Fu Linglong,Sun Mengning,Wen Weizhang,Dong Na,Li Defa
Abstract Small ubiquitin-like modifier (SUMO) tag is widely used to promote soluble expression of exogenous proteins, which can then be cleaved by ubiquitin-like protease 1 (Ulp1) to obtain interested protein. But the application of Ulp1 in large-scale recombinant protein production is limited by complicated purification procedures and high cost. In this study, we describe an efficient and simple method of extracellular production of Ulp1 403-621 using a leaky Escherichia coli BL21(DE3), engineered by deleting the peptidoglycan-associated outer membrane lipoprotein ( pal ) gene. Ulp1 403-621 was successfully leaked into extracellular supernatant by the BL21(DE3)-Δ pal strain after IPTG induction. The addition of 1% glycine increased the extracellular production of Ulp1 403-621 approximately four fold. Moreover, extracellular Ulp1 403-621 without purification had high activities for cleaving SUMO fusion proteins, and antimicrobial peptide pBD2 obtained after cleavage can inhibit the growth of Staphylococcus aureus . The specific activity of extracellular Ulp1 403-621 containing 1 mM EDTA and 8 mM DTT reached 2.0 × 10 6 U/L. Another commonly used protease, human rhinovirus 3C protease, was also successfully secreted by leaky E. coli strains. In conclusion, extracellular production of tool enzymes is an attractive way for producing large-scale active recombinant proteins at a lower cost for pharmaceutical, industrial, and biotechnological applications. Key points
• First report of extracellular production of Ulp1
403-621
in leaky Escherichia coli BL21(DE3) strain.
• One percent glycine addition into cultivation medium increased the extracellular production of Ulp1
403-621
approximately four fold.
• The specific activity of extracellular Ulp1
403-621
produced in this study reached 2.0 × 10
6
U/L.
On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%–93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.
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