Identification of novel salivary candidate protein biomarkers for tuberculosis diagnosis: A preliminary biomarker discovery study

Tuberculosis ◽  
2021 ◽  
pp. 102118
Author(s):  
Hygon Mutavhatsindi ◽  
Bridget Calder ◽  
Shirley McAnda ◽  
Stephanus T. Malherbe ◽  
Kim Stanley ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Protein Biomarkers ◽  
Tuberculosis Diagnosis ◽  
Candidate Protein

scholarly journals Identification of novel salivary candidate protein biomarkers for tuberculosis diagnosis

2020 ◽  
Author(s):  
Hygon Mutavhatsindi ◽  
Bridget Calder ◽  
Shirley McAnda ◽  
Stephanus T. Malherbe ◽  
Kim Stanley ◽  
...  
Keyword(s):  
Diagnostic Tests ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Salivary Protein ◽  
Functional Enrichment ◽  
Protein Biomarkers ◽  
Human Proteins ◽  
Tuberculosis Diagnosis ◽  
Candidate Protein ◽  
Leave One Out

Abstract Background: The diagnosis of TB remains one of the major challenges in the control of the disease, due to limitations in the currently available diagnostic tests. There is an urgent need for new, accurate, rapid, and affordable diagnostic tests. The aim of the present study was to use mass spectrometry to identify new candidate TB diagnostic protein biomarkers in saliva obtained from individuals with TB, and patients with other respiratory diseases (ORD).Methods: Saliva samples were collected from 22 individuals who self-presented with symptoms requiring investigation for TB as part of a larger TB biomarker project. Eleven of the participants were finally diagnosed with TB using routine clinical, radiological and laboratory tests and 11 with ORD. Salivary proteins were concentrated and purified, followed by tryptic digestion. Peptides were analyzed using a QExactive Orbitrap MS coupled to a Dionex liquid chromatography system. Raw files were processed using MaxQuant software against the database of human proteins. Identified proteins were subjected to gene ontology and ingenuity pathway analysis for functional enrichment analysis.Results: We identified 1176 protein groups across all samples, of which 46 (3.91%) were contaminants, 12 (1.02%) were reverse hits and 170 (14.46%) were single-peptide protein groups. After removal of the contaminants, reverse hits and protein groups represented by single peptides, 26 of the remaining 948 proteins significantly discriminated individuals with TB from those with ORD after Benjamini Hochberg correction, with five of these proteins diagnosing TB with AUC ³ 0.80. A 5-protein biosignature comprising of P01011, Q8NCW5, P28072, A0A2Q2TTZ9 and Q99574 diagnosed TB with an AUC of 1.00 (95% CI, 1.00-1.00), sensitivity of 100% (95% CI, 76.2-100%) and specificity of 90.9% (95% CI, 58.7-99.8%) after leave-one-out cross validation. Conclusions: We identified novel salivary protein biomarkers and biosignatures with strong potential as TB diagnostic candidates. Our results are preliminary and require validation in larger studies.

2D-DIGE analysis of sera from transgenic mouse models reveals novel candidate protein biomarkers for human gastric cancer

2012 ◽  
Vol 77 ◽  
pp. 40-58 ◽  
Author(s):  
Megan A.S. Penno ◽  
Manuela Klingler-Hoffmann ◽  
Julie A. Brazzatti ◽  
Alex Boussioutas ◽  
Tracy Putoczki ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Transgenic Mouse ◽  
Mouse Models ◽  
Human Gastric Cancer ◽  
Transgenic Mouse Models ◽  
Protein Biomarkers ◽  
2D Dige ◽  
Candidate Protein ◽  
Dige Analysis

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

2019 ◽  
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.

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

2003 ◽  
Vol 13 (Suppl 2) ◽  
pp. 133-139 ◽  
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.

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

2019 ◽  
Vol 20 (23) ◽  
pp. 6082 ◽  
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.

Exposing the criminal record of every blood sample: Use of SOMAmer technology and sample mapping vectors to mitigate false biomarker discoveries in lung cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21012-e21012
Author(s):  
Rosalynn D Gill ◽  
Steve Williams ◽  
Rachel Ostroff ◽  
Ed Brody ◽  
Alex Stewart ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Blood Sample ◽  
Biomarker Discovery ◽  
Critical Evaluation ◽  
High Risk Population ◽  
Specific Information ◽  
Protein Biomarkers ◽  
Sample Processing ◽  
Control Serum ◽  
Study Sites

e21012 Background: Biomarker discovery studies may fail to translate to the clinic because the study population does not match the intended clinical use or because hidden preanalytic variability in the discovery samples contaminates the apparent disease specific information in the biomarkers. This can arise from differences in blood sample processing between study sites or in samples collected differently at the same study site. Methods: To better understand the effect of different blood sample processing procedures, we evaluated protein measurement bias in a large multi-center lung cancer study using the >1000 protein SOMAscan™ assay. These analyses revealed that perturbations in serum collection and processing result in changes to families of proteins from known biological pathways. We subsequently developed protein biomarker signatures of cell lysis, platelet activation and complement activation and assembled these preanalytic signatures into quantitative multi-dimensional Sample Mapping Vector (SMV) scores. Results: The SMV score provides critical evaluation of the quality of every blood-based sample used in discovery and also enables the evaluation of candidate protein biomarkers for resistance to preanalytic variability. Despite uniform processing protocols for each clinic, the SMV analysis revealed unexpected case/control bias arising from collecting case and control serum from different clinics at the same academic centers, an effect that created false or bias-contaminated disease markers. We therefore used the SMV score to remove bias-susceptible analytes and to define a well-collected, unbiased training set. An improved classifier was developed, resistant to common artifacts in serum processing. Conclusions: . The performance of this classifier to detect lung cancer in a high-risk population is more likely to represent real-world diagnostic results. We believe this approach is generally applicable to clinical investigations in all fields of biomarker discovery and translational medicine.

scholarly journals Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Ming Lu ◽  
Kym F. Faull ◽  
Julian P. Whitelegge ◽  
Jianbo He ◽  
Dejun Shen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cancer Research ◽  
Disease Management ◽  
Early Detection Of Cancer ◽  
Biomarker Discovery ◽  
Protein Biomarkers ◽  
Bioinformatic Tools ◽  
Translational Cancer Research ◽  
Basic Concepts ◽  
Detection And Diagnosis

Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.

scholarly journals Protein Biomarkers in Uveitis

2020 ◽  
Vol 11 ◽  
Author(s):  
Reema Bansal ◽  
Amod Gupta
Keyword(s):  
Biomarker Discovery ◽  
Inflammatory Diseases ◽  
Protein Composition ◽  
Proteome Analysis ◽  
Protein Biomarkers ◽  
Blood Retinal Barrier ◽  
Clinical Appearance ◽  
Ocular Fluids ◽  
A Cell ◽  
Work Done

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).

scholarly journals Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

2020 ◽  
Vol 20 (11) ◽  
Author(s):  
Julia Carrasco Zanini ◽  
Maik Pietzner ◽  
Claudia Langenberg
Keyword(s):  
Type 2 Diabetes ◽  
Drug Targets ◽  
Information Transfer ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Plasma Proteome ◽  
Protein Biomarkers ◽  
Genetic Studies ◽  
Proteomic Data

Abstract Purpose of the Review Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). Recent Findings Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Summary Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

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