Controlling infection-driven inflammation is a major clinical dilemma because of limited therapeutic options and possible adverse effects on microbial clearance. Compounding this difficulty is the continued emergence of drug-resistant bacteria, where experimental strategies aiming to augment inflammatory responses for enhanced microbial killing are not applicable treatment options for infections of vulnerable organs. As with corneal infections, severe or prolonged inflammation jeopardizes corneal transparency, leading to devastating vision loss. We hypothesized that keratin 6a–derived antimicrobial peptides (KAMPs) may be a two-pronged remedy capable of tackling bacterial infection and inflammation at once. We used murine peritoneal neutrophils and macrophages, together with an in vivo model of sterile corneal inflammation, to find that nontoxic and prohealing KAMPs with natural 10– and 18–amino acid sequences suppressed lipoteichoic acid (LTA)– and lipopolysaccharide (LPS)–induced NFκB and IRF3 activation, proinflammatory cytokine production, and phagocyte recruitment independently of their bactericidal function. Mechanistically, KAMPs not only competed with bacterial ligands for cell surface Toll-like receptor (TLR) and co-receptors (MD2, CD14, and TLR2) but also reduced cell surface availability of TLR2 and TLR4 through promotion of receptor endocytosis. Topical KAMP treatment effectively alleviated experimental bacterial keratitis, as evidenced by substantial reductions of corneal opacification, inflammatory cell infiltration, and bacterial burden. These findings reveal the TLR-targeting activities of KAMPs and demonstrate their therapeutic potential as a multifunctional drug for managing infectious inflammatory disease.
Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships.Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events.Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module -MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module -MINR).The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score.Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively.As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis.Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score > 70%.Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively.This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network.This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network.The ME corpus, test corpus, source code, and virtual machine image with pre-configured software are available at www.
Summary: Viruses and hosts are involved in a continuing ‘arms race’. The body deploys multiple defenses; however, viruses utilize generally superior and more rapidly evolving tactics for negating host immune surveillance and viral clearance. In the case of the two major pathogenic human retroviruses, human immunodeficiency virus‐1 (HIV‐1) and human T‐lymphotrophic virus‐I (HTLV‐I), the nuclear factor‐κB (NF‐κB) transcription factor plays a key role in the host’s anti‐viral responses involving both the innate and adaptive arms of the immune response. Similarly, these retroviruses capably exploit NF‐κB for their replication, spread, and pathogenic functions. In this review, we discuss the dynamic and intimate interplay that occurs between NF‐κB and the HTLV‐I and HIV‐1 retroviral pathogens.
ABSTRACT Epithelial cells form a crucial barrier against harmful microbes and inflammatory stimuli. Restraining inflammatory responses at the corneal barrier is necessary for avoiding sight-threatening tissue damage. Yet, epithelial cell-intrinsic mechanisms that dampen inflammation are largely unexplored. Keratin 6a (K6a) is a common type II cytokeratin highly expressed in corneal and other stratified epithelial cells. In a mouse model of sterile corneal inflammation, K6a knockout mice exhibit disease exacerbation. Here, we investigated cell-intrinsic mechanisms by which cytoplasmic K6a curbs corneal inflammation. We stimulated wild-type (WT) and K6a siRNA-knockdown (K6a-KD) human corneal epithelial (hTCEpi) cells with inflammatory P. aeruginosa culture supernatant. Our results showed that, under both basal and inflammatory conditions, K6a-KD cells secreted higher levels of cytokines and chemokines (IL-1α, IL-6, IL-8, CXCL1, CCL20) as compared to WT cells. K6a-KD cells also had increased level of LC3-II, a marker for autophagosomes, while autophagic degradation of SQSTM1/p62 remained unchanged. In K6a-KD cells, the majority of LC3-II puncta were associated with non-acidified autophagosomes rather than acidified autolysosomes. Upon stimulation, IL-8 was found to co-localize with LC3-II by confocal microscopy. Mechanistically, mass spectrometric analysis of K6a immunoprecipitates identified Sec16A, a protein involved in secretory autophagy, as an interacting partner of K6a. Further experiments showed that knocking down key proteins involved in autophagosome formation (ATG5) and the secretory autophagy process (Sec16A, GRASP55, Rab8) abolished the augmentative effect of K6a-KD on cytokine and chemokine secretion. These findings reveal a novel repressive role of K6a in secretory autophagy-mediated proinflammatory cytokine secretion and provide new insights into cell-intrinsic mechanisms of inflammation control at epithelial barriers.
One of the best ways to deal with the problem of knowledge distillation in unstructured text is applying text mining. This machine learning-based approach can provide extracted useful information from large body of texts, in a reasonable time. However, the results are usually in complicated forms, which meant it is a non-trivial task in term of interpretation. In this paper, we present appropriate visualizations and analyses in order to tackle the tangled network representing relationship between entities (i.e. terms extracted from raw text). Conducted on a case study of information extraction in the business management domain, our results reveal the hidden relationships of inter-organizational success factors in a simple structured way. These results can assist companies to understand and generate business strategies, in terms of the collaboration aspect.
Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.
