Noticiero de Vigo : diario independiente de la mañana: Ano XXII Número 9648 - 1906 setembro 3

Here we demonstrate that when <i>Yersinia pesitis</i> is grown in laboratory media, peptides from the medium remain associated with cellular biomass even after washing and inactivation of the bacteria by different methods. These peptides are characteristic of the type of growth medium and of the manufacturer of the medium, reflecting the specific composition of the medium. We analyzed biomass-associated peptides from cultures of two attenuated strains of <i>Yersinia pestis</i> [KIM D27 (<i>pgm-</i>) and KIM D1 (<i>lcr-</i>)] grown in several formulations of 4 different media (tryptic soy broth (TSB), brain–heart infusion (BHI), Luria–Bertani broth (LB), and glucose (G) medium) made from components purchased from different suppliers. Despite the range of growth medium sources and the associated manufacturing processes used in their production, a high degree of peptide similarity was observed for a given medium recipe; however, notable differences in the termination points of select peptides were observed in media formulated using products from some suppliers, presumably reflecting the process by which a manufacturer performed protein hydrolysis for use in culture media. These results may help explain the presence of peptides not explicitly associated with target organisms during proteomic analysis of microbes and other biological systems that require culturing. While the primary aim of this work is to outline the range and type of medium peptides associated with <i>Yersinia pestis</i> biomass and improve the quality of proteomic measurements, these peptides may also represent a potentially useful forensic signature that could provide information about microbial culturing conditions

Regression coefficients of tissue culture “growth” and “wash” experiments, and estimations of metabolic flux.

<p>Regression coefficients of tissue culture “growth” and “wash” experiments, and estimations of metabolic flux.</p

FTIR spectromicroscopy difference spectra of Rat-1 Fibroblasts in lag phase.

<p>FTIR spectra from a cluster of lag phase Rat-1 cells grown on a CaF₂ optical slide. Spectra were recorded (25×25 µm² aperture) over 160 minutes after washing the cells with DMEM prepared with 100% ²H₂O The initial spectrum (defined as t  = 0) was used as the reference. The scale is the same as that used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039685#pone-0039685-g002" target="_blank">Figure 2<i>A</i></a>. The horizontal line indicates that there is essentially no difference between the various spectra.</p

δ²H and δ¹⁸O values of muscle tissue extracted from rats grown on three different waters.

<p>These data are compared with the values obtained from whole body water.</p>*<p>Measurement precision for H isotopes  = 2‰.</p>**<p>Measurement precision for O isotopes  = 0.3‰.</p

FTIR spectromicroscopy difference spectra of Rat-1 Fibroblasts.

<p>FTIR spectra from a cluster of Rat-1 cells grown on a CaF₂ optical slide (∼60% coverage). Spectra were recorded (25×25 µm² aperture) over 360 minutes after washing the cells with DMEM prepared with 100% ²H₂O. <i>A</i>, a two-dimensional plot depicting the absolute absorbance spectra after subtracting the background (an area with ²H-O-²H but no cells). <i>B</i>, the same data plotted in three dimensions in which the initial spectrum (defined as t  = 0) of a cell cluster was used as the reference. In both <i>A</i> and <i>B</i> the spectra are recorded every 5 minutes. The bands at ∼3400 cm⁻¹ (¹H-O-²H stretch) and ∼1450 cm⁻¹ (¹H-O-²H bend) increase over time while the bands at ∼2300 cm⁻¹ (shoulder of the ²H-O-²H stretching mode) and ∼1225 cm⁻¹ (²H-O-²H bend) decrease over time.</p

Regression of the hydrogen isotope ratio of extracted water versus growth medium water.

<p>Rat-1 fibroblasts grown in DMEM were harvested either during exponential growth (30% confluent) or after they reached quiescence (100% confluent). 26% (or 1– slope) of the H atoms in the total water extracted from the cell cakes was isotopically distinct from growth medium water when the cells were in exponential phase. This value dropped to 8% when the cells reached quiescence. Similar values are obtained with oxygen isotopes, and all of these results are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039685#pone-0039685-t001" target="_blank">Table 1</a>.</p

Protein abundances can distinguish between naturally-occurring and laboratory strains of <i>Yersinia pestis</i>, the causative agent of plague

<div><p>The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental (“wild”) strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of <i>Yersinia pestis</i> proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in <i>Y</i>. <i>pestis</i>. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild <i>Y</i>. <i>pestis</i> are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.</p></div

Overview of the samples represented in the assembled data sets.

<p>Overview of the samples represented in the assembled data sets.</p

Output of the LRC to distinguish wild from laboratory-adapted strains using relative protein abundance data.

<p>Each symbol represents the prediction of the LRC for an independent culture. Triangles represent cultures of wild strains. Circles represent laboratory-adapted strains. The horizontal axis value is the predicted probability that a culture is laboratory adapted and is non-linear; points are separated vertically in a random fashion to improve the visualization. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183478#sec003" target="_blank">Methods</a> for an explanation of τ. A. Colors represent wild versus laboratory-adapted. B. Colors represent the facility of preparation and analysis. C. Colors represent the laboratory medium in which the cultures were grown prior to analysis.</p

More protein features than those reported in Table 2 can accurately classify laboratory vs. wild samples.

<p>The Lasso logistic regression classifier (LRC) was constructed in iterations, with the input data for each iteration consisting of all protein features not selected by the LRC in any previous iteration. The plots show the classifier accuracy on the vertical axis plotted against the number of iterations on the horizontal axis. The number of features selected in each iteration is the plotted symbol. <b>A</b>, LRCs using quantitative protein abundance data; <b>B</b>, LRCs using presence/absence data. Note that the accuracy value in the limit of large numbers of iterations is equal to the proportion of laboratory samples in the data, and represents the limit where the features used contain no information useful for classification.</p