Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Triaging of Culture Conditions for Enhanced Secondary Metabolite Diversity from Different Bacteria

Version 1 : Received: 31 August 2020 / Approved: 1 September 2020 / Online: 1 September 2020 (12:24:09 CEST)
Version 2 : Received: 22 December 2020 / Approved: 23 December 2020 / Online: 23 December 2020 (11:19:38 CET)

How to cite: Schwarz, J.; Lütz, S. Triaging of Culture Conditions for Enhanced Secondary Metabolite Diversity from Different Bacteria. Preprints 2020, 2020090019 (doi: 10.20944/preprints202009.0019.v2). Schwarz, J.; Lütz, S. Triaging of Culture Conditions for Enhanced Secondary Metabolite Diversity from Different Bacteria. Preprints 2020, 2020090019 (doi: 10.20944/preprints202009.0019.v2).


Over the past decade, the One Strain Many Compounds (OSMAC) approach has been established for silent gene cluster activation and elicitation of secondary metabolite production, but so far the full secondary metabolome of a biosynthetically promising bacterium has not been elucidated yet. Here, we investigate the ability of seven categories of OSMAC conditions to enhance the diversity of new mass features from bacterial strains with little literature coverage but high biosynthetic potential. The strains Bacillus. amyloliquefaciens DSM7, Corallococcus. coralloides DSM2259, Pyxidicoccus. fallax HKI727, Rhodococcus. jostii DSM44719, and Streptomyces. griseochromogenes DSM40499 were selected after genome mining with antiSMASH. After cultivation under OSMAC conditions, the generated extracts were subjected to LC-MS and MZmine analysis to determine new mass features and evaluate the tested culture conditions. 4 predicted compounds, bacillibactin, desferrioxamine B, myxochelin A, and surfactin, were identified and up to 147 new mass features were detected in the generated extracts, which greatly surpasses the number of predicted gene clusters. Among the new mass features are bioactive compounds that were so far unreported for the strains such as cyclo-(Tyr-Pro) from DSM7 and nocardamin from DSM2259. Furthermore, the tested culture conditions were evaluated regarding their suitability for the generation of new mass features from the selected strains and promising new starting points for further screenings are postulated. Especially culture conditions with little prior literature coverage are responsible for the activation of predicted gene clusters

Subject Areas

mass spectrometry; OSMAC approach; natural products; silent BGC activation; bioinformatics; screening

Comments (1)

Comment 1
Received: 23 December 2020
Commenter: Stephan Lütz
Commenter's Conflict of Interests: Author
Comment: Here is a list of the reviewers comments (in bold type) and our reply, including the changes we have made:

Reviewer 1:The manuscript biomolecules-924160 describes the screening and impact of different culture media conditions in metabolite production from bacterial strains. Overall, the manuscript is not hard to follow, but authors need to perform a careful revision of English style, grammar, and formatting. The methodology seems well performed, but I recommend authors in future works to add an internal standard in the step of sample extraction to guarantee that comparison of feature intensity is only due to differences in bacterial production and not due to sample processing. I believe the manuscript is of great interest to readers of Biomolecules, however, there are some points that need to be addressed before being accepted.
The authors thank the reviewer for his/ her careful revision and his/ her positive assessment of our manuscript. His/ her valuable suggestions have substantially contributed to improve our manuscript. We have indeed noted some bulky sentences and have reviewed and optimized the manuscript in terms of style and grammar. We also thank the reviewer for the positive comment on our methodology and are grateful for the recommendation of an internal standard. We believe, it will be valuable for our future work.
Fig.1S of supplementary material: Authors should include the absolute values of the pizza chart. So, the chart will have both the absolute and percentage values.
We thank the reviewer for his/ her useful suggestion. We included the absolute values, which are indeed necessary for a better understanding of Figure S1.
P4L119-120: a reference is missing for the DSMZ protocol. From where readers can obtain it? Even if it is a pdf from the website it is important to provide the URL (if this is the case…)
We thank the author for pointing out the missing references for the DSMZ protocols and added them including URLs.
P4L160: I believe it is better to use “methanol (LC-MS grade)” than “MS-pure methanol”
We agree and changed the wording according to your suggestion
For standardization in the manuscript, I suggest authors to use “LC-MS” for “liquid chromatography-mass spectrometry”, and “LC-MS/MS” for “liquid chromatography-tandem mass spectrometry”. However, according to the IUPAC recommendations in 2013, the hyphen or slash (LC/MS) can be used interchangeably to indicate combined methods.
Thank you for this remark. We standardized the use of LC-MS and LC-MS/MS throughout the manuscript according to your suggestion.
P5L180: molecular weight symbol commonly used is the MW (both capital letter). However, according to the International System of Units (link:, the molecular weight is obsolete and should be replaced by the equivalent but preferred relative molecular mass, symbol Mr (r subscript, both M and r in italics).
We replaced Mw with the preferred Mr and thank the author for pointing out this shortcoming.
P5, section Evaluation of LC-MS data: please, describe how RAW files were converted to mzXML format
We thank the author for this valuable remark. We added a table with the used parameter settings in the supplementary information (Table S9) and mentioned the used tool msConvert in the Materials and Methods section (P5, L194-195).
P5L189: please, use [M+H]+, [M+NH4]+, [M+Na]+, and [M+k]+ instead of  H+-, NH4+-, Na+- and K+-
We changed the wording according to the reviewer’s suggestion. (P5, L205)
P5L209: Reference error. Please, check other parts of the manuscript too.
We thank the reviewer for the remark and carefully checked and corrected the manuscript.
P6L220-225: problems with formatting
The authors thank the reviewer for pointing out the formatting problems and corrected them.
P7L236-237: For more clarity, authors should indicate in captions of Figs S7, S8 and others, that the image above the MS spectrum is the UV spectrum…
We appreciate this remark and added a sentence according to the reviewer’s suggestion. (Figures S8, S9, S15, S21, S23, S26)
Authors should make clear in the discussion section what means the terms mass based, MS2-fragmentation, and reference compound present in table 3. For instance, i) mass based is referred to only MS data? What the ppm error considered? Isotopic profile too? ii) MS2 is refereed MS/MS match in GNPS database, in silico database or both? How was the score match used? Iii) reference compounds are referred to match in MS, MS/MS, and tR?
The authors thank the reviewer for his/her suggestion, which really improves the manuscript. We added the desired information in the caption of Table 3 and added the ppm error in the table. Yes, mass-based refers to only MS data and of course we considered the isotopic profile as well. The Extracted Ion Chromatograms were used with a tolerance of 0.01 Da. The ppm error was calculated with the measured m/z on the basis of the [M+H]+ calculated by the Compass Data Analysis software. In Table 3, MS/MS match refers to a match to the in silico fragmention pattern generated with MetFrag. “MetFrag” has been added in the heading of the respective column in table 3 to make this clear to the reader. Since both compounds, bacillibactin and nostophycin, were predicted by the antiSMASH analysis, we actively looked for the corresponding masses in the extracts with EICs and used the given PubChem IDs to compare our experimental fragmentation pattern of the putative bacillibactin and nostophycin to in silico fragmentation patterns of the database entries. Both scored 1.0. Here, a match to a reference compound considers the same tR and a match of MS data. The ppm error in comparison to reference compounds was below 5 ppm for all compounds but myxochelin A. For myxochelin A the ppm error was 10.1.
P8L275: Please, correct symbol for retention time (tR and not Rt).
Thank you for this remark. We corrected the term.
Fig. 5: what the letter “c” stands for? Or it should be 0?
We thank the reviewer for his/ her careful revision and this remark. “c” stands for control group and of course it must be indicated in the caption Figure 5. We added the missing information to the caption.
m/z must be in italics, please check the whole manuscript.
Thank you for this remark! We corrected the formatting in the whole manuscript.
Authors must provide a supplementary table containing the MS/MS information of compounds from table 6.
We thank the reviewer for his/ her careful revision. We added a table in the supplementary information with respective MS/MS data. (Table S22)
Authors should use the guide from the metabolomics standards initiative to indicate the level of confidence in the compound “identification”. Please, see DOI: 10.1007/s11306-007-0082-2 and 10.1021/es5002105.
The authors thank the reviewer for pointing out this shortcoming. We added a table with the levels of confidence for all compounds mentioned in our manuscript. (Table S23)
Our assessment of the level of confidence was based on C. Schrimpe-Rutledge, S. G. Codreanu, S. D. Sherrod, J. A. McLean, J. Am. Soc. Mass Spectrom. 2016, 27, 1897–1905.            
 Reviewer 2: This manuscript describes:Based on genome mining, availability and literature research, five strains, B. amyloliquefaciens DSM7, C. coralloides DSM2259, P. fallax HK1727, R. jostii DSM44719 and S. griseochromogenes DSM40499, were selected for the one Strain Many Compounds (OSMAC) experiment to activate silent gene clusters and elicit their secondary metabolite production. Five BGCs, which encoding for surfactin variants and bacillibactin in B. amyloliqeufaciens, myxochelin A and putatively nostophycin in P. fallax, and putatively albaflavenone in S. griseochromogenes, were activated by cultivation under the OSMAC conditions. Four predicted compounds, bacillibacitin, desferrioxamine B, myxochelin A and surfactin, were identified and up to 147 new mass features including bioactive compound, cyclo-(Tyr-Pro) from DSM7 and nocardamin from DMS2259, were detected in the generated extracts.Furthermore, the tested culture conditions were evaluated regarding their suitability for the generation of new mass feature from the selected strains. The substantial shortcomings of the manuscript are addressed as follows:
We thank the reviewer for his/ her valuable revision of our manuscript, which helped to substantially improve it. The manuscript has been revised in response to the reviewer comments. Detailed answers to the reviewer comments are given below. 
While it is interesting to try experimentation using various culture conditions for OSMAC approach, the suggested data and analytic protocol might not be sufficient nor clear to achieve your main objective, which is to search for culture conditions for production and discovery of new secondary metabolites. It would be quite interesting if new secondary metabolites are presented which were produced by new culture conditions. However, the examples are mostly productions of known compounds (known to be produced by the same microorganism) activated compared to control culture (Table 3).
The authors thank the reviewer for his/ her valuable revision of our manuscript. We agree that it would have been interesting if completely new compounds had been found in this study. However, we want to stress, that it was not the main objective of this study to report new compounds from the selected strains. Instead, we wanted to take a first step towards increasing the chemical diversity of the selected bacteria, which we have successfully done and presented in this manuscript. We were apparently not successful in making this clear and thank the reviewer for bringing this to our attention. We have worked on the manuscript wording to make our main objective clearer.Table 3 presents the evaluation of the production of predicted compounds. Table S14 assigns the predicted compounds to specific culture conditions, which trigger their synthesis. We focused only on predicted (and therefore known) compounds, as we wanted to evaluate the impact of varying culture conditions on product scope of the chosen microorganisms. To us, it is quite fascinating that especially those culture conditions, which have previously not been extensively described in literature (here especially limitation experiments and addition of solvents) are responsible for the activation (see Figure 1 and Table S14).
Sizable numbers of new mass features were presented in Table 4, and small portion of them were examined and identified as known compounds, even though they were not detected in control culture (Table 6). The new finding in the case of nocardamin is that coralloides was found to be a new producer of nocardamin. Nocardamin has only been detected in actinomycetes. It would be more impressive if new mass features were finally defined as new metabolites produced by new culture condition.
We completely agree with the reviewer that the finding of new metabolites would have been impressive. However, the identification of a new molecule was not the aim of this study. The main objective of this study was to identify conditions to increase the number and diversity of new mass features. Our manuscript reports a strategy to select culture conditions as promising starting point for future screenings, because these conditions reliably provoke new mass features with a great variety of condition-specific compounds. However, in order to produce and identify a completely novel compound, a different methodology needs to be applied. In our opinion, it is not reasonable to elucidate the identity of all new 590 mass features without any prioritization or indications of pharmaceutical activity. For future research, it would be indeed desirable to identify new compounds and this might be a topic for further studies.  
While the high number of new mass features were showed as an obvious clustering pattern for each of the selected five strains in all experiment, but a small number of new mass feature were not showed a clear separation. These small difference should be validated statistically. In this paper, it was evaluated using only “hit rate” without the statistical analysis for assessment of suitability of tested culture conditions. In order to evaluate effectively the LC-MS based discrimination between activated new mass feature of the selected five strains depending on the tested culture condition and to assess the influence of the selected culture parameters on the number and identity of new mass feature, it is recommended to analyze the data by multivariated statistical methods including principal component analysis (PCA) and supervised partial least-squares discriminate analysis (PLS-DA).
We thank the reviewer for the informative comment and are sure that it will be of great benefit for future research. The clustering patterns shown in the supplementary information (Figures S18-S20) were only used to determine the mass features, which show the greatest peak areas to subsequently assign those dominant mass features via LC-MS/MS analysis. The hit rate was used to evaluate if the tested culture conditions were useful to generate new mass features in all tested strains (hitrate = 1) or only in some of them (hitrate < 1). The authors agree with the reviewer that a statistical assessment of the influence of the selected culture conditions on the identity of the new mass features would be interesting. If most new mass features were detected under several culture conditions and thus peak areas of the same mass feature in different culture conditions could have been compared, we would have surely used statistical analyses to validate our findings and assess this influence. However, in this study, we have noticed that most new mass features were only detected under exactly one culture condition, which is no basis for a statistical analysis of the influence of culture condition on the identity of the new mass features as suggested by the reviewer. To ensure data quality and remove redundant signals, the data filtering including the elimination of signal observed in medium background and control group samples was applied to preprocessed data. Before these data filtering, we recommend as below. Based on the different metabolic expression of microorganisms depending on the environmental changes, the metabolome should be analyzed in biological triplicates to determine the influence of this biological variation on the discrimination between bacterial cell samples.We thank the reviewer for this remark and agree that ideally biological triplicates should be analyzed due to the biological variation. In our study, we worked with duplicates and set strict limits for mass features to be considered as new. They needed to be detected in both flasks and their signal needed to be higher than our intensity threshold of 1∙105 in both cultures. We added this information to the manuscript (P5L200-202) Since we do not aim at quantifying any produced compound or new mass feature in our study, but instead want to see, which mass features were produced reproducibly, duplicates with a strict intensity threshold seem to be sufficient for the present scientific question.
Growth conditions - Depending on OSMAC experiment, the growth rate of all strains are actually different. Some reported papers have noticed on differences in the metabolites of microorganisms along their growth phase. For applying for the same amount of bacteria into next step such as main culture and fermentation extraction, the growth curves of all strains should be determined by measuring the turbidity of fermentation broth at 600 nm as well as the cultivation time before extraction of fermentation broth. The fermentation broths of all strains should be then extracted according to the OD600 value correlated to the growth curve. We recommend to include OD600 value depending on the growth curves of each strain for the EtOAc extraction in the growth condition section and OSMAC experiment
We agree with the reviewer and thank him/ her for this valuable suggestion. We measured OD600 values and included them in the Materials and Methods section. (P4L138-140) Growth curves of the control group are now given in Figure S2 in the supplementary information. In the case of S. griseochromogenes, no OD600 measurement was possible due to extremely clotted growth. The estimation of necessary time of growth was based on the growth of the control groups. The figure shows that all control group cultures were far into the stationary phase before harvesting. We thus decided that the selected time of growth would also be sufficient for conditions provoking slower growth rates if new mass features are produced by the selected bacteria under the selected culture conditions.
OSMAC experiments - For the biotic additives, the cell pellet and the supernatant from fermentation of inducer microbes were used as inducer. Only the volume of the cell pellet and supernatant, which is isolated or centrifuged from the fermentation of inducer microbes according to the regular cultivation protocol, was used for the biotic additive experiment. We recommend to describe the concentration of their cell pellet and supernatant using an accurate indication.
We thank the reviewer for his/ her careful review and suggestion. We followed his/ her suggestion and included a description of the cell pellet and supernatant preparation in the Materials and Methods section of the manuscript. (P4L156-161
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