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Respiratory Proteomics: From Descriptive Studies to Personalized Medicine Luis M Teran, Rosalia Montes-Vizuet, Xinping Li, and Thomas Franz J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr500935s • Publication Date (Web): 10 Nov 2014 Downloaded from http://pubs.acs.org on November 12, 2014

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Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Journal of Proteome Research

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Respiratory Proteomics: From Descriptive Studies to

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Personalized Medicine

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Luis M Teran1,2, Rosalia Montes-Vizuet1, Xinping Li3 and Thomas Franz3

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Instituto Nacional de EnfermedadesRespiratorias Mexico, 2Biomedicine in the

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Postgenomic EraMexico and 3Max Planck Institute for Biology of AgeingGermany.

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Correspondence

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Prof. Luis M Terán

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Calzada Tlalpan 4502

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México, D.F.

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email: [email protected]

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Phone/Fax:+52 (55) 5487-1740

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Abstract

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Respiratory diseases are highly prevalent and affect humankind worldwide causing

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extensive morbidity and mortality with the environment playing an important role.

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Given the complex structure of the airways sophisticated tools are required for

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early diagnosis; initial symptoms are non-specificand the clinical diagnosis is made

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frequently late.Over the last few years, proteomics has made a high technological

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progress in mass spectrometry-based protein identification,and has allowed

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gaining new insights into disease mechanisms and identifying potential novel

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therapeutic targets.This review will highlight the contributions of proteomics

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towards the understanding of the respiratory proteome listing potential biomarkers,

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and its potential application to the clinic.

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proteomics to creating a personalized approach in respiratory medicine.

We also outline the contributions of

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Keywords: Respiratory proteomics, personalized medicine, environment, protein

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biomarkers; mass spectrometry, asthma, COPD, AERD, allergic rhinitis, respiratory

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diseases.

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1. Introduction

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The respiratory system takes up oxygen from the environment and passes it to the

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blood to be then delivered to all parts of the body. However, a number of

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environmental irritants including infectious agents, pollutants, aeroallergens and

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cigarette smoke are deposited into the airways, through the 12,000 L of air inhaled

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daily, causing disease in susceptible individuals. Indeed, respiratory diseases are

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pinned as the third highest cause of deaths in the global scale with the major

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burden shifting from infectious diseases such as, tuberculosis to chronic non-

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infectious disorders including asthma, chronic obstructive pulmonary disease

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(COPD), lung cancer, fibrotic lung disease, and others.1-3 For example, asthma,

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affects over 300 million people around the globe4 while COPD is the fourth most

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important cause of death worldwide.3 The fundamental reason for this situation is

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that these diseases involve complex interactions at the molecular level which has

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made difficult to understand the disease pathogenesis. Proteins are the major

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components of the human body in both health and disease and they are

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the major structural component of all cells. Unraveling the respiratory proteome

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however, has proved difficult as it is not static. Instead, it is in a dynamic state as

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depending on its level of activation a cellular protein may beinvolved in

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many biological functions in separate tissues. Moreover, a single gene can

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generate different proteins through alternative splicing and posttranslational

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modifications generating thousands of protein types in each cell. The main

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objective of using proteomics in medical research is to detect proteins associated

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to disease for therapeutic intervention and to identify novel biomarkers.5,6



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Abiomarker is a measurable molecule that reflects the disease process which can

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be found in body fluids or tissues. Thus, establishing new biomarkers have a range

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of application in respiratory medicine including early detection of disease, patients’

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stratification risk, disease monitoring and patient selection for both selecting the

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most appropriate therapy and evaluating the response to therapy.A successful

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example is the test which allows selecting patients for the appropriate treatment

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with trastuzumab (Herceptin). This antibodybasedtherapeutic approach blocks the

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human epidermal growth factorreceptor 2 (HER2) in breast cancer cells preventing

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tumor development. Unfortunately, very few biomarkers have been taken into the

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clinic. Some of the failures to identify successful biomarkers include small sample

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sizes, lack of standardized sample collection, unequal procedures for handling and

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storage, incomplete clinical data, inadequate experimental design, inconsistent use

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of technologies which may affect proteomic coverage, and deficient data analysis

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methods. Thus, a logical planning process for biomarker discovery should follow

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each of the above parameters in order to achieve final regulatory approval.

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2. Proteomics

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Mass spectrometry proteomics has played an important role in medical research as

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it allows taking a global view on the proteome in health and disease. Expression

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proteomics that involves either 2DE - or [LC/MSanalysis is the predominant

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method used to investigate respiratory diseases. In 2DE, proteins are separated in

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isoelectric focusing strips according to their net charge (first dimension) followed by

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molecular mass separation in a PAGE(second dimension).7,

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fingerprinting along with sequencing of selected peptides using tandem mass

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MS-peptide mass


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spectrometry (also known as MS/MS) is the ideal protein identification method.

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Using this approach, any two samples can be qualitatively and quantitatively

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compared, and the presence or absence of spots provides information about

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differentially expressed proteins including isoforms. Major limitations to this method

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are that it is a laborious procedure and more than one protein can be frequently

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resolved in a single spot making quantification difficult. While the use of 2D PAGE

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is still common, the majority of research work undertaken in the field nowadays

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utilizes LC-MS.

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LC-MS is a powerful technique that it is oriented towards the identification of

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proteins in complex mixtures.9 In this strategy, proteins are enzymatically digested

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before liquid chromatography. Then the mass spectra generated by MS analysis

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are compared with the theoretical MS/MS spectra from the databases (this method

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is also termed shotgun proteomics). Some limitations to this technology are: a)

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most proteins are identified based on few peptides, b) protein isoforms and

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posttranslational modifications are often missed and c) there is a lack of sensitivity

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to perform quantitative proteomics, d) low abundant- and small proteins often will

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be lost. To date, the current shotgun proteomic approach has become a routine

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way to study proteins in biological samples.

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In the search for disease markers, quantitative proteomics has, therefore, arisen.

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Quantitative proteomics can be categorized into relative and absolute quantitation.

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Relative quantitative methods like ICAT iTRACand SILAC (stable isotope labeling

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by amino acids in cell culture), are used to compare proteins (on the level of

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peptide differences): several peptides of a protein must be found to get a reliable 5 ACS Paragon Plus Environment


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relative quantitation between samples. Among these methods, SILAC is the most

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used technique.9-12

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For the detection of low abundant proteins, targeted proteomics technologies have

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been developed including the antibody-based enrichment method named stable

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isotope standards and capture by anti-peptide antibodies (SISCAPA) and the

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selection reaction monitoring (SRM).13 For SISCAPA, antipeptide antibodies

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immobilized on novel nanoaffinity columns are used to concentrate and enrich

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specific peptides along with stable isotope-labeled internal standard of the same

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sequences. On elution from the antipeptide antibody affinity column, ESI-MS is

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then used to determine the quantities of peptides by comparison of the signals

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from the peptides with those of the corresponding stable isotope-labeled internal

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standards. Selected reaction monitoring (SRM, also called multiple reaction

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monitoring) has emerged as a powerful tool in biomarker discovery which can

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detect proteins expressed at concentrations above 50-100 copies per cell14,

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(figure 1). Triple quadrupole MS (TQ-MS) are integrated into the SRM technology,

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the first and the third quadrupoles select predefined m/z values of the targeted

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peptide ion and/or specific fragment, whereas the second quadrupole serves as a

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collision cell. Key features of SRM assays include the ability to target specific

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peptide sequences, quantify protein isoforms or protein post-translational

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modifications and the capacity for multiplexing that allows analysis of hundreds of

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peptides. SRM can be made more quantitative by spiking a known concentration of

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a peptide isotopically labeled which is used as an internal standard, corresponding

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to a specific target protein. The ratio between the endogenous and the synthetic

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peptide is used to calculate the absolute amount of protein after LC-ESI-MS. This

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strategy is known as absolute quantification (termed AQUA) of proteins.

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3. Respiratory Proteomics

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The respiratory airway is a complex structure extending from the nose to the alveoli

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where many cell types produce proteins that under normal conditions maintains the

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proteome biological functions. However, harmful agents affecting the respiratory

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tract alter the proteome leading to disease. Proteomics has used several biological

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samples such as serum, blood cells, BAL fluid (BAL), nasal lavage fluid (NLF),

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sputum, exhaled breath condensate, lung tissue among others. While analysis of

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these samples has provided valuable data, the information obtained from a single

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lung compartment may not mirror the full proteome. For example, BAL fluid

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provides information on the epithelial lining fluid (ELF), but it does not give full

13

information on proteomic changes that may occur in the airway wall. Airway

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diseases usually affect several airway anatomical structures: i.e. asthma affect the

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full airway wall as well as the ELF. Thus, proteomic findings based on the analysis

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of a biological sample may provide a partial picture of the diseased proteome.

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Previous manuscripts have already reviewed the respiratory proteome and some of

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them have focused on a single lung disease. In this article, proteomic clinical

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studies, published over the last 5 years, on nasal- and lung diseases will be

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discussed (pioneered publications will be discussed briefly). A limitation to

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compare some publications is that many proteomic studies focused on discovery in

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few samples and they did not progress to validation. Discovery proteomics

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optimizes protein identification by spending more time and effort per sample and

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reducing the number of samples analyzed while validation-based approaches 7 ACS Paragon Plus Environment


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optimize the proteomics methods to achieve the highest sensitivity and throughput

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for hundreds or thousands of samples. Table 1 summarizes the most relevant

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findings made by the papers discussed in this manuscript.

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3.1 Nasal Proteomics

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The nose filters and humidifies the inhaled air protecting the lower airways

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frompotential pathogens and exogenous particles. Alteration of the fine balance

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between the environment and the respiratory tract may cause a variety of diseases

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including allergic rhinitis, chronic sinusitis, nasal polyps and others (Figure 2). The

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use of proteomics to investigate disease mechanisms has shed some interesting

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findings on the proteome of the upper airways. Early studies undertaken to screen

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proteins in respiratory medicine were made using 2DE gels which were usually

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descriptive as they focused on identifying few proteins. In 1995, Lindahl et al. were

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the first to investigate nasal proteins in NLF using 2D-PAGE and western

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immunoblots, and Edman sequencing16and ever since, technological advances in

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proteomic technology has allowed to gain further insight in respiratory medicine.

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Recently, Mörtstedt et al. used SRM technology to analyze proteins in pooled nasal

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lavage from normal subjects17 allowing identifying 228 nasal proteins. Proteomic

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studies investigating the upper airways are described below (figure 2 provides a

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hypothetical representation of the dynamic nasal proteome).

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3.1.1 Chronic rhinosinusitis

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Chronic rhinosinusitis (CRS) is a multifactorial inflammatory disease which leads to

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a disruption of the intrinsic mucociliary transport system and stagnation of


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secretions creating a favorable environment for bacterial growth. This disease

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affects the nasal and paranasal sinus mucosa and can be divided into two

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subtypes according to the presence of polyps or glandular hypertrophy.18 Clinical

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symptoms last longer than three months and include purulent nasal discharge,

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nasal obstruction, facial pain, and decreased sense of smell. CRS with nasal

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polyps (CRSwNP) is characterized by thickness of the nasal mucosa which is

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replaced with an edematous, eosinophilic, epithelium. On the contrary, CRS

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without nasal polyps is characterized by glandular hypertrophy. To investigate the

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sinusitis proteome, Casado et al., in 2004, showed that during the infection process

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NLF contained high-abundant plasma proteins, glandular serous cell proteins,

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epithelial keratins, and inflammatory cell proteins.19 Interestingly, six days after

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treatment with both, antibiotic and local fluticasone, the acute sinusitis proteome

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was reduced to plasma proteins and lysozyme: author proposed carbohydrate

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sulfotransferase as a potential marker, of successful therapy. Tewfik identified 35

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proteins in nasal mucus from CRS using iTRAQ reagents, most of which were

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related to innate and acquired immunity.20 Potential biomarkers in this study

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included lysozyme C precursor, clara cell phospholipid binding protein (CCPBP)

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also known as CCL10, CCL16, and antileukoproteinase-1 all of which were

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downregulated. In a consecutive study, using Multiple Reaction Monitoring -MS

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the same group confirmed the involvement of most proteins with the exception of

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lysozyme C precursor which was found to be overexpressed in CRS patients.21

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Both, CCPBP and antileukoproteinase exert anti-inflammatory properties. Thus,

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low levels of these proteins may favor an ongoing inflammatory process in CRS. In

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2009, Min-Man et al. analyzed nasal mucosa from nasal polyps, chronic sinusitis, 9 ACS Paragon Plus Environment


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and normal subjects using MALDI-TOF-MS and ESI-Q-TOF-MS.22 Among the 30

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differentially expressed protein spots they identified PLUNC and Copper-and zinc-

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containing superoxide dismutase (Cu/ZnSOD) as good markers for CRS (highly

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expressed in chronic sinusitis, but weakly in nasal polyps). Both PLUNC and

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Cu/ZnSOD may play an important protective role in the upper airways. PLUNC

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protects against infection by binding gram-negative bacterial lipopolysaccharides

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while Cu/ZnSOD exhibits anti-oxidant activity that scavenges reactive oxygen

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species.

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compartments in the nasal airways, their study can provide more comprehensive

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information to uncover potential diagnostic markers in CRS. Recently, Upton et al.

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identified 15 proteins in nasal tissue derived from 3 patients suffering CRSwNP: 8

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proteins were upregulated and 7 downregulated.Eosinophil lysophospholipase

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(CLCP)

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phosphatydilethanolamine-binding protein (PEBP) showed the lowest down

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regulation pattern.23

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3.1.2 Aspirin exacerbated respiratory disease

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Aspirin exacerbated respiratory disease (AERD) affects both upper and lower

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airways: in the initial phase AERD is manifested as nasal congestion which

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progresses to chronic rhinosinusitis, nasal polyposis, asthma and intolerance to

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aspirin or to another non-steroidal anti-inflammatory drugs.24 This disease usually

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appears in 30–40 year-old patients who refer an acute viral-like nasal episode that

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never resolved completely afterwards (rhinovirus has been linked with AERD

23

development). Over-production of cysteinyl-leukotrienes by LTC4 synthase has

Although

was

NLF

the

and

most

nasal

highly

tissue

may

expressed

represent

protein

two

separate

while

the


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been postulated as central components of AERD pathology.

To identify the

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proteins involved in the airway inflammation, Choi et al. analyzed NLF derived from

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18 AERD patients exposed to lysine-aspirin (L-ASA) challenge.25 Using 2DE and

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MALDI-TOF/TOF they found 18 protein peptides differentially expressed which

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upon validation studies, three potential biomarkers were identified including

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apolipoprotein A1(ApoA1), α2 microglobulin (α2M) and ceruloplasmin (CP).

7

Authors speculated that some of these proteins are secreted into NLF, as a result,

8

of increased vascular permeability following aspirin exposure. Studies describing

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potential biomarkers in AERD nasal polyps are discussed below.

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3.1.3 Nasal polyps

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Nasal polyps (NPs) are transparent, pale gray edematous projections that originate

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from nasal ethmoidal mucosa in the vicinity of the middle turbinate.25 Nasal polyps

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are frequently associated to several respiratory diseases including cystic fibrosis,

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AERD, chronic rhinosinusitis and respiratory allergy. Inflammation is recognized as

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a prominent feature of NPs. Still, the fundamental mechanisms underlying this

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disease are partially understood. Proteomic analysis of three types of nasal tissues

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(nasal polyps, chronic sinusitis, and healthy nasal mucosa), showed that plasma

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proteins had the greatest expression in nasal polyps compared with the other two

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tissue types.22 This study proposed that the ongoing inflammatory process which

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characterizes NP could induce both vascular permeability and exudation of plasma

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proteins. Consistent with this observation Choi et al. detected several plasma

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proteins in AERD patients suffering NPs including ApoA1 protein, human serum

23

albumin (HAS), transthyretin (TTR) and fibrinogen gamma chain. In a kinetic study, 11 ACS Paragon Plus Environment


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they showed that APOA1 levels in NLF peaked 1 hour following lysine aspirin

2

challenge.26 ApoA1, is a major component of high-density lipoprotein in the plasma

3

and plays roles in cholesterol transport while it exerts antimicrobial activity in the

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nasal epithelial lyingfluid. Staphilococcus aureus is a common organism in both

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CRS and NPs, and its biofilms appear to be associated to severe disease. Thus,

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ApoA1 may not only protect the sinonasal mucosa from bacterial infection but it

7

could also prevent the inflammatory process associated with the release of

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enterotoxin released from S. aureus which act as superantigens. In a separate

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study, fatty acid-binding protein 1 (FABP1) was found to be highly upregulated in

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AERD (6-fold) in NPs while the heat shock protein 70 (HSP70) was downregulated

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2-fold.26 FABPs are cytoplasmic proteins that bind eicosanoids that have the ability

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to cause cysteinyl leukotriene overproduction and they may enhance the

13

inflammatory response in AERD patients. In relation to HSP70, Zander et al.

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contend with the former study by detecting upregulation of this protein along with

15

beta-adaptin in the nasal polyps derived from aspirin sensitive patients using a

16

protein microarray.27 Differences between these two studies could be explained by

17

the two distinct proteomic technologies used (2DE-LC/MS vs protein microarrays).

18

Interestingly, treating patients suffering NPs with glucocorticoids28 induced

19

upregulation of both HSP70 and HSP90. The HSP family of proteins has

20

cytoprotective effects by preventing aggregation and promotion of correct folding

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and assembly of proteins: when this process is ineffective or downregulated, the

22

misfolded proteins accumulate and trigger apoptosis. While HSPs proteins

23

may reverse the pathological mucosal changes in NP by decreasing misfolding of


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proteins, further studies must define the role of these proteins in AERD patients

2

with nasal polyps.

3

3.1.4 Viral infections

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Upper airway viral infections are the critical cause of the common cold and are

5

recognized as a main health problem world-wide. Moreover, patients infected with

6

virus are prone to develop rhinosinusitis, otitis media and asthma. Viruses are

7

usually transmitted via aerosols exhaled by infected individuals (a single sneeze

8

generates up to 40,000 droplet particles). For example, influenza A remains

9

airborne for prolonged periods increasing the possibilities of infecting individuals.

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We have found a set of proteins in the nasal secretion of children suffering

11

seasonal influenza A virus infection which include proteins involved in innate- and

12

adaptative immune responses.29 The most highly expressed proteins were

13

S100A9, PLUNC, Cystatin S (CST4) and SA (CST2), followed by truncated

14

lactotransferrin (LTF) and lipocalin.

15

proteins in the nasopharyngeal aspirates of infants infected with respiratory

16

syncytial virus (RSV) with a predominant involvement of innate immunity proteins.30

17

In this study authors characterized, a new truncated form of SPLUNC1 in about

18

50% of the aspirates positive to RSV. A thought provoking finding in the above

19

studies is that activation of the innate immune responses leads to the release of

20

antimicrobial peptides that explain the lack bacterial co-infection in the initial

21

phases of the viral respiratory process.

22

3.1.5 Allergic Rhinitis

Similarly, Fornander et al. identified 35



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Allergic rhinitis (AR) is a global health issue that affects about 400 million across

2

the world. As per the ARIA document, allergic rhinitis is described as an

3

inflammatory disorder of the nose, induced by allergen exposure to environmental

4

allergens which is Ig E mediated.The most common allergens include pollens

5

(grasses, trees, and weeds), mites, molds and pets. Psoriasin (S100A7) was the

6

first biomarker identified in AR using proteomic technology31 which was found to be

7

markedly down-regulated in NLF. Like other members of the S100 protein family,

8

psoriasin has calcium-binding properties and potent chemotactic activity for CD4+

9

T lymphocytes and neutrophils. Serum lactoferrinhas been proposed as a

10

biomarker of allergic rhinitis in nasal provocation studies with Dermatophagoides

11

pteronyssinus (DPT)32 Authors speculated that LTF secreted into the nasal mucosa

12

exerts a protective effect to inhibit mast cell-driven inflammation in AR. Lactoferrin

13

(LTF) is a multifunctional protein which possesses antibacterial, antifungal antiviral

14

activities and promotes iron absorption. However, its role in AR is unknown. In the

15

present manuscript, LTF is reported in 6 papers (19, 20, 23, 29, 33, 34). The LTF

16

promoter contains both constitutive and inducible elements which can be activated

17

by specific transcription factors: for example, the COUP/ERE element, is a binding

18

site for estrogen while a GC-rich sequence located at -75/-40 responds to forskolin

19

and epidermal growth factor (EGF) which signal via protein kinase C (PKC) and

20

protein kinase A (PKA) pathways. Increased expression of EGF receptor and EGF

21

ligand is a well-recognized feature of the airway epithelium in asthma. On the

22

other hand, it has been shown that LPS induces-LTF expression in normal mouse

23

mammalian HC-11 cells via PKC, MAPK and NF-kB pathways. These observations

24

altogether reveals the complexity of LTF regulation in different conditions and 14 ACS Paragon Plus Environment

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raised some concerns regarding whether this protein should be used as a

2

biomarker in any specific disease. In order to investigate low abundant proteins,

3

Betson et al removed the most abundant plasma protein from pooled NLF from AR

4

or asthmatic CRS patients. Analysis of pooled samples by combination of

5

bidimensional chromatography and LC-MS/MS showed five proteins, unique to AR

6

patients including α-2M, actin, PLUNC, myosin 1, and myosin and myosin 4.33 In

7

contrast, semenogelin (SEMG) 1 and 2 were only seen in asthmatic CRS, but not

8

in AR. To examine the contribution of CD4+ to the allergic reaction, Blüggel et al.

9

analyzed the proteome of these cells in- and out of the pollen period in allergic

10

rhinitis patients.34 They identified Nipsnap homologue 3A as the most significant

11

expressed among other 5 downregulated proteins out of the of the pollen season

12

(winter) while carbonyl reductase (CBR) 1 and glutathione s-transferase omega

13

(GSTO) were found differentially displayed during the pollen season. Nipsnap

14

homologue 3A has putative roles in vesicular transport whereas GSTO1 and CBR1

15

are involved in the response to oxidative stress (CBR1 is also involved in

16

inflammation). The NLF proteome of AR patients treated with GC has also been

17

investigated. For example, Wang et al characterized the proteome of AR patients

18

according to their response to GCs35: high responders (HR) were defined in

19

patients with the maximum reduction in symptoms following treatment with GCs

20

while low-responders (LR) were those with the lowest symptom reduction. They

21

observed that several proteins differed significantly before and after GC treatment

22

including Oromucoid (ORM), ApoA1, fibrinogen alpha chain (FGA), cathepsin D

23

(CTSD) and SERPINB3. Interestingly, FGA, ORM and ApoA1 were found to

24

decrease in HR, but not in LR, at baseline and following GC treatment. Although 15 ACS Paragon Plus Environment


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1

the studies described above have provided new insights into disease mechanisms

2

affecting the upper airways, they had a limited impact in establishing new

3

diagnostic markers for the clinic. Indeed, many of the proteomic reports described

4

above were conducted in small number of patients without cross validation

5

experiments with other respiratory diseases. Wang et al have shown that some

6

proteins are found at high frequency in 2DE gels suggesting that caution should be

7

exercised to prevent over interpretation of some of the identified proteins. 36 In their

8

study, authors detected 44 proteins regardless of species, in vivo or in vitro. They

9

proposed that the intrinsic cellular stress response could be the universal reason

10

for the differentially expression of many proteins implying that they should not be

11

used as biomarkers.

12

3.2 Lung proteomics

13

The 70 m2 lung surface area in the adult takes up oxygen from the atmosphere and

14

dispenses it to the body. It is well established that the exposure to common lung

15

irritants such as cigarette smoke, aeroallergens, pollutants, and infectious agents

16

can cause alteration of the pulmonary milieu leading to the development of chronic

17

lung disorders such as asthma, chronic pulmonary disease (COPD), fibrotic lung

18

disease and lung cancer. Because early symptoms are non-specific, the clinical

19

diagnosis of lung disorders is made frequently late. Thus, uncovering the lung

20

proteome in health and disease may allow establishing diagnostic- and prognostic

21

markers in lung disease. Over the last decade, several articles have extensively

22

reviewed the contribution of proteomics studies towards understanding lung

23

diseases.37-39 In this review, a summary of the most recent findings on potential 16 ACS Paragon Plus Environment

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1

biomarkers in chronic lung diseases is made including asthma, COPD and

2

idiopathic pulmonary fibrosis as well as lung cancer.

3

3.2.1 Asthma

4

The occurrence and prevalence of asthma is increasing globally and poses an

5

economic burden on society. In children and young adults, this disease is mostly

6

of allergic origin. Exposure of these patients to specific aeroallergens leads to a

7

series of immunological changes culminating in airway inflammation. Moreover,

8

environmental irritants such as diesel fumes, environmental tobacco smoke,

9

endotoxin and air pollution can exacerbate the disease and trigger asthma

10

symptoms. In the nineties, attempts of discovering a protein biomarker for asthma

11

were made using a combination of HPLC and chemotaxis assays, which led to the

12

identification of CCL5 (RANTES) in BAL fluid as a marker of allergic inflammation

13

and eosinophil activation in BAL fluid.40 Subsequent studies using proteomic

14

technology allowed detecting a wide range of proteins in this biological fluid.41- 43 In

15

2005, Wu et al. analyzed BAL fluid after segmental allergen challenge, using SDS-

16

page followed by LC-MS/MS which resulted in the identification of 160 differentially

17

expressed proteins representing a wide range of biological categories including

18

metabolic

19

proteins, matrix metalloproteinases, signaling and proteins involved in lung

20

remodeling.41 MMP 9 was validated as a potential marker. However, only 4

21

asthmatic patients were included in the study. In the search of galectin proteins

22

Cederfur et al. applied galectin affinity chromatography to analyzed BAL fluid and

23

identified a specific glycoformhaptoglobin, among 175 proteins.42In 2012, O’Neil et

24

al. also investigated the asthma proteome profile in bronchial biopsies and reported

enzymes,

serum

proteins,

cytokine/chemokines,

calcium-binding



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seven proteins differentially expressed43 which were involved in cellular movement

2

and immune cell trafficking with specific roles on collagen fibrillogenesis, protein

3

elongation and chemotaxis. In this study treating patients with budesonide induced

4

activation of prothrombin pathway followed by actin motility and signaling witha

5

decreased expression of α2M and vimentin. Authors proposed that the

6

downregulation of these two proteins could have been resulted from the

7

budesonide-induced suppression of inflammatory response. In this study CCL5

8

was also found as a marker of allergic asthma. As the former studies analyzed two

9

different sample types (BALF vs bronchial biopsies) it is not surprising they

10

reported different proteomic profiles. It is well established that the asthmatic

11

response to allergen challenge is characterized by an early phase followed by a

12

late phase which last several hours (this is usually more severe). Singh et al.

13

analyzed plasma samples derived from asthma subjects who were exposed to

14

allergen challenge in order to identify potential markers specific to any of these two

15

asthmatic responses. Interestingly, increased fibronectin and low levels of inter-

16

alpha-inhibitor H4 (ITIH4) were found to characterize dual asthmatic responders

17

(patients who develop both early and late response). However, there was not

18

specific marker which could distinguish early- from late response.44 Fibronectin

19

depositionin the subepitheliumit is a known feature of asthma. Sputum proteomics

20

has been proposed as a good means to investigate the airways in view that it can

21

be obtained in a noninvasive manner. Indeed, a shotgun proteomics (LC-MS/MS)

22

of sputum in 10 asthmatic patients showed 17 expressed proteins among which

23

SERPINA1 (serpin peptidase inhibitor), SCGB1A1 (secretoblin) and SMR3B

24

(submaxillary gland androgen regulated protein 3B) were proposed as potential 18 ACS Paragon Plus Environment

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1

markers: SERPINA1 was up-regulated while SCGB1A1 and SMR3B were down-

2

regulated. In this study, complement 3a was found to be associated with the

3

exercise-induced asthma (EIA) phenotype which is known to occur following

4

vigorous exercise.45 Neutrophilic asthma is a severe asthma phenotype that is

5

associated with lung neutrophilia, lower lung function, air trapping and thicker

6

airway walls. Lee et al. conducted a study in 5 subjects with neutrophilic

7

uncontrolled asthma and 10 subjects with neutrophilic controlled asthma using 2DE

8

(pooled sputum) followed by MALDI-TOF-MS. Among 14 differentially expressed

9

proteins they identified S100A9 (calgranulinB) as a specific marker for neutrophilic

10

uncontrolled asthma.46 CD4+ T lymphocytes derived from uncontrolled asthma

11

patients have also been investigated and found to produce 13 differentially

12

expressed proteins48 including vimentin which was described in O’Neil et al.’s

13

report.43 While inhaled medications keep asthma symptoms under control in most

14

patients a minority of those with severe asthma, do not improve following

15

medication. Brasier et al. have established cytokine relationship with four severe

16

asthma phenotypes using bead-based multiplex cytokine arrays: IL-2 could

17

differentiate “high eosinophil” from “low” eosinophil classes while IP-10, IL-7 and

18

GM-CSF were different between high- and low neutrophil classes.49 Response to

19

bronchodilators was made based on IL-4 concentrations while both IL-1Ra, MIG

20

define the response to methacholine (hyperresponders). In another study,

21

increased levels of MCP-3 (CCL7) and MCP-4 (CCL13) but not MCP-1 (CCL2) and

22

MCP-2 (CCL8)50 were associated to virus exacerbation of asthma.

23

above studies have described several inflammatory mediators, no biomarkers with

24

clinical applications in asthma have emerged so far.

While the



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1

3.2.2 COPD

2

Chronic obstructive pulmonary disease (COPD) is characterized by a gradual

3

decline of pulmonary function and increasing airway obstruction which leads to

4

emphysema. Cigarette smoking is the most frequent cause of COPD although

5

infections and air pollution may exacerbate the disease. A number of potential

6

biomarkers using proteomics technologies have been associated with COPD

7

including neutrophil defensins (NAP) 1 and 2, S100A8, and S100A9, TIMP1,

8

CTSD, the Rho-GDP dissociation inhibitor protein and IL-8 among others.51, Using

9

nano LC-MS Tu et al. identified 76 differentially expressed proteins in BAL fluid

10

from COPD patients which belong to several processes including alcohol

11

metabolism, gluconeogenesis and glycolysis.52 Alcohol exhaled through the lung is

12

routinely used to estimate blood alcohol concentration (BAC). Interestingly, COPD

13

patients show a significant underestimation of BAC. Authors speculated that the

14

alcohol metabolism proteins ADH1B and ALDH2 could account for the BAC

15

changes observed in these patients. In this study however, patients suffering other

16

lung chronic diseases were not included to cross validate findings. In order to

17

investigate more specific markers Pastor et al. studied COPD patients with- and

18

without lung cancer (LC) which allowed detecting four up-regulated proteins

19

common to both COPD and LC/COPD patients.53 However, none of them were

20

unique to COPD. Instead, both CTSD preprotein and Ezrin (EZR) distinguished LC

21

patients from the other two groups. As discussed above, analysis of BAL fluid has

22

provided valuable information on the proteins present on the airway epithelial lining

23

fluid (ELF). However, BAL fluid represents a fraction of the ELF as this is diluted by

24

the BAL (proteins in ELF are released either by epithelial- and inflammatory cells or 20 ACS Paragon Plus Environment

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1

from plasma exudation). In order to obtain higher concentrations of molecules

2

Franciosi et al., obtained ELF directly from the airways and identified 4 proteins as

3

candidate biomarkers of COPD including LTF, coﬁlin-1, HMGB1 and alpha 1-

4

antichymotrypsin (serpin A3).54 In the serum, several inflammatory mediators have

5

also been associated to COPD including CRP (C reactive protein), serum amyloid

6

A (SAA), IL-6, fibrinogen, von Willebrand factor, surfactant protein A (SP-A) and

7

receptor for advanced glycation end products (RAGE).55-56 Fibrinogen is one of the

8

most promising biomarkers in COPD. Bandow et al. identified fibrinogen in serum

9

collected from 24 patients in staged 2a COPD using an improved image analysis

10

workflow for 2DE gel followed by LC-MS/MS.57 Two clinical studies conducted in

11

thousands of patients showed that high fibrinogen levels in serum predict mortality

12

and COPD-related hospitalization.58,59 Fibrinogen is synthesized primarily in the

13

liver and converted by thrombin into fibrin during blood coagulation. Under

14

physiological

15

counterbalanced. However, chronic inflammation as that seen in COPD may

16

results in profound clinical consequences. In vivo evidence for the role of fibrinogen

17

in inflamation derives from fibrinogen-deficient mice in collagen induced arthritis

18

showing fewer affected joints and an overall significant amelioration in disease

19

severity.The role of fibrinogen in COPD has been reviewed recently.60 Additional

20

biomarkers in serum include inter-R-trypsin inhibitor heavy chain H3 (ITI-HC3) and

21

vitamin D-binding protein (VDBP) as early biomarkers in healthy smokers61 and

22

GRP78 and soluble CD163 as inflammatory proteins potentially in involved lung

23

remodeling.62 Lung tissue has also been explored using proteomic technology:

24

Ohlmeier et al. analyzed tissue samples from stage IV COPD and detected high

conditions,

fibrin

formation

anddegradation

are

perfectly



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1

expression of SP-A, and RAGE in two separate studies.63, 64 In the first study, SP-A

2

was found to be associated to COPD, but not to fibrotic lung or normals (these

3

results were further demonstrated by immunohistochemistry and western blotting).

4

In the second report, three different RAGE variants were identified (FL-RAGE,

5

cRAGE, esRAGE) using 2-DE, MS and western blot in lung tissues: FL-RAGE(full

6

length RAGE) and cRAGE(c-terminal processed full length RAGE), but not

7

esRAGE(spliced endogenous secretory RAGE), were declined in COPD lungs.

8

cRAGE expression was found to correlate with COPD progression (when compare

9

with normals cRAGE was 2.1-fold lower and 3.4-fold lower in mild and very severe

10

COPD respectively). In contrast, the three RANGE variants were decreased in

11

IPF(esRAGE expression changed in IPF, but not in COPD). Thus, cRAGE and

12

esRAGE could be used as a marker of COPD progresion and IPF, respectively. In

13

another study, both, MMP-13 and thioredoxin-like 2 (TXL2) were increased in

14

COPD lung tissue obtained from COPD patients who underwent surgery for lung

15

cancer.65 TXNL2 has been found overexpressed in mice with lung cancer.66 This

16

finding suggests that TXNL2 in humans could derived from the lung cancer region

17

in COPD patients. While the number of potential markers in COPD continues to

18

grow, fibrinogen remains one of the most promising candidates as it allows

19

stratifying COPD patients identifying populations at higher risk for poor outcomes,

20

COPD hospitalizations, and death. This protein has been considered for

21

qualification by the US Food and Drug Administration and the European Medicines

22

Agency.67

23

3.2.3 Idiopatic Pulmonary Fibrosis


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1

Idiopathic Pulmonary Fibrosis (IPF) is characterized by progressive lung fibrosis

2

which causes extracellular matrix (ECM) deposition and distortion of the alveolar

3

architecture. Some of the early proposed markers for pulmonary fibrosis include,

4

Apo A1 and RAGE.68,69 In 2011 Ishikawa et al showed low levels of hemoglobin

5

(Hb) α and β in IPF patients as compared with COPD- and normal subjects.70 Hbα

6

and Hbβ were expressed as monomers and complexes in the lung tissues with the

7

Hbα complex completely missing in the IPF lung. MS-analyses revealed Hb α

8

modification at cysteine105, preventing formation of the Hb α complexes. By

9

immunohistochemistry Hb α and β was localized to alveolar cells in both controls

10

and COPD patients but very weak staining was seen in IPF. Kim et al. also

11

reported reduced Hb β along with Apo A1 among 16 differentially expressed

12

proteins in BAL fluid derived from IPF using LC-MS/MS.71 In this last study

13

however, Apo A1 was proposed to play the most significant role in the

14

pathogenesis of IPF in view of its anti-inflammatory activity. By treating sensitized

15

mice with Apo A-I, authors showed substantial inhibition of the bleomycin-induced

16

lung fibrosis which place Apo A-I as a candidate for IPF treatment. In two additional

17

studies Korfei et al analyzed lung tissue.72, 73 In 2011, they compared sporadic IPF

18

with healthy controls using 2-DE, MALDI-TOF-MS and showed 89 differentially,

19

expressed proteins in IPF.72 The predominant markers included unfolded protein

20

response (UPR), heat-shock proteins, and DNA damage stress markers: by

21

immunohistochemistry, UPR was found to localize type-II alveolar epithelial cells.

22

In 2013, they compared the proteomic profile between idiopathic interstitial

23

pneumonias (IPF) and nonspecific interstitial pneumonia (NSIP).73 These idiopathic

24

pneumonias show similar clinical pattern however they exhibit different prognosis 23 ACS Paragon Plus Environment


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1

(IPF patients survive longer).

Results from this last study indicated that both

2

diseases have a similar proteomic profile. However, peroxiredoxin (PRDX)1,

3

PRDX6 and proteasome activator complex subunit 1 (PSME1) were upregulated in

4

fNSIP but downregulated in IPF. These proteins are involved in protection

5

mechanisms against oxidative and ER stress which may explain the better

6

outcome and survival in patients with fNSIP. In a separate study, the BAL fluid

7

proteomic profile of IPF patients was compared with that of another interstitial lung

8

diseases (ILDs) including Sarcoidosis (Sar), idiopathic pulmonary fibrosis (IPF),

9

fibrosis associated with systemic sclerosis (Ssc) and pulmonary Langerhans cell

10

histiocytosis (PLCH)74: principal component analysis clustered all ILDs into six

11

distinct groups with IPF showing differential protein pattern from the other

12

conditions.

13

reflected the severity of fibrotic involvement in this disease. Validation by western

14

blot showed that 14-3-3ε was weakly expressed in IPF as compared with both

15

PLCH and Ssc. In contrast, ANXA3 was up-regulated in IPF, SSc, PLCH with respect to

16

Sar while peroxiredoxin 1 and GSTP1, predominated in Sar, IPF and SSc. Wang et al

17

have quoted that frequently detected proteins such as annexins, and peroxiredoxins

18

are very closely related molecules which exert overlapping functions making

19

difficult to draw conclusion as why they are differentially expressed (36). They

20

proposed that cellular stress can be the universal reason for these proteins to be

21

produced. Multiplex bead-based immunoassay has been used to investigate

22

biomarkers in plasma. Richard et al. investigated 95 proteins in 241 IPF patients

23

and identified five proteins including MMP-7, ICAM-1, IL-8, VCAM-1, and S100A12

24

which predicted poor outcome.75 In this study, the shortest median survival was

Authors hypothesized that the position of the IPF cluster probably


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1

associated with high S100A12 concentrations, (above 26.5 ng/ml) while the longest

2

median survival (4.6 yr) was observed with low concentrations of MMP-7 (below

3

4.3 ng/ml). Experimental evidence for an in vivo role of this metalloproteinase

4

derives from a MMP7 knockout mice study showing that these mice are

5

substantially protected from pulmonary fibrosis in response to intratracheal

6

bleomycin.76 To date however, biomarkers with clinical use in IPF remain to be

7

shown.

8

3.2.4 Lung Cancer

9

Lung cancer,

arise from the

interplay between an

individual’s

genetic

10

susceptibilities and their personal exposure histories. The great majority results

11

from exposure to tobacco smoke although other environmental risk factors can

12

also involve including exposure to radiation, asbestos, heavy metals and polycyclic

13

aromatic hydrocarbons. Detection of lung cancer in early phases is vital as it is a

14

leading cause of death worldwide. Lung cancer is usually classified into small cell

15

lung cancer (SCLC), and non-small-cell lung cancer (NSCLC). NSCLC includes

16

squamous cell carcinoma (SQLC), adenocarcinoma (ADC) and large cell

17

carcinoma (LCC) which accounts for 80% of lung cancers. Lung cancer proteomic

18

studies will be mentioned briefly in view that this subject has recently been

19

reviewed extensively.77 A number of potential markers have been described in sera

20

including haptaglobin (HP) α chain,78, serum amyloid A (SAA)79 and aberrantly

21

glycosylated markers (i.e. fucosylated forms of complement component 9).80 With

22

an aptamer-based proteomic technology Ostroff et al. analyzed serum samples

23

from 1,326 subjects diagnosed with clinical stage I - III NSCLC and identified 12-

24

proteins including CD30 ligand,LRIG3, MIP-4, PRKCI, RGM-C, SCF-sR, sL25 ACS Paragon Plus Environment


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1

Selectin, and YES.81 These proteins efficiently discriminated NSCLC from controls.

2

Similarly, many lung tissue markers with potential clinical applications have been

3

reported; for example, phosphopeptide enrichment followed by MS/MS led to the

4

identification of Cavin-1 and MIF proteins as potential biomarkers in NSCLC.82 By

5

using MALDI- TOF-MS Raham et al. showed that MIF (macrophage migration

6

inhibitory factor) was present in preinvasive NSCLC lesion along with other five

7

potential biomarkers (ubiquitin, thymosin b4, cytochrome c

8

protein (ACBP) and cystatin A (CSTA). The search for prognosis biomarkers have

9

also become of paramount importance as only 10–15% of the patients with lung

10

cancer will ultimately be cured: annexin A2 and Annexin A3 (ANXA2 and ANXA3)

11

were discovered as prognostic markers in lung SCC and ADK,84,

12

Up-regulation of ANXA2 and HSP27 showed more advanced clinical stage and

13

more poor differentiation while ANXA3 was found to be associated with increased

14

relapse rate and decreased survival in ADK cases. The use of prognosis

15

biomarkers in cancer metastasis may help to determine the severity of cancer and

16

the probability of responding to treatment.

17

4. Towards personalized medicine

18

Each individual has a unique code which is under balance and has no undesirable

19

effect on health. However, genetic mutation and environment hazard exposure

20

may cause disease. Over the last decade, personalized medicine has emerged as

21

a medical care approach which uses novel technology in the prevention of

22

diseases aiming to tailor treatments according to the particular needs of each

23

patient. Early investigations on personalized medicine were made using genomic

24

approaches that have eventually led to important discoveries. For example, it has

acyl-coA binding

77

respectively.


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1

recently found that a SNP in the interleukin (IL)-28B gene that codes for interferon-

2

lambda predispose patients to hepatitis C virus. In this study, GWAS demonstrated

3

that patients with IL-28B genotype C/C eliminate the virus spontaneously and after

4

antiviral therapy in about 50% and 80% of affected patients, respectively.85

5

Although genomic technologies have proved useful, many critical molecular events

6

occur at the post-translational level where protein modifications take place. The

7

power of proteomics to analyze expression of proteins globally rather than a single

8

biomarker has also begun to unravel the complexity of the proteome and it is

9

expected to play a significant role in the development of new systems supporting

10

health care. Indeed, while the DNA genome carries the genetic information, it is the

11

proteins that control all biological functions both in health and disease.An example

12

of personalized medicine is the use of Herceptin which targets HER2-positive

13

breast cancer cells blocking their ability to receive chemical signals and

14

precludingthe cells from growing.

15

Lung cancer is one of the diseases where based-proteomic approaches have

16

made substantial progress in identifying biomarkers which have the potential to be

17

used in personalized respiratory medicine. Recently, Taguchi et al investigated

18

plasma protein profiles in four mouse models of lung cancer based on quantitative

19

proteomics that allow the identification Titf1/Nkx2-1, as serum protein signature in

20

lung adenocarcinoma while an epidermal growth factor receptor (EGFR) signature

21

(Adam10 Cdh1 and Cd44) was found in plasma of Non-Small Cell Lung Cancer

22

(NSCLC) EGFR mutant mice model.86 Mutations in the EGFR have been targeted

23

with the inhibitors of the EGFR tyrosine kinase gefitinib and erlotinib which leads to

24

a significant reduction of the tumor size.87 Most of these mutations are in exon 19 27 ACS Paragon Plus Environment


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Page 28 of 47

1

or a missense mutation leading to a substitution of arginine for leucine in exon

2

21.87 In 2007, an 8 peak profile signature was established in a subset of NSCLC

3

patients that classify patients as good or poor by comparing the mass spectra

4

intensity of the eight peaks with the outcome from EGFR-tyrosine kinase inhibitors

5

(TKI) treatment as assessed by progression and overall survival.88 This proteomic

6

profile has evolved into a prognostic test (veristrat)89 which has been used in

7

several studies to investigate patient’s outcome when treated with EGFR-TKI.90-93

8

In a recent biomarker–stratified randomized phase 3 trial, Gregorc et al evaluated

9

the predictive value of veristrat in patients with NSCLC treated with either erlotinib

10

or chemotherapy94 which revealed that patients classified as poor were better

11

treated with chemotherapy in comparison with erlotib in terms of patients survival.

12

In contrast, there was no significant difference in overall survival when the

13

proteomic test was classified as good.

14

stratified subgroups could not have been sufficiently powered for comparison of

15

treatments. Interestingly, serum amyloid A protein 1 (SAA1), together with its two

16

truncated forms, was found to be the protein that generates 4 out of the 8 mass

17

signals which integrate veristrat.95 Salmon et al reported another proteomic profile

18

based on 11 peaks that predicted overall survival and progression-free survival

19

outcome of patients treated with erlotinib.96 Two metabolomic studies have also

20

been able to predict the response to TKIs treatment in NSCLC patients.97,

21

Pharmacoproteomic studies using imaging mass spectrometry have had a great

22

impact as it allows both, localizing the distribution of the drug and determining the

23

deposition of its metabolites in lung tissues, opening a new opportunity in

24

pharmaceutical personalized applications. By applying this technology Marko-

It was proposed that the biomarker

98


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1

Varga et al were able to localize the EGFR tyrosine kinase inhibitors erlotinib and

2

gefitinib to different tissue compartments in three different lung cancer

3

phenotypes.99 The goal of personalized medicine is not only to individualize

4

treatment in order to increase clinical benefits of therapy but also to reduce

5

possible adverse reactions in patients. Proteomics has helped to gain some insight

6

into the proteins associated with interstitial lung disease (ILD) which has been

7

found as an adverse reaction to EGF-TKIs.100-101 Nymberg et al analyzed 43

8

gefitinib-treated NSCLC patients who developed ILD and identified 29 proteins of

9

which 17 belonged to the acute phase response pathway.100 Authors proposed that

10

these 17 proteins could be used as potential biomarkers for the increased risk of

11

developing ILD. Atagi et al have sought to identify proteomic biomarkers in a

12

phase IV erlotinib study where patients developed ILD within 120 days of erlotinib

13

treatment.101 In this study, 3 proteins were differentially expressed between the ILD

14

patients and controls including C3, C4A/C4B, and APOA1: susceptibility to ILD

15

developed was more frequent when C3 levels were higher than the median.

16

Another successful story in lung cancer personalized medicine is the use of the

17

kinase inhibitor crizotinib to treat NSCLC patients who exhibit a chromosomal

18

transformation which activates the anaplastic lymphoma kinase (ALK) gene.102 A

19

phase I trial assessing crizotinib in ALK-positive NSCLC patients showed that

20

60.8% of patients showed a good response with and median progression-free

21

survival was 9.7 months. These observations altogether support the view that

22

proteomics has begun to be translated from the bench to the patients’ bedside, to

23

deliver personalized care in respiratory medicine.

24

5. Conclusions 29 ACS Paragon Plus Environment


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1

The proteomics technology has developed at fast pace over the recent past

2

decade. Indeed, while early proteomic respiratory studies were mostly descriptive

3

detecting few proteins, but it is now possible to characterize thousands of proteins

4

in single MS run. This technological progress has been recently being quoted as

5

the era of next generation proteomics.103 Despite this progress in MS technology,

6

the impact on biomarker discovery has been modest. Some of the failures to

7

identify successful biomarkers include small sample sizes, lack of standardized

8

sample collection, unequal procedures for handling and storage, incomplete clinical

9

data, inadequate experimental design, inconsistent use of technologies which may

10

affect proteomic coverage, and deficient data analysis methods. Thus, a logical

11

planning process for biomarker discovery should be taken as described above in

12

order to achieve final regulatory approval.In respiratory medicine Veristrat is one of

13

the successful proteomic tests with the potential to be taken into the clinic which

14

allowpredicting patient’s outcome when treated with EGFR-TKI. It is expected that

15

proteomics will continue uncovering new diagnostic tests and enabling more

16

precisely targeted treatments providing a careful planning measures are in place in

17

the process of biomarker discovery and validation.

18

19

Acknowledgments

20

Authors are grateful to the Alexander von Humboldt Foundation for the financial

21

support, to Priyadharshini V.S.for critical review of the manuscript, and to Julio

22

Guerrero for helping with figures design.


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Kucherlapati R, Depinho RA, Kemp CJ, Varmus HE, Hanash SM.Lung cancer signatures in plasma based on proteome profiling of mouse tumor models. Cancer Cell 2011;20:289–299. 87. Pallis AG, Syrigos KN. Epidermal growth factor receptor tyrosine kinase inhibitors in the treatment of NSCLC. Lung Cancer 2013;80:120-30. 88. Taguchi F, Solomon B, Gregorc V, Roder H, Gray R, Kasahara K, Nishio M, Brahmer J, Spreafico A, Ludovini V, Massion PP, Dziadziuszko R, Schiller J, Grigorieva J, Tsypin M, Hunsucker SW, Caprioli R, Duncan MW, Hirsch FR, Bunn PA Jr, Carbone DP. Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J Natl Cancer Inst 2007;99:838-46. 89. Kuiper JL, Lind JS, Groen HJ, Roder J, Grigorieva J, Roder H, Dingemans AM, Smit EF. VeriStrat(®) has prognostic value in advanced stage NSCLC patients treated with erlotinib and sorafenib. Br J Cancer 2012;107:1820-5. 90. Stinchcombe TE, Roder J, Peterman AH, Grigorieva J, Lee CB, Moore DT, Socinski MA.A retrospective analysis of VeriStrat status on outcome of a randomized phase II trial of first-line therapy with gemcitabine, erlotinib, or the combination in elderly patients (age 70 years or older) with stage IIIB/IV non-smallcell lung cancer.J ThoracOncol2013; 8:443–51. 91. Amann JM, Lee JW, Roder H, Brahmer J, Gonzalez A, Schiller JH, Carbone DP. Genetic and proteomic features associated with survival after treatment with erlotinib in fi rst-line therapy of non-small cell lung cancer in Eastern Cooperative Oncology Group 3503. J Thorac Oncol2010;5:169–78. 92. Lazzari C, Spreafico A, Bachi A, Roder H, Floriani I, Garavaglia D, Cattaneo A, Grigorieva J, Viganò MG, Sorlini C, Ghio D, Tsypin M, Bulotta A, Bergamaschi L, Gregorc V. Changes in plasma mass spectral profile in course of treatment of non-small cell lung cancer patients with epidermal growth factor receptor tyrosine kinase inhibitors. J ThoracOncol2012; 7: 40–48. 93. Carbone DP, Ding K, Roder H, Grigorieva J, Roder J, Tsao MS, Seymour L, Shepherd FA. Prognostic and predictive role of the VeriStrat plasma test in patients with advanced non-small-cell lung cancer treated with erlotinib or placebo in the NCIC Clinical Trials Group BR.21 trial. J ThoracOncol2012; 7:1653–60. 94. Gregorc V, Novello S, Lazzari C, Barni S, Aieta M, Mencoboni M, Grossi F, Pas TD, de Marinis F, Bearz A, Floriani I, Torri V, Bulotta A, Cattaneo A, Grigorieva J, Tsypin M, Roder J, Doglioni C, Levra MG, Petrelli F, Foti S, Viganò M, BachiA, Roder H. Predictive value of a proteomic signature in patients with non-small-cell lung cancer treated with second-line erlotinib or chemotherapy (PROSE): a biomarker-stratified, randomised phase 3 trial. Lancet Oncol 2014;15:713-21. 39 ACS Paragon Plus Environment


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Figure legends:

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure. 1 Typical SRM analysis of target proteins of bronchoalvelolar lavage fluid. In the triple quadrupole instrument, Q1 selects molecular ions of a specific analyte and Q2 fragments them into daughter ions which are then selected in Q3 and guided to the detector.

Figure 2 Representation of the normal nasal proteome (center) and its hypothetical dynamic variation in different nasal diseases. CRS: chronic rhinosinusitis, AERD: aspirin exacerbated respiratory disease.

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1 2

TABLE 1.MASS SPECTROMETRY BASED PROTEOMIC STUDIES ON RESPIRATORY MEDICINE*

References

Health & Disease

Sample

Method

Al Badaai

CRS

Nasal mucus

MRM MS

1

CRSwNP

Nasal mucosa from polyps

MALDI-TOF-MS ESI-Q-TOF-MS

30

Min-Man Upton Choi Kim

[21]

[22]

[23]

[25]

[28]

Teran [29] Fornander

[30]

Bryborn [31] Choi

Eosinophil lysophospholipase

12 (6 CRS, 6 HCs) 14 (7 CRS wNP, 7 HCs)

2DE-MALD MS

15

AERD

NLF(nasal lavage fluid)

2DE , MALDITOF/TOF

18

ApoA1, a2M and ceruloplasmin.

32 (18 AERD, 14 ATA)

NP

NP tissue

LC-MS/MS

15

FABP1 , HSP 70

13 (8 ATA, 5 AERD)

NP

NP tissue

2DE, MALDI-TOF-MS

20

HSP70, HSP90, RA

3 (no controls)

Viral infection

Nasal aspirates

2-DE-MALDI-MS

1

PLUNC

12 (before & after infection)

Viral Infection

Nasopharyngeal aspirates

2-DE, MS

35

SPLUNC1 isoforms

47 (24 RSV-, 23 RSV+

14

Psoriasin

22 (11 AR, 11 HCs)

11

Serum lactoferrin

107 (23 DPT responders, 22 DPT non responders, 52 HCs)

AR AR

[32]

AR

Blüggel

[34]

AR

Wang

6 (3 CRSwNP, 3 HCs)

[41]

O`Neil Singh

[42]

[43]

[44]

Gharib

[45]

Ko

[48]

Tu

[52]

NLF

CD4+ T cells

2-DE, MALDITOF/TOF-MS

NLF

iTRAQ

133

Allergic asthma

BALF (bronchio alveolar fluid)

SDS-PAGE/nano-LCMS/MS

160

MMP-9

4 mild asthma, 3 HCs

Asthma

BALF

LC-MS/MS

175

Haptoglobin isoform

8 (4 asthmatics, 4 HCs)

Asthma

Bronchial biopsies

iTRAQ, LC-MS/MS

7

α2 macroglobulin and vimentin


Allergic Asthma

Plasma

iTRAQMALDITOF/TOF

21

Fibronectin

8 (4 early- and 4 dual responders)

Asthma

Sputum

LC-MS/MS

17

SERPINA1, SMR3B, SCGB1A1,C3a


Sputum

2DE- MALDI-TOF-MS

14

S100A9

CD4+T-LC

2D-PAGE/LC/MS

13

COPD

BALF

RPLC-MS

76

HSP-70, HSP-90, YWHA CTSD, galectin 3, ADH1B, SAP, ALDH2, and ALDH3A1

COPD, LC

BALF

Bandow (57)

COPD

Plasma

Bortner

SMK

Plasma

2D-PAGE MALDITOF/TOF-MS chipLC-MS/MS, iTRAQR SELDI-TOF Image analysis workflow, LC-MS/MS iTRAQ

COPD

Plasma

COPD COPD

Pastor

[53]

Franciosi

[54]

Bozinovski

Merali

[56]

[61]

[62]

Ohlmeier [65]

[64]

COPD AECOPD

Epithelial lining fluid (ELF) Serum

138

α-2-macroglobulin, actin, PLUNC, myosin 1 and myosin 4. Talin 1, Nipsnap homologue 3A, GCLM, GSTO, CBR1, 2,4dienoyl-CoA redeuctase ORM, APOH, FGA, CTSD and SERPINB3.

LC/MS/MS

Uncontrolled asthma Asthma

[46]

NLF

2-DE, MALDI-TOFMS 2-DE, MALDI-TOFMS

NLF

AR

[35]

Cederfur

3 4 5

PLUNC, Cu/ZnSOD

Subjects studied

Nasal mucosa

[33]

Lee

Main regulated protein(s)/biomarkers proposed Lysozyme C precursor, CCPBP and antileukoproteinase

CRSwNP

Benson

Lee

Proteins (No.)

[26]

Farajzadeh

Wu

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9

16 46 1

CTSD, EZR lLTF, HMGB-1, A1ACT and CFL1 SAA

7

RETB, Fibrinogen, Apoe, ITIH4, Glutation peroxidase ITI-HC3, VDBP

GeLC-MS

31

GRP78, soluble CD163

Lung tissue

2-DE, MS and WB

6

SRAGE

Lung tissue

2DE- MALDI-TOF-MS

12

MMP-13 and Txl-2

8

15 (9 AR, 6 CRS) 20 (10 AR, 10 HCs) 23 (before and after GCs)

95 (20 UA, 35 PC, 21 CA, 21COPD, 8 HCs) 6 asthmatics 10 (10 COPD, 10 HCs) 60 (15 COPD, 15 LC, 15LC&COPD, 15HCs) 16 (8 COPD, 8 non COPD) 62 48 (24 SMK with COPD, 24 SMK without COPD) 14 (7 SMK, 7 NSMK) 10 COPD, 10 HCs 19 (5HCs , 9 COPD, 5 alfa-1 antitripsin deficiency) 22 (8 NSMK, 7 healthy SMK, and 7 COPD SMK)

AR= allergic rhinitis, AERD= aspirin exacerbated respiratory disease, ATA= asprin tolerant asthmatics, CRS= chronic rhinosinusitis, CRSwNP= chronic rhinusinusitis with nasal polyps, HCs= healthy control subjects, NP= nasal polyps, UA= uncontrolled asthmatics, CA= controlled asthmatics, COPD= chronic obstructive pulmonary disease, LC= lung cancer, PC= partially controlled asthma, SMK=smokers, NSMK= non smokers, GCs= glucocorticoids. *Studies which did not use mass spectrometry are not listed.


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1 2

Table 1 - continued

Main regulated protein(s)/biomarkers proposed

Subjects studied

Hbα, Hbβ

12 (4 IPF, 4 COPD, 4 control)

References

Health & disease

Sample

Method

Ishikawa (70)

IPF

Lung tissue, BALF, sputum

2-DE, MS

2

Kim (71)

IPF

BALF

2-DE, LC-MS/MS

16

Korfei (72)

IPF

UPR, Hsp27,UPR

IPF

MALDI-TOF-MS 2-DE/DIGE, MALDITOF-MS

89

Korfei (73)

Lung tissue Lung tissue, BALF

11

PSM1, PRDX1, PRDX6

Landi (74)

IPF, interstitial

BALF

2-DE MALDITOF/TOF-MS

4

14-3-3ε, Annexin A3, GSTP1, Peroxiredoxin 1

Sung (79)

LC

Serum

LC-MS/MS

2

Ostroff (81)

LC

Serum

Aptamer proteomic

12

Gamez-Pozo (82)

LC

Lung tissue

LC-MS/MS

2

Rahman (83)

LC

Lung tissue

MALDI-TOF-MS

6

Lung cells

LCM/2D-DIGE/MS

14

Yao (84)

3 4 5 6

lung disseases

LSC

Proteins

(No.)

Apo A1

SAA1, SAA2 CD30 ligand,LRIG3, MIP-4, PRKCI, RGM-C, SCF-sR, sL-Selectin, and YES

PTRF, Cavin-1

22 (14 IPF, 8 control) 32 (14 IPF, 8 Fibrotic, 10 control) 50 (9 sarcoidosis, 7 systemic sclerosis, 7 IPF, 9 PLCH, 10 NSMK, 8 SMK) 350 985 (213 NSCLC, 772 control) 15 (5 LAC, 5 SCLC, 5 control)

Thymosin β4, Ubiquitin, ACBP, CSTA, Cyt c, MIF

60 (40 LC, 20 control)

Annexin A3, HSP27, CK19, 14-3-3σ

24 (12 non LNM LSC, 12 LNM LSC)

IPF= idiopatic pulmonary fibrosis, LC= lung cancer, PLCH= pulmonary Langerhans cell histiocytosis, COPD= chronic obstructive pulmonary disease, LAC= lung adeno carcinoma, SCLC= small cells lung cancer, NSCLC= non small cells lung cancer, LSC= lung squamous carcinoma, LNM= lymph node metastasis

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Graphical abstract

2


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Q1

Q2

Q3

m/z MS spectrum

LC

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RESPIRATORY PROTEOME


Respiratory Proteomics: From Descriptive Studies ... - ACS Publications

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