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Transcript

  • 1. DNA and Gene Expression
  • 2. Dexoyribonucleic Acid (DNA)
    • Two phosphoric acid sugar strands held apart by pairs of four bases
      • Adenine (A), thymine (T), guanine (G), cytosine (C)
      • A pairs with T, G pairs with C
    • Self replicating molecule
    • Directs synthesis of proteins for body
  • 3. DNA Structure <static.howstuffworks.com/gif/dna-2.jpg> <static.howstuffworks.com/gif/dna-base-pairings.gif>
  • 4. DNA Replication
    • Results in two complete double helixes of DNA
    • How nucleotides are added in DNA replication (animation)
  • 5. Genome
    • Maybe 30,000 genes on human genome
    • Gene range from 1000 to 2 million base pairs
  • 6. Protein Synthesis
    • 20 amino acids, despite 64 possible combinations from 4 base pairs; duplication
    • Codons
      • Various sequences of three base pairs
      • Each codes for an amino acid (or “stop” signal)
    • Amino acids assembled into proteins
    • Interestingly, only about 2% of genome involved in protein synthesis
  • 7. Genetic Code Amino Acid Codons Alanine CGA, CGG, CGT, CGC Arginine GCA, GCG, GCT, GCC, TCT, TCC Aaparagine TTA, TTG Aspartic acid CTA, CTG Cysteine ACA, ACG Glutamic acid CTT, CTC Glutamine GTT, GTC Glycine CCA, CCG, CCT, CCC Histidine GTA, GTG Isoleucine TAA, TAG, TAT Leucine ATT, AAC, GAA, GAG, GAT, GAC Lysine TTT, TTC Methionine TAC Phenylalanine AAA, AAG Proline GGA, GGG, GGT, GGC Serine AGA, AGG, AGT, AGC, TAC, TCG Threonine TGA, TGG, TGT, TGC Tryptophan ACC Tyrosine ATA, ATG Valine CAA, CAG, CAT, CAC (Stop signals) ATT, ATC, ACT
  • 8. Mutations
    • Mistakes made in copying DNA
    • Produces different alleles (called polymorphisms)
    • Mutations occurring in gametes will be transmitted faithfully unless natural selection intervenes
  • 9. Single-Base Mutations
    • Can either change or remove a base from a codon
    • Changing one base for another
      • Generally less likely to have an affect
    • Removal of base
      • More problematic, as it shifts the reading of the triplet code
      • C G A-CTA-TGA --> CAC-TAT-GA…
      • Alanine-aspartic acid-threonine --> valine-isoleucine…
    • Changing amino acid
      • No, small, or large effect on protein production
  • 10. Multi-base Mutations
    • Some genes can have multiple mutations at different locations
    • Complicates matters enormously
    • Both in terms of functionality and for identification of effects by behavioural geneticists
  • 11. RNA
    • Ribonucleic acid
    • Differs from DNA
      • Single-stranded molecule (generally) and is shorter
      • Contains ribose, not deoxyribose, making RNA less stable
      • Complementary nucleotide to adenine is not thymine (as in DNA), but uracil (U)
    • Various forms: mRNA, tRNA, rRNA, non-coding RNA
  • 12. RNA and DNA
    • Actually, the original genetic code
      • Still seen in most viruses
    • But single strand vulnerable to predatory enzymes; double stranded DNA gained selective advantage
    • RNA degrades quickly, is tissue-, age-, and state-specific
  • 13. Gene Expression
    • Transcription of gene
      • Production of mRNA in nucleus from DNA template
    • Translation
      • Assembly of amino acids into peptide chains on basis of information encoded in mRNA
      • Occurs in ribosomes in cell cytoplasm
      • mRNA and tRNA
  • 14. mRNA
    • mRNA exists only for a few minutes
      • Amount of protein produced depends on amount of mRNA available for translation
      • Protein production regulation
    • mRNA carries information about a protein sequence to the ribosomes
      • About 100 amino acids added to protein per second
      • Proteins 100-1000 amino acids long
  • 15. Transcription
    • Transcription animation
  • 16. Translation
    • Translation video
  • 17. Non-Coding RNA
    • Most DNA transcribed into RNA that is not mRNA, so is called non-coding RNA
    • At least 50% of human genome is responsible for non-coding RNA
      • Much of this is involved in directly or indirectly regulating protein-coding genes
  • 18. Introns
    • One type of non-coding RNA
      • DNA sequencers embedded in protein-coding genes
      • Transcribed into RNA, but spliced out before RNA leaves nucleus
      • From 50 to 20,000 base pairs long
    • About 25% of human genome
  • 19. Introns
    • Used to be called “junk” DNA
    • Not the case at all
    • Introns can regulate transcription of genes in which they reside
    • In some cases can also regulate other genes
  • 20. Exons
    • What’s left (and spliced back together) after introns are removed
    • Usually only a few hundred base pairs long
  • 21. MicroRNA
    • Another class of non-coding RNA
    • Usually only 21 base pairs long
      • DNA coding for them is about 80 base pairs
    • Especially important for regulation of genes involved in primate nervous system
    • Bind to, and thus silence, mRNA
    • About 500 microRNA identified which regulate expression of over 30% of all coding mRNA
  • 22. Gene Regulation
    • Can be short-term or long-term
    • Responsive to both environmental factors and expression of other genes
      • i.e., genes can turn each other on and off
  • 23. Polymorphisms
    • Genome is about 3 billion base pairs
    • Millions of base pairs differ among individuals
    • However, about 2 million base pairs differ among at least 1 percent of the population
    • These are the DNA polymorphisms useful for behavioural geneticists
  • 24. Detecting Polymorphisms
    • Genetic markers
      • Traditionally, single genes were identified by their phenotypic protein outcome
    • DNA markers
      • Based on the actual polymorphisms in the DNA
      • Millions of DNA base sequences are polymorphic and can be used in genome-wide DNA studies
      • Identify single-gene disorders
  • 25. DNA Microarrays
    • Gene chips
    • Surfaces the size of a postage stamp
    • Hundreds of thousands of DNA sequences
    • Serve as probes to detect gene expression or single-nucleotide polymorphisms
    • Fodor's gene chip
    <http://www.bio.davidson.edu/ Courses/genomics/chip/chipreal.html> <http://learn.genetics.utah.edu /units/biotech/microarray/>
  • 26. Genetic Screens
    • Test to identify individuals with a phenotype of interest
    • Forward genetic screens
      • Find the genetic basis of a phenotype or trait
    • Reverse genetic screens
      • Identify mutant alleles in genes that are already known
      • Find the possible phenotypes that may derive from specific DNA sequences
    • Traditionally, with non-humans, expose individuals to mutagens to cause mutations, thereby increasing the frequency of unusual alleles
  • 27. Different Types of Screens
    • Basic screens look for a phenotype of interest in the mutated population
    • Enhancer/suppressor screens used when an allele of a gene leads to a weak mutant phenotype
      • A weak effect might be a damaged or abnormal limb, organ, behaviour trait
      • A strong effect might be the total absence of said limb, organ, behaviour
  • 28. Classical Genetic Approach
    • Map mutants by locating a gene on its chromosome through crossbreeding studies
    • Statistics on frequency of traits that co-occur are utilized
    • Now, SNPs (single nucleotide polymorphisms) are used for mapping
  • 29. Reverse Genetic Screens
    • Find the effect of a gene sequence on phenotype
    • Produce disruption in DNA, then look for effect on whole organism
      • Random or directed deletions, insertions, and point mutations produce a mutagenized population
      • Screen population for specific change at the gene of interest
  • 30. Directed Deletions and Point Mutations
    • Gene knockouts
      • Used in yeast and mice
      • Individuals engineered to carry genes made inoperative (“knocked out”)
    • Gene silencing (“gene knockdown”)
      • Uses double stranded RNA to temporarily disrupt gene expression
      • Produces specific knockout effect without mutating the DNA of interest
    • Transgenic organisms
      • Over express normal gene, for example
  • 31. Single Nucleotide Polymorphisms
    • SNPs
      • A variation in DNA sequence when a single nucleotide (A, T, C, G) in the genome differs between individuals or between paired chromosomes of an individual
    • AAGC C TA to AAGC T TA
      • Two alleles here: C and T
    • Almost all common SNPs have only two alleles
    • For a variation to be called an SNP it must occur in at least 1% of the population
  • 32. Amino Acid Sequence
    • SNPs won’t necessarily change the amino acid sequence of a protein
      • Duplicatory nature of codons, might get same amino acid, but have different SNP allele
    • Synonymous SNPs
      • Both forms produce same polypeptide sequence
      • “ Silent mutation”
    • Non-synonymous SNPs
      • Different polypeptide sequences are produced
  • 33. Coding Regions
    • SNPs can exist in both protein coding and non-coding regions of genome
    • Even non-protein coding region SNPs can have effects
      • Gene splicing
      • Transcription factor binding
      • Sequencing of non-coding RNA
  • 34. Example
    • SNP in coding region with subtle/harmless protein change
    • Change the GAU codon to GAG
      • Changes amino acid from aspartic acid to glutamic acid
      • Similar chemical properties, but glutamic acid is a bit bigger
    • This change to a protein is unlikely to be crucial to its function
  • 35. Example
    • SNP in coding region with harmful effect
    • Sickle-cell anemia
    • Changes one nucleotide base in coding region of hemoglobin beta gene
      • Glutamic acid replaced by valine
      • Hemoglobin molecule no longer carrying oxygen as efficiently due to drastic change in protein shape
  • 36. Latent Effects
    • SNP in coding region only switching gene on under certain conditions
    • Under normal conditions, gene is switched off (is latent)
    • Can activate under specific environmental conditions
      • E.g., exposure to precarcinogens or carcinogens
  • 37. SNPs and Cancer
    • SNP changes to genes for proteins regulating rate of absorbing, binding, metabolizing, excreting precarcinogens or carcinogens
    • Small changes can alter an individual’s risk for cancer
    • SNP does no harm itself under normal circumstances, only having an effect when person is exposed to a particular environmental agent
      • E.g., Two people with different SNPs could both smoke, but only one develops cancer, responds to therapy, etc.
  • 38. Smoking and Susceptibility
    • Precarcinogens from tobacco enter lungs
      • Lodge in fat-soluable areas of cells
      • Bind to proteins converting precarcinogens to carcinogens
    • Reactive molecules quickly eliminated
      • Detoxifying proteins make carcinogens water-soluable
      • Excreted in urine before (hopefully) damaging cell
  • 39. SNP Variability
    • Different SNPs may express hyperactive or lazy activator (or something in between)
      • The carcinogen-making protein
      • E.g., Hyperactive ones could “grab” and convert more precarcinogens than usual or do it more rapidly
      • E.g., Influence effectiveness of detoxifying enzymes
      • If more carcinogens build up in lungs, more damage to cells’ DNA is caused
    • Different SNPs could alter individuals’ risk of lung cancer
  • 40. Bladder Cancer
    • Workers in dye industry exposed to arylamines
      • Have increased risk of bladder cancer
    • SNPs may be involved
    • In liver, an acetylator enzymes acts on arylamines, deactivating them for excretion
    • SNPs produce several different slow forms of acetylator enzyme, keeping arylamines in liver for longer
      • More are converted to precarcinogens, increasing risk for cancer
  • 41. Polygenetic Effect
    • SNPs don’t entirely explain this
    • Not all individuals with slow acetylators exposed to arylamines are at increased risk of bladder cancer
      • About half of North American population has slow acetylators
      • Only 1 in 500 develop bladder cancer
    • Other yet undiscovered genes and proteins involved
  • 42. Drug Therapies
    • SNPs could also explain different patient reactions to the same drug treatment
    • Many proteins interact with a drug
      • Transportation through body, absorption into tissues, metabolism into more active or toxic by-products, excretion
    • Having SNPs in one or more of the proteins involved may alter the time the body is exposed to the active form of the drug
      • Could be applied to why individuals with behaviourally similar forms of schizophrenia can react very differently to the same drug therapy
  • 43. SNPs and Gene Mapping
    • SNPs are very common variations throughout the genome
    • Relatively easy to measure
    • Very stable across generations
    • Useful as gene markers
    • Contribute to understanding of complex gene interactions in behaviours and behavioural disorders
  • 44. By Association
    • If SNP located close to gene of interest
    • If gene passed from parent to child, SNP is likely passed too
    • Can infer that when same SNP found in a group of individuals’ genomes that associated gene is also present
  • 45. Sequencing SNPs
    • Sequence the genome of large numbers of people
    • Compare base sequences to discover SNPs
    • Goal is to generate a single map of human genome containing all possible SNPs
  • 46. SNP Profile
    • Each individual has his or her own pattern of SNPs
      • “ SNP profile”
    • By studying SNP profiles in populations correlations will emerge between specific SNP profiles and specific behaviour traits
      • E.g., specific responses to cancer treatments
  • 47. What is a Gene?
    • “ Gene” from “pangenesis” (Darwin’s mechanism of heredity)
    • Greek: genesis (“birth”) or genos (“origin”)
    • First coined by Wilhelm Johannsen in 1909
  • 48. Central Dogma
    • One gene, one protein
    • Information travels from DNA through RNA to protein
    • Gene = DNA region expressed as mRNA, then translated into polypeptide
    • View held through 1960s
  • 49. Extended Dogma
    • Transcribed mRNA produces single polypeptide chain (folds into functional protein)
    • This molecule performs discrete, discernible cellular function
    • Gene regulated by promoter and transcription-factor binding sites on nearby DNA
  • 50. Simplified Extended Dogma From: Seringhaus & Gerstein, 2008
  • 51. Implications
    • Nomenclature
      • Gene named and classified by basic function
    • Traditional classification systems
      • Vertically hierarchical
      • Broad functional categories (e.g., genes whose products catalyze a hydrolysis reaction) to specific functions (e.g., “amylase” describing specific break-down of starch)
    • In 1950s: International Commission on Enzymes Classification, Munich Information Center for Protein Sequences
  • 52.
    • One gene, one protein, one function
    • Straightforward view of subcellular life
    • Allows conception of single protein as indivisible unit in larger cellular network
    • When mapping genes across species, can assume a protein is either fully preserved in organisms or entirely absent
    • Allows easy grouping of related proteins in different species
    • Extended dogma includes regulation, function, and conservation
  • 53. Current View
    • High-throughput experiments
      • Probe activity of millions of bases in genome simultaneously
    • Much more complex than extended dogma
  • 54. Creating RNA Transcript
    • Genes only small fraction of human genome
    • Genome pervasively transcribed ( ENCODE Project , 2007)
    • Non-genic (i.e., genome outside known gene boundaries) transcription very widespread (even including “pseudogenes”)
    • Function of non-gene transcribed material as yet unclear
  • 55. Pseudogenes
    • DNA sequences that look similar to functional genes, but contain genetic lesions (e.g., truncations, premature stop codons), disrupting ability to encode proteins or structural RNA
      • Long considered “fossils” of past genes
    • However, recent work estimates that 5-20% of human pseudogenes can be transcriptionally active (Zheng & Gerstein, 2007)
    • Might achieve functionality via: fusing with mRNAs from nearby functional genes to form chimeric RNAs, having RNA transcript that has regulatory role, combining with new DNA to generate a new gene
  • 56. Introns/Exons
    • Long understood that eukaryote genes composed of short exons, coding regions of DNA separated by long introns
    • Introns transcribed to RNA that is spliced out before proteins produced
    • But, now known that splicing for a gene-containing locus can be done in multiple ways
      • Individual exons left out of final product
      • Only portions of the sequence in an exon are preserved
      • Sequences from outside gene can be spliced in
    • Result is many variants of a single gene
  • 57. Example of Current View From: Seringhaus & Gerstein, 2008
  • 58. Gene Regulation
    • Traditional view
      • Protein-coding portion of gene and regulatory sequence in close proximity on chromosome
    • Doesn’t apply well to mammalian and other higher eukaryote systems
    • Gene activity influenced by epigenetic modifications (changes to DNA itself or to support structures of DNA)
    • Genes can be regulated over 50,000 base pairs away, even beyond adjacent genes
    • Looping and folding of DNA can bring distant spans into close proximity
  • 59. DNA Folding From: Seringhaus & Gerstein, 2008
  • 60. Implications
    • Defining gene functionality much more difficult now
    • Traditionally done by phenotypic effect
    • Doesn’t capture function on molecular level, though
    • Also, pathways a gene product engages in within a cell significant for understanding functionality
  • 61. Classification
    • Non-trivial problem in deciding which qualities of a gene and its products to use
    • Earlier approaches assumed simple hierarchical scheme
    • No longer so simple
    • Recent computer technologies offering solutions
  • 62. Direct Acyclic Graphs (DAGs) Simple hierarchy DAG hierarchy In simple hierarchy a gene has only one “parent” for each node. In the DAG approach each node can have multiple “parents”. Genes can be classified within multiple groups. From: Seringhaus & Gerstein, 2008
  • 63. Naming
    • Cross-species gene identification difficult
    • Naming inconsistent
    • Often, traditionally, have different names for functionally similar (or same) gene in different species
    • Recent increases in computing power and genome sequencing making homology mapping of similar genes across species feasible
  • 64. Example: Notch Pathway
    • Highly conserved among species
    • Defective Notch encodes receptor protein in fruit flies that produces notched wing shape
    • Traditional views of Notch pathway quite limited
    • High throughput experiments in humans identifying many more proteins involved in pathway
    • Hypertext software now makes identifying connections easier
  • 65. Notch Pathway Traditional Current From: Seringhaus & Gerstein, 2008