Novel mutations in TLR genes cause hyporesponsiveness to Mycobacterium avium subsp. paratuberculosis infection

<p>Abstract</p> <p>Background</p> <p>Toll like receptors (TLR) play the central role in the recognition of pathogen associated molecular patterns (PAMPs). Mutations in the TLR1, TLR2 and TLR4 genes may change the ability to recognize PAMPs and cause altered responsiveness to the bacterial pathogens.</p> <p>Results</p> <p>The study presents association between TLR gene mutations and increased susceptibility to <it>Mycobacterium avium </it>subsp. <it>paratuberculosis </it>(MAP) infection. Novel mutations in TLR genes (TLR1- Ser150Gly and Val220Met; TLR2 – Phe670Leu) were statistically correlated with the hindrance in recognition of MAP legends. This correlation was confirmed subsequently by measuring the expression levels of cytokines (IL-4, IL-8, IL-10, IL-12 and IFN-γ) in the mutant and wild type moDCs (mocyte derived dendritic cells) after challenge with MAP cell lysate or LPS. Further <it>in silico </it>analysis of the TLR1 and TLR4 ectodomains (ECD) revealed the polymorphic nature of the central ECD and irregularities in the central LRR (leucine rich repeat) motifs.</p> <p>Conclusion</p> <p>The most critical positions that may alter the pathogen recognition ability of TLR were: the 9^thamino acid position in LRR motif (TLR1–LRR10) and 4^thresidue downstream to LRR domain (exta-LRR region of TLR4). The study describes novel mutations in the TLRs and presents their association with the MAP infection.</p

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Metabolomics-based prediction models of yeast strains for screening of metabolites contributing to ethanol stress tolerance

The increased demand for clean, sustainable and renewable energy resources has driven the development of various microbial systems to produce biofuels. One of such systems is the ethanol-producing yeast. Although yeast produces ethanol naturally using its native pathways, production yield is low and requires improvement for commercial biofuel production. Moreover, ethanol is toxic to yeast and thus ethanol tolerance should be improved to further enhance ethanol production. In this study, we employed metabolomics-based strategy using 30 single-gene deleted yeast strains to construct multivariate models for ethanol tolerance and screen metabolites that relate to ethanol sensitivity/tolerance. The information obtained from this study can be used as an input for strain improvement via metabolic engineering