DNA damage causes TP53-dependent coupling of self-renewal and senescence pathways in embryonal carcinoma cells

Comment on: Wu R, et al. Clin Cancer Res 2011; 17:7359–72     

Effects of food quality on trade‐offs among growth,immunity and survival in the greater wax moth Galleria mellonella

The resources available to an individual in any given environment are finite, and variation in life history traits reflect differential allocation of these resources to competing life functions. Nutritional quality of food is of particular importance in these life history decisions. In this study, we tested trade‐offs among growth, immunity and survival in 3 groups of greater wax moth (Galleria mellonella) larvae fed on diets of high and average nutritional quality. We found rapid growth and weak immunity (as measured by encapsulation response) in the larvae of the high‐energy food group. It took longer to develop on food of average nutritional quality. However, encapsulation response was stronger in this group. The larvae grew longer in the low‐energy food group, and had the strongest encapsulation response. We observed the highest survival rates in larvae of the low‐energy food group, while the highest mortality rates were observed in the high‐energy food group. A significant negative correlation between body mass and the strength of encapsulation response was found only in the high‐energy food group revealing significant competition between growth and immunity only at the highest rates of growth. The results of this study help to establish relationships between types of food, its nutritional value and life history traits of G. mellonella larvae.     

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

     

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.     

Metabolomic analysis of a synthetic metabolic switch in Streptomyces coelicolor A3(2)

The global analysis of metabolism by liquid chromatography coupled to mass spectrometry is often hampered by a large amount of biological and technical variability. Here, we introduce an experimental and analytical strategy that can produce robust metabolome profiles in the face of this challenge. By applying a new computational approach based on concordance analysis to an extremely large number of analytical replicates, we are able to show that the overexpression of an antisense non-coding RNA targeting glutamine synthetase I results in a major reorganization of the metabolism of Streptomyces coelicolor, the model species of antibiotic-producing bacteria. We identified 97 metabolites with statistically significant reproducible dynamic behavior across the time series. The observed metabolic changes are very rapid, specific and widespread across metabolism, but focus on the nitrogen assimilation pathways. Our results demonstrate the power of highly replicated experimental designs for the robust characterization of metabolite dynamics. The identified global rearrangement of metabolism suggests the usefulness of RNA interference as an efficient strategy to manipulate the physiology of bacteria with wider biotechnological applicability in microorganisms.     

Polymerized and polyethylene glycol-conjugated hemoglobins: A globin-based calibration curve for dynamic light scattering analysis

Dynamic light scattering (DLS) is a technique capable of determining the hydrodynamic radius of proteins. From this parameter, a molecular weight can be assessed provided that an appropriate calibration curve is available. To this goal, a globin-based calibration curve was used to determine the polymerization state of a recombinant hemoglobin-based oxygen carrier and to assess the equivalent molecular weight of hemoglobins conjugated with polyethylene glycol molecules. The good agreement between DLS values and those obtained from gel filtration chromatography is a consequence of the high similarity in structure, shape, and density within the globin superfamily. Moreover, globins and heme proteins in general share similar spectroscopic properties, thereby reducing possible systematic errors associated with the absorption of the probe radiation by the chromophore.     

Distribution plasticity of the human estrogen receptor alpha in live cells: distinct imaging of consecutively expressed receptors

An amino-terminal fusion of the human estrogen receptor α (ER) with human O⁶-alkylguanine-DNA alkyltransferase (AGT) enabled the observation and distinction of consecutively expressed ER populations by sequential pulse labeling of the AGT tag with different fluorescent O⁶-alkylguanine derivatives in live cells. The application of agonists and antagonists led to the characteristic speckled redistribution of fluorescent receptors in the nucleus as visualized by confocal microscopy. To investigate where newly synthesized receptors were localized in individual cells with respect to their older relatives in response to extracellular chemical signals, receptor expression was continued for 4 h and newly synthesized receptors were labeled with a new fluorophore spectrally distinct from the first probe. This strategy enabled a time-resolved analysis of the formation of ER-enriched protein complexes in distinct nucleoplasmic compartments. Such complexes represent important but hitherto uncharacterized macromolecular structures involved in ER function. Different, long-lasting effects were observed depending on the type of ligand. For example, 4 h after pulsed application of the partial antagonist 4-hydroxytamoxifen, the second receptor population exhibited a speckled pattern in the cell nucleus that overlapped with the first receptor population pattern. This novel finding suggests that the intranuclear positioning of receptor aggregates is not random but influenced in a ligand-dependent manner. The antagonist ICI 182,780 (7-α-[9-(4.4,5,5,5-pentafluoropentylsulfinyl)nonyl]estra-1,3,5(10)-triene-3,17-β-diol), a potent drug used in cancer treatment, led to down-regulation of the first receptor population and newly expressed receptors accumulated in the cytoplasm. In contrast, the natural agonist 17β-estradiol resulted in significantly shorter effects. Four hours after ligand application, newly expressed receptors were homogeneously distributed in the nucleus as in untreated control cells. We present the pulse labeling of AGT-ER fusion proteins with different fluorophores as a novel tool for investigating the functional regulation of nuclear receptors in individual cells.     

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or "awakening" of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.     

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.