Frontiers in Microbiology
About: Frontiers in Microbiology is an academic journal published by Frontiers Media. The journal publishes majorly in the area(s): Medicine & Biology. It has an ISSN identifier of 1664-302X. It is also open access. Over the lifetime, 29650 publications have been published receiving 756459 citations.
TL;DR: Interaction of VeA with at least four methyltransferase proteins indicates a molecular hub function for VeA that questions: Is there a VeA supercomplex or is VeA part of a highly dynamic cellular control network with many different partners?
Abstract: Fungal secondary metabolism has become an important research topic with great biomedical and biotechnological value. In the postgenomic era, understanding the diversity and the molecular control of secondary metabolites are two challenging tasks addressed by the research community. Discovery of the LaeA methyltransferase 10 years ago opened up a new horizon on the control of secondary metabolite research when it was found that expression of many secondary metabolite gene clusters is controlled by LaeA. While the molecular function of LaeA remains an enigma, discovery of the velvet family proteins as interaction partners further extended the role of the LaeA beyond secondary metabolism. The heterotrimeric VelB-VeA-LaeA complex plays important roles in development, sporulation, secondary metabolism and pathogenicity. Recently, three other methyltransferases have been found to associate with the velvet complex, the LaeA-like methyltransferase F (LlmF) and the methyltransferase heterodimers VipC-VapB. Interaction of VeA with at least four methyltransferase proteins indicates a molecular hub function for VeA that questions: Is there a VeA supercomplex or is VeA part of a highly dynamic cellular control network with many different partners?
TL;DR: The study of antibiotic resistance has been historically concentrated on the analysis of bacterial pathogens and on the consequences of acquiring resistance for human health, but the studies on antibiotic resistance should not be confined to clinical-associated ecosystems.
Abstract: Work in our laboratory is supported by grants BIO2008-00090 from the Spanish Ministry of Science and Innovation and KBBE-227258 (BIOHYPO), HEALTH-F3-2011-282004 (EVOTAR), and HEALTH-F3-2010-241476 (PAR) from European Union.
TL;DR: The different approaches for the synthesis of recombinant proteins in E. coli are reviewed and recent progress in this ever-growing field is discussed.
Abstract: Escherichia coli is the organism of choice for the production of recombinant proteins. Its use as a cell factory is well-established and it has become the most popular expression platform. For this reason, there are many molecular tools and protocols at hand for the high-level production of recombinant proteins, such as a vast catalog of expression plasmids, a great number of engineered strains and many cultivation strategies. We review the different approaches for the synthesis of recombinant proteins in E. coli and discuss recent progress in this ever-growing field.
TL;DR: It is shown that propane metabolism generated terminal and sub-terminal oxidation products such as 1- and 2-propanol, whereas 1-butanol was the only terminal oxidation product detected from n-butane metabolism.
Abstract: Rhodococcus sp. strain BCP1 was initially isolated for its ability to grow on gaseous n-alkanes, which act as inducers for the co-metabolic degradation of low-chlorinated compounds. Here, both molecular and metabolic features of BCP1 cells grown on gaseous and short-chain n-alkanes (up to n-heptane) were examined in detail. We show that propane metabolism generated terminal and sub-terminal oxidation products such as 1- and 2-propanol, whereas 1-butanol was the only terminal oxidation product detected from butane metabolism. Two gene clusters, prmABCD and smoABCD – coding for soluble di-iron monooxgenases (SDIMOs) involved in gaseous n-alkanes oxidation – were detected in the BCP1 genome. By means of reverse transcriptase-quantitative PCR (RT-qPCR) analysis, a set of substrates inducing the expression of the sdimo genes in BCP1 were assessed as well as their transcriptional repression in the presence of sugars, organic acids or during the cell growth on rich medium (Luria Bertani broth). The transcriptional start sites of both the sdimo gene clusters were identified by means of primer extension experiments. Finally, proteomic studies revealed changes in the protein pattern induced by growth on gaseous- (n-butane) and/or liquid (n-hexane) short-chain n-alkanes as compared to growth on succinate. Among the differently expressed protein spots, two chaperonins and an isocytrate lyase were identified along with oxidoreductases involved in oxidation reactions downstream of the initial monooxygenase reaction step.
TL;DR: The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis.
Abstract: Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.