Biomedical subjects

Haifeng Wang

Publications and source records attributed to Haifeng Wang.

4 recordsLinked to original sources

PubMed2026

Chemotactic sensing of extracellular antibiotic resistance genes enables their efficient removal by Stutzerimonas stutzeri.

The dissemination of antibiotic resistance genes (ARGs) in wastewater environments poses a severe threat to public health. Extracellular ARGs (eARGs) persist as free DNA fragments that are refractory to efficient removal by conventional physicochemical treatment technologies. Here, we isolated Stutzerimonas stutzeri CHY07 from municipal sewage and demonstrated that extracellular DNA fragments, including eARGs, can serve as chemoattractants for environmental bacteria. Through genomic mining, molecular docking, surface plasmon resonance (SPR), isothermal titration calorimetry (ITC) and protein-ligand interaction profiling, we identified the chemoreceptor Mcp16 as the primary sensor of extracellular DNA and revealed that it achieves sequence-independent recognition of the DNA phosphate backbone. We further established the endogenous pentapeptide VRSVR as a methylation substrate for CheR and constructed the engineered strain CHY07-2 (mcp16::VRSVR) using an SSB/CRISPR-Cas9 ribonucleoprotein (RNP) system. This strain exhibited significantly enhanced chemotactic responsiveness, achieving 72-h removal efficiencies of 96.56% and 91.60% for low- and high-molecular-weight eARGs in non-sterile WWTP secondary effluent; conversely, mcp16 deletion markedly attenuated both chemotaxis and removal, whereas in situ complementation restored them. These findings reveal a "chemotaxis-contact-removal" cascade - with a proposed self-reinforcing loop - in eARG-removing bacteria, providing both a theoretical framework and a technical paradigm for enhancing pollutant removal through targeted amplification of microbial chemotaxis.

Chemotaxis↗

PubMed2026

Efficient and precise programmable DNA knock-in without double-strand breaks.

Programmable gene knock-in holds substantial promise for treating genetic diseases and advancing cell therapies. However, achieving precise and efficient kilobase-scale DNA fragment integration remains challenging1,2. Here we report CRISPR kilobase-scale nickase-targeting (KNIT) editing for efficient, precise and programmable kilobase-scale DNA insertion without double-strand DNA cleavage, which is enabled through the coupling of a Cas9 nickase with a DNA donor recruiting system. KNIT editing facilitates programmable integration of DNA fragments from 0.7 kb to more than 10 kb and is effective across genomic loci and cell types. It achieves up to 89% efficiency and markedly reduces unintended insertion-deletion mutation (indels) rates, translocations and off-target editing. The system supports repeated insertion editing and multiloci gene knock-in with minimal translocations. Its enhanced version, KNIT editor 2, further improves efficiency via a single transfection. Moreover, in mutant cells with a pathological mutation, KNIT editing restores normal gene expression by inserting a therapeutic gene into a safe harbour locus or its native locus. Notably, KNIT editing enables non-viral and programmable chimeric antigen receptor T cell (CAR-T cell) engineering without double-strand breaks and with clinically relevant efficiencies. Moreover, the engineered CAR-T cells exhibit effective antitumour activity in vitro and in mouse models. Therefore, by achieving programmable and site-specific kilobase-scale DNA insertions without double-strand breaks while reducing unintended outcomes, KNIT editing provides a versatile platform for advancing personalized medicine.

Animals↗

PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗

PubMed2025

Comparative analysis of antibiotic resistance genes between fresh pig manure and composted pig manure in winter, China.

Antibiotic resistance is a critical global public health issue. The gut microbiome acts as a reservoir for numerous antibiotic resistance genes (ARGs), which influence both existing and future microbial populations within a community or ecosystem. However, the differences in ARG expression between fresh and composted feces remain poorly understood. In this study, we collected eight samples from a farm in Kaifeng City, China, comprising both fresh and composted pig manure. Using a high-throughput quantitative PCR array, we analyzed differences in ARG expression between these two types of manure. Our findings revealed significant differences in ARG profiles, as demonstrated by principal coordinate analysis (PCoA). Further analysis identified 39 ARGs (log2FC > 1, p < 0.05) in composted pig manure, with 25 genes downregulated and 14 upregulated. Notably, tetB-01, blaOCH, and blaOXY were the most abundant in composted pig manure compared to fresh manure. Additionally, 16S rRNA species profiling revealed that the composting process significantly altered the microbial community structure, with an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes in composted pig manure. In summary, composting substantially transforms both the microbial community structure and the ARG profile in pig manure, underscoring its potential role in modulating the dynamics of ARGs in agricultural environments.

Animals↗