Acidithiobacillia class members originating at sites within the Pacific Ring of Fire and other tectonically active locations and description of the novel genus 'Igneacidithiobacillus'.

Recent studies have expanded the genomic contours of the Acidithiobacillia, highlighting important lacunae in our comprehension of the phylogenetic space occupied by certain lineages of the class. One such lineage is 'Igneacidithiobacillus', a novel genus-level taxon, represented by 'Igneacidithiobacillus copahuensis' VAN18-1T as its type species, along with two other uncultivated metagenome-assembled genomes (MAGs) originating from geothermally active sites across the Pacific Ring of Fire. In this study, we investigate the genetic and genomic diversity, and the distribution patterns of several uncharacterized Acidithiobacillia class strains and sequence clones, which are ascribed to the same 16S rRNA gene sequence clade. By digging deeper into this data and contributing to novel MAGs emerging from environmental studies in tectonically active locations, the description of this novel genus has been consolidated. Using state-of-the-art genomic taxonomy methods, we added to already recognized taxa, an additional four novel Candidate (Ca.) species, including 'Ca. Igneacidithiobacillus chanchocoensis' (mCHCt20-1TS), 'Igneacidithiobacillus siniensis' (S30A2T), 'Ca. Igneacidithiobacillus taupoensis' (TVZ-G3 TS), and 'Ca. Igneacidithiobacillus waiarikiensis' (TVZ-G4 TS). Analysis of published data on the isolation, enrichment, cultivation, and preliminary microbiological characterization of several of these unassigned or misassigned strains, along with the type species of the genus, plus the recoverable environmental data from metagenomic studies, allowed us to identify habitat preferences of these taxa. Commonalities and lineage-specific adaptations of the seven species of the genus were derived from pangenome analysis and comparative genomic metabolic reconstruction. The findings emerging from this study lay the groundwork for further research on the ecology, evolution, and biotechnological potential of the novel genus 'Igneacidithiobacillus'.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Correction: Vicencio et al. Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis. Int. J. Mol. Sci. 2023, 24, 14835.

In the original publication [...].

Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium.

The human gut is home to trillions of microorganisms that influence several aspects of our health. This dense microbial community targets almost all dietary polysaccharides and releases multiple metabolites, some of which have physiological effects on the host. A healthy equilibrium between members of the gut microbiota, its microbial diversity, and their metabolites is required for intestinal health, promoting regulatory or anti-inflammatory immune responses. In contrast, the loss of this equilibrium due to antibiotics, low fiber intake, or other conditions results in alterations in gut microbiota composition, a term known as gut dysbiosis. This dysbiosis can be characterized by a reduction in health-associated microorganisms, such as butyrate-producing bacteria, enrichment of a small number of opportunistic pathogens, or a reduction in microbial diversity. Bifidobacterium species are key species in the gut microbiome, serving as primary degraders and contributing to a balanced gut environment in various ways. Colonization resistance is a fundamental property of gut microbiota for the prevention and control of infections. This community competes strongly with foreign microorganisms, such as gastrointestinal pathogens, antibiotic-resistant bacteria, or even probiotics. Resistance to colonization is based on microbial interactions such as metabolic cross-feeding, competition for nutrients, or antimicrobial-based inhibition. These interactions are mediated by metabolites and metabolic pathways, representing the inner workings of the gut microbiota, and play a protective role through colonization resistance. This review presents a rationale for how microbial interactions provide resistance to colonization and gut dysbiosis, highlighting the protective role of Bifidobacterium species.

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis.

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

Corrigendum: Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.

[This corrects the article DOI: 10.3389/fgene.2023.1209416.].

Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

First metagenomic analysis of the Andean condor (Vultur gryphus) gut microbiome reveals microbial diversity and wide resistome.

Background: The Andean condor (Vultur gryphus) is the largest scavenger in South America. This predatory bird plays a crucial role in their ecological niche by removing carcasses. We report the first metagenomic analysis of the Andean condor gut microbiome. Methods: This work analyzed shotgun metagenomics data from a mixture of fifteen captive Chilean Andean condors. To filter eukaryote contamination, we employed BWA-MEM v0.7. Taxonomy assignment was performed using Kraken2 and MetaPhlAn v2.0 and all filtered reads were assembled using IDBA-UD v1.1.3. The two most abundant species were used to perform a genome reference-guided assembly using MetaCompass. Finally, we performed a gene prediction using Prodigal and each gene predicted was functionally annotated. InterproScan v5.31-70.0 was additionally used to detect homology based on protein domains and KEGG mapper software for reconstructing metabolic pathways. Results: Our results demonstrate concordance with the other gut microbiome data from New World vultures. In the Andean condor, Firmicutes was the most abundant phylum present, with Clostridium perfringens, a potentially pathogenic bacterium for other animals, as dominating species in the gut microbiome. We assembled all reads corresponding to the top two species found in the condor gut microbiome, finding between 94% to 98% of completeness for Clostridium perfringens and Plesiomonas shigelloides, respectively. Our work highlights the ability of the Andean condor to act as an environmental reservoir and potential vector for critical priority pathogens which contain relevant genetic elements. Among these genetic elements, we found 71 antimicrobial resistance genes and 1,786 virulence factors that we associated with several adaptation processes.

Transcriptional and Histone Acetylation Changes Associated with CRE Elements Expose Key Factors Governing the Regulatory Circuit in the Early Stage of Huntington's Disease Models.

Huntington's disease (HD) is a disorder caused by an abnormal expansion of trinucleotide CAG repeats within the huntingtin (Htt) gene. Under normal conditions, the CREB Binding Protein interacts with CREB elements and acetylates Lysine 27 of Histone 3 to direct the expression of several genes. However, mutant Htt causes depletion of CBP, which in turn induces altered histone acetylation patterns and transcriptional deregulation. Here, we have studied a differential expression analysis and H3K27ac variation in 4- and 6-week-old R6/2 mice as a model of juvenile HD. The analysis of differential gene expression and acetylation levels were integrated into Gene Regulatory Networks revealing key regulators involved in the altered transcription cascade. Our results show changes in acetylation and gene expression levels that are related to impaired neuronal development, and key regulators clearly defined in 6-week-old mice are proposed to drive the downstream regulatory cascade in HD. Here, we describe the first approach to determine the relationship among epigenetic changes in the early stages of HD. We determined the existence of changes in pre-symptomatic stages of HD as a starting point for early onset indicators of the progression of this disease.

Benefiting from the intrinsic role of epigenetics to predict patterns of CTCF binding.

Motivation: One of the most relevant mechanisms involved in the determination of chromatin structure is the formation of structural loops that are also related with the conservation of chromatin states. Many of these loops are stabilized by CCCTC-binding factor (CTCF) proteins at their base. Despite the relevance of chromatin structure and the key role of CTCF, the role of the epigenetic factors that are involved in the regulation of CTCF binding, and thus, in the formation of structural loops in the chromatin, is not thoroughly understood. Results: Here we describe a CTCF binding predictor based on Random Forest that employs different epigenetic data and genomic features. Importantly, given the ability of Random Forests to determine the relevance of features for the prediction, our approach also shows how the different types of descriptors impact the binding of CTCF, confirming previous knowledge on the relevance of chromatin accessibility and DNA methylation, but demonstrating the effect of epigenetic modifications on the activity of CTCF. We compared our approach against other predictors and found improved performance in terms of areas under PR and ROC curves (PRAUC-ROCAUC), outperforming current state-of-the-art methods.

GPR43 stimulation on TCRαß+ intraepithelial colonic lymphocytes inhibits the recruitment of encephalitogenic T-cells into the central nervous system and attenuates the development of autoimmunity.

INTRODUCTION: Gut microbiota plays a critical role in the regulation of immune homeostasis. Accordingly, several autoimmune disorders have been associated with dysbiosis in the gut microbiota. Notably, the dysbiosis associated with central nervous system (CNS) autoimmunity involves a substantial reduction of bacteria belonging to Clostridia clusters IV and XIVa, which constitute major producers of short-chain fatty acids (SCFAs). Here we addressed the role of the surface receptor-mediated effects of SCFAs on mucosal T-cells in the development of CNS autoimmunity. METHODS: To induce CNS autoimmunity, we used the mouse model of experimental autoimmune encephalomyelitis (EAE) induced by immunization with the myelin oligodendrocyte glycoprotein (MOG)-derived peptide (MOG35-55 peptide). To address the effects of GPR43 stimulation on colonic TCRαß+ T-cells upon CNS autoimmunity, mucosal lymphocytes were isolated and stimulated with a selective GPR43 agonist ex vivo and then transferred into congenic mice undergoing EAE. Several subsets of lymphocytes infiltrating the CNS or those present in the gut epithelium and gut lamina propria were analysed by flow cytometry. In vitro migration assays were conducted with mucosal T-cells using transwells. RESULTS: Our results show a sharp and selective reduction of intestinal propionate at the peak of EAE development, accompanied by increased IFN-γ and decreased IL-22 in the colonic mucosa. Further analyses indicated that GPR43 was the primary SCFAs receptor expressed on T-cells, which was downregulated on colonic TCRαß+ T-cells upon CNS autoimmunity. The pharmacologic stimulation of GPR43 increased the anti-inflammatory function and reduced the pro-inflammatory features in several TCRαß+ T-cell subsets in the colonic mucosa upon EAE development. Furthermore, GPR43 stimulation induced the arrest of CNS-autoreactive T-cells in the colonic lamina propria, thus avoiding their infiltration into the CNS and dampening the disease development. Mechanistic analyses revealed that GPR43-stimulation on mucosal TCRαß+ T-cells inhibits their CXCR3-mediated migration towards CXCL11, which is released from the CNS upon neuroinflammation. CONCLUSIONS: These findings provide a novel mechanism involved in the gut-brain axis by which bacterial-derived products secreted in the gut mucosa might control the CNS tropism of autoreactive T-cells. Moreover, this study shows GPR43 expressed on T-cells as a promising therapeutic target for CNS autoimmunity.

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/.

Differentially Expressed Genes and Signaling Pathways Potentially Involved in Primary Resistance to Chemo-Immunotherapy in Advanced-Stage Gastric Cancer Patients.

Recently, the combination of chemotherapy plus nivolumab (chemo-immunotherapy) has become the standard of care for advanced-stage gastric cancer (GC) patients. However, despite its efficacy, up to 40% of patients do not respond to these treatments. Our study sought to identify variations in gene expression associated with primary resistance to chemo-immunotherapy. Diagnostic endoscopic biopsies were retrospectively obtained from advanced GC patients previously categorized as responders (R) or non-responders (NR). Thirty-four tumor biopsies (R: n = 16, NR: n = 18) were analyzed by 3' massive analysis of cDNA ends (3'MACE). We found >30 differentially expressed genes between R and NRs. Subsequent pathway enrichment analyses demonstrated that angiogenesis and the Wnt-ß-catenin signaling pathway were enriched in NRs. Concomitantly, we performed next generation sequencing (NGS) analyses in a subset of four NR patients that confirmed alterations in genes that belonged to the Wnt/ß-catenin and the phosphoinositide 3-kinase (PI3K) pathways. We speculate that angiogenesis, the Wnt, and the PI3K pathways might offer actionable targets. We also discuss therapeutic alternatives for chemo-immunotherapy-resistant advanced-stage GC patients.

Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing.

Leishmania parasites are the causal agent of leishmaniasis, an endemic disease in more than 90 countries worldwide. Over the years, traditional approaches focused on the parasite when developing treatments against leishmaniasis. Despite numerous attempts, there is not yet a universal treatment, and those available have allowed for the appearance of resistance. Here, we propose and follow a host-directed approach that aims to overcome the current lack of treatment. Our approach identifies potential therapeutic targets in the host cell and proposes known drug interactions aiming to improve the immune response and to block the host machinery necessary for the survival of the parasite. We started analyzing transcription factor regulatory networks of macrophages infected with Leishmania major. Next, based on the regulatory dynamics of the infection and available gene expression profiles, we selected potential therapeutic target proteins. The function of these proteins was then analyzed following a multilayered network scheme in which we combined information on metabolic pathways with known drugs that have a direct connection with the activity carried out by these proteins. Using our approach, we were able to identify five host protein-coding gene products that are potential therapeutic targets for treating leishmaniasis. Moreover, from the 11 drugs known to interact with the function performed by these proteins, 3 have already been tested against this parasite, verifying in this way our novel methodology. More importantly, the remaining eight drugs previously employed to treat other diseases, remain as promising yet-untested antileishmanial therapies. IMPORTANCE This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles. Once proteins have been selected, our approach employs a multilayered network methodology that relates proteins to functions to drugs that alter these functions. By applying our novel approach to macrophages during the Leishmania infection process, we both validated our work and found eight drugs already approved for use in humans that to the best of our knowledge were never employed to treat leishmaniasis, rendering our work as a new tool in the box available to the scientific community fighting parasites.

Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study.

Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.

Automated generation of context-specific gene regulatory networks with a weighted approach in Drosophila melanogaster.

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io.

Enforced dimerization between XBP1s and ATF6f enhances the protective effects of the UPR in models of neurodegeneration.

Alteration to endoplasmic reticulum (ER) proteostasis is observed in a variety of neurodegenerative diseases associated with abnormal protein aggregation. Activation of the unfolded protein response (UPR) enables an adaptive reaction to recover ER proteostasis and cell function. The UPR is initiated by specialized stress sensors that engage gene expression programs through the concerted action of the transcription factors ATF4, ATF6f, and XBP1s. Although UPR signaling is generally studied as unique linear signaling branches, correlative evidence suggests that ATF6f and XBP1s may physically interact to regulate a subset of UPR target genes. In this study, we designed an ATF6f/XBP1s fusion protein termed UPRplus that behaves as a heterodimer in terms of its selective transcriptional activity. Cell-based studies demonstrated that UPRplus has a stronger effect in reducing the abnormal aggregation of mutant huntingtin and α-synuclein when compared to XBP1s or ATF6 alone. We developed a gene transfer approach to deliver UPRplus into the brain using adeno-associated viruses (AAVs) and demonstrated potent neuroprotection in vivo in preclinical models of Parkinson's disease and Huntington's disease. These results support the concept in which directing UPR-mediated gene expression toward specific adaptive programs may serve as a possible strategy to optimize the beneficial effects of the pathway in different disease conditions.

Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages.

MOTIVATION: Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. RESULTS: We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. AVAILABILITY AND IMPLEMENTATION: Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pleione: A tool for statistical and multi-objective calibration of Rule-based models.

Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Nevertheless, such models are often difficult to understand. Unlike ODE models, Rule-Based Models (RBMs) utilise formal language to describe reactions as a cumulative number of statements that are easier to understand and correct. They are also gaining popularity because of their conciseness and simulation flexibility. However, RBMs generally lack tools to perform further analysis that requires simulation. This situation arises because exact and approximate simulations are computationally intensive. Translating RBMs into ODEs is commonly used to reduce simulation time, but this technique may be prohibitive due to combinatorial explosion. Here, we present the software called Pleione to calibrate RBMs. Parameter calibration is essential given the incomplete experimental determination of reaction rates and the goal of using models to reproduce experimental data. The software distributes stochastic simulations and calculations and incorporates equivalence tests to determine the fitness of RBMs compared with data. The primary features of Pleione were thoroughly tested on a model of gene regulation in Escherichia coli. Pleione yielded satisfactory results regarding calculation time and error reduction for multiple simulators, models, parameter search strategies, and computing infrastructures.