Erythromycin A is a potent antibiotic long-recognized as a therapeutic option for bacterial infections. The soil-dwelling bacterium Saccharopolyspora erythraea natively produces erythromycin A from a 55 kb gene cluster composed of three large polyketide synthase genes (each ~10 kb) and 17 additional genes responsible for deoxysugar biosynthesis, macrolide tailoring, and resistance. In this study, the erythromycin A gene cluster was systematically transferred from S. erythraea to E. coli for reconstituted biosynthesis, with titers reaching 10 mg/l. Polyketide biosynthesis was then modified to allow the production of two erythromycin analogs. Success establishes E. coli as a viable option for the heterologous production of erythromycin A and more broadly as a platform for the directed production of erythromycin analogs.
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The utility of cycle threshold, at admission, as a predictor of disease severity has not been thoroughly investigated. Methods and findings We conducted a retrospective study of SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS-CoV-2 Ct data within 48 hours of admission (n = 1044). Only patients with complete survival data, discharged (n = 774) or died in hospital (n = 270), were included in our analysis. Laboratory, demographic, and clinical data were extracted from electronic medical records. Multivariable logistic regression was applied to examine the relationship of patient mortality with Ct values while adjusting for established risk factors. Ct was analyzed as continuous variable and subdivided into quartiles to better illustrate its relationship with outcome. Cumulative incidence curves were created to assess whether there was a survival difference in the setting of the competing risks of death versus patient discharge. Mean Ct at admission was higher for survivors (28.6, SD = 5.8) compared to non-survivors (24.8, SD = 6.0, P<0.001). In-hospital mortality significantly differed (p<0.05) by Ct quartile. After adjusting for age, gender, BMI, hypertension and diabetes, increased cycle threshold was associated with decreased odds of in-hospital mortality (0.91, CI 0.89–0.94, p<0.001). Compared to the 4th Quartile, patients with Ct values in the 1st Quartile (Ct <22.9) and 2nd Quartile (Ct 23.0–27.3) had an adjusted odds ratio of in-hospital mortality of 3.8 and 2.6 respectively (p<0.001). The discriminative ability of Ct to predict inpatient mortality was found to be limited, possessing an area under the curve (AUC) of 0.68 (CI 0.63–0.71). Conclusion SARS-CoV-2 Ct was found to be an independent predictor of patient mortality. However, further study is needed on how to best clinically utilize such information given the result variation due to specimen quality, phase of disease, and the limited discriminative ability of the test.
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