Τι είναι η γονιδιωματική;

Η μελέτη του γονιδιώματος

Τι είναι γονιδίωμα;

Η πλήρης γενετική σύσταση ενός οργανισμού

Πόσα είδη γονιδωμάτων υπάρχουν στη γη;

Προκαρυωτικά γονιδιώματα

Ευκαρυωτικά γονιδιώματα

Γονιδιώματα του πυρήνα

Γονιδιώματα του μιτοχονδρίου

Γονιδιώματα του χλωροπλάστη

Πόσα πεδία περιλαμβάνει η γονιδωματική;

Δομική γονιδιωματική

Λειτουργική γονιδιωματική

Συγκριτική γονιδιωματική

Origin of Genomics

• 1976: complete nucleotide sequence of a viral RNA-genome (bacteriophage MS2)

• 1977: 5386 base pairs of Phage Φ-X174, became the first DNA-genome project to be completed, by Fred Sanger

• 1995: The first bacterial genome to be sequenced was that of Haemophilus influenzae

• 1995: the first eukaryotic genome was completed, with the 16 chromosomes of budding yeast Saccharomyces cerevisiae

• 1996: the first genome sequence for an archaeon, Methanococcus jannaschii, was completed

• 2000: A working draft of the Human Genome was announced

Origin of Genomics

• Human Genome Project – Goal: sequence 3 billion base pairs

– High-quality sequence (<1 error per 10 K bases) ACGT

• Immensity of task required new technologies – Automated sequencing

• Decision to sequence other genomes: yeast and bacteria – Beginnings of comparative genomics

Δομική γονιδωματική

Technical foundations of genomics

• Molecular biology:

recombinant-DNA

technology

• DNA sequencing

• Library construction

• PCR amplification

• Hybridization techniques

Log M

W

Distance

. . .

.

Genomics relies on high-throughput technologies

• Automated sequencers

• Fluorescent dyes

• Robotics

– Microarray spotters

– Colony pickers

• High-throughput genetics

Technology Revolution Sequencing by‘synthesis nanotechnology’approach From a Few Billion $ to $5000

Industrial-scale Genomics Lab

Bioinformatics: computational analysis of genomics data

• Uses computational approaches to solve genomics problems

– Sequence analysis

– Gene prediction

– Modeling of biological processes/network

Functional Genomics

• Once we know the sequence of genes, we want to know the function

• The genome is the same in all cells of an individual, except for random mutations

• However, in each cell, only a subset of the genes is expressed – The portion of the genome that is used in each

cell correlates with the cell’s differentiated state

Expression microarrays

• Global expression analysis

• RNA levels of 30x103 genes in the genome analyzed in parallel

• Compare with Northern blot

– Microarrays contain more information by many orders of magnitude

Trends in Genetics, Volume 26, Issue 1, January 2010, Pages 21-28

E. coli regulatory network 1278 genes 2724 interactions 157 genes for TFs 382 metabolic enzyme genes (Blue)

Fixing traits = Fixing gene combinations ? Conceptual challenges from the ‘omics’ era • Epistasis : the gene is part of an interaction network (the ‘interactome’), with complex and moving hierarchies. • Epigenetics : gene regulation, not only the gene itself, is heritable. • Regulatory RNAs : extensive transcription of ‘non-coding’ DNA plays an essential role in gene regulation.

Comparative Genomics • What is conserved between species?

–Genes for basic processes

• Understand the uniqueness between different species

– Their adaptive traits

• What makes closely related species different?

• Analyzing & comparing genetic material from different species to study evolution, gene function, and inherited disease

What is compared?

• Gene location

• Gene structure – Exon number

– Exon lengths

– Intron lengths

– Sequence similarity

• Gene characteristics – Splice sites

– Codon usage

– Conserved synteny

Synteny and genome comparisons

Consensus comparative map data for six legume species.

Choi H et al. PNAS 2004;101:15289-15294

• The 12 chromosomes of rice can be aligned with the ten chromosomes of maize and the basic seven chromosomes of wheat and barley in such a way that any radius drawn around the circles will pass through different versions, known as alleles, of the same genes.

• There are great opportunities to predict the presence and location of a gene in one species from what we know from another.

• The main significance is, however, that we can now pool our knowledge of biochemistry, physiology and genetics and transfer it between crops via synteny.

Synteny and genome comparisons

Nature Reviews Microbiology 11, 349–355 (2013)

Genomics applied to agriculture

• Approaches – Similar for crop plants and farm animals – Relating traits to genes

• Relating genetic maps to physical maps • Rarely monogenic • QTL analysis (Quantitative Trait Loci)

– DNA sequence • Problem of large genome size • So use syntenic relationships • ESTs

– Genes can be manipulated, either through breeding or through genetic engineering, to remove deleterious traits and enhance desirable traits

Πόσο αποτελεσματική μπορεί να είναι σήμερα η εφαρμογή της γονιδιωματικής στη γεωργία;

Quantitative traits:

Unlike a Mendelian trait which is controlled by a single gene locus (monogenic), a quantitative trait is controlled by 3 or more gene loci (polygenic).

• The contribution of each gene to the quantitative trait is additive leading to the appearance of many phenotypic classes in F2. With many genes it becomes difficult to separate the phenotypic classes in F2 and the trait is known as continuous. Example is human height and crop yield.

• Nilsson-Ehle performed the following cross

– P True-breeding red X true-breeding white

– F1 Intermediate red

– F2 Great variation in redness: – White, light red, intermediate red, medium red, dark red

– As shown in Figure 24.3b, Nilsson-Ehle discovered that

the colors fell into a 1:4:6:4:1 ratio

– He concluded that this species is diploid for two different

genes that control hull color

• Each gene exists in two alleles: red or white

– He hypothesized that these two loci must contribute

additively to the color of hull

Trends in Genetics, Volume 29, Issue 1, 2013, 41 - 50

Omic space and related resources in plants.

Mochida K , and Shinozaki K Plant Cell Physiol

2010;51:497-523

Genotype Phenotype Environment Conceptual model showing how knowledge of genotypic variation can reveal the loci underlying phenotypic variation and those under selection by the environment. Alleles at these loci can then be directly selected for optimal phenotypes in appropriate environments jumping limitations of phenotypic selection.