Distinct Protein Expression Profiles Research Articles

Neuromancer1 and Neuromancer2 regulate cell fate specification in the developing embryonic CNS of Drosophila melanogaster

T-box genes encode a large family of transcription factors that regulate many developmental processes in vertebrates and invertebrates. In addition to their roles in regulating embryonic heart and epidermal development in Drosophila, we provide evidence that the T-box transcription factors neuromancer1 (nmr1) and neuromancer2 (nmr2) play key roles in embryonic CNS development. We verify that nmr1 and nmr2 function in a partially redundant manner to regulate neuronal cell fate by inhibiting even-skipped (eve) expression in specific cells in the CNS. Consistent with their redundant function, nmr1 and nmr2 exhibit overlapping yet distinct protein expression profiles within the CNS. Of note, nmr2 transcript and protein are expressed in identical patterns of segment polarity stripes, defined sets of neuroblasts, many ganglion mother cells and discrete populations of neurons. However, while we observe nmr1 transcripts in segment polarity stripes and specific neural precursors in early stages of CNS development, we first detect Nmr1 protein in later stages of CNS development where it is restricted to discrete subsets of Nmr2-positive neurons. Expression studies identify nearly all Nmr1/2 co-expressing neurons as interneurons, while a single Eve-positive U/CQ motor neuron weakly co-expresses Nmr2. Lineage studies map a subset of Nmr1/2-positive neurons to neuroblast lineages 2–2, 6–1, and 6–2 while genetic studies reveal that nmr2 collaborates with nkx6 to regulate eve expression in the CNS. Thus, nmr1 and nmr2 appear to act together as members of the combinatorial code of transcription factors that govern neuronal subtype identity in the CNS.

Proteomic profiling of human liver biopsies: Hepatitis C virus–induced fibrosis and mitochondrial dysfunction

Liver biopsies from hepatitis C virus (HCV)-infected patients offer the unique opportunity to study human liver biology and disease in vivo. However, the low protein yields associated with these small samples present a significant challenge for proteomic analysis. In this study we describe the application of an ultrasensitive proteomics platform for performing robust quantitative proteomic studies on microgram amounts of HCV-infected human liver tissue from 15 patients at different stages of fibrosis. A high-quality liver protein database containing 5,920 unique protein identifications supported high throughput quantitative studies using (16)O/(18)O stable isotope labeling in combination with the accurate mass and time (AMT) tag approach. A total of 1,641 liver biopsy proteins were quantified, and analysis of variance (ANOVA) identified 210 proteins exhibiting statistically significant differences associated with fibrosis stage. Hierarchical clustering showed that biopsies representative of later fibrosis stages (for example, Batts-Ludwig stages 3-4) exhibited a distinct protein expression profile, indicating an apparent down-regulation of many proteins when compared with samples from earlier fibrosis stages (for example, Batts-Ludwig stages 0-2). Functional analysis of these signature proteins suggests that impairment of key mitochondrial processes including fatty acid oxidation and oxidative phosphorylation, and response to oxidative stress and reactive oxygen species occurs during advanced stage 3 to 4 fibrosis. The results reported here represent a significant advancement in clinical proteomics providing to our knowledge, the first demonstration of global proteomic alterations accompanying liver disease progression in patients chronically infected with HCV. Our findings contribute to a generally emerging theme associating oxidative stress and hepatic mitochondrial dysfunction with HCV pathogenesis.

Protein Expression Patterns Associated with Progression of Chronic Obstructive Pulmonary Disease in Bronchoalveolar Lavage of Smokers

We modeled the expression of proteins in baseline bronchoalveolar lavage (BAL) samples from asymptomatic 60-year-old lifelong current smokers or healthy never-smokers, who were reevaluated after 6 to 7 years to record clinical outcome. Applying a technology toolbox consisting of replicate 2-dimensional gel separations, image annotation, and mass spectrometry identification, we catalogued a global set of proteins that were differentially expressed in individuals by presence, absence, and intensity scores. By use of multivariate analysis, we selected a subset of proteins that accurately separated smokers from never-smokers based on composite scoring. Follow-up after 6 to 7 years identified a group of individuals who had progressed to chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease stage 2. The baseline BAL samples of these eventual COPD patients shared a distinct protein expression profile that could be identified using partial least-squares discriminant analysis. This pattern was not observed in BAL samples of asymptomatic smokers free of COPD at 6- to 7-year follow-up. Our model suggests that certain patterns of protein expression occurring in the airways of long-term smokers may be detected in smokers susceptible to a progression of COPD disease, before disease is clinically evident.

Identification of proteins whose expression is up- or down-regulated in the mushroom bodies in the honeybee brain using proteomics

To identify protein(s) with different expression patterns in the mushroom bodies (MBs) in the honeybee brain, we compared the protein profiles of MBs and optic lobes (OLs) using proteomics. Two-dimensional gel electrophoresis revealed that five and three spots were selectively expressed in the MBs or OLs, respectively. Liquid chromatography tandem mass spectrometry analysis identified juvenile hormone diol kinase and glyceraldehyde-3-phosphate dehydrogenase as MB- and OL-selective proteins, respectively. In situ hybridization revealed that jhdk expression was upregulated in MB neuron subsets, whereas gapdh expression was downregulated, indicating that MBs have a distinct gene and protein expression profile in the honeybee brain.

Oropharyngeal squamous cell cancers (OSCC) bearing transcriptionally active human papillomavirus (HPV) display distinct protein expression profile

10028 Background: It is known that among HPV DNA+ OSCCs only the transcriptionally active ones are biologically and clinically relevant. pRb downregulation by HPVE7 results in p16 upregulation. We previously showed (JCO, Feb 2006) that p16 expression defines transcriptionally active HPV+ tumors. Thus, OSCC tumors can be Class I: HPV−/p16−, Class II: HPV+/p16−, or Class III: HPV+/p16+. We hypothesized that tumors classified by these criteria would show different protein expression profiles. Methods: Following institutional review board approval, paraffin-embedded specimens from 77 patients were collected from Yale-New Haven Hospital archives. Patients with OSCC treated with radiotherapy (RT) or surgery and RT were eligible. We used real-time polymerase chain reaction for HPV 16 and immunohistochemistry for p16 protein expression to classify 77 patients with OSCC. Tumors were classified into a 3-class model based on p16 expression and HPV DNA presence: Class I (HPV−, p16−) Class II (HPV+, p16−) and Class III (HPV+, p16+). A tissue microarray including these cases was constructed and examined for 13 target proteins with known involvement in tumor suppression or progression. We used a method of in-situ automated quantitative protein expression analysis (AQUA). Protein signal (AQUA score) was scored on a scale of 0–255.Protein expression between groups was analyzed by analysis of variance (ANOVA). Results: Thirty tumors were class I (39%), 29 were class II (38%), and 18 were class III (23%). There were significant differences for protein expression of Met (p=0.005), β-catenin (p=0.009), pRb (p=0.001), p53(p=0.026), epidermal growth factor receptor (EGFR) (p=0.009) and Vascular Endothelial Growth Factor (VEGF) (p=0.028). There was no difference for ki-67, COX2, p27, p21 or Cyclin D1 expression, while ERK2 and p14 trended towards significance (p=0.09 and 0.054, respectively). Conclusions: We demonstrated that tumors classified by HPV DNA presence and p16 expression status have different molecular phenotypes. Of particular note, class III tumors demonstrated increased expression of Met, β-catenin, EGFR and VEGF. These findings may prove useful for patient selection for molecular-targeted therapies. [Table: see text]

Pathology of Liver Metastases

The liver is the most frequent site of metastatic disease, and metastatic disease to the liver is far more common than primary liver carcinoma in the United States. Pathologic evaluation of biopsy samples is key to establishing a correct diagnosis for patient management. Morphologic and immunoperoxidase studies, which are the standard for pathologic practice, accurately classify most tumors. Subclassification of carcinoma of unknown primary remains problematic. The author reviewed the literature for articles pertaining to liver biopsy, diagnosis of specific tumor types, utility of immunohistochemical markers, and microarray and proteomic analysis. Sampling of liver lesions is best accomplished by combining fine-needle aspiration and needle core biopsy. Many malignancies have distinct morphologic and immunohistochemical patterns and can be correctly subclassified. Adenocarcinoma of unknown primary remains enigmatic since current immunohistochemical markers for this differential diagnosis lack specificity. Microarray analysis and proteomic analysis of tumors can provide distinct gene or protein expression profiles, respectively, for tumor classification. These technologies can be used with fine-needle aspiration and needle core biopsy samples. Most metastatic malignancies in the liver may be correctly diagnosed using standard morphology and immunohistochemical techniques. However, subtyping of some carcinomas and identification of site of unknown primary remains problematic. New technologies may help to further refine our diagnostic capabilities.

ICAT-based comparative proteomic analysis of non-replicating persistent Mycobacterium tuberculosis

The non-replicating persistence (NRP) phenotype of Mycobacterium tuberculosis (NRP-TB) is assumed to be responsible for the maintenance of latent infection and the requirement of a long treatment duration for active tuberculosis. Isotope coded affinity tag-based proteomic analysis was used for the determination of the relative expression of large numbers of M. tuberculosis proteins during oxygen self-depletion under controlled conditions in a multi-chambered fermentor. Expression of the alpha-crystallin homolog protein, acr, was monitored and quantified to confirm entry into NRP. Relative expression of 586 and 628 proteins was determined in log phase vs. early stage NRP (NRP-1) and log phase vs. later stage NRP (NRP-2), respectively. Relative to expression in log phase and using an abundance ratio of +/-2.0 as a cutoff, 6.5% and 20.4% of proteins were found to be upregulated in NRP-1 and NRP-2, respectively while 20.3% and 13.4% were downregulated, respectively. Functional profiling revealed that 42.1%/39.8% of upregulated proteins and 41.2%/45.2% of downregulated proteins in NRP-1/NRP-2, respectively, were involved in small molecule metabolism. Among those proteins the highest proportions of 37.5% in NRP-1 were involved with degradation and of 45.1% in NRP-2 with energy metabolism. These results suggest distinct protein expression profiles in NRP-1 and NRP-2.

Two-dimensional electrophoretic profiling of normal human kidney: differential protein expression in glomerulus, cortex and medulla

The diverse functions of the kidney are fundamentally fulfilled by the nephron, the functional unit of the kidney. The nephron consists of several functionally and structurally different segments, which spans the kidney from the cortex through the medulla. The functional specialization along the nephron and specific localization of the different segments should be reflected in the protein expression profiles of distinct anatomical compartments of the kidney. We performed a two-dimensional gel electrophoresis (2-DE)-based, quantitative, proteomic analysis to identify proteins differentially expressed in the cortex, medulla, and glomeruli purified from cortex of normal human kidney. The protein extracts of the three compartments obtained from 4 normal subjects were separated on 2-DE gels, and analyzed by using a 2-DE gel image analysis software. We detected 1,810 valid protein spots in the glomerulus, 1,758 in the cortex, and 1,275 spots in the medulla. Statistical analysis by one-way ANOVA followed by Tukey's post-hoc test revealed that 616 protein spots were differentially expressed in the glomeruli, 582 in the cortex, and 469 in the medulla. Among these protein spots, spots expressing in considerably higher relative amounts in one of the three compartments were selected and subjected to identification by peptide mass fingerprinting method with a MALDI-TOF mass spectrometer, which led to identification 19 protein spots preferentially expressed in the glomerulus, 35 in the cortex, and 21 in the medulla. The distinct protein expression profiles in different kidney compartments may serve as a resource for further study of physiology and pathophysiology of the human kidney.

ANALYSIS OF PROSTATE CANCER BY PROTEOMICS USING TISSUE SPECIMENS

Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.

Epstein-barr virus-positive gastric carcinoma has a distinct protein expression profile in comparison with epstein-barr virus-negative carcinoma.

EBV has been detected in 2-16% of gastric carcinomas. However, there is little information available about the gene expression profile of EBV-positive gastric carcinomas. EBV infection was examined using EBV-encoded small RNAs (EBERs) in situ hybridization, and 63 (5.6%) of 1127 consecutive gastric carcinomas were found to be EBV-positive. The expressions of 27 tumor-associated proteins were evaluated immunohistochemically in 63 EBV-positive gastric carcinomas and 287 EBV-negative carcinomas using the tissue array method. In addition, the genotype of EBV was investigated by PCR amplification of LMP1 (latent membrane protein 1), Epstein-Barr nuclear antigen 2 (EBNA2), and EBNA3B genes. EBV-positive gastric carcinomas are characterized by the presence of lymphoid stroma, proximal location, and predominance in males. In comparison with EBV-negative carcinomas, EBV-positive carcinomas showed frequent loss of expression of p16, smad4, FHIT, and KAI-1 (kangai 1; P < 0.05), but retained the expression of APC (adenomatous polyposis coli), DCC (deleted in colorectal cancer), and some DNA repair proteins (P < 0.05). There was negative association between EBV infection and the expression of MUC1, MUC2, MUC5AC, p53, CEA, C-erbB2, and smad7. Using hierarchical cluster analysis, we divided EBV-positive gastric carcinomas into two clusters. Those patients with cluster 1 (42 cases) carcinomas had a better prognosis than those with cluster 2 (12 cases; P = 0.0002) or those with EBV-negative carcinomas (280 cases; P = 0.0251). Fifty-one (92.7%) of 55 EBV-positive carcinomas demonstrated the 30-bp deletion in LMP1 gene, and 53 (96.4%) of 55 cases were type 1 for EBNA2 and EBNA3B genes. EBV-positive gastric carcinomas have a distinct protein expression profile as well as distinct clinicopathological features, as compared with EBV-negative carcinomas. The subclassification of EBV-positive carcinomas, by hierarchical cluster analysis, is significantly associated with patient survival.

Ki67, gelsolin and PTEN expression in sarcomatoid renal tumors.

Sarcomatoid renal tumors differ morphologically and prognostically from other renal tumors. Using tissue microarray technology, we show that sarcomatoid renal tumors have a distinct protein expression profile for biomarkers Ki67, gelsolin and PTEN, when compared with clear-cell and papillary renal tumors. Our results confirm the previous reports that Ki67 is highly expressed in sarcomatoid tumors. We also show that gelsolin expression differs between the studied tumor types, suggesting different roles for gelsolin in the carcinogenesis of different renal tumor types.