Transcript No Slide Title

BioSci D145 Lecture #6
• Bruce Blumberg ([email protected])
– 4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment)
– phone 824-8573
• TA – Bassem Shoucri ([email protected])
– 4351 Nat Sci 2, 824-6873, 3116 – office hours M 2-4
• lectures will be posted on web pages after lecture
– http://blumberg.bio.uci.edu/biod145-w2015
– http://blumberg-serv.bio.uci.edu/biod145-w2015
– No office hours on Thursday 2/12
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Midterm results
Mean: 25.3
Median: 27
Maximum: 33
Minimum: 18
Std. dev.: 4.3
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Functional Genomics - The challenge: Many new genes of unknown function
• Where/when are they expressed?
– Known genes (e.g. from genome projects)
• Gene chips (Affymetrix)
• Microarrays (Oligo, cDNA, protein) (Iyer paper week 4)
– Novel genes
• Expression profiling (3 papers this week)
– Genomic tiling microarrays (Kapranov)
– SAGE and related approaches (RIKEN)
– Massively parallel sequencing (RNAseq) (t’Hoen)
• Which genes regulate what other genes?
• Epigenetic modification of gene expression (week 7 papers)
• What is the phenotype of loss-of-function? (week 8 papers)
– Genome wide siRNA (Boutros)
– Genome wide synthetic lethal screens (Luo)
– CRISPR/Cas (Gilbert)
• What do they interact with (week 9 papers)
• ‘Omic approaches and humanized mouse models (week 10 papers)
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Routes to gene identification
• Genome sequences are minimally useful without annotation
– Annotation = description, biological information
• Functional annotation – information on the function
• Structural annotation – identification of genes, sequence elements
– Much annotation is done automatically today
• Via sequence comparisons with various databases
– Gene sequences
– ESTs
• Algorithms predict promoters, splicing, polyadenylation sites and,
most importantly ORFs
• ORFs – open reading frames are putative proteins
– Algorithms miss in both directions
– Source of much disagreement
• Field of bioinformatics has grown to encompass many types of analysis
related to gene function
– www.igb.uci.edu
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
How are genes identified?
• Random
– EST sequencing, select interesting looking gene
– Large scale expression analysis
• http://xenopus.nibb.ac.jp/
• From protein sequences
– Antibody screening
– Reverse translate and oligo screen
• Functional cloning
– Finding a gene by using a functional assay
• Positional cloning
– Find a gene by where it is located, what it is near
• By similarity to other sequences
– Gene family
– Cross-species
– Computer based equivalents
• Bioinformatic analysis that relates back to functional or positional cloning
BioSci D145 lecture 6
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Bruce Blumberg 2011. All rights reserved
How are genes identified?
• Functional cloning (aka expression cloning) – identifying by a functional assay
– What are functional assays?
• Enzyme activity – kinases (add PO4 to proteins)
• Ligand binding – peptide hormone (e.g. glucagon) receptors
• Transport (ions, sugars, etc) – e.g., intestinal glucose transporters
• Mutant rescue – restore function to a cell or embryo
– Introduce cDNA library pools (~10,000 cDNAs)
• via transfection, microinjection, infection
• Perform functional assays
– Robust, sensitive,
accurate is key
– positive pools are subdivided
and retested to obtain pure cDNAs
• cycle is repeated until
single clones obtained
– Applications – many enzymes
transporters and growth factor
receptors cloned this way
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
How are genes identified?
• Positional cloning – identifying by where the gene is located
– from genetic linkage map
– by walking from nearby sequence tags (ESTs, STS, STC, etc)
– Gene trap techniques (week 8)
– Interspecific backcrosses (Mus musculus vs M. spretus)
– When possible – try to rescue phenotype with candidate region
• Positional cloning – ok you have identified a region where your gene of
interest may be located - now what?
– How do we figure out what genes are in this region without knowing
function?
– General problem for annotation of sequences
• Genome sequencing vis a vis positional cloning
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Bruce Blumberg 2011. All rights reserved
How are genes identified? - Case studies
• Duchenne muscular dystrophy (DMD) first gene positionally cloned
– One group did genomic subtraction cloning
• Strategy enriched for regions lost in DMD patient
• Made a library and tested clones by Southern blot to normal and DMD
DNA
– 2nd group cloned breakpoints
• Girl with translocation between X and 21
• 21 was rich in rRNA genes so made a
radiation hybrid panel from patient
• Identified hybrid cell carrying the breakpoint
– made a genomic library from it
• Screened library for clones with both rRNA genes and X chromosome
specific sequences
• Mapped this genomic DNA to male patients with DMD and found
deletions in many of them
– DMD gene is largest known – 2.4 megabases
– cDNA cloning followed – protein is dystrophin
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
How are genes identified? (contd)
• Duchenne muscular dystrophy (contd)
– Today – for a sequenced organism – just go to the database identify
sequences in region of interest and verify by Southern or PCR as above
• Or look in large insert libraries with breakpoints
• Or do cDNA subtraction between tissues from normal individual and
DMD individual
– Presumes knowledge of source of mutation, i.e., the defect
resides in the affected tissue
– Would not detect a defect in inducing factor from other tissue
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Bruce Blumberg 2011. All rights reserved
How are genes identified? (contd)
• Ways to identify genes in regions
– Cross-species hybridization
• Probe another species with this genomic region
• coding sequences are conserved -> should see hybridization where
genes are
• What do you think are limitations to this approach?
– Species must be sufficiently different to reduce “noise” from
overall sequence conservation
» mouse vs human probably not great
» Human vs frog or fish probably good
– Must be sufficiently similar for genes to be conserved
» Human vs frog or fish probably good
» Humans vs yeast only good for common genes
– Target species region needs to be well characterized
• Computer parallels – compare sequence to be annotated with
annotated sequence from a different organism
– e.g., human with Drosophila
– Unknown bacterium with E. coli, etc.
BioSci D145 lecture 6
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Bruce Blumberg 2011. All rights reserved
How are genes identified? (contd)
• Ways to identify genes in regions (contd)
– Hybridization to known genes or coding materials
• What are some examples?
–
–
–
–
Hybridize to mRNA (Northern blots)
Hybridize to cDNA libraries (must be right tissue, cell or stage)
Hybridization to genomic tiling microarrays
Capture cDNAs or mRNAs from solution
• Computer based parallels
– Compare with expressed sequences from other species
– Compare with ESTs
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©copyright
Bruce Blumberg 2011. All rights reserved
How are genes identified? (contd)
• Ways to identify genes in regions (contd)
– Identify features found in typical promoters
• What are promoters?
Regions 5’ to a gene that are required for expression
• CpG islands – regions in eukaryotic genes that are hypomethylated
– Undermethylated
– Remember that methylation of DNA typically inhibits gene
expression
– Digest with enzymes that have CG in recognition site that would
be inhibited if methylated, e.g., SacII CCGCGG, run gel to check
» If nonmethylated (expressed) enzyme will cut, region will be
hypersensitive, get chopped up.
» If methylated (not expressed) enzyme will not cut and
region will not get digested
• DNAse I hypersensitive sites
– Similar principle – transcriptionally active DNA is “open”
– If open, it is more sensitive to DNAse I than non-active DNA
– Test by digestion and gel electrophoresis
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©copyright
Bruce Blumberg 2011. All rights reserved
How are genes identified? (contd)
• The problem with all of these methods is that experiments are required
– What do we do when sequences are coming in at the rate of tens of
gigabases/month?
– Need large-scale, robust, computerized methods to identify genes and
annotate sequences!
• All bioinformatics depends on databases
– UCI bioinformatics groups are among the best at designing and
constructing databases
• http://www.igb.uci.edu/tools/databases.html
• Three major databases of sequences (automatically duplicated)
– GENBANK http://www.ncbi.nlm.nih.gov/Genbank/index.html
– DNA Databank of Japan http://www.ddbj.nig.ac.jp/
– European Molecular Biology Laboratory (EMBL)
http://www.ebi.ac.uk/embl/index.html
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Bruce Blumberg 2011. All rights reserved
Dystrophin as an example
• http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=retrieve&dop
t=default&list_uids=1756
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Genome annotation – how to identify genes?
• Gene identification/prediction is important but difficult
– Large variety of methods and algorithms to predict exons
– To identify genes must first identify open reading frames (ORFs)
• When dealing with cDNAs – look for regions that code for proteins
– Do all genes code for proteins?
– Correct reading frame for a sequence is assumed to be largest
with no stop codons (TGA, TAA, TAG)
• Lots of tricks can be employed
– Codon frequency for an organism
» Coding sequences follow codon usage
» Noncoding sequences do not, often have lots of stop codons
– Consensus sites
» Kozak translational initiation CCRCCATGG
• What is a very important consideration when searching sequences to
predict ORFs?
– Sequence must be accurate
» Incorrect base calls are troublesome
» But indels (insertions or deletions) are disastrous
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Genome annotation – how to identify genes (contd)?
• Computer based gene prediction methods
– Two major methods are in use
• Homology searches
– Compare with other known sequences
• Ab initio (from the beginning) prediction
– Use algorithms to recognize common features and predict genes
» Promoters
» Splice sites
» Polyadenylation sites
» ORFs
– Generally, microbial genomes are much easier to annotate – WHY?
• Simply identify ORFS > 300 bp (100 amino acids)
– Works very well
– But can miss small coding sequences
– Must run on both strands because there are shadow genes
(overlap on two strands)
• Using a variety of programs, can predict genes in bacterial genomes
– Venter Sargasso sea paper
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Genome annotation – how to identify genes (contd)?
• Computer based gene prediction
methods (contd)
– Huge variety of programs
available
• Neural networks –
attempt to model learning process
– Build decision trees, use probabilistic reasoning
• Rule-based systems
– Rules often not clear
– Have trouble with exceptions
• Hidden Markov models
– Break sequences down into small units based on statistical
analysis of composition
» Hexamers appear to be optimal size to search
– Classify sequences into types or “states”
– Identify transitions between states
– Very useful for large number of purposes
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©copyright
Bruce Blumberg 2011. All rights reserved
Genome annotation – how to identify genes (contd)?
• Computer based gene prediction
methods (contd)
– Training sets are used to
“teach” programs how
to solve problems
• Training set is actual data – genes with known features
– Programs use training sets to classify new data
• Neural networks use training data to build decision trees
• Rule-based systems use training data to generate rules
• HMM build table of probabilities for states and transitions
– Pierre Baldi in IGB is UCI expert in machine learning
• How well do gene predicting programs work?
– Extremely well on bacterial genomes
– Fairly well on simply eukaryotic genomes
– Variable on complex genomes
– Rule of thumb – use a group of programs and look for areas of agreement
among them
– The best current programs combine ab initio predictions with similarity
data to define a probability model
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©copyright
Bruce Blumberg 2011. All rights reserved
Identification of gene function
• You have identified a gene – what is its function?
– Always look for similarity to known sequences
• Swiss-prot is fairly well annotated
• GENBANK translated database is most complete
• BLAST is tool to use
• Amino acid searches more sensitive than nucleotide searches
– Because identical amino acid sequences might only be 67%
identical at nucleotide level
– What might you find?
• Match may predict biochemical and physiological function
– e.g., a known enzyme from another organism
• Match may predict biochemical function only
– e.g., a kinase
• Match a gene from another organism with no known function
– May match ESTs or ORFs from other organisms
• Match a known gene with partly characterized function
– Search leads to clarification of function – NifS in book
• Might not match anything at all
– Expect this will happen less and less
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Up-regulated by TTNPB
(> 1.5 Fold, p < 0.01, n=334)
Cytoskeleton
Miscellaneous 4% (14)
1% (4)
Energy metabolism
3% (9)
Extrcellular matrix
3% (10)
Unidentified
21% (73)
Housekeeping
15% (52)
Tumor suppressor
1% (2)
Transcriptional
15% (53)
Signal tranduction
8% (26)
Retinoid metabolism Neural
1% (3)
2% (7)
BioSci D145 lecture 6
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©copyright
Hypothetical
26% (90)
Bruce Blumberg 2011. All rights reserved
Cytoskeleton
Energy metabolism
Extrcellular matrix
Housekeeping
Hypothetical
Neural
Retinoid metabolism
Signal tranduction
Transcriptional
Tumor suppressor
Unidentified
Miscellaneous
Identification of gene function (contd)
• You have identified a gene – what is its function? (contd)
– Does the sequence contain an obvious functional motif?
• Homeobox or other consensus DNA binding domain?
• Kinase domain?
• Serine protease, etc.
– InterPro database allows one to compare a protein sequence with whole
family of structural databases
• http://www.ebi.ac.uk/interpro/
• HICAICGDRSSGKHYGVYSCEGCKGFFKRTVRKDLTYTCRDSKDCMIDKRQRNR
CQYCRYQKCLAMGM
• http://www.ebi.ac.uk/interpro/sequencesearch/iprscan5S20150210-213958-0238-95988468-pg
• Other sorts of similarity searches
• Identify protein secondary structure motifs
– Alpha helix, beta sheets, hydrophobicity
– Amphipathic helices
– Overall polarity of sequences
– Not used much
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Identification of gene function (contd)
• You have identified a gene – what
is its function? (contd)
– Gene ontology – highly
structured vocabulary for gene
classification
• Genes are classified using
this vocabulary
• Relates protein function
with cellular or organismal
functions
– Nucleic acid binding
– Cell division
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Genome annotation
• Extremely important as number of sequences increases
– Goals are to identify
• all of the sequences
• all of the features of each sequence
• All of the functions of the identified genes
– Sometimes annotation does not agree with known function
• Human error
• New and updated information not propagated to database
• Inaccurate sequencing
• Sometimes annotation is correct but protein lacks function under
certain conditions (e.g., need cofactors)
– Gold standard for functional analysis is loss-of-function analysis
• Most accurate annotation
– Common to have “annotation jamborees” where biologists and
bioinformaticians come together to annotate new sequences
• Xenopus tropicalis jamboree was in Spring 2006
• But many genes and gene models are still unannotated
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Which genes regulate what other genes?
• The biggest defect of expression microarrays or transcript profiling is that
neither can distinguish direct targets from indirect targets
– Which genes are a primary response to the treatment vs.
– Which ones have one or more intermediates?
• How can we approach and solve this important problem?
– Identify the genes to which a transcription factor binds
– Identify to which genes RNA polymerase II is recruited
• And from which it is dismissed
• All eukaryotic DNA occurs as chromatin (DNA+histone and other proteins)
– Chromatin
conformation
influences
whether
transcription
can occur
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©copyright
Bruce Blumberg 2011. All rights reserved
Which genes regulate what other genes?
— Open chromatin is accessible to transcriptional machinery
• DNA is unmethylated
• Histones are methylated and acetylated (acetylase activates)
• Many transcriptional co-activators methylate and acetylate histones
and other chromatin associated proteins
− Opens the chromatin
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Which genes regulate what other genes?
— Closed chromatin is inaccessible to transcriptional machinery
• DNA is methylated
• Some histone tails are methylated, others not
• Transcriptional co-repressors recruit histone deacetylases (HDACs)
which lead to chromatin condenstation
• Chromatin condensation leads to gene silencing
• Identification of chromatin-localized proteins is diagnostic for direct target
genes of transcription factors
− Most common application
• Identification of promoters to which RNA Pol II is recruited upon some
treatment is diagnostic for genes directly upregulated by the treatment
− Trickier but very useful
• Identification of promoters from which RNA Pol II is dismissed upon some
treatment is diagnostic for genes downregulated by the treatment
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Chromatin immunoprecipitation - ChIP
• ChIP is the only method for large-scale identification of direct
transcriptional targets
• General strategy
– Crosslink proteins to nearby DNA with formaldehyde
• Works for about 2 angstrom distances
• What does this say about the specificity of the interaction?
—
BioSci D145 lecture 6
Only specific interactions will be reflected in crosslinks
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Bruce Blumberg 2011. All rights reserved
Chromatin immunoprecipitation - ChIP
• ChIP - general strategy (contd)
– Break chromatin into small chunks by sonicating
– Typically want ~500 bp fragments
– Evaluate sonication quality and extent by gel electrophoresis to ensure
that size range is obtained
• Needs MUCH optimization
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©copyright
Bruce Blumberg 2011. All rights reserved
Chromatin immunoprecipitation - ChIP
• ChIP - general strategy (contd)
– Precipitate chromatin with antibody against protein of interest
• Bind antibody, then capture complex
with protein G/protein A beads
• Reverse crosslinks – and remove proteins
with proteinase K digestion
• Purify DNA away from proteins
• Evaluate enrichment of individual
candidate binding sites by PCR
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Chromatin immunoprecipitation - ChIP
• Flavors of ChIP commonly in use
– Standard ChIP – one antibody, few targets analyzed
• Most commonly used method
– ChIP-chip – chromatin immunoprecipitation on chip
• Recovered fragments are used to probe microarray of genomic DNA
• Allows identification of novel binding sites
• Requires good genomic microarrays
— Whole genome requires MANY chips
(at least 7 for human and mouse) = EXPENSIVE
— may not be available for your
target organism
— Affymetrix, Agilent, Nimblegen
are sources
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Chromatin immunoprecipitation - ChIP
• Flavors of ChIP commonly in use
– ChIP-sequencing (ChIP-seq) – chromatin immunoprecipitation sequencing
• Massively parallel sequencing of recovered fragments
• Unbiased method to identify transcription factor binding sites
• Price is fast converging on ChIP-chip
• Requires excellent, well-characterized antibody!
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Computer-based methods that may help to identify binding sites
• Phylogenetic footprinting – What is it?
– Powerful method to identify regulatory elements in DNA sequences
– Central assumption is that protein coding sequences evolve much more
slowly than DNA sequences (or DNA sequences evolve faster)
• Due to selective pressure on protein function
– Sequences conserved in related organisms likely to be functional
– Species selection• Must be sufficiently diverged that functional domains stand out
• Sufficiently conserved to that they can be identified
– A variety of algorithms exist – typical approach is to use multiple
programs and look for what is found in common.
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Bruce Blumberg 2011. All rights reserved
Comparative genomics (contd)
• Phylogenetic footprinting comparison of zebrafish, mouse and xenopus caudal
orthologs (cdx4, cdx4, Xcad3)
– A number of putative conserved elements identified including
– TTCATTTGAATGCAAATGTA
– Absolutely conserved in all 3 promoters
– Compare with database
• http://www.ncbi.nlm.nih.gov/blast/
– Also found in human cdx4 – a good validation of the result
• As more genomes are sequenced and compared, phylogenetic footprinting
becomes a very powerful filter to identify potentially conserved regulatory
sequences
– ECR browser offers precomputed comparisons of conserved elements
• http://ecrbrowser.dcode.org/
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
Routes to gene identification
• Genome sequences are minimally useful without annotation
– Annotation = description, biological information
• Functional annotation – information on the function
• Structural annotation – identification of genes, sequence elements
– Much annotation is done automatically today – “gene models”
• Via sequence comparisons with various databases
– Gene sequences
– ESTs
• Algorithms predict promoters, splicing, polyadenylation sites and,
most importantly ORFs
• ORFs – open reading frames are putative proteins
– Algorithms miss in both directions
– Source of much disagreement
• Field of bioinformatics has grown to encompass many types of analysis
related to gene function
– www.igb.uci.edu
BioSci D145 lecture 6
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©copyright
Bruce Blumberg 2011. All rights reserved
1. (8 points) You are a 4th year senior at UCI and had hoped to stay for a 5th year so that
you could double major in Molecular Biology and Biochemistry together with
Pharmaceutical Sciences. Sadly, the powers that be in the School of Biological Sciences
have decreed that no one can stay for more than 4 years irrespective of the reason. In
order to graduate on time, you need to take an advanced molecular biology
laboratory. The class is a new one at UCI and involves the entire class collaborating on
a project to sequence and characterize the genome of an as yet uncharacterized
organism. UCI is part of the Genome 10K project that aims to sequence 10,000
vertebrate genomes. An enterprising young assistant professor has secured support
from NSF to fund this course. This year’s project is to sequence the genome of
Myrmecophaga tridactyla - the giant anteater (why are you surprised?). This is a one
quarter long project so there is no time to waste.
a) (2 points) The department chair read Craig Venter's autobiography and suggests that
the EST (expressed sequence tag, cDNA) project use a 384-capillary sequencer
(donated by Craig Venter), together with cycle sequencing to generate 200,000 ESTs.
Is this a good idea? Explain why, or why not.
Not a good idea. Standard sequencing can certainly generate 200K ESTs, but in more
like a year using a single machine and Sanger sequencing (200,000/384 = 520 runs).
Therefore, it will be too slow and you will not graduate. It will probably cost much more
than the existing budget, too.
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Bruce Blumberg 2011. All rights reserved
1b) (3 points) Your project is to generate a map of the anteater genome. The department
chair (who is full of ideas) suggests that you conduct large-scale BAC end sequencing
and order these by sequentially hybridizing individual BACs to the library. Amanda, the
course director, thinks this is a bad idea. Who is right? Amanda says that you must
select the method to use and generate a good map by the end of the quarter,
otherwise you are not going to pass the course or graduate. Explain briefly why you
chose the method you did and in succinct terms highlight the main features and
strengths of the method
Amanda is right – you need a new department chair. The only hope of generating a
map in the time that is available will be to use HAPPY mapping. I would map the
ESTs being identified in part a) to genomic DNA using this PCR based method. It will
be tedious, but doable. HAPPY mapping uses isolated genomic DNA and PCR to
determine how close 2 markers are to each other in the genome.
1. c) (3 points) Your significant other is in the group that will generate the actual genome
sequence and asks your advice about how to proceed. Bill, the group leader, proposes to
use nextgen sequencing while George, a very vocal member of the group read Jurassic
Park and insists that sequencing by hybridization is the best and fastest approach (after
all, they cloned dinosaurs!). Who is correct? Why? What key features of the chosen
method might enable the sequence to be generated and assembled in 10 weeks?
Bill is correct – only nextgen sequencing will give yield enough sequence to assemble
the genome in the time available. Sequencing by hybridization is only really applicable
for resequencing regions that you already know the sequence of. The essential
feature of nextgen sequencing is that, depending on which flavor you choose, it can
generate ~108-109 bases per run. Thus, you should be able to sequence the genome
to a high degree of completion without many runs.
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Bruce Blumberg 2011. All rights reserved
2. (12 points) Since you were able to compete your tasks in the molecular biology lab
course and graduated on time, you have been recruited to NASA where you will be part
of a team that characterizes the biological material returned from a probe that has
been secretly sent to collect samples from Europa – a moon of Jupiter that was long
suspected to have liquid water under a crust of ice. Indeed, Europa has an ocean
under the ice and NASA has quietly returned a sample to Earth for characterization.
2a) (3 points) The first task is to determine whether there are any living organisms in the
sample. How might you go about identifying how many different organisms are in the
sample with a high probability of not missing any? What is the main assumption you
must make in order for this to be successful?
This is based on the Venter paper. You will want to extract DNA from the sample and
sequence it directly. The main assumption you have to make is that these organisms will
contain DNA that is sufficiently similar to that of terrestrial organisms so that you can
sequence it using standard methods and assemble into genomes by computer. If you are
lucky it will be and a strategy similar to whole genome shotgun that Venter used will be best.
However, it is 2015 so you would want to use one of the nextgen methods.
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Bruce Blumberg 2011. All rights reserved
2b) (3 points) You are lucky - your sample yields 11,279 sequence assemblies, 8017 of
which are complete, or nearly complete. Are these genomes likely to be prokaryotic or
eukaryotic? Why? How will you determine whether these sequences are bona fide
residents of Europa vs. contaminants introduced on Earth by a sloppy scientist (not
you, of course) ?
If you got 8017 complete genomes, then these are probably small, making it likely that
they will be prokaryotic. I will compare these sequences with those available in the
GENBANK database and determine whether they are identical to any organisms found
on Earth. If they are not, then they have likely originated on Europa. If some are
identical, or highly similar to terrestrial organisms, then you would suspect contamination.
2c) (3 points) Unfortunately, your colleagues on the project develop a strange illness. Orange
fur grows all over their bodies and after two weeks, they develop high fevers, become
delirious and demand to be BioSci majors at UCI. Sadly, they die shortly afterward. You
are the kind of person who makes lemonade out of lemons, instead of complaining that
they are not sweet enough. It is tragic that your colleagues died, but the identification of
Europan DNA sequences on their skin suggests that the microorganisms can be cultured.
Sure enough, you are able to culture two types of bacteria from the skin of the first
deceased patient. Design a diagnostic test to determine whether someone carries these
Europan bacteria on their skin, or whether it is present in any sort of random sample to
be tested? Note the key features of your assay
After sequencing the bacteria, I would develop a RT-PCR assay using Taqman or similar
technology to quickly identify these sequences in any sort of sample you receive. Such
technology uses custom primers to specifically and quantitatively identify matching
sequences in any number of samples. PCR makes it very sensitive. Or, if you had
microarrays containing Europan sequences, you could use those.
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Bruce Blumberg 2011. All rights reserved
3d) (3 point) Clearly, causing a person to develop orange skin and later die is a serious
problem. Describe a relatively thorough approach to identify and track the types of
changes over time induced in patients by exposure to the Europan organisms ?
The approach I was looking for was that of the Chen paper. Since you have learned
about genomic and transcriptomic analyses, it would be appropriate to perform whole
genome sequencing to rule out any mutations induced by the infection and transcriptome
analysis to identify changes in gene expression over time. It would be best if you had
pre-infection samples to compare with those after the person was presumed to be
infected.
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Bruce Blumberg 2011. All rights reserved
3. (8 points) The plot thickens. Patients, doctors and nurses in the local hospital begin to
show signs of the disease – orange fur. Word gets out to the media and suddenly Anderson
Cooper is there broadcasting from in front of the hospital, bravely not wearing hazmat
gear. He is muy macho but not very bright (and does not look good in a hazmat suit
anyway). Intriguingly, some of the patients do not become delirious and die, they simply
have orange fur.
3a) (2 points) The first thing you need to figure out is how the contamination spread from the
original patient to others. You recall that your sarcastic D145 professor said that doctors
are the major source of infections in hospitals, but you aspire to be a doctor and don't
believe him. The patients were immediately put into isolation after arriving in the
hospital so it is very unlikely that the contamination spread without personal contact.
How could you test whether the Europan microorganisms in Dr. Quackenbush (the
attending physician in the ER) are the same as in the initial patients from NASA and in the
others who subsequently become affected? Dr. Quackenbush appears to be unaffected.
Perhaps the best way to do this is to sequence microorganisms collected from the various
patients and swabs from Dr. Quackenbush’s body. If the sequences are identical, AND
present on Dr. Quackenbush, then one appropriate conclusion would be that he is an
intermediate carrier of the infection. You could also use high resolution fingerprinting to
compare the strains with each other. Expression microarrays are not sufficient to
discriminate closely related species as these are likely to be.
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Bruce Blumberg 2011. All rights reserved
3b) (3 points) Intriguingly, Dr. Quackenbush appears to have the Europan
microorganisms on his hands and on his lab coat and gloves, but remains unaffected.
Nevertheless, he is placed into isolation and observed. As time passes and more data
are collected, you notice that there are 3 phenotypes among people who show
evidence of Europan microorganisms on their skin (detected with the test you
developed in 2c). These are a) orange fur, delirious, then dead, b) orange fur but
otherwise healthy, and c) unaffected. This sounds to you like a classic case of a single
gene that differs when it is homozygous for one allele (sensitive, i.e. dead), or the
other allele (resistant), or heterozygous (orange but alive). You perform whole genome
association studies and quickly discover that one region on chromosome 20 (about 1
megabase long) is highly associated with resistance to the Europan microorganisms.
How might you determine whether this region is transcribed into mRNA and, if so, how
large the transcripts are and how much of the transcript is present?
Make RNA from skin of affected people and perform Northern analysis using the region
from chromosome 20 as a probe to detect whether transcripts are present and what size
they are. Add a standard to determine how much is present. Northern is the only way to
detect size but you could use QRT-PCR to quantitate with appropriate standards. Many
people wanted to use RNA-seq and then determine which sequences mapped to this
region. I gave credit for this, but this would be equivalent to killing a fly with a missle.
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Bruce Blumberg 2011. All rights reserved
3c) (3 points)You have identified several families where one parent is totally resistant
to the infection, the other parent gets orange fur and the children are either resistant
or get orange fur but do not die. Your analysis in b) above has shown that there are no
differences in the coding capacity of this region between resistant, partially resistant
or susceptible individuals and that the sequence of the affected region does not change
significantly among patients. The data suggest that a variation in copy number of some
part of the candidate region might be responsible for resistance. Outline how you
would go about identifying copy number variations in this region, determining whether
patients have the CNV or not and if CNVs are linked with the disease?
This would use an approach similar to that in the Redon paper. 1) Use SNP or
chromosome tiling microarrays to identify CNV regions and 2) test this in all sorts of
patients. 3) If the CNV is linked with the disease, then the patients should segregate into
groups where the number of copies is correlated with the severity of disease.
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Bruce Blumberg 2011. All rights reserved
4. (7 points) As you might expect, Anderson Cooper has now developed orange fur. CNN is
desperate to know whether or not he will survive and the ongoing drama is generating big
ratings. They need to know how long the ratings bonanza will continue so that they can
project how much they can charge for commercials. Meanwhile, Larry King has come out
of retirement to anchor the news story since he is so old that no one is worried whether
he will contract the disease (plus it makes him look brave).
4a) (1 point) How could you determine whether Anderson will die, or simply remain orange
(other than waiting and observing what happens) ?
Assuming you showed that CNVs are associated with resistance in 2c, use the same
method to determine which group Anderson falls into.
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Bruce Blumberg 2011. All rights reserved
4b) (3 points) Continuing to make lemonade out of lemons, you hypothesize that
understanding how the Europan microorganisms infect skin cells and lead to the
generation of orange fur might lead you to a cure for baldness and give you financial
security forever. You found that the microorganisms behave somewhat like Chlamydia and
grow as intracellular parasites in skin stem cells. Your analysis suggests that the infected
skin stem cells are instead transformed into a cell type like hair follicle stem cells.
Assuming that you can isolate both cell types in pure form, outline how would you
determine what transcripts are expressed in normal skin stem cells compared with
infected stem cells?
Two methods come to mind. The first would be gene expression microarray analysis. You
would prepare mRNA from both cell populations, label it and probe appropriate microarrays.
You would then compare the genes that are expressed in normal skin stem cell and the
infected, hair follicle stem cell-like cells. Genes that are expressed differently will be the
ones that you focus your attention on. You could also perform exhaustive nextgen
sequencing of cDNA derived from the mRNA to identify which sequences are different
between the 2 populations. This method has the benefit that it would detect microorganism
encoded transcripts that would not appear in your analysis otherwise. Of course, you need
a genome assembly to map the RNA-seq reads against.
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Bruce Blumberg 2011. All rights reserved
4b) c) (3 points) Another possible hypothesis is that something about the infection of skin
cells with the Europan microorganisms has mobilized one or more LINE elements
(retrotransposon-like repeated sequences) allowing them to hop around the genome and
cause the strange effects observed. How could you determine whether LINE elements in
cultured skin cells infected with Europan organisms compared with uninfected cells, move
from their original location in the genome to a new location?
First you need to culture skin cells from regions with orange fur (infected) and regions of
normal skin (presumably uninfected). The most brute force method would be to conduct
genome sequencing from both cells and focus on where LINE elements are located in each.
If they are in different places, then you will have proven that they are moving around.
Alternatively, you could use FISH to show that the chromosomal patterns of LINE
localization are different between the two cell types. Credit was given for a variety of
creative answers.
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Bruce Blumberg 2011. All rights reserved