Briefings in Functional Genomics and Proteomics - Advance Access

Potential misinterpretation of data on differential gene expression in normal and malignant cells in vitro

Ye, X., Lotan, R. — 2008-05-08

High throughput genomic and proteomic methods are often used for comparisons between expression of genes and proteins, respectively in normal cells and malignant counterparts for the identification of potential tumor markers for diagnosis and prognosis. Some experiments use normal and malignant cells cultured in vitro as a source of the mRNA or proteins for analysis. The conditions used for cell culture can exert major effects on the expression of genes and proteins. The interpretation of results of some such studies can be erroneous if normal cells and cancer cells are cultured in serum-free medium (SFM) and serum-supplemented media, respectively as recommended for their optimal growth. The reason for potential complications in the data interpretation is that serum contains different factors that affect gene expression. Likewise, SFM is usually supplemented with specific growth factors as well as bovine pituitary extract. Experimental examples demonstrating the issue include the stimulatory effects of serum on the expression of retinoic acid-inducible genes (e.g. GPRC5A) leading to the potentially erroneous conclusion that such genes are overexpressed in cancer cells. Potential remedy for this problem is to grow the normal and malignant cells in the same medium (serum-free or serum-containing) before analysis.

Network-guided genetic screening: building, testing and using gene networks to predict gene function

Lehner, B., Lee, I. — 2008-04-29

A challenge facing nearly all biologists is to identify the complete set of genes that are important for a process or disease. This applies to scientists investigating fundamental pathways in model organisms, but also to clinicians trying to understand human disease. There are many different types of experimental data that can be used to predict the genes that are important for a process, but these data are normally dispersed across numerous publications and databases, and are of varying and unknown quality. Integrated functional gene networks aim to gather functional information from all of these data into a single intuitive graph model that can be used to predict gene functions. In this approach, the ability of each data set to predict functional associations between genes is first measured using a standard benchmark, and then the scored predictions by each data set are combined. The resulting integrated probabilistic gene network can be used by all researchers to predict gene function, with much greater coverage and accuracy than any individual data set. In this review, we discuss how such integrated gene networks are constructed, how their predictive power for gene function can be tested, and how experimental biologists can use these networks to guide their research. We pay particular attention to such networks constructed for Caenorhabditis elegans, because in this complex multicellular model system functional predictions for genes can be rapidly tested in vivo using RNAi. The approach is, however, widely applicable to any system, and might soon be a common method used to dissect the genetics of human complex diseases.

Studying gene function in Caenorhabditis elegans using RNA-mediated interference

Maine, E. M. — 2008-04-28

The RNA interference (RNAi) method for targeted gene silencing is widely used in Caenorhabditis elegans for large-scale functional genomic studies, analysis of limited gene sets and detailed analysis of individual gene function. The application of RNAi has identified genes that participate in various aspects of development, physiology and cell biology. In addition, RNAi has been used to identify interacting genes and to study functionally redundant genes. This review discusses the various applications of RNAi in C. elegans, focusing particularly on the analysis of developmental processes.

Towards a mutation in every gene in Caenorhabditis elegans

Moerman, D. G., Barstead, R. J. — 2008-04-16

The combined efforts of the Caenorhabditis elegans Knockout Consortium and individuals within the worm community are moving us closer to the goal of identifying mutations in every gene in the nematode C. elegans. At present, we count about 7000 deletion alleles that fall within 5500 genes. The principal method used to detect deletion mutations in the nematode utilizes polymerase chain reaction (PCR). More recently, the Moerman group has incorporated array comparative genome hybridization (aCGH) to detect deletions across the entire coding genome. Other methods used to detect mutant alleles in C. elegans include targeting induced local lesion in genomes (TILLING), transposon tagging, using either Tc1 or Mos1 and resequencing. These combined strategies have improved the overall throughput of the gene-knockout labs, and have broadened the types of mutations that we, and others, can identify. In this review, we will discuss these different approaches.

Proteomics in Caenorhabditis elegans

Audhya, A., Desai, A. — 2008-03-27

Proteomic approaches are increasingly being used to complement genetic and RNA interference-based studies of gene function in Caenorhabditis elegans. Several strategies to isolate protein complexes from whole worms and individual differentiated cell types have been described. In vivo labelling methods have also been developed to quantitatively assess proteome-wide changes depending on genetic composition or developmental stage. Here, we review proteomic approaches that are becoming part of the essential toolbox for studies of gene function in C. elegans and highlight specific examples where their application has led to important new insights.

Large-scale gene expression pattern analysis, in situ, in Caenorhabditis elegans

Bamps, S., Hope, I. A. — 2008-03-09

Observation of gene expression in situ provides a direct connection between the genetic information in the genome sequence and the fully determined developmental cell lineage of Caenorhabditis elegans. Green Fluorescent Protein (GFP) reporters have been fused with many C. elegans genes, in large-scale projects, by conventional DNA ligation, PCR stitching, Gateway recombination and recombineering. These reporter gene fusions have then been used in C. elegans transformation either by microinjection or microprojectile bombardment. So far, the developmental distributions of GFP, as driven by the C. elegans DNA to which the reporter gene has been attached, have been determined simply from direct examination of the transgenic strains by epifluorescence microscopy. Automation of GFP expression pattern determination promises improvements in both quality and quantity of this data type, facilitating the handling of such expression pattern data within computer databases. As with the descriptions of the developmental cell lineage and the genome sequence, a complete description of gene expression patterns will provide a vital knowledge framework through which a full understanding of the development of this animal can emerge.