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Genomics-sequencing of microbial genomes
This lecture illustrates the strategies used in microbial genome sequencing projects, compares genome content and organisation amongst microbes, and shows how to derive information on gene function across genome. Objectives for students: Expected to describe strategies involved in microbial genome sequencing and functional genomics Provide examples of information that can be derived from genomics
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Microbial Genome Sequencing
Genome Sequencing Projects strategy & methods annotation Comparative genomics organisation gene content Functional genomics transcriptome proteome genome-wide mutation Concentrate on strategy & ideas
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Bacterial genome projects
Many completed: Haemophilus influenzae Escherichia coli Bacillus subtilis Mycoplasma genitalium Helicobacter pylori (x2) Campylobacter jejuni Treponema pallidum Neisseria menigitidis Neisseria gonnorhoea Vibrio cholerae E. coli O157 Good link to projects: Genome sequencing progress Complete: Archaeal: 70 (2007&2008: 49&55) Bacterial: 945 (554&728) (Eukaryal: 121) (76&97) Ongoing: Prokaryotic: 3498 Archaeal: 111 (Eukaryotic: 1223) Metagenome projects: 200
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Microbial eukaryote projects
Complete Yeast -Saccharomyces cerevisiae Plasmodium falciparum Aspergillus nidulans, A.niger, A.oryzae & A.fumigatus Trypanosoma cruzi & brucei Leishmania Entamoeba histolytica Giardia lamblia Candida albicans & glabrata Paramecium Underway Pneumocystis carinii Plasmodium vivax some complete chromosomes finished Other species and isolates from completed list
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Why bother? -To sequence or not to sequence (considerations in the pre-genome era)
problems/issues ownership strain choice cost approach data release some now less relevant Post genomic era Comparative genomics Functional genomics piecemeal collection of sequenced genes slow costly ever complete? genome project rational approach efficient and rapid quality assurance address novel questions
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Genome sequencing strategy
Strategy choice large collaborative cosmid/BAC-based projects now better suited for larger genomes slow small insert shotgun approach centralised rapid and efficient choice for bacteria Strain choice fresh isolate vs lab strain clinical vs environmental subsequent genetic analysis
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Yeast genome sequence strategy
Yeast chromosomes (16) individually sequenced several approaches used Make genome library in cosmids order cosmid library which cosmid overlaps with which link cosmid to genome map produced tiled set of cosmids only sequence minimum number Use chromosome specific probe to identify chr-specific cosmids sequence cosmid inserts by subcloning Solve problems by direct PCR sequencing, walking and other libraries (lambda) Telomeres
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Tiled set
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Ordering Clones c1 A B c2 C D c3 E F c4 G H c5 I J c1 c2 c3 c4 c5 A B
c1 c2 c3 c4 c5 A B C D E F G H I J
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100 200 PH011 80 100 120 140 160 180 70512 70893 70266 7202 70265 70449 70515 70871 70124 70463
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Whole genome/chromosome shot-gun strategy (WGS)
Rapid Generation of small insert genomic library Library is not initially ordered DNA sequence ends of inserts Depends on powerful computing to assemble sequence reads
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Main steps in generating a complete genome sequence
Minimum time period (weeks) Isolation 2 Construction 4-6 Shotgun sequencing 2-4 Finishing 12 Annotation 12
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bacterial chromosome vector plasmid random shearing size selection library of clones sequence end of each clone individual clones
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Sequencing individual clones genome sequence with gaps
Assembly genome sequence with gaps
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Automated sequencers: ABI 3700
Made by Applied Biosystems Most widely used automated sequencers: 96 capillaries robot loading from 384-well plates Two to three hours per run 600–700 bases per run robotic arm and syringe 96 glass capillaries 96–well plate load bar
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Automated sequencers: MegaBACE
Made by Amersham 96 capillaries Robotic loading from 384–well plate Two to four hours per run Can read up to 800 bases Source : GE Healthcare Life Science, Uppsala, Sweden
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Automatic gel reading Top image: confocal detection by the MegaBACE sequencer of fluorescently labeled DNA Bottom image: computer image of sequence read by automated sequencer Both automated sequencers detect fluorescently labeled DNA strands as they pass through the capillaries. The MegaBACE sequencer uses the confocal imaging system shown in the top image. The readout is given as peaks of fluorescence, as shown in the lower image.
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Industrialization of sequencing
Most genome sequencing projects divide tasks among different teams Genome libraries Production sequencing Finishing Sequencing machines run 24/7 Many tasks performed by robots The Broad Institute of MIT and Harvard,
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The future is here?..454 sequencing
Reprinted by permission from Macmillan Publishers Ltd: [NATURE] (Margulies et al., 437: 376 copyright (2005)
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454 sequencing: the system
DNA Library Preparation emPCR Sequencing 4.5 hours 8 hours 7.5 hours Well diameter: average 44μm 400,000 reads obtained in parallel A single cloned amplified sstDNA bead is deposited per well 4 bases (TACG) cycled 100 times Chemiluminescent signal generation Signal processing to determine base sequence and quality score Source :454 Sequencing © Roche Diagnostics
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WGS: Just how much effort?
individual sequencing reads accumulate each read about 500bp computing used to assemble reads contiguous sequences called contigs Aim for 8-10 read coverage of genome for accuracy example: H.influenzae 19,687 templates 24,304 reads assembled 11,631,485 bp 9
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Sequencing a genome fragments of sequence overlaps contiguous sequence
luatedgeneticsrel tatedgene ourcesforteach cisahubofevaluatedgenc hprofessionalsandgeneralpub cisahubofevaluatedgen esforteachershealt chershealthprofession atedgene fragments of sequence vgecisahubof bofevaluatedgenetics icsrelatedresourcesforteachershealth lthprofessionalsandgeneralp generalpublic overlaps vgecisahubofevaluatedgeneticsrelatedresourcesforteachershealthprofessionalsandgeneralpublic contiguous sequence
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Gaps Physical Gap Sequence Gap Genome Library clone Sequence read
contig Gaps
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difficult gap bridging
Bridging Gaps Number of reads Number of contigs 1 rapid gap bridging difficult gap bridging Finishing rise in contig number as amount of reads increases steady fall as accumulating sequence bridges gaps between contigs levels off as new reads more likely in known contig than gap start finishing
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Finishing Why are gaps present? Gap bridging Mistakes/poor sequence
sequence gaps sequence gaps –choose appropriate clone and walk physical gaps alternative libraries (which?) PCR across gap Mistakes/poor sequence areas where sequence reads are less than 8-10 repeated sequences -rRNA closure and completion
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Finished Yet? 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Sequencing a genome fragments of sequence overlaps contiguous sequence
luatedgeneticsrel tatedgene ourcesforteach cisahubofevaluatedgenc hprofessionalsandgeneralpub cisahubofevaluatedgen esforteachershealt chershealthprofession atedgene fragments of sequence vgecisahubof bofevaluatedgenetics icsrelatedresourcesforteachershealth lthprofessionalsandgeneralp generalpublic overlaps vgecisahubofevaluatedgeneticsrelatedresourcesforteachershealthprofessionalsandgeneralpublic contiguous sequence VGEC is a hub of evaluated genetics related resources for teachers, health professionals and general public. annotation
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Genome Annotation Find ORFs ORF function Other features
look for ATG-Stop (+alternatives) over certain size overlaps computer based (“Glimmer” & “Orpheus”) and trained eye. ORF function Search databases with predicted translated sequences –BLASTX Consider level of similarity and context Domain comparisons Pfam/Prosite Other features
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Artemis: sequence viewer and annotation tool from the Sanger Centre (
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xBASE is a database for comparative genome analysis of all bacterial genome sequences Chaudhuri RR, Pallen MJ. xBASE, a collection of online databases for bacterial comparative genomics. Nucleic Acids Res Jan 1;34(Database issue):D335-7.
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Finished annotated sequence
A conceptual diagram of the flux and information in a network-based genome-sequencing project Coordinator Working draft sequence DNA Finished sequence Finishing instructions Finished annotated sequence Annotation tasks Shotgun templates S S S S S S Annotations S S S S S S S S S S S S S Finishing sequences S S S S S S S Shotgun sequences S S S S Bioinformatics Lab S S S
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Post Genome Sequence Comparative genomics
comparing genome organisation and content genome size genome repeats/Tn/phages gene content minimal gene content Functional genomics –ascribing gene function across a genome gene function –knowns phenotype prediction gene function –unknowns investigating function Bacteria-Yeast
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Bacteria: Does size matter?
Link genome size to adaptive capability biosynthetic capability synthesis of nutrients Stress resistance resist environmental insults structural complexity surface structures, sporogenesis Regulation –sensing signals and transcriptional responses detect change or requirement and respond appropriately transcriptional regulation
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Not just Size but how you use it…..
Small genomes Mycoplasma genitalium 580,070 bp smallest genome for self-replicating organism free living but only just..infects host cells (guess which!) few biosynthesis and regulatory systems has replication & transcription & translation, metabolism etc functions Borrelia burgdorferi 910,725 bp Lyme disease few cellular biosynthetic systems Mycoplasma pneumoniae (0.8 Mbp); Chlamydia trachomatis (1.0 Mbp);
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bigger genomes Haemophilus influenzae Helicobacter pylori
1.830 Mbp colonises human respiratory tract limited environment Helicobacter pylori 1.667 Mbp colonises human stomach Campylobacter jejuni 1.641 Mbp colonises intestine
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and bigger…. Escherichia coli (K-12) Bacillus subtilis
4.639 Mbp Bacillus subtilis 4.214 Mbp soil/plant organism secondary metabolites Pseudomonas aeruginosa incomplete (5.9 Mbp) Yersinia pestis (4.4 Mbp) Clostridium spp (4-5 Mbp) Mycobacterium tuberculosis 4.411 Mbp slow growing (double in 24h) large proportion of genome on lipid metabolism Streptomyces coelicolor (~8 Mbp) secondary metabolites –antibiotics!
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Organisation Linear chromosomes Multiple chromosomes Plasmids
Borrelia burgdorferi Streptomyces coelicolor Multiple chromosomes Vibrio cholerae Plasmids 17 linear & circular plasmids 50% genome size plasmid replication, “decaying genes”, ?Ag variation Transposons, IS elements, phages found in most genomes Campylobacter has none Repeats
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Replication Origin (oriC) and termination (terC) of replication
OriC often near dnaA gene (replication initiation protein) In Borrelia burgdorferi (linear) oriC (& dnaA) in centre strand bias which strand is each gene on? transcription in same direction as replication –more efficient variation in level of strand bias Mt 55% vs Bs 75%
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Gene Content Annotation Paralogues and Orthologues sequence similarity
gene families regulators, transport, biosynthesis domain matches trans-membrane domains, DNA binding Paralogues and Orthologues Paralogues: Members of same family (homologous) in same genome. Likely to have different exact function Orthologues: homologues (same family) in different genomes May have identical function
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Vibrio cholerae as predicted by genome........
Reprinted by permission from Macmillan Publishers Ltd: [NATURE]( Heidelberg et al, 406 , ), copyright (2000)
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Gene content (cont.) ORFans Gene size –most about 1kb
significant proportion of genome contains ORFs of unknown function some may be orthologues of unknowns in other organisms some unique to organism important for biology of organism examples: H.influenzae: 42% H.pylori: 33% E.coli: 38% M.tuberculosis: 60% to 16% number decreasing Gene size –most about 1kb
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Genomic rearrangements
Example comparison Comparison of: S.e Typhi CT18 with S.e Typhi Ty2 inversion that spans terminus
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Variation by gain and/or loss
Core regions shared by closely related species Additional “flexible” gene pool variable regions acquired from mobile genetic elements First described as pathogenicity islands in non-pathogens too wider role Genomic Islands pathogens commensals symbionts environmental Gain of GI sometimes assoc with gene loss reduction in obligate intracellular pathogens Genome organisation as well as genome content correlates with microbial lifestyle Common bacterial ancestor Genome reduction by deletion events Gene acquisition by HGT Mutations rearrangements Plasmid GEI Intracellular bacterium, obliagate intracellular pathogen, endosymbiont All lifestyles Extracellular bacterium, facultative pathogen, symbiont
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Other tRNA-associated elements: tRNAPProL
Black arrows=Sal+Ec; white arrows=Sal or Ec; grey=strain/serovar specific GC is for S. Typhi Infection and Immunity, May 2002, p , Vol. 70, No. 5
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Other tRNA-associated elements: tRNAArgU
Infection and Immunity, May 2002, p , Vol. 70, No. 5
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The supragenome The distributed-genome hypothesis (DGH)
Bacteria have a (supra) genome much larger than the genome of any single bacterium. Core and non-core gene sets Example: Hiller et al. sequenced 8 strains of Streptococcus pneumoniae + 9 already available Core set of genes in all strains 20-30% genes non-core (not present in all strains) Genetic recombination generates diversity across strains. Also for Haemophilus influenzae (Hogg et al.) ~1400 in core set and ~1300 non-core in subset of strains Hiller et al. Journal of Bacteriology, November 2007, p , Vol. 189, No. 22 Hogg et al. Genome Biology 2007, 8:R103 (doi: /gb r103).
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Yeast 16 chromosomes totalling 12.068Mbp
5885 orfs –6275 but 390 unlikely translated Few introns ~4% Avg gene size 2kb (worm ~6kb and human >30kb) GC vary along chr length low GC at telomere & centromere GC rich correlate with higher recombination Tn and remnants in genome evidence of hotspots 50% orfs known function some exact role unclear
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Copies of the active protein-coding genes
Functional genomics Functional genomics –ascribing gene function across a genome function and inter-relationships strategy [bioinformatic analysis -gene identification] Transcriptome -expression pattern Proteome -expression pattern Mutantome -mutant phenotype Interactome –protein-protein interactions GENOME TRANSCRITOME RNA Copies of the active protein-coding genes PROTEOME The cell’s repertoire
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Arrays: micro and chip Microarrays Chip arrays Redundancy
Glass slides with <10000 individual samples applied in known position Use of robotics Samples can be PCR products or oligos example: oligo/PCR product complementary to each ORF Chip arrays silicon based >10,000 sequences Redundancy fluorescent labels
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Chip Arrays light Laser Individual sequences & ACGTAT TGCATA
bound sample One cell= one specific sequence
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Transcriptome Genome-wide determination of expression level of each ORF when expressed relates to role also assess mutants compare expression of each ORF in different conditions Genome wide expression maps global patterns of expression
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AGGCAT AATGAA AGGCAT TTACTT AATGAA AATGAA TTACTT
orf 1 orf 2 2 x ORF mRNAs When expressed? AGGCAT AATGAA Bacillus genieae isolate mRNAs and make cDNA copy AGGCAT TTACTT AATGAA AATGAA grow in conditions when only orf 2 expressed TTACTT orf 1 orf 2
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Probe array with labelled copy of mRNA
Grow under different conditions extract mRNA Probe array with labelled copy of mRNA
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Differentially labelled probes
Red channel Green channel Combined
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Expression profiling C. jejuni in low iron
Cj0037c Cj1659 (P19) Cj0177
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Proteome Genome-wide determination of protein expression
Gives information stimulons protein expression linked to function assess mutants (regulatory mutants affect several proteins) Grow bacteria under defined conditions Extract proteins 2D-gel electrophoresis Protein spot identification Mass Spectrometry peptide size predictions from Genome data
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Defining the Campylobacter proteome –chasing spots
Which protein? Which conditions? Which other proteins are co-expressed?
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C. jejuni iron example
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* * * * * pI Mol mass Mass Spec digest with protease
pI Mol mass Mass Spec digest with protease * * * * *
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Mass Mutagenesis: mutantome
Mutate every ORF in genome organism specific technology High throughput analysis of phenotype need to analyse many 1000s of mutants under many conditions Signature-tagged technology enables analysis of mutant pools requires array technology for genome-wide projects Association on ORF with mutant phenotypes Regulators might be pleiotropic
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Arrays: micro and chip Microarrays Chip arrays Redundancy
Glass slides with <10000 individual samples applied in known position Use of robotics Samples can be PCR products or oligos example: oligos complementary to each unique Tag example: oligo/PCR product complementary to each ORF Chip arrays silicon based >10,000 sequences Redundancy fluorescent labels
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Chip Arrays light Laser Individual sequences & ACGTAT TGCATA
bound sample One cell= one specific sequence
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Signature Tagged Tags are short unique DNA sequences
ORF X Chromosomal Mutants Tags are short unique DNA sequences Tag linked to mutation Each individual mutant has unique tag Each mutant ORF has unique Tag
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ORF X Chromosomal Mutants Mutant Pools compare condition ‘normal’ functional role ?
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mutant-specific DNA sequence
Bar coding genes “normal, un-mutated Campylobacter mutant 1 mutant-specific DNA sequence mutant 2 mutant 3 mutant 4 and so on… to mutant 1654.
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Which bar codes are missing?
mutant pool post-treatment mutant pool copies of barcodes present ……… 11 21 91 100 + - Bar code Array Which bar coded mutants are missing? Gene involved in process
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Reprinted by permission from Macmillan Publishers Ltd: [NATURE REVIEWS GENETICS] (Mazurkiewicz et al ), copyright (2006)
![Reprinted by permission from Macmillan Publishers Ltd: [NATURE REVIEWS GENETICS] (Mazurkiewicz et al.](http://slideplayer.com/slide/5057137/16/images/73/Reprinted+by+permission+from+Macmillan+Publishers+Ltd%3A+%5BNATURE+REVIEWS+GENETICS%5D+%28Mazurkiewicz+et+al..jpg "), copyright (2006).")
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Interactome Yeast 2 hybrid
Which proteins can interact? Expression library of binding- domain::protein 1 (bait) Expression library of activation- domain::protein 2 (prey) Test combinations of all genome orfs Which combinations turn on the reporter gene?
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Protein-protein interaction networks
Parrish et al A proteome-wide protein interaction map for Campylobacter jejuni. Genome Biol 8:R130.
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Genomotyping or Genomic indexing
11 12 13 14 6 7 8 9 1 2 3 4 15 10 5 Array of all known genes in microbe Genes 1, 2, 3 &14 forms minimal gene set Hybridise array with labelled chromosomal DNA 2 3 1 11 4 5 5 8 6 14 15 9 Isolate 1 Isolate 2 Isolate 3 11 12 13 14 6 7 8 9 1 2 3 4 15 10 5 11 12 13 14 6 7 8 9 1 2 3 4 15 10 5 11 12 13 14 6 7 8 9 1 2 3 4 15 10 5
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