Low-dose apatinib in subjects with renal impairment: A pharmacokinetics study.

OBJECTIVE: In patients with renal impairment, we studied apatinib and its major metabolites (M1-1, M1-2, M1-6, and M9-2) for pharmacokinetics. METHODS: Subjects with different renal functions were given a single oral dose of apatinib mesylate tablets of 250 mg. Pharmacokinetic samples were collected at 1 hour before dosing,0.25, 0.5, 1, 2, 3, 4, 6, 8, 24, 48, 72, and 96 h after dosing. The pharmacokinetic parameters of apatinib and its major metabolites were calculated by noncompartmental analysis. RESULTS: Comparing PK parameters of the mild or moderate renal impairment group with the healthy group: the geometric mean ratios of maximum observed drug concentration (Cmax), the area under the plasma drug concentration-time curve from time 0 to the final quantifiable time (AUC0-t), and the area under the plasma concentration-time curve from time 0 extrapolated to infinity (AUC0-inf) were all about one. No significant effect of mild and moderate renal impairment on apatinib pharmacokinetics was observed. Mild and moderate renal impairment was also not observed to have a significant effect on the pharmacokinetics of metabolites M1-1, M1-2, and M1-6. However, mild and moderate renal impairment had a certain increase in exposure to the metabolite M9-2. Considering that M9-2 has no inhibitory effect on protein tyrosine kinase, it has no clinical significance. In addition, the proportion of cumulative excretion of apatinib and its major metabolites was small and almost negligible in all three groups of subjects. CONCLUSION: Patients with mild and moderate renal impairment do not need to adjust the dose of apatinib when using low dose (250 mg) apatinib.

CKAP4-mediated activation of FOXM1 via phosphorylation pathways regulates malignant behavior of glioblastoma cells.

OBJECTIVE: CKAP4 (Cytoskeleton Associated Protein 4) has been reported as an important regulator of carcinogenesis. A great deal of uncertainty still surrounds the possible molecular mechanism of CKAP4 involvement in GBM. We aimed to specifically elucidate the putative role of CKAP4 in the development of GBM. METHODS: We identified divergent proteomics landscapes of GBM and adjacent normal tissues using mass spectrometry-based label-free quantification. Bioinformatics analysis of differentially expressed proteins (DEPs) led to the identification of CKAP4 as a hub gene. Based on the Chinese Glioma Genome Atlas data, we characterized the elevated expression of CKAP4 in GBM and developed a prognostic model. The influence of CKAP4 on malignant behavior of GBM was detected in vitro and vivo, as well as its downstream target and signaling pathways. RESULTS: The prognosis model displayed accuracy and reliability for the probability of survival of patients with gliomas. CKAP4 knockdown remarkably reduced the malignant potential of GBM cells, whereas its overexpression reversed these effects in GBM cells and xenograft mice. Moreover, we demonstrated that overexpression of CKAP4 leads to increased FOXM1 (Forkhead Box M1) expression in conjunction with an increased level of AKT and ERK phosphorylation. Inhibition of both pathways had synergistic effects, resulting in greater effectiveness of inhibition. CKAP4 could reverse the deregulation of FOXM1 triggered by inhibition of AKT and ERK signaling. CONCLUSIONS: This is the first study to reveal a CKAP4-FOXM1 signaling cascade that contributes to the malignant phenotype of GBMs. The CKAP4-based prognostic model would facilitate individualized treatment decisions for glioma patients.

Advances of multiplex ligation-dependent probe amplification technology in molecular diagnostics.

Multiplex ligation-dependent probe amplification (MLPA) is a multiplex copy number analysis tool which is routinely used to detect large mutations in genetic diseases. With continuous modifications, MLPA has been extended for the detection of DNA methylation variation, single nucleotide polymorphisms, chromosome abnormalities and other forms of genomic variation. The combination with other techniques has even enlarged the application of MLPA in molecular diagnostics of various human diseases. In this review, the principle of MLPA-based techniques as well as their main and latest applications in clinical detection are described. It is believed that with improved automation, increased multiplexing, lower cost and the combination with other technologies, MLPA will play an increasingly important role in molecular diagnosis of human disease.

Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings.

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid-base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein-DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid-nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.

Genome-wide identification and characterization of glucose transporter (glut) genes in spotted sea bass (Lateolabrax maculatus) and their regulated hepatic expression during short-term starvation.

The glucose transporters (GLUTs) are well known for their essential roles in moving the key metabolites, glucose, galactose, fructose and a number of other important substrates in and out of cells. In this study, we identified a total of 21 glut genes in spotted sea bass (Lateolabrax maculatus) through extensive data mining of existing genomic and transcriptomic databases. Glut genes of spotted sea bass were classified into three subfamilies (Class I, Class II and Class III) according to the phylogenetic analysis. Glut genes of spotted sea bass were distributed in 15 out of 24 chromosomes. Deduced gene structure analysis including the secondary structure and the three-dimensional structures, as well as the syntenic analysis further supported their annotations and orthologies. Expression profile in healthy tissues indicated that 9 of 21 glut genes were expressed in liver of spotted sea bass. During short-term starvation, the mRNA expression levels of 3 glut genes (glut2, glut5, and glut10) were significantly up-regulated in liver (P < 0.05), indicating their potential roles in sugar transport and consumption. These findings in our study will facilitate the further evolutionary characterization of glut genes in fish species and provide a theoretical basis for their functional study.