Dynamic proteome in enigmatic preeclampsia: an account of molecular mechanisms and biomarker discovery

The coevolution of genomics and proteomics has led to advancements in the field of diagnosis and molecular mechanisms of disease. Proteomics is now stepping into the field of obstetrics, where early diagnosis of pregnancy complication such as preeclampsia (PE) is imperative. PE is a multifactorial disease characterized by hypertension with proteinuria, which is a leading cause of maternal and neonatal morbidity and mortality occurring in 5-7% of pregnancies worldwide. This review discusses the probable molecular mechanisms that lead to PE and summarizes the proteomics research carried out in understanding the pathogenicity of PE, and for identifying the candidate biomarker for diagnosis of the disease.     

High-resolution biomarker discovery: Moving from large-scale proteome profiling to quantitative validation of lead candidates

Diverse proteomic techniques based on protein MS have been introduced to systematically characterize protein perturbations associated with disease. Progress in clinical proteomics is essential for personalized medicine, wherein treatments will be tailored to individual needs based on patient stratification using noninvasive disease monitoring procedures to reveal the most appropriate therapeutic targets. However, breakthroughs await the successful development and application of a robust proteomic pipeline capable of identifying and rigorously assessing the relevance of multiple candidate proteins as informative diagnostic and prognostic indicators or suitable drug targets involved in a pathological process. While steady progress has been made toward more comprehensive proteome profiling, the emphasis must now shift from in depth screening of reference samples to stringent quantitative validation of selected lead candidates in a broader clinical context. Here, we present an overview of the emerging proteomic strategies for high-throughput protein detection focused primarily on targeted MS/MS as the basis for biomarker verification in large clinical cohorts. We discuss the conceptual promise and practical pitfalls of these methods in terms of achieving higher dynamic range, higher throughput, and more reliable quantification, highlighting research avenues that merit additional inquiry.     

A qualitative proteome investigation of the sediment portion of human urine: Implications in the biomarker discovery process

Inherent to the biomarker discovery process is a comparative analysis of physiological states. It is therefore critical that the proteome detection protocol does not bias the analysis. With urine, the sediment portion, obtained upon thawing frozen urine, is routinely discarded prior to proteome analysis. However, our results demonstrate that such a practice inadvertently induces bias, having significant implications in the biomarker discovery process. We present the first proteome investigation of human urinary sediments, identifying 60 proteins in this phase by MS. Many sediment proteins were also detected in the urinary supernatant, indicating that several proteins partition between the two phases. This partitioning is dependant on the pH of the sample, as well as the degree of sample agitation. As a consequence of discarding the sediment portion of urine, the concentration of potential candidate biomarkers in the supernatant phase will be altered or, in other instances, may be completely removed from the sample. To minimize this, the pH of all samples should first be normalized, and the samples vigorously vortexed prior to discarding the sediments. For more comprehensive biomarker investigations, we suggest that urinary sediments be analyzed along with the supernatant proteins.     

Recognition of kidney glomerulus by dynamic programming matchingmethod

Dynamic programming was applied to locate the glomeruli in microscopic images of kidney tissue section. The glomeruli were modeled by a polygon whose sides could be varied within a given range of lengths. The objects were located by determining the best match of the model according to a so-called optimum criterion in which all possible shapes were evaluated at all possible positions in the input image. The best model was selected according to the maximum average gray level. To increase the probability of obtaining a closed contour, a distance criterion was added and the maximum gray-level requirement was relaxed somewhat. The optimum criterion was modified to include a directionality constraint in which the difference in angle between model segments and the edge values in the image was minimized, thereby increasing the performance of the method. A hierarchical multiresolution strategy was used to reduce calculation time. The cyclical property of a contour is also taken into account     

Non-invasive biomarker sampling and analysis of the exhaled breath proteome

Exhaled breath condensate (EBC) is a biological fluid that contains trace amounts of secreted pulmonary proteins, and is emerging as a potentially valuable and non-invasively obtained source of disease biomarkers. Proteome analysis of these samples could lead to the identification of prognostic indicators of airway diseases. The objective of this study was to develop a protocol for proteome analysis of EBC samples. In this report, an improved procedure for EBC sample preparation and concentration is presented, together with a method for comparison of the protein profiles between two groups. The presented approach enabled to study the condensed exhaled breath proteome for biomarker analysis, and revealed proteins not previously identified in an EBC proteomics approach. In a comparative pilot study, EBC protein profiles obtained from smokers and non-smokers showed distinct differences and are illustrative for its potential in clinical studies. EBC from smokers contained higher concentrations of the more abundant proteins, such as cytokeratins, compared to non-smokers, and calgranulin B was identified uniquely in EBC samples from smokers.     

Quantitative proteomic approaches for biomarker discovery

The rapid advances in proteomic technologies have made possible systematic analysis of hundreds to thousands of proteins in clinical samples with the promise of uncovering novel protein biomarkers for various disease conditions. We will discuss in this review article current MS and protein chip-based quantitative proteomic approaches and their application in biomarker discovery. The emphasis will be placed on new quantification strategies employing stable isotopic labeling coupled with MS/MS, and antibody-based protein chips and nanodevices. The strength and weakness of each technology are briefly highlighted.     

Applications of protein microarrays for biomarker discovery

The search for new biomarkers for diagnosis, prognosis, and therapeutic monitoring of diseases continues in earnest despite dwindling success at finding novel reliable markers. Some of the current markers in clinical use do not provide optimal sensitivity and specificity, with the prostate cancer antigen (PSA) being one of many such examples. The emergence of proteomic techniques and systems approaches to study disease pathophysiology has rekindled the quest for new biomarkers. In particular the use of protein microarrays has surged as a powerful tool for large-scale testing of biological samples. Approximately half the reports on protein microarrays have been published in the last two years especially in the area of biomarker discovery. In this review, we will discuss the application of protein microarray technologies that offer unique opportunities to find novel biomarkers.     

Colorectal cancer proteomics, molecular characterization and biomarker discovery

Colorectal cancer (CRC) is a widespread disease, whose major genetic changes and mutations have been well characterized in the sporadic form. Much less is known at the protein and proteome level. Still, CRC has been the subject of multiple proteomic studies due to the urgent necessity of finding clinically relevant markers and to elucidate the molecular mechanisms underlying the progression of the disease. These proteomic approaches have been limited by different technical issues, mainly related with sensitivity and reproducibility. However, recent advances in proteomic techniques and MS systems have rekindled the quest for new biomarkers in CRC and an improved molecular characterization. In this review, we will discuss the application of different proteomic approaches to the identification of differentially expressed proteins in CRC. In particular, we will make a critical assessment about the use of 2-D DIGE, MS and protein microarray technologies, in their different formats, to identify up- or downregulated proteins and/or autoantibodies profiles that could be useful for CRC characterization and diagnosis. Despite a wide list of potential biomarkers, it is clear that more scientific efforts and technical advances are still needed to cover the range of low-abundant proteins, which may play a key role in CRC diagnostics and progression.     

Immunoproteomics: From biomarker discovery to diagnostic applications

Circulating antibodies reflect a molecular imprint of antigens that are related to autoimmune diseases, cancer or infection. Importantly, serum antibodies are useful clinical markers as they carry diagnostic information from all around the human body. Moreover, the amplification cascade governed by the humoral immune system causes a surplus of circulating antibodies after appearance of the corresponding (low abundance) antigen. In combination with the fact that antibodies are highly stable compared to many other serum proteins, they seem ideal to be implemented in clinical diagnostic assays for the detection of antigen-associated diseases. This review summarises advances in immunoproteomics with respect to technologies for biomarker discovery, with special emphasis on recently developed gel-free MS-based approaches, and looks forward to potential immunoproteomic applications in diagnostic medicine.     

Body fluid proteomics: Prospects for biomarker discovery

Many diseases are caused by perturbations of cellular signaling pathways and related pathway networks as a result of genetic aberrations. These perturbations are manifested by altered cellular protein profiles in the fluids bathing tissue/organs (i.e., the tissue interstitial fluid, TIF). A major challenge of clinical chemistry is to quantitatively map these perturbed protein profiles - the so-called "signatures of disease" - using modern proteomic technologies. This information can be utilized to design protein biomarkers for the early detection of disease, monitoring disease progression and efficacy of drug action. Here, we discuss the use of body fluids in the context of prospective biomarker discovery, and the marked 1000-1500-fold dilution of body fluid proteins, during their passage from TIF to the circulatory system. Further, we discuss proteomics strategies aimed at depleting major serum proteins, especially albumin, in order to focus on low-abundance protein/peptides in plasma. A major limitation of depletion strategies is the removal of low-molecular weight protein/peptides which specifically bind major plasma proteins. We present a prototype model, using albumin, for understanding the multifaceted nature of biomarker research, highlighting the involvement of albumin in Alzheimer's disease. This model underscores the need for a system-level understanding for biomarker research and personalized medicine.     

CE-MS in biomarker discovery, validation, and clinical application

CE coupled MS (CE-MS) has become an increasingly employed technology in proteome analysis with focus on the identification of biomarker peptides in clinical proteomics. In this review, we will cover technical aspects of CE-MS coupling and highlight the improvements made in the last few years. We examine CE-MS from an application point of view, and evaluate its merits and vices for biomarker discovery and clinical applications. We discuss the principal theoretical and practical obstacles encountered when employing CE-MS (and most other proteomic technologies) for the analysis of body fluids for biomarker discovery. We will present several examples of a successful application of CE-MS for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and will discuss current challenges and possible future improvements.     

The interface between biomarker discovery and clinical validation: The tar pit of the protein biomarker pipeline

The application of "omics" technologies to biological samples generates hundreds to thousands of biomarker candidates; however, a discouragingly small number make it through the pipeline to clinical use. This is in large part due to the incredible mismatch between the large numbers of biomarker candidates and the paucity of reliable assays and methods for validation studies. We desperately need a pipeline that relieves this bottleneck between biomarker discovery and validation. This paper reviews the requirements for technologies to adequately credential biomarker candidates for costly clinical validation and proposes methods and systems to verify biomarker candidates. Models involving pooling of clinical samples, where appropriate, are discussed. We conclude that current proteomic technologies are on the cusp of significantly affecting translation of molecular diagnostics into the clinic.     

Prostate cancer biomarker discovery using high performance mass spectral serum profiling

Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION™ Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF™ 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0 ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained.     

The glomerular proteome in a model of chronic kidney disease

Adequate kidney function is crucial in sustaining vertebrate homeostasis. Certain diseases can diminish renal function and lead to end-stage renal disease. Diabetes mellitus and hypertension are the main causes of glomerulosclerosis and albuminuria in adults. The molecular mechanisms that trigger these maladaptive changes are still unsatisfyingly described. We previously introduced 2-D DIGE in combination with focused tissue isolation methods to analyze protein expression in glomeruli. Glomeruli, the crucial compartments in albuminuric renal diseases, were extracted using magnetic particles from subtotally nephrectomized FVB mice (n?=?6); this 5/6 nephrectomy in FVB mice is a model of chronic kidney disease. Analysis of protein expression levels from glomerular protein lysates was performed using 2-D DIGE and compared with glomerular protein lysates from mice that underwent sham surgery. The comparison of about 2100 detectable spots between both groups revealed 48 protein spots that showed significant differential expression. Of those, 33 proteins could be identified using nanoLC-ESI MS. The metalloproteinase meprin 1 alpha, the beta galactoside-binding-lectin galectin-1 and dimethylarginine dimethylaminohydrolase 1, a key enzyme in NO metabolism, were found to be differentially regulated, thus implying a role in the pathogenesis and pathophysiology of progressive kidney disease. In conclusion, 2-D DIGE protein analysis of smallest sample sizes from specific organ compartments provides focused protein expression results, which help in gaining an understanding of the molecular mechanisms of chronic kidney disease.     

Biomarker discovery in glomerular diseases using urinary proteomics

Renal biopsy remains the gold standard test for definitive diagnosis of glomerular diseases. This invasive procedure; however, has a potential risk for serious complications and is contraindicated in some patients. It is therefore essential to search for noninvasive biomarkers for the diagnosis and prognosis of glomerular diseases. The urine is the most appropriate sample for biomarker discovery in glomerular diseases. Urinary proteomics has thus gained a wide acceptance and has been extensively applied to this area. This review focuses mainly on applications of proteomic technologies to urinary proteome profiling for biomarker discovery in various glomerular diseases, including diabetic nephropathy, IgA nephropathy, membranous nephropathy, focal segmental glomerulosclerosis, primary membranoproliferative glomerulonephritis, lupus nephritis, antiglomerular basement membrane disease, minimal change disease, and pediatric nephrotic syndrome. Recent findings from these studies are summarized and discussed. These data clearly underline the great promise of urinary proteomics in biomarker discovery for glomerular diseases.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Customising an antibody leukocyte capture microarray for systemic lupus erythematosus: beyond biomarker discovery

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with diverse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub-grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE-specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell-capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell-capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.     

A peptide-centric approach to breast cancer biomarker discovery utilizing label-free multiple reaction monitoring mass spectrometry

We report an approach for multiplex analysis of cancer biomarkers based on the measurement of diagnostic peptides in whole tissue protein digests. Label-free quantitation with MS3 multiple reaction monitoring (MRM) was developed to afford accurate analysis of prospective marker peptides in a panel of breast tumors. This approach provides an economical and robust alternative to stable isotope-based methods. It is equally applicable to the analysis of samples derived from tissue biopsy, aspirate, or plasma and can be easily translated to clinic.