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  • Oxford University Press (OUP)  (5)
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  • Oxford University Press (OUP)  (5)
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  • 1
    Online Resource
    Online Resource
    High-resolution genome mapping and functional dissection of chlorogenic acid production in Lonicera maackii
    Oxford University Press (OUP) ; 2023
    In:  Plant Physiology Vol. 192, No. 4 ( 2023-08-03), p. 2902-2922
    Details
    In: Plant Physiology, Oxford University Press (OUP), Vol. 192, No. 4 ( 2023-08-03), p. 2902-2922
    Abstract: Amur honeysuckle (Lonicera maackii) is a widely used medicinal plant of the Caprifoliaceae family that produces chlorogenic acid. Research on this plant mainly focuses on its ornamental value and medicinal compounds, but a reference genome sequence and molecular resources for accelerated breeding are currently lacking. Herein, nanopore sequencing and high-throughput chromosome conformation capture (Hi-C) allowed a chromosome-level genome assembly of L. maackii (2n = 18). A global view of the gene regulatory network involved in the biosynthesis of chlorogenic acid and the dynamics of fruit coloration in L. maackii was established through metabolite profiling and transcriptome analyses. Moreover, we identified the genes encoding hydroxycinnamoyl-CoA quinate transferase (LmHQT) and hydroxycinnamoyl-CoA shikimic/quinate transferase (LmHCT), which localized to the cytosol and nucleus. Heterologous overexpression of these genes in Nicotiana benthamiana leaves resulted in elevated chlorogenic acid contents. Importantly, HPLC analyses revealed that LmHCT and LmHQTs recombinant proteins modulate the accumulation of chlorogenic acid (CGA) using quinic acid and caffeoyl CoA as substrates, highlighting the importance of LmHQT and LmHCT in CGA biosynthesis. These results confirmed that LmHQTs and LmHCT catalyze the biosynthesis of CGA in vitro. The genomic data presented in this study will offer a valuable resource for the elucidation of CGA biosynthesis and facilitating selective molecular breeding.
    Type of Medium: Online Resource
    ISSN: 0032-0889 , 1532-2548
    URL: Article
    DOI: 10.1093/plphys/kiad295
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2004346-6
    detail.hit.zdb_id: 208914-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency
    Oxford University Press (OUP) ; 2023
    In:  Briefings in Bioinformatics Vol. 25, No. 1 ( 2023-11-22)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 25, No. 1 ( 2023-11-22)
    Abstract: Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbad486
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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    Online Resource
  • 3
    Online Resource
    Online Resource
    High-throughput site-specific N -glycoproteomics reveals glyco-signatures for liver disease diagnosis
    Oxford University Press (OUP) ; 2023
    In:  National Science Review Vol. 10, No. 1 ( 2023-01-14)
    Details
    In: National Science Review, Oxford University Press (OUP), Vol. 10, No. 1 ( 2023-01-14)
    Abstract: The glycoproteome has emerged as a prominent target for screening biomarkers, as altered glycosylation is a hallmark of cancer cells. In this work, we incorporated tandem mass tag labeling into quantitative glycoproteomics by developing a chemical labeling-assisted complementary dissociation method for the multiplexed analysis of intact N-glycopeptides. Benefiting from the complementary nature of two different mass spectrometry dissociation methods for identification and multiplex labeling for quantification of intact N-glycopeptides, we conducted the most comprehensive site-specific and subclass-specific N-glycosylation profiling of human serum immunoglobulin G (IgG) to date. By analysing the serum of 90 human patients with varying severities of liver diseases, as well as healthy controls, we identified that the combination of IgG1-H3N5F1 and IgG4-H4N3 can be used for distinguishing between different stages of liver diseases. Finally, we used targeted parallel reaction monitoring to successfully validate the expression changes of glycosylation in liver diseases in a different sample cohort that included 45 serum samples.
    Type of Medium: Online Resource
    ISSN: 2095-5138 , 2053-714X
    URL: Article
    DOI: 10.1093/nsr/nwac059
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2745465-4
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  • 4
    Online Resource
    Online Resource
    EnhancerDB: a resource of transcriptional regulation in the context of enhancers
    Oxford University Press (OUP) ; 2019
    In:  Database Vol. 2019 ( 2019-01-01)
    Details
    In: Database, Oxford University Press (OUP), Vol. 2019 ( 2019-01-01)
    Type of Medium: Online Resource
    ISSN: 1758-0463
    URL: Article
    DOI: 10.1093/database/bay141
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2496706-3
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  • 5
    Online Resource
    Online Resource
    scQTLbase: an integrated human single-cell eQTL database
    Oxford University Press (OUP) ; 2023
    In:  Nucleic Acids Research ( 2023-10-04)
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), ( 2023-10-04)
    Abstract: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20–50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkad781
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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