PONE-D-19-26377

Detection of carbapenem-resistant Klebsiella pneumonia on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach

PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Review of: Detection of carbapenem-resistant Klebsiella pneumonia on the basis of matrix assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach

The authors describe a machine learning algorithm that can differentiate between carbapenem resistant K. pneumonia and carbapenem sensitive K. pneumonia. Whole cells were analyzed with MALDI-ToF. While the manuscript could be of potential interest to readers, it needs major revisions to put it in its final form as outlined below.

Major comments:

1- The conclusion in the abstract section is vague. What do the authors mean by “analyzed spectra with the same MALDI-TOF MS settings used for bacterial identification. Furthermore, only one spectrum for an isolate was use to analysis.”

2- The introduction section is superficial and requires thorough revision in terms of the clarity of used language. Sentences are hard to read, although they describe mostly general information. The depth of the content, in my opinion, is not suitable for the audience. Some sections in the introduction also requires the addition of citations to support the authors’ claims. Furthermore, the compilation of the literature review is very confusing. I am not sure what the authors were trying to explain with the four approaches that identify drug resistant bacteria. There was no adequate description of what these approaches do in terms of drug resistant bacteria and how is the approach comparable to the presented work. Also no clear description of the used techniques in these approaches and the associated problems.

3- The authors did not attempt any peak identification at this stage, neither did they correct for isotope, adducts and salts during peak annotation. As such, I don’t see the validity in assessing the significance of the difference in the number of peaks between CSKP and CRKP. As such, figure 2 and discussions in the manuscript on that topic should be removed.

4- The authors selected 80 top ranked unidentified peaks to differentiate between CRKP and CSKP? Can the authors comment on the uniqueness of these peaks to solely carbapenem resistance and whether these peaks are also found in other penicillins that klebsiella is also resistant to such as amoxicillin and ampicillin?

5- Why didn’t the authors attempt for the identification of the peaks, especially with the m/z 9478.87 and 7705.009 as being mostly present in CRKP. Can the authors comment on the availability of data bases.

6- How was the feature selection using t-test conducted, i.e. what was the software used for it? Why did the authors use a univariate statistical test for feature selection? Why multivariate statistical testing such as PCA was not used?

Minor comments:

1- Line 26: “healthcare-associated pathogen” do the authors mean it infect healthcare professionals?

2- Authors should state the full name of the acronym at its first appearance throughout the document, e.g. ToF, CDC, WHO, PCR, MIC, CHCA and RF

3- Line 34: description of the utility of MALDI-ToF should be a part of the introduction and not the methods section of the abstract.

4- Line 40: authors already defined CSKP

5- Line 54: could the authors explain more on the urgency of carbapenem resistance as a threat to healthcare?

6- Line 56: do the authors mean that usually involve a combination of mechanisms at a time, the authors stated once mechanism? Also what is the role of B-Lactamase inhibitors as a combination therapy in addressing this resistance?

7- Line 58: what is KPC?

8- Line 58? What do the authors mean by “KPC-producing bacteria have increasingly been isolated worldwide” this is a very general term and not suitable for a scientific audience.

9- Line 68: the sentence needs to be thoroughly revised to indicate that polypepetides related to drug resistance could alter the spectra usually obtained from susceptible strains.

10- Line 74: what do the authors mean by: “The first three approaches usually detect small molecules (<74 1,000 Da) beyond the detection limit of the MALDI-TOF instruments now commonly employed in clinical microbiology, which spectra were acquired in a linear positive-ion mode at a laser frequency of 60 Hz across a ass/charge (m/z) ratio of 2,000 to 20,000.”

11- Line 89: can the authors comment on the advantages of analyzing whole cells over solvent extraction?

12- Line 109 and line 157: please use SVM abbreviation instead.

13- Line 122: can the authors comment on the possibility of carabapenemase phenotype susceptibility to meropenem or imipenem?

14- Line 125: authors need to be more specific in describing their specimen size. How much is a portion?

15- Line 127: did the authors do any optimization in regards to the MALDI matrix? Was there any matrices tried?

16- Line 131: can the authors reference the manufacturer specification that describes the use of E. Coli as a reference strain?

17- Line 133: I am not clear on the selection criteria for ensuring the quality of the protein extraction. Was 90% a score that was set in-house for assessing the quality of protein extraction or it is a common practice? When isolates were re-analyzed, were the scores from the repeats only used in the analysis or the scores before and after repeating were averaged? In general, was it an n=1 that was used throughout the study?

18- Line 134: did the authors correct for isotopes and salts in their spectral analysis?

19- Line 135: what was the software used for spectral analysis?

20- Line 151: did the authors apply any corrections for the multiple student t-testing?

21- Line 176: was the temperature set at 208 C?

22- Line 188: where were the isolates collected from the patient, are they respiratory fluids? Urine?

23- is there an ethics approval for collecting patient samples?

24- Line 202? What is the unit of dimensionality?

25- Line 245: the authors stated that random forest algorithm outperformed the other techniques, however there was one CRKP that was constantly misclassified using the random forest algorithm while was correctly classified using the other techniques? Can the authors comment on this finding? Did they run any blinded samples to confirm the superior performance of one algorithm over the other?

26- Line 264: the authors mentioned that they did not focus on targeted peaks from enzymes or metabolites related to drug resistance. However, they detected 3 peaks that were differentially present in CRKP and CSKP. Can the authors comment on the significance of their findings in regards to biomarker discovery?

27- Line 306: the authors explicitly detailed the advantages of random forest testing, however, they did not justify the inclusion of other algorithms in this study.

28- Figure 1 and figure 4 were not cited in text

29- Can the authors make a clear discrimination between the specification of the training set and the test set?

30- Figure 3. Can the authors specify what is on the x and y axis in panel A and B?

31- Reference formatting should be corrected for errors

Reviewer #2: In this Manuscript, the authors used MALDI-TOF-MS to differentiate between Carbapenem-resistant and Carbapenem-susceptible Klebsiella pneumonia pathogens (CRKB and CSKB). The authors used statistical techniques to analyze the MALDI-MS data of 46 CRKB and 49 CSKB and to build classifiers for differentiation purposes between these two types of pathogens.

The classifiers MS peaks are shown in table 1. From this list, they found that “ there were none of these peaks distinctive to CRKP or CSKP, although one peak (9478.87 206 Da) was present in 82.6% of the CRKP isolates and only 2.2% of the CSKP isolates, one peak (7705.009 Da) was present in 80.4% of the CRKP 207 isolates and only 2.2% of the CSKP isolates, 208 and the other peak (9541.41 Da) was present in 76.1% of the CRKP isolates and only 2.2% of 209 the CSKP isolates.”

Overall, the work sounds technically good, but it needs statistics experts to assess these statistical techniques used to build these classifiers peaks.

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Reviewer #1: No

Reviewer #2: No

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Detection of carbapenem-resistant Klebsiella pneumonia on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using supervised machine learning approach

PONE-D-19-26377R1

Dear Dr. Chang,

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Joseph Banoub, Ph,D., D. Sc.

Academic Editor

PLOS ONE

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