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Precision run-on sequencing (PRO-seq) for microbiome transcriptomics

Nature Microbiology volume 9, pages 241–250 (2024)Cite this article

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Abstract

Bacteria respond to environmental stimuli through precise regulation of transcription initiation and elongation. Bulk RNA sequencing primarily characterizes mature transcripts, so to identify actively transcribed loci we need to capture RNA polymerase (RNAP) complexed with nascent RNA. However, such capture methods have only previously been applied to culturable, genetically tractable organisms such as E. coli and B. subtilis. Here we apply precision run-on sequencing (PRO-seq) to profile nascent transcription in cultured E. coli and diverse uncultured bacteria. We demonstrate that PRO-seq can characterize the transcription of small, structured, or post-transcriptionally modified RNAs, which are often absent from bulk RNA-seq libraries. Applying PRO-seq to the human microbiome highlights taxon-specific RNAP pause motifs and pause-site distributions across non-coding RNA loci that reflect structure-coincident pausing. We also uncover concurrent transcription and cleavage of CRISPR guide RNAs and transfer RNAs. We demonstrate the utility of PRO-seq for exploring transcriptional dynamics in diverse microbial communities.

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Fig. 1: PRO-seq captures nascent transcripts in E. coli.
Fig. 2: Relative coverage of bacterial species in PRO-seq samples.
Fig. 3: Nascent transcription of an active CRISPR locus observed with PRO-seq.
Fig. 4: Concurrent transcription and cleavage of tRNAs observed with PRO-seq.

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Data availability

Sequencing data produced in this project were uploaded to NCBI’s Sequence Read Archive and are associated with BioProjects PRJNA800038 and PRJNA800070.

Code availability

Scripts and notebooks used to process and visualize sequencing data are available at https://github.com/britolab/PRO-seq.

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Acknowledgements

We thank P. Diebold for helpful discussions regarding cell permeabilization and data visualization. This work was funded by the NIGMS (R01 GM147731-01, awarded to I.L.B.) and the NHGRI (R01 HG009309 and R01 HG010346, awarded to C.G.D.). I.L.B. is a Packard Foundation Fellow and a Pew Biomedical Scholar. A.C.V. is a Cornell Center for Vertebrate Genomics Distinguished Scholar.

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Authors and Affiliations

  1. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA

    Albert C. Vill

  2. Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Edward J. Rice & Charles G. Danko

  3. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    Iwijn De Vlaminck & Ilana L. Brito

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Contributions

A.C.V., I.D.V., C.G.D. and I.L.B. conceptualized the study. A.C.V. and E.J.R. carried out experiments. A.C.V. and I.L.B. analysed the data and wrote the manuscript. All authors provided feedback and comments on the manuscript.

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Correspondence to Ilana L. Brito.

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Extended data

Extended Data Fig. 1 Metagenome characteristics and library type comparisons.

(a) Genus-level relative abundance data for US2 and US3 metagenomic assemblies, calculated with CheckM. Metagenomic bins were assigned taxonomic labels using GTDB-Tk. (b) Percent completeness and percent contamination of each of the high-quality metagenomic bins (<5% contamination; >90% completeness) included in the study for US2 and US3, as determined by CheckM. (c) Phylum-level relative abundance for all library types (terminator exonuclease negative and positive dRNA-seq libraries) calculated from mapped reads using Kraken2 and Bracken. (d) Family-level relative abundance for PRO-seq and RNAseq libraries calculated from mapped reads using Kraken2 and Bracken. Method #1 samples correspond to PRO-seq libraries that were processed without additional enzymes during permeabilization, and Method #2 samples correspond to PRO-seq libraries processed with these enzyme (see Methods). Note that, because of sample limitations, ‘Method #1’ and ‘Method #2’ metagenomes are different samples collected from the same individuals. (e) An UpSet plot showing the overlap of PRO-seq and dRNA-seq peaks coincident with promoter-proximal loci, defined as 50 bp up- and down-stream of the start codon of each open reading frame. The horizontal bar chart shows the total number of loci, and the total number of dRNA-seq peaks and PRO-seq peaks coincident with those loci.

Extended Data Fig. 2 Periodicity observed in CRISPR loci within the PRO-seq data.

Strand-specific RNAseq and PRO-seq read depths, in addition to PRO-seq reads’ 3’- and 5’-ends, are plotted for several well-covered CRISPR loci. Shaded boxes represent repeats. Sequence logos below each plot show repeat conservation. As in Fig. 3a, b, panels (A) and (B) show PRO-seq read 5’ end pile-ups at the same position across repeats. (C) and (D) show PRO-seq read 5’ end pile-ups within spacers.

Extended Data Fig. 3 CRISPR loci in E. coli MG1655 show co-transcriptional cleavage.

(a) One of the two CRISPR loci in E. coli MG1655 is depicted under control (left) and heat-shock (right) conditions. Strand-specific RNAseq and PRO-seq read depths, in addition to PRO-seq reads’ 3’- and 5’-ends, are plotted. Shaded boxes represent repeats. (b) Zoomed-in depiction of the PRO-seq 5’ RNA ends showing pile-ups at consistent positions within repeats. (c) Predicted crRNA repeat secondary structure. The black arrow points to the phosphodiester bond that is possibly cleaved by CasE during pre-crRNA processing, which marks the same position in the repeat as the arrows in (B). (d) PRO-seq captures nascent transcription of the entire CRISPR locus, situated downstream of the crRNA array, including CasE.

Extended Data Fig. 4 PRO-seq traces showing 5’ read end pile-ups within microbiota tRNAs.

(a) tRNA genes were identified in three highly complete US2 bins: Prevotella sp900313215, Prevotella sp002265625 and Prevotella copri. Different colors in the stacked bar plots represent different tRNA isoforms. (b) Representative tRNA genes, listed according to the sample, species annotation, and anticodon, are depicted from the two human microbiome samples. PRO-seq coverage, pile-up of PRO-seq 3’and 5’ read ends, and RNAseq coverage are shown for each tRNA gene (left). A zoomed-in PRO-seq read 5’ end pile-up is shown for each tRNA gene (right). Dotted lines show the boundaries of the tRNA gene.

Extended Data Fig. 5 PRO-seq traces across E. coli tRNAs show PRO-seq 5’ read end pile-ups.

Representative E. coli tRNA genes, listed by isoform, are shown for control (left) and heat shock (right) conditions. PRO-seq coverage, pile-up of PRO-seq 3’and 5’ read ends, and RNAseq coverage are shown for each tRNA gene. Arrows indicate direction of transcription.

Extended Data Fig. 6 Sites of RNAP stalling in tRNA sequences.

All tRNA loci identified in two species, Coprococcus eutactus (US2, top) and Ruminococcus bicirculans (US3, bottom), aligned at the anticodon sequence (vertical black lines). Sequence logos show sequence conservation, and bar plots give counts of PRO-seq 3’-end peaks (Z-score > 5, see Methods) at each aligned position. Secondary structures for representative tRNA sequences (yellow stars) are given at the right, with density plots reiterating the 3’ peak count data overlaid on the tRNA structures.

Extended Data Fig. 7 PRO-seq reveals aborted transcription at invertons and prophages.

(a) Stranded coverage data across four invertons from US2 and US3 is shown, with inverted repeats marked with blue triangles. Coordinates and directionality of coincident genes are given below the coverage plots. Decomposition of PRO-seq reads into 3’ and 5’ ends shows that transcription is initiated within the inverton and terminated just downstream. (b) Four examples of transcription across prophages, highlighting the complementary nature of PRO-seq and RNAseq data for observing the transcription of mobile genetic elements. The bounds of CI-like transcriptional regulators are demarcated by yellow arrows. Teal arrows give the bounds of genes encoding proteins of unknown function.

Extended Data Fig. 8 Phyla-specific pause site motifs.

Logos for clustered sequences surrounding PRO-seq read 3’ end peaks annotated for one Bacteroidota, one Proteobacteria, and two Bacillota species. The number of constituent peaks in a cluster out of the total number of peaks identified per bin is provided, as well as the median Z-score for each cluster and a plot showing the log2(Z-score) distribution for all positions in the −11 to +5 window. Position −1 represents the RNAP pause site and position +1 represents the next nucleotide added.

Extended Data Fig. 9 PRO-seq traces capture transcription of E. coli small regulatory RNAs.

(a) Normalized transcriptome profiles at selected E. coli small non-coding RNA (sRNA) loci. The left panel shows genomic context 2 kb up- and downstream from each sRNA locus (small black arrow). On the right, RNAseq coverage, composite PRO-seq read coverage, 5’ end and 3’ end coverage are shown for the sRNA locus, the bounds and strand of which are given by the large black arrows. (b) Log-log RPKM plots comparing merged PRO-seq and RNAseq libraries for control and heat-shock conditions. Genes are colored by RNA type. Spearman’s rank correlation coefficients (ρ) and Pearson’s correlation coefficients (r) are inset. (c) Box plots show the RPKM distribution for small non-coding RNAs and tRNAs across control and heat-shock conditions; 1 was added to all counts before normalization to facilitate plotting on a log scale. Black lines represent medians. P-values from two-sided Wilcoxon signed-rank tests are reported for each RNA type + treatment pair.

Supplementary information

Reporting Summary

Supplementary Table 1

Sequencing library information. Method #1 samples correspond to PRO-seq libraries that were processed without additional enzymes during permeabilization, and Method #2 samples correspond to PRO-seq libraries processed with these enzymes (see Methods). Note that, because of sample limitations, ‘Method #1’ and ‘Method #2’ metagenomes are different samples collected from the same individuals.

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Vill, A.C., Rice, E.J., De Vlaminck, I. et al. Precision run-on sequencing (PRO-seq) for microbiome transcriptomics. Nat Microbiol 9, 241–250 (2024). https://doi.org/10.1038/s41564-023-01558-w

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