Biomedical subjects

Hui Shen

Publications and source records attributed to Hui Shen.

5 recordsLinked to original sources

PubMed2026

Emergence of ceftazidime-avibactam resistance mediated by KPC variants KPC-71 and KPC-78 in ST463 Pseudomonas aeruginosa.

UNLABELLED: Pseudomonas aeruginosa is a well-recognized opportunistic pathogen and a leading cause of healthcare-associated infections. The shrinking effectiveness of available antimicrobial therapies has intensified the global threat posed by carbapenem-resistant P. aeruginosa (CRPA). Here, we elucidate the mechanisms of ceftazidime-avibactam (CZA) resistance mediated by the rare KPC variants, KPC-71 and KPC-78, identified during the treatment of CRPA infections. Two CZA-resistant P. aeruginosa strains, SY-206885 and HZ-231016032, were isolated from critically ill male patients with severe pneumonia. Whole-genome sequencing assigned both isolates to the high-risk sequence type 463 (ST463). Isolate SY-206885 harbors the blaKPC-71 gene, while HZ-231016032 carries blaKPC-78. Cloning and expression of these genes in P. aeruginosa PAO1 conferred a marked increase in the CZA minimum inhibitory concentration. Notably, expression of KPC-71 or KPC-78 conferred CZA resistance while simultaneously reducing carbapenem hydrolytic activity, a trade-off previously described for some KPC variants but still rarely documented in P. aeruginosa. Structural analysis and kinetic profiling showed that, relative to wild-type KPC-2, both KPC-71 and KPC-78 exhibited reduced catalytic turnover but increased substrate affinity for ceftazidime, together with significantly weakened binding to avibactam. In addition, elevated expression of MexAB-OprM and AmpC-related determinants in the clinical isolates likely further enhanced the high-level CZA resistance phenotype. These findings highlight the capacity of the ST463 CRPA lineage to evolve CZA resistance through KPC structural diversification under antimicrobial pressure and underscore the need for close surveillance during therapy. IMPORTANCE: In this study, we report the detection of the uncommon KPC variants KPC-71 and KPC-78 in clinical sequence type 463 (ST463) carbapenem-resistant Pseudomonas aeruginosa isolates exhibiting resistance to ceftazidime-avibactam (CZA). We demonstrate that CZA resistance is driven by specific structural alterations-a serine insertion between residues 182 and 183 or a D179A substitution within the Ω-loop-that reshape the functional balance of the KPC enzyme. These changes appear to create an evolutionary trade-off by improving ceftazidime recognition while weakening avibactam-mediated inhibition. Given the widespread dissemination of the ST463 lineage in China, the emergence of these variants highlights the urgent need for clinicians to monitor for CZA resistance development during therapy. CLINICAL TRIALS: This study is registered with ClinicalTrials.gov as ChiCTR2500105846.

Ceftazidime↗

PubMed2026

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding-self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.

Spatial Transcriptomics↗

PubMed2026

transFusion: a novel comprehensive platform for integration analysis of single-cell and spatial transcriptomics.

MOTIVATION: Understanding spatial organization, intercellular interactions, and regulatory networks within the spatial context of tissues is crucial for uncovering complex biological processes and disease mechanisms. Spatial transcriptomics technologies have revolutionized this field by enabling the spatially resolved profiling of gene expression. 10× Visium has emerged as the predominant spatial technology, but its low resolution and the complexity of integrating multimodal datasets present significant analytical challenges, particularly for researchers with limited computational and statistical expertise. Current spatial transcriptomics analysis platforms generally fall short of effectively integrating multimodal data and maximizing the utility of spatial information-such as uncovering complex cellular spatial dependencies, multimodal gradient patterns, and spatial coexpression of ligand-receptor pairs and regulatory networks related to disease or biological states-thereby limiting their ability to provide comprehensive end-to-end analytical workflows when analyzing 10× Visium data. RESULTS: To address these limitations, we developed transFusion, a novel, advanced web-based platform specializing in the most comprehensive and effective integration analysis of scRNA-seq and 10× Visium spatial transcriptomics data. transFusion offers 12 key functions, from basic visualization to advanced analyses, including intercellular dependency analysis, ligand-receptor coexpression identification and visualization, and spatial multimodal gradient variation patterns. Two case studies were used to demonstrate transFusion's capabilities in exploring tissue architecture, intercellular communication, dependency networks, and multimodal gradient variation patterns with minimal computational skills and statistical expertise. transFusion provides a flexible and powerful framework for multimodal data integration analysis. AVAILABILITY AND IMPLEMENTATION: transFusion is freely available at https://github.com/WQLin8/transFusion.

Spatial Transcriptomics↗

PubMed2025

Phenotypic and Genetic Associations Between Cardiovascular Disease Subtypes and Alzheimer's Disease.

BACKGROUND: Cardiovascular disease (CVD) and Alzheimer's disease (AD) are major public health concerns that share overlapping risk factors and potential mechanistic pathways. While vascular contributions to cognitive decline are well-documented, the specific relationships between AD and different CVD subtypes remain poorly understood. METHODS: We examined associations between AD and 11 CVD subtypes using logistic regression models in two large biobanks: the UK Biobank (n = 502,133) and the All of Us Research Program (n = 287,011). Models were adjusted for demographic, lifestyle, and clinical covariates. We also explored genetic overlap between AD and CVD traits through colocalization of significant single nucleotide polymorphisms (SNPs) (p < 5×10-8) using genome-wide association study (GWAS) data. RESULTS: Most CVD subtypes were significantly associated with AD in both cohorts. Hypotension had the strongest and most consistent association, followed by hypertension and cerebral infarction. Acute myocardial infarction was the only subtype not significantly linked to AD. Genetic analyses revealed shared loci between AD and CVD-related traits, particularly in regions near APOE, MAPT, and genes influencing myocardial structure and vascular function. CONCLUSIONS: This study identifies subtype-specific CVD associations with AD across two diverse cohorts and highlights shared genetic architecture underlying heart-brain interactions. These findings underscore the importance of vascular health in AD risk and suggest that certain CVD subtypes, especially hypotension, may play underrecognized roles in cognitive decline.

Alzheimer’s disease↗

PubMed2025

Mouse totipotent blastomere-like cells model embryogenesis from zygotic genome activation to post implantation.

Embryo development begins with zygotic genome activation (ZGA), eventually generating blastocysts for implantation. However, in vitro systems modeling the pre-implantation development are still absent and challenging. Here, we used mouse totipotent blastomere-like cells (TBLCs) to develop spontaneous differentiation and blastoid formation systems, respectively. We found Wnt signaling enabled the rapid expansion of TBLCs and the optimization of their culture medium. We successfully developed a TBLC-spontaneous differentiation system in which mouse TBLCs (mTBLCs) firstly converted into two types of ZGA-like cells (ZLCs) distinguished by Zscan4 expression. Surprisingly, Zscan4-, but not Zscan4+, ZLCs further passed through intermediate 4-cell and then 8-cell/morula stages to produce epiblast, primitive endoderm, and trophectoderm lineages. Significantly, single TBLCs underwent expansion, compaction, and polarization to efficiently generate blastocyst-like structures and even post-implantation egg-cylinder-like structures. Conclusively, we established TBLC-based differentiation and embryo-like structure formation systems to model early embryonic development, offering criteria for evaluating and understanding totipotency.

Animals↗