To evaluate the expression patterns of genes involved in iron and oxygen metabolism during magnetosome formation, the profiles of 13 key genes in Magnetospirillum gryphiswaldense MSR-1 cells cultured under high-iron vs. low-iron conditions were examined. Cell growth rates did not differ between the two conditions. Only the high-iron cells produced magnetosomes. Transmission electron microscopy observations revealed that magnetosome formation began at 6 h and crystal maturation occurred from 10 to 18 h. Real-time polymerase chain reaction analysis showed that expression of these genes increased during cell growth and magnetosome synthesis, particularly for ferric reductase gene (fer6) and ferrous transport system-related genes feoAB1, feoAB2, sodB, and katG. The low-iron cells showed increased expression of feoAB1 and feoB2 from 12 to 18 h but no clear expression changes for the other genes. Expression patterns of the genes were divided by hierarchical clustering into four clusters for the high-iron cells and three clusters for the low-iron cells. Each cluster included both iron and oxygen metabolism genes showing similar expression patterns. The findings indicate the coordination and co-dependence of iron and oxygen metabolism gene activity to achieve a balance during the biomineralization process. Future transcriptome analysis will help elucidate the mechanism of biomineralization in MSR-1 magnetosome formation.

Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC 50) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC 50 values beyond the drug safety window.