Biparatopic Nanobody-Based Immunosorbent for the Highly Selective Elimination of Tumor Necrosis Factor-α.

Removing the overexpressed TNF-α by hemoperfusion positively affects clinical treatments for diseases such as autoimmune disease and sepsis. However, clearance ratios of adsorbents targeting TNF-α were limited by the extremely low concentration of TNF-α (mostly <1000 ng/L in sepsis) and hydrophobic interactions. In this work, biparatopic nanobodies (NbC21) with a high affinity of 19.9 pM, which bind to two distinct sites of TNF-α, were constructed as high-affinity ligands for the immunosorbent. The theoretical maximum adsorption capacity estimated from the Langmuir isotherm was up to 18.22 mg/g gel. The prepared immunosorbent (NbC21-sorbent) had an outstanding TNF-α clearance ratio of approximately 96% during the dynamic adsorption test, with a sorbent-to-serum ratio of 1:1000. Additionally, it demonstrated favorable hemocompatibility and a prolonged storage capability. The results indicated that the biparatopic nanobody immunosorbent exhibited significant potential for clinical applications as it met the stringent criteria for both efficacy and safety.

Bait-trap chip for accurate and ultrasensitive capture of living circulating tumor cells.

Accurate analysis of living circulating tumor cells (CTCs) plays a crucial role in cancer diagnosis and prognosis evaluation. However, it is still challenging to develop a facile method for accurate, sensitive, and broad-spectrum isolation of living CTCs. Herein, inspired by the filopodia-extending behavior and clustered surface-biomarker of living CTCs, we present a unique bait-trap chip to achieve accurate and ultrasensitive capture of living CTCs from peripheral blood. The bait-trap chip is designed with the integration of nanocage (NCage) structure and branched aptamers. The NCage structure could "trap" the extended filopodia of living CTCs and resist the adhesion of filopodia-inhibited apoptotic cells, thus realizing the accurate capture (∼95% accuracy) of living CTCs independent of complex instruments. Using an in-situ rolling circle amplification (RCA) method, branched aptamers were easily modified onto the NCage structure, and served as "baits" to enhance the multi-interactions between CTC biomarker and chips, leading to ultrasensitive (99%) and reversible cell capture performance. The bait-trap chip successfully detects living CTCs in broad-spectrum cancer patients and achieves high diagnostic sensitivity (100%) and specificity (86%) of early prostate cancer. Therefore, our bait-trap chip provides a facile, accurate, and ultrasensitive strategy for living CTC isolation in clinical. STATEMENT OF SIGNIFICANCE: A unique bait-trap chip integrated with precise nanocage structure and branched aptamers was developed for the accurate and ultrasensitive capture of living CTCs. Compared with the current CTC isolation methods that are unable to distinguish CTC viability, the nanocage structure could not only "trap" the extended-filopodia of living CTCs, but also resist the adhesion of filopodia-inhibited apoptotic cells, thus realizing the accurate capture of living CTCs. Additionally, benefiting from the "bait-trap" synergistic effects generated by aptamer modification and nanocage structure, our chip achieved ultrasensitive, reversible capture of living CTCs. Moreover, this work provided a facile strategy for living CTC isolation from the blood of patients with early-stage and advanced cancer, exhibiting high consistency with the pathological diagnosis.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins.

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.