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Biomedical subjects

Shengnan Li

Publications and source records attributed to Shengnan Li.

4 recordsLinked to original sources

Engineered genomic attachment sites for site-specific recombinases enable high-efficiency integration in plants and human cells.

Here we developed a DNA-centric strategy for optimizing site-specific recombination by rationally engineering chimeric attachment sites. The high-activity att variants enhance Bxb1-mediated integration efficiency in human cells and plants. Among these att variants, the engineered attB(V111) site achieved 51.9% integration efficiency in HEK293T cells (1.7-fold versus wild-type attB) and 35.6% in rice protoplasts (4.4-fold versus wild-type attB). When paired with an engineered single protein mutant in the Bxb1 catalytic domain, the optimized system achieved targeted integration efficiencies of 31% for a CD19 chimeric antigen receptor cassette and 25% for an ornithine transcarbamylase expression cassette in human cells. In rice, these engineered variants enabled integration of a 5.8 kb herbicide-resistance cassette at a targeted genomic locus, with stable integration detected in 24% of regenerated plants. Oxford Nanopore-based long-read sequencing of edited plants reveals complete and precise insertion with high specificity. Propagation of edited seedlings to T1 plants confirms heritable editing to future generations. This approach provides a safe, broadly applicable approach for recombinase-based genome editing.

Journal Article

Development and validation of whole-genome SSR markers in sugar beet (Beta vulgaris L.).

Sugar beet (Beta vulgaris L.) is an important sugar and cash crop worldwide. To systematically characterize SSR (Simple Sequence Repeat) loci across sugar beet chromosomes and enable the precise identification of germplasm resources, this study conducted a genome-wide scan for SSR loci, analyzed their distribution patterns, and determined their genotypes using resequencing data from 123 sugar beet varieties. The results revealed an abundance of SSR loci in the sugar beet genome, with a total of 135, 379 identified, from which 135, 344 pairs of SSR primers were designed (135, 344 primer pairs successfully designed; 35 loci failed to meet design criteria). Specifically, 31, 748 primer pairs were designed based on SSRs located in unassigned scaffolds, and 103, 596 primer pairs from SSRs assigned to the nine chromosomes. Through bioinformatic analysis, we identified 28, 768 SSR primers located in multi-copy genes with PIC (Polymorphism Information Content) ≥ 0.5, and 2, 326 SSR markers located in single-copy genes residing in various genic regions (among which 543 had PIC ≥ 0.5, with the highest reaching 0.776). PCR (Polymerase Chain Reaction) validation confirmed 20 robust and polymorphic markers producing clear and reproducible bands. Among them, 10 SSR primers located in multi-copy genes exhibited three or more polymorphic types, and 10 markers located in single-copy genes displayed 2-3 polymorphic types. The most polymorphic marker, YCD-4-2, detected 11 polymorphic types across 48 varieties. Furthermore, to explore markers with potential functional significance, we annotated the genes harboring SSR markers located in single-copy genes. The results showed that 1, 264 SSRs located in single-copy genes were localized to 967 genes, which are significantly enriched in pathways related to carbohydrate metabolism, stress responses, and plant-pathogen interactions. The 20 validated markers and the 2, 326 SSRs located in single-copy genes provided in this study can be directly applied to fingerprinting of sugar beet varieties, seed purity testing, and marker-assisted selection, thus representing a practical resource for molecular breeding.

genome-wide

Causal relationship between frailty and diabetes subtypes: A bidirectional Mendelian randomization study.

Frailty and diabetes mellitus (DM) are closely linked, but their causal relationship remains unclear. This study aims to determine the bidirectional causal relationship between frailty and different DM subtypes using Mendelian randomization (MR). We performed a 2-sample MR analysis using summary statistics from large-scale genome-wide association studies. The inverse-variance weighting method was the primary analytical approach, with MR-Egger regression and weighted median methods for sensitivity analysis. Horizontal pleiotropy and heterogeneity were assessed using MR-PRESSO and Cochran Q test. Genetically predicted frailty was significantly associated with an increased risk of type 2 diabetes (T2DM) and gestational diabetes (GDM) (odds ratio [OR]&#x2005;=&#x2005;2.142, 95% confidence interval [CI]: 1.751-2.621, P&#x2005;<&#x2005;.001; OR&#x2005;=&#x2005;2.280, 95% CI: 1.368-3.800, P&#x2005;=&#x2005;.002), but no causal relationship was observed for type 1 diabetes or glycemic traits (P&#x2005;>&#x2005;.05). Conversely, genetically predicted type 1 diabetes, T2DM, GDM, and postprandial glucose levels (2-hour post-load glucose) increased the risk of frailty (OR&#x2005;=&#x2005;1.026, 95% CI: 1.014-1.038, P&#x2005;<&#x2005;.001; OR&#x2005;=&#x2005;1.046, 95% CI: 1.033-1.058, P&#x2005;<&#x2005;.001; OR&#x2005;=&#x2005;1.068, 95% CI: 1.040-1.096, P&#x2005;<&#x2005;.001; OR&#x2005;=&#x2005;1.095, 95% CI: 1.049-1.144, P&#x2005;<&#x2005;.001). Sensitivity analyses confirmed the robustness of these findings. This study provides genetic evidence supporting a bidirectional causal relationship between frailty and diabetes, particularly T2DM and GDM. These findings highlight the need for early frailty screening in diabetic patients and better metabolic management in frail populations.

Humans