Dramatic down-regulation of oxidoreductases in human hepatocellular carcinoma hepG2 cells: proteomics and gene ontology unveiling new frontiers in cancer enzymology

Background

Oxidoreductases are enzymes that catalyze many redox reactions in normal and neoplastic cells. Their actions include catalysis of the transformation of free, neutral oxygen gas into oxygen free radicals, superoxide, hydroperoxide, singlet oxygen and hydrogen peroxide. These activated forms of oxygen contribute to oxidative stress that modifies lipids, proteins, DNA and carbohydrates. On the other hand, oxidoreductases constitute one of the most important free radical scavenger systems typified by catalase, superoxide dismutase and glutathione peroxidase. In this work, proteomics, Gene Ontology mapping and Directed Acyclic Graphs (DAG) are employed to detect and quantify differential oxidoreductase enzyme expressions between HepG2 cells and normal human liver tissues.

Results

For the set of bioinformatics calculations whose BLAST searches are performed using the BLAST program BLASTP 2.2.13 [Nov-27-2005], DAG of the Gene Ontology's Molecular Function annotations show that oxidoreductase activity parent node of the liver proteome contains 331 annotated protein sequences, 7 child nodes and an annotation score of 188.9, whereas that of HepG2 cells has 188 annotated protein sequences, 3 child nodes and an annotation score of only 91.9. Overwhelming preponderance of oxidoreductases in the liver is additionally supported by the isomerase DAGs: nearly all the reactions described in the normal liver isomerase DAG are oxidoreductase isomerization reactions, whereas only one of the three child nodes in the HepG2 isomerase DAG is oxidoreductase. Upon normalization of the annotation scores to the parent Molecular Function nodes, oxidoreductases are down-regulated in HepG2 cells by 58%. Similarly, for the set of bioinformatics calculations whose BLAST searches are carried out using BLASTP 2.2.15 [Oct-15-2006], oxidoreductases are down-regulated in HepG2 cells by 56%.

Conclusion

Proteomics and Gene Ontology reveal, for the first time, differential enzyme activities between HepG2 cells and normal human liver tissues, which may be a promising new prognostic marker of Hepatocellular carcinoma. Two independent sets of bioinformatics calculations that employ two BLAST program versions, and searched different databases, arrived at essentially the same conclusion: oxidoreductases are down-regulated in HepG2 cells by approximately 57%, when compared to normal human liver tissues. Down-regulation of oxidoreductases in hepatoma is additionally supported by Gene Ontology analysis of isomerises.     

Sample prep for proteomics of breast cancer: proteomics and gene ontology reveal dramatic differences in protein solubilization preferences of radioimmunoprecipitation assay and urea lysis buffers

Background

An important step in the proteomics of solid tumors, including breast cancer, consists of efficiently extracting most of proteins in the tumor specimen. For this purpose, Radio-Immunoprecipitation Assay (RIPA) buffer is widely employed. RIPA buffer's rapid and highly efficient cell lysis and good solubilization of a wide range of proteins is further augmented by its compatibility with protease and phosphatase inhibitors, ability to minimize non-specific protein binding leading to a lower background in immunoprecipitation, and its suitability for protein quantitation.

Results

In this work, the insoluble matter left after RIPA buffer extraction of proteins from breast tumors are subjected to another extraction step, using a urea-based buffer. It is shown that RIPA and urea lysis buffers fractionate breast tissue proteins primarily on the basis of molecular weights. The average molecular weight of proteins that dissolve exclusively in urea buffer is up to 60% higher than in RIPA. Gene Ontology (GO) and Directed Acyclic Graphs (DAG) are used to map the collective biological and biophysical attributes of the RIPA and urea proteomes. The Cellular Component and Molecular Function annotations reveal protein solubilization preferences of the buffers, especially the compartmentalization and functional distributions. It is shown that nearly all extracellular matrix proteins (ECM) in the breast tumors and matched normal tissues are found, nearly exclusively, in the urea fraction, while they are mostly insoluble in RIPA buffer. Additionally, it is demonstrated that cytoskeletal and extracellular region proteins are more soluble in urea than in RIPA, whereas for nuclear, cytoplasmic and mitochondrial proteins, RIPA buffer is preferred. Extracellular matrix proteins are highly implicated in cancer, including their proteinase-mediated degradation and remodelling, tumor development, progression, adhesion and metastasis. Thus, if they are not efficiently extracted by RIPA buffer, important information may be missed in cancer research.

Conclusion

For proteomics of solid tumors, a two-step extraction process is recommended. First, proteins in the tumor specimen should be extracted with RIPA buffer. Second, the RIPA-insoluble material should be extracted with the urea-based buffer employed in this work.     

A comparison of alternative methods to compute conditional genotype probabilities for genetic evaluation with finite locus models

An increased availability of genotypes at marker loci has prompted the development of models that include the effect of individual genes. Selection based on these models is known as marker-assisted selection (MAS). MAS is known to be efficient especially for traits that have low heritability and non-additive gene action. BLUP methodology under non-additive gene action is not feasible for large inbred or crossbred pedigrees. It is easy to incorporate non-additive gene action in a finite locus model. Under such a model, the unobservable genotypic values can be predicted using the conditional mean of the genotypic values given the data. To compute this conditional mean, conditional genotype probabilities must be computed. In this study these probabilities were computed using iterative peeling, and three Markov chain Monte Carlo (MCMC) methods – scalar Gibbs, blocking Gibbs, and a sampler that combines the Elston Stewart algorithm with iterative peeling (ESIP). The performance of these four methods was assessed using simulated data. For pedigrees with loops, iterative peeling fails to provide accurate genotype probability estimates for some pedigree members. Also, computing time is exponentially related to the number of loci in the model. For MCMC methods, a linear relationship can be maintained by sampling genotypes one locus at a time. Out of the three MCMC methods considered, ESIP, performed the best while scalar Gibbs performed the worst.     

Flying lemurs – The 'flying tree shrews'? Molecular cytogenetic evidence for a Scandentia-Dermoptera sister clade

Background  

Flying lemurs or Colugos (order Dermoptera) represent an ancient mammalian lineage that contains only two extant species. Although molecular evidence strongly supports that the orders Dermoptera, Scandentia, Lagomorpha, Rodentia and Primates form a superordinal clade called Supraprimates (or Euarchontoglires), the phylogenetic placement of Dermoptera within Supraprimates remains ambiguous.     

Ligand-dependent differences in estrogen receptor beta-interacting proteins identified in lung adenocarcinoma cells corresponds to estrogenic responses

Background

A recent epidemiological study demonstrated a reduced risk of lung cancer mortality in breast cancer patients using antiestrogens. These and other data implicate a role for estrogens in lung cancer, particularly nonsmall cell lung cancer (NSCLC). Approximately 61% of human NSCLC tumors express nuclear estrogen receptor β (ERβ); however, the role of ERβ and estrogens in NSCLC is likely to be multifactorial. Here we tested the hypothesis that proteins interacting with ERβ in human lung adenocarcinoma cells that respond proliferatively to estradiol (E₂) are distinct from those in non-E₂-responsive cells.

Methods

FLAG affinity purification of FLAG-ERβ-interacting proteins was used to isolate ERβ-interacting proteins in whole cell extracts from E₂ proliferative H1793 and non-E₂-proliferative A549 lung adenocarcinoma cell lines. Following trypsin digestion, proteins were identified using liquid chromatography electrospray ionization tandem mass spectrometry (LC-MS/MS). Proteomic data were analyzed using Ingenuity Pathway Analysis. Select results were confirmed by coimmunoprecipitation.

Results

LC-MS/MS identified 27 non-redundant ERβ-interacting proteins. ERβ-interacting proteins included hsp70, hsp60, vimentin, histones and calmodulin. Ingenuity Pathway Analysis of the ERβ-interacting proteins revealed differences in molecular and functional networks between H1793 and A549 lung adenocarcinoma cells. Coimmunoprecipitation experiments in these and other lung adenocarcinoma cells confirmed that ERβ and EGFR interact in a gender-dependent manner and in response to E₂ or EGF. BRCA1 interacted with ERβ in A549 cell lines and in human lung adenocarcinoma tumors, but not normal lung tissue.

Conclusion

Our results identify specific differences in ERβ-interacting proteins in lung adenocarcinoma cells corresponding to ligand-dependent differences in estrogenic responses.

     

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

Background

The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.

Methods

The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.

Results

Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.     

Apoptosis,cytokine and chemokine induction by non-structural 1 (NS1) proteins encoded by different influenza subtypes

Background

Influenza pandemic remains a serious threat to human health. Viruses of avian origin, H5N1, H7N7 and H9N2, have repeatedly crossed the species barrier to infect humans. Recently, a novel strain originated from swine has evolved to a pandemic. This study aims at improving our understanding on the pathogenic mechanism of influenza viruses, in particular the role of non-structural (NS1) protein in inducing pro-inflammatory and apoptotic responses.

Methods

Human lung epithelial cells (NCI-H292) was used as an in-vitro model to study cytokine/chemokine production and apoptosis induced by transfection of NS1 mRNA encoded by seven infleunza subtypes (seasonal and pandemic H1, H2, H3, H5, H7, and H9), respectively.

Results

The results showed that CXCL-10/IP10 was most prominently induced (> 1000 folds) and IL-6 was slightly induced (< 10 folds) by all subtypes. A subtype-dependent pattern was observed for CCL-2/MCP-1, CCL3/MIP-1α, CCL-5/RANTES and CXCL-9/MIG; where induction by H5N1 was much higher than all other subtypes examined. All subtypes induced a similar temporal profile of apoptosis following transfection. The level of apoptosis induced by H5N1 was remarkably higher than all others. The cytokine/chemokine and apoptosis inducing ability of the 2009 pandemic H1N1 was similar to previous seasonal strains.

Conclusions

In conclusion, the NS1 protein encoded by H5N1 carries a remarkably different property as compared to other avian and human subtypes, and is one of the keys to its high pathogenicity. NCI-H292 cells system proves to be a good in-vitro model to delineate the property of NS1 proteins.

     

BAC libraries construction from the ancestral diploid genomes of the allotetraploid cultivated peanut

Background

Cultivated peanut, Arachis hypogaea is an allotetraploid of recent origin, with an AABB genome. In common with many other polyploids, it seems that a severe genetic bottle-neck was imposed at the species origin, via hybridisation of two wild species and spontaneous chromosome duplication. Therefore, the study of the genome of peanut is hampered both by the crop's low genetic diversity and its polyploidy. In contrast to cultivated peanut, most wild Arachis species are diploid with high genetic diversity. The study of diploid Arachis genomes is therefore attractive, both to simplify the construction of genetic and physical maps, and for the isolation and characterization of wild alleles. The most probable wild ancestors of cultivated peanut are A. duranensis and A. ipaënsis with genome types AA and BB respectively.

Results

We constructed and characterized two large-insert libraries in Bacterial Artificial Chromosome (BAC) vector, one for each of the diploid ancestral species. The libraries (AA and BB) are respectively c. 7.4 and c. 5.3 genome equivalents with low organelle contamination and average insert sizes of 110 and 100 kb. Both libraries were used for the isolation of clones containing genetically mapped legume anchor markers (single copy genes), and resistance gene analogues.

Conclusion

These diploid BAC libraries are important tools for the isolation of wild alleles conferring resistances to biotic stresses, comparisons of orthologous regions of the AA and BB genomes with each other and with other legume species, and will facilitate the construction of a physical map.