Analysis and comparison of the bacterial σ54 regulon: Evidence of phylogenetic trends in gene regulation

Maricela Carrera-Reyna; Edna Cruz-Flores; Enrique Merino

doi:10.1371/journal.pone.0327805

Abstract

While the role of σ⁵⁴ in regulating genes involved in nitrogen metabolism, flagellar biosynthesis, and stress responses in Pseudomonadota is well established, its involvement in regulating alternative metabolic pathways and cellular processes in other phyla has been less explored. By employing position-specific scoring matrices (PSSMs) to identify promoter sequences regulated by the σ⁵⁴ factor, we successfully predicted genes under its control across 33 taxonomic classes spanning 16 distinct phyla. For the first time, we conducted a comprehensive statistical assessment of σ⁵⁴ regulation across major bacterial phylogenetic groups. Our findings provide an extensive perspective on the regulatory role of σ⁵⁴ beyond nitrogen metabolism and reveal the different trends in which metabolic and biological processes can be regulated by this sigma factor depending on the phylogenetic group. The main findings of our study are available on the aRpoNDB webpage (https://biocomputo.ibt.unam.mx/arpondb/).

Citation: Carrera-Reyna M, Cruz-Flores E, Merino E (2025) Analysis and comparison of the bacterial σ⁵⁴ regulon: Evidence of phylogenetic trends in gene regulation. PLoS One 20(8): e0327805. https://doi.org/10.1371/journal.pone.0327805

Editor: Shengwei Sun, KTH Royal Institute of Technology, SWEDEN

Received: February 11, 2025; Accepted: June 21, 2025; Published: August 1, 2025

Copyright: © 2025 Carrera-Reyna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is available on our webpage at https://biocomputo.ibt.unam.mx/arpondb/ and at the figshare repository at https://figshare.com/projects/Analysis_and_comparison_of_the_bacterial_54_regulon/251936

Funding: Funder Name: DGAPA – Universidad Nacional Autónoma de México (UNAM) Grant Number: IN222423 Grant Recipient: Dr. Enrique Merino (E.M.) Additionally, M.C. is a Ph.D. student in the Programa de Doctorado en Ciencias Bioquímicas at UNAM and is supported by a CONACYT fellowship (731966).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sigma factors are specialized proteins that determine the specificity of promoter recognition and influence the efficiency of RNA polymerase to initiate gene transcription. All bacteria possess a primary sigma factor that directs the transcription of most of their genes during exponential growth, including those involved in essential housekeeping functions. In E. coli, this role is fulfilled by σ⁷⁰. In addition to this main sigma factor, many bacterial species encode one or more alternative sigma factors, which regulate the transcription of genes involved in specific physiological responses. These include adaptation to stress conditions, transitions between growth phases, morphological differentiation, or specialized cellular processes, such as sporulation and germination [1]. The number and diversity of alternative sigma factors vary widely among various bacterial species and typically reflect their ecological niches and lifestyles. For instance, obligate intracellular organisms residing in stable environments often possess only the essential housekeeping sigma factor. Conversely, free-living bacteria, such as Streptomyces coelicolor, which experience dynamic and heterogeneous environmental conditions, may encode dozens of alternative sigma factors to facilitate adaptive responses [2].

From an evolutionary and mechanistic perspective, sigma factors can be categorized into two primary groups. The first group is the most widely distributed and abundant, corresponds to the σ⁷⁰ family. Members of this sigma factor enhance the specific interaction between RNA polymerase (RNAP) and promoter DNA, which typically include two conserved elements located approximately −35 and −10 nucleotides upstream from the transcription start site. The second group corresponds to the σ⁵⁴ family, which is evolutionarily distinct and currently has no known homologs. σ⁵⁴ differs from σ⁷⁰ in many instances [3]. It binds to RNAP to form a complex with promoter DNA that is transcriptionally inactive. Transition from this close complex to an open complex, required for transcription initiation, depends on the ATPase activity of a family of enhancer-binding proteins (EBPs) [4,5]. These EBPs bind to upstream enhancer-like sequence in the promoter region in response to specific environmental signals or stress conditions. In addition, the σ⁵⁴-recognized promoter differs from that of σ⁷⁰ by containing two conserved elements located at positions −24 and −12 relative to the transcription start site instead of the −35 and −10 elements typical of σ⁷⁰-dependent promoters [6].

The transcriptional regulatory factor σ⁵⁴, also known as RpoN, SigL, NtrA, or σN, is broadly distributed across the bacterial kingdom [7,8]. Although σ⁵⁴ is typically classified as an alternative sigma factor, its importance varies across different organisms [9]. In certain microorganisms, σ⁵⁴ is essential for survival under challenging conditions and plays a central role as a global regulator within complex transcriptional networks that govern diverse cellular processes [9–12]. Notably, deletion of σ⁵⁴ is lethal only in Myxococcus xanthus [13]. While σ⁵⁴ regulates the expression of essential genes, these genes are often subject to additional regulatory inputs, including other sigma factors and regulatory mechanisms that collectively contribute to their overall gene expression [14].

σ⁵⁴ has historically been associated with the regulation of nitrogen metabolism, having been initially identified as a positive regulatory factor required for the expression of glutamine synthetase in enterobacteria [15]. Shortly thereafter, its role was expanded to include the regulation of genes involved in amino acid and sugar transport, the catabolism of compounds such as xylene and toluene, the utilization of alternative carbon sources, extracellular alginate production, flagellar synthesis, bacterial growth, and stress responses. In both pathogenic and phytopathogenic bacteria, σ⁵⁴ also contributes to virulence by regulating antibiotic resistance, type IV pili synthesis (T4Ps), motility (twitching and swimming) [7,8]. It also regulates the expression of type III secretion systems (T3SS) [16–19] and type VI secretion systems (T6SS), as well as biofilm formation, quorum sensing, and the transcription of genes involved in toxin production, adhesion, and colonization, all of which enhance pathogenicity [12,16,17,20–29]. More recently, in organisms such as Clostridioides difficile (formerly Clostridium difficile), a member of the class Clostridia within the phylum Bacillota, σ⁵⁴ has been shown to play a crucial role in regulating functions related to metabolism, stress responses, and sporulation, a characteristic feature of these bacteria. Members of this class are known for their ability to form into highly resistant dormant forms called endospores, which enables survival under adverse environmental conditions. σ⁵⁴ governs the expression of genes involved in the initiation of sporulation, septum formation during sporulation, and spore coat development [26].

Transcription driven by the ^σ54 promoter involves three main components: a) the RNAP-σ⁵⁴ holoenzyme, b) the σ⁵⁴ promoter, and c) EBPs (Fig 1).

Download:

Fig 1. Schematic representation of the architecture of the functional domains of Escherichia coli σ⁵⁴.

The functional domains of σ⁵⁴ and their associated roles in transcription initiation are indicated in the figure. Region I is involved in core RNA polymerase, enhancer, and DNA binding and localizes to the activator interacting domain (AID, Pfam number PF00309). Region II, located between Regions I and III, is not essential and is a variable region. Nevertheless, the complete or partial removal of this region can have a significant impact on the activity of σ⁵⁴, i.e., on DNA binding and holoenzyme formation. Region III is subdivided into several conserved subregions that interact with core RNA polymerase (CBD, Pfam 04963), and DNA at the −12 and the −24 consensus motifs, which are highlighted in the DNA-binding domain (DBD, Pfam PF04552).

https://doi.org/10.1371/journal.pone.0327805.g001

a) The RNAP-σ⁵⁴ holoenzyme. When concerning the first player, the RNAP-σ⁵⁴ holoenzyme, it is crucial to underscore the fact that σ⁵⁴ encompasses two conserved functional regions, region I and region III, both of which are essential for its proper function. At its N-terminal end lies the activator interaction domain (AID), while the core binding domain (CBD) and the DNA-binding domain (DBD) reside in region III at the C-terminal end. The sequence conservation of these domains is captured in hidden Markov models (HMMs) in the Pfam database [30], denoted as the PF00309, PF04963, and PF04552 profile-HMMs. The AID domain plays an essential role in regulating σ⁵⁴ activity and functions as a molecular switch. In its inactive state, AID inhibits RNA polymerase activity by impeding the spontaneous isomerization of the open promoter complex. However, upon activation triggered by specific molecules (EBPs), AID switches its role to facilitate transcription initiation [31]. Some Neisseria species possess truncated RpoN proteins lacking one or more of the domains, including the DNA-binding domain (DBD). This loss likely explains the observed 80% reduction in σ⁵⁴-dependent transcripts [32].
b) The σ⁵⁴ promoter. The promoter element is the second player and consists of two highly conserved sequences that are separated by 5 to 7 nucleotides: 5`-TGGCAC-3` and 5`-TGC-3`. Typically, these conserved motifs are centered at −12 and −24 base pairs relative to the transcription start of the target gene [8].
c) EBPs. The third component in σ⁵⁴-dependent transcription involves EBPs. These proteins have two primary domains: an N-terminal regulatory domain and a C-terminal DNA-binding domain. The N-terminal regulatory domain is responsible for sensing and responding to specific signals or stimuli. EBPs often contain signal perception domains or sensory modules such as CheY-like response regulatory domains, PAS domains (Per-Arnt-Sim), GAF domains (cGMP-specific phosphodiesterases, adenylyl cyclases, and FhlA), PRD modules, and V4R domains. These domains enable EBPs to interact with signaling molecules or undergo conformational changes in response to environmental signals. The C-terminal DNA-binding domain of EBPs recognizes and binds to specific DNA sequences in enhancers. This domain frequently contains a helix-turn-helix (HTH) motif. Although EBPs have different domain structures, maintaining the sequences and structures in their DNA-binding domain is important so that their exact binding sites can be found and contribute to σ⁵⁴-mediated transcriptional activation [33,34].

Each EBP may have unique characteristics, but the “GAFTGA” motif is a defining feature that is invariant in all σ⁵⁴-specific EBPs since this motif is essential for interactions with the σ⁵⁴-RNA polymerase complex and is located within the N-terminal regulatory domain [35,36]. Bacteria most frequently possess one σ⁵⁴ gene, occasionally have two, while multiple EBPs constitute exceptional cases [33]. A single σ⁵⁴ can interact with various EBPs to modulate gene expression in response to specific environmental conditions across different bacterial phyla [17].

Analyses of σ⁵⁴-dependent regulons have been conducted across a range of bacterial groups. These regulons have been effectively characterized using various techniques, including microarrays [37], RNA-seq, and in silico approaches. Typically, these studies have been presented independently for each taxonomic group. Some extensively studied groups include Clostridiales [25] and Pseudomonadota, with a focus on classes such as α-Proteobacteria [10,38,39] and γ-Proteobacteria [17,21,28,40–42]. One notable study in this context is the recent comprehensive report by Yu Chao, and their colleagues, representing one of the earliest investigations into the global significance of σ⁵⁴ in a wide range of phytopathogenic bacteria [16]. This in-depth review of σ⁵⁴-dependent regulatory functions, coupled with insights into newly sequenced organisms from less-studied bacterial groups, provides a foundation for further elucidating the intricate regulatory network governed by σ⁵⁴. Nevertheless, a comprehensive global analysis of the regulatory role of σ⁵⁴, including the primary bacterial taxa and newly sequenced organisms from less-studied bacterial groups, has not yet been conducted. Our study, encompassing the examination of σ⁵⁴-dependent regulatory functions in 1,414 organisms from 33 phylogenetic classes spanning 16 distinct phyla, is a significant step toward providing in-depth review of σ⁵⁴-dependent regulatory functions and a more comprehensive understanding of the regulatory role of this sigma factor.

Methods

Genome sequence datasets

From a set of 6,889 genomic sequences of bacteria from the KEGG GENOME database [43] we selected nonredundant organisms at the genus phylogenetic level, focusing on those with a greater number of coding genes. Additionally, we included the sequences of the model organisms Escherichia coli K-12 MG1655, Bacillus subtilis 168, and Pseudomonas aeruginosa UCBPP-PA14 as reference points. This selection ensured that a nonredundant and representative dataset was used for our analysis. We clustered these organisms into their respective phylogenetic classes. Initially, 86 phylogenetic classes were considered. Ultimately, we refined our selection to phylogenetic classes containing a minimum of four organisms with an rpoN gene. Our final dataset for the study included 1,414 organisms, representing 33 classes from 16 prokaryotic phyla, which are listed in S1 Table.

Taxonomic classification

The taxonomic assignment of organisms was carried out according to the KEGG GENOME database [43] (https://www.genome.jp/kegg/genome/).

COG assignments

Proteins from the 1,414 nonredundant organisms in this study were analyzed using a four-step computational procedure to identify their corresponding orthologous groups (Clusters of Orthologous Genes, COGs) [44]. First, we retrieved all proteins for a given COG in the COG database available from the NCBI (https://www.ncbi.nlm.nih.gov/research/cog). Second, we aligned the proteins in each set of COG groups using the MUSCLE 4.1 program [45]. Third, we constructed a hidden Markov model for each COG using the hmmerbuild program via the HMMER package v3.3.2 [46]. Fourth, we utilized the HMM matrices created for each COG and the hmmscan program, also via the HMMER package v3.3.2., to systematically scan all protein sequences within our set of organisms; the protein domains that exhibited the closest alignment with a COG HMM model was identified. The parameters used for this search included [-E 0.001] to set the E-value threshold in the model output to 0.001; [--domE 0.001] to specify an E-value threshold of 0.001 in the domain output; [--noali] to request that output alignments not be displayed; and [--domtblout] to save a parsable table that includes per-domain information.

Operon predictions

The transcription units that constitute operons in the set of our studied organisms were predicted based on our previously described algorithm [47], which utilizes an artificial neural network with two input variables: a) intergenic distances between gene pairs transcribed in the same direction and b) the functional relationship between the protein products of adjacent genes as defined in the STRING database [48]. Operon prediction is a key step in our statistical enrichment analysis of genes regulated by σ⁵⁴ since it considers all the genes within an operon rather than solely focusing on the first gene located immediately downstream of the σ⁵⁴ promoter of the transcription unit. Fig 2 provides an overview of the protocol employed to identify potential σ⁵⁴-dependent promoters. The process is structured into three distinct phases.

Download:

Fig 2. Methodology overview.

Our methodology was divided into three phases: 1) data preparation and construction of the seed matrix for σ⁵⁴-dependent promoters, 2) iterative process of pattern finding and pattern discovery, and 3) statistical analysis and functional interpretation. This comprehensive approach ensured the construction of a robust seed matrix that could be used to identify potential σ⁵⁴-dependent promoters and further investigate their regulatory mechanisms and biological significance.

https://doi.org/10.1371/journal.pone.0327805.g002

Data preparation and construction of the seed matrix for σ⁵⁴-dependent promoters. As shown in Fig 2, our protocol for identifying potential σ⁵⁴-dependent promoter sequences across phylogenetic groups begins with the construction of a position weight matrix (PWM) to initiate an iterative promoter search. To minimize potential bias toward E. coli or other model organisms, we curated a diverse dataset of experimentally validated RpoN-dependent promoter sequences from major regulatory databases, including RegPrecise 3.0 [49], DBTBS [50], and EcoCyc [51], as well as from the comprehensive compilation by Barrios et al. (1999) [52]. When promoter sequences appeared in multiple sources, only a single representative was retained to avoid redundancy. The resulting seed dataset comprises 720 experimentally validated σ⁵⁴-dependent promoter sequences derived from 56 bacterial species (S2 Table) and served as the basis for constructing an initial positional weight matrix (PWM) that represents the primary consensus sequence motif, also referred to as the “seed matrix”, for σ⁵⁴-dependent promoters (S3 Table).
Promoter identification. The second phase involved identifying σ⁵⁴-dependent promoters and a cyclic process composed of two steps:
1. I. Pattern-finding. This step was conducted by employing Motif Alignment and Search Tool (MAST) [53] and a MEME Position-Specific Scoring Matrix (PSSM) [54], matrix for σ⁵⁴ promoter sequences was obtained in the pattern discovery step.
2. II. Pattern discovery. This was performed with a PSSM matrix constructed using Multiple Expectation-Maximization for Motif Elicitation (MEME) software [54], with the σ⁵⁴ promoter sequences identified in the MAST search step used as input.

This cyclic process of pattern-finding/pattern-discovery was independently executed five times to improve the accuracy of the results and was performed independently for each of the 33 phylogenetic classes scrutinized in our study. The first cycle of analysis was conducted with the initial seed matrix.

For MEME analysis, the parameters employed were as follows: [-dna] to specify the usage of the DNA alphabet; [-w 30] to set a 30-nucleotide window size to encompass the two conserved boxes of the bacterial σ⁵⁴ consensus promoters; [-nmotifs 2] to extract the top two motifs; [-markov_order 1] to indicate the use of a first-order Markov model; [-bfile <bfile>] to specify the background Markov model file name; and [-text] to request the output in text format [54].

Regarding MAST analysis, the parameters utilized were [-norc] to exclude the reverse complement DNA strand scoring and [-bfile <bfile>] to indicate the background Markov model file name. In addition, with the aim of determining the best E-value cutoff values for MAST analysis, the performance of the process was evaluated using ascending E-value cutoffs of 0.625, 1.25, 2.5, and 5. The results from cycles with statistically significant coefficients of sensitivity and specificity were selected. To assess the accuracy of our prediction program, we compared its results against curated σ⁵⁴ regulon annotations from two phylogenetically distinct model organisms: Escherichia coli (Pseudomonadota) and Bacillus subtilis (Bacillota). For this comparison, we used reference data obtained from the EcoCyc [51] and DBTBS [50] databases, respectively. By comparing the real values (genes regulated by σ⁵⁴, annotated with strong experimental evidence) with the predicted values, we calculated Matthew’s Correlation Coefficient (MCC), as a statistical indicator of our protocol´s performance.

3. Statistical analysis and functional interpretation. In the third phase, our focus shifted to performing a comprehensive statistical analysis of the overrepresentation of σ⁵⁴-regulated genes across the different phylogenetic classes in our study, followed by in-depth interpretation of their biological functions. To achieve this goal, we identified orthologous gene groups in all the genomes based on their orthologous relationships using COG assignments. These assignments were established by employing more than 4,500 Markov models, each corresponding to a COG group and the hmmscan program. Next, we employed a PERL program (https://figshare.com/projects/Analysis_and_comparison_of_the_bacterial_54_regulon/251936) that uses the phyper and dhyper functions from the R package version 4.2.2 to evaluate the hypergeometric distribution, which quantifies the enrichment of each COG protein group within one of the 33 phylogenetic classes under scrutiny. This distribution reveals how effectively σ⁵⁴ transcribes genes associated with a particular COG across diverse phylogenetic classes compared to the expectations set by the entire gene pool spanning all organisms in the study. This comparative analysis enabled us to identify universally conserved and lineage-specific σ⁵⁴-dependent genes, offering insights into the regulon’s evolutionary conservation.

Identification of σ⁵⁴-specific EBPs

To ensure accurate identification of enhancer-binding proteins that interact with σ⁵⁴ (σ⁵⁴-specific EBPs), we developed a computational pipeline that integrates both domain-based detection (via PF00158) and conserved motif architecture analysis. The pipeline consists of the following steps:

Initial EBP Identification: We searched for the Pfam domain PF00158 (Sigma 54_activat) within the Pfam database [30] and utilized the hmmsearch program in the HMMER package v3.3.2 [46], with an E-value threshold of 1e-51 to ensure the inclusion of EBP candidates with high confidence and significant similarity to the PF00158 domain. This domain is conserved in EBP proteins involved in ATP-dependent interactions with σ⁵⁴.
Sequence Redundancy Reduction. To reduce sequence redundancy, the PF00158-containing proteins were clustered using CD-HIT [55] with the following parameters: -c 0.6 (60% identity threshold); -n 3 (word length); -g 1 (accurate clustering mode).
Motif Discovery. The resulting non-redundant sequences were analyzed with MEME to identify conserved motifs, using the following settings: -w 12 (motif width of 12); -nmotifs 10 (maximum of 10 motifs); -protein (protein sequence input)
Motif Scanning Across Proteomes. We then used MAST to scan all proteomes for the presence of the 10 MEME-defined motifs, using an E-value cutoff of -ev 0.1.
Filtering for GAFTGA Motif Presence. Given that the “GAFTGA” motif is experimentally validated as essential for σ⁵⁴-specific EBP function [35,36], we retained only sequences containing this motif. Notably, these sequences typically also contained all 10 MEME-identified motifs and displayed the most significant E-values in the MAST analysis. Furthermore, the motifs appeared in a consistent and conserved order.
Final High-Confidence σ⁵⁴-specific EBP Selection. We parsed the MAST results to select sequences that contained all 10 motifs arranged in the following conserved order: (2) – (10) – (6) – (3) – (4) – (9) – (7) – (5) – (8) – (1).

Sequences that passed all of the above filters were classified as bona fide σ⁵⁴-specific EBPs.

To evaluate the specificity of our protocol for detecting σ⁵⁴-specific EBP proteins, we used a negative control set consisting of 380 archaeal and 36 fungal (Eukaryota) proteomes, totaling and 1,169,896 protein sequences. No σ⁵⁴-specific EBPs were identified in this group, indicating that our method achieved 100% specificity. The list of organisms used as a negative control is provided in S4 Table.

Identification of σ⁵⁴ protein domains

The σ⁵⁴ holoenzyme, which is an enhancer-dependent RNA polymerase complex, is characterized by three functional domains:

a) The AID plays a crucial role in facilitating the interaction between σ⁵⁴ and the activator while acting as a transcriptional inhibitor by preventing initiation before activator binding, thereby blocking the entry of the template DNA strand.
b) The DBD directly interacts with the central RNA polymerase.
c) The CBD directly interacts with the core RNA polymerase to form an enhancer-dependent σ⁵⁴ holoenzyme.

To identify these domains in σ⁵⁴ protein sequences, we employed the hmmsearch program in the HMMER package v3.3.2 2 [46] with the Pfam motif matrices PF00309, PF04552, and PF04963, using specific E-value threshold values of 1.6e-14, 2.8e-50, and 2.3e-70, respectively. These cutoff values were determined based on the results of the bona fide σ⁵⁴ sequence analysis.

Identification of σ⁵⁴ protein domains

The 16S rRNA sequences from five randomly selected organisms within each of the 33 phylogenetic classes included in our study were aligned using MAFFT v7.5+ [56], employing the L-INS-i strategy, which is optimized for sequences with globally conserved regions. This multiple sequence alignment was then used to generate a phylogenetic tree with IQ-TREE v2.2+ [57], utilizing its automatic model selection feature to determine the best-fitting evolutionary model. The final tree was visualized using the iTOL web tool [58].

Web page construction

To visualize the information generated by the methodology proposed in this study, we developed ARpoN-DB, an interactive web-based platform specifically designed to centralize the query and exploration of data related to σ⁵⁴-dependent regulons across multiple bacterial phylogenetic phyla. The interface of ARpoN-DB was implemented using a combination of modern web development technologies including HTML5 for structuring content, including key images and downloadable tables, CSS for responsive design and an intuitive user experience and JavaScript to enable interactive features, such as navigation, dynamic content display, and seamless downloading of data in CSV format. On the backend, PHP processes user requests and manages advanced functionalities. The platform is hosted on an Apache HTTP Server (v2.4), ensuring robust performance, reliability, and efficient resource accessibility. The homepage showcases representative visualizations derived from statistical analyses performed using Perl, Python, and R, providing an overview of the data and its insights. These visualizations and downloadable resources are integral to the platform, facilitating further analysis and exploration by the scientific community.

Results and discussion

Phylogenetic distribution of σ⁵⁴

As described in the Methods section, the identification of σ⁵⁴ protein sequences in our studied organisms was based on the detection of Pfam motifs corresponding to its three functional domains: AID (PF00309), DBD (PF04552), and CBD (PF04963). From our dataset of 1,414 organisms of study, we identify 1,150 organisms, which accounted for approximately 81.3%, contained one or more copies of the rpoN gene. These organisms spanned 33 phylogenetic classes across 16 prokaryotic phyla (Fig 3 and S1 Table). In general, most organisms typically possessed a single copy of the rpoN gene in their genomes, although significant exceptions to this trend were observed (Fig 3B). Approximately 5.04% of the organisms studied had multiple copies of the σ⁵⁴ factor gene, as indicated in S5 Table. Notably, six organisms had more than two paralogous copies of σ⁵⁴. Among these, Rhodobacter sphaeroides 2.4.1 (rsp), which belongs to the α-Proteobacteria class, is noteworthy for having up to four rpoN genes, as shown in Fig 3B. A previous study conducted by Poggio et al. in 2002 provided evidence of the distinct functions of gene copies within R. sphaeroides 2.4.1. Specifically, two of these genes, rpoN1 and rpoN2, were found to play unique roles in gene transcription. RpoN1 was shown to play a crucial role in transcribing a subset of genes under nitrogen-limiting conditions, while RpoN2 was found to be essential for the expression of flagellar genes in this bacterium [59]. These findings offer valuable insights into the functional diversity and specialization of multiple rpoN paralogous gene copies within specific bacterial species.

Download:

Fig 3. Analysis of rpoN genes in bacteria grouped by phylogenetic class.

A) Percentage of organisms within each phylogenetic class that carry at least one rpoN gene encoding the σ⁵⁴ factor. The number of organisms in the phylogenetic class is denoted by the value “n”, shown in parentheses. Phylogenetic classes without an rpoN gene are not included. B) Distribution of the number of σ⁵⁴ genes per organism, with most bacteria having only one copy of the rpoN gene. C) Conservation of domains within σ⁵⁴ proteins, identifying the Pfam domains AID, DBD, and CBD across various bacterial phyla, and illustrating the diverse architectural arrangements of these domains in RpoN proteins.

https://doi.org/10.1371/journal.pone.0327805.g003

Conversely, Actinomycetota stands out for its near-complete absence of the σ⁵⁴ factor. Our initial search for rpoN genes within this phylum identified only 4 out of 173 representative organisms (<2.31%): Arthrobacter citreus NEB 577 (acit), Alloactinosynnema sp. L-07 (alo), Modestobacter marinus BC501 (mmar), and Citricoccus sp. SGAir0253−1 (cig) (Fig 3C). However, among these, only Arthrobacter citreus NEB 577 encodes an RpoN protein with sequence similarities to other bona fide RpoN proteins and significant Pfam scores across all three conserved domains, PF00309 (AID), PF04552 (CBD), and PF04963 (DBD) (Fig 3C and S6 and S7 Tables). Further analysis, including the construction of a 16S rRNA species phylogenetic tree (S1 Fig) and an extensive review of NCBI database annotations, revealed a significant misclassification: Arthrobacter citreus NEB 577, initially assigned to Actinomycetota, aligns more closely with the Bacillota phylum. Taken together, these findings indicate that Actinomycetota effectively lacks a functional RpoN sigma factor. These results align with previous observation regarding the almost inexistent presence of rpoN gene in Actinomycetota [9].

A plausible explanation for the absence of the rpoN gene in Actinomycetota may be found in their high genomic GC content, which averages around 65%. Additionally, the consensus sequence of σ⁵⁴-dependent promoters is notably GC-rich, with a GC content of 62.5%. We hypothesize that this overlap in high GC content between the genome and σ⁵⁴-dependent promoter sequences may impair σ⁵⁴’s ability to distinguish promoters from random genomic sequences reliably. This limitation could impose higher regulatory costs for transcription, contributing to the reduced prevalence of rpoN in this phylum. Furthermore, phylogenetic analyses [60], place Actinobacteria within the Terrabacteria branch of the Tree of Life [58], which also includes Cyanobacteria. Interestingly, these phyla rarely possess the rpoN gene, with exceptions found in Gloeocapsa sp. PCC 7428 and Leptolyngbya sp. O-77 (specifically, the Glo7428_4143 and O77CONTIG1_04029 genes, respectively).

The extensive distribution of σ⁵⁴ factors across various bacterial phyla (as illustrated in Fig 3), combined with the absence of sigma factors in archaea and eukaryotes [2], points to the parsimonious origin of σ⁵⁴ at the early stage of bacterial evolution, followed by the partial or total loss of this sigma factor in specific bacterial phyla.

Phylogenetic distribution of σ⁵⁴-specific EBPs

To identify σ⁵⁴ -specific EBPs within the proteome sequences of our dataset of 1,628 organisms, we used a computational pipeline that integrates both, domain-based detection by searching for the Pfam Sigma54_activat motif (PF00158), and the motif architecture analysis of ten conserved sequence motives present in bona-fide σ⁵⁴ protein sequences (see Methods section). The distribution of σ⁵⁴ -specific EBPs across different phylogenetic classes and phyla is shown in Fig 4 and detailed in S8 Table. Notably, organisms from the Desulfobacteria class within the Thermodesulfobacteriota phylum possess the most significant number of EBPs genes, including Desulfosarcina ovata subsp. sediminis 28bB2T (dov), Desulfoluna sp. ASN36 (dek), and Desulforapulum autotrophicum HRM2 (dat), with 69, 53 and 52 EBP genes, respectively. In addition, members of the Clostridia class within the Bacillota phylum that displayed a remarkably high abundance of EBPs in their genomes are Desulfosporosinus orientis DSM 765 (dor), and Candidatus Formimonas warabiya DCMF (fwa) with 67 and 59 paralogous EBPs copies, respectively In contrast, organisms within Actinomycetes, Erysipelotrichia and Chlamydia tended to lack EBPs in their genomes, as shown in Fig 4 and S9 Table. This absence of EBPs corresponds with the notable scarcity of genes encoding σ⁵⁴ proteins, as illustrated in Fig 3. This analysis highlights the variable distribution of EBPs across different phylogenetic groups, with certain lineages, such as Myxococcota and Desulfobacteria, showing higher counts of these proteins, potentially linked to their distinct genomic and ecological characteristics.

Download:

Fig 4. Distribution of σ⁵⁴-specific EBPs in bacterial classes and phyla.

The distribution of EBPs across bacterial classes and phyla is illustrated using a box and whisker plot. This plot visually represents the number of EBPs per genome within different organisms in each phylogenetic class and phylum. In the plot, a vertical line denotes the median value, while the left and right sides of the box indicate the first and third quartiles, respectively, thus providing insights into the dispersion of the data. Outliers, represented by red circles outside the whiskers, may indicate unique characteristics or variations in the number of EBP proteins among specific organisms or classes. To facilitate easy comparisons both within and between phyla, each class within the same phylum is assigned the same color. Consistent color coding across phyla ensures uniformity and clarity in visualization. The arrangement of the phyla-class names follows the sequence depicted in Fig 3 for coherence and ease of reference.

https://doi.org/10.1371/journal.pone.0327805.g004

We analyzed the co-occurrence of genes encoding both EBPs and σ⁵⁴ across all organisms in our study, revealing a consistent correlation: σ⁵⁴ was always present alongside EBPs (Figs 3 and 4). This finding underscores the indispensable role of EBPs in σ⁵⁴-dependent transcription. While the necessity of EBPs for isomerization—converting the RNAP-σ⁵⁴ closed complex into an open complex via ATP hydrolysis—has been previously documented [31,34,61,62], our study provides the first large-scale genomic evidence of this dependency, based on over 1,400 nonredundant genomes.

In silico identification of σ⁵⁴-dependent promoters

The in silico protocol used to identify σ⁵⁴-dependent promoters in our study involved a five-step iterative process of pattern-finding and motif discovery. This process was applied separately to each of the 33 phylogenetic classes to improve precision and minimize bias toward any single model organism. While the accuracy of these predictions may vary, experimental validation is typically required to confirm the functionality of predicted σ⁵⁴ promoters. Nevertheless, our method showed strong performance when tested on two model organisms from distinct phylogenetic groups: E. coli (Pseudomonadota) and B. subtilis (Bacillota). In E. coli, the algorithm achieved a Matthew correlation coefficient (MCC) of 0.672, with a precision of 100%, specificity of 100% and a sensitivity of 45%. For B. subtilis, similar values were observed, with an MCC of 0.738, 100% precision of 54%, specificity of 100%, and a sensitivity of 99.8%. The low sensitivity of our method can be attributed to two main factors. First, we used stringent cut-off values to optimize specificity and so be sure that all calculated σ⁵⁴-dependent promoter sites are true positives. Second, the method has limited capacity to predict promoters that significantly deviate from the consensus sequence, TGGCACN5–7TTGCT, such as in the case of the E. coli’s promoter sequences of dcuD (TGGCAGN7CTCTT), xapBp3 (TGGCAAN6ACGAT), glpQp (TGGACTN6GCTGG). These promoters exhibit specific variations that challenge the predictive accuracy of the algorithm. Such deviations underline the challenge of predicting non-canonical promoters, as the algorithm relies heavily on sequence conservation and spacing constraints characteristic of σ⁵⁴-regulated promoters. The complete set of experimentally validated σ⁵⁴ promoters that our method failed to detect, along with their specific deviations from the consensus sequence, is provided in S10 Table.

It is worth noting that, unlike other computational methods for identifying σ⁵⁴-regulated promoters, which rely on a single position weight matrix (PWM) derived from experimentally characterized σ⁵⁴ promoters such as PromScan (https://molbiol-tools.ca/promscan/), our approach uses a cyclic process in which the initial PWM serves only as a starting point. Subsequent PWMs are independently generated for each phylogenetic class, based on the regulatory regions of the organisms within those classes. We believe that this design, substantially reduces the risk of overfitting. In some cases, bioinformatic methods enhance their predictive accuracy by considering the presence of genes encoding enhancer-binding proteins (EBPs) near DNA sequences that match the σ⁵⁴ promoter consensus, as demonstrated in Pirellula species strain 1 studied [27] and Salmonella enterica serovar Typhimurium LT2 [63]. However, information on the specificity of EBPs for particular σ⁵⁴-dependent promoters is not available for all the organisms included in our study. More broadly, the task of identifying σ⁵⁴-dependent promoters can be framed as a pattern recognition problem that is well-suited to artificial intelligence (AI) and machine learning (ML) techniques, particularly those capable of learning complex sequence features. A recent study evaluated various model architectures, including random forests, support vector machines, and artificial neural networks, among others, to determine the most effective approach for predicting σ⁵⁴ promoter sequences. The resulting tool, implemented as a web server, bases its predictions on a hybrid architecture combining convolutional and recurrent deep neural networks [64,65].We have compiled the complete set of σ⁵⁴-dependent promoter sequences identified in our study, along with their corresponding σ⁵⁴-regulated operons in a downable zip file available from our web server aRpoNDB (https://biocomputo.ibt.unam.mx/arpondb/) and in the figshare repository (https://figshare.com/projects/Analysis_and_comparison_of_the_bacterial_54_regulon/251936). For each gene, the table includes its identifier, the COG annotation of its protein product (classified by cellular or biological process), a functional description, and the source of the promoter sequence, whether derived from the literature, public databases, or identified in this study.

Phylogenetic diversity in σ⁵⁴-mediated regulation of biological processes

To analyze the taxonomic distribution of genes regulated by the RNAP-σ⁵⁴ complex and uncover principles underlying its evolutionary assembly, we conducted a statistical enrichment analysis of function-related genes transcribed by RNAP-σ⁵⁴ across bacterial phylogenetic classes. This involved calculating the fraction of genes transcribed by RNAP-σ⁵⁴ within each class and clustering genes based on orthologous relationships defined in the COG database [44]. Statistical significance was assessed using the hypergeometric distribution. Results are presented by COG functional categories: 1) metabolism, 2) cellular processes and signaling, 3) information storage and processing, and 4) poorly characterized. Brief explanations of these groups highlight their functional and evolutionary relevance.

Metabolism.

For historical reasons and due to their significant importance, the first category of genes regulated by σ⁵⁴ that we discuss in our work is the metabolism category. Based on the specific functions of these σ⁵⁴-regulated genes, they can be further categorized into eight groups. The relative enrichment of σ⁵⁴ regulation in these groups is presented in Fig 5 and compiled in S2 Fig. This graph displays the taxonomic distribution of RNAP-σ⁵⁴-regulated genes within metabolic subcategories based on the COG hierarchy. Each data point, represented by a circle, varies in size to reflect the negative log-transformed hypergeometric p-value, indicating the enrichment significance. Subcategories are color-coded for clarity, as detailed in the legend.

Download:

Fig 5. Functional enrichment analysis of RNAP-σ⁵⁴-regulated genes in the metabolism category.

This graph illustrates the taxonomic distribution of genes regulated by the RNAP-σ⁵⁴ complex across functional subcategories within the metabolic category based on the COG hierarchy. Specifically, this graph showcases the enrichment of predictions within distinct COG subcategories associated with metabolism. Each data point is depicted as a circle, with the size of the circle indicating the negative log-transformed hypergeometric distribution value. This size variation provides a visual representation of the significance of the enrichment for the corresponding COG, thereby facilitating a more straightforward interpretation. To aid in differentiation, colors were assigned to each COG subcategory:. Red: nitrogen metabolism. Orange: amino acid transport and metabolism. Green: carbohydrate transport and metabolism. Blue: lipid transport and metabolism. Purple: energy production and conversion. Brown: coenzyme transport and metabolism. Yellow: inorganic ion transport and metabolism.

https://doi.org/10.1371/journal.pone.0327805.g005

Nitrogen metabolism: Nitrogen metabolism has long been recognized as one of the key processes that is regulated by σ⁵⁴, initially leading to the association of this sigma factor with the name σN. Consistent with previous reports [10,37,66–68] our results indicate that the nitrogen metabolism regulon stands out as highly enriched, particularly in α, β, and γ-Proteobacteria, its enrichment diminishes to a lesser degree in certain genes related to nitrogen fixation among the Opitutia, Desulfobacteria, Chlorobiia, Spirochaetia, Aquificia, Clostridia, and Bacilli classes. This trend further declined until it was completely absent in the other examined classes, where the RNAP-σ⁵⁴ complex showed a preference for regulating other metabolic pathways and cellular functions.

The first enriched COG group regulated by the RNAP-σ⁵⁴ complex, predominantly in Pseudomonadota, includes components of the nitrogenase enzyme complex critical for nitrogen fixation, specifically the molybdenum-iron nitrogenase protein (COG2710) and the nitrogenase subunit NifH (COG1348).

Glutamine synthetase (COG0174), primarily regulated by RNAP-σ⁵⁴ in Pseudomonadota, catalyzes the synthesis of glutamine from ammonia. Glutamine is crucial for nitrogen metabolism and serves as a key nitrogen carrier and building block for amino acids, nucleotides, and other biomolecules.

A third COG family in Pseudomonadota, regulated by the RNAP-σ⁵⁴ complex, is involved in nitrogen uptake. It includes genes encoding the ammonia permease protein (COG0004) and components of the ABC-type nitrate transport system, such as the permease (COG0600), ATPase (COG1116), and periplasmic (COG0715) components.

The enrichment of transcription regulation by σ⁵⁴ in Pseudomonadota was also observed for the genes encoding NAD(P)H-nitrite reductase (COG1251). This enzyme is essential for the assimilation of inorganic nitrogen by catalyzing the reduction of nitrite, a common form of inorganic nitrogen, to ammonia [69,70].

In addition to genes encoding enzymes and membrane transporters related to nitrogen metabolism, our study primarily identified, within Pseudomonadota, σ⁵⁴-dependent genes encoding regulatory proteins, such as the nitrogen regulatory protein PII (COG0347). Additionally, our findings revealed significant σ⁵⁴ regulation, albeit to a lesser extent, of the urease complex proteins UreA, UreB, UreC, UreD, UreE, UreF, UreF, and UreJ, clustered in the COG0831, COG0832, COG0804, COG0829, COG2370, COG2371, COG0830, and COG0378. The urease enzyme is a key element of nitrogen metabolism since it is responsible for breaking down urea into ammonia, which bacteria utilize as a nitrogen source. This enrichment is predominantly observed in the α and β-Proteobacteria classes.

Conversely, in Bacillota, the COG groups mainly regulated by σ⁵⁴ include glutamate dehydrogenase enzymes involved in ammonia incorporation (COG0334) and arginase and agmatinase enzymes that process nitrogen-rich substrates like arginine and agmatine, aiding in nitrogen recycling and regulation across various metabolic pathways (COG0010).

Amino acid transport and metabolism: In the Metabolism category, the second group of COGs regulated by the RNAP-σ⁵⁴ complex was associated with Amino acid transport and metabolism and is represented by orange circles in Fig 5. Many of these COGs correspond to the ABC-type transport system, a class of membrane transporters found in bacteria that is responsible for the uptake of various nitrogen-containing nutrients, amino acids, ions, and metabolites.

We identified significant enrichment in σ⁵⁴ transcriptional regulation within specific phylogenetic classes. For example, genes encoding ABC-like transport system in several COGs, were found primarily in the α, β, and γ-Proteobacteria classes (COG0410, COG0683, COG0559, COG0765, COG0834, COG1126, COG4177, COG4160, COG4215, COG4597, COG4598, COG0444, COG0601, COG0747, COG1173, COG1176, COG1177, COG3842, COG4608), as well as genes encoding for the hydantoin racemase (COG4126), cysteine sulfinate desulfinase (COG1104), Spermidine/putrescine-binding periplasmic protein (COG0687). Conversely, enrichment of genes predominantly regulated by σ⁵⁴ in the Bacilli class are the N-methylhydantoinase A (COG0145), 4-aminobutyrate aminotransferase (COG0160), proline dehydrogenase (COG0506), proline racemase (COG3938), Lysine 2,3-aminomutase (COG1509), and ornithine/acetylornithine aminotransferase (COG4992).

Carbohydrate transport and metabolism: Carbohydrate transport in bacteria involves acquiring and internalizing carbohydrates, with the phosphotransferase system (PTS) being a key mechanism. The PTS allows cells to transport and phosphorylate specific sugars, serving as an essential pathway for obtaining both energy and carbon sources, using energy from phosphoenolpyruvate phosphorylation [19,66,71]. Our study revealed significant enrichment of genes encoding PTS components could be transcribed by the RNAP-σ⁵⁴ complex, which are categorized under carbohydrate transport and metabolism and represented by green circles in Fig 5. This enrichment was most prominent in classes within the Bacillota phylum, including Bacilli, Clostridia, and Erysipelotrichia. Interestingly, as previously documented, RNAP-σ⁵⁴ complex also regulates the transcription of PTS component genes in β- [42] and γ-Proteobacteria [63]. These genes include those encoding components for various sugars like mannitol/fructose, mannose/fructose/N-acetylgalactosamine, cellobiose, and galactitol, highlighting the diversity of sugars metabolized through the PTS and σ⁵⁴’s versatility in regulating carbohydrate metabolism.

Beyond the PTS system, other carbohydrate transport and metabolism-related genes regulated by σ⁵⁴ in the Bacillota phylum included dihydroxyacetone kinase (COG2376) in Clostridia, the H + /gluconate symporter (COG2610), altronate dehydratase (COG2721), and permeases of the major facilitator superfamily (COG0477). Conversely, in β-Proteobacteria, σ⁵⁴ regulation prominently enriched genes encoding glucose dehydrogenase (COG4993).

Lipid transport and metabolism: Lipid metabolism plays a crucial role in biofilm formation, with σ⁵⁴ acting as a key regulator. It influences motility and metabolic adaptations to nutrient conditions within biofilms, composed of extracellular DNA, lipids, proteins, and polysaccharides [25,72,73]. As depicted in Fig 5 by pink circles, genes associated with lipid transport and metabolism and regulated by σ⁵⁴ were predominantly enriched in the Clostridia and Bacilli classes within the Bacillota phylum. Among these, several enzymes associated with β-oxidation, such as acyl-CoA dehydrogenases (COG1960) and 3-hydroxyacyl-CoA dehydrogenases (COG1250), were identified as significantly enriched. These enzymes play a central role in breaking down fatty acids into acetyl-CoA for energy production.

Our study identified several lipid metabolism-associated proteins regulated by the RNAP-σ⁵⁴ complex, including acetyl-CoA acetyltransferase (COG0183), which is involved in both fatty acid synthesis and degradation, enoyl-CoA hydratase/carnitine racemase (COG1024), which supports β-oxidation, and acyl-CoA:acetate/3-ketoacid CoA transferase α and β subunits (COG1788 and COG2057), essential for ketone body utilization.

Energy production and conversion: Our analysis of σ⁵⁴-regulated metabolic genes reveals a diverse array of enzymes critical for energy production and conversion. These processes, depicted by blue circles in Fig 5, are notably enriched in Bacilli and Clostridia. In Bacilli, the pyruvate/2-oxoglutarate dehydrogenase complex (COG1071, COG0508, COG0022, COG1249) connects glycolysis to the Krebs cycle, generating acetyl-CoA, NADH, and FADH2 for ATP synthesis. Additionally, delta 1-pyrroline-5-carboxylate dehydrogenase (COG4230) facilitates proline and arginine catabolism into glutamate, maintaining nitrogen balance. Phosphotransacetylase (COG0280) supports ATP production via substrate-level phosphorylation, emphasizing the regulatory role of σ⁵⁴ in metabolic processes.

In Clostridia, enzymes such as butyrate kinase (COG3426) and acetyl-CoA hydrolase (COG0427) are crucial for energy metabolism, alongside electron transfer flavoprotein subunits (COG2025 and COG2086) that play a role in electron transport chains. Alcohol dehydrogenase, class IV (COG1454), is found in both Bacilli and Clostridia, where it contributes to the oxidation and reduction of alcohols in energy-yielding pathways.

In Proteobacteria, σ⁵⁴-dependent regulation affects various classes, including Ni,Fe-hydrogenase small (COG1740) and large (COG0374) subunits, which catalyze hydrogen oxidation or evolution, and Na + /H + -dicarboxylate symporters (COG1301), which import dicarboxylates like succinate and fumarate, important for the TCA cycle [74,75].

Enzymes like NAD-dependent aldehyde dehydrogenases (COG1012) are regulated by σ⁵⁴ in Bacilli and γ-Proteobacteria, playing a role in oxidizing aldehydes during glycolysis and carbon compound breakdown. Additionally, hydrogenase components (COG4237, COG1142) and formate hydrogenlyase subunits (COG0651) in γ-Proteobacteria emphasize σ⁵⁴’s role in energy metabolism across bacterial clades, highlighting its broad regulatory impact.

Coenzyme transport and metabolism: Three COG groups associated with the Coenzyme transport and metabolism category are regulated by σ⁵⁴ and are represented in Fig 5 by purple circles. The first group includes the trimethylamine:corrinoid methyltransferase enzyme, also known as MttB (COG5598), found in the Clostridia class. This enzyme is crucial for coenzyme biosynthesis, specifically in the production of coenzyme B12 (cobalamin), as it facilitates the transfer of a methyl group from trimethylamine (TMA) to a corrinoid protein. The second COG, aspartate 1-decarboxylase (COG0853), plays a key role in coenzyme A (CoA) biosynthesis by converting L-aspartate into β-alanine, a precursor for pantothenate, an essential component for CoA synthesis. This group of orthologous genes shows the highest enrichment in Flavobacteriia, a class within the phylum Bacteroidota.

Inorganic ion transport and metabolism: Fig 5 shows RNAP-σ⁵⁴-mediated regulation of genes in Inorganic ion transport and metabolism (yellow circles). Cyanate lyase (COG1513) plays a crucial role in detoxification by breaking down cyanate into carbon dioxide and ammonia, with enrichment in α and γ-Proteobacteria. In Aquificia, the β subunit of sulfite reductase (COG0155) reduces sulfite to sulfide, aiding sulfur incorporation into essential compounds, with significant enrichment highlighting the importance of sulfur metabolism in this group.

Secondary metabolites biosynthesis/transport and catabolism: The final category within the metabolism group encompasses the Biosynthesis, transport, and catabolism of secondary metabolites. Within this category, several COGs show significant transcriptional regulation by the RNAP-σ⁵⁴ complex. These include COG1335 in α-Proteobacteria, COG0318 and COG1028 in Bacilli, and COG2368 in Clostridia, with the latter being visually represented by the brown circles in Fig 5.

COG1335, encompassing amidases like nicotinamidase, indirectly supports nitrogen metabolism by hydrolyzing nicotinamide into nicotinic acid and releasing ammonia (NH₃) as a byproduct. This ammonia contributes to the cellular nitrogen pool, aiding nitrogen assimilation and metabolic processes. While not directly fixing nitrogen, nicotinamidase plays a key role in the NAD⁺ salvage pathway, particularly in nitrogen-limited environments, where its ability to release ammonia helps maintain nitrogen balance.

The enrichment of acyl-CoA synthetases involved in fatty acid catabolism (COG1318) and dehydrogenases with varying specificities that sustain energy-yielding redox reactions (COG1028) in the Bacilli class underscores their enhanced capabilities for energy production and fatty acid metabolism. These metabolic adaptations are particularly advantageous in nutrient-limited environments or during periods of rapid growth. Together, these COGs form an interconnected network, with acyl-CoA synthetases initiating fatty acid breakdown and dehydrogenases supporting biosynthesis and cellular maintenance, contributing to the adaptability of Bacilli in diverse ecological niches.

COG2368, regulated by σ⁵⁴ primarily in Clostridia, includes flavin-dependent oxidoreductases such as aromatic ring hydroxylase and 4-hydroxybutyryl-CoA dehydratase. These enzymes play roles in aromatic compound degradation, energy production, secondary metabolite synthesis, and detoxification. Notably, 3-hydroxybutyryl-CoA, a fermentation intermediate, is σ⁵⁴-regulated in Clostridial species [25].

Cellular processes and signaling.

The second functional category examined in our analysis encompasses fundamental cellular processes. The enrichment of genes regulated by σ⁵⁴ is shown in Fig 6 and S3 Fig. This category includes the following classes:

Download:

Fig 6. Functional enrichment analysis of RNAP-σ⁵⁴-regulated genes in the cellular processes and signaling category.

Each circle represents a data point, with its diameter proportional to the negative log-transformed hypergeometric distribution (P value). Larger circles indicate greater enrichment for a specific COG subcategory, highlighting a stronger association with RNAP-σ⁵⁴ regulation. The figure’s legend uses distinct colors to differentiate between functional groups for clarity. Green: cell wall/membrane/envelope biogenesis; red: cell motility. Orange: intracellular trafficking, secretion, and vesicular transport. Blue: posttranslational modification, protein turnover, and chaperones. Yellow: signal transduction mechanisms. Pink: defense mechanisms. Purple: cell cycle control, cell division, and chromosome partitioning.

https://doi.org/10.1371/journal.pone.0327805.g006

Cell wall/membrane and envelope biogenesis: The cell wall, membrane, and envelope constitute a crucial protective barrier for the cell. Their biogenesis relies on coordinated activity by various proteins to ensure structural integrity, selective transport, responses to stress, and virulence. In this study, we identified several proteins associated with these processes that show potential transcriptional regulation by the RNAP-σ⁵⁴ complex. The corresponding COGs are represented by green circles in Fig 6.

In the Myxococcia class, σ⁵⁴ regulation was enriched in COGs related to cell wall synthesis and maintenance, including lytic murein transglycosylases (COG0741), glycosyltransferases (COG0438), periplasmic polysaccharide export proteins (COG1596), UDP-3-O-acyl-N-acetylglucosamine deacetylases (COG0774), and membrane glycosyltransferases (COG2943). These COGs contribute to structural integrity, immune evasion, adhesion, and stress responses, supporting bacterial growth, adaptability, and pathogenicity. In the γ-Proteobacteria class, σ⁵⁴ regulation was enriched in COGs involved in cell wall synthesis and maintenance, including teichoic acid biosynthesis proteins (COG1922), pyridoxal phosphate-dependent enzymes (COG0399), UDP-N-acetylglucosamine 2-epimerase (COG0381), D-alanyl-D-alanine dipeptidase (COG2173), polysaccharide chain length determinant proteins (COG3765), and rod-binding proteins (COG3951). These COGs play critical roles in cell wall integrity, morphology, antibiotic resistance, and environmental defense. Notably, COG3951 enrichment was also detected in α-Proteobacteria and Desulfuromonadia, indicating conserved functions across taxa.

Proteins linked to structural integrity and nutrient transport showed class-specific enrichment. In Flavobacteriia, the orthologous group COG0810, which includes TonB, was prominent. TonB, a periplasmic protein in Gram-negative bacteria, aids in importing nutrients and iron complexes by interacting with outer membrane receptors and energy-providing inner membrane complexes [76]. Although TonB’s presence in Flavobacteriia is well-documented, its transcriptional regulation by the RNAP-σ⁵⁴ complex remains unconfirmed. In the Planctomycetia class, COG0668, linked to cell wall and membrane biogenesis, showed unexpected enrichment in RNAP-σ⁵⁴-dependent regulation. This group includes genes encoding mechanosensitive (MscS) channels, which help cells respond to mechanical stress by modulating ion flow. MscS channels are essential for osmoregulation, infection, and antibiotic resistance in pathogenic bacteria [23]. Given the ecological diversity of Planctomycetia, MscS channels may have evolved additional functions, although osmoregulation likely remains a key role due to their frequent exposure to osmotic stress. In addition, in Salmonella Typhi, Escherichia coli, and Yersinia pestis, members of the γ-Proteobacteria class, sequence homology analysis and predicted promoter sites have identified virulence-associated genes potentially regulated by σ⁵⁴ [63].

Cell motility: Cellular motility in bacteria, which includes swimming, swarming, and twitching, enables bacteria to explore their environment and respond to stimuli [12]. σ⁵⁴ regulates flagellar genes involved in flagellar synthesis and motor function, highlighting its importance in bacterial motility, as supported by previous studies [18,77] and findings in the current research. The regulatory influence of σ⁵⁴ on the expression of flagellar genes has been observed in human pathogens such as Rhodobacter sphaeroides [59], Vibrio cholerae [40,78], Helicobacter pylori [79], and Campylobacter jejuni [12], as well as in phytopathogens like Xanthomonas oryzae and Ralstonia solanacearum [17].

Fig 6 shows the distribution of genes related to cell motility (red circles) across bacterial phyla, with notable enrichment across various bacterial classes. These include a GTP-binding protein (COG1419) essential for energy transduction, motor switch proteins (COG1868 and COG1536) that regulate flagellar rotation, and motor components (COG1291 and COG1360) required for torque generation. Enrichment is most pronounced in γ-Proteobacteria, particularly in COG1419 and COG1868, followed by α- and β-Proteobacteria, Desulfobacteria, and Desulfuromonadia.

Patterns of enrichment in genes coding for flagellar basal body proteins underscore their critical role in motility across bacterial taxa. γ-Proteobacteria show the highest enrichment levels, particularly in proteins such as the basal body rod protein (COG4786), P-ring protein (COG1706), L-ring protein (COG2063), and FlaG (COG1334), which are vital for the structural integrity of the flagellar apparatus. Strong representation is also observed in α- and β-Proteobacteria, including the basal body components encoded by COG1815 and COG1558, highlighting their structural and functional conservation. Notably, the P-ring biosynthesis protein (COG1261) exhibits especially high enrichment in γ-Proteobacteria, emphasizing their specialization in basal body assembly. Enrichment patterns in Desulfobacteria, Desulfuromonadia, Planctomycetia, and Deferribacteres further underscore the remarkable conservation of these essential components across diverse bacterial lineages.

Our also results highlight the significant regulatory role of σ⁵⁴ in coordinating the synthesis of flagellar capping and hook-related proteins across bacterial classes, including α-, β-, and γ-Proteobacteria, as well as Desulfobacteria, Desulfovibrionia, and Thermodesulfobacteria. Within γ-Proteobacteria, the highest enrichment is observed in flagellin and hook-associated proteins essential for filament assembly and structural stability (COG1344) and the flagellar hook capping protein (COG1843). Additional enrichment in the flagellar hook protein FlgE (COG1749), the hook-length control protein (COG3144), and the hook-associated protein (COG1256) underscores their critical roles in hook assembly and length regulation.

Genes involved in flagellar assembly, particularly in β- and γ-Proteobacteria, show significant enrichment. These include the flagellin-specific chaperone FliS, which is essential for proper filament assembly (COG1516), as well as proteins associated with flagellar biosynthesis and the type III secretion system (COG3418 and COG2882).

Additionally, σ⁵⁴-dependent transcription was found to regulate motility to a lesser but still notable extent in other classes, including Desulfuromonadia, Desulfobacteria, Thermodesulfobacteria, Desulfovibrionia, and Desulfobulbia, all within the Thermodesulfobacteriota phylum; Opitutia within the Verrucomicrobiota phylum; Planctomycetia within the Planctomycetota phylum; and Deferribacteres within the Deferribacterota phylum.

Although the RNAP-σ⁵⁴ complex did not show statistically significant enrichment in regulating specific orthologous COG groups related to cell motility across certain bacterial phyla, a closer examination of individual organisms revealed σ⁵⁴-dependent promoters in motility-related genes. For example, in Liquorilactobacillus nagelii (Bacillota), including genes for flagellar motor switch phosphatase (COG1776), flagellar biosynthesis anti-sigma factor (COG2747), and flagellar hook-associated proteins (COG1256 and COG1344), and in Anoxybacillus flavithermus, including a gene encoding a flagellar hook (COG). More details are available in our repository at https://biocomputo.ibt.unam.mx/arpondb/.

Intracellular trafficking/secretion and vesicular transport: The RNAP-σ⁵⁴ complex plays a nuanced regulatory role in protein secretion systems. While its direct control over all genes in Type II (T2SS) and Type III (T3SS) secretion systems is not fully established, evidence indicates its involvement in specific genes across various bacterial species, including Campylobacter jejuni, Chlamydia trachomatis, Pseudomonas aeruginosa, Yersinia pseudotuberculosis, Erwinia amylovora, Erwinia carotovora, and Pseudomonas syringae.

Secretion systems like T2SS and T3SS are pivotal for bacterial pathogenesis, enabling host adhesion, barrier traversal, immune evasion, and toxin production. Our findings support RNAP-σ⁵⁴ complex’s role in regulating pathogenicity-associated genes in plant and animal/human pathogens. Enrichment of COGs linked to secretion systems was observed across several bacterial classes, including α, β, and γ -Proteobacteria, Desulfobacteria, Planctomycetia, Polyangia, and Myxococcia, as illustrated by orange circles in Fig 6.

The T2SS facilitates intracellular trafficking and vesicular transport and are composed by different protein components (COG2165, COG3031, COG4795, COG1450, COG1459, COG2804, COG3156, and COG4972) which are predominantly enriched in Myxococcia. Lesser enrichment is noted in Polyangia and Planctomycetia for COG2165.

The T3SS primarily functions to deliver effector proteins directly into host cells. Our study identified statistically significant enrichment of genes associated with this secretion system (COG1886, COG1766, COG4789, COG1317) predominantly in γ-Proteobacteria, with lower levels of enrichment observed in α-Proteobacteria, β-Proteobacteria, and Desulfobacteria.

Genes related to flagellar biosynthesis and pilus assembly, highlight σ⁵⁴’s role in motility and interactions with host cells. For instance, Tfp Pilus Assembly Protein PilA (COG4969), strongly enriched in β-Proteobacteria, with γ-Proteobacteria following. Flagellar Components, FliR (COG1684), FlhB (COG1377), and FliQ (COG1987) exhibit peak enrichment in γ-Proteobacteria, with moderate presence in Desulfobacteria, β, and γ-Proteobacteria.

Posttranslational modification/protein turnover and chaperones: Chaperones play a vital role in protein folding, stabilization, transport, and degradation, contributing to the maintenance of cellular homeostasis. In Fig 6 (blue circles), two key protein clusters are highlighted within the posttranslational modification, protein turnover, and chaperone category.

The first cluster includes heat shock proteins such as the molecular chaperonin GroEL (COG0459), the small heat shock protein IbpA (COG0071), the co-chaperonin GroES (COG0234), the nucleotide exchange factor GrpE (COG0576), and the chaperone complex proteins DnaJ (COG0484) and DnaK (COG0443). These proteins operate in a coordinated manner: DnaJ initially binds to misfolded proteins, transferring them to DnaK, which stabilizes and assists in refolding substrates through ATP-driven conformational changes. The substrates are subsequently handed off to the GroEL-GroES chaperonin system for final folding, a process facilitated by GrpE [80].

This cluster is significantly enriched in the Planctomycetota and Bacteroidota phyla, with the highest representation in the Flavobacteriia class of Bacteroidota, followed by Bacteroidia and Cytophagia. In Planctomycetota, enrichment is concentrated in Planctomycetia, with a smaller presence in Phycisphaerae. Notably, the transcription of GrpE in Bacteroides thetaiotaomicron and Flavobacterium psychrophilum, as well as GroES in B. thetaiotaomicron, is σ⁵⁴-dependent [80]. Additionally, COG0326, part of the HSP90 family, is enriched in the Bacteroidia class. Elements linked to protein turnover were also identified within these phyla, including ATP-dependent proteases COG0542, COG0466, and COG1066. These proteases are enriched in the Flavobacteriia and Bacteroidia classes of Bacteroidota and in the Planctomycetia class of Planctomycetota.

The second cluster includes proteins involved in nitrogenase and hydrogenase enzyme assembly, oxidative stress response, and degradation of misfolded proteins. Key members of this cluster are NifU (COG0694), hydrogenase maturation factors (COG0298, COG0409, COG0309), proteasome-type protease (COG3484), and peroxiredoxin (COG0450), with significant enrichment observed in α, β, and γ-Proteobacteria. NifU assembles Fe-S clusters, which are essential for electron transfer, enzyme catalysis, and regulation. Hydrogenase maturation factors, such as HypC/HupF, assist in hydrogenase enzyme maturation by incorporating metal cofactors like Fe-S clusters. Proteasome-type proteases ensure protein quality by degrading misfolded proteins, thus maintaining cellular homeostasis. Notably, a chaperone-specific cluster enriched in Spirochaetia includes proteases with potential chaperone activity for protein degradation (COG0533).

Signal transduction mechanisms: This subcategory within Cellular processes and signaling comprises COGs associated with proteins that are crucial for bacterial signal transduction pathways. These COGs are represented by yellow circles in Fig 6, particularly within two-component systems (TCSs). TCSs consist of a sensor histidine kinase (HK) and a response regulator (RR), enabling bacteria to adapt to environmental stimuli and regulate virulence factors. These systems detect environmental changes and trigger signal transduction cascades that activate or repress target genes, including those involved in stress responses [81].

Notable COGs overrepresented in this subcategory include those related to stress response (COG0589), transcriptional regulation (COG2204, COG3707, and COG3852), and signal transduction (COG4191). For instance, COG1551, associated with carbon storage regulation, intersects with quorum sensing and motility, reflecting its broader regulatory roles. Prominent mechanisms also include histidine kinases (COG0642, COG5000) and CheY-like receivers (COG0784), underscoring a dynamic regulatory network across bacterial classes such as Planctomycetia, Desulfobacteria, and Gammaproteobacteria.

In Planctomycetia, notable enrichment of COG0589 includes proteins such as the UspA domain protein, T2SS protein E, and putative universal stress proteins. UspA proteins help bacteria adapt to nutrient scarcity, oxidative stress, and toxins by integrating environmental signals into cellular responses [82]. Protein E, part of the T2SS system, plays a role in secretion and may be regulated in response to stress signals [83].

In β- and γ-Proteobacteria, COG3852 includes histidine kinases like NtrB, key for nitrogen fixation. The process is regulated by a two-component system, with NtrB detecting nitrogen availability and activating NtrC. NtrC, containing PAS-type domains, senses environmental signals (e.g., nitrogen levels) and undergoes conformational changes. This activates NtrB, which phosphorylates NtrC. The phosphorylated NtrC (NtrC-P) then binds DNA promoters, activating transcription of genes required for nitrogen fixation [33]. Furthermore, our method identified promoters not only in NtrB but also in homologous NtrC genes within COG2204, a cluster of DNA-binding transcriptional response regulators. These regulators, part of the NtrC family, are characterized by REC, AAA-type ATPase, and Fis-type DNA-binding domains, and also include the transcriptional regulator ZraR. The analysis revealed significant enrichment of 414 genes in the COG2204 cluster with potential σ⁵⁴-dependent promoters, notably in Pseudomonadota and other phyla like Planctomycetota and Thermodesulfobacteriota.

In Pseudomonadota, nitrogen fixation is regulated by the NtrB/NtrC two-component system, but its conservation and functional presence in Planctomycetota, Thermodesulfobacteriota, and Spirochaeotota are limited. In Planctomycetota, the predicted genes correspond to ZraR, a response regulator in the ZraS/ZraR two-component system, involved in stress responses to zinc and heavy metals. ZraR, activated by ZraS, modulates gene expression, often working with σ⁵⁴ to initiate transcription in response to environmental signals [84]. It can also autoregulate the operon encoding ZraS and ZraR, contributing to feedback regulation.

Another σ⁵⁴-dependent regulated COG related to nitrogen metabolism is COG3707, which groups a cluster of genes encoding proteins with AmiR and NasR Transcription Antitermination Regulator (ANTAR) domains, RNA-binding domains involved in transcription antitermination. These domains are found in response regulators of two-component systems and single-component sensory regulators. The analysis predicts 66 σ⁵⁴-dependent promoters in proteins grouped in this COG, primarily in β and γ-Proteobacteria. Notably, the COG includes NasT, which regulates the nasAB operon, responsible for nitrate and nitrite reductases, by antitermination through binding to stem‒loop structures in the operon’s leader region.

Our analysis showed significant enrichment of COG4191, which includes various response regulators, particularly in Desulfobacteria. This COG groups proteins involved in signal transduction and C4-dicarboxylate transport, primarily those signaling through histidine kinases. C4-dicarboxylate transport sensor proteins regulate the uptake of C4-dicarboxylates, such as fumarate, succinate, malate, and aspartate, which are essential for bacterial metabolism in specific environmental conditions. These bacteria use C4-dicarboxylates as carbon sources and electron acceptors, enabling them to detect and transport these compounds.

There was notable enrichment of COG1551 within the Planctomycetia class, which includes proteins associated with the carbon storage regulator (CsrA). CsrA plays a key role in bacterial metabolic regulation, swarming behavior, and quorum sensing. These RNA-binding proteins manage carbon storage and allocation, influencing bacterial motility and community behavior. They regulate gene expression post-transcriptionally by modulating mRNA stability and translation involved in carbon metabolism. The csrA gene has been shown to have σ⁵⁴-dependent promoters in Pirellula species strain 1, a member of Planctomycetota, where the σ⁵⁴ regulon has been studied [27].

The presence of RNAP-σ⁵⁴-dependent promoters in genes such as ntrC-ntrB, ntrY-ntrX, nasT, and other EBPs and their sensor kinases suggests coordinated regulation in response to environmental conditions. This regulation ensures the simultaneous production of sensors (such as NtrB) and response regulators (NtrC), enabling the proper detection and transduction of signals. The functional specialization of these EBPs and sensor kinases allows bacteria to adapt to various environmental conditions.

Defense mechanisms: COGs exhibiting significant enrichment of σ⁵⁴-mediated transcription in this category are represented by pink circles in Fig 6. These include COG0577, which corresponds to the permease component of the ABC-type antimicrobial peptide transport system. Notably, this COG was enriched in the γ-Proteobacteria, Opitutia, and Bacteroidia classes within the Pseudomonadota, Verrucomicrobiota, and Bacteroidota phyla. Permease components are crucial for transporting various molecules across the cell membrane and display considerable structural diversity and substrate specificity [85–87]. This adaptation is especially relevant for pathogens like Pseudomonas aeruginosa, which use ABC transporters to evade host immune defenses [88–90].

In Opitutia, we observed enrichment of COG1136, which includes genes encoding the ATPase component of ABC-type antimicrobial peptide transport systems. These ATPases hydrolyze ATP to power substrate transport, crucial for bacterial survival. In soil-dwelling bacteria like Opitutia, these systems also aid in nutrient acquisition and biofilm formation, showcasing their functional versatility beyond antimicrobial defense.

COG0842, enriched in Bacteroidia, represents the permease component of ABC-type multidrug transport systems, key to bacterial multidrug resistance (MDR). These permeases transport various substrates across membranes, and their upregulation in response to environmental stresses or antibiotics boosts bacterial survival, especially in gut microbiota exposed to xenobiotics and antibiotics.

The FtsX-like permease family, regulated by the RNAP-σ⁵⁴ complex, plays a crucial role in maintaining cell envelope integrity, preventing membrane damage, and preserving cell viability under antimicrobial stress by regulating the influx or efflux of antimicrobial peptides (AMPs). This study provides the first evidence linking RNAP-σ⁵⁴-mediated regulation to this family of ABC-type systems, revealing a previously unrecognized regulatory axis.

The differential enrichment of COGs across bacterial classes reflects ecological and evolutionary adaptations. In Opitutia, the presence of permease (COG0577) and ATPase (COG1136) components suggests a strong antimicrobial defense system, likely crucial for survival in competitive environments. In Bacteroidia, the prominence of multidrug transport systems (COG0842) aligns with their role in gut microbiota, where exposure to various xenobiotics and antibiotics is common.

Cell cycle control/cell division and chromosome partitioning: In this subcategory, COG1192 and COG0455, enriched in γ-Proteobacteria, Desulfobacteria, and Desulfuromonadia, cluster proteins related to ATPase activity, crucial for energy-driven processes. These enrichments are visually represented by purple circles in Fig 6. Notably, ParA/MinD proteins, involved in chromosome segregation during cell division, form a dynamic gradient in the bacterial cell, aiding in the movement of replicated chromosomes to opposite poles [91]. These proteins interact with FtsZ to coordinate chromosome segregation and septum formation [92]

Information storage and processing.

In our analysis, the third functional category concerned genes involved in fundamental processes associated with DNA replication, repair, recombination, chromatin remodeling, transcription, ribosome biogenesis, and genes responsible for maintaining and utilizing the genetic material within cells. Fig 7 illustrates the enrichment of genes within this category that could be transcriptionally regulated by σ⁵⁴.

Download:

Fig 7. Functional enrichment analysis of RNAP-σ⁵⁴-regulated genes in the Information Storage and Processing category.

This figure highlights functional enrichment analysis results for RNAP-σ⁵⁴-regulated genes within the Information Storage and Processing category of the COG database. Each circle represents a data point, with its size proportional to the negative log-transformed hypergeometric P value, signifying enrichment levels. Larger circles indicate stronger associations with RNAP-σ⁵⁴ regulation. A color-coded legend distinguishes COG subcategories, while the red dashed line marks COG1508, representing the orthologous group of genes encoding the σ⁵⁴ or RpoN sigma factor. Functional groups are represented using distinct colors in the legend to facilitate interpretation. Green: transcription. Pink: translation, ribosomal structure, and biogenesis. Purple: recombination and repair.

https://doi.org/10.1371/journal.pone.0327805.g007

Translation/ribosomal structure and biogenesis: The first subcategory within the Information Storage and Processing category, which focuses on the translation/ribosomal structure and biogenesis group and is represented by pink circles in Fig 7. Many of the orthologous clusters identified as significantly regulated by RNAP-σ⁵⁴ complex were associated with ribosomal proteins, including L17, S8, L6P/L9E, S5, L15, L18, S14, S11, L24, L36, S4, and L30/L7E (COG0203, COG0096, COG0097, COG0098, COG0200, COG0256, COG0199, COG0100, COG0198, COG0257, COG0522, and COG1841, respectively). This enrichment of RNAP-σ⁵⁴ complex transcriptional regulation of genes encoding ribosomal proteins is predominantly observed within the Flavobacteria phylum, with significant enrichment also in ε, α and β-proteobacteria. Additionally, we observed significant enrichment of RNAP-σ⁵⁴ complex regulation of genes encoding the Asp-tRNA_Asn/Glu-tRNA_Gln amidotransferase (COG0154) in the α, β, and γ-Proteobacteria classes, along with translation initiation factor 1 (IF-1) (COG0361) within the Flavobacteria and ε-Proteobacteria classes.

Transcription: Within the transcription subcategory, a distinct pattern emerges in the regulation of the RNAP-σ⁵⁴ complex. This pattern includes various specialized sigma subunits of DNA-directed RNA polymerase, which are represented by green circles in Fig 7. Notably, the COG1191 subtype predominates, particularly among α, and γ-Proteobacteria. Similarly, other subsets of specialized DNA-directed RNA polymerase sigma subunits, such as those encoding σ⁵⁴ (COG1508) or σ⁷⁰/σ³² (COG0568), exhibited a consistent association with σ⁵⁴ regulation. Furthermore, the enrichment of genes related to σ⁵⁴ regulation included genes responsible for transcriptional regulation (COG1802, COG1221, COG1737, COG1802 and COG1392), transcriptional anti-terminators (COG3933, COG2747 and COG1954), the DNA-directed RNA polymerase α subunit/40 kD subunit (COG0202), and cold shock proteins (COG1278) and phage shock protein A (COG1842).

In specific bacterial classes, such as Bacilli, Clostridia, Erysipelotrichia, Verrucomicrobiia, Plantomycetia, α, β, and γ-Proteobacteria, a notable enrichment of genes encoding σ⁵⁴ (RpoN sigma factor, COG1508) and transcribed via σ⁵⁴ promoters was observed, implying autoregulation of the transcription of this sigma factor. While this regulatory phenomenon is not significant in other phyla, it has been documented in select organisms such as Geobacter sulfurreducens, δ-Proteobacterium [28], and Dokdonia sp. MED134, a member of the Bacteroidetes phylum, where the rpoN gene is part of the σ⁵⁴ regulon [93].

Replication/recombination and repair: Fig 8 highlights the subcategory of replication, recombination, and repair COGs, represented by purple circles. Notably, we observe enrichment of DNA repair proteins within COGs such as COG2003, COG125, and COG119. These COGs play a critical role in repairing DNA damage and preserving genomic integrity. Their consistently low E-value across diverse bacterial phyla underscores their evolutionary conservation and importance. By maintaining genome stability, these proteins enable bacteria to endure environmental stresses that could otherwise compromise DNA integrity. This universal conservation across bacterial phyla suggests that these repair mechanisms are fundamental for bacterial survival and evolutionary success.

Download:

Fig 8. Functional enrichment analysis of RNAP-σ⁵⁴-regulated genes in the Poorly characterized category.

This figure shows the results of a functional enrichment analysis focusing on genes regulated by the RNA polymerase σ⁵⁴ within the poorly characterized category in the COG database. Each data point is represented by a circle, with the circle’s diameter directly proportional to the negative log-transformed hypergeometric distribution (P value) value. Larger circles correspond to a greater degree of enrichment for a particular COG subcategory, indicating a significant association with RNAP-σ⁵⁴ regulation. The legend accompanying the figure indicates the unique color that is assigned to each COG subcategory depicted on the graph to allow for clear differentiation of functional groups. Blue: general function prediction only. Yellow: function unknown.

https://doi.org/10.1371/journal.pone.0327805.g008

In addition, our analysis identifies three COGs associated with transposases and related proteins (COG3666, COG3464, and COG3385). These proteins are crucial for DNA rearrangement and the activity of mobile genetic elements, driving horizontal gene transfer and genetic diversity. Such mechanisms significantly enhance bacterial adaptability and evolution. Transposases exhibit notable enrichment in taxa like Spirochaetia and Bacteroidia, where low E-values reflect their functional importance. Interestingly, COG3385 is significantly enriched in the Planctomycetia class. However, caution is warranted when interpreting these results due to the repetitive nature of transposons, which can obscure specific regulatory associations, such as those mediated by RNAP-σ⁵⁴ complex.

Finally, we identify COGs associated with ATPases involved in DNA replication and repair, including COG126, COG125, and COG127. These ATPases are essential for processes such as DNA unwinding during replication and facilitating repair mechanisms. They are particularly prominent in Spirochaetia and Myxococcia, where low E-values underscore their significant roles. Their conservation highlights their importance in ensuring bacterial genome stability and supporting core cellular functions.

Poorly characterized.

The term poorly characterized in the COG database refers to a group of proteins that exhibit sequence conservation across diverse organisms. These proteins, despite their conservation, have not been thoroughly studied or characterized, leading to a lack of comprehensive functional annotation or detailed experimental data. However, their potential significance cannot be overstated. Although these proteins are believed to possess conserved functions based on their sequence similarity, their precise roles or functions remain largely unknown. The COG database considers two different classes of proteins within this category—function unknown and general function prediction only. The relative enrichment of σ⁵⁴ regulation in these groups is presented in Fig 8.

General function prediction only: The general function prediction only category refers to those COGs for which limited information is available regarding the functions of their protein members; these are represented in Fig 8 by blue circles. For example, COG3383 is annotated as “uncharacterized anaerobic dehydrogenase”; however, from a closer look at the individual description of the 176 members regulated by σ⁵⁴ in this COG, two different classes of enzymes emerge within this COG: 42% correspond to nitrate reductase enzymes, while 32% correspond to formate dehydrogenases. The transcriptional regulation of this COG by the RNAP-σ⁵⁴ complex is enriched in α, β, and γ-Proteobacteria and Bacilli. COG0402 is referred to in the COG database as “cytosine deaminase and related metal-dependent hydrolases”. Nevertheless, a detailed analysis of the gene descriptions revealed that these COG clusters cytosine deaminase (42%) and guanine deaminase (32%) enzymes. Members of COG3383 and COG0402 are regulated by σ⁵⁴ in the α, β, and γ-Proteobacteria classes.

Others COGs such as COG1744, COG460, COG1079, COG3845, etc. refer to predicted orthologous genes with shared ancestry and potentially similar functions. These are annotated as poorly characterized in general function prediction only, but suggest they play roles in ABC transport systems, indicating potential but unclear roles in cellular processes. They have a significant enrichment across different classes of Proteobacteria (in α, β, and γ-Proteobacteria).

The Planctomycetes phylum exhibits a fascinating diversity in its COGs, many of which are linked to poorly characterized proteins with intriguing potential. A number of these COGs, such as COG0457 (TPR repeat) and COG0517 (CBS domain), are involved in general function predictions, hinting at novel mechanisms of protein-protein interactions and cellular regulation that remain to be fully explored. Many of these COGs are associated with membrane transport and periplasmic proteins, such as predicted permeases and lipoproteins, suggesting a complex network of nutrient acquisition, particularly in nutrient-poor environments. Additionally, some COGs predict proteins related to nitrogen metabolism, including those akin to glutamine synthetase and glycosyltransferases, which may contribute to Planctomycetes’ roles in nitrogen cycling, particularly in wastewater treatment and symbiotic relationships. The high number of uncharacterized proteins reflects the unique and still-mysterious roles these organisms play in their ecological niches, reinforcing their potential for biotechnological applications, such as bioremediation and symbiotic interactions with plants or other microorganisms. These findings highlight Planctomycetes’ evolutionary novelty and adaptability, pointing to their critical roles in environmental processes and their potential for future discovery in microbiology.

Function unknown: The COGs categorized under function unknown represent genes without associated information. However, our findings suggest potential coordinated regulation of their transcription by σ⁵⁴ and are represented by yellow circles in Fig 8. Despite being labeled as an uncharacterized conserved protein, a closer examination of the functional definitions of these genes reveals that, for specific genomes, more precise knowledge exists about their functions. For instance, out of the 102 genes regulated by σ⁵⁴ in α, β, and γ-Proteobacteria within COG3665, 59% were identified as “urea carboxylase.” Similarly, despite COG3501 being referred to as an uncharacterized conserved protein in the COG database, we determined that 72% of its members are associated with the “type VI secretion protein VgrG,” a structural component that is crucial for the assembly of the T6SS apparatus and is primarily regulated by the RNAP-σ⁵⁴ complex in the γ-Proteobacteria class. Proteins clustered in COG1288, COG2170, and COG3221 are predicted to be associated with membranes, which implies involvement in processes like transport, signaling, or interaction with the environment. These predictions are often made based on hydrophobic domain analyses.

Other groups, such as proteins with SCP/PR1 domains (COG2340) in Planctomycetes and proteins with CXXC pairs (COG3880) in Opitutia, offer potential functional insights. SCP/PR1 domains are linked to pathogenesis and may also possess enzymatic functions in certain contexts [94]. These findings indicate that uncharacterized proteins are not simply evolutionary remnants but could represent essential components of bacterial physiology, awaiting further functional characterization.

Online access to our data and analyses through the aRpoN-DB Web Page

The aRpoN-DB Web Page is a web-based platform designed to store, visualize, and explore data derived from a comprehensive comparative analysis of σ⁵⁴-dependent regulons across multiple bacterial phyla. This freely accessible platform, available at https://biocomputo.ibt.unam.mx/arpondb/, offers an intuitive interface and interactive tools that provide researchers with valuable insights into the cellular functions regulated by σ⁵⁴.

The main page presents global analyses through interactive graphs and tables, including a summary of bacterial classes studied. Each class links to a list of organisms organized by genus, which in turn redirects to detailed organism-specific pages. These pages display putative σ⁵⁴-dependent regulons, including information such as predicted promoter sequences, cognate DNA motifs, statistical enrichment values (E-values), COG-based functional classifications, and downstream operon genes with functional annotations. All data is downloadable in CSV format for custom analyses.

The platform provides visualizations covering five key aspects of σ⁵⁴-regulated pathways: (1) the phylogenetic distribution of rpoN genes; enrichment analyses of the RNAP-σ⁵⁴ regulon in (2) metabolism-related genes, (3) Cellular processes and signaling related genes, (4) Information storage and processing related genes, and (5) genes with poorly characterized functions. Additionally, a curated set of 33 logos representing conserved DNA motifs in promoter sequences, categorized by taxonomic class, is available in an interactive grid format for download, supporting further research into gene regulation.

Distribution of COG functional categories across phylogenetic classes

To summarize our main findings on the diverse regulatory roles of σ⁵⁴ across a wide range of bacterial lineages, we present the distribution of COG functional categories associated with σ⁵⁴-regulated genes across different phylogenetic classes (Fig 9). This overview not only validates earlier reports on the role of σ⁵⁴ in regulating specific metabolic and cellular processes within certain phylogenetic groups but also highlights previously unrecognized taxa where σ⁵⁴ may play important regulatory roles.

Download:

Fig 9. COG functional categories among phylogenetic classes.

σ⁵⁴ regulated genes were first grouped in relation to their respective COG orthologous groups, which were then subcategorized into broader functional classes. The relative abundance of each functional category within each phylogenetic class is shown as percentage values.

https://doi.org/10.1371/journal.pone.0327805.g009

Conclusion

Nearly three decades have passed since the functional characterization of the product encoded by the rpoN gene as an RNA polymerase sigma factor [95]. Over this time span, the role of σ⁵⁴ in various cellular processes has been extensively documented. These processes include nitrogen metabolism, flagellar biosynthesis and motility, urease activity formation, flagellar biosynthesis and motility, stress response pathways, virulence factors, and carbon metabolism. The role of σ⁵⁴ in regulating these and other cellular functions has been documented and reviewed, with a focus on specific cellular processes or phylogenetic groups.

In our study, we exploited the diverse and vast set of available bacterial genome sequences and developed a bioinformatic approach to elucidate the regulatory network of σ⁵⁴ across various phylogenetic groups. Our approach was initially based on the construction of a position-specific scoring matrix (PSSM) derived from the regulatory regions of 720 experimentally determined rpoN genes of 56 organisms from different phylogenetic origins. Afterwards, we implemented an iterative motif discovery and refinement process, combining MEME and MAST within each of our 33 phylogenetic classes across 16 distinct bacterial phyla. Through rigorous statistical analysis, we identified the most significantly enriched group of orthologous genes transcribed by σ⁵⁴ within each major taxonomic class to provide novel insights into the regulatory landscape governed by σ⁵⁴ across diverse bacterial lineages.

As anticipated, our bioinformatic approach led to the successful identification of all the cellular functions previously documented to be transcriptionally regulated by σ⁵⁴ within the major phylogenetic groups. Furthermore, our analysis revealed the full extent of the σ⁵⁴ regulon in additional groups in which previous information was lacking, thereby providing a statistical assessment of the prevalence of σ⁵⁴ regulation within each phylogenetic clade. In addition, our study revealed unreported cellular processes that were regulated by σ⁵⁴ in particular phylogenetic clades, such as the ABC-type antimicrobial peptide transport system used as a defense mechanism in the γ-Proteobacteria and Bacteroidia classes, the genes encoding ribosomal proteins within the Flavobacteria phylum, or the genes involved in the chromosome partitioning process, a process essential for precise cell cycle control and division, in Thermodesulfobacteria and the α, ∂, and γ-Proteobacteria classes.

As extensively documented, EBPs play an essential role in σ⁵⁴-dependent transcription by facilitating the transition from the stable closed complex of the DNA-bound RNA polymerase-σ⁵⁴ holoenzyme to an open complex to initiate the transcription process. Through a comprehensive sequence analysis involving over 1,400 nonredundant genomes, we demonstrated a clear correlation between the presence of these EBPs in bacterial genomes and that of one of the σ⁵⁴ coding genes. This correlation underscores a coevolutionary process involving these regulatory elements.

Although our inference regarding σ⁵⁴ regulons across different phyla relies solely on sequence analysis, the accuracy of our bioinformatics prediction model was significant when benchmarked against experimentally defined genes transcribed by σ⁵⁴ in E. coli that were documented in the EcoCyc database. Furthermore, our predictions align well with the established understanding of the primary cellular processes that are regulated by σ⁵⁴, which has been extensively documented. This finding elucidates the differential prevalence of σ⁵⁴ regulation among the main bacterial phyla because of the selective processes throughout evolution that have shaped the regulatory landscape of this sigma factor, which is of unique importance in the regulation of genes in bacteria.

Supporting information

S1 Fig. Phylogenetic analysis based on 16S rRNA sequences shows that Arthrobacter citreus NEB 577 aligns more closely with the Bacillota phyla than with Actinomycetota.

The 16S rRNA sequences from five representative organisms within each of the 33 phylogenetic classes included in our study were aligned and used to construct the phylogenetic tree shown in the figure. This tree reveals that Arthrobacter citreus NEB 577 clusters more closely with members of the Bacillota phylum (red) than with those of the Actinomycetota (yellow).

https://doi.org/10.1371/journal.pone.0327805.s001

(TIF)

S2 Fig. Distribution of COGs associated with σ⁵⁴-regulated genes across phylogenetic classes in the metabolism category.

The bar chart illustrates the number of COGs per phylogenetic class, organized by their functional descriptions. Metabolism-related COGs are further subdivided into functional subcategories to highlighting enriched classes. The COGs included in each category were selected based on their statistical enrichment values, as determined by hypergeometric distribution statistics, as detailed in Fig 5.

https://doi.org/10.1371/journal.pone.0327805.s002

(TIF)

S3 Fig. Distribution of COGs associated with σ⁵⁴-regulated genes across phylogenetic classes in the Cellular processes and signaling category.

The bar chart illustrates the number of COGs per phylogenetic class, organized by their functional descriptions. Cellular processes and signaling-related COGs are further subdivided into functional subcategories to highlighting enriched classes. The COGs included in each category were selected based on their statistical enrichment values, as determined by hypergeometric distribution statistics, as detailed in Fig 6.

https://doi.org/10.1371/journal.pone.0327805.s003

(TIF)

S1 Table. Study organisms from 33 classes from 16 prokaryotic phyla.

This table displays organisms grouped by their class and phylum, with one representative organism per genus. Organisms were organized according to their respective phylogenetic classes across 16 prokaryotic phyla. The “Class” column categorizes each organism based on its phylogenetic classification. Only classes containing at least four organisms encoding the rpoN gene were included, refining the dataset to a more focused and relevant group. The final dataset comprises 1,414 organisms, offering a comprehensive and representative sample of prokaryotic diversity. Additionally, model organisms such as Escherichia coli (eco) and Bacillus subtilis (bsu) were included as reference points for comparative analysis. Each organism is identified by a three-letter code derived from the KEGG GENOME Database.

https://doi.org/10.1371/journal.pone.0327805.s004

(XLS)

S2 Table. Initial set of seed RpoN-dependent promoter sequences used to generate the seed MEME-derived position-specific scoring matrix (PSSM).

720 experimentally validated σ⁵⁴-dependent promoter sequences derived from 56 bacterial species were compiled from major regulatory databases, including RegPrecise 3.0 [49], DBTBS [50], and EcoCyc [51], as well as from the comprehensive compilation by Barrios et al. (1999) [52].

https://doi.org/10.1371/journal.pone.0327805.s005

(XLSX)

S3 Table. Seed position-specific scoring matrix (PSSM).

The initial set of seed 720 experimentally validated σ⁵⁴-dependent promoter sequences were used to generate the seed MEME-derived position-specific scoring matrix (PSSM).

https://doi.org/10.1371/journal.pone.0327805.s006

(TXT)

S4 Table. List of organisms used as a negative control in our σ⁵⁴-specific EBP search.

380 genome sequences form Archaea and 36 Eukaryota, were used as negative control to identify σ⁵⁴-specific EBP proteins.

https://doi.org/10.1371/journal.pone.0327805.s007

(XLSX)

S5 Table. Distribution of Organisms with Multiple rpoN Genes by Phylogenetic Class.

This table presents the organisms in the studied dataset that harbor more than one rpoN gene. They have been grouped by their respective phylogenetic classes. Each organism is identified by a three-letter code derived from the KEGG genome database. Additionally, the table provides the number of organisms in each class that contain the rpoN gene alongside the total number of organisms per class.

https://doi.org/10.1371/journal.pone.0327805.s008

(XLS)

S6 Table. RpoN sequence similarities.

The amino acid sequences of RpoN proteins from model organisms and Actinomycetota were compared, and the percentage similarities are summarized in separate tables for the AID, CBD, and DBD domains.

https://doi.org/10.1371/journal.pone.0327805.s009

(XLSX)

S7 Table. Pfam scores for the AID, CBD, and DBD domains of RpoN proteins from Actinomycetota species.

The amino acid sequences of RpoN proteins from both model organisms and Actinomycetota species were analyzed using the hmmsearch program to identify their corresponding conserved domains. The Pfam scores for these domains are presented in the table.

https://doi.org/10.1371/journal.pone.0327805.s010

(XLSX)

S8 Table. Distribution of EBPs across prokaryotic classes.

He proteomes of the organisms included in our study were analyzed to identify enhancer-binding proteins (EBPs) using a pipeline that combines domain-based detection using the hmmsearch program and the Pfam Sigma54_activat motif (PF00158), with domain architecture analysis using MAST, as described in the Methods section.

https://doi.org/10.1371/journal.pone.0327805.s011

(XLSX)

S9 Table. Architectural arrangements of Pfam domains in RpoN proteins across phylogenetic classes.

This table details the diverse architectural arrangements of Pfam domains within RpoN proteins, identifying the PF00309 (AID), PF04552 (DBD), and PF04963 (CBD) for domain analysis. The architectural arrangements are categorized and labeled as AID-DBD-CBD, AID-CBD, AID-DBD, and CBD-DBD, highlighting the distribution of each RpoN protein arrangement across different classes.

https://doi.org/10.1371/journal.pone.0327805.s012

(XLSX)

S10 Table. Sequence variations of false negative σ⁵⁴-promoters of our study.

Bona fide promoter sequences that were not detected by our computational protocol were analyzed to identify deviations from the consensus promoter sequence.

https://doi.org/10.1371/journal.pone.0327805.s013

(XLSX)

Acknowledgments

We sincerely thank Shirley Ainsworth for her bibliographical services, Arturo Ocádiz and Juan Manuel Hurtado for their computer support, and María Luisa Tabche for her laboratory management.

References

1. Burgess RR. Sigma factors. In: Encyclopedia of genetics. Elsevier; 2001. p. 1831–1834.
- View Article
- Google Scholar
2. Chávez J, Devos DP, Merino E. Complementary tendencies in the use of regulatory elements (transcription factors, sigma factors, and riboswitches) in bacteria and archaea. J Bacteriol. 2020;203(2):e00413-20. pmid:33077635
- View Article
- PubMed/NCBI
- Google Scholar
3. Burrows PC, Severinov K, Buck M, Wigneshweraraj SR. Reorganisation of an RNA polymerase-promoter DNA complex for DNA melting. EMBO J. 2004;23(21):4253–63. pmid:15470504
- View Article
- PubMed/NCBI
- Google Scholar
4. Burgess RR, Travers AA, Dunn JJ, Bautz EK. Factor stimulating transcription by RNA polymerase. Nature. 1969;221(5175):43–6. pmid:4882047
- View Article
- PubMed/NCBI
- Google Scholar
5. Bose D, Pape T, Burrows PC, Rappas M, Wigneshweraraj SR, Buck M, et al. Organization of an activator-bound RNA polymerase holoenzyme. Mol Cell. 2008;32(3):337–46. pmid:18995832
- View Article
- PubMed/NCBI
- Google Scholar
6. Helmann JD. Where to begin? Sigma factors and the selectivity of transcription initiation in bacteria. Mol Microbiol. 2019;112(2):335–47. pmid:31119812
- View Article
- PubMed/NCBI
- Google Scholar
7. Feklístov A, Sharon BD, Darst SA, Gross CA. Bacterial sigma factors: a historical, structural, and genomic perspective. Annu Rev Microbiol. 2014;68:357–76. pmid:25002089
- View Article
- PubMed/NCBI
- Google Scholar
8. Wigneshweraraj S, Bose D, Burrows PC, Joly N, Schumacher J, Rappas M, et al. Modus operandi of the bacterial RNA polymerase containing the sigma54 promoter-specificity factor. Mol Microbiol. 2008;68(3):538–46. pmid:18331472
- View Article
- PubMed/NCBI
- Google Scholar
9. Francke C, Groot Kormelink T, Hagemeijer Y, Overmars L, Sluijter V, Moezelaar R, et al. Comparative analyses imply that the enigmatic Sigma factor 54 is a central controller of the bacterial exterior. BMC Genomics. 2011;12:385. pmid:21806785
- View Article
- PubMed/NCBI
- Google Scholar
10. Tsoy OV, Ravcheev DA, Čuklina J, Gelfand MS. Nitrogen fixation and molecular oxygen: comparative genomic reconstruction of transcription regulation in Alphaproteobacteria. Front Microbiol. 2016;7:1343. pmid:27617010
- View Article
- PubMed/NCBI
- Google Scholar
11. Reitzer L, Schneider BL. Metabolic context and possible physiological themes of sigma(54)-dependent genes in Escherichia coli. Microbiol Mol Biol Rev. 2001;65(3):422–44, table of contents. pmid:11528004
- View Article
- PubMed/NCBI
- Google Scholar
12. Tsang J, Hoover TR. Themes and variations: regulation of RpoN-dependent flagellar genes across diverse bacterial species. Scientifica (Cairo). 2014;2014:681754. pmid:24672734
- View Article
- PubMed/NCBI
- Google Scholar
13. Keseler IM, Kaiser D. 54, a vital protein for Myxococcus xanthus (transcription factor RNA polymerase promoter activator proteins). Proc Natl Acad Sci. 1997;94:1979–84.
- View Article
- Google Scholar
14. Bervoets I, Charlier D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol Rev. 2019;43(3):304–39. pmid:30721976
- View Article
- PubMed/NCBI
- Google Scholar
15. Garcia E, Bancroft S, Rhee SG, Kustu S. The product of a newly identified gene, gInF, is required for synthesis of glutamine synthetase in Salmonella. Proc Natl Acad Sci U S A. 1977;74(4):1662–6. pmid:16262
- View Article
- PubMed/NCBI
- Google Scholar
16. Ancona V, Li W, Zhao Y. Alternative sigma factor RpoN and its modulation protein YhbH are indispensable for Erwinia amylovora virulence. Mol Plant Pathol. 2014;15(1):58–66. pmid:23937726
- View Article
- PubMed/NCBI
- Google Scholar
17. Yu C, Yang F, Xue D, Wang X, Chen H. The regulatory functions of σ54 factor in phytopathogenic bacteria. Int J Mol Sci. 2021;22(23):12692. pmid:34884502
- View Article
- PubMed/NCBI
- Google Scholar
18. Phale PS, Malhotra H, Shah BA. Degradation strategies and associated regulatory mechanisms/features for aromatic compound metabolism in bacteria. Adv Appl Microbiol. 2020;112:1–65. pmid:32762865
- View Article
- PubMed/NCBI
- Google Scholar
19. Gruber TM, Gross CA. Multiple sigma subunits and the partitioning of bacterial transcription space. Annu Rev Microbiol. 2003;57:441–66. pmid:14527287
- View Article
- PubMed/NCBI
- Google Scholar
20. Sher AA, Jerome JP, Bell JA, Yu J, Kim HY, Barrick JE, et al. Experimental evolution of Campylobacter jejuni leads to loss of motility, rpoN (σ54) deletion and genome reduction. Front Microbiol. 2020;11:579989. pmid:33240235
- View Article
- PubMed/NCBI
- Google Scholar
21. Mahmud AKMF, Navais R, Nilsson K, Choudhury R, Avican K, Fallman M. RpoN is required for a functional type III secretion system in Yersinia pseudotuberculosis. bioRxiv. 2022:2022.02.11.480049.
- View Article
- Google Scholar
22. Kazakov AE, Rajeev L, Chen A, Luning EG, Dubchak I, Mukhopadhyay A, et al. σ54-dependent regulome in Desulfovibrio vulgaris Hildenborough. BMC Genomics. 2015;16:919. pmid:26555820
- View Article
- PubMed/NCBI
- Google Scholar
23. Baum A, Singer JE, Baum C. Stress and environment. In: Evans GW, editor. Environmental stress. Cambridge: Cambridge University Press; 1982. p. 15–44.
24. Wiley J. Stress and environmental regulation of gene expression and adaptation in bacteria. New Jersey; 2016.
25. Nie X, Dong W, Yang C. Genomic reconstruction of σ54 regulons in Clostridiales. BMC Genomics. 2019;20(1):565. pmid:31288763
- View Article
- PubMed/NCBI
- Google Scholar
26. Clark AE, Adamson CC, Carothers KE, Roxas BAP, Viswanathan VK, Vedantam G. The alternative sigma factor SigL influences Clostridioides difficile toxin production, sporulation, and cell surface properties. Front Microbiol. 2022;13:871152. pmid:35633701
- View Article
- PubMed/NCBI
- Google Scholar
27. Studholme DJ, Dixon R. In silico analysis of the sigma54-dependent enhancer-binding proteins in Pirellula species strain 1. FEMS Microbiol Lett. 2004;230(2):215–25. pmid:14757243
- View Article
- PubMed/NCBI
- Google Scholar
28. Leang C, Krushkal J, Ueki T, Puljic M, Sun J, Juárez K, et al. Genome-wide analysis of the RpoN regulon in Geobacter sulfurreducens. BMC Genomics. 2009;10:331. pmid:19624843
- View Article
- PubMed/NCBI
- Google Scholar
29. Waters CM, Bassler BL. Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol. 2005;21:319–46. pmid:16212498
- View Article
- PubMed/NCBI
- Google Scholar
30. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, et al. The Pfam protein families database. Nucleic Acids Res. 2010;38(Database issue):D211-22. pmid:19920124
- View Article
- PubMed/NCBI
- Google Scholar
31. Zhang N, Darbari VC, Glyde R, Zhang X, Buck M. The bacterial enhancer-dependent RNA polymerase. Biochem J. 2016;473(21):3741–53. pmid:27789741
- View Article
- PubMed/NCBI
- Google Scholar
32. Lobanovska M, Tang CM, Exley RM. Crossm pilE in Neisseria meningitidis. 2019;201:1–16.
33. Bush M, Dixon R. The role of bacterial enhancer binding proteins as specialized activators of σ54-dependent transcription. Microbiol Mol Biol Rev. 2012;76(3):497–529. pmid:22933558
- View Article
- PubMed/NCBI
- Google Scholar
34. Ghosh T, Bose D, Zhang X. Mechanisms for activating bacterial RNA polymerase. FEMS Microbiol Rev. 2010;34(5):611–27. pmid:20629756
- View Article
- PubMed/NCBI
- Google Scholar
35. Buck M, Gallegos MT, Studholme DJ, Guo Y, Gralla JD. The bacterial enhancer-dependent sigma(54) (sigma(N)) transcription factor. J Bacteriol. 2000;182(15):4129–36. pmid:10894718
- View Article
- PubMed/NCBI
- Google Scholar
36. Dago AE, Wigneshweraraj SR, Buck M, Morett E. A role for the conserved GAFTGA motif of AAA+ transcription activators in sensing promoter DNA conformation. J Biol Chem. 2007;282(2):1087–97. pmid:17090527
- View Article
- PubMed/NCBI
- Google Scholar
37. da Silva Neto JF, Koide T, Gomes SL, Marques MV. Global gene expression under nitrogen starvation in Xylella fastidiosa: contribution of the σ54 regulon. BMC Microbiol. 2010;10:231. pmid:20799976
- View Article
- PubMed/NCBI
- Google Scholar
38. Dombrecht B, Marchal K, Vanderleyden J, Michiels J. Prediction and overview of the RpoN-regulon in closely related species of the Rhizobiales. Genome Biol. 2002;3(12):RESEARCH0076. pmid:12537565
- View Article
- PubMed/NCBI
- Google Scholar
39. López-Leal G, Cevallos MA, Castillo-Ramírez S. Evolution of a sigma factor: an all-in-one of gene duplication, horizontal gene transfer, purifying selection, and promoter differentiation. Front Microbiol. 2016;7:581. pmid:27199915
- View Article
- PubMed/NCBI
- Google Scholar
40. Dong TG, Mekalanos JJ. Characterization of the RpoN regulon reveals differential regulation of T6SS and new flagellar operons in Vibrio cholerae O37 strain V52. Nucleic Acids Res. 2012;40(16):7766–75. pmid:22723378
- View Article
- PubMed/NCBI
- Google Scholar
41. Cases I, Ussery DW, de Lorenzo V. The sigma54 regulon (sigmulon) of Pseudomonas putida. Environ Microbiol. 2003;5(12):1281–93. pmid:14641574
- View Article
- PubMed/NCBI
- Google Scholar
42. Bono AC, Hartman CE, Solaimanpour S, Tong H, Porwollik S, McClelland M. Novel DNA binding and regulatory activities for σ54 (RpoN) in Salmonella. J Bacteriol. 2017;199:1–24.
- View Article
- Google Scholar
43. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545–51. pmid:33125081
- View Article
- PubMed/NCBI
- Google Scholar
44. Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–6. pmid:10592175
- View Article
- PubMed/NCBI
- Google Scholar
45. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113. pmid:15318951
- View Article
- PubMed/NCBI
- Google Scholar
46. Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7(10):e1002195. pmid:22039361
- View Article
- PubMed/NCBI
- Google Scholar
47. Taboada B, Verde C, Merino E. High accuracy operon prediction method based on STRING database scores. Nucleic Acids Res. 2010;38(12):e130. pmid:20385580
- View Article
- PubMed/NCBI
- Google Scholar
48. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37(Database issue):D412-6. pmid:18940858
- View Article
- PubMed/NCBI
- Google Scholar
49. Novichkov PS, Kazakov AE, Ravcheev DA, Leyn SA, Kovaleva GY, Sutormin RA, et al. RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics. 2013;14:745. pmid:24175918
- View Article
- PubMed/NCBI
- Google Scholar
50. Sierro N, Makita Y, de Hoon M, Nakai K. DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res. 2008;36(Database issue):D93-6. pmid:17962296
- View Article
- PubMed/NCBI
- Google Scholar
51. Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martínez C, et al. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 2013;41(Database issue):D605-12. pmid:23143106
- View Article
- PubMed/NCBI
- Google Scholar
52. Barrios H, Valderrama B, Morett E. Compilation and analysis of sigma(54)-dependent promoter sequences. Nucleic Acids Res. 1999;27(22):4305–13. pmid:10536136
- View Article
- PubMed/NCBI
- Google Scholar
53. Bailey TL, Gribskov M. Combining evidence using p-values: application to sequence homology searches. Bioinformatics. 1998;14(1):48–54. pmid:9520501
- View Article
- PubMed/NCBI
- Google Scholar
54. Bailey TL, Williams N, Misleh C, Li WW. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 2006;34(Web Server issue):W369-73. pmid:16845028
- View Article
- PubMed/NCBI
- Google Scholar
55. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680–2. pmid:20053844
- View Article
- PubMed/NCBI
- Google Scholar
56. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80. pmid:23329690
- View Article
- PubMed/NCBI
- Google Scholar
57. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–4. pmid:32011700
- View Article
- PubMed/NCBI
- Google Scholar
58. Letunic I, Bork P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024;52(W1):W78–82. pmid:38613393
- View Article
- PubMed/NCBI
- Google Scholar
59. Poggio S, Osorio A, Dreyfus G, Camarena L. The four different sigma(54) factors of Rhodobacter sphaeroides are not functionally interchangeable. Mol Microbiol. 2002;46(1):75–85. pmid:12366832
- View Article
- PubMed/NCBI
- Google Scholar
60. Battistuzzi FU, Feijao A, Hedges SB. A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evol Biol. 2004;4:44. pmid:15535883
- View Article
- PubMed/NCBI
- Google Scholar
61. Studholme DJ, Buck M. The biology of enhancer-dependent transcriptional regulation in bacteria: insights from genome sequences. FEMS Microbiol Lett. 2000;186(1):1–9.
- View Article
- Google Scholar
62. Chen B, Sysoeva TA, Chowdhury S, Nixon BT. Regulation and action of the bacterial enhancer-binding protein AAA+ domains. Biochem Soc Trans. 2008;36:89–93.
- View Article
- Google Scholar
63. Studholme DJ. Enhancer-dependent transcription in Salmonella enterica Typhimurium: new members of the σN regulon inferred from protein sequence homology and predicted promoter sites. J Mol Microbiol Biotechnol. 2002;4:367–74.
- View Article
- Google Scholar
64. Achterberg T, de Jong A. ProPr54 web server: predicting σ54 promoters and regulon with a hybrid convolutional and recurrent deep neural network. NAR Genom Bioinform. 2025;7(1):lqae188. pmid:39781509
- View Article
- PubMed/NCBI
- Google Scholar
65. Amin R, Rahman CR, Ahmed S, Sifat MHR, Liton MNK, Rahman MM, et al. iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters. Bioinformatics. 2020;36(19):4869–75. pmid:32614400
- View Article
- PubMed/NCBI
- Google Scholar
66. Bowlin MQ, Long AR, Huffines JT, Gray MJ. The role of nitrogen-responsive regulators in controlling inorganic polyphosphate synthesis in Escherichia coli. Microbiology (Reading). 2022;168(4):001185. pmid:35482529
- View Article
- PubMed/NCBI
- Google Scholar
67. Merrick MJ, Edwards RA. Nitrogen control in bacteria. Microbiol Rev. 1995;59(4):604–22.
- View Article
- Google Scholar
68. Schulze J. How are nitrogen fixation rates regulated in legumes? Z Pflanzenernähr Bodenk. 2004;167(2):125–37.
- View Article
- Google Scholar
69. Studholme DJ, Wigneshwereraraj SR, Gallegos MT, Buck M. Functionality of purified sigma(N) (sigma(54)) and a NifA-like protein from the hyperthermophile Aquifex aeolicus. J Bacteriol. 2000;182(6):1616–23. pmid:10692367
- View Article
- PubMed/NCBI
- Google Scholar
70. Wang B, Pierson LS 3rd, Rensing C, Gunatilaka MK, Kennedy C. NasT-mediated antitermination plays an essential role in the regulation of the assimilatory nitrate reductase operon in Azotobacter vinelandii. Appl Environ Microbiol. 2012;78(18):6558–67. pmid:22773651
- View Article
- PubMed/NCBI
- Google Scholar
71. Nie X, Yang B, Zhang L, Gu Y, Yang S, Jiang W, et al. PTS regulation domain-containing transcriptional activator CelR and sigma factor σ(54) control cellobiose utilization in Clostridium acetobutylicum. Mol Microbiol. 2016;100(2):289–302. pmid:26691835
- View Article
- PubMed/NCBI
- Google Scholar
72. Stevens MJA, Molenaar D, de Jong A, De Vos WM, Kleerebezem M. sigma54-Mediated control of the mannose phosphotransferase sytem in Lactobacillus plantarum impacts on carbohydrate metabolism. Microbiology (Reading). 2010;156(Pt 3):695–707. pmid:19942662
- View Article
- PubMed/NCBI
- Google Scholar
73. Hayrapetyan H, Tempelaars M, Nierop Groot M, Abee T. Bacillus cereus ATCC 14579 RpoN (Sigma 54) is a pleiotropic regulator of growth, carbohydrate metabolism, motility, biofilm formation and toxin production. PLoS One. 2015;10(8):e0134872. pmid:26241851
- View Article
- PubMed/NCBI
- Google Scholar
74. Lundgren BR, Connolly MP, Choudhary P, Brookins-Little TS, Chatterjee S, Raina R, et al. Defining the metabolic functions and roles in virulence of the rpoN1 and rpoN2 genes in Ralstonia solanacearum GMI1000. PLoS One. 2015;10(12):e0144852. pmid:26659655
- View Article
- PubMed/NCBI
- Google Scholar
75. Gouzy A, Poquet Y, Neyrolles O. Nitrogen metabolism in Mycobacterium tuberculosis physiology and virulence. Nat Rev Microbiol. 2014;12(11):729–37. pmid:25244084
- View Article
- PubMed/NCBI
- Google Scholar
76. Noinaj N, Guillier M, Barnard TJ, Buchanan SK. TonB-dependent transporters: regulation, structure, and function. Annu Rev Microbiol. 2010;64:43–60. pmid:20420522
- View Article
- PubMed/NCBI
- Google Scholar
77. Zhao K, Liu M, Burgess RR. Promoter and regulon analysis of nitrogen assimilation factor, sigma54, reveal alternative strategy for E. coli MG1655 flagellar biosynthesis. Nucleic Acids Res. 2010;38(4):1273–83. pmid:19969540
- View Article
- PubMed/NCBI
- Google Scholar
78. Prouty MG, Correa NE, Klose KE. The novel sigma54- and sigma28-dependent flagellar gene transcription hierarchy of Vibrio cholerae. Mol Microbiol. 2001;39(6):1595–609. pmid:11260476
- View Article
- PubMed/NCBI
- Google Scholar
79. Pereira LE, Tsang J, Mrázek J, Hoover TR. The zinc-ribbon domain of Helicobacter pylori HP0958: requirement for RpoN accumulation and possible roles of homologs in other bacteria. Microb Inform Exp. 2011;1(8):1–10. pmid:22408721
- View Article
- PubMed/NCBI
- Google Scholar
80. Sabree ZL, Kambhampati S, Moran NA. Nitrogen recycling and nutritional provisioning by Blattabacterium, the cockroach endosymbiont. Proc Natl Acad Sci U S A. 2009;106(46):19521–6. pmid:19880743
- View Article
- PubMed/NCBI
- Google Scholar
81. Lazar JT, Tabor JJ. Bacterial two-component systems as sensors for synthetic biology applications. Curr Opin Syst Biol. 2021;28:100398. pmid:34917859
- View Article
- PubMed/NCBI
- Google Scholar
82. Farewell A, Kvint K, Nyström T. Negative regulation by RpoS: a case of sigma factor competition. Mol Microbiol. 1998;29(4):1039–51. pmid:9767572
- View Article
- PubMed/NCBI
- Google Scholar
83. Zhang R, Slamti L, Tong L, Verplaetse E, Ma L, Lemy C, et al. The stationary phase regulator CpcR activates cry gene expression in non-sporulating cells of Bacillus thuringiensis. Mol Microbiol. 2020;113(4):740–54. pmid:31793098
- View Article
- PubMed/NCBI
- Google Scholar
84. Shimada T, Furuhata S, Ishihama A. Whole set of constitutive promoters for RpoN sigma factor and the regulatory role of its enhancer protein NtrC in Escherichia coli K-12. Microb Genom. 2021;7(11):000653. pmid:34787538
- View Article
- PubMed/NCBI
- Google Scholar
85. Higgins CF. ABC transporters: from microorganisms to man. Annu Rev Cell Biol. 1992;8:67–113. pmid:1282354
- View Article
- PubMed/NCBI
- Google Scholar
86. Feng L, Bi W, Chen S, Zhu J, Liu X. Regulatory function of sigma factors RpoS/RpoN in adaptation and spoilage potential of Shewanella baltica. Food Microbiol. 2021;97:103755. pmid:33653528
- View Article
- PubMed/NCBI
- Google Scholar
87. Chang G. Multidrug resistance ABC transporters. FEBS Lett. 2003;555(1):102–5. pmid:14630327
- View Article
- PubMed/NCBI
- Google Scholar
88. Chevalier S, Bouffartigues E, Bazire A, Tahrioui A, Duchesne R, Tortuel D, et al. Extracytoplasmic function sigma factors in Pseudomonas aeruginosa. Biochim Biophys Acta Gene Regul Mech. 2019;1862(7):706–21. pmid:29729420
- View Article
- PubMed/NCBI
- Google Scholar
89. Al Ahmar R, Kirby BD, Yu HD. Pyrimidine biosynthesis regulates the small-colony variant and mucoidy in Pseudomonas aeruginosa through sigma factor competition. J Bacteriol. 2018;201(1):e00575-18. pmid:30322853
- View Article
- PubMed/NCBI
- Google Scholar
90. Lloyd MG, Vossler JL, Nomura CT, Moffat JF. Blocking RpoN reduces virulence of Pseudomonas aeruginosa isolated from cystic fibrosis patients and increases antibiotic sensitivity in a laboratory strain. Sci Rep. 2019;9(1):6677. pmid:31040330
- View Article
- PubMed/NCBI
- Google Scholar
91. Lutkenhaus J. The ParA/MinD family puts things in their place. Trends Microbiol. 2012;20(9):411–8. pmid:22672910
- View Article
- PubMed/NCBI
- Google Scholar
92. Taviti AC, Beuria TK. MinD directly interacting with FtsZ at the H10 helix suggests a model for robust activation of MinC to destabilize FtsZ polymers. Biochem J. 2017;474(18):3189–205. pmid:28743721
- View Article
- PubMed/NCBI
- Google Scholar
93. González JM, Pinhassi J, Fernández-Gómez B, Coll-Lladó M, González-Velázquez M, Puigbò P, et al. Genomics of the proteorhodopsin-containing marine flavobacterium Dokdonia sp. strain MED134. Appl Environ Microbiol. 2011;77(24):8676–86. pmid:22003006
- View Article
- PubMed/NCBI
- Google Scholar
94. Lu S, Faris JD, Sherwood R, Friesen TL, Edwards MC. A dimeric PR-1-type pathogenesis-related protein interacts with ToxA and potentially mediates ToxA-induced necrosis in sensitive wheat. Mol Plant Pathol. 2014;15(7):650–63. pmid:24433289
- View Article
- PubMed/NCBI
- Google Scholar
95. Taylor M, Butler R, Chambers S, Casimiro M, Badii F, Merrick M. The RpoN-box motif of the RNA polymerase sigma factor sigma N plays a role in promoter recognition. Mol Microbiol. 1996;22(5):1045–54. pmid:8971724
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Burgess RR. Sigma factors. In: Encyclopedia of genetics. Elsevier; 2001. p. 1831–1834.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Chávez J, Devos DP, Merino E. Complementary tendencies in the use of regulatory elements (transcription factors, sigma factors, and riboswitches) in bacteria and archaea. J Bacteriol. 2020;203(2):e00413-20. pmid:33077635
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref3] 3. Burrows PC, Severinov K, Buck M, Wigneshweraraj SR. Reorganisation of an RNA polymerase-promoter DNA complex for DNA melting. EMBO J. 2004;23(21):4253–63. pmid:15470504
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. Burgess RR, Travers AA, Dunn JJ, Bautz EK. Factor stimulating transcription by RNA polymerase. Nature. 1969;221(5175):43–6. pmid:4882047
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Bose D, Pape T, Burrows PC, Rappas M, Wigneshweraraj SR, Buck M, et al. Organization of an activator-bound RNA polymerase holoenzyme. Mol Cell. 2008;32(3):337–46. pmid:18995832
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Helmann JD. Where to begin? Sigma factors and the selectivity of transcription initiation in bacteria. Mol Microbiol. 2019;112(2):335–47. pmid:31119812
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Feklístov A, Sharon BD, Darst SA, Gross CA. Bacterial sigma factors: a historical, structural, and genomic perspective. Annu Rev Microbiol. 2014;68:357–76. pmid:25002089
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Wigneshweraraj S, Bose D, Burrows PC, Joly N, Schumacher J, Rappas M, et al. Modus operandi of the bacterial RNA polymerase containing the sigma54 promoter-specificity factor. Mol Microbiol. 2008;68(3):538–46. pmid:18331472
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Francke C, Groot Kormelink T, Hagemeijer Y, Overmars L, Sluijter V, Moezelaar R, et al. Comparative analyses imply that the enigmatic Sigma factor 54 is a central controller of the bacterial exterior. BMC Genomics. 2011;12:385. pmid:21806785
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Tsoy OV, Ravcheev DA, Čuklina J, Gelfand MS. Nitrogen fixation and molecular oxygen: comparative genomic reconstruction of transcription regulation in Alphaproteobacteria. Front Microbiol. 2016;7:1343. pmid:27617010
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Reitzer L, Schneider BL. Metabolic context and possible physiological themes of sigma(54)-dependent genes in Escherichia coli. Microbiol Mol Biol Rev. 2001;65(3):422–44, table of contents. pmid:11528004
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Tsang J, Hoover TR. Themes and variations: regulation of RpoN-dependent flagellar genes across diverse bacterial species. Scientifica (Cairo). 2014;2014:681754. pmid:24672734
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Keseler IM, Kaiser D. 54, a vital protein for Myxococcus xanthus (transcription factor RNA polymerase promoter activator proteins). Proc Natl Acad Sci. 1997;94:1979–84.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref14] 14. Bervoets I, Charlier D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol Rev. 2019;43(3):304–39. pmid:30721976
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Garcia E, Bancroft S, Rhee SG, Kustu S. The product of a newly identified gene, gInF, is required for synthesis of glutamine synthetase in Salmonella. Proc Natl Acad Sci U S A. 1977;74(4):1662–6. pmid:16262
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Ancona V, Li W, Zhao Y. Alternative sigma factor RpoN and its modulation protein YhbH are indispensable for Erwinia amylovora virulence. Mol Plant Pathol. 2014;15(1):58–66. pmid:23937726
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Yu C, Yang F, Xue D, Wang X, Chen H. The regulatory functions of σ54 factor in phytopathogenic bacteria. Int J Mol Sci. 2021;22(23):12692. pmid:34884502
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Phale PS, Malhotra H, Shah BA. Degradation strategies and associated regulatory mechanisms/features for aromatic compound metabolism in bacteria. Adv Appl Microbiol. 2020;112:1–65. pmid:32762865
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Gruber TM, Gross CA. Multiple sigma subunits and the partitioning of bacterial transcription space. Annu Rev Microbiol. 2003;57:441–66. pmid:14527287
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Sher AA, Jerome JP, Bell JA, Yu J, Kim HY, Barrick JE, et al. Experimental evolution of Campylobacter jejuni leads to loss of motility, rpoN (σ54) deletion and genome reduction. Front Microbiol. 2020;11:579989. pmid:33240235
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref21] 21. Mahmud AKMF, Navais R, Nilsson K, Choudhury R, Avican K, Fallman M. RpoN is required for a functional type III secretion system in Yersinia pseudotuberculosis. bioRxiv. 2022:2022.02.11.480049.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref22] 22. Kazakov AE, Rajeev L, Chen A, Luning EG, Dubchak I, Mukhopadhyay A, et al. σ54-dependent regulome in Desulfovibrio vulgaris Hildenborough. BMC Genomics. 2015;16:919. pmid:26555820
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Baum A, Singer JE, Baum C. Stress and environment. In: Evans GW, editor. Environmental stress. Cambridge: Cambridge University Press; 1982. p. 15–44.

[ref24] 24. Wiley J. Stress and environmental regulation of gene expression and adaptation in bacteria. New Jersey; 2016.

[ref25] 25. Nie X, Dong W, Yang C. Genomic reconstruction of σ54 regulons in Clostridiales. BMC Genomics. 2019;20(1):565. pmid:31288763
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref26] 26. Clark AE, Adamson CC, Carothers KE, Roxas BAP, Viswanathan VK, Vedantam G. The alternative sigma factor SigL influences Clostridioides difficile toxin production, sporulation, and cell surface properties. Front Microbiol. 2022;13:871152. pmid:35633701
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref27] 27. Studholme DJ, Dixon R. In silico analysis of the sigma54-dependent enhancer-binding proteins in Pirellula species strain 1. FEMS Microbiol Lett. 2004;230(2):215–25. pmid:14757243
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref28] 28. Leang C, Krushkal J, Ueki T, Puljic M, Sun J, Juárez K, et al. Genome-wide analysis of the RpoN regulon in Geobacter sulfurreducens. BMC Genomics. 2009;10:331. pmid:19624843
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref29] 29. Waters CM, Bassler BL. Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol. 2005;21:319–46. pmid:16212498
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref30] 30. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, et al. The Pfam protein families database. Nucleic Acids Res. 2010;38(Database issue):D211-22. pmid:19920124
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. Zhang N, Darbari VC, Glyde R, Zhang X, Buck M. The bacterial enhancer-dependent RNA polymerase. Biochem J. 2016;473(21):3741–53. pmid:27789741
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref32] 32. Lobanovska M, Tang CM, Exley RM. Crossm pilE in Neisseria meningitidis. 2019;201:1–16.

[ref33] 33. Bush M, Dixon R. The role of bacterial enhancer binding proteins as specialized activators of σ54-dependent transcription. Microbiol Mol Biol Rev. 2012;76(3):497–529. pmid:22933558
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref34] 34. Ghosh T, Bose D, Zhang X. Mechanisms for activating bacterial RNA polymerase. FEMS Microbiol Rev. 2010;34(5):611–27. pmid:20629756
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref35] 35. Buck M, Gallegos MT, Studholme DJ, Guo Y, Gralla JD. The bacterial enhancer-dependent sigma(54) (sigma(N)) transcription factor. J Bacteriol. 2000;182(15):4129–36. pmid:10894718
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref36] 36. Dago AE, Wigneshweraraj SR, Buck M, Morett E. A role for the conserved GAFTGA motif of AAA+ transcription activators in sensing promoter DNA conformation. J Biol Chem. 2007;282(2):1087–97. pmid:17090527
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref37] 37. da Silva Neto JF, Koide T, Gomes SL, Marques MV. Global gene expression under nitrogen starvation in Xylella fastidiosa: contribution of the σ54 regulon. BMC Microbiol. 2010;10:231. pmid:20799976
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref38] 38. Dombrecht B, Marchal K, Vanderleyden J, Michiels J. Prediction and overview of the RpoN-regulon in closely related species of the Rhizobiales. Genome Biol. 2002;3(12):RESEARCH0076. pmid:12537565
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref39] 39. López-Leal G, Cevallos MA, Castillo-Ramírez S. Evolution of a sigma factor: an all-in-one of gene duplication, horizontal gene transfer, purifying selection, and promoter differentiation. Front Microbiol. 2016;7:581. pmid:27199915
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref40] 40. Dong TG, Mekalanos JJ. Characterization of the RpoN regulon reveals differential regulation of T6SS and new flagellar operons in Vibrio cholerae O37 strain V52. Nucleic Acids Res. 2012;40(16):7766–75. pmid:22723378
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref41] 41. Cases I, Ussery DW, de Lorenzo V. The sigma54 regulon (sigmulon) of Pseudomonas putida. Environ Microbiol. 2003;5(12):1281–93. pmid:14641574
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref42] 42. Bono AC, Hartman CE, Solaimanpour S, Tong H, Porwollik S, McClelland M. Novel DNA binding and regulatory activities for σ54 (RpoN) in Salmonella. J Bacteriol. 2017;199:1–24.
View Article
Google Scholar

[154] View Article

[155] Google Scholar

[ref43] 43. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545–51. pmid:33125081
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref44] 44. Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–6. pmid:10592175
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref45] 45. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113. pmid:15318951
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref46] 46. Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7(10):e1002195. pmid:22039361
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref47] 47. Taboada B, Verde C, Merino E. High accuracy operon prediction method based on STRING database scores. Nucleic Acids Res. 2010;38(12):e130. pmid:20385580
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref48] 48. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37(Database issue):D412-6. pmid:18940858
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref49] 49. Novichkov PS, Kazakov AE, Ravcheev DA, Leyn SA, Kovaleva GY, Sutormin RA, et al. RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics. 2013;14:745. pmid:24175918
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref50] 50. Sierro N, Makita Y, de Hoon M, Nakai K. DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res. 2008;36(Database issue):D93-6. pmid:17962296
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref51] 51. Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martínez C, et al. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 2013;41(Database issue):D605-12. pmid:23143106
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref52] 52. Barrios H, Valderrama B, Morett E. Compilation and analysis of sigma(54)-dependent promoter sequences. Nucleic Acids Res. 1999;27(22):4305–13. pmid:10536136
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref53] 53. Bailey TL, Gribskov M. Combining evidence using p-values: application to sequence homology searches. Bioinformatics. 1998;14(1):48–54. pmid:9520501
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref54] 54. Bailey TL, Williams N, Misleh C, Li WW. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 2006;34(Web Server issue):W369-73. pmid:16845028
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref55] 55. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26(5):680–2. pmid:20053844
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

[ref56] 56. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80. pmid:23329690
View Article
PubMed/NCBI
Google Scholar

[209] View Article

[210] PubMed/NCBI

[211] Google Scholar

[ref57] 57. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–4. pmid:32011700
View Article
PubMed/NCBI
Google Scholar

[213] View Article

[214] PubMed/NCBI

[215] Google Scholar

[ref58] 58. Letunic I, Bork P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 2024;52(W1):W78–82. pmid:38613393
View Article
PubMed/NCBI
Google Scholar

[217] View Article

[218] PubMed/NCBI

[219] Google Scholar

[ref59] 59. Poggio S, Osorio A, Dreyfus G, Camarena L. The four different sigma(54) factors of Rhodobacter sphaeroides are not functionally interchangeable. Mol Microbiol. 2002;46(1):75–85. pmid:12366832
View Article
PubMed/NCBI
Google Scholar

[221] View Article

[222] PubMed/NCBI

[223] Google Scholar

[ref60] 60. Battistuzzi FU, Feijao A, Hedges SB. A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evol Biol. 2004;4:44. pmid:15535883
View Article
PubMed/NCBI
Google Scholar

[225] View Article

[226] PubMed/NCBI

[227] Google Scholar

[ref61] 61. Studholme DJ, Buck M. The biology of enhancer-dependent transcriptional regulation in bacteria: insights from genome sequences. FEMS Microbiol Lett. 2000;186(1):1–9.
View Article
Google Scholar

[229] View Article

[230] Google Scholar

[ref62] 62. Chen B, Sysoeva TA, Chowdhury S, Nixon BT. Regulation and action of the bacterial enhancer-binding protein AAA+ domains. Biochem Soc Trans. 2008;36:89–93.
View Article
Google Scholar

[232] View Article

[233] Google Scholar

[ref63] 63. Studholme DJ. Enhancer-dependent transcription in Salmonella enterica Typhimurium: new members of the σN regulon inferred from protein sequence homology and predicted promoter sites. J Mol Microbiol Biotechnol. 2002;4:367–74.
View Article
Google Scholar

[235] View Article

[236] Google Scholar

[ref64] 64. Achterberg T, de Jong A. ProPr54 web server: predicting σ54 promoters and regulon with a hybrid convolutional and recurrent deep neural network. NAR Genom Bioinform. 2025;7(1):lqae188. pmid:39781509
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref65] 65. Amin R, Rahman CR, Ahmed S, Sifat MHR, Liton MNK, Rahman MM, et al. iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters. Bioinformatics. 2020;36(19):4869–75. pmid:32614400
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref66] 66. Bowlin MQ, Long AR, Huffines JT, Gray MJ. The role of nitrogen-responsive regulators in controlling inorganic polyphosphate synthesis in Escherichia coli. Microbiology (Reading). 2022;168(4):001185. pmid:35482529
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref67] 67. Merrick MJ, Edwards RA. Nitrogen control in bacteria. Microbiol Rev. 1995;59(4):604–22.
View Article
Google Scholar

[250] View Article

[251] Google Scholar

[ref68] 68. Schulze J. How are nitrogen fixation rates regulated in legumes? Z Pflanzenernähr Bodenk. 2004;167(2):125–37.
View Article
Google Scholar

[253] View Article

[254] Google Scholar

[ref69] 69. Studholme DJ, Wigneshwereraraj SR, Gallegos MT, Buck M. Functionality of purified sigma(N) (sigma(54)) and a NifA-like protein from the hyperthermophile Aquifex aeolicus. J Bacteriol. 2000;182(6):1616–23. pmid:10692367
View Article
PubMed/NCBI
Google Scholar

[256] View Article

[257] PubMed/NCBI

[258] Google Scholar

[ref70] 70. Wang B, Pierson LS 3rd, Rensing C, Gunatilaka MK, Kennedy C. NasT-mediated antitermination plays an essential role in the regulation of the assimilatory nitrate reductase operon in Azotobacter vinelandii. Appl Environ Microbiol. 2012;78(18):6558–67. pmid:22773651
View Article
PubMed/NCBI
Google Scholar

[260] View Article

[261] PubMed/NCBI

[262] Google Scholar

[ref71] 71. Nie X, Yang B, Zhang L, Gu Y, Yang S, Jiang W, et al. PTS regulation domain-containing transcriptional activator CelR and sigma factor σ(54) control cellobiose utilization in Clostridium acetobutylicum. Mol Microbiol. 2016;100(2):289–302. pmid:26691835
View Article
PubMed/NCBI
Google Scholar

[264] View Article

[265] PubMed/NCBI

[266] Google Scholar

[ref72] 72. Stevens MJA, Molenaar D, de Jong A, De Vos WM, Kleerebezem M. sigma54-Mediated control of the mannose phosphotransferase sytem in Lactobacillus plantarum impacts on carbohydrate metabolism. Microbiology (Reading). 2010;156(Pt 3):695–707. pmid:19942662
View Article
PubMed/NCBI
Google Scholar

[268] View Article

[269] PubMed/NCBI

[270] Google Scholar

[ref73] 73. Hayrapetyan H, Tempelaars M, Nierop Groot M, Abee T. Bacillus cereus ATCC 14579 RpoN (Sigma 54) is a pleiotropic regulator of growth, carbohydrate metabolism, motility, biofilm formation and toxin production. PLoS One. 2015;10(8):e0134872. pmid:26241851
View Article
PubMed/NCBI
Google Scholar

[272] View Article

[273] PubMed/NCBI

[274] Google Scholar

[ref74] 74. Lundgren BR, Connolly MP, Choudhary P, Brookins-Little TS, Chatterjee S, Raina R, et al. Defining the metabolic functions and roles in virulence of the rpoN1 and rpoN2 genes in Ralstonia solanacearum GMI1000. PLoS One. 2015;10(12):e0144852. pmid:26659655
View Article
PubMed/NCBI
Google Scholar

[276] View Article

[277] PubMed/NCBI

[278] Google Scholar

[ref75] 75. Gouzy A, Poquet Y, Neyrolles O. Nitrogen metabolism in Mycobacterium tuberculosis physiology and virulence. Nat Rev Microbiol. 2014;12(11):729–37. pmid:25244084
View Article
PubMed/NCBI
Google Scholar

[280] View Article

[281] PubMed/NCBI

[282] Google Scholar

[ref76] 76. Noinaj N, Guillier M, Barnard TJ, Buchanan SK. TonB-dependent transporters: regulation, structure, and function. Annu Rev Microbiol. 2010;64:43–60. pmid:20420522
View Article
PubMed/NCBI
Google Scholar

[284] View Article

[285] PubMed/NCBI

[286] Google Scholar

[ref77] 77. Zhao K, Liu M, Burgess RR. Promoter and regulon analysis of nitrogen assimilation factor, sigma54, reveal alternative strategy for E. coli MG1655 flagellar biosynthesis. Nucleic Acids Res. 2010;38(4):1273–83. pmid:19969540
View Article
PubMed/NCBI
Google Scholar

[288] View Article

[289] PubMed/NCBI

[290] Google Scholar

[ref78] 78. Prouty MG, Correa NE, Klose KE. The novel sigma54- and sigma28-dependent flagellar gene transcription hierarchy of Vibrio cholerae. Mol Microbiol. 2001;39(6):1595–609. pmid:11260476
View Article
PubMed/NCBI
Google Scholar

[292] View Article

[293] PubMed/NCBI

[294] Google Scholar

[ref79] 79. Pereira LE, Tsang J, Mrázek J, Hoover TR. The zinc-ribbon domain of Helicobacter pylori HP0958: requirement for RpoN accumulation and possible roles of homologs in other bacteria. Microb Inform Exp. 2011;1(8):1–10. pmid:22408721
View Article
PubMed/NCBI
Google Scholar

[296] View Article

[297] PubMed/NCBI

[298] Google Scholar

[ref80] 80. Sabree ZL, Kambhampati S, Moran NA. Nitrogen recycling and nutritional provisioning by Blattabacterium, the cockroach endosymbiont. Proc Natl Acad Sci U S A. 2009;106(46):19521–6. pmid:19880743
View Article
PubMed/NCBI
Google Scholar

[300] View Article

[301] PubMed/NCBI

[302] Google Scholar

[ref81] 81. Lazar JT, Tabor JJ. Bacterial two-component systems as sensors for synthetic biology applications. Curr Opin Syst Biol. 2021;28:100398. pmid:34917859
View Article
PubMed/NCBI
Google Scholar

[304] View Article

[305] PubMed/NCBI

[306] Google Scholar

[ref82] 82. Farewell A, Kvint K, Nyström T. Negative regulation by RpoS: a case of sigma factor competition. Mol Microbiol. 1998;29(4):1039–51. pmid:9767572
View Article
PubMed/NCBI
Google Scholar

[308] View Article

[309] PubMed/NCBI

[310] Google Scholar

[ref83] 83. Zhang R, Slamti L, Tong L, Verplaetse E, Ma L, Lemy C, et al. The stationary phase regulator CpcR activates cry gene expression in non-sporulating cells of Bacillus thuringiensis. Mol Microbiol. 2020;113(4):740–54. pmid:31793098
View Article
PubMed/NCBI
Google Scholar

[312] View Article

[313] PubMed/NCBI

[314] Google Scholar

[ref84] 84. Shimada T, Furuhata S, Ishihama A. Whole set of constitutive promoters for RpoN sigma factor and the regulatory role of its enhancer protein NtrC in Escherichia coli K-12. Microb Genom. 2021;7(11):000653. pmid:34787538
View Article
PubMed/NCBI
Google Scholar

[316] View Article

[317] PubMed/NCBI

[318] Google Scholar

[ref85] 85. Higgins CF. ABC transporters: from microorganisms to man. Annu Rev Cell Biol. 1992;8:67–113. pmid:1282354
View Article
PubMed/NCBI
Google Scholar

[320] View Article

[321] PubMed/NCBI

[322] Google Scholar

[ref86] 86. Feng L, Bi W, Chen S, Zhu J, Liu X. Regulatory function of sigma factors RpoS/RpoN in adaptation and spoilage potential of Shewanella baltica. Food Microbiol. 2021;97:103755. pmid:33653528
View Article
PubMed/NCBI
Google Scholar

[324] View Article

[325] PubMed/NCBI

[326] Google Scholar

[ref87] 87. Chang G. Multidrug resistance ABC transporters. FEBS Lett. 2003;555(1):102–5. pmid:14630327
View Article
PubMed/NCBI
Google Scholar

[328] View Article

[329] PubMed/NCBI

[330] Google Scholar

[ref88] 88. Chevalier S, Bouffartigues E, Bazire A, Tahrioui A, Duchesne R, Tortuel D, et al. Extracytoplasmic function sigma factors in Pseudomonas aeruginosa. Biochim Biophys Acta Gene Regul Mech. 2019;1862(7):706–21. pmid:29729420
View Article
PubMed/NCBI
Google Scholar

[332] View Article

[333] PubMed/NCBI

[334] Google Scholar

[ref89] 89. Al Ahmar R, Kirby BD, Yu HD. Pyrimidine biosynthesis regulates the small-colony variant and mucoidy in Pseudomonas aeruginosa through sigma factor competition. J Bacteriol. 2018;201(1):e00575-18. pmid:30322853
View Article
PubMed/NCBI
Google Scholar

[336] View Article

[337] PubMed/NCBI

[338] Google Scholar

[ref90] 90. Lloyd MG, Vossler JL, Nomura CT, Moffat JF. Blocking RpoN reduces virulence of Pseudomonas aeruginosa isolated from cystic fibrosis patients and increases antibiotic sensitivity in a laboratory strain. Sci Rep. 2019;9(1):6677. pmid:31040330
View Article
PubMed/NCBI
Google Scholar

[340] View Article

[341] PubMed/NCBI

[342] Google Scholar

[ref91] 91. Lutkenhaus J. The ParA/MinD family puts things in their place. Trends Microbiol. 2012;20(9):411–8. pmid:22672910
View Article
PubMed/NCBI
Google Scholar

[344] View Article

[345] PubMed/NCBI

[346] Google Scholar

[ref92] 92. Taviti AC, Beuria TK. MinD directly interacting with FtsZ at the H10 helix suggests a model for robust activation of MinC to destabilize FtsZ polymers. Biochem J. 2017;474(18):3189–205. pmid:28743721
View Article
PubMed/NCBI
Google Scholar

[348] View Article

[349] PubMed/NCBI

[350] Google Scholar

[ref93] 93. González JM, Pinhassi J, Fernández-Gómez B, Coll-Lladó M, González-Velázquez M, Puigbò P, et al. Genomics of the proteorhodopsin-containing marine flavobacterium Dokdonia sp. strain MED134. Appl Environ Microbiol. 2011;77(24):8676–86. pmid:22003006
View Article
PubMed/NCBI
Google Scholar

[352] View Article

[353] PubMed/NCBI

[354] Google Scholar

[ref94] 94. Lu S, Faris JD, Sherwood R, Friesen TL, Edwards MC. A dimeric PR-1-type pathogenesis-related protein interacts with ToxA and potentially mediates ToxA-induced necrosis in sensitive wheat. Mol Plant Pathol. 2014;15(7):650–63. pmid:24433289
View Article
PubMed/NCBI
Google Scholar

[356] View Article

[357] PubMed/NCBI

[358] Google Scholar

[ref95] 95. Taylor M, Butler R, Chambers S, Casimiro M, Badii F, Merrick M. The RpoN-box motif of the RNA polymerase sigma factor sigma N plays a role in promoter recognition. Mol Microbiol. 1996;22(5):1045–54. pmid:8971724
View Article
PubMed/NCBI
Google Scholar

[360] View Article

[361] PubMed/NCBI

[362] Google Scholar

Figures

Abstract

Introduction

Methods

Genome sequence datasets

Taxonomic classification

COG assignments

Operon predictions

Identification of σ54-specific EBPs

Identification of σ54 protein domains

Identification of σ54 protein domains

Web page construction

Results and discussion

Phylogenetic distribution of σ54

Phylogenetic distribution of σ54-specific EBPs

In silico identification of σ54-dependent promoters

Phylogenetic diversity in σ54-mediated regulation of biological processes

Metabolism.

Cellular processes and signaling.

Information storage and processing.

Poorly characterized.

Online access to our data and analyses through the aRpoN-DB Web Page

Distribution of COG functional categories across phylogenetic classes

Conclusion

Supporting information

S1 Fig. Phylogenetic analysis based on 16S rRNA sequences shows that Arthrobacter citreus NEB 577 aligns more closely with the Bacillota phyla than with Actinomycetota.

S2 Fig. Distribution of COGs associated with σ54-regulated genes across phylogenetic classes in the metabolism category.

S3 Fig. Distribution of COGs associated with σ54-regulated genes across phylogenetic classes in the Cellular processes and signaling category.

S1 Table. Study organisms from 33 classes from 16 prokaryotic phyla.

S2 Table. Initial set of seed RpoN-dependent promoter sequences used to generate the seed MEME-derived position-specific scoring matrix (PSSM).

S3 Table. Seed position-specific scoring matrix (PSSM).

S4 Table. List of organisms used as a negative control in our σ54-specific EBP search.

S5 Table. Distribution of Organisms with Multiple rpoN Genes by Phylogenetic Class.

S6 Table. RpoN sequence similarities.

S7 Table. Pfam scores for the AID, CBD, and DBD domains of RpoN proteins from Actinomycetota species.

S8 Table. Distribution of EBPs across prokaryotic classes.

S9 Table. Architectural arrangements of Pfam domains in RpoN proteins across phylogenetic classes.

S10 Table. Sequence variations of false negative σ54-promoters of our study.

Acknowledgments

References

Identification of σ⁵⁴-specific EBPs

Identification of σ⁵⁴ protein domains

Identification of σ⁵⁴ protein domains

Phylogenetic distribution of σ⁵⁴

Phylogenetic distribution of σ⁵⁴-specific EBPs

In silico identification of σ⁵⁴-dependent promoters

Phylogenetic diversity in σ⁵⁴-mediated regulation of biological processes

S2 Fig. Distribution of COGs associated with σ⁵⁴-regulated genes across phylogenetic classes in the metabolism category.

S3 Fig. Distribution of COGs associated with σ⁵⁴-regulated genes across phylogenetic classes in the Cellular processes and signaling category.

S4 Table. List of organisms used as a negative control in our σ⁵⁴-specific EBP search.

S10 Table. Sequence variations of false negative σ⁵⁴-promoters of our study.