Use of Time-Resolved Fluorescence To Improve Sensitivity and Dynamic Range of Gel-Based Proteomics

Limitations in the sensitivity and dynamic range of two-dimensional gel electrophoresis (2-DE) are currently hampering its utility in global proteomics and biomarker discovery applications. In the current study, we present proof-of-concept analyses showing that introducing time-resolved fluorescence in the image acquisition step of in-gel protein quantification provides a sensitive and accurate method for subtracting confounding background fluorescence at the photon level. In-gel protein detection using the minimal difference gel electrophoresis workflow showed improvements in lowest limit of quantification in terms of CyDye molecules per pixel of 330-fold in the blue-green region (Cy2) and 8000-fold in the red region (Cy5) over conventional state-of-the-art image acquisition instrumentation, here represented by the Typhoon 9400 instrument. These improvements make possible the detection of low-abundance proteins present at sub-attomolar levels, thereby representing a quantum leap for the use of gel-based proteomics in biomarker discovery. These improvements were achieved using significantly lower laser powers and overall excitation times, thereby drastically decreasing photobleaching during repeated scanning. The single-fluorochrome detection limits achieved by the cumulative time-resolved emission two-dimensional electrophoresis (CuTEDGE) technology facilitates in-depth proteomics characterization of very scarce samples, for example, primary human tissue materials collected in clinical studies. The unique information provided by high-sensitivity 2-DE, including positional shifts due to post-translational modifications, may increase the chance to detect biomarker signatures of relevance for identification of disease subphenotypes

Building Multivariate Systems Biology Models

Systems biology methods using large-scale “omics” data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate “omics” data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography–mass spectrometry (LC–MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals

Data_Sheet_1_The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries.docx

BackgroundThe global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR.MethodCross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1–30% in each country.ResultsOverall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03–0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs.ConclusionBooster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.</p

Allergic Asthmatics Show Divergent Lipid Mediator Profiles from Healthy Controls Both at Baseline and following Birch Pollen Provocation

<div><h3>Background</h3><p>Asthma is a respiratory tract disorder characterized by airway hyper-reactivity and chronic inflammation. Allergic asthma is associated with the production of allergen-specific IgE and expansion of allergen-specific T-cell populations. Progression of allergic inflammation is driven by T-helper type 2 (Th2) mediators and is associated with alterations in the levels of lipid mediators.</p> <h3>Objectives</h3><p>Responses of the respiratory system to birch allergen provocation in allergic asthmatics were investigated. Eicosanoids and other oxylipins were quantified in the bronchoalveolar lumen to provide a measure of shifts in lipid mediators associated with allergen challenge in allergic asthmatics.</p> <h3>Methods</h3><p>Eighty-seven lipid mediators representing the cyclooxygenase (COX), lipoxygenase (LOX) and cytochrome P450 (CYP) metabolic pathways were screened via LC-MS/MS following off-line extraction of bronchoalveolar lavage fluid (BALF). Multivariate statistics using OPLS were employed to interrogate acquired oxylipin data in combination with immunological markers.</p> <h3>Results</h3><p>Thirty-two oxylipins were quantified, with baseline asthmatics possessing a different oxylipin profile relative to healthy individuals that became more distinct following allergen provocation. The most prominent differences included 15-LOX-derived ω-3 and ω-6 oxylipins. Shared-and-Unique-Structures (SUS)-plot modeling showed a correlation (R² = 0.7) between OPLS models for baseline asthmatics (R²Y[cum] = 0.87, Q²[cum] = 0.51) and allergen-provoked asthmatics (R²Y[cum] = 0.95, Q²[cum] = 0.73), with the majority of quantified lipid mediators and cytokines contributing equally to both groups. Unique structures for allergen provocation included leukotrienes (LTB₄ and 6-<em>trans</em>-LTB₄), CYP-derivatives of linoleic acid (epoxides/diols), and IL-10.</p> <h3>Conclusions</h3><p>Differences in asthmatic relative to healthy profiles suggest a role for 15-LOX products of both ω-6 and ω-3 origin in allergic inflammation. Prominent differences at baseline levels indicate that non-symptomatic asthmatics are subject to an underlying inflammatory condition not observed with other traditional mediators. Results suggest that oxylipin profiling may provide a sensitive means of characterizing low-level inflammation and that even individuals with mild disease display distinct phenotypic profiles, which may have clinical ramifications for disease.</p> </div

Sum of 15-LOX metabolites.

<p>(<b>A</b>) Sum of 15-LOX metabolites from both ω-3 and ω-6 pathways. (<b>B</b>) Sum of 15-LOX metabolites from ω-6 fatty acids (12-and 15-HETE, 15-KETE, 5,15-DiHETE, 13-HODE, 9,10,13- and 9,12,13-TriHOME, 9-HODE, 9-KODE and 15-HETrE), (<b>C</b>) Sum of 15-LOX metabolites from ω-3 fatty acids (9- and 13-HOTE, 12- and 15-HEPE and 17-HDoHE). Data are provided as concentration in BALF (pM). HC: Healthy controls, AC: Asthmatic controls, AFP: Asthmatics following provocation. The p-values obtained using Student's T-test (HC vs. AC and HC vs. AFP) and one-sided Cochran-Armitage trend test (HC, AC and AFP) are indicated in the figure.</p

Oxylipin composition based on polyunsaturated fatty acid class.

<p>(<b>A</b>) Healthy controls (HC) (<b>B</b>) Asthmatic controls (AC) and (<b>C</b>) Asthmatics following provocation (AFP). Oxylipins were grouped into the following classes based upon their fatty acid substrate: linoleic acid (LA), dihomo-γ-linolenic acid (DGLA), arachidonic acid (AA), α-linolenic acid (α-LA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Healthy controls and asthmatics following provocation evidenced significant differences for LA (p = 0.04), EPA (p = 0.02) and DHA (p = 0.04). The proportion of EPA metabolites was also significantly higher in asthmatic controls compared to healthy controls (p = 0.04).</p

Cytokine and BAL cell levels in BALF from 1) Healthy controls 2) Asthmatic controls and 3) Asthmatics following provocation.^a

a<p>Selected variables displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone-0033780-g004" target="_blank">Figure 4</a>, cell populations are defined in Methods. Units for each of the variable groups are defined as: age (years), cell populations (% of total population in BAL cells, except for mast cells, which are given as the number of cells per 10 visual fields in a Bürker chamber), immunological markers (proportion of cells expressing a set of markers among another defined cell population as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780.s007" target="_blank">Tables S4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780.s008" target="_blank">S5</a>), and cytokines (fg/ml BALF) as reported in Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a>.</p>b<p>Statistical significance was calculated with either an unpaired or paired Student's t-test. Values with p<0.05 are shown in italics with two significant figures.</p>c<p>The median levels among the healthy controls (HC) regarding each variable were used as a cut off limit for all three groups (<i>i.e.</i>, Healthy Controls, Asthmatic Controls and Asthmatics Following Provocation). All p-values regarding trend are one-sided.</p>d<p>Average.</p>e<p>Coefficient of variance.</p>f<p>N.D. = value not determined.</p>g<p>The IL-10 data consisted of a significant range in individual values, with one healthy individual possessing a value of 26.1 fg/ml, which did not pass a Q_crit test. However, since this individual was included in the original Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a> paper, it was not removed from this analysis. Exclusion of this individual from the trend test gave p = 0.042 and the resulting Student's t-test values were: HC/AC p = 0.08, HC/AFP p = 0.04, and AC/AFP p = 0.04.</p

Shared and Unique Structures (SUS) plot.

<p>SUS plot correlating the OPLS models of healthy controls versus asthmatic controls (Baseline Asthmatics, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone-0033780-g003" target="_blank">Figure 3A, X</a>-axis) and healthy controls versus asthmatics following provocation (Provoked Asthmatics, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone-0033780-g003" target="_blank">Figure 3B, Y</a>-axis). A complete list of p(corr) values for both models is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780.s010" target="_blank">Table S7</a>. Abbreviations are as follows: 15-lipoxygenease (15-LOX), cyclooxygenase (COX), 5-lipoxygenase (5-LOX), cytochrome P450 (CYP), ω-6 fatty acid (ω-6), ω-3 fatty acid (ω-3), healthy controls (HC), asthmatic controls (AC) and asthmatics following provocation (AFP). Colors are as follows: 15-LOX-derived ω-3 oxylipins (blue), 15-LOX-derived ω-6 oxylipins (red), COX-derived ω-6 oxylipins (green), 5-LOX-derived ω-6 oxylipins (orange), CYP-derived ω-6 oxylipins (gold). Variables with p(corr)≥|0.4| are labeled, while those ≤|0.4| are shown as symbols.</p

Clinical data of participating subjects.

a<p>Subject numbering is presented as originally published by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a>.</p>b<p>Asthma (A), Healthy (H).</p>c<p>Age in years at time of study inclusion.</p>d<p>Female (F), Male (M).</p>e<p>FEV₁% values at baseline. A Student's t-test of the groups indicated that there was no different between the two populations (p = 0.11).</p>f<p>kU/l required for 20% drop in FEV₁%, as reported by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a>.</p>g<p>Oxylipin profiling of subject 7 and 8 was performed in a pooled sample.</p>h<p>Individual 9 was incorrectly reported by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a> and is a 22 year old male healthy individual.</p

OPLS score and loading column plots with respect to separation according to diagnosis.

<p>Loading column plots visualize variables correlating with healthy (−) or asthmatics (+), error bars indicate 95% confidence interval. The number of variables correlating with the asthmatic population with 95% confidence increases following provocation from n = 21 to n = 32. (<b>A</b>) Healthy controls (green) and asthmatic controls (yellow) (R²Y[cum] = 0.87, Q²[cum] = 0.51). (<b>B</b>) Healthy controls (green) and asthmatics following provocation (red) (R²Y[cum] = 0.95, Q²[cum] = 0.73).</p