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Pick a bug, any bug.

Pick a bug, any bug. Comprehensive Aligned Sequence Construction for Automated Design of Effective Probes (CASCADE-P). The design and practical use of rDNA oligonucleotide microarrays to identify microbes in complex samples Todd DeSantis, Igor Dubosarskiy (Ceres), Sonya Murray, Gary Andersen

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Pick a bug, any bug.

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  1. Pick a bug, any bug. Comprehensive Aligned Sequence Construction for Automated Design of Effective Probes (CASCADE-P) The design and practical use of rDNA oligonucleotide microarrays to identify microbes in complex samples Todd DeSantis, Igor Dubosarskiy (Ceres), Sonya Murray, Gary Andersen Environmental Molecular Microbiology Group BBRP - LLNL

  2. The worries of an Italian parent Is he getting enough sleep? Does he feel accepted? Is there sufficient diversity in his lower G.I. bacterial community? Are the straps on his car seat irritating his neck? Are there archaeal organisms are in the aerosols he breathes? Enzo Salvatore DeSantis Jenny DiGiovanni DeSantis

  3. Every soiled diaper sacrificed to the Diaper Genie is lost data. • But who wants to do all the work? • Culture • anaerobes • non-cultivable • Sequencing 16S rDNA • Need to create, clone, & process hundreds of samples • Can we create a simple, quantitative, comprehensive microbial test?

  4. Outline • Goals • Experimental approach • Why create a new 16S rDNA database? • How do you align >65,000 sequences? • Organization of sequences into types • Designing probes for each type • Reassessing probe specificity as database grows • Using 16S GeneChip for quantitative aerosol analysis

  5. Project Overview • Create a single GeneChip® capable of detecting and quantifying bacterial and/or archaeal organisms in a complex sample. • What is in a sample, as opposed to what isnot in a sample. • Approach • Combinatorial power of multiple probes for sequence-specific hybridization

  6. General Protocol Sample Air Soil Feces Blood Random hexamer total gDNA amplification Allen Christian gDNA Universal 16S rDNA PCR rRNA Contains probes adhered to glass surface in grid pattern.

  7. 16S rRNA gene (16S rDNA) • Used to identify and classify organisms by gene sequence variations. • Variations have been used in design of DNA probes for the detection of: • taxonomic domains, divisions, groups … • specific organisms

  8. The Ribosome rDNA rRNA (functional molecule) LSU SSU 16s or 18s

  9. The Ribosome • Folded secondary structure • Essential functional component • Conserved spans • structure must be retained for viability • targeted for universal/group-specific PCR primers and probes • Variable regions • spans not fundamental to the folded structure • receive less pressure from natural selection • probed for genus and species level discrimination

  10. Building upon two decades of 16S gene cataloging • Over 75,000 16S records housed at NCBI. • Grows every week. • Don’t need to build a reference library for organism ID. • FAME • MS • Surface Antigen Identification

  11. What could be amplified? • Universal 16S PCR primers  complex population of amplicons. • Must define the targets to consider as the Potential Amplicon Set or PAS. • “Give me a file of all the amplicon sequences possible if we used universal primers upon a sample containing every prokaryote.” Tom Kusmarski Variable

  12. Difficulties defining the PAS • Why Entrez search for “16S” is insufficient: • non-16S sequences are errantly deposited as 16S • 16S sequences may be concealed within longer records that cover entire operons or genomes • anti-sense strands aren't specified as such

  13. Difficulties defining the PAS • For each sequence, need to trim away bases that won’t be amplified. • Problem: Most 16S records are “partial”. • Can primer pattern matching within sequences allow for proper trimming? • What does it mean when a primer search fails? • Primer locus present in record but mutated, or • Primer locus outside the sequence span deposited

  14. Difficulties defining the PAS • Aligned sequences were necessary. • A 16S MSA arranged as horizontal rows of characters allows vertical slices to be extracted between columns of primer annealing positions.

  15. Existing 16S aligned databases • Under 20,000 sequences among 3 databases. • Updates occurred annually or worse. • Others focused on hand-aligning complete sequences. • Structure predictions • Phylogeny assessment • Comprehensive Aligned Sequence Construction for Automated Design of Effective Probes (CASCADE-P) • need up-to-date records • need to include partial sequences • add “inertia” to region considered conserved • increase the likelihood of detecting a polymorphism • searched for unwanted cross-hybridizations with a tentative probe

  16. 2.28.3.27.2 2.30.9.2.10 1st Level: BACTERIA 1st Level: BACTERIA 2nd Level: PROTEOBACTERIA 2nd Level: GRAM_POSITIVE_BACTERIA 3rd Level: GAMMA_SUBDIVISION 3rd Level: CLOSTRIDIUM_AND_RELATIVES 4th Level: ENTERICS_AND_RELATIVES (Group) 4th Level: C.BOTULINUM_GROUP 5th Level: ESCHERICHIA_SUBGROUP 5th Level: C.ACETOBUTYLICUM_SUBGROUP U85138 clone ACK-SA7 AE000452 Escherichia coli str. K-12 Er.trachep Erwinia tracheiphila LMG 2906 (T) E.coliK12 Escherichia coli [gene=rrnG gene] Haf.alvei3 Hafnia alvei S.tymuriu3 Salmonella typhimurium str. Stm1 Shi.boydii Shigella boydii AF084835 str. KN4 S.enterit4 Salmonella enteritidis str. SE22 S.ptyphi6 Salmonella paratyphi S.typhi3 Salmonella typhi str. St111 S.bovismrb Salmonella bovis morbificans Sbm1 Alt.agrlyt Alterococcus agarolyticus str. ADT3 Shi.flxne2 Shigella flexneri ATCC 29903 (T) Clostridium collagenovorans DSM 3089 (T)  Clostridium sardiniensis ATCC 33455 (T)  Clostridium acetobutylicum ATCC 824 (T)  Clostridium acetobutylicum DSM 792 (T)  Clostridium acetobutylicum ATCC 824 (T)  Clostridium acetobutylicum NCDO 1712  Clostridium acetobutylicum DSM 1731  RDP alignment and tree – a great skeleton • Ribosomal Database Project (Michigan State) • 16S MSA of 16 277 seqs (v8.1) • trimmed of extra-16S • top-strand oriented • 1,541 bases stretched to 4,218 characters • Each placed within a hierarchical phylogenetic tree

  17. Sequence pre-processing • Download ‘16S’ candidates via “ESearch” • BLAST compare to 16S/18S RDP “standards”. • Candidates were rejected if • the longest match length was <300 base pairs • the highest scoring BLAST subject was eukaryotic • candidate matched sequences in two or more RDP terminal tree branches equally well • Phylocode assigned from top HSP

  18. Sequence pre-processing

  19. Sequence pre-processing • Candidate trimmed of extra-16S seq data • tRNA genes, intergenic spacer regions, and 23S rDNA • based on HSP boundries • If HSP paired opposite strands, candidate was reverse complemented.

  20. Sequence pre-processing

  21. Sequence pre-processing • The "template" was assigned from the top HSP from a second BLAST process • G=1, E=1. • Favors longer, but less identical matches.

  22. Prokaryotic Multiple Sequence AlignmentprokMSA • Essentially, the prokMSA was a merger built serially by aligning each candidate to its closest relative in the RDP tree. • Two Steps • Align0 • Publicly available, pair-wise SW aligner • Candidate expansion • NAST • Nearest Alignment Space Termination • Novel algorithm • Candidate compression

  23. NAST DEFINE St= post-Align0 template sequence. Sc= post-Align0 candidate sequence. Ht = alignment space (hyphen) inserted into Stby Align0. Hc = alignment space (hyphen) inserted into Scby Align0. WHILE (St contains one or more Ht) DO LHt = character index of distal 5' Ht within St L5' = character index of Hc within Sc which is 5' proximal to Ht L3' = character index of Hc within Sc which is 3' proximal to Ht IF ((LHt – L5') > (L3' – LHt)) Delete Hc found at L3' ELSE Delete Hc found at L5' Delete template gap character. END WHILE

  24. December 28th, 2002

  25. Operational Taxonomic Units • We did not desire to design probes for each sequence. • Many sequences are nearly identical. • Desired 20 probe per sequence: • 60,000 seqs * ( 20 probes + 20 probes) = • 2.4 million probes (not possible, yet) • We did desire to design probes for each type of sequence. • Need to group sequences into types amenable to probe design.

  26. Operational Taxonomic Units • Avoid groupings based on historical nomenclature. • Sequence-dependent classification by transitive similarity clustering at 98%. • Create groupings into Operational Taxonomic Units (OTU). • Each sequence must be in exactly 1 OTU if x R y & y R z  x R z

  27. Sequences Clustered Is = IB Lm / min (La, Lb) BACTERIA (2) GRAM_POSITIVE_BACTERIA (2.30) BACILLUS-LACTO-STREPTOCOC_SUBDIVISION (2.30.7) CARNOBACTERIUM_GROUP (2.30.7.18) CRN.DIVERGENS_SUBGROUP (2.30.7.18.2) OTU 2.30.7.18.2.012 (11 sequence records) AF244371 Nostocoida limicola I Ben200 AF244372 Nostocoida limicola I Ben201 AF244375 Nostocoida limicola I Ben77 AF255736 Nostocoida limicola I AF276462 Uncultured bacterium clone RFLP 102E AF394926 Lactosphaera sp. PMagG1 AJ296179 Ruminococcus palustris DSM 9172T AJ306612 Trichococcus collinsii 37AN3 L76599 Lactosphaera pasteurii ATCC 35945 X87150 Lactosphaera pasteurii DSM 2381 Y17301 Trichococcus flocculiformis DSM 2094

  28. Some OTUs contain hundreds of sequences. Example: Many isolates of a human pathogen. Some “species” are found in over 20 OTUs. • Bioinformatics manuscript input • Sonya Murray • Peter Agron • Sadhana Chauhan

  29. http://greengenes.llnl.gov/16S • Comprehensive Aligned Sequence Construction for Automated Design of Effective Probes • Igor Dubosarskiy • Java implementations • Tim Harsch • RDBMS consultations • Lisa Corsetti • Apache module management • Kevin Melissare • Graphics

  30. “My Interest List” • Able to define a region of the tree that is important to you. • Your list will be remembered between visits.

  31. Picking Probes for GeneChip Microarray 1492R pA 27 1507 Slice taken from prokMSA • Select 20 to 28 probe pairs for each of 8,432 OTUs • Ideal Perfect Match Probe • 25mer • Present in all sequences of the OTU • Not present outside the OTU • Unable to X-hybe with seqs in other OTUs • Ideal Mis-match Control Probe • Unable to X-hybe within entire PAS

  32. Scoring Probe Candidates • Score is calculated for each potential probe pair. • Product of 3 factors: • Locus Specific Prevalence Factor • PerfectMatch X-hybe Factor • The closer the tree distance, the lower the factor • MisMatch X-hybe Factor • 3 bases available • use base which produces highest factor

  33. 22/22 25/25 20/25 OTU composed of 26sequences Locus Specific Prevalence Scoring

  34. Cross Hybridization Central Data Structure: $17mer_hash{AGCTATTATAGCTGCAG}{‘2.30.7.12.4.004’} = 1 $17mer_hash{AGCTATTATAGCTGCAG}{‘2.30.7.12.4.009’} = 1 $17mer_hash{AGCTATTATAGCTGCAG}{‘2.30.7.12.3.001’} = 1 … 1.2 Gb Allowed rapid lookup of all OTUs containing a particular 17mer Consultations: Mark Wagner Tom Slezak Tom Kuczmarski Mike Mittman, Affymetrix

  35. OTU-2.30.7.18.2.002 Sample Probe Rankings RANK

  36. OTU % pos pairs 2.30.7.12.1.013* 100 2.30.7.12.1.014 46 – 57 2.30.7.12.1.015 54 - 61 2.30.7.12.1.016 39 – 54 2.30.7.12.1.017 18 2.30.7.12.2.002 11 2.30.7.12.2.003 14 2.30.7.12.2.005 14 – 32 2.30.7.12.2.006 18 – 32 2.30.7.12.2.007 21 – 25 2.30.7.12.2.008 14 – 29 2.30.7.12.3.001 7 – 25 2.30.7.12.3.002 8 2.30.7.12.3.003 4 2.30.7.12.3.004 7 – 11 2.30.7.12.3.005 4 – 14 2.30.7.12.3.006 11 2.30.7.12.3.007 14 – 29 2.30.7.12.3.008 7 2.30.7.12.3.009 4 – 11 2.30.7.12.3.010 0 - 4 2.30.7.12.4.001 21 – 36 2.30.7.12.4.004* 100 2.30.7.12.4.005 0 – 11 2.30.7.12.4.006 29 – 54 2.30.7.12.4.007 11 – 14 2.30.7.12.4.008 11 Combinatorial scoring of “Probe Sets” are able to categorize mixed samples. S. aureus spike Art Koybayashi – Simulation package B. anthracis spike

  37. OTU % pos pairs 2.30.7.12.1.013* 100 2.30.7.12.1.014 46 – 57 2.30.7.12.1.015 54 - 61 2.30.7.12.1.016 39 – 54 2.30.7.12.1.017 18 2.30.7.12.2.002 11 2.30.7.12.2.003 14 2.30.7.12.2.005 14 – 32 2.30.7.12.2.006 18 – 32 2.30.7.12.2.007 21 – 25 2.30.7.12.2.008 14 – 29 2.30.7.12.3.001 7 – 25 2.30.7.12.3.002 8 2.30.7.12.3.003 4 2.30.7.12.3.004 7 – 11 2.30.7.12.3.005 4 – 14 2.30.7.12.3.006 11 2.30.7.12.3.007 14 – 29 2.30.7.12.3.008 7 2.30.7.12.3.009 4 – 11 2.30.7.12.3.010 0 - 4 2.30.7.12.4.001 21 – 36 2.30.7.12.4.004* 100 2.30.7.12.4.005 0 – 11 2.30.7.12.4.006 29 – 54 2.30.7.12.4.007 11 – 14 2.30.7.12.4.008 11 Combinatorial scoring of “Probe Sets” are able to categorize mixed samples. Hybridization results from spike-in experiment done in triplicate. Sonya Murray Aubree Hubbel Percent of probe-pairs scored positive for each probe set in the Staphylococcus Group.

  38. Opportunities PAS is a moving target • Problems • New 16S sequence data is constantly being deposited to public databases. • Mismatch Probes can become Perfect Matches. • Phylogenetic groupings (OTUs) can change. • New transitive links may be discovered

  39. Dynamic probe associations • Ability to “re-map” chip to up-to-date 16S data • Many of the existing probes on the physical array are complementary to “new” sequences. • Probes originally deemed MM can become PM to some organisms. • mySQL database used for association maintenance original (static) dynamic Name=2.30.7.12.004 BlockNumber=1 NumAtoms=33 NumCells=66 CellHeader=X Y PROBE FEAT QUAL Cell1=6 59 TCAAACATTGCGGGCTTCAG 2.30.7.12.004 Cell2=6 58 TCAAACATTGTGGGCTTCAG 2.30.7.12.004 Cell3=171 202 AAACATTGTGGGCTTCAGCC 2.30.7.12.004 Cell4=171 203 AAACATTGTGTGCTTCAGCC 2.30.7.12.004 Cell5=197 163 CATTGTGGGCATCAGCCACC 2.30.7.12.004 Cell6=197 162 CATTGTGGGCTTCAGCCACC 2.30.7.12.004 Cell7=151 175 ATTGTGGGCTGCAGCCACCC 2.30.7.12.004 Cell8=151 174 ATTGTGGGCTTCAGCCACCC 2.30.7.12.004 Cell9=228 2 TTGTGGGCTTCAGCCACCCC 2.30.7.12.004 Cell10=228 3 TTGTGGGCTTTAGCCACCCC 2.30.7.12.004 Cell11=139 22 TGTGGGCTTCAGCCACCCCA 2.30.7.12.004 Cell12=139 23 TGTGGGCTTCGGCCACCCCA 2.30.7.12.004 Cell13=94 76 GGGCTTCAGCCACCCCATTG 2.30.7.12.004 Cell14=94 77 GGGCTTCAGCTACCCCATTG 2.30.7.12.004 Cell15=111 118 CTTCAGCCACCCCATTGGAA 2.30.7.12.004 … … … … … …

  40. Finding groupings probes sequences Consider A – O to be 16S sequences. Consider 1 – 24 to be probes already embedded on the chip. First, associate all available probes with all available sequences. Let probe similarities drive sequence groupings.

  41. Finding groupings Consider A – O to be 16S sequences. Consider 1 – 24 to be probes already embedded on the chip. First, associate all available probes with all available sequences. Let probe similarities drive sequence groupings.

  42. Finding groupings Consider A – O to be 16S sequences. Consider 1 – 24 to be probes already embedded on the chip. First, associate all available probes with all available sequences. Let probe similarities drive sequence groupings.

  43. Finding groupings Consider A – O to be 16S sequences. Consider 1 – 24 to be probes already embedded on the chip. First, associate all available probes with all available sequences. Let probe similarities drive sequence groupings.

  44. Finding groupings Consider A – O to be 16S sequences. Consider 1 – 24 to be probes already embedded on the chip. First, associate all available probes with all available sequences. Let probe similarities drive sequence groupings.

  45. Progressive Cyclical Grouping DEFINEuGBpp as the number of useful probe pairs which globally differentiate a cluster from all other sequences. FORuGBpplock (11 .. 4) DO FORuPWppsep (1 .. 10) DO Determine uGBppclustfor each cluster. Lock all clusters where uGBppclust ≥ uGBpplock. Pair-wise (PW) compare each non-locked cluster (clustA, clustB) uPWppclustA = number of useful probe pairs which PW differentiate clustA from clustB uPWppclustB = number of useful probe pairs which PW differentiate clustB from clustA Merge sequences of clustA and clustB into one cluster unless uPWppclustA≥ uPWppsep AND uPWppclustB≥ uPWppsep END FOR Separate all sequences in non-locked clusters so that each sequence is the sole element of its own cluster. END FOR

  46. Quantitative Analysis • Could the concentration of each amplicon in a sample be measured by fluorescence intensity? • Experimental setup for 20 point calibration: SPIKE CONCENTRATION (pM in Hybridization Solution) Sonya Murray Carol Stone * 18uL of products from 30 cycle universal 16S PCR of gDNA extracted from U.K. air sample.

  47. Quantitative Analysis Log2 transformed Linear Least Squares Regression Pearson’s corr coeff was significant (df=18) 95% confidence intervals calculated according to: National Measurement System Valid Analytical Measurement Programme (VAM)

  48. Quantitative Analysis • Environmental community is measured with confidence intervals.

  49. Summary • prokMSA contains 65,000 aligned sequences and growing (largest collection). • Over 8,000 distinct OTUs have been found. • Global probe-picking was completed. • “DOE 16S” GeneChip® was manufactured. • Ability to correctly categorize spike-ins is being validated. • Detected amplicons can be quantified.

  50. Acknowledgements • Gary Andersen – Group Leader (The Tangent Terminator) • Carol Stone – Sample collection, hybridization (DSLT) • Aubree Hubbel – Spike synthesis • Sonya Murray - Hybridizations • Peter Agron – ms advise • Sadhana Chauhan – ms advice • Mike Mittman – probe selection constraints (Affymetrix) • Art Koybayashi – Hyb simulation package, ms advice • Tom Kusmarski - PAS, algorithm optimization • Tom Slezak - algorithm optimization • Mark Wagner - algorithm optimization • Igor Dubosarskiy – Java, web front-end (Ceres) • Tim Harsch - RDBMS • Lisa Corsetti – Apache administration • Allen Christensen – genomic sample amplification • This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory, under contract no. W-7405-Eng-48.

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