Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry

Proteome signature for differential diagnosis of patients with bilateral lung infiltrates

Background

The differential diagnosis of bilateral lung infiltrates and prognosis prediction can be challenging for clinicians in the intensive care unit (ICU). We analysed the proteome from bronchoalveolar lavage fluid (BALF) and determined its usefulness for evaluating the infectious causes and mortality associated with bilateral lung infiltrates.

Methods

In the ICU cohort, 136 patients with bilateral infiltrate on chest radiographs were selected, and bronchoscopy with bronchoalveolar lavage (BAL) was performed. Proteomic profiling of the exosomes in the BALF (n=20) was conducted to identify candidate protein biomarkers potentially associated with infection or mortality. The BAL samples (n=116) were used to measure the candidate biomarker levels.

Results

The candidate biomarkers, CD20, CLIC4, SCFD1 and TAP1, were selected for the differential diagnosis of infection or mortality. The levels of CD20 were significantly elevated in patients with non-infectious causes, compared with those with infectious causes (248.6±154.5 versus 177.6±150.9 ng·mL-1, p=0.014). The levels of CLIC4, SCFD1 and TAP1 did not differ between the two groups. As per the receiver operating characteristic analysis, CD20 was a significant predictor of non-infectious causes (area under curve 0.668; 95% confidence interval 0.567-0.769; p=0.002; cut-off value 167.6 ng·mL-1; sensitivity 74.1%; specificity 63.2%). There were no significant differences in the concentrations of the biomarkers between survivors and non-survivors.

Conclusions

Our results suggest that CD20 levels in BALF might be a useful biomarker for differentiating non-infectious and infectious diseases in patients with bilateral lung infiltrates.

Research status and development trends of omics in neuroblastoma a bibliometric and visualization analysis

Background

Neuroblastoma (NB), a prevalent extracranial solid tumor in children, stems from the neural crest. Omics technologies are extensively employed in NB, and We analyzed published articles on NB omics to understand the research trends and hot topics in NB omics.

Method

We collected all articles related to NB omics published from 2005 to 2023 from the Web of Science Core Collection database. Subsequently, we conducted analyses using VOSviewer, CiteSpace, Bibliometrix, and the Bibliometric online analysis platform (https://bibliometric.com/ ).

Results

We included a total of 514 articles in our analysis. The increasing number of publications in this field since 2020 indicates growing attention to NB omics, gradually entering a mature development stage. These articles span 50 countries and 1,000 institutions, involving 3,669 authors and 292 journals. The United States has the highest publication output and collaboration with other countries, with Germany being the most frequent collaborator. Capital Medical University and the German Cancer Research Center are the institutions with the highest publication count. The Journal of Proteome Research and the Journal of Biological Chemistry are the most prolific journal and most co-cited journal, respectively. Wang, W, and Maris, JM are the scholars with the highest publication count and co-citations in this field. "Neuroblastoma" and "Expression" are the most frequent keywords, while "classification," "Metabolism," "Cancer," and "Diagnosis" are recent key terms. The article titled "Neuroblastoma" by John M. Maris is the most cited reference in this analysis.

Conclusion

The continuous growth in NB omics research underscores its increasing significance in the scientific community. Omics technologies have facilitated the identification of potential biomarkers, advancements in personalized medicine, and the development of novel therapeutic strategies. Despite these advancements, the field faces significant challenges, including tumor heterogeneity, data standardization issues, and the translation of research findings into clinical practice.

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

CZE-MS peptide mapping: To desalt or not to desalt?

BACKGROUND

In "shotgun" approaches involving high-performance liquid chromatography or capillary zone electrophoresis (CZE), matrix removal prior to sample analysis is considered as an indispensable tool. Despite the fact that CZE offers a high tolerance towards salts, most publications reported on the use of desalting. There seems to be no clear consensus on the utilization of desalting in the CZE-MS community, most probably due to the absence of works addressing the comparison of desalted and non-desalted digests. Our aim was to fill this research gap using protein samples of varying complexity in different sample matrices.

RESULTS

First, standard protein digests were analyzed to build the knowledge on the effect of sample clean-up by solid-phase extraction (SPE) pipette tips and the possible stacking phenomena induced by different sample matrices. Desalting led to a somewhat altered peptide profile, the procedure affected mostly the hydrophilic peptides (although not to a devastating extent). Nevertheless, desalting samples allowed remarkable stacking efficiency owing to their low-conductivity sample background, enabling a so-called field-amplified sample stacking phenomenon. Non-desalted samples also produced a stacking event, the mechanism of which is based on transient-isotachophoresis due to the presence of high-mobility ions in the digestion buffer itself. Adding either extra ammonium ions or acetonitrile into the non-desalted digests enhanced the stacking efficiency. A complex sample (yeast cell lysate) was also analyzed with the optimal conditions, which yielded similar tendencies.

SIGNIFICANCE

Based on these results, we propose that sample clean-up in the bottom-up sample preparation process prior to CZE-MS analysis can be omitted. The preclusion of desalting can even enhance detection sensitivity, separation efficiency or sequence coverage.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields

MOTIVATION

Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning.

RESULTS

To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena.

AVAILABILITY AND IMPLEMENTATION

All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

Trends in metabolic dysfunction in polycystic ovary syndrome: a bibliometric analysis

Polycystic ovary syndrome (PCOS) is a very common chronic disease and causes reproductive disorders in women of childbearing age worldwide. The cause of metabolic dysfunction in PCOS is unknown, and there is a lack of systematic bibliometric analysis for this disease. This study included 3,972 articles on metabolic dysfunction in PCOS published from 2012 to 2021. We applied the VOSviewer and the CiteSpace scientometric analysis software to analyze the data regarding the publication of the articles, countries, authors, institutions, scientific categories, cited journals, and keywords. Through this analysis, we determined the research efforts and their developing trends and anticipated the progress in understanding PCOS-related metabolic dysfunction.

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.

Research trends of omics in ulcerative colitis: A bibliometric analysis

Background

Omics has emerged as a promising biological science to shed light on the etiology, pathogenesis, and treatment of ulcerative colitis (UC). At present, although research on the omics of UC has drawn global attention, there is still a lack of bibliometric analysis in this field. This study aimed to access the trends and hotspots of omics in UC research.

Method

Publications related to omics in UC from 1 January 2000 to 15 October 2022 were retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the online bibliometric analysis platform “Bibliometrix” were adopted to extract and visualize information.

Results

A total of 385 publications were finally included and the annual number of publications fluctuated. The trend in publications increased rapidly after 2019. The United States showed its dominant position in several publications, total citations, and international collaborations. The top five research organizations for publications on the research of omics in UC were Harvard Medical School, the Icahn School of Medicine at Mount Sinai, Karolinska Institutet, the Brigham and Women's Hospital, and the Massachusetts General Hospital. Ashwin Ananthakrishnan from the Massachusetts General Hospital was the most productive author, and Séverine Vermeire from the Catholic University of Leuven was co-cited most often. Inflammatory bowel disease was the most popular and co-cited journal in this field. The reference with citation bursts and trend topics showed that “ulcerative colitis,” “inflammatory bowel disease,” “microbiome,” “transcriptomics,” “genomics,” “metabolomics,” “proteomics,” “dysbiosis,” “biomarkers,” “loci,” and “therapy” are currently research hotspots.

Conclusion

Our study presents several important insights into the research trends and developments in the field of omics in UC, which will provide key information for further research.

Recent Advances in Proteomics-Based Approaches to Studying Age-Related Macular Degeneration: A Systematic Review

Age-related macular degeneration (AMD) is a common ocular disease characterized by degeneration of the central area of the retina in the elderly population. Progression and response to treatment are influenced by genetic and non-genetic factors. Proteomics is a powerful tool to study, at the molecular level, the mechanisms underlying the progression of the disease, to identify new therapeutic targets and to establish biomarkers to monitor progression and treatment effectiveness. In this work, we systematically review the use of proteomics-based approaches for the study of the molecular mechanisms underlying the development of AMD, as well as the progression of the disease and on-treatment patient monitoring. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guidelines were followed. Proteomic approaches have identified key players in the onset of the disease, such as complement components and proteins involved in lipid metabolism and oxidative stress, but also in the progression to advanced stages, including factors related to extracellular matrix integrity and angiogenesis. Although anti-vascular endothelial growth factor (anti-VEGF)-based therapy has been crucial in the treatment of neovascular AMD, it is necessary to deepen our understanding of the underlying disease mechanisms to move forward to next-generation therapies for later-stage forms of this multifactorial disease.

Integrating bioinformatic strategies in spatial life science research

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

Meiotic DNA breaks activate a streamlined phospho-signaling response that largely avoids protein-level changes

Meiotic cells introduce a numerous programmed DNA breaks into their genome to stimulate meiotic recombination and ensure controlled chromosome inheritance and fertility. A checkpoint network involving key kinases and phosphatases coordinates the repair of these DNA breaks, but the precise phosphorylation targets remain poorly understood. It is also unknown whether meiotic DNA breaks change gene expression akin to the canonical DNA-damage response. To address these questions, we analyzed the meiotic DNA break response in Saccharomyces cerevisiae using multiple systems-level approaches. We identified 332 DNA break-dependent phosphorylation sites, vastly expanding the number of known events during meiotic prophase. Less than half of these events occurred in recognition motifs for the known meiotic checkpoint kinases Mec1 (ATR), Tel1 (ATM), and Mek1 (CHK2), suggesting that additional kinases contribute to the meiotic DNA-break response. We detected a clear transcriptional program but detected only very few changes in protein levels. We attribute this dichotomy to a decrease in transcript levels after meiotic entry that dampens the effects of break-induced transcription sufficiently to cause only minimal changes in the meiotic proteome.

The crosstalk signals of Sodium Tanshinone ⅡA Sulfonate in rats with cerebral ischemic stroke: Insights from proteomics

BACKGROUND

Stroke could cause long-term disability, even mortality around the world. Recently, Sodium tanshinone IIA sulfonate (STS), identified from Salvia miltiorrhiza Bunge and was found to have unique efficiency in clinical practice as a potential therapeutic agent for ischemic cerebral infarction. However, systematic investigation about the biological mechanism is still lacking. Herein, we utilized high-throughput proteomics approach to identify the underlying targets for the treatment of STS in stroke.

METHODS

We investigated the effect of STS on stroke outcomes on rat model of the Middle Cerebral Artery Occlusion and Reperfusion (MCAO/R), assessing by Z-Longa score, infarct volume and HE staining. Pharmacoproteomic profiling of ischemic penumbra in cortical (IPC) was performed using DIA-based label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) technique. Bioinformatics analysis was processed for further investigation. The expression of core proteins was semi-quantified by DIA, and the major protein correlating with stroke was examined using parallel reaction monitoring (PRM).

RESULTS

Rats in the MCAO/R group showed neurological function deterioration, which was improved by STS. There were 423 differentially expressed proteins (DEPs) in IPC being detected and quantified in both the sham group and the MCAO/R group. Meanwhile, 285 proteins were significantly changed in the STS treated group, compared to the MCAO/R model. Protein-protein interaction (PPI) network, pathway and biological function enrichment were processed for the DEPs across each two groups, the results of which were integrated for analysis. Alb, mTOR, Dync1h1, Stxbp1, Cltc, and Sptan1 were contained as the core proteins. Altered molecules were discovered to be enriched in 18 signal pathways such as phosphatidylinositol signaling system, PI3K/AKT signal pathway and HIF-1 signal pathway. The results also showed the correlation with sleep disturbances and depression post-stroke.

CONCLUSIONS

We concluded that STS could prevent penumbra from progressively ongoing damage and improve neurological deficits in MCAO/R model rats. The intersected pathways and protein networks predicted by proteomics might provide much more detailed information for the therapeutic mechanisms of STS in the treatment of CIS.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

The impact of genomic variation on protein phosphorylation states and regulatory networks

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well-defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

SWATH-based quantitative proteomic analysis of Morus alba L. leaves after exposure to ultraviolet-B radiation and incubation in the dark

Morus alba is a woody shrub of the family Moraceae and used as traditional Chinese medicine for a long history. Ultraviolet-B (UV-B) radiation, as a kind of abiotic stress factor, affected the growth and secondary metabolism in M. alba. Previous studies indicated that the contents of several secondary metabolites such as moracin N, chalcomaricin were significantly increased under high level UV-B radiation and dark incubation in M. alba leaves. To reveal the response mechanism under UV-B radiation and dark incubation in M. alba leaves, SWATH-based quantitative proteomic analysis was performed. Totally, 716 proteins were identified and quantified in the control, UVB, and UVD groups. Among them, 123 proteins and 96 proteins were identified as differentially abundant proteins in UVB group and UVD groups, respectively. Proteins related to photosynthesis, amino acid biosynthesis, and tocopherol biosynthesis were significantly altered in UVB group, while proteins related to the biosynthesis of phenolic compounds were significantly altered in UVD group. In addition, the abundances of proteins involved in the ubiquitin-proteasome system (UPS) were significantly increased in both UVB and UVD groups, indicating that UPS combined with secondary mechanism participated in the resistance to UV-B radiation and dark incubation. The obtained results provide novel insight into the effects of high level UV-B radiation on M. alba leaves and on the strategies used for maximizing the chemical constituents and the medicinal value of the M. alba leaves.

Alteration of EIF2 Signaling, Glycolysis, and Dopamine Secretion in Form-Deprived Myopia in Response to 1% Atropine Treatment: Evidence From Interactive iTRAQ-MS and SWATH-MS Proteomics Using a Guinea Pig Model

Purpose: Atropine, a non-selective muscarinic antagonist, effectively slows down myopia progression in human adolescents and several animal models. However, the underlying molecular mechanism is unclear. The current study investigated retinal protein changes of form-deprived myopic (FDM) guinea pigs in response to topical administration of 1% atropine gel (10 g/L).

Methods: At the first stage, the differentially expressed proteins were screened using fractionated isobaric tags for a relative and absolute quantification (iTRAQ) approach, coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) (n = 24, 48 eyes) using a sample pooling technique. At the second stage, retinal tissues from another cohort with the same treatment (n = 12, 24 eyes) with significant ocular changes were subjected to label-free sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics for orthogonal protein target confirmation. The localization of Alpha-synuclein was verified using immunohistochemistry and confocal imaging.

Results: A total of 1,695 proteins (8,875 peptides) were identified with 479 regulated proteins (FC ≥ 1.5 or ≤0.67) found from FDM eyes and atropine-treated eyes receiving 4-weeks drug treatment using iTRAQ-MS proteomics. Combining the iTRAQ-MS and SWATH-MS datasets, a total of 29 confident proteins at 1% FDR were consistently quantified and matched, comprising 12 up-regulated and 17 down-regulated proteins which differed between FDM eyes and atropine treated eyes (iTRAQ: FC ≥ 1.5 or ≤0.67, SWATH: FC ≥ 1.4 or ≤0.71, p-value of ≤0.05). Bioinformatics analysis using IPA and STRING databases of these commonly regulated proteins revealed the involvement of the three commonly significant pathways: EIF2 signaling; glycolysis; and dopamine secretion. Additionally, the most significantly regulated proteins were closely connected to Alpha-synuclein (SNCA). Using immunostaining (n = 3), SNCA was further confirmed in the inner margin of the inner nuclear layer (INL) and spread throughout the inner plexiform layer (IPL) of the retina of guinea pigs.

Conclusion: The molecular evidence using next-generation proteomics (NGP) revealed that retinal EIF2 signaling, glycolysis, and dopamine secretion through SNCA are implicated in atropine treatment of myopia in the FDM-induced guinea pig model.

Skyline Batch: An Intuitive User Interface for Batch Processing with Skyline

Skyline Batch is a newly developed Windows forms application that enables the easy and consistent reprocessing of data with Skyline. Skyline has made previous advances in this direction; however, none enable seamless automated reprocessing of local and remote files. Skyline keeps a log of all of the steps that were taken in the document; however, reproducing these steps takes time and allows room for human error. Skyline also has a command-line interface, enabling it to be run from a batch script, but using the program in this way requires expertise in editing these scripts. By formalizing the workflow of a highly used set of batch scripts into an intuitive and powerful user interface, Skyline Batch can reprocess data stored in remote repositories just by opening and running a Skyline Batch configuration file. When run, a Skyline Batch configuration downloads all necessary remote files and then runs a four-step Skyline workflow. By condensing the steps needed to reprocess the data into one file, Skyline Batch gives researchers the opportunity to publish their processing along with their data and other analysis files. These easily run configuration files will greatly increase the transparency and reproducibility of published work. Skyline Batch is freely available at https://skyline.ms/batch.url.

Serum KLKB1 as a Potential Prognostic Biomarker for Hepatocellular Carcinoma Based on Data-Independent Acquisition and Parallel Reaction Monitoring

Purpose

With the advancement of minimally invasive surgery and catheters for hepatocellular carcinoma (HCC), it is becoming more and more inconvenient to get tissues or the tissues gained are insufficient for testing. Screening of blood-derived markers is of great significance for prognosis assessment.

Patients and Methods

Data-independent acquisition (DIA) and parallel reaction monitoring (PRM) were implemented to identify valuable prognostic HCC biomarkers in 48 patients with different prognosis. The potential candidate biomarkers were examined in 205 HCC patients using enzyme-linked immunosorbent assay (ELISA) and then validated in The Cancer Genome Atlas (TCGA) HCC cohort.

Results

DIA screened 86 significantly differentially regulated proteins between patients with poor prognosis and those with good prognosis. Eight proteins from the DIA proteomic analyses were quantified by PRM, and six of them (KLKB1, IGFBP3, SHBG, SAA1, C7, and CD44) presented consistent expression trends between DIA and PRM. Then, the results of ELISA indicated that KLKB1 was abnormally expressed in HCC patients, and the serum level of KLKB1 also exhibited significant changes before and after treatment (P = 0.016). Patients with higher KLKB1 serum levels had significantly superior overall survival (P = 0.035) and progression-free survival (P = 0.027) than those with lower KLKB1 expression. In the TCGA-HCC cohort, Cox regression analysis suggested that KLKB1 was an independent prognostic factor for overall survival (P = 0.032) of HCC patients.

Conclusion

Aberrant expression of KLKB1 was strongly associated with the prognosis of HCC patients. KLKB1 may be used to evaluate the prognosis and guide the treatment for HCC.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Identification and Validation of Combination Plasma Biomarker of Afamin, Fibronectin and Sex Hormone-Binding Globulin to Predict Pre-eclampsia

The purpose of the present study was to identify a plasma protein biomarker able to predict pre-eclampsia (PE). Comprehensive quantitative proteomics using mass spectrometry with sequential window acquisition of all theoretical fragment ion spectra (SWATH-MS) was applied to plasma samples of 7 PE and 14 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE), and 11 proteins were selected as candidates potentially able to differentiate the two groups. Plasmas collected at gestational weeks 14-24 from 36 PE and 120 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE) were used to conduct selected reaction monitoring quantification analysis, optimize protein combinations and conduct internal validation, which consisted of 30 iterations of 10-fold cross-validation using multivariate logistic regression and receiver operating characteristic (ROC) analysis. The combination of afamin, fibronectin, and sex-hormone-binding globulin was selected as the best candidate. The 3-protein combination predictive model (predictive equation and cut-off value) generated using the internal validation subjects was successfully validated in another group of validation subjects (36 PE and 54 healthy (for PE subjects, plasma samples were taken before onset of PE)) and showed good predictive performance, with the area under the curve (AUC) 0.835 and odds ratio 13.43. In conclusion, we newly identified a 3-protein combination biomarker and established a predictive equation and cut-off value that can predict the onset of PE based on analysis of plasma samples collected during gestational weeks 14-24.

SWATH Based Quantitative Proteomics Reveals Significant Lipid Metabolism in Early Myopic Guinea Pig Retina

Most of the previous myopic animal studies employed a single-candidate approach and lower resolution proteomics approaches that were difficult to detect minor changes, and generated limited systems-wide biological information. Hence, a complete picture of molecular events in the retina involving myopic development is lacking. Here, to investigate comprehensive retinal protein alternations and underlying molecular events in the early myopic stage, we performed a data-independent Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH) based proteomic analysis coupled with different bioinformatics tools in pigmented guinea pigs after 4-day lens-induced myopia (LIM). Myopic eyes compared to untreated contralateral control eyes caused significant changes in refractive error and choroid thickness (p < 0.05, n = 5). Relative elongation of axial length and the vitreous chamber depth were also observed. Using pooled samples from all individuals (n = 10) to build a species-specific retinal ion library for SWATH analysis, 3202 non-redundant proteins (with 24,616 peptides) were identified at 1% global FDR. For quantitative analysis, the 10 individual retinal samples (5 pairs) were analyzed using a high resolution Triple-TOF 6600 mass spectrometry (MS) with technical replicates. In total, 37 up-regulated and 21 down-regulated proteins were found significantly changed after LIM treatment (log2 ratio (T/C) > 0.26 or < -0.26; p ≤ 0.05). Data are accepted via ProteomeXchange with identifier PXD025003. Through Ingenuity Pathways Analysis (IPA), "lipid metabolism" was found as the top function associated with the differentially expressed proteins. Based on the protein abundance and peptide sequences, expression patterns of two regulated proteins (SLC6A6 and PTGES2) identified in this pathway were further successfully validated with high confidence (p < 0.05) using a novel Multiple Reaction Monitoring (MRM) assay on a QTRAP 6500+ MS. In summary, through an integrated discovery and targeted proteomic approach, this study serves as the first report to detect and confirm novel retinal protein changes and significant biological functions in the early LIM mammalian guinea pigs. The study provides new workflow and insights for further research to myopia control.

Exosomal Cargo May Hold the Key to Improving Reproductive Outcomes in Dairy Cows

The reproductive status of dairy cows remains a challenge for dairy farmers worldwide, with impaired fertility linked to a significant reduction in herd profitability, due in part to impaired immunity, increased metabolic pressure, and longer postpartum anestrous interval (PPAI). Exosomes are nanovesicles released from a variety of cell types and end up in circulation, and carry proteins, bioactive peptides, lipids, and nucleic acids specific to the place of origin. As such, their role in health and disease has been investigated in humans and animals. This review discusses research into exosomes in the context of reproduction in dairy herds and introduces recent advances in mass-spectrometry (MS) based proteomics that have a potential to advance quantitative profiling of exosomal protein cargo in a search for early biomarkers of cattle fertility.

Benchmarking Quantitative Performance in Label-Free Proteomics

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.

Nα-terminal acetylation of proteins by NatA and NatB serves distinct physiological roles in Saccharomyces cerevisiae

N-terminal (Nt) acetylation is a highly prevalent co-translational protein modification in eukaryotes, catalyzed by at least five Nt acetyltransferases (Nats) with differing specificities. Nt acetylation has been implicated in protein quality control, but its broad biological significance remains elusive. We investigate the roles of the two major Nats of S. cerevisiae, NatA and NatB, by performing transcriptome, translatome, and proteome profiling of natAΔ and natBΔ mutants. Our results reveal a range of NatA- and NatB-specific phenotypes. NatA is implicated in systemic adaptation control, because natAΔ mutants display altered expression of transposons, sub-telomeric genes, pheromone response genes, and nuclear genes encoding mitochondrial ribosomal proteins. NatB predominantly affects protein folding, because natBΔ mutants, to a greater extent than natA mutants, accumulate protein aggregates, induce stress responses, and display reduced fitness in the absence of the ribosome-associated chaperone Ssb. These phenotypic differences indicate that controlling Nat activities may serve to elicit distinct cellular responses.

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

Uncovering proteome variations of differently heat-treated sea cucumber (Apostichopus japonicus) by label-free mass spectrometry

The effects of heat treatment on the proteome of Apostichopus japonicus have been evaluated using label-free quantitative proteomics by ultrahigh performance liquid chromatography-quadrupole/time of flight (UHPLC-Q/TOF) mass spectrometry with sequential window acquisition of all the theoretical fragment ion (SWATH) acquisition mode. Chemometric tools are integrated to reveal proteomic changes by mining the protein quantitation data from fresh and differently heat-treated samples. SWATH allows the quantitation of 548 proteins, of which 24 proteins are significantly sensitive to heat treatment and 13 proteins vary significantly responding to different heat procedures (boiling, steaming, and microwave heating), and 5 of them are sharing proteins. Gene ontology (GO) annotation of the differentiating proteins highlights most of them are relevant to molecular functions. The results can be favorable to evaluate the effects of heat treatment on the nutrition and function of processed sea cucumbers and facilitate the selection of an optimal thermal treatment.

Natural Shrimp (Litopenaeus vannamei) Tropomyosin Shows Higher Allergic Properties than Recombinant Ones as Compared through SWATH-MS-Based Proteomics and Immunological Response

Tropomyosin (TM) is the major shrimp allergen that could trigger anaphylactic reactions. Recently, recombinant TM (rTM) has been accepted widely in the field of allergen-specific immunotherapy, but the allergenicity of rTM has not been compared with natural TM (nTM) based on an in vitro digestion profile. In this work, IgG-/IgE binding, allergen peptides, and degranulation ability of the digested samples in simulated gastric fluid/simulated intestinal fluid/gastrointestinal models from nTM and rTM were evaluated by immunoassays, proteomics, and basophil degranulation assay. Results showed that pepsin-digested and trypsin-digested samples of rTM exhibited lower IgG-/IgE binding and degranulation than those of nTM. More peptides of the digested samples from rTM (57.8%) matched shrimp allergic epitopes than those from nTM (33.3%). However, the peptide SITDELDQTF (269-278) appeared most frequently. These findings would supply foundation data for epitope-based immunotherapy to shrimp allergic individuals.

Multiomics Profiling Reveals Protective Function of Schisandra Lignans against Acetaminophen-Induced Hepatotoxicity

The action principles of traditional Chinese medicines (TCMs) feature multiactive components, multitarget sites, and weak combination with action targets. In the present study, we performed an integrated analysis of metabonomics, proteomics, and lipidomics to establish a scientific research system on the underlying mechanism of TCMs, and Schisandra lignan extract (SLE) was selected as a model TCM. In metabonomics, several metabolic pathways were found to mediate the liver injury induced by acetaminophen (APAP), and SLE could regulate the disorder of lipid metabolism. The proteomic study further proved that the hepatoprotective effect of SLE was closely related to the regulation of lipid metabolism. Indeed, the results of lipidomics demonstrated that SLE dosing has an obvious callback effect on APAP-induced lipidic profile shift. The contents of 25 diglycerides (DAGs) and 21 triglycerides (TAGs) were enhanced significantly by APAP-induced liver injury, which could further induce liver injury and inflammatory response by upregulating protein kinase C (PKCβ, PKCγ, PKCδ, and PKCθ). The upregulated lipids and PKCs could be reversed to the normal level by SLE dosing. More importantly, phosphatidic acid phosphatase, fatty acid transport protein 5, and diacylglycerol acyltransferase 2 were proved to be positively associated with the regulation of DAGs and TAGs. SIGNIFICANCE STATEMENT: Integrated multiomics was first used to reveal the mechanism of APAP-induced acute liver failure (ALF) and the hepatoprotective role of SLE. The results showed that the ALF caused by APAP was closely related to lipid regulation and that SLE dosing could exert a hepatoprotective role by reducing intrahepatic diglyceride and triglyceride levels. Our research can not only promote the application of multicomponent technology in the study of the mechanism of traditional Chinese medicines but also provide an effective approach for the prevention and treatment of ALF.

From classical to new generation approaches: An excursus of -omics methods for investigation of protein-protein interaction networks

Functional Proteomics aims to the identification of in vivo protein-protein interaction (PPI) in order to piece together protein complexes, and therefore, cell pathways involved in biological processes of interest. Over the years, proteomic approaches used for protein-protein interaction investigation have relied on classical biochemical protocols adapted to a global overview of protein-protein interactions, within so-called "interactomics" investigation. In particular, their coupling with advanced mass spectrometry instruments and innovative analytical methods led to make great strides in the PPIs investigation in proteomics. In this review, an overview of protein complexes purification strategies, from affinity purification approaches, including proximity-dependent labeling techniques and cross-linking strategy for the identification of transient interactions, to Blue Native Gel Electrophoresis (BN-PAGE) and Size Exclusion Chromatography (SEC) employed in the "complexome profiling", has been reported, giving a look to their developments, strengths and weakness and providing to readers several recent applications of each strategy. Moreover, a section dedicated to bioinformatic databases and platforms employed for protein networks analyses was also included.

MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

Salivary proteome signatures in the early and middle stages of human pregnancy with term birth outcome

The establishment and maintenance of pregnancy in humans proceed through a continuous change of biochemical and biophysical processes. It requires a constant interaction between the fetus and the maternal system. The present prospective study aims to elucidate changes in salivary proteome from the early to middle stages of term pregnancy, and establishing an expressional trajectory for modulated proteins. To date, a comprehensive characterization of the longitudinal salivary proteome in pregnancy has not been performed and it is our immediate interest. In the discovery phase, maternal saliva (N = 20) at 6–13, 18–21, and 26–29 weeks of gestation was analyzed using level-free proteomics (SWATH-MS) approach. The expression levels of 65 proteins were found to change significantly with gestational age and distributed into two distinct clusters with a unique expression trajectory. The results revealed that altered proteins are involved in maternal immune modulation, metabolism, and host defense mechanism. Further, verification of 12 proteins was employed using targeted mass spectrometry (MRM-MS) in a separate subset of saliva (N = 14). The MRM results of 12 selected proteins confirmed a similar expression pattern as in SWATH-MS analysis. Overall, the results not only demonstrate the longitudinal maternal saliva proteome for the first time but also set the groundwork for comparative analysis between term birth and adverse pregnancy outcomes.

Revealing Dynamic Protein Acetylation across Subcellular Compartments

Protein acetylation is a widespread post-translational modification implicated in many cellular processes. Recent advances in mass spectrometry have enabled the cataloging of thousands of sites throughout the cell; however, identifying regulatory acetylation marks have proven to be a daunting task. Knowledge of the kinetics and stoichiometry of site-specific acetylation is an important factor to uncover function. Here, an improved method of quantifying acetylation stoichiometry was developed and validated, providing a detailed landscape of dynamic acetylation stoichiometry within cellular compartments. The dynamic nature of site-specific acetylation in response to serum stimulation was revealed. In two distinct human cell lines, growth factor stimulation led to site-specific, temporal acetylation changes, revealing diverse kinetic profiles that clustered into several groups. Overlap of dynamic acetylation sites among two different human cell lines suggested similar regulatory control points across major cellular pathways that include splicing, translation, and protein homeostasis. Rapid increases in acetylation on protein translational machinery suggest a positive regulatory role under progrowth conditions. Finally, higher median stoichiometry was observed in cellular compartments where active acetyltransferases are well described. Data sets can be accessed through ProteomExchange via the MassIVE repository (ProteomExchange: PXD014453; MassIVE: MSV000084029).

Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.

Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics

Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.

SWATH Differential Abundance Proteomics and Cellular Assays Show In Vitro Anticancer Activity of Arachidonic Acid- and Docosahexaenoic Acid-Based Monoacylglycerols in HT-29 Colorectal Cancer Cells

Colorectal cancer (CRC) is one of the most common and mortal types of cancer. There is increasing evidence that some polyunsaturated fatty acids (PUFAs) exercise specific inhibitory actions on cancer cells through different mechanisms, as a previous study on CRC cells demonstrated for two very long-chain PUFA. These were docosahexaenoic acid (DHA, 22:6n3) and arachidonic acid (ARA, 20:4n6) in the free fatty acid (FFA) form. In this work, similar design and technology have been used to investigate the actions of both DHA and ARA as monoacylglycerol (MAG) molecules, and results have been compared with those obtained using the corresponding FFA. Cell assays revealed that ARA- and DHA-MAG exercised dose- and time-dependent antiproliferative actions, with DHA-MAG acting on cancer cells more efficiently than ARA-MAG. Sequential window acquisition of all theoretical mass spectra (SWATH) - mass spectrometry massive quantitative proteomics, validated by parallel reaction monitoring and followed by pathway analysis, revealed that DHA-MAG had a massive effect in the proteasome complex, while the ARA-MAG main effect was related to DNA replication. Prostaglandin synthesis also resulted as inhibited by DHA-MAG. Results clearly demonstrated the ability of both ARA- and DHA-MAG to induce cell death in colon cancer cells, which suggests a direct relationship between chemical structure and antitumoral actions.

Mass Spectrometry-Based Analysis of Time-Resolved Proteome Quantification

The aspect of time is essential in biological processes and thus it is important to be able to monitor signaling molecules through time. Proteins are key players in cellular signaling and they respond to many stimuli and change their expression in many time-dependent processes. Mass spectrometry (MS) is an important tool for studying proteins, including their posttranslational modifications and their interaction partners-both in qualitative and quantitative ways. In order to distinguish the different trends over time, proteins, modification sites, and interacting proteins must be compared between different time points, and therefore relative quantification is preferred. In this review, the progress and challenges for MS-based analysis of time-resolved proteome dynamics are discussed. Further, aspects on model systems, technologies, sampling frequencies, and presentation of the dynamic data are discussed.

Extremely Fast and Accurate Open Modification Spectral Library Searching of High-Resolution Mass Spectra Using Feature Hashing and Graphics Processing Units

Open modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides. We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. On the basis of these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

RNA sampling from tissue sections using infrared laser ablation

RNA was obtained from discrete locations of frozen rat brain tissue sections through infrared (IR) laser ablation using a 3-μm wavelength in transmission geometry. The ablated plume was captured in a microcentrifuge tube containing RNAse-free buffer and processed using a commercial RNA purification kit. RNA transfer efficiency and integrity were evaluated based on automated electrophoresis in microfluidic chips. Reproducible IR-laser ablation of intact RNA was demonstrated with purified RNA at laser fluences of 3-5 kJ/m² (72 ± 12% transfer efficiency) and with tissue sections at a laser fluence of 13 kJ/m² (79 ± 14% transfer efficiency); laser energies were attenuated ∼20% by the soda-lime glass slides used to support the samples. RNA integrity from tissue ablation was >90% of its original RIN value (∼7) and the purified RNA was sufficiently intact for conversion to cDNA and subsequent qPCR assay.

Methodologies and Applications of Proteomics for Study of Yeast Strains: An Update

Yeasts are one of the mostly used microorganisms as models in several studies. A wide range of applications in different processes can be attributed to their intrinsic characteristics. They are eukaryotes and therefore valuable expression hosts that require elaborate post-translational modifications. Their arsenal of proteins has become a valuable biochemical tool for the catalysis of several reactions of great value to the food (beverages), pharmaceutical and energy industries. Currently, the main challenge in systemic yeast biology is the understanding of the expression, function and regulation of the protein pool encoded by such microorganisms. In this review, we will provide an overview of the proteomic methodologies used in the analysis of yeasts. This research focuses on the advantages and improvements in their most recent applications with an understanding of the functionality of the proteins of these microorganisms, as well as an update of the advances of methodologies employed in mass spectrometry.

Development of Targeted Mass Spectrometry-Based Approaches for Quantitation of Proteins Enriched in the Postsynaptic Density (PSD)

The postsynaptic density (PSD) is a structural, electron-dense region of excitatory glutamatergic synapses, which is involved in a variety of cellular and signaling processes in neurons. The PSD is comprised of a large network of proteins, many of which have been implicated in a wide variety of neuropsychiatric disorders. Biochemical fractionation combined with mass spectrometry analyses have enabled an in-depth understanding of the protein composition of the PSD. However, the PSD composition may change rapidly in response to stimuli, and robust and reproducible methods to thoroughly quantify changes in protein abundance are warranted. Here, we report on the development of two types of targeted mass spectrometry-based assays for quantitation of PSD-enriched proteins. In total, we quantified 50 PSD proteins in a targeted, parallel reaction monitoring (PRM) assay using heavy-labeled, synthetic internal peptide standards and identified and quantified over 2100 proteins through a pre-determined spectral library using a data-independent acquisition (DIA) approach in PSD fractions isolated from mouse cortical brain tissue.

Comparative analysis of proteomic and metabolomic profiles of different species of Paris

An extract prepared from species of Paris is the most widely consumed herbal product in China. The genus Paris includes a variety of genotypes with different medicinal component contents but only two are defined as official sources. Closely related species have different medicinal properties because of differential expression of proteins and metabolites. To better understand the molecular basis of these differences, we examined proteomic and metabolomic changes in rhizomes of P. polyphylla var. chinensis, P. polyphylla var. yunnanensis, and P. fargesii var. fargesii using a technique known as sequential window acquisition of all theoretical mass spectra as well as gas chromatography-time-of-flight mass spectrometry. In total, 419 proteins showed significant abundance changes, and 33 metabolites could be used to discriminate Paris species. A complex analysis of proteomic and metabolomic data revealed a higher efficiency of sucrose utilization and an elevated protein abundance in the sugar metabolic pathway of P. polyphylla var. chinensis. The pyruvate content and efficiency of acetyl-CoA-utilization in saponin biosynthesis were also higher in P. polyphylla var. chinensis than in the other two species. The results expand our understanding of the proteome and metabolome of Paris and offer new insights into the species-specific traits of these herbaceous plants. SIGNIFICANCE: The traditional Chinese medicine Paris is the most widely consumed herbal product for the treatment of joint pain, rheumatoid arthritis and antineoplastic. All Paris species have roughly the same morphological characteristics; however, different members have different medicinal compound contents. Efficient exploitation of genetic diversity is a key factor in the development of rare medicinal plants with improved agronomic traits and malleability to challenging environmental conditions. Nevertheless, only a partial understanding of physiological and molecular mechanisms of different plants of Paris can be achieved without proteomics. To better understand the molecular basis of these differences and facilitate the use of other Paris species, we examine proteomic metabolomic changes in rhizomes of Paris using the technique known as SWATH-MS and GC/TOF-MS. Our research has provided information that can be used in other studies to compare metabolic traits in different Paris species. Our findings can also serve as a theoretical basis for the selection and cultivation of other Paris species with a higher medicinal value.