Metabolomics Signatures of serotonin reuptake inhibitor (Escitalopram), serotonin norepinephrine reuptake inhibitor (Duloxetine) and Cognitive Behavior Therapy on Key Neurotransmitter Pathways in Major Depressive Disorder.

Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive Behavior Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways. Changes in metabolite concentrations related to each treatment arm were identified and compared to define metabolic signatures of exposure. In addition, association between metabolites and depressive symptom severity (assessed with the 17-item Hamilton Rating Scale for Depression [HRSD17]) and anxiety symptom severity (assessed with the 14-item Hamilton Rating Scale for Anxiety [HRSA14]) were evaluated, both at baseline and after 12 weeks of treatment. Significant reductions in serum serotonin level and increases in tryptophan-derived indoles that are gut bacterially derived were observed with escitalopram and duloxetine arms but not in CBT arm. These include indole-3-propionic acid (I3PA), indole-3-lactic acid (I3LA) and Indoxyl sulfate (IS), a uremic toxin. Purine-related metabolites were decreased across all arms. Different metabolites correlated with improved symptoms in the different treatment arms revealing potentially different mechanisms between response to antidepressant medications and to CBT.

The DIRAC framework: Geometric structure underlies roles of diversity and accuracy in combining classifiers.

Combining classification systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Similar to improving binary classification with fusion, fusing ranking systems most commonly increases Pearson or Spearman correlations with a target when the input classifiers are "sufficiently good" (generalized as "accuracy") and "sufficiently different" (generalized as "diversity"), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Building on our previous empirical work establishing the DIRAC (DIversity of Ranks and ACcuracy) framework, which accurately predicts the outcome of fusing binary classifiers, we demonstrate that the DIRAC framework similarly explains the outcome of fusing ranking systems. Specifically, precise geometric representation of diversity and accuracy as angle-based distances within rank-based combinatorial structures (permutahedra) fully captures their synergistic roles in rank approximation, uncouples them from the specific metrics of a given problem, and represents them as generally as possible.

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner.

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.

Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation.

Hao et al. (2022) present DTox (deep learning for toxicology), a neural network designed to predict and probe the sites and potential mechanisms underlying chemical toxicity; results provide a map to facilitate modular testing and improvements across multiple disparate applications.

Ranks underlie outcome of combining classifiers: Quantitative roles for diversity and accuracy.

Combining classifier systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Classification most commonly improves when the classifiers are "sufficiently good" (generalized as " accuracy ") and "sufficiently different" (generalized as " diversity "), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Beginning with simulated data, we develop the DIRAC framework (DIversity of Ranks and ACcuracy), which accurately predicts outcome of both score-based fusions originating from exponentially modified Gaussian distributions and rank-based fusions, which are inherently distribution independent. DIRAC was validated using biological dual-energy X-ray absorption and magnetic resonance imaging data. The DIRAC framework is domain independent and has expected utility in far-ranging areas such as clinical biomarker development/personalized medicine, clinical trial enrollment, insurance pricing, portfolio management, and sensor optimization.

Optimization of Electrospray Ionization Source Parameters for Lipidomics To Reduce Misannotation of In-Source Fragments as Precursor Ions.

Lipidomics requires the accurate annotation of lipids in complex samples to enable determination of their biological relevance. We demonstrate that unintentional in-source fragmentation (ISF, common in lipidomics) generates ions that have identical masses to other lipids. Lysophosphatidylcholines (LPC), for example, generate in-source fragments with the same mass as free fatty acids and lysophosphatidylethanolamines (LPE). The misannotation of in-source fragments as true lipids is particularly insidious in complex matrixes since most masses are initially unannotated and comprehensive lipid standards are unavailable. Indeed, we show such LPE/LPC misannotations are incorporated in the data submitted to the National Institute of Standards and Technology (NIST) interlaboratory comparison exercise. Computer simulations exhaustively identified potential misannotations. The selection of in-source fragments of highly abundant lipids as features, instead of the correct recognition of trace lipids, can potentially lead to (i) missing the biologically relevant lipids (i.e., a false negative) and/or (ii) incorrect assignation of a phenotype to an incorrect lipid (i.e., false positive). When ISF is not eliminated in the negative ion mode, ∼40% of the 100 most abundant masses corresponding to unique phospholipids measured in plasma were artifacts from ISF. We show that chromatographic separation and ion intensity considerations assist in distinguishing precursor ions from in-source fragments, suggesting ISF may be especially problematic when complex samples are analyzed via shotgun lipidomics. We also conduct a systematic evaluation of electrospray ionization (ESI) source parameters on an Exactive equipped with a heated electrospray ionization (HESI-II) source with the objective of obtaining uniformly appropriate source conditions for a wide range of lipids, while, at the same time, reducing in-source fragmentation.

Simple LC-MS Method for Differentiation of Isobaric Phosphatidylserines and Phosphatidylcholines with Deuterated Mobile Phase Additives.

Lipids from different classes sometimes can exhibit the same exact mass upon electrospray ionization; this presents an analytical challenge in lipidomics. In the negative ionization mode, for example, this can occur with phosphatidylcholines (PCs) and phosphatidylserines (PSs), making them indistinguishable in the absence of fragmentation data. PSs are found at low concentrations in biological samples, making MS/MS spectra difficult to obtain. Moreover, while PCs and PSs are distinguishable in the positive mode, PSs do not ionize as well as PCs, and their ionization is suppressed by the PCs. Here, we show that, in the negative ionization mode, substituting protiated LC-MS additives with their deuterated forms provides a way to distinguish PCs and PSs without chemical derivatization. The method described leverages the differential ionization mechanism of PCs and PSs. PCs are ionized via adduction with salts, whereas PSs ionize via hydrogen abstraction. Substituting the salts used for LC-MS with their deuterated form shifts the mass of PCs by the number of deuterium atoms in the salt, while the mass of PSs remains the same. This comparative shift enables their direct differentiation. We demonstrate that the use of deuterated formate shifts the mass of PCs and provides a direct method to distinguish PCs and PSs, even at biologically relevant low concentrations. The utility of the method was established and validated in the simultaneous analysis of PCs and PSs in lipid extracts from isolated liver mitochondria in two different rat strains. Thirteen low concentration PSs were identified that would otherwise not have been distinguishable from low concentration PCs.

Dietary macronutrients modulate the fatty acyl composition of rat liver mitochondrial cardiolipins.

The interaction of dietary fats and carbohydrates on liver mitochondria were examined in male FBNF1 rats fed 20 different low-fat isocaloric diets. Animal growth rates and mitochondrial respiratory parameters were essentially unaffected, but mass spectrometry-based mitochondrial lipidomics profiling revealed increased levels of cardiolipins (CLs), a family of phospholipids essential for mitochondrial structure and function, in rats fed saturated or trans fat-based diets with a high glycemic index. These mitochondria showed elevated monolysocardiolipins (a CL precursor/product of CL degradation), elevated ratio of trans-phosphocholine (PC) (18:1/18:1) to cis-PC (18:1/18:1) (a marker of thiyl radical stress), and decreased ubiquinone Q9; the latter two of which imply a low-grade mitochondrial redox abnormality. Extended analysis demonstrated: i) dietary fats and, to a lesser extent, carbohydrates induce changes in the relative abundance of specific CL species; ii) fatty acid (FA) incorporation into mature CLs undergoes both positive (>400-fold) and negative (2.5-fold) regulation; and iii) dietary lipid abundance and incorporation of FAs into both the CL pool and specific mature tetra-acyl CLs are inversely related, suggesting previously unobserved compensatory regulation. This study reveals previously unobserved complexity/regulation of the central lipid in mitochondrial metabolism.

Serum lipidomics profiling using LC-MS and high-energy collisional dissociation fragmentation: focus on triglyceride detection and characterization.

There is a growing need both clinically and experimentally to improve the characterization of blood lipids. A liquid chromatography-mass spectrometry (LC-MS) method, developed for the qualitative and semiquantitative detection of lipids in biological samples and previously validated in mitochondrial samples, was now evaluated for the profiling of serum lipids. Data were acquired using high-resolution, full scan MS and high-energy, collisional dissociation (HCD), all ion fragmentation. The method was designed for efficient separation and detection in both positive and negative ionization mode and evaluated using standards spanning seven lipid classes. Platform performance, related to the identification and characterization of serum triglycerides (TGs), was assessed using extracted ion chromatograms with mass tolerance windows of 5 ppm or less from full scan exact mass measurements determined using SIEVE nondifferential LC-MS analysis software. The platform showed retention time coefficients of variation (CV) of <0.3%, mass accuracy values of <2 ppm error, and peak area CV of <13%, with the majority of that error coming from sample preparation and extraction rather than the LC-MS analysis, and linearity was shown to be over 4 orders of magnitude (r(2) = 0.999) for the standard TG (15:0)(3) spiked into serum. Instrument mass accuracy and precision were critical to the identification of unknown TG species, in part because these parameters enabled us to reduce false positives. In addition to detection and relative quantitation of TGs in serum, TG structures were characterized through the use of alternating HCD scans at different energies to produce diagnostic fragmentations on all ions in the analysis. The lipidomics method was applied to serum samples from 192 rats maintained on diets differing in macronutrient composition. The analysis identified 86 TG species with 81 unique masses that varied over 3.5 orders of magnitude and showed diet-dependency, consistent with TGs linking diet and disease risk.

Lipidomics profiling by high-resolution LC-MS and high-energy collisional dissociation fragmentation: focus on characterization of mitochondrial cardiolipins and monolysocardiolipins.

A liquid chromatography-mass spectrometry (LC-MS) method was used for separation of lipid classes as well as both qualitative and semiquantitative detection of individual lipids in biological samples. Data were acquired using high-resolution full-scan MS and high-energy collisional dissociation (HCD) all ion fragmentation. The method was evaluated for efficient separation and detection in both positive and negative ionization mode using standards spanning six lipid classes. Platform linearity and robustness, related to the mitochondrial lipid cardiolipin (CL), were assessed using extracted ion chromatograms with mass tolerance windows of 5 ppm or less from full scan exact mass measurements. The platform CL limit of detection was determined to be 5 pmol (0.9 µM) on the column, with mass accuracy <1.5 ppm, retention time coefficients of variation (CV) < 0.5%, and area CV < 13%. This mass accuracy was critical to the identification of unknown CL species in mitochondria samples, through the elimination of false positives. In addition to detection and relative quantitation of CL species in mitochondria, CL structures were characterized through the use of alternating HCD scans at different energies to produce diagnostic fragmentations on all ions in the analysis. The developed lipid profiling method was applied to mitochondrial samples from an animal study related to the linkages between diet, mitochondrial function, and disease. The analysis identified 28 unique CL species and two monolysocardiolipin species that are often associated with mitochondrial stress and dysfunction.

Current approaches for global post-translational modification discovery and mass spectrometric analysis.

More and more attention is being focused on the analysis of post-translational modifications (PTMs) on proteins as researchers are continually learning how essential they are for proper cellular function. As there are hundreds of different types of known PTMs, traditional methods of modification analysis are incapable of comprehensively monitoring for post-translational modifications, a task which is a necessity for truly understanding a cell's biology. This review highlights recent developments in novel multiplexed methods of PTM analysis including: fluorescent stain and immuno-based methods, hardware-based mass spectrometric methods and computational-based mass spectrometric methods. Many of these techniques show great promise and will likely be a valuable resource for the biological community.

A multiplexed post-translational modification monitoring approach on a matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometer.

Post-translational modifications (PTMs) of proteins are essential for proper function, as they regulate many aspects of a protein's activity and interaction with substrates. When analyzing modified peptides derived from such proteins by mass spectrometry, these modifications can dissociate, producing either a marker ion or neutral loss characteristic of the modification, which have conventionally been monitored with a precursor ion scan or neutral loss scan, respectively. Although powerful, both precursor ion scans and neutral loss scans can only screen for one particular modification at a time. This has led to the development of multiple neutral loss monitoring (MNM) for neutral losses and multiple precursor ion monitoring (MPM) for marker ions on electrospray instruments. Here, we report their implementation on a matrix-assisted laser desorption/ionization (MALDI) instrument as well as the inception of a novel scan strategy termed targeted multiple precursor ion monitoring (tMPM). This latter scan strategy has been developed on a MALDI tandem time-of-flight (TOF/TOF) mass spectrometer for the identification of multiple PTMs via their associated marker ions by manipulating certain components of the instrument, notably the timed ion selector and the delayed extraction source 2. Targeted MPM combined with a second approach, multiple neutral loss monitoring (MNM), is shown to be a successful approach in the identification of PTMs, identifying multiple modified peptides in a complex sample matrix.

Advances in the analysis of dynamic protein complexes by proteomics and data processing.

Signal transduction governs virtually every cellular function of multicellular organisms, and its deregulation leads to a variety of diseases. This intricate network of molecular interactions is mediated by proteins that are assembled into complexes within individual signaling pathways, and their composition and function is often regulated by different post-translational modifications. Proteomic approaches are commonly used to analyze biological complexes and networks, but often lack the specificity to address the dynamic and hence transient nature of the interactions and the influence of the multiple post-translational modifications that govern these processes. Here we review recent developments in proteomic research to address these limitations, and discuss several technologies that have been developed for this purpose. The synergy between these proteomic and computational tools, when applied together with global methods to the analysis of individual proteins, complexes and pathways, may allow researchers to unravel the underlying mechanisms of signaling networks in greater detail than previously possible.

Correlation and convolution analysis of peptide mass spectra.

As proteomics continues to establish itself as an effective postgenomic research tool, there is an increasingly urgent need for efficient, automated analysis techniques capable of effectively dealing with the vast amounts of data generated via mass spectrometry. Wholesale analysis packages, often used to deal with these enormous amounts of data, may benefit from supplementary, targeted analyses as current research begins to emphasize posttranscriptional/translational protein modifications, protein truncations, and poorly characterized mutations. We demonstrate the application of a new analysis technique based on mathematical correlation that is computationally efficient and robust against different instruments, noise levels, and experimental conditions. We have previously shown that this technique is able to extract pertinent mass shift signals from MS data, corresponding to the neutral loss of a modification from a peptide, e.g., a loss of 79.97 Th from phosphorylated tyrosine. Here we show that an extension of this method is applicable to MS and MS/MS data in general, allowing visualization of ions that produce a particular mass shift signal, be it from differential stable isotope labeling, overlap of fragment ions in a series, or ions that produce a neutral loss. The application of this method allows the researcher to discover individual features, such as the presence of specific modified or isotopically labeled peptides, to eliminate overlapping fragment ion series, and to localize specific sites of modification.

Multiple neutral loss monitoring (MNM): a multiplexed method for post-translational modification screening.

Post-translational modifications of proteins are involved in determining the activity of proteins and are essential for proper protein function. Current mass spectrometric strategies require one to specify a particular type of modification, in some cases also a particular charge state of a protein or peptide that is to be studied before the actual analysis. Due to these requirements, most of the modifications on proteins are not considered in such an experiment and, thus, a series of similar analyses need to be performed to ensure a more extensive characterization. A novel scan strategy has been developed, multiple neutral loss monitoring (MNM), allowing for the comprehensive screening of post-translational modifications (PTM) on proteins that fragment as neutral losses in a mass spectrometer. MNM method parameters were determined by performing product ion scans on a number of modified peptides over a range of collision energies, providing neutral loss energy profiles and optimal collision energies (OCE) for each modification, supplying valuable information pertaining to the fragmentation of these modifications and the necessary parameters that would be required to obtain the best analysis. As the optimal collision energy was highly dependent on the type of modification and the charge state of the peptide, the MNM scan was operated with a collision energy gradient. Autocorrelation analyses identified the type of modification, and convolution mapping analyses identified the associated peptide. The MNM scan with the new collision energy parameters was successfully applied to a mixture of four modified peptides in a BSA digest. The implementation of this technique will allow for comprehensive screening of all modifications that fragment as neutral losses.

Statistical evaluation of electrospray tandem mass spectra for optimized peptide fragmentation.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the method of choice for the analysis of complex peptide mixtures. It combines the separation power of nanoflow LC with highly specific sequence analysis, allowing automated peptide sequencing with high resolution and throughput. For peptide fragmentation, the current experimental setup uses predefined parameters based on the mass-to-charge ratio of the individual precursor. Suitable parameters are typically established by empirical evaluation of fragment spectra of individual peptides used as standards. As a result, nonoptimal fragment spectra are obtained if peptides show fragmentation behavior different from these standards, which often result in the loss of sequence-specific fragment ion information. Here we describe a statistical approach for the systematic evaluation of the quality of individual peptide fragment spectra based on the calculation of their arithmetic mean and standard deviation. The method utilizes the dependence of these parameters on the difference in electric potential across the collision cell to determine the value that results in maximum information content. We show that the method is applicable to fragment spectra generated from a variety of multiply-charged tryptic peptides, over a wide concentration range, and on different types of mass analyzers. We also show how this novel approach can be used to define optimized collision energy settings over a wide mass-to-charge range.