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Biomedical subjects

Huan Li

Publications and source records attributed to Huan Li.

5 recordsLinked to original sources

Post-infection colonization and recurrent infections by ST11-KL64 carbapenem-resistant Klebsiella pneumoniae: a study of within-host evolution.

Bacteria pose a serious threat to hosts through adaptive mutations that confer stress resistance and promote persistent colonization. Here, we describe an adaptive evolution event involving eight highly similar ST11-KL64 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains, isolated from a non-infected inpatient who acquired two distinct CRKP strains, CRKP-F1 and CRKP-S2 during the first hospitalization, recovered, and was discharged after receiving antimicrobial therapy but subsequently experienced two additional recurrent febrile episodes and re-admission. The strain CRKP-S2 showed significantly enhanced resistance to oxidative stress, survival within macrophages, and internalization ability, and carried an additional ~72 kb fragment containing oxidative stress response factors (including NAD(P)-dependent oxidoreductases), and a ~ 19kb plasmid fragment harboring catA2, sul2, umuC/D genes, compared to the initial strain CRKP-F1. All four strains, CRKP-B3, CRKP-U4, CRKP-F5 and CRKP-S6, from the second hospitalization exhibited higher genetic similarity to CRKP-S2 than each other, and each of these strains has its own unique mutations compared to CRKP-S2. The third-hospitalization strain CRKP-U7 displayed the highest average nucleotide identity (ANI) with CRKP-S2, and possessed unique mutations in cecR, rlmA1, and selB, distinct from second-hospitalization strains. However, the last strain, CRKP-B8, carries a new gene mutation based on CRKP-U7 and exhibits greater host adaptability than all other isolates. While these findings are suggestive, whether the ~72-kb and ~19-kb fragments and mutations in CRKP-S2 drove enhanced colonization, and whether subsequent mutations contributed to subclones linked to recurrent febrile, or merely coincided, remains unclear. The possibility of mixed colonization by co-circulating subclones cannot be excluded, and functional validation is needed.

Klebsiella pneumoniae

Serum Proteomic Profiling Reveals Renin-Associated Immune and Cytoskeletal Dysregulation in Post-COVID-19 Condition Patients with Secondary Adrenal Insufficiency.

Post-COVID-19 condition (PCC) with secondary adrenal insufficiency (SAI) involves multiorgan dysfunction, potentially linked to renin-angiotensin-aldosterone system dysregulation. The molecular basis of renin-associated pathology remains unclear. Here, PCC+SAI patients were stratified by upright renin into low- (<38.8&#x202f;pg/mL) and high-renin (&#x2265;38.8&#x202f;pg/mL) groups. Clinical, endocrine, and proteomic analyses were performed. We found that high-renin patients showed increased BMI, lipids, renin, and aldosterone, but reduced aldosterone-to-renin ratio. Proteomic annalysis identified 20 differentially expressed proteins (DEPs), including 17 upregulated and 3 downregulated proteins in Ren-H patients. Functional annotation revealed that 15 DEPs were immune-related (e.g., APOC4, APOE, C4BPA, CFAH, CFHR3, PF4V, PLF4), while FLNA and COF1 represented cytoskeletal proteins. These DEPs were primarily involved in immune response, complement and coagulation cascades, and MAPK signaling pathways. Correlation analyses indicated that upright renin was positively correlated with complement-related proteins and platelet-derived immune factors, while cytoskeletal proteins (FLNA, COF1) showed positive associations with serum Na+ levels. Additionally, white blood cell and platelet counts were positively correlated with the majority of DEPs. In conclusion, exploratory proteomic analyses suggest that elevated upright renin in PCC+SAI may be associated with immune dysregulation, complement activation, and cytoskeletal remodeling, offering novel insights into the endocrine-immune interactions driving postviral sequelae.

Humans

Hyperlactate-Associated Lysine Lactylome Remodeling in Laryngeal Squamous Cell Carcinoma.

Laryngeal squamous cell carcinoma (LSCC) lacks reliable biomarkers, and the roles of lactate metabolism and lysine lactylation (Kla) remain largely unknown. We profiled the lysine lactylome of LSCC, paired it with adjacent normal tissues, and integrated the data with quantitative proteomic and transcriptomic analyses. LSCC exhibited a hyperlactate-associated phenotype characterized by dysregulated lactate-related genes (LRGs), altered protein abundance, increased tissue lactate, and globally increased Kla levels. Data-independent acquisition mass spectrometry (DIA-MS) identified 1616 Kla sites on 1468 peptides from 688 proteins, with most differential sites being upregulated in tumors. Differentially lactylated proteins were enriched in cell-matrix adhesion, cell migration, chromatin remodeling, and gene-regulatory processes and were clustered into cytoskeletal and nuclear regulatory modules. Multiple Kla sites were also detected on the core histones. Immunoblotting and tissue microarray analyses confirmed increased pan-Kla expression in the LSCC. Pan-Kla levels were independent of sex and age but positively correlated with the tumor stage and lymph-node metastasis. These findings provide a systematic resource for hyperlactate-associated lactylome remodeling in LSCCs and identify candidate Kla-related molecular features associated with clinicopathological progression for future functional and clinical evaluation.

Humans

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Humans

Exploring prognostic genes in the immune microenvironment of acute myeloid leukemia via weighted gene co-expression network analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous blood cancer that arises from transformed myeloid precursor cells in a compromised bone marrow microenvironment. This environment is essential for AML initiation, progression, and relapse. Alongside oncogenic changes in hematopoietic cells, immunological dysregulation also contributes to leukemogenesis. The present study is aimed to identify prognostic genes in stromal and immune cells associated with AML using the weighted gene co-expression network analysis (WGCNA). METHODS: Gene expression profiles were retrieved from The Cancer Genome Atlas database, and immune and stromal cell scores were calculated using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) method. These scores helped identify differentially expressed genes (DEGs), which were then used to create gene clusters through WGCNA. To explore the functions of genes linked to AML subtypes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. A protein-protein interaction network was developed to identify hub genes. The top 18 hub genes were identified using the cytoHubba plug-in in Cytoscape software, and survival analysis was conducted with the Gene Expression Profiling Interactive Analysis 2 online tool. RESULTS: A total of 1097 DEGs were identified, with 601 being upregulated and 496 downregulated. WGCNA analysis indicated that the gray module, comprising 165 genes, had the strongest association with AML subtypes (Cor&#x2005;>&#x2005;0.3; P&#x2005;<&#x2005;.05). Gene Ontology enrichment analysis demonstrated that the 18 identified hub genes were predominantly associated with neutrophil activation, immune response, secretory granule membrane, and pattern recognition receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the DEGs were mainly involved in pathways related to phagosome, lysosome, tuberculosis, leishmaniasis, and neutrophil extracellular trap formation. Kaplan-Meier survival analysis of the top 18 hub genes indicated that ITGAM, IL10, and CD163 were significantly correlated with survival outcomes in AML. CONCLUSION: Key stromal and immune-related genes influencing AML patient outcomes were identified, highlighting their potential as therapeutic targets. These discoveries provide deeper insights into the molecular mechanisms driving AML pathogenesis and subtype differentiation.

Leukemia, Myeloid, Acute