Quantitative proteome profiling reveals molecular hallmarks of egg quality in Atlantic halibut: impairments of transcription and protein folding impede protein and energy homeostasis during early development.

BACKGROUND: Tandem mass tag spectrometry (TMT labeling-LC-MS/MS) was utilized to examine the global proteomes of Atlantic halibut eggs at the 1-cell-stage post fertilization. Comparisons were made between eggs judged to be of good quality (GQ) versus poor quality (BQ) as evidenced by their subsequent rates of survival for 12 days. Altered abundance of selected proteins in BQ eggs was confirmed by parallel reaction monitoring spectrometry (PRM-LC-MS/MS). Correspondence of protein levels to expression of related gene transcripts was examined via qPCR. Potential mitochondrial differences between GQ and BQ eggs were assessed by transmission electron microscopy (TEM) and measurements of mitochondrial DNA (mtDNA) levels. RESULTS: A total of 115 proteins were found to be differentially abundant between GQ and BQ eggs. Frequency distributions of these proteins indicated higher protein folding activity in GQ eggs compared to higher transcription and protein degradation activities in BQ eggs. BQ eggs were also significantly enriched with proteins related to mitochondrial structure and biogenesis. Quantitative differences in abundance of several proteins with parallel differences in their transcript levels were confirmed in egg samples obtained over three consecutive reproductive seasons. The observed disparities in global proteome profiles suggest impairment of protein and energy homeostasis related to unfolded protein response and mitochondrial stress in BQ eggs. TEM revealed BQ eggs to contain significantly higher numbers of mitochondria, but differences in corresponding genomic mtDNA (mt-nd5 and mt-atp6) levels were not significant. Mitochondria from BQ eggs were significantly smaller with a more irregular shape and a higher number of cristae than those from GQ eggs. CONCLUSION: The results of this study indicate that BQ Atlantic halibut eggs are impaired at both transcription and translation levels leading to endoplasmic reticulum and mitochondrial disorders. Observation of these irregularities over three consecutive reproductive seasons in BQ eggs from females of diverse background, age and reproductive experience indicates that they are a hallmark of poor egg quality. Additional research is needed to discover when in oogenesis and under what circumstances these defects may arise. The prevalence of this suite of markers in BQ eggs of diverse vertebrate species also begs investigation.

Comparing Efficiency of Lysis Buffer Solutions and Sample Preparation Methods for Liquid Chromatography-Mass Spectrometry Analysis of Human Cells and Plasma.

The use of a proper sample processing methodology for maximum proteome coverage and high-quality quantitative data is an important choice to make before initiating a liquid chromatography-mass spectrometry (LC-MS)-based proteomics study. Popular sample processing workflows for proteomics involve in-solution proteome digestion and single-pot, solid-phase-enhanced sample preparation (SP3). We tested them on both HeLa cells and human plasma samples, using lysis buffers containing SDS, or guanidinium hydrochloride. We also studied the effect of using commercially available depletion mini spin columns before SP3, to increase proteome coverage in human plasma samples. Our results show that the SP3 protocol, using either buffer, achieves the highest number of quantified proteins in both the HeLa cells and plasma samples. Moreover, the use of depletion mini spin columns before SP3 results in a two-fold increase of quantified plasma proteins. With additional fractionation, we quantified nearly 1400 proteins, and examined lower-abundance proteins involved in neurodegenerative pathways and mitochondrial metabolism. Therefore, we recommend the use of the SP3 methodology for biological sample processing, including those after depletion of high-abundance plasma proteins.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows.

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Proteomic Comparison of Bone Marrow Derived Osteoblasts and Mesenchymal Stem Cells.

Mesenchymal stem cells (MSCs) can differentiate into osteoblasts, and therapeutic targeting of these cells is considered both for malignant and non-malignant diseases. We analyzed global proteomic profiles for osteoblasts derived from ten and MSCs from six healthy individuals, and we quantified 5465 proteins for the osteoblasts and 5420 proteins for the MSCs. There was a large overlap in the profiles for the two cell types; 156 proteins were quantified only in osteoblasts and 111 proteins only for the MSCs. The osteoblast-specific proteins included several extracellular matrix proteins and a network including 27 proteins that influence intracellular signaling (Wnt/Notch/Bone morphogenic protein pathways) and bone mineralization. The osteoblasts and MSCs showed only minor age- and sex-dependent proteomic differences. Finally, the osteoblast and MSC proteomic profiles were altered by ex vivo culture in serum-free media. We conclude that although the proteomic profiles of osteoblasts and MSCs show many similarities, we identified several osteoblast-specific extracellular matrix proteins and an osteoblast-specific intracellular signaling network. Therapeutic targeting of these proteins will possibly have minor effects on MSCs. Furthermore, the use of ex vivo cultured osteoblasts/MSCs in clinical medicine will require careful standardization of the ex vivo handling of the cells.

Development of robust targeted proteomics assays for cerebrospinal fluid biomarkers in multiple sclerosis.

BACKGROUND: Verification of cerebrospinal fluid (CSF) biomarkers for multiple sclerosis and other neurological diseases is a major challenge due to a large number of candidates, limited sample material availability, disease and biological heterogeneity, and the lack of standardized assays. Furthermore, verification studies are often based on a low number of proteins from a single discovery experiment in medium-sized cohorts, where antibodies and surrogate peptides may differ, thus only providing an indication of proteins affected by the disease and not revealing the bigger picture or concluding on the validity of the markers. We here present a standard approach for locating promising biomarker candidates based on existing knowledge, resulting in high-quality assays covering the main biological processes affected by multiple sclerosis for comparable measurements over time. METHODS: Biomarker candidates were located in CSF-PR (proteomics.uib.no/csf-pr), and further filtered based on estimated concentration in CSF and biological function. Peptide surrogates for internal standards were selected according to relevant criteria, parallel reaction monitoring (PRM) assays created, and extensive assay quality testing performed, i.e. intra- and inter-day variation, trypsin digestion status over time, and whether the peptides were able to separate multiple sclerosis patients and controls. RESULTS: Assays were developed for 25 proteins, represented by 72 peptides selected according to relevant guidelines and available literature and tested for assay peptide suitability. Stability testing revealed 64 peptides with low intra- and inter-day variations, with 44 also being stably digested after 16 h of trypsin digestion, and 37 furthermore showing a significant difference between multiple sclerosis and controls, thereby confirming literature findings. Calibration curves and the linear area of measurement have, so far, been determined for 17 of these peptides. CONCLUSIONS: We present 37 high-quality PRM assays across 21 CSF-proteins found to be affected by multiple sclerosis, along with a recommended workflow for future development of new assays. The assays can directly be used by others, thus enabling better comparison between studies. Finally, the assays can robustly and stably monitor biological processes in multiple sclerosis patients over time, thus potentially aiding in diagnosis and prognosis, and ultimately in treatment decisions.

CSF-PR 2.0: An Interactive Literature Guide to Quantitative Cerebrospinal Fluid Mass Spectrometry Data from Neurodegenerative Disorders.

The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.

Long-term smoking alters abundance of over half of the proteome in bronchoalveolar lavage cell in smokers with normal spirometry, with effects on molecular pathways associated with COPD.

BACKGROUND: Smoking represents a significant risk factor for many chronic inflammatory diseases, including chronic obstructive pulmonary disease (COPD). METHODS: To identify dysregulation of specific proteins and pathways in bronchoalveolar lavage (BAL) cells associated with smoking, isobaric tags for relative and absolute quantitation (iTRAQ)-based shotgun proteomics analyses were performed on BAL cells from healthy never-smokers and smokers with normal lung function from the Karolinska COSMIC cohort. Multivariate statistical modeling, multivariate correlations with clinical data, and pathway enrichment analysis were performed. RESULTS: Smoking exerted a significant impact on the BAL cell proteome, with more than 500 proteins representing 15 molecular pathways altered due to smoking. The majority of these alterations occurred in a gender-independent manner. The phagosomal- and leukocyte trans endothelial migration (LTM) pathways significantly correlated with FEV1/FVC as well as the percentage of CD8+ T-cells and CD8+CD69+ T-cells in smokers. The correlations to clinical parameters in healthy never-smokers were minor. CONCLUSION: The significant correlations of proteins in the phagosome- and LTM pathways with activated cytotoxic T-cells (CD69+) and the level of airway obstruction (FEV1/FVC) in smokers, both hallmarks of COPD, suggests that these two pathways may play a role in the molecular events preceding the development of COPD in susceptible smokers. Both pathways were found to be further dysregulated in COPD patients from the same cohort, thereby providing further support to this hypothesis. Given that not all smokers develop COPD in spite of decades of smoking, it is also plausible that some of the molecular pathways associated with response to smoking exert protective mechanisms to smoking-related pathologies in resilient individuals. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT02627872 ; Retrospectively registered on December 9, 2015.

Proteomic profiling of lung immune cells reveals dysregulation of phagocytotic pathways in female-dominated molecular COPD phenotype.

BACKGROUND: Smoking is the main risk factor for chronic obstructive pulmonary disease (COPD). Women with COPD who smoke experienced a higher risk of hospitalization and worse decline of lung function. Yet the mechanisms of these gender-related differences in clinical presentations in COPD remain unknown. The aim of our study is to identify proteins and molecular pathways associated with COPD pathogenesis, with emphasis on elucidating molecular gender difference. METHOD: We employed shotgun isobaric tags for relative and absolute quantitation (iTRAQ) proteome analyses of bronchoalveolar lavage (BAL) cells from smokers with normal lung function (n = 25) and early stage COPD patients (n = 18). Multivariate modeling, pathway enrichment analysis, and correlation with clinical characteristics were performed to identify specific proteins and pathways of interest. RESULTS: More pronounced alterations both at the protein- and pathway- levels were observed in female COPD patients, involving dysregulation of the FcγR-mediated phagocytosis-lysosomal axis and increase in oxidative stress. Alterations in pathways of the phagocytosis-lysosomal axis associated with a female-dominated COPD phenotype correlated well with specific clinical features: FcγR-mediated phagocytosis correlated with FEV1/FVC, the lysosomal pathway correlated with CT < -950 Hounsfield Units (HU), and regulation of actin cytoskeleton correlated with FEV1 and FEV1/FVC in female COPD patients. Alterations observed in the corresponding male cohort were minor. CONCLUSION: The identified molecular pathways suggest dysregulation of several phagocytosis-related pathways in BAL cells in female COPD patients, with correlation to both the level of obstruction (FEV1/FVC) and disease severity (FEV1) as well as emphysema (CT < -950 HU) in women. TRIAL REGISTRATION: No.: NCT02627872 , retrospectively registered on December 9, 2015.

In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis.

In the current study, we conducted a quantitative in-depth proteome and deglycoproteome analysis of cerebrospinal fluid (CSF) from relapsing-remitting multiple sclerosis (RRMS) and neurological controls using mass spectrometry and pathway analysis. More than 2000 proteins and 1700 deglycopeptides were quantified, with 484 proteins and 180 deglycopeptides significantly changed between pools of RRMS and pools of controls. Approximately 300 of the significantly changed proteins were assigned to various biological processes including inflammation, extracellular matrix organization, cell adhesion, immune response, and neuron development. Ninety-six significantly changed deglycopeptides mapped to proteins that were not found changed in the global protein study. In addition, four mapped to the proteins oligo-myelin glycoprotein and noelin, which were found oppositely changed in the global study. Both are ligands to the nogo receptor, and the glycosylation of these proteins appears to be affected by RRMS. Our study gives the most extensive overview of the RRMS affected processes observed from the CSF proteome to date, and the list of differential proteins will have great value for selection of biomarker candidates for further verification.

Exploring the potential of public proteomics data.

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Label-free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis.

The aims of the study were to: (i) identify differentially regulated proteins in cerebrospinal fluid (CSF) between multiple sclerosis (MS) patients and non-MS controls; (ii) examine the effect of matching the CSF samples on either total protein amount or volume, and compare four protein normalization strategies for CSF protein quantification. CSF from MS patients (n = 37) and controls (n = 64), consisting of other noninflammatory neurological diseases (n = 50) and non neurological spinal anesthetic subjects (n = 14), were analyzed using label-free proteomics, quantifying almost 800 proteins. In total, 122 proteins were significantly regulated (p < 0.05), where 77 proteins had p-value <0.01 or AUC value >0.75. Hierarchical clustering indicated that there were two main groups of MS patients, those with increased levels of inflammatory response proteins and decreased levels of proteins involved in neuronal tissue development (n = 30), and those with normal protein levels for both of these protein groups (n = 7). The main subgroup of controls clustering with the MS patients showing increased inflammation and decreased neuronal tissue development were patients suffering from chronic fatigue. Our data indicate that the preferable way to quantify proteins in CSF is to first match the samples on total protein amount and then normalize the data based on the median intensities, preferably from the CNS-enriched proteins.

Cerebrospinal fluid proteomics in multiple sclerosis.

Multiple sclerosis (MS) is an immune mediated chronic inflammatory disease of the central nervous system usually initiated during young adulthood, affecting approximately 2.5 million people worldwide. There is currently no cure for MS, but disease modifying treatment has become increasingly more effective, especially when started in the first phase of the disease. The disease course and prognosis are often unpredictable and it can be challenging to determine an early diagnosis. The detection of novel biomarkers to understand more of the disease mechanism, facilitate early diagnosis, predict disease progression, and find treatment targets would be very attractive. Over the last decade there has been an increasing effort toward finding such biomarker candidates. One promising strategy has been to use state-of-the-art quantitative proteomics approaches to compare the cerebrospinal fluid (CSF) proteome between MS and control patients or between different subgroups of MS. In this review we summarize and discuss the status of CSF proteomics in MS, including the latest findings with a focus on the last five years. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Erratum to: Association of chromosome 19 to lung cancer genotypes and phenotypes.

Erratum to: Cancer and Metastasis Review, DOI 10.1007/s10555-015-9556-2. There are changes in authors' affiliations and a new affiliations for Carol L. Nilsson and Thomas E. Fehniger has been added. The corresponding author also missed out to include Peter Horvatovich as a co-author of this work. The complete list of authors is now listed above.

Association of chromosome 19 to lung cancer genotypes and phenotypes.

The Chromosome 19 Consortium, a part of the Chromosome-Centric Human Proteome Project (C-HPP, http://www.C-HPP.org ), is tasked with the understanding chromosome 19 functions at the gene and protein levels, as well as their roles in lung oncogenesis. Comparative genomic hybridization (CGH) studies revealed chromosome aberration in lung cancer subtypes, including ADC, SCC, LCC, and SCLC. The most common abnormality is 19p loss and 19q gain. Sixty-four aberrant genes identified in previous genomic studies and their encoded protein functions were further validated in the neXtProt database ( http://www.nextprot.org/ ). Among those, the loss of tumor suppressor genes STK11, MUM1, KISS1R (19p13.3), and BRG1 (19p13.13) is associated with lung oncogenesis or remote metastasis. Gene aberrations include translocation t(15, 19) (q13, p13.1) fusion oncogene BRD4-NUT, DNA repair genes (ERCC1, ERCC2, XRCC1), TGFß1 pathway activation genes (TGFB1, LTBP4), Dyrk1B, and potential oncogenesis protector genes such as NFkB pathway inhibition genes (NFKBIB, PPP1R13L) and EGLN2. In conclusion, neXtProt is an effective resource for the validation of gene aberrations identified in genomic studies. It promises to enhance our understanding of lung cancer oncogenesis.

In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR).

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies.

Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies.

Database Search Engines: Paradigms, Challenges and Solutions.

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Tandem Mass Spectrum Sequencing: An Alternative to Database Search Engines in Shotgun Proteomics.

Protein identification via database searches has become the gold standard in mass spectrometry based shotgun proteomics. However, as the quality of tandem mass spectra improves, direct mass spectrum sequencing gains interest as a database-independent alternative. In this chapter, the general principle of this so-called de novo sequencing is introduced along with pitfalls and challenges of the technique. The main tools available are presented with a focus on user friendly open source software which can be directly applied in everyday proteomic workflows.

Visualization, Inspection and Interpretation of Shotgun Proteomics Identification Results.

Shotgun proteomics is a high throughput technique for protein identification able to identify up to several thousand proteins from a single sample. In order to make sense of this large amount of data, proteomics analysis software is needed, aimed at making the data intuitively accessible to beginners as well as experienced scientists. This chapter provides insight on where to start when analyzing shotgun proteomics data, with a focus on explaining the most common pitfalls in protein identification analysis and how to avoid them. Finally, the move to seeing beyond the list of identified proteins and to putting the results into a bigger biological context is discussed.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.