Discovery of Genes for Improved Cellulose and Cellulose-Extractability from Poplar Secondary Xylem

Jill L WegrzynJill L Wegrzyn11, Jennifer M. Lee, Jennifer M. Lee22, Andrew J. Eckert, Andrew J. Eckert22, Charlyn J. Suarez, Charlyn J. Suarez22

Brian J. StantonBrian J. Stanton33, Mark F. Davis, Mark F. Davis44, Chung-Jui Tsai, Chung-Jui Tsai55, David B. Neale, David B. Neale11

11Department of Plant Sciences, University of California at Davis, Davis, CADepartment of Plant Sciences, University of California at Davis, Davis, CA22Department of Evolution and Ecology, University of California at Davis, Davis, CADepartment of Evolution and Ecology, University of California at Davis, Davis, CA

33Genetic Resources Conservation Program, Greenwood Resources, Portland, ORGenetic Resources Conservation Program, Greenwood Resources, Portland, OR44National Renewable Energy Lab, Golden, CONational Renewable Energy Lab, Golden, CO

55School of Forest Resouces, Michigan Technical University, Hougton, MISchool of Forest Resouces, Michigan Technical University, Hougton, MI

Project Objectives• Resequence 40 candidate genes using a discovery

panel of 15 unrelated poplar individuals• Identify SNPs in the 40 genes using an automated

alignment and SNP calling bioinformatics pipeline• SNP genotype 456 poplar clones for 1536 SNPs

(Illumina Golden Gate assay)• Harvest wood increment cores from 2-3 ramets of

each of the 456 poplar clones (1100 trees in total)• Molecular Beam Mass Spectrometry (MBMS)

analysis on all 1100 wood cores to develop secondary xylem metabolomic profiles

• Association genetics analyses to identify genes controlling cellulose quantity and quality phenotypic variation in poplar

Poplar Biofuels Genome ProjectProject Overview

Gene Family Gene Names phenylalanine ammonia-lyase (PAL) PAL2, PAL4, PAL5 cinnamate 4-hydroxylase (C4H) C4H1, C4H2 4-coumarate:CoA ligase (4CL) 4CL1, 4CL3, 4CL5 hydroxycinnamoyl-CoA quinate/shikimate hydroxycinnamoyltransferase (HCT) HCT1, HCT6 coumarate 3-hydroxylase (C3H) C3H3 ferulate 5-hydroxylase (F5H) F5H1, F5H2 caffeate O-methyltransferase (COMT) COMT1, COMT2 caffeoyl CoA O-methyltransferase (CCoAOMT) CCoAOMT1, CCoAMT2 cinnamoyl-CoA reductase (CCR) CCR cinnamyl alcool dehydrogenase (CAD) CAD laccase (LAC) LAC1a, LAC2, LAC90a alpha-tubulin (TUA) TUA1, TUA5 beta-tubulin (TUB) TUB15, TUB9, TUB16 cellulose synthase (CesA) CesA1A, CesA2A, CesA1B, CesA2B, CesA3A

sucrose synthase (SUSY) SUSY1 cellulase (KOR) KOR1 glycine decarboxylase complex, H subunit (gdcH) gdcH1 glycine decarboxylase complex, T subunit (gdcT) gdcT2 S-adenosylmethionine synthetase (SAMS) SAMS1 Serine hydroxymethyltransferase (SHMT) SHMT1, SHMT3, SHMT6

Selected Candidate Genes40 Genes highly expressed in wood-forming tissues and associated

with lignin and cellulose biosynthesis

Genomic sequences of ~179,000 bp covering the entire protein-coding regions, including introns, and 1,000 bp upstream and 300 bp downstream, were retrieved from JGI

Primer Design and SequencingAgencourt Biosciences

Primer Design:

mRNA sequences were used to direct custom software to use 1000 bp upstream along with intronic sequence from the poplar genome

517 primers were designed across 40 genes

203 non-overlapping primers were finally selected based on: quality score, position, homopolymer regions (bioinformatic validation)

Goal: Fully re-sequence 40 candidate genes to facilitate SNP discovery~ between 3 and 12 amplicons/gene~ total of 202 amplicons from Agencourt~ forward and reverse sequencing

Candidate Genes Re-Sequenced from a Panel of 15 Unrelated Poplar Clones

DNA landmarks responsible for extraction

• AlignmentCritical in the automation of base calls– Commonly used Phrap (from PhredPhrap) is an assembler and is NOT ideal for

alignments

– Many commonly used aligners work best with protein sequences or with a reference sequence

– Preservation of quality scores for input into SNP identification programs– Speed for high-throughput programs

• Automated SNP Calls- Reference Sequence Required- Traditional approaches without reference sequence include “eSNPs” (human,

maize, and pine) -Very little redundancy outside of abundant genes-Overall high number of false positives (single pass reads)

- Not specific to frequencies observed in different organisms- High number of false positives in currently accepted methods

- Polybayes & PolyPhred

Alignment and SNP Calling PipelineChallenges in High-Throughput SNP Identification

Re-Sequencing datafrom Agencourt:

Initial Processing

Base Calling

Sequence Alignment

SNP Identification

Machine Learning

Data Storage & Release

Identification of SNPs in the 40 Candidate GenesAutomated Alignment and SNP identification Pipeline

Base Calling and Sequence Alignment

Modified PhredPhrapallows for trimming of bases from start and end of sequence based on trace quality

Ace2FASTAConverts native PhredPhrap output (ace file) into an unaligned FASTA file

ProbconsRNAOptimal DNA sequence alignment program

AlignedContig2ReadFASTAProvides single multifasta file with all reads aligned to the contig from PhredPhrap AND the contigs alignment to the other contigs from probconsRNA

FASTA2AceConverts resulting FASTA file back into ace file for SNP Identification

• Examine features to improve the accuracy of SNP location prediction

• Utilize machine learning to apply the features

• Refine the accuracy of the learning algorithm through adjustments to feature representation

• Utilize the classifier against the large re-sequenced set to improve accuracy of SNP calls originating from Polybayes and Polyphred

Alignment and SNP IdentificationSNP Identification Overview

• Polyphred: • http://droog.mbt.washington.edu/PolyPhred.html

– PolyPhred identifies potential SNPs using the base calls and peak information provided by Phred and the sequence alignments provided by Phrap

– SNP score based on base quality and sequence depth• Polybayes:• http://genome.wustl.edu/tools/software/polybayes.cgi

- Fully probabilistic SNP detection algorithm that identifies SNPs based on discrepancies at a given location of a multiple alignment.

- SNP score is based on a Bayesian-statistical formulation and can take-in prior frequency information

Alignment and SNP IdentificationExisting SNP Identification Software

Description Representation

Sequence Depth Continuous

Variation Type Categorical

Polybayes Score Continuous

Polyphred Score Continuous

Freq of major/minor alleles Continuous

Max quality of major/minor alleles Continuous

Local average quality Continuous

Overall average quality Continuous

Alignment Quality Continuous

Alignment and SNP IdentificationFeature Selection

• Sequence depth - Count of number of sequences in the alignment at the position of variation. – All sequences in the alignment may not overlap at the position of variation;

number is different from the total number of the sequences in the alignment

• Variation type– Variation type can be SNP or INDEL.

• PolyBayes score– PolyBayes program assigns a Bayesian posterior probability value for each called

SNP using the frequency priors given for observing a variation at that position.

Alignment and SNP IdentificationFeature Representation

• Polyphred score– Polyphred assigns a score calculated primarily from sequence depth and quality score.

• Base frequencies– The number of occurrences of different bases at the position of variation is important in

determining a polymorphic position.

– Frequencies of the first (major allele) and the second (minor allele) represented as ratio to sequence depth.

• Relative distance– Sequence quality at the ends of the alignment tends to be poor due to inherent limitations of

current sequencing technology.

– SNP position was represented as the ratio of the distance in the consensus sequence from the closest end, or the relative distance

Alignment and SNP IdentificationFeature Representation

• Sequence quality– Variation is observed because of a poor quality base. – Based on the base frequencies calculated: – maximum qualities of the major and minor alleles– average qualities of major and minor alleles

• Alignment quality– Misalignment of bases caused by sequence alignment programs

sometimes result in an erroneous SNP call. – In the neighborhood of the SNP (+/- 10 bases) all the

mismatches with the consensus sequence are given a penalty and the penalty is more if the mismatch is continuous

Alignment and SNP IdentificationFeature Representation

• Training set for loblolly pine was composed of a total of 300 validated sequences. – Divided to represent the relative percentages of sequence

source– Testing set is composed of 120 validated sequence sets

• Training set for poplar was composed of 42 validated sequences selected at random– Testing set is composed of a total of 30 validated sequence

sets.

• Validation = manually observed FP, FN, TP, and TN SNP calls through observation of tracefiles in Consed.

Alignment and SNP IdentificationSNP Identification Datasets

GOAL: Prediction

Learn a function or set of functions that assign a record to one of

several predefined classes.

Decision tree C4.5 program is open-source C code (WEKA) - J48

– At each point in the construction of the decision tree, C4.5 selects the feature to test based on maximum information gain.

– The goal is to generate a minimum size tree that correctly classifies all the SNP calls in the training set.

Alignment and SNP IdentificationClassification

• Accuracy = (TP + TN)/total

• Sensitivity = TP/(TP + FN)

• Specificity = TN/(FP + TN)

Alignment and SNP IdentificationEvaluation Criteria

Evaluation J48 Polyphred Polybayes

Accuracy 93.6 76.25 78.02

Sensitivity 88.21 83.22 86.54

Specificity 98.73 N/A N/A

Evaluation J48 Polyphred Polybayes

Accuracy 94.6 79.35 80.24

Sensitivity 90.54 85.01 88.14

Specificity 97.23 N/A N/A

PineSAP

• PineSAP alignment improves– Inaccuracies introduced by using Phrap to align

sequences– Time which would be required by using a aligner such as

ProbconsRNA or ClustalW on its own– PineSAP has a 98% success rate when used to align loblolly

resequencing data.• PineSAP identified a success list of features to enhance

polymorphism predictions• PineSAP obtained an overall prediction accuracy of 93% in

SNP Identification• PineSAP provided a full alignment and polymorphism

detection system that can be adapted to specific genomes

• Total of 202 amplicons• Number of SNPs Identified - 1486

– Meet a minimum confidence score from the PineSAP pipeline

• Average number of SNPs/amplicon ~ 7• Amplicon length ~ 600 - 700bp• Remaining SNPs generated from 232 additional

genes.– Utilized an eSNP method with publicly available EST

data and reference genome from JGI.– Identified a total of 1,232 potential SNPs

Alignment and SNP IdentificationSNPs Identified

Polyphred style output is transformed into Illumina style input

-adding IUPAC codes for SNPs in flanking sequence

Alignment and SNP IdentificationSNP Formatting

SNP GenotypingIllumina GoldenGate Assay

Alignment and SNP IdentificationIllumina Design

• All SNP and amplicon information is databased.• SQL queries can be used to select specific SNPs

– Pair-wise comparisons of all SNPs – Scores were assigned to each pair of SNPs in each amplicon,

accounting for distance between the SNPs, Illumina score for both SNPs, and frequency of minor allele

• We can also use SQL queries to select SNPs and minimize additional SNPs in flanking sequence

Alignment and SNP IdentificationSNP Selection

Mass spectrometer

Transfer line and interface

Autosampler

48 sample tray

Mass spectrometer

Transfer line and interface

Autosampler

48 sample tray

Pyrolysis Molecular Beam Mass Spectrometry Analysis

cell wall chemistry

lignin

hemicellulose

cellulose

Acknowledgements

Chung-Jui TsaiMike DavisDavid NealeJill WegrzynJennifer Lee

Andrew EckertJohn Liechty

Funding:

Brian StantonRich Shuren