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1.  H5N1 Virus Causes Significant Perturbations in Host Proteome Very Early in Influenza Virus-Infected Primary Human Monocyte-Derived Macrophages 
The Journal of Infectious Diseases  2012;206(5):640-645.
H5N1 influenza viruses, which cause disease in humans, have unusually high pathogenicity. The temporal response of primary human monocyte-derived macrophages infected with highly pathogenic H5N1 and seasonal H1N1 influenza viruses was evaluated using mass spectrometry–based quantitative proteomic profiling. This was done in order to demonstrate significant perturbation of the host proteome upon viral infection, as early as 1 hour after infection. This early host response distinguished H5N1 infection from H1N1 infection, the latter inducing less of a response. The most pronounced effect was observed on the translational machinery, suggesting that H5N1 might gain advantage in replication by using the cell protein synthesis machinery early in the infection.
doi:10.1093/infdis/jis423
PMCID: PMC3491741  PMID: 22822004
2.  Quantitative Proteomic Analysis of HIV-1 Infected CD4+ T Cells Reveals an Early Host Response in Important Biological Pathways: Protein Synthesis, Cell Proliferation, and T-cell Activation 
Virology  2012;429(1):37-46.
Human immunodeficiency virus (HIV-1) depends upon host-encoded proteins to facilitate its replication while at the same time inhibiting critical components of innate and/or intrinsic immune response pathways. To characterize the host cell response on protein levels in CD4+ lymphoblastoid SUP-T1 cells after infection with HIV-1 strain LAI, we used mass spectrometry (MS)-based global quantitation with iTRAQ (isobaric tag for relative and absolute quantification). We found 266, 60 and 22 proteins differentially expressed (DE) (P-value ≤ 0.05) at 4, 8, and 20 hours post-infection (hpi), respectively, compared to time-matched mock-infected samples. The majority of changes in protein abundance occurred at an early stage of infection well before the de novo production of viral proteins. Functional analyses of these DE proteins showed enrichment in several biological pathways including protein synthesis, cell proliferation, and T-cell activation. Importantly, these early changes before the time of robust viral production have not been described before.
doi:10.1016/j.virol.2012.03.026
PMCID: PMC3358407  PMID: 22542004
Protein quantification; iTRAQ; HIV-1; T-cells; LC-MS/MS; Ribosomal proteins
3.  Early transcriptional programming links progression to hepatitis C virus-induced severe liver disease in transplant patients 
Hepatology (Baltimore, Md.)  2012;56(1):17-27.
Liver failure due to chronic hepatitis C virus infection is a major cause for liver transplantation worldwide. Recurrent infection of the graft is universal in HCV patients following transplant and results in rapid progression to severe fibrosis and end-stage liver disease in one-third of all patients. No single clinical variable, or combination thereof, has so far proven accurate in identifying patients at risk of hepatic decompensation in the transplant setting. A combination of longitudinal, dimensionality reduction, and categorical analysis of the transcriptome from 111 liver biopsy specimens taken from 57 HCV-infected patients over time identified a molecular signature of gene expression of patients at risk of developing severe fibrosis. Significantly, alterations in gene expression occur prior to histologic evidence of liver disease progression, suggesting that events which occur during the acute phase of infection influence patient outcome. Additionally, a common precursor state for different severe clinical outcomes was identified. Hence, based on this patient cohort, incidence of severe liver disease is a process initiated early during HCV infection of the donor organ. The probable cellular network at the basis of the initial transition to severe liver disease was identified and characterized.
doi:10.1002/hep.25612
PMCID: PMC3349763  PMID: 22278598
viral hepatitis; microarray; hepatic fibrosis
4.  Proteome and Computational Analyses Reveal New Insights into the Mechanisms of Hepatitis C Virus Mediated Liver Disease Post-Transplantation 
Hepatology (Baltimore, Md.)  2012;56(1):28-38.
Liver transplant tissues offer the unique opportunity to model the longitudinal protein abundance changes occurring during hepatitis C virus (HCV)-associated liver disease progression in vivo. In this study, our goal was to identify molecular signatures, and potential key regulatory proteins, representative of the processes influencing early progression to fibrosis. We performed global protein profiling analyses on 24 liver biopsy specimens obtained from 15 HCV+ liver transplant recipients at 6 and/or 12 months post-transplantation. Differentially regulated proteins associated with early progression to fibrosis were identified by analysis of the area under the receiver operating characteristic curve (AUC). Analysis of serum metabolites was performed on samples obtained from an independent cohort of 60 HCV+ liver transplant patients. Computational modeling approaches were applied to identify potential key regulatory proteins of liver fibrogenesis. Among 4,324 proteins identified, 250 exhibited significant differential regulation in patients with rapidly progressive fibrosis. Patients with rapid fibrosis progression exhibited enrichment in differentially regulated proteins associated with various immune, hepatoprotective, and fibrogenic processes. The observed increase in pro-inflammatory activity and impairment in anti-oxidant defenses suggests that patients who develop significant liver injury experience elevated oxidative stresses. This was supported by an independent study demonstrating the altered abundance of oxidative stress associated serum metabolites in patients who develop severe liver injury. Computational modeling approaches further highlight a potentially important link between HCV-associated oxidative stress and epigenetic regulatory mechanisms impacting on liver fibrogenesis. In conclusion, our proteome and metabolome analyses provide new insights into the role for increased oxidative stress in the rapid fibrosis progression observed in HCV+ liver transplant recipients. These findings may prove useful in prognostic applications for predicting early progression to fibrosis.
doi:10.1002/hep.25649
PMCID: PMC3387320  PMID: 22331615
liver biopsy; systems biology; protein bottleneck
5.  Proteome and Transcriptome Profiles of a Her2/Neu-driven Mouse Model of Breast Cancer 
Proteomics. Clinical applications  2011;5(3-4):179-188.
Purpose
We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens.
Experimental design
Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias.
Results
In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue.
Conclusions and clinical relevance
These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
doi:10.1002/prca.201000037
PMCID: PMC3069718  PMID: 21448875
Breast cancer; Her2; mouse; proteome; transcriptome
6.  Comprehensive Proteomic Analysis of Influenza Virus Polymerase Complex Reveals a Novel Association with Mitochondrial Proteins and RNA Polymerase Accessory Factors▿† 
Journal of Virology  2011;85(17):8569-8581.
The trimeric RNA polymerase complex (3P, for PA-PB1-PB2) of influenza A virus (IAV) is an important viral determinant of pathogenicity and host range restriction. Specific interactions of the polymerase complex with host proteins may be determining factors in both of these characteristics and play important roles in the viral life cycle. To investigate this question, we performed a comprehensive proteomic analysis of human host proteins associated with the polymerase of the well-characterized H5N1 Vietnam/1203/04 isolate. We identified over 400 proteins by liquid chromatography-tandem mass spectrometry (LC-MS/MS), of which over 300 were found to bind to the PA subunit alone. The most intriguing and novel finding was the large number of mitochondrial proteins (∼20%) that associated with the PA subunit. These proteins mediate molecular transport across the mitochondrial membrane or regulate membrane potential and may in concert with the identified mitochondrion-associated apoptosis inducing factor (AIFM1) have roles in the induction of apoptosis upon association with PA. Additionally, we identified host factors that associated with the PA-PB1 (68 proteins) and/or the 3P complex (34 proteins) including proteins that have roles in innate antiviral signaling (e.g., ZAPS or HaxI) or are cellular RNA polymerase accessory factors (e.g., polymerase I transcript release factor [PTRF] or Supt5H). IAV strain-specific host factor binding to the polymerase was not observed in our analysis. Overall, this study has shed light into the complex contributions of the IAV polymerase to host cell pathogenicity and allows for direct investigations into the biological significance of these newly described interactions.
doi:10.1128/JVI.00496-11
PMCID: PMC3165779  PMID: 21715506
7.  A targeted proteomics–based pipeline for verification of biomarkers in plasma 
Nature biotechnology  2011;29(7):625-634.
High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
doi:10.1038/nbt.1900
PMCID: PMC3232032  PMID: 21685906
8.  Occurrence of Autoantibodies to Annexin I, 14-3-3 Theta and LAMR1 in Prediagnostic Lung Cancer Sera 
Journal of Clinical Oncology  2008;26(31):5060-5066.
Purpose
We have implemented a high throughput platform for quantitative analysis of serum autoantibodies, which we have applied to lung cancer for discovery of novel antigens and for validation in prediagnostic sera of autoantibodies to antigens previously defined based on analysis of sera collected at the time of diagnosis.
Materials and Methods
Proteins from human lung adenocarcinoma cell line A549 lysates were subjected to extensive fractionation. The resulting 1,824 fractions were spotted in duplicate on nitrocellulose-coated slides. The microarrays produced were used in a blinded validation study to determine whether annexin I, PGP9.5, and 14-3-3 theta antigens previously found to be targets of autoantibodies in newly diagnosed patients with lung cancer are associated with autoantibodies in sera collected at the presymptomatic stage and to determine whether additional antigens may be identified in prediagnostic sera. Individual sera collected from 85 patients within 1 year before a diagnosis of lung cancer and 85 matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were hybridized to individual microarrays.
Results
We present evidence for the occurrence in lung cancer sera of autoantibodies to annexin I, 14-3-3 theta, and a novel lung cancer antigen, LAMR1, which precede onset of symptoms and diagnosis.
Conclusion
Our findings suggest potential utility of an approach to diagnosis of lung cancer before onset of symptoms that includes screening for autoantibodies to defined antigens.
doi:10.1200/JCO.2008.16.2388
PMCID: PMC2652098  PMID: 18794547
9.  A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development 
PLoS Medicine  2008;5(6):e123.
Background
The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.
Methods and Findings
Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer.
Conclusions
Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
Samir Hanash and colleagues identify proteins that are increased at an early stage of pancreatic tumor development in a mouse model and may be a useful tool in detecting early tumors in humans.
Editors' Summary
Background.
Cancers are life-threatening, disorganized masses of cells that can occur anywhere in the human body. They develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer is detected when it is still small and has not metastasized, surgery can often provide a cure. Unfortunately, many cancers are detected only when they are large enough to press against surrounding tissues and cause pain or other symptoms. By this time, surgical removal of the original (primary) tumor may be impossible and there may be secondary cancers scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. One cancer type for which late detection is a particular problem is pancreatic adenocarcinoma. This cancer rarely causes any symptoms in its early stages. Furthermore, the symptoms it eventually causes—jaundice, abdominal and back pain, and weight loss—are seen in many other illnesses. Consequently, pancreatic cancer has usually spread before it is diagnosed, and most patients die within a year of their diagnosis.
Why Was This Study Done?
If a test could be developed to detect pancreatic cancer in its early stages, the lives of many patients might be extended. Tumors often release specific proteins—“cancer biomarkers”—into the blood, a bodily fluid that can be easily sampled. If a protein released into the blood by pancreatic cancer cells could be identified, it might be possible to develop a noninvasive screening test for this deadly cancer. In this study, the researchers use a “proteomic” approach to identify potential biomarkers for early pancreatic cancer. Proteomics is the study of the patterns of proteins made by an organism, tissue, or cell and of the changes in these patterns that are associated with various diseases.
What Did the Researchers Do and Find?
The researchers started their search for pancreatic cancer biomarkers by studying the plasma proteome (the proteins in the fluid portion of blood) of mice genetically engineered to develop cancers that closely resemble human pancreatic tumors. Through the use of two techniques called high-resolution mass spectrometry and acrylamide isotopic labeling, the researchers identified 165 proteins that were present in larger amounts in plasma collected from mice with early and/or advanced pancreatic cancer than in plasma from control mice. Then, to test whether any of these protein changes were relevant to human pancreatic cancer, the researchers analyzed blood samples collected from patients with pancreatic cancer. These samples, they report, contained larger amounts of some of these proteins than blood collected from patients with chronic pancreatitis, a condition that has similar symptoms to pancreatic cancer. Finally, using blood samples collected during a clinical trial, the Carotene and Retinol Efficacy Trial (a cancer-prevention study), the researchers showed that the measurement of five of the proteins present in increased amounts at an early stage of tumor development in the mouse model discriminated between people with pancreatic cancer and matched controls up to 13 months before cancer diagnosis.
What Do These Findings Mean?
These findings suggest that in-depth proteomic analysis of genetically engineered mouse models of human cancer might be an effective way to identify biomarkers suitable for the early detection of human cancers. Previous attempts to identify such biomarkers using human samples have been hampered by the many noncancer-related differences in plasma proteins that exist between individuals and by problems in obtaining samples from patients with early cancer. The use of a mouse model of human cancer, these findings indicate, can circumvent both of these problems. More specifically, these findings identify a panel of proteins that might allow earlier detection of pancreatic cancer and that might, therefore, extend the life of some patients who develop this cancer. However, before a routine screening test becomes available, additional markers will need to be identified and extensive validation studies in larger groups of patients will have to be completed.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050123.
The MedlinePlus Encyclopedia has a page on pancreatic cancer (in English and Spanish). Links to further information are provided by MedlinePlus
The US National Cancer Institute has information about pancreatic cancer for patients and health professionals (in English and Spanish)
The UK charity Cancerbackup also provides information for patients about pancreatic cancer
The Clinical Proteomic Technologies for Cancer Initiative (a US National Cancer Institute initiative) provides a tutorial about proteomics and cancer and information on the Mouse Proteomic Technologies Initiative
doi:10.1371/journal.pmed.0050123
PMCID: PMC2504036  PMID: 18547137
10.  Prediction of Clinical Outcome Using Gene Expression Profiling and Artificial Neural Networks for Patients with Neuroblastoma 
Cancer research  2004;64(19):6883-6891.
Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children’s Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children’s Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P = 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.
doi:10.1158/0008-5472.CAN-04-0695
PMCID: PMC1298184  PMID: 15466177
11.  cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCN-amplification in neuroblastoma 
BMC Genomics  2004;5:70.
Background
Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression.
Results
We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the MYCN amplified samples. There were 6 independent non-contiguous amplicons (10.4–69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by NAG and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without MYCN amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype.
Conclusions
cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and MYCN amplification.
doi:10.1186/1471-2164-5-70
PMCID: PMC520814  PMID: 15380028

Results 1-11 (11)