156 research outputs found
Network Properties for Ranking Predicted miRNA Targets in Breast Cancer
MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs it is important to identify their target genes. The small number of experimentally validated target genes makes computer prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an unpredictable number of false positives. In this paper, we propose and evaluate two approaches for ranking the biological relevance of putative targets of miRNAs which are associated with breast cancer
How to Predict Molecular Interactions between Species?
Organisms constantly interact with other species through physical contact which leads to chan-ges on the molecular level, for example the transcriptome. These changes can be monitored forall genes, with the help of high-throughput experiments such as RNA-seq or microarrays. Theadaptation of the gene expression to environmental changes within cells is mediated throughcomplex gene regulatory networks. Often, our knowledge of these networks is incomplete. Netw-ork inference predicts gene regulatory interactions based on transcriptome data. An emergingapplication of high-throughput transcriptome studies are dual transcriptomics experiments. Here,the transcriptome of two or more interacting species is measured simultaneously. Based ona dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candidaalbicans, the software tool NetGenerator was applied to predict an inter-species gene regulatorynetwork. To promote further investigations of molecular inter-species interactions, we recentlydiscussed dual RNA-seq experiments for host-pathogen interactions and extended the appliedtool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use ofmeasurement variances in the algorithmic procedure and accepts gene expression time seriesdata with missing values. Additionally, we tested multiple modeling scenarios regarding the stimulifunctions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015)and put it into a broader context. We review various studies making use of the dual transcriptomicsapproach to investigate the molecular basis of interacting species. Besides the application tohost-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic andcommensalistic interactions. Furthermore, we give a short introduction into additional approachesfor the prediction of gene regulatory networks and discuss their application to dual transcriptomicsdata. We conclude that the application of network inference on dual-transcriptomics data is apromising approach to predict molecular inter-species interactions
Data- and knowledge-based modeling of gene regulatory networks: an update
Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions
Editorial: The Transition Era to New Sequencing Technologies and Their Application to Integrative Omics in Molecular Surveillance
Peer Reviewe
Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach
Comparative Study on Alternative Splicing in Human Fungal Pathogens Suggests Its Involvement During Host Invasion
Alternative splicing (AS) is an important regulatory mechanism in eukaryotes but only little is known about its impact in fungi. Human fungal pathogens are of high clinical interest causing recurrent or life-threatening infections. AS can be well-investigated genome-wide and quantitatively with the powerful technology of RNA-Seq. Here, we systematically studied AS in human fungal pathogens based on RNA-Seq data. To do so, we investigated its effect in seven fungi during conditions simulating ex vivo infection processes and during in vitro stress. Genes undergoing AS are species-specific and act independently from differentially expressed genes pointing to an independent mechanism to change abundance and functionality. Candida species stand out with a low number of introns with higher and more varying lengths and more alternative splice sites. Moreover, we identified a functional difference between response to host and other stress conditions: During stress, AS affects more genes and is involved in diverse regulatory functions. In contrast, during response-to-host conditions, genes undergoing AS have membrane functionalities and might be involved in the interaction with the host. We assume that AS plays a crucial regulatory role in pathogenic fungi and is important in both response to host and stress conditions
Comparison of Illumina and Oxford Nanopore Technology for genome analysis of Francisella tularensis, Bacillus anthracis, and Brucella suis
Background
Bacterial epidemiology needs to understand the spread and dissemination of strains in a One Health context. This is important for highly pathogenic bacteria such as Bacillus anthracis, Brucella species, and Francisella tularensis. Whole genome sequencing (WGS) has paved the way for genetic marker detection and high-resolution genotyping. While such tasks are established for Illumina short-read sequencing, Oxford Nanopore Technology (ONT) long-read sequencing has yet to be evaluated for such highly pathogenic bacteria with little genomic variations between strains. In this study, three independent sequencing runs were performed using Illumina, ONT flow cell version 9.4.1, and 10.4 for six strains of each of Ba. anthracis, Br. suis and F. tularensis. Data from ONT sequencing alone, Illumina sequencing alone and two hybrid assembly approaches were compared.
Results
As previously shown, ONT produces ultra-long reads, while Illumina produces short reads with higher sequencing accuracy. Flow cell version 10.4 improved sequencing accuracy over version 9.4.1. The correct (sub-)species were inferred from all tested technologies, individually. Moreover, the sets of genetic markers for virulence, were almost identical for the respective species. The long reads of ONT allowed to assemble not only chromosomes of all species to near closure, but also virulence plasmids of Ba. anthracis. Assemblies based on nanopore data alone, Illumina data alone, and both hybrid assemblies correctly detected canonical (sub-)clades for Ba. anthracis and F. tularensis as well as multilocus sequence types for Br. suis.
For F. tularensis, high-resolution genotyping using core-genome MLST (cgMLST) and core-genome Single-Nucleotide-Polymorphism (cgSNP) typing produced highly comparable results between data from Illumina and both ONT flow cell versions. For Ba. anthracis, only data from flow cell version 10.4 produced similar results to Illumina for both high-resolution typing methods. However, for Br. suis, high-resolution genotyping yielded larger differences comparing Illumina data to data from both ONT flow cell versions.
Conclusions
In summary, combining data from ONT and Illumina for high-resolution genotyping might be feasible for F. tularensis and Ba. anthracis, but not yet for Br. suis. The ongoing improvement of nanopore technology and subsequent data analysis may facilitate high-resolution genotyping for all bacteria with highly stable genomes in future.Peer Reviewe
Comparative in silico genome analysis of Clostridium perfringens unravels stable phylogroups with different genome characteristics and pathogenic potential
Clostridium perfringens causes a plethora of devastating infections, with toxin production being the underlying mechanism of pathogenicity in various hosts. Genomic analyses of 206 public-available C. perfringens strains ' sequence data identified a substantial degree of genomic variability in respect to episome content, chromosome size and mobile elements. However, the position and order of the local collinear blocks on the chromosome showed a considerable degree of preservation. The strains were divided into five stable phylogroups (I-V). Phylogroup I contained human food poisoning strains with chromosomal enterotoxin (cpe) and a Darmbrand strain characterized by a high frequency of mobile elements, a relatively small genome size and a marked loss of chromosomal genes, including loss of genes encoding virulence traits. These features might correspond to the adaptation of these strains to a particular habitat, causing human foodborne illnesses. This contrasts strains that belong to phylogroup II where the genome size points to the acquisition of genetic material. Most strains of phylogroup II have been isolated from enteric lesions in horses and dogs. Phylogroups III, IV and V are heterogeneous groups containing a variety of different strains, with phylogroup III being the most abundant (65.5%). In conclusion, C. perfringens displays five stable phylogroups reflecting different disease involvements, prompting further studies on the evolution of this highly important pathogen
Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis
Campylobacter (C.) jejuni is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of C. jejuni recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq® technology was used to sequence 66 C. jejuni isolates obtained between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. Phylogeny, resistome, plasmidome and virulome profiles were analyzed using whole-genome sequencing data. Genetic resistance markers were identified with bioinformatics tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance. The isolates were assigned to 28 different sequence types and 11 clonal complexes. The average pairwise single nucleotide-polymorphisms distance of 14,585 SNPs (range: 0–26,540 SNPs) revealed a high genetic distinction between the isolates. Thirteen virulence-associated genes were identified in C. jejuni isolates. Most of the isolates harbored the genes flaA (83.3%) and flaB (78.8%). The wlaN gene associated with the Guillain–Barré syndrome was detected in nine (13.6%) isolates. The genes for resistance to ampicillin (blaOXA), tetracycline [tet(O)], neomycin [aph(3')-IIIa], streptomycin (aadE) and streptothricin (sat4) were detected in isolated C. jejuni using WGS. A gene cluster comprising the genes sat4, aph(3′)-IIIa and aadE was present in six isolates. The single point mutation T86I in the housekeeping gene gyrA conferring resistance to quinolones was retrieved in 93.6% of phenotypically fluoroquinolone-resistant isolates. Five phenotypically erythromycin-susceptible isolates carried the mutation A103V in the gene for the ribosomal protein L22 inferring macrolide resistance. An assortment of 13 β-lactam resistance genes (blaOXA variants) was detected in 58 C. jejuni isolates. Out of 66 sequenced isolates, 28 (42.4%) carried plasmid-borne contigs. Six isolates harbored a pTet-like plasmid-borne contig which carries the tet(O) gene. This study emphasized the potential of whole-genome sequencing to ameliorate the routine surveillance of C. jejuni. Whole-genome sequencing can predict antimicrobial resistance with a high degree of accuracy. However, resistance gene databases need curation and updates to revoke inaccuracy when using WGS-based analysis pipelines for AMR detection
Campylobacter concisus Impairs Sodium Absorption in Colonic Epithelium via ENaC Dysfunction and Claudin-8 Disruption
The epithelial sodium channel (ENaC) can increase the colonic absorptive capacity for salt and water. Campylobacter concisus is a common pathogenic epsilonproteobacterium, causing enteritis and diarrhea. It can induce barrier dysfunction in the intestine, but its influence on intestinal transport function is still unknown. Therefore, our study aimed to characterize C. concisus effects on ENaC using the HT-29/B6-GR/MR (epithelial cell line HT-29/B6 transfected with glucocorticoid and mineralocorticoid receptors) cell model and mouse colon. In Ussing chambers, C. concisus infection inhibited ENaC-dependent Na+ transport as indicated by a reduction in amiloride-sensitive short circuit current (-55%, n = 15, p < 0.001). This occurred via down-regulation of β- and γ-ENaC mRNA expression and ENaC ubiquitination due to extracellular signal-regulated kinase (ERK)1/2 activation, predicted by Ingenuity Pathway Analysis (IPA). In parallel, C. concisus reduced the expression of the sealing tight junction (TJ) protein claudin-8 and induced claudin-8 redistribution off the TJ domain of the enterocytes, which facilitates the back leakage of Na+ ions into the intestinal lumen. In conclusion, C. concisus caused ENaC dysfunction via interleukin-32-regulated ERK1/2, as well as claudin-8-dependent barrier dysfunction-both of which contribute to Na+ malabsorption and diarrhea
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