Homogenous growth of gold nanocrystals for quantification of PSA protein biomarker

We report on another alternative sensing platform for the detection of protein biomarker (PSA–ACT complex) based on homogenous growth of Au nanocrystals in solution phase. The immuno-recognition event is translated into the gold nanoparticle growth signal which can be intuitively recognized by an unaided eye, or quantitatively measured by an UV–vis spectrophotometric analysis. Surface plasmonic signature and kinetics of the Au nanogrowth in the homogenous phase containing of HAuCl4, AA, and CTAB have also been studied to provide suitable parameters for the immunoassay. As a result, detection limit of PSA–ACT complex was determined to be 10 fM. The result indicated that this is a very sensitive, robust, simple, and economic strategy to detect protein biomarkers, and it has great potential to detect other biological interactions.

Potential Urinary Protein Biomarker Candidates for the Accurate Detection of Prostate Cancer among Benign Prostatic Hyperplasia Patients

Globally, Prostate cancer (PCa) is the most frequently occurring non-cutaneous cancer, and is the second highest cause of cancer mortality in men. Serum prostate specific antigen (PSA) has been the standard in PCa screening since its approval by the American Food & Drug Administration (FDA) in 1994. Currently, PSA is used as an indicator for PCa - patients with a serum PSA level above 4ng/mL will often undergo prostate biopsy to confirm cancer. Unfortunately fewer than similar to 30% of these men will biopsy positive for cancer, meaning that the majority of men undergo invasive biopsy with little benefit. Despite PSA's notoriously poor specificity (33%), there is still a significant lack of credible alternatives. Therefore an ideal biomarker that can specifically detect PCa at an early stage is urgently required. The aim of this study was to investigate the potential of using deregulation of urinary proteins in order to detect Prostate Cancer (PCa) among Benign Prostatic Hyperplasia (BPH). To identify the protein signatures specific for PCa, protein expression profiling of 8 PCa patients, 12 BPH patients and 10 healthy males was carried out using LC-MS/MS. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR. This approach revealed that significant the down-regulation of Fibronectin and TP53INP2 was a characteristic event among PCa patients. Fibronectin mRNA down-regulation, was identified as offering improved specificity (50%) over PSA, albeit with a slightly lower although still acceptable sensitivity (75%) for detecting PCa. As for TP53INP2 on the other hand, its down-regulation was moderately sensitive (75%), identifying many patients with PCa, but was entirely non-specific (7%), designating many of the benign samples as malignant and being unable to accurately identify more than one negative.

Graphene-based protein biomarker detection

An ability to detect and quantify protein molecules, harbingers of specific pathologies, potentially underpins both early disease diagnosis and an assessment of treatment efficacy. However, the specific detection of a particular protein biomarker in a complex environment is by no means an easy task and requires a progressive improvement in sensor technology. The high surface area, volume, electrical conductance, atomic level thickness and apparent biocompatibility of graphene makes it potentially an exceedingly powerful transducer of biorecognition events; the demands of its application in biosensing, and progress to date are reviewed herein.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Effect of growth-promoting 17ß estradiol, 19 nortestosterone and dexamethasone on circulating levels of nine potential biomarker candidates in veal calves

The use of screening methods based on the detection of biological effects of growth promoters is a promising approach to assist residue monitoring. To reveal useful effects on protein metabolism, male and female veal calves at 10 weeks of age were treated thrice with a combination of 25 mg 17ß-estradiol 3-benzoate and 150 mg 19-nortestosterone decanoate with 2 weeks intervals and finally once with 4 mg dexamethasone. Hormone-treated calves showed a significant accelerated growth rate over 6 weeks. Plasma samples of treated and control calves were analysed for immunoreactive inhibin (ir-inhibin), osteocalcin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor-binding protein 2 (IGFBP-2), IGFBP-3, luteinzing hormone (LH), follicle-stimulating hormone (FSH) and prolactin using immunoaffinity assays. Hormone treatment did not affect levels of IGF-1, IGFBP-2, IGFBP-3, LH, FSH and prolactin. The concentration of circulating ir-inhibin decreased, however, significantly (P < 0.05) in bull calves upon administration of the sex steroids, whereas it remained unchanged in the female animals. Dexamethasone treatment decreased significantly (P < 0.05) circulating levels of osteocalcin in both female and male animals. Ir-inhibin and osteocalcin were, therefore, considered as candidates for a protein biomarker-based screening assay for detection of abuse of estrogens, androgens and/or glucocorticoids in cattle fattening, which is being developed in the framework of EU research project BioCop

Detection of cardiac biomarker proteins using a disposable based on a molecularly imprinted polymer grafted onto graphite

A low-cost disposable was developed for rapid detection of the protein biomarker myoglobin (Myo) as a model analyte. A screen printed electrode was modified with a molecularly imprinted material grafted on a graphite support and incorporated in a matrix composed of poly(vinyl chloride) and the plasticizer o-nitrophenyloctyl ether. The protein-imprinted material (PIM) was produced by growing a reticulated polymer around a protein template. This is followed by radical polymerization of 4-styrenesulfonic acid, 2-aminoethyl methacrylate hydrochloride, and ethylene glycol dimethacrylate. The polymeric layer was then covalently bound to the graphitic support, and Myo was added during the imprinting stage to act as a template. Non-imprinted control materials (CM) were also prepared by omitting the Myo template. Morphological and structural analysis of PIM and CM by FTIR, Raman, and SEM/EDC microscopies confirmed the modification of the graphite support. The analytical performance of the SPE was assessed by square wave voltammetry. The average limit of detection is 0.79 μg of Myo per mL, and the slope is −0.193 ± 0.006 μA per decade. The SPE-CM cannot detect such low levels of Myo but gives a linear response at above 7.2 μg · mL−1, with a slope of −0.719 ± 0.02 μA per decade. Interference studies with hemoglobin, bovine serum albumin, creatinine, and sodium chloride demonstrated good selectivity for Myo. The method was successfully applied to the determination of Myo urine and is conceived to be a promising tool for screening Myo in point-of-care patients with ischemia.

Nonculture-based identification of bacteria in milk by protein fingerprinting

Traditional methods for bacterial identification include Gram staining, culturing, and biochemical assays for phenotypic characterization of the causative organism. These methods can be time-consuming because they require in vitro cultivation of the microorganisms. Recently, however, it has become possible to obtain chemical profiles for lipids, peptides, and proteins that are present in an intact organism, particularly now that new developments have been made for the efficient ionization of biomolecules. MS has therefore become the state-of-the-art technology for microorganism identification in microbiological clinical diagnosis. Here, we introduce an innovative sample preparation method for nonculture-based identification of bacteria in milk. The technique detects characteristic profiles of intact proteins (mostly ribosomal) with the recently introduced MALDI SepsityperTM Kit followed by MALDI-MS. In combination with a dedicated bioinformatics software tool for databank matching, the method allows for almost real-time and reliable genus and species identification. We demonstrate the sensitivity of this protocol by experimentally contaminating pasteurized and homogenized whole milk samples with bacterial loads of 10(3)-10(8) colony-forming units (cfu) of laboratory strains of Escherichia coli, Enterococcus faecalis, and Staphylococcus aureus. For milk samples contaminated with a lower bacterial load (104 cfu mL-1), bacterial identification could be performed after initial incubation at 37 degrees C for 4 h. The sensitivity of the method may be influenced by the bacterial species and count, and therefore, it must be optimized for the specific application. The proposed use of protein markers for nonculture-based bacterial identification allows for high-throughput detection of pathogens present in milk samples. This method could therefore be useful in the veterinary practice and in the dairy industry, such as for the diagnosis of subclinical mastitis and for the sanitary monitoring of raw and processed milk products.

Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens-to-hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma, and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and 7 control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to sub-nanogram/mL sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and inter-laboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy isotope labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an inter-laboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality c`ontrol measures, enables sensitive, specific, reproducible and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.

Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Resonant Rayleigh light scattering response of individual Au nanoparticles to antigen--antibody interaction

A proof-of-concept study was reported on analysis of antigen–antibody recognition based on resonant Rayleigh scattering response of single Au nanoparticles in an imaging chamber. As benefited by a traditional dark-field microscope and a spectrograph, individual Au nanoparticles (30 nm) were observed with high signal-to-noise ratio and they were effectively utilized to monitor changes in refractive index induced by specific binding of the adsorbates. Using PSA antigen as a model, a LSPR ?max shift of about 2.85 nm was recorded for a molecular binding corresponding to 0.1 pg ml-1 of the protein biomarker. This result successfully demonstrates a non-labeling detection system for proteins as well as thousands of different chemical or biological species, and it possesses a great potential as a sensitive, on-chip and multiplexing detection.

A new method for non-labeling attomolar detection of diseases based on an individual gold nanorod immunosensor

Herein, we present the use of a single gold nanorod sensor for detection of diseases on an antibody-functionalized surface, based on antibody–antigen interaction and the localized surface plasmon resonance (LSPR) ?max shifts of the resonant Rayleigh light scattering spectra. By replacing the cetyltrimethylammonium bromide (CTAB), a tightly packed self-assembled monolayer of HS(CH2)11(OCH2CH2)6OCH2COOH(OEG6) has been successfully formed on the gold nanorod surface prior to the LSPR sensing, leading to the successful fabrication of individual gold nanorod immunosensors. Using prostate specific antigen (PSA) as a protein biomarker, the lowest concentration experimentally detected was as low as 111 aM, corresponding to a 2.79 nm LSPR ?max shift. These results indicate that the detection platform is very sensitive and outperforms detection limits of commercial tests for PSA so far. Correlatively, its detection limit can be equally compared to the assays based on DNA biobarcodes. This study shows that a gold nanorod has been used as a single nanobiosensor to detect antigens for the first time; and the detection method based on the resonant Rayleigh scattering spectrum of individual gold nanorods enables a simple, label-free detection with ultrahigh sensitivity.

Molecular imprinted nanoelectrodes for ultra sensitive detection of ovarian cancer marker

The relentless discovery of cancer biomarkers demands improved methods for their detection. In this work, we developed protein imprinted polymer on three-dimensional gold nanoelectrode ensemble (GNEE) to detect epithelial ovarian cancer antigen-125 (CA 125), a protein biomarker associated with ovarian cancer. CA 125 is the standard tumor marker used to follow women during or after treatment for epithelial ovarian cancer. The template protein CA 125 was initially incorporated into the thin-film coating and, upon extraction of protein from the accessible surfaces on the thin film, imprints for CA 125 were formed. The fabrication and analysis of the CA 125 imprinted GNEE was done by using cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. The surfaces of the very thin, protein imprinted sites on GNEE are utilized for immunospecific capture of CA 125 molecules, and the mass of bound on the electrode surface can be detected as a reduction in the faradic current from the redox marker. Under optimal conditions, the developed sensor showed good increments at the studied concentration range of 0.5–400 U mL−1. The lowest detection limit was found to be 0.5 U mL−1. Spiked human blood serum and unknown real serum samples were analyzed. The presence of non-specific proteins in the serum did not significantly affect the sensitivity of our assay. Molecular imprinting using synthetic polymers and nanomaterials provides an alternative approach to the trace detection of biomarker proteins.

Estudo metaloproteômico do mércurio em amostras de tecido hepático de peixes coletados na área de influência do AHE JIRAU - Bacia do rio Madeira

Pós-graduação em Biologia Geral e Aplicada - IBB

Proteomics-driven approach for the detection of breast cancer biomarkers

Breast cancer (BC) is the most often diagnosed cancer entity of women worldwide. No molecular biomarkers are usable in the clinical routine for the early detection of BC. Proteomics is one of the dynamic tools for the successful examination of changes on the protein level. In this thesis different proteomics-based investigations were performed for the detection of protein and autoantibody biomarkers in serum samples of BC and healthy (CTRL) subjects. First, protein levels of candidates from previous profiling studies were investigated via antibody-microarray platform. Three proteins were found in distinct levels in both groups: secretoglobin family 1D member 1, alpha-2 macroglobulin and inter-alpha-trypsin inhibitor heavy chain family member 4. The second part was dedicated to the de novo exploration of potentially immunogenic tumor antigens (TA’s) with immunoprecipitation and Western immunoblotting followed by identification over mass spectrometry. Autoantibody levels were verified in individual serum profiling via the protein microarray platform. Two autoantibody’ cohorts (anti-Histone 2B and anti-Recoverin) were found in different levels in both groups. The findings of this PhD thesis underline deregulated serum protein and autoantibody levels in the presence of BC. Further investigations are needed to confirm the results in an independent study population.