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The central dogma of biology describes the method by which information is taken from genes and used to create proteins. DNA transcription produces RNA, then RNA translation makes proteins. This process is known as gene expression and most life forms use it to create the building blocks of life from genetic information [1]. Note that some RNA molecules, such as rRNA, tRNA, and other non-coding RNAs, are not translated into proteins, but have structural functions.

A cell expresses only a selection of the genes it contains at a certain time, which means that the cell can interpret its genetic code in different ways. Controlling which genes are expressed enables the cell to control its size, shape and functions. The ways in which an organism's cells express the genes they contain affects the organism’s phenotype, e.g., which color hair a mouse has, or whether it has hair at all [2].

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What is a gene expression profile?

 
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A gene expression profile measures which genes are being expressed in a cell at any given moment. This method can measure thousands of genes at a time; some experiments can measure the complete genome at once [3]. Gene expression profiling measures mRNA levels, showing the pattern of genes expressed by a cell at the transcription level [4]. This often means measuring relative mRNA amounts in two or more experimental conditions, then assessing which conditions resulted in specific genes being expressed.

Different techniques are used to determine gene expression. These include DNA microarrays and sequencing technologies. The former measures the activity of specific genes of interest and the latter enables researchers to determine active genes in a cell [5].

A gene expression profile tells us how a cell is functioning at a specific time. This is because cell gene expression is influenced by external and internal stimuli, including whether the cell is dividing, what factors are present in the cell's environment, the signals it is receiving from other cells, and even the time of day [6].

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Why use gene expression profiling?

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Gene expression profiling enables you to investigate the effects of different conditions on gene expression by altering the environment to which the cell is exposed, and determining which genes are expressed. Alternatively, if you already know a gene is involved in a certain cell behavior, gene expression profiling helps you to determine whether a cell is carrying out this function. For example, certain genes are known to be involved in cell division; if these genes are active in a cell, you can tell the cell is undergoing division, or whether a cell is differentiated [7,8].

Gene expression profiling is often used in hypothesis generation. If very little is known about when and why a gene will be expressed, expression profiling under different conditions can help design a hypothesis to test in future research experiments. For example, if gene A is expressed only when the cell is exposed to other cells, this gene may be involved in intercellular communication. Further experiments could determine whether this is the case [4].

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Gene expression profiling methods

There are three extensively used methods to view and measure the expression profile of genes. Each of these methods has unique advantages and applications, making them valuable tools in molecular biology research.

RNA sequencing

Method #1: RNA sequencing

RNA expression patterns are key to predicting and classifying human disease based on specific biomarkers. To understand cellular responses, we must determine how gene expression changes are affected in relation to external stimuli, different environmental conditions, and genetic lesions [10].

RNA-seq, using next-generation sequencing, let us discover differentially expressed genes without requiring knowledge of which genes are involved [11]. It is a sequencing technology that provides a more comprehensive analysis of gene expression compared to microarrays.

Protein-coding RNAs (transcripts) are an important source of information. Next-generation RNA sequencing enables such analysis, along with:

  • ‘Digital counting’ of RNA molecules for highly quantitative and precise measurements
  • Dynamic ranges to fully capture relevant biological changes
  • Discovery of unknown RNAs (novel transcripts, splice variants, and gene fusions)
Quantitative real-time PCR

Method #2: Real-time PCR (qPCR)

Quantification of mRNA using qPCR can be done using TaqMan probe-based analysis and SYBR Green dye-based analysis plus using digital PCR, as discussed subsequently [13].

qPCR is a extensively used technique for verifying differential gene expression profiles and helps enable:

  • Quantitation of gene products
  • Microarray analysis
  • Pathway analysis
  • Studies of developmental biology
  • Quality control and assay verification
  • siRNA/RNAi experiments
  • Low-fold copy number discrimination (down to two-fold)
  • Mid- to high-throughput profiling using the OpenArray platform
Quantifying rare targets and small-fold changes with digital PCR (dPCR)

Method #3: dPCR for gene expression profiling

Although qPCR is effective for detecting gene expression changes of two-fold or more, a different approach is needed for measuring less than two-fold changes. Digital PCR (dPCR) can be used to resolve low-fold gene expression changes. dPCR facilitates:

  • Absolute quantification of nucleic acid standards and next-generation sequencing libraries
  • Rare target detection
  • Enrichment and separation of mixtures


Caveats in gene expression profiling

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Knowing a cell is expressing certain genes provides a lot of information about how a cell is functioning, and potentially new insights into which genes (and therefore proteins) are involved in certain cellular behaviors. However, a gene does not code for just one protein [14]. There are around 20,000 protein-coding genes in the human genome that produce many more proteins, probably in the order of 2 million [15]. This is partly because cells use post-translational modification to change proteins after they have been created by the transcription-translation process, and because alternative splicing produces different proteins from the same gene [16].

We need more information than just the mRNA profile of a cell to establish a cell's function. It may be helpful, for example, to work out which proteins a cell makes through proteomics experiments [17]. However, gene-expression profiling is still among the most effective method to determine a cell's function from a single experiment.

When reporting gene expression profiling studies, it is typical to report the genes that had significantly different expression profiles under the experimental conditions. This is limiting because:

  1. Cellular differentiation means different cells express different genes at baseline; this is what gives them their different structures and functions.
  2. Many genes do not change because they are required in their original form for cell survival or are not affected by the experimental condition
  3. Altering levels of mRNA (measured during gene expression profiling) is not the only way in which a cell changes the genes it uses. Post-translational modification, for example, changes a protein that is made from the same gene. Changing mRNA levels are not necessarily associated with changing levels of protein [16].

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Analysis of gene expression profiling data

Analysis of the data gathered in gene expression profiling experiments can be complex. However, expression profiling can give you information on the genes expressed under different conditions, helping you to develop and test your hypothesis. Analyzing the data can be an interdisciplinary task and may require a biostatistician with multivariate statistical analysis to provide essential support.


References
  1. Crick C (1970) Central dogma of molecular biology. Nature 227:561–563. 
  2. Papatheodorou I, Oellrich A, Smedley D (2015) Linking gene expression to phenotypes via pathway information. J Biomed Semant 6:17. doi: 10.1186/s13326-015-0013-5. 
  3. Metsis A, Andersson U, Bauren G et al. (2004) Whole-genome expression profiling through fragment display and combinatorial gene identification. Nucleic Acids Res 32(16):e127. doi: 10.1093/nar/gnh126. 
  4. Fielden MR, Zacharewski TR (2001) Challenges and limitations of gene expression profiling in mechanistic and predictive toxicology. Toxicol Sci 60(1):6–10. doi: 10.1093/toxsci/60.1.6. 
  5. Hurd PJ, Nelson CJ (2009) Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief Funct Genomic and Proteomic 8(3):174–183. doi: 10.1093/bfgp/elp013. 
  6. Stahlberg A, Kubista M, Aman P (2011) Single-cell gene-expression profiling and its potential diagnostic applications. Expert Rev Mol Diagn 11(7):735–740. doi: 10.1586/erm.11.60. 
  7. Underhill GH, George D, Bremer EG et al. (2003) Gene expression profiling reveals a highly specialized genetic program of plasma cells. Blood 101(10):4013–4021. doi: 10.1182/blood-2002-08-2673. 
  8. Richard C, Granier C, Inze D et al. (2001) Analysis of cell division parameters and cell cycle gene expression during the cultivation of Arabidpsis thaliana cell suspensions. J Exp Bot. 52(361):1625–1633. doi: 10.1093/jxb/52.361.1625. 
  9. Bertucci F, Finetti P, Rougemont J et al. (2004) Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy. Cancer Res64(23):8558–8565. doi: 10.1158/0008-5472.CAN-04-2696. 
  10. Gracey AY (2007) Interpreting physiological responses to environmental change through gene expression profiling. J Exp Biol. 210(9):1584–1592. doi: 10.1242/jeb.004333. 
  11. Finotello F, Di Camillo B (2015) Measuring differential gene expression with RNA-seq: Challenges and strategies for data analysis. Brief Funct Genomics 14(2):130–142. doi: 10.1093/bfgp/elu035. 
  12. Arrigoni A, Ranzani V, Rossetti G et al. (2016) Analysis RNA-seq and noncoding RNA. Methods Mol Biol 1480:125–135. doi: 10.1007/978-1-4939-6380-5_11. 
  13. Bernardo V, Riberio Pinto LF et al. (2013) Gene expression analysis by real-time PCR: Experimental demonstration of PCR detection limits. Anal Biochem 432(2):131–133. doi: 10.1016/j.ab.2012.09.029. 
  14. Mouilleron H, Delcourt V, Roucou X (2015) Death of a dogma: Eukaryotic mRNAs can code for more than one protein. Nucleic Acids Res 44(1):14–23. doi: 10.1093/nar/gkv1218. 
  15. Ezkurdia I, Juan D, Rodriguez JM et al. (2014) Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet 23(22):5866–5878. doi: 10.1093/hmg/ddu309. 
  16. Sheng JJ, Jin JP (2014) Gene regulation, alternative splicing, and posttranslational modification of troponin subunits in cardiac development and adaptation: A focused review. Front Physiol 5:165. doi: 10.3389/fphys.2014.00165. 
  17. Chandramouli K, Qian PY (2009) Proteomics: Challenges, techniques and possibilities to overcome biological sample complexity. Hum Genomics Proteomics 1:239204. doi: 10.4061/2009/239204. 

 

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