Metabolism of the anthelmintic drug niclosamide by cytochrome P450 enzymes and UDP-glucuronosyltransferases: metabolite elucidation and main contributions from CYP1A2 and UGT1A1

<div><p></p><p>1. Niclosamide is an old anthelmintic drug that shows potential in fighting against cancers. Here, we characterized the metabolism of niclosamide by cytochrome P450 enzymes (CYPs) and UDP-glucuronosyltransferases (UGTs) using human liver microsomes (HLM) and expressed enzymes.</p><p>2. NADPH-supplemented HLM (and liver microsomes from various animal species) generated one hydroxylated metabolite (M1) from niclosamide; and UDPGA-supplemented liver microsomes generated one mono-<i>O</i>-glucuronide (M2). The chemical structures of M1 (3-hydroxy niclosamide) and M2 (niclosamide-2-<i>O</i>-glucuronide) were determined through LC–MS/MS and/or NMR analyses.</p><p>3. Reaction phenotyping revealed that CYP1A2 was the main enzyme responsible for M1 formation. The important role of CYP1A2 in niclosamide metabolism was further confirmed by activity correlation analyses as well as inhibition experiments using specific inhibitors.</p><p>4. Although seven UGT enzymes were able to catalyze glucuronidation of niclosamide, UGT1A1 and 1A3 were the enzymes showed the highest metabolic activities. Activity correlation analyses demonstrated that UGT1A1 played a predominant role in hepatic glucuronidation of niclosamide, whereas the role of UGT1A3 was negligible.</p><p>5. In conclusion, niclosamide was subjected to efficient metabolic reactions hydroxylation and glucuronidation, wherein CYP1A2 and UGT1A1 were the main contributing enzymes, respectively.</p></div

Aged gp78 knockout mice develop obesity and gp78 is unregulated upon ER stress.

<p>(<b>A</b>) Schematic of gp78-targeting strategy. The gp78 allele disrupted by the insertion of a gene trap vector (OmniBank Vector 76) in the first intron. Genomic DNA isolated from WT, heterozygous and homozygous was genotyped by PCR. Primer set including double reverse primers was designed for internal PCR quality. (<b>B</b>) Total RNA and lysates were prepared in embryo and adult liver and were analyzed in RT-PCR (Top), both lysates were immunoprecipitated with rabbit anti-gp78 antibodies (epitope: 524–537) and then immunoblotted with monoclonal anti-gp78 antibodies (epitope: 451–551) to identify gp78 protein (bottom). (<b>C</b>) Immunohistochemistry (IHC) of liver with monoclonal anti-gp78 antibody. Arrow indicates gp78 positive stain. PV, portal vein. Original image, x400. (<b>D</b>) Upregulation of gp78 in response to ER stress. Immortalized THLE-3 and cancerous HepG2 liver cells were treated with tunicamycin (1ug/ml) for 24 hrs. and lysates were immunoprecipitated with anti-gp78 antibody and immunoblotted. GRP78/BIP, a chaperon of UPR pathways. (<b>F</b>) Photography of abdomen of 1-year-old mice (left). Comparison of body weight of WT and gp78-KO mice at 3 months (n = 25), 6 months (n = 25), 12 months (n = 25) old (right). Asterisk indicates a significance determined by Student’s test (*, p < 0.05).</p

Gp78-KO mice spontaneously develop hepatic steatosis with inflammatory infiltrates.

<p>(<b>A</b>) Livers of 1-year-old gp78 KO mice, grown with normal diet were stained with H&E and visualized as indicated magnification. (a) Steatohepatitis shows swelling cytoplasm with lipid droplets (black arrow) and infiltrating cells. (b) Mild lipid droplets and infiltrating cells (Red arrow). (c) Infiltrate cells gather in the absence of lipid droplets. (<b>B</b>) Oil red stain. Arrow indicates lipid droplets. (<b>C</b>) Trichrome stain was preformed to identify blue colored collagen fibers. PV; portal vein, FL; fatty liver area. (<b>D</b>) Percentage of incidence including mild fatty liver, inflammation in 1-year-old KO mice (n = 25 mice per each group).</p

Gp78-KO mice with acute ER stress progresses to severe fatty liver through UPR-driving SREBP-1 activation.

<p>(<b>A</b>) 1mg/kg body weight of tunicamycin (TM) was intraperitoneally injected to 6 months-old gp78-KO mice. Body weight is represented after normalization to the starting weight as the meant ± SE (n = 3 per group). (<b>B</b>) Radish livers are brown colored after TM injection. Liver of gp78-KO at 11 days was not recovered. (<b>C</b>) Acute ER stress potentiates entire fatty liver of gp78-KO. H&E stain at 3 days after TM injection. Ballooned cells (b) are typically bigger size than WT hepatocytes (a). Cytoplasmic lipid droplets (arrow) (40x). (<b>D</b>) Prolonged fatty liver and fibrosis in gp78-KO. H&E, oil red and Trichrome stains at 11 days after TM injection. White ballooned cells (arrows) were maintained on H&E (200x). Frozen tissues were stained with oil-red O. Irregular fibrosis was extended from connective tissue of portal vein surrounding accumulated lipid droplets (white spots) at pinkish gp78-KO liver (100x). (<b>E</b>) Persistent SREBP-1 activation along with UPR up regulation is responsible for fatty liver of gp78-KO. TM-injected mice were scarified respectively (n = 3). Liver extracts at indicated days were subjected to immunoblots. GRP78, Glucose-Regulated Protein; PDI, Protein Disulfide Isomerase, SERBP; Sterol Regulatory Element Binding Transcription Factor; Insig, Insulin Induced Gene. (<b>F</b>) Chop-mediated apoptosis. Cell survival was analyzed with viable counting in gp78-KO mouse embryonic fibroblast (MEF) cells treated with TM (1μg/ml) as indicated times (top). Induction of UPR was analyzed in immunoblots and gp78 expression was visualized after its immunoprecipitation (bottom). Chop; ER stress-mediated apoptosis marker.</p

High-Performance Wet Adhesion of Wood with Chitosan

Strong adhesion is desirable when using wood with a wide range of moisture contents, but most of the existing adhesives face challenges in bonding wood under high-humidity conditions. Here, we report a simple strategy that involves the one-step dissolution of chitosan powder in acetic acid at room temperature, followed by direct use of the resulting chitosan slurry as an adhesive on dry/wet wood veneers. Mechanical interlocks and hydrogen bonds at cell wall interfaces provided strong adhesion. Moreover, heat treatment induced recrystallization and cross-linking of chitosan chains, resulting in a high cohesion. Meanwhile, heat treatment caused the acetylation reaction between the protonated amino groups (NH3+) of chitosan and acetate groups (CH3COO–) to produce hydrophobic acetyl groups. In addition, we prepared wooden products such as plywood (dry veneers) and wooden straws (wet veneers) using wood veneers with different moisture contents. The tensile shear strengths under 63 °C water and under boiling water of plywood were 1.12 and 0.81 MPa, respectively. The compressive strength of wooden straws is up to 35.32 MPa, which was higher than that of existing commercial straws (such as paper straws, polypropylene straws, and plastic straws). The chitosan wet adhesive showed good water resistance, high bonding strength, environmental degradability, and nontoxicity, thus providing a highly promising alternative to traditional wood composite adhesives

Table_3_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p

Table_2_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p

Table_1_Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.docx

ObjectiveThe recent World Endoscopy Organization (WEO) guidelines now recognize precursor lesions of colorectal cancer (CRC) as legitimate screening targets. However, an optimal screening method for detecting advanced adenoma (AA), a significant precursor lesion, remains elusive.MethodsWe employed five machine learning methods, using clinical and laboratory data, to develop and validate a diagnostic model for identifying patients with AA (569 AAs vs. 3228 controls with normal colonoscopy). The best-performing model was selected based on sensitivity and specificity assessments. Its performance in recognizing adenoma-carcinoma sequence was evaluated in line with guidelines, and adjustable thresholds were established. For comparison, the Fecal Occult Blood Test (FOBT) was also selected.ResultsThe XGBoost model demonstrated superior performance in identifying AA, with a sensitivity of 70.8% and a specificity of 83.4%. It successfully detected 42.7% of non-advanced adenoma (NAA) and 80.1% of CRC. The model-transformed risk assessment scale provided diagnostic performance at different positivity thresholds. Compared to FOBT, the XGBoost model better identified AA and NAA, however, was less effective in CRC.ConclusionThe XGBoost model, compared to FOBT, offers improved accuracy in identifying AA patients. While it may not meet the recommendations of some organizations, it provides value for individuals who are unable to use FOBT for various reasons.</p