Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, whereas mass spectra are acquired in about 100 microseconds with time-of-flight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive because of its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 600,000 fragmentation spectra in standard 120 min LC runs are achievable, which can be used for near exhaustive precursor selection in complex mixtures or accumulating the signal of weak precursors. In 120 min single runs of HeLa digest, MaxQuant identified more than 6,000 proteins without matching to a library and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

Single-cell Proteomics: Progress and Prospects

MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows

A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method's applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows. Graphical Abstract.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines

Within the past decade numerous methods for quantitative proteome analysis have been developed of which all exhibit particular advantages and disadvantages. Here, we present the results of a study aiming for a comprehensive comparison of ion-intensity based label-free proteomics and two label-based approaches using isobaric tags incorporated at the peptide and protein levels, respectively. As model system for our quantitative analysis we used the three hepatoma cell lines HepG2, Hep3B and SK-Hep-1. Four biological replicates of each cell line were quantitatively analyzed using an RPLC-MS/MS setup. Each quantification experiment was performed twice to determine technical variances of the different quantification techniques. We were able to show that the label-free approach by far outperforms both TMT methods regarding proteome coverage, as up to threefold more proteins were reproducibly identified in replicate measurements. Furthermore, we could demonstrate that all three methods show comparable reproducibility concerning protein quantification, but slightly differ in terms of accuracy. Here, label-free was found to be less accurate than both TMT approaches. It was also observed that the introduction of TMT labels at the protein level reduces the effect of underestimation of protein ratios, which is commonly monitored in case of TMT peptide labeling. Previously reported differences in protein expression between the particular cell lines were furthermore reproduced, which confirms the applicability of each investigated quantification method to study proteomic differences in such biological systems. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides

Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox.

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts.

Molecular-level insights into aging processes of skin elastin

Skin aging is characterized by different features including wrinkling, atrophy of the dermis and loss of elasticity associated with damage to the extracellular matrix protein elastin. The aim of this study was to investigate the aging process of skin elastin at the molecular level by evaluating the influence of intrinsic (chronological aging) and extrinsic factors (sun exposure) on the morphology and susceptibility of elastin towards enzymatic degradation. Elastin was isolated from biopsies derived from sun-protected or sun-exposed skin of differently aged individuals. The morphology of the elastin fibers was characterized by scanning electron microscopy. Mass spectrometric analysis and label-free quantification allowed identifying differences in the cleavage patterns of the elastin samples after enzymatic digestion. Principal component analysis and hierarchical cluster analysis were used to visualize differences between the samples and to determine the contribution of extrinsic and intrinsic aging to the proteolytic susceptibility of elastin. Moreover, the release of potentially bioactive peptides was studied. Skin aging is associated with the decomposition of elastin fibers, which is more pronounced in sun-exposed tissue. Marker peptides were identified, which showed an age-related increase or decrease in their abundances and provide insights into the progression of the aging process of elastin fibers. Strong age-related cleavage occurs in hydrophobic tropoelastin domains 18, 20, 24 and 26. Photoaging makes the N-terminal and central parts of the tropoelastin molecules more susceptible towards enzymatic cleavage and, hence, accelerates the age-related degradation of elastin.

gpGrouper: A Peptide Grouping Algorithm for Gene-Centric Inference and Quantitation of Bottom-Up Proteomics Data

In quantitative mass spectrometry, the method by which peptides are grouped into proteins can have dramatic effects on downstream analyses. Here we describe gpGrouper, an inference and quantitation algorithm that offers an alternative method for assignment of protein groups by gene locus and improves pseudo-absolute iBAQ quantitation by weighted distribution of shared peptide areas. We experimentally show that distributing shared peptide quantities based on unique peptide peak ratios improves quantitation accuracy compared with conventional winner-take-all scenarios. Furthermore, gpGrouper seamlessly handles two-species samples such as patient-derived xenografts (PDXs) without ignoring the host species or species-shared peptides. This is a critical capability for proper evaluation of proteomics data from PDX samples, where stromal infiltration varies across individual tumors. Finally, gpGrouper calculates peptide peak area (MS1) based expression estimates from multiplexed isobaric data, producing iBAQ results that are directly comparable across label-free, isotopic, and isobaric proteomics approaches.

Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob).

SIGNIFICANCE

The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.

High-Throughput Analysis of IgG Fc Glycopeptides by LC-MS

This chapter contains a nanoscale liquid chromatography-mass spectrometry method for the glycoform profiling of the conserved Fc N-glycosylation site of monoclonal and polyclonal immunoglobulin G (IgG). It describes in detail LaCyTools, a program for automated data (pre-)processing of the obtained LC-MS data. The minimal sample preparation necessary is explained as well as an optional method for affinity purification of (polyclonal) antibodies from serum or plasma.After (optional) affinity purification, the pure IgG is cleaved with trypsin. The tryptic glycopeptides are separated almost exclusively on their peptide backbone. This ensures similar response factors for all glycoforms in the MS detection and allows the collection of separate glycoform profiles for different IgG isoforms or allotypes. LaCyTools automatically performs label-free (relative) quantitation of the obtained data after minimal manual input and additionally calculates several quality criteria which can be used for data curation at the level of both individual analytes and entire LC-MS runs.

Quantitative Proteomics Identification of Seminal Fluid Proteins in Male Drosophila melanogaster

Seminal fluid contains some of the fastest evolving proteins currently known. These seminal fluid proteins (Sfps) play crucial roles in reproduction, such as supporting sperm function, and particularly in insects, modifying female physiology and behavior. Identification of Sfps in small animals is challenging, and often relies on samples taken from the female reproductive tract after mating. A key pitfall of this method is that it might miss Sfps that are of low abundance because of dilution in the female-derived sample or rapid processing in females. Here we present a new and complementary method, which provides added sensitivity to Sfp identification. We applied label-free quantitative proteomics to Drosophila melanogaster, male reproductive tissue - where Sfps are unprocessed, and highly abundant - and quantified Sfps before and immediately after mating, to infer those transferred during copulation. We also analyzed female reproductive tracts immediately before and after copulation to confirm the presence and abundance of known and candidate Sfps, where possible. Results were cross-referenced with transcriptomic and sequence databases to improve confidence in Sfp detection. Our data were consistent with 125 previously reported Sfps. We found nine high-confidence novel candidate Sfps, which were both depleted in mated versus, unmated males and identified within the reproductive tract of mated but not virgin females. We also identified 42 more candidates that are likely Sfps based on their abundance, known expression and predicted characteristics, and revealed that four proteins previously identified as Sfps are at best minor contributors to the ejaculate. The estimated copy numbers for our candidate Sfps were lower than for previously identified Sfps, supporting the idea that our technique provides a deeper analysis of the Sfp proteome than previous studies. Our results demonstrate a novel, high-sensitivity approach to the analysis of seminal fluid proteomes, whose application will further our understanding of reproductive biology.

Genomic and proteomic analysis of lignin degrading and polyhydroxyalkanoate accumulating β-proteobacterium Pandoraea sp. ISTKB

BACKGROUND

Lignin is a major component of plant biomass and is recalcitrant to degradation due to its complex and heterogeneous aromatic structure. The biomass-based research mainly focuses on polysaccharides component of biomass and lignin is discarded as waste with very limited usage. The sustainability and success of plant polysaccharide-based biorefinery can be possible if lignin is utilized in improved ways and with minimal waste generation. Discovering new microbial strains and understanding their enzyme system for lignin degradation are necessary for its conversion into fuel and chemicals. The Pandoraea sp. ISTKB was previously characterized for lignin degradation and successfully applied for pretreatment of sugarcane bagasse and polyhydroxyalkanoate (PHA) production. In this study, genomic analysis and proteomics on aromatic polymer kraft lignin and vanillic acid are performed to find the important enzymes for polymer utilization.

RESULTS

Genomic analysis of Pandoraea sp. ISTKB revealed the presence of strong lignin degradation machinery and identified various candidate genes responsible for lignin degradation and PHA production. We also applied label-free quantitative proteomic approach to identify the expression profile on monoaromatic compound vanillic acid (VA) and polyaromatic kraft lignin (KL). Genomic and proteomic analysis simultaneously discovered Dyp-type peroxidase, peroxidases, glycolate oxidase, aldehyde oxidase, GMC oxidoreductase, laccases, quinone oxidoreductase, dioxygenases, monooxygenases, glutathione-dependent etherases, dehydrogenases, reductases, and methyltransferases and various other recently reported enzyme systems such as superoxide dismutases or catalase-peroxidase for lignin degradation. A strong stress response and detoxification mechanism was discovered. The two important gene clusters for lignin degradation and three PHA polymerase spanning gene clusters were identified and all the clusters were functionally active on KL-VA.

CONCLUSIONS

The unusual aerobic '-CoA'-mediated degradation pathway of phenylacetate and benzoate (reported only in 16 and 4-5% of total sequenced bacterial genomes), peroxidase-accessory enzyme system, and fenton chemistry based are the major pathways observed for lignin degradation. Both ortho and meta ring cleavage pathways for aromatic compound degradation were observed in expression profile. Genomic and proteomic approaches provided validation to this strain's robust machinery for the metabolism of recalcitrant compounds and PHA production and provide an opportunity to target important enzymes for lignin valorization in future.

Proteomics analysis to investigate the impact of diversified thermal processing on meat tenderness in Hengshan goat meat

During the thermal processing, proteins of Hengshan goat meat undergo structural modifications such as degradation, oxidation and denaturation, ultimately affect the palatability and acceptability. The results of several objective metrics demonstrated that thermal processing exhibited significant impacts on the tenderness of goat meat. The 551, 84, 72, and 121 proteins were identified in the control and thermal processed groups (boiled, steamed, and roasted), respectively. Compared with the control group, the 101, 98, and 109 differentially-expressed proteins were explored in the treatment groups. Furthermore, the functions of metabolic and skeletal muscle proteome were investigated and discussed. Sensory evaluation and proteomics analysis showed that steaming and boiling treatment had no significant effect on the tenderness of goat meat, while roasting significantly reduced the tenderness, indicating that the available thermal processing methods to ensure the tenderness of goat meat were steaming and boiling treatments. Thus, the established proteomics database of goat meat provided the valuable reference for rational selection of thermal processing methods.

In-depth evaluation of software tools for data-independent acquisition based label-free quantification

Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).

Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences

Disease biomarkers are the measurable changes associated with a pathophysiological process. Without homeostatic control, urine accumulates systematic changes in the body. Thus, urine is an attractive biological material for the discovery of disease biomarkers. One of the major bottlenecks in urinary biomarker discovery is that the concentration and composition of urinary proteins are influenced by many physiological factors. To elucidate the individual variation and related factors influencing the urinary proteome, we comprehensively analyzed the urine samples from healthy adult donors (aged 20-69 years). Co-expression network analysis revealed protein clusters representing the metabolic status, gender-related differences and age-related differences in urinary proteins. In particular, we demonstrated that gender is a crucial factor contributing to individual variation. Proteins that were increased in the male urine samples include prostate-secreted proteins and TIMP1, a protein whose abundance alters under various cancers and renal diseases; however, the proteins that were increased in the female urine samples have known functions in the immune system. Nine gender-related proteins were validated on 85 independent samples by multiple reaction monitoring. Five of these proteins were further used to build a model that could accurately distinguish male and female urine samples with an area under curve value of 0.94. Based on the above results, we strongly suggest that future biomarker investigations should consider gender as a crucial factor in experimental design and data analysis. Finally, reference intervals of each urinary protein were estimated, providing a baseline for the discovery of abnormalities.

Proteins, Transcripts, and Genetic Architecture of Seminal Fluid and Sperm in the Mosquito Aedes aegypti

The yellow fever mosquito, Aedes aegypti,, transmits several viruses causative of serious diseases, including dengue, Zika, and chikungunya. Some proposed efforts to control this vector involve manipulating reproduction to suppress wild populations or to replace them with disease-resistant mosquitoes. The design of such strategies requires an intimate knowledge of reproductive processes, yet our basic understanding of reproductive genetics in this vector remains largely incomplete. To accelerate future investigations, we have comprehensively catalogued sperm and seminal fluid proteins (SFPs) transferred to females in the ejaculate using tandem mass spectrometry. By excluding female-derived proteins using an isotopic labeling approach, we identified 870 sperm proteins and 280 SFPs. Functional composition analysis revealed parallels with known aspects of sperm biology and SFP function in other insects. To corroborate our proteome characterization, we also generated transcriptomes for testes and the male accessory glands-the primary contributors to Ae. aegypti, sperm and seminal fluid, respectively. Differential gene expression of accessory glands from virgin and mated males suggests that transcripts encoding proteins involved in protein translation are upregulated post-mating. Several SFP transcripts were also modulated after mating, but >90% remained unchanged. Finally, a significant enrichment of SFPs was observed on chromosome 1, which harbors the male sex determining locus in this species. Our study provides a comprehensive proteomic and transcriptomic characterization of ejaculate production and composition and thus provides a foundation for future investigations of Ae. aegypti, reproductive biology, from functional analysis of individual proteins to broader examination of reproductive processes.

Stress-induced Changes in the S-palmitoylation and S-nitrosylation of Synaptic Proteins

The precise regulation of synaptic integrity is critical for neuronal network connectivity and proper brain function. Essential aspects of the activity and localization of synaptic proteins are regulated by posttranslational modifications. S-palmitoylation is a reversible covalent modification of the cysteine with palmitate. It modulates affinity of the protein for cell membranes and membranous compartments. Intracellular palmitoylation dynamics are regulated by crosstalk with other posttranslational modifications, such as S-nitrosylation. S-nitrosylation is a covalent modification of cysteine thiol by nitric oxide and can modulate protein functions. Therefore, simultaneous identification of endogenous site-specific proteomes of both cysteine modifications under certain biological conditions offers new insights into the regulation of functional pathways. Still unclear, however, are the ways in which this crosstalk is affected in brain pathology, such as stress-related disorders. Using a newly developed mass spectrometry-based approach Palmitoylation And Nitrosylation Interplay Monitoring (PANIMoni), we analyzed the endogenous S-palmitoylation and S-nitrosylation of postsynaptic density proteins at the level of specific single cysteine in a mouse model of chronic stress. Among a total of 813 S-PALM and 620 S-NO cysteine sites that were characterized on 465 and 360 proteins, respectively, we sought to identify those that were differentially affected by stress. Our data show involvement of S-palmitoylation and S-nitrosylation crosstalk in the regulation of 122 proteins including receptors, scaffolding proteins, regulatory proteins and cytoskeletal components. Our results suggest that atypical crosstalk between the S-palmitoylation and S-nitrosylation interplay of proteins involved in synaptic transmission, protein localization and regulation of synaptic plasticity might be one of the main events associated with chronic stress disorder, leading to destabilization in synaptic networks.

A Draft Map of the Human Ovarian Proteome for Tissue Engineering and Clinical Applications

Fertility preservation research in women today is increasingly taking advantage of bioengineering techniques to develop new biomimetic materials and solutions to safeguard ovarian cell function and microenvironment in vitro, and in vivo,. However, available data on the human ovary are limited and fundamental differences between animal models and humans are hampering researchers in their quest for more extensive knowledge of human ovarian physiology and key reproductive proteins that need to be preserved. We therefore turned to multi-dimensional label-free mass spectrometry to analyze human ovarian cortex, as it is a high-throughput and conclusive technique providing information on the proteomic composition of complex tissues like the ovary. In-depth proteomic profiling through two-dimensional liquid chromatography-mass spectrometry, Western blotting, histological and immunohistochemical analyses, and data mining helped us to confidently identify 1508 proteins. Moreover, our method allowed us to chart the most complete representation so far of the ovarian matrisome, defined as the ensemble of extracellular matrix proteins and associated factors, including more than 80 proteins. In conclusion, this study will provide a better understanding of ovarian proteomics, with a detailed characterization of the ovarian follicle microenvironment, in order to enable bioengineers to create biomimetic scaffolds for transplantation and three-dimensional in vitro, culture. By publishing our proteomic data, we also hope to contribute to accelerating biomedical research into ovarian health and disease in general.

Insulin Induces Microtubule Stabilization and Regulates the Microtubule Plus-end Tracking Protein Network in Adipocytes

Insulin-stimulated glucose uptake is known to involve microtubules, although the function of microtubules and the microtubule-regulating proteins involved in insulin action are poorly understood. CLASP2, a plus-end tracking microtubule-associated protein (+TIP) that controls microtubule dynamics, was recently implicated as the first +TIP associated with insulin-regulated glucose uptake. Here, using protein-specific targeted quantitative phosphoproteomics within 3T3-L1 adipocytes, we discovered that insulin regulates phosphorylation of the CLASP2 network members G2L1, MARK2, CLIP2, AGAP3, and CKAP5 as well as EB1, revealing the existence of a previously unknown microtubule-associated protein system that responds to insulin. To further investigate, G2L1 interactome studies within 3T3-L1 adipocytes revealed that G2L1 coimmunoprecipitates CLASP2 and CLIP2 as well as the master integrators of +TIP assembly, the end binding (EB) proteins. Live-cell total internal reflection fluorescence microscopy in adipocytes revealed G2L1 and CLASP2 colocalize on microtubule plus-ends. We found that although insulin increases the number of CLASP2-containing plus-ends, insulin treatment simultaneously decreases CLASP2-containing plus-end velocity. In addition, we discovered that insulin stimulates redistribution of CLASP2 and G2L1 from exclusive plus-end tracking to "trailing" behind the growing tip of the microtubule. Insulin treatment increases α-tubulin Lysine 40 acetylation, a mechanism that was observed to be regulated by a counterbalance between GSK3 and mTOR, and led to microtubule stabilization. Our studies introduce insulin-stimulated microtubule stabilization and plus-end trailing of +TIPs as new modes of insulin action and reveal the likelihood that a network of microtubule-associated proteins synergize to coordinate insulin-regulated microtubule dynamics.

Phenotypic Adaption of Pseudomonas aeruginosa by Hacking Siderophores Produced by Other Microorganisms

Bacteria secrete siderophores to access iron, a key nutrient poorly bioavailable and the source of strong competition between microorganisms in most biotopes. Many bacteria also use siderophores produced by other microorganisms (exosiderophores) in a piracy strategy. Pseudomonas aeruginosa, an opportunistic pathogen, produces two siderophores, pyoverdine and pyochelin, and is also able to use a panel of exosiderophores. We first investigated expression of the various iron-uptake pathways of P. aeruginosa in three different growth media using proteomic and RT-qPCR approaches and observed three different phenotypic patterns, indicating complex phenotypic plasticity in the expression of the various iron-uptake pathways. We then investigated the phenotypic plasticity of iron-uptake pathway expression in the presence of various exosiderophores (present individually or as a mixture) under planktonic growth conditions, as well as in an epithelial cell infection assay. In all growth conditions tested, catechol-type exosiderophores were clearly more efficient in inducing the expression of their corresponding transporters than the others, showing that bacteria opt for the use of catechol siderophores to access iron when they are present in the environment. In parallel, expression of the proteins of the pyochelin pathway was significantly repressed under most conditions tested, as well as that of proteins of the pyoverdine pathway, but to a lesser extent. There was no effect on the expression of the heme and ferrous uptake pathways. Overall, these data provide precise insights on how P. aeruginosa adjusts the expression of its various iron-uptake pathways (phenotypic plasticity and switching) to match varying levels of iron and competition.

Data-independent Acquisition Improves Quantitative Cross-linking Mass Spectrometry

Quantitative cross-linking mass spectrometry (QCLMS) reveals structural detail on altered protein states in solution. On its way to becoming a routine technology, QCLMS could benefit from data-independent acquisition (DIA), which generally enables greater reproducibility than data-dependent acquisition (DDA) and increased throughput over targeted methods. Therefore, here we introduce DIA to QCLMS by extending a widely used DIA software, Spectronaut, to also accommodate cross-link data. A mixture of seven proteins cross-linked with bis[sulfosuccinimidyl] suberate (BS3) was used to evaluate this workflow. Out of the 414 identified unique residue pairs, 292 (70%) were quantifiable across triplicates with a coefficient of variation (CV) of 10%, with manual correction of peak selection and boundaries for PSMs in the lower quartile of individual CV values. This compares favorably to DDA where we quantified cross-links across triplicates with a CV of 66%, for a single protein. We found DIA-QCLMS to be capable of detecting changing abundances of cross-linked peptides in complex mixtures, despite the ratio compression encountered when increasing sample complexity through the addition of E. coli cell lysate as matrix. In conclusion, the DIA software Spectronaut can now be used in cross-linking and DIA is indeed able to improve QCLMS.

Extracellular vesicle proteomes reflect developmental phases of Bacillus subtilis

BACKGROUND

Extracellular vesicles (EV) are spherical membrane-bound vesicles with nano-scale diameters, which are shed to the extracellular region by most eukaryotic and prokaryotic cells. Bacterial EV are proposed to contribute to intercellular communication, bacterial survival and human pathogenesis as a novel secretion system. EV have been characterized from many Gram-negative species and, more recently, from several vegetative Gram-positive bacteria. Further characterization of EV and their molecular cargos will contribute to understanding bacterial physiology and to developing therapeutic approaches.

RESULTS

Bacillus subtilis were observed to release EV to a similar extent during sporulation as during the vegetative growth phase. However, the two vesicular cargos show qualitatively and quantitatively different proteomes. Among 193 total proteins identified across both samples, 61 were shown to be significantly more abundant in EV shed by sporulating cells, with (log) ratio of spectral counts RSC > 1 and Fisher-exact test FDR < 5 %. Sixty-two proteins were found to be significantly more abundant in EV shed by vegetative cells. Membrane fusion was shown to take place between these EVs and Gram-positive cells.

CONCLUSION

Biogenesis of EV is a continuous process over the entire life cycle of this sporulating bacterium. The formation of EV during sporulation is strongly supported by the delineation of protein content that differs from the proteome of EV formed by vegetative spores.