Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels

Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal–bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.Subject terms: Fungi, Infectious diseases, Metagenomics, Gastrointestinal diseases, Microbiome     

Epigenetic effects of selenium and their implications for health

Alterations of epigenetic marks are linked to normal development and cellular differentiation as well as to the progression of common chronic diseases. The plasticity of these marks provides potential for disease therapies and prevention strategies. Macro- and micro-nutrients have been shown to modulate disease risk in part via effects on the epigenome. The essential micronutrient selenium affects human health outcomes, e.g., cancers, cardiovascular and autoimmune diseases, via selenoproteins and through a range of biologically active dietary selenocompounds and metabolism products thereof. This review provides an assessment of the current literature regarding epigenetic effects of dietary and synthetic selenocompounds, which include the modulation of marks and editors of epigenetic information and interference with one-carbon metabolism, which provides the methyl donor for DNA methylation. The relevance of a selenium-epigenome interaction for human health is discussed, and we also indicate where future studies will be helpful to gain a deeper understanding of epigenetic effects elicited by selenium.     

Effects of plating density and age in culture on growth and cell division of neonatal rat heart primary cultures

Summary The cell-type composition of the initial cell population from protease-dispersed neonatal rat heart tissue has been evaluated using time lapse photography and identification of cell type-specific functions. The effects of two commonly employed plating densities on growth and cell division of the two major cell types were examined. Total protein synthesis rates were not affected by plating density but did change with age in culture. Maximum protein synthesis rates were observed during the period of maximum cell division and cell growth (increase in total cell protein), which was from 24 h in culture to the 4th d in culture. After 6 d in culture, synthesis rates for total proteins remained constant for at least 2 wk. Sizing of cells by Coulter counter analysis indicated that essentially all the cells were increasing in size with age in culture. Measurements of cell numbers and rate of DNA synthesis indicated that the extent of cell division was dependent on plating density. Cells disaggregated from neonatal rat hearts consisted of approximately 75% muslce cells and 25% nonmuscle cells. This composition approximates the cell-type composition of the intact neonatal rat heart. In high density cultures there is little cell division and the relative proportionsof the cell types are preserved with time in culture. In low density cultures, proliferation of nonmuscle cells is a significant process and the composition of the cell population changes drastically during the first 2 to 3 d in culture. These results suggest that the low plating density used by many researchers may limit correlation of data derived from such cultures with the physiological state. It also indicates that plating densities should be given in published accounts for comparisons to be made with results from other laboratories. This work was supported in part by U.S. Public Health Service Grant HL10018 and The Pennsylvania State University Agricultural Experiment Station and was authorized for publication as Paper 5490 in the journal series of the Pennsylvania Agricultural Experiment Station.     

Estimation of steady-state flux rates in metabolic systems by computer simulations of radioactive tracer experiments

The dynamics of radioactive tracers in metabolic systems canbe mathematically modelled by coupled systems of differentialequations for both the concentrations and the specific radio-activitiesof the metabolites involved. Computer simulations, i.e. directfitting of numerical solutions of these equations to observedtime-courses of metabolites and spec jflc radioactivities, enablesone to estimate unknown kinetic parameters of the system. Thetheoretical fundamentals of this approach and the main stepstowards the quantitative assessment of flux rates in stationarymetabolic systems are outlined. An application of the methodto the purine nucleotide metabolism of Ehrlich ascites twnorcells is given based on a simp4fied model of this pathway. Theobtained steady-state flux rates indicate a marked increasein the metabolism of the adenine nucleotides during the proliferatingphase in comparison to the resting phase of tumor growth.     

The effects of estramustine on metaphase and anaphase in DU 145 prostatic carcinoma cells

The chemotherapeutic drug, estramustine, has been shown to cause the disassembly of microtubules via binding to microtubule-associated proteins. In this report, estramustine is shown to be a potent inhibitor of mitotic progression in the human prostatic carcinoma cell line, DU 145. Examination of individual living cells via video-enhanced differential interference contrast (DIC) optics shows that the drug delays the onset of anaphase, reduces anaphase spindle-pole elongation (anaphase B), and delays cytokinesis. In addition, immunofluorescent studies demonstrate that estramustine causes a rapid disorganization of the mitotic apparatus at significantly lower concentrations than those reported previously. Electron microscopic studies show that microtubule bundles are present in drug-treated mitotic cells in association with kinetochores and centrioles.     

Model‐based identifiable parameter determination applied to a simultaneous saccharification and fermentation process model for bio‐ethanol production

In this work, a methodology for the model‐based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over‐parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill‐posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio‐ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross‐validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:1064–1082, 2013