Publications by authors named "Stephen G Wu"

To achieve dual-reuse of both enzyme and support in enzyme immobilization, hybrid nanoflowers (hNFs) were synthesized and crystallized in aqueous solution using calcium phosphate as inorganic component and enzyme as organic component. When hNFs lost their catalytic activity after reuse for times, they underwent dissolution and recrystallization to achieve the dual-cycle of enzyme and support. Six enzymes including papain, bromelain, trypsin, Lipase from Porcine Pancreas (PPL), Lipase from (TLL) and Lipase B from (CALB) were chose as model enzymes and the obtained hNFs all presented high catalytic activity and thermal stability.

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To develop an efficient and green immobilized biocatalyst for promiscuous catalysis which has a broad scope of applications, hybrid nanoflower (hNF) confined papain as a biocatalyst has been proposed and characterized in this study. hNFs were firstly prepared through mixing CuSO aqueous solution with papain in phosphate saline (PBS) at room temperature. The resulting hNFs were characterized by SEM and verified through a hydrolysis reaction with -benzoyl-dl-arginine amide as substrate.

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In China, the majority of ovarian cancer patients (80%-90%) are women who are diagnosed with epithelial ovarian cancer. The SYNPO2 gene has recently been reported to be associated with epithelial ovarian cancer in Europeans. To investigate the association of common variants of SYNPO2 gene with epithelial ovarian cancer in Han Chinese individuals, we designed a case-control study with 719 epithelial ovarian cancer patients and 1568 unrelated healthy controls of Han Chinese descent.

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Osteosarcoma (OS) is a common malignant tumor, which exists widely in the bone of children and adolescents, and genetic factors may influence its susceptibility. Recently, the gene has been reported to be associated with OS in a Caucasian population. To investigate the association of common variants in with OS risk in Han Chinese individuals, we designed a two-stage case-control study with 392 OS patients and 1,578 unrelated healthy controls of Han Chinese individuals.

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Objective: In a recent study single nucleotide polymorphisms (SNPs) in the COL11A1 gene were found to be associated with hip osteoarthritis (OA) among European populations. In this study, our aim was to identify common genetic variants in COL11A1 predisposing to primary hip OA in Han Chinese individuals.

Methods: We designed a case-control study that included 313 hip OA patients and 911 unrelated healthy controls.

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Background: Flux analyses, including flux balance analysis (FBA) and C-metabolic flux analysis (C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB.

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Our recent C-metabolic flux analysis ( C-MFA) study indicates that energy metabolism becomes a rate-limiting factor for fatty acid overproduction in E. coli strains (after "Push-Pull-Block" based genetic modifications). To resolve this bottleneck, Vitreoscilla hemoglobin (VHb, a membrane protein facilitating O transport) was introduced into a fatty-acid-producing strain to promote oxygen supply and energy metabolism.

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Article Synopsis
  • 13C metabolic flux analysis (13C-MFA) helps researchers measure metabolic rates in microorganisms and predict their behavior based on environmental and genetic factors.
  • The authors introduce MFlux, a web platform that uses machine learning to analyze data from about 100 studies on bacterial metabolism, employing methods like Support Vector Machine (SVM) for improved accuracy.
  • MFlux effectively predicts metabolic flux profiles based on various factors such as species and growth conditions, although its accuracy could improve with more diverse studies on non-model organisms.
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Background: Genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions.

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In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes.

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