scholarly journals The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

2020 ◽  
Vol 49 (D1) ◽  
pp. D529-D535 ◽  
Author(s):  
Matthew E Berginski ◽  
Nienke Moret ◽  
Changchang Liu ◽  
Dennis Goldfarb ◽  
Peter K Sorger ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Knowledge Generation ◽  
Data Resource ◽  
Functional Relationships ◽  
External Data ◽  
Level Data ◽  
Numerous Cell ◽  
Therapeutic Development

Abstract Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

scholarly journals Distinct p53 acetylation cassettes differentially influence gene-expression patterns and cell fate

2006 ◽  
Vol 173 (4) ◽  
pp. 533-544 ◽  
Author(s):  
Chad D. Knights ◽  
Jason Catania ◽  
Simone Di Giovanni ◽  
Selen Muratoglu ◽  
Ricardo Perez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Biological Activities ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
P53 Gene ◽  
Protein Protein Interactions

The activity of the p53 gene product is regulated by a plethora of posttranslational modifications. An open question is whether such posttranslational changes act redundantly or dependently upon one another. We show that a functional interference between specific acetylated and phosphorylated residues of p53 influences cell fate. Acetylation of lysine 320 (K320) prevents phosphorylation of crucial serines in the NH2-terminal region of p53; only allows activation of genes containing high-affinity p53 binding sites, such as p21/WAF; and promotes cell survival after DNA damage. In contrast, acetylation of K373 leads to hyperphosphorylation of p53 NH2-terminal residues and enhances the interaction with promoters for which p53 possesses low DNA binding affinity, such as those contained in proapoptotic genes, leading to cell death. Further, acetylation of each of these two lysine clusters differentially regulates the interaction of p53 with coactivators and corepressors and produces distinct gene-expression profiles. By analogy with the “histone code” hypothesis, we propose that the multiple biological activities of p53 are orchestrated and deciphered by different “p53 cassettes,” each containing combination patterns of posttranslational modifications and protein–protein interactions.

scholarly journals Molecular Characterization and Elucidation of Pathways to Identify Novel Therapeutic Targets in Pulmonary Arterial Hypertension

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoting Yao ◽  
Tian Jing ◽  
Tianxing Wang ◽  
Chenxin Gu ◽  
Xi Chen ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Lung Tissue ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Signatures ◽  
Protein Protein Interactions ◽  
Pulmonary Arterial ◽  
Microarray Datasets

Background: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there are limited studies reflecting the available biomarkers from separate gene expression profiles in PAH. This study explored two microarray datasets by an integrative analysis to estimate the molecular signatures in PAH.Methods: Two microarray datasets (GSE53408 and GSE113439) were exploited to compare lung tissue transcriptomes of patients and controls with PAH and to estimate differentially expressed genes (DEGs). According to common DEGs of datasets, gene and protein overrepresentation analyses, protein–protein interactions (PPIs), DEG–transcription factor (TF) interactions, DEG–microRNA (miRNA) interactions, drug–target protein interactions, and protein subcellular localizations were conducted in this study.Results: We obtained 38 common DEGs for these two datasets. Integration of the genome transcriptome datasets with biomolecular interactions revealed hub genes (HSP90AA1, ANGPT2, HSPD1, HSPH1, TTN, SPP1, SMC4, EEA1, and DKC1), TFs (FOXC1, FOXL1, GATA2, YY1, and SRF), and miRNAs (hsa-mir-17-5p, hsa-mir-26b-5p, hsa-mir-122-5p, hsa-mir-20a-5p, and hsa-mir-106b-5p). Protein–drug interactions indicated that two compounds, namely, nedocromil and SNX-5422, affect the identification of PAH candidate biomolecules. Moreover, the molecular signatures were mostly localized in the extracellular and nuclear areas.Conclusions: In conclusion, several lung tissue-derived molecular signatures, highlighted in this study, might serve as novel evidence for elucidating the essential mechanisms of PAH. The potential drugs associated with these molecules could thus contribute to the development of diagnostic and therapeutic strategies to ameliorate PAH.

scholarly journals Comprehensive Clinical and Molecular Characterization of KRASG12C-Mutant Colorectal Cancer

2021 ◽  
pp. 613-621
Author(s):  
Jason T. Henry ◽  
Oluwadara Coker ◽  
Saikat Chowdhury ◽  
John Paul Shen ◽  
Van K. Morris ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trials ◽  
De Novo ◽  
Expression Profiles ◽  
Single Gene ◽  
Gene Expression Profiles ◽  
Progression Free Survival ◽  
Cancer Center ◽  
Data Set ◽  
External Data

PURPOSE KRAS p.G12C mutations occur in approximately 3% of metastatic colorectal cancers (mCRC). Recently, two allosteric inhibitors of KRAS p.G12C have demonstrated activity in early phase clinical trials. There are no robust studies examining the behavior of this newly targetable population. METHODS We queried the MD Anderson Cancer Center data set for patients with colorectal cancer who harbored KRAS p.G12C mutations between January 2003 and September 2019. Patients were analyzed for clinical characteristics, overall survival (OS), and progression-free survival (PFS) and compared against KRAS nonG12C. Next, we analyzed several internal and external data sets to assess immune signatures, gene expression profiles, hypermethylation, co-occurring mutations, and proteomics. RESULTS Among the 4,632 patients with comprehensive molecular profiling, 134 (2.9%) were found to have KRAS p.G12C mutations. An additional 53 patients with single gene sequencing were included in clinical data but excluded from prevalence analysis allowing for 187 total patients. Sixty-five patients had de novo metastatic disease and received a median of two lines of chemotherapy without surgical intervention. For the first three lines of chemotherapy, the median PFS was 6.4 months (n = 65; 95% CI, 5.0 to 7.4 months), 3.9 months (n = 47; 95% CI, 2.9 to 5.9 months), and 3.0 months (n = 21; 95% CI, 2.0 to 3.4 months), respectively. KRAS p.G12C demonstrated higher rates of basal EGFR activation compared with KRAS nonG12C. When compared with an internal cohort of KRAS nonG12C, KRAS p.G12C patients had worse OS. CONCLUSION PFS is poor for patients with KRAS p.G12C metastatic colorectal cancer. OS was worse in KRAS p.G12C compared with KRAS nonG12C patients. Our data highlight the innate resistance to chemotherapy for KRAS p.G12C patients and serve as a historical comparator for future clinical trials.

scholarly journals Intra- and intergenerational changes in the cortical DNA methylome in response to therapeutic intermittent hypoxia in mice

2020 ◽  
Vol 52 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Krystal Courtney D. Belmonte ◽  
Jarrod C. Harman ◽  
Nicholas A. Lanson ◽  
Jeffrey M. Gidday
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interactions ◽  
Dna Methylome ◽  
Methylated Dna ◽  
Modified Dna ◽  
F1 Generation

Recent evidence from our laboratory documents functional resilience to retinal ischemic injury in untreated mice derived from parents exposed to repetitive hypoxic conditioning (RHC) before breeding. To begin to understand the epigenetic basis of this intergenerational protection, we used methylated DNA immunoprecipitation and sequencing to identify genes with differentially methylated promoters (DMGPs) in the prefrontal cortex of mice treated directly with the same RHC stimulus (F0-RHC) and in the prefrontal cortex of their untreated F1-generation offspring (F1-*RHC). Subsequent bioinformatic analyses provided key mechanistic insights into how changes in gene expression secondary to promoter hypo- and hypermethylation might afford such protection within and across generations. We found extensive changes in DNA methylation in both generations consistent with the expression of many survival-promoting genes, with twice the number of DMGPs in the cortex of F1*RHC mice relative to their F0 parents that were directly exposed to RHC. In contrast to our hypothesis that similar epigenetic modifications would be realized in the cortices of both F0-RHC and F1-*RHC mice, we instead found relatively few DMGPs common to both generations; in fact, each generation manifested expected injury resilience via distinctly unique gene expression profiles. Whereas in the cortex of F0-RHC mice, predicted protein-protein interactions reflected activation of an anti-ischemic phenotype, networks activated in F1-*RHC cortex comprised networks indicative of a much broader cytoprotective phenotype. Altogether, our results suggest that the intergenerational transfer of an acquired phenotype to offspring does not necessarily require the faithful recapitulation of the conditioning-modified DNA methylome of the parent.

scholarly journals BrassicaEDB: A Gene Expression Database for Brassica Crops

2020 ◽  
Vol 21 (16) ◽  
pp. 5831 ◽  
Author(s):  
Haoyu Chao ◽  
Tian Li ◽  
Chaoyu Luo ◽  
Hualei Huang ◽  
Yingfei Ruan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Level ◽  
Level Data ◽  
Rapeseed Cultivar ◽  
Transcriptome Level

The genus Brassica contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB) for the research community. In the current BrassicaEDB, we only focused on the transcriptome level in rapeseed. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full-bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including “eFP”, “Treatment”, “Coexpression”, and “SRA Project” modules based on gene expression profiles and “Gene Feature”, “qPCR Primer”, and “BLAST” modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for rapeseed researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.

scholarly journals Identification of potential core genes in Kawasaki disease using bioinformatics analysis

2019 ◽  
Vol 47 (9) ◽  
pp. 4051-4058
Author(s):  
Wenbin Wu ◽  
Keyou You ◽  
Jinchan Zhong ◽  
Zhanwei Ruan ◽  
Bubu Wang
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Kawasaki Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Humoral Response ◽  
Hub Genes ◽  
Potential Core

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

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