Modulation of the central carbon metabolism of Corynebacterium glutamicum improves malonyl-CoA availability and increases plant polyphenol synthesis

In recent years microorganisms have been engineered towards synthesizing interesting plant polyphenols such as flavonoids and stilbenes from glucose. Currently, the low endogenous supply of malonyl-CoA, indispensable for plant polyphenol synthesis, impedes high product titers. Usually, limited malonyl-CoA availability during plant polyphenol production is avoided by supplementing fatty acid synthesis-inhibiting antibiotics such as cerulenin, which are known to increase the intracellular malonyl-CoA pool as a side effect. Motivated by the goal of microbial polyphenol synthesis being independent of such expensive additives, we used rational metabolic engineering approaches to modulate regulation of fatty acid synthesis and flux into the tricarboxylic acid cycle (TCA cycle) in Corynebacterium glutamicum strains capable of flavonoid and stilbene synthesis. Initial experiments showed that sole overexpression of genes coding for the native malonyl-CoA-forming acetyl-CoA carboxylase is not sufficient for increasing polyphenol production in C. glutamicum. Hence, the intracellular acetyl-CoA availability was also increased by reducing the flux into the TCA cycle through reduction of citrate synthase activity. In defined cultivation medium, the constructed C. glutamicum strains accumulated 24 mg·L ⁻¹ (0.088 mM) naringenin or 112 mg·L ⁻¹ (0.49 mM) resveratrol from glucose without supplementation of phenylpropanoid precursor molecules or any inhibitors of fatty acid synthesis.     

Warming and water deficit impact leaf photosynthesis and decrease forage quality and digestibility of a C4 tropical grass

Global warming is predicted to cause more intense extreme events such as heat waves, flooding and severe droughts, producing significant effects on agriculture. In tropics, climate change will severely impact livestock production affecting water availability, forage quality and food for cattle. We investigated the isolated and combined effects of soil water deficit (wS) and + 2°C increase in canopy temperature (eT) on leaf gas exchange, chlorophyll fluorescence, carbohydrate content, forage quality and in vitro dry matter digestibility (IVDMD) of a field‐grown C4 tropical forage grass Panicum maximum Jacq. using a temperature‐free air‐controlled enhancement (T‐FACE) system. The wS and eT treatments showed no effects on photosystem II photochemistry. However, wS under ambient temperature decreased net photosynthesis rate (A), stomatal conductance (g_s) and maximum rate of carboxylation of Rubisco (V_cmax), leading to a reduced starch content in leaves. A 16% reduction in leaf dry mass (LDM) and reduction in forage quality by increasing fibers, reducing crude protein (CP) and decreasing the IVDMD was also observed by effect of wS. Warming under adequate soil moisture (eT) significantly increased LDM by 25% but reduced the forage quality, increasing the lignin content and reducing starch, CP and digestibility. The combined wSeT treatment reduced A, g_s, V_cmax and the forage quality. When compared to control, the lignin content in leaves increased by 43, 28 and 17% in wS, eT and wSeT, respectively, causing a significant reduction in IVDMD. We concluded that despite physiological mechanisms to acclimate to warming, both warming and water deficit will impair the quality and digestibility of C4 tropical pastures.     

Identification of Biomarkers of Mercury Contamination in Brachyplatystoma filamentosum of the Madeira River,Brazil, Using Metalloproteomic Strategies

Biological Trace Element Research - Predator fish can accumulate high levels of mercury, which qualifies them as potential indicators of this toxic metal. The predatory species Brachyplatystoma...     

Loss of heterozygosity in tumor cells requires re-evaluation: the data are biased by the size-dependent differential sensitivity of allele detection

Normal tissue contamination of tumors may eclipse the detection of loss of heterozygosity (LOH) by microsatellite analysis and may also hamper isolation of tumor suppressor genes. To test the potential impact of this problem, we prepared artificial mixtures of mouse-human microcell hybrid lines that carried different alleles of the same chromosome 3 marker. After performing an allele titration assay, we found a consistent difference between the LOH of a high molecular weight (H) allele and the LOH of a low molecular weight (L) allele of the same CA repeat marker. It follows that normal tissue admixtures will be less of a problem when LOH affects a H allele than with a L allele. Random screening of 100 papers published between 1994 and 1999 revealed that the loss of a L allele was recorded at about half the frequency (52%) of loss of a H allele. To avoid this bias, we have developed rules for the evaluation of LOH data. We suggest that the loss of a L allele should be given more weight than the loss of a H allele in LOH studies using microsatellite markers.     

Neural networks learning with sliding mode control: the sliding mode backpropagation algorithm

Based on the classical backpropagation weight update equations, sliding mode control theory is introduced as a technique to adapt weights of a multi-layer perceptron. As will be demonstrated, the introduction of sliding mode has resulted in a much faster version of the standard backpropagation. The results show also that the proposed algorithm presents some important features of sliding mode control, which are robustness and high speed of learning. In addition to that, this paper shows also how control theory can be applied to train neural networks.     

Alteration in the endogenous intestinal flora of Swiss Webster mice by experimental Angiostrongylus costaricensis infection

The association between worm infections and bacterial diseases has only recently been emphasized. This study examined the effect of experimental Angiostrongylus costaricensis infection on endogenous intestinal flora of Swiss Webster mice. Eight mice aging six weeks were selected for this experiment. Four were infected with A. costaricensis and the other four were used as controls. Twenty eight days after the worm infection, all mice in both groups were sacrificed and samples of the contents of the ileum and colon were obtained and cultured for aerobic and anaerobic bacteria. In the mice infected with A. costaricensis there was a significant increase in the number of bacteria of the endogenous intestinal flora, accompanied by a decrease in the number of Peptostreptococcus spp. This alteration in the intestinal flora of mice infected by the nematode may help to understand some bacterial infections described in humans.     

Slow-binding inhibition of 2-keto-3-deoxy-6-phosphogluconate (KDPG) aldolase

2-Keto-3-deoxy-6-phosphogluconate (KDPG) aldolase is a key enzyme in the Entner-Doudoroff pathway of bacteria. It catalyzes the reversible production of KDPG from pyruvate and D-glyceraldehyde 3-phosphate through a class I Schiff base mechanism. On the basis of aldolase mechanistic pathway, various pyruvate analogues bearing beta-diketo structures were designed and synthesized as potential inhibitors. Their capacity to inhibit aldolase catalyzed reaction by forming stabilized iminium ion or conjugated enamine were investigated by enzymatic kinetics and UV-vis difference spectroscopy. Depending of the substituent R (methyl or aromatic ring), a competitive or a slow-binding inhibition takes place. These results were examined on the basis of the three-dimensional structure of the enzyme.     

Recent advances in the MOBJ algorithm for training artificial neural networks

This paper presents a new scheme for training MLPs which employs a relaxation method for multi-objective optimization. The algorithm works by obtaining a reduced set of solutions, from which the one with the best generalization is selected. This approach allows balancing between the training error and norm of network weight vectors, which are the two objective functions of the multi-objective optimization problem. The method is applied to classification and regression problems and compared with Weight Decay (WD), Support Vector Machines (SVMs) and standard Backpropagation (BP). It is shown that the systematic procedure for training proposed results on good generalization neural models, and outperforms traditional methods.