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Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications
Sanjeev Dahal,Jiao Zhao,Laurence Yang 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.6
Genome-scale models (GEMs) are predictive tools to study genotype-phenotype relationships in biological systems. Initially, genome-scale models were used for predicting the metabolic state of the organism given the nutrient condition and genetic perturbation (if any). Such metabolic (M-) models have been successfully developed for diverse organisms in both prokaryotes and eukaryotes. In this review, we focus our attention to genome-scale models of metabolism and macromolecular expression or ME-models. ME-models expand the scope of M-models by incorporating macromolecular biosynthesis pathways of transcription and translation. ME-models can predict the proteome investment in metabolism under any given condition. Therefore, ME-models significantly improve the quantitative prediction of gene expression. Unlike Mmodels that can predict biological properties in only nutrient-limited condition, ME-models can do so in both nutrient- and proteome-limited conditions. There are a few limitations of ME-models, many of which have now been largely overcome, making them more attractive to the broader research community. We finally discuss the applications of GEMs in general, and how they have been applied for biomedical, bioengineering and bioremediation purposes.
Groeneveld, Dionysius C.,Tavoularis, Stavros,Raogudla, Prassada,Yang, Sun-Kyu,Leung, Laurence K.H. Korean Nuclear Society 2008 Nuclear Engineering and Technology Vol.40 No.2
The present paper describes the preliminary compilation, assessment and examination of the supercritical heat transfer(SCHT) database. The availability and reliability of the SCHT data are discussed. Similarities in thermodynamic supercritical properties and SCHT behaviour of water, $CO_{2}$ and R-134a have been examined and some tentative conclusions are made. Finally, the future experimental and analytical program at the University of Ottawa is described.
Liu Na,Feng Yuchen,Liu Huicheng,Wu Wenliang,Liang Yuxia,Li Pingfei,Wei Zhengping,Wu Min,Tang Zhao-Hui,Han Junyan,Cheng Xiang,Liu Zheng,Laurence Arian,Li Huabin,Zhen Guohua,Yang Xiang-Ping 대한천식알레르기학회 2021 Allergy, Asthma & Immunology Research Vol.13 No.3
Purpose Macrophages are important regulators of environmental allergen-induced airway inflammation and asthma. ATP6V0d2 is a subunit of vacuolar ATPase highly expressed in macrophages. However, the functions of ATP6V0d2 in the regulation of pathogenesis of allergic asthma remain unclear. The aim of this study is to determine the function and related molecular mechanisms of macrophage protein ATP6V0d2 in allergic asthma. Methods We compared the disease severity between female C57BL/6 wild-type and ATP6V0d2−/− mice in an ovalbumin (OVA)-induced asthma model. We also investigated the association of expression of ATP6V0d2, PU.1 and CCL17 with disease severity among asthmatic patients. Results The expression of ATP6V0d2 in sputum cells of asthmatic patients and in the lungs of OVA-challenged mice was enhanced compared to healthy subjects and their counterparts, respectively. However, ATP6V0d2-deficient mice exaggerated inflammatory cell infiltration as well as enhanced alternative activated macrophage (AAM) polarization and mucus production in an OVA-induced asthma model. Furthermore, we found that Atp6v0d2 promoted lysosomal degradation of Pu.1, which induced AAM polarization and Ccl17 production. Among asthma patients, ATP6V0d2 expression was inversely associated with disease severity, whereas PU.1 and CCL17 expression was positively associated with disease severity. Conclusions Our results identify macrophage Atp6v0d2, as an induced feedback inhibitor of asthma disease severity by promoting Pu.1 lysosomal degradation, which may in turn leads to reduced AAM polarization and Ccl17 production.
Systematic discovery of uncharacterized transcription factors in <i>Escherichia coli</i> K-12 MG1655
Gao, Ye,Yurkovich, James T,Seo, Sang Woo,Kabimoldayev, Ilyas,Drä,ger, Andreas,Chen, Ke,Sastry, Anand V,Fang, Xin,Mih, Nathan,Yang, Laurence,Eichner, Johannes,Cho, Byung-Kwan,Kim, Donghyuk,Palsson, Oxford University Press 2018 Nucleic acids research Vol.46 No.20
<P><B>Abstract</B></P><P>Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in <I>Escherichia coli</I> K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the <I>E. coli</I> transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to (i) identify 16 candidate TFs from over a hundred uncharacterized genes; (ii) capture a total of 255 DNA binding peaks for ten candidate TFs resulting in six high-confidence binding motifs; (iii) reconstruct the regulons of these ten TFs by determining gene expression changes upon deletion of each TF and (iv) identify the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of <SMALL>L</SMALL>-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron-limited conditions, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.</P>