To identify and characterize a serologic glycoprotein biomarker for hepatocellular carcinoma (HCC), multi-lectin affinity chromatography was used to isolate intracellular N-linked glycoprotein fractions from five paired non-tumor and tumor tissues. From the series of 2-D DIGE targeted differentially expressed N-linked glycoproteins, we identified human liver carboxylesterase 1 (hCE1), which was remarkably down-regulated in tumor tissues, a finding confirmed by Western blot, a quantitative real-time RT-PCR, and immunohistochemical staining of non-tumor and tumor tissues from total 58 HCC patients. To investigate whether hCE1 is also present in human plasma, we employed a magnetic bead-based immunoprecipitation followed by nano-LC-MS/MS analysis, and we found for the first time that hCE1 is present in human plasma as opposed to that in liver tissues. That is, from normalization of hCE1 signal by the immunoprecipitation and Western blot analysis, hCE1 levels were increased in plasma specimens from HCC patients than in plasma from other disease patient groups (e.g. liver cirrhosis, chronic hepatitis, cholangiocarcinoma, stomach cancer, and pancreatic cancer). From the receiver operating characteristic analysis in HCC, both sensitivity and specificity were shown to be greater than 70.0 and 85.0%, respectively. Thus, the high-resolution proteomic approach demonstrates that hCE1 is a good candidate for further validation as a serologic glycoprotein biomarker for HCC.
A simple, rapid, and reliable UPLC-MS/MS method was developed and validated for the determination of tadalafil in human plasma. The plasma samples were deproteinized with acetonitrile. Chromatographic separation was performed on a Shiseido C18 (100 × 2.1 mm, 2.7 μm) column with isocratic elution using 2.0 mM ammonium acetate and acetonitrile (55:45, v/v) with 0.1% formic acid at a flow rate of 0.7 mL/min. The total run time was 1 min per sample. The quantitative analysis was performed using multiple reaction monitoring at transition of m/z 390.4 → 268.3 for tadalafil and m/z 475.3 → 283.3 for sildenafil as an internal standard. The method was fully validated over a concentration range of 5-1,000 ng/mL with a lower quantification limit of 5 ng/mL. Intra-and inter-day precision (relative standard deviation, %RSD) were within 8.4% and accuracy (relative error, %RE) was lower than -3.2%. The developed and validated method was successfully applied to a pharmacokinetic study of tadalafil (20 mg) in Korean healthy male subjects (n = 12).
Autism spectrum disorder (ASD) is a major neurodevelopmental disorder in which patients present with core symptoms of social communication impairment, restricted interest, and repetitive behaviors. Although various studies have been performed to identify ASD-related mechanisms, ASD pathology is still poorly understood. CNTNAP2 genetic variants have been found that represent ASD genetic risk factors, and disruption of Cntnap2 expression has been associated with ASD phenotypes in mice. In this study, we performed an integrative multi-omics analysis by combining quantitative proteometabolomic data obtained with Cntnap2 knockout (KO) mice with multi-omics data obtained from ASD patients and forebrain organoids to elucidate Cntnap2-dependent molecular networks in ASD. To this end, a mass spectrometry-based proteometabolomic analysis of the medial prefrontal cortex in Cntnap2 KO mice led to the identification of Cntnap2-associated molecular features, and these features were assessed in combination with multi-omics data obtained on the prefrontal cortex in ASD patients to identify bona fide ASD cellular processes. Furthermore, a reanalysis of single-cell RNA sequencing data obtained from forebrain organoids derived from patients with CNTNAP2-associated ASD revealed that the aforementioned identified ASD processes were mainly linked to excitatory neurons. On the basis of these data, we constructed Cntnap2-associated ASD network models showing mitochondrial dysfunction, axonal impairment, and synaptic activity. Our results may shed light on the Cntnap2-dependent molecular networks in ASD.
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