Dinosaur: A Refined Open-Source Peptide MS Feature Detector

The quantification of zebrafish ocular-associated proteins provides hints for sex-biased visual impairments and perception

Biochemical differences between sexes can also be seen in non-sexual organs and may affect organ functions and susceptibility to diseases. It has been shown that there are sex-biased visual perceptions and impairments. Abundance differences of eye proteins could provide explanations for some of these. Exploration of the ocular proteome was performed to find sex-based protein abundance differences in zebrafish Danio rerio. A label-free protein quantification workflow using high-resolution mass spectrometry was employed to find proteins with significant differences between the sexes. In total, 3740 unique master proteins were identified and quantified, and 49 proteins showed significant abundance differences between the eyes of male and female zebrafish. Those proteins belong to lipoproteins, immune system, blood coagulation, antioxidants, iron and heme-binding proteins, ion channels, pumps and exchangers, neuronal and photoreceptor proteins, and the cytoskeleton. An extensive literature review provided clues for the possible links between the sex-biased level of proteins and visual perception and impairments. In conclusion, sexual dimorphism at the protein level was discovered for the first time in the eye of zebrafish and should be accounted for in ophthalmological studies. Data are available via ProteomeXchange with identifier PXD033338.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Accurate and comprehensive peptide precursor ions are crucial to tandem mass-spectrometry-based peptide identification. An identification engine can derive great advantages from the search space reduction enabled by credible and detailed precursors. Furthermore, by considering multiple precursors per spectrum, both the number of identifications and the spectrum explainability can be substantially improved. Here, we introduce PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for subsequent peptide identification. PepPre is evaluated both on regular and cross-linked peptide data sets and compared with 11 methods. The experimental results show that PepPre achieves a remarkable increase of 203% in PSM and 68% in peptide identifications compared to instrument software for regular peptides and 99% in PSM and 27% in peptide pair identifications for cross-linked peptides, surpassing the performance of all other evaluated methods. In addition to the increased identification numbers, further credibility evaluations evidence the reliability of the identified results. Moreover, by widening the isolation window of data acquisition from 2 to 8 Th, with PepPre, an engine is able to identify at least 64% more PSMs, thereby demonstrating the potential advantages of wide-window data acquisition. PepPre is open-source and available at http://peppre.ctarn.io.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

An exhaustive multiple knockout approach to understanding cell wall hydrolase function in Bacillus subtilis

IMPORTANCE

In order to grow, bacterial cells must both create and break down their cell wall. The enzymes that are responsible for these processes are the target of some of our best antibiotics. Our understanding of the proteins that break down the wall- cell wall hydrolases-has been limited by redundancy among the large number of hydrolases many bacteria contain. To solve this problem, we identified 42 cell wall hydrolases in Bacillus subtilis and created a strain lacking 40 of them. We show that cells can survive using only a single cell wall hydrolase; this means that to understand the growth of B. subtilis in standard laboratory conditions, it is only necessary to study a very limited number of proteins, simplifying the problem substantially. We additionally show that the ∆40 strain is a research tool to characterize hydrolases, using it to identify three "helper" hydrolases that act in certain stress conditions.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

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.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics

Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail, and (5) verify that the hyperparameter estimations are sensible.

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics

Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.

A de novo MS1 feature detector for the Bruker timsTOF Pro

Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706).

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

Multiomic Profiling Identified EGF Receptor Signaling as a Potential Inhibitor of Type I Interferon Response in Models of Oncolytic Therapy by Vesicular Stomatitis Virus

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Day and night isotope labelling reveal metabolic pathway specific regulation of protein synthesis rates in Arabidopsis

Plants have a diurnal separation of metabolic fluxes and a need for differential maintenance of protein machinery in the day and night. To directly assess the output of the translation process and to estimate the ATP investment involved, the individual rates of protein synthesis and degradation of hundreds of different proteins need to be measured simultaneously. We quantified protein synthesis and degradation through pulse labelling with heavy hydrogen in Arabidopsis thaliana rosettes to allow such an assessment of ATP investment in leaf proteome homeostasis on a gene-by-gene basis. Light-harvesting complex proteins were synthesised and degraded much faster in the day (approximately 10:1), while carbon metabolism and vesicle trafficking components were translated at similar rates day or night. Few leaf proteins changed in abundance between the day and the night despite reduced protein synthesis rates at night, indicating that protein degradation rates are tightly coordinated. The data reveal how the pausing of photosystem synthesis and degradation at night allows the redirection of a decreased energy budget to a selective night-time maintenance schedule.

Leaf Apoplast of Field-Grown Potato Analyzed by Quantitative Proteomics and Activity-Based Protein Profiling

Multiple biotic and abiotic stresses challenge plants growing in agricultural fields. Most molecular studies have aimed to understand plant responses to challenges under controlled conditions. However, studies on field-grown plants are scarce, limiting application of the findings in agricultural conditions. In this study, we investigated the composition of apoplastic proteomes of potato cultivar Bintje grown under field conditions, i.e., two field sites in June-August across two years and fungicide treated and untreated, using quantitative proteomics, as well as its activity using activity-based protein profiling (ABPP). Samples were clustered and some proteins showed significant intensity and activity differences, based on their field site and sampling time (June-August), indicating differential regulation of certain proteins in response to environmental or developmental factors. Peroxidases, class II chitinases, pectinesterases, and osmotins were among the proteins more abundant later in the growing season (July-August) as compared to early in the season (June). We did not detect significant differences between fungicide Shirlan treated and untreated field samples in two growing seasons. Using ABPP, we showed differential activity of serine hydrolases and β-glycosidases under greenhouse and field conditions and across a growing season. Furthermore, the activity of serine hydrolases and β-glycosidases, including proteins related to biotic stress tolerance, decreased as the season progressed. The generated proteomics data would facilitate further studies aiming at understanding mechanisms of molecular plant physiology in agricultural fields and help applying effective strategies to mitigate biotic and abiotic stresses.

Deep neural network for detecting arbitrary precision peptide features through attention based segmentation

A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.

High-Resolution Proteomic Profiling Shows Sexual Dimorphism in Zebrafish Heart-Associated Proteins

Understanding the molecular basis of sexual dimorphism in the cardiovascular system may contribute to the improvement of the outcome in biological, pharmacological, and toxicological studies as well as on the development of sex-based drugs and therapeutic approaches. Label-free protein quantification using high-resolution mass spectrometry was applied to detect sex-based proteome differences in the heart of zebrafish Danio rerio. Out of almost 3000 unique identified proteins in the heart, 79 showed significant abundance differences between male and female fish. The functional differences were mapped using enrichment analyses. Our results suggest that a large amount of materials needed for reproduction (e.g., sugars, lipids, proteins, etc.) may impose extra pressure on blood, vessels, and heart on their way toward the ovaries. In the present study, the female's heart shows a clear sexual dimorphism by changing abundance levels of numerous proteins, which could be a way to safely overcome material-induced elevated pressures. These proteins belong to the immune system, oxidative stress response, drug metabolization, detoxification, energy, metabolism, and so on. In conclusion, we showed that sex can induce dimorphism at the molecular level in nonsexual organs such as heart and must be considered as an important factor in cardiovascular research. Data are available via ProteomeXchange with identifier PXD023506.

MSTracer: A Machine Learning Software Tool for Peptide Feature Detection from Liquid Chromatography-Mass Spectrometry Data

Liquid chromatography with tandem mass spectrometry (MS/MS) has been widely used in proteomics. Although a typical experiment includes both MS and MS/MS scans, existing bioinformatics research has focused far more on MS/MS data than on MS data. In MS data, each peptide produces a few trails of signal peaks, which are collectively called a peptide feature. Here, we introduce MSTracer, a new software tool for detecting peptide features from MS data. The software incorporates two scoring functions based on machine learning: one for detecting the peptide features and the other for assigning a quality score to each detected feature. The software was compared with several existing tools and demonstrated significantly better performance.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.

SMITER-A Python Library for the Simulation of LC-MS/MS Experiments

SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently.

Stable bull fertility protein markers in seminal plasma

Bull fertility is an important trait in breeding as the semen of one bull can, potentially, be used to perform thousands of inseminations. The high number of inseminations needed to obtain reliable measures from Non-Return Rates to oestrus creates difficulties in assessing fertility accurately. Improving molecular knowledge of seminal properties may provide ways to facilitate selection of bulls with good semen quality. In this study, liquid chromatography mass spectrometry (LC-MS/MS) was used to analyze the protein content from the seminal plasma of 20 bulls with Non-Return Rates between 35 and 60%, sampled across three seasons. Overall, 1343 proteins were identified and proteins with consistent correlation to fertility across multiple seasons found. From these, nine protein groups had a significant Pearson correlation (p < 0.1) with fertility in all three seasons and 34 protein groups had a similar correlation in at least two seasons. Among notable proteins showing a high and consistent correlation across seasons were Osteopontin, a lipase (LIPA) and N-acetylglucosamine-1phosphotransferase subunit gamma. Three proteins were combined in a multiple linear regression to predict fertility (r = 0.81). These sets of proteins represent potential markers, which could be used by the breeding industry to phenotype bull fertility. SIGNIFICANCE: The ability of bull spermatozoa to fertilize oocytes is crucial for breeding efficiency. However, the reliability of this trait from field measures is relatively low and the prediction of fertility given by conventional methods to evaluate sperm quality is currently not very accurate. In this work, we identify sets of proteins in bull seminal plasma from repeated samples collected at different times of the year that correlate to fertility in a consistent way. We combined these individual proteins to build a molecular signature predictive of fertility. This study provides an overview of proteins linked to fertility in seminal plasma, thereby increasing knowledge of the bull seminal plasma proteome. Protein signatures from the latter, potentially related to fertility, may be of use to predict fertility for individual bulls.

Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support

RATIONALE

One of the important steps in initial data processing of peptide mass spectra is the detection of peptide features in full-range mass spectra. Ion mobility offers advantages over previous methods performing this detection by providing an additional structure-specific separation dimension. However, there is a lack of open-source software that utilizes these advantages and detects peptide features in mass spectra acquired along with ion mobility data using new instruments such as timsTOF and/or FAIMS-Orbitrap.

METHODS

Recently, a utility called Dinosaur was presented, which provides an efficient way for feature detection in peptide ion mass spectra. In this work we extended its functionality by developing Biosaur software to fully employ the additional information provided by ion mobility data. Biosaur was developed using the Python 3.8 programming language.

RESULTS

Biosaur supports the processing of data acquired using mass spectrometers with ion mobility capabilities, specifically timsTOF and FAIMS. In addition, it processes mass spectra obtained in negative ion mode and reports cosine correlation table for peptide features which is useful for differentiation between in-source fragments and semi-tryptic peptides.

CONCLUSIONS

Biosaur is a utility for detecting peptide features in liquid chromatography-mass spectra with ion mobility and negative ion supports. The software is distributed with an open-source APACHE 2.0 license and is freely available on Github: https://github.com/abdrakhimov1/Biosaur.

In vivo Cross-Linking MS of the Complement System MAC Assembled on Live Gram-Positive Bacteria

Protein–protein interactions are central in many biological processes, but they are challenging to characterize, especially in complex samples. Protein cross-linking combined with mass spectrometry (MS) and computational modeling is gaining increased recognition as a viable tool in protein interaction studies. Here, we provide insights into the structure of the multicomponent human complement system membrane attack complex (MAC) using in vivo cross-linking MS combined with computational macromolecular modeling. We developed an affinity procedure followed by chemical cross-linking on human blood plasma using live Streptococcus pyogenes to enrich for native MAC associated with the bacterial surface. In this highly complex sample, we identified over 100 cross-linked lysine–lysine pairs between different MAC components that enabled us to present a quaternary model of the assembled MAC in its native environment. Demonstrating the validity of our approach, this MAC model is supported by existing X-ray crystallographic and electron cryo-microscopic models. This approach allows the study of protein–protein interactions in native environment mimicking their natural milieu. Its high potential in assisting and refining data interpretation in electron cryo-tomographic experiments will be discussed.

Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology

Streptococcus pyogenes (Group A streptococcus; GAS) is an important human pathogen responsible for mild to severe, life-threatening infections. GAS expresses a wide range of virulence factors, including the M family proteins. The M proteins allow the bacteria to evade parts of the human immune defenses by triggering the formation of a dense coat of plasma proteins surrounding the bacteria, including IgGs. However, the molecular level details of the M1-IgG interaction have remained unclear. Here, we characterized the structure and dynamics of this interaction interface in human plasma on the surface of live bacteria using integrative structural biology, combining cross-linking mass spectrometry and molecular dynamics (MD) simulations. We show that the primary interaction is formed between the S-domain of M1 and the conserved IgG Fc-domain. In addition, we show evidence for a so far uncharacterized interaction between the A-domain and the IgG Fc-domain. Both these interactions mimic the protein G-IgG interface of group C and G streptococcus. These findings underline a conserved scavenging mechanism used by GAS surface proteins that block the IgG-receptor (FcγR) to inhibit phagocytic killing. We additionally show that we can capture Fab-bound IgGs in a complex background and identify XLs between the constant region of the Fab-domain and certain regions of the M1 protein engaged in the Fab-mediated binding. Our results elucidate the M1-IgG interaction network involved in inhibition of phagocytosis and reveal important M1 peptides that can be further investigated as future vaccine targets.

Streptococcus pyogenes is a human specific pathogen causing both mild and invasive infections. It employs sophisticated mechanisms to evade and circumvent parts of the host’s immune defenses, in part via its major surface associated virulence factor, the family of M proteins. Of these, the M1 protein is the most prevalent serotype. The M1 protein creates a dense coat-like structure with multiple host proteins on the bacterial surface to disguise itself from opsonizing antibodies. It specifically interacts in a non-immune way with human immunoglobulin G (IgG) Fc-domains to disarm their receptor binding site. The molecular level details of this interaction have not been characterized. Here, we describe these interactions from minimally perturbed samples of human plasma adsorbed onto living bacteria using an integrative structural biology approach including cross-linking mass spectrometry, molecular modeling, and molecular dynamics simulations. We identify two distinct M1-peptides that bind IgGs and reveal the stability of these interactions. We show that both peptides block the Fc-receptor binding sites through capturing IgGs via their Fc-domains. These results highlight the importance of describing novel pathogen-derived peptides mediating host immune evasion as potential vaccine targets in future studies.

Proteome-Wide Analysis of ADAR-Mediated Messenger RNA Editing during Fruit Fly Ontogeny

Adenosine-to-inosine RNA editing is an enzymatic post-transcriptional modification which modulates immunity and neural transmission in multicellular organisms. In particular, it involves editing of mRNA codons with the resulting amino acid substitutions. We identified such sites for developmental proteomes of Drosophila melanogaster at the protein level using available data for 15 stages of fruit fly development from egg to imago and 14 time points of embryogenesis. In total, 40 sites were obtained, each belonging to a unique protein, including four sites related to embryogenesis. The interactome analysis has revealed that the majority of the editing-recoded proteins were associated with synaptic vesicle trafficking and actomyosin organization. Quantitation data analysis suggested the existence of a phase-specific RNA editing regulation with yet unknown mechanisms. These findings supported the transcriptome analysis results, which showed that a burst in the RNA editing occurs during insect metamorphosis from pupa to imago. Finally, targeted proteomic analysis was performed to quantify editing-recoded and genomically encoded versions of five proteins in brains of larvae, pupae, and imago insects, which showed a clear tendency toward an increase in the editing rate for each of them. These results will allow a better understanding of the protein role in physiological effects of RNA editing.

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

Quantitative Evaluation of Ion Chromatogram Extraction Algorithms

Extracted ion chromatograms (XIC) are the fundamental signal unit in mass spectrometry. There are many algorithms for analyzing raw mass spectrometry data tasked with distinguishing real isotopic signals from noise. While one or more of the available algorithms are typically chained together for end-to-end mass spectrometry analysis, analysis of each algorithm in isolation provides a specific measurement of the strengths and weaknesses of each approach. Though qualitative opinions on extraction algorithm performance abound, quantitative performance has never been publicly ascertained. Quantitative evaluation has not occurred partly due to the lack of an available quantitative ground truth MS1 data set. Using a recently published, manually extracted XICs as ground truth data, we evaluate the quality of popular XIC algorithms, including MaxQuant, MZMine2, and several methods from XCMS. The manually curated data set comprises 48 human proteins stratified over 6 abundance orders of magnitude. Signals in the sample were manually curated into XIC using a commercial tool for visually identifying XIC and isotopic envelopes. XIC algorithms were applied to the manually extracted data using a grid search of possible parameters. Performance varied greatly between different parameter settings, though nearly all algorithms with parameter settings optimized with respect to the number of true positives recovered over 10 000 XICs.

Interactive proteogenomic exploration of response to Fusarium head blight in oat varieties with different resistance

Fusarium species are cereal pathogens that cause the Fusarium Head Blight (FHB) disease. FHB can reduce yield, cause mycotoxin accumulation in the grain and reduce germination efficiency of the harvested seeds. Understanding the biochemical interactions between the host plants and the pathogen is crucial for controlling the disease and for the development of cultivars with improved tolerance to FHB. Here, we studied morphological and proteomic differences between the susceptible oat variety Belinda and the more resistant variety Argamak using variety-specific transcriptome assemblies as references. Measurements of deoxynivalenol toxin levels confirmed the partial resistance in Argamak and the susceptibility in Belinda. To jointly investigate the proteomics- and sequence data, we developed an RShiny-based interface for interactive exploration of the dataset using univariate and multivariate statistics. When applying this interface to the dataset, quantitative protein differences between Belinda and Argamak were detected, and eighteen peptides were found uniquely in Argamak during infection, among them several lipoxygenases. Such proteins can be developed as markers for Fusarium resistance breeding. In conclusion, this study provides the first proteogenomic insight on molecular Fusarium-oat interactions at both morphological and molecular levels and the data are openly available through an interactive interface for further inspection. SIGNIFICANCE: Fusarium head blight causes widespread damage to crops, and chronic and acute toxicity to human and livestock due to the accumulation of toxins during infection. In the present study, two oat varieties with differing resistance were challenged with Fusarium to understand the disease better, and studied both at morphological and molecular levels, identifying proteins which could play a role in the defense mechanism. Furthermore, a proteogenomics approach allows joint profiling of expression and sequence level differences to identify potentially functionally differing mutations. Here such analysis is made openly available through an interactive interface which allows other scientists to draw further findings from the data. This study may both serve as a basis for understanding oat disease response and developing breeding markers for Fusarium resistant oat and future proteogenomic studies using the interactive approach described.

An integrated transcriptomic- and proteomic-based approach to evaluate the human skin sensitization potential of glyphosate and its commercial agrochemical formulations

We investigated the skin sensitization hazard of glyphosate, the surfactant polyethylated tallow amine (POEA) and two commercial glyphosate-containing formulations using different omics-technologies based on a human dendritic cell (DC)-like cell line. First, the GARD™skin assay, investigating changes in the expression of 200 transcripts upon cell exposure to xenobiotics, was used for skin sensitization prediction. POEA and the formulations were classified as skin sensitizers while glyphosate alone was classified as a non-sensitizer. Interestingly, the mixture of POEA together with glyphosate displayed a similar sensitizing prediction as POEA alone, indicating that glyphosate likely does not increase the sensitizing capacity when associated with POEA. Moreover, mass spectrometry analysis identified differentially regulated protein groups and predicted molecular pathways based on a proteomic approach in response to cell exposures with glyphosate, POEA and the glyphosate-containing formulations. Based on the protein expression data, predicted pathways were linked to immunologically relevant events and regulated proteins further to cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. In summary, we here present an integrative analysis involving advanced technologies to elucidate the molecular mechanisms behind DC activation in the skin sensitization process triggered by the investigated agrochemical materials. SIGNIFICANCE: The use of glyphosate has increased worldwide, and much effort has been made to improve risk assessments and to further elucidate the mechanisms behind any potential human health hazard of this chemical and its agrochemical formulations. In this context, omics-based techniques can provide a multiparametric approach, including several biomarkers, to expand the mechanistic knowledge of xenobiotics-induced toxicity. Based on this, we performed the integration of GARD™skin and proteomic data to elucidate the skin sensitization hazard of POEA, glyphosate and its two commercial mixtures, and to investigate cellular responses more in detail on protein level. The proteomic data indicate the regulation of immune response-related pathways and proteins associated with cholesterol biosynthesis and homeostasis as well as to autophagy, identifying novel aspects of DC responses after exposure to xenobiotics. Therefore, our data show the applicability of a multiparametric integrated approach for the mechanism-based hazard evaluation of xenobiotics, eventually complementing decision making in the holistic risk assessment of chemicals regarding their allergenic potential in humans.

DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.

Proteomics of PTI and Two ETI Immune Reactions in Potato Leaves

Plants have a variety of ways to defend themselves against pathogens. A commonly used model of the plant immune system is divided into a general response triggered by pathogen-associated molecular patterns (PAMPs), and a specific response triggered by effectors. The first type of response is known as PAMP triggered immunity (PTI), and the second is known as effector-triggered immunity (ETI). To obtain better insight into changes of protein abundance in immunity reactions, we performed a comparative proteomic analysis of a PTI and two different ETI models (relating to Phytophthora infestans) in potato. Several proteins showed higher abundance in all immune reactions, such as a protein annotated as sterol carrier protein 2 that could be interesting since Phytophthora species are sterol auxotrophs. RNA binding proteins also showed altered abundance in the different immune reactions. Furthermore, we identified some PTI-specific changes of protein abundance, such as for example, a glyoxysomal fatty acid beta-oxidation multifunctional protein and a MAR-binding protein. Interestingly, a lysine histone demethylase was decreased in PTI, and that prompted us to also analyze protein methylation in our datasets. The proteins upregulated explicitly in ETI included several catalases. Few proteins were regulated in only one of the ETI interactions. For example, histones were only downregulated in the ETI-Avr2 interaction, and a putative multiprotein bridging factor was only upregulated in the ETI-IpiO interaction. One example of a methylated protein that increased in the ETI interactions was a serine hydroxymethyltransferase.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

The Interactome of Palmitoyl-Protein Thioesterase 1 (PPT1) Affects Neuronal Morphology and Function

Palmitoyl-protein thioesterase 1 (PPT1) is a depalmitoylation enzyme that is mutated in cases of neuronal ceroid lipofuscinosis (NCL). The hallmarks of the disease include progressive neurodegeneration and blindness, as well as seizures. In the current study, we identified 62 high-confident PPT1-binding proteins. These proteins included a self-interaction of PPT1, two V-type ATPases, calcium voltage-gated channels, cytoskeletal proteins and others. Pathway analysis suggested their involvement in seizures and neuronal morphology. We then proceeded to demonstrate that hippocampal neurons from Ppt1−/− mice exhibit structural deficits, and further investigated electrophysiology parameters in the hippocampi of mutant mice, both in brain slices and dissociated postnatal primary cultures. Our studies reveal new mechanistic features involved in the pathophysiology of this devastating neurodegenerative disease.

Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics

Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differential proteins use intermediate filters to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered data sets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical data set we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

CDK and MAPK Synergistically Regulate Signaling Dynamics via a Shared Multi-site Phosphorylation Region on the Scaffold Protein Ste5

We report an unanticipated system of joint regulation by cyclin-dependent kinase (CDK) and mitogen-activated protein kinase (MAPK), involving collaborative multi-site phosphorylation of a single substrate. In budding yeast, the protein Ste5 controls signaling through a G1 arrest pathway. Upon cell-cycle entry, CDK inhibits Ste5 via multiple phosphorylation sites, disrupting its membrane association. Using quantitative time-lapse microscopy, we examined Ste5 membrane recruitment dynamics at different cell-cycle stages. Surprisingly, in S phase, where Ste5 recruitment should be blocked, we observed an initial recruitment followed by a steep drop-off. This delayed inhibition revealed a requirement for both CDK activity and negative feedback from the pathway MAPK Fus3. Mutagenesis, mass spectrometry, and electrophoretic analyses suggest that the CDK and MAPK modify shared sites, which are most extensively phosphorylated when both kinases are active and able to bind their docking sites on Ste5. Such collaborative phosphorylation can broaden regulatory inputs and diversify output dynamics of signaling pathways.

Comparative Membrane-Associated Proteomics of Three Different Immune Reactions in Potato

Plants have evolved different types of immune reactions but large-scale proteomics about these processes are lacking, especially in the case of agriculturally important crop pathosystems. We have established a system for investigating PAMP-triggered immunity (PTI) and two different effector-triggered immunity (ETI; triggered by Avr2 or IpiO) responses in potato. The ETI responses are triggered by molecules from the agriculturally important Phytophthora infestans interaction. To perform large-scale membrane protein-based comparison of these responses, we established a method to extract proteins from subcellular compartments in leaves. In the membrane fractions that were subjected to quantitative proteomics analysis, we found that most proteins regulated during PTI were also regulated in the same way in ETI. Proteins related to photosynthesis had lower abundance, while proteins related to oxidative and biotic stress, as well as those related to general antimicrobial defense and cell wall degradation, were found to be higher in abundance. On the other hand, we identified a few proteins—for instance, an ABC transporter-like protein—that were only found in the PTI reaction. Furthermore, we also identified proteins that were regulated only in ETI interactions. These included proteins related to GTP binding and heterotrimeric G-protein signaling, as well as those related to phospholipase signaling.

Fast, axis-agnostic, dynamically summarized storage and retrieval for mass spectrometry data

Mass spectrometry, a popular technique for elucidating the molecular contents of experimental samples, creates data sets comprised of millions of three-dimensional (m/z, retention time, intensity) data points that correspond to the types and quantities of analyzed molecules. Open and commercial MS data formats are arranged by retention time, creating latency when accessing data across multiple m/z. Existing MS storage and retrieval methods have been developed to overcome the limitations of retention time-based data formats, but do not provide certain features such as dynamic summarization and storage and retrieval of point meta-data (such as signal cluster membership), precluding efficient viewing applications and certain data-processing approaches. This manuscript describes MzTree, a spatial database designed to provide real-time storage and retrieval of dynamically summarized standard and augmented MS data with fast performance in both m/z and RT directions. Performance is reported on real data with comparisons against related published retrieval systems.

Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.