Exploration of the normal human bronchoalveolar lavage fluid proteome

We obtained insight into normal lung function by proteome analysis of bronchoalveolar lavage fluid (BALF) from six normal human subjects using a "Lyse-N-Go' shotgun proteomic protocol. Intra-sample variation was calculated using three different label-free methods, (i) protein sequence coverage; (ii) peptide spectral counts and (iii) peptide single-ion current areas (PICA), which generates protein expression data by summation of the area under the curve for a given peptide single-ion current trace and then adding values for all peptides from that same parent protein. PICA gave the least intra-subject variability and was used to calculate differences in protein expression between the six subjects. We observed an average threefold inter-sample variability, which affects analysis of changes in protein expression that occur in different diseases. We detected 167 unique proteins with >100 proteins detected in each of the six individual BAL samples, 42 of which were common to all six subjects. Gene ontology analysis demonstrated enrichment of several biological processes in the lung, reflecting its expected role in gas exchange and host defense as an immune organ. The same biological processes were enriched compared to either plasma or total genome proteome, suggesting an active enrichment of plasma proteins in the lung rather than passive capillary leak.     

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

Shotgun MS proteomic analysis of bronchoalveolar lavage fluid in normal subjects

We provide a review of proteomic techniques used to characterize the bronchoalveolar lavage fluid (BALF) proteome of normal healthy subjects. Bronchoalveolar lavage (BAL) is the most common technique for sampling the components of the alveolar space. The proteomic techniques used to study normal BALF include protein separation by 2DE, whereby proteins were identified by comparison to a reference gel as well as high pressure liquid chromatography (HPLC)-MS/MS, also known as shotgun proteomics. We summarize recent progress using shotgun MS technologies to define the normal BALF proteome. Surprisingly, we find that despite advances in shotgun proteomic technologies over the course of the last 10 years, which have resulted in greater numbers of proteins being identified, the functional landscape of normal BALF proteome was similarly described by all methods examined.     

Proteomics of nipple aspirate fluid, breast cyst fluid, milk, and colostrum

Proteomic analysis of breast fluids has wide-ranging clinical applications. Protein expression in nipple aspirate fluid and breast cyst fluid may prove valuable for the detection and monitoring of breast cancer, but has been hampered by the lack of a single marker with sufficient breast cancer sensitivity and specificity to be clinically useful. The assessment of multiple proteins may offer a more powerful cancer detection tool. Breast cancer is particularly difficult to detect in women who are lactating. The identification of cancer predicting proteins in milk may prove very helpful in an early cancer detection in this group of women. A better understanding of the protein composition of milk and colostrum should improve infant care and nutrition, and lead to alternatives for individuals with milk allergies.     

Proteomics of human cerebrospinal fluid - the good, the bad, and the ugly

The development of MALDI ESI in the late 1980s has revolutionized the biological sciences and facilitated the emergence of a new discipline called proteomics. Application of proteomics to human cerebrospinal fluid (CSF) has greatly hastened the advancement of characterizing the CSF proteome as well as revealing novel protein biomarkers that are diagnostic of various neurological diseases. While impressive progressions have been made in this field, it has become increasingly clear that proteomics results generated by various laboratories are highly variable. The underlying issues are vast, including limitations and complications with heterogeneity of patients/testing subjects, experimental design, sample processing, as well as current proteomics technology. Accordingly, this review not only summarizes the current status of characterization of the human CSF proteome and biomarker discovery for major neurodegenerative disorders, i.e., Alzheimer's disease and Parkinson's disease, but also addresses a few essential caveats involved in several steps of CSF proteomics that may contribute to the variable/contradicting results reported by different laboratories. The potential future directions of CSF proteomics are also discussed with this analysis.     

Bronchoalveolar lavage fluid proteomic patterns of sulfur mustard-exposed patients

Sulfur mustard is an alkylating agent that reacts with ocular, respiratory, cutaneous, and bone marrow tissues. Main late respiratory complications are chronic obstructive pulmonary disease, bronchiectasis, asthma, and bronchiolitis obliterans. The aim of the present study was to identify differentially expressed proteins in bronchoalveolar lavage (BAL) fluid of control healthy and sulfur mustard-exposed lung disease patients. The BAL protein profile of ten healthy and 30 exposed patients with mild, moderate, and severe conditions (ten males in each group) were separated with 2-D SDS-PAGE and differentially expressed protein spots were successfully identified with MALDI TOF TOF MS. Among the differentially expressed proteins we observed a significant increase in vitamin D binding protein isoforms, haptoglobin isoforms, and fibrinogen especially in exposed moderate and severe lung diseases patients (p<0.01). Moreover, compared with healthy controls, significant decreases was noted in calcyphosine, surfactant protein A, and transthyretin in these patients (p<0.01). Apolipoprotein A1 was detected in all patients' BAL fluid but none of the healthy controls. Furthermore, S100 calcium-binding protein A8 was only detected in BAL fluid of moderate and severe groups. These findings will be useful to improve current methods of monitoring and helps to identify new therapeutic targets for treatment of this complicated illness.     

Proteomics in the Diagnosis of Endometriosis: Opportunities and Challenges

The non‐surgical diagnosis of endometriosis is still challenging for the clinician. Ultrasonography and magnetic resonance imaging can be used to diagnose ovarian endometriotic cysts and deep infiltrating endometriosis; but their performance is poor in the diagnosis of initial stages of endometriosis. CA‐125 and other serum markers (such as CA 19‐9, serum protein PP14, interleukins, and angiogenetic factors) have been measured in women with endometriosis but they are not reliable for the diagnosis of the disease. Although several studies used proteomics technologies to identify plasmatic markers of endometriosis, the non‐invasive diagnosis of endometriosis is far from being achieved. In this issue, Manousopoulou et al. compare the integrated quantitative proteomic profile of eutopic endometrium and serum of women with endometriosis and controls. 1214 proteins are differentially expressed in the eutopic endometrium and 404 proteins in the serum of the two study groups. 21 proteins are aberrantly expressed in both eutopic endometrium and serum of women with endometriosis. More work is needed to assess if the differentially expressed proteins identified in this study can be used as clinical markers of endometriosis.     

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow‐up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.     

Proteomics of bacterial pathogenicity: therapeutic implications

Identification of the molecular mechanisms of host-pathogen interaction is becoming a key focus of proteomics. Analysis of these interactions holds promise for significant developments in the identification of new therapeutic strategies to combat infectious diseases, a process that will also benefit parallel improvements in molecular diagnostics, biomarker identification and drug discovery. This review highlights recent advances in functional proteomics initiatives in infectious disease with emphasis on studies undertaken within physiologically relevant parameters that enable identification of the infectious proteome rather than that of the vegetative state. Deciphering the molecular details of what constitutes physiologically relevant host-pathogen interactions remains an underdeveloped aspect of research into infectious disease. The magnitude of this deficit will be largely influenced by the ease with which model systems can be established to investigate such interactions. As the selective pressures exerted by the host on an infecting pathogen are numerous, the adequacy of certain model systems should be considered carefully.     

Proteomics: A new perspective for the understanding of pterygia

Pterygia is a common ocular surface disease bothering both the patient because of its unsightly appearance and the surgeon because of its tendency to recur. The pathogenesis of pterygia is complex and the exact mechanism(s), especially at the molecular level, remains unknown. The use of modern proteomic techniques such as iTRAQ may yield new knowledge on the underlying pathogenesis of pterygia. In this issue of Proteomics Clinical Applications, Linghu et al. (article number 1600094) identified a total of 156 proteins that expressed differently between the pterygia and healthy conjunctiva using the isobaric tags for relative and absolute quantification based quantitative proteomic analysis. Most significantly, western blotting confirmed that two candidate proteins matrix metalloproteinase 10 (MMP‐10) and CD34 were significantly upregulated in pterygia, suggesting that they have potential roles in the pathogenesis of pterygia. The findings in Linhu's study may provide a new perspective for the understanding of pterygia and develop a new therapeutic target.     

Challenges in Computational Analysis of Mass Spectrometry Data for Proteomics

Mass spectrometry is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification and quantification, and post translational modification characterization in proteomics research. Both the size and the complexity of the data produced by this experimental technique impose great computational challenges in the data analysis. This article reviews some of these challenges and serves as an entry point for those who want to study the area in ...     

A mixed formulation of the Bingham fluid flow problem: Analysis and numerical solution

In this paper we introduce a mixed formulation of the Bingham fluid flow problem. We consider both the original and a regularized version of the problem, where a parameter ε is introduced, forcing the entire domain to be formally a fluid region. In general, common solvers for the regularized problem experience a performance degradation when the parameter ε gets smaller. The method studied here introduces an auxiliary tensor variable and shows enhanced numerical properties for small values of ε. A good performance is also observed for the non-regularized case. The well posedness for the regularized problem and the equivalence – at the continuous level – between the original (primitive variables) and the mixed formulation are demonstrated. We analyze properties of linearized problems that are relevant for the convergence of numerical solvers. A finite element method for the mixed formulation is discussed. Numerical results confirm the predicted better performances of the mixed formulation when compared to the primitive variables formulation. A comparison to a non-regularized solver based on the augmented Duvaut–Lions–Glowinski formulation of the problem is carried out as well.     

Proteomics of semen and its constituents

Semen is a complex fluid comprising sperm and other products of the testes together with secretions from the accessory sex glands including the prostate, seminal vesicles, and the bulbourethral gland. Studies of the protein components of seminal fluid, with or without the sperm present, can improve our understanding of reproductive biology; have potential clinical applications in the assessment and treatment of infertility; can assist with the diagnosis and management of urological diseases; and, at times, can offer important forensic insights. This review examines the application of proteomic methods to the study of spermatozoa and seminal fluid and highlights some of the unique challenges associated with the collection, fractionation, and analysis of this fluid. The discussion is restricted to issues relating to human semen, although we make occasional reference to important studies from animals.