Functional Genomics

1. Functional Genomics Unit II

2. Introduction Genomics – It is the study of genomes. • The field of genomics comprises of two main areas: • Structural genomics • Functional genomics

3. Structural genomics • It deals with genome structures with a focus on the study of genome mapping and assembly as well as genome annotation and comparison.

4. Functional genomics • It is largely experiment based with a focus on gene functions at the whole genome level using high throughput approaches. • Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures.

5. Techniques and applications • Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism (such as single nucleotide polymorphism (SNP) analysis), as well as measurement of molecular activities. • It also comprise a number of "-omics" such as transcriptomics (gene expression), proteomics (protein production), and metabolomics.

6. Functional genomics uses mostly multiplex techniques to measure the abundance of many or all gene products such as mRNAs or proteins within a biological sample.

8. Genetic Interaction Mapping • Systematic pairwise deletion of genes or inhibition of gene expression can be used to identify genes with related function, even if they do not interact physically. • Epistasis refers to genetic interactions in which the mutation of one gene masks the phenotypic effects of a mutation at another locus. • Systematic analysis of these epistatic interactions can provide insight into the structure and function of genetic pathways.

9. Epistasis • Epistasis refers to the fact that effects for two different gene knockouts may not be additive; that is, the phenotype that results when two genes are inhibited may be different from the sum of the effects of single knockouts. • Epistasis has a large influence on the shape of evolutionary landscapes, which leads to profound consequences for evolution and evolvability of phenotypic traits.

10. Systematic analysis of these epistatic interactions can provide insight into the structure and function of genetic pathways. • Examining the phenotypes resulting from pairs of mutations helps in understanding how the function of these genes intersects. • Genetic interactions are generally classified as either Positive/Alleviating or Negative/ Aggravating.

11. Fitness epistasis (an interaction between non-allelic genes) is positive, when a loss of function mutation of two given genes results in exceeding the fitness predicted from individual effects of deleterious mutations, and it is negative (aggravating) when it decreases fitness.

12. Detection Methods • High-throughput methods of analyzing these types of interactions have been useful in expanding our knowledge of genetic interactions. • Synthetic genetic arrays (SGA),  • Diploid based synthetic lethality analysis on microarrays (dSLAM), and  • Epistaticminiarray profiles (E-MAP)

13. Advantages • This systematic approach to studying epistasis on a genome wide scale has significant implications for functional genomics and mapping of genetic interactions. • By identifying the negative and positive interactions between an unknown gene and a set genes within a known pathway, these methods can elucidate the function of previously uncharacterized genes within the context of a metabolic or developmental pathway.

14. Genetic interactions revealed by conditional mapping. Genome plot generated using circos software

15. Transcriptome analysis • Transcriptome analysis can be conducted by two approaches: • Sequence based approaches • Microarray based approaches

16. Sequence based approaches • Expressed sequence tags: ESTs are short sequences of cDNA typically 200-400 nucleotides in length. • Obtained from either 5’end or 3’end of cDNA inserts of cDNA library.

17. Advantages of EST • Provide a rough estimate of genes that are actively expressed in a genome under a particular physiological condition. • Help in discovering new genes, due to random sequencing of cDNA clones. • EST libraries can be easily generated.

18. Drawbacks • Automatically generated without verification thus contain high error rates. • It is always contaminated by vector sequence, introns, ribosomal RNA, mitochondrial RNA. • Weakly expressed genes are hardly found in EST sequencing survey. • ESTs represent only partial sequences of genes.

19. SAGE(Serial Analysis of Gene Expression)

26. Protocol • The mRNA of an input sample (e.g. a tumour) is isolated and a reverse transcriptase and biotinylated primers are used to synthesize cDNA from mRNA. • The cDNA is bound to Streptavidin beads via interaction with the biotin attached to the primers, and is then cleaved using a restriction endonuclease called an anchoring enzyme (AE). The location of the cleavage site and thus the length of the remaining cDNA bound to the bead will vary for each individual cDNA (mRNA).

27. The cleaved cDNA downstream from the cleavage site is then discarded, and the remaining immobile cDNA fragments upstream from cleavage sites are divided in half and exposed to one of two adapter oligonucleotides (A or B) containing several components in the following order upstream from the attachment site: • 1) Sticky ends with the AE cut site to allow for attachment to cleaved cDNA; • 2) A recognition site for a restriction endonuclease known as the tagging enzyme (TE), which cuts about 15 nucleotides downstream of its recognition site (within the original cDNA/mRNA sequence); • 3) A short primer sequence unique to either adapter A or B, which will later be used for further amplification via PCR.

28. After adapter ligation, cDNA are cleaved using TE to remove them from the beads, leaving only a short "tag" of about 11 nucleotides of original cDNA (15 nucleotides minus the 4 corresponding to the AE recognition site). • The cleaved cDNA tags are then repaired with DNA polymerase to produce blunt end cDNA fragments

29. These cDNA tag fragments (with adapter primers and AE and TE recognition sites attached) are ligated, sandwiching the two tag sequences together, and flanking adapters A and B at either end. These new constructs, called ditags, are then PCR amplified using anchor A and B specific primers. • The ditags are then cleaved using the original AE, and allowed to link together with other ditags, which will be ligated to create a cDNAconcatemer with each ditag being separated by the AE recognition site.

30. These concatemers are then transformed into bacteria for amplification through bacterial replication. • The cDNAconcatemers can then be isolated and sequenced using modern high-throughput DNA sequencers, and these sequences can be analysed with computer programs which quantify the recurrence of individual tags.

31. Analysis • The output of SAGE is a list of short sequence tags and the number of times it is observed. Using sequence databases a researcher can usually determine, from which original mRNA (and therefore which gene) the tag was extracted. • Statistical methods can be applied to tag and count lists from different samples in order to determine which genes are more highly expressed. For example, a normal tissue sample can be compared against a corresponding tumor to determine which genes tend to be more (or less) active.

47. Microarray

48. Summary of DNA microarray

49. A DNA microarray (also commonly known as DNAchip or biochip) is a collection of microscopic DNA spots attached to a solid surface. • DNA microarraysis used to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome

50. A typical microarray experiment involves the hybridization of an mRNA molecule to the DNA template from which it is originated. • Many DNA samples are used to construct an array. Each DNA spot contains picomoles (10−12moles) of a specific DNA sequence, known as probes (or reporters or oligos). • These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions. • The amount of mRNA bound to each site on the array indicates the expression level of the various genes. • All the data is collected and a profile is generated for gene expression in the cell.