OpenMS: a flexible open-source software platform for mass spectrometry data analysis

Bioinformatics Pipeline for Processing Single-Cell Data

Single-cell proteomics can offer valuable insights into dynamic cellular interactions, but identifying proteins at this level is challenging due to their low abundance. In this chapter, we present a state-of-the-art bioinformatics pipeline for single-cell proteomics that combines the search engine Sage (via SearchGUI), identification rescoring with MS2Rescore, quantification through FlashLFQ, and differential expression analysis using MSqRob2. MS2Rescore leverages LC-MS/MS behavior predictors, such as MS2PIP and DeepLC, to recalibrate scores with Percolator or mokapot. Combining these tools into a unified pipeline, this approach improves the detection of low-abundance peptides, resulting in increased identifications while maintaining stringent FDR thresholds.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Data-Independent Acquisition Peptidomics

In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework "Nextflow" ( https://nextflow.io ). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user's interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.

PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbitrap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combinations of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.•A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.•Possible spectrum annotation are described as independent consistent networks, with annotations for each fragment as nodes, and annotations for the mass difference between fragments as edges.•Annotation process is described as the selection of the most connected fragments network.

Diverse electron carriers drive syntrophic interactions in an enriched anaerobic acetate-oxidizing consortium

In many anoxic environments, syntrophic acetate oxidation (SAO) is a key pathway mediating the conversion of acetate into methane through obligate cross-feeding interactions between SAO bacteria (SAOB) and methanogenic archaea. The SAO pathway is particularly important in engineered environments such as anaerobic digestion (AD) systems operating at thermophilic temperatures and/or with high ammonia. Despite the widespread importance of SAOB to the stability of the AD process, little is known about their in situ physiologies due to typically low biomass yields and resistance to isolation. Here, we performed a long-term (300-day) continuous enrichment of a thermophilic (55 °C) SAO community from a municipal AD system using acetate as the sole carbon source. Over 80% of the enriched bioreactor metagenome belonged to a three-member consortium, including an acetate-oxidizing bacterium affiliated with DTU068 encoding for carbon dioxide, hydrogen, and formate production, along with two methanogenic archaea affiliated with Methanothermobacter_A. Stable isotope probing was coupled with metaproteogenomics to quantify carbon flux into each community member during acetate conversion and inform metabolic reconstruction and genome-scale modeling. This effort revealed that the two Methanothermobacter_A species differed in their preferred electron donors, with one possessing the ability to grow on formate and the other only consuming hydrogen. A thermodynamic analysis suggested that the presence of the formate-consuming methanogen broadened the environmental conditions where ATP production from SAO was favorable. Collectively, these results highlight how flexibility in electron partitioning during SAO likely governs community structure and fitness through thermodynamic-driven mutualism, shedding valuable insights into the metabolic underpinnings of this key functional group within methanogenic ecosystems.

Computational Expansion of High-Resolution-MSn Spectral Libraries

Commonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS2 analysis, such as MSn fragmentation, can be applied to probe metabolites for additional structural information. In MSn fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS2 spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (m/z) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSn spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSn spectra by converting existing low-resolution-MSn spectra using complementary high-resolution-MS2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSn spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.

Genetic deletion of MMP12 ameliorates cardiometabolic disease by improving insulin sensitivity, systemic inflammation, and atherosclerotic features in mice

BACKGROUND

Matrix metalloproteinase 12 (MMP12) is a macrophage-secreted protein that is massively upregulated as a pro-inflammatory factor in metabolic and vascular tissues of mice and humans suffering from cardiometabolic diseases (CMDs). However, the molecular mechanisms explaining the contributions of MMP12 to CMDs are still unclear.

METHODS

We investigated the impact of MMP12 deficiency on CMDs in a mouse model that mimics human disease by simultaneously developing adipose tissue inflammation, insulin resistance, and atherosclerosis. To this end, we generated and characterized low-density lipoprotein receptor (Ldlr)/Mmp12-double knockout (DKO) mice fed a high-fat sucrose- and cholesterol-enriched diet for 16-20 weeks.

RESULTS

DKO mice showed lower cholesterol and plasma glucose concentrations and improved insulin sensitivity compared with LdlrKO mice. Untargeted proteomic analyses of epididymal white adipose tissue revealed that inflammation- and fibrosis-related pathways were downregulated in DKO mice. In addition, genetic deletion of MMP12 led to alterations in immune cell composition and a reduction in plasma monocyte chemoattractant protein-1 in peripheral blood which indicated decreased low-grade systemic inflammation. Aortic en face analyses and staining of aortic valve sections demonstrated reduced atherosclerotic plaque size and collagen content, which was paralleled by an improved relaxation pattern and endothelial function of the aortic rings and more elastic aortic sections in DKO compared to LdlrKO mice. Shotgun proteomics revealed upregulation of anti-inflammatory and atheroprotective markers in the aortas of DKO mice, further supporting our data. In humans, MMP12 serum concentrations were only weakly associated with clinical and laboratory indicators of CMDs.

CONCLUSION

We conclude that the genetic deletion of MMP12 ameliorates obesity-induced low-grade inflammation, white adipose tissue dysfunction, biomechanical properties of the aorta, and the development of atherosclerosis. Therefore, therapeutic strategies targeting MMP12 may represent a promising approach to combat CMDs.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics

BACKGROUND

Liquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.

RESULTS

To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Short-term acidification promotes diverse iron acquisition and conservation mechanisms in upwelling-associated phytoplankton

Coastal upwelling regions are among the most productive marine ecosystems but may be threatened by amplified ocean acidification. Increased acidification is hypothesized to reduce iron bioavailability for phytoplankton thereby expanding iron limitation and impacting primary production. Here we show from community to molecular levels that phytoplankton in an upwelling region respond to short-term acidification exposure with iron uptake pathways and strategies that reduce cellular iron demand. A combined physiological and multi-omics approach was applied to trace metal clean incubations that introduced 1200 ppm CO2 for up to four days. Although variable, molecular-level responses indicate a prioritization of iron uptake pathways that are less hindered by acidification and reductions in iron utilization. Growth, nutrient uptake, and community compositions remained largely unaffected suggesting that these mechanisms may confer short-term resistance to acidification; however, we speculate that cellular iron demand is only temporarily satisfied, and longer-term acidification exposure without increased iron inputs may result in increased iron stress.

Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale

The growing complexity and volume of proteomics data necessitate the development of efficient software tools for peptide identification and quantification from mass spectra. Given their central role in proteomics, it is imperative that these tools are auditable and extensible─requirements that are best fulfilled by open-source and permissively licensed software. This work presents Sage, a high-performance, open-source, and freely available proteomics pipeline. Scalable and cloud-ready, Sage matches the performance of state-of-the-art software tools while running an order of magnitude faster.

A robust nanoscale RP HPLC-MS approach for sensitive Fc proteoform profiling of IgG allotypes

The conserved region (Fc) of IgG antibodies dictates the interactions with designated receptors thus defining the immunological effector functions of IgG. Amino acid sequence variations in the Fc, recognized as subclasses and allotypes, as well as post-translational modifications (PTMs) modulate these interactions. Yet, the high similarity of Fc sequences hinders allotype-specific PTM analysis by state-of-the-art bottom-up methods and current subunit approaches lack sensitivity and face co-elution of near-isobaric allotypes. To circumvent these shortcomings, we present a nanoscale reversed-phase (RP) HPLC-MS workflow of intact Fc subunits for comprehensive characterization of Fc proteoforms in an allotype- and subclass-specific manner. Polyclonal IgGs were purified from individuals followed by enzymatic digestion releasing single chain Fc subunits (Fc/2) that were directly subjected to analysis. Chromatographic conditions were optimized to separate Fc/2 subunits of near-isobaric allotypes and subclasses allowing allotype and proteoform identification and quantification across all four IgG subclasses. The workflow was complemented by a semi-automated data analysis pipeline based on the open-source software Skyline followed by post-processing in R. The approach revealed pronounced differences in Fc glycosylation between donors, besides inter-subclass and inter-allotype variability within donors. Notably, partial occupancy of the N-glycosylation site in the CH3 domain of IgG3 was observed that is generally neglected by established approaches. The described method was benchmarked across several hundred runs and showed good precision and robustness. This methodology represents a first mature Fc subunit profiling approach allowing truly subclass- and allotype-specific Fc proteoform characterization beyond established approaches. The comprehensive information obtained paired with the high sensitivity provided by the miniaturization of the approach guarantees applicability to a broad range of research questions including clinically relevant (auto)antibody characterization or pharmacokinetics assessment of therapeutic IgGs.

Organic contaminants in bio-based fertilizer treated soil: Target and suspect screening approaches

Using bio-based fertilizer (BBF) in agricultural soil can reduce the dependency on chemical fertilizer and increase sustainability by recycling nutrient-rich side-streams. However, organic contaminants in BBFs may lead to residues in the treated soil. This study assessed the presence of organic contaminants in BBF treated soils, which is essential for evaluating sustainability/risks of BBF use. Soil samples from two field studies amended with 15 BBFs from various sources (agricultural, poultry, veterinary, and sludge) were analyzed. A combination of QuEChERS-based extraction, liquid chromatography quadrupole time of flight mass spectrometry-based (LC-QTOF-MS) quantitative analysis, and an advanced, automated data interpretation workflow was optimized to extract and analyze organic contaminants in BBF-treated agricultural soil. The comprehensive screening of organic contaminants was performed using target analysis and suspect screening. Of the 35 target contaminants, only three contaminants were detected in the BBF-treated soil with concentrations ranging from 0.4 ng g-1 to 28.7 ng g-1; out of these three detected contaminants, two were also present in the control soil sample. Suspect screening using patRoon (an R-based open-source software platform) workflows and the NORMAN Priority List resulted in tentative identification of 20 compounds (at level 2 and level 3 confidence level), primarily pharmaceuticals and industrial chemicals, with only one overlapping compound in two experimental sites. The contamination profiles of the soil treated with BBFs sourced from veterinary and sludge were similar, with common pharmaceutical features identified. The suspect screening results suggest that the contaminants found in BBF-treated soil might come from alternative sources other than BBFs.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Comprehensive proteomics and meta-analysis of COVID-19 host response

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.

Mobile phase and column chemistry selection for high sensitivity non-targeted LC/ESI/HRMS screening of water

Systematic selection of mobile phase and column chemistry type can be critical for achieving optimal chromatographic separation, high sensitivity, and low detection limits in liquid chromatography electrospray high resolution mass spectrometry (LC/MS). However, the selection process is challenging for non-targeted screening where the compounds of interest are not preselected nor available for method optimization. To provide general guidance, twenty different mobile phase compositions and four columns were compared for the analysis of 78 compounds with a wide range of physicochemical properties (logP range from -1.46 to 5.48), and analyte sensitivity was compared between methods. The pH, additive type, column, and organic modifier had significant effects on the analyte response factors, and acidic mobile phases (e.g. 0.1% formic acid) yielded highest sensitivity. In some cases, the effect was attributable to the difference in organic modifier content at the time of elution, depending on the mobile phase and column chemistry. Based on these findings, 0.1% formic acid, 0.1% ammonia and 5.0 mM ammonium fluoride were further evaluated for their performance in non-targeted LC/ESI/HRMS analysis of wastewater treatment plan influent and effluent, using a data dependent MS² acquisition and two different data processing workflows (MS-DIAL, patRoon 2.1) to compare number of detected features and sensitivity. Both data-processing workflows indicated that 0.1% formic acid yielded the highest number of features in full scan spectrum (MS¹)^, as well as the highest number of features that triggered fragmentation spectra (MS²) when dynamic exclusion was used.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability

Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.

Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences

Proteomic studies typically involve the use of different types of software for annotating experimental tandem mass spectrometric data (MS/MS) and thereby simplifying the process of peptide and protein identification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursor and fragment ions, for which a database of protein sequences must be provided as an input file. The calculated m/z values are stored as another database, which the user usually cannot view. Database Creator for Mass Analysis of Peptides and Proteins (DC-MAPP) is a novel standalone software that can create custom databases for "viewing" the calculated m/z values of precursor and fragment ions, prior to the database search. It contains three modules. Peptide/Protein sequences as per user's choice can be entered as input to the first module for creating a custom database. In the second module, m/z values must be queried-in, which are searched within the custom database to identify protein/peptide sequences. The third module is suited for peptide mass fingerprinting, which can be used to analyze both ESI and MALDI mass spectral data. The feature of "viewing" the custom database can be helpful not only for better understanding the search engine processes, but also for designing multiple reaction monitoring (MRM) methods. Post-translational modifications and protein isoforms can also be analyzed. Since, DC-MAPP relies on the protein/peptide "sequences" for creating custom databases, it may not be applicable for the searches involving spectral libraries. Python language was used for implementation, and the graphical user interface was built with Page/Tcl, making this tool more user-friendly. It is freely available at https://vit.ac.in/DC-MAPP/.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Impact of Asialoglycoprotein Receptor and Mannose Receptor Deficiency on Murine Plasma N-glycome Profiles

The asialoglycoprotein receptor (ASGPR) and the mannose receptor C-type 1 (MRC1) are well known for their selective recognition and clearance of circulating glycoproteins. Terminal galactose and N-Acetylgalactosamine are recognized by ASGPR, while terminal mannose, fucose, and N-Acetylglucosamine are recognized by MRC1. The effects of ASGPR and MRC1 deficiency on the N-glycosylation of individual circulating proteins have been studied. However, the impact on the homeostasis of the major plasma glycoproteins is debated and their glycosylation has not been mapped with high molecular resolution in this context. Therefore, we evaluated the total plasma N-glycome and plasma proteome of ASGR1 and MRC1 deficient mice. ASGPR deficiency resulted in an increase in O-acetylation of sialic acids accompanied by higher levels of apolipoprotein D, haptoglobin, and vitronectin. MRC1 deficiency decreased fucosylation without affecting the abundance of the major circulating glycoproteins. Our findings confirm that concentrations and N-glycosylation of the major plasma proteins are tightly controlled and further suggest that glycan-binding receptors have redundancy, allowing compensation for the loss of one major clearance receptor.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant

RATIONALE

Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation.

METHODS

We developed an interactive R script for guided mass spectrometry (MS) data processing. The "MQ_Assistant" is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and preview the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst.

RESULTS

Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of three biological replicates of a plant-microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data.

CONCLUSIONS

The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The stepwise operation with visual feedback also suggests the use of the MQ_Assistant in education.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs). Here, we developed a deep learning-based pure ion chromatogram method (DeepPIC) to find PICs using a customized U-Net from centroid mode data of LC-MS directly and automatically. A model was trained, validated, and tested on the Arabidopsis thaliana dataset with 200 input-label pairs. DeepPIC was integrated into KPIC2. The combination enables the entire processing pipeline from raw data to discriminant models for metabolomics datasets. The KPIC2 with DeepPIC was compared against other competing methods (XCMS, FeatureFinderMetabo, and peakonly) on the MM48, simulated MM48, and quantitative datasets. These comparisons showed that DeepPIC outperforms XCMS, FeatureFinderMetabo, and peakonly in recall rates and correlation with sample concentrations. Five datasets of different instruments and samples were used to evaluate the quality of PICs and the universal applicability of DeepPIC, and 95.12% of the found PICs could precisely match their manually labeled PICs. Therefore, KPIC2+DeepPIC is an automatic, practical, and off-the-shelf method to extract features from raw data directly, exceeding traditional methods with careful parameter tuning. It is publicly available at https://github.com/yuxuanliao/DeepPIC.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

AlphaPeptStats: an open-source Python package for automated and scalable statistical analysis of mass spectrometry-based proteomics

SUMMARY

The widespread application of mass spectrometry (MS)-based proteomics in biomedical research increasingly requires robust, transparent, and streamlined solutions to extract statistically reliable insights. We have designed and implemented AlphaPeptStats, an inclusive Python package with currently with broad functionalities for normalization, imputation, visualization, and statistical analysis of label-free proteomics data. It modularly builds on the established stack of Python scientific libraries and is accompanied by a rigorous testing framework with 98% test coverage. It imports the output of a range of popular search engines. Data can be filtered and normalized according to user specifications. At its heart, AlphaPeptStats provides a wide range of robust statistical algorithms such as t-tests, analysis of variance, principal component analysis, hierarchical clustering, and multiple covariate analysis-all in an automatable manner. Data visualization capabilities include heat maps, volcano plots, and scatter plots in publication-ready format. AlphaPeptStats advances proteomic research through its robust tools that enable researchers to manually or automatically explore complex datasets to identify interesting patterns and outliers.

AVAILABILITY AND IMPLEMENTATION

AlphaPeptStats is implemented in Python and part of the AlphaPept framework. It is released under a permissive Apache license. The source code and one-click installers are freely available and on GitHub at https://github.com/MannLabs/alphapeptstats.

Integrative Multi-Omics Analysis of Oncogenic EZH2 Mutants: From Epigenetic Reprogramming to Molecular Signatures

Somatic heterozygous mutations in the active site of the enhancer of zeste homolog 2 (EZH2) are prevalent in diffuse large B-cell lymphoma (DLBCL) and acute myeloid leukemia (AML). The methyltransferase activity of EZH2 towards lysine 27 on histone H3 (H3K27) and non-histone proteins is dysregulated by the presence of gain-of-function (GOF) and loss-of-function (LOF) mutations altering chromatin compaction, protein complex recruitment, and transcriptional regulation. In this study, a comprehensive multi-omics approach was carried out to characterize the effects of differential H3K27me3 deposition driven by EZH2 mutations. Three stable isogenic mutants (EZH2Y641F, EZH2A677G, and EZH2H689A/F667I) were examined using EpiProfile, H3K27me3 CUT&Tag, ATAC-Seq, transcriptomics, label-free proteomics, and untargeted metabolomics. A discrete set of genes and downstream targets were identified for the EZH2 GOF and LOF mutants that impacted pathways involved in cellular proliferation, differentiation, and migration. Disruption of protein networks and metabolic signatures able to sustain aberrant cell behavior was observed in response to EZH2 mutations. This systems biology-based analysis sheds light on EZH2-mediated cell transformative processes, from the epigenetic to the phenotypic level. These studies provide novel insights into aberrant EZH2 function along with targets that can be explored for improved diagnostics/treatment in hematologic malignancies with mutated EZH2.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.

Disease phenotype prediction in multiple sclerosis

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.

NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter

The present study introduces NOMspectra, a Python package for processing high resolution mass spectrometry data on complex systems of natural organic matter (NOM). NOM is characterized by multicomponent composition reflected as thousands of signals producing very complex patterns in high resolution mass spectra. This complexity sets special demands on the methods of data processing used for analysis. The developed NOMspectra package offers a comprehensive workflow for processing, analyzing, and visualizing information-rich mass spectra of NOM and HS including algorithms for filtering spectra, recalibrating, and assigning elemental compositions to molecular ions. Additionally, the package includes functions for calculating various molecular descriptors and methods for data visualization. A graphical user interface (GUI) has been developed to make a user-friendly interface for the proposed package.

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

BACKGROUND

The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.

RESULTS

The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.

CONCLUSIONS

Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.

Viral proteogenomic and expression profiling during productive replication of a skin-tropic herpesvirus in the natural host

Efficient transmission of herpesviruses is essential for dissemination in host populations; however, little is known about the viral genes that mediate transmission, mostly due to a lack of natural virus-host model systems. Marek's disease is a devastating herpesviral disease of chickens caused by Marek's disease virus (MDV) and an excellent natural model to study skin-tropic herpesviruses and transmission. Like varicella zoster virus that causes chicken pox in humans, the only site where infectious cell-free MD virions are efficiently produced is in epithelial skin cells, a requirement for host-to-host transmission. Here, we enriched for heavily infected feather follicle epithelial skin cells of live chickens to measure both viral transcription and protein expression using combined short- and long-read RNA sequencing and LC/MS-MS bottom-up proteomics. Enrichment produced a previously unseen breadth and depth of viral peptide sequencing. We confirmed protein translation for 84 viral genes at high confidence (1% FDR) and correlated relative protein abundance with RNA expression levels. Using a proteogenomic approach, we confirmed translation of most well-characterized spliced viral transcripts and identified a novel, abundant isoform of the 14 kDa transcript family via IsoSeq transcripts, short-read intron-spanning sequencing reads, and a high-quality junction-spanning peptide identification. We identified peptides representing alternative start codon usage in several genes and putative novel microORFs at the 5' ends of two core herpesviral genes, pUL47 and ICP4, along with strong evidence of independent transcription and translation of the capsid scaffold protein pUL26.5. Using a natural animal host model system to examine viral gene expression provides a robust, efficient, and meaningful way of validating results gathered from cell culture systems.

Chaperonin CCT controls extracellular vesicle production and cell metabolism through kinesin dynamics

Cell proteostasis includes gene transcription, protein translation, folding of de novo proteins, post-translational modifications, secretion, degradation and recycling. By profiling the proteome of extracellular vesicles (EVs) from T cells, we have found the chaperonin complex CCT, involved in the correct folding of particular proteins. By limiting CCT cell-content by siRNA, cells undergo altered lipid composition and metabolic rewiring towards a lipid-dependent metabolism, with increased activity of peroxisomes and mitochondria. This is due to dysregulation of the dynamics of interorganelle contacts between lipid droplets, mitochondria, peroxisomes and the endolysosomal system. This process accelerates the biogenesis of multivesicular bodies leading to higher EV production through the dynamic regulation of microtubule-based kinesin motors. These findings connect proteostasis with lipid metabolism through an unexpected role of CCT.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS² database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

Effects of Rheum rhaponticum and Rheum rhabarbarum extracts on haemostatic activity of blood plasma components and endothelial cells in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional medicine recommends the use of Rheum rhaponticum L. and R. rhabarbarum L. to treat over thirty complaints, including disorders related to the cardiovascular system such as heartache, pains in the pericardium, epistaxis and other types of haemorrhage, blood purification as well as disorders of venous circulation.

AIM OF THE STUDY

This work was dedicated to examining for the first time the effects of extracts from petioles and roots of R. rhaponticum and R. rhabarbarum, as well as two stilbene compounds (rhapontigenin and rhaponticin) on the haemostatic activity of endothelial cells and functionality of blood plasma components of the haemostatic system.

MATERIALS AND METHODS

The study was based on three main experimental modules, including the activity of proteins of the human blood plasma coagulation cascade and the fibrinolytic system as well as analyses of the haemostatic activity of human vascular endothelial cells. Additionally, interactions of the main components of the rhubarb extracts with crucial serine proteases of the coagulation cascade and fibrinolysis (i.e. thrombin, the coagulation factor Xa and plasmin) were analyzed in silico.

RESULTS

The examined extracts displayed anticoagulant properties and significantly reduced the tissue factor-induced clotting of human blood plasma (by about 40%). Inhibitory effects of the tested extracts on thrombin and the coagulation factor Xa (FXa) were found as well. For the extracts, the IC₅₀ was ranging from 20.26 to 48.11 μg/ml. Modulatory effects on the haemostatic response of endothelial cells, including the release of von Willebrand factor, tissue-type plasminogen activator and the plasminogen activator inhibitor-1, have been also found.

CONCLUSIONS

Our results indicated for the first time that the examined Rheum extracts influenced the haemostatic properties of blood plasma proteins and endothelial cells, with the prevalence of the anticoagulant action. The anticoagulant effect of the investigated extracts may be partly attributed to the inhibition of the FXa and thrombin activities, the key serine proteases of the blood coagulation cascade.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Development and application of a data processing method for food metabolomics analysis

Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although technologies exist to analyze these data and different tools exist for various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into the workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualization. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow are included in this method. Compared with conventional approaches, the results of MS1&MS2 spectra-based identification workflows are combined in this approach via the tolerance of retention times and mass to charge ratios (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while retaining 90% of the correct identification. The results demonstrated that the developed method is a rapid and reliable method for food metabolomics data processing.

InSpectra - A platform for identifying emerging chemical threats

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Extended DNA threading through a dual-engine motor module of the activating signal co-integrator 1 complex

Activating signal co-integrator 1 complex (ASCC) subunit 3 (ASCC3) supports diverse genome maintenance and gene expression processes, and contains tandem Ski2-like NTPase/helicase cassettes crucial for these functions. Presently, the molecular mechanisms underlying ASCC3 helicase activity and regulation remain unresolved. We present cryogenic electron microscopy, DNA-protein cross-linking/mass spectrometry as well as in vitro and cellular functional analyses of the ASCC3-TRIP4 sub-module of ASCC. Unlike the related spliceosomal SNRNP200 RNA helicase, ASCC3 can thread substrates through both helicase cassettes. TRIP4 docks on ASCC3 via a zinc finger domain and stimulates the helicase by positioning an ASC-1 homology domain next to the C-terminal helicase cassette of ASCC3, likely supporting substrate engagement and assisting the DNA exit. TRIP4 binds ASCC3 mutually exclusively with the DNA/RNA dealkylase, ALKBH3, directing ASCC3 for specific processes. Our findings define ASCC3-TRIP4 as a tunable motor module of ASCC that encompasses two cooperating NTPase/helicase units functionally expanded by TRIP4.

PyC2MC: An Open-Source Software Solution for Visualization and Treatment of High-Resolution Mass Spectrometry Data

Complex molecular mixtures are encountered in almost all research disciplines, such as biomedical 'omics, petroleomics, and environmental sciences. State-of-the-art characterization of sample materials related to these fields, deploying high-end instrumentation, allows for gathering large quantities of molecular composition data. One established technological platform is ultrahigh-resolution mass spectrometry, e.g., Fourier-transform mass spectrometry (FT-MS). However, the huge amounts of data acquired in FT-MS often result in tedious data treatment and visualization. FT-MS analysis of complex matrices can easily lead to single mass spectra with more than 10,000 attributed unique molecular formulas. Sophisticated software solutions to conduct these treatment and visualization attempts from commercial and noncommercial origins exist. However, existing applications have distinct drawbacks, such as focusing on only one type of graphic representation, being unable to handle large data sets, or not being publicly available. In this respect, we developed a software, within the international complex matrices molecular characterization joint lab (IC2MC), named "python tools for complex matrices molecular characterization" (PyC2MC). This piece of software will be open-source and free to use. PyC2MC is written under python 3.9.7 and relies on well-known libraries such as pandas, NumPy, or SciPy. It is provided with a graphical user interface developed under PyQt5. The two options for execution, (1) a user-friendly route with a prepacked executable file or (2) running the main python script through a Python interpreter, ensure a high applicability but also an open characteristic for further development by the community. Both are available on the GitHub platform (https://github.com/iC2MC/PyC2MC_viewer).

Oral supplementation of nicotinamide riboside alters intestinal microbial composition in rats and mice, but not humans

The gut microbiota impacts systemic levels of multiple metabolites including NAD⁺ precursors through diverse pathways. Nicotinamide riboside (NR) is an NAD⁺ precursor capable of regulating mammalian cellular metabolism. Some bacterial families express the NR-specific transporter, PnuC. We hypothesized that dietary NR supplementation would modify the gut microbiota across intestinal sections. We determined the effects of 12 weeks of NR supplementation on the microbiota composition of intestinal segments of high-fat diet-fed (HFD) rats. We also explored the effects of 12 weeks of NR supplementation on the gut microbiota in humans and mice. In rats, NR reduced fat mass and tended to decrease body weight. Interestingly, NR increased fat and energy absorption but only in HFD-fed rats. Moreover, 16S rRNA gene sequencing analysis of intestinal and fecal samples revealed an increased abundance of species within Erysipelotrichaceae and Ruminococcaceae families in response to NR. PnuC-positive bacterial strains within these families showed an increased growth rate when supplemented with NR. The abundance of species within the Lachnospiraceae family decreased in response to HFD irrespective of NR. Alpha and beta diversity and bacterial composition of the human fecal microbiota were unaltered by NR, but in mice, the fecal abundance of species within Lachnospiraceae increased while abundances of Parasutterella and Bacteroides dorei species decreased in response to NR. In conclusion, oral NR altered the gut microbiota in rats and mice, but not in humans. In addition, NR attenuated body fat mass gain in rats, and increased fat and energy absorption in the HFD context.

Analysis of differentially expressed proteins after EHP-infection and characterization of caspase 3 protein in the whiteleg shrimp (Litopenaeus vannamei)

Whiteleg shrimp (Litopenaeus vannamei) is the most important species of shrimp farmed worldwide in terms of its economic value. Enterocytozoon hepatopenaei (EHP) infects the hepatopancreas, resulting in the hepatopancreatic microsporidiosis (HPM) of the host, which causes slow growth of the shrimp and poses a threat to the farming industry. In this study, differentially expressed proteins (DEPs) between EHP-infected and uninfected shrimp were investigated through proteomics sequencing. A total of 9908 peptides and 2092 proteins were identified. A total of 69 DEPs were identified in the hepatopancreas (HP), of which, 28 were upregulated and 41 were downregulated. Our results showed that the differences among the level of multiple proteins involved in the apoptosis were significant after the EHP infection, which indicated that the apoptosis pathway was activated in whiteleg shrimp. In addition, expression leve of caspase 3 gene were identified related to the EHP infection. Furthermore, predictions of spatial structure, analysis of phylogeny and chromosome-level linearity of the caspase 3 protein were performed as well. In conclusion, a relatively complete proteomic data set of hepatopancreas tissues in whiteleg shrimp were established in this study. Findings about genes involved in the apoptosis here will provide a further understanding of the molecular mechanism of EHP infection in the internal immunity of whiteleg shrimp.

Quantitation of small molecules from liquid chromatography-mass spectrometric accurate mass datasets using CycloBranch

Gaussian and exponentially modified Gaussian functions were incorporated into integrating algorithms used by an open-source, cross-platform tool called CycloBranch. The quantitation is demonstrated on bacterial pyoverdines separated by fine isotope features. Using our algorithm, we can separate the m/z values 694.25802 and 694.26731 (a 0.009 Da difference), where the former belongs to the most intense peak of pyoverdine D (PvdD), and the latter to the second most intense peak of pyoverdine E (PvdE) in the respective isotopic clusters of [M + Fe-H]²⁺ ions. The areas under chromatographic curves of standards were analyzed for the limit of detection (LOD), limit of quantitation (LOQ), and regression coefficient calculations. The quantitative module returned a LOD and LOQ of 1.4 and 4.3 ng/mL, respectively, for both PvdD and PvdE in human urine. If present and detected in mass spectra, the intensities of user-defined [M + H]⁺, [M + Na]⁺, [M + K]⁺, [M + Fe-H]²⁺, or other ion types, can be accumulated and used for quantitation. The quantitation result is returned by CycloBranch in seconds or minutes, contrary to an hours-long manual approach, prone to user-born errors originating from necessary copying among various software environments. Native Bruker, Waters, Thermo, txt, mgf, mzML, and mzXML data formats are supported in CycloBranch, which is freely available at https://ms.biomed.cas.cz/cyclobranch.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.