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To determine how many genes were necessary for
Any manipulation that reduces expression of a specific gene.
Gene knockdowns approaches
- -Overexpress a dominant negative
- -Inject a morpholino
- -Homologous recombination
Overexpress a dominant negative Example
- Tyrosine kinase receptor
- Inject mRNA that will be translated into a mutant subunit protein
- that lacks the site needed to initiate signaling
- Ligand binds to dimer, but no signaling activated
- •Like RNA but ribose replaced with morpholine group;
- phosphodiester backbone modified.
- •Resists degradation by cell.
- •Complementary to RNA sequences.
Translation blocking morpholinos
- Binds near initiation site (overlapping portions of the 5’UTR
- and the first exon), interfering with ribosome assembly
- and prevents translation.
Homologous recombination (knockout)
- Relies on recombination between a vector carrying positive and negative
- selectable markers.
- Recombination occurs between identical sequences in vector and chromosomal DNA.
Often organisms appear not to “need” many of their genes
- -If you knockdown or knockout any single gene in a mouse, most of
- the time the mouse will survive.
- -Only about 30% of the genes are necessary for survival!
- -In yeast, more than 4,700 single-gene knockouts were performed in
- homozygous diploid lines. Only 10.7% exhibited reduced growth/
- viability. Growth of 83.5% of the knockouts was unaffected
redundancy refers to pairs of homologous genes
- with functional overlap where one can compensate
- for loss of the other.
Sources of genetic redundancy?
- -Gene duplication
- -Genome duplication
- -Convergent evolution
Implications of high occurrence of redundancy in signaling components?
- •Functional overlap in redundant genes may be beneficial in
- maintaining ability to signal.
Examples of redundant signaling proteins?
- -Hox genes
- -Wnt proteins
- -Myogenic regulators (MyoD, Myf5, myogenin, Mrf4)
-In study of 59 pairs of redundant genes (yeast), the
redundant forms were
- not expressed at the same
- time and/or place. Expression patterns didn’t overlap.
In vertebrate developmental pathways, redundant
- expressed in spatially or temporally
- distinct areas.
Knocking out one gene often results in
- upregulation of the
- redundant partner.
Redundancy may allow
compensation when one isoform lost
- -Early in vertebrate development, segmented blocks of tissue appear on either side of the nerve cord
- -The somites form muscle, tendons, endothelial cells, dermis, and cartilage.
Portions of the somite forming
adult skeletal muscle divided
into two domains…
- -Epaxial: dorso-medial region (ep)
- -Hypaxial: ventrolateral region (hyp)
Redundancy may allow adaptation to
Redundancy can be used to increase ability of cell
- to sense changes in
- environment and respond.
Redundancy may improve
processing of external info
Gene redundancy may provide important opportunities
for the organism…
- -Compensating for loss of another molecule
- -Adapting to changing external factors
- -Improved processing of external information
-How are new gene families created?
A) Divergent evolution
B) Concerted evolution
C) Birth-and-death evolution
- A group of genes is duplicated.
- -Evolution of gene A doesn’t
- affect evolution of gene B.
- -Each gene gradually diverges
- as mutations accumulate.
- -Duplicate genes assume new
- Ex: alpha and beta globins
- -Gene family members do not evolve
- independently. Evolve at same time.
- -In ribosomal RNA of frogs, find that
- intergenic regions more similar in
- different rRNAs of same species than
- in two related Xenopus species.
- -Why? If one gene acquires a mutation,
- it spreads to the other rRNAs by
- unequal crossing over or by
- nonreciprocal recombination.
- Ex: 5S rRNA in Xenopus
- Primate U2 snRNA
- -New genes made by gene duplication.
- -Some remain, others become
- pseudogenes or are deleted.
- -Pattern in phylogenetic tree is more
- difficult to interpret.
- Ex: Major histocompatibility complex genes
- T-cell receptors
- MADS-box genes
- Ubiquitins, etc.
- Instead of focusing on genes, look at evolution
- of protein domains.
- All the domains from a dataset is the “domainome” for that
- group of sequences.
How can the g-value paradox be explained?
- -More potential combinations between proteins
- -Multifunctional genes
- -Alternative splicing
- -Transcriptional control
- -Posttranslational modification
- -Roles of non-coding RNA
- assumes that once a complex trait is lost in a lineage, it cannot be
Domains acting in cell regulation
- increased during
- eukaryotic evolution.
Domains related to metabolism
- decreased during
- eukaryotic evolution.
- Suggests that roles of metabolic domains may be taken over by symbionts
High number of domains in basal groups supports idea that
- the last
- common ancestor of eukaryotes was complex.
Class I elements
Class II elements
Insertion of transposable elements can harm the host by
- Insertional mutagensis, chimeric transcript production, antisense effects, and illegitimate recombination
How does the genome defend itself from transposable elements?
- Cytosine methylation
- Defensive mutagenesis
- 1- In cytoplasm, Dicer binds
- 2- Dicer cleaves dsRNA to
- form small interfering RNAs
- (siRNAs, 21-23 bp long w/2 bp
- overhang at 5’ end).
- 3- The RNA-induced silencing
- complex (RISC) forms when the
- antisense strand of siRNA
- associates with Argonaute 2
- (AGO2) protein. May also include
- other protein types.
- 4- RISC complex scans RNAs
- to find complementary
- sequence to siRNA. siRNA
- binds sense strand of
- target mRNA and RISC
- complex cleaves target.
- 1- MicroRNA is transcribed from DNA.
- 2-These short RNA sequences form
- haipin loops and are transported to
- the cytoplasm by exportin5.
- 3- In the cytoplasm, Dicer trims the
- dsRNA (22 bp seq with overhang).
- 4- miRNAs silence gene expression by
- binding complementary regions in
- the 3’ UTR of target mRNAs
- (animals) or by binding coding
- regions of target mRNAs (plants).
- Base pairing is often partial and binding of the miRNA affects multiple mRNA types.
- Effects? Inhibiting translation, causing loss of poly-A tail, interfering with methylated cap/
- poly-A tail interactions, or causing mRNA degradation by exonucleases.
- -Bacteria use DNA methylation to protect the genome from
- degradation by restriction enzymes.
- -Restriction enzymes cannot destroy methylated restriction sites
- but can cleave unmethylated restriction sites.
- -Endogenous methyltransferases attach methyl groups to
- cytosines or adenines.
- -In eukaryotes, modified 5-methyl-
- cytosine is made by adding a methyl
- group to the 5 position of cytosine.
- -Causes transcriptional silencing when
- promoter region methylated. Good for
- long-term silencing. Doesn’t appear to
- be reversible.
- -Repetitive DNA in plants and
- mammals is usually methylated.
- -A method to block retroviral replication.
- -In primates, during reverse transcription,
- the host protein APOBEC3G is incorporated
- in 1st strand cDNA.
- -It deaminates cytosines in the retrovirus
- cDNA strand, converting them to uracils.
- -During second strand synthesis, uracil is
- recognized as thymidine, so adenine is
- inserted in the new DNA strand.
- -Deactivates virus by mutating up to 25%
- of cDNA guanine residues.
- -Not as effective in HIV-1 retrovirus. This
- virus makes Vif (viral interference factor)
- which inhibits activity of the APOBEC3G protein.
WHY Protein alignments are more useful if you’re comparing distantly
- -Peptide sequences are more likely to be conserved than nucleotide
- sequences since there are multiple codons for the same amino acid.
- - Some amino acids have similar biophysical properties. Similarities can
- be accounted for in the protein scoring matrices.
- -Similarities arising early in the evolutionary process may be detectable
- using a protein alignment, but not a DNA alignment.
WHY Nucleotide alignments can be more useful if you’re comparing closely
- -If sequences are very closely related, the amino acid sequences may
- not vary much. Nucleotide sequence will vary more, allowing easier
- detection of differences.
Global Sequence Alignment
- -Tries to align two sequences along entire length.
- -Best for highly similar sequences of same length.
- -As similarities decrease, misses important relationships.
Local Sequence Alignment
- -Looks for the most similar regions in sequence instead
- of trying to align entire length.
- -May return more than 1 result if there is more than
- 1 subsequence in common.
- -Good method to use if sequences differ in length or
- share partial similarity.
- -A simple way to visually compare 2 sequences to
- find local alignments, direct repeats, inverted
- repeats, insertions, deletions or low-complexity regions
Why aren’t dot plots used more?
Do not provide a measure of statistical similarity
- -Score is the log of an odds ratio. Considers how often, in nature,
- a particular residue is substituted for another versus how often
- this substitution would occur if by random chance.
- Si,j = log [ ( qi,j ) / ( pi
- pj ) ]
- Si,j is the score for a replacement of residue i with residue j.
- qi,j represents how often the two amino acids align with each
- other in multiple sequence alignments of protein groups.
- is the probability that residue i will occur among all proteins
- pj is the probability that residue j will occur among all proteins
- Relies on “seeding”. Looks for a short query word. Finds this and
- related words.
- -To score related words, uses a scoring matrix called “The neighborhood.”
- -Threshold setting controls how many options allowed in the neighborhood.
- -Then performs local alignment. Extends until gaps and mismatches decrease
- score below the score threshold (S)…This info recorded by BLAST.
- -If enter a sequence from a low complexity region can
- inflate BLAST scores.
- (Masking with DUST or SEG counter this.)
- -The hit list contains entries that represent hypothetical
- -Hits to ESTs should be treated with caution. Sequencing
- accuracy is lower than in “finished”
How does BLAST determine length
- Program measures cumulative
- score as alignment extended
- •If angle of drop off after a peak
- exceeds a threshold value (X),
- extension terminated and trims
- alignment to preceding peak in
- •HSP = high-scoring segment pair
- and is the trimmed alignment •Then calculates E value to determine if alignment
- is significant.