10 research outputs found

    Using spin to understand the formation of LIGO's black holes

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    With the detection of four candidate binary black hole (BBH) mergers by the Advanced LIGO detectors thus far, it is becoming possible to constrain the properties of the BBH merger population in order to better understand the formation of these systems. Black hole (BH) spin orientations are one of the cleanest discriminators of formation history, with BHs in dynamically formed binaries in dense stellar environments expected to have spins distributed isotropically, in contrast to isolated populations where stellar evolution is expected to induce BH spins preferentially aligned with the orbital angular momentum. In this work we propose a simple, model-agnostic approach to characterizing the spin properties of LIGO's BBH population. Using measurements of the effective spin of the binaries, which is LIGO's best constrained spin parameter, we introduce a simple parameter to quantify the fraction of the population that is isotropically distributed, regardless of the spin magnitude distribution of the population. Once the orientation characteristics of the population have been determined, we show how measurements of effective spin can be used to directly constrain the underlying BH spin magnitude distribution. Although we find that the majority of the current effective spin measurements are too small to be informative, with LIGO's four BBH candidates we find a slight preference for an underlying population with aligned spins over one with isotropic spins (with an odds ratio of 1.1). We argue that it will be possible to distinguish symmetric and anti-symmetric populations at high confidence with tens of additional detections, although mixed populations may take significantly more detections to disentangle. We also derive preliminary spin magnitude distributions for LIGO's black holes, under the assumption of aligned or isotropic populations

    PGC-1α-Mediated Branched-Chain Amino Acid Metabolism in the Skeletal Muscle

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    <div><p>Peroxisome proliferator-activated receptor (PPAR) γ coactivator 1α (PGC-1α) is a coactivator of various nuclear receptors and other transcription factors, which is involved in the regulation of energy metabolism, thermogenesis, and other biological processes that control phenotypic characteristics of various organ systems including skeletal muscle. PGC-1α in skeletal muscle is considered to be involved in contractile protein function, mitochondrial function, metabolic regulation, intracellular signaling, and transcriptional responses. Branched-chain amino acid (BCAA) metabolism mainly occurs in skeletal muscle mitochondria, and enzymes related to BCAA metabolism are increased by exercise. Using murine skeletal muscle overexpressing PGC-1α and cultured cells, we investigated whether PGC-1α stimulates BCAA metabolism by increasing the expression of enzymes involved in BCAA metabolism. Transgenic mice overexpressing PGC-1α specifically in the skeletal muscle had increased the expression of branched-chain aminotransferase (BCAT) 2, branched-chain α-keto acid dehydrogenase (BCKDH), which catabolize BCAA. The expression of BCKDH kinase (BCKDK), which phosphorylates BCKDH and suppresses its enzymatic activity, was unchanged. The amount of BCAA in the skeletal muscle was significantly decreased in the transgenic mice compared with that in the wild-type mice. The amount of glutamic acid, a metabolite of BCAA catabolism, was increased in the transgenic mice, suggesting the activation of muscle BCAA metabolism by PGC-1α. In C2C12 cells, the overexpression of PGC-1α significantly increased the expression of BCAT2 and BCKDH but not BCKDK. Thus, PGC-1α in the skeletal muscle is considered to significantly contribute to BCAA metabolism.</p></div

    Gene expression of BCAA metabolic enzyme in skeletal muscle of PGC-1α Tg mice.

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    <p>Expression of A) BCAT2, B) BCKDH, and C) BCKDK genes in WT (control; open columns, N = 9) and PGC-1α Tg (filled columns, N = 7) mice by quantitative real-time RT-PCR. RNA was obtained from mice with feeding condition. These samples were as used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone.0091006-Tadaishi1" target="_blank">[4]</a>. In the sample, PGC-1α expression was 30 fold higher in Tg mice than in WT mice (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone-0091006-g001" target="_blank">Fig. 1</a> of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone.0091006-Tadaishi1" target="_blank">[4]</a>). The relative values are shown (the control is set as 100). ***P<0.001.</p

    Gene expression of BCAA metabolic enzymes in cultured C2C12 cells overexpressing PGC-1α.

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    <p>Total RNA was isolated from the cells and analyzed by quantitative real-time RT-PCR with primers for A) PGC-1α, B) BCAT2, C) BCKDHa, and D) BCKDK. Open columns represent mock cells (N = 3), and filled columns represent PGC-1α-overexpressed cells (N = 3). Each value represents mean ± SE (N = 3). The relative values are shown (the control is set as 100). For PGC-1α expression, the value was set as 100 in the PGC-1α overexpressed cells. ***P<0.001, **P<0.01.</p

    BCAA content in skeletal muscle and blood of PGC-1α Tg mice.

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    <p>Val, Leu, and Ile levels in (A) skeletal muscle and (B) blood. Open columns represent for WT (N = 4) and filled columns represent Tg (N = 4). ***P<0.001, **P<0.01. T.R., trace level.</p

    Amino acid content in blood of PGC-1α Tg mice.

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    <p>The samples were used as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone-0091006-g004" target="_blank">Figure 4</a>.</p><p>*P<0.05.</p

    Bioinformatics analysis of transcription factors enriched in the BCAA metabolic pathway genes up-regulated in PGC-1α Tg mice.

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    <p>List of transcription factors, which are statistically identified as ones that can be recruited to the BCAA metabolic genes, up-regulated in PGC-1α Tg mice. Target genes were previously found in ChIP assay for interacting with indicated transcription factors in the literature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone.0091006-Lachmann1" target="_blank">[27]</a>. Abbreviations of the transcription factors are as follows, KLF4, Krueppel-like factor 4; PPARG, Constitutive coactivator of peroxisome proliferator-activated receptor gamma (Constitutive coactivator of PPAR-gamma) (Constitutive coactivator of PPARG); EKLF, Krueppel-like factor 1 (Erythroid krueppel-like transcription factor); ESRRB, Steroid hormone receptor ERR2 (Estrogen receptor-like 2) (Estrogen-related receptor beta) (ERR-beta); PPARD, Peroxisome proliferator-activated receptor delta (PPAR-delta); ZFP42, Zinc finger protein 42; WT1, Wilms tumor protein; NR0B1, Nuclear receptor subfamily 0 group B member 1 (Nuclear receptor DAX-1); TET1, Methylcytosine dioxygenase TET1 (EC 1.14.11.n2) (CXXC-type zinc finger protein 6) (Ten-eleven translocation 1 gene protein homolog); GATA4, Transcription factor GATA-4 (GATA-binding factor 4).</p

    Amino acid content in C2C12 cells overexpressing of PGC-1α.

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    <p>The samples were used as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#pone-0091006-g005" target="_blank">Figure 5</a>.</p><p>*P<0.05. TR, trace level. ND, not detected.</p

    Pathway analysis.

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    <p>Compared with WT mice, 315 genes were found to be up-regulated in PGC-1α Tg mice by microarray and classified into KEGG pathway analysis as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091006#s2" target="_blank">Methods</a>.</p

    Pathway map of Val, Leu, and Ile degradation.

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    <p>Individual gene name of the KEGG pathway of Val, Leu, and Ile degradation extracted by pathway analysis is shown as a metabolic map. Red asterisks indicate increased gene expression by microarray of Tg mice. Gene names corresponding to enzyme numbers with red asterisks are as follows: 2.8.3.5, 3-oxoacid CoA transferase 1; 2.3.1.16, acetyl-Coenzyme A acyltransferase 2 (mitochondrial 3-oxoacyl-Coenzyme A thiolase); 1.3.8.1, acyl-Coenzyme A dehydrogenase, short chain; 2.6.1.42, branched chain aminotransferase 2, mitochondrial; 1.2.4.4, branched chain ketoacid dehydrogenase E1, alpha polypeptide; 1.8.1.4, dihydrolipoamide dehydrogenase; 1.1.1.35, hydroxyacyl-Coenzyme A dehydrogenase; 4.2.1.17, hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase (trifunctional protein), alpha subunit; 5.1.99.1, methylmalonyl CoA epimerase; 1.1.1.31, 3-hydroxyisobutyrate dehydrogenase.</p
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