Collagen type I alters the proteomic signature of macrophages in a collagen morphology-dependent manner.

Idiopathic pulmonary fibrosis is a progressive lung disease that causes scarring and loss of lung function. Macrophages play a key role in fibrosis, but their responses to altered morphological and mechanical properties of the extracellular matrix in fibrosis is relatively unexplored. Our previous work showed functional changes in murine fetal liver-derived alveolar macrophages on fibrous or globular collagen morphologies. In this study, we applied differential proteomics to further investigate molecular mechanisms underlying the observed functional changes. Macrophages cultured on uncoated, fibrous, or globular collagen-coated plastic were analyzed by liquid chromatography-mass spectrometry. The presence of collagen affected expression of 77 proteins, while 142 were differentially expressed between macrophages grown on fibrous or globular collagen. Biological process and pathway enrichment analysis revealed that culturing on any type of collagen induced higher expression of enzymes involved in glycolysis. However, this did not lead to a higher rate of glycolysis, probably because of a concomitant decrease in activity of these enzymes. Our data suggest that macrophages sense collagen morphologies and can respond with changes in expression and activity of metabolism-related proteins. These findings suggest intimate interactions between macrophages and their surroundings that may be important in repair or fibrosis of lung tissue.

BluePrint breast cancer molecular subtyping recognizes single and dual subtype tumors with implications for therapeutic guidance.

PURPOSE: BluePrint (BP) is an 80-gene molecular subtyping test that classifies early-stage breast cancer (EBC) into Basal, Luminal, and HER2 subtypes. In most cases, breast tumors have one dominant subtype, representative of a single activated pathway. However, some tumors show a statistically equal representation of more than one subtype, referred to as dual subtype. This study aims to identify and examine dual subtype tumors by BP to understand their biology and possible implications for treatment guidance. METHODS: The BP scores of over 15,000 tumor samples from EBC patients were analyzed, and the differences between the highest and the lowest scoring subtypes were calculated. Based upon the distribution of the differences between BP scores, a threshold was determined for each subtype to identify dual versus single subtypes. RESULTS: Approximately 97% of samples had one single activated BluePrint molecular subtype, whereas ~ 3% of samples were classified as BP dual subtype. The most frequently occurring dual subtypes were the Luminal-Basal-type and Luminal-HER2-type. Luminal-Basal-type displays a distinct biology from the Luminal single type and Basal single type. Burstein's classification of the single and dual Basal samples showed that the Luminal-Basal-type is mostly classified as 'luminal androgen receptor' and 'mesenchymal' subtypes, supporting molecular evidence of AR activation in the Luminal-Basal-type tumors. Tumors classified as Luminal-HER2-type resemble features of both Luminal-single-type and HER2-single-type. However, patients with dual Luminal-HER2-type have a lower pathological complete response after receiving HER2-targeted therapies in addition to chemotherapy in comparison with patients with a HER2-single-type. CONCLUSION: This study demonstrates that BP identifies tumors with two active functional pathways (dual subtype) with specific transcriptional characteristics and highlights the added value of distinguishing BP dual from single subtypes as evidenced by distinct treatment response rates.

Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort.

OBJECTIVES: Our aim was to derive reference intervals for all Sysmex XN hematology analyzer parameters. The rationale behind the study was the lack of reference intervals for the XN analyzer cell population data (CPD) and functional parameters. METHODS: Fresh fasting blood samples from 18,484 participants in the Dutch Lifelines study were analyzed using two automated XN analyzers. Structured health questionnaire data were used to select a subgroup of 15,803 apparently healthy individuals for inclusion in the reference population. The Latent Abnormal Values Exclusion (LAVE) approach was used to reduce the influence of latent diseases in the reference population on the resulting reference intervals. We applied analysis of variance to judge the need for partitioning of the reference intervals by sex or age. RESULTS: We report reference intervals for 105 XN analyzer hematological parameters with and without applying LAVE. Sex-related partitioning was required for red blood cells, (RBC, RBC-O), hemoglobin (HGB, HGB-O), hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), reticulocyte production index (RPI), and side scattered light intensity of the red blood cell population in the RET channel (RBC-Z). Partitioning for age was not warranted. Body mass index (BMI) and smoking had moderate influence on a minority of the parameters. CONCLUSIONS: We provide reference intervals for all Sysmex XN analyzer routine, CPD and functional parameters, using a direct approach in a large cohort in the Netherlands.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Treatment response and 5-year distant metastasis-free survival outcome in breast cancer patients after the use of MammaPrint and BluePrint to guide preoperative systemic treatment decisions.

AIM: In the prospective neoadjuvant NBREaST II study, we measured the response to preoperative treatment and 5-year survival outcome in the molecular subgroups as determined by combining the MammaPrint and BluePrint. METHODS: Between 2012 and 2016 we included 256 patients for whom MammaPrint and BluePrint were performed on pre-treatment core needle biopsies. The primary objective of the NBREaST II trial was to measure chemosensitivity or endocrine sensitivity in the molecular subgroups. Distant metastasis-free survival (DMFS), relapse-free survival (RFS) and breast cancer-specific survival (BCSS) were the endpoints for long-term follow-up. RESULTS: MammaPrint and BluePrint molecular sub-typing reclassified 9% (24/256) of tumours, reassigning more responsive patients to the HER2-Type and Basal-Type, and less responsive patients to the Luminal-Type category. Patients with Luminal A-Type tumours (n = 67, 26% of the total cohort) had a poor response when treated with neoadjuvant chemotherapy (NCT), but had the highest 5-year DMFS outcome (91.4%; 95% CI 78.6-96.7) of all molecular subgroups. Out of the IHC/FISH defined HER2+ tumours (n = 41), 37% were not classified as HER2-Type by BluePrint. Notably, in BluePrint HER2-Type tumours, we observed a higher pCR rate, whereas the 5-year DMFS was lower compared to IHC/FISH-defined HER2+ tumours. The pCR rate and 5-year outcome for patients with Basal-Type tumours were similar to IHC/FISH-defined TN tumours. CONCLUSION: These findings suggest that MammaPrint and BluePrint can predict chemosensitivity and 5-year outcomes more accurately compared to traditional pathological sub-typing, supporting informed decision-making.

Combined Metabolic and Chemical (CoMetChem) Labeling Using Stable Isotopes-a Strategy to Reveal Site-Specific Histone Acetylation and Deacetylation Rates by LC-MS.

Histone acetylation is an important, reversible post-translational protein modification and a hallmark of epigenetic regulation. However, little is known about the dynamics of this process, due to the lack of analytical methods that can capture site-specific acetylation and deacetylation reactions. We present a new approach that combines metabolic and chemical labeling (CoMetChem) using uniformly 13C-labeled glucose and stable isotope-labeled acetic anhydride. Thereby, chemically equivalent, fully acetylated histone species are generated, enabling accurate relative quantification of site-specific lysine acetylation dynamics in tryptic peptides using high-resolution mass spectrometry. We show that CoMetChem enables site-specific quantification of the incorporation or loss of lysine acetylation over time, allowing the determination of reaction rates for acetylation and deacetylation. Thus, the CoMetChem methodology provides a comprehensive description of site-specific acetylation dynamics.

Pipelines and Systems for Threshold-Avoiding Quantification of LC-MS/MS Data.

The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins.

Dynamic binning peak detection and assessment of various lipidomics liquid chromatography-mass spectrometry pre-processing platforms.

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics generates large datasets that need to be interpreted using high-performance data pre-processing tools such as XCMS, mzMine, and Progenesis. These pre-processing tools rely heavily on accurate peak detection, which depends on proper setting of the peak detection mass tolerance (PDMT). The PDMT is usually set with a fixed value in either ppm or Da units. However, this fixed value may result in duplicates or missed peak detection and inaccurate peak quantification. To improve the accuracy of peak detection, we developed the dynamic binning method, which considers peak broadening described by the physics of ion separation and sets the PDMT dynamically in function of m/z. In our method, the PDMT is proportional to (mz)2 for Fourier-transform ion cyclotron resonance (FTICR), to (mz)1.5 for Orbitrap and to m/z for Quadrupole time-of-flight (Q-TOF), and is a constant for Quadrupole mass analyzer. The dynamic binning method was implemented in XCMS [1,2], and the adopted source code is available in GitHub at https://github.com/xiaodfeng/DynamicXCMS. We have compared the performance of the XCMS implemented dynamic binning with different popular lipidomics pre-processing tools to find differential compounds. We generated set samples with 43 lipid internal standards that were differentially spiked to aliquots of one human plasma lipid sample using Orbitrap LC-MS/MS. The performance of various pipelines using matched parameter sets was quantified by a quality score system that reflects the ability of a pre-processing pipeline to detect differential peaks spiked at various concentrations. The quality score indicated that our dynamic binning method improves the quantification performance of XCMS (maximum p-value 9.8·10-3 of two-sample Wilcoxon test) over its original implementation. We also showed that the XCMS with dynamic binning found differential spiked-in lipids better or with similar performance as mzMine and Progenesis do.

Lipid peroxidation is involved in calcium dependent upregulation of mitochondrial metabolism in skeletal muscle.

BACKGROUND: Skeletal muscle cells continuously generate reactive oxygen species (ROS). Excessive ROS can affect lipids resulting in lipid peroxidation (LPO). Here we investigated the effects of myotube intracellular calcium-induced signaling eliciting contractions on the LPO induction and the impact of LPO-product 4-hydroxynonenal (4-HNE) on physiology/pathology of myotubes using C2C12 myoblasts. METHODS: C2C12 myoblasts were differentiated into myotubes, stimulated with caffeine and analyzed for the induction of LPO and formation of 4-HNE protein adducts. Further effects of 4-HNE on mitochondrial bioenergetics, NADH level, mitochondrial density and expression of mitochondrial metabolism genes were determined. RESULTS: Short and long-term caffeine stimulation of myotubes promoted superoxide production, LPO and formation of 4-HNE protein adducts. Furthermore, low 4-HNE concentrations had no effect on myotube viability and cellular redox homeostasis, while concentrations from 10 µM and above reduced myotube viability and significantly disrupted homeostasis. A time and dose-dependent 4-HNE effect on superoxide production and mitochondrial NADH-autofluorescence was observed. Finally, 4-HNE had strong impact on maximal respiration, spare respiratory capacity, ATP production, coupling efficiency of mitochondria and mitochondrial density. CONCLUSION: Data presented in this work make evident for the first time that pathological 4-HNE levels elicit damaging effects on skeletal muscle cells while acute exposure to physiological 4-HNE induces transient adaptation. GENERAL SIGNIFICANCE: This work suggests an important role of 4-HNE on the regulation of myotube's mitochondrial metabolism and cellular energy production. It further signifies the importance of skeletal muscle cells hormesis in response to acute stress in order to maintain essential biological functions.

Dietary exposure of the Qatari population to food mycotoxins and reflections on the regulation limits.

The article estimates the exposure of the population of Qatar to aflatoxins, fumonisins and ochratoxins, measured in food samples collected from the markets of Doha. The mycotoxin extractions were performed using the "Quick Easy Cheap Effective Rugged Safe" Method (QuEChERS) and the extract measured with LC-MS/MS. High contamination with aflatoxins was detected in Nuts and Spices (e.g. 534.15 ng/g and 371.6 ng/g respectively). The estimated daily intake (EDI) level was estimated using statistical data on average Qatari population. Additionally, the probability to exceed TDI was calculated for fumonisins and ochratoxins. The results indicate high exposure to aflatoxins (with alarming values of margin of exposure, i.e. MoE). The article points to the necessity of a regular assessment and reevaluation of the concentration limits allowed in food products accounting for statistics on the population (i.e. food consumption), economic growth of the country, and monitoring of mycotoxin contamination of food as much as technological, financial and human resources of a country allows.

Aflatoxin, fumonisin, ochratoxin, zearalenone and deoxynivalenol biomarkers in human biological fluids: A systematic literature review, 2001-2018.

Human exposure to mycotoxins occurs mostly through dietary intake, although exposure through dermal and inhalation routes has also been shown. Depending on the type of mycotoxins, the applied dose and duration of exposure, a particular toxin can cause either chronic or acute illnesses such as kidney failure and cancer. Thus, understanding the biotransformation of mycotoxins and identification of reliable biomarkers in the human body is important for accurate risk assessment of mycotoxin exposure. This review provides a comprehensive overview of worldwide aflatoxins, fumonisins, ochratoxin, zearalenone and deoxynivalenol mycotoxin biomonitoring studies reported in the last 18 years. The studies performed in Africa, Europe, Asia and America are based on the measurement of a limited number of mycotoxin biomarkers and do not provide a comprehensive risk assessment of the mycotoxin exposure. Although the findings represent a small segment of a much larger health risk of mycotoxins exposure, it is acknowledged that a multianalyte approach covering bioconjugated and other metabolites of most often occurring mycotoxins would better reflect the extent of the global exposure problems with these highly toxic compounds.

Whole Blood Storage in CPDA1 Blood Bags Alters Erythrocyte Membrane Proteome.

Autologous blood transfusion (ABT) has been frequently abused in endurance sport and is prohibited since the mid-1980s by the International Olympic Committee. Apart from any significant performance-enhancing effects, the ABT may pose a serious health issue due to aging erythrocyte-derived "red cell storage lesions." The current study investigated the effect of blood storage in citrate phosphate dextrose adenine (CPDA1) on the red blood cell (RBC) membrane proteome. One unit of blood was collected in CPDA1 blood bags from 6 healthy female volunteers. RBC membrane protein samples were prepared on days 0, 14, and 35 of storage. Proteins were digested in gel and peptides separated by nanoliquid chromatography coupled to tandem mass spectrometry resulting in the confident identification of 33 proteins that quantitatively change during storage. Comparative proteomics suggested storage-induced translocation of cytoplasmic proteins to the membrane while redox proteomics analysis identified 14 proteins prone to storage-induced oxidation. The affected proteins are implicated in the RBC energy metabolism and membrane vesiculation and could contribute to the adverse posttransfusion outcomes. Spectrin alpha chain, band 3 protein, glyceraldehyde-3-phosphate dehydrogenase, and ankyrin-1 were the main proteins affected by storage. Although potential biomarkers of stored RBCs were identified, the stability and lifetime of these markers posttransfusion remain unknown. In summary, the study demonstrated the importance of studying storage-induced alterations in the erythrocyte membrane proteome and the need to understand the clearance kinetics of transfused erythrocytes and identified protein markers.

Application of Displacement Chromatography to Online Two-Dimensional Liquid Chromatography Coupled to Tandem Mass Spectrometry Improves Peptide Separation Efficiency and Detectability for the Analysis of Complex Proteomes.

The complexity of mammalian proteomes is a challenge in bottom-up proteomics. For a comprehensive proteome analysis, multidimensional separation strategies are necessary. Online two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) combining strong cation exchange (SCX) in the first dimension with reversed-phase (RP) chromatography in the second dimension provides a powerful approach to analyze complex proteomes. Although the combination of SCX with RP chromatography provides a good orthogonality, only a moderate separation is achieved in the first dimension for peptides with two (+2) or three (+3) positive charges. The aim of this study was to improve the performance of online SCX-RP-MS/MS by applying displacement chromatography to the first separation dimension. Compared to gradient chromatography mode (GCM), displacement chromatography mode (DCM) was expected to improve the separation of +2-peptides and +3-peptides, thus reducing complexity and increasing ionization and detectability. The results show that DCM provided a separation of +2-peptides and +3-peptides in remarkably sharp zones with a low degree of coelution, thus providing fractions with significantly higher purities compared to GCM. In particular, +2-peptides were separated over several fractions, which was not possible to achieve in GCM. The better separation in DCM resulted in a higher reproducibility and significantly higher identification rates for both peptides and proteins including a 2.6-fold increase for +2-peptides. The higher number of identified peptides in DCM resulted in significantly higher protein sequence coverages and a considerably higher number of unique peptides per protein. Compared to conventionally used salt-based GCM, DCM increased the performance of online SCX-RP-MS/MS and enabled comprehensive proteome profiling in the low microgram range.

Bayesian peak tracking: A novel probabilistic approach to match GCxGC chromatograms.

A novel peak tracking method based on Bayesian statistics is proposed. The method consists of assigning (i.e. tracking) peaks from two GCxGC-FID data sets of the same sample taken in different conditions. Opposed to traditional (i.e. deterministic) peak tracking algorithms, in which the assignment problem is solved with a unique solution, the proposed algorithm is probabilistic. In other words, we quantify the uncertainty of matching two peaks without excluding other possible candidates, ranking the possible peak assignments regarding their posterior probability. This represents a significant advantage over existing deterministic methods. Two algorithms are presented: the blind peak tracking algorithm (BPTA) and peak table matching algorithm (PTMA). PTMA method was able to assign correctly 78% of a selection of peaks in a GCxGC-FID chromatogram of a diesel sample and proved to be extremely fast.

Use of Bayesian Statistics for Pairwise Comparison of Megavariate Data Sets: Extracting Meaningful Differences between GCxGC-MS Chromatograms Using Jensen-Shannon Divergence.

A new method for comparison of GCxGC-MS is proposed. The method is aimed at spotting the differences between two GCxGC-MS injections, in order to highlight the differences between two samples, in order to flag differences in composition, or to spot compounds only present in one of the samples. The method is based on application of the Jensen-Shannon divergence (JS) analysis combined with Bayesian hypothesis testing. In order to make the method robust against misalignment in both time dimensions, a moving-window approach is proposed. Using a Bayesian framework, we provide a probabilistic visual map (i.e., log likelihood ratio map) of the significant differences between two data sets consequently excluding the deterministic (i.e., "yes" or "no") decision. We proved this approach to be a versatile tool in GCxGC-MS data analysis, especially when the differences are embedded inside a complex matrix. We tested the approach to spot contamination of diesel samples.

Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples.

Accurate analysis of chromatographic data often requires the removal of baseline drift. A frequently employed strategy strives to determine asymmetric weights in order to fit a baseline model by regression. Unfortunately, chromatograms characterized by a very high peak saturation pose a significant challenge to such algorithms. In addition, a low signal-to-noise ratio (i.e. s/n<40) also adversely affects accurate baseline correction by asymmetrically weighted regression. We present a baseline estimation method that leverages a probabilistic peak detection algorithm. A posterior probability of being affected by a peak is computed for each point in the chromatogram, leading to a set of weights that allow non-iterative calculation of a baseline estimate. For extremely saturated chromatograms, the peak weighted (PW) method demonstrates notable improvement compared to the other methods examined. However, in chromatograms characterized by low-noise and well-resolved peaks, the asymmetric least squares (ALS) and the more sophisticated Mixture Model (MM) approaches achieve superior results in significantly less time. We evaluate the performance of these three baseline correction methods over a range of chromatographic conditions to demonstrate the cases in which each method is most appropriate.

Post-polymerization photografting on methacrylate-based monoliths for separation of intact proteins and protein digests with comprehensive two-dimensional liquid chromatography hyphenated with high-resolution mass spectrometry.

Post-polymerization photografting is a versatile tool to alter the surface chemistry of organic-based monoliths so as to obtain desired stationary phase properties. In this study, 2-acrylamido-2-methyl-1-propanesulfonic acid was grafted to a hydrophobic poly(butyl methacrylate-co-ethylene glycol dimethacrylate) monolith to create a strong cation exchange stationary phase. Both single-step and two-step photografting were addressed, and the effects of grafting conditions were assessed. An experimental design has been applied in an attempt to optimize three of the key parameters of the two-step photografting chemistry, i.e. the grafting time of the initiator, the monomer concentration and the monomer irradiation time. The photografted columns were implemented in a comprehensive two-dimensional column liquid chromatography ( (t) LC × (t) LC) workflow and applied for the separation of intact proteins and peptides. A baseline separation of 11 intact proteins was obtained within 20 min by implementing a gradient across a limited RP composition window in the second dimension. (t) LC × (t) LC with UV detection was used for the separation of cytochrome c digest, bovine serum insulin digest and a digest of a complex protein mixture. A semi-quantitative estimation of the occupation of separation space, the orthogonality, of the (t) LC × (t) LC system yielded 75%. The (t) LC × (t) LC setup was hyphenated to a high-resolution Fourier transform ion cyclotron resonance mass spectrometer instrument to identify the bovine serum insulin tryptic peptides and to demonstrate the compatibility with MS analysis.

Retention time prediction in temperature-programmed, comprehensive two-dimensional gas chromatography: modeling and error assessment.

In this paper we present a model relating experimental factors (column lengths, diameters and thickness, modulation times, pressures and temperature programs) with retention times. Unfortunately, an analytical solution to calculate the retention in temperature programmed GC × GC is impossible, making thus necessary to perform a numerical integration. In this paper we present a computational physical model of GC × GC, capable of predicting with a high accuracy retention times in both dimensions. Once fitted (e.g., calibrated), the model is used to make predictions, which are always subject to error. In this way, the prediction can result rather in a probability distribution of (predicted) retention times than in a fixed (most likely) value. One of the most common problems that can occur when fitting unknown parameters using experimental data is overfitting. In order to detect overfitting situations and assess the error, the K-fold cross-validation technique was applied. Another technique of error assessment proposed in this article is the use of error propagation using Jacobians. This method is based on estimation of the accuracy of the model by the partial derivatives of the retention time prediction with respect to the fitted parameters (in this case entropy and enthalpy for each component) in a set of given conditions. By treating the predictions of the model in terms of intervals rather than as precise values, it is possible to considerably increase the robustness of any optimization algorithm.