A genome-wide study of two-component signal transduction systems in eight newly sequenced mutans streptococci strains

<p>Abstract</p> <p>Background</p> <p>Mutans streptococci are a group of gram-positive bacteria including the primary cariogenic dental pathogen <it>Streptococcus mutans </it>and closely related species. Two component systems (TCSs) composed of a signal sensing histidine kinase (HK) and a response regulator (RR) play key roles in pathogenicity, but have not been comparatively studied for these oral bacterial pathogens.</p> <p>Results</p> <p>HKs and RRs of 8 newly sequenced mutans streptococci strains, including <it>S. sobrinus </it>DSM20742, <it>S. ratti </it>DSM20564 and six <it>S. mutans </it>strains, were identified and compared to the TCSs of <it>S. mutans </it>UA159 and NN2025, two previously genome sequenced <it>S. mutans </it>strains. Ortholog analysis revealed 18 TCS clusters (HK-RR pairs), 2 orphan HKs and 2 orphan RRs, of which 8 TCS clusters were common to all 10 strains, 6 were absent in one or more strains, and the other 4 were exclusive to individual strains. Further classification of the predicted HKs and RRs revealed interesting aspects of their putative functions. While TCS complements were comparable within the six <it>S. mutans </it>strains, <it>S. sobrinus </it>DSM20742 lacked TCSs possibly involved in acid tolerance and fructan catabolism, and <it>S. ratti </it>DSM20564 possessed 3 unique TCSs but lacked the quorum-sensing related TCS (ComDE). Selected computational predictions were verified by PCR experiments.</p> <p>Conclusions</p> <p>Differences in the TCS repertoires of mutans streptococci strains, especially those of <it>S. sobrinus </it>and <it>S. ratti </it>in comparison to <it>S. mutans</it>, imply differences in their response mechanisms for survival in the dynamic oral environment. This genomic level study of TCSs should help in understanding the pathogenicity of these mutans streptococci strains.</p

Targeted interplay between bacterial pathogens and host autophagy

Due to the critical role played by autophagy in pathogen clearance, pathogens have developed diverse strategies to subvert autophagy. Despite previous key findings of bacteria-autophagy interplay, a systems level insight into selective targeting by the host and autophagy modulation by the pathogens is lacking. We predicted potential interactions between human autophagy proteins and effector proteins from 56 pathogenic bacterial species by identifying bacterial proteins predicted to have recognition motifs for selective autophagy receptors p62/NDP52 and LC3. Conversely, using structure-based interaction prediction methods, we identified bacterial effector proteins that could putatively modify core autophagy components. Our analysis revealed that autophagy receptors in general potentially target mostly genus specific proteins, and not those present in multiple genera. We also show that the complementarity between the predicted p62 and NDP52 targets, which has been shown for Salmonella, Listeria and Shigella, could be observed across other pathogens. Using literature evidence, we hypothesize that this complementarity potentially leave the host more susceptible to chronic infections upon the mutation of one of the autophagy receptors. To check any bias caused by our pathogenic protein selection criteria, control analysis using proteins derived from entero-toxigenic and non-toxigenic Bacillus outer membrane vesicles indicated that autophagy targets pathogenic proteins rather than non-pathogenic ones. We also observed a pathogen specific pattern as to which autophagy phase could be modulated by specific genera. We found intriguing examples of bacterial proteins which could modulate autophagy, and in turn capable of being targeted by the autophagy receptors and LC3 as a host defence mechanism. To demonstrate the validity of our predictions, we confirmed experimentally with in vitro Salmonella invasion assays the bi-directional interactions underlying the interplay between a Salmonella protease, YhjJ and autophagy. Our comparative meta-analysis points out key commonalities and differences in how pathogens could affect autophagy and how autophagy potentially recognises these pathogenic effectors

Extracellular vesicles produced by the human commensal gut bacterium Bacteroides thetaiotaomicron affect host immune pathways in a cell-type specific manner that are altered in inflammatory bowel disease

The gastrointestinal (GI) tract harbours a complex microbial community, which contributes to its homeostasis. A disrupted microbiome can cause GI-related diseases, including inflammatory bowel disease (IBD), therefore identifying host-microbe interactions is crucial for better understanding gut health. Bacterial extracellular vesicles (BEVs), released into the gut lumen, can cross the mucus layer and access underlying immune cells. To study BEV-host interactions, we examined the influence of BEVs generated by the gut commensal bacterium, Bacteroides thetaiotaomicron, on host immune cells. Single-cell RNA sequencing data and host-microbe protein-protein interaction networks were used to predict the effect of BEVs on dendritic cells, macrophages and monocytes focusing on the Toll-like receptor (TLR) pathway. We identified biological processes affected in each immune cell type and cell-type specific processes including myeloid cell differentiation. TLR pathway analysis highlighted that BEV targets differ among cells and between the same cells in healthy versus disease (ulcerative colitis) conditions. The in silico findings were validated in BEV-monocyte co-cultures demonstrating the requirement for TLR4 and Toll-interleukin-1 receptor domain-containing adaptor protein (TIRAP) in BEV-elicited NF-kB activation. This study demonstrates that both cell-type and health status influence BEV-host communication. The results and the pipeline could facilitate BEV-based therapies for the treatment of IBD

Evolution of regulatory networks associated with traits under selection in cichlids

Background Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. Results To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait—the visual system—for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. Conclusions Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits

Konstruierung und Verifizierung eines Transkriptionsregulationsnetzwerks von Streptococcus mutans in Reaktion auf die Behandlung mit dem Biofilminhibitor Carolacton

Streptococcus mutans ist ein oraler Krankheitserreger, der für die Entstehung des Zahnkaries bei Menschen verantwortlich ist. Die pathogenen Eigenschaften von S. mutans sind eng mit seiner Fähigkeit zur Biofilmbildung verbunden. Carolacton, ein neu identifizierter Sekundärmetabolit zeigt inhibierende Wirkung auf das Wachstum des Biofilms von S. mutans. Ein systembiologischer Ansatz wurde angewendet, um mittels Reverse Engineering aus den zeitaufgelösten Transkriptom-Daten in Kombination mit Transkriptionsfaktor-Bindungsmotifs-daten sowie experimentelle Validierungen die angestrebte Konstruktion des regulatorischen Transkriptionsnetzwerks zu realisieren. Die Deletion des Transkriptionsregulators cysR, der die höchste Konnektivität unter den identifizierten regulatorischen Netzwerkknoten aufwies, führte zu einem Mutant, auf den Carolacton keine Wirkung mehr zeigte.Streptococcus mutans is a biofilm forming oral pathogen primarily responsible for causing human dental caries. To infer the underlying network mediating the growth inhibitory effect of a newly identified secondary metabolite carolacton on S. mutans biofilms, the transcriptional regulatory response network (TRRN) of S. mutans biofilms upon carolacton treatment was constructed using a systems biology approach combining time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation. From the inferred TRRN, predicted transcriptional regulatory interactions were confirmed using interaction studies. Sensitivity testing of deletion mutants of key regulators from the predicted network identified the cysteine metabolism regulator CysR as being essential for the response of S. mutans biofilms to carolacton

What we learned from big data for autophagy research

Autophagy is the process by which cytoplasmic components are sequestered in autophagosomal vesicles and delivered to the lysosome for degradation. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organisation of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large scale multi-omics approaches (such as transcriptomics, proteomics, lipidomics and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating a multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems

What We Learned From Big Data for Autophagy Research

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome–Host Interactions

Microbiome&ndash;host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe&ndash;host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions&mdash;as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn&rsquo;s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub

Essential O-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions.

Identification of the gene network through which Pseudomonas aeruginosa PAO1 (PA) adapts to altered oxygen-availability environments is essential for a better understanding of stress responses and pathogenicity of PA. We performed high-time-resolution (HTR) transcriptome analyses of PA in a continuous cultivation system during the transition from high oxygen tension to low oxygen tension (HLOT) and the reversed transition from low to high oxygen tension (LHOT). From those genes responsive to both transient conditions, we identified 85 essential oxygen-availability responsive genes (EORGs), including the expected ones (arcDABC) encoding enzymes for arginine fermentation. We then constructed the regulatory network for the EORGs of PA by integrating information from binding motif searching, literature and HTR data. Notably, our results show that only the sub-networks controlled by the well-known oxygen-responsive transcription factors show a very high consistency between the inferred network and literature knowledge, e.g. 87.5% and 83.3% of the obtained sub-network controlled by the anaerobic regulator (ANR) and a quorum sensing regulator RhIR, respectively. These results not only reveal stringent EORGs of PA and their transcription regulatory network, but also highlight that achieving a high accuracy of the inferred regulatory network might be feasible only for the apparently affected regulators under the given conditions but not for all the expressed regulators on a genome scale