SUMMARY To identify FDA-approved agents targeting leukemic cells, we performed a chemical screen on two human leukemic cell lines and identified the antimicrobial tigecycline. A genome-wide screen in yeast identified mitochondrial translation inhibition as the mechanism of tigecycline-mediated lethality. Tigecycline selectively killed leukemia stem and progenitor cells compared to their normal counterparts and also showed anti-leukemic activity in mouse models of human leukemia. ShRNA-mediated knockdown of EF-Tu mitochondrial translation factor in leukemic cells reproduced the anti-leukemia activity of tigecycline. These effects were derivative of mitochondrial biogenesis which, together with an increased basal oxygen consumption, proved to be enhanced in AML versus normal hematopoietic cells and were also important for their difference in tigecycline sensitivity.

Summary From an shRNA screen, we identified ClpP as a member of the mitochondrial proteome whose knockdown reduced the viability of K562 leukemic cells. Expression of this mitochondrial protease that has structural similarity to the cytoplasmic proteosome is increased in the leukemic cells from approximately half of patients with AML. Genetic or chemical inhibition of ClpP killed cells from both human AML cell lines and primary samples in which the cells showed elevated ClpP expression, but did not affect their normal counterparts. Importantly, Clpp knockout mice were viable with normal hematopoiesis. Mechanistically, we found ClpP interacts with mitochondrial respiratory chain proteins and metabolic enzymes, and knockdown of ClpP in leukemic cells inhibited oxidative phosphorylation and mitochondrial metabolism.

An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R 0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of R t . In the first assumption, R t was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with R t = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively, by 29 February 2020. In the second assumption, R t was assumed to gradually decrease at different phases from high level of transmission (R t = 3.1, 2.6, and 1.9) to below 1 (R t = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077-84,520 or 55,869-81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce R t to an ideal level and control the infection.