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Background: Lincomycin, produced by Streptomyces lincolnensis, is a lincosamide antibiotic and widely used for the treatment of the infective diseases caused by Gram-positive bacteria. The mechanisms of lincomycin biosynthesis have been deeply explored in recent years. However, the regulatory effects of LmbU that is a transcriptional regulator in lincomycin biosynthetic (lmb) gene cluster have not been fully addressed. Results: LmbU was used to search for homologous LmbU (LmbU-like) proteins in the genomes of actinobacteria, and the results showed that LmbU-like proteins are highly distributed regulators in the biosynthetic gene clusters (BGCs) of secondary metabolites or/and out of the BGCs in actinomycetes. The overexpression, inactivation and complementation of the lmbU gene indicated that LmbU positively controls lincomycin biosynthesis in S. lincolnensis. Comparative transcriptomic analysis further revealed that LmbU activates the 28 lmb genes at whole lmb cluster manner. Furthermore, LmbU represses the transcription of the non-lmb gene hpdA in the biosynthesis of l-tyrosine, the precursor of lincomycin. LmbU up-regulates nineteen non-lmb genes, which would be involved in multi-drug flux to self-resistance, nitrate and sugar transmembrane transport and utilization, and redox metabolisms. Conclusions: LmbU is a significant pleiotropic transcriptional regulator in lincomycin biosynthesis by entirely activating the lmb cluster and regulating the non-lmb genes in Streptomyces lincolnensis. Our results first revealed the pleiotropic regulatory function of LmbU, and shed new light on the transcriptional effects of LmbU-like family proteins on antibiotic biosynthesis in actinomycetes. [ABSTRACT FROM AUTHOR]

Due to the outbreak of coronavirus disease 2019 (COVID-19), the Chinese government implemented strict lockdown measures to control the spread of infection. The impact of the COVID-19 lockdown on eating habits and lifestyles in the general population is unclear. This cross-sectional study was conducted via an online survey to obtain an overview of the food access, food intake, and physical activity of Chinese residents during the initial stage of the COVID-19 lockdown, and to investigate the association between staying at home/working from home and changes in eating habits and lifestyles. A total of 2702 participants (70.7% women) were included. Most of the participants maintained their habitual diet, while 38.2% increased their snack intake, 54.3% reported reduced physical activity, and 45.5% had increased sleep duration. Most people (70.1%) reported no change in body weight, while 25.0% reported an increase. Always staying at home/working from home was associated with an increase in animal product, vegetable, fruit, mushroom, nut, water, and snack intake, as well as sleep duration and frequency of skipping breakfast (odds ratio (OR) 1.54, 1.62, 1.58, 1.53, 1.57, 1.52, 1.77, 2.29, and 1.76 respectively). Suggestions should be made to encourage people to reduce their snack intake, maintain the daily consumption of breakfast, and increase physical activity during future lockdown periods. [ABSTRACT FROM AUTHOR]

Background: With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline.Results: Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.Conclusion: PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html. [ABSTRACT FROM AUTHOR]