Demands for faster and more accurate methods to analyze microbial communities from natural and clinical samples have been increasing in the medical and healthcare industry. Recent advances in next-generation sequencing technologies have facilitated the elucidation of the microbial community composition with higher accuracy and greater throughput than was previously achievable; however, the short sequencing reads often limit the microbial composition analysis at the species level due to the high similarity of 16S rRNA amplicon sequences. To overcome this limitation, we used the nanopore sequencing platform to sequence full-length 16S rRNA amplicon libraries prepared from the mouse gut microbiota. A comparison of the nanopore and short-read sequencing data showed that there were no significant differences in major taxonomic units (89%) except one phylotype and three taxonomic units. Moreover, both sequencing data were highly similar at all taxonomic resolutions except the species level. At the species level, nanopore sequencing allowed identification of more species than short-read sequencing, facilitating the accurate classification of the bacterial community composition. Therefore, this method of full-length 16S rRNA amplicon sequencing will be useful for rapid, accurate and efficient detection of microbial diversity in various biological and clinical samples.

Individual Streptomyces species have the genetic potential to produce a diverse array of natural products of commercial, medical and veterinary interest. However, these products are often not detectable under laboratory culture conditions. To harness their full biosynthetic potential, it is important to develop a detailed understanding of the regulatory networks that orchestrate their metabolism. Here we integrate nucleotide resolution genome-scale measurements of the transcriptome and translatome of Streptomyces coelicolor, the model antibiotic-producing actinomycete. Our systematic study determines 3,570 transcription start sites and identifies 230 small RNAs and a considerable proportion (∼21%) of leaderless mRNAs; this enables deduction of genome-wide promoter architecture. Ribosome profiling reveals that the translation efficiency of secondary metabolic genes is negatively correlated with transcription and that several key antibiotic regulatory genes are translationally induced at transition growth phase. These findings might facilitate the design of new approaches to antibiotic discovery and development.

Streptomyces are a large and valuable resource of bioactive and complex secondary metabolites, many of which have important clinical applications. With the advances in high throughput genome sequencing methods, various in silico genome mining strategies have been developed and applied to the mapping of the Streptomyces genome. These studies have revealed that Streptomyces possess an even more significant number of uncharacterized silent secondary metabolite biosynthetic gene clusters (smBGCs) than previously estimated. Linking smBGCs to their encoded products has played a critical role in the discovery of novel secondary metabolites, as well as, knowledge-based engineering of smBGCs to produce altered products. In this mini review, we discuss recent progress in Streptomyces genome sequencing and the application of genome mining approaches to identify and characterize smBGCs. Furthermore, we discuss several challenges that need to be overcome to accelerate the genome mining process and ultimately support the discovery of novel bioactive compounds.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain’s systems biology.

Among CO ₂ -fixing metabolic pathways in nature, the linear Wood–Ljungdahl pathway (WLP) in phylogenetically diverse acetate-forming acetogens comprises the most energetically efficient pathway, requires the least number of reactions, and converts CO ₂ to formate and then into acetyl-CoA. Despite two genes encoding glycine synthase being well-conserved in WLP gene clusters, the functional role of glycine synthase under autotrophic growth conditions has remained uncertain. Here, using the reconstructed genome-scale metabolic model i SL771 based on the completed genome sequence, transcriptomics, ¹³ C isotope-based metabolite-tracing experiments, biochemical assays, and heterologous expression of the pathway in another acetogen, we discovered that the WLP and the glycine synthase pathway are functionally interconnected to fix CO ₂ , subsequently converting CO ₂ into acetyl-CoA, acetyl-phosphate, and serine. Moreover, the functional cooperation of the pathways enhances CO ₂ consumption and cellular growth rates via bypassing reducing power required reactions for cellular metabolism during autotrophic growth of acetogens.