Abstract
Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells4. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription–replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.
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Data availability
All counts matrices and raw sequencing reads used to perform the scRNA-seq analysis are available in the Gene Expression Omnibus (GEO) under the accession number GSE217715. Previously published counts matrices used for bulk analysis of C. crescentus expression are available in the GEO under the accession number GSE46915.
Code availability
Example code used to generate the analyses and plots is available at https://github.com/yanailab/TRIPs.
References
Bervoets, I. & Charlier, D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol. Rev. 43, 304–339 (2019).
Vilar, J. M. G., Guet, C. C. & Leibler, S. Modeling network dynamics: the lac operon, a case study. J. Cell Biol. 161, 471–476 (2003).
Narula, J., Devi, S. N., Fujita, M. & Igoshin, O. A. Ultrasensitivity of the Bacillus subtilis sporulation decision. Proc. Natl Acad. Sci. USA 109, E3513–E3522 (2012).
Homberger, C., Hayward, R. J., Barquist, L. & Vogel, J. Improved bacterial single-cell RNA-seq through automated MATQ-seq and Cas9-based removal of rRNA reads. mBio 14, e0355722 (2023).
Balakrishnan, R. et al. Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria. Science 378, eabk2066 (2022).
Cooper, S. & Helmstetter, C. E. Chromosome replication and the division cycle of Escherichia coli B/r. J. Mol. Biol. 31, 519–540 (1968).
Schaechter, M., Bentzon, M. W. & Maaloe, O. Synthesis of deoxyribonucleic acid during the division cycle of bacteria. Nature 183, 1207–1208 (1959).
Wang, M., Zhang, J., Xu, H. & Golding, I. Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality. Nat. Microbiol. 4, 2118–2127 (2019).
Narula, J. et al. Chromosomal arrangement of phosphorelay genes couples sporulation and DNA replication. Cell 162, 328–337 (2015).
Slager, J. & Veening, J.-W. Hard-wired control of bacterial processes by chromosomal gene location. Trends Microbiol. 24, 788–800 (2016).
Peterson, J. R., Cole, J. A., Fei, J., Ha, T. & Luthey-Schulten, Z. A. Effects of DNA replication on mRNA noise. Proc. Natl Acad. Sci. USA 112, 15886–15891 (2015).
Laub, M. T., McAdams, H. H., Feldblyum, T., Fraser, C. M. & Shapiro, L. Global analysis of the genetic network controlling a bacterial cell cycle. Science 290, 2144–2148 (2000).
Fang, G. et al. Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution. BMC Genom. 14, 450 (2013).
Zhou, B. et al. The global regulatory architecture of transcription during the Caulobacter cell cycle. PLoS Genet. 11, e1004831 (2015).
De Nisco, N. J., Abo, R. P., Wu, C. M., Penterman, J. & Walker, G. C. Global analysis of cell cycle gene expression of the legume symbiont Sinorhizobium meliloti. Proc. Natl Acad. Sci. USA 111, 3217–3224 (2014).
Bandekar, A. C., Subedi, S., Ioerger, T. R. & Sassetti, C. M. Cell-cycle-associated expression patterns predict gene function in Mycobacteria. Curr. Biol. 30, 3961–3971.e6 (2020).
Cooper, S. The synchronization manifesto: a critique of whole-culture synchronization. FEBS J. 286, 4650–4656 (2019).
Blattman, S. B., Jiang, W., Oikonomou, P. & Tavazoie, S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201 (2020).
Kuchina, A. et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science 371, eaba5257 (2021).
Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A.-E. & Vogel, J. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206 (2020).
Homberger, C., Barquist, L. & Vogel, J. Ushering in a new era of single-cell transcriptomics in bacteria. microLife 3, uqac020 (2022).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).
Bremer, H. & Dennis, P. P. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus 3, https://doi.org/10.1128/ecosal.5.2.3 (2008).
Michelsen, O., Teixeira de Mattos, M. J., Jensen, P. R. & Hansen, F. G. Precise determinations of C and D periods by flow cytometry in Escherichia coli K-12 and B/r. Microbiology 149, 1001–1010 (2003).
Wang, X., Lesterlin, C., Reyes-Lamothe, R., Ball, G. & Sherratt, D. J. Replication and segregation of an Escherichia coli chromosome with two replication origins. Proc. Natl Acad. Sci. USA 108, E243–E250 (2011).
Dimude, J. U. et al. Origins left, right, and centre: increasing the number of initiation sites in the chromosome. Genes 9, 376 (2018).
Ivanova, D. et al. Shaping the landscape of the Escherichia coli chromosome: replication-transcription encounters in cells with an ectopic replication origin. Nucleic Acids Res. 43, 7865–7877 (2015).
Khodursky, A. B. et al. Analysis of topoisomerase function in bacterial replication fork movement: use of DNA microarrays. Proc. Natl Acad. Sci. USA 97, 9419–9424 (2000).
Pham, T. M. et al. A single-molecule approach to DNA replication in Escherichia coli cells demonstrated that DNA polymerase III is a major determinant of fork speed. Mol. Microbiol. 90, 584–596 (2013).
Kjeldgaard, N. O., Maaloe, O. & Schaechter, M. The transition between different physiological states during balanced growth of Salmonella typhimurium. J. Gen. Microbiol. 19, 607–616 (1958).
Skinner, S. O., Sepúlveda, L. A., Xu, H. & Golding, I. Measuring mRNA copy number in individual Escherichia coli cells using single-molecule fluorescent in situ hybridization. Nat. Protoc. 8, 1100–1113 (2013).
Proshkin, S., Rahmouni, A. R., Mironov, A. & Nudler, E. Cooperation between translating ribosomes and RNA polymerase in transcription elongation. Science 328, 504–508 (2010).
Pomerantz, R. T. & O’Donnell, M. The replisome uses mRNA as a primer after colliding with RNA polymerase. Nature 456, 762–766 (2008).
de la Fuente, A., Palacios, P. & Vicente, M. Transcription of the Escherichia coli dcw cluster: evidence for distal upstream transcripts being involved in the expression of the downstream ftsZ gene. Biochimie 83, 109–115 (2001).
Flärdh, K., Palacios, P. & Vicente, M. Cell division genes ftsQAZ in Escherichia coli require distant cis-acting signals upstream of ddlB for full expression. Mol. Microbiol. 30, 305–315 (1998).
Lutkenhaus, J. F., Wolf-Watz, H. & Donachie, W. D. Organization of genes in the ftsA–envA region of the Escherichia coli genetic map and identification of a new fts locus (ftsZ). J. Bacteriol. 142, 615–620 (1980).
Zaslaver, A., Mayo, A., Ronen, M. & Alon, U. Optimal gene partition into operons correlates with gene functional order. Phys. Biol. 3, 183–189 (2006).
Zhu, M., Mu, H., Han, F., Wang, Q. & Dai, X. Quantitative analysis of asynchronous transcription–translation and transcription processivity in under various growth conditions. iScience 24, 103333 (2021).
Zhu, M., Mori, M., Hwa, T. & Dai, X. Disruption of transcription–translation coordination in Escherichia coli leads to premature transcriptional termination. Nat. Microbiol. 4, 2347–2356 (2019).
Sharpe, M. E., Hauser, P. M., Sharpe, R. G. & Errington, J. Bacillus subtilis cell cycle as studied by fluorescence microscopy: constancy of cell length at initiation of DNA replication and evidence for active nucleoid partitioning. J. Bacteriol. 180, 547–555 (1998).
Golding, I. Revisiting replication-induced transcription in Escherichia coli. Bioessays 42, e1900193 (2020).
Guptasarma, P. Does replication-induced transcription regulate synthesis of the myriad low copy number proteins of Escherichia coli? Bioessays 17, 987–997 (1995).
Ray, A., Edmonds, K. A., Palmer, L. D., Skaar, E. P. & Giedroc, D. P. Glucose-induced biofilm accessory protein A (GbaA) is a monothiol-dependent electrophile sensor. Biochemistry 59, 2882–2895 (2020).
Van Loi, V. et al. The two-Cys-type TetR repressor GbaA confers resistance under disulfide and electrophile stress in Staphylococcus aureus. Free Radic. Biol. Med. 177, 120–131 (2021).
Campbell, J. L. & Kleckner, N. E. coli oriC and the dnaA gene promoter are sequestered from dam methyltransferase following the passage of the chromosomal replication fork. Cell 62, 967–979 (1990).
Theisen, P. W., Grimwade, J. E., Leonard, A. C., Bogan, J. A. & Helmstetter, C. E. Correlation of gene transcription with the time of initiation of chromosome replication in Escherichia coli. Mol. Microbiol. 10, 575–584 (1993).
Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).
Cooper, S. The Escherichia coli cell cycle. Res. Microbiol. 141, 17–29 (1990).
Garrido, T., Sánchez, M., Palacios, P., Aldea, M. & Vicente, M. Transcription of ftsZ oscillates during the cell cycle of Escherichia coli. EMBO J. 12, 3957–3965 (1993).
Zhou, P. & Helmstetter, C. E. Relationship between ftsZ gene expression and chromosome replication in Escherichia coli. J. Bacteriol. 176, 6100–6106 (1994).
Lin, J. & Amir, A. Homeostasis of protein and mRNA concentrations in growing cells. Nat. Commun. 9, 4496 (2018).
Rosenfeld, N., Elowitz, M. B. & Alon, U. Negative autoregulation speeds the response times of transcription networks. J. Mol. Biol. 323, 785–793 (2002).
Ma, P. et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell 186, 877–891.e14 (2023).
Brennan, M. A. & Rosenthal, A. Z. Single-Cell RNA sequencing elucidates the structure and organization of microbial communities. Front. Microbiol. 12, 713128 (2021).
Xu, Z. et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat. Commun. 14, 5130 (2023).
Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).
Jun, S., Si, F., Pugatch, R. & Scott, M. Fundamental principles in bacterial physiology-history, recent progress, and the future with focus on cell size control: a review. Rep. Prog. Phys. 81, 056601 (2018).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Bergen, V., Soldatov, R. A., Kharchenko, P. V. & Theis, F. J. RNA velocity-current challenges and future perspectives. Mol. Syst. Biol. 17, e10282 (2021).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).
Levin, M. et al. The mid-developmental transition and the evolution of animal body plans. Nature 531, 637–641 (2016).
Zalts, H. & Yanai, I. Developmental constraints shape the evolution of the nematode mid-developmental transition. Nat. Ecol. Evol. 1, 113 (2017).
Keseler, I. M. et al. The EcoCyc database in 2021. Front. Microbiol. 12, 711077 (2021).
Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief. Bioinform. 20, 1085–1093 (2019).
Fuchs, S. et al. AureoWiki—the repository of the Staphylococcus aureus research and annotation community. Int. J. Med. Microbiol. 308, 558–568 (2018).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Chen, J., Yoong, P., Ram, G., Torres, V. J. & Novick, R. P. Single-copy vectors for integration at the SaPI1 attachment site for Staphylococcus aureus. Plasmid 76, 1–7 (2014).
Benson, M. A. et al. Staphylococcus aureus regulates the expression and production of the staphylococcal superantigen-like secreted proteins in a Rot-dependent manner. Mol. Microbiol. 81, 659–675 (2011).
Acknowledgements
The authors thank Y. Pilpel, T. Lionnet and F. Matheis for critical discussions on the project and the manuscript; S. Tavazoie, S. Blattman and W. Jiang for initial advice on implementing PETRI-seq; C. Rudolph and his laboratory for providing the E. coli strains; P. Eichenberger for advice on B. subtilis growth; and M. Wang and D. Barkley, J. Wang and other members of the Yanai and Golding laboratories for advice and suggestions. The following funding was provided by the National Institutes of Health: R21AI169350 (I.Y.), R01AI143290 (I.Y.), R01AI137336 (B.S., V.J.T. and I.Y.), R35 GM140709 (I.G.). I.G. also received funding from the Alfred P. Sloan Foundation, and K.J.C.M. was supported by the Ralph O. Simmons Undergraduate Research Scholarship.
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A.W.P. and I.Y. conceived the project. A.W.P. generated and analysed the scRNA-seq data, with contributions from P.J. A.W.P., P.J. and M.P. produced the S. aureus strains. T.Y. and I.G. designed the smFISH approach with T.Y. and E.H. performing the experiments and Y.G., E.H. and T.Y. performing the analysis. Y.G., K.J.C.M. and T.Y. performed the biophysical modelling analysis. I.Y., I.G., B.S. and V.J.T. contributed funding and resources to the project. The original draft was written by A.W.P. with contributions from I.Y., I.G., T.Y., E.H., Y.G. and K.J.C.M., and additional reviewing and editing were provided by P.J., B.S., V.J.T. and M.P.
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Extended data figures and tables
Extended Data Fig. 1 Growth and single cell analysis of E. coli and S. aureus.
A) mRNAs captured per cell by PETRI-seq. mRNA captured is quantified as unique molecular identifiers (UMI) per unique cell barcode combination in S. aureus in TSB (left, Dataset D3) and E. coli in different media (right, Dataset D1). B) Growth curves of bacterial strains. Left: E. coli in three medium conditions, LB (n = 4), M9GA (n = 4), and M9G (n = 3). Doubling times were calculated based on the linear portions of growth (marked as fitted lines). Center: Growth of S. aureus under standard growth conditions, with the time and log2(A600) values when exponential and stationary phase samples were taken (Datasets D3 & D4) marked with dotted lines. The line is fitted to the mean at each time point, with the gray area representing standard deviation (n = 5). Right: Growth of S. aureus under balanced growth conditions (n = 3). The black line indicates the linear portion from which doubling time was estimated. C) Spearman correlations from Fig. 1c without binning by chromosome position. D) Correlations from Fig. 1c without the use of scVI, binning in 200 kb bins by chromosome position. E) Spearman correlations in exponential S. aureus data from Dataset D4, averaged in 50 kb bins, as for Dataset D3 in Fig. 1c. F) Initial correlations from unbinned, scVI-denoised gene expression data. Sample “S. aureus exponential 2” is from Dataset D4, whereas E. coli LB replicates 1 and 2 are from Dataset D1 and Dataset D2, respectively. G) Gene-gene correlations in 50 kb bins as depicted in Fig. 1c but for stationary phase S. aureus (Dataset D4). H) Schematic figure of the simulation. Each “arm” of the circular chromosome is represented as an array of integers (initially ones), representing each gene. Replication proceeds stepwise from origin to terminus, doubling copy number as it does (steps 1 to 2). At high replication rates, a second round of replication will initiate before the first has finished (step 3). When one round of replication reaches the terminus, that round finishes and after a given time interval copy numbers are globally halved, reflecting cell division (steps 4 to 5). Figures on the right indicate the represented states on the circular chromosome. See Methods for details. I) Simulation of DNA copy number effects predicts the global gene covariance pattern. For 1,000 simulated, unsynchronized cells where the doubling time td is equal to the C-period, the normalized, scaled gene expression matrix (left) is used to calculate gene-gene correlations (right). J) Quantitative comparison between simulated and observed datasets. For each simulated and observed dataset, expression was first averaged in 40 bins by chromosome position. After calculating the correlation matrix of this binned data, the lower triangle was taken (excluding self-self correlations along the central diagonal) and flattened into a one-dimensional vector. These vectors were then used to calculate Spearman correlations. For all E. coli samples, the strongest correlations are between matched simulated and observed pairs. K) Simulated gene copy abundance data with cells ordered by increasing total gene dosage in S. aureus (left, from simulation in (I)) and E. coli (right, from simulation in Fig. 1eLeft panel). Fork positions are indicated by black dots. Note that because our simulation results in subsets of cells that diverge from the normal chromosomal copy number range (because of the lack of checkpoints between replication and division that would be present in real bacterial cells), the raw sum of total gene copies is first divided by the copy number of genes at the terminus to provide the total gene dosage used here. L) Gene expression correlations in synchronized C. crescentus bulk RNA-seq from. Scaled gene expression is averaged into 100 kb bins. M) The relationship between origin distance and expression levels. For each E. coli growth condition, the average fraction of total mRNA UMI from each gene was calculated and log2-transformed. A linear regression model (black line) was fitted between log-fraction counts and origin distance. Spearman correlations are −0.13 (LB, P = 3.8 ×10−10), −0.09 (M9GA, P = 2.2 ×10−5), and −0.07 (M9G, P = 6.0 ×10−4). P-values indicate the statistical significance of the Spearman correlation based on a two-sided test using the “spearmanr” function in the Python package scipy v1.9.3. See Methods for details. N) The gradient of the linear model fits in (M). Note that in each case, there is a negative relationship, with a steeper gradient for faster growth rates. This is expected given that at fast growth rates, genes near the origin may attain higher copy number states (>2) than at slow growth rates. O) Histogram showing that length-adjusted average gene expression varies over several orders of magnitude in exponentially growing S. aureus. This is a broad distribution that would not be expected from genomic DNA. Raw expression counts were normalized by library size (to sum to 1 per barcode) and the average expression was calculated. Length correction was performed as expression divided by gene length then multiplied by median gene length. P) Spearman correlations between genes in the top and bottom 20% of genes ranked by expression level (Left: S. aureus; Right: E. coli). Genes are arranged by chromosomal order. Shown for both unbinned expression (Top) and expression grouped in 50 kb bins as in Fig. 1c (Bottom). If the pattern was driven by low-level contaminating genomic DNA, it would be expected to be more evident in low-expressed genes (since a higher proportion of reads from these genes should come from genomic DNA) than in high-expressed genes. The opposite is true, with a much stronger pattern in high-expressed genes (presumably due to less noise in these measurements). Taken together, these observations strongly support that the pattern is driven by variation in the transcriptome rather than contaminating genomic DNA.
Extended Data Fig. 2 Cell and gene angle analysis to model replication-dependent gene expression.
A) UMAP analysis of LB-grown E. coli based on scVI-predicted expression. B) UMAP of S. aureus with gene expression averaged in 50 kb bins by chromosome position. Cells are colored by the cell angle θc between UMAP dimensions relative to the center of the projection. C) UMAP of E. coli genes, performed on the same data as the PCA in Fig. 2d. Gene angles shown are those derived from PCA. D) The relationship between θg and origin distance for E. coli grown in M9 + glucose + amino acids (M9GA) or M9 + glucose (M9G). The black line indicates the model fit as described in SI Methods Section “Modeling the gene angle-origin distance relationship”. E) Predicted replication patterns as for Fig. 2g but for E. coli under slower growth conditions. F) Gradients of the gene angle-origin distance relationship and estimates of DNA polymerase speed from these gradients. See Methods for details. G) Expression in LB-grown E. coli is first averaged in 100 bins by θc then averaged in 100 bins by θg to yield the 100 ×100 matrix represented here as a heatmap. This is used to train the model to predict gene expression at a given point in the cell cycle (θc) for a given gene (θg). H) Conceptual representation of the cell cycle expression parameterization. Cells are ordered in their cell cycle state by θc, whereas genes are ordered by their cell cycle expression by θg. Cell cycle expression can be described as the concurrent cycling of cells and genes ordered by these metrics. I) Predicting gene expression dynamics based on distance from the origin. The following pipeline predicts cell cycle expression for a given gene based only on its distance from the origin of replication. A regression model predicts gene angle θg-pred based on origin distance alone (left) and this is converted into a prediction of expression by cell angle θc using a second regression model (middle). Ordering genes by chromosome position (right) shows a smoothed version of the expression pattern in Fig. 2b. The bar at the top of this figure shows the real and predicted gene angles. Data are from E. coli grown in LB. See Methods for full details.
Extended Data Fig. 3 Transcriptional and replication dynamics during environmental shifts.
A-D) Nutritional shift-up in E. coli from M9GA (here, td measured as 36.0 min) to a 1:1 mix of M9GA and 2x LB (LB/M9GA, td measured as 24.2 min). Samples LB 15 min and LB 30 min are 15 min and 30 min, respectively, after cells grown in M9GA are mixed 1:1 with 2x LB. Samples M9GA and LB/M9GA are in balanced growth conditions. See Methods for details. A) Growth curve. After 120 min, cells were diluted 1:1 into either the same growth medium (for M9GA and LB/M9GA conditions) or M9GA into 2x LB (M9GA - > LB). A600 measurements are corrected for this 2x dilution. Arrows represent the times at which PETRI-seq samples were taken, colored according to the condition of these samples. Lines represent linear model fits to data from 30−180 min (M9GA & LB/M9GA conditions) or from 135-280 min (M9GA - > LB condition). Data are from three biological replicates. B) Shifts in the transcriptome upon nutritional shift-up. Left: PCA of all cells, colored by sample. The top is a plot of density along the PC−1 axis, the bottom is a scatter plot of PCs 1 and 2. Prior to PCA, scVI denoising was performed jointly on all samples. Annotated arrows at the top summarize enriched KEGG pathways based on gene set enrichment analysis (GSEA). GSEA was performed on the PC-1 loadings, where the direction of the arrows indicates the sign of the GSEA normalized enrichment score. PC-1 represents the transition from M9GA to LB/M9GA since PC-1 loadings strongly correlated with the fold change in gene expression between these samples (Spearman’s r = 0.88). Right: Direct sample-sample Spearman correlations between bulk-averaged per-sample gene expression (measured as total UMI-normalized counts). Note that M9GA has a relatively low correlation to other samples, suggesting that the shift in the transcriptome has already occurred 15 min after the shift-up. C) Chromosome-wide expression correlations measured between 50 kb regions as in Fig. 1c. Note that the noisy pattern for the M9GA sample is caused by the low UMI/cell acquired for this sample (42 UMI/cell). D) Replication pattern statistics. Left: Estimated overlap in rounds of replication for each sample. Right: Inferred doubling time, td. By assuming that the C-period is unchanged from the M9GA sample (here, 52 min), we can convert replication patterns into inferred estimates of doubling time based on replication alone. See Methods. E-H) Inhibition of S. aureus growth by vancomycin. S. aureus cells in balanced growth in TSB were exposed to vancomycin and measured over time. E) Growth curve after addition of vancomycin. Arrows represent the times at which PETRI-seq samples were taken, colored according to the condition of these samples. Lines represent sample averages of three biological replicates at each timepoint. F) Shifts in the S. aureus transcriptome after vancomycin treatment. See (B) for details. G) Chromosome-wide expression correlations measured between 50 kb regions as in Fig. 1c. H) Replication pattern statistics. See (D) for details.
Extended Data Fig. 4 Correcting for and measuring divergence from predicted replication-associated patterns.
A) Two-dimensional histogram for E. coli showing the relationship between observed expression from scVI and replication-predicted expression. Expression is averaged in 100 bins by cell angle θc. The red line indicates x = y i.e. the case where expression in both matrices is identical. Overall, there is a rough 1:1 correspondence between observed and predicted expression, indicating a good model fit (Pearson’s r = 0.59). B) Gene-gene correlations in LB-grown E. coli across θc-binned expression data (100 bins) for the full scVI observed model (left), the replication-only model (middle), and the corrected model that is the difference of the two expression matrices (right). C) The mean-variance relationship in E. coli of log-transformed normalized counts. The black line indicates the locally weighted scatterplot smoothing (LOWESS)-fitted values and red points are genes classed as highly variable. See Methods for further details. D) Comparison of the divergence score σcorrected between LB-grown E. coli in Datasets D1 & D2 of genes classed as highly variable in both datasets (287 genes). Red indicates replication-divergent genes (σcorrected > 0.6). E) Comparison of σcorrected (standard deviation of divergence from the replication model) between LB-grown E. coli in Dataset D1 and Dataset D2 of all genes present in both datasets. Red indicates σcorrected > 0.6 in both datasets, meaning that they are considered replication-divergent. F) Two-dimensional histogram as in (A) but for S. aureus (Pearson’s r = 0.67). G) Gene-gene correlation plots as for (B) but for S. aureus. H & I) Comparison of σcorrected (standard deviation of divergence from the replication model) between S. aureus in Dataset D5 and Dataset D6 for highly variable genes in both datasets (H) (Pearson’s r = 0.66) and all genes (I) (Pearson’s r = 0.48). Red indicates σcorrected > 0.5 in both datasets, meaning that they are considered replication-divergent. J) Gene expression plots of select genes based on unsmoothed counts. Raw counts were normalized by total UMI/cell and then converted to percentage mRNA fraction by multiplying by 100. Expression was then averaged in 20 bins by cell angle θc and summed across genes within each operon.
Extended Data Fig. 5 smFISH analysis of cell cycle gene expression correlates with phase-shifted scRNA-seq data.
A) Negative control for smFISH labeling. E. coli cells labeled against bacteriophage lambda cI mRNA. smFISH signal is shown using the same contrast as in Fig. 2i & Extended Data Fig. 6A. Representative of two independent experiments. See SI Methods Section “Oligonucleotide probe design”. B) The distribution of mRNA copy-number per cell for each gene. See SI Methods Section “mRNA quantification”. Red line, fit to a negative binomial distribution plus a “zero spike”. C) The distribution of cell length in each sample. Black line, fit to the theoretical model, see SI Methods Section “Modeling the distribution of cell length”. D) Comparison of the population-averaged mRNA fraction, as measured using scRNA-seq, with mRNA concentration, as measured using smFISH. Markers indicate averages of two biological replicates by each method. Blue line, fit to a function y = axb. E) Estimation of the cell-cycle phase difference between scRNA-seq and smFISH. The phase of each dataset was estimated as described in SI Methods Section “Cell-cycle analysis of mRNA concentration”. Left: markers indicate averages of two biological replicates by each method. Blue line, fit to a linear function, indicating a constant phase difference φ. Right: the estimated phase difference across the six genes examined. The bars represent the average of four pairwise differences calculated from two biological replicates of each method. F) Top: the theoretically predicted cellular DNA contents as a function of cell age, see SI Methods Section “Inferring cell-cycle phase from the DAPI signal”. Bottom: DAPI-measured DNA content per cell as a function of cell length. Single-cell data was binned based on cell length (moving average ± SEM, 21 cells per bin), Blue line, fit to the theoretical model. Inset, the distribution of the inferred cell length where oriC replicates, estimated from all smFISH samples. G) Divergent genes exhibit a larger amplitude of cell-cycle fluctuations. The ratio between the maximum and minimum expression level of different genes, as measured using scRNA-seq and smFISH. The mRNA fraction (scRNA-seq) and concentration (smFISH) were obtained as in Fig. 2i & Extended Data Fig. 6A, 2nd and 3rd columns. The maximum and minimum levels were determined from the binned data. Markers indicate averages of two biological replicates by each method. H) Consistency between spot-based and cell-based smFISH quantification. Comparison of the mRNA levels inferred from smFISH data using spot-based and cell-based mRNA quantification. Both methods are described in SI Methods Section “mRNA quantification”. Left: mRNA concentration. Right: mRNA copy number per cell. Markers indicate mean values from each smFISH sample. Black line, fit to a linear function.
Extended Data Fig. 6 Cell cycle analysis of smFISH and scRNA-seq shows good agreement across biological replicates.
A) Comparison between scRNA-seq and smFISH with relative and absolute expression, as in Fig. 2i but for other genes shown in Fig. 2h. From left to right: 1) Microscopy images of E. coli cells labeled using smFISH against the indicated gene (cspA is visualized with alternative contrast, for negative control see Extended Data Fig. 5A); 2) scRNA-seq expression shown as fraction of total cellular mRNA (expression is averaged in 100 bins by θc); 3) mRNA concentration, measured using smFISH, as a function of cell length. Single-cell data (scatter plot) was binned by cell length (shaded curve, moving average ± SEM, 10% sample size per bin). Dashed lines indicate the twofold length range where most cells reside, used to infer the mean values at birth and division; 4) Alignment of scaled data from smFISH and scRNA-seq measurements; 5) Absolute mRNA copy number, measured using smFISH, as a function of cell length. Single-cell data was processed as in column 3 (5% sample size per bin). Black line, fit to a sum of two Hill functions, corresponding to two gene replication rounds. B) Pairwise comparison between two smFISH and two scRNA-seq datasets. Analysis as in Fig. 2i & (A), 4th column. See SI Methods Section “mRNA quantification”. C) Absolute mRNA copy number, measured using smFISH, as a function of cell length. Displayed as for Fig. 2i & (A), 5th column, but for smFISH replicate 2.
Extended Data Fig. 7 The relationship between distance from the transcriptional start site and gene expression timing and amplitude.
A) Cell cycle gene expression plots for operons showing “delayed” genes as in Fig. 3b but for LB-grown WT E. coli from Dataset D2. The red line indicates predicted expression. B) Amplitude measured as peak/trough fold change (SI Methods Section “Identifying genes with high divergence from predicted expression”) as a function of distance from the promoter for two large operons, nuoA->nuoN and mraZ->ftsZ. The positions of promoters on each operon are marked by arrows. All are supported by direct experimental evidence (evidence code ‘EV-EXP-IDA’) except for nuoMp (dotted arrow), which is inferred from expression pattern only (evidence code ‘EV-EXP-IEP’). For a description of the evidence ontology, see http://brg.ai.sri.com/evidence-ontology/downloads/evidence.html. C) Plots of maximum distance from a transcriptional start site against difference between predicted and observed angles as in Fig. 3c. Red line indicates the linear model fit and red points indicate averages of 2 kb bins. Data are shown for additional E. coli replicates and all S. aureus replicates. P-values are based on a two-sided statistical test with the null hypothesis of no correlation. See Methods for details. D) Plots as in (C) but of maximum distance from a transcriptional start site against the log2-transformed peak/trough ratio in gene expression, calculated as described in SI Methods Section “Identifying genes with high divergence from predicted expression”. E) Plots as in (C) but using manual operon annotation. Here, any tandem, contiguous stretch of genes with an intergenic distance less than 40 bp is considered an operon. Transcriptional start sites are defined as the start position of the first gene in the operon. F) Plot of divergence from predictions against the difference between predicted and observed angles, as in Fig. 3a but for S. aureus.
Extended Data Fig. 8 Cell cycle analysis of the B. subtilis transcriptome.
A) Growth curve of B. subtilis in LB. Black line indicates linear portion used to calculate growth rate. Data shown are from four biological replicates. B) UMI per barcode distribution for B. subtilis (Dataset D9 Rep 1). Axes have been clipped for visualization purposes. The dashed black line indicates the minimum threshold of 20 UMI/barcode for this sample. C) Presence of an additional cluster associated with PBSX prophage mobilization. Left: UMAP visualization of initial clustering in B. subtilis. UMAP was performed on the latent space representation generated by scVI22. Red indicates a cluster driven by PBSX mobilization that was excluded from downstream analyses. Green is the cluster used for downstream analysis. Leiden clustering was performed using scanpy. Right: Same UMAP visualization but represented as the fraction of mRNA counts mapping to the PBSX prophage (based on annotation from phaster.ca). Note that 1.5% of cells contain at least three transcripts mapping to this prophage. D) Chromosome-wide correlation of gene expression in 50 kb bins as in Fig. 1c. Analysis was performed after the PBSX cluster was removed. E) Origin distance-gene angle relationship as in Fig. 2e. Overlap between rounds of replications is visualized as in Fig. 2g. F) Relationship between angle difference and divergence from predictions for all highly variable genes. As in Fig. 3a. G) Relationship between angle difference and distance from the transcriptional start site as in Fig. 3c. Spearman correlation and significance of the relationship are shown. P-values are based on a two-sided statistical test with the null hypothesis of no correlation. See Methods for details. H) Relationship between expression amplitude (peak/trough fold change) and distance from the transcriptional start site as in Extended Data Fig. 7D. Spearman correlation and significance of the relationship are shown. P-values are based on a two-sided statistical test with the null hypothesis of no correlation. See Methods for details. I) Comparison between long operons from different species. Angle difference is converted to “Time delay” by dividing by 360° and multiplying by the doubling time in minutes, allowing standardization across species. Distance is measured as distance of the first base position of each gene in the operon from the first base position of the first gene in the operon. Inferred RNAP speeds from linear model fits to the individual operons are shown.
Extended Data Fig. 9 Transcription-replication interactions in E. coli and S. aureus.
A) Clusters as in Fig. 4b but colored according to their average, length-corrected expression. This was determined by a gene’s mean fraction of total mRNA that was length-corrected by dividing by its length and multiplying by the median gene length across genes. B) Left: Clusters as in Fig. 4b but with coloring only for those clusters that are enriched for repressed genes (FDR = 0.1, hypergeometric test) colored. Non-significant clusters are in black. Right: Histogram of the number of annotated repressor targets by transcription factor. Those with 20 or more target genes were excluded from the hypergeometric analysis. See Methods Section “Clustering of TRIPs” for further details. C) Plot as in Fig. 4b except for mean expression of S. aureus clusters. Genes situated on mobile genetic elements were removed prior to clustering analysis. D) Plots of individual genes from clusters indicated in (C). E) Mean cluster expression of TRIPs partitioned into 20 clusters by aligned gene expression (θc-rep). Clusters in red are those that only contain genes located within MGEs. Left: Reproducible genes; Right: all genes included in the scVI model (regardless of reproducibility). The cluster assignments on the right are used for (F). F) Expression of S. aureus genes within mobile elements. Genes are colored based on whether they are in MGE-exclusive clusters from (E) (Right panel) (red) or other clusters (black). Top: schematic figure of MGE gene content. The x-axis represents chromosomal coordinate and + and - strands are plotted separately by y-axis position. Predicted attachment sites attL and attR denote the predicted boundaries of the MGE and are annotated in blue. Annotation for MGEs was taken from the online tool Phaster. Bottom: Plots of MGE genes by aligned gene expression (θc-rep) as represented in Fig. 4. Gray genes represent the non-MGE background. Note that phage ΦSa2 is disabled and expression of its MGE-specific (“red”) cluster genes is low (0.002% of cells contain at least three transcripts) compared to the staphylococcal pathogenicity island (SaPI) 5 (0.4%) and phage ΦSa3 (0.07%), potentially contributing to the less clear delineation between expression profiles by gene type. MGE-specific expression patterns may arise due to MGE mobilization and these patterns may represent rare events that are not effectively captured by our cell cycle analysis, meaning that the plots here should be interpreted with caution. G) Aligned expression profiles for genes within the GbaA regulon for USA300 LAC WT. Plotted as in Fig. 4d, with mean-adjusted expression shown and all other reproducible genes shown in gray. The structure of the regulon is shown as a schematic figure above. H) Expression fold change of genes in the GbaA regulon after gbaA transposon insertion. Genes of the GbaA-L operon increase in expression >100–fold. However, due to the location of the transposon insertion towards the 5′ end of gbaA, induction of GbaA-R genes is not observed. Genes with names starting with SAUSA300_RS are truncated to give only the unique number. I) Average expression of GbaA-L genes and sGFP in reporter strains (compared to JE2 in measurements from the same experiment). Average expression measured as fraction of total mRNA was length-corrected as elsewhere by dividing by the gene length and multiplying by the median gene length across all genes. Note that sGFP expression in JE2 PGbaA-L-sGFP is approximately fourfold higher than that of GbaA-L genes, and the derepressed form in gbaA- PGbaA-L-sGFP is also fourfold lower (possibly reflecting lower copy number due to its further distance from the origin). Therefore, while repression of the GbaA-L locus is ~96-fold, repression of sGFP by GbaA is only 5.3-fold. J) Comparison of aligned expression (θc-rep) (as in Fig. 4) for GbaA regulon genes and sGFP in the two reporter constructs. Thick black and gray lines represent average expression across all reproducible genes. The schematic figure represents the relative positions of the GbaA regulon and the PGbaA-L-sGFP integration site.
Extended Data Fig. 10 Biophysical modeling of cell cycle gene expression dynamics.
A) Conversion from fraction of total mRNA per cell as a function of cell angle to the estimated absolute number of mRNA per cell as a function of time since cell division. Data are shown for the individual genes displayed in Fig. 2i & Extended Data Fig. 6A. B) R2 values for all reproducible genes (correlation between replicates >0.7) from fitting to the null model, compared between the two replicates for E. coli grown in LB. The Spearman correlation between the two replicates is shown. Grey dashed lines indicate R2 = 0.9, which we use as a threshold for good fit with the data. C) In order to assess fit to the null model, we rely on an additional assumption that gene dosage-driven expression values should increase monotonically within the cell cycle. To evaluate monotonicity, we calculate the Spearman correlation between number of mRNA per cell and time. Genes that have both a good fit to the model (R2 > 0.9) and good monotonicity with time (Spearman’s r > 0.9) are considered null model genes as indicated in green and quantified in (I). D) Comparison of degradation rates (kd) for all genes with a good fit to the null model between fitted values in this study and those inferred from rapamycin responses in Balakrishnan et al. Data are from Dataset D1 but are representative of two independent biological experiments (also Dataset D2). E) Fitted and expected replication times and their relation to gene expression. A heatmap is shown for scaled expression as a function of time and chromosome position (similar to Fig. 2b). Superimposed on this are expected replication times (tr) of the null model genes, based on predicted expression minima (black dots) (Methods Section “Defining gene Transcription-Replication Interaction Profiles (TRIPs)”) or based on fits to the null model (green dots) (SI Methods Section “The null model”). F) Comparing fits between activation and repression models. These models have more parameters than the null model and this flexibility means they tend to give very good fits based on R2. Since R2 values are highly correlated between models (left panel), this limits the usefulness of R2 to distinguish between gene profiles. By contrast, the ability of the models to infer accurately the predicted replication time, tr, differs greatly between models, with no correlation between the errors in each model for fitted tr compared to expectations (right panel). Data are from Dataset D1 but are representative of two independent biological experiments (also Dataset D2). G) Average null model fit (R2) for clusters defined in Fig. 4b. Key clusters are highlighted by their colors in Fig. 4b. Note that for activation and repression models, all clusters have average R2 > 0.9. H) Absolute values of errors in prediction of replication timing tr as a fraction of td, averaged across Fig. 4b clusters. Values for activation and repression models are compared. Error in fitted tr compared to tr based on the predicted expression minimum was calculated as in (E) for activation and repression models. Absolute values of these errors were averaged across genes within each cluster. There is a significant negative correlation between these, implying that replication timings of clusters well-predicted by the activation model are poorly predicted by the repression model and vice versa. Highlighted clusters are colored as in Fig. 4b. Data are from Dataset D1 but are representative of two independent biological experiments (also Dataset D2). I) Number of genes among reproducible genes that are well-fit by either null, activation, or repression models (or no good fit to any). See Methods for decision criteria used. For each one, the portion of genes whose midpoint is >2 kb from the transcriptional start site (TSS) is indicated. Only the repression model is enriched for genes >2 kb from their TSS (hypergeometric test, P = 5e-15, compared to P > 0.4 for all other models). J) Absolute values of error in prediction of tr (as a fraction of td) for all three models as a function of distance from the TSS for all genes (left panel) or genes within the nuo operon (right panel). K) Alternative model fits for genes fitting the activation (nemA) or repression (nuoN) models. For these genes, the null model can be rejected because of the poor R2, whereas the repression or activation models can be rejected for nemA and nuoN, respectively, because of their poor prediction of tr (see Fig. 5 for comparison). L) Expression profiles and model fits for the lacZ gene. Note that while the fit of lacZ is imperfect to any model (possibly due to its very low expression under these conditions, which reduces data quality), the activation model does the best when both R2 and prediction of tr are considered. M) Additional plots for two genes with good fits to the repression model. N) Expression dynamics of genes in the IclR regulon. IclR is encoded by iclR and is adjacent to the aceBAK operon. IclR represses its own transcription as well as that of the nearby aceBAK operon. Regulation of the gene arpA is not described but it is downstream of iclR. arpA is annotated as belonging to a single gene transcript, but the similar expression patterns suggest that it may actually share an operon with iclR. All genes fit to the activation model, however iclR and arpA exhibit early heterochrony (Ec17, Class 2a, Fig. 4c) whereas aceBAK does not (Ec9, Class 2b, Fig. 4d). The activation model fit predicts a shorter window of activation (right panel) in iclR/arpA compared to aceBAK. The bar plot represents the mean activation window duration with points representing the values of individual replicates.
Supplementary information
Supplementary Information
This file contains Supplementary Methods and Supplementary Tables 1–4
Supplementary Table 5
Gene-specific parameters from cell cycle analysis.
Supplementary Table 6
Transcription-replication interaction profiles of individual genes.
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Pountain, A.W., Jiang, P., Yao, T. et al. Transcription–replication interactions reveal bacterial genome regulation. Nature 626, 661–669 (2024). https://doi.org/10.1038/s41586-023-06974-w
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DOI: https://doi.org/10.1038/s41586-023-06974-w
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