The role of the mesenchymal stromal cell- (MSC-) derived secretome is becoming increasingly intriguing from a clinical perspective due to its ability to stimulate endogenous tissue repair processes as well as its effective regulation of the immune system, mimicking the therapeutic effects produced by the MSCs. The secretome is a composite product secreted by MSC in vitro (in conditioned medium) and in vivo (in the extracellular milieu), consisting of a protein soluble fraction (mostly growth factors and cytokines) and a vesicular component, extracellular vesicles (EVs), which transfer proteins, lipids, and genetic material. MSC-derived secretome differs based on the tissue from which the MSCs are isolated and under specific conditions (e.g., preconditioning or priming) suggesting that clinical applications should be tailored by choosing the tissue of origin and a priming regimen to specifically correct a given pathology. MSC-derived secretome mediates beneficial angiogenic effects in a variety of tissue injury-related diseases. This supports the current effort to develop cell-free therapeutic products that bring both clinical benefits (reduced immunogenicity, persistence in vivo, and no genotoxicity associated with long-term cell cultures) and manufacturing advantages (reduced costs, availability of large quantities of off-the-shelf products, and lower regulatory burden). In the present review, we aim to give a comprehensive picture of the numerous components of the secretome produced by MSCs derived from the most common tissue sources for clinical use (e.g., AT, BM, and CB). We focus on the factors involved in the complex regulation of angiogenic processes.
Mammalian cell cultures are the predominant system for the production of recombinant proteins requiring post-translational modifications. As protein yields are a function of growth performance (among others), and performance varies greatly between culture medium (e.g., different growth rates and peak cell densities), an understanding of the biological mechanisms underpinning this variability would facilitate rational medium and process optimization, increasing product yields, and reducing costs. We employed a metabolomics approach to analyze differences in metabolite concentrations of CHO cells cultivated in three different media exhibiting different growth rates and maximum viable cell densities. Analysis of intra- and extracellular metabolite concentrations over the course of the cultures using a combination of HPLC and GC-MS, readily detected medium specific and time dependent changes. Using multivariate data analysis, we identified a range of metabolites correlating with growth rate, illustrating how metabolomics can be used to relate gross phenotypic changes to the fine details of cellular metabolism.
Genome-scale metabolic reconstructions are routinely used for the analysis and design of metabolic engineering strategies for production of primary metabolites. The use of such reconstructions for metabolic engineering of antibiotic production is not common due to the lack of simple design algorithms in the absence of a cellular growth objective function. Here, we present the metabolic network reconstruction for the erythromycin producer Saccharopolyspora erythraea NRRL23338. The model was manually curated for primary and secondary metabolism pathways and consists of 1,482 reactions (2,075 genes) and 1,646 metabolites. As part of the model validation, we explored the potential benefits of supplying amino acids and identified five amino acids "compatible" with erythromycin production, whereby if glucose is supplemented with this amino acid on a carbon mole basis, the in silico model predicts that high erythromycin yield is possible without lowering biomass yield. Increased erythromycin titre was confirmed for four of the five amino acids, namely valine, isoleucine, threonine and proline. In bioreactor experiments, supplementation with 2.5 % carbon mole of valine increased the growth rate by 20 % and simultaneously the erythromycin yield on biomass by 50 %. The model presented here can be used as a framework for the future integration of high-throughput biological data sets in S. erythraea and ultimately to realise strain designs capable of increasing erythromycin production closer to the theoretical yield.
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