Peptides in body fluids and tissues as markers of disease

Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

Comparative peptidomics analysis of preeclamptic placenta and the identification of a novel bioactive SERPINA1 C-terminal peptide

Preeclampsia (PE) is a life-threatening disease that severely harms pregnant women and infants' health but has a poorly understood etiology. Peptidomics can supply important information about the occurrence of diseases. However, application of peptidomics in preeclamptic placentas has never been reported. We conducted a comparative peptidomics analysis of PE placentas and performed bio-informatics analysis on differentially expressed peptides. Effects of differential peptide 405SPLFMGKVVNPTQK418 on the behaviors of trophoblasts and angiogenesis were assessed by CCK8, transwell assays, and tube network formation assays. And we also confirmed the role of peptide in the zebrafish xenograft model. A total of 3582 peptide were identified. 48 peptides were differentially expressed. Bioinformatics analysis indicated that precursor proteins of these differentially expressed peptides correlate with "complement and coagulation cascades," and "platelet activation" pathways. Of the 48 differential peptides, we found that peptide 405SPLFMGKVVNPTQK418 can significantly increase proliferation, migration of trophoblasts and stimulate angiogenesis of HUVECs in vitro and zebrafish model. These findings suggest peptidomes can aid in understanding the pathogenesis of PE more comprehensively. Peptide 405SPLFMGKVVNPTQK418 can be novel target and strategy to alleviate the condition of preeclampsia.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

Application of serum peptidomics for Parkinson's disease in SNCA-A30P mice

Intraneuronal inclusions of alpha-synuclein (α-synuclein, α-syn) are commonly found in the brain of patients with Parkinson's disease (PD). The pathogenesis of the abundant α-syn protein in the blood has been extensively studied to understand its properties better. In recent years, peptidome analysis has received increasing attention. In this study, we identified and analyzed serum peptides from wild-type (WT) and the (Thy-1)-h[A30P] alpha-synuclein transgenic mice (SNCA-A30P mice) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). One thousand eight hundred fifty-six peptides from 771 proteins were analyzed. Among them, 151 peptides from 107 proteins were significantly differentially expressed. The glycoprotein VI platelet pathway (GP6) was the pathway's most significant differentially expressed signaling pathway. Cleavage sites of the differentially expressed peptides may reflect protease distribution and activity. We selected the most significantly differentially expressed peptide, VGGDPI, and found that it contained cathepsin K (Ctsk) and trypsin-1 cleavage sites, suggesting that Ctsk and trypsin-1 may be key peptidases in PD. α-syn is a protein associated with the pathogenesis of PD. mutations in several genes, including SNCA, which encodes α-syn, are associated with the development of PD. Bioinformatics analysis of the physiological pathways related to SNCA genes and apoptosis genes found the five most markedly up-regulated proteins: formin homology 2 domain-containing 1 (FHOD1), insulin receptor substrate 1(IRS1), TRPM8 channel-associated factor 1 (TCAF1), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and interleukin-16 (IL-16). Therefore, the differentially expressed peptides in the five precursor protein domains may be potential bioactive peptides associated with α-syn and apoptosis. This study provides a validated peptidomics profile of SNCA-A30P mice and identifies potentially bioactive peptides linked to α-syn and apoptosis.

Selective Detection and Characterization of Small Cysteine-Containing Peptides with Cluster-Modified Nanopore Sensing

It was recently demonstrated that one can monitor ligand-induced structure fluctuations of individual thiolate-capped gold nanoclusters using resistive-pulse nanopore sensing. The magnitude of the fluctuations scales with the size of the capping ligand, and it was later shown one can observe ligand exchange in this nanopore setup. We expand on these results by exploring the different types of current fluctuations associated with peptide ligands attaching to tiopronin-capped gold nanoclusters. We show here that the fluctuations can be used to identify the attaching peptide through either the magnitude of the peptide-induced current jumps or the onset of high-frequency current fluctuations. Importantly, the peptide attachment process requires that the peptide contains a cysteine residue. This suggests that nanopore-based monitoring of peptide attachments with thiolate-capped clusters could provide a means for selective detection of cysteine-containing peptides. Finally, we demonstrate the cluster-based protocol with various peptide mixtures to show that one can identify more than one cysteine-containing peptide in a mixture.

To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.

Development of Peptide Biopharmaceuticals in Russia

Peptides are low-molecular-weight substances that participate in numerous important physiological functions, such as human growth and development, stress, regulation of the emotional state, sexual behavior, and immune responses. Their mechanisms of action are based on receptor–ligand interactions, which result in highly selective effects. These properties and low toxicity enable them to be considered potent drugs. Peptide preparations became possible at the beginning of the 20th century after a method was developed for selectively synthesizing peptides; however, after synthesis of the first peptide drugs, several issues related to increasing the stability, bioavailability, half-life, and ability to move across cell membranes remain unresolved. Here, we briefly review the history of peptide production and development in the biochemical industry and outline potential areas of peptide biopharmaceutical applications and modern approaches for creating pharmaceuticals based on synthetic peptides and their analogs. We also focus on original peptide drugs and the approaches used for their development by the Russian Federation.

Tunning Intermolecular Interaction of Peptide-Conjugated AIEgen in Nano-Confined Space for Quantitative Detection of Tumor Marker Secreted from Cells

Determining the expression level of biomarkers is crucial for disease diagnosis. However, the low abundance of biomarkers in the early stage makes the detection extremely difficult by traditional aggregation-induced emission (AIE)-based fluorescent probes. Here, by tuning the intermolecular interaction, a two steps-based MP/NPs-SLIPS sensing system is designed for ultrasensitive detection of the tumor marker matrix metalloproteinase-2 (MMP-2). During the sensing process, aggregation of AIE residual could be intensified through the electrostatic absorption by negatively charged nanoparticles (NPs), as well as the confined space formed by the self-assembly of NPs to photonic crystals (PCs) on slippery lubricant-infused porous substrates (SLIPS). The fluorescent signals obviously increased with a strengthened aggregation degree, which contributes to improved sensitivity. Thus, the limit of detection is decreased to 3.7 ng/mL for MP/NPs-SLIPS sensing system, which could be used for detecting the MMP-2 secreted by tumor cells directly. This strategy also demonstrated its potential applications as high-throughput detection devices and will be of significance for the ultrasensitive analysis of biomarkers.

Plasma nontargeted peptidomics discovers potential biomarkers for major depressive disorder

PURPOSE

There are great demands for identifying biomarkers of major depressive disorder (MDD), a common mental illness with a prevalence of approximately 6%. Finding potential biomarkers to aid MDD diagnosis is in high demand.

EXPERIMENTAL DESIGN

In this study, a combination of pretreatment methods named salt-out assisted liquid-liquid extraction (SALLE) and nontargeted peptidomics based on nano-LC-Orbitrap/MS was primarily employed to discover the candidate peptide markers from the plasma of 238 subjects.

RESULTS

Many peptides were enriched and identified from the plasma, 42 of which showed significant differences between MDD patients and controls by univariate statistical analysis. A binary logistic regression (BLR) model combined four peptide markers (P1, P9, P17, P29) was established, yielding an overall prediction accuracy of 91.7% and 82.2% in the discovery and validation sets, respectively.

CONCLUSIONS AND CLINICAL RELEVANCE

In conclusion, the excellent performance of the BLR model in both discovery and validation sets demonstrates the robustness of the four peptide markers panel. It is very valuable for quantification of the absolute content of four peptides and further verification.

Peptidomics Analysis Discloses That Novel Bioactive Peptides Participate in Necrotizing Enterocolitis in a Rat Model

Necrotizing enterocolitis (NEC) is a common devastating gastrointestinal disease in premature infants, the molecular mechanisms of which have not been fully elucidated. Recently, endogenous peptides have garnered much attention owing to their role in diagnosis and treatment. However, changes in the peptide expression of NEC intestinal tissues remain poorly understood. In the present study, a comparative peptidomics profiling analysis was performed between NEC and control intestinal tissues via liquid chromatography-tandem mass spectrometry (LC-MS). In total, 103 upregulated and 73 downregulated peptides were identified in the intestinal tissues (fold change ≥ 1.5, p < 0.05). Bioinformatics analysis revealed that these differentially expressed peptides were significantly associated with NEC pathophysiology, including apoptosis, the TGF-β signaling pathway, the Wnt signaling pathway, and the MAPK signaling pathway. Furthermore, two putative peptides could inhibit apoptosis and promote the migration of intestinal epithelial cells induced by lipopolysaccharide; these peptides were derived from the protein domains MT1 and EZRI, respectively. In conclusion, our study revealed that endogenous peptides are involved in the pathophysiologic mechanism of NEC; nevertheless, further exploration is required in this regard.

AntiCP 2.0: an updated model for predicting anticancer peptides

Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides (ACPs). ACPs residue composition analysis show the preference of A, F, K, L and W. Positional preference analysis revealed that residues A, F and K are favored at N-terminus and residues L and K are preferred at C-terminus. Motif analysis revealed the presence of motifs like LAKLA, AKLAK, FAKL and LAKL in ACPs. Machine learning models were developed using various input features and implementing different machine learning classifiers on two datasets main and alternate dataset. In the case of main dataset, dipeptide composition based ETree classifier model achieved maximum Matthews correlation coefficient (MCC) of 0.51 and 0.83 area under receiver operating characteristics (AUROC) on the training dataset. In the case of alternate dataset, amino acid composition based ETree classifier performed best and achieved the highest MCC of 0.80 and AUROC of 0.97 on the training dataset. Five-fold cross-validation technique was implemented for model training and testing, and their performance was also evaluated on the validation dataset. Best models were implemented in the webserver AntiCP 2.0, which is freely available at https://webs.iiitd.edu.in/raghava/anticp2/. The webserver is compatible with multiple screens such as iPhone, iPad, laptop and android phones. The standalone version of the software is available at GitHub; docker-based container also developed.

Comparative peptidomic profile between human hypertrophic scar tissue and matched normal skin for identification of endogenous peptides involved in scar pathology

Endogenous peptides recently attract increasing attention for their participation in various biological processes. Their roles in the pathogenesis of human hypertrophic scar remains poorly understood. In this study, we used liquid chromatography-tandem mass spectrometry to construct a comparative peptidomic profiling between human hypertrophic scar tissue and matched normal skin. A total of 179 peptides were significantly differentially expressed in human hypertrophic scar tissue, with 95 upregulated and 84 downregulated peptides between hypertrophic scar tissue and matched normal skin. Further bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis) indicated that precursor proteins of these differentially expressed peptides correlate with cellular process, biological regulation, cell part, binding and structural molecule activity ribosome, and PPAR signaling pathway occurring during pathological changes of hypertrophic scar. Based on prediction database, we found that 78 differentially expressed peptides shared homology with antimicrobial peptides and five matched known immunomodulatory peptides. In conclusion, our results show significantly altered expression profiles of peptides in human hypertrophic scar tissue. These peptides may participate in the etiology of hypertrophic scar and provide beneficial scheme for scar evaluation and treatments.

Mass Spectrometric Identification of Endogenous Peptides

Peptidomics is an emerging field focused in the analysis of endogenous peptides. Naturally occurring peptides are often endogenously produced protein fragments. Cleavage of precursor proteins by proteases generates peptides that may gain specialized functions not ascribed to their precursors, and which could reflect the state of a cell under certain physiological conditions or pathological processes.Since peptides are found in complex matrices (e.g., serum, tear, urine, cerebrospinal fluid), they need to be isolated from the matrix and concentrated before they can be analyzed on mass spectrometry. This chapter describes methods for sample preparation prior to mass spectrometry analysis. In addition, different peptide fragmentation techniques are described which are complementary when analyzing naturally occurring peptides by liquid chromatography coupled online to tandem mass spectrometry.

Endogenous peptides profiles of human infantile hemangioma tissue and their clinical significance for treatment

Endogenous peptides play crucial roles in various biological processes. Their effects on the pathogenesis of human infantile hemangioma remains poorly understood. In this study, we construct a comparative peptidomic profiling between human infantile hemangioma tissue and matched normal skin using liquid chromatography-tandem mass spectrometry. A total of 192 peptides were significantly differentially expressed in human infantile hemangioma tissue, with 182 upregulated, and 10 downregulated peptides between infantile hemangioma tissue and matched normal skin. Performing bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis), we found that precursor proteins of these differentially expressed peptides correlate with metabolic process, biological regulation, binding, catalytic activity, and pathways in cancer occurring during pathological changes of infantile hemangioma. Furthermore, 89 differentially expressed peptides shared homology with antimicrobial peptides and 13 matched known immunomodulatory peptides based on prediction database. In conclusion, our results reveal significantly altered expression profiles of peptides in human infantile hemangioma tissue. These peptides may participate in the etiology of infantile hemangioma and provide beneficial scheme for clinical treatments.

CancerPDF: A repository of cancer-associated peptidome found in human biofluids

CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.

Human Antimicrobial Peptides in Bodily Fluids: Current Knowledge and Therapeutic Perspectives in the Postantibiotic Era

Antimicrobial peptides (AMPs) are an integral part of the innate immune defense mechanism of many organisms. Due to the alarming increase of resistance to antimicrobial therapeutics, a growing interest in alternative antimicrobial agents has led to the exploitation of AMPs, both synthetic and isolated from natural sources. Thus, many peptide-based drugs have been the focus of increasing attention by many researchers not only in identifying novel AMPs, but in defining mechanisms of antimicrobial peptide activity as well. Herein, we review the available strategies for the identification of AMPs in human body fluids and their mechanism(s) of action. In addition, an overview of the distribution of AMPs across different human body fluids is provided, as well as its relation with microorganisms and infectious conditions.

Recent applications of CE- and HPLC-MS in the analysis of human fluids

The present review intends to cover the literature on the use of CE-/LC-MS for the analysis of human fluids, from 2010 until present. It has been planned to provide an overview of the most recent practical applications of these techniques to less extensively used human body fluids, including, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate, tear fluid, breast fluid, amniotic fluid, and cerumen. Potential pitfalls related to fluid collection and sample preparation, with particular attention to sample clean-up procedures, and methods of analysis, from the research laboratory to a clinical setting will also be addressed. While being apparent that proteomics/metabolomics represent the most prominent approaches for global identification/quantification of putative biomarkers for a variety of human diseases, evidence is also provided of the suitability of these sophisticated techniques for the detection of heterogeneous components carried by these fluids.

Current peptidomics: applications, purification, identification, quantification, and functional analysis

Peptidomics is an emerging field branching from proteomics that targets endogenously produced protein fragments. Endogenous peptides are often functional within the body-and can be both beneficial and detrimental. This review covers the use of peptidomics in understanding digestion, and identifying functional peptides and biomarkers. Various techniques for peptide and glycopeptide extraction, both at analytical and preparative scales, and available options for peptide detection with MS are discussed. Current algorithms for peptide sequence determination, and both analytical and computational techniques for quantification are compared. Techniques for statistical analysis, sequence mapping, enzyme prediction, and peptide function, and structure prediction are explored.

Biological sample collection for clinical proteomics: existing SOPs

Proteomic study of clinical samples aims at the better understanding of physiological and pathological conditions, as well as the discovery of diagnostic and prognostic markers for the latter. Quantitative and/or qualitative variations of body fluid proteome may reflect health- or disease-associated events connected to the adjacent or distant body regions of the fluid production/secretion/excretion and/or systemic reactions to the presence of disease. Sample collection and preparation is a critical step in order to obtain useful and valid information in clinical proteomics analysis. In this chapter, we present the current protocols and guidelines for human body fluid collection and storage, prior to proteomic analysis. A variety of body fluids that are currently being used in proteomic analysis and have potential interest in clinical practice are presented including blood plasma and serum, urine, cerebrospinal fluid, cerumen, nasal secretions, saliva, tears, breast milk, bronchoalveolar fluid, nipple aspirate fluid, amniotic fluid, bile, cervico-vaginal fluid, and seminal plasma. With no doubt these body fluids differ in the extent of their application in clinical proteomics investigations, hence in some cases the presented SOPs are established following more extensive testing (e.g., plasma, serum, urine, CSF) than others (nasal secretions, saliva, tears, breast milk, bronchoalveolar fluid, nipple aspirate fluid, amniotic fluid, bile, cervico-vaginal fluid, and seminal plasma). However, even in these latter cases, the presented protocols were reported by at least two independent groups according to the literature. We hope they can thus serve as a reliable guide for sample collection based on our current knowledge in the field and excellent starting points for proteomics investigators. It should also be pointed that variations to these protocols exist and their further refinement in the future is foreseen following the evolution of the proteomics technologies.

Peptidomics of Urine and Other Biofluids for Cancer Diagnostics

BACKGROUND

Cancer is a leading cause of death worldwide. The low diagnostic sensitivity and specificity of most current cancer biomarkers make early cancer diagnosis a challenging task. The comprehensive study of peptides and small proteins in a living system, known as “peptidomics,” represents an alternative technological approach to the discovery of potential biomarkers for the assessment of a wide variety of pathologies. This review examines the current status of peptidomics for several body fluids, with a focus on urine, for cancer diagnostics applications.

CONTENT

Several studies have used high-throughput technologies to characterize the peptide content of different body fluids. Because of its noninvasive collection and high stability, urine is a valuable source of candidate cancer biomarkers. A wide variety of preanalytical issues concerning patient selection and sample handling need to be considered, because not doing so can affect the quality of the results by introducing bias and artifacts. Optimization of both the analytical strategies and the processing of bioinformatics data is also essential to minimize the false-discovery rate.

SUMMARY

Peptidomics-based studies of urine and other body fluids have yielded a number of biomolecules and peptide panels with potential for diagnosing different types of cancer, especially of the ovary, prostate, and bladder. Large-scale studies are needed to validate these molecules as cancer biomarkers.

Size-exclusive magnetic graphene/mesoporous silica composites with titanium(IV)-immobilized pore walls for selective enrichment of endogenous phosphorylated peptides

Developing an effective separation method is necessary for identifying low-abundant endogenous phosphorylated peptides with the removal of proteins. In this work, we prepared size-exclusive magnetic graphene/mesoporous silica composites with titanium(IV)-immobilized pore walls (denoted as Ti(4+)-MGMSs) for capturing endogenous phosphorylated peptides for mass spectrometry analysis. The introduction of hydrophilic polydopamine simplified the synthetic process of Ti(4+)-MGMSs, and the ordered mesoporous channels are beneficial to the trapping of endogenous phosphopeptides while large-size proteins are excluded. Furthermore, the magnetic performance greatly simplifies the entire process of enrichment. With all of the advances, the novel Ti(4+)-MGMSs present high enrichment efficiency either from the low concentration of β-casein tryptic digest (0.5 fmol/μL) or the mixture of β-casein tryptic digest and α-casein (or plus bovine serum albumin, with a mass ratio up to 1:500). Besides, Ti(4+)-MGMSs have also been successfully applied to enrich endogenous phosphorylated peptides from human serum and human saliva.

A combo-pore approach for the programmable extraction of peptides/proteins

A novel combo-pore approach has been designed for the programmable purification, minimisation of sample complexity, enrichment and sensitive detection of peptides in biosamples. This approach has a superior performance to conventional protocols and commercial products.

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.

ELISA microarray technology as a high-throughput system for cancer biomarker validation

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

Serum dihydroxyacetone kinase peptide m/z 520.3 as predictor of disease severity in patients with compensated chronic hepatitis B

Background & aim

Due to known limitations of liver biopsy, reliable non-invasive serum biomarkers for chronic liver diseases are needed. We performed serum peptidomics for such investigation in compensated chronic hepatitis B (CHB) patients.

Methods

Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed peptides in sera from 40 CHB patients (20 with S0G0-S1G1 and 20 with S3G3-S4G4). Ion pair quantification from differentially expressed peptides in a validation set of sera from 86 CHB patients was done with multiple reaction monitoring (MRM).

Results

21 differentially represented peptide peaks were found through LC-MS/MS. Ion pairs generated from eleven of these peptides (m/z < 800) were quantified by MRM. Summed peak area ratios of 6 ion pairs from peptide m/z 520.3 (176.1, 353.7, 459.8, 503.3, 351.3, 593.1), which was identified as dihydroxyacetone kinase (DAK) fragment, decreased from mild to advanced stages of fibrosis or inflammation. Area Under Receiver Operating Characteristic Curves (AUROCs) of five ion models discriminating fibrosis degrees were 0.871 ~ 0.915 (S2-4 versus S0-1) and 0.804 ~ 0.924 (S3-4 versus S0-2). AUROCs discriminating inflammation grades were 0.840 ~ 0.902 (G2-4 versus G0-1) and 0.787 ~ 0.888 (G3-4 versus G0-2). The diagnostic power of these models provides improved sensitivity and specificity for predicting disease progression as compared to aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Forn’s index and serum DAK protein.

Conclusions

The peptide fragment (m/z 520.3) of DAK is a promising biomarker to guide timing of antiviral treatment and to avoid liver biopsy in compensated CHB patients.

Observation of peptide differences between cancer and control in gastric juice

Biomarkers for various diseases have been extensively searched for the past 5 years. Nevertheless, most efforts were focused on the search for protein biomarkers from serum samples. In this work, we tried to look for peptide biomarkers from gastric juice samples with MALDI-TOF-MS. More than 200 gastric juice samples from healthy people, gastric ulcer patients, duodenal ulcer patients, and cancer patients were examined. There were clear pattern differences of mass spectra among samples from healthy people and patients with different gastric diseases. We found five peptides for gastric cancer diagnosis with high sensitivity and specificity. Sequences of these five peptides, including two pepsinogen fragments, leucine zipper protein fragment, albumin fragment, and α-1-antitrypsin fragment, have been identified by mass spectrometric analysis and immuno-deplete assay with antibodies.

Surrogate end points: how well do they represent patient-relevant end points?

This review takes a critical look at the concept of replacing patient-relevant end points, such as morbidity or mortality, with surrogate end points in clinical trials. Surrogate end points can be measured earlier in the course of a clinical trial and so are thought to accelerate the drug development process. Furthermore, they might be beneficial to the patients themselves by allowing faster adjustment of therapeutic strategies. However, the fact that in the past several promising surrogate end points have not fulfilled their expectations emphasizes the importance of applying strict evaluation criteria. The evaluation of the candidate surrogate end point prostate-specific antigen using the Prentice criteria and a meta-analytic approach is discussed. Prostate-specific antigen is often used to replace overall or progression-free survival in prostate cancer trials testing the benefit of medical interventions.

Biological and methodical challenges of blood-based proteomics in the field of neurological research

Biomarker discovery is an application of major importance in today's proteomic research. There is an urgent need for suitable biomarkers to improve diagnostic tools and treatment in various neurological diseases, such as neurodegenerative disorders. Recent years have witnessed an enormous interest in proteomics, which is currently seen as an invaluable tool to shed more light on complex interacting signalling pathways and molecular networks involved in several neuropathological conditions. However, while first results of proteomic research studies have sparked much public attention, the momentum of further proteomic biomarker research in neurological disorders may suffer by its very complex methodology which is sensitive to various sources of artefacts. A major source of variability is proteome perturbation caused by sample handling/preservation (preanalytical phase) and processing/measurement (analytical phase). The aim of the present review is to summarize the current literature focusing on the crucial role played by preanalytical and analytical factors that affect the quality of samples and the reliability of the data produced in blood-based proteomic biomarker research in neurology, which may apply to Alzheimer's disease (AD) as well as other neurological disorders. Procedures for sample preparation and protocols for the analysis of serum and plasma samples will be delineated. Finally, the potential usefulness of bioinformatics--allowing for the assembly, store, and processing of data--as well as its contribution to the execution of proteomic studies will be critically discussed.

Micro OS-ELISA: Rapid noncompetitive detection of a small biomarker peptide by open-sandwich enzyme-linked immunosorbent assay (OS-ELISA) integrated into microfluidic device

A novel detection system that combines the merits of open-sandwich (OS) enzyme-linked immunoadsorbent assay (ELISA) and a microfluidic sensor chip system, and which enables rapid and noncompetitive immunodetection of small antigens of less than 1000 in molecular weight, has been proposed. Equipped with a sensitive thermal lens microscope, a minute amount of the carboxyl-terminal peptide of human osteocalcin (BGP), a biomarker for bone metabolism, was quantified utilizing antigen-dependent stabilization of an antibody variable region (OS principle). In a short analysis time (approximately 12 min), we could attain a detection limit comparable to that of the microplate-based OS ELISA (1 microg L(-1)). In addition, the effects of several pretreatments for serum-derived samples were investigated: an albumin absorption resin, addition of a protease inhibitor cocktail and heat treatment. Each pretreatment was found to be effective. Consequently, intrinsic BGP and its fragments could be detected in healthy human serum with a superior detection limit and working range compared to those of the conventional competitive ELISA method.

Recent advances in mass spectrometry-based peptidome analysis

The peptidome, which is the low-molecular-weight subset of the proteome, has attracted increasing attention in recent years. However, with the interference of high-abundance protein components in complex biological mixtures (e.g., serum), selective extraction of endogenous peptides is the first and most important step before analyzing the peptidome. A number of methods and technologies have now been developed for the selective enrichment, fractionation, quantitative analysis of the endogenous peptides and their application in the potential biomarker discovery. This review will cover the methods and technologies developed in recent years for the peptidome analysis on the selective extraction, multidimensional separation and quantitative analysis, as well as their application for clinical diagnosis and biomarker discovery. The future prospects of the peptidome are also discussed.

Biomarker discovery in psychiatric disorders

Schizophrenia is a multifaceted neuropsychiatric disorder. Its onset is the result of complex interactions between genetic, developmental and environmental factors. It almost certainly presents a heterogeneous group of aetiologies which may not be reflected in the symptomatic/clinical presentation of patients. Therefore, a better molecular understanding of the disease onset and progression is urgently needed. The high complexity of the disorder and the heterogeneity of patient populations account for the slow progress of biomarker discovery approaches. Multi-omics profiling approaches can be employed to investigate large numbers of patient and control samples in a single experiment. These large scale experiments are required to identify disease intrinsic molecular signatures as well as patient subgroups with potentially distinct biochemical pathways underpinning their symptoms. In this overview, we describe some of the most important challenges for biomarker discovery for psychiatric disorders and emphasize how these problems contribute to the requirement of large sample numbers. Results of MS-based protein profiling studies in schizophrenia research are reviewed and technical advantages and difficulties of the methodologies described. We outline recent technological advances that generated impressive results in other areas of research and point to their applicability for biomarker discovery in psychiatric disorders.

A mass spectrometry-based strategy for detecting and characterizing endogenous proteinase activities in complex biological samples

Endogenous proteinases in biological fluids such as human saliva produce a rich peptide repertoire that reflects a unique combination of enzymes, substrates, and inhibitors/activators. Accordingly, this subproteome is an interesting source of biomarkers for disease processes that either directly or indirectly involve proteolysis. However, the relevant proteinases, typically very low abundance molecules, are difficult to classify and identify. We hypothesized that a sensitive technique for monitoring accumulated peptide products in an unbiased, global manner would be very useful for detecting and profiling proteolytic activities in complex biological samples. Building on the longstanding use of 18O isotope-based approaches for the classification of proteolytic and other enzymatic processes we devised a new method for evaluating endogenous proteinases. Specifically, we showed that upon ex vivo incubation endogenous proteinases in human parotid saliva introduced 18O from isotopically enriched water into the C-terminal carboxylic groups of their peptide products. Subsequent peptide sequence determination and inhibitor profiling enabled the detection of discrete subsets of proteolytic products that were generated by different enzymes. As a proof-of-principle we used one of these fingerprints to identify the relevant activity as tissue kallikrein. We termed this technique PALeO. Our results suggest that PALeO is a rapid and highly sensitive method for globally assessing proteinase activities in complex biological samples.

Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

Surface-enhanced laser desorption time of flight mass spectrometry (SELDI-TOF-MS) is an important proteomic technology that is immediately available for the high throughput analysis of complex protein samples. Over the last few years, several studies have demonstrated that comparative protein profiling using SELDI-TOF-MS breaks new ground in diagnostic protein analysis particularly with regard to the identification of novel biomarkers. Importantly, researchers have acquired a better understanding also of the limitations of this technology and various pitfalls in biomarker discovery. Bearing these in mind, great emphasis must be placed on the development of rigorous standards and quality control procedures for the pre-analytical as well as the analytical phase and subsequent bioinformatics applied to analysis of the data. To avoid the risk of false-significant results studies must be designed carefully and control groups accurately selected. In addition, appropriate tools, already established for analysis of highly complex microarray data, need to be applied to protein profiling data. To validate the significance of any candidate biomarker derived from pilot studies in appropriately designed prospective multi-center studies is mandatory; reproducibility of the clinical results must be shown over time and in different diagnostic settings. SELDI-TOF-MS-based studies that are in compliance with these requirements are now required; only a few have been published so far. In the meantime, further evaluation and optimization of both technique and marker validation strategies are called for before MS-based proteomic algorithms can be translated into routine laboratory testing.

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.

Neuropeptidomics: expanding proteomics downwards

Biological function is mainly carried out by a dynamic population of proteins and peptides which may be used as markers for disease diagnosis, prognosis and as a guide for effective treatment. The study of proteins is called proteomics and it is generally performed by two-dimensional gel electrophoresis and mass spectrometric methods. However, gel-based proteomics is methodologically restricted from the low mass region, which includes important endogenous peptides. The study of endogenous peptides, peptidomics, is complicated by protein fragments produced post-mortem during conventional sample handling. Nanoflow liquid chromatography and MS, together with improved methods for sample preparation, have been used to semi-quantitatively monitor endogenous peptides in brain tissue. When rapidly heat-denatured brain tissue was analysed, these methods enabled simultaneous detection of hundreds of peptides and the identification of several endogenous peptides not previously described in the literature. In an application of the MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) model for Parkinson's disease, the expression of the small protein PEP-19 was compared with controls. The levels were found to be significantly decreased in the striatum of MPTP-treated animals.