Transcript Slide 1
From ome to ome: revolutions in current biology Deri Tomos (Ysgol Gwyddorau Biolegol, Prifysgol Cymru Bangor) Friedrich Wohler 1828 Gregor Mendel 1866 (de Vries 1900) The Substance of Life & Inheritance ? Proteins – the most complex bio-molecules From Protein to DNA 1944-1953 - 9 anni mirabili Watson & Crick (Franklin, Wilkins ……. & Herbert Wilson) - 1953 Crick Wilson Self replicating C=G A=T Okazaki fragments A new biology A universal language Haemoglobin Reading the Genome Virus X 174 - 8 genes - 5386 letters (1976) Escherischia coli - 4.6 million Yeast - 23 million So many sequences ! Nematode Human (3 billion letters) Mouse Drosophila Zebra Fish Arabidopsis Rice Automated sequencing Three types of “reading” Gene sequencing Genetic mapping Fingerprinting Genetic fingerprinting Unique sequences – cf car registration numbers or NI numbers Catching criminals/Disaster identification/families Genetic mapping Whole series of tabloid headline “genes for” …… Dangerous (?) statistical correlations In plant and animal breeding – marker assisted breeding “Real” gene identification Genetic disease – eg CF, PKU (some 10,000 examples) Each has already raised moral issues – eg insurance, genetic councelling etc) What does the Genome do ? Central Dogma – a “photocopy” ! Translation Transcription DNA RNA Protein Genome Hans Winkler, 1920 Genomics was introduced 24 years ago by Victor McKusick and Frank Ruddle, as the catchword for the new journal of that name they had just founded Proteome 1994 Marc Wilkins (Proteome Systems) Jeremy Nicholson "metabolomics"-- 1996 Minimum number of genes ~ 300 ? The Central Dogma today ? Outcomes of reading the genome in 1980s. Gene (DNA) copy, cut and splice Transcript (RNA) Introns The gene for one type of collagen found in chickens is split into 52 separate exons. The gene for dystrophin, which is mutated in boys with muscular dystrophy, has 79 exons. Even the genes for rRNA and tRNA are split. Only 2% of the Human Genome codes for proteins ! and 25,000 such genes produce 100,000 proteins ! We need to know what genes are actually making Studying the transcriptome (RNA) Microarrays Each spot is an active gene In humans : ~ 25,000 Enormous power – the bits of the book that are being read at any time What makes a Queen Bee ? 9 genes Disease and design of new treatments Doctor’s surgery soon Non-protein RNA. “Junk genes” (Steve Jones) ? 50% of human genome - “transposons” Internalised viruses Epigenetics Non-genome inheritance. Imprinting Chromosome structure On / Off switches Epigenetics Lamarck ? Stem cell role – resetting the clock. Another Solid Gold sheep story Epigenetics at work The need to look directly at the Proteins that are made. Proteomics Proteome Gel electrophoresis $700 million 1999 $5.6 billion 2005 Robots essential for “high throughput” Robots cut out spots and feed them to powerful mass spectrometers Fragments can be identified by reference to the genome, if known, prediction. But needs powerful computers ! BIOINFORMATICS This Biology is BIG and expensive So why do we bother ? New drugs are harder and harder to develop. In 2000 $30 billion and R&D – only 30 drugs approved. But ultimately it is the small molecules - the metabolites - that matter. Metabolomics Chromatography and nmr - but again need high throughput analysis Pharmaceutical companies need millions of analyses per year. Powerful - and expensive - mass spectrometers In Fig A is depicted the metabolomic analysis of a wide variety of compounds across 11 different tissues from a mouse. The height of each dot represents the relative concentration of each compound. The distribution of a single compound across all 11 tissues is depicted in Fig B. but 2,000 - 20,000 per tissue type ? Drug targets and effects Proteome Transcriptome Metabolome Genome Serious bioinformatics challenges ! This Biology is Multidisciplinary Where will this approach take us ? Genetic (metabolic) diseases Food production, nutrition and environmental protection Cancer and development New pharmaceuticals Brain and consciousness INDIVIDUALISATION of treatments Functional imaging ? NNB. Expected time scale for our students But remember Wohler ! Diolch Thank you In physics, probably starting with Faraday's ion, cation, anion, the -on suffix has tended to signify an elementary particle, later materially focused on the photon, electron, proton, meson, etc., whereas -ome in biology has the opposite intellectual function, of directing attention to a holistic abstraction, an eventual goal, of which only a few parts may be initially at hand. [ Joshua Lederberg and Alexa T. McCray "'Ome Sweet 'Omics: A Genealogical Treasury of Words" Scientist 15 (7): 8 April 2, 2001] http://www.thescientist.com/yr2001/apr/comm_010402.html Peirianneg Genynnau Yr Offer Ensymau “cyfyngu” Lladd dafad ddall Peirianneg Genynnau A B