International journal of proteomics最新文献

In a high-resolution MALDI-TOF mass spectrum, a peptide produces multiple peaks, corresponding to the isotopic variants of the molecules. An overlap occurs when two peptides appear in the vicinity of the mass coordinate, resulting in the difficulty of quantifying the relative abundance and the exact masses of these peptides. To address the problem, two factors need to be considered: (1) the variability pertaining to the abundances of the isotopic variants (2) extra information content needed to supplement the information contained in data. We propose a Bayesian model for the incorporation of prior information. Such information exists, for example, for the distribution of the masses of peptides and the abundances of the isotopic variants. The model we develop allows for the correct estimation of the parameters of interest. The validity of the modeling approach is verified by a real-life case study from a controlled mass spectrometry experiment and by a simulation study.

Protein glycosylation is one of the most common posttranslational modifications in mammalian cells. It is involved in many biological pathways and molecular functions and is well suited for proteomics-based disease investigations. Aberrant protein glycosylation may be associated with disease processes. Specific glycoforms of glycoproteins may serve as potential biomarkers for the early detection of disease or as biomarkers for the evaluation of therapeutic efficacy for treatment of cancer, diabetes, and other diseases. Recent technological developments, including lectin affinity chromatography and mass spectrometry, have provided researchers the ability to obtain detailed information concerning protein glycosylation. These in-depth investigations, including profiling and quantifying glycoprotein expression, as well as comprehensive glycan structural analyses may provide important information leading to the development of disease-related biomarkers. This paper describes methodologies for the detection of cancer-related glycoprotein and glycan structural alterations and briefly summarizes several current cancer-related findings.

Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.

The present clinical staging of melanoma stratifies patients into heterogeneous groups, resulting in the application of aggressive therapies to large populations, diluting impact and increasing toxicity. To move to a new era of therapeutic decisions based on highly specific tumor profiling, the discovery and validation of new prognostic and predictive biomarkers in melanoma is critical. Genomic profiling, which is showing promise in other solid tumors, requires fresh tissue from a large number of primary tumors, and thus faces a unique challenge in melanoma. For this and other reasons, proteomics appears to be an ideal choice for the discovery of new melanoma biomarkers. Several approaches to proteomics have been utilized in the search for clinically relevant biomarkers, but to date the results have been relatively limited. This article will review the present work using both tissue and serum proteomics in the search for melanoma biomarkers, highlighting both the relative advantages and disadvantages of each approach. In addition, we review several of the major obstacles that need to be overcome in order to advance the field.

Proteomic profiles of RAST(+) subjects with severe food allergies and RAST(-) subjects were compared using 2D-DIGE analysis to obtain candidate biomarkers specific to food allergies. Our analysis highlighted 52 proteins that were differentially expressed between the RAST(+) and RAST(-) groups of which 37 were successfully identified that include chondroitin sulfates, zinc finger proteins, C-type lectins, retinoic acid binding proteins, heat shock proteins, myosin, cytokines, mast cell expressed proteins, and MAP kinases. Biological network analysis tool Metacore revealed that most of these regulated proteins play a role in immune tolerance, hypersensitivity and modulate cytokine patterns inducing a Th2 response that typically results in IgE-mediated allergic response which has a direct or indirect biological link to food allergy. Identifying unique biomarkers associated with certain allergic phenotypes and potentially cross-reactive proteins through bioinformatics analyses will provide enormous insight into the mechanisms that underlie allergic response in patients with food allergies.

We present a versatile user-friendly software tool, PolyAlign, for the alignment of multiple LC-MS signal maps with the option of manual landmark setting or automated alignment. One of the spectral images is selected as a reference map, and after manually setting the landmarks, the program warps the images using either polynomial or Hermite transformation. The software provides an option for automated landmark finding. The software includes a very fast zoom-in function synchronized between the images, which facilitate detecting correspondences between the adjacent images. Such an interactive visual process enables the analyst to decide when the alignment is satisfactory and to correct known irregularities. We demonstrate that the software provides significant improvements in the alignment of LC-MALDI data, with 10-15 landmark pairs, and it is also applicable to correcting electrospray LC-MS data. The results with practical data show substantial improvement in peak alignment compared to MZmine, which was among the best analysis packages in a recent assessment. The PolyAlign software is freely available and easily accessible as an integrated component of the popular MZmine software, and also as a simpler stand-alone Perl implementation to preview data and apply landmark directed polynomial transformation.

Repair of double-stranded breaks (DSBs) is vital to maintaining genomic stability. In mammalian cells, DSBs are resolved in one of the following complex repair pathways: nonhomologous end-joining (NHEJ), homologous recombination (HR), or the inclusive DNA damage response (DDR). These repair pathways rely on factors that utilize reversible phosphorylation of proteins as molecular switches to regulate DNA repair. Many of these molecular switches overlap and play key roles in multiple pathways. For example, the NHEJ pathway and the DDR both utilize DNA-PK phosphorylation, whereas the HR pathway mediates repair with phosphorylation of RPA2, BRCA1, and BRCA2. Also, the DDR pathway utilizes the kinases ATM and ATR, as well as the phosphorylation of H2AX and MDC1. Together, these molecular switches regulate repair of DSBs by aiding in DSB recognition, pathway initiation, recruitment of repair factors, and the maintenance of repair mechanisms.

Proteomics is a powerful method used to identify, characterize, and quantify proteins within biologic samples. Pancreatic cystic neoplasms are a common clinical entity and represent a diagnostic and management challenge due to difficulties in accurately diagnosing cystic lesions with malignant potential and assessing the risk of malignant degeneration. Currently, cytology and other biomarkers in cyst fluid have had limited success in accurately distinguishing both the type of cystic neoplasm and the presence of malignancy. Emerging data suggests that the use of protein-based biomarkers may have greater utility in helping clinicians correctly diagnose the type of cyst and to identify which cystic neoplasms are malignant. Several candidate proteins have been identified within pancreatic cystic neoplasms as potential biomarkers. Future studies will be needed to validate these findings and move these biomarkers into the clinical setting.

Myogenesis, the formation of skeletal muscle, is a multistep event that commences with myoblast proliferation, followed by cell-cycle arrest, and finally the formation of multinucleated myotubes via fusion of mononucleated myoblasts. Each step is orchestrated by well-documented intracellular factors, such as cytoplasmic signalling molecules and nuclear transcription factors. Regardless, the key step in getting a more comprehensive understanding of the regulation of myogenesis is to explore the extracellular factors that are capable of eliciting the downstream intracellular factors. This could further provide valuable insight into the acute cellular response to extrinsic cues in maintaining normal muscle development. In this paper, we survey the intracellular factors that respond to extracellular cues that are responsible for the cascades of events during myogenesis: myoblast proliferation, cell-cycle arrest of myoblasts, and differentiation of myoblasts into myotubes. This focus on extracellular perspective of muscle development illustrates our mass spectrometry-based proteomic approaches to identify differentially expressed secreted factors during skeletal myogenesis.

Better prognostic and predictive markers in melanoma are needed to select patients for therapy. We utilized a dual-lectin affinity chromatography and a natural protein microarray-based analysis to select a subproteome of target glycoproteins to profile serum antibodies against melanoma associated antigens that may predict nodal positivity. We identified 5 melanoma-associated antigens using this microarray coupled to mass spectrometry; GRP75, GRP94, ASAH1, CTSD and LDHB. We evaluated their predictive value for nodal status adjusting for age, gender, Breslow thickness, mitotic rate and ulceration using standard logistic regression. After adjustment, ASAH1, CTSD and LDHB were significantly negatively associated with nodal status (P = 0.0008) and GRP94 was significantly positively associated (P = 0.014). Our best multivariate model for nodal positivity included Breslow thickness, presence of serum anti-ASAH1, anti-LDHB or anti-CTSD, and presence of serum anti-GRP94, with an area under the ROC curve of 0.869. If validated, these results show promise for selecting clinically node negative patients for SLN biopsy. In addition, there is strong potential for glycoprotein microarray to screen serum autoantibodies that may identify patients at high risk of distant metastases or those likely or unlikely to respond to treatment, and these proteins may serve as targets for intervention.