MicroRNAs (miRNAs) have been shown to be major regulators of eukaryotic gene expression. However, bacterial RNAs comparable in size to eukaryotic miRNAs (18–22 nucleotides) have received little ...attention. Recently, a novel class of small RNAs similar in size to miRNAs (miRNA-size, small RNAs or msRNAs) have also been found in several bacteria. Like miRNAs, msRNAs are approximately 15 to 25 nucleotides in length, and their precursors are predicted to form a hairpin loop secondary structure. Here, we identified msRNAs in the periodontal pathogens Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Treponema denticola. We examined these msRNAs using a deep sequencing method and characterized dozens of msRNAs through bioinformatic analysis. Highly expressed msRNAs were selected for further validation. The findings suggest that this class of small RNAs is well conserved across the domains of life. Indeed, msRNAs secreted via bacterial outer membrane vesicles (OMVs) were detected. The ability of bacterial OMVs to deliver RNAs into eukaryotic cells was also observed. These msRNAs in OMVs allowed us to identify their potential human immune-related target genes. Furthermore, we found that exogenous msRNAs could suppress expression of certain cytokines in Jurkat T cells. We propose msRNAs may function as novel bacterial signaling molecules that mediate bacteria-to-human interactions. Furthermore, this study may provide fresh insight into bacterial pathogenic mechanisms of periodontal diseases.
The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between ...them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the ...exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson's correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.
Background
Recent evidence indicates that Staphylococcus aureus, one of the most important human pathogens, secretes vesicles into the extracellular milieu.
Objective
To evaluate whether inhalation ...of S. aureus‐derived extracellular vesicles (EV) is causally related to the pathogenesis of inflammatory pulmonary diseases.
Methods
Staphylococcus aureus EV were prepared by sequential ultrafiltration and ultracentrifugation. The innate immune response was evaluated in vitro after the application of EV to airway epithelial cells and alveolar macrophages. In vivo innate and adaptive immune responses were evaluated after airway exposure to EV. Adjuvant effects of EV on the development of hypersensitivity to inhaled allergens were also evaluated after airway sensitization with S. aureus EV and ovalbumin (OVA).
Results
Staphylococcus aureus and S. aureus EV were detected in house dust. Alveolar macrophages produced both tumor necrosis α (TNF‐α) and interleukin 6 (IL‐6) after in vitro stimulation with S. aureus EV, whereas airway epithelial cells produced only IL‐6. Repeated airway exposure to S. aureus EV induced both Th1 and Th17 cell responses and neutrophilic pulmonary inflammation, mainly via a Toll‐like receptor 2 (TLR2)‐dependent mechanism. In terms of adjuvant effects, airway sensitization with S. aureus EV and OVA resulted in neutrophilic pulmonary inflammation after OVA challenge alone. This phenotype was partly reversed by the absence of interferon γ (IFN‐γ) or IL‐17.
Conclusion
Staphylococcus aureus EV can induce Th1 and Th17 neutrophilic pulmonary inflammation, mainly in a TLR2‐dependent manner. Additionally, S. aureus EV enhance the development of airway hypersensitivity to inhaled allergens.
For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool ...called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the “missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
To cite this article: Hong S-W, Kim M-R, Lee E-Y, Kim JH, Kim Y-S, Jeon SG, Yang J-M, Lee B-J, Pyun B-Y, Gho YS, Kim Y-K. Extracellular vesicles derived from Staphylococcus aureus induce atopic ...dermatitis-like skin inflammation. Allergy 2011; 66: 351-359. ABSTRACT: Background: Recently, we found that Staphylococcus aureus produces extracellular vesicles (EV) that contain pathogenic proteins. Although S. aureus infection has been linked with atopic dermatitis (AD), the identities of the causative agents from S. aureus are controversial. We evaluated whether S. aureus-derived EV are causally related to the pathogenesis of AD. Methods: Extracellular vesicles were isolated by the ultracentrifugation of S. aureus culture media. The EV were applied three times per week to tape-stripped mouse skin. Inflammation and immune dysfunction were evaluated 48 h after the final application in hairless mice. Extracellular vesicles-specific IgE levels were measured by ELISA in AD patients and healthy subjects. Results: The in vitro application of S. aureus EV increased the production of pro-inflammatory mediators (IL-6, thymic stromal lymphopoietin, macrophage inflammatory protein-1α, and eotaxin) by dermal fibroblasts. The in vivo application of S. aureus EV after tape stripping caused epidermal thickening with infiltration of the dermis by mast cells and eosinophils in mice. These changes were associated with the enhanced cutaneous production of IL-4, IL-5, IFN-γ, and IL-17. Interestingly, the serum levels of S. aureus EV-specific IgE were significantly increased in AD patients relative to healthy subjects. Conclusion: These results indicate that S. aureus EV induce AD-like inflammation in the skin and that S. aureus-derived EV are a novel diagnostic and therapeutic target for the control of AD.
Abstract
Summary
Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an ...open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.
Availability and implementation
RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home
Supplementary information
Supplementary data are available at Bioinformatics online.
Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of ...labels by imposing connectivity between the labels. Recent studies for BioNER have reported state-of-the-art performance by combining deep learning-based models (e.g., bidirectional Long Short-Term Memory) and CRF. The deep learning-based models in the CRF-based methods are dedicated to estimating individual labels, whereas the relationships between connected labels are described as static numbers; thereby, it is not allowed to timely reflect the context in generating the most plausible label-label transitions for a given input sentence. Regardless, correctly segmenting entity mentions in biomedical texts is challenging because the biomedical terms are often descriptive and long compared with general terms. Therefore, limiting the label-label transitions as static numbers is a bottleneck in the performance improvement of BioNER.
We introduce DTranNER, a novel CRF-based framework incorporating a deep learning-based label-label transition model into BioNER. DTranNER uses two separate deep learning-based networks: Unary-Network and Pairwise-Network. The former is to model the input for determining individual labels, and the latter is to explore the context of the input for describing the label-label transitions. We performed experiments on five benchmark BioNER corpora. Compared with current state-of-the-art methods, DTranNER achieves the best F1-score of 84.56% beyond 84.40% on the BioCreative II gene mention (BC2GM) corpus, the best F1-score of 91.99% beyond 91.41% on the BioCreative IV chemical and drug (BC4CHEMD) corpus, the best F1-score of 94.16% beyond 93.44% on the chemical NER, the best F1-score of 87.22% beyond 86.56% on the disease NER of the BioCreative V chemical disease relation (BC5CDR) corpus, and a near-best F1-score of 88.62% on the NCBI-Disease corpus.
Our results indicate that the incorporation of the deep learning-based label-label transition model provides distinctive contextual clues to enhance BioNER over the static transition model. We demonstrate that the proposed framework enables the dynamic transition model to adaptively explore the contextual relations between adjacent labels in a fine-grained way. We expect that our study can be a stepping stone for further prosperity of biomedical literature mining.
As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between ...random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for example, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p-value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p-value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers.
Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric ...Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.
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