Correlation-associated peptide networks of human cerebrospinal fluid

Exploring Human Milk Dynamics: Interindividual Variation in Milk Proteome, Peptidome, and Metabolome

Human milk is a dynamic biofluid, and its detailed composition receives increasing attention. While most studies focus on changes over time or differences between maternal characteristics, interindividual variation receives little attention. Nevertheless, a comprehensive insight into this can help interpret human milk studies and help human milk banks provide targeted milk for recipients. This study aimed to map interindividual variation in the human milk proteome, peptidome, and metabolome and to investigate possible explanations for this variation. A set of 286 milk samples was collected from 29 mothers in the third month postpartum. Samples were pooled per mother, and proteins, peptides, and metabolites were analyzed. A substantial coefficient of variation (>100%) was observed for 4.6% and 36.2% of the proteins and peptides, respectively. In addition, using weighted correlation network analysis (WGCNA), 5 protein and 11 peptide clusters were obtained, showing distinct characteristics. With this, several associations were found between the different data sets and with specific sample characteristics. This study provides insight into the dynamics of human milk protein, peptide, and metabolite composition. In addition, it will support future studies that evaluate the effect size of a parameter of interest by enabling a comparison with natural variability.

Origins, Technological Development, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.

Peptidomics analysis of human blood specimens for biomarker discovery

This review addresses the concepts, limitations and perspectives for the application of peptidomics science and technologies to discover putative biomarkers in blood specimens. Peptidomics can be defined as the comprehensive multiplex analysis of endogenous peptides contained within a biological sample under defined conditions to describe the multitude of native peptides in a biological compartment. In addition to the discovery of disease associated biomarkers, an emerging field in peptidomics is the analysis of peptides to describe in vivo effects of protease inhibitors. The development and application of peptidomics technologies represent an arena of biomarker research that has the potential for adding significant clinical value.

Peptidomics: identification of pathogenic and marker peptides

Recent years have seen great advances in mass spectrometry and proteomics, the science dealing with the analysis of proteins, their structure and function. A branch of proteomics dealing with naturally occurring peptides is often referred to as peptidomics. Direct analysis of peptides produced by processing or degradation of proteins might be useful for example for detecting and identifying pathogenic and/or biomarker peptides in body fluids like blood. In this paper, we introduce one of the standard protocols for comprehensive analysis of serum-derived peptides, which consists of methods for purification of serum peptides, detection of peptides, pattern recognition and clustering (bioinformatics), and identification of peptide sequences. Peptide identification should be followed by the investigation of their pathogenic roles using for example synthetic peptides and the establishment of their usefulness as bioclinical markers.

Introduction to omics

Exploiting the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes has currently been receiving a lot of attention. In recent years, most of the effort has been put into demonstrating the possible clinical applications of the various omics fields. The cost-effectiveness analysis has been, so far, rather neglected. The cost of omics-derived applications is still very high, but future technological improvements are likely to overcome this problem. In this chapter, we will give a general background of the main omics fields and try to provide some examples of the most successful applications of omics that might be used in clinical diagnosis and in a therapeutic context.

A computational platform for MALDI-TOF mass spectrometry data: application to serum and plasma samples

BACKGROUND

Mass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data.

RESULTS

MASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population.

CONCLUSIONS

The analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf.

Peptide screening of cerebrospinal fluid in patients with glioblastoma multiforme

AIMS

To apply modern mass spectrometry based technology to identify possible CSF peptide markers of glioblastoma multiforme (GBM).

METHODS

Mass spectrometry based peptidomics technology enables a systematic and comprehensive screening of cerebrospinal fluid (CSF) with regard to its peptide composition. Differential Peptide Display (DPD) allows the identification of single marker peptides for a target disease. Using both, we analyzed CSF samples of 11 patients harbouring a glioblastoma multiforme in comparison to 13 normal controls.

RESULTS

Four CSF peptides which significantly distinguished GBM from controls in all applied statistic tests could be identified out of more than 2,000 detected CSF peptides. They were specific C-terminal fragments of alpha-1-antichymotrypsin, osteopontin, and transthyretin as well as a N-terminal residue of albumin. All molecules are constituents of normal CSF, but none has previously been reported to be significantly elevated in CSF of GBM patients.

CONCLUSION

The study showed that peptidomics technology is able to identify possible biomarkers of neoplastic CNS disease. It remains to be determined if the identified elevated CSF peptides are specific for GBM. With regard to GBM, however, the more important role of CSF peptide biomarkers than aiding initial diagnosis might be early recognition of disease recurrence or monitoring of efficacy of adjuvant therapy protocols.

Peptidomic analysis of blood plasma after in vivo treatment with protease inhibitors--a proof of concept study

Native peptides can be regarded as surrogate markers for protease activity in biological samples. Analysis of peptides by peptidomics allows to monitor protease activity in vivo and to describe the influence of protease inhibition. To elucidate the potential of peptides as markers for in vivo protease inhibition we analyzed plasma samples from animals treated with either the indirect FXa inhibitor FONDAPARINUX or the dipeptidylpeptidase IV inhibitor AB192. Signals correlating with the treatment were subsequently identified and assessed with respect to protease-dependent consensus cleavage motifs and occurrence of downstream targets. It could be shown that regulated peptides were either substrates, products or downstream targets of the inhibited protease. The results from the present study demonstrate that the in vivo analysis of peptides by peptidomics has the potential to broaden the knowledge of inhibitor related effects in vivo and that this method may pave the way to develop predictive biomarkers.

Proteomics-driven cancer biomarker discovery: looking to the future

Availability of a suite of biomarkers for early detection, stratification into distinct subtypes, and monitoring progression or response to therapy promises significant improvements in clinical outcomes for cancer patients. However, despite the recent progress in proteomics technologies based on mass spectrometry (MS), discovery of novel clinical assessment tools has been slow. This is, partly due to the inherent difficulties in working with blood as the biospecimen for candidate discovery. A better understanding of the limitations of blood for comparative protein profiling and a better appreciation of the advantages of cancer tissue or cancer cell secretomes have the potential to greatly enhance the progress.

Analysis of cerebrospinal fluid inflammatory mediators in chronic complex regional pain syndrome related dystonia

OBJECTIVES

There is compelling evidence of central nervous system involvement in neuropathic pain and movement disorders in patients with complex regional pain syndrome (CRPS). Previously, elevated cerebrospinal fluid (CSF) levels of interleukin-1beta and interleukin-6 were found in CRPS patients with and without movement disorders. The aim of the present study was to replicate these findings and to search for additional CSF biomarkers in chronic CRPS patients with dystonia.

METHODS

CSF samples of 20 patients and 29 controls who underwent spinal anesthesia for surgical interventions participated. We measured interleukin-1beta, interleukin-6, interferon-gamma inducible protein-10, RANTES (regulated upon activation, normal T-cell expressed and secreted), complement C3, mannose-binding lectin, complement C1q, soluble intercellular adhesion molecule-1, endothelin-1, nitric oxide, human lactoferrin, and hypocretin-1 levels in these samples.

RESULTS

No differences in the CSF levels of these effector mediators between patients and controls were found.

CONCLUSION

Our CSF findings do not support a role of a variety of inflammatory mediators or hypocretin-1 in chronic CRPS patients with dystonia.

Alpha-1-antitrypsin and complement component C7 are involved in asthma exacerbation

Asthma is the most common chronic disorder in childhood and asthma exacerbation is an important cause of childhood morbidity and hospitalization. Allergic responses are known to be biased toward T-helper type 2 in asthmatics; however, the pathogenesis of asthma is not simple, and our understanding of the disease mechanism remains incomplete. The aim of the present study was to identify protein expression signatures that reflect acute exacerbation of asthma. Plasma was taken twice from pediatric asthmatic patients, once during asthma exacerbation and once during a stable period. Plasma was also taken from healthy children as a control. The protein profiles of plasma during asthma exacerbation were analyzed by 2-DE and 49 spots were differentially expressed during asthma exacerbation. Thirty-eight of the spots were successfully identified by MALDI-TOF MS. Proteins up- or down-regulated during asthma exacerbation were involved in responses to stress and pathogens, in the complement and coagulation cascades, and in acute-phase responses. Among the differentially expressed proteins, up-regulation of alpha-1-antitrypsin and complement component C7 was confirmed by nephelometry and ELISA. Our present results suggest that protease inhibitors and complement components may be involved in asthma exacerbation, and plasma level of alpha-1-antitrypsin may be a potential biomarker for asthma.

Protein identification and Peptide expression resolver: harmonizing protein identification with protein expression data

Proteomic discovery platforms generate both peptide expression information and protein identification information. Peptide expression data are used to determine which peptides are differentially expressed between study cohorts, and then these peptides are targeted for protein identification. In this paper, we demonstrate that peptide expression information is also a powerful tool for enhancing confidence in protein identification results. Specifically, we evaluate the following hypothesis: tryptic peptides originating from the same protein have similar expression profiles across samples in the discovery study. Evidence supporting this hypothesis is provided. This hypothesis is integrated into a protein identification tool, PIPER (Protein Identification and Peptide Expression Resolver), that reduces erroneous protein identifications below 5%. PIPER's utility is illustrated by application to a 72-sample biomarker discovery study where it is demonstrated that false positive protein identifications can be reduced below 5%. Consequently, it is recommended that PIPER methodology be incorporated into proteomic studies where both protein expression and identification data are collected.

Peptidomic analysis of human peripheral monocytes persistently infected by Chlamydia trachomatis

Peptidomic analysis using Differential Peptide Display (DPD) of human peripheral blood mononuclear cells (PBMC) mock-infected or persistently infected by Chlamydia trachomatis (CT) revealed 10 peptides, expressed upon CT infection. Analysis of these 10 candidates by tandem mass spectrometry enabled the determination of seven candidates as fragments from the precursors (I) ferritin heavy chain subunit, (II) HLA class II histocompatibility antigen, (III) vimentin, (IV) indoleamine 2,3-dioxygenase, (V and VI) pre-B cell enhancing factor (PBEF), and (VII) Interleukin-8 (CXCL8). The identified candidates proved the presence of anti-bacterial and immunologically active monocytic proteins after CT infection.

Optimization and evaluation of surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry for protein profiling of cerebrospinal fluid

Cerebrospinal fluid (CSF) potentially carries an archive of peptides and small proteins relevant to pathological processes in the central nervous system (CNS) and surrounding brain tissue. Proteomics is especially well suited for the discovery of biomarkers of diagnostic potential in CSF for early diagnosis and discrimination of several neurodegenerative diseases. ProteinChip surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one such approach which offers a unique platform for high throughput profiling of peptides and small proteins in CSF. In this study, we evaluated methodologies for the retention of CSF proteins < 20 kDa in size, and identify a strategy for screening small proteins and peptides in CSF. ProteinChip array types, along with sample and binding buffer conditions, and matrices were investigated. By coupling the processing of arrays to a liquid handler reproducible and reliable profiles, with mean peak coefficients of variation < 20%, were achieved for intra- and inter-assays under selected conditions. Based on peak m/z we found a high degree of overlap between the tested array surfaces. The combination of CM10 and IMAC30 arrays was sufficient to represent between 80–90% of all assigned peaks when using either sinapinic acid or α-Cyano-4-hydroxycinnamic acid as the energy absorbing matrices. Moreover, arrays processed with SPA consistently showed better peak resolution and higher peak number across all surfaces within the measured mass range. We intend to use CM10 and IMAC30 arrays prepared in sinapinic acid as a fast and cost-effective approach to drive decisions on sample selection prior to more in-depth discovery of diagnostic biomarkers in CSF using alternative but complementary proteomic strategies.

Peptidomic analysis of breast cancer reveals a putative surrogate marker for estrogen receptor-negative carcinomas

Estrogen-receptor status provides a major biomarker in breast cancer classification and has an important impact on prognosis and treatment options. The aim of this study was to investigate peptide profiles of invasive breast cancer with positive (n=39) and negative receptor status (n=41). Peptide profiles were generated by 'Differential Peptide Display', which is an offline-coupled combination of reversed-phase-HPLC and MALDI mass spectrometry. Mass spectrometric data were correlated with the immunohistochemically determined receptor state. Identification of peptides of interest was carried out by additional mass spectrometric methods (eg MALDI-TOF-TOF-MS-MS). Approximately 3000-7000 signals were detected per sample and thymosin alpha-1, an asparaginyl endopeptidase generated cleavage product of the ubiquitous acidic protein prothymosin-alpha, was found to differentiate the tumor samples according to their receptor status with the highest specificity. The concentration of Thymosin alpha-1 was found to be upregulated (n=37) in estrogen-negative cancer samples and downregulated (n=32) in estrogen-positive breast cancer samples. The expression of the precursor protein (Prothymosin-alpha) has been discussed previously as a prognostic factor in breast cancer. It is involved in the ER signal transduction pathway as an anti-coactivator-inhibitor. From our findings we conclude that Thymosin alpha-1 could serve as a surrogate marker in breast cancers and may indicate ER functionality.