| 1. |
- Dai, Juncheng, et al.
(författare)
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Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk
- 2020
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Ingår i: International Journal of Cancer. - : John Wiley & Sons. - 0020-7136 .- 1097-0215. ; 146:10, s. 2855-2864
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung ncer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant netic polymorphisms in the human genome. INDELs are highly associated with multiple human seases, including lung cancer. However, limited studies with large-scale samples have been available to stematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large-scale meta- alysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional notations were performed to further explore the potential function of lung cancer risk INDELs. nditional analysis was used to clarify the relationship between INDELs and SNPs. Four new risk loci re identified in genome-wide INDEL analysis (1p13.2: rs5777156, Insertion, OR = 0.92, p = 9.10 x 10(- ; 4q28.2: rs58404727, Deletion, OR = 1.19, p = 5.25 x 10(-7); 12p13.31: rs71450133, Deletion, OR = 09, p = 8.83 x 10(-7); and 14q22.3: rs34057993, Deletion, OR = 0.90, p = 7.64 x 10(-8)). The eQTL alysis and functional annotation suggested that INDELs might affect lung cancer susceptibility by gulating the expression of target genes. After conducting conditional analysis on potential causal SNPs, e INDELs in the new loci were still nominally significant. Our findings indicate that INDELs could be tentially functional genetic variants for lung cancer risk. Further functional experiments are needed to tter understand INDEL mechanisms in carcinogenesis.
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| 2. |
- Li, Feiran, 1993, et al.
(författare)
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Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for alpha-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.
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