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Secretome Dynamics in a Gram-Positive Bacterial Model.

ABSTRACT: Protein secretion is a central biological process in all organisms. Most studies dissecting bacterial secretion mechanisms have focused on Gram-negative cell envelopes such as that of Escherichia coli However, proteomics analyses in Gram negatives is hampered by their outer membrane. Here we studied protein secretion in the Gram-positive bacterium Streptomyces lividans TK24, in which most of the secretome is released in the growth medium. We monitored changes of the secretome as a function of growth phase and medium. We determined distinct protein classes of "house-keeping" secreted proteins that do not change their appearance or abundance in the various media and growth phases. These comprise mainly enzymes involved in cell wall maintenance and basic transport. In addition, we detected significant abundance and content changes to a sub-set of the proteome, as a function of growth in the different media. These did not depend on the media being minimal or rich. Transcriptional regulation but not changes in export machinery components can explain some of these changes. However, additional downstream mechanisms must be important for selective secretome funneling. These observations lay the foundations of using S. lividans as a model organism to study how metabolism is linked to optimal secretion and help develop rational optimization of heterologous protein production.

SUBMITTER: Tsolis KC

PROVIDER: S-EPMC6398212 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Secretome Dynamics in a Gram-Positive Bacterial Model.

Tsolis Konstantinos C KC Hamed Mohamed Belal MB Simoens Kenneth K Koepff Joachim J Busche Tobias T Rückert Christian C Oldiges Marco M Kalinowski Jörn J Anné Jozef J Kormanec Jan J Bernaerts Kristel K Karamanou Spyridoula S Economou Anastassios A

Molecular & cellular proteomics : MCP 20181129 3

Protein secretion is a central biological process in all organisms. Most studies dissecting bacterial secretion mechanisms have focused on Gram-negative cell envelopes such as that of <i>Escherichia coli</i> However, proteomics analyses in Gram negatives is hampered by their outer membrane. Here we studied protein secretion in the Gram-positive bacterium <i>Streptomyces lividans</i> TK24, in which most of the secretome is released in the growth medium. We monitored changes of the secretome as a ...[more]

PMID: 30498012

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Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Project description:Alkene monooxygenases (MOs) are soluble di-iron-containing enzymes found in bacteria that grow on alkenes. Here, we report improved heterologous expression systems for the propene MO (PmoABCD) and ethene MO (EtnABCD) from Mycobacterium chubuense strain NBB4. Strong functional expression of PmoABCD and EtnABCD was achieved in Mycobacterium smegmatis mc2155, yielding epoxidation activities (62 and 27 nmol/min/mg protein, respectively) higher than any reported to date for heterologous expression of a di-iron MO system. Both PmoABCD and EtnABCD were specialized for the oxidation of gaseous alkenes (C2 to C4), and their activity was much lower on liquid alkenes (C5 to C8). Despite intensive efforts to express the complete EtnABCD enzyme in Escherichia coli, this was not achieved, although recombinant EtnB and EtnD proteins could be purified individually in soluble form. The biochemical function of EtnD as an oxidoreductase was confirmed (1.36 μmol cytochrome c reduced/min/mg protein). Cloning the EtnABCD gene cluster into Pseudomonas putida KT2440 yielded detectable epoxidation of ethene (0.5 nmol/min/mg protein), and this could be stimulated (up to 1.1 nmol/min/mg protein) by the coexpression of cpn60 chaperonins from either Mycobacterium spp. or E. coli Successful expression of the ethene MO in a Gram-negative host was validated by both whole-cell activity assays and peptide mass spectrometry of induced proteins seen on SDS-PAGE gels.IMPORTANCE Alkene MOs are of interest for their potential roles in industrial biocatalysis, most notably for the stereoselective synthesis of epoxides. Wild-type bacteria that grow on alkenes have high activities for alkene oxidation but are problematic for biocatalysis, since they tend to consume the epoxide products. Using recombinant biocatalysts is the obvious alternative, but a major bottleneck is the low activities of recombinant alkene MOs. Here, we provide new high-activity recombinant biocatalysts for alkene oxidation, and we provide insights into how to further improve these systems.

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Secretome Dynamics in a Gram-Positive Bacterial Model.

Publications

Secretome Dynamics in a Gram-Positive Bacterial Model.

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