Mycobacterium versus Streptomyces - we are different, we are the same. Curr Opinion Microbiol

Department of Molecular Biology and Microbiology, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA.
Current opinion in microbiology (Impact Factor: 5.9). 10/2009; 12(6):699-707. DOI: 10.1016/j.mib.2009.10.003
Source: PubMed


At first glance, bacteria that belong to the two genera Streptomyces and Mycobacterium of the phylum Actinobacteria show no sign of similarity. Whereas Streptomyces species are generally classified as spore-forming, filamentous bacteria, species of the Mycobacterium genus have been considered non-sporulating, rod-like shaped. However, recent studies in genetics and cell biology of Streptomyces and Mycobacterium have revealed striking analogies in the developmental and morphological hallmarks of their life cycles. Understanding the mechanisms underlying these similarities, as well as variations in morphogenesis and development of these two groups of bacteria may not only provide important insights in the evolution of cell shapes in Actinobacteria, but also lead to medical interventions that impact human health.

Download full-text


Available from: Liem Nguyen, Oct 04, 2015
58 Reads
  • Source
    • "Generally, comparative modelling as described here could lead to a more systematic approach towards the identification of suitable “universal hosts” for heterologous expression of gene clusters [47]–[49]. Specifically, this preliminary analysis already suggests that free-living mycobacteria might be an attractive starting point for the generation of a minimal actinobacterial genome for use in synthetic biology approaches [45], [46], especially as all three of them belong to the fast-growing mycobacteria. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from
    PLoS ONE 12/2012; 7(12):e51511. DOI:10.1371/journal.pone.0051511 · 3.23 Impact Factor
  • Source
    • "Strains of Streptomyces sp. are well known as good producers of extracellular recombinant proteins; these strains also have the ability to glycosylate their own proteins, as well as heterologous proteins [15-22]. Recent studies in genetics and cell biology have revealed analogies between Streptomyces sp. and Mycobacterium sp., both belonging to the phylum Actinobacteria [23]. Moreover, S. lividans allows the production, both in shake flasks [15] and in bioreactor [24], of large amounts of glycosylated rAPA suitable for biochemical studies and immunological assays. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The Ala-Pro-rich O-glycoprotein known as the 45/47 kDa or APA antigen from Mycobacterium tuberculosis is an immunodominant adhesin restricted to mycobacterium genus and has been proposed as an alternative candidate to generate a new vaccine against tuberculosis or for diagnosis kits. In this work, the recombinant O-glycoprotein APA was produced by the non-pathogenic filamentous bacteria Streptomyces lividans, evaluating three different culture conditions. This strain is known for its ability to produce heterologous proteins in a shorter time compared to M. tuberculosis. Three different shake flask geometries were used to provide different shear and oxygenation conditions; and the impact of those conditions on the morphology of S. lividans and the production of rAPA was characterized and evaluated. Small unbranched free filaments and mycelial clumps were found in baffled and coiled shake flasks, but one order of magnitude larger pellets were found in conventional shake flasks. The production of rAPA is around 3 times higher in small mycelia than in larger pellets, most probably due to difficulties in mass transfer inside pellets. Moreover, there are four putative sites of O-mannosylation in native APA, one of which is located at the carboxy-terminal region. The carbohydrate composition of this site was determined for rAPA by mass spectrometry analysis, and was found to contain different glycoforms depending on culture conditions. Up to two mannoses residues were found in cultures carried out in conventional shake flasks, and up to five mannoses residues were determined in coiled and baffled shake flasks. The shear and/or oxygenation parameters determine the bacterial morphology, the productivity, and the O-mannosylation of rAPA in S. lividans. As demonstrated here, culture conditions have to be carefully controlled in order to obtain recombinant O-glycosylated proteins with similar "quality" in bacteria, particularly, if the protein activity depends on the glycosylation pattern. Furthermore, it will be an interesting exercise to determine the effect of shear and oxygen in shake flasks, to obtain evidences that may be useful in scaling-up these processes to bioreactors. Another approach will be using lab-scale bioreactors under well-controlled conditions, and study the impact of those on rAPA productivity and quality.
    Microbial Cell Factories 12/2011; 10(1):110. DOI:10.1186/1475-2859-10-110 · 4.22 Impact Factor
  • Source
    • "Compared to, for example B. subtilis spores, those of Streptomyces are less resistant (Flärdh and Buttner 2009). In a recent review Scherr and Nguyen (2009) discussed the similarities and differences between Mycobacterium and Streptomyces (Scherr and Nguyen 2009). Other actinomycetes bacteria produce non-motile spores without forming aerial hyphaes (Asano and Kawamoto 1986; Ara and Kudo 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Bacteria have the ability to adapt to different growth conditions and to survive in various environments. They have also the capacity to enter into dormant states and some bacteria form spores when exposed to stresses such as starvation and oxygen deprivation. Sporulation has been demonstrated in a number of different bacteria but Mycobacterium spp. have been considered to be non-sporulating bacteria. We recently provided evidence that Mycobacterium marinum and likely also Mycobacterium bovis bacillus Calmette-Guérin can form spores. Mycobacterial spores were detected in old cultures and our findings suggest that sporulation might be an adaptation of lifestyle for mycobacteria under stress. Here we will discuss our current understanding of growth, cell division, and sporulation in mycobacteria.
    Antonie van Leeuwenhoek 05/2010; 98(2):165-77. DOI:10.1007/s10482-010-9446-0 · 1.81 Impact Factor
Show more