DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information

Regulator DegU can remarkably influence alkaline protease AprE biosynthesis in Bacillus licheniformis 2709

Alkaline protease AprE, produced by Bacillus licheniformis 2709 is an important edible hydrolase, which has potential applications in nutrient acquisition and medicine. The expression of AprE is finely regulated by a complex transcriptional regulation system. However, there is little study on transcriptional regulation mechanism of AprE biosynthesis in Bacillus licheniformis, which limits system engineering and further enhancement of AprE. Here, the severely depressed expression of aprE in degU and degS deletion mutants illustrated that the regulator DegU and its phosphorylation played a crucial part in AprE biosynthesis. Further electrophoretic mobility shift assay (EMSA) in vitro indicated that phosphorylated DegU can directly bind to the regulatory region though the DNase I foot-printing experiments failed to observe protected region. The plasmid-mediated overexpression of degU32 (Hy) obviously improved the yield of AprE by 41.6 % compared with the control strain, which demonstrated the importance of phosphorylation state of DegU on the transcription of aprE in vivo. In this study, the putative binding sequence of aprE (5'-TAAAT……AAAAT…….AACAT…TAAAA-3') located upstream -91 to -87 bp, -101 to -97 bp, -195 to -191 bp, -215 to -211 bp of the transcription start site (TSS) in B. licheniformis was computationally identified based on the DNA-binding sites of DegU in Bacillus subtilis. Overall, we systematically investigated the influence of the interplay between phosphorylated DegU and its cognate DNA sequence on expression of aprE, which not only contributes to the further AprE high-production in a genetically modified host in the future, but also significantly increases our understanding of the aprE transcription mechanism.

Transcriptional regulation of cellobiose utilization by PRD-domain containing Sigma54-dependent transcriptional activator (CelR) and catabolite control protein A (CcpA) in Bacillus thuringiensis

Cellobiose, a β-1,4-linked glucose dimer, is a major cellodextrin resulting from the enzymatic hydrolysis of cellulose. It is a major source of carbon for soil bacteria. In bacteria, the phosphoenolpyruvate (PEP): carbohydrate phosphotransferase system (PTS), encoded by the cel operon, is responsible for the transport and utilization of cellobiose. In this study, we analyzed the transcription and regulation of the cel operon in Bacillus thuringiensis (Bt). The cel operon is composed of five genes forming one transcription unit. β-Galactosidase assays revealed that cel operon transcription is induced by cellobiose, controlled by Sigma54, and positively regulated by CelR. The HTH-AAA+ domain of CelR recognized and specifically bound to three possible binding sites in the celA promoter region. CelR contains two PTS regulation domains (PRD1 and PRD2), which are separated by two PTS-like domains-the mannose transporter enzyme IIA component domain (EIIAMan) and the galactitol transporter enzyme IIB component domain (EIIBGat). Mutations of His-546 on the EIIAMan domain and Cys-682 on the EIIBGat domain resulted in decreased transcription of the cel operon, and mutations of His-839 on PRD2 increased transcription of the cel operon. Glucose repressed the transcription of the cel operon and catabolite control protein A (CcpA) positively regulated this process by binding the cel promoter. In the celABCDE and celR mutants, PTS activities were decreased, and cellobiose utilization was abolished, suggesting that the cel operon is essential for cellobiose utilization. Bt has been widely used as a biological pesticide. The metabolic properties of Bt are critical for fermentation. Nutrient utilization is also essential for the environmental adaptation of Bt. Glucose is the preferred energy source for many bacteria, and the presence of the phosphotransferase system allows bacteria to utilize other sugars in addition to glucose. Cellobiose utilization pathways have been of particular interest owing to their potential for developing alternative energy sources for bacteria. The data presented in this study improve our understanding of the transcription patterns of cel gene clusters. This will further help us to better understand how cellobiose is utilized for bacterial growth.

Comparative genomics sheds light on transcription factor-mediated regulation in the extreme acidophilic Acidithiobacillia representatives

Extreme acidophiles thrive in acidic environments, confront a multitude of challenges, and demonstrate remarkable adaptability in their metabolism to cope with the ever-changing environmental fluctuations, which encompass variations in temperature, pH levels, and the availability of electron acceptors and donors. The survival and proliferation of members within the Acidithiobacillia class rely on the deployment of transcriptional regulatory systems linked to essential physiological traits. The study of these transcriptional regulatory systems provides valuable insights into critical processes, such as energy metabolism and nutrient assimilation, and how they integrate into major genetic-metabolic circuits. In this study, we examined the transcriptional regulatory repertoires and potential interactions of forty-three Acidithiobacillia complete and draft genomes, encompassing nine species. To investigate the function and diversity of Transcription Factors (TFs) and their DNA Binding Sites (DBSs), we conducted a genome-wide comparative analysis, which allowed us to identify these regulatory elements in representatives of Acidithiobacillia. We classified TFs into gene families and compared their occurrence among all representatives, revealing conservation patterns across the class. The results identified conserved regulators for several pathways, including iron and sulfur oxidation, the main pathways for energy acquisition, providing new evidence for viable regulatory interactions and branch-specific conservation in Acidithiobacillia. The identification of TFs and DBSs not only corroborates existing experimental information for selected species, but also introduces novel candidates for experimental validation. Moreover, these promising candidates have the potential for further extension to new representatives within the class.

Mechanism of δ Mediated Transcription Activation in Bacillus subtilis: Interaction with α CTD of RNA Polymerase Stabilizes δ and Successively Facilitates the Open Complex Formation

The α CTD (C-terminal domain of the α subunit) of RNA polymerase (RNAP) is a target for transcriptional regulators. In the transcription activation at Class I, Class II, and Class III promoters of bacteria, the transcriptional regulator, binds to DNA at different sites and interacts with the α CTD to stabilize the RNAP at the promoter or it binds to the α CTD to form a prerecruitment complex that searches for its cognate binding site. This 'simple recruitment mechanism' of the transcriptional machinery at the promoter is responsible for the activation of transcription. Strikingly, in B. subtilis the binding of RNAP at the promoter stabilizes the transcriptional regulator, δ at the -41 site of the promoter DNA through an interaction with its α CTD and successively facilitates the open complex formation. Two residues R293 and K294 of α CTD (equivalent to K297 and K298 of E. coli) are involved in the interactions with δ and essential for the activation of transcription. R293 is responsible for the stabilization of δ, while K294 is responsible for facilitating the open complex formation. Based on our data we propose a new model of transcription activation by δ of B. subtilis that is similar to (its binding location and interaction with α CTD), but distinct from (the recruitment of transcription factor by RNAP at the DNA, and enhancement of the open complex formation) the model Class II promoters in bacteria.

Exploring and Engineering Novel Strong Promoters for High-Level Protein Expression in Bacillus subtilis DB104 through Transcriptome Analysis

Bacillus subtilis is widely employed for recombinant protein expression. B. subtilis DB104 offers a distinct advantage as a protein expression host because it is an extracellular protease-deficient derivative of B. subtilis 168. We have conducted a time-course transcriptome analysis of B. subtilis DB104 in a prior study. In the present study, we identified 10 genes that exhibited strong expression at each time point or all, based on transcriptome data. Subsequently, we assessed the strength of 12 promoters that transcribe these genes using enhanced green fluorescent protein (eGFP) as a reporter. Among these promoters, Psdp and PskfA had the highest expression levels. At 24 h, these two promoters exhibited 34.5- and 38.8-fold higher strength, respectively, than the strength of P43, the control promoter. Consequently, these two promoters were selected for further development. We enhanced these promoters by optimizing spacer length, promoter sequence, Shine-Dalgarno sequence, regulator binding sites, and terminator sequences. As a result, we successfully engineered the most potent protein expression cassette, Psdp-4, which exhibited a 3.84-fold increase in strength compared to the original Psdp promoter. Furthermore, we constructed an expression cassette for a human epidermal growth factor (hEGF) using Psdp-4 to evaluate its general application. The expression level of His tagged hEGF, quantified using ImageJ analysis and applied to SDS-PAGE, reached the highest yield of 103.9 μg/mL under the control of Psdp-4 at 24 h. The expressed hEGF protein was purified, and its bioactivity was confirmed through a cell proliferation assay using HT-29 cells. Our work demonstrates the construction of a highly efficient expression system for B. subtilis DB104 based on transcriptome data and promoter engineering. This system enables rapid, inducer-free protein expression within 24 h. It can be used as a valuable tool for various industrial applications.

Prediction of prokaryotic and eukaryotic promoters based on information-theoretic features

Promoters are DNA regulatory elements located near the transcription start site and are responsible for regulating the transcription of genes. DNA fragments arranged in a certain order form specific functional regions with different information contents. Information theory is the science that studies the extraction, measurement and transmission of information. The genetic information contained in DNA follows the general laws of information storage. Therefore, method in information theory can be used for the analysis of promoters carrying genetic information. In this study, we introduced the concept of information theory to the study of promoter prediction. We used 107 features extracted based on information theory methods and a backpropagation neural network to build a classifier. Then, the trained classifier was applied to predict the promoters of 6 organisms. The average AUCs of the 6 organisms obtained by using hold-out validation and ten-fold cross-validation were 0.885 and 0.886, respectively. The results verified the effectiveness of information-theoretic features in promoter prediction. Considering the possible redundancy in the feature set, we performed feature selection and obtained key feature subsets related to promoter characteristics. The results indicate the potential utility of information-theoretic features in promoter prediction.

Glucose controls manganese homeostasis through transcription factors regulating known and newly identified manganese transporter genes in Bacillus subtilis

Mn2+ is an essential nutrient whose concentration is tightly controlled in bacteria. In Bacillus subtilis, the Mn2+-activated transcription factor MntR controls Mn2+ transporter genes. However, factors regulating intracellular Mn2+ concentration are incompletely understood. Here, we found that glucose addition induces an increase in intracellular Mn2+ concentration. We determined this upshift was mediated by glucose induction of the major Mn2+ importer gene mntH by the transcription factor AhrC, which is known to be involved in arginine metabolism and to be indirectly induced by glucose. In addition, we identified novel AhrC-regulated genes encoding the Mn2+ importer YcsG and the ABC-type exporter YknUV. We found the expression of these genes was also regulated by glucose and contributes to the glucose induction of Mn2+ concentrations. ycsG expression is regulated by MntR as well. Furthermore, we analyzed the interaction of AhrC and MntR with the promoter driving ycsG expression and examined the Mn2+-dependent induction of this promoter to identify the transcription factors responsible for the Mn2+ induction. RNA-Seq revealed that disruption of ahrC and mntR affected the expression of 502 and 478 genes, respectively (false discovery rate, <0.001, log2[fold change] ≥ |2|. The AhrC- and/or MntR-dependent expression of twenty promoters was confirmed by LacZ analysis, and AhrC or MntR binding to some of these promoters was observed via EMSA. The finding that glucose promotes an increase in intracellular Mn2+ levels without changes in extracellular Mn2+ concentrations is reasonable for the bacterium, as intracellular Mn2+ is required for enzymes and pathways mediating glucose metabolism.

Time-Course Transcriptome Analysis of Bacillus subtilis DB104 during Growth

Bacillus subtilis DB104, an extracellular protease-deficient derivative of B. subtilis 168, is widely used for recombinant protein expression. An understanding of the changes in gene expression during growth is essential for the commercial use of bacterial strains. Transcriptome and proteome analyses are ideal methods to study the genomic response of microorganisms. In this study, transcriptome analysis was performed to monitor changes in the gene expression level of B. subtilis DB104 while growing on a complete medium. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, K-mean cluster analysis, gene ontology (GO) enrichment analysis, and the function of sigma factors were used to divide 2122 differentially expressed genes (DEGs) into 10 clusters and identified gene functions according to expression patterns. The results of KEGG pathway analysis indicated that ABC transporter is down-regulated during exponential growth and metabolic changes occur at the transition point where sporulation starts. At this point, several stress response genes were also turned on. The genes involved in the lipid catabolic process were up-regulated briefly at 15 h as an outcome of the programmed cell death that postpones sporulation. The results suggest that changes in the gene expression of B. subtilis DB104 were dependent on the initiation of sporulation. However, the expression timing of the spore coat gene was only affected by the relevant sigma factor. This study can help to understand gene expression and regulatory mechanisms in B. subtilis species by providing an overall view of transcriptional changes during the growth of B. subtilis DB104.

MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors

All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.

Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database ‘EcoPromDB’ has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

Prediction and validation of novel SigB regulon members in Bacillus subtilis and regulon structure comparison to Bacillales members

BACKGROUND

Sigma factor B (SigB) is the central regulator of the general stress response in Bacillus subtilis and regulates a group of genes in response to various stressors, known as the SigB regulon members. Genes that are directly regulated by SigB contain a promotor binding motif (PBM) with a previously identified consensus sequence.

RESULTS

In this study, refined SigB PBMs were derived and different spacer compositions and lengths (N₁₂-N₁₇) were taken into account. These were used to identify putative SigB-regulated genes in the B. subtilis genome, revealing 255 genes: 99 had been described in the literature and 156 genes were newly identified, increasing the number of SigB putative regulon members (with and without a SigB PBM) to > 500 in B. subtilis. The 255 genes were assigned to five categories (I-V) based on their similarity to the original SigB consensus sequences. The functionalities of selected representatives per category were assessed using promoter-reporter fusions in wt and ΔsigB mutants upon exposure to heat, ethanol, and salt stress. The activity of the P_rsbV (I) positive control was induced upon exposure to all three stressors. P_ytoQ (II) showed SigB-dependent activity only upon exposure to ethanol, whereas P_pucI (II) with a N₁₇ spacer and P_ylaL (III) with a N₁₆ spacer showed mild induction regardless of heat/ethanol/salt stress. P_ywzA (III) and P_yaaI (IV) displayed ethanol-specific SigB-dependent activities despite a lower-level conserved - 10 binding motif. P_gtaB (V) was SigB-induced under ethanol and salt stress while lacking a conserved - 10 binding region. The activities of P_ygaO and P_ykaA (III) did not show evident changes under the conditions tested despite having a SigB PBM that highly resembled the consensus. The identified extended SigB regulon candidates in B. subtilis are mainly involved in coping with stress but are also engaged in other cellular processes. Orthologs of SigB regulon candidates with SigB PBMs were identified in other Bacillales genomes, but not all showed a SigB PBM. Additionally, genes involved in the integration of stress signals to activate SigB were predicted in these genomes, indicating that SigB signaling and regulon genes are species-specific.

CONCLUSION

The entire SigB regulatory network is sophisticated and not yet fully understood even for the well-characterized organism B. subtilis 168. Knowledge and information gained in this study can be used in further SigB studies to uncover a complete picture of the role of SigB in B. subtilis and other species.

Poly-γ-Glutamic Acid Production by Engineering a DegU Quorum-Sensing Circuit in Bacillus subtilis

As a natural biological macromolecule, γ-polyglutamic acid (γ-PGA) plays a significant role in medicine, food, and cosmetic industries owing to its unique properties of biocompatibility, biodegradability, water solubility, and viscosity. Although many strategies have been adopted to increase the yield of γ-PGA in Bacillus subtilis, the effectiveness of these common approaches is not high because the strong viscosity affects cell growth. However, dynamic regulation based on quorum sensing (QS) has been extensively applied as a fundamental tool for fine-tuning gene expression in reaction to changes in cell density without adding expensive inducers. A modular PhrQ-RapQ-DegU QS system is developed based on promoter P_D4, which is upregulated by phosphorylated DegU (DegU-P). In this study, first, we analyzed the DegU-based gene expression regulation system in B. subtilis 168. We constructed a promoter library of different abilities, selected suitable promoters from the library, and performed mutation screening on the selected promoters and degU region. Furthermore, we constructed a PhrQ-RapQ-DegU QS system to dynamically control the synthesis of γ-PGA in BS168. Cell growth and efficient synthesis of the target product can be dynamically balanced by the QS system. Our dynamic adjustment approach increased the yield of γ-PGA to 6.53-fold of that by static regulation in a 3 L bioreactor, which verified the effectiveness of this strategy. In summary, the PhrQ-RapQ-DegU QS system has been successfully integrated with biocatalytic functions to achieve dynamic metabolic pathway control in BS168, which can be stretched to a large number of microorganisms to fine-tune gene expression and enhance the production of metabolites.

iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called "iProm-phage" for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.

Draft Genome Sequence of the Tomato Stem Endophyte Bacillus safensis TS3

Tomato stem endophyte Bacillus safensis TS3 was isolated from surface-sterilized stems of greenhouse tomato plants. Here, we sequenced the complete genome of this strain to understand the molecular mechanisms underlying its beneficial activities.

GroovDB: A Database of Ligand-Inducible Transcription Factors

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.

Homology-based reconstruction of regulatory networks for bacterial and archaeal genomes

Gene regulation is a key process for all microorganisms, as it allows them to adapt to different environmental stimuli. However, despite the relevance of gene expression control, for only a handful of organisms is there related information about genome regulation. In this work, we inferred the gene regulatory networks (GRNs) of bacterial and archaeal genomes by comparisons with six organisms with well-known regulatory interactions. The references we used are: Escherichia coli K-12 MG1655, Bacillus subtilis 168, Mycobacterium tuberculosis, Pseudomonas aeruginosa PAO1, Salmonella enterica subsp. enterica serovar typhimurium LT2, and Staphylococcus aureus N315. To this end, the inferences were achieved in two steps. First, the six model organisms were contrasted in an all-vs-all comparison of known interactions based on Transcription Factor (TF)-Target Gene (TG) orthology relationships and Transcription Unit (TU) assignments. In the second step, we used a guilt-by-association approach to infer the GRNs for 12,230 bacterial and 649 archaeal genomes based on TF-TG orthology relationships of the six bacterial models determined in the first step. Finally, we discuss examples to show the most relevant results obtained from these inferences. A web server with all the predicted GRNs is available at https://regulatorynetworks.unam.mx/ or http://132.247.46.6/.

From a large-scale genomic analysis of insertion sequences to insights into their regulatory roles in prokaryotes

Background

Insertion sequences (ISs) are mobile repeat sequences and most of them can copy themselves to new host genome locations, leading to genome plasticity and gene regulation in prokaryotes. In this study, we present functional and evolutionary relationships between IS and neighboring genes in a large-scale comparative genomic analysis.

Results

IS families were located in all prokaryotic phyla, with preferential occurrence of IS3, IS4, IS481, and IS5 families in Alpha-, Beta-, and Gammaproteobacteria, Actinobacteria and Firmicutes as well as in eukaryote host-associated organisms and autotrophic opportunistic pathogens. We defined the concept of the IS-Gene couple (IG), which allowed to highlight the functional and regulatory impacts of an IS on the closest gene. Genes involved in transcriptional regulation and transport activities were found overrepresented in IG. In particular, major facilitator superfamily (MFS) transporters, ATP-binding proteins and transposases raised as favorite neighboring gene functions of IS hotspots. Then, evolutionary conserved IS-Gene sets across taxonomic lineages enabled the classification of IS-gene couples into phylum, class-to-genus, and species syntenic IS-Gene couples. The IS5, IS21, IS4, IS607, IS91, ISL3 and IS200 families displayed two to four times more ISs in the phylum and/or class-to-genus syntenic IGs compared to other IS families. This indicates that those families were probably inserted earlier than others and then subjected to horizontal transfer, transposition and deletion events over time. In phylum syntenic IG category, Betaproteobacteria, Crenarchaeota, Calditrichae, Planctomycetes, Acidithiobacillia and Cyanobacteria phyla act as IS reservoirs for other phyla, and neighboring gene functions are mostly related to transcriptional regulators. Comparison of IS occurrences with predicted regulatory motifs led to ~ 26.5% of motif-containing ISs with 2 motifs per IS in average. These results, concomitantly with short IS-Gene distances, suggest that those ISs would interfere with the expression of neighboring genes and thus form strong candidates for an adaptive pairing.

Conclusions

All together, our large-scale study provide new insights into the IS genetic context and strongly suggest their regulatory roles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08678-3.

Bacterial Transcriptional Regulators: A Road Map for Functional, Structural, and Biophysical Characterization

The different niches through which bacteria move during their life cycle require a fast response to the many environmental queues they encounter. The sensing of these stimuli and their correct response is driven primarily by transcriptional regulators. This kind of protein is involved in sensing a wide array of chemical species, a process that ultimately leads to the regulation of gene transcription. The allosteric-coupling mechanism of sensing and regulation is a central aspect of biological systems and has become an important field of research during the last decades. In this review, we summarize the state-of-the-art techniques applied to unravel these complex mechanisms. We introduce a roadmap that may serve for experimental design, depending on the answers we seek and the initial information we have about the system of study. We also provide information on databases containing available structural information on each family of transcriptional regulators. Finally, we discuss the recent results of research about the allosteric mechanisms of sensing and regulation involving many transcriptional regulators of interest, highlighting multipronged strategies and novel experimental techniques. The aim of the experiments discussed here was to provide a better understanding at a molecular level of how bacteria adapt to the different environmental threats they face.

PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning

BACKGROUND

Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm.

RESULTS

To train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs.

CONCLUSION

PredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Prediction of DNA-Binding Transcription Factors in Bacteria and Archaea Genomes

DNA-binding transcription factors (TFs) play a central role in the gene expression of all organisms, from viruses to humans, including bacteria and archaea. The role of these proteins is the fate of gene expression in the context of environmental challenges. Because thousands of genomes have been sequenced to date, predictions of the encoded proteins are validated through the use of bioinformatics tools to obtain the necessary experimental, posterior knowledge. In this chapter, we describe three approaches to identify TFs in protein sequences. The first approach integrates the results of sequence comparisons and PFAM assignments, using as reference a manually curated collection of TFs. The second approach considers the prediction of DNA-binding structures, such as the classical helix-turn-helix (HTH); and the third approach considers a deep learning model. We suggest that all approaches must be considered together to increase the possibility of identifying new TFs in bacterial and archaeal genomes.

Dormant phages communicate via arbitrium to control exit from lysogeny

Temperate bacterial viruses (phages) can transition between lysis-replicating and killing the host-and lysogeny, that is, existing as dormant prophages while keeping the host viable. Recent research showed that on invading a naïve cell, some phages communicate using a peptide signal, termed arbitrium, to control the decision of entering lysogeny. Whether communication can also serve to regulate exit from lysogeny (known as phage induction) is unclear. Here we show that arbitrium-coding prophages continue to communicate from the lysogenic state by secreting and sensing the arbitrium signal. Signalling represses DNA damage-dependent phage induction, enabling prophages to reduce the induction rate when surrounded by other lysogens. We show that in certain phages, DNA damage and communication converge to regulate the expression of the arbitrium-responsive gene aimX, while in others integration of DNA damage and communication occurs downstream of aimX expression. Additionally, signalling by prophages tilts the decision of nearby infecting phages towards lysogeny. Altogether, we find that phages use small-molecule communication throughout their entire life cycle to sense the abundance of lysogens in the population, thus avoiding lysis when they are likely to encounter established lysogens rather than permissive uninfected hosts.

PRODORIC: state-of-the-art database of prokaryotic gene regulation

PRODORIC is worldwide one of the largest collections of prokaryotic transcription factor binding sites from multiple bacterial sources with corresponding interpretation and visualization tools. With the introduction of PRODORIC2 in 2017, the transition to a modern web interface and maintainable backend was started. With this latest PRODORIC release the database backend is now fully API-based and provides programmatical access to the complete PRODORIC data. The visualization tools Genome Browser and ProdoNet from the original PRODORIC have been reintroduced and were integrated into the PRODORIC website. Missing input and output options from the original Virtual Footprint were added again for position weight matrix pattern-based searches. The whole PRODORIC dataset was reannotated. Every transcription factor binding site was re-evaluated to increase the overall database quality. During this process, additional parameters, like bound effectors, regulation type and different types of experimental evidence have been added for every transcription factor. Additionally, 109 new transcription factors and 6 new organisms have been added. PRODORIC is publicly available at https://www.prodoric.de.

Rapid preparation of 6S RNA-free B. subtilis σA-RNA polymerase and σA

The regulatory 6S-1 and 6S-2 RNAs of B. subtilis bind to the housekeeping RNA polymerase holoenzyme (σ^A-RNAP) with submicromolar affinity. We observed copurification of endogenous 6S RNAs from a published B. subtilis strain expressing a His-tagged RNAP. Such 6S RNA contaminations in σ^A-RNAP preparations reduce the fraction of enzymes that are accessible for binding to DNA promoters. In addition, this leads to background RNA synthesis by σ^A-RNAP utilizing copurified 6S RNA as template for the synthesis of short abortive transcripts termed product RNAs (pRNAs). To avoid this problem we constructed a B. subtilis strain expressing His-tagged RNAP but carrying deletions of the two 6S RNA genes. The His-tagged, 6S RNA-free σ^A-RNAP holoenzyme can be prepared with sufficient purity and activity by a single affinity step. We also report expression and separate purification of B. subtilis σ^A that can be added to the His-tagged RNAP to maximize the amount of holoenzyme and, by inference, in vitro transcription activity.

Identification of an RNA sponge that controls the RoxS riboregulator of central metabolism in Bacillus subtilis

sRNAs are a taxonomically-restricted but transcriptomically-abundant class of post-transcriptional regulators. While of major importance for adaption to the environment, we currently lack global-scale methodology enabling target identification, especially in species without known RNA hub proteins (e.g. Hfq). Using psoralen RNA cross-linking and Illumina-sequencing we identify RNA-RNA interacting pairs in vivo in Bacillus subtilis, resolving previously well-described interactants. Although sRNA-sRNA pairings are rare (compared with sRNA-mRNA), we identify a robust example involving the conserved sRNA RoxS and an unstudied sRNA RosA (Regulator of sRNA A). We show RosA to be the first confirmed RNA sponge described in a Gram-positive bacterium. RosA interacts with at least two sRNAs, RoxS and FsrA. The RosA/RoxS interaction not only affects the levels of RoxS but also its processing and regulatory activity. We also found that the transcription of RosA is repressed by CcpA, the key regulator of carbon-metabolism in B. subtilis. Since RoxS is already known to be transcriptionally controlled by malate via the transcriptional repressor Rex, its post-transcriptional regulation by CcpA via RosA places RoxS in a key position to control central metabolism in response to varying carbon sources.

A novel computational framework for genome-scale alternative transcription units prediction

Alternative transcription units (ATUs) are dynamically encoded under different conditions and display overlapping patterns (sharing one or more genes) under a specific condition in bacterial genomes. Genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques because of the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in Escherichia coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. In addition, the proportion of 5'- or 3'-end genes of the predicted ATUs, having documented transcription factor binding sites and transcription termination sites, was three times greater than that of no 5'- or 3'-end genes. We further evaluated the predicted ATUs by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. The results suggested that gene pairs frequently encoded in the same ATUs are more functionally related than those that can belong to two distinct ATUs. Overall, these results demonstrated the high reliability of predicted ATUs. We expect that the new insights derived by SeqATU will not only improve the understanding of the transcription mechanism of bacteria but also guide the reconstruction of a genome-scale transcriptional regulatory network.

Sigma factor dependent translational activation in Bacillus subtilis

Sigma factors are an important class of bacterial transcription factors that lend specificity to RNA polymerases by binding to distinct promoter elements for genes in their regulons. Here we show that activation of the general stress sigma factor, σB, in Bacillus subtilis paradoxically leads to dramatic induction of translation for a subset of its regulon genes. These genes are translationally repressed when transcribed by the housekeeping sigma factor, σA, owing to extended RNA secondary structures as determined in vivo using DMS-MaPseq. Transcription from σB-dependent promoters ablates the secondary structures and activates translation, leading to dual induction. Translation efficiencies between σB- and σA-dependent RNA isoforms can vary by up to 100-fold, which in multiple cases exceeds the magnitude of transcriptional induction. These results highlight the role of long-range RNA folding in modulating translation and demonstrate that a transcription factor can regulate protein synthesis beyond its effects on transcript levels.

Bagasse minority pathway expression: Real time study of GH2 β-mannosidases from bacteroidetes

After being isolated from a sugarcane pile, the bacterium Chitinophaga sp. CB10 demonstrated to be a rich source of carbohydrases, with 350 predicted CAZyme domains. CB10 was able to grow on carbohydrates of different structural complexities: glucose, carboxymethylcellulose, corn starch, galactomannan, Aloe vera gum and sugarcane bagasse. The sugarcane bagasse is a rich source of complex polymers, and the diversity of metabolites released by its enzymatic hydrolysis has an important role for green chemistry, including minority pathways such as the degradation of mannan conjugates. In this sense, CB10 demonstrated considerable levels of gene expression for mannanases, and was stable for a period of 96-144 hours in the presence of sugarcane bagasse as sole carbon source. The bacterium showed respectively 4.8x and 5.6x expression levels for two genes predicted for GH2 β-mannosidase: one located within a gene cluster identified as "polysaccharide utilization loci" (PUL), and another a classic β-mannosidase. These enzymes shared less than 45% of identity with enzymes characterized from the genus Chitinophaga belonging to the phylum Bacteroidetes. The degree of novelty-as demonstrated by the low identity with previously characterized enzymes; the remarkable capability to grow in different substrates; mannanase activity, evidenced by the release of residual oligosaccharides in the cultivation with galactomannan (HPLC-RID, 12.3 mMol); associated to the ability of mannanases expression in a low concentration of inductor conditions (sugarcane bagasse, 0.2%) indicate the high potential for the application of CB10 as a source of enzymes in the production of oligosaccharides from biomass. This capacity might prove to be very valuable for the biorefinery process of pre-biotic precursors and other functional oligosaccharides focused on the food and pharmaceutical industries.

ComHub: Community predictions of hubs in gene regulatory networks

BACKGROUND

Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

RESULTS

We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.

CONCLUSIONS

In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

BSGatlas: a unified Bacillus subtilis genome and transcriptome annotation atlas with enhanced information access

A large part of our current understanding of gene regulation in Gram-positive bacteria is based on Bacillus subtilis, as it is one of the most well studied bacterial model systems. The rapid growth in data concerning its molecular and genomic biology is distributed across multiple annotation resources. Consequently, the interpretation of data from further B. subtilis experiments becomes increasingly challenging in both low- and large-scale analyses. Additionally, B. subtilis annotation of structured RNA and non-coding RNA (ncRNA), as well as the operon structure, is still lagging behind the annotation of the coding sequences. To address these challenges, we created the B. subtilis genome atlas, BSGatlas, which integrates and unifies multiple existing annotation resources. Compared to any of the individual resources, the BSGatlas contains twice as many ncRNAs, while improving the positional annotation for 70 % of the ncRNAs. Furthermore, we combined known transcription start and termination sites with lists of known co-transcribed gene sets to create a comprehensive transcript map. The combination with transcription start/termination site annotations resulted in 717 new sets of co-transcribed genes and 5335 untranslated regions (UTRs). In comparison to existing resources, the number of 5' and 3' UTRs increased nearly fivefold, and the number of internal UTRs doubled. The transcript map is organized in 2266 operons, which provides transcriptional annotation for 92 % of all genes in the genome compared to the at most 82 % by previous resources. We predicted an off-target-aware genome-wide library of CRISPR-Cas9 guide RNAs, which we also linked to polycistronic operons. We provide the BSGatlas in multiple forms: as a website (https://rth.dk/resources/bsgatlas/), an annotation hub for display in the UCSC genome browser, supplementary tables and standardized GFF3 format, which can be used in large scale -omics studies. By complementing existing resources, the BSGatlas supports analyses of the B. subtilis genome and its molecular biology with respect to not only non-coding genes but also genome-wide transcriptional relationships of all genes.

Detecting operons in bacterial genomes via visual representation learning

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

Embryo-Like Features in Developing Bacillus subtilis Biofilms

Correspondence between evolution and development has been discussed for more than two centuries. Recent work reveals that phylogeny-ontogeny correlations are indeed present in developmental transcriptomes of eukaryotic clades with complex multicellularity. Nevertheless, it has been largely ignored that the pervasive presence of phylogeny-ontogeny correlations is a hallmark of development in eukaryotes. This perspective opens a possibility to look for similar parallelisms in biological settings where developmental logic and multicellular complexity are more obscure. For instance, it has been increasingly recognized that multicellular behavior underlies biofilm formation in bacteria. However, it remains unclear whether bacterial biofilm growth shares some basic principles with development in complex eukaryotes. Here we show that the ontogeny of growing Bacillus subtilis biofilms recapitulates phylogeny at the expression level. Using time-resolved transcriptome and proteome profiles, we found that biofilm ontogeny correlates with the evolutionary measures, in a way that evolutionary younger and more diverged genes were increasingly expressed toward later timepoints of biofilm growth. Molecular and morphological signatures also revealed that biofilm growth is highly regulated and organized into discrete ontogenetic stages, analogous to those of eukaryotic embryos. Together, this suggests that biofilm formation in Bacillus is a bona fide developmental process comparable to organismal development in animals, plants, and fungi. Given that most cells on Earth reside in the form of biofilms and that biofilms represent the oldest known fossils, we anticipate that the widely adopted vision of the first life as a single-cell and free-living organism needs rethinking.

Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes

The herbivore digestive tract is home to a complex community of anaerobic microbes that work together to break down lignocellulose. These microbiota are an untapped resource of strains, pathways and enzymes that could be applied to convert plant waste into sugar substrates for green biotechnology. We carried out more than 400 parallel enrichment experiments from goat faeces to determine how substrate and antibiotic selection influence membership, activity, stability and chemical productivity of herbivore gut communities. We assembled 719 high-quality metagenome-assembled genomes (MAGs) that are unique at the species level. More than 90% of these MAGs are from previously unidentified herbivore gut microorganisms. Microbial consortia dominated by anaerobic fungi outperformed bacterially dominated consortia in terms of both methane production and extent of cellulose degradation, which indicates that fungi have an important role in methane release. Metabolic pathway reconstructions from MAGs of 737 bacteria, archaea and fungi suggest that cross-domain partnerships between fungi and methanogens enabled production of acetate, formate and methane, whereas bacterially dominated consortia mainly produced short-chain fatty acids, including propionate and butyrate. Analyses of carbohydrate-active enzyme domains present in each anaerobic consortium suggest that anaerobic bacteria and fungi employ mostly complementary hydrolytic strategies. The division of labour among herbivore anaerobes to degrade plant biomass could be harnessed for industrial bioprocessing.

Transcriptome based functional identification and application of regulator AbrB on alkaline protease synthesis in Bacillus licheniformis 2709

Bacillus licheniformis 2709 is the major alkaline protease producer, which has great potential value of industrial application, but how the high-producer can be regulated rationally is still not completely understood. It's meaningful to understand the metabolic processes during alkaline protease production in industrial fermentation medium. Here, we collected the transcription database at various enzyme-producing stages (preliminary stage, stable phase and decline phase) to specifically research the synthesized and regulatory mechanism of alkaline protease in B. licheniformis. The RNA-sequencing analysis showed differential expression of numerous genes related to several processes, among which genes correlated with regulators were concerned, especially the major differential gene abrB on enzyme (AprE) synthesis was investigated. It was further verified that AbrB is a repressor of AprE by plasmid-mediated over-expression due to the severely descending enzyme activity (11,300 U/mL to 2695 U/mL), but interestingly it is indispensable for alkaline protease production because the enzyme activity of the null abrB mutant was just about 2279 U/mL. Thus, we investigated the aprE transcription by eliminating the theoretical binding site (TGGAA) of AbrB protein predicated by computational strategy, which significantly improved the enzyme activity by 1.21-fold and gene transcription level by 1.77-fold in the mid-log phase at a cultivation time of 18 h. Taken together, it is of great significance to improve the production strategy, control the metabolic process and oriented engineering by rational molecular modification of regulatory network based on the high throughput sequencing and computational prediction.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

Deciphering the functional diversity of DNA-binding transcription factors in Bacteria and Archaea organisms

DNA-binding Transcription Factors (TFs) play a central role in regulation of gene expression in prokaryotic organisms, and similarities at the sequence level have been reported. These proteins are predicted with different abundances as a consequence of genome size, where small organisms contain a low proportion of TFs and large genomes contain a high proportion of TFs. In this work, we analyzed a collection of 668 experimentally validated TFs across 30 different species from diverse taxonomical classes, including Escherichia coli K-12, Bacillus subtilis 168, Corynebacterium glutamicum, and Streptomyces coelicolor, among others. This collection of TFs, together with 111 hidden Markov model profiles associated with DNA-binding TFs collected from diverse databases such as PFAM and DBD, was used to identify the repertoire of proteins putatively devoted to gene regulation in 1321 representative genomes of Archaea and Bacteria. The predicted regulatory proteins were posteriorly analyzed in terms of their genomic context, allowing the prediction of functions for TFs and their neighbor genes, such as genes involved in virulence, enzymatic functions, phosphorylation mechanisms, and antibiotic resistance. The functional analysis associated with PFAM groups showed diverse functional categories were significantly enriched in the collection of TFs and the proteins encoded by the neighbor genes, in particular, small-molecule binding and amino acid transmembrane transporter activities associated with the LysR family and proteins devoted to cellular aromatic compound metabolic processes or responses to drugs, stress, or abiotic stimuli in the MarR family. We consider that with the increasing data derived from new technologies, novel TFs can be identified and help improve the predictions for this class of proteins in complete genomes. The complete collection of experimentally characterized and predicted TFs is available at http://web.pcyt.unam.mx/EntrafDB/.

The functional determinants in the organization of bacterial genomes

Bacterial genomes are now recognized as interacting intimately with cellular processes. Uncovering organizational mechanisms of bacterial genomes has been a primary focus of researchers to reveal the potential cellular activities. The advances in both experimental techniques and computational models provide a tremendous opportunity for understanding these mechanisms, and various studies have been proposed to explore the organization rules of bacterial genomes associated with functions recently. This review focuses mainly on the principles that shape the organization of bacterial genomes, both locally and globally. We first illustrate local structures as operons/transcription units for facilitating co-transcription and horizontal transfer of genes. We then clarify the constraints that globally shape bacterial genomes, such as metabolism, transcription and replication. Finally, we highlight challenges and opportunities to advance bacterial genomic studies and provide application perspectives of genome organization, including pathway hole assignment and genome assembly and understanding disease mechanisms.

Genome-Wide Transcription Start Site Mapping and Promoter Assignments to a Sigma Factor in the Human Enteropathogen Clostridioides difficile

The emerging human enteropathogen Clostridioides difficile is the main cause of diarrhea associated with antibiotherapy. Regulatory pathways underlying the adaptive responses remain understudied and the global view of C. difficile promoter structure is still missing. In the genome of C. difficile 630, 22 genes encoding sigma factors are present suggesting a complex pattern of transcription in this bacterium. We present here the first transcriptional map of the C. difficile genome resulting from the identification of transcriptional start sites (TSS), promoter motifs and operon structures. By 5′-end RNA-seq approach, we mapped more than 1000 TSS upstream of genes. In addition to these primary TSS, this analysis revealed complex structure of transcriptional units such as alternative and internal promoters, potential RNA processing events and 5′ untranslated regions. By following an in silico iterative strategy that used as an input previously published consensus sequences and transcriptomic analysis, we identified candidate promoters upstream of most of protein-coding and non-coding RNAs genes. This strategy also led to refine consensus sequences of promoters recognized by major sigma factors of C. difficile. Detailed analysis focuses on the transcription in the pathogenicity locus and regulatory genes, as well as regulons of transition phase and sporulation sigma factors as important components of C. difficile regulatory network governing toxin gene expression and spore formation. Among the still uncharacterized regulons of the major sigma factors of C. difficile, we defined the SigL regulon by combining transcriptome and in silico analyses. We showed that the SigL regulon is largely involved in amino-acid degradation, a metabolism crucial for C. difficile gut colonization. Finally, we combined our TSS mapping, in silico identification of promoters and RNA-seq data to improve gene annotation and to suggest operon organization in C. difficile. These data will considerably improve our knowledge of global regulatory circuits controlling gene expression in C. difficile and will serve as a useful rich resource for scientific community both for the detailed analysis of specific genes and systems biology studies.

Advances on systems metabolic engineering of Bacillus subtilis as a chassis cell

The Gram-positive model bacterium Bacillus subtilis, has been broadly applied in various fields because of its low pathogenicity and strong protein secretion ability, as well as its well-developed fermentation technology. B. subtilis is considered as an attractive host in the field of metabolic engineering, in particular for protein expression and secretion, so it has been well studied and applied in genetic engineering. In this review, we discussed why B. subtilis is a good chassis cell for metabolic engineering. We also summarized the latest research progress in systematic biology, synthetic biology and evolution-based engineering of B. subtilis, and showed systemic metabolic engineering expedite the harnessing B. subtilis for bioproduction.

Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of meta-curated, historical, bacterial regulatory networks, their completeness and system-level characterization

Some organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species (Mycobacterium tuberculosis, Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pyogenes, Streptococcus pneumoniae, and Streptomyces coelicolor), containing 8459 regulons and 4335 modules.

Database URL: https://abasy.ccg.unam.mx/.

Impact of Fatty-Acid Labeling of Bacillus subtilis Membranes on the Cellular Lipidome and Proteome

Developing cultivation methods that yield chemically and isotopically defined fatty acid (FA) compositions within bacterial cytoplasmic membranes establishes an in vivo experimental platform to study membrane biophysics and cell membrane regulation using novel approaches. Yet before fully realizing the potential of this method, it is prudent to understand the systemic changes in cells induced by the labeling procedure itself. In this work, analysis of cellular membrane compositions was paired with proteomics to assess how the proteome changes in response to the directed incorporation of exogenous FAs into the membrane of Bacillus subtilis. Key findings from this analysis include an alteration in lipid headgroup distribution, with an increase in phosphatidylglycerol lipids and decrease in phosphatidylethanolamine lipids, possibly providing a fluidizing effect on the cell membrane in response to the induced change in membrane composition. Changes in the abundance of enzymes involved in FA biosynthesis and degradation are observed; along with changes in abundance of cell wall enzymes and isoprenoid lipid production. The observed changes may influence membrane organization, and indeed the well-known lipid raft-associated protein flotillin was found to be substantially down-regulated in the labeled cells – as was the actin-like protein MreB. Taken as a whole, this study provides a greater depth of understanding for this important cell membrane experimental platform and presents a number of new connections to be explored in regard to modulating cell membrane FA composition and its effects on lipid headgroup and raft/cytoskeletal associated proteins.

Conserved DNA Methyltransferases: A Window into Fundamental Mechanisms of Epigenetic Regulation in Bacteria

An increasing number of studies have reported that bacterial DNA methylation has important functions beyond the roles in restriction-modification systems, including the ability of affecting clinically relevant phenotypes such as virulence, host colonization, sporulation, biofilm formation, among others. Although insightful, such studies have a largely ad hoc nature and would benefit from a systematic strategy enabling a joint functional characterization of bacterial methylomes by the microbiology community. In this opinion article, we propose that highly conserved DNA methyltransferases (MTases) represent a unique opportunity for bacterial epigenomic studies. These MTases are rather common in bacteria, span various taxonomic scales, and are present in multiple human pathogens. Apart from well-characterized core DNA MTases, like those from Vibrio cholerae, Salmonella enterica, Clostridioides difficile, or Streptococcus pyogenes, multiple highly conserved DNA MTases are also found in numerous human pathogens, including those belonging to the genera Burkholderia and Acinetobacter. We discuss why and how these MTases can be prioritized to enable a community-wide, integrative approach for functional epigenomic studies. Ultimately, we discuss how some highly conserved DNA MTases may emerge as promising targets for the development of novel epigenetic inhibitors for biomedical applications.

CoryneRegNet 7, the reference database and analysis platform for corynebacterial gene regulatory networks

We present the newest version of CoryneRegNet, the reference database for corynebacterial regulatory interactions, available at www.exbio.wzw.tum.de/coryneregnet/. The exponential growth of next-generation sequencing data in recent years has allowed a better understanding of bacterial molecular mechanisms. Transcriptional regulation is one of the most important mechanisms for bacterial adaptation and survival. These mechanisms may be understood via an organism's network of regulatory interactions. Although the Corynebacterium genus is important in medical, veterinary and biotechnological research, little is known concerning the transcriptional regulation of these bacteria. Here, we unravel transcriptional regulatory networks (TRNs) for 224 corynebacterial strains by utilizing genome-scale transfer of TRNs from four model organisms and assigning statistical significance values to all predicted regulations. As a result, the number of corynebacterial strains with TRNs increased twenty times and the back-end and front-end were reimplemented to support new features as well as future database growth. CoryneRegNet 7 is the largest TRN database for the Corynebacterium genus and aids in elucidating transcriptional mechanisms enabling adaptation, survival and infection.

Spo0A can efficiently enhance the expression of the alkaline protease gene aprE in Bacillus licheniformis by specifically binding to its regulatory region

The expression of enzymes in Bacillus licheniformis, such as the valuable extracellular alkaline protease AprE, is highly regulated by a complex transcriptional regulation mechanism. Here, we found that the transcript abundance of aprE varies >343-fold in response to the supply of nutrients or to environmental challenges. To identify the underlying regulatory mechanism, the core promoter of aprE and several important upstream regulatory regions outside the promoter were firstly confirmed by 5'-RACE and mutagenesis experiments. The specific proteins that bind to the identified sequences were subsequently captured by DNA pull-down experiments, which yielded the transcriptional factors (TFs) Spo0A, CggR, FruR, YhcZ, as well as fragments of functionally unassigned proteins. Further electrophoretic mobility shift assay (EMSA) and DNase I foot-printing experiments indicated that Spo0A can directly bind to the region from -92 to -118 nucleotides upstream of the transcription start site, and the deletion of this specific region drastically decreased the production of AprE. Taken together, these results indicated that the expression of aprE was mainly regulated by the interplay between Spo0A and its cognate DNA sequence, which was successfully applied to overproduce AprE in a genetically modified host harboring three aprE expression cassettes. The DNA binding proteins may serve to increase the efficiency of transcription by creating an additional binding site for RNA polymerase. The discovery of this mechanism significantly increases our understanding of the aprE transcription mechanism, which is of great importance for AprE overproduction.

Antibiotic drug discovery: Challenges and perspectives in the light of emerging antibiotic resistance

Amid a rising threat of antimicrobial resistance in a global scenario, our huge investments and high-throughput technologies injected for rejuvenating the key therapeutic scaffolds to suppress these rising superbugs has been diminishing severely. This has grasped world-wide attention, with increased consideration being given to the discovery of new chemical entities. Research has now proven that the relatively tiny and simpler microbes possess enhanced capability of generating novel and diverse chemical constituents with huge therapeutic leads. The usage of these beneficial organisms could help in producing new chemical scaffolds that govern the power to suppress the spread of obnoxious superbugs. Here in this review, we have explicitly focused on several appealing strategies employed for the generation of new chemical scaffolds. Also, efforts on providing novel insights on some of the unresolved questions in the production of metabolites, metabolic profiling and also the serendipity of getting "hit molecules" have been rigorously discussed. However, we are highly aware that biosynthetic pathway of different classes of secondary metabolites and their biosynthetic route is a vast topic, thus we have avoided discussion on this topic.