Respiratory proteomics: from descriptive studies to personalized medicine

Current Insights on the Impact of Proteomics in Respiratory Allergies

Respiratory allergies affect humans worldwide, causing extensive morbidity and mortality. They include allergic rhinitis (AR), asthma, pollen food allergy syndrome (PFAS), aspirin-exacerbated respiratory disease (AERD), and nasal polyps (NPs). The study of respiratory allergic diseases requires new technologies for early and accurate diagnosis and treatment. Omics technologies provide the tools required to investigate DNA, RNA, proteins, and other molecular determinants. These technologies include genomics, transcriptomics, proteomics, and metabolomics. However, proteomics is one of the main approaches to studying allergic disorders' pathophysiology. Proteins are used to indicate normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In this field, the principal goal of proteomics has been to discover new proteins and use them in precision medicine. Multiple technologies have been applied to proteomics, but that most used for identifying, quantifying, and profiling proteins is mass spectrometry (MS). Over the last few years, proteomics has enabled the establishment of several proteins for diagnosing and treating respiratory allergic diseases.

Differential urine proteome analysis of a ventilator-induced lung injury rat model by label-free quantitative and parallel reaction monitoring proteomics

Urine is a promising resource for biomarker research. Therefore, the purpose of this study was to investigate potential urinary biomarkers to monitor the disease activity of ventilator-induced lung injury (VILI). In the discovery phase, a label-free data-dependent acquisition (DDA) quantitative proteomics method was used to profile the urinary proteomes of VILI rats. For further validation, the differential proteins were verified by parallel reaction monitoring (PRM)-targeted quantitative proteomics. In total, 727 high-confidence proteins were identified with at least 1 unique peptide (FDR ≤ 1%). Compared to the control group, 110 proteins (65 upregulated, 45 downregulated) were significantly changed in the VILI group (1.5-fold change, P < 0.05). The canonical pathways and protein-protein interaction analyses revealed that the differentially expressed proteins were enriched in multiple functions, including oxidative stress and inflammatory responses. Finally, thirteen proteins were identified as candidate biomarkers for VILI by PRM validation. Among these PRM-validated proteins, AMPN, MEP1B, LYSC1, DPP4 and CYC were previously reported as lung-associated disease biomarkers. SLC31, MEP1A, S15A2, NHRF1, XPP2, GGT1, HEXA, and ATPB were newly discovered in this study. Our results suggest that the urinary proteome might reflect the pathophysiological changes associated with VILI. These differential proteins are potential urinary biomarkers for the activity of VILI.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.

Omics for the future in asthma

Asthma is a common, complex, multifaceted disease. It comprises multiple phenotypes, which might benefit from treatment with different types of innovative targeted therapies. Refining these phenotypes and understanding their underlying biological structure would help to apply precision medicine approaches. Using different omics methods, such as (epi)genomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, allowed to view and investigate asthma from diverse angles. Technological advancement led to a large increase in the application of omics studies in the asthma field. Although the use of omics technologies has reduced the gap between bench to bedside, several design and methodological challenges still need to be tackled before omics can be applied in asthma patient care. Collaborating under a centralized harmonized work frame (such as in consortia, under consistent methodologies) could help worldwide research teams to tackle these challenges. In this review, we discuss the transition of single biomarker research to multi-omics studies. In addition, we deliberate challenges such as the lack of standardization of sampling and analytical methodologies and validation of findings, which comes in between omics and personalized patient care. The future of omics in asthma is encouraging but not completely clear with some unanswered questions, which have not been adequately addressed before. Therefore, we highlight these questions and emphasize on the importance of fulfilling them.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

Searching for biomarkers of chronic obstructive pulmonary disease using proteomics: The current state

Detection of proteins which may be potential biomarkers of disorders represents a big step forward in understanding the molecular mechanisms that underlie pathological processes. In this context proteomics plays the important role of opening a path for the identification of molecular signatures that can potentially assist in early diagnosis of several clinical disturbances. Aim of this report is to provide an overview of the wide variety of proteomic strategies that have been applied to the investigation of chronic obstructive pulmonary disease (COPD), a severe disorder that causes an irreversible damage to the lungs and for which there is no cure yet. The results in this area published over the past decade show that proteomics indeed has the ability of monitoring alterations in expression profiles of proteins from fluids/tissues of patients affected by COPD and healthy controls. However, these data also suggest that proteomics, while being an attractive tool for the identification of novel pathological mediators of COPD, remains a technique mainly generated and developed in research laboratories. Great efforts dedicated to the validation of these biological signatures will result in the proof of their clinical utility.

Differential Proteomic Analysis between Small Cell Lung Carcinoma (SCLC) and Pulmonary Carcinoid Tumors Reveals Molecular Signatures for Malignancy in Lung Cancer

PURPOSE

The molecular underpinnings that may prognosticate survival and increase our understanding of tumor development and progression are still poorly understood. This study aimed to define the molecular signatures for malignancy in small cell lung carcinoma (SCLC), which is known for its highly aggressive clinical features and poor prognosis.

EXPERIMENTAL DESIGN

Using clinical specimens, the authors perform a comparative proteomic analysis of high-grade SCLCs and low-grade pulmonary carcinoid tumors (PCTs), both of which are types of neuroendocrine tumors. A label-free LC-MS-based quantitative proteomic analysis is applied to tumor cells laser-microdissected from their formalin-fixed paraffin-embedded (FFPE) tissues obtained from six patients each.

RESULTS

Overall, 1991 proteins are identified from tumor cells in the FFPE tissues. Through the protein-protein interaction network analysis of 201 proteins significantly, the authors find that SCLC is functionally characterized by activation of molecular pathways for spliceosome, RNA transport, and DNA replication and cell cycle. Particularly, 11 proteins involved in tumor proliferation (MCM2, 4, 6, 7, and MSH2), metastasis (RCC2, CORO1C, CHD4, and IPO9), and cancer metabolism (PHGDH and TYMP) are identified as SCLC-specific proteins. Furthermore, their prognostic significances are demonstrated by online Kaplan-Meier survival analysis.

CONCLUSIONS AND CLINICAL RELEVANCE

These clinical tissue proteomic approach for SCLC reveals the proteins associated with aggressiveness and poor prognosis. The identified SCLC-specific proteins represent potential therapeutic targets. Moreover, MCMs and PHGDH can be poor prognostic factors for lung cancer.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague

The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental ("wild") strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

INTRODUCTION

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

Asthma Metabolomics and the Potential for Integrative Omics in Research and the Clinic

Asthma is a complex disease well-suited to metabolomic profiling, both for the development of novel biomarkers and for the improved understanding of pathophysiology. In this review, we summarize the 21 existing metabolomic studies of asthma in humans, all of which reported significant findings and concluded that individual metabolites and metabolomic profiles measured in exhaled breath condensate, urine, plasma, and serum could identify people with asthma and asthma phenotypes with high discriminatory ability. There was considerable consistency across the studies in terms of the reported biomarkers, regardless of biospecimen, profiling technology, and population age. In particular, acetate, adenosine, alanine, hippurate, succinate, threonine, and trans-aconitate, and pathways relating to hypoxia response, oxidative stress, immunity, inflammation, lipid metabolism and the tricarboxylic acid cycle were all identified as significant in at least two studies. There were also a number of nonreplicated results; however, the literature is not yet sufficiently developed to determine whether these represent spurious findings or reflect the substantial heterogeneity and limited statistical power in the studies and their methods to date. This review highlights the need for additional asthma metabolomic studies to explore these issues, and, further, the need for standardized methods in the way these studies are conducted. We conclude by discussing the potential of translation of these metabolomic findings into clinically useful biomarkers and the crucial role that integrated omics is likely to play in this endeavor.

The path to personalized medicine in asthma

INTRODUCTION

Asthma is a common respiratory disorder, since about 10% of the population suffer from this disease, and up to 10% have a severe form. Recent findings have allowed a greater and deeper understanding of the pathophysiological mechanisms, distinguishing two groups of patients according to the prevalent cellular population that drives the inflammatory process, and consequentially, to intervene on different cellular targets.

AREAS COVERED

Currently, several biological drugs directly interfering with these pathophysiological mechanisms (namely IgE, IL-4, IL-5, IL-13, and IL-17) are under investigation. Expert commentary: With the elucidation of mechanisms, new-targeted drugs have been developed. Asthma therapy is changing from a 'one size fits all' therapy to a 'precision medicine' model, where we may prescribe the most appropriate treatment for each patient. Moreover, in the near future, the possibility to act a 'sequential bio-combination therapy' can be envisaged, using different biological drugs in the same patient to act on different pathophysiological mechanisms.

Personalized Medicine in Respiratory Disease: Role of Proteomics

Respiratory diseases affect humanity globally, with chronic lung diseases (e.g., asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, among others) and lung cancer causing extensive morbidity and mortality. These conditions are highly heterogeneous and require an early diagnosis. However, initial symptoms are nonspecific, and the clinical diagnosis is made late frequently. Over the last few years, personalized medicine has emerged as a medical care approach that uses novel technology aiming to personalize treatments according to the particular patient's medical needs. This review highlights the contributions of proteomics toward the understanding of personalized medicine in respiratory disease and its potential applications in the clinic.

Identification of Ligustrum lucidum pollen allergens using a proteomics approach

BACKGROUND

Ligustrum spp. are members of the Oleaceae family, one of the most prominent allergic families worldwide. The genus Ligustrum contains approximately fifty species, including Ligustrum lucidum, which have been widely cultivated as ornamental plants, and its pollen is a source of inhalant allergens associated with respiratory allergic diseases. Little is known about the presence of allergenic proteins in L. lucidum.

METHODS

The L. lucidum pollen proteins were extracted by a modified phenolic extraction method. A pool of four sera from mono sensitive patients was analyzed by 2DE immunoblotting and mass spectrometric analysis was performed on 6 immunoreactive protein spots.

RESULTS

SDS-PAGE of L. lucidum pollen extract revealed proteins in ranges of 15-150 kDa. The 2DE gel profile of the L. lucidum pollen protein extract showed approximately 180 spots, and the 2DE immunoblots obtained using sera from Ligustrum monosensitive patients as the source of IgE antibodies revealed six allergen protein spots, corresponding to Profilin, Enolase, Fra e 9.01 (β-1,3-glucanase), Pollen-specific Polygalacturonases, Alanine aminotransferase, and two ATP synthase beta subunits.

CONCLUSION

We report for the first time the identification of IgE-reactive proteins from L. lucidum.

Asthma Pharmacogenomics: 2015 Update

There is evidence that genetic factors are implicated in the observed differences in therapeutic responses to the common classes of asthma therapy such as β2-agonists, corticosteroids, and leukotriene modifiers. Pharmacogenomics explores the roles of genetic variation in drug response and continues to be a field of great interest in asthma therapy. Prior studies have focused on candidate genes and recently emphasized genome-wide association analyses. Newer integrative omics and system-level approaches have recently revealed novel understanding of drug response pathways. However, the current known genetic loci only account for a fraction of variability in drug response and ongoing research is needed. While the field of asthma pharmacogenomics is not yet fully translatable to clinical practice, ongoing research should hopefully achieve this goal in the near future buttressed by the recent precision medicine efforts in the USA and worldwide.