Comprehensive comparative analysis of the periodontal pathogen Porphyromonas gingivalis: exploring the pan-genome, the reconstruction of the gene regulatory network and genome-scale metabolic network.

Porphyromonas gingivalis is a nonmotile, obligate anaerobic, Gram-negative bacterium known for its association with periodontal disease and its involvement in systemic diseases such as atherosclerosis, cardiovascular disease, colon cancer and Alzheimer's disease. This bacterium produces several virulence factors, including capsules, fimbriae, lipopolysaccharides, proteolytic enzymes and hemagglutinins. A comparative genomic analysis revealed the open pangenome of P. gingivalis and identified complete type IV secretion systems (T4SS) in strain KCOM2805 and almost complete type VI secretion systems (T6SS) in strains KCOM2798 and ATCC49417, which is a new discovery as previous studies did not find the proteins involved in secretion systems IV and VI. Conservation of some virulence factors between different strains was observed, regardless of their genetic diversity and origin. In addition, we performed for the first time a reconstruction analysis of the gene regulatory network (GRN), identifying transcription factors and proteins involved in the regulatory mechanisms of bacterial pathogenesis. In particular, QseB regulates the expression of hemagglutinin and arginine deaminase, while Rex may suppress the release of gingipain through interactions with PorV and the formatum/nitrate transporter. Our study highlights the central role of conserved virulence factors and regulatory pathways, particularly QseB and Rex, in P. gingivalis and provides insights into potential therapeutic targets.

DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.

The interaction of InvF-RNAP is mediated by the chaperone SicA in Salmonella sp: an in silico prediction.

In this work we carried out an in silico analysis to understand the interaction between InvF-SicA and RNAP in the bacterium Salmonella Typhimurium strain LT2. Structural analysis of InvF allowed the identification of three possible potential cavities for interaction with SicA. This interaction could occur with the structural motif known as tetratricopeptide repeat (TPR) 1 and 2 in the two cavities located in the interface of the InvF and α-CTD of RNAP. Indeed, molecular dynamics simulations showed that SicA stabilizes the Helix-turn-Helix DNA-binding motifs, i.e., maintaining their proper conformation, mainly in the DNA Binding Domain (DBD). Finally, to evaluate the role of amino acids that contribute to protein-protein affinity, an alanine scanning mutagenesis approach, indicated that R177 and R181, located in the DBD motif, caused the greatest changes in binding affinity with α-CTD, suggesting a central role in the stabilization of the complex. However, it seems that the N-terminal region also plays a key role in the protein-protein interaction, especially the amino acid R40, since we observed conformational flexibility in this region allowing it to interact with interface residues. We consider that this analysis opens the possibility to validate experimentally the amino acids involved in protein-protein interactions and explore other regulatory complexes where chaperones are involved.

Differential Impact of CD43 and CD28 on T-Cell Differentiation Depending on the Order of Engagement with the TCR.

The combination of signals from the T-cell receptor (TCR) and co-stimulatory molecules triggers transcriptional programs that lead to proliferation, cytokine secretion, and effector functions. We compared the impact of engaging the TCR with CD28 and/or CD43 at different time points relative to TCR engagement on T-cell function. TCR and CD43 simultaneous engagement resulted in higher CD69 and PD-1 expression levels than in TCR and CD28-stimulated cells, with a cytokine signature of mostly effector, inflammatory, and regulatory cytokines, while TCR and CD28-activated cells secreted all categories of cytokines, including stimulatory cytokines. Furthermore, the timing of CD43 engagement relative to TCR ligation, and to a lesser degree that of CD28, resulted in distinct patterns of expression of cytokines, chemokines, and growth factors. Complete cell activation was observed when CD28 or CD43 were engaged simultaneously with or before the TCR, but ligating the TCR before CD43 or CD28 failed to complete a cell activation program regarding cytokine secretion. As the order in which CD43 or CD28 and the TCR were engaged resulted in different combinations of cytokines that shape distinct T-cell immune programs, we analyzed their upstream sequences to assess whether the combinations of cytokines were associated with different sets of regulatory elements. We found that the order in which the TCR and CD28 or CD43 are engaged predicts the recruitment of specific sets of chromatin remodelers and TFSS, which ultimately regulate T-cell polarization and plasticity. Our data underscore that the combination of co-stimulatory molecules and the time when they are engaged relative to the TCR can change the cell differentiation program.

CDBProm: the Comprehensive Directory of Bacterial Promoters.

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.

Interactions between the AraC/XylS-like transcriptional activator InvF of Salmonella Typhimurium, the RNA polymerase alpha subunit and the chaperone SicA.

Salmonella enterica serovar Typhimurium causes gastroenteritis and systemic infections in humans. For this bacterium the expression of a type III secretion system (T3SS) and effector proteins encoded in the Salmonella pathogenicity island-1 (SPI-1), is keystone for the virulence of this bacterium. Expression of these is controlled by a regulatory cascade starting with the transcriptional regulators HilD, HilC and RtsA that induce the expression of HilA, which then activates expression of the regulator InvF, a transcriptional regulator of the AraC/XylS family. InvF needs to interact with the chaperone SicA to activate transcription of SPI-1 genes including sicA, sopB, sptP, sopE, sopE2, and STM1239. InvF very likely acts as a classical activator; however, whether InvF interacts with the RNA polymerase alpha subunit RpoA has not been determined. Results from this study confirm the interaction between InvF with SicA and reveal that both proteins interact with the RNAP alpha subunit. Thus, our study further supports that the InvF/SicA complex acts as a classical activator. Additionally, we showed for the first time an interaction between a chaperone of T3SS effectors (SicA) and the RNAP.

Screening and Structural Characterization of Heat Shock Response Elements (HSEs) in Entamoeba histolytica Promoters.

Entamoeba histolytica (E. histolytica) exhibits a remarkable capacity to respond to thermal shock stress through a sophisticated genetic regulation mechanism. This process is carried out via Heat Shock Response Elements (HSEs), which are recognized by Heat Shock Transcription Factors (EhHSTFs), enabling fine and precise control of gene expression. Our study focused on screening for HSEs in the promoters of the E. histolytica genome, specifically analyzing six HSEs, including Ehpgp5, EhrabB1, EhrabB4, EhrabB5, Ehmlbp, and Ehhsp100. We discovered 2578 HSEs, with 1412 in promoters of hypothetical genes and 1166 in coding genes. We observed that a single promoter could contain anywhere from one to five HSEs. Gene ontology analysis revealed the presence of HSEs in essential genes for the amoeba, including cysteine proteinases, ribosomal genes, Myb family DNA-binding proteins, and Rab GTPases, among others. Complementarily, our molecular docking analyses indicate that these HSEs are potentially recognized by EhHSTF5, EhHSTF6, and EhHSTF7 factors in their trimeric conformation. These findings suggest that E. histolytica has the capability to regulate a wide range of critical genes via HSE-EhHSTFs, not only for thermal stress response but also for vital functions of the parasite. This is the first comprehensive study of HSEs in the genome of E. histolytica, significantly contributing to the understanding of its genetic regulation and highlighting the complexity and precision of this mechanism in the parasite's survival.

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.

Identifying similarities at metabolic pathways with a strategy of Enzymatic Step Sequences.

An easy and fast strategy to compare functionally the metabolic maps is described. The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm. To do this, the KGML files are retrieved, and directed graph representations are created; where the nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is the 'product' from one reaction and a 'substrate' for the next. Then, a set of initialization nodes are selected, and used as the root for the construction of the BFS tree. This tree is used as a guide to the construction of the ESS. From each leaf (terminal node), the path is traced backwards until it reaches the root metabolic map and with two or fewer neighbors in the graph. In a second step, the ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score. The dissimilarity values between two EC numbers ranged from 0 to 1, where 0 indicates similar EC numbers, and 1 indicates different EC numbers. Finally, the alignment is evaluated by using the normalized entropy-based function, considering a threshold of ≤ 0.27 as significant.•The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm.•Nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is 'product' from one reaction and a 'substrate' for the next.•The ESS are compared with a Dynamic Programing algorithm, considering an "ad hoc" substitution matrix, and minimizing the global score.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides.

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.

Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulation of their gene expression is essential. A key momentum in regulating genes responsible for the life maintenance of archaea is when transcription factor proteins bind to the promoter element. This DNA segment is conserved, which enables its exploration by machine learning techniques. Here, we trained and tested a support vector machine with 3935 known archaeal promoter sequences. All promoter sequences were coded into DNA Duplex Stability. After, we performed a model interpretation task to map the decision pattern of the classification procedure. We also used a dataset of known-promoter sequences for validation. Our results showed that an AT rich region around position - 27 upstream (relative to the start TSS) is the most conserved in the analyzed organisms. In addition, we were able to identify the BRE element (- 33), the PPE (at - 10) and a position at + 3, that provides a more understandable picture of how promoters are organized in all the archaeal organisms. Finally, we used the interpreted model to identify potential promoter sequences of 135 unannotated organisms, delivering regulatory regions annotation of archaea in a scale never accomplished before ( https://pcyt.unam.mx/gene-regulation/ ). We consider that this approach will be useful to understand how gene regulation is achieved in other organisms apart from the already established transcription factor binding sites.

Construction and analysis of gene co-expression network in the pathogenic fungus Ustilago maydis.

Introduction: Biological systems respond to environmental disturbances and a wide range of compounds through complex gene interaction networks. The enormous growth of experimental information obtained using large-scale genomic techniques such as microarrays and RNA sequencing led to the construction of a wide variety of gene co-expression networks in recent years. These networks allow the discovery of clusters of co-expressed genes that potentially work in the same process linking them to biological processes often of interest to industrial, medicinal, and academic research. Methods: In this study, we built the gene co-expression network of Ustilago maydis from the gene expression data of 168 samples belonging to 19 series, which correspond to the GPL3681 platform deposited in the NCBI using WGCNA software. This network was analyzed to identify clusters of co-expressed genes, gene hubs and Gene Ontology terms. Additionally, we identified relevant modules through a hypergeometric approach based on a predicted set of transcription factors and virulence genes. Results and Discussion: We identified 13 modules in the gene co-expression network of U. maydis. The TFs enriched in the modules of interest belong to the superfamilies of Nucleic acid-binding proteins, Winged helix DNA-binding, and Zn2/Cys6 DNA-binding. On the other hand, the modules enriched with virulence genes were classified into diseases related to corn smut, Invasive candidiasis, among others. Finally, a large number of hypothetical, a large number of hypothetical genes were identified as highly co-expressed with virulence genes, making them possible experimental targets.

A landscape of gene regulation in the parasitic amoebozoa Entamoeba spp.

Entamoeba are amoeboid extracellular parasites that represent an important group of organisms for which the regulatory networks must be examined to better understand how genes and functional processes are interrelated. In this work, we inferred the gene regulatory networks (GRNs) in four Entamoeba species, E. histolytica, E. dispar, E. nuttalli, and E. invadens, and the GRN topological properties and the corresponding biological functions were evaluated. From these analyses, we determined that transcription factors (TFs) of E. histolytica, E. dispar, and E. nuttalli are associated mainly with the LIM family, while the TFs in E. invadens are associated with the RRM_1 family. In addition, we identified that EHI_044890 regulates 121 genes in E. histolytica, EDI_297980 regulates 284 genes in E. dispar, ENU1_120230 regulates 195 genes in E. nuttalli, and EIN_249270 regulates 257 genes in E. invadens. Finally, we identified that three types of processes, Macromolecule metabolic process, Cellular macromolecule metabolic process, and Cellular nitrogen compound metabolic process, are the main biological processes for each network. The results described in this work can be used as a basis for the study of gene regulation in these organisms.

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/.

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.

Gene Regulatory Network Inference and Gene Module Regulating Virulence in Fusarium oxysporum.

In this work, we inferred the gene regulatory network (GRN) of the fungus Fusarium oxysporum by using the regulatory networks of Aspergillus nidulans FGSC A4, Neurospora crassa OR74A, Saccharomyces cerevisiae S288c, and Fusarium graminearum PH-1 as templates for sequence comparisons. Topological properties to infer the role of transcription factors (TFs) and to identify functional modules were calculated in the GRN. From these analyzes, five TFs were identified as hubs, including FOXG_04688 and FOXG_05432, which regulate 2,404 and 1,864 target genes, respectively. In addition, 16 communities were identified in the GRN, where the largest contains 1,923 genes and the smallest contains 227 genes. Finally, the genes associated with virulence were extracted from the GRN and exhaustively analyzed, and we identified a giant module with ten TFs and 273 target genes, where the most highly connected node corresponds to the transcription factor FOXG_05265, homologous to the putative bZip transcription factor CPTF1 of Claviceps purpurea, which is involved in ergotism disease that affects cereal crops and grasses. The results described in this work can be used for the study of gene regulation in this organism and open the possibility to explore putative genes associated with virulence against their host.

Machine learning and statistics shape a novel path in archaeal promoter annotation.

BACKGROUND: Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques. RESULTS AND DISCUSSIONS: In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control. CONCLUDING REMARKS: The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.