Species of the genus Streptomyces are known for their ability to produce multiple secondary metabolites; their genomes have been extensively explored to discover new bioactive compounds. The richness of genomic data currently available allows filtering for high quality genomes, which in turn permits reliable comparative genomics studies and an improved prediction of biosynthetic gene clusters (BGCs) through genome mining approaches. In this work, we used 121 genome sequences of the genus Streptomyces in a comparative genomics study with the aim of estimating the genomic diversity by protein domains content, sequence similarity of proteins and conservation of Intergenic Regions (IGRs). We also searched for BGCs but prioritizing those with potential antibiotic activity. Our analysis revealed that the pan-genome of the genus Streptomyces is clearly open, with a high quantity of unique gene families across the different species and that the IGRs are rarely conserved. We also described the phylogenetic relationships of the analyzed genomes using multiple markers, obtaining a trustworthy tree whose relationships were further validated by Average Nucleotide Identity (ANI) calculations. Finally, 33 biosynthetic gene clusters were detected to have potential antibiotic activity and a predicted mode of action, which might serve up as a guide to formulation of related experimental studies.

Clavulanic acid (CA) is a potent inhibitor of class A β-lactamase enzymes produced by Streptomyces clavuligerus (S. clavuligerus) as a defense mechanism. Due to its industrial interest, the process optimization is under continuous investigation. This work aimed at identifying the potential relationship that might exist between S. clavuligerus ATCC 27064 morphology and CA biosynthesis. For this, modified culture conditions such as source, size, and age of inoculum, culture media, and geometry of fermentation flasks were tested. We observed that high density spore suspensions (1 × 107 spores/mL) represent the best inoculum source for S. clavuligerus cell suspension culture. Further, we studied the life cycle of S. clavuligerus in liquid medium, using optic, confocal, and electron microscopy; results allowed us to observe a potential relationship that might exist between the accumulation of CA and the morphology of disperse hyphae. Reactor geometries that increase shear stress promote smaller pellets and a quick disintegration of these in dispersed secondary mycelia, which begins the pseudosporulation process, thus easing CA accumulation. These outcomes greatly contribute to improving the understanding of antibiotic biosynthesis in the Streptomyces genus.

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The performance of software tools for de novo transcriptome assembly greatly depends on the selection of software parameters. Up to now, the development of de novo transcriptome assembly for prokaryotes has not been as remarkable as that for eukaryotes. In this contribution, Rockhopper2 was used to perform a comparative transcriptome analysis of Streptomyces clavuligerus exposed to diverse environmental conditions. The study focused on assessing the incidence of software parameters on software performance for the identification of differentially expressed genes as a final goal. For this, a statistical optimization was performed using the Transrate Assembly Score (TAS). TAS was also used for evaluating the software performance and for comparing it with related tools, e.g., Trinity. Transcriptome redundancy and completeness were also considered for this analysis. Rockhopper2 and Trinity reached a TAS value of 0.55092 and 0.58337, respectively. Trinity assembles transcriptomes with high redundancy, with 55.6% of transcripts having some duplicates. Additionally, we observed that the total number of differentially expressed genes (DEG) and their annotation greatly depends on the method used for removing redundancy and the tools used for transcript quantification. To our knowledge, this is the first work aimed at assessing de novo assembly software for prokaryotic organisms. complete transcription map [1]. De novo transcriptome assembly is necessary for organisms whose genomes have been neither sequenced nor annotated, e.g., for non-model organisms, when analyzing complex microbial communities, in meta-transcriptome studies, or while investigating uncultivable microorganisms [5][6][7]. Many software tools have been developed to assemble transcriptomes using the de novo strategy. The most commonly used are: Trinity [3,8], Oases [6], Bridger [9], SOAPdenovo-Trans [10], IDBA-Trans [11], SSP [12], Shannon [13], BinPacker [14] and Rockhopper2 [5].De novo assembly is very sensitive to software parameters due to the lack of a genome to guide the assembly and the type of algorithms used which are mostly based on the De Bruijn graphs. Thus, they depend on the k-mer length [14] or on the minimum k-mer coverage [3]. Moreover, the consistency and biological relevance of the data, obtained from different sources, make it challenging to select the most accurate assembly [15,16], and the same data can generate substantially different assemblies, both within and between assembly methods, affecting the biological analysis and its conclusions [17]. In this regard, some authors have undertaken the task of evaluating the impact of different methodologies and software configurations on the quality of the assembled transcriptome [18][19][20][21][22].Different software are available for evaluating the quality of a de novo assembly, e.g., SCAN [23], rnaQUAST [24], DETONATE [16], Transrate [17] and recently, a topology-based method called Branching Measure [25]. For the case of Transrate, it renders a Transrate Assembly Score (TAS) that allow...

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