded 16S rRNA and total barcode. In\ncontrast, barcoded 16S rRNA and 16S rRNA concentrations lack a strong correlation (R2\n= 0.01). No significant difference in 16S rRNA concentration was found across the\nconstructs or species (all p-values > 0.05 via pairwise Wilcox rank sum rest). The figures\nshown below were incorporated into the supplementary figures document and a statement\nwas added to the main text referencing these correlations.\n(Line 149-151) Concentrations of barcoded 16S rRNA and total barcode, that is\nbarcode in unreacted ribozyme and barcoded 16S rRNA, were positively correlated\n(R2 = 0.8), and no correlation was observed between barcoded 16S rRNA and total\n16S rRNA concentration (R2 = 0.01; Fig. S5).\nSupplementary Figure S5. Barcoded 16S rRNA correlates with total barcode but\nnot total 16S rRNA in monocultures exposed to different phage and phage-derived\nparticles. a, Total barcode (barcode in unreacted ribozyme and barcoded 16S rRNA)\nconcentration and barcoded 16S rRNA concentration in individual samples. b, 16S rRNA\nconcentration and barcoded 16S rRNA concentration in individual samples. These results\nsuggest that the concentration of 16S rRNA does not affect the total amount of barcoded\n16S rRNA, likely due to 16S rRNA being in excess in the RAM ribozyme + 16S rRNA\n→ Barcoded 16S rRNA chemical reaction.\nLine 204: P1 infection was already demonstrated for S enterica (i.e. line 182-185), so it’s not\nclear why this sentence suggests these results extends the host range for P1 to S enterica. Do the\nauthors mean something else (e.g. pBBR1 host range)? If so, consider rephrasing.\nWe meant to indicate a novel demonstration of transduction by a P1 phage-derived\nparticle, followed by the stable maintenance of the transduced pBBR1 phagemid in\nSalmonella enterica. We modified our text to reflect this, and we updated some of the\ntext as we were unable to find previous demonstrations of P1 phage/phagemid infection\nof Enterobacter cloacae and Shigella sonnei elsewhere in the literature.\n(Line 187) RAM captures novel transduction hosts in a synthetic community\n(Line 189-193) In addition to the four microorganisms tested as isolates, we included\nEnterobacter cloacae and Shigella sonnei, neither of which have reported\nsusceptibility to P1 transduction. We also included Salmonella enterica and\nPseudomonas aeruginosa, as P1 phage and P1 phagemid infection, respectively, has\nbeen observed with related strains.25,33\n(Line 209-212) These results show that RAM allows for facile comparisons of phage and\nphagemid host range in a community, and they extend the stable transduction range of\nP1 phage-derived particles carrying pBBR1 phagemids to S. enterica.\nLine 235-239: While these unique ASVs may be only detectable because of they represent a small\nportion of the community, there is also the possibility they are just sequencing error. Are these\nsimilar to other sequences in the community (more likely sequencing error) or substantially\ndifferent from any observed in the community but similar to other known species (such that they\nare likely just underrepresented)?\nWe used DADA2 to generate ASVs from raw amplicon sequencing data. DADA2\ngenerates ASVs using a two-step probabilistic process that first builds a model of\nsequencing errors and then uses that model to “denoise” reads and identify true biological\nsequences.6 Thus, while it is possible that low-abundance ASVs are due to sequencing\nerror, it is unlikely given the way ASVs are generated.\nLine 257-262: While the authors state that this technique doesn’t necessarily identify organisms\nthat can undergo lytic infections in the barcoded hosts, now that they have the an Aeromonas\nbarcoded strain in culture, they should state whether P1 can completely infect in this strain (e.g.\nform plaques).\nOur goal was to demonstrate the use of RAM to identify transduction hosts, and thus we\ndid not attempt to determine whether P1 could make functional phage particles from the\nAeromonas strain (or other tested species/strains). While an interesting question in itself,\nwe consider identifying the full infection cycle in Aeromonas to be outside the scope of\nthis study.\nLine 312-314: Is a weighted Unifrac the right approach to apply for this analysis? It seems like\nthe weighted approach will be subject to variations in rRNA between species more than host\nrange, which is not the goal of the analysis. Using an unweighted analysis seems like a better\napproach to get the information you are interested in (different in host range).\nWe chose to use a weighted Unifrac analysis because it provides information on relative\ninfection efficiency across the different constructs/replicates, which is an element of host\nrange that is important in many contexts. Below we show the weighted and unweighted\nUnifrac analysis of the data presented in Figures 3f and 4g, along with results of\nPERMANOVA analysis on the same data sets. All comparisons between different\nexperimental groups (except for mini P1 and P1 in Figure 3) showed statistically\nsignificant clustering for both weighted and unweighted Unifrac analysis in Figure 3 and\nFigure 4. In regards to 16S rRNA abundances, our assumption is that 16S rRNA is in\nexcess relative to the other molecules in the splicing reaction (see Fig. S4, 16S rRNA is\n102-106 more concentrated than total RNA barcode in the sample), and would therefore\nnot significantly impact concentrations of barcoded 16S rRNA. We added text in the\nmethods section to justify our choice of a weighted Unifrac analysis.\n(Line 589-591) We chose to report the weighted Unifrac analysis over the\nunweighted Unifrac analysis because it includes information on relative infection\nefficiency, an important element of host range.\n(Top-Left) Figure 3f weighted Unifrac; (Top-Right) Figure 3f unweighted Unifrac;\n(Bottom-Left) Figure 4g weighted Unifrac; (Bottom-Right) Figure 4g unweighted\nUnifrac\n3f - weighted unifrac permanova 4f - unweighted unifrac permanova\nGroup 1 Group 2 p-value q-value Group 1 Group 2 p-value q-value\nP1 mini P1 0.003 0.0045 P1 mini P1 0.287 0.2870\npBBR1 0.008 0.008 pBBR1 0.021 0.0315\nmini P1 pBBR1 0.002 0.0045 mini P1 pBBR1 0.023 0.0315\n4g - weighted unifrac permanova 4g - unweighted unifrac permanova\nGroup 1 Group 2 p-value q-value Group 1 Group 2 p-value q-value\nS S' 0.039 0.039 S S' 0.016 0.02\nWildtype 0.008 0.012 Wildtype 0.01 0.02\nS' Wildtype 0.005 0.012 S' Wildtype 0.02 0.02\nPERMANOVA tests on weighted and unweighted Unifrac analysis from Figure 3f and\nFigure 4g. All metrics, except for the P1 and mini P1 clusters in the unweighted Unifrac\nPERMANOVA, suggest statistically significant clustering between the respective groups\n(p-value < 0.05, q-value < 0.05)\nLine 315: At what level was the ANCOM-BC approach applied? At ASVs level?\nANCOM-BC analysis was applied at the genus level. Text was added to clarify this.\n(Line 327-329) To identify taxonomic groups that were significantly enriched or depleted\nbetween the tail fibers’ host communities, an ANCOM-BC analysis was performed at the\ngenus level (Fig. 4h).\nLine 376-377: Not sure how this approach would be applicable to culture collections compared\nto the cheaper methods of standard plaque assays. Are the authors proposing to construct\nsynthetic communities from the culture collection and then apply this technique? It should be\nclarified.\nOur vision was to pool organisms from a culture collection together (construct a synthetic\ncommunity), and then expose the pool to a phage encoded with RAM. Then through\nshallow amplicon sequencing (currently ~$50/sample), identify susceptible hosts. This\napproach could save money and time, obviating the need for plating, and assaying many\norganisms in parallel in a single reaction. We have added additional text expounding on\nwhat this might look like.\n(Line 391-393) Direct incorporation of RAM into phage genomes could allow for\nscreening of phages against pools of organisms from culture collections to generate\nbiobanks of phages with well-defined hosts.27,49–51\nReferences\n1. Calderón-Franco, D., van Loosdrecht, M. C. M., Abeel, T. & Weissbrodt, D. G. Catch me if\nyou can: capturing microbial community transformation by extracellular DNA using Hi-C\nsequencing. Antonie Van Leeuwenhoek 116, 667–685 (2023).\n2. Nordgård, L. et al. Lack of detectable DNA uptake by bacterial gut isolates grown in vitro and\nby Acinetobacter baylyi colonizing rodents in vivo. Environ. Biosafety Res. 6, 149–160\n(2007).\n3. Rizzi, A. et al. The Stability and Degradation of Dietary DNA in the Gastrointestinal Tract of\nMammals: Implications for Horizontal Gene Transfer and the Biosafety of GMOs. Crit. Rev.\nFood Sci. Nutr. 52, 142–161 (2012).\n4. Chowdhury, N. N. et al. Measuring Microbial Community-Wide Antibiotic Resistance\nPropagation via Natural Transformation in the Human Gut Microbiome. 2024.11.26.625464\nPreprint at https://doi.org/10.1101/2024.11.26.625464 (2024).\n5. Kalvapalle, P. B. et al. Information storage across a microbial community using universal\nRNA barcoding. Nat. Biotechnol. 1–8 (2025) doi:10.1038/s41587-025-02593-0.\n6. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data.\nNat. Methods 13, 581–583 (2016).\nREVIEWER #2:\nIn this manuscript, LaTurner et al., adapted a recently reported microbiome editing tool, RNA-\naddressable modification (RAM), into temperate plasmid-phage P1 and phagemids thereof. This\nenables new lines of investigation into phage-host range biology through RT-qPCR of host-rRNA\nbarcoding (introduced by the edited RAM-Phage-derived particles). They showcase the\ntechnology’s functionality across a range of microbial complexity: isolate culture, synthetic\nmicrobial communities, and complex, wastewater microbial communities. Consistent with being\nP1-derived, these rRNA edits only delivered/functioned in gammaproteobacteria. However,\naided by the sensitivity of RAM barcoding, the authors succeeded in identifying new potential\ntransduction hosts for phage P1, Salmonella enterica and Aeromonas species. Finally, they\nprovide a nice demonstration of how to assay the impact of different tail fibers on P1\ntransduction host range.\nOverall, this manuscript presents a creative use of phage-derived particles for studying the\nmicrobiome and phage gene contributions to host range. Both topics are of strong interest for\nmicrobiome science and phage biology communities. I have a few major comments (below) that I\nbelieve can be easily addressed through text and figure edits. When addressed, I believe this\nmanuscript will be in good condition to publish in Nature Communications.\nMajor comments:\nThe discussion lacks inclusion of recent, pertinent phage-derived particles microbiome\nengineering literature. For instance, engineered phages (and phage-derived particles) have\ndelivered Cas9-targeting capability, base-editors, Cas transposases in both monoculture and\ncomplex microbiome environments. Many of these endeavors are well-known from the last few\nyears and contextualizes the timeliness of this manuscript. I was looking forward to seeing a\ndiscussion section comparing and highlighting opportunities for these technologies as delivered\nby phages, but was saddened to not see these discussion points.\nWe appreciate the reviewers comments and suggestions. We added citations referencing\ndelivery of different payloads for microbiome engineering applications including those\nmentioned in the comment (see references 44-48 and 52-57 in the text below). We\nenvision that RAM can be applied to advance microbiome engineering by revealing\nwhich microbial community members can receive genetic constructs via horizontal gene\ntransfer, and thus could be paired with tools for engineering microbes. We thus do not\ncompare RAM against these tools, but instead highlight opportunities to integrate them in\nmicrobiome engineering applications.\n(Line 389-395) RAM is expected to be advantageous for microbiome engineering\napplications, as it enables high-throughput screens to design synthetic phages with user-\ndefined characteristics.18,44–48 Direct incorporation of RAM into phage genomes could\nallow for screening of phages against pools of organisms from culture collections to\ngenerate biobanks of phages with well-defined hosts.27,49–51 Also, knock-in studies that\nadd genes of interest or knock-down studies (i.e. phage functional genomics) that\ninterrupt specific genes could be paired with RAM measurements to identify phage genes\nessential for infections across different species in a community.52–57\n44. Brödel, A. K. et al. In situ targeted base editing of bacteria in the mouse gut.\nNature 632, 877–884 (2024).\n45. Citorik, R. J., Mimee, M. & Lu, T. K. Sequence-specific antimicrobials using\nefficiently delivered RNA-guided nucleases. Nat Biotechnol 32, 1141–1145 (2014).\n46. Bikard, D. et al. Exploiting CRISPR-Cas nucleases to produce sequence-specific\nantimicrobials. Nat Biotechnol 32, 1146–1150 (2014).\n47. Roberts, A., Adler, B. A., Cress, B. F., Doudna, J. A. & Barrangou, R. Phage-\nbased delivery of CRISPR-associated transposases for targeted bacterial editing.\nProceedings of the National Academy of Sciences 122, e2504853122 (2025).\n48. Gelsinger, D. R. et al. Metagenomic editing of commensal bacteria in vivo using\nCRISPR-associated transposases. Science 390, eadx7604 (2025).\n52. Chen, J. et al. Systematic, high-throughput characterization of bacteriophage\ngene essentiality on diverse hosts. Cell Host & Microbe 33, 1363-1380.e11 (2025).\n53. Humolli, D., Ransome, J., Piel, D., Veening, J.-W. & Harms, A. Systematic\nmapping of bacteriophage gene essentiality with HIDEN-SEQ. 2025.11.20.689424\nPreprint at https://doi.org/10.1101/2025.11.20.689424 (2025).\n54. Chan, A. et al. Bacteriophage genome-wide transposon mutagenesis.\n2025.11.23.690004 Preprint at https://doi.org/10.1101/2025.11.23.690004 (2025).\n55. Yosef, I., Goren, M. G., Globus, R., Molshanski-Mor, S. & Qimron, U.\nExtending the Host Range of Bacteriophage Particles for DNA Transduction.\nMolecular Cell 66, 721-728.e3 (2017).\n56. Ando, H., Lemire, S., Pires, D. P. & Lu, T. K. Engineering Modular Viral\nScaffolds for Targeted Bacterial Population Editing. Cell Systems 1, 187–196 (2015).\n57. Silas, S. et al. Anti-restriction functions of injected phage proteins revealed by\npeeling back layers of bacterial immunity. Nat Commun 16, 7828 (2025).\nA key limitation of the technology as presented in this manuscript is the sole use of P1-derived\nphage scaffolds. P1 itself is a temperate plasmid-phage and may not be as aggressive in killing\nthe target bacterium after transduction. It also benefits from being one of the longest studied\nphages with 75 years of research behind it. This robust history of knowledge enables complex\ncircuitry behind phagemids. Are temperate or nonlytic “lifestyles” required to use RAM in a\nphage? Are there limitations of phage engineering that prevent choice of other scaffolds? What\nare the impacts on host-range that these phages can target? I don’t think the authors necessarily\nneed to show that they can do this with experiments, but discussing these points will facilitate\nadoption of the technology instead of limiting potential users to preexisting phagemids.\nWe thank the reviewer for this thoughtful comment and agree that the discussion of\nphage scaffold choice is important for broad adoption of RAM. Our use of P1-derived\nphagemids and P1 reflects a deliberate starting point rather than a fundamental constraint\nof the technology. RAM does not require a temperate or non-lytic lifestyle. Because RNA\nis central to all phage lifecycles, RAM could be applied in diverse phage types, including\nlytic phages. We selected P1 because of its well-characterized biology and genetic\ntractability. Importantly, none of the molecular components of RAM are inherently\nspecific to P1. More generally, scaffold choice involves tradeoffs between infection\ndynamics, payload capacity, host range, and ease of engineering. Implementing RAM in\ndifferent phage types may require navigating these tradeoffs and phage engineering\nchallenges, rather than intrinsic limitations of RAM itself. We have expanded the\nDiscussion to clarify these points.\n(Line 419-427) Although this study focuses on P1 and P1-derived phagemids, this\nchoice reflects a practical starting point rather than a fundamental limitation of\nRAM. P1’s large packaging capacity and genetic tractability made it an ideal\nstarting point to demonstrate the use of RAM to report on phage host range.\nBecause RNA is central to all phage life cycles, RAM could be implemented in many\nother phage types, including lytic and RNA phages. Extension of RAM to other\nphage scaffolds involves tradeoffs common to phage engineering, including infection\ndynamics, payload capacity, and host range. Ongoing advances in phage editing\ntechniques, including CRISPR-based genetic modification, recombitrons, and in\nvivo or cell free rebooting,56,73–77 are rapidly expanding the set of accessible phages\nfor engineering.\nRelatedly, there is need to be careful with the ambiguity of terms relating to phage biology when\ndiscussing transduction agents such as a packaged phagemid as “phage particles”. “Phage-\nderived particle” is a field-standard term to refer to phagemid-derived phages that may not have\na complete infectious cycle, but “phage particles” typically refers to phage virions that package\nreplicative phage genomes unless also describing the modification of interest to clarify. The\nshortened use of “phage particles” without immediate clarification reads ambiguous to a phage\nbiologist in many places and deviates from phage engineering field standards. I would\nrecommend using the formalisms adopted by Brodel and Charpenay et al., 2024\n(https://doi.org/10.1038/s41586-024-07681-w). While this is, at its core, a semantic critique,\naddressing this should be simple (text edits would be sufficient) and will greatly increase the\nclarity of the manuscript. This will increase recognition within the phage biology community.\nThere are many places in the manuscript this slightly impacts including figures.\nTo improve the precision of terminology used in the paper, all uses of the terms phage\nand phage-derived particles, as well as infection and transduction have been updated. We\nuse “phage particles” to refer to particles containing the P1 genome and capable of\ncompleting a full phage lifecycle. We use “phage-derived particles” to refer to particles\ncontaining the phagemid constructs. In some instances, the more general term “particle”\nis used to refer to both phage and phage-derived particles. Additionally, “infection” is\nonly used to refer to transduction of phage DNA capable of completing a full phage life\ncycle (i.e. the P1 genome). The term “transduction” is used to refer to instances when any\nDNA, whether capable of completing a full phage lifecycle or not, is injected into a host.\nWe use “host” to refer to organisms that have been infected or transduced by DNA.\nAll of the citations in the Methods section appear to be incorrectly assigned to seemingly random\npapers. Maybe this is a citation-editor mix up? Please check through the citations carefully.\nThere are likely more places that got swapped.\nWe apologize - the citations in the methods section were misaligned. These citations have\nbeen updated to indicate correct references. Specific changes can be found in response to\nreviewer comments below.\nMinor comments\n1. The genetic diagram in Figure 1b is quite confusing while describing some pretty advanced\nconcepts central to the manuscript. In particular, the piece that is more confusing than it needs\nto be is that it is unclear (without pausing for 10 minutes) which elements are circular in the cell\n(i.e. phage episome, phagemid, plasmid), genomic or packaged by the phage. These elements\ndo not appear to be organized by time either (until Fig1c). Providing more cues linking the\nelements in Fig 1b to Fig 1c would greatly enhance readability.\nWe clarified and simplified the conceptual diagrams in Figure 1 and made parallel\nchanges in Figures 2 and 3 to match. Panels 1b and 1c have been combined to remove\nredundancy that may have complicated understanding. Our diagrams follow\nrecommendations from the Synthetic Biology Open Language (SBOL) for standardized\ndepiction of genetic circuits. Representations of plasmids in this system are meant to have\ninwards curving arcs at either end of the circuit, while representations of genomes are\nmeant to have S shaped arcs. The P1 genome can be referred to as a plasmid itself, but we\nopted for depicting it as a genome to distinguish its complexity from our completely\nsynthetic plasmids. We also made corresponding changes to the caption for 1b and where\n1b is referred to in the main body of the text.\n(Figure 1, Line 162-173) b, The hosts and genetic constructs used to make particles\nwith either the P1 genome + RAM or a phagemid + RAM. Left: E. coli MG1655\ncontaining the lysis plasmid is lysogenized with P1 phage containing RAM in the\ngenome. Genomically integrated RAM (P1 genome + RAM) includes TmpR in the\nRAM operon to allow for selection and maintenance of modified lysogens. The lysis\nplasmid contains the c one inactivator (coi) gene under arabinose inducible\nexpression. Coi represses the C1 repressor, which itself represses the lytic cycle of\nP1. Addition of arabinose induces the lytic cycle of P1, leading to production of P1\nphage particles containing the P1 genome + RAM. Right: E. coli MG1655\ncontaining the lysis plasmid, phagemid, and P1 lysogen. Phagemids include a P1\npackaging site so they will be incorporated into phage particles. In this system,\naddition of arabinose induces the lytic cycle of P1, leading to a mixture of phage-\nderived particles, some containing phagemid and some containing P1 genome\nwithout RAM.\n2. “mini P1” should be “mini-P1” to be consistent with its original source material.\nAll references to the P1 oriR containing phagemid have been changed to, “mini P1”, for\nconsistency with our initial naming.\n3. Early in the manuscript, the authors assess whether or not differences in transduction\nefficiency from Fig 1e comes from heterogeneous particles. However, the authors propose an\nanswer to this question with another measurement of phage transduction in Fig S2. While this is\na valid measurement of transduction efficiency, it is not a physical measurement of\nphage particles as described in the manuscript. There are a lot of ways to address this ranging from\ndirect TEM measurements, indirect measurements such as dynamic light scattering or statements\nof packaging efficiency such as qPCR. As it stands however, there is only evidence of\ntransduction heterogeneity agnostic to particle identity. The claim of heterogeneous particles\ndoes not appear appropriately substantiated in the current form of the manuscript.\nThe reviewer raises an important point about measuring phage and phage-derived particle\nconcentration via selective plating. We considered absolute measurements of phage and\nphage-derived particle concentration to be out of the scope of this project because we are\nnot deriving quantitative relationships between phage and phage-derived particle\nconcentration and RAM signal. However, we made changes to the early section in the\nmanuscript as well as some text in Supplementary Figure 2 to clarify the implications of\nthe results from selective plating for different cell populations infected with phagemid\nlysates.\n(Line 132-142) We evaluated E. coli transduced by phagemid lysates to investigate\nwhether some phage-derived particles still contained wildtype P1 genome (Fig. S2).\nA large number of E. coli exposed to either phagemid lysate contained wildtype P1,\n7.3E8 CFUs/mL lysate for pBBR1 and 8.7E6 CFUs/mL lysate for mini P1 as measured\nby selective plating on E. coli (relative to 1.1E8 CFUs/mL lysate and 1.2E7 CFUs/mL\nlysate containing the respective phagemids). These values were lower than\ntransduction of P1 phage lysate alone (1.2E10 CFUs/mL lysate), suggesting\ndifferences in the absolute amount or relative transduction efficiency of phage and\nphage-derived particles in phagemid lysates. These findings show that P1 phage and\nphagemids can infect a variety of microbes, but variation in RAM signal was observed\nacross constructs due to differences arising during phage-derived particle generation\nand/or transduction.\n4. Line 186: “P1 phagemid infection has been observed with this strain “phagemids do not\npossess infection properties, just transduction properties\nUsage of the words transduction and infection have been updated throughout the\nmanuscript. See our third response to the reviewer’s major comments.\n5. Line 225: “environmental community” reads ambiguous. I would recommend “environmental\nmicrobial community”.\nThe text has been modified to reduce ambiguity.\n(Line 233-236) To understand P1 host range in an environmental microbial community,\nthe rRNA-barcoding phage and phagemids were added to a microbial community derived\nfrom wastewater influent sampled from a wastewater treatment plant in Houston, Texas\n(Fig. 3a).\n6. The color palette used in Fig 3d (and Fig S5), is repeating, making interpretation ambiguous.\nPlease use a more appropriate color palette for this figure.\nThe color palettes used in Fig 3d, Fig 4e, and Fig S5 have been updated so that each\ntaxonomic group has a unique color. See Fig 3d below as an example.\n7. For Fig 3d (and Fig S5), It may be advisable to (1) lump low abundance assignments to an\n“other” category and/or (2) pick a single level of genus, family, order in its own right. Because\nof the nature of rRNA readout having overlap between close clades, it might be worth\nconsidering an unassigned category (ex. “unassigned Enerobacteriaceae”)\nTo include as much information as possible, we chose to show the lowest taxonomic\nlevel that our assignment software could confidently assign an ASV too. If we were to\ngroup low abundance assignments or pick a single taxonomic level to display, we would\nlose some of the information that we believe is valuable to provide in the plot.\n8. Lines 256-257: “Many isolates were antibiotic resistant, 20% for trimethoprim and 70% for\ncarbenicillin, limiting the use of selections to monitor transduction.” My understanding is that\nthese microbes being antibiotic resistant isn’t a real technical limitation here as kanamycin was\ntypically used to measure transduction efficiency via selection. I agree that there is value to\nRAM-encoding phage-derived particles, specifically on this point, but is more appropriate to\nhighlight in the discussion.\nKanamycin was used to measure the presence of P1 genome (without RAM) transduction\nfrom phagemid lysates. Trimethoprim was used to measure transduction of RAM\ncontaining P1 genomes and carbenicillin transduction of RAM containing phagemids.\nWe specifically mention trimethoprim and carbenicillin here because those were the\nantibiotics used to select for RAM containing constructs, rather than kanamycin. We have\nadded text to clarify this point.\n(Line 266-268) Many isolates were antibiotic resistant, 20% for trimethoprim and 70%\nfor carbenicillin (the antibiotics used to select for cells with P1 containing RAM and\nphagemids containing RAM, respectively), limiting the use of selections to monitor\ntransduction.\n9. Line 258: “one was susceptible to P1 infection”. Data is not shown for this claim in Fig. S6.\nData is only shown for transduction. Please correct phrasing.\nThe phrasing has been clarified to indicate transduction as opposed to infection.\n(Line 268-270) Among the eight trimethoprim-susceptible isolates, one was susceptible\nto P1 transduction as measured by selective plating (Fig. S8).\n10. Line 262- 263: (similar to above) this is a statement about transduction. Please correct to\naddress this.\nAs noted above, usage of the words transduction and infection have been updated\nthroughout the manuscript.\n11. Considering the observation in Fig 4e, that S’ tail fibers infect a wide range of species\nrelative to S tail fibers, I would limit the usage of the word “only” when describing host range\nimparted by S’ recognition of hosts.\nThe phrasing has been updated to indicate that S’ could infect one of the two microbes\ntested in this experimental context, not necessarily indicating S’ has a smaller\ntransduction range generally.\n(Line 308-311) Transduction monitored using selection (Fig. 4c) and barcoding (Fig.\nS10) revealed S particles transduced both microbes, similar to particles produced using\nan unmodified P1 genome (designated wildtype), while S’ transduced S. flexneri 2a\n24570 alone, as previously observed.\n12. Fig 4h: Please report what the log fold change is relative to.\nThree different sets of comparisons are shown in the figure, indicated by the top section\nwith a white background, the middle section with the grey background, and the bottom\nsection with the white background. For the top, log fold change is for genera found in the\nS tail fiber group relative to genera found in the S’ tail fiber group; the middle shows\nWildtype relative to S, and the bottom shows Wildtype relative to S’. Only genera with\nstatistically significant log fold change between the two tail fiber groups being compared\nare shown in the figure. Additional text has been added in the figure caption to clarify\nthis.\n(Figure 4, Line 363-368) h, Log fold change of differentially abundant taxa between the\nthree tail fiber constructs as revealed by ANCOM-BC analysis. Positive values indicate\nenrichment of a taxonomic group in one set of tail fiber constructs relative to another.\nSpecific tail fiber construct comparisons (S’ vs S, Wildtype vs S, Wildtype vs S’)\nare indicated in the bottom right corner of each section. Bars represent point estimates and\nerror bars the standard error for five biological replicates.\n13. I would suggest not phrasing organisms as “unculturable”, rather “uncultivated” or\n“difficult to cultivate”.\nAll instances of “unculturable” in the text have been changed to “uncultivated” to better\nreflect the status of these microorganisms.\n(Line 371-372) Our results illustrate how RAM is a facile and sensitive tool that enables\nthe study of phage host range in uncultivated microorganisms and environmental\nmicrobiomes.\n(Line 386-388) As such, RAM can advance the discovery of uncultivated hosts in other\ncommunities and reveal how phages indirectly influence community outcomes through\ntransduction in different environmental contexts.\n(Line 401-403) These approaches could be expanded to other elements of phage biology,\nsuch as anti-defense systems, and paired with RAM to improve throughput or build phage\nparticles to target uncultivated bacteria.\n14. Several claims in the discussion are either too speculative or missing citations\nLines 370-371: “The ability to monitor barcoding in diverse communities is expected to be\nscalable, enabling high-throughput studies in diverse microbiomes in parallel.”\nWe added an additional citation for our previous work where we used multiple barcodes\nto track different plasmid constructs with RAM. Given the ease with which sequencing-\nbased approaches are scaled and the ease with which barcodes can be generated to tag\nspecific experimental conditions, we think it reasonable to suggest that RAM is\nextensively scalable. Sequencing approaches are multiplexable, allowing for the\nprocessing of many samples in parallel.\nLines 371-372: “RAM can advance the discovery of unculturable hosts in other communities”.\nThis seems a bit speculative given the limitations of the current technologies to not only\ncultivated hosts but also ones with well-defined phagemids.\nWe consider our deployment of RAM in natural wastewater communities as an ample\ndemonstration of RAMs ability to identify uncultivated hosts. We also had success\ninserting RAM into the genome of P1, suggesting that well-defined phagemids are not a\nprerequisite for using RAM in a phage. The phage must be engineerable if RAM is to be\nadded to the genome, which is one limitation, but new tools for manipulating phage\ngenomes are rapidly expanding our abilities to do so.\nLines 377-380: “Also, knock-in studies that add genes of interest or knock-down studies that\ninterrupt specific genes could be paired with RAM measurements to identify phage genes\nessential for infections…”\nWe included additional citations after this sentence of phage knock-in/knock-down\nstudies that could be paired with RAM.\n52. Chen, J. et al. Systematic, high-throughput characterization of bacteriophage\ngene essentiality on diverse hosts. Cell Host & Microbe 33, 1363-1380.e11 (2025).\n53. Humolli, D., Ransome, J., Piel, D., Veening, J.-W. & Harms, A. Systematic\nmapping of bacteriophage gene essentiality with HIDEN-SEQ. 2025.11.20.689424\nPreprint at https://doi.org/10.1101/2025.11.20.689424 (2025).\n54. Chan, A. et al. Bacteriophage genome-wide transposon mutagenesis.\n2025.11.23.690004 Preprint at https://doi.org/10.1101/2025.11.23.690004 (2025).\n55. Yosef, I., Goren, M. G., Globus, R., Molshanski-Mor, S. & Qimron, U.\nExtending the Host Range of Bacteriophage Particles for DNA Transduction.\nMolecular Cell 66, 721-728.e3 (2017).\n56. Ando, H., Lemire, S., Pires, D. P. & Lu, T. K. Engineering Modular Viral\nScaffolds for Targeted Bacterial Population Editing. Cell Systems 1, 187–196 (2015).\n57. Silas, S. et al. Anti-restriction functions of injected phage proteins revealed by\npeeling back layers of bacterial immunity. Nat Commun 16, 7828 (2025).\nLines 385-386: “These approaches could be expanded to other elements of phage biology, such\nas anti-defense systems”\nWe included additional citations after this sentence demonstrating phage/phagemid based\napproaches for manipulating host range using phage elements outside of tail fibers.\n44. Brödel, A. K. et al. In situ targeted base editing of bacteria in the mouse gut.\nNature 632, 877–884 (2024).\n57. Silas, S. et al. Anti-restriction functions of injected phage proteins revealed by\npeeling back layers of bacterial immunity. Nat Commun 16, 7828 (2025).\n15. All citations in the methods section appear to be misaligned\n[Several places] “BioDesignER strain”. Is this from a company and missing a company\nacknowledgement or a paper and missing a citation? Looking closer, there appears to be a\nmisaligned citation (update: see major comments).\n[Several places] Please add a citation for CAST.\nCitations for source phagemids seem to be missing in the Methods section.\nThe BioDesignER strain is from a paper that was cited, though it was in the fraction of\ncitations that were misaligned and have since been updated. We have added additional\ncitations for the BioDesignerER strain and the CAST systems when they are referenced\nanew.\n(Line 447-448) Modifications to the P1 genome were implemented through the CAST\nsystem or the BioDesignER E. coli strain, a λ-Red based system.30,70\nThe packaging sites used to make P1 phagemids were gifts from a colleague included in\nthe acknowledgements. The exact architecture of the packaging site we used has not\nbeen previously published, but is functionally the same as prior work. We added\nsentences and a citation in the methods clarifying this.\n(Line 466-469) The packaging site used to generate P1 phagemids was a gift from the\nsame collaborator and is slightly different in overall architecture, but functionally\nthe same as previously published packaging sites used for P1 phagemids.32 Exact\npackaging site architecture can be found in Supplementary Figure S1b."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The reviewers have addressed my comments and I have no further comments. It is an exciting manuscript, worthy of publication Nature Communications.", "reviewer_2": "I appreciate the authors' efforts to address the reviews. The additions to the discussion are valuable, though I have some remaining concerns about citation accuracy and one unresolved comment on lytic vs. temperate phages.\nMy remaining comments primarily have to do with the added discussion, which is out of touch with what is possible in the broader phage biotechnology field. Some of the added citations are strange choices in the context of their updated discussion. An additional major comment about lytic versus temperate phage choice was not sufficiently addressed. Considering Reviewer #1 also sees that “One drawback is that it requires the use of a genetically modified phage, limiting this approach to the small fraction of phage that are amenable to genetic manipulation.” It remains important that the authors portray this part of the field accurately.\n(Line 389-395 addition) During discussion of knock-in readouts using anti-defense proteins, the authors cite Silas et al., 2025. This is a strange choice given this citation doesn’t have a single knock-in experiment in phage genomes. Knock-ins of anti-CRISPR proteins (ex. PMID: 31474367, PMID: 36316452) and, more broadly, anti-defense proteins (ex. PMID: 39197447, 40894804) to increase phage host-range have numerous high-profile cases in the field.\n(Line 389-395 addition) The authors also mention “phage functional genomics” as an opportunity to pair with RAM measurements. While this is a pretty interesting thought, it is unclear how (or even if it is possible) pooled phage knockout mutants get linked to the RNA readout provided by RAM without considerable automation and novel sequencing pipelines. Furthermore, the authors refer to knock-down methods in phage functional genomics without citing any relevant knock-down methods in phage functional genomics including CRISPRi, CRISPRi-ART and anti-sense oligonucleotides (PMIDs: 38048319, 40011704, 40931073). Currently, the citations exclusively refer to knock-out or transposon mutagenesis (knockout via disruption) methods.\n(Line 419-427 addition) If the authors wish to cite “CRISPR-based genetic modification of phages… rapidly expanding the set of accessible phages for engineering”, the authors should cite the advances of Cas13 phage editing techniques (stemming from PMIDs: 36316452, 36316451) and not a review. These two papers are the primary advance in the field that has unlocked tractability of new phage species for phage editing (for instance, the key technologies used for many of the advances cited in 47, 53, 54 are built upon these techniques).\n(Line 419-427 addition) I don’t think this addition fully addresses my comment. I appreciate the commentary of P1 and P1-phagemids as a practical starting point. However, this does not reflect the challenges of lytic phage scaffolds. Unencumbered lytic phages such as T7 can lyse the host within 20minutes and critically impact readouts that require intact hosts such as RT-qPCR. As currently reported, the authors expose communities to phage for 2 hours. This poses an infection timescale problem that goes beyond \"advances in phage editing techniques” that may require advanced modification of the wildtype phage."}, "author_response": "Response to Reviewer Comments (Round 2)\nManuscript Reference Number: NCOMMS-25-67744-T\nTitle: Cross-order detection of bacteriophage transduction in microbial communities using\nRNA barcoding\nZachary W. LaTurner, Matt J. Dysart, Samuel K. Schwartz, Elizabeth Zeng, James Chappell,\nJonathan J. Silberg, Lauren B. Stadler\nWe thank the reviewers for the productive dialogue related to our manuscript. We provide a\npoint-by-point response to their additional comments below, corresponding revisions in the\nmanuscript are highlighted in blue. Bolded blue text indicates specific changes.\nReviewer #1:\nThe reviewers have addressed my comments and I have no further comments. It is an exciting\nmanuscript, worthy of publication Nature Communications.\nWe thank the reviewer for their comments and feedback.\nReviewer #2:\nI appreciate the authors' efforts to address the reviews. The additions to the discussion are\nvaluable, though I have some remaining concerns about citation accuracy and one unresolved\ncomment on lytic vs. temperate phages.\nMy remaining comments primarily have to do with the added discussion, which is out of touch\nwith what is possible in the broader phage biotechnology field. Some of the added citations are\nstrange choices in the context of their updated discussion. An additional major comment about\nlytic versus temperate phage choice was not sufficiently addressed. Considering Reviewer #1\nalso sees that “One drawback is that it requires the use of a genetically modified phage, limiting\nthis approach to the small fraction of phage that are amenable to genetic manipulation.” It\nremains important that the authors portray this part of the field accurately.\n(Line 389-395 addition) During discussion of knock-in readouts using anti-defense proteins, the\nauthors cite Silas et al., 2025. This is a strange choice given this citation doesn’t have a single\nknock-in experiment in phage genomes. Knock-ins of anti-CRISPR proteins (ex. PMID:\n31474367, PMID: 36316452) and, more broadly, anti-defense proteins (ex. PMID:\n39197447, 40894804) to increase phage host-range have numerous high-profile cases in the field.\nThe rationale behind citing Silas et al. 2025 was portraying a situation where knock-in of\ndefense genes in the host could be paired with RAM in phages to rapidly assay the effect\nof many defense genes against many phages at once. This would require incorporation of\nRAM into multiple phages and barcoding of a different RNA molecule in the host than\n16S rRNA. We realize that this type of experiment may be outside the scope of what is\nsuggested in the text and beyond what could realistically be done with RAM in phages at\nthis time. We have decided to remove the Silas et al. 2025 citation, and have incorporated\nthe relevant citations suggested by the reviewer.\n(Line 393-35) Also, knock-in studies that add genes of interest or knock-down studies\n(i.e. phage functional genomics) that interrupt specific genes could be paired with RAM\nmeasurements to identify phage genes essential for infections across different species in a\ncommunity.52–63\n57. Stanley, S. Y. et al. Anti-CRISPR-Associated Proteins Are Crucial Repressors of\nAnti-CRISPR Transcription. Cell 178, 1452-1464.e13 (2019).\n58. Guan, J. et al. Bacteriophage genome engineering with CRISPR-Cas13a. Nat.\nMicrobiol. 7, 1956–1966 (2022).\n59. Hobbs, S. J., Nomburg, J., Doudna, J. A. & Kranzusch, P. J. Animal and\nbacterial viruses share conserved mechanisms of immune evasion. Cell 187, 5530-\n5539.e8 (2024).\n60. Hadary, R. et al. Functional diversity of phage sponge proteins that sequester\nhost immune signals. BioRxiv Prepr. Serv. Biol. 2025.08.24.671296 (2025)\ndoi:10.1101/2025.08.24.671296.\n(Line 389-395 addition) The authors also mention “phage functional genomics” as an\nopportunity to pair with RAM measurements. While this is a pretty interesting thought, it is\nunclear how (or even if it is possible) pooled phage knockout mutants get linked to the RNA\nreadout provided by RAM without considerable automation and novel sequencing pipelines.\nFurthermore, the authors refer to knock-down methods in phage functional genomics without\nciting any relevant knock-down methods in phage functional genomics including CRISPRi,\nCRISPRi-ART and anti-sense oligonucleotides (PMIDs: 38048319, 40011704, 40931073).\nCurrently, the citations exclusively refer to knock-out or transposon mutagenesis (knockout via\ndisruption) methods.\nWe specifically included the knockout via disruption methods as opposed to other\nmethods because we see them as starting points for deploying RAM in phage functional\ngenomics. These methods involve insertion of DNA into the phage, and in theory, RAM\ncould be encoded in this inserted DNA. As well, these methods generally insert a DNA\nbarcode to track unique insert locations, and this same barcode could be included in the\nRAM reporter. As the reviewer points out, there are other approaches for phage\nfunctional genomics and we have added citations to those as well.\n(Line 393-35) Also, knock-in studies that add genes of interest or knock-down studies\n(i.e. phage functional genomics) that interrupt specific genes could be paired with RAM\nmeasurements to identify phage genes essential for infections across different species in a\ncommunity.52–63\n61. Piya, D. et al. Systematic and scalable genome-wide essentiality mapping to\nidentify nonessential genes in phages. PLOS Biol. 21, e3002416 (2023).\n62. Adler, B. A. et al. CRISPRi-ART enables functional genomics of diverse\nbacteriophages using RNA-binding dCas13d. Nat. Microbiol. 10, 694–709 (2025).\n63. Gerovac, M. et al. Programmable antisense oligomers for phage functional\ngenomics. Nature 646, 1195–1203 (2025).\n(Line 419-427 addition) If the authors wish to cite “CRISPR-based genetic modification of\nphages… rapidly expanding the set of accessible phages for engineering”, the authors should\ncite the advances of Cas13 phage editing techniques (stemming from PMIDs: 36316452,\n36316451) and not a review. These two papers are the primary advance in the field that\nhas unlocked tractability of new phage species for phage editing (for instance, the key technologies\nused for many of the advances cited in 47, 53, 54 are built upon these techniques).\nWe have removed the review and added the primary citations as suggested by the\nreviewer. We also included a statement and citations on the development of Cas13\nselection, which, as the reviewer points out, enables many of the editing techniques.\nOngoing advances in phage editing techniques, including CRISPR-based genetic\nmodification, recombitrons, and in vivo or cell free rebooting,56,77–80 concomitantly\ndeveloped with Cas13 selection for genomic edits,81,82 are rapidly expanding the set of\naccessible phages for engineering.\n81. Guan, J. et al. Bacteriophage genome engineering with CRISPR-Cas13a. Nat.\nMicrobiol. 7, 1956–1966 (2022).\n82. Adler, B. A. et al. Broad-spectrum CRISPR-Cas13a enables efficient phage\ngenome editing. Nat. Microbiol. 7, 1967–1979 (2022).\n(Line 419-427 addition) I don’t think this addition fully addresses my comment. I appreciate the\ncommentary of P1 and P1-phagemids as a practical starting point. However, this does not reflect\nthe challenges of lytic phage scaffolds. Unencumbered lytic phages such as T7 can lyse the host\nwithin 20 minutes and critically impact readouts that require intact hosts such as RT-qPCR. As\ncurrently reported, the authors expose communities to phage for 2 hours. This poses an\ninfection timescale problem that goes beyond \"advances in phage editing techniques” that may require\nadvanced modification of the wildtype phage.\nWe appreciate the reviewer’s reasonable critic of deploying RAM in lytic phages. With\nregards to timing, we point the reviewer to our time series experiment in Supplementary\nFigure S3 (shown below), where we detected RAM signal 30 minutes after addition of\nphage particles to a monoculture of E. coli. This data suggests we could likely detect\nRAM even earlier than that (i.e., within 20 minutes of adding T7). Additionally, and\ncoincidentally, as it pertains to the reviewer's comment, we have another ongoing project\nwhere we have successfully implemented RAM in a lytic phage and measured host\nbarcoding in monoculture and in a microbial community, but this work is outside the\nscope of this manuscript.\nSupplementary Figure S3. Effect of incubation time on the RAM signal. P1 phage with\ngenomically encoded RAM was added to a suspension of E. coli MG1655 at time zero, and\nbarcoded rRNA was quantified using RT-qPCR in samples of the suspension collected\nevery 30 minutes. As a control, RAM signal was also measured from E. coli without the\naddition of phages (no signal was detected). Based on this data, an incubation time of two\nhours was selected to incubate RAM containing phages in bacterial cultures before\nharvesting RNA."}]

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