Protein Biomarkers in Uveitis

The diseases affecting the retina or uvea (iris, ciliary body, or choroid) generate changes in the biochemical or protein composition of ocular fluids/tissues due to disruption of blood-retinal barrier. Ocular infections and inflammations are sight-threatening diseases associated with various infectious and non-infectious etiologies. Several etiological entities cause uveitis, a complex intraocular inflammatory disease. These causes of uveitis differ in different populations due to geographical, racial, and socioeconomic variations. While clinical appearance is sufficiently diagnostic in many diseases, some of the uveitic entities manifest nonspecific or atypical clinical presentation. Identification of biomarkers in such diseases is an important aid in their diagnostic armamentarium. Different diseases and their different severity states release varying concentrations of proteins, which can serve as biomarkers. Proteomics is a high throughput technology and a powerful screening tool for serum biomarkers in various diseases that identifies proteins by mass spectrometry and helps to improve the understanding of pathogenesis of a disease. Proteins determine the biological state of a cell. Once identified as biomarkers, they serve as future diagnostic and pharmaceutical targets. With a potential to redirect the diagnosis of idiopathic uveitis, ocular proteomics provide a new insight into the pathophysiology and therapeutics of various ocular inflammatory diseases. Tears, aqueous and vitreous humor represent potential repositories for proteomic biomarkers discovery in uveitis. With an extensive proteomics work done on animal models of uveitis, various types of human uveitis are being subjected to proteome analysis for biomarker discovery in different ocular fluids (vitreous, aqueous, or tears).

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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

International Journal of Molecular Sciences ◽

10.3390/ijms22031399 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1399

Author(s):

Salim Ghannoum ◽

Waldir Leoncio Netto ◽

Damiano Fantini ◽

Benjamin Ragan-Kelley ◽

Amirabbas Parizadeh ◽

...

Keyword(s):

Single Cell ◽

Biomarker Discovery ◽

Enrichment Analysis ◽

Myxoid Liposarcoma ◽

R Package ◽

Differential Analysis ◽

A Cell ◽

Reproducible Analysis ◽

Transcriptomic Level ◽

User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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Transcriptome and Proteome Research in Veterinary Science: What Is Possible and What Questions Can Be Asked?

The Scientific World JOURNAL ◽

10.1100/2012/254962 ◽

2012 ◽

Vol 2012 ◽

pp. 1-14 ◽

Cited By ~ 12

Author(s):

Robert Klopfleisch ◽

Achim D. Gruber

Keyword(s):

Sequential Analysis ◽

Biomarker Discovery ◽

Expression Patterns ◽

Proteome Analysis ◽

Shotgun Proteomics ◽

Complete Analysis ◽

Biological Research ◽

Veterinary Research ◽

Gene Sets ◽

2D Gel

In recent years several technologies for the complete analysis of the transcriptome and proteome have reached a technological level which allows their routine application as scientific tools. The principle of these methods is the identification and quantification of up to ten thousands of RNA and proteins species in a tissue, in contrast to the sequential analysis of conventional methods such as PCR and Western blotting. Due to their technical progress transcriptome and proteome analyses are becoming increasingly relevant in all fields of biological research. They are mainly used for the explorative identification of disease associated complex gene expression patterns and thereby set the stage for hypothesis-driven studies. This review gives an overview on the methods currently available for transcriptome analysis, that is, microarrays, Ref-Seq, quantitative PCR arrays and discusses their potentials and limitations. Second, the most powerful current approaches to proteome analysis are introduced, that is, 2D-gel electrophoresis, shotgun proteomics, MudPIT and the diverse technological concepts are reviewed. Finally, experimental strategies for biomarker discovery, experimental settings for the identification of prognostic gene sets and explorative versus hypothesis driven approaches for the elucidation of diseases associated genes and molecular pathways are described and their potential for studies in veterinary research is highlighted.

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Accelerated Protein Biomarker Discovery from FFPE tissue samples using Single-shot, Short Gradient Microflow SWATH MS

10.1101/675348 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rui Sun ◽

Christie Hunter ◽

Chen Chen ◽

Weigang Ge ◽

Nick Morrice ◽

...

Keyword(s):

Prostate Cancer ◽

Biomarker Discovery ◽

Single Shot ◽

Protein Biomarkers ◽

Differential Abundance ◽

Tissue Samples ◽

Peptide Precursors ◽

High Consistency ◽

Method R ◽

Acquisition Method

ABSTRACTWe report and evaluated a microflow, single-shot, short gradient SWATH MS method intended to accelerate the discovery and verification of protein biomarkers in clinical specimens. The method uses 15-min gradient microflow-LC peptide separation, an optimized SWATH MS window configuration and OpenSWATH software for data analysis.We applied the method to a cohort 204 of FFPE prostate tissue samples from 58 prostate cancer patients and 10 prostatic hyperplasia patients. Altogether we identified 27,976 proteotypic peptides and 4,043 SwissProt proteins from these 204 samples. Compared to a reference SWATH method with 2-hour gradient the accelerated method consumed only 27% instrument time, quantified 80% proteins and showed reduced batch effects. 3,800 proteins were quantified by both methods in two different instruments with relatively high consistency (r = 0.77). 75 proteins detected by the accelerated method with differential abundance between clinical groups were selected for further validation. A shortlist of 134 selected peptide precursors from the 75 proteins were analyzed using MRM-HR, exhibiting high quantitative consistency with the 15-min SWATH method (r = 0.89) in the same sample set. We further verified the capacity of these 75 proteins in separating benign and malignant tissues (AUC = 0.99) in an independent prostate cancer cohort (n=154).Overall our data show that the single-shot short gradient microflow-LC SWATH MS method achieved about 4-fold acceleration of data acquisition with reduced batch effect and a moderate level of protein attrition compared to a standard SWATH acquisition method. Finally, the results showed comparable ability to separate clinical groups.

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Eyelid Diseases

10.4018/978-1-7998-6937-5.ch001 ◽

2022 ◽

pp. 1-37

Keyword(s):

Dry Eye ◽

Malignant Tumors ◽

Inflammatory Diseases ◽

Meibomian Gland ◽

Benign Tumors ◽

Clinical Appearance ◽

Characteristic Appearance ◽

Lipoid Proteinosis ◽

Evaporative Dry Eye ◽

Eyelid Diseases

This chapter reviews eyelid malpositions, benign and malignant tumors, infections, and inflammatory diseases of the eyelid. Eyelid lesions can originate in any layer or structure within the eyelid and eyelid adnexa. Benign tumors of the eyelid can often be diagnosed based on their characteristic appearance. A biopsy should be performed if an eyelid lesion is not easily diagnosed based on clinical appearance. Blepharitis and meibomitis are very common disorders. Despite their prevalence, these diseases are often overlooked and misdiagnosed. Meibomian gland dysfunction is a major cause of evaporative dry eye and can occur along with aqueous deficient dry eye. Bacteria, fungi, viruses, and parasites can cause infection of the eyelids in different locations, anterior, posterior, angular, which can have different courses, acute, intermediate, or chronic. This chapter also contains photos with a rare disorder, Urbach-Wiethe disease, demonstrating the lesions of lipoid proteinosis at the lower and upper eyelids.

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Management and Analysis of Mass Spectrometry Proteomics Data on the Grid

Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare ◽

10.4018/978-1-60566-374-6.ch011 ◽

2011 ◽

pp. 206-227

Author(s):

Mario Cannataro ◽

Pietro Hiram Guzzi ◽

Giuseppe Tradigo ◽

Pierangelo Veltri

Keyword(s):

Mass Spectrometry ◽

Protein Identification ◽

Biomarker Discovery ◽

Integrated Management ◽

Molecular Signature ◽

Proteomics Data ◽

Computational Proteomics ◽

Manual Inspection ◽

A Cell ◽

Grid Based

Recent advances in high throughput technologies analysing biological samples enabled the researchers to collect a huge amount of data. In particular, mass spectrometry-based proteomics uses the mass spectrometry to investigate proteins expressed in an organism or a cell. The manual inspection of spectra is unfeasible, so the need to introduce a set of algorithms, tools and platforms to manage and analyze them arises. Computational Proteomics regards the computational methods for analyzing spectra data in qualitative (i.e. peptide/protein identification in tandem mass spectrometry), and quantitative proteomics (i.e. protein expression in samples), as well as in biomarker discovery (i.e. the identification of a molecular signature of a disease directly from spectra). This chapter presents main standards, tools, and technologies for building scalable, reusable, and portable applications in this field. The chapter surveys available solutions for computational proteomics and includes a deep description of MS-Analyzer, a Grid-based software platform for the integrated management and analysis of spectra data. MS-Analyzer provides efficient spectra management through a specialized spectra database, and supports the semantic composition of pre-processing and data mining services to analyze spectra on the Grid.

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Proteomic analysis for early detection of ovarian cancer: A realistic approach?

International Journal of Gynecological Cancer ◽

10.1136/ijgc-00009577-200311001-00001 ◽

2003 ◽

Vol 13 (Suppl 2) ◽

pp. 133-139 ◽

Cited By ~ 5

Author(s):

E. V. Stevens ◽

L. A. Liotta ◽

E. C. Kohn

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Serum Protein ◽

Mass Spectroscopy ◽

Biomarker Discovery ◽

Protein Pattern ◽

Protein Biomarkers ◽

Promising Tool ◽

Chip Technology ◽

Stage Disease

Ovarian cancer is a multifaceted disease wherein most women are diagnosed with advanced stage disease. One of the most imperative issues in ovarian cancer is early detection. Biomarkers that allow cancer detection at stage I, a time when the disease is amenable to surgical and chemotherapeutic cure in over 90% of patients, can dramatically alter the horizon for women with this disease. Recent developments in mass spectroscopy and protein chip technology coupled with bioinformatics have been applied to biomarker discovery. The complexity of the proteome is a rich resource from which the patterns can be gleaned; the pattern rather than its component parts is the diagnostic. Serum is a key source of putative protein biomarkers, and, by its nature, can reflect organ-confined events. Pioneering use of mass spectroscopy coupled with bioinformatics has been demonstrated as being capable of distinguishing serum protein pattern signatures of ovarian cancer in patients with early- and late-stage disease. This is a sensitive, precise, and promising tool for which further validation is needed to confirm that ovarian cancer serum protein signature patterns can be a robust biomarker approach for ovarian cancer diagnosis, yielding improved patient outcome and reducing the death and suffering from ovarian cancer.

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Decellularized Human Gut as a Natural 3D Platform for Research in Intestinal Fibrosis

Inflammatory Bowel Diseases ◽

10.1093/ibd/izz115 ◽

2019 ◽

Vol 25 (11) ◽

pp. 1740-1750 ◽

Cited By ~ 2

Author(s):

Paolo Giuffrida ◽

Marco Curti ◽

Walid Al-Akkad ◽

Carin Biel ◽

Claire Crowley ◽

...

Keyword(s):

Cell Cultures ◽

Drug Testing ◽

Biomarker Discovery ◽

Disease Modeling ◽

3D Culture ◽

Protein Composition ◽

Intestinal Fibrosis ◽

3D Cell Cultures ◽

3D Architecture ◽

Inflammatory Bowel

Abstract Background The current methodologies for the identification of therapeutic targets for inflammatory bowel disease (IBD) are limited to conventional 2-dimensional (2D) cell cultures and animal models. The use of 3D decellularized human intestinal scaffolds obtained from surgically resected intestine and engineered with human intestinal cells may provide a major advancement in the development of innovative intestinal disease models. The aim of the present study was to design and validate a decellularization protocol for the production of acellular 3D extracellular matrix (ECM) scaffolds from the human duodenum. Methods Scaffolds were characterized by verifying the preservation of the ECM protein composition and 3D architecture of the native intestine and were employed for tissue engineering with primary human intestinal myofibroblasts for up to 14 days. Results Engrafted cells showed the ability to grow and remodel the surrounding ECM. mRNA expression of key genes involved in ECM turnover was significantly different when comparing primary human intestinal myofibroblasts cultured in 3D scaffolds with those cultured in standard 2D cultures on plastic dishes. Moreover, incubation with key profibrogenic growth factors such as TGFβ1 and PDGF-BB resulted in markedly different effects in standard 2D vs 3D cultures, further emphasizing the importance of using 3D cell cultures. Conclusions These results confirm the feasibility of 3D culture of human intestinal myofibroblasts in intestinal ECM scaffolds as an innovative platform for disease modeling, biomarker discovery, and drug testing in intestinal fibrosis.

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Gel-Based Proteomics of Clinical Samples Identifies Potential Serological Biomarkers for Early Detection of Colorectal Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms20236082 ◽

2019 ◽

Vol 20 (23) ◽

pp. 6082 ◽

Cited By ~ 3

Author(s):

Stine Thorsen ◽

Irina Gromova ◽

Ib Christensen ◽

Simon Fredriksson ◽

Claus Andersen ◽

...

Keyword(s):

Colorectal Cancer ◽

Early Detection ◽

Biomarker Discovery ◽

Real Life ◽

Clinical Samples ◽

Plasma Samples ◽

Healthy Individuals ◽

Protein Biomarkers ◽

2D Gel ◽

Cancer Diseases

The burden of colorectal cancer (CRC) is considerable—approximately 1.8 million people are diagnosed each year with CRC and of these about half will succumb to the disease. In the case of CRC, there is strong evidence that an early diagnosis leads to a better prognosis, with metastatic CRC having a 5-year survival that is only slightly greater than 10% compared with up to 90% for stage I CRC. Clearly, biomarkers for the early detection of CRC would have a major clinical impact. We implemented a coherent gel-based proteomics biomarker discovery platform for the identification of clinically useful biomarkers for the early detection of CRC. Potential protein biomarkers were identified by a 2D gel-based analysis of a cohort composed of 128 CRC and site-matched normal tissue biopsies. Potential biomarkers were prioritized and assays to quantitatively measure plasma expression of the candidate biomarkers were developed. Those biomarkers that fulfilled the preset criteria for technical validity were validated in a case-control set of plasma samples, including 70 patients with CRC, adenomas, or non-cancer diseases and healthy individuals in each group. We identified 63 consistently upregulated polypeptides (factor of four-fold or more) in our proteomics analysis. We selected 10 out of these 63 upregulated polypeptides, and established assays to measure the concentration of each one of the ten biomarkers in plasma samples. Biomarker levels were analyzed in plasma samples from healthy individuals, individuals with adenomas, CRC patients, and patients with non-cancer diseases and we identified one protein, tropomyosin 3 (Tpm3) that could discriminate CRC at a significant level (p = 0.0146). Our results suggest that at least one of the identified proteins, Tpm3, could be used as a biomarker in the early detection of CRC, and further studies should provide unequivocal evidence for the real-life clinical validity and usefulness of Tpm3.

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Targeting Opposing Immunological Roles of the Junctional Adhesion Molecule-A in Autoimmunity and Cancer

Frontiers in Immunology ◽

10.3389/fimmu.2020.602094 ◽

2020 ◽

Vol 11 ◽

Author(s):

Caio S. Bonilha ◽

Robert A. Benson ◽

James M. Brewer ◽

Paul Garside

Keyword(s):

Immune Responses ◽

Adhesion Molecule ◽

Therapeutic Potential ◽

Inflammatory Diseases ◽

Clinical Pathology ◽

Surface Adhesion ◽

Pathological Conditions ◽

Relevant Role ◽

A Cell ◽

Cell Surface Adhesion Molecule

The junctional adhesion molecule-A (JAM-A) is a cell surface adhesion molecule expressed on platelets, epithelial cells, endothelial cells and leukocytes (e. g. monocytes and dendritic cells). JAM-A plays a relevant role in leukocyte trafficking and its therapeutic potential has been studied in several pathological conditions due to its capacity to induce leukocyte migration out of inflamed sites or infiltration into tumor sites. However, disruption of JAM-A pathways may worsen clinical pathology in some cases. As such, the effects of JAM-A manipulation on modulating immune responses in the context of different diseases must be better understood. In this mini-review, we discuss the potential of JAM-A as a therapeutic target, summarizing findings from studies manipulating JAM-A in the context of inflammatory diseases (e.g. autoimmune diseases) and cancer and highlighting described mechanisms.

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