Operomics: Integrated genomic and proteomic profiling of cells and tissues

In the post-genome era, technologies are becoming availablethat allow the profiling of tissues and cell populations atmultiple levels including genomic (DNA and RNA), proteomic (proteinsand peptides) and post-proteomic (eg metabolomic). Operomicsrefers to the molecular analysis of tissues and cells at thethree levels that are connected through the coding process —namely, DNA, RNA and protein. The premise is that no one levelor type of analysis fully captures gene expression and thatfunctional changes at the proteome level cannot be simply predictedfrom analyses at the DNA or RNA levels. An important determinantthat weakens a direct link between RNA and protein levels istranslational control that differentially regulates mRNA translation.In this paper, the approaches for genomic and proteomic profilingand the contribution of translational control are reviewed.      

Functional materials for microscale genomic and proteomic analyses

The design of functional materials for genomic and proteomic analyses in microscale systems has begun to mature, from materials designed for capillary-based electrophoresis systems to those tailored for microfluidic-based or 'chip-based' platforms. In particular, recent research has focused on evaluating different polymer chemistries for microchannel surface passivation and improved DNA separation matrix performance. Additionally, novel bioconjugate materials designed specifically for electrophoretic separations in microscale channels are facilitating new separation modalities.     

Integrated analysis of genetic, genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.     

Integrated analysis of genetic,genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.     

Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma

Similar to other malignancies, urothelial carcinoma (UC) is characterized by specific recurrent chromosomal aberrations and gene mutations. However, the interconnection between specific genomic alterations, and how patterns of chromosomal alterations adhere to different molecular subgroups of UC, is less clear. We applied tiling resolution array CGH to 146 cases of UC and identified a number of regions harboring recurrent focal genomic amplifications and deletions. Several potential oncogenes were included in the amplified regions, including known oncogenes like E2F3, CCND1, and CCNE1, as well as new candidate genes, such as SETDB1 (1q21), and BCL2L1 (20q11). We next combined genome profiling with global gene expression, gene mutation, and protein expression data and identified two major genomic circuits operating in urothelial carcinoma. The first circuit was characterized by FGFR3 alterations, overexpression of CCND1, and 9q and CDKN2A deletions. The second circuit was defined by E3F3 amplifications and RB1 deletions, as well as gains of 5p, deletions at PTEN and 2q36, 16q, 20q, and elevated CDKN2A levels. TP53/MDM2 alterations were common for advanced tumors within the two circuits. Our data also suggest a possible RAS/RAF circuit. The tumors with worst prognosis showed a gene expression profile that indicated a keratinized phenotype. Taken together, our integrative approach revealed at least two separate networks of genomic alterations linked to the molecular diversity seen in UC, and that these circuits may reflect distinct pathways of tumor development.     

Integrated transcriptomic and proteomic analyses for the characterization of parathyroid oxyphil cells in uremic patients

Amino Acids - Chief cells are the predominant cells in parathyroid glands of healthy adults; however, parathyroid oxyphil cells, whose function is unknown, increase dramatically in patients with...     

Integrated analyses of copy number variations and gene expression in lung adenocarcinoma

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Identification of prognostic biomarkers for lung cancer using gene expression microarrays poses a major challenge in that very few overlapping genes have been reported among different studies. To address this issue, we have performed concurrent genome-wide analyses of copy number variation and gene expression to identify genes reproducibly associated with tumorigenesis and survival in non-smoking female lung adenocarcinoma. The genomic landscape of frequent copy number variable regions (CNVRs) in at least 30% of samples was revealed, and their aberration patterns were highly similar to several studies reported previously. Further statistical analysis for genes located in the CNVRs identified 475 genes differentially expressed between tumor and normal tissues (p<10⁻⁵). We demonstrated the reproducibility of these genes in another lung cancer study (p = 0.0034, Fisher''s exact test), and showed the concordance between copy number variations and gene expression changes by elevated Pearson correlation coefficients. Pathway analysis revealed two major dysregulated functions in lung tumorigenesis: survival regulation via AKT signaling and cytoskeleton reorganization. Further validation of these enriched pathways using three independent cohorts demonstrated effective prediction of survival. In conclusion, by integrating gene expression profiles and copy number variations, we identified genes/pathways that may serve as prognostic biomarkers for lung tumorigenesis.     

Integrated analyses of copy number variations and gene differential expression in lung squamous-cell carcinoma

Background

Although numerous efforts have been made, the pathogenesis underlying lung squamous-cell carcinoma (SCC) remains unclear. This study aimed to identify the CNV-driven genes by an integrated analysis of both the gene differential expression and copy number variation (CNV).

Results

A higher burden of the CNVs was found in 10–50 kb length. The 16 CNV-driven genes mainly located in chr 1 and chr 3 were enriched in immune response [e.g. complement factor H (CFH) and Fc fragment of IgG, low affinity IIIa, receptor (FCGR3A)], starch and sucrose metabolism [e.g. amylase alpha 2A (AMY2A)]. Furthermore, 38 TFs were screened for the 9 CNV-driven genes and then the regulatory network was constructed, in which the GATA-binding factor 1, 2, and 3 (GATA1, GATA2, GATA3) jointly regulated the expression of TP63.

Conclusions

The above CNV-driven genes might be potential contributors to the development of lung SCC.     

哺乳类细胞基因表达系统

真核基因的转录和转译调控是哺乳类细胞基因表达系统建立和发展的基础,外源基因的表达水平不仅决定于启动子/增强子的强弱,还与剪接信号、终止信号和poly(A)信号以及质粒的拷贝数等因素有关。另外,在基因治疗的研究中,也已寻找到多种具有组织或肿瘤特异性的启动子,来达到特异性肿瘤基因治疗的目的。     

Comparative proteomic and radiobiological analyses in human lung adenocarcinoma cells

In clinic, many non-small cell lung cancer (NSCLC) patients receive radiation therapy after chemotherapy failure. However, whether the multidrug resistance (MDR) can elevate the radioresistance (RDR) remains unclear. To evaluate the MDR's effect on the RDR, screen MDR- and RDR-related proteins in human lung adenocarcinoma (HLA) cells and tissues A549, and A549/DDP cells after irradiation were analyzed by colony-forming assay and flow cytometry. Two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were utilized to identify differentially expressed proteins (DEPs) between them. The value of D0, Dq, and SF2 increased, the mean percentage in G2 phase and apoptosis rate significantly decreased in A549/DDP cells compared with A549 cells. 40 DEP points were found, and among them 27 were identified through proteomics. Four up-regulated proteins (HSPB1, Vimentin, Cofilin-1, and Annexin A4) in MDR cells compared with non-MDR cells, were confirmed by Western blot. Immuno-histochemistry showed that they were also over-expressed in MDR tissues compared with non-MDR counterparts of HLA. These results proved that the MDR in HLA cells and tissues increased the RDR. HSPB1, Vimentin, Cofilin-1, and Annexin A4 are potential biomarkers for predicting HLA response to MDR and RDR, and novel treatment targets of HLA.     

Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses

ABSTRACT: BACKGROUND: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. RESULTS: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. CONCLUSIONS: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.     

脑缺血/缺氧预适应引起的基因、蛋白质表达谱的变化

缺血,缺氧预适应保护作用的分子机制目前尚未充分阐明。最近的研究在大鼠和小鼠脑缺血,缺氧预适应的模型上采用了基因芯片、双向电泳结合质谱分析技术,揭示了大鼠和小鼠脑缺血,缺氧预适应引起的基因和蛋白质表达谱的变化。这些研究发现预适应引起一些新的基因和蛋白质的表达发生改变,深入研究这些基因和蛋白质有可能发现治疗脑中风新的药物靶标。     

Respiratory protein interactions in Dehalobacter sp. strain 8M revealed through genomic and native proteomic analyses

Dehalobacter (Firmicutes) encompass obligate organohalide-respiring bacteria used for bioremediation of groundwater contaminated with halogenated organics. Various aspects of their biochemistry remain unknown, including the identities and interactions of respiratory proteins. Here, we sequenced the genome of Dehalobacter sp. strain 8M and analysed its protein expression. Strain 8M encodes 22 reductive dehalogenase homologous (RdhA) proteins. RdhA D8M_v2_40029 (TmrA) was among the two most abundant proteins during growth with trichloromethane and 1,1,2-trichloroethane. To examine interactions of respiratory proteins, we used blue native gel electrophoresis together with dehalogenation activity tests and mass spectrometry. The highest activities were found in gel slices with the highest abundance of TmrA. Protein distributions across gel lanes provided biochemical evidence that the large and small subunits of the membrane-bound [NiFe] uptake hydrogenase (HupL and HupS) interacted strongly and that HupL/S interacted weakly with RdhA. Moreover, the interaction of RdhB and membrane-bound b-type cytochrome HupC was detected. RdhC proteins, often encoded in rdh operons but without described function, migrated in a protein complex not associated with HupL/S or RdhA. This study provides the first biochemical evidence of respiratory protein interactions in Dehalobacter, discusses implications for the respiratory architecture and advances the molecular comprehension of this unique respiratory chain.