Novel and natural knockout lung cancer cell lines for the LKB1/STK11 tumor suppressor gene

Germline mutations of the LKB1 gene are responsible for Peutz-Jeghers syndrome (PJS), an autosomal dominant inherited disorder bestowing an increased risk of cancer. We have recently demonstrated that LKB1 inactivating mutations are not confined to PJS, but also appear in lung adenocarcinomas of sporadic origin, including primary tumors and lung cancer cell lines. To accurately determine the frequency of inactivating LKB1 gene mutations in lung tumors we have sequenced the complete coding region of LKB1 in 21 additional lung cancer cell lines. Here we describe the mutational status of LKB1 gene in 30 lung cancer cell lines from different histopathological types, including 11 lung adenocarcinomas (LADs) and 11 small cell lung cancers (SCLCs). LKB1 gene alterations were present in six (54%) of the LAD cell lines tested but in none of the other histological types. Similar to our previous observations in primary tumors, all point mutations were of the nonsense or frameshift type, leading to an abnormal, truncated protein. Moreover, 2 cell lines (A427 and H2126) harbored large gene deletions that spanned several exons. Hence, we have identified additional lung cancer cell lines carrying inactivating mutations of the LKB1 tumor suppressor gene, further attesting to the significance of this gene in the development of LADs and providing new natural LKB1 knockouts for studies of the biological function of the LKB1 protein

Water extract of Cryphaea heteromalla (Hedw.) D. Mohr bryophyte as a natural powerful source of biologically active compounds

Bryophytes comprise of the mosses, liverworts, and hornworts. Cryphaea heteromalla, (Hedw.) D. Mohr, is a non-vascular lower plant belonging to mosses group. To the date, the most chemically characterized species belong to the liverworts, while only 3.2% and 8.8% of the species belonging to the mosses and hornworts, respectively, have been investigated. In this work, we present Folin–Ciocalteu and oxygen radical absorbance capacity (ORAC) data related to crude extracts of C. heteromalla obtained by three different extraction solvents: pure water (WT), methanol:water (80:20 v/v) (MET), and ethanol:water (80:20 v/v) (ETH). The water extract proved to be the best solvent showing the highest content of biophenols and the highest ORAC value. The C. heteromalla-WT extract was investigated by HPLC-TOF/MS (High Performance Liquid Chromatography-Time of Flight/Mass Spectrometry) allowing for the detection of 14 compounds, five of which were phenolic compounds, derivatives of benzoic, caffeic, and coumaric acids. Moreover, the C. heteromalla WT extract showed a protective effect against reactive oxygen species (ROS) generation induced by tert-butyl hydroperoxide (TBH) on the murine NIH-3T3 fibroblast cell line

Extraction of the antioxidant phytocomplex from wine-making by-products and sustainable loading in phospholipid vesicles specifically tailored for skin protection

The present study is aimed at valorizing grape pomace, one of the most abundant winery-making by-products of the Mediterranean area, through the extraction of the main bioactive compounds from the skin of grape pomace and using them to manufacture innovative nanoformulations capable of both avoiding skin damages and promoting skincare. The phytochemicals were recovered through maceration in hydroethanolic solution. Catechin, quercetin, fisetin and gallic acid, which are known for their antioxidant power, were detected as the main compounds of the extract. Liposomes and phospholipid vesicles modified with glycerol or Montanov 82® or a combination of both, were used as carriers for the extract. The vesicles were small (~183 nm), slightly polydispersed (PI ≥ 0.28), and highly negatively charged (~−50 mV). The extract was loaded in high amounts in all vesicles (~100%) irrespective of their composition. The antioxidant activity of the extract, measured by using the DPPH (2,2-Diphenyl-1-picrylhydrazyl) test, was 84 ± 1%, and slightly increased when loaded into the vesicles (~89%, P < 0.05). The grape pomace extract loaded vesicles were highly biocompatible and able to protect fibroblasts (3T3) from the oxidative stress induced by hydrogen peroxide

Incorporation of lippia citriodora microwave extract into total-green biogelatin-phospholipid vesicles to improve its antioxidant activity

Phytochemicals from Lippia citriodora leaves were extracted by applying an innovative technology based on the use of microwaves, which represents an alternative method to extract bioactive substances. The obtained extract was incorporated into phospholipid vesicles in order to promote the antioxidant effect of the bioactive molecules present in L. citriodora extract. The extract was analyzed by High Performance Liquid Chromatography coupled to Time-Of-Flight mass spectrometer by electrospray (HPLC-ESI-TOF-MS) and different phytochemicals were detected and quantified. The whole extract was incorporated in liposomes, glycerosomes (liposomes modified with glycerol) and propylene glycol-containing vesicles (PG-PEVs). Moreover, a biopolymer obtained from fish by-product, that is Thunnus albacares skin, was added to improve the bioactivity of the formulations. The in vitro biocompatibility and the antioxidant efficacy of the extract in solution or loaded in the vesicles were tested in primary mouse embryonic fibroblasts (3T3). The results showed the superior bioactivity of the vesicle formulations over the aqueous solution of the extract, which points to an interesting strategy for the treatment of skin disorders

Specific interaction of methionine adenosyltransferase with free radicals

Although free radicals have been traditionally implicated in cell injury, and associated to pathophysiological processes, recent data implicate them in cell signaling events. Free radicals are naturally occurring oxygen-,nitrogen-and sulfur-derived species with an unpaired electron, such as superoxide, hydroxyl radical or nitric oxide. In order to assess the role of free radicals in cell signaling, we have studies the modulator effect of oxygen and nitrogen active species on liver methionine adenosyltransferase (MAT), a key metabolic enzyme. The presence of 10 cysteine residues per subunit, makes liver MAT a sensitive target for oxidation/nitrosylation. Here we show that purified MAT from rat liver is nitrosylated and oxidized in vitro. Incubation with H202 or the NO donor S-nitrosylated GSH (GSNO), diminish MAT activity in a dose-and time-dependent manner. Furthermore, the inactivation derived from both oxidation and nitrosylation, was reverted by GSH. MAT inactivation originates on the specific and covalent modification of the sulphydryl group of cysteine residue 121. We also studied how free radicals modulate MAT activity in vivo. It was previously shown that MAT activity is strongly dependent on cellular GSH levels. Generation of oxygen and nitrogen active species in rats by injection of LPS, induced a decrease of liver MAT activity. This effect might derive from nitrosylation and/or oxidation of the enzyme. Modulation of liver MAT by NO is further supported by the inactivation of this enzyme observed in experimental models in which NO is produced; such as the administration of NO donors to rats and in hepatocytes cultured in hypoxia, a condition that induces the expression of the inducible nitric oxide synthase (iNOS). Oxidation also controls liver MAT activity in a cell environment as shown in CHO cells stably transfected with rat liver MAT cDNA upon addition of H2O2 to the culture medium. This effect depends upon the generation of the hydroxyl radical. On the basis of the metabolic implications of liver MAT, together with the structural features accounting for the sensitivity of this enzyme to active oxygen and nitrogen species, we propose that modulation of MAT by these agents could be a mechanism to regulate the consumption of ATP in the liver, and thus preserve cellular viability under different stress conditions

Brown dwarf census with the Dark Energy Survey year 3 data and the thin disc scale height of early L types

27 pages, 18 figuresIn this paper we present a catalogue of 11 745 brown dwarfs with spectral types ranging from L0 to T9, photometrically classified using data from the Dark Energy Survey (DES) year 3 release matched to the Vista Hemisphere Survey (VHS) DR3 and Wide-field Infrared Survey Explorer (WISE) data, covering ≈2400 deg2 up to iAB = 22. The classification method follows the same phototype method previously applied to SDSS-UKIDSS-WISE data. The most significant difference comes from the use of DES data instead of SDSS, which allow us to classify almost an order of magnitude more brown dwarfs than any previous search and reaching distances beyond 400 pc for the earliest types. Next, we also present and validate the GalmodBD simulation, which produces brown dwarf number counts as a function of structural parameters with realistic photometric properties of a given survey. We use this simulation to estimate the completeness and purity of our photometric LT catalogue down to iAB = 22, as well as to compare to the observed number of LT types. We put constraints on the thin disc scale height for the early L (L0–L3) population to be around 450 pc, in agreement with previous findings. For completeness, we also publish in a separate table a catalogue of 20 863 M dwarfs that passed our colour cut with spectral types greater than M6. Both the LT and the late M catalogues are found at DES release page https://des.ncsa.illinois.edu/releases/other/y3-mlt.Peer reviewedFinal Published versio

The PAU Survey: Photometric redshifts using transfer learning from simulations

In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-$z$) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. \textsc{Deepz} reduces the $\sigma_{68}$ scatter statistic by 50\% at $i_{\rm AB}=22.5$ compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-$z$ scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.Comment: Accepted versio

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA