The cancer cell’s “power plants” as promising therapeutic targets: An overview

This introductory article to the review series entitled “The Cancer Cell’s Power Plants as Promising Therapeutic Targets” is written while more than 20 million people suffer from cancer. It summarizes strategies to destroy or prevent cancers by targeting their energy production factories, i.e., “power plants.” All nucleated animal/human cells have two types of power plants, i.e., systems that make the “high energy” compound ATP from ADP and P _i . One type is “glycolysis,” the other the “mitochondria.” In contrast to most normal cells where the mitochondria are the major ATP producers (>90%) in fueling growth, human cancers detected via Positron Emission Tomography (PET) rely on both types of power plants. In such cancers, glycolysis may contribute nearly half the ATP even in the presence of oxygen (“Warburg effect”). Based solely on cell energetics, this presents a challenge to identify curative agents that destroy only cancer cells as they must destroy both of their power plants causing “necrotic cell death” and leave normal cells alone. One such agent, 3-bromopyruvate (3-BrPA), a lactic acid analog, has been shown to inhibit both glycolytic and mitochondrial ATP production in rapidly growing cancers (Ko et al., Cancer Letts., 173, 83–91, 2001), leave normal cells alone, and eradicate advanced cancers (19 of 19) in a rodent model (Ko et al., Biochem. Biophys. Res. Commun., 324, 269–275, 2004). A second approach is to induce only cancer cells to undergo “apoptotic cell death.” Here, mitochondria release cell death inducing factors (e.g., cytochrome c). In a third approach, cancer cells are induced to die by both apoptotic and necrotic events. In summary, much effort is being focused on identifying agents that induce “necrotic,” “apoptotic” or apoptotic plus necrotic cell death only in cancer cells. Regardless how death is inflicted, every cancer cell must die, be it fast or slow.     

Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

We propose a growing network model that consists of two tunable mechanisms: growth by merging modules which are represented as complete graphs and a fitness-driven preferential attachment. Our model exhibits the three prominent statistical properties are widely shared in real biological networks, for example gene regulatory, protein-protein interaction, and metabolic networks. They retain three power law relationships, such as the power laws of degree distribution, clustering spectrum, and degree-degree correlation corresponding to scale-free connectivity, hierarchical modularity, and disassortativity, respectively. After making comparisons of these properties between model networks and biological networks, we confirmed that our model has inference potential for evolutionary processes of biological networks.     

Exploration of the conformational space of myosin recovery stroke via molecular dynamics

Muscle contractions are driven by cyclic conformational changes of myosin, whose molecular mechanisms of operation are being elucidated by recent advances in crystallographic studies and single molecule experiments. To complement such structural studies and consider the energetics of the conformational changes of myosin head, umbrella sampling molecular dynamics (MD) simulations were performed with the all-atom model of the scallop myosin sub-fragment 1 (S1) with a bound ATP in solution in explicit water using the crystallographic near-rigor and transition state conformations as two references. The constraints on RMSD reaction coordinates used for the umbrella sampling were found to steer the conformational changes efficiently, and relatively close correlations have been observed between the set of characteristic structural changes including the lever arm rotation and the closing of the nucleotide binding pocket. The lever arm angle and key residue interaction distances in the nucleotide binding pocket and the relay helix show gradual changes along the recovery stroke reaction coordinate, consistent with previous crystallographic and computational minimum energy studies. Thermal fluctuations, however, appear to make the switch-2 coordination of ATP more flexible than suggested by crystal structures. The local solvation environment of the fluorescence probe, Trp 507 (scallop numbering), also appears highly mobile in the presence of thermal fluctuations.     

Development of sequential multiple comparison procedure for dose response test

We propose a multiple comparison procedure to identify the minimum effective dose level by sequentially comparing each dose level with the zero dose level in the dose finding test. If we can find the minimum effective dose level at an early stage in the sequential test, it is possible to terminate the procedure in the dose finding test after a few group observations up to the dose level. Thus, the procedure is viable from an economical point of view when high costs are involved in obtaining the observations. In the procedure, we present an integral formula to determine the critical values for satisfying a predefined type I familywise error rate. Furthermore, we show how to determine the required sample size in order to guarantee the power of the test in the procedure. In practice, we compare the power of the test and the required sample size for various configurations of the population means in simulation studies and adopt our sequential procedure to the dose response test in a case study.     

Microarthropod density and diversity respond little to spatial isolation

The effect of spatial isolation on the soil microarthropod community of a deciduous forest was investigated for 16 months. Soil animals were confined in plastic tubes (diameter: 7 cm; length: 15 cm). We expected the density and diversity of most microarthropods to decrease with time in isolated habitats and this decline to be more pronounced in species of high trophic level. We also expected that species that are top-down controlled, such as collembolans, would benefit from reduced predator densities whereas species suffering little from enemies, such as oribatid mites, would be little affected. In contrast to these hypotheses, the density and diversity of almost all microarthropod taxa (Gamasina, most groups of Oribatida and Collembola) were not significantly reduced by isolation. Also in contrast to our expectation, the density of predators (Gamasina) increased in isolated habitats. This increase may have resulted from the release of predator pressure, due to the exclusion of macrofauna predators. We conclude that soil microarthropods in the studied forest are insensitive to spatial isolation. Food generalism and parthenogenetic reproduction may enable them to persist in isolated communities.     

Power generation using spinel manganese-cobalt oxide as a cathode catalyst for microbial fuel cell applications

This study focused on the use of spinel manganese-cobalt (Mn-Co) oxide, prepared by a solid state reaction, as a cathode catalyst to replace platinum in microbial fuel cells (MFCs) applications. Spinel Mn-Co oxides, with an Mn/Co atomic ratios of 0.5, 1, and 2, were prepared and examined in an air cathode MFCs which was fed with a molasses-laden synthetic wastewater and operated in batch mode. Among the three Mn-Co oxide cathodes and after 300 h of operation, the Mn-Co oxide catalyst with Mn/Co atomic ratio of 2 (MnCo-2) exhibited the highest power generation 113 mW/m2 at cell potential of 279 mV, which were lower than those for the Pt catalyst (148 mW/m2 and 325 mV, respectively). This study indicated that using spinel Mn-Co oxide to replace platinum as a cathodic catalyst enhances power generation, increases contaminant removal, and substantially reduces the cost of MFCs.     

Systematic error is of minor importance to feedback structure estimates derived from time series of nonlinear population indices

Most modern population dynamics analyses of time series use simple population indices for ecological inference. These indices, collected for many years for various agricultural pests or game animals, are generally believed not to distort systematically feedback estimates because the assumption of linearity to population size roughly holds. To assess the relative importance of this assumption, we examined the effect of nonlinearity in a burrow index for voles on feedback estimates obtained through autoregressive modeling. We show that the issue of linearity is of less importance to ecological inference because the feedback estimates are routinely obtained on a logarithmic scale. Transforming data to logs has a strong linearization effect, removing most of the nonlinearity observed on the original scale. We conclude that the statistical tools for ecological inference, such as autoregressive log-linear models, are sufficiently robust to the systematic error imposed by index nonlinearity and that indices are valuable sources of ecological information even in situations when the assumed linear functional forms to population size were not exactly validated. We suggest that for time series modelers, the issue of a large sampling variation in small “noisy” populations is by far a more burning one than the systematic error due to index nonlinearity.     

The role of statistical power analysis in quantitative proteomics

Designing an experiment for quantitative proteomic analysis is not a trivial task. One of the key factors influencing the success of such studies is the number of biological replicates included in the analysis. This, along with the measured variation will determine the statistical power of the analysis. Presented is a simple yet powerful analysis to determine the appropriate sample size required for reliable and reproducible results, based on the total variation (technical and biological). This approach can also be applied retrospectively for the interpretation of results as it takes into account both significance (p value) and quantitative difference (fold change) of the results.     

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.