28 research outputs found

    Metabolic bioactivation of antidepressants: advance and underlying hepatotoxicity

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    Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which toxicity is induced may be difficult to determine, identifying reactive metabolites that cause toxicity can offer valuable insights for decreasing the bioactivation potential of candidates during the drug discovery process. A comprehensive understanding of drug metabolic pathways can mitigate adverse drug-drug interactions that may be caused by elevated formation of reactive metabolites. This review provides a comprehensive overview of the current state of knowledge on antidepressant bioactivation, the metabolizing enzymes responsible for the formation of reactive metabolites, and their potential implication in hepatotoxicity. This information can be a valuable resource for medicinal chemists, toxicologists, and clinicians engaged in the fields of antidepressant development, toxicity, and depression treatment.</p

    Increase in olfactory bulb granule cell layer neurogenesis in 10 week old <i>Hmgb2−/−</i> mice.

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    <p>A) Immunostaining for BrdU (red) and NeuN (green) and the merged image in the OB granule cell layer (GCL) of 10 week old mice after 14 days of differentiation. Quantification of (B) BrdU+, (C) NeuN+, and (D) BrdU+/NeuN+ cell densities in the GCL of WT and <i>Hmgb2−/−</i>10 week old mice. Quantification of the relative fraction of newborn GCL neurons (BrdU+/NeuN+) among (E) all BrdU+ cells and (F) all NeuN+ cells. (G) Immunostaining of OB glomerular layer (GL) neurogenesis after 14 days differentiation <i>in vivo</i>. (H-L) Quantification of GL neurogenesis at 2.5 months. All figures are mean+/− SEM, n = 2 WT and n = 3 <i>Hmgb2−/−</i> per group; the Kruskal-Wallis test was used. (p = 0.0008 for panel B), but no significance for all other panels.</p

    Heightened DCX expression in the SVZ of 10 week old <i>Hmgb2−/−</i> mice.

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    <p>Mash1 and Nestin expression in lateral <i>Hmgb2+/+</i> and <i>Hmgb2−/−</i> brain sections and surface plots of pixel density distributions (A,B). Doublecortin (DCX) in lateral and medial <i>Hmgb2+/+</i> and <i>Hmgb2−/−</i> brain sections (C,D) with quantification of DCX+ cells/SVZ hpf in WT and <i>Hmgb2−/−</i> medial brain sections (E). The values are mean+/− SEM, **p = 0.036.</p

    Pluripotency and migration/differentiation factors are variably expressed in the <i>Hmgb2−/−</i> SVZ neural stem cell niche.

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    <p>The protein levels of different factors were examined in SVZ wholemount tissue lysates from wt and <i>Hmgb2</i><b><i>−/−</i></b> mice. A) Oct4; B) pAkt (ser); C) p21 (two representative immunoblots are provided); D) NCAM (n = 3); E) Cartoon depicting the suggested role of HMGB2 in NSC maintenance.</p

    Hyperproliferation and altered NSC/NPC composition <i>in vivo</i> in 10 week old <i>Hmgb2−/−</i> mice.

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    <p>A) Immunostaining for BrdU (red) and Ki67 (green) in the anterior SVZ of 10 week old WT and <i>Hmgb2−/−</i> mice. B) Collapsed image of confocal Z-stacks from brain sections of compound <i>Hmgb2+/+</i>NestinGFP+ and <i>Hmgb2−/−</i>NestinGFP+ transgenic mice stained for GFAP, a marker of SVZ NSCs. GFAP (red), Nestin (green), DAPI (blue). (C) High magnification orthogonal view of a single confocal plane from brain sections of compound transgenic mice stained with GFAP at the SVZ/RMS junction. D) Quantification of SVZ BrdU+ cells and cell density in (A), E) Ki67+ cells and cell density in (A), F) BrdU+/Ki67+ cells and cell density in (A). All figures are mean+/− SEM, *p≤0.05, **p≤0.005. n = 4 WT and n = 5 <i>Hmgb2−/−</i> per group. (G) Quantification of Nestin+ and GFAP+ staining in WT and <i>Hmgb2−/−</i> SVZ in (B). All figures are mean+/− SEM; the Kruskal-Wallis test was used. ***p = 0.0002. n = 4 WT and n = 5 <i>Hmgb2−/−</i> per group. (H) Reconstruction of z-stacks used to generate the image in panel B of GFAP staining in the SVZ of <i>Hmgb2+/+</i> (WT) and <i>Hmgb2−/−</i> mice. DAPI (blue), GFAP (red), and Nestin (green).</p

    Ligand Independent and Subtype-Selective Actions of Thyroid Hormone Receptors in Human Adipose Derived Stem Cells

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    <div><p>Thyroid hormone (TH) receptors (TRs α and β) are homologous ligand-dependent transcription factors (TFs). While the TRs display distinct actions in development, metabolic regulation and other processes, comparisons of TRα and TRβ dependent gene regulation mostly reveal similar mechanisms of action and few TR subtype specific genes. Here, we show that TRα predominates in multipotent human adipose derived stem cells (hADSC) whereas TRβ is expressed at lower levels and is upregulated during hADSC differentiation. The TRs display several unusual properties in parental hADSC. First, TRs display predominantly cytoplasmic intracellular distribution and major TRα variants TRα1 and TRα2 colocalize with mitochondria. Second, knockdown experiments reveal that endogenous TRs influence hADSC cell morphology and expression of hundreds of genes in the absence of hormone, but do not respond to exogenous TH. Third, TRα and TRβ affect hADSC in completely distinct ways; TRα regulates cell cycle associated processes while TRβ may repress aspects of differentiation. TRα splice variant specific knockdown reveals that TRα1 and TRα2 both contribute to TRα-dependent gene expression in a gene specific manner. We propose that TRs work in a non-canonical and hormone independent manner in hADSC and that prominent subtype-specific activities emerge in the context of these unusual actions.</p></div

    Subcellular partitioning of TRα1 and TRα2 in hADSC to mitochondria and endoplasmic reticulum.

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    <p>Confocal images of hADSC showing double immunostaining for TRα1 and TRα2 (green) with mitochondrial marker COX IV (red—A, B) or endoplasmic reticulum marker—calnexin (red—C, D). Magnified view of TRα1 and TRα2 localization in cisternal structures (endoplasmic reticulum) at the cell periphery (green arrows) and perinuclear region (green arrowheads) (B) and their colocalization with calnexin—(D—yellow arrows, yellow arrowheads). Green arrows on D) pointing to TRα1 and TRα2 localization outside of endoplasmic reticulum. Bar: A, B = 25 μm.</p

    Verification of hADSC differentiation along adipogenic, chondrogenic or osteogenic paths.

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    <p>(A) Image of hADSC. (B, C) qRT-PCR analysis showing decreased expression of stem cell markers (B) and emergence of specific differentiation markers (C). The error bars represent the SD. Asterisks show statistically significant changes (***, <i>p</i> < 0.001; **, <i>p</i> < 0.01; *, <i>p</i> < 0.05). (D) Stained images of cells confirming appropriate differentiation. Venn diagrams representing differential gene expression after adipogenesis, chondrogenesis and osteogenesis as revealed by microarray (E) and transcription factor (TF) analysis.</p

    Unliganded TRα and TRβ regulate distinct genes in hADSC.

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    <p>(A) Differential gene regulation in hADSC cells after TRα and TRβ KD. (B-G) Effects of TRα and TRβ KD at representative target genes. All data are represented as mean ± SD. ***, <i>p</i> < 0.001; **, <i>p</i> < 0.01; *, <i>p</i> < 0.05.</p

    TR mediated processes.

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    <p>Canonical processes obtained from GeneCodis using SlimProcess database. Gene co-occurence annotation found by Genecodis for the genes differentially expressed (FC > 2, <i>P</i> < 0.05 corrected for multiple testing) between siCtrl versus siTR hADSC samples. <i>P</i>-values have been obtained through hypergeometric analysis (Hyp) corrected by FDR method (Hyp*). Microarray data have been deposited in NCBI’s Gene Expression Omnibus; accession number GSE75692.</p
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