Biomedical subjects

Jianping Li

Publications and source records attributed to Jianping Li.

4 recordsLinked to original sources

PubMed2026

Genetic evidence supports the combined targeting of lipoprotein(a) and LDL cholesterol to reduce coronary artery disease risk.

Distinct genetic mechanisms govern how lipoprotein(a) (Lp(a)) and low-density lipoprotein cholesterol (LDL-C) promote atherosclerosis. It remains unclear whether targeting both provides additive cardiovascular benefits. Here we use coding loss-of-function variants in LPA and PCSK9 and genetic scores associated with Lp(a) and LDL-C levels to evaluate the effects of lowering Lp(a) and LDL-C on coronary artery disease (CAD) risk. Among 408,039 individuals from the UK Biobank, LPA or PCSK9 loss-of-function carriers have lower CAD risk than noncarriers (odds ratio (OR) 0.91 and 0.81). Carriers of both variants have even lower CAD risk (OR 0.73). Genetic lowering of Lp(a) and LDL-C showed a stronger reduction of CAD risk (OR 0.70) than either trait individually (OR 0.85 and 0.81) in the two-factor genetic score analysis. Among statin users, Lp(a) reduction was linearly associated with CAD risk. A phenome-wide association study revealed that combined therapy was associated with cardiometabolic benefits without adverse effects. The additive benefits were replicated in 65,171 individuals from the Mass General Brigham Biobank.

Humans↗

PubMed2026

Genome-Wide Characterization of the ZIP Transporter Family in Sea Island Cotton (Gossypium barbadense L.) and Expression Profiling Under Heavy Metal and Pathogen Stresses.

G. barbadense represents an indispensable germplasm resource for high-quality textile fiber and disease resistance; nevertheless, systematic information regarding its ZRT/IRT-like protein (ZIP) gene family remains limited. Here, a total of 46 GbZIP genes were identified across the G. barbadense genome. Comprehensive bioinformatic investigations revealed uneven chromosomal distribution and confirmed that segmental/whole-genome duplications, supplemented by localized tandem duplications, drove family expansion. Members clustered within the same phylogenetic clades shared conserved motif organization and gene architecture, while promoter regions harbored abundant cis-acting elements associated with phytohormone and stress signaling. Transcriptome profiling indicated distinct expression patterns across vegetative/reproductive tissues, fiber and ovule developmental stages, and diverse abiotic stress conditions (cold, hot, drought, and salt). Quantitative Real-Time PCR (qRT-PCR) further validated that several GbZIP candidates exhibited temporal expression variations upon exposure to cadmium toxicity, V. dahliae infection, and combined Cd-V. dahliae stress. Specifically, GbZIP13, GbZIP18, GbZIP27, and GbZIP36 displayed prominent broad-spectrum responses to all three stress conditions, whereas GbZIP16, GbZIP29, and GbZIP30 showed stress-specific regulatory divergence. Overall, this study aims to systematically analyze the evolutionary characteristics and expression patterns of the GbZIP family, and to specifically evaluate the response differences under Cd stress, V. dahliae stress, and combined stress, in order to identify potential key candidate genes.

Gossypium barbadense↗

PubMed2026

Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.

The escalating generation of global vegetable waste represents a critical loss of bioactive resources, necessitating a paradigm shift from passive disposal to active nutrient upcycling. However, the industrial conversion of this heterogeneous biomass into standardized functional food ingredients is currently impeded by significant techno-economic barriers, primarily structural recalcitrance, compositional inconsistency, and the presence of toxic fermentation inhibitors. This review provides a comprehensive analysis of the synergistic application of microbial engineering and artificial intelligence (AI) to resolve these bioprocessing bottlenecks within a food-to-food closed-loop framework (as shown in the graphical abstract). We evaluate recent advances in engineering food-grade microbial chassis (e.g., Saccharomyces cerevisiae and Escherichia coli) to enhance lignocellulose degradation and stress tolerance. Concurrently, we examine the integration of AI across the entire value chain, covering deep learning-based rational enzyme design, genome-scale metabolic modeling, and intelligent process control for precision fermentation. Current evidence demonstrates that the hardware-software coupling of engineered strains and AI algorithms significantly enhances conversion efficiency and process robustness. Key findings highlight that AI-driven Design-Build-Test-Learn cycles facilitate the de novo creation of enzymes with superior kinetics and strains with adaptive stress response capabilities against toxins. Moreover, dynamic digital twin models effectively mitigate the impact of substrate variability, ensuring the batch-to-batch consistency required for food applications. We conclude that this data-driven synergistic paradigm is pivotal for establishing a resilient circular bioeconomy, enabling the reliable bioconversion of waste into high-value single-cell proteins, natural flavor additives, and sustainable packaging materials.

Artificial Intelligence↗

PubMed2022

Inhibition of RAS-driven signaling and tumorigenesis with a pan-RAS monobody targeting the Switch I/II pocket.

RAS mutants are major therapeutic targets in oncology with few efficacious direct inhibitors available. The identification of a shallow pocket near the Switch II region on RAS has led to the development of small-molecule drugs that target this site and inhibit KRAS(G12C) and KRAS(G12D). To discover other regions on RAS that may be targeted for inhibition, we have employed small synthetic binding proteins termed monobodies that have a strong propensity to bind to functional sites on a target protein. Here, we report a pan-RAS monobody, termed JAM20, that bound to all RAS isoforms with nanomolar affinity and demonstrated limited nucleotide-state specificity. Upon intracellular expression, JAM20 potently inhibited signaling mediated by all RAS isoforms and reduced oncogenic RAS-mediated tumorigenesis in vivo. NMR and mutation analysis determined that JAM20 bound to a pocket between Switch I and II, which is similarly targeted by low-affinity, small-molecule inhibitors, such as BI-2852, whose in vivo efficacy has not been demonstrated. Furthermore, JAM20 directly competed with both the RAF(RBD) and BI-2852. These results provide direct validation of targeting the Switch I/II pocket for inhibiting RAS-driven tumorigenesis. More generally, these results demonstrate the utility of tool biologics as probes for discovering and validating druggable sites on challenging targets.

Biological Products↗