Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.

Gas phase detection of explosives is an ongoing trend in the detection sciences. The conception of gas phase detection devices requires knowledge about gas phase concentration of the target analytes. Nitrate esters are well performing explosives with a high potential for misuse in improvised explosive devices that need to be detected at vulnerable infrastructures. With respect to this the six nitrate esters, ethyl nitrate (1), ethylene glycol dinitrate (2), glycerol trinitrate (3), meso‐erythritol tetranitrate (4), d‐mannitol hexanitrate (5) and pentaerythritol tetranitrate (6) were investigated in terms of detectability by vacuum outlet‐GC/MS as potential components in improvised explosive devices. All compounds besides 5 could be detected using vacuum outlet GC/MS and their limits of detection were determined according to DIN 32645 : 2008. The vapor pressure of 2–4 was measured using the transpiration method. It was observed that the introduction of a CHONO2 unit lowers the vapor pressure of the nitrate esters by about two orders of magnitude. For compound 4 the saturation concentration (73 ng L−1) was compared with a vapor pressure based estimation of its concentration in diffusion equilibrium (0.385 pg L−1).

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Over the past decades, virus‐like particle (VLP)‐based gene therapy (GT) evolved as a promising approach to cure inherited diseases or cancer. Tremendous costs due to inefficient production processes remain one of the key challenges despite considerable efforts to improve titers. This review aims to link genome‐scale metabolic models (GSMMs) to cell lines used for VLP synthesis for the first time. We summarize recent advances and challenges of GSMMs for Chinese hamster ovary (CHO) cells and provide an overview of potential cell lines used in GT. Although GSMMs in CHO cells led to significant improvements in growth rates and recombinant protein (RP)‐production, no GSMM has been established for VLP production so far. To facilitate the generation of GSMM for these cell lines we further provide an overview of existing omics data and the highest production titers so far reported.