9 research outputs found

    LADUMA: looking at the distant universe with the MeerKAT array

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    The cosmic evolution of galaxies’ neutral atomic gas content is a major science driver for the Square Kilometre Array (SKA), as well as for its South African (MeerKAT) and Australian (ASKAP) precursors. Among the H I large survey programs (LSPs) planned for ASKAP and MeerKAT, the deepest and narrowest tier of the “wedding cake” will be defined by the combined L-band+UHF-band Looking At the Distant Universe with the MeerKAT Array (LADUMA) survey, which will probe H I in emission within a single “cosmic vuvuzela” that extends to z = 1.4, when the universe was only a third of its present age. Through a combination of individual and stacked detections (the latter relying on extensive multi-wavelength studies of the survey’s target field), LADUMA will study the redshift evolution of the baryonic Tully–Fisher relation and the cosmic H I density, the variation of the H I mass function with redshift and environment, and the connection between H I content and galaxies’ stellar properties (mass, age, etc.). The survey will also build a sample of OH megamaser detections that can be used to trace the cosmic merger history. This proceedings contribution provides a brief introduction to the survey, its scientific aims, and its technical implementation, deferring a more complete discussion for a future article after the implications of a recent review of MeerKAT LSP project plans are fully worked out

    Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

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    Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature

    Looking at the Distant Universe with the MeerKAT Array: Discovery of a Luminous OH Megamaser at z > 0.5

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    In the local universe, OH megamasers (OHMs) are detected almost exclusively in infrared-luminous galaxies, with a prevalence that increases with IR luminosity, suggesting that they trace gas-rich galaxy mergers. Given the proximity of the rest frequencies of OH and the hyperfine transition of neutral atomic hydrogen (H i), radio surveys to probe the cosmic evolution of H i in galaxies also offer exciting prospects for exploiting OHMs to probe the cosmic history of gas-rich mergers. Using observations for the Looking At the Distant Universe with the MeerKAT Array (LADUMA) deep H i survey, we report the first untargeted detection of an OHM at z > 0.5, LADUMA J033046.20-275518.1 (nicknamed "Nkalakatha"). The host system, WISEA J033046.26-275518.3, is an infrared-luminous radio galaxy whose optical redshift z ≈ 0.52 confirms the MeerKAT emission-line detection as OH at a redshift z OH = 0.5225 ± 0.0001 rather than H i at lower redshift. The detected spectral line has 18.4σ peak significance, a width of 459 ± 59 km s-1, and an integrated luminosity of (6.31 ± 0.18 [statistical] ± 0.31 [systematic]) × 103 L ⊙, placing it among the most luminous OHMs known. The galaxy's far-infrared luminosity L FIR = (1.576 ±0.013) × 1012 L ⊙ marks it as an ultraluminous infrared galaxy; its ratio of OH and infrared luminosities is similar to those for lower-redshift OHMs. A comparison between optical and OH redshifts offers a slight indication of an OH outflow. This detection represents the first step toward a systematic exploitation of OHMs as a tracer of galaxy growth at high redshifts

    Identification of genetic elements in metabolism by high-throughput mouse phenotyping.  

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    Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome
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