<i>Corynebacterium glutamicum</i>regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

et al. 2021

Preprint

Self Cite

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a metacurated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. We found a low level of direct experimental validation for the regulatory interactions reported in the literature and curated in this work. Only ~6% (533/9687) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called “strong” evidence. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions’ reliability, finding the inference from DNA sequence data to be the most trustworthy. Finally, we show two possible applications of the inferred and the curated network. The inferred one allowed us to identify putative novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated one allows us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Curation, inference, and assessment of a globally reconstructed gene regulatory network forStreptomyces coelicolor

et al. 2021

Preprint

Self Cite

“…We applied the regulogs analysis 59 with one-to-one orthology relationships to alleviate network incompleteness and make them comparable. As prior networks, we used Curated_FL(S)-DBSCR(S) (534 interactions) for S. coelicolor and 196627_v2020_s21_eStrong from Abasy Atlas 19 (2941 interactions) for C. glutamicum 16 . After the regulogs analysis, we ended up with 2966 interactions in C. glutamicum and 692 interactions in S. coelicolor.…”

Section: Comparative Analysis With Corynebacterium Glutamicum Shows Coherent System-level Components Conservationmentioning

confidence: 99%

“…Even though network curation could be approached with text mining 11 , it would still require manual intervention for those articles where interactions are not clearly defined. Alternatively, GRN inference has been applied in diverse bacteria to provide a deeper understanding of their regulatory mechanisms, propose selective experimental validation of putative interactions, analyze bacterial GRN evolution, and build biological models for biotechnological processes [12][13][14][15][16] . A GRN inference for S. coelicolor was performed by Castro-Melchor, et.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of a Global Gene Regulatory Network for Streptomyces Coelicolor: Curation, Inference, and Assessment

et al. 2021

Preprint

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). We performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics to tackle network incompleteness, using a community integration approach to reduce false positives. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions’ reliability, finding the inference from DNA sequence data to be the most trustworthy. The inferences allowed us to identify putative novel transcription factors for key Streptomyces antibiotic regulatory proteins (SARPs). Finally, we studied the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum and identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as supplementary material.

Curation, inference, and assessment of a globally reconstructed gene regulatory network for Streptomyces coelicolor

et al. 2022

Sci Rep

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. Even though S. coelicolor has an outstanding variety of regulators among bacteria, little effort to globally study its transcription has been made. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~ 41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. Only ~ 6% (534/9707) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called “strong” evidence. While for the remaining interactions there is no confirmation of direct binding. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif identification in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the reliability of the predictions, finding the inference from DNA sequence data to be the most trustworthy approach. Finally, we show two applications of the inferred and the curated networks. The inference allowed us to propose novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated network allowed us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.