Articles Online (Volume 8, Issue 3)

Review

Transcriptomics and Proteomics in the Study of H1N1 2009

Lijun Zhang, Xiaojun Zhang, Qing Ma, Fang Ma, Honghao Zhou

Influenza A virus (H1N1) 2009, a new swine-origin influenza A virus, has been spread worldwidely and caused great public fear. High-throughput transcriptomics and proteomics methods are now being used to identify H1N1 and H1N1-host interaction. This article reviews recent transcriptomics and proteomics research in H1N1 diagnosis, treatment, and H1N1 virus-host interaction, to offer some help for further understanding the infection mechanism and controlling H1N1 transmission.

Page 139–144


Article

Proteomic Analysis of Bovine Nucleolus

Amrutlal K. Patel, Doug Olson, Suresh K. Tikoo

Nucleolus is the most prominent subnuclear structure, which performs a wide variety of functions in the eukaryotic cellular processes. In order to understand the structural and functional role of the nucleoli in bovine cells, we analyzed the proteomic composition of the bovine nucleoli. The nucleoli were isolated from Madin Darby bovine kidney cells and subjected to proteomic analysis by LC-MS/MS after fractionation by SDS-PAGE and strong cation exchange chromatography. Analysis of the data using the Mascot database search and the GPM database search identified 311 proteins in the bovine nucleoli, which contained 22 proteins previously not identified in the proteomic analysis of human nucleoli. Analysis of the identified proteins using the GoMiner software suggested that the bovine nucleoli contained proteins involved in ribosomal biogenesis, cell cycle control, transcriptional, translational and post-translational regulation, transport, and structural organization.

Page 145–158


Review Article

A Novel Interpretation of Structural Dot Plots of Genomes Derived from the Analysis of Two Strains of Neisseria meningitidis

Wilfred R. Cuff, Venkata R.S.K. Duvvuri, Binhua Liang, Bhargavi Duvvuri, Gillian E. Wu, Jianhong Wu, Raymond S.W. Tsang

Neisseria meningitidis is the agent of invasive meningococcal disease, including cerebral meningitis and septicemia. Because the diseases caused by different clonal groups (sequence types) have their own epidemiological characteristics, it is important to understand the differences among the genomes of the N. meningitidis clonal groups. To this end, a novel interpretation of a structural dot plot of genomes was devised and applied; exact nucleotide matches between the genomes of N. meningitidis serogroup A strain Z2491 and serogroup B strain MC58 were identified, leading to the specification of various structural regions. Known and putative virulence genes for each N. meningitidis strain were then classified into these regions. We found that virulence genes of MC58 tend more to the translocated regions (chromosomal segments in new sequence contexts) than do those of Z2491, notably tending towards the interface between one of the translocated regions and the collinear region. Within the col-linear region, virulence genes tend to occur within 16 kb of gaps in the exact matches. Verification of these tendencies using genes clustered in the cps locus was sufficiently supportive to suggest that these tendencies can be used to focus the search for and understanding of virulence genes and mechanisms of pathogenicity in these two organisms.

Page 159–169


Review Article

Regulation of U6 Promoter Activity by Transcriptional Interference in Viral Vector-Based RNAi

Linghu Nie, Meghna Das Thakur, Yumei Wang, Qin Su, Yongliang Zhao, Yunfeng Feng

The direct negative impact of the transcriptional activity of one component on the second one in cis is referred to as transcriptional interference (TI). U6 is a type III RNA polymerase III promoter commonly used for driving small hairpin RNA (shRNA) expression in vector-based RNAi. In the design and construction of viral vectors, multiple transcription units may be arranged in close proximity in a space-limited vector. Determining if U6 promoter activity can be affected by TI is critical for the expression of target shRNA in gene therapy or loss-of-function studies. In this research, we designed and implemented a modified retroviral system where shRNA and exogenous gene expressions were driven by two independent transcriptional units. We arranged U6 promoter driving shRNA expression and UbiC promoter in two promoter arrangements. In primary macrophages, we found U6 promoter activity was inhibited by UbiC promoter when in the divergent arrangement but not in tandem. In contrast, PKG promoter had no such negative impact. Instead of enhancing U6 promoter activity, CMV enhancer had significant negative impact on U6 promoter activity in the presence of UbiC promoter. Our results indicate that U6 promoter activity can be affected by TI in a proximal promoter-specific and arrangement-dependent manner.

Page 170–179


Review Article

Follow the Leader: Preference for Specific Amino Acids Directly Following the Initial Methionine in Proteins of Different Organisms

Ronen Shemesh , Amit Novik, Yossi Cohen

It is well established that the vast majority of proteins of all taxonomical groups and species are initiated by an AUG codon, translated into the amino acid methionine (Met). Many attempts were made to evaluate the importance of the sequences surrounding the initiation codon, mostly focusing on the RNA sequence. However, the role and importance of the amino acids following the initiating Met residue were rarely investigated, mostly in bacteria and fungi. Herein, we computationally examined the protein sequences of all major taxonomical groups represented in the Swiss-Prot database, and evaluated the preference of each group to specific amino acids at the positions directly following the initial Met. The results indicate that there is a species-specific preference for the second amino acid of the majority of protein sequences. Interestingly, the preference for a certain amino acid at the second position changes throughout evolution from lysine in prokaryotes, through serine in lower eukaryotes, to alanine in higher plants and animals.

Page 180–189


Review Article

Structural and Functional Analysis of NS1 and NS2 Proteins of H1N1 Subtype

Parveen Salahuddin, Asad U. Khan

Influenza A virus (H1N1), a genetic reassortment of endemic strains of human, avian and swine flu, has crossed species barrier to human and apparently acquired the capability of human to human transmission. Some strains of H5N1 subtype are highly virulent because NS1 protein inhibits antiviral interferon α/β production. Another protein NS2 mediates export of viral ribonucleoprotein from nucleus to the cytoplasm through export signal. In this paper, we have studied structure-function relationships of these proteins of H1N1 subtype and have determined the cause of their pathogenicity. Our results showed that non-conservative mutations slightly stabilized or destabilized structural domains of NS1 or NS1-dsRNA complex, hence slightly increased or decreased the function of NS1 protein and consequently enhanced or reduced the pathogenicity of the H1N1 virus. NS2 protein of different strains carried non-conservative mutations in different domains, resulting in slight loss of function. These mutations slightly decreased the pathogenicity of the virus. Thus, the results confirm the structure-function relationships of these viral proteins.

Page 190–199


Method

Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decomposition

Ferran Reverter, Esteban Vegas, Pedro Sánchez

The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene expression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the input matrix and incorporates an additional step that involves KPCA. The main properties of our method are the extraction of nonlinear features and the preservation of the input variables (genes) in the output display. We apply this algorithm to colon tumor, leukemia and lymphoma datasets. Our approach reveals the underlying structure of the gene expression profiles and provides a more intuitive understanding of the gene and sample association.

Page 200–210