New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Alpha C protein of group B Streptococcus as a potential vaccine candidate

Group B Streptococcus (GBS) is a leading cause of life-threatening infection in neonates and young infants, pregnant women, and non-pregnant adults with underlying medical conditions. Immunization has theoretical potential to prevent significant morbidity and mortality from GBS disease. Alpha C protein (α C), found in 70% of non-type III capsule polysaccharide group B Streptococcus, elicits antibodies protective against α C-expressing strains in experimental animals and is an appealing carrier for a GBS conjugate vaccine. We determined whether natural exposure to α C elicits antibodies in women and if high maternal α C-specific serum antibody at delivery is associated with protection against neonatal disease. An ELISA was designed to measure α C-specific IgM and IgG in human sera. A case-control design (1:3 ratio) was used to match α C-expressing GBS colonized and non-colonized women by age and compare quantified serum α C-specific IgM and IgG. Sera also were analyzed from bacteremic neonates and their mothers and from women with invasive GBS disease. Antibody concentrations were compared using t-tests on log-transformed data. Geometric mean concentrations of α C-specific IgM and IgG were similar in sera from 58 α C strain colonized and 174 age-matched non-colonized women (IgG 245 and 313 ng/ml; IgM 257 and 229 ng/ml, respectively). Delivery sera from mothers of 42 neonates with GBS α C sepsis had similar concentrations of α C-specific IgM (245 ng/ml) and IgG (371 ng/ml), but acute sera from 13 women with invasive α C-expressing GBS infection had significantly higher concentrations (IgM 383 and IgG 476 ng/ml [p=0.036 and 0.038, respectively]). Convalescent sera from 5 of these women 16-49 days later had high α C-specific IgM and IgG concentrations (1355 and 4173 ng/ml, respectively). In vitro killing of α C-expressing GBS correlated with total α C-specific antibody concentration. Invasive disease but not colonization elicits α C-specific IgM and IgG in adults. Whether α C-specific IgG induced by vaccine would protect against disease in neonates merits further investigation. ^

THEORETICAL STUDIES ON THE METHODS OF RECONSTRUCTING PHYLOGENETIC TREES FROM DNA SEQUENCE DATA (MOLECULAR EVOLUTION, HOMINOID EVOLUTION, COMPUTER SIMULATION)

(1) A mathematical theory for computing the probabilities of various nucleotide configurations is developed, and the probability of obtaining the correct phylogenetic tree (model tree) from sequence data is evaluated for six phylogenetic tree-making methods (UPGMA, distance Wagner method, transformed distance method, Fitch-Margoliash's method, maximum parsimony method, and compatibility method). The number of nucleotides (m*) necessary to obtain the correct tree with a probability of 95% is estimated with special reference to the human, chimpanzee, and gorilla divergence. m* is at least 4,200, but the availability of outgroup species greatly reduces m* for all methods except UPGMA. m* increases if transitions occur more frequently than transversions as in the case of mitochondrial DNA. (2) A new tree-making method called the neighbor-joining method is proposed. This method is applicable either for distance data or character state data. Computer simulation has shown that the neighbor-joining method is generally better than UPGMA, Farris' method, Li's method, and modified Farris method on recovering the true topology when distance data are used. A related method, the simultaneous partitioning method, is also discussed. (3) The maximum likelihood (ML) method for phylogeny reconstruction under the assumption of both constant and varying evolutionary rates is studied, and a new algorithm for obtaining the ML tree is presented. This method gives a tree similar to that obtained by UPGMA when constant evolutionary rate is assumed, whereas it gives a tree similar to that obtained by the maximum parsimony tree and the neighbor-joining method when varying evolutionary rate is assumed. ^

Alterations in redox and energy metabolism in Ras-transformed cells: Mechanisms and therapeutic implications

Increasing attention has been given to the connection between metabolism and cancer. Under aerobic conditions, normal cells predominantly use oxidative phosphorylation for ATP generation. In contrast, increase of glycolytic activity has been observed in various tumor cells, which is known as Warburg effect. Cancer cells, compared to normal cells, produce high levels of Reactive Oxygen Species (ROS) and hence are constantly under oxidative stress. Increase of oxidative stress and glycolytic activity in cancer cells represent major biochemical alterations associated with malignant transformation. Despite prevalent upregulation of ROS production and glycolytic activity observed in various cancer cells, underlying mechanisms still remain to be defined. Oncogenic signals including Ras has been linked to regulation of energy metabolism and ROS production. Current study was initiated to investigate the mechanism by which Ras oncogenic signal regulates cellular metabolism and redox status. A doxycycline inducible gene expression system with oncogenic K-ras transfection was constructed to assess the role played by Ras activation in any given studied parameters. Data obtained here reveals that K-ras activation directly caused mitochondrial dysfunction and ROS generation, which appeared to be mechanistically associated with translocation of K-ras to mitochondria and the opening of the mitochondrial permeability transition pore. K-ras induced mitochondrial dysfunction led to upregulation of glycolysis and constitutive activation of ROS-generating NAD(P)H Oxidase (NOX). Increased oxidative stress, upregulation of glycolytic activity, and constitutive activated NOX were also observed in the pancreatic K-ras transformed cancer cells compared to their normal counterparts. Compared to non-transformed cells, the pancreatic K-ras transformed cancer cells with activated NOX exhibited higher sensitivity to capsaicin, a natural compound that appeared to target NOX and cause preferential accumulation of oxidative stress in K-ras transformed cells. Taken together, these findings shed new light on the role played by Ras in the road to cancer in the context of oxidative stress and metabolic alteration. The mechanistic relationship between K-ras oncogenic signals and metabolic alteration in cancer will help to identify potential molecular targets such as NAD(P)H Oxidase and glycolytic pathway for therapeutic intervention of cancer development. ^