Search

Abstract Recombinant proteins are gaining increasing popularity for treating human diseases. The clinical effectiveness of recombinant proteins is directly related to their biological activity, which is an important indicator in drug development and quality control. However, certain recombinant proteins have unclear or complex signal pathways, making detecting their activity in vitro difficult. For instance, recombinant human endostatin (endostatin), a new antitumor drug developed in China, lacks a sensitive and stable assay for its biological activity since being market approval. To address this issue, we performed a genome‐wide screening of immortalized human umbilical vein endothelial cells (HUVECs) using a CRISPR/Cas9 knockout library containing 20,000 targeted genes. We identified two potential endostatin‐resistant genes, NEPSPP and UTS2, and successfully constructed a highly sensitive cell line, HUVEC‐UTS2‐3#, by knocking down the UTS2 gene. Based on the optimized parameters of HUVEC‐UTS2‐3# cells, we established a new method for detecting the biological activity of endostatin. The method was validated, and it produced results consistent with primary HUVEC cells but with higher sensitivity and more stable data. The use of gene‐editing technology provides a novel solution for detecting the biological activity of recombinant proteins that other methods cannot detect.

Owing to the outbreak of the novel coronavirus (SARS-CoV-2) worldwide at the end of 2019, the development of a SARS-CoV-2 vaccine became an urgent need. In this study, we developed a type 9 adeno-associated virus vectored vaccine candidate expressing a dimeric receptor binding domain (RBD) of the SARS-CoV-2 spike protein (S protein) and evaluated its immunogenicity in a murine model. The vaccine candidate, named AAV9-RBD virus, was constructed by inserting a signal peptide to the N-terminus of two copies of RBD, spaced by a linker, into the genome of a type 9 adeno-associated virus. In vitro assays showed that HeLa cells infected by the recombinant AAV virus expressed high levels of the recombinant RBD protein, mostly found in the cell culture supernatant. The recombinant AAV9-RBD virus was cultured and purified. The genome titer of the purified recombinant AAV9-RBD virus was determined to be 2.4 × 1013 genome copies/mL (GC/mL) by Q-PCR. Balb/c mice were immunized with the virus by intramuscular injection or nasal drip administration. Eight weeks after immunization, neutralizing antibodies against the new coronavirus pseudovirus were detected in the sera of all mice; the mean neutralizing antibody EC50 values were 517.7 ± 292.1 (n=10) and 682.8 ± 454.0 (n=10) in the intramuscular injection group and nasal drip group, respectively. The results of this study showed that the recombinant AAV9-RBD virus may be used for the development of a SARS-CoV-2 vaccine.

Accurate determination of biological activity is essential in quality control of recombinant human brain natriuretic peptide (rhBNP). In previous study, we successfully developed a genetically modified cell line 293GCAC3-based ELISA assay for rhBNP. But ELISA procedure is still tedious, so this study was aimed to develop a rapid and simple bioassay for rhBNP using GloSensor technology, which provides a platform of flexible luciferase-based biosensors for real-time detection of signaling events in live cells, including cGMP production. A reporter cell line 293GCAGlo-G1 was constructed by transfecting pGloSensor™ 40 F plasmid into 293GCAC3. The reporter assay based on 293GCAGlo-G1 showed high precision with intra-assay CV being 8.3% and inter-assay CV being 14.1%; high accuracy with 80%, 100% and 120% recovery rate being 99.2%, 102.4% and 99.0% respectively; and great linearity with R2 of linear fitting equation being 0.99. Besides, no significant difference was found in test results of reporter assay and 293GCAC3-based ELISA assay (paired t test, p = 0.630). All these results suggested that the reporter assay was a viable assay for biological determination of rhBNP. Keywords: RhBNP, cGMP, GloSensor technology, Reporter assay

The long-acting growth hormone (LAGH) is a promising alternative biopharmaceutical to treat growth hormone (GH) deficiency in children, and it was developed using a variety of technologies by several pharmaceutical companies. Most LAGH preparations, such as Fc fusion protein, are currently undergoing preclinical study and clinical trials. Accurate determination of bioactivity is critical for the efficacy of quality control systems of LAGH. The current in vivo rat weight gain assays used to determine the bioactivity of recombinant human GH (rhGH) in pharmacopoeias are time-consuming, expensive, and imprecise, and there are no recommended bioassays for LAGH bioactivity in pharmacopoeias. Therefore, we developed a cell-based bioassay for bioactivity determination of therapeutic long-acting Fc-fusion recombinant human growth hormone (rhGH-Fc) based on the luciferase reporter gene system, which is involved in the full-length human GH receptor (hGHR) and the SG (SIE and GAS) response element. The established bioassay was comprehensively validated according to the International Council for Harmonization (ICH) Q2 (R1) guidelines and the Chinese Pharmacopoeia, and is highly precise, time-saving, simple, and robust. The validated bioassay could be qualified for bioactivity determination during the research, development, and manufacture of rhGH-Fc, and other LAGH formulations.

The therapeutic recombinant human keratinocyte growth factor 1 (rhKGF-1) was approved by the FDA for oral mucositis resulting from hematopoietic stem cell transplantation for hematological malignancies in 2004. However, no recommended bioassay for rhKGF-1 bioactivity has been recorded in the U.S. Pharmacopoeia. In this study, we developed an rhKGF-1-dependent bioassay for determining rhKGF-1 bioactivity based on HEK293 and HaCat cell lines that stably expressed the luciferase reporter driven by the serum response element (SRE) and human fibroblast growth factor receptor (FGFR2) IIIb. A good responsiveness to rhKGF-1 and rhKGF-2 shared by target HEK293/HaCat cell lines was demonstrated. Our stringent validation was completely focused on specificity, linearity, accuracy, precision, and robustness according to the International Council for Harmonization (ICH) Q2 (R1) guidelines, AAPS/FDA Bioanalytical Workshop and the Chinese Pharmacopoeia. We confirmed the reliability of the method in determining rhKGF bioactivity. The validated method is highly timesaving, sensitive, and simple, and is especially valuable for providing information for quality control during the manufacture, research, and development of therapeutic rhKGF.

BACKGROUND: Recombinant human brain natriuretic peptide (rhBNP) is an important peptide-based therapeutic drug indicated for the treatment of acute heart failure. Accurate determination of the potency of therapeutic rhBNP is crucial for the safety and efficacy of the drug. The current bioassay involves use of rabbit aortic strips, with experiments being complicated and time-consuming and markedly variable in results. Animal-less methods with better precision and accuracy should be explored. We have therefore developed an alternative cell-based assay, which relies on the ability of BNP to induce cGMP production in HEK293 cells expressing BNP receptor guanylyl cyclase-A. METHODOLOGY/PRINCIPAL FINDINGS: An alternative assay based on the measurement of BNP-induced cGMP production was developed. Specifically, the bioassay employs cells engineered to express BNP receptor guanylyl cyclase-A (GCA). Upon rhBNP stimulation, the levels of the second messager cGMP in these cells drastically increased and subsequently secreted into culture supernatants. The quantity of cGMP, which corresponds to the rhBNP activity, was determined using a competitive ELISA developed by us. Compared with the traditional assay, the novel cell-based assay demonstrated better reproducibility and precision. CONCLUSION/SIGNIFICANCE: The optimized cell-based assay is much simpler, more rapid and precise compared with the traditional assay using animal tissues. To our knowledge, this is the first report on a novel and viable alternative assay for rhBNP potency analysis.

As human uricase has been silenced during evolution, counterparts from other species become an alternative for the treatment of hyperuricemia. Candida uricase is a promising option among them, but its aggregation propensity remains a major obstacle to clinical use. In this study, we designed two mutations according to homology-modeled 3D structure of Candida uricase: Cys249Ser substitution and C-terminal Leu deletion. The wild-type uricase and three mutants containing either or both of the mutations were expressed in Escherichia coli BL21 and validated by mass spectrometry. Size-exclusion chromatography and electrophoresis analysis demonstrated that aggregation was induced by interchain disulfide bonds and could be significantly avoided by Cys249Ser substitution. In combination with Cys249Ser substitution, deletion of Leu increased the enzymatic activity by 8%. Taken together, mutant containing both mutations is chosen as our target protein which is comparatively more suitable for therapeutic use. In addition, homology-modeled 3D structure was proved to be an efficient approach for protein engineering. [ABSTRACT FROM AUTHOR]

With the increasing number of remote sensing satellites, the diversification of observation modals, and the continuous advancement of artificial intelligence algorithms, historically opportunities have been brought to the applications of earth observation and information retrieval, including climate change monitoring, natural resource investigation, ecological environment protection, and territorial space planning. Over the past decade, artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation. Therefore, this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth's surface. Firstly, it introduces the basic framework of semantic segmentation network models for land use/land cover mapping. Then, we summarize the development of semantic segmentation models in geographical field, focusing on spatial and semantic feature extraction, context relationship perception, multi-scale effects modelling, and the transferability of models under geographical differences. Then, the application of semantic segmentation models in agricultural management, building boundary extraction, single tree segmentation and inter-species classification are reviewed. Finally, we discuss the future development prospects of deep learning technology in the context of remote sensing big data.