Simplified captopril analogues as NDM-1 inhibitors.

Captopril is a New Delhi metallo-ß-lactamase-1 (NDM-1) inhibitor with an IC50 value of 7.9µM. It is composed of two units: a 3-mercapto-2-methylpropanoyl fragment and a proline residue. In this study, we synthesized simple amide derivatives of 3-mercapto-2-methylpropanoic acid, and then tested them as NDM-1 inhibitors in order to identify the pharmacophore for NDM-1 inhibition. We found that the lead compound 22 had an IC50 value of 1.0µM. Further structure simplification provided compounds 31 and 32, which had IC50 values of 15 and 10µM, respectively. As compound 32 is a clinically used antidote for metal poisoning, it has great potential to be repurposed to treat bacterial infections.

Refined spatial temporal epigenomic profiling reveals intrinsic connection between PRDM9-mediated H3K4me3 and the fate of double-stranded breaks.

Meiotic recombination is initiated by the formation of double-strand breaks (DSBs), which are repaired as either crossovers (COs) or noncrossovers (NCOs). In most mammals, PRDM9-mediated H3K4me3 controls the nonrandom distribution of DSBs; however, both the timing and mechanism of DSB fate control remain largely undetermined. Here, we generated comprehensive epigenomic profiles of synchronized mouse spermatogenic cells during meiotic prophase I, revealing spatiotemporal and functional relationships between epigenetic factors and meiotic recombination. We find that PRDM9-mediated H3K4me3 at DSB hotspots, coinciding with H3K27ac and H3K36me3, is intimately connected with the fate of the DSB. Our data suggest that the fate decision is likely made at the time of DSB formation: earlier formed DSBs occupy more open chromatins and are much more competent to proceed to a CO fate. Our work highlights an intrinsic connection between PRDM9-mediated H3K4me3 and the fate decision of DSBs, and provides new insight into the control of CO homeostasis.

Single-cell RNA-seq uncovers dynamic processes and critical regulators in mouse spermatogenesis.

A systematic interrogation of male germ cells is key to complete understanding of molecular mechanisms governing spermatogenesis and the development of new strategies for infertility therapies and male contraception. Here we develop an approach to purify all types of homogeneous spermatogenic cells by combining transgenic labeling and synchronization of the cycle of the seminiferous epithelium, and subsequent single-cell RNA-sequencing. We reveal extensive and previously uncharacterized dynamic processes and molecular signatures in gene expression, as well as specific patterns of alternative splicing, and novel regulators for specific stages of male germ cell development. Our transcriptomics analyses led us to discover discriminative markers for isolating round spermatids at specific stages, and different embryo developmental potentials between early and late stage spermatids, providing evidence that maturation of round spermatids impacts on embryo development. This work provides valuable insights into mammalian spermatogenesis, and a comprehensive resource for future studies towards the complete elucidation of gametogenesis.

Redescription of Oedichirus flammeus Koch, and description of two new Oedichirus species from China (Coleoptera, Staphylinidae, Paederinae, Pinophilini).

Oedichirus flammeus Koch, 1939 (Zhejiang, East China), O. damingensis sp. n. (Guangxi, South China), and O. pengzhongi sp. n. (Hainan, South China) are (re-)described and illustrated. The genus is now represented in China by eight described species, six of them are micropterous. The latest key to the Oedichirus species of China is modified to include the new species. 

Lung cancer risk prediction method based on feature selection and artificial neural network.

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.