Proteomic analysis of differentially expressed serum proteins in lung cancer

Lung cancer continues to represent a major public health concern with high morbidity and mortality worldwide. Early detection of lung cancer is problematic due to a lack of diagnostic markers with high sensitivity and specificity. To determine the differently expressed proteins in the serum of lung cancer and identify the function of such proteins, two-dimensional electrophoresis (2DE) and liquid chromatography mass spectrometry (LC-MS) were used to screen the serum of lung cancer model induced by 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanone (NNK). A total of 25 protein spots were qualitatively different and 6 were quantitatively different in the serum from rats bearing induced lung cancer when compared with normal controls. Two of the proteins that showed major changes in concentration in sera were identified to be Immunoglobulin γ 2A chain C region (heavy chain) and Transferrin by LC-MS/MS.     

RegStatGel: proteomic software for identifying differentially expressed proteins based on 2D gel images

Image analysis of two-dimensional gel electrophoresis is a key step in proteomic workflow for identifying proteins that change under different experimental conditions. Since there are usually large amount of proteins and variations shown in the gel images, the use of software for analysis of 2D gel images is inevitable. We developed open-source software with graphical user interface for differential analysis of 2D gel images. The user-friendly software, RegStatGel, contains fully automated as well as interactive procedures. It was developed and has been tested under Matlab 7.01. AVAILABILITY: The database is available for free at http://www.mediafire.com/FengLi/2DGelsoftware.     

Identification of differentially expressed proteins in colorectal cancer by proteomics: down-regulation of secretagogin

To identify proteins with colorectal cancer-specific regulation, comparative 2-DE of individual-matched normal and neoplastic colorectal tissue specimens was performed. We found 15 protein spots with concordantly increased and 20 protein spots with concordantly decreased intensity in tumor tissue (expression regulation more than fivefold). Nine of these proteins were identified by MS/MS. Interestingly, one of the proteins, which exhibited a marked down-regulation in colorectal cancer tissues, was the recently identified endocrine cell-expressed protein secretagogin. The reduction of the secretagogin content in colorectal cancer tissues was confirmed by comparative immunoblotting (n = 17) and RT-PCR (n = 22) as well as by immunohistochemistry (n = 45) of individual-matched neoplastic and normal colorectal tissue specimens. Immunohistochemistry revealed absence of secretagogin-expressing cells in most of the colorectal cancer tissue specimens. However, some colorectal cancers were characterized by secretagogin-expressing cells. In normal mucosa, positively stained cells exhibited a neuroendocrine cell-characteristic morphology and mucosal location. In colorectal cancer tissues, secretagogin-expressing cells were characterized by a malignant morphology. Our findings might represent the basis for the clinical application of secretagogin as a biomarker for a distinct subgroup of colorectal cancers.     

Analysis of glycated serum proteins in type 2 diabetes patients with nephropathy

The aim of this study was to screen for proteins that are susceptible to glycation under hyperglycemic conditions in patients with type 2 diabetic nephropathy. Serum proteins were analyzed by a proteomic approach using two-dimensional electrophoresis (2-DE) and ESI-Q-TOF MS/MS. Gels were stained with Pro-Q Emerald 488 to analyze the serum glycoproteome, followed by silver nitrate to examine the total serum proteome. Patient sera were divided into four groups according to their microalbuminuria index: type 2 diabetics with normoalbuminuria, microalbuminuria, and overt nephropathy, and healthy subjects. When the HbA1c levels of the diabetic groups were examined, groups with higher HbA1c exhibited higher fructosamine levels, suggesting that the loss of glycemic control affected the glycation of serum proteins. The proteins that became glycated under poor glycemic control were PEDF, apolipoprotein J precursor, hemopexin, immunoglobulin mu heavy chain, and immunoglobulin kappa chain. As albuminuria increased, a marker of kidney damage, the levels of glycated prekallikrein and complement factor C4B3 also increased. The glycated proteins identified in this study may provide the foundation for the development of novel markers of diabetes, hyperglycemia, and diabetic complications.     

Comparative proteomics analysis of differentially expressed proteins in soybean cell wall during flooding stress

Flooding is a major problem for soybean crop as it reduces the growth and grain yield. To investigate the function of the soybean cell wall in the response to flooding stress, cell wall proteins were analyzed. Cell wall proteins from roots and hypocotyls of soybeans, which were germinated for 2 days and subjected to 2 days of flooding, were purified, separated by two-dimensional polyacrylamide gel electrophoresis and stained with Coomassie brilliant blue. Sixteen out of 204 cell wall proteins showed responses to flooding stress. Of these, two lipoxygenases, four germin-like protein precursors, three stem 28/31 kDa glycoprotein precursors, and one superoxide dismutase [Cu–Zn] were downregulated. A copper amine oxidase was found to have shifted from the basic to acidic zone following flooding stress. Based on these results, it was confirmed by the lignin staining that the lignification was suppressed in the root of soybean under the flooding stress. These results suggest that the roots and hypocotyls of soybean caused the suppression of lignification through decrease of these proteins by downregulation of reactive oxygen species and jasmonate biosynthesis under flooding stress.     

The use of LC-MS to identify differentially expressed proteins in docetaxel-resistant prostate cancer cell lines

Docetaxel is a taxane-derived chemotherapy drug that has been approved for treatment of prostate cancer. While docetaxel is frequently used as a treatment for hormone-refractory prostate cancer, a subset of patients either do not respond to this treatment or those that do respond eventually become resistant to the drug over time. Resistance to docetaxel is complex and multi-factoral and further understanding of the cellular biochemistry underlying resistance is vital to improve treatment efficacy. To identify proteins altered in the resistant phenotype, three parental cell lines DU145, 22RV1 and PC-3, as well as their docetaxel resistant sub-lines, were subjected to quantitative label-free LC-MS proteomic profiling. A total of 189 significant (p < 0.05) protein abundance changes were identified in the DU145 resistant sub-lines, 254 in the 22RV1 sub-lines, and 51 and 72 in the 8 and 12 nM resistant PC-3 sub-lines, respectively. From these, 29 proteins demonstrated a significant (p < 0.05) fold change across two or more resistant variants. These included proteins indicative of an epithelial-to-mesenchemyl transition as well as altered heat shock response elements.     

The role of iron in type 2 diabetes in humans

The role of micronutrients in the etiology of type 2 diabetes is not well established. Several lines of evidence suggest that iron play may a role in the pathogenesis of type 2 diabetes. Iron is a strong pro-oxidant and high body iron levels are associated with increased level of oxidative stress that may elevate the risk of type 2 diabetes. Several epidemiological studies have reported a positive association between high body iron stores, as measured by circulating ferritin level, and the risk of type 2 diabetes and of other insulin resistant states such as the metabolic syndrome, gestational diabetes and polycystic ovarian syndrome. In addition, increased dietary intake of iron, especially that of heme iron, is associated with risk of type 2 diabetes in apparently healthy populations. Results from studies that have evaluated the association between genetic mutations related to iron metabolism have been inconsistent. Further, several clinical trials have suggested that phlebotomy induced reduction in body iron levels may improve insulin sensitivity in humans. However, no interventional studies have yet directly evaluated the effect of reducing iron intake or body iron levels on the risk of developing type 2 diabetes. Such studies are required to prove the causal relationship between moderate iron overload and diabetes risk.     

Nonparametric methods for identifying differentially expressed genes in microarray data

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.     

A proteomics approach for identifying osmotic-stress-related proteins in rice

Osmotic stress can endanger the survival of plants. To investigate the mechanisms of how plants respond to osmotic stress, rice protein profiles from mannitol-treated plants, were monitored using a proteomics approach. Two-week-old rice seedlings were treated with 400mM mannitol for 48h. After separation of proteins from the basal part of leaf sheaths by two-dimensional polyacrylamide gel electrophoresis, 327 proteins were detected. The levels of 12 proteins increased and the levels of three proteins decreased with increasing concentration or duration, of mannitol treatment. Levels of a heat shock protein and a dnaK-type molecular chaperone were reduced under osmotic, cold, salt and drought stresses, and ABA treatment, whereas a 26S proteasome regulatory subunit was found to be responsive only to osmotic stress. Furthermore, proteins whose accumulation was sensitive to osmotic stress are present in an osmotic-tolerant cultivar. These results indicate that specific proteins expressed in the basal part of rice leaf sheaths show a coordinated response to cope with osmotic stress.     

Analysis of N-glycan in serum glycoproteins from db/db mice and humans with type 2 diabetes

Glycosylation has an important role in regulating properties of proteins and is associated with many diseases. To examine the alteration of serum N-glycans in type 2 diabetes, we used the db/db mouse model. Serum N-glycans were fluorescence labeled and applied to HPLC. There were reproducible differences in N-glycan profiles between the db/db mouse model and the db/+ control. The structures of the oligosaccharides, which had changed in their amounts, were analyzed by a two-dimensional mapping method, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry, and exoglycosidase digestion. Those analyses revealed an increase in the N-glycans possessing alpha1,6-fucose in the serum of db/db mice. The level of alpha1,6-fucosyltransferase mRNA was increased in the liver of the db/db mice. The ratio of a biantennary N-glycan with alpha1,6-fucose to that without alpha1,6-fucose in the liver tissue of the db/db mouse was increased relative to the db/+ control. Next, we analyzed the serum N-glycan profile in human subjects with type 2 diabetes and found an increased amount of a biantennary N-glycan that had an alpha1,6-fucose with a bisecting N-acetylglucosamine. In conclusion, the increase in alpha1,6-fucosylation is a striking change in the serum N-glycans of the db/db mice, whereas the change in the fucosylation in humans with type 2 diabetes was small, albeit statistically significant. It is likely that the change is caused, at least partially, by the increase in the alpha1,6-fucosyltransferase mRNA level in the liver. The increased alpha1,6-fucosylation may affect protein properties associated with the pathophysiology of type 2 diabetes.     

Identification of differentially expressed proteins in Ficedula flycatchers

This is the first report on the large-scale identification and comparison of proteins in non-model organisms, Ficedula flycatchers. It highlights the potential of proteomics approaches in both non-sequenced and non-model organisms for identification of differentially expressed proteins. Not surprisingly, more than 55% of the proteins failed to be identified even though the MS spectra were of high quality. Nevertheless, the protein information obtained in this study will serve as a valuable resource for continued research.     

Quantitative proteomics analysis reveals that proteins differentially expressed in chronic pancreatitis are also frequently involved in pancreatic cancer

The effective treatment of pancreatic cancer relies on the diagnosis of the disease at an early stage, a difficult challenge. One major obstacle in the development of diagnostic biomarkers of early pancreatic cancer has been the dual expression of potential biomarkers in both chronic pancreatitis and cancer. To better understand the limitations of potential protein biomarkers, we used ICAT technology and tandem mass spectrometry-based proteomics to systematically study protein expression in chronic pancreatitis. Among the 116 differentially expressed proteins identified in chronic pancreatitis, most biological processes were responses to wounding and inflammation, a finding consistent with the underlining inflammation and tissue repair associated with chronic pancreatitis. Furthermore 40% of the differentially expressed proteins identified in chronic pancreatitis have been implicated previously in pancreatic cancer, suggesting some commonality in protein expression between these two diseases. Biological network analysis further identified c-MYC as a common prominent regulatory protein in pancreatic cancer and chronic pancreatitis. Lastly five proteins were selected for validation by Western blot and immunohistochemistry. Annexin A2 and insulin-like growth factor-binding protein 2 were overexpressed in cancer but not in chronic pancreatitis, making them promising biomarker candidates for pancreatic cancer. In addition, our study validated that cathepsin D, integrin beta1, and plasminogen were overexpressed in both pancreatic cancer and chronic pancreatitis. The positive involvement of these proteins in chronic pancreatitis and pancreatic cancer will potentially lower the specificity of these proteins as biomarker candidates for pancreatic cancer. Altogether our study provides some insights into the molecular events in chronic pancreatitis that may lead to diverse strategies for diagnosis and treatment of these diseases.     

Proteomic analysis of differentially expressed skin proteins in iRhom2Uncv mice

A mouse homozygous for the spontaneous mutation uncovered (Uncv) has a hairless phenotype. A 309-bp non-frameshift deletion mutation in the N-terminal cytoplasmic domain of iRhom2 was identified in Uncv mice (iRhom2^Uncv) using target region sequencing. The detailed molecular basis for how the iRhom2 mutation causes the hairless phenotype observed in the homozygous iRhom2^Uncv mouse remains unknown. To identify differentially expressed proteins in the skin of wild-type and homozygous iRhom2^Uncv littermates at postnatal day 5, proteomic approaches, including two-dimensional gel electrophoresis and mass spectrometry were used. Twelve proteins were differentially expressed in the skin in a comparison between wild-type and homozygous iRhom2^Uncv mice. A selection of the proteomic results were tested and verified using qRT-PCR, western blot and immunohistochemistry. These data indicate that differentially expressed proteins, especially KRT73, MEMO1 and Coro-1, might participate in the mechanism by which iRhom2 regulates the development of murine skin. [BMB Reports 2015; 48(1): 19-24]     

A proteomic approach to identifying proteins differentially expressed in conidia and mycelium of the entomopathogenic fungus Metarhizium acridum

Metarhizium spp. is an important worldwide group of entomopathogenic fungi used as an interesting alternative to chemical insecticides in programs of agricultural pest and disease vector control. Metarhizium conidia are important in fungal propagation and also are responsible for host infection. Despite their importance, several aspects of conidial biology, including their proteome, are still unknown. We have established conidial and mycelial proteome reference maps for Metarhizium acridum using two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF MS). In all, 1130±102 and 1200±97 protein spots were detected in ungerminated conidia and fast-growing mycelia, respectively. Comparison of the two protein-expression profiles reveled that only 35% of the protein spots were common to both developmental stages. Out of 94 2-DE protein spots (65 from conidia, 25 from mycelia and two common to both) analyzed using mass spectrometry, seven proteins from conidia, 15 from mycelia and one common to both stages were identified. The identified protein spots exclusive to conidia contained sequences similar to known fungal stress-protector proteins (such as heat shock proteins (HSP) and 6-phosphogluconate dehydrogenase) plus the fungal allergen Alt a 7, actin and the enzyme cobalamin-independent methionine synthase. The identified protein spots exclusive to mycelia included proteins involved in several cell housekeeping biological processes. Three proteins (HSP 90, 6-phosphogluconate dehydrogenase and allergen Alt a 7) were present in spots in conidial and mycelial gels, but they differed in their locations on the two gels.     

Sample size for identifying differentially expressed genes in microarray experiments.

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.