BACKGROUND: Type A aortic dissection (TAAD) is a life-threatening condition characterized by complex pathophysiology, in which macrophages play a critical but not yet fully understood role. This study focused on the role of endothelial cells with elevated expression of ACKR1 (atypical chemokine receptor 1) and their interaction with proinflammatory macrophages in TAAD development. METHODS: Single-cell transcriptomic analysis of human aortic tissues was used to identify cellular heterogeneity in TAAD. Clinical and animal studies evaluated the relationship between ACKR1 expression and TAAD severity. Gain- and loss-of-function experiments, involving modulation of ACKR1 expression in ECs, investigated its role in macrophage regulation. Molecular docking and in vitro/in vivo studies identified and tested potential drugs targeting ACKR1. RESULTS: TAAD tissues exhibited increased ECs with high ACKR1 expression and proinflammatory macrophages. High ACKR1 levels were strongly associated with TAAD severity. Knockdown of ACKR1 suppressed the NF-κB (nuclear factor-κB) signaling pathway and SPP1 (secreted phosphoprotein 1) expression, reducing macrophage migration and polarization, thereby inhibiting TAAD progression. Conversely, overexpression of ACKR1 exacerbated TAAD. Amikacin, identified as an ACKR1 targeted drug, regulated macrophage behavior via the ACKR1/NF-κB/SPP1 pathway, attenuating TAAD progression and improving survival in mice. CONCLUSIONS: This study reveals how endothelial cells exhibiting high ACKR1 expression modulate macrophage migration and proinflammatory polarization through the ACKR1/NF-κB/SPP1 signaling pathway, a crucial mechanism in TAAD progression. Targeting ACKR1 through both functional and pharmacological approaches effectively suppressed TAAD progression and extended survival in TAAD mice, offering promising new intervention strategies for clinical evaluation.

Simple sequence repeats (SSRs) are known as microsatellites, and consist of tandem 1–6-base motifs. They have become one of the most popular molecular markers, and are widely used in molecular ecology, conservation biology, molecular breeding, and many other fields. Previously reported methods identify monomorphic and polymorphic SSRs and determine the polymorphic SSRs via experimental validation, which is potentially time-consuming and costly. Herein, we present a new strategy named insertion/deletion (INDEL) SSR (IDSSR) to identify polymorphic SSRs by integrating SSRs with nucleotide insertions/deletions (INDEL) solely based on a single genome sequence and the sequenced pair-end reads. These INDEL indexes and polymorphic SSRs were identified, as well as the number of repeats, repeat motifs, chromosome location, annealing temperature, and primer sequences, enabling future experimental approaches to determine the correctness and polymorphism. Experimental validation with the giant panda demonstrated that our method has high reliability and stability. The efficient SSR pipeline would help researchers obtain high-quality genetic markers for plants and animals of interest, save labor, and reduce costly marker-screening experiments. IDSSR is freely available at https://github.com/Allsummerking/IDSSR.

Background: Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. Methods: MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis. Results: A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as “collagen catabolic process”, “positive regulation of pathway-restricted SMAD protein phosphorylation”, “osteoblast differentiation”, “regulation of release of cytochrome c from mitochondria” and “interleukin-1 production”. The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells. Conclusions: Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS.

In most solid tumors, cellular energy metabolism is primarily dominated by aerobic glycolysis, which fulfills the high demand for biomacromolecules at the expense of reduced ATP production efficiency. Elucidation of the mechanisms by which rapidly proliferating malignant cells acquire sufficient energy in this state of inefficient ATP production from glycolysis could enable the development of metabolism-targeted therapeutic strategies. In this study, we observed a significant association between elevated expression levels of the long noncoding RNA small nuclear RNA host gene 17 (SNHG17) and unfavorable prognosis in breast cancer. SNHG17 promoted breast cancer cell proliferation by augmenting mitochondrial ATP production. Mechanistically, SNHG17 directly interacted with the P65 subunit of NF-κB and phosphorylated P65 at the threonine 505 site. SNHG17 bound to P65 at its truncated loop2 site, recruited P65 to mitochondria, and coregulated the transcriptional activation of mitochondrial DNA to promote ATP production. Accordingly, targeting SNHG17 with an antisense oligonucleotide significantly reduced breast cancer tumor growth both in vitro and in vivo. Overall, these results established a role for SNHG17 in promoting breast cancer progression by increasing ATP production and provided insights into the reprogramming of energy metabolism in solid tumors. Significance: SNHG17 cooperates with NF-κB to induce expression of mitochondrial DNA and boost ATP production in breast cancer, suggesting that targeting SNHG17 could reverse metabolic reprogramming to suppress tumor progression.

The clinical significance of mutation in multiple pulmonary nodules is largely limited by single gene mutation-directed analysis and lack of validation of gene expression profiles. New analytic strategy is urgently needed for comprehensive understanding of genomic data in multiple pulmonary nodules. In this study, we performed whole exome sequencing in 16 multiple lung nodules and 5 adjacent normal tissues from 4 patients with multiple pulmonary nodules and decoded the mutation information from a perspective of cellular functions and signaling pathways. Mutated genes as well as mutation patterns shared in more than two lesions were identified and characterized. We found that the number of mutations or mutated genes and the extent of protein structural changes caused by different mutations is positively correlated with the degree of malignancy. Moreover, the mutated genes in the nodules are associated with the molecular functions or signaling pathways related to cell proliferation and survival. We showed a developing pattern of quantity (the number of mutations/mutated genes) and quality (the extent of protein structural changes) in multiple pulmonary nodules. The mutation and mutated genes in multiple pulmonary nodules are associated with cell proliferation and survival related signaling pathways. This study provides a new perspective for comprehension of genomic mutational data and might shed new light on deciphering molecular evolution of early stage lung adenocarcinoma.

Background: Respiratory syncytial virus (RSV) is a leading cause of hospitalization in children, but predictors of critical illness remain poorly defined. This study aimed to identify risk factors for critical RSV pneumonia and to develop a predictive model. Methods: A retrospective analysis of 12,035 hospitalized children with RSV infection (2019-2025) yielded 304 eligible patients after exclusions. Based on critical illness criteria, 30 critical cases and 90 randomly selected non critical controls from the remaining patients were enrolled. Clinical characteristics, laboratory parameters, and co-infection patterns were compared. Univariate, Lasso, and multivariable logistic regression analyses were performed to identify independent predictors, which were then incorporated into a nomogram. Model performance was assessed using ROC curve, calibration plot, and decision curve analysis. Results: Among the 304 eligible children, 30 (9.9%) had critical illness. Co infection with three or more pathogens was most common in the critical group (43.3%), whereas single RSV infection predominated in the non critical group (38.9%). Multivariable logistic regression identified four independent predictors of critical illness: interleukin 6 (IL 6), creatine kinase MB (CK MB), serum bilirubin excretion (SBE), and neutrophil percentage. The nomogram combining these factors exhibited excellent discriminative ability (AUC = 0.921, 95% CI: 0.868-0.974). The calibration curve agreed well with the 45° reference line (Hosmer-Lemeshow χ² = 3.233, p = 0.919), and decision curve analysis demonstrated clinical benefit across threshold probabilities ranging from 0.01 to 0.99. Conclusions: Elevated IL-6, CK-MB, neutrophil percentage, and SBE are independent predictors of critical RSV infection in children. The nomogram based on these readily available biomarkers provides a robust tool for early risk stratification and clinical decision-making.