Characterization and functional gene analysis of a newly isolated indole-degrading bacterium Burkholderia sp. IDO3.

J Hazard Mater. 2018 Dec 19;367:144-151

Authors: Ma Q, Liu Z, Yang B, Dai C, Qu Y

Abstract
Indole is a common N-heterocyclic pollutant as well as a signaling molecule widespread in various environmental matrices. Several bacterial strains have been reported to be able to degrade indole, while the degradation capacity and functional enzymes are poorly documented. Herein, the degradation characteristics of a newly-isolated indole-degrading strain Burkholderia sp. IDO3 were carefully investigated. Strain IDO3 exhibited superior degradation ability which could completely remove 100 mg/L indole within 14 h in mineral salt medium. It maintained comparable degradation performance under conditions of pH 4.0-9.0, temperature 25-35 °C and rotary speed 0-250 r/min, and most of the tested heavy metals and organic pollutants did not significantly affect the degradation process. Two important intermediates, i.e. isatin and anthranilate, were identified in indole degradation process. The genomic clone library technique with indigo-based screening method was successfully applied to screen the functional genes. Heterologous expression assay proved that recombinant E. coli strain carrying indole oxygenase and reductase genes iifCD could transform indole to indigo. Bioinformatic analyses indicated that the newly obtained enzyme IifC_IDO3 formed a phylogenetically separate branch from other related aromatic oxygenases. This study provides new insights into our understanding of indole degradation by Burkholderia strains and offers efficient bacterial resource for indole bioremediation.

PMID: 30594713 [PubMed - as supplied by publisher]

Graphene oxide-quenching-based fluorescence in situ hybridization (G-FISH) to detect RNA in tissue: Simple and fast tissue RNA diagnostics.

Nanomedicine. 2018 Dec 27;:

Authors: Hwang DW, Choi YR, Kim D, Park HY, Kim KW, Kim MY, Park CK, Lee DS

Abstract
FISH-based RNA detection in paraffin-embedded tissue can be challenging, with complicated procedures producing uncertain results and poor image quality. Here, we developed a robust RNA detection method based on graphene oxide (GO) quenching and recovery of fluorescence in situ hybridization (G-FISH) in formalin-fixed paraffin-embedded (FFPE) tissues. Using a fluorophore-labeled peptide nucleic acid (PNA) attached to GO, the endogenous long noncoding RNA BC1, the constitutive protein β-actin mRNA, and miR-124a and miR-21 could be detected in the cytoplasm of a normal mouse brain, primary cultured hippocampal neurons, an Alzheimer's disease model mouse brain, and glioblastoma multiforme tumor tissues, respectively. Coding and non-coding RNAs, either long or short, could be detected in deparaffinized FFPE or frozen tissues, as well as in clear lipid-exchanged anatomically rigid imaging/immunostaining-compatible tissue hydrogel (CLARITY)-transparent brain tissues. The fluorescence recovered by G-FISH correlated highly with the amount of miR-21, as measured by quantitative real time RT-PCR. We propose G-FISH as a simple, fast, inexpensive, and sensitive method for RNA detection, with a very low background, which could be applied to a variety of research or diagnostic purposes.

PMID: 30594658 [PubMed - as supplied by publisher]

Metabolic profiling of early lactation dairy cows using milk mid-infrared spectra.

J Dairy Sci. 2018 Dec 26;:

Authors: Luke TDW, Rochfort S, Wales WJ, Bonfatti V, Marett L, Pryce JE

Abstract
Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of mid-infrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of south-eastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R2) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R2 ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.

PMID: 30594377 [PubMed - as supplied by publisher]

A Blueprint for Systems Biology.

Clin Chem. 2018 Dec 28;:

Authors: Ideker T, Hood L

PMID: 30593465 [PubMed - as supplied by publisher]

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

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Proteomics turns functional.

J Proteomics. 2018 Dec 13;:

Authors: Monti C, Zilocchi M, Colugnat I, Alberio T

Abstract
Proteomics is acquiring a pivotal role in the comprehensive understanding of human biology. Biochemical processes involved in complex diseases, such as neurodegenerative diseases, diabetes and cancer, can be identified by combining proteomics analysis and bioinformatics tools. In the last ten years, the main output of differential proteomics investigations evolved from long lists of proteins to the generation of new hypotheses and their functional verification. The Journal of Proteomics participated to this progress, reporting more and more biologically-oriented papers with functional interpretation of proteomics data. This change in the field was due to both technological development and novel strategies in exploiting the deep characterization of proteomes. In this review, we explore several approaches that allow proteomics to turn functional. In particular, systems biology tools for data analysis are now routinely used to interpret results, thus defining the biological meaning of differentially abundant proteins. Moreover, by considering the importance of protein-protein interactions and the composition of macromolecular complexes, interactomics is complementing the information given by differential quantitative proteomics. Eventually, terminomics is unveiling new functions for cleaved proteoforms, by analyzing the effect of proteolysis globally. SIGNIFICANCE: Proteomics is rapidly evolving not only technologically but also strategically. The correct interpretation of proteomics data can reveal new functions of proteins in several biological backgrounds. Systems biology tools allow researchers to formulate new hypotheses to be further functionally tested. Interactomics is shedding new light on protein complexes truly involved in biochemical pathways and how their alteration can lead to dysfunctionality (in disease pathogenesis, for example). Terminomics is revealing the function of new discovered proteoforms and attributing a novel role to proteolysis. This review would provide the biologist important insights into current applications of several proteomic approaches that could offer new strategies to investigate biological systems.

PMID: 30553948 [PubMed - as supplied by publisher]

A novel computational solution to the health risk assessment of air pollution via joint toxicity prediction: A case study on selected PAH binary mixtures in particulate matters.

Ecotoxicol Environ Saf. 2018 Dec 13;170:427-435

Authors: Liu X, Zhang H, Pan W, Xue Q, Fu J, Liu G, Zheng M, Zhang A

Abstract
Regional haze episode has already caused overwhelming public concern. Unraveling the health effects of the representative composition mixtures of atmospheric fine particulate matters (PM2.5) becomes a top priority. In this study, a novel computational solution integrating chemical-induced genomic residual effect prediction with in vitro-based risk assessment is proposed to obtain the cumulative health risk of typical chemical mixtures of particulate matters (PM). The joint toxicity of binary mixtures is estimated by analyzing both genomic similarity and dose-response curve of relevant pollutants for the chemical-induced genomic residual effect. Specifically, the modified relative potency factor (mRPF) of mixtures is introduced for this purpose, and the ratio of activation (RA) value is defined to assess the corresponding health risks of the mixtures. As a methodology demonstration, the health risk of typical binary polycyclic aromatic hydrocarbon (PAH) mixtures in PM, containing Benzo[a]pyrene (BaP) as a component, is assessed using the proposed solution. Our results indicate that the combined effect of pairwise PAHs of BaP with Benzo[b]fluoranthene (BbF) and Benz[a]anthracene (BaA) is synergistic on p53 pathway, and that the health risk of the such mixtures increases compared to that of the individual ones. Obviously, the cumulative health risk of environmental mixtures will be underestimated when the synergistic effect is wrongly assumed to be additive. To our knowledge, this is the first study ever report on a computational solution to the health risk assessment of environmental pollution via joint toxicity prediction. The novel methodology proposed here makes full use of the open-access in vitro assay data and transcriptomic information in literatures and provides a successful demonstration of the concept of systems biology and translational science.

PMID: 30553920 [PubMed - as supplied by publisher]

CellMinerCDB for Integrative Cross-Database Genomics and Pharmacogenomics Analyses of Cancer Cell Lines.

iScience. 2018 Nov 30;:

Authors: Rajapakse VN, Luna A, Yamade M, Loman L, Varma S, Sunshine M, Iorio F, Sousa FG, Elloumi F, Aladjem MI, Thomas A, Sander C, Kohn KW, Benes CH, Garnett M, Reinhold WC, Pommier Y

Abstract
CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness.

PMID: 30553813 [PubMed - as supplied by publisher]