The correlation between the central carbon metabolic flux distribution and the number of shared enzyme regulators in Saccharomyces cerevisiae

The central carbon metabolic system is the upstream energy source for microbial fermentation. In addition, it is a master switch for increasing the production of metabolites and an important part of the microbial metabolic network. Investigation into the relationship between genes, environmental factors, and metabolic networks is a main focus of systems biology, which significantly impacts research in biochemistry, metabolic engineering, and synthetic biology. To this end, the central carbon metabolic flux under a variety of growth conditions or using strains with various genetic modifications was previously measured in Saccharomyces cerevisiae using 13C tracer technology. However, the measured values were not integrated and investigated further. In this study, we collected and analyzed the metabolic flux rates of the central carbon metabolic system in S. cerevisiae measured in recent studies. We carried out preliminary analyses of flux values of each pathway, performed regression analyses on relationship between different fluxes, and extracted principal component factors of the flux variables. Based on the results, the general characteristics of pathway flux distribution were clustered and explored, and the effects of environmental and genetic factors on the flux distribution were analyzed. Furthermore, this study explored the relationship between similarity in the enzyme's transcriptional regulation and the correlations in the enzyme's reaction flux. Our results provide a foundation for further studies on the control of the central carbon metabolic flux and facilitate the search for targets in metabolic engineering research.

Value of Golgi protein 73 monoclonal antibody in diagnosis of hepatocellular carcinoma / 中国医学科学院学报

<p><b>OBJECTIVE</b>To explore the sensitivity and specificity of Golgi protein 73 (GP73) monoclonal antibody in the diagnosis of hepatocellular carcinoma (HCC).</p><p><b>METHODS</b>Self-prepared GP73 monoclonal antibody was used as the primary antibody for detecting the serum GP73 levels in healthy controls(n=31)and HCC patients (n=59). The baseline level of the healthy controls was determined by semiquantitative analysis. The results were compared with those from GP73 polyclonal antibody and alpha-fetoprotein (AFP).</p><p><b>RESULTS</b>The GP73 level of healthy controls was 1.2 (0.9-1.7) relative unit (RU), which was significantly lower than that of HCC patients [5.7 (2.5-7.8) RU] (P<0.001) with monoclonal antibody. Using polyclonal antibody, the GP73 level of HCC patients was also significantly higher than healthy controls [7.8 (3.0-12.4) RU vs. 1.1 (1.0-2.0) RU, P<0.001]. The sensitivity and specificity of GP73 monoclonal antibody in diagnosis of HCC were 84.7% and 93.5%; on the contrary, those of GP73 polyclonal antibody were 78.0% and 93.5%, respectively. The sensitivity and specificity of AFP (67.8% and 74.2%, respectively) in the HCC patients were markedly lower than those of GP73. Logistic regression analysis showed that the odds ratio (OR) of GP73 monoclonal antibody was 7.18 and that of GP73 polyclonal antibody was 1.51.</p><p><b>CONCLUSIONS</b>Our self-prepared monoclonal antibody can effectively detect GP73 serum level in HCC patients, and has higher sensitivity and specificity than AFP. It may be superior to the currently used GP73 polyclonal antibody. The results lay the foundation for the further development of ELISA methods by using this monoclonal antibody.</p>

An elementary proof of MinVol(Rn) = 0 for n ≥ 3

In this paper, we give an elementary proof of the result that the minimal volumes of R³ and R4 are zero. The approach is to construct a sequence of explicit complete metrics on them such that the sectional curvatures are bounded in absolute value by 1 and the volumes tend to zero. As a direct consequence, we get that MinVol (Rn) = 0 for n > 3.

Neste artigo fornecemos uma demonstração elementar do resultado de que os volumes minimais de R³ e R4 são ambos iguais a zero. A abordagem consiste na construção de uma seqüência de métricas completas explícitas nesses espaços cujas curvaturas seccionais são limitadas em valor absoluto por 1 e os volumes tendem a zero. Como conseqüência direta, estabelecemos que MinVol(Rn) = 0 para n > 3.