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Prediction of novel non-coding RNAs relevant for the growth of Pseudomonas putida in a bioreactor

ABSTRACT: Pseudomonas putida is a microorganism with great potential for industry due to its stress-endurance traits and easy manipulation of the metabolism. However, optimization is still required to improve production yields. In the last years, manipulation of bacterial small non-coding RNAs (ncRNAs) has been recognized as an effective tool to improve the production of industrial compounds. So far, very few ncRNAs are annotated in P. putida beyond the generally conserved. In the present study, P. putida was cultivated in a two-compartment scale-down bioreactor that simulates large-scale industrial bioreactors. We performed RNA-Seq of samples collected at distinct locations and time-points to predict novel and potentially important ncRNAs for the adaptation of P. putida to bioreactor stress conditions. Instead of using a purely genomic approach, we have rather identified regions of putative ncRNAs with high expression levels using two different programs (Artemis and sRNA detect). Only the regions identified with both approaches were considered for further analysis and, in total, 725 novel ncRNAs were predicted. We also found that their expression was not constant throughout the bioreactor, showing different patterns of expression with time and position. This is the first work focusing on the ncRNAs whose expression is triggered in a bioreactor environment. This information is of great importance for industry, since it provides possible targets to engineer more effective P. putida strains for large-scale production.

ORGANISM(S): Pseudomonas putida

PROVIDER: GSE129947 | GEO | 2019/11/19

REPOSITORIES: GEO

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Project description:There is a great need for setting novel measurable attributes at the cell physiological level in a scalable biopharmaceutical production process to be able to predict the process outcomes and improve process understanding. In a biologic production process, changes in culture environment due to several factors such as shear and bubble induced damage from gas sparging and agitation are known to occur. There is a gap in the knowledge of cellular response due to varying bioreactor environment itself during the course of cell culture, from lag-phase to log-phase to stationary-phase in culture. With the emergence of micro-arrays as tools for exploring cell physiological changes, it opens the possibility for studying the effect of bioreactor culture environment itself on the cell substrate. Such information could be eventually used to designate gene transcripts as biomarkers for cell status in a controlled bioreactor system. A model 5L bench-scale bubble aerated and impeller agitated bioreactor system was used to study gene expression profiles of a hybridoma cell line during the time-course of batch culture. Gene expression profiles that were variable from early-to-late in batch culture, as well as invariant gene profiles were summarized using microarray findings. Typical cellular functions studied were oxidative stress response, DNA damage response, apoptosis, antioxidant activity, cellular metabolism, and protein folding. These findings were also verified with a more rigorous semi-quantitative RT-PCR technique. The results of this study suggest that under predefined bioreactor culture conditions, significant gene changes from lag to log to stationary phase could be identified, which could then be used to track the culture state. We ran consecutive 5L bioreactor runs, each with an independent vial thaw, to achieve multiple biological replicates per time-point. Bioreactors were sampled approximately every 12 hours for RNA extraction. For the 5L bioreactors, microarray samples were run for day 1 (n=2), day 2 (n=2), day 3 (n=3), and day 3.5 (n=3). Here 2 or 3 of the three biological replicates run for each time-point were included in the analysis, based on >70% genes found. We define early exponential as day 1, peak exponential as day 2 and day 3 and late stationary as day 3.5.

Project description:Nogales2008 - Genome-scale metabolic network of Pseudomonas putida (iJN746) This model is described in the article: A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. Nogales J, Palsson BØ, Thiele I. BMC Syst Biol 2008; 2: 79 Abstract: BACKGROUND: Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. RESULTS: We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. CONCLUSION: Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production. This model is hosted on BioModels Database and identified by: MODEL1507180068. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Pseudomonas putida KT2440 has received increasing attention as an important biocatalyst for the conversion of diverse carbon sources to multiple products, including the olefinic diacid, cis,cis-muconic acid (muconate). P. putida has been previously engineered to produce muconate from glucose; however, periplasmic oxidation of glucose causes substantial 2-ketogluconate accumulation, reducing product yield and selectivity. Deletion of the glucose dehydrogenase gene (gcd) prevents 2-ketogluconate accumulation, but dramatically slows growth and muconate production. In this work, we employed adaptive laboratory evolution to improve muconate production in strains incapable of producing 2-ketogluconate. Growth-based selection improved growth, but reduced muconate titer. A new muconate-responsive biosensor was therefore developed to enable muconate-based screening using fluorescence activated cell sorting. Sorted clones demonstrated both improved growth and muconate production. Mutations identified by whole genome resequencing of these isolates indicated that glucose metabolism may be dysregulated in strains lacking gcd. Using this information, we used targeted engineering to recapitulate improvements achieved by evolution. Deletion of the transcriptional repressor gene hexR improved strain growth and increased the muconate production rate, and the impact of this deletion was investigated using transcriptomics. The genes gntZ and gacS were also disrupted in several evolved clones, and deletion of these genes further improved strain growth and muconate production. Together, these targets provide a suite of modifications that improve glucose conversion to muconate by P. putida in the context of gcd deletion. Prior to this work, our engineered strain lacking gcd generated 7.0 g/L muconate at a productivity of 0.07 g/L/h and a 38% yield (mol/mol) in a fed-batch bioreactor. Here, the resulting strain with the deletion of hexR, gntZ, and gacS achieved 22.0 g/L at 0.21 g/L/h and a 35.6% yield (mol/mol) from glucose in similar conditions. These strategies enabled enhanced muconic acid production and may also improve production of other target molecules from glucose in P. putida.

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Prediction of novel non-coding RNAs relevant for the growth of Pseudomonas putida in a bioreactor

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