Editorial: MALDI-TOF MS Application in Microbial Ecology Studies

Rahi, Praveen; Vaishampayan, Parag

doi:10.3389/fmicb.2019.02954

Cited by 23 publications

(15 citation statements)

References 11 publications

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“…The technology has some of the same caveats as ASV identification from sequence amplicons introduced above, however: amino acid variants must exist between the strains of interest in the profiled proteins, at a level detectable above experimental noise, and must be classifiable to a taxon of origin in a reference database or by clustering [ 159 , 160 ]. While in principle the same types of strain-level protein variants could be detected using MALDI-TOF MS technologies in culture-independent community extracts, such applications remain extremely challenging, and instead, community proteomics are currently more commonly analyzed in a gene- or taxon-centric way [ 161 ].…”

Section: Strategies and Approaches To Identifying Community Strain DImentioning

confidence: 99%

Strain-level epidemiology of microbial communities and the human microbiome

et al. 2020

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The biological importance and varied metabolic capabilities of specific microbial strains have long been established in the scientific community. Strains have, in the past, been largely defined and characterized based on microbial isolates. However, the emergence of new technologies and techniques has enabled assessments of their ecology and phenotypes within microbial communities and the human microbiome. While it is now more obvious how pathogenic strain variants are detrimental to human health, the consequences of subtle genetic variation in the microbiome have only recently been exposed. Here, we review the operational definitions of strains (e.g., genetic and structural variants) as they can now be identified from microbial communities using different high-throughput, often culture-independent techniques. We summarize the distribution and diversity of strains across the human body and their emerging links to health maintenance, disease risk and progression, and biochemical responses to perturbations, such as diet or drugs. We list methods for identifying, quantifying, and tracking strains, utilizing highthroughput sequencing along with other molecular and "culturomics" technologies. Finally, we discuss implications of population studies in bridging experimental gaps and leading to a better understanding of the health effects of strains in the human microbiome.

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Section: Strategies and Approaches To Identifying Community Strain DImentioning

confidence: 99%

Strain-level epidemiology of microbial communities and the human microbiome

et al. 2020

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“…At present, there is growing interest in bacterial endophytes since they can exert a positive effect on plant growth, but those of canola plants have rarely been studied, and their identification has been carried out primarily through rrs gene sequence analysis [11][12][13][14][15][16]. In this work, we applied, for the first time, MALDI-TOF MS for the identification of canola root endophytes, as it is a rapid and reliable methodology for bacterial identification [28,32,33]. We completed this identification through rrs gene sequencing, as has been reported for the identification of bacterial endophytes of other plant species [20,23,25].…”

Section: Discussionmentioning

confidence: 99%

Identification of Canola Roots Endophytic Bacteria and Analysis of Their Potential as Biofertilizers for Canola Crops with Special Emphasis on Sporulating Bacteria

et al. 2021

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Canola (Brassica napus L. var. oleracea) is the third most common oil-producing crop worldwide after palm and soybean. Canola cultivation requires the use of chemical fertilizers, but the amount required can be reduced by applying plant growth-promoting bacteria (PGPB). Among PGPB, endophytic bacteria have certain advantages as biofertilizers, but canola endophytic bacteria have rarely been studied. In this work, we identified a collection of bacterial endophytes isolated from canola roots using MALDI-TOF MS, a technique that is still rarely used for the identification of such bacteria, and rrs gene sequencing, a methodology that is commonly used to identify canola endophytes. The results demonstrated that some bacterial isolates from canola roots belonged to the genera Bacillus, Neobacillus, Peribacillus (Pe.), and Terribacillus, but most isolates belonged to the genera Paenibacillus (P.) and Pseudomonas (Ps.). Inoculation of these isolates indicated that several of them could efficiently promote canola seedling growth in hydroponic conditions. These results were then confirmed in a microcosm experiment using agricultural soil, which demonstrated that several isolates of Pseudomonas thivervalensis, Paenibacillus amylolyticus, Paenibacillus polymyxa, Paenibacillus sp. (Paenibacillus glucanolyticus/Paenibacillus lautus group), and Peribacillus simplex (previously Bacillus simplex) could efficiently promote canola shoot growth under greenhouse conditions. Among them, the isolates of Paenibacillus and Peribacillus were the most promising biofertilizers for canola crops as they are sporulated rods, which is an advantageous trait when formulating biofertilizers.

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“…The classification algorithm for classifying a bacterial MS database plays a critical role in the identification approach ( Fangous et al, 2014 ; Mesureur et al, 2018 ). Manufacturer-provided software, such as FlexAnalysis and ClinProTools from Bruker Daltonics, are widely used for classification ( Epperson et al, 2018 ; Rahi and Vaishampayan, 2019 ). A large proportion of classification studies were performed using FlexAnalysis and ClinProTools with preprogrammed machine learning algorithms including the SVM, spiking neural network (SNN), and quantum clustering (QC) ( Weis et al, 2020 ; Delavy et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification

Jin

Shao

et al. 2020

Front. Microbiol.

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Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis is a rapid and reliable method for bacterial identification. Classification algorithms, as a critical part of the MALDI-TOF MS analysis approach, have been developed using both traditional algorithms and machine learning algorithms. In this study, a method that combined helix matrix transformation with a convolutional neural network (CNN) algorithm was presented for bacterial identification. A total of 14 bacterial species including 58 strains were selected to create an in-house MALDI-TOF MS spectrum dataset. The 1D array-type MALDI-TOF MS spectrum data were transformed through a helix matrix transformation into matrix-type data, which was fitted during the CNN training. Through the parameter optimization, the threshold for binarization was set as 16 and the final size of a matrix-type data was set as 25 × 25 to obtain a clean dataset with a small size. A CNN model with three convolutional layers was well trained using the dataset to predict bacterial species. The filter sizes for the three convolutional layers were 4, 8, and 16. The kernel size was three and the activation function was the rectified linear unit (ReLU). A back propagation neural network (BPNN) model was created without helix matrix transformation and a convolution layer to demonstrate whether the helix matrix transformation combined with CNN algorithm works better. The areas under the receiver operating characteristic (ROC) curve of the CNN and BPNN models were 0.98 and 0.87, respectively. The accuracies of the CNN and BPNN models were 97.78 ± 0.08 and 86.50 ± 0.01, respectively, with a significant statistical difference ( p < 0.001). The results suggested that helix matrix transformation combined with the CNN algorithm enabled the feature extraction of the bacterial MALDI-TOF MS spectrum, which might be a proposed solution to identify bacterial species.

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Editorial: MALDI-TOF MS Application in Microbial Ecology Studies

Cited by 23 publications

References 11 publications

Strain-level epidemiology of microbial communities and the human microbiome

Strain-level epidemiology of microbial communities and the human microbiome

Identification of Canola Roots Endophytic Bacteria and Analysis of Their Potential as Biofertilizers for Canola Crops with Special Emphasis on Sporulating Bacteria

Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification

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