Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

     

Toll-like receptor 4 deficiency: Smaller infarcts,but nogain in function

Backgound  

It has been reported that Toll-like receptor 4 (TLR4) deficiency reduces infarct size after myocardial ischemia/reperfusion (MI/R). However, measurement of MI/R injury was limited and did not include cardiac function. In a chronic closed-chest model we assessed whether cardiac function is preserved in TLR4-deficient mice (C3H/HeJ) following MI/R, and whether myocardial and systemic cytokine expression differed compared to wild type (WT).     

Glyceraldehyde-3-phosphate dehydrogenase from Thermotoga maritima: Strategies of protein stabilization

Abstract: The molecular origin of protein stability has been the subject of active research for more than a generation (R. Jaenicke (1991) Eur. J. Biochem. 202, 715–728). Faced with the discovery of extremophiles, in recent years the problem has gained momentum, especially because of its biotechnological potential. In analyzing a number of enzymes from the hyperthermophilic bacterium Thermotoga maritima , it has become clear that the excess free energy of stabilization is equivalent to only a few weak bonds ( ΔΔG _stab≈ 50 kJ/mol). As taken from the comparison of homologous enzymes from mesophiles, thermophiles and hyperthermophiles, these accumulate from local interactions (especially ion pairs), enhanced secondary or supersecondary structure, and improved packing of domains and/or subunits, without significantly altering the overall topology. In this review, glyceraldehyde-3-phosphate dehydrogenase will be discussed as a representative example to illustrate possible adaptive strategies to the extreme thermal stress in hydrothermal vents.     

17 beta-Hydroxysteroid dehydrogenase in the human prostate: properties and distribution between epithelium and stroma in benign hyperplastic tissue

In order to delineate differences in the mechanism of androgen action in epithelium (E) and stroma (S) of the human prostate, we studied the 17 beta-hydroxysteroid dehydrogenase (17 beta-HSDH) in these tissues of benign prostatic hyperplasia (BPH). Tissue was obtained by suprapubic prostatectomy. E and S were separated; samples were homogenized in buffer and incubated with [3H] steroids (4-androstenedione (Ae), estrone (E1), or dehydroepiandrosterone (DHEA] and NADH (4.2 mmol/l) as cosubstrate for 60 min at 37 degrees C. Separation and quantification of the metabolites were performed by TLC and LSC, respectively. The main results were: (1) Following incubation with DHEA and E1, only the metabolites 5-androstene-3 beta,17 beta-diol and estradiol, respectively, were found. Following incubation with Ae, testosterone, 5 alpha-dihydrotestosterone and 5 alpha-androstane-3 alpha-(beta),17 beta-diol were detected as metabolites (the sum of these metabolites were used for calculations). (2) The Michaelis constants were identical in E and S (mean +/- SEM (n), mumol/l, Ae 6.92 +/- 1.01, E1 7.84 +/- 0.69, DHEA 3.73 +/- 0.38). (3) The maximum velocity rate for the three substrates in E was 5-10-fold that in S (P at least less than 0.01), the value in the whole tissue homogenate (WT) being intermediate (pmol/mg protein h), for Ae: E 383 +/- 56, S 40 +/- 3, WT 75 +/- 13; for E1: E 362 +/- 71, S 33 +/- 4, WT 63 +/- 8; for DHEA: E 132 +/- 21, S 26 +/- 4, WT 36 +/- 4. On the basis of these results the role of 17 beta-HSDH in forming active androgens and estrogens from less potent precursors is discussed in the stromal and epithelial compartment of the human prostate.