Promotech: a general tool for bacterial promoter recognition

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compare Promotech’s performance with the performance of five other promoter prediction methods. Promotech outperforms these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech.

Identification and characterization of short leader and trailer RNAs synthesized by the Ebola virus RNA polymerase

Transcription of non-segmented negative sense (NNS) RNA viruses follows a stop-start mechanism and is thought to be initiated at the genome’s very 3’-end. The synthesis of short abortive leader transcripts (leaderRNAs) has been linked to transcription initiation for some NNS viruses. Here, we identified the synthesis of abortive leaderRNAs (as well as trailer RNAs) that are specifically initiated opposite to (anti)genome nt 2; leaderRNAs are predominantly terminated in the region of nt ~ 60–80. LeaderRNA synthesis requires hexamer phasing in the 3’-leader promoter. We determined a steady-state NP mRNA:leaderRNA ratio of ~10 to 30-fold at 48 h after Ebola virus (EBOV) infection, and this ratio was higher (70 to 190-fold) for minigenome-transfected cells. LeaderRNA initiation at nt 2 and the range of termination sites were not affected by structure and length variation between promoter elements 1 and 2, nor the presence or absence of VP30. Synthesis of leaderRNA is suppressed in the presence of VP30 and termination of leaderRNA is not mediated by cryptic gene end (GE) signals in the 3’-leader promoter. We further found different genomic 3’-end nucleotide requirements for transcription versus replication, suggesting that promoter recognition is different in the replication and transcription mode of the EBOV polymerase. We further provide evidence arguing against a potential role of EBOV leaderRNAs as effector molecules in innate immunity. Taken together, our findings are consistent with a model according to which leaderRNAs are abortive replicative RNAs whose synthesis is not linked to transcription initiation. Rather, replication and transcription complexes are proposed to independently initiate RNA synthesis at separate sites in the 3’-leader promoter, i.e., at the second nucleotide of the genome 3’-end and at the more internally positioned transcription start site preceding the first gene, respectively, as reported for Vesicular stomatitis virus.

Supervised Promoter Recognition: A benchmark framework

Deep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models have continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are being evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each model’s true performance difficult to assess.We present a framework called Supervised Promoter Recognition Framework (‘SUPR REF’) capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models in a systematic manner. SUPR REF includes the creation of biologically relevant benchmark datasets to be used in the evaluation process of deep learning promoter recognition models. We showcase this framework by comparing the models’ performance on alternative datasets, and properly evaluate previously published models on new benchmark datasets. Our results show that the reliability of deep learning ab initio promoter recognition models on eukaryotic genomic sequences is still not at a sufficient level, as precision is severely lacking. Furthermore, given the observational nature of these data, cross-validation results from small datasets need to be interpreted with caution.AvailabilitySource code and documentation of the framework is available online at https://github.com/ivanpmartell/suprref

Independent Promoter Recognition by TcpP Precedes Cooperative Promoter Activation by TcpP and ToxR

Vibrio cholerae continues to be a public health threat throughout much of the world. Its ability to cause disease is governed by an unusual complex of regulatory proteins in the membrane of the cell, including ToxR and TcpP.

Template strand deoxyuridine promoter recognition by a viral RNA polymerase

Bacillus subtilis bacteriophage AR9 employs two strategies for efficient host takeover control and host antiviral defense evasion - it encodes two unique DNA-dependent RNA polymerases (RNAPs) that function at different stages of virus morphogenesis in the cell, and its double stranded (ds) DNA genome contains uracils instead of thymines throughout. Unlike any known RNAP, the AR9 non-virion RNAP (nvRNAP), which transcribes late phage genes, recognizes promoters in the template strand of dsDNA and efficiently differentiates obligatory uracils from thymines in its promoters3. Here, using structural data obtained by cryo-electron microscopy and X-ray crystallography on the AR9 nvRNAP core, holoenzyme, and a promoter complex, and a variety of in vitro transcription assays, we elucidate a unique mode of uracil-specific, template strand-dependent promoter recognition. It is achieved by a tripartite interaction between the promoter specificity subunit, the core enzyme, and DNA adopting a unique conformation. We also show that interaction with the non-template strand plays a critical role in the process of AR9 nvRNAP promoter recognition in dsDNA, and that the AR9 nvRNAP core and a part of its promoter specificity subunit that interacts with the core are structurally similar to their bacterial RNAP counterparts. Our work demonstrates the extent to which viruses can evolve new functional mechanisms to control acquired multisubunit cellular enzymes and make these enzymes serve their needs.

NbPsbO1 Interacts Specifically with Bamboo Mosaic Virus Subgenomic RNA Promoter and Is Required for Efficient BaMV SgRNA Transcription

Many positive-strand (+) RNA viruses produce subgenomic RNAs (sgRNAs) in the infection cycle through the combined activities of viral replicase and host proteins. However, knowledge about host proteins involved in direct sgRNA promoter recognition is limited. Here, in the partially purified replicase complexes from Bamboo mosaic virus (BaMV)-infected tissue, we have identified Nicotiana benthamiana Photosystem II oxygen-evolving complex protein, NbPsbO1, which specifically interacted with the promoter of sgRNA but not that of genomic RNA (gRNA). Silencing of NbPsbO1 expression suppressed BaMV accumulation in N. benthamiana protoplasts without affecting viral gRNA replication. Overexpression of wild-type NbPsbO1 stimulated BaMV sgRNA accumulation. Fluorescent microscopy examination revealed that the fluorescence associated with NbPsbO1 was redistributed from chloroplast granal thylakoids to stroma in BaMV-infected cells. Overexpression of a mis-localized mutant of NbPsbO1, dTPPsbO1-T7, inhibited BaMV RNA accumulation in N. benthamiana , whereas overexpression of an NbPsbO1 derivative, sPsbO1-T7, designed to be targeted to chloroplast stroma, upregulated sgRNA level. Furthermore, depletion of NbPsbO1 in BaMV RdRp preparation significantly inhibited sgRNA synthesis in vitro , but exerted no effect on (+) or (-) gRNA synthesis, which indicates that NbPsbO1 is required for efficient sgRNA synthesis. These results reveal a novel role for NbPsbO1 in the selective enhancement of BaMV sgRNA transcription, most likely via direct interaction with the sgRNA promoter. IMPORTANCE Production of subgenomic RNAs (sgRNAs) for efficiently translating of downstream viral proteins is one of the major strategies adapted for viruses that contain multicistronic RNA genome. Both viral genomic RNA (gRNA) replication and sgRNA transcription rely on the combined activities of viral replicase and host proteins, which recognize promoter regions for the initiation of RNA synthesis. However, compared to the cis -acting elements involved in the regulation of sgRNA synthesis, the host factors involved in sgRNA promoter recognition mostly remain to be elucidated. Here, we found a chloroplast protein, NbPsbO1, which specifically interacts with Bamboo mosaic virus (BaMV) sgRNA promoter. We showed that NbPsbO1 is relocated to the BaMV replication site in BaMV infected cells, and demonstrated that NbPsbO1 is required for efficient BaMV sgRNA transcription, but exerts no effect on gRNA replication. This study provides a new insight into the regulating mechanism of viral gRNA and sgRNA synthesis.

Switching Promotor Recognition of Phage RNA Polymerase in Silico Following Path along Lab Directed Evolution

In this work we computationally investigated how a viral RNA polymerase (RNAP) from bacteriophage T7 evolves into RNAP variants under lab-directed evolution to switch recognition from T7 promoter to T3 promoter in transcription initiation. We first constructed a closed initiation complex for the wild-type T7 RNAP, and then for six mutant RNAPs discovered from phage assisted continuous evolution experiments. All-atom molecular dynamics (MD) simulations up to one microsecond each were conducted on these RNAPs in complex with T7/T3 promoter. Our simulations show notably that protein-DNA electrostatic interactions or stabilities at the RNAP-DNA promoter interface well dictate the promoter recognition preference of the RNAP and variants. Key residues and structural elements that contribute significantly to switching the promoter recognition were identified. Followed by a first point mutation N748D on the specificity loop to slightly disengage the RNAP from the promoter to hinder the original recognition, we found an auxiliary helix (206-225) that takes over switching the promoter recognition upon further mutations (E222K and E207K), by forming additional charge interactions with the promoter DNA and reorientating differently on the T7 and T3 promoter. Further mutations on the AT-rich loop and the specificity loop can fully switch the RNAP-promoter recognition to the T3 promoter. Overall, our studies reveal energetics and structural dynamics details along an exemplary directed evolutionary path of the phage RNAP variants for a rewired promoter recognition function. The findings demonstrate underlying physical mechanisms and are expected to assist knowledge/data learning or rational redesign of the protein enzyme structure-function.

DNA structural and physical properties reveal peculiarities in promoter sequences of the bacterium Escherichia coli K-12

The gene transcription of bacteria starts with a promoter sequence being recognized by a transcription factor found in the RNAP enzyme, this process is assisted through the conservation of nucleotides as well as other factors governing these intergenic regions. Faced with this, the coding of genetic information into physical aspects of the DNA such as enthalpy, stability, and base-pair stacking could suggest promoter activity as well as protrude differentiation of promoter and non-promoter data. In this work, a total of 3131 promoter sequences associated to six different sigma factors in the bacterium E. coli were converted into numeric attributes, a strong set of control sequences referring to a shuffled version of the original sequences as well as coding regions is provided. Then, the parameterized genetic information was normalized, exhaustively analyzed through statistical tests. The results suggest that strong signals in the promoter sequences match the binding site of transcription factor proteins, indicating that promoter activity is well represented by its conversion into physical attributes. Moreover, the features tested in this report conveyed significant variances between promoter and control data, enabling these features to be employed in bacterial promoter classification. The results produced here may aid in bacterial promoter recognition by providing a robust set of biological inferences.

Promotech: A general tool for bacterial promoter recognition

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compared Promotech's performance with the performance of five other promoter prediction methods. Promotech outperformed these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech.