Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P06889 (Mol)
 630,302 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Bone morphogenetic proteins (BMP) are firmly implicated as intra-ovarian regulators of follicle function and steroidogenesis but information is lacking regarding the regulation of BMP signalling by extracellular binding proteins co-expressed in the ovary. In this study we compared the abilities of four BMP binding proteins (gremlin, noggin, chordin, follistatin) to antagonize the action of four different BMPs (BMP2 BMP4, BMP6, BMP7) on LH-induced androstenedione secretion by bovine theca cells in primary culture. Expression of the four BMP binding proteins and BMPs investigated here has previously been documented in bovine follicles. All four BMPs suppressed androstenedione secretion by >85%. Co-treatment with gremlin antagonized BMP2- and, less potently, BMP4-induced suppression of androgen secretion but did not affect responses to BMP6 and BMP7. Noggin antagonized the effects of three BMPs (rank order: BMP4 > BMP2 > BMP7) but did not affect the response to BMP6. Follistatin partially reversed the suppressive effects of BMP6 on androgen secretion but did not affect BMP2, BMP4 and BMP7 action. Chordin had no effect on the response to any of the four BMPs. BMP6 treatment upregulated thecal expression of GREM1, NOG, CHRD and SMAD6 mRNA whilst inhibiting expression of the four BMPs. Taken together with previous work documenting the intra-ovarian expression of different BMPs, BMP binding proteins and signalling receptors, these observations reinforce the conclusion that extracellular binding proteins selectively modulate BMP-dependent alterations in thecal steroidogenesis. As such they likely constitute an important regulatory component of this, and other intra-ovarian actions of BMPs.
J Mol Endocrinol 2018 Oct 01
PMID:Gremlin, Noggin, Chordin and follistatin differentially modulate BMP induced suppression of androgen secretion by bovine ovarian theca cells. 3040 42

Nowadays, breast cancer is one of the most widespread malignancies in women, and the second leading cause of cancer death among women. The progesterone receptor (PR) is one of the treatment targets in breast cancer, and can be blocked with selective progesterone receptor modulators (SPRMs). Since administration of chemical drugs can cause serious side effects, and patients, especially those undergoing long-term treatment, can suffer harmful consequences, there is an urgent need to discover novel potent drugs. Large-scale structural diversity is a feature of natural compounds. Accordingly, in the present study, we selected a library of 20,000 natural compounds from the ZINC database, and screened them against the PR for binding affinity and efficacy. In addition, we evaluated the pharmacodynamics and ADMET properties of the compounds and performed molecular docking. Moreover, molecular dynamics (MD) simulation was carried out in order to examine the stability of the protein. In addition, principal component analysis (PCA) was performed to study the motions of the protein. Finally, the MMPBSA method was applied in order to estimate the binding free energy. Our docking results reveal that compounds ZINC00936598, ZINC00869973 and ZINC01020370 have the highest binding energy into the PR binding site, comparable with that of Levonorgestrel (positive control). Moreover, RMSD, RMSF, Rg and H-bond analysis demonstrate that the lead compounds preserve stability in complex with PR during simulation. Our PCA analysis results were in accordance with MD results and the binding free energies support the docking results. This study paves the way for discovery of novel drugs from natural sources and with optimal efficacy, targeting the PR. Graphical Abstract The binding mode of new progesterone receptor inhibitors.
J Mol Model 2018 Nov 10
PMID:In silico assessment of new progesterone receptor inhibitors using molecular dynamics: a new insight into breast cancer treatment. 3041 81

Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.
Hum Mol Genet 2019 11 01
PMID:SNV identification from single-cell RNA sequencing data. 3150 20


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