Genome function

Related Terms

Bioinformatics, biological process, comparative genomics, deoxyribonucleic acid, genetic variation, genome function, genome sequencing, genome, genomics, homology, Human Genome Project, metabolic profiling networks, metabolomics, molecular function, mutation, pathways, personalized medicine, phylogenomics, physiological genomics, polymorphism, protein, protein analyses, proteomics, single nucleotide polymorphisms, structural genomics, transcriptomics.

Background

Functional genomics (or physiological genomics) is defined as the study of genes, their resulting proteins, and the role played by these proteins in the body's biochemical processes. Functional genomicsaims to discover the biological function of particular genes and to uncover how sets of genes and their protein products work together in health and disease. By combining many traditional genetic and other biological approaches, functional genomics seeks to expand the scope of biological investigation from studying single genes or proteins to studying all genes or proteins at once in a systematic fashion.
Functional genomics is a field of molecular biology that seeks to understand and define gene and protein functions and interactions by using data from genome sequencing projects. In contrast to genomics and proteomics, functional genomics emphasizes the dynamic aspects, such as gene transcription (the synthesis of an RNA (translates the genetic code of DNA into protein) copy from a sequence of DNA (the chemical containing the instructions to develop and direct the activities of the entire organism); the first step in gene expression), translation (the process in which the genetic code carried by mRNA directs the synthesis of proteins from amino acids), and protein-protein interactions, compared with unchanging aspects of the genomic information, such as DNA sequence or structures.
Functional genomics uses information and analytic methods provided by structural genomics (the construction of genetic, physical and transcript maps of an organism), and employs large-scale research methods and analysis of the results. The basic strategy of functional genomics is to expand the understanding of biological processes from studying single genes or proteins to studying all genes or proteins at once in a systematic fashion.
A complete set of nuclear DNA in an organism is called the genome. Every cell in the human body contains a complete copy of the about three billion DNA base pairs, or letters, that make up the human genome. A gene is the unit of DNA that carries the instructions for making a specific protein or set of proteins. Organs and tissue are made of protein, and proteins control chemical reactions and carry signals between cells. Abnormal protein may be produced if a cell's DNA is mutated, disrupting the body's normal function and leading to diseases such as cancer.
Most disease in humans has a genetic basis. The Human Genome Project (HGP) was designed to study the genetic variations that increase the risk of specific diseases, such as cancer, diabetes, and cardiovascular disease that constitute most of the health problems in the United States.
The goal of genomics is to identify genes and how they relate to drug treatment, leading to therapies that target the actual biology of the disease and not just the symptoms. This individualized treatment based on the genetic makeup of the person is called personalized medicine. Functional genomics is one of the new technologies developed from the HGP and the effort to understand DNA and form the foundation of personalized medicine.
Completion of the HGP was the first step in understanding humans at the molecular level. Ongoing research is helping to determine the function of the estimated 30,000 human genes, the role of single nucleotide polymorphisms (SNPs)-single DNA base changes within the genome, and the role of noncoding regions and repeats in the genome. Effective and rapid interpretation of human gene function and other DNA sequences requires that resources and strategies be developed to enable large-scale investigations across whole genomes.
A method used to cope with the complexity of functional genomics information is to average the data from many different genes into broad 'omic categories. For instance, instead of looking at how the level of expression of an individual gene changes over a time, all the genes in a functional category (e.g. glycolysis) are averaged together. This gives a more powerful answer about the degree to which a functional system changes over the time period.
Functional genomics includes functional aspects of the genome, such as mutation (inherited change in DNA sequence) and polymorphism (difference in DNA sequence among individuals that may underlie differences in health) analysis, and measurement of molecular activities. The last category comprises several areas of research that end with "-omics" that include transcriptomics (gene expression), proteomics (protein expression), and metabolomics (metabolic expression). Another category includes phylogenomics which studies the relationship between the function and evolution of genes. Together, these areas of study provide the understanding of gene and protein functions and interactions.
The technology goals of functional genomics are to produce sets of full-length complementary DNA (cDNA; DNA synthesized in the laboratory from a messenger RNA template) clones and sequences that represent human genes and model organisms, to support research on approaches for studying the functions of nonprotein-coding sequences, to develop technology for comprehensive analysis of gene expression, to improve methods for genome-wide mutagenesis, and to develop technology for large-scale protein analyses.

Methods

Functional genomics research is conducted with model organisms (laboratory animals useful for research), such as mice. Model organisms give the researcher a cost-effective approach to follow the inheritance of genes very similar to human genes through several generations in a brief time period. Model organisms studied in the HGP included the bacteria Escherichia coli, the yeast Saccharomyces cerevisiae, the roundworm Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the laboratory mouse.
Mice are very useful in genome research because they are genetically very similar to humans, reproduce rapidly, have short life spans, are inexpensive and easy to handle, and may be genetically manipulated at the molecular level. Knockout mice are transgenic (experimentally produced) mice whose genetic code has been changed by the insertion of foreign genetic material into their DNA. Knockout mice allow researchers to target specific genes by causing them to be expressed or inactivated. The offspring of these mice create a population with the trait. Researchers may isolate human genes with unknown functions and create knockout mice with these genes. Observing these results helps researchers understand health and disease by observing how these genes work in cells.
Functional genomics uses high-throughput (a fast method of determining the order of bases in DNA) techniques to characterize gene products, such as mRNA and proteins. Technology platforms that are used to achieve this include DNA microarrays (sets of miniaturized chemical reaction areas that may also be used to test DNA fragments, antibodies, or proteins), serial analysis of gene expression (SAGE; a database that helps identify and define new transcription units) for mRNA, two-dimensional gel electrophoresis (a method to isolate proteins in a sample and compare levels to other samples), and mass spectrometry (a method that identifies proteins when combined with two-dimensional gel electrophoresis) for protein. Bioinformatics (the science of analyzing genomic research data) is crucial to this type of analysis because of the large quantity of data produced by these techniques and the objective of finding biologically meaningful patterns in the data.

Research

Two of the greatest challenges in functional genomics may be to assign potential protein functions and to understand which proteins may perform related activities. Homology refers to two or more genes or gene products which may have a common ancestry. Often, homologous proteins are identified based on translations of DNA sequences and comparisons of the primary amino acid sequences. However, often this identification process does not work, in part because similar protein structures may be derived from primary amino acids sequences that are quite dissimilar. Thus, the ability to perform high-throughput protein structure analysis is needed to generate data which may help identify functionally-related proteins. In response to this need, nuclear magnetic resonance spectroscopy (NMR spectroscopy) is applied to understand relationships between protein structure and function. Laboratories automate analysis of protein NMR data with the idea that, through structure determination and application of new bioinformatics methods, biochemical functions may be discovered for previously unknown proteins discovered in genome sequencing efforts.
The agent that causes tuberculosis (TB) is Mycobacterium tuberculosis (MTB). Although many efforts to control this disease have failed, the availability of genome sequences of several MTB strains has led to an understanding of its functional genomics. This information has led to research that has allowed scientists to understand the mechanisms used by MTB to persist and cause disease. Comparative genomics of different MTB strains has been performed to test the gene expression profiles of MTB growing under different conditions. This data may be used to design new drugs, especially against multidrug-resistant (MDR) strains, develop improved vaccines, and identify biomarkers of TB for better diagnosis.
The parasite Plasmodium falciparum is the agent of mosquito-transmitted malaria, and causes more than one million deaths every year in tropical areas world-wide. Many of these deaths occur in Sub-Saharan Africa. New strategies are needed to counter the increases in drug resistance and insecticides. Prediction of the gene products of P. falciparum is now available, making possible the application of functional genomics to malaria research with the final goal of providing a complete survey of the Plasmodium life cycle. Genome-wide approaches to the study of transcriptome (the set of all mRNA molecules made in one or more cells) or proteome (the full set of protein produced by all the genes in a genome, cell, tissue or organism) have been applied to the malaria parasite, promising new drugs and vaccines in the near future.

Implications

Functional genomics has the potential to impact the drug development process and the clinical practice of medicine in a variety of ways. These include potentially improving the ability to diagnose disease and predict disease risk, determining whether a treatment is effective, detecting early signs of disease in healthy people, producing safer drugs by anticipating side effects, targeting patient subgroups most likely to benefit from a drug or be harmed by it, providing researchers with a comprehensive picture of events taking place within a cell, and producing new structural materials forming the basis of more effective and safer medical products.

Limitations

Personalized medicine is very new and remains in the early stages. Drug manufacturers may be reluctant to invest the time and money developing pharmacogenomic tests to determine who might respond to a treatment and who should not receive a treatment when this would narrow the market for the drug. Also, identifying the huge number of the genetic variations could take many years. Medication response may be determined by several genes and their products interacting with each other instead of just one gene. Functional genomics is likely to be expensive and time-consuming.
Given the need for expertise in genomics, and the biological,physical and engineering sciences needed with functional genomics, it may be impractical to attempt to develop a large number of scientists with expertise in all these areas.
Functional genomics faces technical challenges stemming from the need to involve biological systems that each bring their own amount of complexity. This is in part related to the requirement, with a given disease, of a comprehensive molecular knowledge of all cell components in the afflicted tissue or organ and how they interface to produce overall phenotypes. For example, skin cancer would require complete knowledge not only of the epithelial cells that give rise to the cancer, but of their molecular changes as they become precancerous and cancerous.

Future research

The induced sputum technique to study lung conditions allows access to specific cell types from the airways, which could be used in studies of functional genomics and proteomics. As the functional state of a cell reflects the expressed genes of that cell, and the expressed genes may be used to define cell type, stage of development, and responses to stimuli, cell cultures obtained from induced sputum samples may allow functional genomics studies to be performed. This would expand the scope of biological investigation from a single gene to studying all simultaneously expressed genes in a systematic fashion.
Drug discovery researchers are in a "post-genomic," functional genomics era that requires an understanding of how genes and their protein products work, how they interact in pathways within the cell and the organism, and what roles they play in health and disease. Many leading pharmaceutical companies are making or have made major investments in the field of functional genomics, often partnering with small genomics and biotechnology companies that are developing functional genomics platform technologies.
Research on aging is also entering the post-genomic, functional genomics era. A long list of genes that are associated with various models of aging in humans has recently emerged. The next task is to evaluate the importance of these genes, mainly with respect to normal aging and to understand the functions of these genes and how they affect the process of aging. Also, the concept of lifespan plasticity (the ability to change and adapt to the environment) is now becoming accepted, with epigenetics (non-genetic factors that cause an organism's genes to express differently) believed to contribute roughly 75% to the aging process.

Author information

This information has been edited and peer-reviewed by contributors to the Natural Standard Research Collaboration (www.naturalstandard.com).

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