Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Lim, Hyun Gyu; Rychel, Kevin; Sastry, Anand V.; Bentley, Gayle J.; Mueller, Jenna L.; Schindel, Heidi S.; Larsen, Peter E.; Laible, Philip D.; Guss, Adam M.; Niu, Wei; Johnson, Christopher W.; Beckham, Gregg T.; Feist, Adam M.; Palsson, Bernhard Ø.

doi:10.1016/j.ymben.2022.04.004

Cited by 38 publications

(28 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taken together, PRECISE-1K and iModulons extracted from it highlight the central role that top-down, data-driven methods must take in transcriptional regulatory network discovery across organisms. Indeed, iModulons have already successfully generated top-down regulatory networks for other organisms (Chauhan et al, 2021; Lim et al, 2022; Poudel et al, 2020; Rajput et al, 2022; Rychel et al, 2020; Sastry et al, 2019; Yoo et al, 2022). The success of PRECISE-1K serves to further cement both the importance of pursuing such efforts and the reliability of the results.…”

Section: Discussionmentioning

confidence: 99%

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Sastry

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Uncovering the structure of the transcriptional regulatory network (TRN) that modulates gene expression in prokaryotes remains an important challenge. Transcriptomics data is plentiful, necessitating the development of scalable methods for converting this data into useful knowledge about the TRN. Previously, we published the PRECISE dataset for Escherichia coli K-12 MG1655, containing 278 RNA-seq datasets created using a standardized protocol. Here, we present PRECISE 2.0, which is nearly three times the size of the original PRECISE dataset and also created using a standardized protocol. We analyze PRECISE 2.0 at multiple scales, demonstrating multiple analytical strategies for extracting knowledge from this dataset. Specifically, we: (1) highlight patterns in gene expression across the dataset; (2) utilize independent component analysis to extract 218 independently modulated groups of genes (iModulons) that describe the TRN at the systems level; (3) demonstrate the utility of iModulons over traditional differential expression analysis; and (4) uncover 6 new potential regulons. Thus, PRECISE 2.0 is a large-scale, high-quality transcriptomics dataset which may be analyzed at multiple scales to yield important biological insights.

show abstract

Section: Discussionmentioning

confidence: 99%

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Sastry

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…iModulon activity levels can be used to infer the activity of their underlying regulators, and thus enable quantitative interrogation of the cell's sensory systems. This approach has provided valuable insights into the TRNs of Escherichia coli 10,11 and several other organisms [12][13][14][15][16][17][18][19] . iModulon analysis is supported by a developed codebase and online knowledgebase (iModulonDB.org) 20,21 , which are publicly available.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Rychel

Tan

Patel

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance.

show abstract

“…coli lpp promoter, which was commonly used in the genetic code expansion with the Mb PylRS/PylT pair. − Meanwhile, its strength in KT2440 is unknown. To identify alternate tRNA promoters, we analyzed an RNA-seq dataset for the expression level of endogenous tRNAs when KT2440 cells were cultured to the exponential growth phase. The two tRNAs with the highest expression level were a Tyr tRNA (PP_t01) and a Leu tRNA (PP_t68).…”

Section: Resultsmentioning

confidence: 99%

Genetic Code Expansion in Pseudomonas putida KT2440

Gao

Chen

et al. 2022

ACS Synth. Biol.

Self Cite

View full text Add to dashboard Cite

Pseudomonas putida KT2440 is an emerging microbial chassis for biobased chemical production from renewable feedstocks and environmental bioremediation. However, tools for studying, engineering, and modulating protein complexes and biosynthetic enzymes in this organism are largely underdeveloped. Genetic code expansion for the incorporation of unnatural amino acids (unAAs) into proteins can advance such efforts and, furthermore, enable additional controls of biological processes of the strain. In this work, we established the orthogonality of two widely used archaeal tRNA synthetase and tRNA pairs in KT2440. Following the optimization of decoding systems, four unAAs were incorporated into proteins in response to a UAG stop codon at 34.6–78% efficiency. In addition, we demonstrated the utility of genetic code expansion through the incorporation of a photocross-linking amino acid, p-benzoyl-l-phenylalanine (pBpa), into glutathione S-transferase (GstA) and a chemosensory response regulator (CheY) for protein–protein interaction studies in KT2440. This work reported the successful genetic code expansion in KT2440 for the first time. Given the diverse structure and functions of unAAs that have been added to protein syntheses using the archaeal systems, our research lays down a solid foundation for future work to study and enhance the biological functions of KT2440.

show abstract

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Cited by 38 publications

References 61 publications

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

A multi-scale transcriptional regulatory network knowledge base forEscherichia coli

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Genetic Code Expansion in Pseudomonas putida KT2440

Contact Info

Product

Resources

About